test.list.pkl

(lp1
ccopy_reg
_reconstructor
p2
(cBio.Medline
Record
p3
c__builtin__
dict
p4
(dp5
S'LID'
p6
S'10.1016/j.jtbi.2016.08.016 [doi] S0022-5193(16)30246-6 [pii]'
p7
sS'STAT'
p8
S'In-Data-Review'
p9
sS'DEP'
p10
S'20160811'
p11
sS'JID'
p12
S'0376342'
p13
sS'DA'
p14
S'20160924'
p15
sS'AID'
p16
(lp17
S'S0022-5193(16)30246-6 [pii]'
p18
aS'10.1016/j.jtbi.2016.08.016 [doi]'
p19
asS'FAU'
p20
(lp21
S'Taylor, Bradford P'
p22
aS'Dushoff, Jonathan'
p23
aS'Weitz, Joshua S'
p24
asS'DP'
p25
S'2016 Nov 7'
p26
sS'OWN'
p27
S'NLM'
p28
sS'PT'
p29
(lp30
S'Journal Article'
p31
asS'LA'
p32
(lp33
S'eng'
p34
asS'CRDT'
p35
(lp36
S'2016/08/16 06:00'
p37
asS'JT'
p38
S'Journal of theoretical biology'
p39
sS'PG'
p40
S'145-54'
p41
sS'TI'
p42
S'Stochasticity and the limits to confidence when estimating R0 of Ebola and other emerging infectious diseases.'
p43
sS'PL'
p44
S'England'
p45
sS'TA'
p46
S'J Theor Biol'
p47
sS'CI'
p48
(lp49
S'Copyright (c) 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.'
p50
asS'AB'
p51
S'Dynamic models - often deterministic in nature - were used to estimate the basic reproductive number, R0, of the 2014-5 Ebola virus disease (EVD) epidemic outbreak in West Africa. Estimates of R0 were then used to project the likelihood for large outbreak sizes, e.g., exceeding hundreds of thousands of cases. Yet fitting deterministic models can lead to over-confidence in the confidence intervals of the fitted R0, and, in turn, the type and scope of necessary interventions. In this manuscript we propose a hybrid stochastic-deterministic method to estimate R0 and associated confidence intervals (CIs). The core idea is that stochastic realizations of an underlying deterministic model can be used to evaluate the compatibility of candidate values of R0 with observed epidemic curves. The compatibility is based on comparing the distribution of expected epidemic growth rates with the observed epidemic growth rate given "process noise", i.e., arising due to stochastic transmission, recovery and death events. By applying our method to reported EVD case counts from Guinea, Liberia and Sierra Leone, we show that prior estimates of R0 based on deterministic fits appear to be more confident than analysis of stochastic trajectories suggests should be possible. Moving forward, we recommend including process noise among other sources of noise when estimating R0 CIs of emerging epidemics. Our hybrid procedure represents an adaptable and easy-to-implement approach for such estimation.'
p52
sS'AD'
p53
S'School of Physics, Georgia Institute of Technology, Atlanta, GA, USA. Electronic address: bradfordptaylor@gmail.com. Department of Biology and Institute for Infectious Disease Research, McMaster University, Hamilton, Canada. School of Physics, Georgia Institute of Technology, Atlanta, GA, USA; School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA. Electronic address: jsweitz@gatech.edu.'
p54
sS'VI'
p55
S'408'
p56
sS'IS'
p57
S'1095-8541 (Electronic) 0022-5193 (Linking)'
p58
sS'AU'
p59
(lp60
S'Taylor BP'
p61
aS'Dushoff J'
p62
aS'Weitz JS'
p63
asS'MHDA'
p64
S'2016/08/16 06:00'
p65
sS'PHST'
p66
(lp67
S'2016/01/20 [received]'
p68
aS'2016/07/28 [revised]'
p69
aS'2016/08/10 [accepted]'
p70
aS'2016/08/11 [aheadofprint]'
p71
asS'OTO'
p72
(lp73
S'NOTNLM'
p74
asS'EDAT'
p75
S'2016/08/16 06:00'
p76
sS'SO'
p77
S'J Theor Biol. 2016 Nov 7;408:145-54. doi: 10.1016/j.jtbi.2016.08.016. Epub 2016 Aug 11.'
p78
sS'SB'
p79
S'IM'
p80
sS'PMID'
p81
S'27524644'
p82
sS'OT'
p83
(lp84
S'Confidence intervals'
p85
aS'Demographic stochasticity'
p86
aS'Infectious diseases'
p87
asS'PST'
p88
S'ppublish'
p89
stRp90
ag2
(g3
g4
(dp91
S'STAT'
p92
S'Publisher'
p93
sS'AB'
p94
S"Despite the availability of inexpensive antimicrobial treatment, syphilis remains prevalent worldwide, affecting millions of individuals. Furthermore, syphilis infection is suspected of increasing both susceptibility to, and tendency to transmit, HIV. Development of a syphilis vaccine would be a potentially promising step towards control, but the value of dedicating resources to vaccine development should be evaluated in the context of the anticipated benefits. Here, we use a detailed mathematical model to explore the potential impact of rolling out a hypothetical syphilis vaccine on morbidity from both syphilis and HIV and compare it to the impact of expanded 'screen and treat' programmes using existing treatments. Our results suggest that an efficacious vaccine has the potential to sharply reduce syphilis prevalence under a wide range of scenarios, while expanded treatment interventions are likely to be substantially less effective. Our modelled interventions in our simulated study populations are expected to have little effect on HIV, and in some scenarios lead to small increases in HIV incidence, suggesting that interventions against syphilis should be accompanied with interventions against other sexually transmitted infections to prevent the possibility that lower morbidity or lower perceived risk from syphilis could lead to increases in other sexually transmitted diseases."
p95
sS'AD'
p96
S'School of Computational Science and Engineering,McMaster University,Hamilton,Ontario,Canada. Department of Biochemistry and Microbiology,University of Victoria,Victoria,British Columbia,Canada. Department of Pathology and Molecular Medicine,McMaster University,Hamilton,Ontario,Canada. Department of Theoretical Biology,McMaster University,Hamilton,Ontario,Canada.'
p97
sS'DEP'
p98
S'20160801'
p99
sS'IS'
p100
S'1469-4409 (Electronic) 0950-2688 (Linking)'
p101
sS'JID'
p102
S'8703737'
p103
sS'DA'
p104
S'20160801'
p105
sS'AU'
p106
(lp107
S'Champredon D'
p108
aS'Cameron CE'
p109
aS'Smieja M'
p110
aS'Dushoff J'
p111
asS'AID'
p112
(lp113
S'S0950268816001643 [pii]'
p114
aS'10.1017/S0950268816001643 [doi]'
p115
asS'FAU'
p116
(lp117
S'Champredon, D'
p118
aS'Cameron, C E'
p119
aS'Smieja, M'
p120
aS'Dushoff, J'
p121
asS'DP'
p122
S'2016 Aug 1'
p123
sS'MHDA'
p124
S'2016/08/02 06:00'
p125
sS'AUID'
p126
(lp127
S'ORCID: http://orcid.org/0000-0002-7090-8757'
p128
asS'OWN'
p129
S'NLM'
p130
sS'PT'
p131
(lp132
S'JOURNAL ARTICLE'
p133
asS'LA'
p134
(lp135
S'ENG'
p136
asS'OTO'
p137
(lp138
S'NOTNLM'
p139
asS'CRDT'
p140
(lp141
S'2016/08/02 06:00'
p142
asS'EDAT'
p143
S'2016/08/02 06:00'
p144
sS'JT'
p145
S'Epidemiology and infection'
p146
sS'LR'
p147
S'20160801'
p148
sS'PG'
p149
S'1-9'
p150
sS'TI'
p151
S'Epidemiological impact of a syphilis vaccine: a simulation study.'
p152
sS'SO'
p153
S'Epidemiol Infect. 2016 Aug 1:1-9.'
p154
sS'PMID'
p155
S'27477823'
p156
sS'OT'
p157
(lp158
S'Co-infection'
p159
aS'HIV'
p160
aS'immunization'
p161
aS'mathematical modelling'
p162
aS'public health'
p163
aS'syphilis'
p164
asS'PST'
p165
S'aheadofprint'
p166
sS'TA'
p167
S'Epidemiol Infect'
p168
stRp169
ag2
(g3
g4
(dp170
S'LID'
p171
S'10.1093/aje/kwv303 [doi]'
p172
sS'STAT'
p173
S'In-Data-Review'
p174
sS'DEP'
p175
S'20160504'
p176
sS'CI'
p177
(lp178
S'(c) The Author 2016. Published by Oxford University Press on behalf of the Johns'
p179
aS'Hopkins Bloomberg School of Public Health. All rights reserved. For permissions,'
p180
aS'please e-mail: journals.permissions@oup.com.'
p181
asS'DA'
p182
S'20160702'
p183
sS'AID'
p184
(lp185
S'kwv303 [pii]'
p186
aS'10.1093/aje/kwv303 [doi]'
p187
asS'FAU'
p188
(lp189
S'Sempa, Joseph B'
p190
aS'Dushoff, Jonathan'
p191
aS'Daniels, Michael J'
p192
aS'Castelnuovo, Barbara'
p193
aS'Kiragga, Agnes N'
p194
aS'Nieuwoudt, Martin'
p195
aS'Bellan, Steven E'
p196
asS'DP'
p197
S'2016 Jul 1'
p198
sS'OWN'
p199
S'NLM'
p200
sS'PT'
p201
(lp202
S'Journal Article'
p203
asS'LA'
p204
(lp205
S'eng'
p206
asS'CRDT'
p207
(lp208
S'2016/05/19 06:00'
p209
asS'JT'
p210
S'American journal of epidemiology'
p211
sS'LR'
p212
S'20160708'
p213
sS'PG'
p214
S'67-77'
p215
sS'TI'
p216
S'Reevaluating Cumulative HIV-1 Viral Load as a Prognostic Predictor: Predicting Opportunistic Infection Incidence and Mortality in a Ugandan Cohort.'
p217
sS'IS'
p218
S'1476-6256 (Electronic) 0002-9262 (Linking)'
p219
sS'PL'
p220
S'United States'
p221
sS'TA'
p222
S'Am J Epidemiol'
p223
sS'JID'
p224
S'7910653'
p225
sS'AB'
p226
S"Recent studies have evaluated cumulative human immunodeficiency virus type 1 (HIV-1) viral load (cVL) for predicting disease outcomes, with discrepant results. We reviewed the disparate methodological approaches taken and evaluated the prognostic utility of cVL in a resource-limited setting. Using data on the Infectious Diseases Institute (Makerere University, Kampala, Uganda) cohort, who initiated antiretroviral therapy in 2004-2005 and were followed up for 9 years, we calculated patients' time-updated cVL by summing the area under their viral load curves on either a linear scale (cVL1) or a logarithmic scale (cVL2). Using Cox proportional hazards models, we evaluated both metrics as predictors of incident opportunistic infections and mortality. Among 489 patients analyzed, neither cVL measure was a statistically significant predictor of opportunistic infection risk. In contrast, cVL2 (but not cVL1) was a statistically significant predictor of mortality, with each log10 increase corresponding to a 1.63-fold (95% confidence interval: 1.02, 2.60) elevation in mortality risk when cVL2 was accumulated from baseline. However, whether cVL is predictive or not hinges on difficult choices surrounding the cVL metric and statistical model employed. Previous studies may have suffered from confounding bias due to their focus on cVL1, which strongly correlates with other variables. Further methodological development is needed to illuminate whether the inconsistent predictive utility of cVL arises from causal relationships or from statistical artifacts."
p227
sS'IP'
p228
S'1'
sS'PMCR'
p229
(lp230
S'2017/07/01 00:00'
p231
asS'PMC'
p232
S'PMC4929240'
p233
sS'AU'
p234
(lp235
S'Sempa JB'
p236
aS'Dushoff J'
p237
aS'Daniels MJ'
p238
aS'Castelnuovo B'
p239
aS'Kiragga AN'
p240
aS'Nieuwoudt M'
p241
aS'Bellan SE'
p242
asS'VI'
p243
S'184'
p244
sS'MHDA'
p245
S'2016/05/18 06:00'
p246
sS'PHST'
p247
(lp248
S'2015/04/23 [received]'
p249
aS'2015/10/29 [accepted]'
p250
aS'2016/05/04 [aheadofprint]'
p251
asS'OTO'
p252
(lp253
S'NOTNLM'
p254
asS'OID'
p255
(lp256
S'NLM: PMC4929240 [Available on 07/01/17]'
p257
asS'EDAT'
p258
S'2016/05/18 06:00'
p259
sS'SO'
p260
S'Am J Epidemiol. 2016 Jul 1;184(1):67-77. doi: 10.1093/aje/kwv303. Epub 2016 May 4.'
p261
sS'SB'
p262
S'IM'
p263
sS'PMID'
p264
S'27188943'
p265
sS'OT'
p266
(lp267
S'Cox proportional hazards models'
p268
aS'HIV'
p269
aS'Martingale residuals'
p270
aS'human immunodeficiency virus'
p271
aS'mortality'
p272
aS'opportunistic infections'
p273
aS'viral load'
p274
aS'viremia copy-years'
p275
asS'PST'
p276
S'ppublish'
p277
stRp278
ag2
(g3
g4
(dp279
S'LID'
p280
S'10.4049/jimmunol.1502343 [doi]'
p281
sS'STAT'
p282
S'In-Data-Review'
p283
sS'DEP'
p284
S'20160513'
p285
sS'CI'
p286
(lp287
S'Copyright (c) 2016 by The American Association of Immunologists, Inc.'
p288
asS'DA'
p289
S'20160604'
p290
sS'AID'
p291
(lp292
S'jimmunol.1502343 [pii]'
p293
aS'10.4049/jimmunol.1502343 [doi]'
p294
asS'FAU'
p295
(lp296
S'Ndifon, Wilfred'
p297
aS'Dushoff, Jonathan'
p298
asS'DP'
p299
S'2016 Jun 15'
p300
sS'OWN'
p301
S'NLM'
p302
sS'PT'
p303
(lp304
S'Journal Article'
p305
asS'LA'
p306
(lp307
S'eng'
p308
asS'CRDT'
p309
(lp310
S'2016/05/17 06:00'
p311
asS'JT'
p312
S'Journal of immunology (Baltimore, Md. : 1950)'
p313
sS'PG'
p314
S'4999-5004'
p315
sS'TI'
p316
S'The Hayflick Limit May Determine the Effective Clonal Diversity of Naive T Cells.'
p317
sS'PL'
p318
S'United States'
p319
sS'TA'
p320
S'J Immunol'
p321
sS'JID'
p322
S'2985117R'
p323
sS'AB'
p324
S'Having a large number of sufficiently abundant T cell clones is important for adequate protection against diseases. However, as shown in this paper and elsewhere, between young adulthood and >70 y of age the effective clonal diversity of naive CD4/CD8 T cells found in human blood declines by a factor of >10. (Effective clonal diversity accounts for both the number and the abundance of T cell clones.) The causes of this observation are incompletely understood. A previous study proposed that it might result from the emergence of certain rare, replication-enhancing mutations in T cells. In this paper, we propose an even simpler explanation: that it results from the loss of T cells that have attained replicative senescence (i.e., the Hayflick limit). Stochastic numerical simulations of naive T cell population dynamics, based on experimental parameters, show that the rate of homeostatic T cell proliferation increases after the age of approximately 60 y because naive T cells collectively approach replicative senescence. This leads to a sharp decline of effective clonal diversity after approximately 70 y, in agreement with empirical data. A mathematical analysis predicts that, without an increase in the naive T cell proliferation rate, this decline will occur >50 yr later than empirically observed. These results are consistent with a model in which exhaustion of the proliferative capacity of naive T cells causes a sharp decline of their effective clonal diversity and imply that therapeutic potentiation of thymopoiesis might either prevent or reverse this outcome.'
p325
sS'AD'
p326
S'African Institute for Mathematical Sciences, Muizenberg 7945, Cape Town, South Africa; African Institute for Mathematical Sciences, Legon, Accra, Ghana; Stellenbosch University, Matieland 7602, Stellenbosch, South Africa; and wndifon@aims.ac.za. Department of Biology, McMaster University, Hamilton, Ontario L8S 4K1, Canada.'
p327
sS'AUID'
p328
(lp329
S'ORCID: http://orcid.org/0000-0003-0506-4794'
p330
asS'IP'
p331
S'12'
p332
sS'IS'
p333
S'1550-6606 (Electronic) 0022-1767 (Linking)'
p334
sS'AU'
p335
(lp336
S'Ndifon W'
p337
aS'Dushoff J'
p338
asS'VI'
p339
S'196'
p340
sS'MHDA'
p341
S'2016/05/18 06:00'
p342
sS'PHST'
p343
(lp344
S'2015/11/04 [received]'
p345
aS'2016/04/18 [accepted]'
p346
aS'2016/05/13 [aheadofprint]'
p347
asS'EDAT'
p348
S'2016/05/18 06:00'
p349
sS'SO'
p350
S'J Immunol. 2016 Jun 15;196(12):4999-5004. doi: 10.4049/jimmunol.1502343. Epub 2016 May 13.'
p351
sS'SB'
p352
S'AIM IM'
p353
sS'PMID'
p354
S'27183600'
p355
sS'PST'
p356
S'ppublish'
p357
stRp358
ag2
(g3
g4
(dp359
S'LID'
p360
S'10.1098/rsif.2015.0666 [doi] 20150666 [pii]'
p361
sS'STAT'
p362
S'In-Process'
p363
sS'JT'
p364
S'Journal of the Royal Society, Interface / the Royal Society'
p365
sS'CI'
p366
(lp367
S'(c) 2016 The Author(s).'
p368
asS'DA'
p369
S'20160225'
p370
sS'AID'
p371
(lp372
S'rsif.2015.0666 [pii]'
p373
aS'10.1098/rsif.2015.0666 [doi]'
p374
asS'FAU'
p375
(lp376
S'Miller, Ezer'
p377
aS'Dushoff, Jonathan'
p378
aS'Huppert, Amit'
p379
asS'DP'
p380
S'2016 Feb'
p381
sS'OWN'
p382
S'NLM'
p383
sS'PT'
p384
(lp385
S'Journal Article'
p386
aS"Research Support, Non-U.S. Gov't"
p387
asS'LA'
p388
(lp389
S'eng'
p390
asS'CRDT'
p391
(lp392
S'2016/02/26 06:00'
p393
asS'LR'
p394
S'20160319'
p395
sS'PG'
p396
S'20150666'
p397
sS'TI'
p398
S'The risk of incomplete personal protection coverage in vector-borne disease.'
p399
sS'IS'
p400
S'1742-5662 (Electronic) 1742-5662 (Linking)'
p401
sS'PL'
p402
S'England'
p403
sS'TA'
p404
S'J R Soc Interface'
p405
sS'JID'
p406
S'101217269'
p407
sS'AB'
p408
S'Personal protection (PP) techniques, such as insecticide-treated nets, repellents and medications, include some of the most important and commonest ways used today to protect individuals from vector-borne infectious diseases. In this study, we explore the possibility that a PP intervention with partial coverage may have the counterintuitive effect of increasing disease burden at the population level, by increasing the biting intensity on the unprotected portion of the population. To this end, we have developed a dynamic model which incorporates parameters that describe the potential effects of PP on vector searching and biting behaviour and calculated its basic reproductive rate, R0. R0 is a well-established threshold of disease risk; the higher R0 is above unity, the stronger the disease onset intensity. When R0 is below unity, the disease is typically unable to persist. The model analysis revealed that partial coverage with popular PP techniques can realistically lead to a substantial increase in the reproductive number. An increase in R0 implies an increase in disease burden and difficulties in eradication efforts within certain parameter regimes. Our findings therefore stress the importance of studying vector behavioural patterns in response to PP interventions for future mitigation of vector-borne diseases.'
p409
sS'AD'
p410
S'The Biostatistics Unit, Gertner Institute for Epidemiology and Health Policy Research, Tel Hashomer, Israel Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel ezermiller@gmail.com. Department of Biology, McMaster University, Hamilton, Ontario, Canada. The Biostatistics Unit, Gertner Institute for Epidemiology and Health Policy Research, Tel Hashomer, Israel Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.'
p411
sS'IP'
p412
S'115'
p413
sS'PMCR'
p414
(lp415
S'2017/02/01 00:00'
p416
asS'PMC'
p417
S'PMC4780561'
p418
sS'AU'
p419
(lp420
S'Miller E'
p421
aS'Dushoff J'
p422
aS'Huppert A'
p423
asS'VI'
p424
S'13'
p425
sS'MHDA'
p426
S'2016/02/26 06:00'
p427
sS'OTO'
p428
(lp429
S'NOTNLM'
p430
asS'OID'
p431
(lp432
S'NLM: PMC4780561 [Available on 02/01/17]'
p433
asS'EDAT'
p434
S'2016/02/26 06:00'
p435
sS'SO'
p436
S'J R Soc Interface. 2016 Feb;13(115):20150666. doi: 10.1098/rsif.2015.0666.'
p437
sS'SB'
p438
S'IM'
p439
sS'PMID'
p440
S'26911486'
p441
sS'OT'
p442
(lp443
S'bednets'
p444
aS'disease transmission'
p445
aS'insect repellents'
p446
aS'insecticide-treated nets'
p447
aS'vector ecology'
p448
aS'vector-borne infectious diseases'
p449
asS'PST'
p450
S'ppublish'
p451
stRp452
ag2
(g3
g4
(dp453
S'LID'
p454
S'10.1016/j.jtbi.2016.01.022 [doi] S0022-5193(16)00055-2 [pii]'
p455
sS'STAT'
p456
S'In-Process'
p457
sS'DEP'
p458
S'20160215'
p459
sS'JID'
p460
S'0376342'
p461
sS'DA'
p462
S'20160411'
p463
sS'AID'
p464
(lp465
S'S0022-5193(16)00055-2 [pii]'
p466
aS'10.1016/j.jtbi.2016.01.022 [doi]'
p467
asS'FAU'
p468
(lp469
S'Keegan, Lindsay T'
p470
aS'Dushoff, Jonathan'
p471
asS'DP'
p472
S'2016 May 21'
p473
sS'OWN'
p474
S'NLM'
p475
sS'PT'
p476
(lp477
S'Journal Article'
p478
asS'LA'
p479
(lp480
S'eng'
p481
asS'CRDT'
p482
(lp483
S'2016/02/20 06:00'
p484
asS'JT'
p485
S'Journal of theoretical biology'
p486
sS'PG'
p487
S'1-12'
p488
sS'TI'
p489
S'Estimating finite-population reproductive numbers in heterogeneous populations.'
p490
sS'PL'
p491
S'England'
p492
sS'TA'
p493
S'J Theor Biol'
p494
sS'CI'
p495
(lp496
S'Copyright (c) 2016 Elsevier Ltd. All rights reserved.'
p497
asS'AB'
p498
S'The basic reproductive number, R0, is one of the most important epidemiological quantities. R0 provides a threshold for elimination and determines when a disease can spread or when a disease will die out. Classically, R0 is calculated assuming an infinite population of identical hosts. Previous work has shown that heterogeneity in the host mixing rate increases R0 in an infinite population. However, it has been suggested that in a finite population, heterogeneity in the mixing rate may actually decrease the finite-population reproductive numbers. Here, we outline a framework for discussing different types of heterogeneity in disease parameters, and how these affect disease spread and control. We calculate "finite-population reproductive numbers" with different types of heterogeneity, and show that in a finite population, heterogeneity has complicated effects on the reproductive number. We find that simple heterogeneity decreases the finite-population reproductive number, whereas heterogeneity in the intrinsic mixing rate (which affects both infectiousness and susceptibility) increases the finite-population reproductive number when R0 is small relative to the size of the population and decreases the finite-population reproductive number when R0 is large relative to the size of the population. Although heterogeneity has complicated effects on the finite-population reproductive numbers, its implications for control are straightforward: when R0 is large relative to the size of the population, heterogeneity decreases the finite-population reproductive numbers, making disease control or elimination easier than predicted by R0.'
p499
sS'AD'
p500
S'615 North Wolfe St, The Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland, USA. Electronic address: keeganlt@mcmaster.ca. 1280 Main Street West, McMaster University, Hamilton, Ontario, Canada.'
p501
sS'VI'
p502
S'397'
p503
sS'IS'
p504
S'1095-8541 (Electronic) 0022-5193 (Linking)'
p505
sS'AU'
p506
(lp507
S'Keegan LT'
p508
aS'Dushoff J'
p509
asS'MHDA'
p510
S'2016/02/20 06:00'
p511
sS'PHST'
p512
(lp513
S'2015/07/21 [received]'
p514
aS'2016/01/13 [revised]'
p515
aS'2016/01/16 [accepted]'
p516
aS'2016/02/15 [aheadofprint]'
p517
asS'OTO'
p518
(lp519
S'NOTNLM'
p520
asS'EDAT'
p521
S'2016/02/20 06:00'
p522
sS'SO'
p523
S'J Theor Biol. 2016 May 21;397:1-12. doi: 10.1016/j.jtbi.2016.01.022. Epub 2016 Feb 15.'
p524
sS'SB'
p525
S'IM'
p526
sS'PMID'
p527
S'26891919'
p528
sS'OT'
p529
(lp530
S'Basic reproductive number'
p531
aS'Heterogeneity'
p532
aS'Super-spreaders'
p533
asS'PST'
p534
S'ppublish'
p535
stRp536
ag2
(g3
g4
(dp537
S'LID'
p538
S'10.1098/rspb.2015.2026 [doi] 20152026 [pii]'
p539
sS'STAT'
p540
S'In-Process'
p541
sS'JT'
p542
S'Proceedings. Biological sciences / The Royal Society'
p543
sS'CI'
p544
(lp545
S'(c) 2015 The Authors.'
p546
asS'DA'
p547
S'20151217'
p548
sS'AID'
p549
(lp550
S'rspb.2015.2026 [pii]'
p551
aS'10.1098/rspb.2015.2026 [doi]'
p552
asS'FAU'
p553
(lp554
S'Champredon, David'
p555
aS'Dushoff, Jonathan'
p556
asS'DP'
p557
S'2015 Dec 22'
p558
sS'AD'
p559
S'School of Computational Science and Engineering, McMaster University, Hamilton, Canada L8S 4L8 david.champredon@gmail.com. Department of Biology, McMaster University, Hamilton, Canada L8S 4L8.'
p560
sS'OWN'
p561
S'NLM'
p562
sS'PT'
p563
(lp564
S'Journal Article'
p565
aS"Research Support, Non-U.S. Gov't"
p566
asS'LA'
p567
(lp568
S'eng'
p569
asS'CRDT'
p570
(lp571
S'2015/12/18 06:00'
p572
asS'LR'
p573
S'20160127'
p574
sS'PG'
p575
S'20152026'
p576
sS'TI'
p577
S'Intrinsic and realized generation intervals in infectious-disease transmission.'
p578
sS'PL'
p579
S'England'
p580
sS'TA'
p581
S'Proc Biol Sci'
p582
sS'JID'
p583
S'101245157'
p584
sS'AB'
p585
S"The generation interval is the interval between the time when an individual is infected by an infector and the time when this infector was infected. Its distribution underpins estimates of the reproductive number and hence informs public health strategies. Empirical generation-interval distributions are often derived from contact-tracing data. But linking observed generation intervals to the underlying generation interval required for modelling purposes is surprisingly not straightforward, and misspecifications can lead to incorrect estimates of the reproductive number, with the potential to misguide interventions to stop or slow an epidemic. Here, we clarify the theoretical framework for three conceptually different generation-interval distributions: the 'intrinsic' one typically used in mathematical models and the 'forward' and 'backward' ones typically observed from contact-tracing data, looking, respectively, forward or backward in time. We explain how the relationship between these distributions changes as an epidemic progresses and discuss how empirical generation-interval data can be used to correctly inform mathematical models."
p586
sS'GR'
p587
(lp588
S'IER-143486/Canadian Institutes of Health Research/Canada'
p589
asS'IP'
p590
S'1821'
p591
sS'IS'
p592
S'1471-2954 (Electronic) 0962-8452 (Linking)'
p593
sS'PMC'
p594
S'PMC4707754'
p595
sS'AU'
p596
(lp597
S'Champredon D'
p598
aS'Dushoff J'
p599
asS'VI'
p600
S'282'
p601
sS'MHDA'
p602
S'2015/12/18 06:00'
p603
sS'AUID'
p604
(lp605
S'ORCID: http://orcid.org/0000-0002-7090-8757'
p606
aS'ORCID: http://orcid.org/0000-0003-0506-4794'
p607
asS'OTO'
p608
(lp609
S'NOTNLM'
p610
asS'OID'
p611
(lp612
S'NLM: PMC4707754'
p613
asS'EDAT'
p614
S'2015/12/18 06:00'
p615
sS'SI'
p616
(lp617
S'Dryad/4DD3S'
p618
asS'SO'
p619
S'Proc Biol Sci. 2015 Dec 22;282(1821):20152026. doi: 10.1098/rspb.2015.2026.'
p620
sS'SB'
p621
S'IM'
p622
sS'PMID'
p623
S'26674948'
p624
sS'OT'
p625
(lp626
S'contact-tracing'
p627
aS'epidemiological model'
p628
aS'generation interval'
p629
aS'reproductive number'
p630
asS'PST'
p631
S'ppublish'
p632
stRp633
ag2
(g3
g4
(dp634
S'LID'
p635
S'10.1016/S1473-3099(15)00303-5 [doi] S1473-3099(15)00303-5 [pii]'
p636
sS'STAT'
p637
S'MEDLINE'
p638
sS'DEP'
p639
S'20150920'
p640
sS'MID'
p641
(lp642
S'NIHMS774869'
p643
asS'DA'
p644
S'20151014'
p645
sS'AID'
p646
(lp647
S'S1473-3099(15)00303-5 [pii]'
p648
aS'10.1016/S1473-3099(15)00303-5 [doi]'
p649
asS'CRDT'
p650
(lp651
S'2015/10/14 06:00'
p652
asS'DP'
p653
S'2015 Oct'
p654
sS'CON'
p655
(lp656
S'Lancet Infect Dis. 2015 Jul;15(7):762-3. PMID: 26122441'
p657
asS'OWN'
p658
S'NLM'
p659
sS'PT'
p660
(lp661
S'Comment'
p662
aS'Letter'
p663
aS'Research Support, N.I.H., Extramural'
p664
aS"Research Support, Non-U.S. Gov't"
p665
aS"Research Support, U.S. Gov't, Non-P.H.S."
p666
asS'LA'
p667
(lp668
S'eng'
p669
asS'FAU'
p670
(lp671
S'Pulliam, Juliet R C'
p672
aS'Bellan, Steve E'
p673
aS'Gambhir, Manoj'
p674
aS'Meyers, Lauren Ancel'
p675
aS'Dushoff, Jonathan'
p676
asS'JT'
p677
S'The Lancet. Infectious diseases'
p678
sS'LR'
p679
S'20160427'
p680
sS'PG'
p681
S'1134'
p682
sS'TI'
p683
S'Evaluating Ebola vaccine trials: insights from simulation.'
p684
sS'RN'
p685
(lp686
S'0 (Ebola Vaccines)'
p687
asS'PL'
p688
S'United States'
p689
sS'TA'
p690
S'Lancet Infect Dis'
p691
sS'JID'
p692
S'101130150'
p693
sS'GR'
p694
(lp695
S'R25 GM102149/GM/NIGMS NIH HHS/United States'
p696
aS'R25GM102149/GM/NIGMS NIH HHS/United States'
p697
aS'U01 GM087719/GM/NIGMS NIH HHS/United States'
p698
aS'U01GM087719/GM/NIGMS NIH HHS/United States'
p699
aS'Canadian Institutes of Health Research/Canada'
p700
asS'IP'
p701
S'10'
p702
sS'IS'
p703
S'1474-4457 (Electronic) 1473-3099 (Linking)'
p704
sS'PMC'
p705
S'PMC4830262'
p706
sS'DCOM'
p707
S'20160125'
p708
sS'AU'
p709
(lp710
S'Pulliam JR'
p711
aS'Bellan SE'
p712
aS'Gambhir M'
p713
aS'Meyers LA'
p714
aS'Dushoff J'
p715
asS'AD'
p716
S'Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA. Electronic address: pulliam@ufl.edu. Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX, USA. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; Division of Preparedness and Emerging Infections, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA. Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA; The Santa Fe Institute, Santa Fe, NM, USA. Department of Biology, McMaster University, Hamilton, ON, Canada.'
p717
sS'VI'
p718
S'15'
p719
sS'MHDA'
p720
S'2016/01/26 06:00'
p721
sS'PHST'
p722
(lp723
S'2015/07/25 [received]'
p724
aS'2015/08/24 [accepted]'
p725
aS'2015/09/20 [aheadofprint]'
p726
asS'OID'
p727
(lp728
S'NLM: NIHMS774869'
p729
aS'NLM: PMC4830262'
p730
asS'MH'
p731
(lp732
S'*Disease Outbreaks'
p733
aS'Ebola Vaccines/*immunology/*isolation & purification'
p734
aS'Ebolavirus/*immunology'
p735
aS'Hemorrhagic Fever, Ebola/*epidemiology/*prevention & control'
p736
aS'Humans'
p737
asS'EDAT'
p738
S'2015/10/16 06:00'
p739
sS'SO'
p740
S'Lancet Infect Dis. 2015 Oct;15(10):1134. doi: 10.1016/S1473-3099(15)00303-5. Epub 2015 Sep 20.'
p741
sS'SB'
p742
S'IM'
p743
sS'PMID'
p744
S'26461945'
p745
sS'PST'
p746
S'ppublish'
p747
stRp748
ag2
(g3
g4
(dp749
S'LID'
p750
S'10.1016/j.epidem.2015.04.001 [doi] S1755-4365(15)00049-3 [pii]'
p751
sS'STAT'
p752
S'MEDLINE'
p753
sS'DEP'
p754
S'20150427'
p755
sS'CI'
p756
(lp757
S'Copyright (c) 2015 The Authors. Published by Elsevier B.V. All rights reserved.'
p758
asS'DA'
p759
S'20150907'
p760
sS'AID'
p761
(lp762
S'S1755-4365(15)00049-3 [pii]'
p763
aS'10.1016/j.epidem.2015.04.001 [doi]'
p764
asS'CRDT'
p765
(lp766
S'2015/09/07 06:00'
p767
asS'DP'
p768
S'2015 Sep'
p769
sS'OWN'
p770
S'NLM'
p771
sS'PT'
p772
(lp773
S'Journal Article'
p774
aS'Research Support, N.I.H., Extramural'
p775
aS"Research Support, Non-U.S. Gov't"
p776
aS"Research Support, U.S. Gov't, Non-P.H.S."
p777
asS'LA'
p778
(lp779
S'eng'
p780
asS'DCOM'
p781
S'20160725'
p782
sS'JT'
p783
S'Epidemics'
p784
sS'FAU'
p785
(lp786
S'Birger, Ruthie'
p787
aS'Kouyos, Roger'
p788
aS'Dushoff, Jonathan'
p789
aS'Grenfell, Bryan'
p790
asS'TI'
p791
S'Modeling the effect of HIV coinfection on clearance and sustained virologic response during treatment for hepatitis C virus.'
p792
sS'PL'
p793
S'Netherlands'
p794
sS'PG'
p795
S'1-10'
p796
sS'JID'
p797
S'101484711'
p798
sS'AB'
p799
S'BACKGROUND: HIV/hepatitis C (HCV) coinfection is a major concern in global health today. Each pathogen can exacerbate the effects of the other and affect treatment outcomes. Understanding the within-host dynamics of these coinfecting pathogens is crucial, particularly in light of new, direct-acting antiviral agents (DAAs) for HCV treatment that are becoming available. METHODS AND FINDINGS: In this study, we construct a within-host mathematical model of HCV/HIV coinfection by adapting a previously published model of HCV monoinfection to include an immune system component in infection clearance. We explore the effect of HIV-coinfection on spontaneous HCV clearance and sustained virologic response (SVR) by building in decreased immune function with increased HIV viral load. Treatment is modeled by modifying HCV burst-size, and we use clinically-relevant parameter estimates. Our model replicates real-world patient outcomes; it outputs infected and uninfected target cell counts, and HCV viral load for varying treatment and coinfection scenarios. Increased HIV viral load and reduced CD4(+) count correlate with decreased spontaneous clearance and SVR chances. Treatment efficacy/duration combinations resulting in SVR are calculated for HIV-positive and negative patients, and crucially, we replicate the new findings that highly efficacious DAAs reduce treatment differences between HIV-positive and negative patients. However, we also find that if drug efficacy decays sufficiently over treatment course, SVR differences between HIV-positive and negative patients reappear. CONCLUSIONS: Our model shows theoretical evidence of the differing outcomes of HCV infection in cases where the immune system is compromised by HIV. Understanding what controls these outcomes is especially important with the advent of efficacious but often prohibitively expensive DAAs. Using a model to predict patient response can lend insight into optimal treatment design, both in helping to identify patients who might respond well to treatment and in helping to identify treatment pathways and pitfalls.'
p800
sS'AD'
p801
S'Department of Ecology and Evolutionary Biology, Princeton University, 106a Guyot Hall, Princeton, NJ 08544, USA. Electronic address: rbirger@princeton.edu. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Ramistr. 100, CH-8091 Zurich, Switzerland; Institute of Medical Virology, University of Zurich, Zurich, Switzerland. Department of Biology, McMaster University, 1280 Main St. West, Hamilton, Ontario L8S 4K1, Canada. Department of Ecology and Evolutionary Biology, Princeton University, 106a Guyot Hall, Princeton, NJ 08544, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA.'
p802
sS'VI'
p803
S'12'
p804
sS'IS'
p805
S'1878-0067 (Electronic) 1878-0067 (Linking)'
p806
sS'AU'
p807
(lp808
S'Birger R'
p809
aS'Kouyos R'
p810
aS'Dushoff J'
p811
aS'Grenfell B'
p812
asS'MHDA'
p813
S'2016/07/28 06:00'
p814
sS'PHST'
p815
(lp816
S'2014/05/09 [received]'
p817
aS'2015/04/18 [revised]'
p818
aS'2015/04/20 [accepted]'
p819
aS'2015/04/27 [aheadofprint]'
p820
asS'OT'
p821
(lp822
S'Antiviral therapy'
p823
aS'Coinfection'
p824
aS'HCV-HIV'
p825
aS'Immune response'
p826
aS'Mathematical model'
p827
asS'OTO'
p828
(lp829
S'NOTNLM'
p830
asS'MH'
p831
(lp832
S'HIV Infections/*complications/drug therapy'
p833
aS'Hepatitis C/complications/drug therapy/immunology/*virology'
p834
aS'Humans'
p835
aS'*Models, Theoretical'
p836
asS'EDAT'
p837
S'2015/09/08 06:00'
p838
sS'SO'
p839
S'Epidemics. 2015 Sep;12:1-10. doi: 10.1016/j.epidem.2015.04.001. Epub 2015 Apr 27.'
p840
sS'SB'
p841
S'IM'
p842
sS'PMID'
p843
S'26342237'
p844
sS'TA'
p845
S'Epidemics'
p846
sS'PST'
p847
S'ppublish'
p848
stRp849
ag2
(g3
g4
(dp850
S'LID'
p851
S'10.1186/s12879-015-0961-5 [doi]'
p852
sS'STAT'
p853
S'MEDLINE'
p854
sS'DEP'
p855
S'20150630'
p856
sS'DA'
p857
S'20150630'
p858
sS'AID'
p859
(lp860
S'10.1186/s12879-015-0961-5 [doi]'
p861
aS'10.1186/s12879-015-0961-5 [pii]'
p862
asS'CRDT'
p863
(lp864
S'2015/07/01 06:00'
p865
asS'DP'
p866
S'2015'
p867
sS'AD'
p868
S'McMaster University, Hamilton, ON, Canada, Austin, TX, USA. david.champredon@gmail.com. Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX, USA. The South African DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa. International Centre for Reproductive Health (ICRH), Ghent University, Ghent, Belgium. McMaster University, Hamilton, ON, Canada, Austin, TX, USA. McMaster University, Hamilton, ON, Canada, Austin, TX, USA. McMaster University, Hamilton, ON, Canada, Austin, TX, USA. McMaster University, Hamilton, ON, Canada, Austin, TX, USA.'
p869
sS'OWN'
p870
S'NLM'
p871
sS'PT'
p872
(lp873
S'Journal Article'
p874
aS'Meta-Analysis'
p875
aS"Research Support, Non-U.S. Gov't"
p876
aS'Review'
p877
asS'LA'
p878
(lp879
S'eng'
p880
asS'DCOM'
p881
S'20160502'
p882
sS'JT'
p883
S'BMC infectious diseases'
p884
sS'LR'
p885
S'20150702'
p886
sS'FAU'
p887
(lp888
S'Champredon, David'
p889
aS'Bellan, Steven E'
p890
aS'Delva, Wim'
p891
aS'Hunt, Spencer'
p892
aS'Shi, Chyun-Fung'
p893
aS'Smieja, Marek'
p894
aS'Dushoff, Jonathan'
p895
asS'TI'
p896
S'The effect of sexually transmitted co-infections on HIV viral load amongst individuals on antiretroviral therapy: a systematic review and meta-analysis.'
p897
sS'PL'
p898
S'England'
p899
sS'PG'
p900
S'249'
p901
sS'JID'
p902
S'100968551'
p903
sS'AB'
p904
S'BACKGROUND: Antiretroviral therapy (ART) markedly reduces HIV transmission, and testing and treatment programs have been advocated as a method for decreasing transmission at the population level. Little is known, however, about the extent to which sexually transmitted infections (STIs), which increase the HIV infectiousness of untreated individuals, may decrease the effectiveness of treatment as prevention. METHODS: We searched major bibliographic databases to August 12(th), 2014 and identified studies reporting differences in HIV transmission rate or in viral load between individuals on ART who either were or were not co-infected with another STI. We used hierarchical Bayesian models to estimate viral load differences between individuals with and without STI co-infections. RESULTS: The search strategy retrieved 1630 unique citations of which 14 studies (reporting on 4607 HIV viral load measurements from 2835 unique individuals) met the inclusion criteria. We did not find any suitable studies that estimated transmission rates directly in both groups. Our meta-analysis of HIV viral load measurements among treated individuals did not find a statistically significant effect of STI co-infection; viral loads were, on average, 0.11 log10 (95% CI -0.62 to 0.83) higher among co-infected versus non-co-infected individuals. CONCLUSIONS: Direct evidence about the effects of STI co-infection on transmission from individuals on ART is very limited. Available data suggests that the average effect of STI co-infection on HIV viral load in individuals on ART is less than 1 log10 difference, and thus unlikely to decrease the effectiveness of treatment as prevention. However, there is not enough data to rule out the possibility that particular STIs pose a larger threat.'
p905
sS'GR'
p906
(lp907
S'Canadian Institutes of Health Research/Canada'
p908
asS'VI'
p909
S'15'
p910
sS'IS'
p911
S'1471-2334 (Electronic) 1471-2334 (Linking)'
p912
sS'PMC'
p913
S'PMC4486691'
p914
sS'AU'
p915
(lp916
S'Champredon D'
p917
aS'Bellan SE'
p918
aS'Delva W'
p919
aS'Hunt S'
p920
aS'Shi CF'
p921
aS'Smieja M'
p922
aS'Dushoff J'
p923
asS'MHDA'
p924
S'2016/05/03 06:00'
p925
sS'PHST'
p926
(lp927
S'2014/12/11 [received]'
p928
aS'2015/05/21 [accepted]'
p929
aS'2015/06/30 [aheadofprint]'
p930
asS'OID'
p931
(lp932
S'NLM: PMC4486691'
p933
asS'MH'
p934
(lp935
S'*Coinfection'
p936
aS'Female'
p937
aS'HIV Infections/complications/drug therapy/*epidemiology'
p938
aS'Humans'
p939
aS'Male'
p940
aS'Sexually Transmitted Diseases/complications/*epidemiology'
p941
aS'Viral Load'
p942
asS'EDAT'
p943
S'2015/07/01 06:00'
p944
sS'SO'
p945
S'BMC Infect Dis. 2015 Jun 30;15:249. doi: 10.1186/s12879-015-0961-5.'
p946
sS'SB'
p947
S'IM'
p948
sS'PMID'
p949
S'26123030'
p950
sS'TA'
p951
S'BMC Infect Dis'
p952
sS'PST'
p953
S'epublish'
p954
stRp955
ag2
(g3
g4
(dp956
S'LID'
p957
S'10.1371/journal.pntd.0003786 [doi]'
p958
sS'STAT'
p959
S'PubMed-not-MEDLINE'
p960
sS'DEP'
p961
S'20150511'
p962
sS'DA'
p963
S'20150512'
p964
sS'AID'
p965
(lp966
S'10.1371/journal.pntd.0003786 [doi]'
p967
aS'PNTD-D-15-00703 [pii]'
p968
asS'CRDT'
p969
(lp970
S'2015/05/12 06:00'
p971
asS'DP'
p972
S'2015 May'
p973
sS'OWN'
p974
S'NLM'
p975
sS'PT'
p976
(lp977
S'Published Erratum'
p978
asS'LA'
p979
(lp980
S'eng'
p981
asS'FAU'
p982
(lp983
S'Hampson, Katie'
p984
aS'Coudeville, Laurent'
p985
aS'Lembo, Tiziana'
p986
aS'Sambo, Maganga'
p987
aS'Kieffer, Alexia'
p988
aS'Attlan, Michael'
p989
aS'Barrat, Jacques'
p990
aS'Blanton, Jesse D'
p991
aS'Briggs, Deborah J'
p992
aS'Cleaveland, Sarah'
p993
aS'Costa, Peter'
p994
aS'Freuling, Conrad M'
p995
aS'Hiby, Elly'
p996
aS'Knopf, Lea'
p997
aS'Leanes, Fernando'
p998
aS'Meslin, Francois-Xavier'
p999
aS'Metlin, Artem'
p1000
aS'Miranda, Mary Elizabeth'
p1001
aS'Muller, Thomas'
p1002
aS'Nel, Louis H'
p1003
aS'Recuenco, Sergio'
p1004
aS'Rupprecht, Charles E'
p1005
aS'Schumacher, Carolin'
p1006
aS'Taylor, Louise'
p1007
aS'Antonio, Marco'
p1008
aS'Vigilato, Natal'
p1009
aS'Zinsstag, Jakob'
p1010
aS'Dushoff, Jonathan'
p1011
asS'JT'
p1012
S'PLoS neglected tropical diseases'
p1013
sS'LR'
p1014
S'20150706'
p1015
sS'PG'
p1016
S'e0003786'
p1017
sS'TI'
p1018
S'Correction: Estimating the global burden of endemic canine rabies.'
p1019
sS'IS'
p1020
S'1935-2735 (Electronic) 1935-2727 (Linking)'
p1021
sS'PL'
p1022
S'United States'
p1023
sS'TA'
p1024
S'PLoS Negl Trop Dis'
p1025
sS'JID'
p1026
S'101291488'
p1027
sS'CN'
p1028
(lp1029
S'Global Alliance for Rabies Control Partners for Rabies Prevention'
p1030
asS'IP'
p1031
S'5'
sS'EFR'
p1032
(lp1033
S'PLoS Negl Trop Dis. 2015 Apr;9(4):e0003709. PMID: 25881058'
p1034
asS'PMC'
p1035
S'PMC4427501'
p1036
sS'DCOM'
p1037
S'20150713'
p1038
sS'AU'
p1039
(lp1040
S'Hampson K'
p1041
aS'Coudeville L'
p1042
aS'Lembo T'
p1043
aS'Sambo M'
p1044
aS'Kieffer A'
p1045
aS'Attlan M'
p1046
aS'Barrat J'
p1047
aS'Blanton JD'
p1048
aS'Briggs DJ'
p1049
aS'Cleaveland S'
p1050
aS'Costa P'
p1051
aS'Freuling CM'
p1052
aS'Hiby E'
p1053
aS'Knopf L'
p1054
aS'Leanes F'
p1055
aS'Meslin FX'
p1056
aS'Metlin A'
p1057
aS'Miranda ME'
p1058
aS'Muller T'
p1059
aS'Nel LH'
p1060
aS'Recuenco S'
p1061
aS'Rupprecht CE'
p1062
aS'Schumacher C'
p1063
aS'Taylor L'
p1064
aS'Antonio M'
p1065
aS'Vigilato N'
p1066
aS'Zinsstag J'
p1067
aS'Dushoff J'
p1068
asS'VI'
p1069
S'9'
sS'MHDA'
p1070
S'2015/05/12 06:01'
p1071
sS'PHST'
p1072
(lp1073
S'2015/05 [ecollection]'
p1074
aS'2015/05/11 [epublish]'
p1075
asS'OID'
p1076
(lp1077
S'NLM: PMC4427501'
p1078
asS'EDAT'
p1079
S'2015/05/12 06:00'
p1080
sS'SO'
p1081
S'PLoS Negl Trop Dis. 2015 May 11;9(5):e0003786. doi: 10.1371/journal.pntd.0003786. eCollection 2015 May.'
p1082
sS'PMID'
p1083
S'25961848'
p1084
sS'PST'
p1085
S'epublish'
p1086
stRp1087
ag2
(g3
g4
(dp1088
S'LID'
p1089
S'10.1016/S1473-3099(15)70139-8 [doi] S1473-3099(15)70139-8 [pii]'
p1090
sS'STAT'
p1091
S'MEDLINE'
p1092
sS'DEP'
p1093
S'20150414'
p1094
sS'MID'
p1095
(lp1096
S'NIHMS754200'
p1097
asS'DA'
p1098
S'20150526'
p1099
sS'AID'
p1100
(lp1101
S'S1473-3099(15)70139-8 [pii]'
p1102
aS'10.1016/S1473-3099(15)70139-8 [doi]'
p1103
asS'CRDT'
p1104
(lp1105
S'2015/04/19 06:00'
p1106
asS'DP'
p1107
S'2015 Jun'
p1108
sS'AD'
p1109
S'Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX, USA. Electronic address: steve.bellan@gmail.com. Department of Biology, University of Florida, Gainesville, FL, USA; Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA. Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA. School of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada. Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA. Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, Yale University, New Haven, CT, USA. Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, Yale University, New Haven, CT, USA; Department of Ecology and Evolution, Yale University, New Haven, CT, USA. Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia; Division of Preparedness and Emerging Infections, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA; IHRC Inc, Atlanta, GA, USA. Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA; Rollins School of Public Health, Emory University, Atlanta, GA, USA. Francis I Proctor Foundation, University of California, San Francisco, CA, USA; Department of Ophthalmology, University of California, San Francisco, CA, USA; Department of Epidemiology & Biostatistics, University of California, San Francisco, CA, USA. Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA; The Santa Fe Institute, Santa Fe, NM, USA. Department of Biology, McMaster University, Hamilton, ON, Canada.'
p1110
sS'OWN'
p1111
S'NLM'
p1112
sS'PT'
p1113
(lp1114
S'Journal Article'
p1115
aS'Research Support, N.I.H., Extramural'
p1116
aS"Research Support, Non-U.S. Gov't"
p1117
aS"Research Support, U.S. Gov't, Non-P.H.S."
p1118
asS'LA'
p1119
(lp1120
S'eng'
p1121
asS'FAU'
p1122
(lp1123
S'Bellan, Steven E'
p1124
aS'Pulliam, Juliet R C'
p1125
aS'Pearson, Carl A B'
p1126
aS'Champredon, David'
p1127
aS'Fox, Spencer J'
p1128
aS'Skrip, Laura'
p1129
aS'Galvani, Alison P'
p1130
aS'Gambhir, Manoj'
p1131
aS'Lopman, Ben A'
p1132
aS'Porco, Travis C'
p1133
aS'Meyers, Lauren Ancel'
p1134
aS'Dushoff, Jonathan'
p1135
asS'JT'
p1136
S'The Lancet. Infectious diseases'
p1137
sS'LR'
p1138
S'20160406'
p1139
sS'PG'
p1140
S'703-10'
p1141
sS'TI'
p1142
S'Statistical power and validity of Ebola vaccine trials in Sierra Leone: a simulation study of trial design and analysis.'
p1143
sS'RN'
p1144
(lp1145
S'0 (Ebola Vaccines)'
p1146
asS'PL'
p1147
S'United States'
p1148
sS'TA'
p1149
S'Lancet Infect Dis'
p1150
sS'JID'
p1151
S'101130150'
p1152
sS'AB'
p1153
S"BACKGROUND: Safe and effective vaccines could help to end the ongoing Ebola virus disease epidemic in parts of west Africa, and mitigate future outbreaks of the virus. We assess the statistical validity and power of randomised controlled trial (RCT) and stepped-wedge cluster trial (SWCT) designs in Sierra Leone, where the incidence of Ebola virus disease is spatiotemporally heterogeneous, and is decreasing rapidly. METHODS: We projected district-level Ebola virus disease incidence for the next 6 months, using a stochastic model fitted to data from Sierra Leone. We then simulated RCT and SWCT designs in trial populations comprising geographically distinct clusters at high risk, taking into account realistic logistical constraints, and both individual-level and cluster-level variations in risk. We assessed false-positive rates and power for parametric and non-parametric analyses of simulated trial data, across a range of vaccine efficacies and trial start dates. FINDINGS: For an SWCT, regional variation in Ebola virus disease incidence trends produced increased false-positive rates (up to 0.15 at alpha=0.05) under standard statistical models, but not when analysed by a permutation test, whereas analyses of RCTs remained statistically valid under all models. With the assumption of a 6-month trial starting on Feb 18, 2015, we estimate the power to detect a 90% effective vaccine to be between 49% and 89% for an RCT, and between 6% and 26% for an SWCT, depending on the Ebola virus disease incidence within the trial population. We estimate that a 1-month delay in trial initiation will reduce the power of the RCT by 20% and that of the SWCT by 49%. INTERPRETATION: Spatiotemporal variation in infection risk undermines the statistical power of the SWCT. This variation also undercuts the SWCT's expected ethical advantages over the RCT, because an RCT, but not an SWCT, can prioritise vaccination of high-risk clusters. FUNDING: US National Institutes of Health, US National Science Foundation, and Canadian Institutes of Health Research."
p1154
sS'GR'
p1155
(lp1156
S'R25 GM102149/GM/NIGMS NIH HHS/United States'
p1157
aS'R25GM102149/GM/NIGMS NIH HHS/United States'
p1158
aS'U01 GM087719/GM/NIGMS NIH HHS/United States'
p1159
aS'U01 GM087728/GM/NIGMS NIH HHS/United States'
p1160
aS'U01GM087719/GM/NIGMS NIH HHS/United States'
p1161
aS'U01GM087728/GM/NIGMS NIH HHS/United States'
p1162
aS'Canadian Institutes of Health Research/Canada'
p1163
asS'IP'
p1164
S'6'
sS'IS'
p1165
S'1474-4457 (Electronic) 1473-3099 (Linking)'
p1166
sS'PMC'
p1167
S'PMC4815262'
p1168
sS'DCOM'
p1169
S'20150803'
p1170
sS'CIN'
p1171
(lp1172
S'Lancet Infect Dis. 2015 Jun;15(6):624-6. PMID: 25886800'
p1173
asS'AU'
p1174
(lp1175
S'Bellan SE'
p1176
aS'Pulliam JR'
p1177
aS'Pearson CA'
p1178
aS'Champredon D'
p1179
aS'Fox SJ'
p1180
aS'Skrip L'
p1181
aS'Galvani AP'
p1182
aS'Gambhir M'
p1183
aS'Lopman BA'
p1184
aS'Porco TC'
p1185
aS'Meyers LA'
p1186
aS'Dushoff J'
p1187
asS'VI'
p1188
S'15'
p1189
sS'MHDA'
p1190
S'2015/08/04 06:00'
p1191
sS'PHST'
p1192
(lp1193
S'2015/04/14 [aheadofprint]'
p1194
asS'OID'
p1195
(lp1196
S'NLM: NIHMS754200'
p1197
aS'NLM: PMC4815262'
p1198
asS'CI'
p1199
(lp1200
S'Copyright (c) 2015 Elsevier Ltd. All rights reserved.'
p1201
asS'MH'
p1202
(lp1203
S'Biomedical Research/*methods'
p1204
aS'Biostatistics/*methods'
p1205
aS'Ebola Vaccines/*administration & dosage/*immunology'
p1206
aS'Hemorrhagic Fever, Ebola/immunology/*prevention & control'
p1207
aS'Humans'
p1208
aS'*Randomized Controlled Trials as Topic'
p1209
aS'Sierra Leone'
p1210
asS'EDAT'
p1211
S'2015/04/19 06:00'
p1212
sS'SO'
p1213
S'Lancet Infect Dis. 2015 Jun;15(6):703-10. doi: 10.1016/S1473-3099(15)70139-8. Epub 2015 Apr 14.'
p1214
sS'SB'
p1215
S'IM'
p1216
sS'PMID'
p1217
S'25886798'
p1218
sS'PST'
p1219
S'ppublish'
p1220
stRp1221
ag2
(g3
g4
(dp1222
S'LID'
p1223
S'10.1371/journal.pntd.0003709 [doi]'
p1224
sS'STAT'
p1225
S'MEDLINE'
p1226
sS'DEP'
p1227
S'20150416'
p1228
sS'CIN'
p1229
(lp1230
S'BMJ. 2015;350:h2189. PMID: 25908640'
p1231
asS'DA'
p1232
S'20150417'
p1233
sS'AID'
p1234
(lp1235
S'10.1371/journal.pntd.0003709 [doi]'
p1236
aS'PNTD-D-14-00532 [pii]'
p1237
asS'CRDT'
p1238
(lp1239
S'2015/04/17 06:00'
p1240
asS'DP'
p1241
S'2015 Apr'
p1242
sS'EIN'
p1243
(lp1244
S'PLoS Negl Trop Dis. 2015 May;9(5):e0003786. PMID: 25961848'
p1245
asS'OWN'
p1246
S'NLM'
p1247
sS'PT'
p1248
(lp1249
S'Journal Article'
p1250
aS"Research Support, Non-U.S. Gov't"
p1251
asS'LA'
p1252
(lp1253
S'eng'
p1254
asS'FAU'
p1255
(lp1256
S'Hampson, Katie'
p1257
aS'Coudeville, Laurent'
p1258
aS'Lembo, Tiziana'
p1259
aS'Sambo, Maganga'
p1260
aS'Kieffer, Alexia'
p1261
aS'Attlan, Michael'
p1262
aS'Barrat, Jacques'
p1263
aS'Blanton, Jesse D'
p1264
aS'Briggs, Deborah J'
p1265
aS'Cleaveland, Sarah'
p1266
aS'Costa, Peter'
p1267
aS'Freuling, Conrad M'
p1268
aS'Hiby, Elly'
p1269
aS'Knopf, Lea'
p1270
aS'Leanes, Fernando'
p1271
aS'Meslin, Francois-Xavier'
p1272
aS'Metlin, Artem'
p1273
aS'Miranda, Mary Elizabeth'
p1274
aS'Muller, Thomas'
p1275
aS'Nel, Louis H'
p1276
aS'Recuenco, Sergio'
p1277
aS'Rupprecht, Charles E'
p1278
aS'Schumacher, Carolin'
p1279
aS'Taylor, Louise'
p1280
aS'Vigilato, Marco Antonio Natal'
p1281
aS'Zinsstag, Jakob'
p1282
aS'Dushoff, Jonathan'
p1283
asS'JT'
p1284
S'PLoS neglected tropical diseases'
p1285
sS'LR'
p1286
S'20150422'
p1287
sS'PG'
p1288
S'e0003709'
p1289
sS'TI'
p1290
S'Estimating the global burden of endemic canine rabies.'
p1291
sS'RN'
p1292
(lp1293
S'0 (Rabies Vaccines)'
p1294
asS'PL'
p1295
S'United States'
p1296
sS'TA'
p1297
S'PLoS Negl Trop Dis'
p1298
sS'JID'
p1299
S'101291488'
p1300
sS'AB'
p1301
S'BACKGROUND: Rabies is a notoriously underreported and neglected disease of low-income countries. This study aims to estimate the public health and economic burden of rabies circulating in domestic dog populations, globally and on a country-by-country basis, allowing an objective assessment of how much this preventable disease costs endemic countries. METHODOLOGY/PRINCIPAL FINDINGS: We established relationships between rabies mortality and rabies prevention and control measures, which we incorporated into a model framework. We used data derived from extensive literature searches and questionnaires on disease incidence, control interventions and preventative measures within this framework to estimate the disease burden. The burden of rabies impacts on public health sector budgets, local communities and livestock economies, with the highest risk of rabies in the poorest regions of the world. This study estimates that globally canine rabies causes approximately 59,000 (95% Confidence Intervals: 25-159,000) human deaths, over 3.7 million (95% CIs: 1.6-10.4 million) disability-adjusted life years (DALYs) and 8.6 billion USD (95% CIs: 2.9-21.5 billion) economic losses annually. The largest component of the economic burden is due to premature death (55%), followed by direct costs of post-exposure prophylaxis (PEP, 20%) and lost income whilst seeking PEP (15.5%), with only limited costs to the veterinary sector due to dog vaccination (1.5%), and additional costs to communities from livestock losses (6%). CONCLUSIONS/SIGNIFICANCE: This study demonstrates that investment in dog vaccination, the single most effective way of reducing the disease burden, has been inadequate and that the availability and affordability of PEP needs improving. Collaborative investments by medical and veterinary sectors could dramatically reduce the current large, and unnecessary, burden of rabies on affected communities. Improved surveillance is needed to reduce uncertainty in burden estimates and to monitor the impacts of control efforts.'
p1302
sS'AD'
p1303
S'Boyd Orr Centre for Population and Ecosystem Health, Institute for Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow, United Kingdom. Sanofi Pasteur, Lyon, France. Boyd Orr Centre for Population and Ecosystem Health, Institute for Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow, United Kingdom. Ifakara Health Institute, Ifakara, Tanzania. Sanofi Pasteur, Lyon, France. Sanofi Pasteur, Lyon, France. ANSES-French Agency for Food, Environmental and Occupational Health and Safety, Rabies and Wildlife laboratory of Nancy, Atton, France. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America. Global Alliance for Rabies Control, Manhattan, Kansas, United States of America. Boyd Orr Centre for Population and Ecosystem Health, Institute for Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow, United Kingdom. Global Alliance for Rabies Control, Manhattan, Kansas, United States of America. Friedrich-Loeffler-Institute-Federal Research Institute for Animal Health, Greifswald-Insel Riems, Germany. International Companion Animal Management Coalition, Cambridge, United Kingdom. Global Alliance for Rabies Control, Manhattan, Kansas, United States of America. Pan-American Health Organization, Rio de Janeiro, Brazil. Independent rabies scientist, Geneva, Switzerland. Institut Pasteur in Cambodia, Phnom Penh, Cambodia. Global Alliance for Rabies Control, Manhattan, Kansas, United States of America. Friedrich-Loeffler-Institute-Federal Research Institute for Animal Health, Greifswald-Insel Riems, Germany. University of Pretoria, Pretoria, South Africa. Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America. Global Alliance for Rabies Control, Manhattan, Kansas, United States of America; Ross University School of Veterinary Medicine, Basseterre, St. Kitts, West Indes. Merial, Lyon, France. Global Alliance for Rabies Control, Manhattan, Kansas, United States of America. Pan-American Health Organization, Rio de Janeiro, Brazil. Swiss Tropical and Public Health Institute, Basel, Switzerland. McMaster University, Hamilton, Ontario, Canada.'
p1304
sS'CN'
p1305
(lp1306
S'Global Alliance for Rabies Control Partners for Rabies Prevention'
p1307
asS'IP'
p1308
S'4'
sS'IS'
p1309
S'1935-2735 (Electronic) 1935-2727 (Linking)'
p1310
sS'PMC'
p1311
S'PMC4400070'
p1312
sS'DCOM'
p1313
S'20160121'
p1314
sS'AU'
p1315
(lp1316
S'Hampson K'
p1317
aS'Coudeville L'
p1318
aS'Lembo T'
p1319
aS'Sambo M'
p1320
aS'Kieffer A'
p1321
aS'Attlan M'
p1322
aS'Barrat J'
p1323
aS'Blanton JD'
p1324
aS'Briggs DJ'
p1325
aS'Cleaveland S'
p1326
aS'Costa P'
p1327
aS'Freuling CM'
p1328
aS'Hiby E'
p1329
aS'Knopf L'
p1330
aS'Leanes F'
p1331
aS'Meslin FX'
p1332
aS'Metlin A'
p1333
aS'Miranda ME'
p1334
aS'Muller T'
p1335
aS'Nel LH'
p1336
aS'Recuenco S'
p1337
aS'Rupprecht CE'
p1338
aS'Schumacher C'
p1339
aS'Taylor L'
p1340
aS'Vigilato MA'
p1341
aS'Zinsstag J'
p1342
aS'Dushoff J'
p1343
asS'VI'
p1344
S'9'
sS'MHDA'
p1345
S'2016/01/23 06:00'
p1346
sS'PHST'
p1347
(lp1348
S'2015/04 [ecollection]'
p1349
aS'2014/03/31 [received]'
p1350
aS'2015/03/18 [accepted]'
p1351
aS'2015/04/16 [epublish]'
p1352
asS'OID'
p1353
(lp1354
S'NLM: PMC4400070'
p1355
asS'MH'
p1356
(lp1357
S'Animals'
p1358
aS'Dog Diseases/epidemiology/prevention & control/*virology'
p1359
aS'Dogs'
p1360
aS'Endemic Diseases/*veterinary'
p1361
aS'Global Health'
p1362
aS'Humans'
p1363
aS'Post-Exposure Prophylaxis/economics'
p1364
aS'Public Health/economics'
p1365
aS'Rabies/economics/epidemiology/prevention & control/*veterinary'
p1366
aS'Rabies Vaccines/economics/*immunology'
p1367
aS'Vaccination/economics'
p1368
asS'EDAT'
p1369
S'2015/04/17 06:00'
p1370
sS'GR'
p1371
(lp1372
S'095787/Z/11/Z/Wellcome Trust/United Kingdom'
p1373
asS'SO'
p1374
S'PLoS Negl Trop Dis. 2015 Apr 16;9(4):e0003709. doi: 10.1371/journal.pntd.0003709. eCollection 2015 Apr.'
p1375
sS'SB'
p1376
S'IM'
p1377
sS'PMID'
p1378
S'25881058'
p1379
sS'PST'
p1380
S'epublish'
p1381
stRp1382
ag2
(g3
g4
(dp1383
S'LID'
p1384
S'10.1371/journal.pmed.1001801 [doi]'
p1385
sS'STAT'
p1386
S'MEDLINE'
p1387
sS'DEP'
p1388
S'20150317'
p1389
sS'CIN'
p1390
(lp1391
S'PLoS Med. 2015 Mar;12(3):e1001803. PMID: 25781617'
p1392
asS'DA'
p1393
S'20150318'
p1394
sS'AID'
p1395
(lp1396
S'10.1371/journal.pmed.1001801 [doi]'
p1397
aS'PMEDICINE-D-14-01583 [pii]'
p1398
asS'CRDT'
p1399
(lp1400
S'2015/03/18 06:00'
p1401
asS'DP'
p1402
S'2015 Mar'
p1403
sS'AD'
p1404
S'Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America. Department of Biology, McMaster University, Hamilton, Ontario, Canada. Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America; Department of Ecology and Evolution, Yale University, New Haven, Connecticut, United States of America. Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America; The Santa Fe Institute, Santa Fe, New Mexico, United States of America.'
p1405
sS'OWN'
p1406
S'NLM'
p1407
sS'PT'
p1408
(lp1409
S'Journal Article'
p1410
aS'Research Support, N.I.H., Extramural'
p1411
aS"Research Support, Non-U.S. Gov't"
p1412
asS'LA'
p1413
(lp1414
S'eng'
p1415
asS'FAU'
p1416
(lp1417
S'Bellan, Steve E'
p1418
aS'Dushoff, Jonathan'
p1419
aS'Galvani, Alison P'
p1420
aS'Meyers, Lauren Ancel'
p1421
asS'JT'
p1422
S'PLoS medicine'
p1423
sS'LR'
p1424
S'20150415'
p1425
sS'PG'
p1426
S'e1001801'
p1427
sS'TI'
p1428
S'Reassessment of HIV-1 acute phase infectivity: accounting for heterogeneity and study design with simulated cohorts.'
p1429
sS'PL'
p1430
S'United States'
p1431
sS'TA'
p1432
S'PLoS Med'
p1433
sS'JID'
p1434
S'101231360'
p1435
sS'AB'
p1436
S"BACKGROUND: The infectivity of the HIV-1 acute phase has been directly measured only once, from a retrospectively identified cohort of serodiscordant heterosexual couples in Rakai, Uganda. Analyses of this cohort underlie the widespread view that the acute phase is highly infectious, even more so than would be predicted from its elevated viral load, and that transmission occurring shortly after infection may therefore compromise interventions that rely on diagnosis and treatment, such as antiretroviral treatment as prevention (TasP). Here, we re-estimate the duration and relative infectivity of the acute phase, while accounting for several possible sources of bias in published estimates, including the retrospective cohort exclusion criteria and unmeasured heterogeneity in risk. METHODS AND FINDINGS: We estimated acute phase infectivity using two approaches. First, we combined viral load trajectories and viral load-infectivity relationships to estimate infectivity trajectories over the course of infection, under the assumption that elevated acute phase infectivity is caused by elevated viral load alone. Second, we estimated the relative hazard of transmission during the acute phase versus the chronic phase (RHacute) and the acute phase duration (dacute) by fitting a couples transmission model to the Rakai retrospective cohort using approximate Bayesian computation. Our model fit the data well and accounted for characteristics overlooked by previous analyses, including individual heterogeneity in infectiousness and susceptibility and the retrospective cohort's exclusion of couples that were recorded as serodiscordant only once before being censored by loss to follow-up, couple dissolution, or study termination. Finally, we replicated two highly cited analyses of the Rakai data on simulated data to identify biases underlying the discrepancies between previous estimates and our own. From the Rakai data, we estimated RHacute = 5.3 (95% credibility interval [95% CrI]: 0.79-57) and dacute = 1.7 mo (95% CrI: 0.55-6.8). The wide credibility intervals reflect an inability to distinguish a long, mildly infectious acute phase from a short, highly infectious acute phase, given the 10-mo Rakai observation intervals. The total additional risk, measured as excess hazard-months attributable to the acute phase (EHMacute) can be estimated more precisely: EHMacute = (RHacute - 1) x dacute, and should be interpreted with respect to the 120 hazard-months generated by a constant untreated chronic phase infectivity over 10 y of infection. From the Rakai data, we estimated that EHMacute = 8.4 (95% CrI: -0.27 to 64). This estimate is considerably lower than previously published estimates, and consistent with our independent estimate from viral load trajectories, 5.6 (95% confidence interval: 3.3-9.1). We found that previous overestimates likely stemmed from failure to account for risk heterogeneity and bias resulting from the retrospective cohort study design. Our results reflect the interaction between the retrospective cohort exclusion criteria and high (47%) rates of censorship amongst incident serodiscordant couples in the Rakai study due to loss to follow-up, couple dissolution, or study termination. We estimated excess physiological infectivity during the acute phase from couples data, but not the proportion of transmission attributable to the acute phase, which would require data on the broader population's sexual network structure. CONCLUSIONS: Previous EHMacute estimates relying on the Rakai retrospective cohort data range from 31 to 141. Our results indicate that these are substantial overestimates of HIV-1 acute phase infectivity, biased by unmodeled heterogeneity in transmission rates between couples and by inconsistent censoring. Elevated acute phase infectivity is therefore less likely to undermine TasP interventions than previously thought. Heterogeneity in infectiousness and susceptibility may still play an important role in intervention success and deserves attention in future analyses."
p1437
sS'GR'
p1438
(lp1439
S'R25 GM102149/GM/NIGMS NIH HHS/United States'
p1440
aS'R25GM102149/GM/NIGMS NIH HHS/United States'
p1441
aS'U01 GM087719/GM/NIGMS NIH HHS/United States'
p1442
aS'U01GM087719/GM/NIGMS NIH HHS/United States'
p1443
asS'IP'
p1444
S'3'
sS'IS'
p1445
S'1549-1676 (Electronic) 1549-1277 (Linking)'
p1446
sS'PMC'
p1447
S'PMC4363602'
p1448
sS'DCOM'
p1449
S'20160113'
p1450
sS'AU'
p1451
(lp1452
S'Bellan SE'
p1453
aS'Dushoff J'
p1454
aS'Galvani AP'
p1455
aS'Meyers LA'
p1456
asS'VI'
p1457
S'12'
p1458
sS'MHDA'
p1459
S'2016/01/14 06:00'
p1460
sS'PHST'
p1461
(lp1462
S'2015/03 [ecollection]'
p1463
aS'2014/05/13 [received]'
p1464
aS'2015/02/04 [accepted]'
p1465
aS'2015/03/17 [epublish]'
p1466
asS'OID'
p1467
(lp1468
S'NLM: PMC4363602'
p1469
asS'MH'
p1470
(lp1471
S'Acute Disease'
p1472
aS'*Acute-Phase Reaction'
p1473
aS'Bayes Theorem'
p1474
aS'*Bias (Epidemiology)'
p1475
aS'Cohort Studies'
p1476
aS'Computer Simulation'
p1477
aS'Family Characteristics'
p1478
aS'HIV Infections/pathology/*transmission/virology'
p1479
aS'HIV Seropositivity/virology'
p1480
aS'*HIV-1'
p1481
aS'Heterosexuality'
p1482
aS'Humans'
p1483
aS'Patient Selection'
p1484
aS'*Research Design/statistics & numerical data'
p1485
aS'Retrospective Studies'
p1486
aS'Risk'
p1487
aS'Sexual Behavior'
p1488
aS'*Sexual Partners'
p1489
aS'Uganda'
p1490
aS'*Viral Load'
p1491
asS'EDAT'
p1492
S'2015/03/18 06:00'
p1493
sS'SO'
p1494
S'PLoS Med. 2015 Mar 17;12(3):e1001801. doi: 10.1371/journal.pmed.1001801. eCollection 2015 Mar.'
p1495
sS'SB'
p1496
S'IM'
p1497
sS'PMID'
p1498
S'25781323'
p1499
sS'PST'
p1500
S'epublish'
p1501
stRp1502
ag2
(g3
g4
(dp1503
S'LID'
p1504
S'10.1038/srep08751 [doi]'
p1505
sS'STAT'
p1506
S'MEDLINE'
p1507
sS'DEP'
p1508
S'20150304'
p1509
sS'DA'
p1510
S'20150304'
p1511
sS'AID'
p1512
(lp1513
S'srep08751 [pii]'
p1514
aS'10.1038/srep08751 [doi]'
p1515
asS'CRDT'
p1516
(lp1517
S'2015/03/05 06:00'
p1518
asS'DP'
p1519
S'2015'
p1520
sS'OWN'
p1521
S'NLM'
p1522
sS'PT'
p1523
(lp1524
S'Journal Article'
p1525
aS"Research Support, Non-U.S. Gov't"
p1526
asS'LA'
p1527
(lp1528
S'eng'
p1529
asS'DCOM'
p1530
S'20160401'
p1531
sS'JT'
p1532
S'Scientific reports'
p1533
sS'LR'
p1534
S'20150311'
p1535
sS'FAU'
p1536
(lp1537
S'Weitz, Joshua S'
p1538
aS'Dushoff, Jonathan'
p1539
asS'TI'
p1540
S'Modeling post-death transmission of Ebola: challenges for inference and opportunities for control.'
p1541
sS'PL'
p1542
S'England'
p1543
sS'PG'
p1544
S'8751'
p1545
sS'JID'
p1546
S'101563288'
p1547
sS'AB'
p1548
S'Multiple epidemiological models have been proposed to predict the spread of Ebola in West Africa. These models include consideration of counter-measures meant to slow and, eventually, stop the spread of the disease. Here, we examine one component of Ebola dynamics that is of ongoing concern - the transmission of Ebola from the dead to the living. We do so by applying the toolkit of mathematical epidemiology to analyze the consequences of post-death transmission. We show that underlying disease parameters cannot be inferred with confidence from early-stage incidence data (that is, they are not "identifiable") because different parameter combinations can produce virtually the same epidemic trajectory. Despite this identifiability problem, we find robustly that inferences that don\'t account for post-death transmission tend to underestimate the basic reproductive number - thus, given the observed rate of epidemic growth, larger amounts of post-death transmission imply larger reproductive numbers. From a control perspective, we explain how improvements in reducing post-death transmission of Ebola may reduce the overall epidemic spread and scope substantially. Increased attention to the proportion of post-death transmission has the potential to aid both in projecting the course of the epidemic and in evaluating a portfolio of control strategies.'
p1549
sS'AD'
p1550
S'1] School of Biology, Georgia Institute of Technology, Atlanta, GA, USA [2] School of Physics, Georgia Institute of Technology, Atlanta, GA, USA. 1] Department of Biology, McMaster University, Hamilton, ON, Canada [2] Institute of Infectious Disease Research, McMaster University, Hamilton, ON, Canada.'
p1551
sS'VI'
p1552
S'5'
sS'IS'
p1553
S'2045-2322 (Electronic) 2045-2322 (Linking)'
p1554
sS'PMC'
p1555
S'PMC4348651'
p1556
sS'AU'
p1557
(lp1558
S'Weitz JS'
p1559
aS'Dushoff J'
p1560
asS'MHDA'
p1561
S'2016/04/02 06:00'
p1562
sS'PHST'
p1563
(lp1564
S'2014/12/09 [received]'
p1565
aS'2015/02/02 [accepted]'
p1566
asS'OID'
p1567
(lp1568
S'NLM: PMC4348651'
p1569
asS'MH'
p1570
(lp1571
S'*Algorithms'
p1572
aS'Disease Outbreaks'
p1573
aS'Epidemics'
p1574
aS'Hemorrhagic Fever, Ebola/epidemiology/*prevention & control/*transmission'
p1575
aS'Humans'
p1576
aS'*Models, Theoretical'
p1577
aS'Risk Factors'
p1578
aS'Time Factors'
p1579
asS'EDAT'
p1580
S'2015/03/05 06:00'
p1581
sS'SO'
p1582
S'Sci Rep. 2015 Mar 4;5:8751. doi: 10.1038/srep08751.'
p1583
sS'SB'
p1584
S'IM'
p1585
sS'PMID'
p1586
S'25736239'
p1587
sS'TA'
p1588
S'Sci Rep'
p1589
sS'PST'
p1590
S'epublish'
p1591
stRp1592
ag2
(g3
g4
(dp1593
S'LID'
p1594
S'10.1136/bmj.g7518 [doi]'
p1595
sS'STAT'
p1596
S'MEDLINE'
p1597
sS'DEP'
p1598
S'20141210'
p1599
sS'DA'
p1600
S'20141216'
p1601
sS'CRDT'
p1602
(lp1603
S'2014/12/16 06:00'
p1604
asS'DP'
p1605
S'2014'
p1606
sS'AD'
p1607
S'Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA steve.bellan@gmail.com. Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA. Department of Biology and Institute of Infectious Disease Research, McMaster University, Hamilton, ON, Canada. Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA.'
p1608
sS'OWN'
p1609
S'NLM'
p1610
sS'PT'
p1611
(lp1612
S'Letter'
p1613
asS'LA'
p1614
(lp1615
S'eng'
p1616
asS'DCOM'
p1617
S'20150219'
p1618
sS'JT'
p1619
S'BMJ (Clinical research ed.)'
p1620
sS'LR'
p1621
S'20160218'
p1622
sS'FAU'
p1623
(lp1624
S'Bellan, Steve E'
p1625
aS'Pulliam, Juliet R C'
p1626
aS'Dushoff, Jonathan'
p1627
aS'Meyers, Lauren Ancel'
p1628
asS'TI'
p1629
S'Ebola virus vaccine trials: the ethical mandate for a therapeutic safety net.'
p1630
sS'RN'
p1631
(lp1632
S'0 (Ebola Vaccines)'
p1633
asS'PL'
p1634
S'England'
p1635
sS'PG'
p1636
S'g7518'
p1637
sS'JID'
p1638
S'8900488'
p1639
sS'GR'
p1640
(lp1641
S'R25 GM102149/GM/NIGMS NIH HHS/United States'
p1642
asS'VI'
p1643
S'349'
p1644
sS'IS'
p1645
S'1756-1833 (Electronic) 0959-535X (Linking)'
p1646
sS'PMC'
p1647
S'PMC4707566'
p1648
sS'AU'
p1649
(lp1650
S'Bellan SE'
p1651
aS'Pulliam JR'
p1652
aS'Dushoff J'
p1653
aS'Meyers LA'
p1654
asS'MHDA'
p1655
S'2015/02/20 06:00'
p1656
sS'OID'
p1657
(lp1658
S'NLM: PMC4707566'
p1659
asS'MH'
p1660
(lp1661
S'Adolescent'
p1662
aS'Adult'
p1663
aS'*Ebola Vaccines'
p1664
aS'Hemorrhagic Fever, Ebola/mortality/*prevention & control'
p1665
aS'Humans'
p1666
aS'Patient Safety'
p1667
aS'Randomized Controlled Trials as Topic/ethics/methods/mortality'
p1668
aS'Treatment Outcome'
p1669
aS'Young Adult'
p1670
asS'EDAT'
p1671
S'2014/12/17 06:00'
p1672
sS'SO'
p1673
S'BMJ. 2014 Dec 10;349:g7518. doi: 10.1136/bmj.g7518.'
p1674
sS'SB'
p1675
S'AIM IM'
p1676
sS'PMID'
p1677
S'25498325'
p1678
sS'TA'
p1679
S'BMJ'
p1680
sS'PST'
p1681
S'epublish'
p1682
stRp1683
ag2
(g3
g4
(dp1684
S'LID'
p1685
S'10.1016/S0140-6736(14)61839-0 [doi] S0140-6736(14)61839-0 [pii]'
p1686
sS'STAT'
p1687
S'MEDLINE'
p1688
sS'DEP'
p1689
S'20141015'
p1690
sS'MID'
p1691
(lp1692
S'NIHMS774868'
p1693
asS'DA'
p1694
S'20141113'
p1695
sS'AID'
p1696
(lp1697
S'S0140-6736(14)61839-0 [pii]'
p1698
aS'10.1016/S0140-6736(14)61839-0 [doi]'
p1699
asS'CRDT'
p1700
(lp1701
S'2014/11/13 06:00'
p1702
asS'DP'
p1703
S'2014 Oct 25'
p1704
sS'AD'
p1705
S'Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA. Electronic address: steve.bellan@gmail.com. Department of Biology and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA; Fogarty International Center, National Institutes of Health, Bethesda, MD, USA. Department of Biology and Institute of Infectious Disease Research, McMaster University, Hamilton, ON, Canada. Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA; Santa Fe Institute, Santa Fe, NM, USA.'
p1706
sS'OWN'
p1707
S'NLM'
p1708
sS'PT'
p1709
(lp1710
S'Letter'
p1711
asS'LA'
p1712
(lp1713
S'eng'
p1714
asS'FAU'
p1715
(lp1716
S'Bellan, Steve E'
p1717
aS'Pulliam, Juliet R C'
p1718
aS'Dushoff, Jonathan'
p1719
aS'Meyers, Lauren Ancel'
p1720
asS'JT'
p1721
S'Lancet (London, England)'
p1722
sS'LR'
p1723
S'20160505'
p1724
sS'PG'
p1725
S'1499-500'
p1726
sS'TI'
p1727
S'Ebola control: effect of asymptomatic infection and acquired immunity.'
p1728
sS'PL'
p1729
S'England'
p1730
sS'TA'
p1731
S'Lancet'
p1732
sS'JID'
p1733
S'2985213R'
p1734
sS'GR'
p1735
(lp1736
S'R25 GM102149/GM/NIGMS NIH HHS/United States'
p1737
asS'IP'
p1738
S'9953'
p1739
sS'IS'
p1740
S'1474-547X (Electronic) 0140-6736 (Linking)'
p1741
sS'PMC'
p1742
S'PMC4829342'
p1743
sS'DCOM'
p1744
S'20150128'
p1745
sS'AU'
p1746
(lp1747
S'Bellan SE'
p1748
aS'Pulliam JR'
p1749
aS'Dushoff J'
p1750
aS'Meyers LA'
p1751
asS'VI'
p1752
S'384'
p1753
sS'MHDA'
p1754
S'2015/01/30 06:00'
p1755
sS'PHST'
p1756
(lp1757
S'2014/10/15 [aheadofprint]'
p1758
asS'OID'
p1759
(lp1760
S'NLM: NIHMS774868'
p1761
aS'NLM: PMC4829342'
p1762
asS'MH'
p1763
(lp1764
S'Adaptive Immunity/*physiology'
p1765
aS'*Asymptomatic Infections'
p1766
aS'Disease Outbreaks/*prevention & control'
p1767
aS'Hemorrhagic Fever, Ebola/immunology/*prevention & control'
p1768
aS'Humans'
p1769
aS'Liberia/epidemiology'
p1770
asS'EDAT'
p1771
S'2014/11/13 06:00'
p1772
sS'SO'
p1773
S'Lancet. 2014 Oct 25;384(9953):1499-500. doi: 10.1016/S0140-6736(14)61839-0. Epub 2014 Oct 15.'
p1774
sS'SB'
p1775
S'AIM IM'
p1776
sS'PMID'
p1777
S'25390569'
p1778
sS'PST'
p1779
S'ppublish'
p1780
stRp1781
ag2
(g3
g4
(dp1782
S'LID'
p1783
S'10.1007/s00125-014-3390-x [doi]'
p1784
sS'STAT'
p1785
S'MEDLINE'
p1786
sS'DEP'
p1787
S'20141008'
p1788
sS'DA'
p1789
S'20141205'
p1790
sS'AID'
p1791
(lp1792
S'10.1007/s00125-014-3390-x [doi]'
p1793
asS'CRDT'
p1794
(lp1795
S'2014/10/09 06:00'
p1796
asS'DP'
p1797
S'2015 Jan'
p1798
sS'OWN'
p1799
S'NLM'
p1800
sS'PT'
p1801
(lp1802
S'Journal Article'
p1803
aS"Research Support, Non-U.S. Gov't"
p1804
asS'LA'
p1805
(lp1806
S'eng'
p1807
asS'FAU'
p1808
(lp1809
S'Alyass, Akram'
p1810
aS'Almgren, Peter'
p1811
aS'Akerlund, Mikael'
p1812
aS'Dushoff, Jonathan'
p1813
aS'Isomaa, Bo'
p1814
aS'Nilsson, Peter'
p1815
aS'Tuomi, Tiinamaija'
p1816
aS'Lyssenko, Valeriya'
p1817
aS'Groop, Leif'
p1818
aS'Meyre, David'
p1819
asS'JT'
p1820
S'Diabetologia'
p1821
sS'PG'
p1822
S'87-97'
p1823
sS'TI'
p1824
S'Modelling of OGTT curve identifies 1 h plasma glucose level as a strong predictor of incident type 2 diabetes: results from two prospective cohorts.'
p1825
sS'RN'
p1826
(lp1827
S'0 (Blood Glucose)'
p1828
asS'PL'
p1829
S'Germany'
p1830
sS'TA'
p1831
S'Diabetologia'
p1832
sS'JID'
p1833
S'0006777'
p1834
sS'AB'
p1835
S'AIMS/HYPOTHESIS: The relevance of the OGTT in predicting type 2 diabetes is unclear. We assessed the performance of 14 OGTT glucose traits in type 2 diabetes prediction. METHODS: We studied 2,603 and 2,386 Europeans from the Botnia study and Malmo Prevention Project (MPP) cohorts with baseline OGTT data. Over a follow-up period of 4.94 years and 23.5 years, 155 (5.95%) and 467 (19.57%) participants, respectively, developed type 2 diabetes. The main outcome was incident type 2 diabetes. RESULTS: One-hour plasma glucose (1h-PG) was a fair/good predictor of incident type 2 diabetes in the Botnia study and MPP (AUC for receiver operating characteristic [AUCROC] 0.80 [0.77, 0.84] and 0.70 [0.68, 0.73]). 1h-PG alone outperformed the prediction model of multiple clinical risk factors (age, sex, BMI, family history of type 2 diabetes) in the Botnia study and MPP (AUCROC 0.75 [0.72, 0.79] and 0.67 [0.64, 0.70]). The same clinical risk factors added to 1h-PG modestly increased prediction for incident type 2 diabetes (Botnia, AUCROC 0.83 [0.80, 0.86]; MPP, AUCROC 0.74 [0.72, 0.77]). 1h-PG also outperformed HbA1c in predicting type 2 diabetes in the Botnia cohort. A 1h-PG value of 8.9 mmol/l and 8.4 mmol/l was the optimal cut-point for initial screening and selection of high-risk individuals in the Botnia study and MPP, respectively, and represented 30% and 37% of all participants in these cohorts. High-risk individuals had a substantially increased risk of incident type 2 diabetes (OR 8.0 [5.5, 11.6] and 3.8 [3.1, 4.7]) and captured 75% and 62% of all incident type 2 diabetes in the Botnia study and MPP. CONCLUSIONS/INTERPRETATION: 1h-PG is a valuable prediction tool for identifying adults at risk for future type 2 diabetes.'
p1836
sS'AD'
p1837
S'Department of Clinical Epidemiology and Biostatistics, McMaster University, Michael DeGroote Centre for Learning & Discovery, Room 3205, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.'
p1838
sS'IP'
p1839
S'1'
sS'IS'
p1840
S'1432-0428 (Electronic) 0012-186X (Linking)'
p1841
sS'DCOM'
p1842
S'20160412'
p1843
sS'AU'
p1844
(lp1845
S'Alyass A'
p1846
aS'Almgren P'
p1847
aS'Akerlund M'
p1848
aS'Dushoff J'
p1849
aS'Isomaa B'
p1850
aS'Nilsson P'
p1851
aS'Tuomi T'
p1852
aS'Lyssenko V'
p1853
aS'Groop L'
p1854
aS'Meyre D'
p1855
asS'VI'
p1856
S'58'
p1857
sS'MHDA'
p1858
S'2016/04/14 06:00'
p1859
sS'PHST'
p1860
(lp1861
S'2014/07/02 [received]'
p1862
aS'2014/08/29 [accepted]'
p1863
aS'2014/10/08 [aheadofprint]'
p1864
asS'MH'
p1865
(lp1866
S'Adult'
p1867
aS'Blood Glucose/*analysis'
p1868
aS'Canada/epidemiology'
p1869
aS'Diabetes Mellitus, Type 2/*blood/*diagnosis/epidemiology'
p1870
aS'Female'
p1871
aS'Follow-Up Studies'
p1872
aS'Glucose Tolerance Test'
p1873
aS'Humans'
p1874
aS'Incidence'
p1875
aS'Male'
p1876
aS'Middle Aged'
p1877
aS'*Models, Theoretical'
p1878
aS'Prognosis'
p1879
aS'Prospective Studies'
p1880
asS'EDAT'
p1881
S'2014/10/09 06:00'
p1882
sS'SO'
p1883
S'Diabetologia. 2015 Jan;58(1):87-97. doi: 10.1007/s00125-014-3390-x. Epub 2014 Oct 8.'
p1884
sS'SB'
p1885
S'IM'
p1886
sS'PMID'
p1887
S'25292440'
p1888
sS'PST'
p1889
S'ppublish'
p1890
stRp1891
ag2
(g3
g4
(dp1892
S'LID'
p1893
S'10.1007/s11538-014-9950-x [doi]'
p1894
sS'STAT'
p1895
S'MEDLINE'
p1896
sS'DEP'
p1897
S'20140423'
p1898
sS'DA'
p1899
S'20140508'
p1900
sS'AID'
p1901
(lp1902
S'10.1007/s11538-014-9950-x [doi]'
p1903
asS'CRDT'
p1904
(lp1905
S'2014/04/24 06:00'
p1906
asS'DP'
p1907
S'2014 May'
p1908
sS'OWN'
p1909
S'NLM'
p1910
sS'PT'
p1911
(lp1912
S'Journal Article'
p1913
asS'LA'
p1914
(lp1915
S'eng'
p1916
asS'FAU'
p1917
(lp1918
S'Keegan, Lindsay'
p1919
aS'Dushoff, Jonathan'
p1920
asS'JT'
p1921
S'Bulletin of mathematical biology'
p1922
sS'LR'
p1923
S'20150806'
p1924
sS'PG'
p1925
S'1143-54'
p1926
sS'TI'
p1927
S'Analytic calculation of finite-population reproductive numbers for direct- and vector-transmitted diseases with homogeneous mixing.'
p1928
sS'PL'
p1929
S'United States'
p1930
sS'TA'
p1931
S'Bull Math Biol'
p1932
sS'JID'
p1933
S'0401404'
p1934
sS'AB'
p1935
S'The basic reproductive number, R0, provides a foundation for evaluating how various factors affect the incidence of infectious diseases. Recently, it has been suggested that, particularly for vector-transmitted diseases, R0 should be modified to account for the effects of finite host population within a single disease transmission generation. Here, we use a transmission factor approach to calculate such "finite-population reproductive numbers," under the assumption of homogeneous mixing, for both vector-borne and directly transmitted diseases. In the case of vector-borne diseases, we estimate finite-population reproductive numbers for both host-to-host and vector-to-vector generations, assuming that the vector population is effectively infinite. We find simple, interpretable formulas for all three of these quantities. In the direct case, we find that finite-population reproductive numbers diverge from R0 before R0 reaches half of the population size. In the vector-transmitted case, we find that the host-to-host number diverges at even lower values of R0, while the vector-to-vector number diverges very little over realistic parameter ranges.'
p1936
sS'AD'
p1937
S'Department of Biology, McMaster University, Hamilton, ON, Canada, keeganlt@mcmaster.ca.'
p1938
sS'IP'
p1939
S'5'
sS'IS'
p1940
S'1522-9602 (Electronic) 0092-8240 (Linking)'
p1941
sS'PMC'
p1942
S'PMC4013491'
p1943
sS'DCOM'
p1944
S'20150408'
p1945
sS'AU'
p1946
(lp1947
S'Keegan L'
p1948
aS'Dushoff J'
p1949
asS'VI'
p1950
S'76'
p1951
sS'MHDA'
p1952
S'2015/04/09 06:00'
p1953
sS'PHST'
p1954
(lp1955
S'2013/05/25 [received]'
p1956
aS'2014/03/13 [accepted]'
p1957
aS'2014/04/23 [aheadofprint]'
p1958
asS'OID'
p1959
(lp1960
S'NLM: PMC4013491'
p1961
asS'MH'
p1962
(lp1963
S'Animals'
p1964
aS'*Basic Reproduction Number'
p1965
aS'Communicable Diseases/epidemiology/*transmission'
p1966
aS'Computer Simulation'
p1967
aS'*Disease Outbreaks'
p1968
aS'*Disease Vectors'
p1969
aS'Humans'
p1970
aS'Incidence'
p1971
aS'*Models, Theoretical'
p1972
aS'Population Density'
p1973
asS'EDAT'
p1974
S'2014/04/24 06:00'
p1975
sS'SO'
p1976
S'Bull Math Biol. 2014 May;76(5):1143-54. doi: 10.1007/s11538-014-9950-x. Epub 2014 Apr 23.'
p1977
sS'SB'
p1978
S'IM'
p1979
sS'PMID'
p1980
S'24756856'
p1981
sS'PST'
p1982
S'ppublish'
p1983
stRp1984
ag2
(g3
g4
(dp1985
S'LID'
p1986
S'10.1086/675716 [doi]'
p1987
sS'STAT'
p1988
S'MEDLINE'
p1989
sS'DEP'
p1990
S'20140328'
p1991
sS'DA'
p1992
S'20140417'
p1993
sS'AID'
p1994
(lp1995
S'10.1086/675716 [doi]'
p1996
asS'CRDT'
p1997
(lp1998
S'2014/04/18 06:00'
p1999
asS'DP'
p2000
S'2014 May'
p2001
sS'OWN'
p2002
S'NLM'
p2003
sS'PT'
p2004
(lp2005
S'Journal Article'
p2006
aS"Research Support, Non-U.S. Gov't"
p2007
aS"Research Support, U.S. Gov't, Non-P.H.S."
p2008
asS'LA'
p2009
(lp2010
S'eng'
p2011
asS'FAU'
p2012
(lp2013
S'Winfree, Rachael'
p2014
aS'Williams, Neal M'
p2015
aS'Dushoff, Jonathan'
p2016
aS'Kremen, Claire'
p2017
asS'JT'
p2018
S'The American naturalist'
p2019
sS'PG'
p2020
S'600-11'
p2021
sS'TI'
p2022
S'Species abundance, not diet breadth, drives the persistence of the most linked pollinators as plant-pollinator networks disassemble.'
p2023
sS'PL'
p2024
S'United States'
p2025
sS'TA'
p2026
S'Am Nat'
p2027
sS'JID'
p2028
S'2984688R'
p2029
sS'AB'
p2030
S"Theoretical and simulation studies predict that the order of species loss from mutualist networks with respect to how linked species are to other species within the network will determine the rate at which networks collapse. However, the empirical order of species loss with respect to linkage has rarely been investigated. Furthermore, a species' linkage is a composite of its diet breadth and its abundance, yet the relative importance of these two factors in determining species loss order is poorly known. Here we explore the order of pollinator species loss in two contrasting study systems undergoing land-use intensification, using >20,000 pollinator specimens. We found that a pollinator species' linkage, as measured independently within plant-pollinator networks, positively predicted its persistence at human-disturbed sites in three of four analyses. The strongest predictor of persistence in all analyses was pollinator species abundance. In contrast, diet breadth poorly predicted persistence. Overall, our results suggest that community disassembly order buffers plant-pollinator networks against environmental change by retaining the highly linked species that make a disproportionate contribution to network robustness. Furthermore, these highly linked species likely persist because they are also the most common species, not because they are dietary generalists."
p2031
sS'AD'
p2032
S'Department of Ecology, Evolution and Natural Resources, Rutgers University, New Brunswick, New Jersey 08901.'
p2033
sS'IP'
p2034
S'5'
sS'IS'
p2035
S'1537-5323 (Electronic) 0003-0147 (Linking)'
p2036
sS'DCOM'
p2037
S'20141230'
p2038
sS'AU'
p2039
(lp2040
S'Winfree R'
p2041
aS'Williams NM'
p2042
aS'Dushoff J'
p2043
aS'Kremen C'
p2044
asS'VI'
p2045
S'183'
p2046
sS'MHDA'
p2047
S'2014/12/31 06:00'
p2048
sS'PHST'
p2049
(lp2050
S'2014/03/28 [aheadofprint]'
p2051
asS'MH'
p2052
(lp2053
S'*Angiosperms'
p2054
aS'Animals'
p2055
aS'*Biodiversity'
p2056
aS'Biota'
p2057
aS'California'
p2058
aS'*Diet'
p2059
aS'Ecosystem'
p2060
aS'*Insects'
p2061
aS'New Jersey'
p2062
aS'Pollination'
p2063
asS'EDAT'
p2064
S'2014/04/18 06:00'
p2065
sS'SO'
p2066
S'Am Nat. 2014 May;183(5):600-11. doi: 10.1086/675716. Epub 2014 Mar 28.'
p2067
sS'SB'
p2068
S'IM'
p2069
sS'PMID'
p2070
S'24739193'
p2071
sS'PST'
p2072
S'ppublish'
p2073
stRp2074
ag2
(g3
g4
(dp2075
S'LID'
p2076
S'10.1371/journal.pone.0082906 [doi]'
p2077
sS'STAT'
p2078
S'MEDLINE'
p2079
sS'DEP'
p2080
S'20131220'
p2081
sS'DA'
p2082
S'20131230'
p2083
sS'AID'
p2084
(lp2085
S'10.1371/journal.pone.0082906 [doi]'
p2086
aS'PONE-D-13-28167 [pii]'
p2087
asS'CRDT'
p2088
(lp2089
S'2013/12/31 06:00'
p2090
asS'DP'
p2091
S'2013'
p2092
sS'AD'
p2093
S'School of Computational Science and Engineering, McMaster University, Hamilton, Canada. Center for Computational Biology and Bioinformatics, University of Texas at Austin, Austin, Texas, United States of America. Department of Biology, McMaster University, Hamilton, Canada.'
p2094
sS'OWN'
p2095
S'NLM'
p2096
sS'PT'
p2097
(lp2098
S'Journal Article'
p2099
aS'Research Support, N.I.H., Extramural'
p2100
aS"Research Support, Non-U.S. Gov't"
p2101
asS'LA'
p2102
(lp2103
S'eng'
p2104
asS'FAU'
p2105
(lp2106
S'Champredon, David'
p2107
aS'Bellan, Steve'
p2108
aS'Dushoff, Jonathan'
p2109
asS'JT'
p2110
S'PloS one'
p2111
sS'LR'
p2112
S'20150422'
p2113
sS'PG'
p2114
S'e82906'
p2115
sS'TI'
p2116
S'HIV sexual transmission is predominantly driven by single individuals rather than discordant couples: a model-based approach.'
p2117
sS'PL'
p2118
S'United States'
p2119
sS'TA'
p2120
S'PLoS One'
p2121
sS'JID'
p2122
S'101285081'
p2123
sS'AB'
p2124
S'Understanding the relative contribution to HIV transmission from different social groups is important for public-health policy. Information about the importance of stable serodiscordant couples (when one partner is infected but not the other) relative to contacts outside of stable partnerships in spreading disease can aid in designing and targeting interventions. However, the overall importance of within-couple transmission, and the determinants and correlates of this importance, are not well understood. Here, we explore how mechanistic factors - like partnership dynamics and rates of extra-couple transmission - affect various routes of transmission, using a compartmental model with parameters based on estimates from Sub-Saharan Africa. Under our assumptions, when sampling model parameters within a realistic range, we find that infection of uncoupled individuals is usually the predominant route (median 0.62, 2.5%-97.5% quantiles: 0.26-0.88), while transmission within discordant couples is usually important, but rarely represents the majority of transmissions (median 0.33, 2.5%-97.5% quantiles: 0.10-0.67). We find a strong correlation between long-term HIV prevalence and the contact rate of uncoupled individuals, implying that this rate may be a key driver of HIV prevalence. For a given level of prevalence, we find a negative correlation between the proportion of discordant couples and the within-couple transmission rate, indicating that low discordance in a population may reflect a relatively high rate of within-couple transmission. Transmission within or outside couples and among uncoupled individuals are all likely to be important in sustaining heterosexual HIV transmission in Sub-Saharan Africa. Hence, intervention policies should be broadly targeted when practical.'
p2125
sS'GR'
p2126
(lp2127
S'U01GM087719/GM/NIGMS NIH HHS/United States'
p2128
asS'IP'
p2129
S'12'
p2130
sS'IS'
p2131
S'1932-6203 (Electronic) 1932-6203 (Linking)'
p2132
sS'PMC'
p2133
S'PMC3869741'
p2134
sS'DCOM'
p2135
S'20141006'
p2136
sS'AU'
p2137
(lp2138
S'Champredon D'
p2139
aS'Bellan S'
p2140
aS'Dushoff J'
p2141
asS'VI'
p2142
S'8'
sS'MHDA'
p2143
S'2014/10/07 06:00'
p2144
sS'PHST'
p2145
(lp2146
S'2013 [ecollection]'
p2147
aS'2013/07/08 [received]'
p2148
aS'2013/11/07 [accepted]'
p2149
aS'2013/12/20 [epublish]'
p2150
asS'OID'
p2151
(lp2152
S'NLM: PMC3869741'
p2153
asS'MH'
p2154
(lp2155
S'Africa South of the Sahara/epidemiology'
p2156
aS'Family Characteristics'
p2157
aS'Female'
p2158
aS'HIV Infections/epidemiology/*transmission'
p2159
aS'Heterosexuality'
p2160
aS'Humans'
p2161
aS'Male'
p2162
aS'*Models, Statistical'
p2163
aS'Prevalence'
p2164
aS'*Sexual Partners'
p2165
asS'EDAT'
p2166
S'2014/01/01 06:00'
p2167
sS'SO'
p2168
S'PLoS One. 2013 Dec 20;8(12):e82906. doi: 10.1371/journal.pone.0082906. eCollection 2013.'
p2169
sS'SB'
p2170
S'IM'
p2171
sS'PMID'
p2172
S'24376602'
p2173
sS'PST'
p2174
S'epublish'
p2175
stRp2176
ag2
(g3
g4
(dp2177
S'LID'
p2178
S'10.1007/s11538-013-9918-2 [doi]'
p2179
sS'STAT'
p2180
S'MEDLINE'
p2181
sS'DEP'
p2182
S'20131123'
p2183
sS'DA'
p2184
S'20140115'
p2185
sS'AID'
p2186
(lp2187
S'10.1007/s11538-013-9918-2 [doi]'
p2188
asS'CRDT'
p2189
(lp2190
S'2013/11/26 06:00'
p2191
asS'DP'
p2192
S'2014 Jan'
p2193
sS'OWN'
p2194
S'NLM'
p2195
sS'PT'
p2196
(lp2197
S'Journal Article'
p2198
aS"Research Support, Non-U.S. Gov't"
p2199
asS'LA'
p2200
(lp2201
S'eng'
p2202
asS'FAU'
p2203
(lp2204
S'Ma, Junling'
p2205
aS'Dushoff, Jonathan'
p2206
aS'Bolker, Benjamin M'
p2207
aS'Earn, David J D'
p2208
asS'JT'
p2209
S'Bulletin of mathematical biology'
p2210
sS'PG'
p2211
S'245-60'
p2212
sS'TI'
p2213
S'Estimating initial epidemic growth rates.'
p2214
sS'PL'
p2215
S'United States'
p2216
sS'TA'
p2217
S'Bull Math Biol'
p2218
sS'JID'
p2219
S'0401404'
p2220
sS'AB'
p2221
S'The initial exponential growth rate of an epidemic is an important measure of disease spread, and is commonly used to infer the basic reproduction number [Formula: see text]. While modern techniques (e.g., MCMC and particle filtering) for parameter estimation of mechanistic models have gained popularity, maximum likelihood fitting of phenomenological models remains important due to its simplicity, to the difficulty of using modern methods in the context of limited data, and to the fact that there is not always enough information available to choose an appropriate mechanistic model. However, it is often not clear which phenomenological model is appropriate for a given dataset. We compare the performance of four commonly used phenomenological models (exponential, Richards, logistic, and delayed logistic) in estimating initial epidemic growth rates by maximum likelihood, by fitting them to simulated epidemics with known parameters. For incidence data, both the logistic model and the Richards model yield accurate point estimates for fitting windows up to the epidemic peak. When observation errors are small, the Richards model yields confidence intervals with better coverage. For mortality data, the Richards model and the delayed logistic model yield the best growth rate estimates. We also investigate the width and coverage of the confidence intervals corresponding to these fits.'
p2222
sS'AD'
p2223
S'Mathematics and Statistics, University of Victoria, Victoria, BC, Canada, junlingm@uvic.ca.'
p2224
sS'IP'
p2225
S'1'
sS'IS'
p2226
S'1522-9602 (Electronic) 0092-8240 (Linking)'
p2227
sS'DCOM'
p2228
S'20150515'
p2229
sS'AU'
p2230
(lp2231
S'Ma J'
p2232
aS'Dushoff J'
p2233
aS'Bolker BM'
p2234
aS'Earn DJ'
p2235
asS'VI'
p2236
S'76'
p2237
sS'MHDA'
p2238
S'2015/05/16 06:00'
p2239
sS'PHST'
p2240
(lp2241
S'2013/05/22 [received]'
p2242
aS'2013/10/24 [accepted]'
p2243
aS'2013/11/23 [aheadofprint]'
p2244
asS'MH'
p2245
(lp2246
S'Basic Reproduction Number'
p2247
aS'Computer Simulation'
p2248
aS'Confidence Intervals'
p2249
aS'Epidemics/*statistics & numerical data'
p2250
aS'Humans'
p2251
aS'Likelihood Functions'
p2252
aS'Logistic Models'
p2253
aS'Mathematical Concepts'
p2254
aS'*Models, Biological'
p2255
aS'Mortality'
p2256
asS'EDAT'
p2257
S'2013/11/26 06:00'
p2258
sS'SO'
p2259
S'Bull Math Biol. 2014 Jan;76(1):245-60. doi: 10.1007/s11538-013-9918-2. Epub 2013 Nov 23.'
p2260
sS'SB'
p2261
S'IM'
p2262
sS'PMID'
p2263
S'24272389'
p2264
sS'PST'
p2265
S'ppublish'
p2266
stRp2267
ag2
(g3
g4
(dp2268
S'LID'
p2269
S'10.1093/aje/kwt242 [doi]'
p2270
sS'STAT'
p2271
S'MEDLINE'
p2272
sS'DEP'
p2273
S'20131014'
p2274
sS'CIN'
p2275
(lp2276
S'Am J Epidemiol. 2014 Jan 15;179(2):262-3. PMID: 24511629'
p2277
asS'DA'
p2278
S'20131227'
p2279
sS'AID'
p2280
(lp2281
S'kwt242 [pii]'
p2282
aS'10.1093/aje/kwt242 [doi]'
p2283
asS'CRDT'
p2284
(lp2285
S'2013/10/16 06:00'
p2286
asS'DP'
p2287
S'2014 Jan 15'
p2288
sS'CON'
p2289
(lp2290
S'Am J Epidemiol. 2012 Sep 15;176(6):497-505. PMID: 22875753'
p2291
asS'OWN'
p2292
S'NLM'
p2293
sS'PT'
p2294
(lp2295
S'Comment'
p2296
aS'Letter'
p2297
aS'Research Support, N.I.H., Extramural'
p2298
aS"Research Support, Non-U.S. Gov't"
p2299
aS"Research Support, U.S. Gov't, Non-P.H.S."
p2300
asS'LA'
p2301
(lp2302
S'eng'
p2303
asS'FAU'
p2304
(lp2305
S'Pulliam, Juliet R C'
p2306
aS'Dushoff, Jonathan'
p2307
asS'JT'
p2308
S'American journal of epidemiology'
p2309
sS'LR'
p2310
S'20150422'
p2311
sS'PG'
p2312
S'261-2'
p2313
sS'TI'
p2314
S'Re: "The \'case-chaos study\' L adjunct or alternative to conventional case-control study methodology".'
p2315
sS'PL'
p2316
S'United States'
p2317
sS'TA'
p2318
S'Am J Epidemiol'
p2319
sS'JID'
p2320
S'7910653'
p2321
sS'GR'
p2322
(lp2323
S'Canadian Institutes of Health Research/Canada'
p2324
asS'IP'
p2325
S'2'
sS'IS'
p2326
S'1476-6256 (Electronic) 0002-9262 (Linking)'
p2327
sS'PMC'
p2328
S'PMC3873109'
p2329
sS'DCOM'
p2330
S'20140211'
p2331
sS'AU'
p2332
(lp2333
S'Pulliam JR'
p2334
aS'Dushoff J'
p2335
asS'AD'
p2336
S'Department of Biology, College of Liberal Arts and Sciences, University of Florida, Gainesville, FL 32611.'
p2337
sS'VI'
p2338
S'179'
p2339
sS'MHDA'
p2340
S'2014/02/12 06:00'
p2341
sS'PHST'
p2342
(lp2343
S'2013/10/14 [aheadofprint]'
p2344
asS'OID'
p2345
(lp2346
S'NLM: PMC3873109'
p2347
asS'MH'
p2348
(lp2349
S'*Case-Control Studies'
p2350
aS'*Epidemiologic Research Design'
p2351
aS'Humans'
p2352
aS'Risk Assessment/*methods'
p2353
asS'EDAT'
p2354
S'2013/10/16 06:00'
p2355
sS'SO'
p2356
S'Am J Epidemiol. 2014 Jan 15;179(2):261-2. doi: 10.1093/aje/kwt242. Epub 2013 Oct 14.'
p2357
sS'SB'
p2358
S'IM'
p2359
sS'PMID'
p2360
S'24125919'
p2361
sS'PST'
p2362
S'ppublish'
p2363
stRp2364
ag2
(g3
g4
(dp2365
S'LID'
p2366
S'10.1111/1574-6941.12231 [doi]'
p2367
sS'STAT'
p2368
S'MEDLINE'
p2369
sS'DEP'
p2370
S'20131025'
p2371
sS'CI'
p2372
(lp2373
S'(c) 2013 Federation of European Microbiological Societies. Published by John'
p2374
aS'Wiley & Sons Ltd. All rights reserved.'
p2375
asS'DA'
p2376
S'20140408'
p2377
sS'AID'
p2378
(lp2379
S'10.1111/1574-6941.12231 [doi]'
p2380
asS'CRDT'
p2381
(lp2382
S'2013/10/15 06:00'
p2383
asS'DP'
p2384
S'2014 Feb'
p2385
sS'OWN'
p2386
S'NLM'
p2387
sS'PT'
p2388
(lp2389
S'Journal Article'
p2390
aS"Research Support, Non-U.S. Gov't"
p2391
asS'LA'
p2392
(lp2393
S'eng'
p2394
asS'FAU'
p2395
(lp2396
S'Bartram, Andrea K'
p2397
aS'Jiang, Xingpeng'
p2398
aS'Lynch, Michael D J'
p2399
aS'Masella, Andre P'
p2400
aS'Nicol, Graeme W'
p2401
aS'Dushoff, Jonathan'
p2402
aS'Neufeld, Josh D'
p2403
asS'JT'
p2404
S'FEMS microbiology ecology'
p2405
sS'PG'
p2406
S'403-15'
p2407
sS'TI'
p2408
S'Exploring links between pH and bacterial community composition in soils from the Craibstone Experimental Farm.'
p2409
sS'RN'
p2410
(lp2411
S'0 (RNA, Bacterial)'
p2412
aS'0 (RNA, Ribosomal, 16S)'
p2413
aS'0 (Soil)'
p2414
asS'PL'
p2415
S'England'
p2416
sS'TA'
p2417
S'FEMS Microbiol Ecol'
p2418
sS'JID'
p2419
S'8901229'
p2420
sS'AB'
p2421
S"Soil pH is an important determinant of microbial community composition and diversity, yet few studies have characterized the specific effects of pH on individual bacterial taxa within bacterial communities, both abundant and rare. We collected composite soil samples over 2 years from an experimentally maintained pH gradient ranging from 4.5 to 7.5 from the Craibstone Experimental Farm (Craibstone, Scotland). Extracted nucleic acids were characterized by bacterial and group-specific denaturing gradient gel electrophoresis and next-generation sequencing of bacterial 16S rRNA genes. Both methods demonstrated comparable and reproducible shifts within higher taxonomic bacterial groups (e.g. Acidobacteria, Alphaproteobacteria, Verrucomicrobia, and Gammaproteobacteria) across the pH gradient. In addition, we used non-negative matrix factorization (NMF) for the first time on 16S rRNA gene data to identify positively interacting (i.e. co-occurring) operational taxonomic unit (OTU) clusters (i.e. 'components'), with abundances that correlated strongly with pH, and sample year to a lesser extent. All OTUs identified by NMF were visualized within principle coordinate analyses of UNIFRAC distances and subjected to taxonomic network analysis (SSUnique), which plotted OTU abundance and similarity against established taxonomies. Most pH-dependent OTUs identified here would not have been identified by previous methodologies for microbial community profiling and were unrelated to known lineages."
p2422
sS'AD'
p2423
S'Department of Biology, University of Waterloo, Waterloo, ON, Canada.'
p2424
sS'IP'
p2425
S'2'
sS'IS'
p2426
S'1574-6941 (Electronic) 0168-6496 (Linking)'
p2427
sS'DCOM'
p2428
S'20140528'
p2429
sS'AU'
p2430
(lp2431
S'Bartram AK'
p2432
aS'Jiang X'
p2433
aS'Lynch MD'
p2434
aS'Masella AP'
p2435
aS'Nicol GW'
p2436
aS'Dushoff J'
p2437
aS'Neufeld JD'
p2438
asS'VI'
p2439
S'87'
p2440
sS'MHDA'
p2441
S'2014/05/29 06:00'
p2442
sS'PHST'
p2443
(lp2444
S'2013/07/05 [received]'
p2445
aS'2013/09/23 [revised]'
p2446
aS'2013/09/24 [accepted]'
p2447
aS'2013/10/25 [aheadofprint]'
p2448
asS'OTO'
p2449
(lp2450
S'NOTNLM'
p2451
asS'MH'
p2452
(lp2453
S'Agriculture'
p2454
aS'Bacteria/*classification/genetics'
p2455
aS'Denaturing Gradient Gel Electrophoresis'
p2456
aS'Genes, Bacterial'
p2457
aS'Genes, rRNA'
p2458
aS'Hydrogen-Ion Concentration'
p2459
aS'Molecular Sequence Data'
p2460
aS'Polymerase Chain Reaction'
p2461
aS'RNA, Bacterial/genetics'
p2462
aS'RNA, Ribosomal, 16S/genetics'
p2463
aS'Scotland'
p2464
aS'Sequence Analysis, DNA/methods'
p2465
aS'Soil/*chemistry'
p2466
aS'*Soil Microbiology'
p2467
asS'EDAT'
p2468
S'2013/10/15 06:00'
p2469
sS'SI'
p2470
(lp2471
S'SRA/SRP007517'
p2472
asS'SO'
p2473
S'FEMS Microbiol Ecol. 2014 Feb;87(2):403-15. doi: 10.1111/1574-6941.12231. Epub 2013 Oct 25.'
p2474
sS'SB'
p2475
S'IM'
p2476
sS'PMID'
p2477
S'24117982'
p2478
sS'OT'
p2479
(lp2480
S'16S rRNA genes'
p2481
aS'agriculture'
p2482
aS'microbial diversity'
p2483
aS'pH'
p2484
aS'rare biosphere'
p2485
aS'soil bacteria'
p2486
asS'PST'
p2487
S'ppublish'
p2488
stRp2489
ag2
(g3
g4
(dp2490
S'LID'
p2491
S'10.1098/rspb.2013.1174 [doi]'
p2492
sS'STAT'
p2493
S'MEDLINE'
p2494
sS'DEP'
p2495
S'20130911'
p2496
sS'DA'
p2497
S'20130912'
p2498
sS'AID'
p2499
(lp2500
S'rspb.2013.1174 [pii]'
p2501
aS'10.1098/rspb.2013.1174 [doi]'
p2502
asS'CRDT'
p2503
(lp2504
S'2013/09/13 06:00'
p2505
asS'DP'
p2506
S'2013 Nov 7'
p2507
sS'AD'
p2508
S"Department of Applied Mathematics, Hong Kong Polytechnic University, , Hung Hom, Kowloon, Hong Kong (SAR), People's Republic of China, Department of Biology, McMaster University, , Hamilton, Ontario, Canada , L8S 4L8, M.G. DeGroote Institute for Infectious Disease Research, McMaster University, , Hamilton, Ontario, Canada , L8S 4L8, Division of Mathematics, University of Dundee, , Dundee DD1 4HN, UK, Department of Mathematics and Statistics, McMaster University, , Hamilton, Ontario, Canada , L8S 4K1."
p2509
sS'OWN'
p2510
S'NLM'
p2511
sS'PT'
p2512
(lp2513
S'Journal Article'
p2514
aS"Research Support, Non-U.S. Gov't"
p2515
asS'LA'
p2516
(lp2517
S'eng'
p2518
asS'FAU'
p2519
(lp2520
S'He, Daihai'
p2521
aS'Dushoff, Jonathan'
p2522
aS'Eftimie, Raluca'
p2523
aS'Earn, David J D'
p2524
asS'JT'
p2525
S'Proceedings. Biological sciences / The Royal Society'
p2526
sS'LR'
p2527
S'20150422'
p2528
sS'PG'
p2529
S'20131174'
p2530
sS'TI'
p2531
S'Patterns of spread of influenza A in Canada.'
p2532
sS'PL'
p2533
S'England'
p2534
sS'TA'
p2535
S'Proc Biol Sci'
p2536
sS'JID'
p2537
S'101245157'
p2538
sS'AB'
p2539
S'Understanding spatial patterns of influenza transmission is important for designing control measures. We investigate spatial patterns of laboratory-confirmed influenza A across Canada from October 1999 to August 2012. A statistical analysis (generalized linear model) of the seasonal epidemics in this time period establishes a clear spatio-temporal pattern, with influenza emerging earlier in western provinces. Early emergence is also correlated with low temperature and low absolute humidity in the autumn. For the richer data from the 2009 pandemic, a mechanistic mathematical analysis, based on a transmission model, shows that both school terms and weather had important effects on pandemic influenza transmission.'
p2540
sS'GR'
p2541
(lp2542
S'Canadian Institutes of Health Research/Canada'
p2543
asS'IP'
p2544
S'1770'
p2545
sS'IS'
p2546
S'1471-2954 (Electronic) 0962-8452 (Linking)'
p2547
sS'PMC'
p2548
S'PMC3779324'
p2549
sS'DCOM'
p2550
S'20140404'
p2551
sS'AU'
p2552
(lp2553
S'He D'
p2554
aS'Dushoff J'
p2555
aS'Eftimie R'
p2556
aS'Earn DJ'
p2557
asS'VI'
p2558
S'280'
p2559
sS'MHDA'
p2560
S'2014/04/05 06:00'
p2561
sS'PHST'
p2562
(lp2563
S'2013 [ppublish]'
p2564
asS'OTO'
p2565
(lp2566
S'NOTNLM'
p2567
asS'OID'
p2568
(lp2569
S'NLM: PMC3779324'
p2570
asS'MH'
p2571
(lp2572
S'Canada/epidemiology'
p2573
aS'Humans'
p2574
aS'Humidity'
p2575
aS'Incidence'
p2576
aS'Influenza A Virus, H1N1 Subtype/*physiology'
p2577
aS'Influenza, Human/*epidemiology/microbiology/*transmission'
p2578
aS'Linear Models'
p2579
aS'Models, Theoretical'
p2580
aS'*Pandemics'
p2581
aS'Seasons'
p2582
aS'Temperature'
p2583
aS'Time Factors'
p2584
asS'EDAT'
p2585
S'2013/09/13 06:00'
p2586
sS'SO'
p2587
S'Proc Biol Sci. 2013 Sep 11;280(1770):20131174. doi: 10.1098/rspb.2013.1174. Print 2013 Nov 7.'
p2588
sS'SB'
p2589
S'IM'
p2590
sS'PMID'
p2591
S'24026815'
p2592
sS'OT'
p2593
(lp2594
S'influenza'
p2595
aS'pandemic'
p2596
aS'school term'
p2597
aS'spatial pattern'
p2598
aS'vaccination'
p2599
aS'weather'
p2600
asS'PST'
p2601
S'epublish'
p2602
stRp2603
ag2
(g3
g4
(dp2604
S'LID'
p2605
S'10.1186/1471-2334-13-428 [doi]'
p2606
sS'STAT'
p2607
S'MEDLINE'
p2608
sS'DEP'
p2609
S'20130911'
p2610
sS'DA'
p2611
S'20131115'
p2612
sS'AID'
p2613
(lp2614
S'1471-2334-13-428 [pii]'
p2615
aS'10.1186/1471-2334-13-428 [doi]'
p2616
asS'CRDT'
p2617
(lp2618
S'2013/09/13 06:00'
p2619
asS'DP'
p2620
S'2013'
p2621
sS'OWN'
p2622
S'NLM'
p2623
sS'PT'
p2624
(lp2625
S'Journal Article'
p2626
asS'LA'
p2627
(lp2628
S'eng'
p2629
asS'DCOM'
p2630
S'20140623'
p2631
sS'JT'
p2632
S'BMC infectious diseases'
p2633
sS'LR'
p2634
S'20150422'
p2635
sS'FAU'
p2636
(lp2637
S'Keegan, Lindsay T'
p2638
aS'Dushoff, Jonathan'
p2639
asS'TI'
p2640
S'Population-level effects of clinical immunity to malaria.'
p2641
sS'PL'
p2642
S'England'
p2643
sS'PG'
p2644
S'428'
p2645
sS'JID'
p2646
S'100968551'
p2647
sS'AB'
p2648
S'BACKGROUND: Despite a resurgence in control efforts, malaria remains a serious public-health problem, causing millions of deaths each year. One factor that complicates malaria-control efforts is clinical immunity, the acquired immune response that protects individuals from symptoms despite the presence of parasites. Clinical immunity protects individuals against disease, but its effects at the population level are complex. It has been previously suggested that under certain circumstances, malaria is bistable: it can persist, if established, in areas where it would not be able to invade. This phenomenon has important implications for control: in areas where malaria is bistable, if malaria could be eliminated until immunity wanes, it would not be able to re-invade. METHODS: Here, we formulate an analytically tractable, dynamical model of malaria transmission to explore the possibility that clinical immunity can lead to bistable malaria dynamics. We summarize what is known and unknown about the parameters underlying this simple model, and solve the model to find a criterion that determines under which conditions we expect bistability to occur. RESULTS: We show that bistability can only occur when clinically immune individuals are more "effective" at transmitting malaria than naive individuals are. We show how this "effectiveness" includes susceptibility, ability to transmit, and duration of infectiousness. We also show that the amount of extra effectiveness necessary depends on the ratio between the duration of infectiousness and the time scale at which immunity is lost. Thus, if the duration of immunity is long, even a small amount of extra transmission effectiveness by clinically immune individuals could lead to bistability. CONCLUSIONS: We demonstrate a simple, plausible mechanism by which clinical immunity may be causing bistability in human malaria transmission. We suggest that simple summary parameters--in particular, the relative transmission effectiveness of clinically immune individuals and the time scale at which clinical immunity is lost--are key to determining where and whether bistability is happening. We hope these findings will guide future efforts to measure transmission parameters and to guide malaria control efforts.'
p2649
sS'AD'
p2650
S'Department of Biology, McMaster University, Hamilton, Ontario, Canada. keeganlt@mcmaster.ca.'
p2651
sS'VI'
p2652
S'13'
p2653
sS'IS'
p2654
S'1471-2334 (Electronic) 1471-2334 (Linking)'
p2655
sS'PMC'
p2656
S'PMC3848694'
p2657
sS'AU'
p2658
(lp2659
S'Keegan LT'
p2660
aS'Dushoff J'
p2661
asS'MHDA'
p2662
S'2014/06/24 06:00'
p2663
sS'PHST'
p2664
(lp2665
S'2013/03/22 [received]'
p2666
aS'2013/08/28 [accepted]'
p2667
aS'2013/09/11 [aheadofprint]'
p2668
asS'OID'
p2669
(lp2670
S'NLM: PMC3848694'
p2671
asS'MH'
p2672
(lp2673
S'Humans'
p2674
aS'Malaria/*immunology/prevention & control/transmission'
p2675
aS'Models, Biological'
p2676
aS'Population Surveillance'
p2677
asS'EDAT'
p2678
S'2013/09/13 06:00'
p2679
sS'SO'
p2680
S'BMC Infect Dis. 2013 Sep 11;13:428. doi: 10.1186/1471-2334-13-428.'
p2681
sS'SB'
p2682
S'IM'
p2683
sS'PMID'
p2684
S'24024630'
p2685
sS'TA'
p2686
S'BMC Infect Dis'
p2687
sS'PST'
p2688
S'epublish'
p2689
stRp2690
ag2
(g3
g4
(dp2691
S'LID'
p2692
S'10.1371/journal.pntd.0002372 [doi]'
p2693
sS'STAT'
p2694
S'MEDLINE'
p2695
sS'DEP'
p2696
S'20130822'
p2697
sS'DA'
p2698
S'20130830'
p2699
sS'AID'
p2700
(lp2701
S'10.1371/journal.pntd.0002372 [doi]'
p2702
aS'PNTD-D-13-00157 [pii]'
p2703
asS'CRDT'
p2704
(lp2705
S'2013/08/31 06:00'
p2706
asS'DP'
p2707
S'2013'
p2708
sS'AD'
p2709
S'Boyd Orr Centre for Population and Ecosystem Health, Institute for Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, Scotland, United Kingdom. sunnytownsend@gmail.com'
p2710
sS'OWN'
p2711
S'NLM'
p2712
sS'PT'
p2713
(lp2714
S'Journal Article'
p2715
aS"Research Support, Non-U.S. Gov't"
p2716
asS'LA'
p2717
(lp2718
S'eng'
p2719
asS'FAU'
p2720
(lp2721
S'Townsend, Sunny E'
p2722
aS'Sumantra, I Putu'
p2723
aS'Pudjiatmoko'
p2724
aS'Bagus, Gusti Ngurah'
p2725
aS'Brum, Eric'
p2726
aS'Cleaveland, Sarah'
p2727
aS'Crafter, Sally'
p2728
aS'Dewi, Ayu P M'
p2729
aS'Dharma, Dewa Made Ngurah'
p2730
aS'Dushoff, Jonathan'
p2731
aS'Girardi, Janice'
p2732
aS'Gunata, I Ketut'
p2733
aS'Hiby, Elly F'
p2734
aS'Kalalo, Corlevin'
p2735
aS'Knobel, Darryn L'
p2736
aS'Mardiana, I Wayan'
p2737
aS'Putra, Anak Agung Gde'
p2738
aS'Schoonman, Luuk'
p2739
aS'Scott-Orr, Helen'
p2740
aS'Shand, Mike'
p2741
aS'Sukanadi, I Wayan'
p2742
aS'Suseno, Pebi Purwo'
p2743
aS'Haydon, Daniel T'
p2744
aS'Hampson, Katie'
p2745
asS'JT'
p2746
S'PLoS neglected tropical diseases'
p2747
sS'LR'
p2748
S'20150423'
p2749
sS'PG'
p2750
S'e2372'
p2751
sS'TI'
p2752
S'Designing programs for eliminating canine rabies from islands: Bali, Indonesia as a case study.'
p2753
sS'RN'
p2754
(lp2755
S'0 (Rabies Vaccines)'
p2756
asS'PL'
p2757
S'United States'
p2758
sS'TA'
p2759
S'PLoS Negl Trop Dis'
p2760
sS'JID'
p2761
S'101291488'
p2762
sS'AB'
p2763
S'BACKGROUND: Canine rabies is one of the most important and feared zoonotic diseases in the world. In some regions rabies elimination is being successfully coordinated, whereas in others rabies is endemic and continues to spread to uninfected areas. As epidemics emerge, both accepted and contentious control methods are used, as questions remain over the most effective strategy to eliminate rabies. The Indonesian island of Bali was rabies-free until 2008 when an epidemic in domestic dogs began, resulting in the deaths of over 100 people. Here we analyze data from the epidemic and compare the effectiveness of control methods at eliminating rabies. METHODOLOGY/PRINCIPAL FINDINGS: Using data from Bali, we estimated the basic reproductive number, R(0), of rabies in dogs, to be ~1 . 2, almost identical to that obtained in ten-fold less dense dog populations and suggesting rabies will not be effectively controlled by reducing dog density. We then developed a model to compare options for mass dog vaccination. Comprehensive high coverage was the single most important factor for achieving elimination, with omission of even small areas (<0.5% of the dog population) jeopardizing success. Parameterizing the model with data from the 2010 and 2011 vaccination campaigns, we show that a comprehensive high coverage campaign in 2012 would likely result in elimination, saving ~550 human lives and ~$15 million in prophylaxis costs over the next ten years. CONCLUSIONS/SIGNIFICANCE: The elimination of rabies from Bali will not be achieved through achievable reductions in dog density. To ensure elimination, concerted high coverage, repeated, mass dog vaccination campaigns are necessary and the cooperation of all regions of the island is critical. Momentum is building towards development of a strategy for the global elimination of canine rabies, and this study offers valuable new insights about the dynamics and control of this disease, with immediate practical relevance.'
p2764
sS'GR'
p2765
(lp2766
S'095787/Z/11/Z/Wellcome Trust/United Kingdom'
p2767
aS'G0901135/Medical Research Council/United Kingdom'
p2768
asS'IP'
p2769
S'8'
sS'IS'
p2770
S'1935-2735 (Electronic) 1935-2727 (Linking)'
p2771
sS'PMC'
p2772
S'PMC3749988'
p2773
sS'DCOM'
p2774
S'20140228'
p2775
sS'AU'
p2776
(lp2777
S'Townsend SE'
p2778
aS'Sumantra IP'
p2779
aS'Pudjiatmoko'
p2780
aS'Bagus GN'
p2781
aS'Brum E'
p2782
aS'Cleaveland S'
p2783
aS'Crafter S'
p2784
aS'Dewi AP'
p2785
aS'Dharma DM'
p2786
aS'Dushoff J'
p2787
aS'Girardi J'
p2788
aS'Gunata IK'
p2789
aS'Hiby EF'
p2790
aS'Kalalo C'
p2791
aS'Knobel DL'
p2792
aS'Mardiana IW'
p2793
aS'Putra AA'
p2794
aS'Schoonman L'
p2795
aS'Scott-Orr H'
p2796
aS'Shand M'
p2797
aS'Sukanadi IW'
p2798
aS'Suseno PP'
p2799
aS'Haydon DT'
p2800
aS'Hampson K'
p2801
asS'VI'
p2802
S'7'
sS'MHDA'
p2803
S'2014/03/01 06:00'
p2804
sS'PHST'
p2805
(lp2806
S'2013/08 [ecollection]'
p2807
aS'2013/01/31 [received]'
p2808
aS'2013/07/02 [accepted]'
p2809
aS'2013/08/22 [epublish]'
p2810
asS'OID'
p2811
(lp2812
S'NLM: PMC3749988'
p2813
asS'MH'
p2814
(lp2815
S'Animals'
p2816
aS'Basic Reproduction Number'
p2817
aS'Disease Eradication/methods/*organization & administration'
p2818
aS'Dog Diseases/*epidemiology/*prevention & control'
p2819
aS'Dogs'
p2820
aS'Indonesia/epidemiology'
p2821
aS'Mass Vaccination/methods'
p2822
aS'Models, Statistical'
p2823
aS'Population Density'
p2824
aS'Rabies/epidemiology/prevention & control/*veterinary'
p2825
aS'Rabies Vaccines/administration & dosage/immunology'
p2826
asS'EDAT'
p2827
S'2013/08/31 06:00'
p2828
sS'SO'
p2829
S'PLoS Negl Trop Dis. 2013 Aug 22;7(8):e2372. doi: 10.1371/journal.pntd.0002372. eCollection 2013.'
p2830
sS'SB'
p2831
S'IM'
p2832
sS'PMID'
p2833
S'23991233'
p2834
sS'PST'
p2835
S'epublish'
p2836
stRp2837
ag2
(g3
g4
(dp2838
S'STAT'
p2839
S'PubMed-not-MEDLINE'
p2840
sS'AB'
p2841
S'We consider the dynamics of an extension of the influential Granovetter model of social behavior, where individuals are affected by their personal preferences and observation of the neighbors\' behavior. Individuals are arranged in a network (usually the square lattice), and each has a state and a fixed threshold for behavior changes. We simulate the system asynchronously by picking a random individual and we either update its state or exchange it with another randomly chosen individual (mixing). We describe the dynamics analytically in the fast-mixing limit by using the mean-field approximation and investigate it mainly numerically in the case of finite mixing. We show that the dynamics converge to a manifold in state space, which determines the possible equilibria, and show how to estimate the projection of this manifold by using simulated trajectories, emitted from different initial points. We show that the effects of considering the network can be decomposed into finite-neighborhood effects, and finite-mixing-rate effects, which have qualitatively similar effects. Both of these effects increase the tendency of the system to move from a less-desired equilibrium to the "ground state." Our findings can be used to probe shifts in behavioral norms and have implications for the role of information flow in determining when social norms that have become unpopular in particular communities (such as foot binding or female genital cutting) persist or vanish.'
p2842
sS'JID'
p2843
S'101136452'
p2844
sS'AD'
p2845
S'Theoretical Biology Laboratory, Department of Biology, McMaster University, Hamilton, Ontario L8S4K1, Canada. aakhmetz@um2.fr'
p2846
sS'DEP'
p2847
S'20130725'
p2848
sS'IP'
p2849
S'1'
sS'IS'
p2850
S'1550-2376 (Electronic) 1539-3755 (Linking)'
p2851
sS'DCOM'
p2852
S'20140408'
p2853
sS'DA'
p2854
S'20130815'
p2855
sS'AU'
p2856
(lp2857
S'Akhmetzhanov AR'
p2858
aS'Worden L'
p2859
aS'Dushoff J'
p2860
asS'PST'
p2861
S'ppublish'
p2862
sS'AID'
p2863
(lp2864
S'10.1103/PhysRevE.88.012816 [doi]'
p2865
asS'CRDT'
p2866
(lp2867
S'2013/08/16 06:00'
p2868
asS'VI'
p2869
S'88'
p2870
sS'DP'
p2871
S'2013 Jul'
p2872
sS'MHDA'
p2873
S'2013/08/16 06:01'
p2874
sS'PHST'
p2875
(lp2876
S'2013/04/18 [received]'
p2877
aS'2013/07/25 [aheadofprint]'
p2878
asS'OWN'
p2879
S'NLM'
p2880
sS'PT'
p2881
(lp2882
S'Journal Article'
p2883
aS"Research Support, Non-U.S. Gov't"
p2884
asS'LA'
p2885
(lp2886
S'eng'
p2887
asS'FAU'
p2888
(lp2889
S'Akhmetzhanov, Andrei R'
p2890
aS'Worden, Lee'
p2891
aS'Dushoff, Jonathan'
p2892
asS'EDAT'
p2893
S'2013/08/16 06:00'
p2894
sS'JT'
p2895
S'Physical review. E, Statistical, nonlinear, and soft matter physics'
p2896
sS'SO'
p2897
S'Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Jul;88(1):012816. Epub 2013 Jul 25.'
p2898
sS'PG'
p2899
S'012816'
p2900
sS'TI'
p2901
S'Effects of mixing in threshold models of social behavior.'
p2902
sS'PMID'
p2903
S'23944527'
p2904
sS'PL'
p2905
S'United States'
p2906
sS'TA'
p2907
S'Phys Rev E Stat Nonlin Soft Matter Phys'
p2908
stRp2909
ag2
(g3
g4
(dp2910
S'LID'
p2911
S'10.1098/rspb.2013.1345 [doi]'
p2912
sS'STAT'
p2913
S'MEDLINE'
p2914
sS'DEP'
p2915
S'20130907'
p2916
sS'DA'
p2917
S'20130711'
p2918
sS'AID'
p2919
(lp2920
S'rspb.2013.1345 [pii]'
p2921
aS'10.1098/rspb.2013.1345 [doi]'
p2922
asS'CRDT'
p2923
(lp2924
S'2013/07/12 06:00'
p2925
asS'DP'
p2926
S'2013 Sep 7'
p2927
sS'OWN'
p2928
S'NLM'
p2929
sS'PT'
p2930
(lp2931
S'Journal Article'
p2932
aS"Research Support, Non-U.S. Gov't"
p2933
asS'LA'
p2934
(lp2935
S'eng'
p2936
asS'FAU'
p2937
(lp2938
S'He, Daihai'
p2939
aS'Dushoff, Jonathan'
p2940
aS'Day, Troy'
p2941
aS'Ma, Junling'
p2942
aS'Earn, David J D'
p2943
asS'JT'
p2944
S'Proceedings. Biological sciences / The Royal Society'
p2945
sS'LR'
p2946
S'20150423'
p2947
sS'PG'
p2948
S'20131345'
p2949
sS'TI'
p2950
S'Inferring the causes of the three waves of the 1918 influenza pandemic in England and Wales.'
p2951
sS'PL'
p2952
S'England'
p2953
sS'TA'
p2954
S'Proc Biol Sci'
p2955
sS'JID'
p2956
S'101245157'
p2957
sS'AB'
p2958
S'Past influenza pandemics appear to be characterized by multiple waves of incidence, but the mechanisms that account for this phenomenon remain unclear. We propose a simple epidemic model, which incorporates three factors that might contribute to the generation of multiple waves: (i) schools opening and closing, (ii) temperature changes during the outbreak, and (iii) changes in human behaviour in response to the outbreak. We fit this model to the reported influenza mortality during the 1918 pandemic in 334 UK administrative units and estimate the epidemiological parameters. We then use information criteria to evaluate how well these three factors explain the observed patterns of mortality. Our results indicate that all three factors are important but that behavioural responses had the largest effect. The parameter values that produce the best fit are biologically reasonable and yield epidemiological dynamics that match the observed data well.'
p2959
sS'AD'
p2960
S"Department of Applied Mathematics, Hong Kong Polytechnic University Hung Hom, , Kowloon, Hong Kong (SAR), People's Republic of China. hedaihai@gmail.com"
p2961
sS'IP'
p2962
S'1766'
p2963
sS'IS'
p2964
S'1471-2954 (Electronic) 0962-8452 (Linking)'
p2965
sS'PMC'
p2966
S'PMC3730600'
p2967
sS'DCOM'
p2968
S'20140224'
p2969
sS'AU'
p2970
(lp2971
S'He D'
p2972
aS'Dushoff J'
p2973
aS'Day T'
p2974
aS'Ma J'
p2975
aS'Earn DJ'
p2976
asS'VI'
p2977
S'280'
p2978
sS'MHDA'
p2979
S'2014/02/25 06:00'
p2980
sS'PHST'
p2981
(lp2982
S'2013 [ppublish]'
p2983
asS'OTO'
p2984
(lp2985
S'NOTNLM'
p2986
asS'OID'
p2987
(lp2988
S'NLM: PMC3730600'
p2989
asS'MH'
p2990
(lp2991
S'*Disease Outbreaks'
p2992
aS'England/epidemiology'
p2993
aS'Humans'
p2994
aS'Incidence'
p2995
aS'Influenza, Human/*epidemiology/mortality'
p2996
aS'Models, Theoretical'
p2997
aS'Schools'
p2998
aS'Seasons'
p2999
aS'Temperature'
p3000
aS'Time Factors'
p3001
aS'Wales/epidemiology'
p3002
asS'EDAT'
p3003
S'2013/07/12 06:00'
p3004
sS'SO'
p3005
S'Proc Biol Sci. 2013 Sep 7;280(1766):20131345. doi: 10.1098/rspb.2013.1345. Print 2013 Sep 7.'
p3006
sS'SB'
p3007
S'IM'
p3008
sS'PMID'
p3009
S'23843396'
p3010
sS'OT'
p3011
(lp3012
S'Spanish flu'
p3013
aS'behavioural response'
p3014
aS'iterated filtering'
p3015
aS'pandemic influenza'
p3016
aS'school closure'
p3017
aS'weather'
p3018
asS'PST'
p3019
S'epublish'
p3020
stRp3021
ag2
(g3
g4
(dp3022
S'LID'
p3023
S'10.1186/1471-2458-13-512 [doi]'
p3024
sS'STAT'
p3025
S'MEDLINE'
p3026
sS'DEP'
p3027
S'20130528'
p3028
sS'DA'
p3029
S'20130708'
p3030
sS'AID'
p3031
(lp3032
S'1471-2458-13-512 [pii]'
p3033
aS'10.1186/1471-2458-13-512 [doi]'
p3034
asS'CRDT'
p3035
(lp3036
S'2013/05/29 06:00'
p3037
asS'DP'
p3038
S'2013'
p3039
sS'AD'
p3040
S'Department of Biology, McMaster University, Hamilton, ON L8S 4K1, Canada.'
p3041
sS'OWN'
p3042
S'NLM'
p3043
sS'PT'
p3044
(lp3045
S'Journal Article'
p3046
aS"Research Support, Non-U.S. Gov't"
p3047
asS'LA'
p3048
(lp3049
S'eng'
p3050
asS'DCOM'
p3051
S'20140921'
p3052
sS'JT'
p3053
S'BMC public health'
p3054
sS'LR'
p3055
S'20151119'
p3056
sS'FAU'
p3057
(lp3058
S'Shi, Chyun-Fung'
p3059
aS'Kouyoumdjian, Fiona G'
p3060
aS'Dushoff, Jonathan'
p3061
asS'TI'
p3062
S'Intimate partner violence is associated with HIV infection in women in Kenya: a cross-sectional analysis.'
p3063
sS'PL'
p3064
S'England'
p3065
sS'PG'
p3066
S'512'
p3067
sS'JID'
p3068
S'100968562'
p3069
sS'AB'
p3070
S"BACKGROUND: The relationship between intimate partner violence (IPV) and women's risk of HIV infection has attracted much recent attention, with varying results in terms of whether there is an association and what the magnitude of association is. Understanding this relationship is important for HIV surveillance and intervention programs. METHODS: We analyzed data from the 2008-2009 Demographic and Health Survey (DHS) in Kenya, on 1,904 women aged 15-49. A generalized linear mixed model was adapted to explore the relationship between IPV and HIV prevalence, controlling for sociodemographic variables, and treating DHS survey clusters, province and ethnicity as random effects. We used principal components analysis (PCA) to calculate a single IPV score for each woman. The effect of HIV risk behaviours on the association between IPV and HIV was also assessed. RESULTS: Controlling for relevant sociodemographic factors, we found that HIV risk was significantly associated with IPV (P <0.01). After adjustment for risk factors as well as sociodemographic variables, the positive association between IPV and HIV remained significant (P=0.035). The estimated effect size of this model corresponds to an odds ratio of 1.55 for HIV infection comparing a woman who experienced no IPV and a woman at the 95th percentile for our IPV index. CONCLUSION: This study provides further evidence that IPV and HIV are associated. In addition, we found that this association remains even when we controlled for several HIV risk factors. This implies that IPV can be used as a marker of potential HIV risk, and may be causally associated with HIV risk. Further, these results suggest that IPV monitoring and prevention may have a useful role in HIV prevention in Kenya. Further research, ideally based on longitudinal observations, is needed to disentangle these relationships."
p3071
sS'GR'
p3072
(lp3073
S'Canadian Institutes of Health Research/Canada'
p3074
asS'VI'
p3075
S'13'
p3076
sS'IS'
p3077
S'1471-2458 (Electronic) 1471-2458 (Linking)'
p3078
sS'PMC'
p3079
S'PMC3702473'
p3080
sS'AU'
p3081
(lp3082
S'Shi CF'
p3083
aS'Kouyoumdjian FG'
p3084
aS'Dushoff J'
p3085
asS'MHDA'
p3086
S'2014/09/23 06:00'
p3087
sS'PHST'
p3088
(lp3089
S'2013/04/08 [received]'
p3090
aS'2013/05/10 [accepted]'
p3091
aS'2013/05/28 [aheadofprint]'
p3092
asS'OID'
p3093
(lp3094
S'NLM: PMC3702473'
p3095
asS'MH'
p3096
(lp3097
S'Adolescent'
p3098
aS'Adult'
p3099
aS'Cluster Analysis'
p3100
aS'Cross-Sectional Studies'
p3101
aS'Domestic Violence/psychology/*statistics & numerical data'
p3102
aS'Female'
p3103
aS'HIV Infections/*epidemiology/psychology/transmission'
p3104
aS'Humans'
p3105
aS'Kenya/epidemiology'
p3106
aS'Linear Models'
p3107
aS'Middle Aged'
p3108
aS'Principal Component Analysis'
p3109
aS'Risk Factors'
p3110
aS'Risk-Taking'
p3111
aS'*Sexual Partners'
p3112
aS'Socioeconomic Factors'
p3113
aS'Surveys and Questionnaires'
p3114
aS"*Women's Health"
p3115
aS'Young Adult'
p3116
asS'EDAT'
p3117
S'2013/05/29 06:00'
p3118
sS'SO'
p3119
S'BMC Public Health. 2013 May 28;13:512. doi: 10.1186/1471-2458-13-512.'
p3120
sS'SB'
p3121
S'IM'
p3122
sS'PMID'
p3123
S'23711189'
p3124
sS'TA'
p3125
S'BMC Public Health'
p3126
sS'PST'
p3127
S'epublish'
p3128
stRp3129
ag2
(g3
g4
(dp3130
S'LID'
p3131
S'10.1093/gbe/evt065 [doi]'
p3132
sS'STAT'
p3133
S'MEDLINE'
p3134
sS'JT'
p3135
S'Genome biology and evolution'
p3136
sS'DA'
p3137
S'20130515'
p3138
sS'AID'
p3139
(lp3140
S'evt065 [pii]'
p3141
aS'10.1093/gbe/evt065 [doi]'
p3142
asS'CRDT'
p3143
(lp3144
S'2013/05/07 06:00'
p3145
asS'DP'
p3146
S'2013'
p3147
sS'OWN'
p3148
S'NLM'
p3149
sS'PT'
p3150
(lp3151
S'Journal Article'
p3152
aS"Research Support, Non-U.S. Gov't"
p3153
asS'LA'
p3154
(lp3155
S'eng'
p3156
asS'FAU'
p3157
(lp3158
S'Shen, Jiangshan J'
p3159
aS'Dushoff, Jonathan'
p3160
aS'Bewick, Adam J'
p3161
aS'Chain, Frederic J J'
p3162
aS'Evans, Ben J'
p3163
asS'LR'
p3164
S'20150427'
p3165
sS'PG'
p3166
S'998-1009'
p3167
sS'TI'
p3168
S'Genomic dynamics of transposable elements in the western clawed frog (Silurana tropicalis).'
p3169
sS'RN'
p3170
(lp3171
S'0 (DNA Transposable Elements)'
p3172
asS'PL'
p3173
S'England'
p3174
sS'TA'
p3175
S'Genome Biol Evol'
p3176
sS'JID'
p3177
S'101509707'
p3178
sS'AB'
p3179
S'Transposable elements (TEs) are repetitive DNA sequences that can make new copies of themselves that are inserted elsewhere in a host genome. The abundance and distributions of TEs vary considerably among phylogenetically diverse hosts. With the aim of exploring the basis of this variation, we evaluated correlations between several genomic variables and the presence of TEs and non-TE repeats in the complete genome sequence of the Western clawed frog (Silurana tropicalis). This analysis reveals patterns of TE insertion consistent with gene disruption but not with the insertional preference model. Analysis of non-TE repeats recovered unique features of their genome-wide distribution when compared with TE repeats, including no strong correlation with exons and a particularly strong negative correlation with GC content. We also collected polymorphism data from 25 TE insertion sites in 19 wild-caught S. tropicalis individuals. DNA transposon insertions were fixed at eight of nine sites and at a high frequency at one of nine, whereas insertions of long terminal repeat (LTR) and non-LTR retrotransposons were fixed at only 4 of 16 sites and at low frequency at 12 of 16. A maximum likelihood model failed to attribute these differences in insertion frequencies to variation in selection pressure on different classes of TE, opening the possibility that other phenomena such as variation in rates of replication or duration of residence in the genome could play a role. Taken together, these results identify factors that sculpt heterogeneity in TE distribution in S. tropicalis and illustrate that genomic dynamics differ markedly among TE classes and between TE and non-TE repeats.'
p3180
sS'AD'
p3181
S'Department of Biology, McMaster University, Hamilton, Ontario, Canada.'
p3182
sS'IP'
p3183
S'5'
sS'IS'
p3184
S'1759-6653 (Electronic) 1759-6653 (Linking)'
p3185
sS'PMC'
p3186
S'PMC3673623'
p3187
sS'DCOM'
p3188
S'20131209'
p3189
sS'AU'
p3190
(lp3191
S'Shen JJ'
p3192
aS'Dushoff J'
p3193
aS'Bewick AJ'
p3194
aS'Chain FJ'
p3195
aS'Evans BJ'
p3196
asS'VI'
p3197
S'5'
sS'MHDA'
p3198
S'2013/12/16 06:00'
p3199
sS'OTO'
p3200
(lp3201
S'NOTNLM'
p3202
asS'OID'
p3203
(lp3204
S'NLM: PMC3673623'
p3205
asS'MH'
p3206
(lp3207
S'Animals'
p3208
aS'Base Sequence'
p3209
aS'Chromosome Mapping'
p3210
aS'DNA Transposable Elements/*genetics'
p3211
aS'*Evolution, Molecular'
p3212
aS'Genome'
p3213
aS'Genome Components'
p3214
aS'*Mutagenesis, Insertional'
p3215
aS'Terminal Repeat Sequences/genetics'
p3216
aS'Xenopus/*genetics'
p3217
asS'EDAT'
p3218
S'2013/05/07 06:00'
p3219
sS'SO'
p3220
S'Genome Biol Evol. 2013;5(5):998-1009. doi: 10.1093/gbe/evt065.'
p3221
sS'SB'
p3222
S'IM'
p3223
sS'PMID'
p3224
S'23645600'
p3225
sS'OT'
p3226
(lp3227
S'African clawed frogs'
p3228
aS'GC content'
p3229
aS'Xenopus tropicalis'
p3230
aS'gene expression'
p3231
aS'genome evolution'
p3232
aS'natural selection'
p3233
asS'PST'
p3234
S'ppublish'
p3235
stRp3236
ag2
(g3
g4
(dp3237
S'LID'
p3238
S'10.1038/ismej.2013.10 [doi]'
p3239
sS'STAT'
p3240
S'MEDLINE'
p3241
sS'DEP'
p3242
S'20130214'
p3243
sS'DA'
p3244
S'20130521'
p3245
sS'AID'
p3246
(lp3247
S'ismej201310 [pii]'
p3248
aS'10.1038/ismej.2013.10 [doi]'
p3249
asS'CRDT'
p3250
(lp3251
S'2013/02/15 06:00'
p3252
asS'DP'
p3253
S'2013 Jun'
p3254
sS'OWN'
p3255
S'NLM'
p3256
sS'PT'
p3257
(lp3258
S'Journal Article'
p3259
aS"Research Support, Non-U.S. Gov't"
p3260
asS'LA'
p3261
(lp3262
S'eng'
p3263
asS'FAU'
p3264
(lp3265
S'Haegeman, Bart'
p3266
aS'Hamelin, Jerome'
p3267
aS'Moriarty, John'
p3268
aS'Neal, Peter'
p3269
aS'Dushoff, Jonathan'
p3270
aS'Weitz, Joshua S'
p3271
asS'JT'
p3272
S'The ISME journal'
p3273
sS'LR'
p3274
S'20150219'
p3275
sS'PG'
p3276
S'1092-101'
p3277
sS'TI'
p3278
S'Robust estimation of microbial diversity in theory and in practice.'
p3279
sS'PL'
p3280
S'England'
p3281
sS'TA'
p3282
S'ISME J'
p3283
sS'JID'
p3284
S'101301086'
p3285
sS'AB'
p3286
S"Quantifying diversity is of central importance for the study of structure, function and evolution of microbial communities. The estimation of microbial diversity has received renewed attention with the advent of large-scale metagenomic studies. Here, we consider what the diversity observed in a sample tells us about the diversity of the community being sampled. First, we argue that one cannot reliably estimate the absolute and relative number of microbial species present in a community without making unsupported assumptions about species abundance distributions. The reason for this is that sample data do not contain information about the number of rare species in the tail of species abundance distributions. We illustrate the difficulty in comparing species richness estimates by applying Chao's estimator of species richness to a set of in silico communities: they are ranked incorrectly in the presence of large numbers of rare species. Next, we extend our analysis to a general family of diversity metrics ('Hill diversities'), and construct lower and upper estimates of diversity values consistent with the sample data. The theory generalizes Chao's estimator, which we retrieve as the lower estimate of species richness. We show that Shannon and Simpson diversity can be robustly estimated for the in silico communities. We analyze nine metagenomic data sets from a wide range of environments, and show that our findings are relevant for empirically-sampled communities. Hence, we recommend the use of Shannon and Simpson diversity rather than species richness in efforts to quantify and compare microbial diversity."
p3287
sS'AD'
p3288
S'Centre for Biodiversity Theory and Modelling, Experimental Ecology Station, Centre National de Recherche Scientifique, Moulis, France. bart.haegeman@ecoex-moulis.cnrs.fr'
p3289
sS'IP'
p3290
S'6'
sS'IS'
p3291
S'1751-7370 (Electronic) 1751-7362 (Linking)'
p3292
sS'PMC'
p3293
S'PMC3660670'
p3294
sS'DCOM'
p3295
S'20131219'
p3296
sS'AU'
p3297
(lp3298
S'Haegeman B'
p3299
aS'Hamelin J'
p3300
aS'Moriarty J'
p3301
aS'Neal P'
p3302
aS'Dushoff J'
p3303
aS'Weitz JS'
p3304
asS'VI'
p3305
S'7'
sS'MHDA'
p3306
S'2013/12/20 06:00'
p3307
sS'PHST'
p3308
(lp3309
S'2013/02/14 [aheadofprint]'
p3310
asS'OID'
p3311
(lp3312
S'NLM: PMC3660670'
p3313
asS'MH'
p3314
(lp3315
S'Archaea/classification'
p3316
aS'Bacteria/*classification'
p3317
aS'*Biodiversity'
p3318
aS'Computer Simulation'
p3319
aS'Metagenomics'
p3320
aS'Regression Analysis'
p3321
aS'Seawater/*microbiology'
p3322
aS'*Soil Microbiology'
p3323
asS'EDAT'
p3324
S'2013/02/15 06:00'
p3325
sS'SO'
p3326
S'ISME J. 2013 Jun;7(6):1092-101. doi: 10.1038/ismej.2013.10. Epub 2013 Feb 14.'
p3327
sS'SB'
p3328
S'IM'
p3329
sS'PMID'
p3330
S'23407313'
p3331
sS'PST'
p3332
S'ppublish'
p3333
stRp3334
ag2
(g3
g4
(dp3335
S'LID'
p3336
S'10.1016/S0140-6736(12)61960-6 [doi] S0140-6736(12)61960-6 [pii]'
p3337
sS'STAT'
p3338
S'MEDLINE'
p3339
sS'DEP'
p3340
S'20130205'
p3341
sS'MID'
p3342
(lp3343
S'NIHMS485512'
p3344
asS'DA'
p3345
S'20130506'
p3346
sS'AID'
p3347
(lp3348
S'S0140-6736(12)61960-6 [pii]'
p3349
aS'10.1016/S0140-6736(12)61960-6 [doi]'
p3350
asS'CRDT'
p3351
(lp3352
S'2013/02/09 06:00'
p3353
asS'DP'
p3354
S'2013 May 4'
p3355
sS'AD'
p3356
S'Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA. steve.bellan@gmail.com'
p3357
sS'OWN'
p3358
S'NLM'
p3359
sS'PT'
p3360
(lp3361
S'Journal Article'
p3362
aS'Research Support, N.I.H., Extramural'
p3363
aS"Research Support, Non-U.S. Gov't"
p3364
aS"Research Support, U.S. Gov't, Non-P.H.S."
p3365
asS'LA'
p3366
(lp3367
S'eng'
p3368
asS'FAU'
p3369
(lp3370
S'Bellan, Steve E'
p3371
aS'Fiorella, Kathryn J'
p3372
aS'Melesse, Dessalegn Y'
p3373
aS'Getz, Wayne M'
p3374
aS'Williams, Brian G'
p3375
aS'Dushoff, Jonathan'
p3376
asS'JT'
p3377
S'Lancet (London, England)'
p3378
sS'LR'
p3379
S'20150616'
p3380
sS'PG'
p3381
S'1561-9'
p3382
sS'TI'
p3383
S'Extra-couple HIV transmission in sub-Saharan Africa: a mathematical modelling study of survey data.'
p3384
sS'PL'
p3385
S'England'
p3386
sS'TA'
p3387
S'Lancet'
p3388
sS'JID'
p3389
S'2985213R'
p3390
sS'AB'
p3391
S'BACKGROUND: The proportion of heterosexual HIV transmission in sub-Saharan Africa that occurs within cohabiting partnerships, compared with that in single people or extra-couple relationships, is widely debated. We estimated the proportional contribution of different routes of transmission to new HIV infections. As plans to use antiretroviral drugs as a strategy for population-level prevention progress, understanding the importance of different transmission routes is crucial to target intervention efforts. METHODS: We built a mechanistic model of HIV transmission with data from Demographic and Health Surveys (DHS) for 2003-2011, of 27,201 cohabiting couples (men aged 15-59 years and women aged 15-49 years) from 18 sub-Saharan African countries with information about relationship duration, age at sexual debut, and HIV serostatus. We combined this model with estimates of HIV survival times and country-specific estimates of HIV prevalence and coverage of antiretroviral therapy (ART). We then estimated the proportion of recorded infections in surveyed cohabiting couples that occurred before couple formation, between couple members, and because of extra-couple intercourse. FINDINGS: In surveyed couples, we estimated that extra-couple transmission accounted for 27-61% of all HIV infections in men and 21-51% of all those in women, with ranges showing intercountry variation. We estimated that in 2011, extra-couple transmission accounted for 32-65% of new incident HIV infections in men in cohabiting couples, and 10-47% of new infections in women in such couples. Our findings suggest that transmission within couples occurs largely from men to women; however, the latter sex have a very high-risk period before couple formation. INTERPRETATION: Because of the large contribution of extra-couple transmission to new HIV infections, interventions for HIV prevention should target the general sexually active population and not only serodiscordant couples. FUNDING: US National Institutes of Health, US National Science Foundation, and J S McDonnell Foundation.'
p3392
sS'GR'
p3393
(lp3394
S'GM83863/GM/NIGMS NIH HHS/United States'
p3395
aS'R01 GM083863/GM/NIGMS NIH HHS/United States'
p3396
aS'R24 TW008822/TW/FIC NIH HHS/United States'
p3397
aS'R24TW008822/TW/FIC NIH HHS/United States'
p3398
asS'IP'
p3399
S'9877'
p3400
sS'IS'
p3401
S'1474-547X (Electronic) 0140-6736 (Linking)'
p3402
sS'PMC'
p3403
S'PMC3703831'
p3404
sS'DCOM'
p3405
S'20130530'
p3406
sS'CIN'
p3407
(lp3408
S'Lancet. 2013 May 4;381(9877):1519-21. PMID: 23391463'
p3409
asS'AU'
p3410
(lp3411
S'Bellan SE'
p3412
aS'Fiorella KJ'
p3413
aS'Melesse DY'
p3414
aS'Getz WM'
p3415
aS'Williams BG'
p3416
aS'Dushoff J'
p3417
asS'VI'
p3418
S'381'
p3419
sS'MHDA'
p3420
S'2013/06/01 06:00'
p3421
sS'PHST'
p3422
(lp3423
S'2013/02/05 [aheadofprint]'
p3424
asS'OID'
p3425
(lp3426
S'NLM: NIHMS485512'
p3427
aS'NLM: PMC3703831'
p3428
asS'CI'
p3429
(lp3430
S'Copyright (c) 2013 Elsevier Ltd. All rights reserved.'
p3431
asS'MH'
p3432
(lp3433
S'Adolescent'
p3434
aS'Adult'
p3435
aS'Africa South of the Sahara/epidemiology'
p3436
aS'Family Characteristics'
p3437
aS'Female'
p3438
aS'HIV Infections/*epidemiology/*transmission'
p3439
aS'HIV Seropositivity/epidemiology/transmission'
p3440
aS'Health Surveys'
p3441
aS'Humans'
p3442
aS'Male'
p3443
aS'Middle Aged'
p3444
aS'Models, Biological'
p3445
aS'Young Adult'
p3446
asS'EDAT'
p3447
S'2013/02/09 06:00'
p3448
sS'SO'
p3449
S'Lancet. 2013 May 4;381(9877):1561-9. doi: 10.1016/S0140-6736(12)61960-6. Epub 2013 Feb 5.'
p3450
sS'SB'
p3451
S'AIM IM'
p3452
sS'PMID'
p3453
S'23391466'
p3454
sS'PST'
p3455
S'ppublish'
p3456
stRp3457
ag2
(g3
g4
(dp3458
S'LID'
p3459
S'10.1371/journal.pone.0043866 [doi]'
p3460
sS'STAT'
p3461
S'MEDLINE'
p3462
sS'DEP'
p3463
S'20120918'
p3464
sS'DA'
p3465
S'20121010'
p3466
sS'AID'
p3467
(lp3468
S'10.1371/journal.pone.0043866 [doi]'
p3469
aS'PONE-D-12-04836 [pii]'
p3470
asS'CRDT'
p3471
(lp3472
S'2012/10/11 06:00'
p3473
asS'DP'
p3474
S'2012'
p3475
sS'OWN'
p3476
S'NLM'
p3477
sS'PT'
p3478
(lp3479
S'Journal Article'
p3480
aS"Research Support, Non-U.S. Gov't"
p3481
aS"Research Support, U.S. Gov't, Non-P.H.S."
p3482
asS'LA'
p3483
(lp3484
S'eng'
p3485
asS'FAU'
p3486
(lp3487
S'Jiang, Xingpeng'
p3488
aS'Langille, Morgan G I'
p3489
aS'Neches, Russell Y'
p3490
aS'Elliot, Marie'
p3491
aS'Levin, Simon A'
p3492
aS'Eisen, Jonathan A'
p3493
aS'Weitz, Joshua S'
p3494
aS'Dushoff, Jonathan'
p3495
asS'JT'
p3496
S'PloS one'
p3497
sS'LR'
p3498
S'20150222'
p3499
sS'PG'
p3500
S'e43866'
p3501
sS'TI'
p3502
S'Functional biogeography of ocean microbes revealed through non-negative matrix factorization.'
p3503
sS'RN'
p3504
(lp3505
S'0 (Proteins)'
p3506
asS'PL'
p3507
S'United States'
p3508
sS'TA'
p3509
S'PLoS One'
p3510
sS'JID'
p3511
S'101285081'
p3512
sS'AB'
p3513
S'The direct "metagenomic" sequencing of genomic material from complex assemblages of bacteria, archaea, viruses and microeukaryotes has yielded new insights into the structure of microbial communities. For example, analysis of metagenomic data has revealed the existence of previously unknown microbial taxa whose spatial distributions are limited by environmental conditions, ecological competition, and dispersal mechanisms. However, differences in genotypes that might lead biologists to designate two microbes as taxonomically distinct need not necessarily imply differences in ecological function. Hence, there is a growing need for large-scale analysis of the distribution of microbial function across habitats. Here, we present a framework for investigating the biogeography of microbial function by analyzing the distribution of protein families inferred from environmental sequence data across a global collection of sites. We map over 6,000,000 protein sequences from unassembled reads from the Global Ocean Survey dataset to [Formula: see text] protein families, generating a protein family relative abundance matrix that describes the distribution of each protein family across sites. We then use non-negative matrix factorization (NMF) to approximate these protein family profiles as linear combinations of a small number of ecological components. Each component has a characteristic functional profile and site profile. Our approach identifies common functional signatures within several of the components. We use our method as a filter to estimate functional distance between sites, and find that an NMF-filtered measure of functional distance is more strongly correlated with environmental distance than a comparable PCA-filtered measure. We also find that functional distance is more strongly correlated with environmental distance than with geographic distance, in agreement with prior studies. We identify similar protein functions in several components and suggest that functional co-occurrence across metagenomic samples could lead to future methods for de-novo functional prediction. We conclude by discussing how NMF, and other dimension reduction methods, can help enable a macroscopic functional description of marine ecosystems.'
p3514
sS'AD'
p3515
S'College of Information Science and Technology, Drexel University, Philadelphia, Pennsylvania, USA.'
p3516
sS'IP'
p3517
S'9'
sS'IS'
p3518
S'1932-6203 (Electronic) 1932-6203 (Linking)'
p3519
sS'PMC'
p3520
S'PMC3445553'
p3521
sS'DCOM'
p3522
S'20130422'
p3523
sS'AU'
p3524
(lp3525
S'Jiang X'
p3526
aS'Langille MG'
p3527
aS'Neches RY'
p3528
aS'Elliot M'
p3529
aS'Levin SA'
p3530
aS'Eisen JA'
p3531
aS'Weitz JS'
p3532
aS'Dushoff J'
p3533
asS'VI'
p3534
S'7'
sS'MHDA'
p3535
S'2013/04/23 06:00'
p3536
sS'PHST'
p3537
(lp3538
S'2012/02/12 [received]'
p3539
aS'2012/07/30 [accepted]'
p3540
aS'2012/09/18 [epublish]'
p3541
asS'OID'
p3542
(lp3543
S'NLM: PMC3445553'
p3544
asS'MH'
p3545
(lp3546
S'*Algorithms'
p3547
aS'Aquatic Organisms/*classification/genetics'
p3548
aS'Archaea/*classification/genetics'
p3549
aS'Atlantic Ocean'
p3550
aS'Bacteria/*classification/genetics'
p3551
aS'Eukaryotic Cells/*classification/metabolism'
p3552
aS'Metagenomics'
p3553
aS'Microbial Consortia/genetics'
p3554
aS'Pacific Ocean'
p3555
aS'Phylogeny'
p3556
aS'Phylogeography'
p3557
aS'Proteins/*classification/genetics'
p3558
aS'Viruses/*classification/genetics'
p3559
asS'EDAT'
p3560
S'2012/10/11 06:00'
p3561
sS'SO'
p3562
S'PLoS One. 2012;7(9):e43866. doi: 10.1371/journal.pone.0043866. Epub 2012 Sep 18.'
p3563
sS'SB'
p3564
S'IM'
p3565
sS'PMID'
p3566
S'23049741'
p3567
sS'PST'
p3568
S'ppublish'
p3569
stRp3570
ag2
(g3
g4
(dp3571
S'LID'
p3572
S'10.1371/journal.pbio.1001295 [doi]'
p3573
sS'STAT'
p3574
S'MEDLINE'
p3575
sS'DEP'
p3576
S'20120403'
p3577
sS'DA'
p3578
S'20120417'
p3579
sS'AID'
p3580
(lp3581
S'10.1371/journal.pbio.1001295 [doi]'
p3582
aS'PBIOLOGY-D-11-04141 [pii]'
p3583
asS'CRDT'
p3584
(lp3585
S'2012/04/18 06:00'
p3586
asS'DP'
p3587
S'2012'
p3588
sS'GR'
p3589
(lp3590
S'GM83863/GM/NIGMS NIH HHS/United States'
p3591
aS'R24TW008822/TW/FIC NIH HHS/United States'
p3592
asS'OWN'
p3593
S'NLM'
p3594
sS'PT'
p3595
(lp3596
S'Journal Article'
p3597
aS'Research Support, N.I.H., Extramural'
p3598
aS"Research Support, Non-U.S. Gov't"
p3599
aS"Research Support, U.S. Gov't, Non-P.H.S."
p3600
asS'LA'
p3601
(lp3602
S'eng'
p3603
asS'FAU'
p3604
(lp3605
S'Bellan, Steve E'
p3606
aS'Pulliam, Juliet R C'
p3607
aS'Scott, James C'
p3608
aS'Dushoff, Jonathan'
p3609
asS'JT'
p3610
S'PLoS biology'
p3611
sS'LR'
p3612
S'20150225'
p3613
sS'PG'
p3614
S'e1001295'
p3615
sS'TI'
p3616
S'How to make epidemiological training infectious.'
p3617
sS'PL'
p3618
S'United States'
p3619
sS'TA'
p3620
S'PLoS Biol'
p3621
sS'FIR'
p3622
(lp3623
S'Bellan, Steve E'
p3624
aS'Pulliam, Juliet R C'
p3625
aS'Scott, James C'
p3626
aS'Dushoff, Jonathan'
p3627
aS'Porco, Travis C'
p3628
aS'Williams, Brian G'
p3629
aS'Hargrove, John W'
p3630
aS'Welte, Alex'
p3631
aS'Delva, Wim'
p3632
aS'Hitchcock, Gavin'
p3633
aS'Adams, Bibi'
p3634
aS'Blows, Linsay'
p3635
aS'Fanucchi, Dario'
p3636
aS'Irunde, Jacob Ismail'
p3637
aS'Nezar Gennrich, Jessica'
p3638
aS'Jones, Piet'
p3639
aS'Maluta, Eric'
p3640
aS'Marutla, Geoffrey'
p3641
aS'Mazinu, Cynthia'
p3642
aS'Mudimu, Edinah'
p3643
aS'Nakakawa, Juliet'
p3644
aS'Nelufule, Nthatheni Norman'
p3645
aS'Ngwenya, Olina'
p3646
aS'Pascal, Dany'
p3647
aS'Reddy, Tarylee'
p3648
aS'Sekgobela, Wilcan'
p3649
aS'Serumula, Valrie Mabu'
p3650
aS'Seuneu, Milaine'
p3651
aS'Shabangu, Patrick'
p3652
aS'Van Rensburg, Marinel Janse'
p3653
aS'Wilson, Ben'
p3654
asS'JID'
p3655
S'101183755'
p3656
sS'AB'
p3657
S'Modern infectious disease epidemiology builds on two independently developed fields: classical epidemiology and dynamical epidemiology. Over the past decade, integration of the two fields has increased in research practice, but training options within the fields remain distinct with few opportunities for integration in the classroom. The annual Clinic on the Meaningful Modeling of Epidemiological Data (MMED) at the African Institute for Mathematical Sciences has begun to address this gap. MMED offers participants exposure to a broad range of concepts and techniques from both epidemiological traditions. During MMED 2010 we developed a pedagogical approach that bridges the traditional distinction between classical and dynamical epidemiology and can be used at multiple educational levels, from high school to graduate level courses. The approach is hands-on, consisting of a real-time simulation of a stochastic outbreak in course participants, including realistic data reporting, followed by a variety of mathematical and statistical analyses, stemming from both epidemiological traditions. During the exercise, dynamical epidemiologists developed empirical skills such as study design and learned concepts of bias while classical epidemiologists were trained in systems thinking and began to understand epidemics as dynamic nonlinear processes. We believe this type of integrated educational tool will prove extremely valuable in the training of future infectious disease epidemiologists. We also believe that such interdisciplinary training will be critical for local capacity building in analytical epidemiology as Africa continues to produce new cohorts of well-trained mathematicians, statisticians, and scientists. And because the lessons draw on skills and concepts from many fields in biology--from pathogen biology, evolutionary dynamics of host--pathogen interactions, and the ecology of infectious disease to bioinformatics, computational biology, and statistics--this exercise can be incorporated into a broad array of life sciences courses.'
p3658
sS'AD'
p3659
S'Department of Environmental Science, Policy & Management, University of California, Berkeley, California, United States of America. steve.bellan@gmail.com'
p3660
sS'CN'
p3661
(lp3662
S'MMED Organizing Committee'
p3663
asS'IP'
p3664
S'4'
sS'IS'
p3665
S'1545-7885 (Electronic) 1544-9173 (Linking)'
p3666
sS'IR'
p3667
(lp3668
S'Bellan SE'
p3669
aS'Pulliam JR'
p3670
aS'Scott JC'
p3671
aS'Dushoff J'
p3672
aS'Porco TC'
p3673
aS'Williams BG'
p3674
aS'Hargrove JW'
p3675
aS'Welte A'
p3676
aS'Delva W'
p3677
aS'Hitchcock G'
p3678
aS'Adams B'
p3679
aS'Blows L'
p3680
aS'Fanucchi D'
p3681
aS'Irunde JI'
p3682
aS'Nezar Gennrich J'
p3683
aS'Jones P'
p3684
aS'Maluta E'
p3685
aS'Marutla G'
p3686
aS'Mazinu C'
p3687
aS'Mudimu E'
p3688
aS'Nakakawa J'
p3689
aS'Nelufule NN'
p3690
aS'Ngwenya O'
p3691
aS'Pascal D'
p3692
aS'Reddy T'
p3693
aS'Sekgobela W'
p3694
aS'Serumula VM'
p3695
aS'Seuneu M'
p3696
aS'Shabangu P'
p3697
aS'Van Rensburg MJ'
p3698
aS'Wilson B'
p3699
asS'DCOM'
p3700
S'20120807'
p3701
sS'AU'
p3702
(lp3703
S'Bellan SE'
p3704
aS'Pulliam JR'
p3705
aS'Scott JC'
p3706
aS'Dushoff J'
p3707
asS'VI'
p3708
S'10'
p3709
sS'MHDA'
p3710
S'2012/08/08 06:00'
p3711
sS'PHST'
p3712
(lp3713
S'2012/04/03 [epublish]'
p3714
asS'OID'
p3715
(lp3716
S'NLM: PMC3317897'
p3717
asS'MH'
p3718
(lp3719
S'Data Interpretation, Statistical'
p3720
aS'Epidemics/statistics & numerical data'
p3721
aS'Epidemiologic Factors'
p3722
aS'Epidemiology/*education'
p3723
aS'Humans'
p3724
aS'Models, Biological'
p3725
aS'Models, Statistical'
p3726
aS'*Problem-Based Learning'
p3727
asS'EDAT'
p3728
S'2012/04/18 06:00'
p3729
sS'PMC'
p3730
S'PMC3317897'
p3731
sS'SO'
p3732
S'PLoS Biol. 2012;10(4):e1001295. doi: 10.1371/journal.pbio.1001295. Epub 2012 Apr 3.'
p3733
sS'SB'
p3734
S'IM'
p3735
sS'PMID'
p3736
S'22509129'
p3737
sS'PST'
p3738
S'ppublish'
p3739
stRp3740
ag2
(g3
g4
(dp3741
S'LID'
p3742
S'10.7326/0003-4819-156-3-201202070-00005 [doi]'
p3743
sS'STAT'
p3744
S'MEDLINE'
p3745
sS'JT'
p3746
S'Annals of internal medicine'
p3747
sS'CIN'
p3748
(lp3749
S'Ann Intern Med. 2012 Feb 7;156(3):238-40. PMID: 22312144'
p3750
asS'DA'
p3751
S'20120207'
p3752
sS'AID'
p3753
(lp3754
S'156/3/173 [pii]'
p3755
aS'10.7326/0003-4819-156-3-201202070-00005 [doi]'
p3756
asS'CRDT'
p3757
(lp3758
S'2012/02/08 06:00'
p3759
asS'DP'
p3760
S'2012 Feb 7'
p3761
sS'AD'
p3762
S'McMaster University, Hamilton, Ontario, Canada. earn@math.mcmaster.ca'
p3763
sS'OWN'
p3764
S'NLM'
p3765
sS'PT'
p3766
(lp3767
S'Journal Article'
p3768
aS"Research Support, Non-U.S. Gov't"
p3769
asS'LA'
p3770
(lp3771
S'eng'
p3772
asS'SPIN'
p3773
(lp3774
S'Ann Intern Med. 2012 Feb 7;156(3):I28. PMID: 22312154'
p3775
asS'FAU'
p3776
(lp3777
S'Earn, David J D'
p3778
aS'He, Daihai'
p3779
aS'Loeb, Mark B'
p3780
aS'Fonseca, Kevin'
p3781
aS'Lee, Bonita E'
p3782
aS'Dushoff, Jonathan'
p3783
asS'LR'
p3784
S'20130625'
p3785
sS'PG'
p3786
S'173-81'
p3787
sS'TI'
p3788
S'Effects of school closure on incidence of pandemic influenza in Alberta, Canada.'
p3789
sS'PL'
p3790
S'United States'
p3791
sS'TA'
p3792
S'Ann Intern Med'
p3793
sS'JID'
p3794
S'0372351'
p3795
sS'AB'
p3796
S'BACKGROUND: Control of pandemic influenza by social-distancing measures, such as school closures, is a controversial aspect of pandemic planning. However, investigations of the extent to which these measures actually affect the progression of a pandemic have been limited. OBJECTIVE: To examine correlations between the incidence of pandemic H1N1 (pH1N1) influenza in Alberta, Canada, in 2009 and school closures or weather changes, and to estimate the effects of school closures and weather changes on pH1N1 transmission. DESIGN: Mathematical transmission models were fit to data that compared the pattern of confirmed pH1N1 cases with the school calendar and weather patterns. SETTING: Alberta, Canada, from 19 April 2009 to 2 January 2010. DATA SOURCES: 2009 virologic test results, 2006 census data, 2009 daily temperature and humidity data, and 2009 school calendars. MEASUREMENTS: Age-specific daily counts of positive results for pH1N1 from the complete database of 35 510 specimens submitted to the Alberta Provincial Laboratory for Public Health for virologic testing from 19 April 2009 to 2 January 2010. RESULTS: The ending and restarting of school terms had a major effect in attenuating the first wave and starting the second wave of pandemic influenza cases. Mathematical models suggested that school closure reduced transmission among school-age children by more than 50% and that this was a key factor in interrupting transmission. The models also indicated that seasonal changes in weather had a significant effect on the temporal pattern of the epidemic. LIMITATIONS: Data probably represent a small sample of all viral infections. The mathematical models make simplifying assumptions in order to make simulations and analysis feasible. CONCLUSION: Analysis of data from unrestricted virologic testing during an influenza pandemic provides compelling evidence that closing schools can have dramatic effects on transmission of pandemic influenza. School closure seems to be an effective strategy for slowing the spread of pandemic influenza in countries with social contact networks similar to those in Canada. PRIMARY FUNDING SOURCE: Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, and Public Health Agency of Canada.'
p3797
sS'GR'
p3798
(lp3799
S'Canadian Institutes of Health Research/Canada'
p3800
asS'IP'
p3801
S'3'
sS'IS'
p3802
S'1539-3704 (Electronic) 0003-4819 (Linking)'
p3803
sS'DCOM'
p3804
S'20120703'
p3805
sS'AU'
p3806
(lp3807
S'Earn DJ'
p3808
aS'He D'
p3809
aS'Loeb MB'
p3810
aS'Fonseca K'
p3811
aS'Lee BE'
p3812
aS'Dushoff J'
p3813
asS'VI'
p3814
S'156'
p3815
sS'MHDA'
p3816
S'2012/07/04 06:00'
p3817
sS'MH'
p3818
(lp3819
S'Adolescent'
p3820
aS'Alberta/epidemiology'
p3821
aS'Child'
p3822
aS'Child, Preschool'
p3823
aS'Female'
p3824
aS'Humans'
p3825
aS'Incidence'
p3826
aS'*Influenza A Virus, H1N1 Subtype'
p3827
aS'Influenza, Human/*epidemiology/prevention & control/*transmission'
p3828
aS'Likelihood Functions'
p3829
aS'Male'
p3830
aS'*Pandemics/prevention & control'
p3831
aS'*Schools'
p3832
aS'Seasons'
p3833
aS'Social Isolation'
p3834
asS'EDAT'
p3835
S'2012/02/09 06:00'
p3836
sS'SO'
p3837
S'Ann Intern Med. 2012 Feb 7;156(3):173-81. doi: 10.7326/0003-4819-156-3-201202070-00005.'
p3838
sS'SB'
p3839
S'AIM IM'
p3840
sS'PMID'
p3841
S'22312137'
p3842
sS'PST'
p3843
S'ppublish'
p3844
stRp3845
ag2
(g3
g4
(dp3846
S'LID'
p3847
S'10.1371/journal.pone.0028608 [doi]'
p3848
sS'STAT'
p3849
S'MEDLINE'
p3850
sS'DEP'
p3851
S'20111220'
p3852
sS'DA'
p3853
S'20111229'
p3854
sS'AID'
p3855
(lp3856
S'10.1371/journal.pone.0028608 [doi]'
p3857
aS'PONE-D-11-12844 [pii]'
p3858
asS'CRDT'
p3859
(lp3860
S'2011/12/30 06:00'
p3861
asS'DP'
p3862
S'2011'
p3863
sS'OWN'
p3864
S'NLM'
p3865
sS'PT'
p3866
(lp3867
S'Journal Article'
p3868
aS"Research Support, Non-U.S. Gov't"
p3869
asS'LA'
p3870
(lp3871
S'eng'
p3872
asS'FAU'
p3873
(lp3874
S'Dushoff, Jonathan'
p3875
aS'Patocs, Audrey'
p3876
aS'Shi, Chyun-Fung'
p3877
asS'JT'
p3878
S'PloS one'
p3879
sS'LR'
p3880
S'20150129'
p3881
sS'PG'
p3882
S'e28608'
p3883
sS'TI'
p3884
S'Modeling the population-level effects of male circumcision as an HIV-preventive measure: a gendered perspective.'
p3885
sS'PL'
p3886
S'United States'
p3887
sS'TA'
p3888
S'PLoS One'
p3889
sS'JID'
p3890
S'101285081'
p3891
sS'AB'
p3892
S'BACKGROUND: Evidence from biological, epidemiological, and controlled intervention studies has demonstrated that male circumcision (MC) protects males from HIV infection, and MC is now advocated as a public-health intervention against HIV. MC provides direct protection only to men, but is expected to provide indirect protection to women at risk of acquiring HIV from heterosexual transmission. How such indirect protection interacts with the possibility that MC campaigns will lead to behavior changes, however, is not yet well understood. Our objective here is to investigate the link between individual-level effects of MC campaigns and long-term population-level outcomes resulting from disease dynamics, looking at both genders separately, over a broad range of parameters. METHODS AND FINDINGS: We use simple mathematical models of heterosexual transmission to investigate the potential effects of a circumcision scale-up, combined with possible associated behavioral disinhibition. We examine patterns in expected long-term prevalence using a simple equilibrium model based on transmission factors, and validate our results with ODE-based simulations, focusing on the link between effects on females and those on males.We find that the long-term population-level effects on females and males are not strongly linked: there are many possible ways in which an intervention which reduces prevalence in males might nonetheless increase prevalence in females. CONCLUSIONS: Since an intervention that reduces long-term male prevalence could nonetheless increase long-term female prevalence, MC campaigns should explicitly consider both the short-term and long-term effects of MC interventions on females. Our findings strongly underline the importance of pairing MC programs with education, support programs and HIV testing and counseling, together with other prevention measures.'
p3893
sS'AD'
p3894
S'Department of Biology and Institute of Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada. dushoff@mcmaster.ca'
p3895
sS'IP'
p3896
S'12'
p3897
sS'IS'
p3898
S'1932-6203 (Electronic) 1932-6203 (Linking)'
p3899
sS'PMC'
p3900
S'PMC3243682'
p3901
sS'DCOM'
p3902
S'20120423'
p3903
sS'AU'
p3904
(lp3905
S'Dushoff J'
p3906
aS'Patocs A'
p3907
aS'Shi CF'
p3908
asS'VI'
p3909
S'6'
sS'MHDA'
p3910
S'2012/04/24 06:00'
p3911
sS'PHST'
p3912
(lp3913
S'2011/07/05 [received]'
p3914
aS'2011/11/11 [accepted]'
p3915
aS'2011/12/20 [epublish]'
p3916
asS'OID'
p3917
(lp3918
S'NLM: PMC3243682'
p3919
asS'MH'
p3920
(lp3921
S'*Circumcision, Male'
p3922
aS'Disease Progression'
p3923
aS'Female'
p3924
aS'HIV Infections/epidemiology/physiopathology/*prevention & control/transmission'
p3925
aS'Heterosexuality'
p3926
aS'Humans'
p3927
aS'Male'
p3928
aS'*Models, Theoretical'
p3929
aS'Probability'
p3930
aS'Reproduction'
p3931
aS'Sex Factors'
p3932
aS'Virus Latency'
p3933
asS'EDAT'
p3934
S'2011/12/30 06:00'
p3935
sS'SO'
p3936
S'PLoS One. 2011;6(12):e28608. doi: 10.1371/journal.pone.0028608. Epub 2011 Dec 20.'
p3937
sS'SB'
p3938
S'IM'
p3939
sS'PMID'
p3940
S'22205956'
p3941
sS'PST'
p3942
S'ppublish'
p3943
stRp3944
ag2
(g3
g4
(dp3945
S'LID'
p3946
S'10.1186/1471-2164-12-599 [doi]'
p3947
sS'STAT'
p3948
S'MEDLINE'
p3949
sS'DEP'
p3950
S'20111212'
p3951
sS'CI'
p3952
(lp3953
S'(c) 2011 Chain et al; licensee BioMed Central Ltd.'
p3954
asS'DA'
p3955
S'20120116'
p3956
sS'AID'
p3957
(lp3958
S'1471-2164-12-599 [pii]'
p3959
aS'10.1186/1471-2164-12-599 [doi]'
p3960
asS'CRDT'
p3961
(lp3962
S'2011/12/14 06:00'
p3963
asS'DP'
p3964
S'2011'
p3965
sS'OWN'
p3966
S'NLM'
p3967
sS'PT'
p3968
(lp3969
S'Journal Article'
p3970
aS"Research Support, Non-U.S. Gov't"
p3971
asS'LA'
p3972
(lp3973
S'eng'
p3974
asS'DCOM'
p3975
S'20120501'
p3976
sS'JT'
p3977
S'BMC genomics'
p3978
sS'LR'
p3979
S'20150129'
p3980
sS'FAU'
p3981
(lp3982
S'Chain, Frederic J J'
p3983
aS'Dushoff, Jonathan'
p3984
aS'Evans, Ben J'
p3985
asS'TI'
p3986
S'The odds of duplicate gene persistence after polyploidization.'
p3987
sS'PL'
p3988
S'England'
p3989
sS'PG'
p3990
S'599'
p3991
sS'JID'
p3992
S'100965258'
p3993
sS'AB'
p3994
S'BACKGROUND: Gene duplication is an important biological phenomenon associated with genomic redundancy, degeneration, specialization, innovation, and speciation. After duplication, both copies continue functioning when natural selection favors duplicated protein function or expression, or when mutations make them functionally distinct before one copy is silenced. RESULTS: Here we quantify the degree to which genetic parameters related to gene expression, molecular evolution, and gene structure in a diploid frog - Silurana tropicalis - influence the odds of functional persistence of orthologous duplicate genes in a closely related tetraploid species - Xenopus laevis. Using public databases and 454 pyrosequencing, we obtained genetic and expression data from S. tropicalis orthologs of 3,387 X. laevis paralogs and 4,746 X. laevis singletons - the most comprehensive dataset for African clawed frogs yet analyzed. Using logistic regression, we demonstrate that the most important predictors of the odds of duplicate gene persistence in the tetraploid species are the total gene expression level and evenness of expression across tissues and development in the diploid species. Slow protein evolution and information density (fewer exons, shorter introns) in the diploid are also positively correlated with duplicate gene persistence in the tetraploid. CONCLUSIONS: Our findings suggest that a combination of factors contribute to duplicate gene persistence following whole genome duplication, but that the total expression level and evenness of expression across tissues and through development before duplication are most important. We speculate that these parameters are useful predictors of duplicate gene longevity after whole genome duplication in other taxa.'
p3995
sS'AD'
p3996
S'Department of Biology, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada. chain@evolbio.mpg.de'
p3997
sS'VI'
p3998
S'12'
p3999
sS'IS'
p4000
S'1471-2164 (Electronic) 1471-2164 (Linking)'
p4001
sS'PMC'
p4002
S'PMC3258412'
p4003
sS'AU'
p4004
(lp4005
S'Chain FJ'
p4006
aS'Dushoff J'
p4007
aS'Evans BJ'
p4008
asS'MHDA'
p4009
S'2012/05/02 06:00'
p4010
sS'PHST'
p4011
(lp4012
S'2011/05/27 [received]'
p4013
aS'2011/12/12 [accepted]'
p4014
aS'2011/12/12 [aheadofprint]'
p4015
asS'OID'
p4016
(lp4017
S'NLM: PMC3258412'
p4018
asS'MH'
p4019
(lp4020
S'Animals'
p4021
aS'Anura/genetics'
p4022
aS'Evolution, Molecular'
p4023
aS'Gene Duplication'
p4024
aS'*Polyploidy'
p4025
aS'Species Specificity'
p4026
asS'EDAT'
p4027
S'2011/12/14 06:00'
p4028
sS'SO'
p4029
S'BMC Genomics. 2011 Dec 12;12:599. doi: 10.1186/1471-2164-12-599.'
p4030
sS'SB'
p4031
S'IM'
p4032
sS'PMID'
p4033
S'22151890'
p4034
sS'TA'
p4035
S'BMC Genomics'
p4036
sS'PST'
p4037
S'epublish'
p4038
stRp4039
ag2
(g3
g4
(dp4040
S'LID'
p4041
S'10.1016/j.jtbi.2011.08.003 [doi]'
p4042
sS'STAT'
p4043
S'MEDLINE'
p4044
sS'DEP'
p4045
S'20110816'
p4046
sS'CI'
p4047
(lp4048
S'Copyright (c) 2011 Elsevier Ltd. All rights reserved.'
p4049
asS'DA'
p4050
S'20110929'
p4051
sS'AID'
p4052
(lp4053
S'S0022-5193(11)00389-4 [pii]'
p4054
aS'10.1016/j.jtbi.2011.08.003 [doi]'
p4055
asS'CRDT'
p4056
(lp4057
S'2011/08/23 06:00'
p4058
asS'DP'
p4059
S'2011 Nov 7'
p4060
sS'AD'
p4061
S'Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.'
p4062
sS'OWN'
p4063
S'NLM'
p4064
sS'PT'
p4065
(lp4066
S'Journal Article'
p4067
aS"Research Support, Non-U.S. Gov't"
p4068
asS'LA'
p4069
(lp4070
S'eng'
p4071
asS'DCOM'
p4072
S'20111215'
p4073
sS'JT'
p4074
S'Journal of theoretical biology'
p4075
sS'FAU'
p4076
(lp4077
S'Ma, Junling'
p4078
aS'Dushoff, Jonathan'
p4079
aS'Earn, David J D'
p4080
asS'TI'
p4081
S'Age-specific mortality risk from pandemic influenza.'
p4082
sS'RN'
p4083
(lp4084
S'0 (Antigens, Viral)'
p4085
aS'0 (Cytokines)'
p4086
asS'PL'
p4087
S'England'
p4088
sS'PG'
p4089
S'29-34'
p4090
sS'JID'
p4091
S'0376342'
p4092
sS'AB'
p4093
S'Younger age groups account for proportionally more mortality in influenza pandemics than in seasonal influenza epidemics. Mechanisms that might explain this include young people suffering from an over-reactive immune system ("cytokine storm"), older people benefiting from cross-immunity from a wider variety of previous influenza infections ("antigenic history"), and lifetime immune responses in all people being shaped by their first influenza A infection ("antigenic imprinting" or "original antigenic sin"). We examined whether these mechanisms can explain age-specific influenza mortality patterns, using the complete database of individual deaths in Canada from 1951 to 1999. The mortality pattern during the 1957 pandemic indicates that antigenic imprinting plays an important role in determining age-specific influenza virulence and that both shift years and major drift years contribute significantly to antigenic imprints. This information should help pandemic planners to identify age groups that might respond differently to novel influenza strains.'
p4094
sS'GR'
p4095
(lp4096
S'Canadian Institutes of Health Research/Canada'
p4097
asS'VI'
p4098
S'288'
p4099
sS'IS'
p4100
S'1095-8541 (Electronic) 0022-5193 (Linking)'
p4101
sS'AU'
p4102
(lp4103
S'Ma J'
p4104
aS'Dushoff J'
p4105
aS'Earn DJ'
p4106
asS'MHDA'
p4107
S'2011/12/16 06:00'
p4108
sS'PHST'
p4109
(lp4110
S'2011/02/09 [received]'
p4111
aS'2011/07/22 [revised]'
p4112
aS'2011/08/02 [accepted]'
p4113
aS'2011/08/16 [aheadofprint]'
p4114
asS'MH'
p4115
(lp4116
S'Adolescent'
p4117
aS'Adult'
p4118
aS'Age Distribution'
p4119
aS'Aged'
p4120
aS'Aged, 80 and over'
p4121
aS'Antigens, Viral/immunology'
p4122
aS'Canada/epidemiology'
p4123
aS'Child'
p4124
aS'Child, Preschool'
p4125
aS'Cytokines/biosynthesis'
p4126
aS'Humans'
p4127
aS'Infant'
p4128
aS'Influenza, Human/immunology/*mortality'
p4129
aS'Middle Aged'
p4130
aS'Mortality/trends'
p4131
aS'Orthomyxoviridae/immunology'
p4132
aS'Pandemics'
p4133
aS'Risk Assessment/methods'
p4134
aS'Young Adult'
p4135
asS'EDAT'
p4136
S'2011/08/23 06:00'
p4137
sS'SO'
p4138
S'J Theor Biol. 2011 Nov 7;288:29-34. doi: 10.1016/j.jtbi.2011.08.003. Epub 2011 Aug 16.'
p4139
sS'SB'
p4140
S'IM'
p4141
sS'PMID'
p4142
S'21856313'
p4143
sS'TA'
p4144
S'J Theor Biol'
p4145
sS'PST'
p4146
S'ppublish'
p4147
stRp4148
ag2
(g3
g4
(dp4149
S'LID'
p4150
S'10.1098/rsif.2011.0223 [doi]'
p4151
sS'STAT'
p4152
S'MEDLINE'
p4153
sS'DEP'
p4154
S'20110601'
p4155
sS'DA'
p4156
S'20111123'
p4157
sS'AID'
p4158
(lp4159
S'rsif.2011.0223 [pii]'
p4160
aS'10.1098/rsif.2011.0223 [doi]'
p4161
asS'CRDT'
p4162
(lp4163
S'2011/06/03 06:00'
p4164
asS'DP'
p4165
S'2012 Jan 7'
p4166
sS'GR'
p4167
(lp4168
S'R01-TW05869/TW/FIC NIH HHS/United States'
p4169
asS'OWN'
p4170
S'NLM'
p4171
sS'PT'
p4172
(lp4173
S'Journal Article'
p4174
aS'Research Support, N.I.H., Extramural'
p4175
aS"Research Support, Non-U.S. Gov't"
p4176
aS"Research Support, U.S. Gov't, Non-P.H.S."
p4177
asS'LA'
p4178
(lp4179
S'eng'
p4180
asS'FAU'
p4181
(lp4182
S'Pulliam, Juliet R C'
p4183
aS'Epstein, Jonathan H'
p4184
aS'Dushoff, Jonathan'
p4185
aS'Rahman, Sohayati A'
p4186
aS'Bunning, Michel'
p4187
aS'Jamaluddin, Aziz A'
p4188
aS'Hyatt, Alex D'
p4189
aS'Field, Hume E'
p4190
aS'Dobson, Andrew P'
p4191
aS'Daszak, Peter'
p4192
asS'JT'
p4193
S'Journal of the Royal Society, Interface / the Royal Society'
p4194
sS'LR'
p4195
S'20150204'
p4196
sS'PG'
p4197
S'89-101'
p4198
sS'TI'
p4199
S'Agricultural intensification, priming for persistence and the emergence of Nipah virus: a lethal bat-borne zoonosis.'
p4200
sS'PL'
p4201
S'England'
p4202
sS'TA'
p4203
S'J R Soc Interface'
p4204
sS'JID'
p4205
S'101217269'
p4206
sS'AB'
p4207
S"Emerging zoonoses threaten global health, yet the processes by which they emerge are complex and poorly understood. Nipah virus (NiV) is an important threat owing to its broad host and geographical range, high case fatality, potential for human-to-human transmission and lack of effective prevention or therapies. Here, we investigate the origin of the first identified outbreak of NiV encephalitis in Malaysia and Singapore. We analyse data on livestock production from the index site (a commercial pig farm in Malaysia) prior to and during the outbreak, on Malaysian agricultural production, and from surveys of NiV's wildlife reservoir (flying foxes). Our analyses suggest that repeated introduction of NiV from wildlife changed infection dynamics in pigs. Initial viral introduction produced an explosive epizootic that drove itself to extinction but primed the population for enzootic persistence upon reintroduction of the virus. The resultant within-farm persistence permitted regional spread and increased the number of human infections. This study refutes an earlier hypothesis that anomalous El Nino Southern Oscillation-related climatic conditions drove emergence and suggests that priming for persistence drove the emergence of a novel zoonotic pathogen. Thus, we provide empirical evidence for a causative mechanism previously proposed as a precursor to widespread infection with H5N1 avian influenza and other emerging pathogens."
p4208
sS'AD'
p4209
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.'
p4210
sS'CN'
p4211
(lp4212
S'Henipavirus Ecology Research Group (HERG)'
p4213
asS'IP'
p4214
S'66'
p4215
sS'IS'
p4216
S'1742-5662 (Electronic) 1742-5662 (Linking)'
p4217
sS'PMC'
p4218
S'PMC3223631'
p4219
sS'DCOM'
p4220
S'20120827'
p4221
sS'AU'
p4222
(lp4223
S'Pulliam JR'
p4224
aS'Epstein JH'
p4225
aS'Dushoff J'
p4226
aS'Rahman SA'
p4227
aS'Bunning M'
p4228
aS'Jamaluddin AA'
p4229
aS'Hyatt AD'
p4230
aS'Field HE'
p4231
aS'Dobson AP'
p4232
aS'Daszak P'
p4233
asS'VI'
p4234
S'9'
sS'MHDA'
p4235
S'2012/08/28 06:00'
p4236
sS'PHST'
p4237
(lp4238
S'2011/06/01 [aheadofprint]'
p4239
asS'MH'
p4240
(lp4241
S'Agriculture'
p4242
aS'Animals'
p4243
aS'Chiroptera/*virology'
p4244
aS'Communicable Diseases, Emerging/*epidemiology/transmission'
p4245
aS'*Disease Outbreaks'
p4246
aS'Disease Vectors'
p4247
aS'Encephalitis, Viral/*epidemiology/transmission'
p4248
aS'Henipavirus Infections/*epidemiology/transmission/veterinary'
p4249
aS'Humans'
p4250
aS'*Nipah Virus'
p4251
aS'Swine/virology'
p4252
aS'Swine Diseases/*epidemiology/transmission/virology'
p4253
aS'Zoonoses/*epidemiology/transmission/virology'
p4254
asS'EDAT'
p4255
S'2011/06/03 06:00'
p4256
sS'SO'
p4257
S'J R Soc Interface. 2012 Jan 7;9(66):89-101. doi: 10.1098/rsif.2011.0223. Epub 2011 Jun 1.'
p4258
sS'SB'
p4259
S'IM'
p4260
sS'PMID'
p4261
S'21632614'
p4262
sS'PST'
p4263
S'ppublish'
p4264
stRp4265
ag2
(g3
g4
(dp4266
S'LID'
p4267
S'10.1007/s00285-011-0428-2 [doi]'
p4268
sS'STAT'
p4269
S'MEDLINE'
p4270
sS'DEP'
p4271
S'20110601'
p4272
sS'DA'
p4273
S'20120208'
p4274
sS'AID'
p4275
(lp4276
S'10.1007/s00285-011-0428-2 [doi]'
p4277
asS'CRDT'
p4278
(lp4279
S'2011/06/02 06:00'
p4280
asS'DP'
p4281
S'2012 Mar'
p4282
sS'OWN'
p4283
S'NLM'
p4284
sS'PT'
p4285
(lp4286
S'Journal Article'
p4287
aS"Research Support, U.S. Gov't, Non-P.H.S."
p4288
asS'LA'
p4289
(lp4290
S'eng'
p4291
asS'FAU'
p4292
(lp4293
S'Jiang, Xingpeng'
p4294
aS'Weitz, Joshua S'
p4295
aS'Dushoff, Jonathan'
p4296
asS'JT'
p4297
S'Journal of mathematical biology'
p4298
sS'PG'
p4299
S'697-711'
p4300
sS'TI'
p4301
S'A non-negative matrix factorization framework for identifying modular patterns in metagenomic profile data.'
p4302
sS'PL'
p4303
S'Germany'
p4304
sS'TA'
p4305
S'J Math Biol'
p4306
sS'JID'
p4307
S'7502105'
p4308
sS'AB'
p4309
S'Metagenomic studies sequence DNA directly from environmental samples to explore the structure and function of complex microbial and viral communities. Individual, short pieces of sequenced DNA ("reads") are classified into (putative) taxonomic or metabolic groups which are analyzed for patterns across samples. Analysis of such read matrices is at the core of using metagenomic data to make inferences about ecosystem structure and function. Non-negative matrix factorization (NMF) is a numerical technique for approximating high-dimensional data points as positive linear combinations of positive components. It is thus well suited to interpretation of observed samples as combinations of different components. We develop, test and apply an NMF-based framework to analyze metagenomic read matrices. In particular, we introduce a method for choosing NMF degree in the presence of overlap, and apply spectral-reordering techniques to NMF-based similarity matrices to aid visualization. We show that our method can robustly identify the appropriate degree and disentangle overlapping contributions using synthetic data sets. We then examine and discuss the NMF decomposition of a metabolic profile matrix extracted from 39 publicly available metagenomic samples, and identify canonical sample types, including one associated with coral ecosystems, one associated with highly saline ecosystems and others. We also identify specific associations between pathways and canonical environments, and explore how alternative choices of decompositions facilitate analysis of read matrices at a finer scale.'
p4310
sS'AD'
p4311
S'Department of Biology, McMaster University, Hamilton, Ontario, Canada.'
p4312
sS'IP'
p4313
S'4'
sS'IS'
p4314
S'1432-1416 (Electronic) 0303-6812 (Linking)'
p4315
sS'DCOM'
p4316
S'20120615'
p4317
sS'AU'
p4318
(lp4319
S'Jiang X'
p4320
aS'Weitz JS'
p4321
aS'Dushoff J'
p4322
asS'VI'
p4323
S'64'
p4324
sS'MHDA'
p4325
S'2012/06/16 06:00'
p4326
sS'PHST'
p4327
(lp4328
S'2010/10/04 [received]'
p4329
aS'2011/03/18 [revised]'
p4330
aS'2011/06/01 [aheadofprint]'
p4331
asS'MH'
p4332
(lp4333
S'Animals'
p4334
aS'Databases, Genetic'
p4335
aS'*Ecosystem'
p4336
aS'Metagenomics/*methods'
p4337
aS'*Numerical Analysis, Computer-Assisted'
p4338
aS'Pattern Recognition, Automated/*methods'
p4339
asS'EDAT'
p4340
S'2011/06/02 06:00'
p4341
sS'SO'
p4342
S'J Math Biol. 2012 Mar;64(4):697-711. doi: 10.1007/s00285-011-0428-2. Epub 2011 Jun 1.'
p4343
sS'SB'
p4344
S'IM'
p4345
sS'PMID'
p4346
S'21630089'
p4347
sS'PST'
p4348
S'ppublish'
p4349
stRp4350
ag2
(g3
g4
(dp4351
S'LID'
p4352
S'10.1007/s11538-011-9653-5 [doi]'
p4353
sS'STAT'
p4354
S'MEDLINE'
p4355
sS'DEP'
p4356
S'20110408'
p4357
sS'DA'
p4358
S'20111110'
p4359
sS'AID'
p4360
(lp4361
S'10.1007/s11538-011-9653-5 [doi]'
p4362
asS'CRDT'
p4363
(lp4364
S'2011/04/09 06:00'
p4365
asS'DP'
p4366
S'2011 Dec'
p4367
sS'OWN'
p4368
S'NLM'
p4369
sS'PT'
p4370
(lp4371
S'Journal Article'
p4372
aS"Research Support, Non-U.S. Gov't"
p4373
asS'LA'
p4374
(lp4375
S'eng'
p4376
asS'FAU'
p4377
(lp4378
S'Eftimie, Raluca'
p4379
aS'Dushoff, Jonathan'
p4380
aS'Bridle, Byram W'
p4381
aS'Bramson, Jonathan L'
p4382
aS'Earn, David J D'
p4383
asS'JT'
p4384
S'Bulletin of mathematical biology'
p4385
sS'PG'
p4386
S'2932-61'
p4387
sS'TI'
p4388
S'Multi-stability and multi-instability phenomena in a mathematical model of tumor-immune-virus interactions.'
p4389
sS'RN'
p4390
(lp4391
S'0 (Antigens, Neoplasm)'
p4392
aS'0 (Cancer Vaccines)'
p4393
aS'0 (Viral Vaccines)'
p4394
asS'PL'
p4395
S'United States'
p4396
sS'TA'
p4397
S'Bull Math Biol'
p4398
sS'JID'
p4399
S'0401404'
p4400
sS'AB'
p4401
S'Recent advances in virology, gene therapy, and molecular and cell biology have provided insight into the mechanisms through which viruses can boost the anti-tumor immune response, or can infect and directly kill tumor cells. A recent experimental report (Bridle et al. in Molec. Ther. 18(8):1430-1439, 2010) showed that a sequential treatment approach that involves two viruses that carry the same tumor antigen leads to an improved anti-tumor response compared to the effect of each virus alone. In this article, we derive a mathematical model to investigate the anti-tumor effect of two viruses, and their interactions with the immune cells. We discuss the conditions necessary for permanent tumor elimination and, in this context, we stress the importance of investigating the long-term effect of non-linear interactions. In particular, we discuss multi-stability and multi-instability, two complex phenomena that can cause abrupt transitions between different states in biological and physical systems. In the context of cancer immunotherapies, the transitions between a tumor-free and a tumor-present state have so far been associated with the multi-stability phenomenon. Here, we show that multi-instability can also cause the system to switch from one state to the other. In addition, we show that the multi-stability is driven by the immune response, while the multi-instability is driven by the presence of the virus.'
p4402
sS'AD'
p4403
S'Department of Mathematics and Statistics, McMaster University, Hamilton, ON, L8S 4K1, Canada. reftimie@math.mcmaster.ca'
p4404
sS'IP'
p4405
S'12'
p4406
sS'IS'
p4407
S'1522-9602 (Electronic) 0092-8240 (Linking)'
p4408
sS'DCOM'
p4409
S'20120322'
p4410
sS'AU'
p4411
(lp4412
S'Eftimie R'
p4413
aS'Dushoff J'
p4414
aS'Bridle BW'
p4415
aS'Bramson JL'
p4416
aS'Earn DJ'
p4417
asS'VI'
p4418
S'73'
p4419
sS'MHDA'
p4420
S'2012/03/23 06:00'
p4421
sS'PHST'
p4422
(lp4423
S'2010/10/28 [received]'
p4424
aS'2011/03/15 [accepted]'
p4425
aS'2011/04/08 [aheadofprint]'
p4426
asS'MH'
p4427
(lp4428
S'Antigens, Neoplasm'
p4429
aS'Cancer Vaccines/therapeutic use'
p4430
aS'Humans'
p4431
aS'Immunologic Memory'
p4432
aS'Mathematical Concepts'
p4433
aS'Models, Biological'
p4434
aS'Neoplasms/*immunology/*therapy/virology'
p4435
aS'Nonlinear Dynamics'
p4436
aS'*Oncolytic Virotherapy'
p4437
aS'Viral Vaccines/therapeutic use'
p4438
asS'EDAT'
p4439
S'2011/04/09 06:00'
p4440
sS'SO'
p4441
S'Bull Math Biol. 2011 Dec;73(12):2932-61. doi: 10.1007/s11538-011-9653-5. Epub 2011 Apr 8.'
p4442
sS'SB'
p4443
S'IM'
p4444
sS'PMID'
p4445
S'21476110'
p4446
sS'PST'
p4447
S'ppublish'
p4448
stRp4449
ag2
(g3
g4
(dp4450
S'LID'
p4451
S'10.1371/journal.pgen.1001301 [doi]'
p4452
sS'STAT'
p4453
S'MEDLINE'
p4454
sS'DEP'
p4455
S'20110217'
p4456
sS'CIN'
p4457
(lp4458
S'Nat Rev Genet. 2011 Apr;12(4):227. PMID: 21358743'
p4459
asS'DA'
p4460
S'20110310'
p4461
sS'AID'
p4462
(lp4463
S'10.1371/journal.pgen.1001301 [doi]'
p4464
asS'CRDT'
p4465
(lp4466
S'2011/03/11 06:00'
p4467
asS'DP'
p4468
S'2011 Feb'
p4469
sS'AD'
p4470
S'Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.'
p4471
sS'OWN'
p4472
S'NLM'
p4473
sS'PT'
p4474
(lp4475
S'Journal Article'
p4476
aS'Research Support, N.I.H., Extramural'
p4477
aS"Research Support, Non-U.S. Gov't"
p4478
asS'LA'
p4479
(lp4480
S'eng'
p4481
asS'FAU'
p4482
(lp4483
S'Kryazhimskiy, Sergey'
p4484
aS'Dushoff, Jonathan'
p4485
aS'Bazykin, Georgii A'
p4486
aS'Plotkin, Joshua B'
p4487
asS'JT'
p4488
S'PLoS genetics'
p4489
sS'LR'
p4490
S'20150204'
p4491
sS'PG'
p4492
S'e1001301'
p4493
sS'TI'
p4494
S'Prevalence of epistasis in the evolution of influenza A surface proteins.'
p4495
sS'RN'
p4496
(lp4497
S'0 (Hemagglutinin Glycoproteins, Influenza Virus)'
p4498
aS'0 (Membrane Proteins)'
p4499
aS'0 (Viral Proteins)'
p4500
aS'20O93L6F9H (Oseltamivir)'
p4501
aS'EC 3.2.1.18 (Neuraminidase)'
p4502
asS'PL'
p4503
S'United States'
p4504
sS'TA'
p4505
S'PLoS Genet'
p4506
sS'JID'
p4507
S'101239074'
p4508
sS'AB'
p4509
S'The surface proteins of human influenza A viruses experience positive selection to escape both human immunity and, more recently, antiviral drug treatments. In bacteria and viruses, immune-escape and drug-resistant phenotypes often appear through a combination of several mutations that have epistatic effects on pathogen fitness. However, the extent and structure of epistasis in influenza viral proteins have not been systematically investigated. Here, we develop a novel statistical method to detect positive epistasis between pairs of sites in a protein, based on the observed temporal patterns of sequence evolution. The method rests on the simple idea that a substitution at one site should rapidly follow a substitution at another site if the sites are positively epistatic. We apply this method to the surface proteins hemagglutinin and neuraminidase of influenza A virus subtypes H3N2 and H1N1. Compared to a non-epistatic null distribution, we detect substantial amounts of epistasis and determine the identities of putatively epistatic pairs of sites. In particular, using sequence data alone, our method identifies epistatic interactions between specific sites in neuraminidase that have recently been demonstrated, in vitro, to confer resistance to the drug oseltamivir; these epistatic interactions are responsible for widespread drug resistance among H1N1 viruses circulating today. This experimental validation demonstrates the predictive power of our method to identify epistatic sites of importance for viral adaptation and public health. We conclude that epistasis plays a large role in shaping the molecular evolution of influenza viruses. In particular, sites with , which would normally not be identified as positively selected, can facilitate viral adaptation through epistatic interactions with their partner sites. The knowledge of specific interactions among sites in influenza proteins may help us to predict the course of antigenic evolution and, consequently, to select more appropriate vaccines and drugs.'
p4510
sS'GR'
p4511
(lp4512
S'2U54AI057168/AI/NIAID NIH HHS/United States'
p4513
asS'IP'
p4514
S'2'
sS'IS'
p4515
S'1553-7404 (Electronic) 1553-7390 (Linking)'
p4516
sS'PMC'
p4517
S'PMC3040651'
p4518
sS'DCOM'
p4519
S'20110715'
p4520
sS'AU'
p4521
(lp4522
S'Kryazhimskiy S'
p4523
aS'Dushoff J'
p4524
aS'Bazykin GA'
p4525
aS'Plotkin JB'
p4526
asS'VI'
p4527
S'7'
sS'MHDA'
p4528
S'2011/07/16 06:00'
p4529
sS'PHST'
p4530
(lp4531
S'2010/08/04 [received]'
p4532
aS'2011/01/07 [accepted]'
p4533
aS'2011/02/17 [epublish]'
p4534
asS'OID'
p4535
(lp4536
S'NLM: PMC3040651'
p4537
asS'MH'
p4538
(lp4539
S'Drug Resistance, Viral/drug effects/genetics'
p4540
aS'*Epistasis, Genetic/drug effects'
p4541
aS'*Evolution, Molecular'
p4542
aS'Hemagglutinin Glycoproteins, Influenza Virus/genetics'
p4543
aS'Humans'
p4544
aS'Influenza A Virus, H1N1 Subtype/drug effects/genetics'
p4545
aS'Influenza A Virus, H3N2 Subtype/drug effects/genetics'
p4546
aS'Influenza A virus/enzymology/*genetics'
p4547
aS'Membrane Proteins/*genetics'
p4548
aS'Mutation/genetics'
p4549
aS'Neuraminidase/genetics'
p4550
aS'Oseltamivir/pharmacology'
p4551
aS'Phylogeny'
p4552
aS'Viral Proteins/*genetics'
p4553
asS'EDAT'
p4554
S'2011/03/11 06:00'
p4555
sS'SO'
p4556
S'PLoS Genet. 2011 Feb;7(2):e1001301. doi: 10.1371/journal.pgen.1001301. Epub 2011 Feb 17.'
p4557
sS'SB'
p4558
S'IM'
p4559
sS'PMID'
p4560
S'21390205'
p4561
sS'PST'
p4562
S'ppublish'
p4563
stRp4564
ag2
(g3
g4
(dp4565
S'STAT'
p4566
S'MEDLINE'
p4567
sS'AB'
p4568
S"Geographically extensive forest inventories, such as the USDA Forest Service's Forest Inventory and Analysis (FIA) program, contain millions of individual tree growth and mortality records that could be used to develop broad-scale models of forest dynamics. A limitation of inventory data, however, is that individual-level measurements of light (L) and other environmental factors are typically absent. Thus, inventory data alone cannot be used to parameterize mechanistic models of forest dynamics in which individual performance depends on light, water, nutrients, etc. To overcome this limitation, we developed methods to estimate species-specific parameters (thetaG) relating sapling growth (G) to L using data sets in which G, but not L, is observed for each sapling. Our approach involves: (1) using calibration data that we collected in both eastern and western North America to quantify the probability that saplings receive different amounts of light, conditional on covariates x that can be obtained from inventory data (e.g., sapling crown class and neighborhood crowding); and (2) combining these probability distributions with observed G and x to estimate thetaG using Bayesian computational methods. Here, we present a test case using a data set in which G, L, and x were observed for saplings of nine species. This test data set allowed us to compare estimates of thetaG obtained from the standard approach (where G and L are observed for each sapling) to our method (where G and x, but not L, are observed). For all species, estimates of thetaG obtained from analyses with and without observed L were similar. This suggests that our approach should be useful for estimating light-dependent growth functions from inventory data that lack direct measurements of L. Our approach could be extended to estimate parameters relating sapling mortality to L from inventory data, as well as to deal with uncertainty in other resources (e.g., water or nutrients) or environmental factors (e.g., temperature)."
p4569
sS'JID'
p4570
S'9889808'
p4571
sS'AD'
p4572
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey 08544, USA. JWL@princeton.edu'
p4573
sS'JT'
p4574
S'Ecological applications : a publication of the Ecological Society of America'
p4575
sS'IP'
p4576
S'3'
sS'IS'
p4577
S'1051-0761 (Print) 1051-0761 (Linking)'
p4578
sS'DCOM'
p4579
S'20100525'
p4580
sS'DA'
p4581
S'20100504'
p4582
sS'AU'
p4583
(lp4584
S'Lichstein JW'
p4585
aS'Dushoff J'
p4586
aS'Ogle K'
p4587
aS'Chen A'
p4588
aS'Purves DW'
p4589
aS'Caspersen JP'
p4590
aS'Pacala SW'
p4591
asS'CRDT'
p4592
(lp4593
S'2010/05/05 06:00'
p4594
asS'VI'
p4595
S'20'
p4596
sS'DP'
p4597
S'2010 Apr'
p4598
sS'MHDA'
p4599
S'2010/05/26 06:00'
p4600
sS'OWN'
p4601
S'NLM'
p4602
sS'PT'
p4603
(lp4604
S'Evaluation Studies'
p4605
aS'Journal Article'
p4606
aS"Research Support, Non-U.S. Gov't"
p4607
aS"Research Support, U.S. Gov't, Non-P.H.S."
p4608
asS'LA'
p4609
(lp4610
S'eng'
p4611
asS'MH'
p4612
(lp4613
S'*Models, Biological'
p4614
aS'*Models, Statistical'
p4615
aS'North America'
p4616
aS'*Sunlight'
p4617
aS'Trees/*growth & development'
p4618
aS'Uncertainty'
p4619
asS'FAU'
p4620
(lp4621
S'Lichstein, Jeremy W'
p4622
aS'Dushoff, Jonathan'
p4623
aS'Ogle, Kiona'
p4624
aS'Chen, Anping'
p4625
aS'Purves, Drew W'
p4626
aS'Caspersen, John P'
p4627
aS'Pacala, Stephen W'
p4628
asS'EDAT'
p4629
S'2010/05/05 06:00'
p4630
sS'PST'
p4631
S'ppublish'
p4632
sS'SO'
p4633
S'Ecol Appl. 2010 Apr;20(3):684-99.'
p4634
sS'PG'
p4635
S'684-99'
p4636
sS'TI'
p4637
S'Unlocking the forest inventory data: relating individual tree performance to unmeasured environmental factors.'
p4638
sS'SB'
p4639
S'IM'
p4640
sS'PMID'
p4641
S'20437956'
p4642
sS'PL'
p4643
S'United States'
p4644
sS'TA'
p4645
S'Ecol Appl'
p4646
stRp4647
ag2
(g3
g4
(dp4648
S'LID'
p4649
S'10.1073/pnas.0902958106 [doi]'
p4650
sS'STAT'
p4651
S'MEDLINE'
p4652
sS'DEP'
p4653
S'20091218'
p4654
sS'DA'
p4655
S'20100118'
p4656
sS'AID'
p4657
(lp4658
S'0902958106 [pii]'
p4659
aS'10.1073/pnas.0902958106 [doi]'
p4660
asS'CRDT'
p4661
(lp4662
S'2010/01/19 06:00'
p4663
asS'DP'
p4664
S'2009 Dec 22'
p4665
sS'AD'
p4666
S'Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. egoldste@hsph.harvard.edu'
p4667
sS'OWN'
p4668
S'NLM'
p4669
sS'PT'
p4670
(lp4671
S'Historical Article'
p4672
aS'Journal Article'
p4673
aS'Research Support, N.I.H., Extramural'
p4674
aS"Research Support, Non-U.S. Gov't"
p4675
asS'LA'
p4676
(lp4677
S'eng'
p4678
asS'FAU'
p4679
(lp4680
S'Goldstein, Edward'
p4681
aS'Dushoff, Jonathan'
p4682
aS'Ma, Junling'
p4683
aS'Plotkin, Joshua B'
p4684
aS'Earn, David J D'
p4685
aS'Lipsitch, Marc'
p4686
asS'JT'
p4687
S'Proceedings of the National Academy of Sciences of the United States of America'
p4688
sS'LR'
p4689
S'20141204'
p4690
sS'PG'
p4691
S'21825-9'
p4692
sS'TI'
p4693
S'Reconstructing influenza incidence by deconvolution of daily mortality time series.'
p4694
sS'PL'
p4695
S'United States'
p4696
sS'TA'
p4697
S'Proc Natl Acad Sci U S A'
p4698
sS'JID'
p4699
S'7505876'
p4700
sS'AB'
p4701
S'We propose a mathematically straightforward method to infer the incidence curve of an epidemic from a recorded daily death curve and time-to-death distribution; the method is based on the Richardson-Lucy deconvolution scheme from optics. We apply the method to reconstruct the incidence curves for the 1918 influenza epidemic in Philadelphia and New York State. The incidence curves are then used to estimate epidemiological quantities, such as daily reproductive numbers and infectivity ratios. We found that during a brief period before the official control measures were implemented in Philadelphia, the drop in the daily number of new infections due to an average infector was much larger than expected from the depletion of susceptibles during that period; this finding was subjected to extensive sensitivity analysis. Combining this with recorded evidence about public behavior, we conclude that public awareness and change in behavior is likely to have had a major role in the slowdown of the epidemic even in a city whose response to the 1918 influenza epidemic is considered to have been among the worst in the U.S.'
p4702
sS'GR'
p4703
(lp4704
S'1U54GM088588/GM/NIGMS NIH HHS/United States'
p4705
aS'3U54AI057168-06S1/AI/NIAID NIH HHS/United States'
p4706
aS'5U01GM076497/GM/NIGMS NIH HHS/United States'
p4707
aS'U54 GM088558/GM/NIGMS NIH HHS/United States'
p4708
aS'Canadian Institutes of Health Research/Canada'
p4709
asS'IP'
p4710
S'51'
p4711
sS'IS'
p4712
S'1091-6490 (Electronic) 0027-8424 (Linking)'
p4713
sS'PMC'
p4714
S'PMC2796142'
p4715
sS'DCOM'
p4716
S'20100218'
p4717
sS'AU'
p4718
(lp4719
S'Goldstein E'
p4720
aS'Dushoff J'
p4721
aS'Ma J'
p4722
aS'Plotkin JB'
p4723
aS'Earn DJ'
p4724
aS'Lipsitch M'
p4725
asS'VI'
p4726
S'106'
p4727
sS'MHDA'
p4728
S'2010/02/19 06:00'
p4729
sS'PHST'
p4730
(lp4731
S'2009/12/18 [aheadofprint]'
p4732
asS'OID'
p4733
(lp4734
S'NLM: PMC2796142'
p4735
asS'MH'
p4736
(lp4737
S'Disease Outbreaks/history'
p4738
aS'History, 20th Century'
p4739
aS'Humans'
p4740
aS'Incidence'
p4741
aS'Influenza, Human/*epidemiology/mortality'
p4742
aS'New York/epidemiology'
p4743
aS'Philadelphia/epidemiology'
p4744
asS'EDAT'
p4745
S'2010/01/19 06:00'
p4746
sS'SO'
p4747
S'Proc Natl Acad Sci U S A. 2009 Dec 22;106(51):21825-9. doi: 10.1073/pnas.0902958106. Epub 2009 Dec 18.'
p4748
sS'SB'
p4749
S'IM'
p4750
sS'PMID'
p4751
S'20080801'
p4752
sS'PST'
p4753
S'ppublish'
p4754
stRp4755
ag2
(g3
g4
(dp4756
S'LID'
p4757
S'10.1016/j.tpb.2009.10.003 [doi]'
p4758
sS'STAT'
p4759
S'MEDLINE'
p4760
sS'DEP'
p4761
S'20091018'
p4762
sS'CI'
p4763
(lp4764
S'Copyright 2009 Elsevier Inc. All rights reserved.'
p4765
asS'DA'
p4766
S'20100129'
p4767
sS'AID'
p4768
(lp4769
S'S0040-5809(09)00113-0 [pii]'
p4770
aS'10.1016/j.tpb.2009.10.003 [doi]'
p4771
asS'CRDT'
p4772
(lp4773
S'2009/10/22 06:00'
p4774
asS'DP'
p4775
S'2010 Feb'
p4776
sS'OWN'
p4777
S'NLM'
p4778
sS'PT'
p4779
(lp4780
S'Journal Article'
p4781
aS"Research Support, Non-U.S. Gov't"
p4782
aS"Research Support, U.S. Gov't, Non-P.H.S."
p4783
asS'LA'
p4784
(lp4785
S'eng'
p4786
asS'FAU'
p4787
(lp4788
S'Boettiger, Carl'
p4789
aS'Dushoff, Jonathan'
p4790
aS'Weitz, Joshua S'
p4791
asS'JT'
p4792
S'Theoretical population biology'
p4793
sS'PG'
p4794
S'6-13'
p4795
sS'TI'
p4796
S'Fluctuation domains in adaptive evolution.'
p4797
sS'PL'
p4798
S'United States'
p4799
sS'TA'
p4800
S'Theor Popul Biol'
p4801
sS'JID'
p4802
S'0256422'
p4803
sS'AB'
p4804
S'We derive an expression for the variation between parallel trajectories in phenotypic evolution, extending the well known result that predicts the mean evolutionary path in adaptive dynamics or quantitative genetics. We show how this expression gives rise to the notion of fluctuation domains-parts of the fitness landscape where the rate of evolution is very predictable (due to fluctuation dissipation) and parts where it is highly variable (due to fluctuation enhancement). These fluctuation domains are determined by the curvature of the fitness landscape. Regions of the fitness landscape with positive curvature, such as adaptive valleys or branching points, experience enhancement. Regions with negative curvature, such as adaptive peaks, experience dissipation. We explore these dynamics in the ecological scenarios of implicit and explicit competition for a limiting resource.'
p4805
sS'AD'
p4806
S'Center for Population Biology, University of California, Davis, United States. cboettig@ucdavis.edu'
p4807
sS'IP'
p4808
S'1'
sS'IS'
p4809
S'1096-0325 (Electronic) 0040-5809 (Linking)'
p4810
sS'DCOM'
p4811
S'20100422'
p4812
sS'AU'
p4813
(lp4814
S'Boettiger C'
p4815
aS'Dushoff J'
p4816
aS'Weitz JS'
p4817
asS'VI'
p4818
S'77'
p4819
sS'MHDA'
p4820
S'2010/04/23 06:00'
p4821
sS'PHST'
p4822
(lp4823
S'2009/04/22 [received]'
p4824
aS'2009/09/26 [revised]'
p4825
aS'2009/10/07 [accepted]'
p4826
aS'2009/10/18 [aheadofprint]'
p4827
asS'MH'
p4828
(lp4829
S'Adaptation, Physiological/*genetics'
p4830
aS'Ecosystem'
p4831
aS'*Evolution, Molecular'
p4832
aS'*Genetic Fitness'
p4833
aS'Genetic Variation'
p4834
aS'Genetics, Population'
p4835
aS'Humans'
p4836
aS'*Models, Genetic'
p4837
aS'Models, Statistical'
p4838
aS'*Mutation'
p4839
aS'Phenotype'
p4840
aS'Thermodynamics'
p4841
asS'EDAT'
p4842
S'2009/10/22 06:00'
p4843
sS'SO'
p4844
S'Theor Popul Biol. 2010 Feb;77(1):6-13. doi: 10.1016/j.tpb.2009.10.003. Epub 2009 Oct 18.'
p4845
sS'SB'
p4846
S'IM'
p4847
sS'PMID'
p4848
S'19843477'
p4849
sS'PST'
p4850
S'ppublish'
p4851
stRp4852
ag2
(g3
g4
(dp4853
S'LID'
p4854
S'10.1186/1471-2105-10-316 [doi]'
p4855
sS'STAT'
p4856
S'MEDLINE'
p4857
sS'DEP'
p4858
S'20091002'
p4859
sS'DA'
p4860
S'20091023'
p4861
sS'AID'
p4862
(lp4863
S'1471-2105-10-316 [pii]'
p4864
aS'10.1186/1471-2105-10-316 [doi]'
p4865
asS'CRDT'
p4866
(lp4867
S'2009/10/06 06:00'
p4868
asS'DP'
p4869
S'2009'
p4870
sS'OWN'
p4871
S'NLM'
p4872
sS'PT'
p4873
(lp4874
S'Journal Article'
p4875
aS"Research Support, U.S. Gov't, Non-P.H.S."
p4876
asS'LA'
p4877
(lp4878
S'eng'
p4879
asS'DCOM'
p4880
S'20100119'
p4881
sS'JT'
p4882
S'BMC bioinformatics'
p4883
sS'LR'
p4884
S'20141207'
p4885
sS'FAU'
p4886
(lp4887
S'Kislyuk, Andrey'
p4888
aS'Bhatnagar, Srijak'
p4889
aS'Dushoff, Jonathan'
p4890
aS'Weitz, Joshua S'
p4891
asS'TI'
p4892
S'Unsupervised statistical clustering of environmental shotgun sequences.'
p4893
sS'PL'
p4894
S'England'
p4895
sS'PG'
p4896
S'316'
p4897
sS'JID'
p4898
S'100965194'
p4899
sS'AB'
p4900
S"BACKGROUND: The development of effective environmental shotgun sequence binning methods remains an ongoing challenge in algorithmic analysis of metagenomic data. While previous methods have focused primarily on supervised learning involving extrinsic data, a first-principles statistical model combined with a self-training fitting method has not yet been developed. RESULTS: We derive an unsupervised, maximum-likelihood formalism for clustering short sequences by their taxonomic origin on the basis of their k-mer distributions. The formalism is implemented using a Markov Chain Monte Carlo approach in a k-mer feature space. We introduce a space transformation that reduces the dimensionality of the feature space and a genomic fragment divergence measure that strongly correlates with the method's performance. Pairwise analysis of over 1000 completely sequenced genomes reveals that the vast majority of genomes have sufficient genomic fragment divergence to be amenable for binning using the present formalism. Using a high-performance implementation, the binner is able to classify fragments as short as 400 nt with accuracy over 90% in simulations of low-complexity communities of 2 to 10 species, given sufficient genomic fragment divergence. The method is available as an open source package called LikelyBin. CONCLUSION: An unsupervised binning method based on statistical signatures of short environmental sequences is a viable stand-alone binning method for low complexity samples. For medium and high complexity samples, we discuss the possibility of combining the current method with other methods as part of an iterative process to enhance the resolving power of sorting reads into taxonomic and/or functional bins."
p4901
sS'AD'
p4902
S'School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA. kislyuk@gatech.edu'
p4903
sS'VI'
p4904
S'10'
p4905
sS'IS'
p4906
S'1471-2105 (Electronic) 1471-2105 (Linking)'
p4907
sS'PMC'
p4908
S'PMC2765972'
p4909
sS'AU'
p4910
(lp4911
S'Kislyuk A'
p4912
aS'Bhatnagar S'
p4913
aS'Dushoff J'
p4914
aS'Weitz JS'
p4915
asS'MHDA'
p4916
S'2010/01/20 06:00'
p4917
sS'PHST'
p4918
(lp4919
S'2009/01/29 [received]'
p4920
aS'2009/10/02 [accepted]'
p4921
aS'2009/10/02 [aheadofprint]'
p4922
asS'OID'
p4923
(lp4924
S'NLM: PMC2765972'
p4925
asS'MH'
p4926
(lp4927
S'Algorithms'
p4928
aS'*Cluster Analysis'
p4929
aS'Genome'
p4930
aS'Genomics/*methods'
p4931
aS'Sequence Analysis, DNA/methods'
p4932
asS'EDAT'
p4933
S'2009/10/06 06:00'
p4934
sS'SO'
p4935
S'BMC Bioinformatics. 2009 Oct 2;10:316. doi: 10.1186/1471-2105-10-316.'
p4936
sS'SB'
p4937
S'IM'
p4938
sS'PMID'
p4939
S'19799776'
p4940
sS'TA'
p4941
S'BMC Bioinformatics'
p4942
sS'PST'
p4943
S'epublish'
p4944
stRp4945
ag2
(g3
g4
(dp4946
S'LID'
p4947
S'10.1111/j.1750-2659.2009.00100.x [doi]'
p4948
sS'STAT'
p4949
S'MEDLINE'
p4950
sS'JT'
p4951
S'Influenza and other respiratory viruses'
p4952
sS'MID'
p4953
(lp4954
S'NIHMS292493'
p4955
asS'DA'
p4956
S'20090825'
p4957
sS'AID'
p4958
(lp4959
S'IRV100 [pii]'
p4960
aS'10.1111/j.1750-2659.2009.00100.x [doi]'
p4961
asS'CRDT'
p4962
(lp4963
S'2009/08/26 09:00'
p4964
asS'DP'
p4965
S'2009 Sep'
p4966
sS'AD'
p4967
S'Division of Mathematical Modeling, British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada. babak.pourbohloul@bccdc.ca'
p4968
sS'OWN'
p4969
S'NLM'
p4970
sS'PT'
p4971
(lp4972
S'Journal Article'
p4973
aS'Research Support, N.I.H., Extramural'
p4974
aS"Research Support, Non-U.S. Gov't"
p4975
aS"Research Support, U.S. Gov't, Non-P.H.S."
p4976
asS'LA'
p4977
(lp4978
S'eng'
p4979
asS'FAU'
p4980
(lp4981
S'Pourbohloul, Babak'
p4982
aS'Ahued, Armando'
p4983
aS'Davoudi, Bahman'
p4984
aS'Meza, Rafael'
p4985
aS'Meyers, Lauren A'
p4986
aS'Skowronski, Danuta M'
p4987
aS'Villasenor, Ignacio'
p4988
aS'Galvan, Fernando'
p4989
aS'Cravioto, Patricia'
p4990
aS'Earn, David J D'
p4991
aS'Dushoff, Jonathan'
p4992
aS'Fisman, David'
p4993
aS'Edmunds, W John'
p4994
aS'Hupert, Nathaniel'
p4995
aS'Scarpino, Samuel V'
p4996
aS'Trujillo, Jesus'
p4997
aS'Lutzow, Miguel'
p4998
aS'Morales, Jorge'
p4999
aS'Contreras, Ada'
p5000
aS'Chavez, Carolina'
p5001
aS'Patrick, David M'
p5002
aS'Brunham, Robert C'
p5003
asS'LR'
p5004
S'20141207'
p5005
sS'PG'
p5006
S'215-22'
p5007
sS'TI'
p5008
S'Initial human transmission dynamics of the pandemic (H1N1) 2009 virus in North America.'
p5009
sS'PL'
p5010
S'England'
p5011
sS'TA'
p5012
S'Influenza Other Respir Viruses'
p5013
sS'JID'
p5014
S'101304007'
p5015
sS'AB'
p5016
S'BACKGROUND: Between 5 and 25 April 2009, pandemic (H1N1) 2009 caused a substantial, severe outbreak in Mexico, and subsequently developed into the first global pandemic in 41 years. We determined the reproduction number of pandemic (H1N1) 2009 by analyzing the dynamics of the complete case series in Mexico City during this early period. METHODS: We analyzed three mutually exclusive datasets from Mexico City Distrito Federal which constituted all suspect cases from 15 March to 25 April: confirmed pandemic (H1N1) 2009 infections, non-pandemic influenza A infections and patients who tested negative for influenza. We estimated the initial reproduction number from 497 suspect cases identified prior to 20 April, using a novel contact network methodology incorporating dates of symptom onset and hospitalization, variation in contact rates, extrinsic sociological factors, and uncertainties in underreporting and disease progression. We tested the robustness of this estimate using both the subset of laboratory-confirmed pandemic (H1N1) 2009 infections and an extended case series through 25 April, adjusted for suspected ascertainment bias. RESULTS: The initial reproduction number (95% confidence interval range) for this novel virus is 1.51 (1.32-1.71) based on suspected cases and 1.43 (1.29-1.57) based on confirmed cases before 20 April. The longer time series (through 25 April) yielded a higher estimate of 2.04 (1.84-2.25), which reduced to 1.44 (1.38-1.51) after correction for ascertainment bias. CONCLUSIONS: The estimated transmission characteristics of pandemic (H1N1) 2009 suggest that pharmaceutical and non-pharmaceutical mitigation measures may appreciably limit its spread prior the development of an effective vaccine.'
p5017
sS'GR'
p5018
(lp5019
S'1-U01-GM087719-01/GM/NIGMS NIH HHS/United States'
p5020
aS'MOP-81273/Canadian Institutes of Health Research/Canada'
p5021
aS'PAP-93425/Canadian Institutes of Health Research/Canada'
p5022
aS'PTL-93146/Canadian Institutes of Health Research/Canada'
p5023
aS'PTL-97125/Canadian Institutes of Health Research/Canada'
p5024
aS'U01 GM087719/GM/NIGMS NIH HHS/United States'
p5025
aS'U01 GM087719-01/GM/NIGMS NIH HHS/United States'
p5026
asS'IP'
p5027
S'5'
sS'IS'
p5028
S'1750-2659 (Electronic) 1750-2640 (Linking)'
p5029
sS'PMC'
p5030
S'PMC3122129'
p5031
sS'DCOM'
p5032
S'20110701'
p5033
sS'AU'
p5034
(lp5035
S'Pourbohloul B'
p5036
aS'Ahued A'
p5037
aS'Davoudi B'
p5038
aS'Meza R'
p5039
aS'Meyers LA'
p5040
aS'Skowronski DM'
p5041
aS'Villasenor I'
p5042
aS'Galvan F'
p5043
aS'Cravioto P'
p5044
aS'Earn DJ'
p5045
aS'Dushoff J'
p5046
aS'Fisman D'
p5047
aS'Edmunds WJ'
p5048
aS'Hupert N'
p5049
aS'Scarpino SV'
p5050
aS'Trujillo J'
p5051
aS'Lutzow M'
p5052
aS'Morales J'
p5053
aS'Contreras A'
p5054
aS'Chavez C'
p5055
aS'Patrick DM'
p5056
aS'Brunham RC'
p5057
asS'VI'
p5058
S'3'
sS'MHDA'
p5059
S'2011/07/02 06:00'
p5060
sS'OID'
p5061
(lp5062
S'NLM: NIHMS292493'
p5063
aS'NLM: PMC3122129'
p5064
asS'MH'
p5065
(lp5066
S'Contact Tracing'
p5067
aS'*Disease Outbreaks'
p5068
aS'Epidemiologic Methods'
p5069
aS'Humans'
p5070
aS'Influenza A Virus, H1N1 Subtype/*pathogenicity'
p5071
aS'Influenza, Human/epidemiology/physiopathology/*transmission/virology'
p5072
aS'Mexico/epidemiology'
p5073
aS'North America/epidemiology'
p5074
aS'*Pandemics'
p5075
asS'EDAT'
p5076
S'2009/08/26 09:00'
p5077
sS'SO'
p5078
S'Influenza Other Respir Viruses. 2009 Sep;3(5):215-22. doi: 10.1111/j.1750-2659.2009.00100.x.'
p5079
sS'SB'
p5080
S'IM'
p5081
sS'PMID'
p5082
S'19702583'
p5083
sS'PST'
p5084
S'ppublish'
p5085
stRp5086
ag2
(g3
g4
(dp5087
S'LID'
p5088
S'10.1371/journal.pcbi.1000472 [doi]'
p5089
sS'STAT'
p5090
S'MEDLINE'
p5091
sS'DEP'
p5092
S'20090821'
p5093
sS'DA'
p5094
S'20090821'
p5095
sS'AID'
p5096
(lp5097
S'10.1371/journal.pcbi.1000472 [doi]'
p5098
asS'CRDT'
p5099
(lp5100
S'2009/08/22 09:00'
p5101
asS'DP'
p5102
S'2009 Aug'
p5103
sS'OWN'
p5104
S'NLM'
p5105
sS'PT'
p5106
(lp5107
S'Journal Article'
p5108
aS"Research Support, Non-U.S. Gov't"
p5109
aS"Research Support, U.S. Gov't, Non-P.H.S."
p5110
asS'LA'
p5111
(lp5112
S'eng'
p5113
asS'FAU'
p5114
(lp5115
S'Ndifon, Wilfred'
p5116
aS'Plotkin, Joshua B'
p5117
aS'Dushoff, Jonathan'
p5118
asS'JT'
p5119
S'PLoS computational biology'
p5120
sS'LR'
p5121
S'20141207'
p5122
sS'PG'
p5123
S'e1000472'
p5124
sS'TI'
p5125
S'On the accessibility of adaptive phenotypes of a bacterial metabolic network.'
p5126
sS'PL'
p5127
S'United States'
p5128
sS'TA'
p5129
S'PLoS Comput Biol'
p5130
sS'JID'
p5131
S'101238922'
p5132
sS'AB'
p5133
S"The mechanisms by which adaptive phenotypes spread within an evolving population after their emergence are understood fairly well. Much less is known about the factors that influence the evolutionary accessibility of such phenotypes, a pre-requisite for their emergence in a population. Here, we investigate the influence of environmental quality on the accessibility of adaptive phenotypes of Escherichia coli's central metabolic network. We used an established flux-balance model of metabolism as the basis for a genotype-phenotype map (GPM). We quantified the effects of seven qualitatively different environments (corresponding to both carbohydrate and gluconeogenic metabolic substrates) on the structure of this GPM. We found that the GPM has a more rugged structure in qualitatively poorer environments, suggesting that adaptive phenotypes could be intrinsically less accessible in such environments. Nevertheless, on average approximately 74% of the genotype can be altered by neutral drift, in the environment where the GPM is most rugged; this could allow evolving populations to circumvent such ruggedness. Furthermore, we found that the normalized mutual information (NMI) of genotype differences relative to phenotype differences, which measures the GPM's capacity to transmit information about phenotype differences, is positively correlated with (simulation-based) estimates of the accessibility of adaptive phenotypes in different environments. These results are consistent with the predictions of a simple analytic theory that makes explicit the relationship between the NMI and the speed of adaptation. The results suggest an intuitive information-theoretic principle for evolutionary adaptation; adaptation could be faster in environments where the GPM has a greater capacity to transmit information about phenotype differences. More generally, our results provide insight into fundamental environment-specific differences in the accessibility of adaptive phenotypes, and they suggest opportunities for research at the interface between information theory and evolutionary biology."
p5134
sS'AD'
p5135
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA. ndifon@gmail.com'
p5136
sS'IP'
p5137
S'8'
sS'IS'
p5138
S'1553-7358 (Electronic) 1553-734X (Linking)'
p5139
sS'PMC'
p5140
S'PMC2716542'
p5141
sS'DCOM'
p5142
S'20091202'
p5143
sS'AU'
p5144
(lp5145
S'Ndifon W'
p5146
aS'Plotkin JB'
p5147
aS'Dushoff J'
p5148
asS'VI'
p5149
S'5'
sS'MHDA'
p5150
S'2009/12/16 06:00'
p5151
sS'PHST'
p5152
(lp5153
S'2008/10/30 [received]'
p5154
aS'2009/07/16 [accepted]'
p5155
aS'2009/08/21 [epublish]'
p5156
asS'OID'
p5157
(lp5158
S'NLM: PMC2716542'
p5159
asS'MH'
p5160
(lp5161
S'Adaptation, Biological/*genetics'
p5162
aS'Algorithms'
p5163
aS'Computer Simulation'
p5164
aS'Escherichia coli/*genetics/*metabolism'
p5165
aS'Evolution, Molecular'
p5166
aS'Genotype'
p5167
aS'Metabolic Networks and Pathways/*genetics'
p5168
aS'*Models, Genetic'
p5169
aS'Phenotype'
p5170
asS'EDAT'
p5171
S'2009/08/22 09:00'
p5172
sS'SO'
p5173
S'PLoS Comput Biol. 2009 Aug;5(8):e1000472. doi: 10.1371/journal.pcbi.1000472. Epub 2009 Aug 21.'
p5174
sS'SB'
p5175
S'IM'
p5176
sS'PMID'
p5177
S'19696877'
p5178
sS'PST'
p5179
S'ppublish'
p5180
stRp5181
ag2
(g3
g4
(dp5182
S'LID'
p5183
S'10.1016/j.vaccine.2009.02.047 [doi]'
p5184
sS'STAT'
p5185
S'MEDLINE'
p5186
sS'DEP'
p5187
S'20090224'
p5188
sS'CIN'
p5189
(lp5190
S'Vaccine. 2009 Aug 13;27(37):5033-4. PMID: 19524615'
p5191
asS'DA'
p5192
S'20090416'
p5193
sS'AID'
p5194
(lp5195
S'S0264-410X(09)00310-7 [pii]'
p5196
aS'10.1016/j.vaccine.2009.02.047 [doi]'
p5197
asS'CRDT'
p5198
(lp5199
S'2009/04/17 09:00'
p5200
asS'DP'
p5201
S'2009 Apr 21'
p5202
sS'OWN'
p5203
S'NLM'
p5204
sS'PT'
p5205
(lp5206
S'Journal Article'
p5207
aS"Research Support, Non-U.S. Gov't"
p5208
asS'LA'
p5209
(lp5210
S'eng'
p5211
asS'FAU'
p5212
(lp5213
S'Ndifon, Wilfred'
p5214
aS'Dushoff, Jonathan'
p5215
aS'Levin, Simon A'
p5216
asS'JT'
p5217
S'Vaccine'
p5218
sS'LR'
p5219
S'20090727'
p5220
sS'PG'
p5221
S'2447-52'
p5222
sS'TI'
p5223
S'On the use of hemagglutination-inhibition for influenza surveillance: surveillance data are predictive of influenza vaccine effectiveness.'
p5224
sS'RN'
p5225
(lp5226
S'0 (Antigens, Viral)'
p5227
aS'0 (Influenza Vaccines)'
p5228
asS'PL'
p5229
S'Netherlands'
p5230
sS'TA'
p5231
S'Vaccine'
p5232
sS'JID'
p5233
S'8406899'
p5234
sS'AB'
p5235
S'The hemagglutination-inhibition (HI) assay is the main tool used by epidemiologists to quantify antigenic differences between circulating influenza virus strains, with the goal of selecting suitable vaccine strains. However, such quantitative measures of antigenic difference were recently shown to have poor predictive accuracy with respect to influenza vaccine effectiveness (VE) in healthy adults. Here, we re-examine those results using a more rigorous criterion for predictive accuracy -- considering only cases when the vaccine (V) and dominant (D) circulating strains are antigenically different -- and greater numbers of HI titers. We find that the Archetti -- Horsfall measure of antigenic difference, which is based on both the normalized HI titer (NHI) of D relative to antisera raised against V and the NHI of V relative to D, predicts VE very well (R(2)=0.62, p=4.1x10(-3)). In contrast, the predictive accuracies of the NHI of D relative to V alone (R(2)=0.01), and two other measures of antigenic difference based on the amino acid sequence of influenza virus hemagglutinin (R(2)=0.03 for both measures) are relatively poor. Furthermore, while VE in the elderly is generally high in cases when D and V are antigenically identical (VE=35%, S.E.=5%), in other cases VE appears to increase with the antigenic difference between D and V (R(2)=0.90, p=2.5x10(-5)). This paradoxical observation could reflect the confounding effects of prior immunity on estimates of VE in the elderly. Together, our results underscore the need for consistently accurate selection of suitable vaccine strains. We suggest directions for further studies aimed at improving vaccine-strain selection and present a large collection of HI titers that will be useful to such studies.'
p5236
sS'AD'
p5237
S'Department of Ecology & Evolutionary Biology, Princeton University, NJ 08544, United States. ndifon@gmail.com'
p5238
sS'IP'
p5239
S'18'
p5240
sS'IS'
p5241
S'0264-410X (Print) 0264-410X (Linking)'
p5242
sS'DCOM'
p5243
S'20090512'
p5244
sS'AU'
p5245
(lp5246
S'Ndifon W'
p5247
aS'Dushoff J'
p5248
aS'Levin SA'
p5249
asS'VI'
p5250
S'27'
p5251
sS'MHDA'
p5252
S'2009/05/13 09:00'
p5253
sS'PHST'
p5254
(lp5255
S'2008/12/02 [received]'
p5256
aS'2009/02/08 [revised]'
p5257
aS'2009/02/18 [accepted]'
p5258
aS'2009/02/24 [aheadofprint]'
p5259
asS'MH'
p5260
(lp5261
S'Antigens, Viral/*analysis'
p5262
aS'Hemagglutination Inhibition Tests/*methods'
p5263
aS'Humans'
p5264
aS'Influenza Vaccines/*immunology'
p5265
aS'Orthomyxoviridae/*immunology'
p5266
asS'EDAT'
p5267
S'2009/04/17 09:00'
p5268
sS'SO'
p5269
S'Vaccine. 2009 Apr 21;27(18):2447-52. doi: 10.1016/j.vaccine.2009.02.047. Epub 2009 Feb 24.'
p5270
sS'SB'
p5271
S'IM'
p5272
sS'PMID'
p5273
S'19368786'
p5274
sS'PST'
p5275
S'ppublish'
p5276
stRp5277
ag2
(g3
g4
(dp5278
S'LID'
p5279
S'10.1086/596510 [doi]'
p5280
sS'STAT'
p5281
S'MEDLINE'
p5282
sS'AB'
p5283
S'Understanding viral factors that promote cross-species transmission is important for evaluating the risk of zoonotic emergence. We constructed a database of viruses of domestic artiodactyls and examined the correlation between traits linked in the literature to cross-species transmission and the ability of viruses to infect humans. Among these traits--genomic material, genome segmentation, and replication without nuclear entry--the last is the strongest predictor of cross-species transmission. This finding highlights nuclear entry as a barrier to transmission and suggests that the ability to complete replication in the cytoplasm may prove to be a useful indicator of the threat of cross-species transmission.'
p5284
sS'JID'
p5285
S'0413675'
p5286
sS'GR'
p5287
(lp5288
S'P50 GM071508/GM/NIGMS NIH HHS/United States'
p5289
asS'JT'
p5290
S'The Journal of infectious diseases'
p5291
sS'IP'
p5292
S'4'
sS'IS'
p5293
S'0022-1899 (Print) 0022-1899 (Linking)'
p5294
sS'DCOM'
p5295
S'20090402'
p5296
sS'DA'
p5297
S'20090313'
p5298
sS'AU'
p5299
(lp5300
S'Pulliam JR'
p5301
aS'Dushoff J'
p5302
asS'AID'
p5303
(lp5304
S'10.1086/596510 [doi]'
p5305
asS'CRDT'
p5306
(lp5307
S'2009/03/14 09:00'
p5308
asS'VI'
p5309
S'199'
p5310
sS'DP'
p5311
S'2009 Feb 15'
p5312
sS'AD'
p5313
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA. pulliamjuliet@mail.nih.gov'
p5314
sS'MHDA'
p5315
S'2009/04/03 09:00'
p5316
sS'OWN'
p5317
S'NLM'
p5318
sS'PT'
p5319
(lp5320
S'Journal Article'
p5321
aS'Research Support, N.I.H., Extramural'
p5322
aS"Research Support, Non-U.S. Gov't"
p5323
aS"Research Support, U.S. Gov't, Non-P.H.S."
p5324
asS'LA'
p5325
(lp5326
S'eng'
p5327
asS'MH'
p5328
(lp5329
S'Animals'
p5330
aS'Animals, Domestic/virology'
p5331
aS'Artiodactyla/*virology'
p5332
aS'Cytoplasm/*virology'
p5333
aS'Databases, Genetic'
p5334
aS'Humans'
p5335
aS'Logistic Models'
p5336
aS'Virus Diseases/*transmission/veterinary/virology'
p5337
aS'*Virus Replication'
p5338
aS'Viruses/*growth & development'
p5339
aS'*Zoonoses'
p5340
asS'FAU'
p5341
(lp5342
S'Pulliam, Juliet R C'
p5343
aS'Dushoff, Jonathan'
p5344
asS'EDAT'
p5345
S'2009/03/14 09:00'
p5346
sS'PST'
p5347
S'ppublish'
p5348
sS'SO'
p5349
S'J Infect Dis. 2009 Feb 15;199(4):565-8. doi: 10.1086/596510.'
p5350
sS'PG'
p5351
S'565-8'
p5352
sS'TI'
p5353
S'Ability to replicate in the cytoplasm predicts zoonotic transmission of livestock viruses.'
p5354
sS'SB'
p5355
S'AIM IM'
p5356
sS'PMID'
p5357
S'19281304'
p5358
sS'PL'
p5359
S'United States'
p5360
sS'TA'
p5361
S'J Infect Dis'
p5362
stRp5363
ag2
(g3
g4
(dp5364
S'LID'
p5365
S'10.1371/journal.pbio.1000053 [doi]'
p5366
sS'STAT'
p5367
S'MEDLINE'
p5368
sS'JT'
p5369
S'PLoS biology'
p5370
sS'DA'
p5371
S'20090402'
p5372
sS'AID'
p5373
(lp5374
S'08-PLBI-RA-2505 [pii]'
p5375
aS'10.1371/journal.pbio.1000053 [doi]'
p5376
asS'CRDT'
p5377
(lp5378
S'2009/03/13 09:00'
p5379
asS'DP'
p5380
S'2009 Mar 10'
p5381
sS'AD'
p5382
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA. K.Hampson@Shef.ac.uk'
p5383
sS'OWN'
p5384
S'NLM'
p5385
sS'PT'
p5386
(lp5387
S'Journal Article'
p5388
aS'Research Support, N.I.H., Extramural'
p5389
aS"Research Support, Non-U.S. Gov't"
p5390
aS"Research Support, U.S. Gov't, Non-P.H.S."
p5391
asS'LA'
p5392
(lp5393
S'eng'
p5394
asS'FAU'
p5395
(lp5396
S'Hampson, Katie'
p5397
aS'Dushoff, Jonathan'
p5398
aS'Cleaveland, Sarah'
p5399
aS'Haydon, Daniel T'
p5400
aS'Kaare, Magai'
p5401
aS'Packer, Craig'
p5402
aS'Dobson, Andy'
p5403
asS'LR'
p5404
S'20141210'
p5405
sS'PG'
p5406
S'e53'
p5407
sS'TI'
p5408
S'Transmission dynamics and prospects for the elimination of canine rabies.'
p5409
sS'RN'
p5410
(lp5411
S'0 (Rabies Vaccines)'
p5412
asS'PL'
p5413
S'United States'
p5414
sS'TA'
p5415
S'PLoS Biol'
p5416
sS'JID'
p5417
S'101183755'
p5418
sS'AB'
p5419
S'Rabies has been eliminated from domestic dog populations in Western Europe and North America, but continues to kill many thousands of people throughout Africa and Asia every year. A quantitative understanding of transmission dynamics in domestic dog populations provides critical information to assess whether global elimination of canine rabies is possible. We report extensive observations of individual rabid animals in Tanzania and generate a uniquely detailed analysis of transmission biology, which explains important epidemiological features, including the level of variation in epidemic trajectories. We found that the basic reproductive number for rabies, R0, is very low in our study area in rural Africa (approximately 1.2) and throughout its historic global range (<2). This finding provides strong support for the feasibility of controlling endemic canine rabies by vaccination, even near wildlife areas with large wild carnivore populations. However, we show that rapid turnover of domestic dog populations has been a major obstacle to successful control in developing countries, thus regular pulse vaccinations will be required to maintain population-level immunity between campaigns. Nonetheless our analyses suggest that with sustained, international commitment, global elimination of rabies from domestic dog populations, the most dangerous vector to humans, is a realistic goal.'
p5420
sS'GR'
p5421
(lp5422
S'082715/Wellcome Trust/United Kingdom'
p5423
aS'Wellcome Trust/United Kingdom'
p5424
asS'IP'
p5425
S'3'
sS'IS'
p5426
S'1545-7885 (Electronic) 1544-9173 (Linking)'
p5427
sS'PMC'
p5428
S'PMC2653555'
p5429
sS'DCOM'
p5430
S'20090515'
p5431
sS'AU'
p5432
(lp5433
S'Hampson K'
p5434
aS'Dushoff J'
p5435
aS'Cleaveland S'
p5436
aS'Haydon DT'
p5437
aS'Kaare M'
p5438
aS'Packer C'
p5439
aS'Dobson A'
p5440
asS'VI'
p5441
S'7'
sS'MHDA'
p5442
S'2009/05/16 09:00'
p5443
sS'PHST'
p5444
(lp5445
S'2008/06/23 [received]'
p5446
aS'2009/01/21 [accepted]'
p5447
asS'OID'
p5448
(lp5449
S'NLM: PMC2653555'
p5450
asS'MH'
p5451
(lp5452
S'Animals'
p5453
aS'Dog Diseases/epidemiology/prevention & control/*transmission'
p5454
aS'Dogs'
p5455
aS'Incidence'
p5456
aS'Population Dynamics'
p5457
aS'Rabies/epidemiology/transmission/*veterinary'
p5458
aS'*Rabies Vaccines'
p5459
aS'Tanzania/epidemiology'
p5460
aS'Vaccination'
p5461
asS'EDAT'
p5462
S'2009/03/13 09:00'
p5463
sS'SO'
p5464
S'PLoS Biol. 2009 Mar 10;7(3):e53. doi: 10.1371/journal.pbio.1000053.'
p5465
sS'SB'
p5466
S'IM'
p5467
sS'PMID'
p5468
S'19278295'
p5469
sS'PST'
p5470
S'ppublish'
p5471
stRp5472
ag2
(g3
g4
(dp5473
S'LID'
p5474
S'10.1073/pnas.0803596105 [doi]'
p5475
sS'STAT'
p5476
S'MEDLINE'
p5477
sS'DEP'
p5478
S'20081210'
p5479
sS'DA'
p5480
S'20081217'
p5481
sS'AID'
p5482
(lp5483
S'0803596105 [pii]'
p5484
aS'10.1073/pnas.0803596105 [doi]'
p5485
asS'CRDT'
p5486
(lp5487
S'2008/12/18 09:00'
p5488
asS'DP'
p5489
S'2008 Dec 16'
p5490
sS'AD'
p5491
S'Miller Institute for Basic Research in Science, and Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA.'
p5492
sS'OWN'
p5493
S'NLM'
p5494
sS'PT'
p5495
(lp5496
S'Journal Article'
p5497
aS'Research Support, N.I.H., Extramural'
p5498
aS"Research Support, Non-U.S. Gov't"
p5499
aS"Research Support, U.S. Gov't, Non-P.H.S."
p5500
asS'LA'
p5501
(lp5502
S'eng'
p5503
asS'FAU'
p5504
(lp5505
S'Livnat, Adi'
p5506
aS'Papadimitriou, Christos'
p5507
aS'Dushoff, Jonathan'
p5508
aS'Feldman, Marcus W'
p5509
asS'JT'
p5510
S'Proceedings of the National Academy of Sciences of the United States of America'
p5511
sS'LR'
p5512
S'20140901'
p5513
sS'PG'
p5514
S'19803-8'
p5515
sS'TI'
p5516
S'A mixability theory for the role of sex in evolution.'
p5517
sS'PL'
p5518
S'United States'
p5519
sS'TA'
p5520
S'Proc Natl Acad Sci U S A'
p5521
sS'JID'
p5522
S'7505876'
p5523
sS'AB'
p5524
S'The question of what role sex plays in evolution is still open despite decades of research. It has often been assumed that sex should facilitate the increase in fitness. Hence, the fact that it may break down highly favorable genetic combinations has been seen as a problem. Here, we consider an alternative approach. We define a measure that represents the ability of alleles to perform well across different combinations and, using numerical iterations within a classical population-genetic framework, show that selection in the presence of sex favors this ability in a highly robust manner. We also show that the mechanism responsible for this effect has been out of the purview of previous theory, because it operates during the evolutionary transient, and that the breaking down of favorable genetic combinations is an integral part of it. Implications of these results and more to evolutionary theory are discussed.'
p5525
sS'GR'
p5526
(lp5527
S'GM28016/GM/NIGMS NIH HHS/United States'
p5528
asS'IP'
p5529
S'50'
p5530
sS'IS'
p5531
S'1091-6490 (Electronic) 0027-8424 (Linking)'
p5532
sS'PMC'
p5533
S'PMC2604923'
p5534
sS'DCOM'
p5535
S'20090112'
p5536
sS'AU'
p5537
(lp5538
S'Livnat A'
p5539
aS'Papadimitriou C'
p5540
aS'Dushoff J'
p5541
aS'Feldman MW'
p5542
asS'VI'
p5543
S'105'
p5544
sS'MHDA'
p5545
S'2009/01/13 09:00'
p5546
sS'PHST'
p5547
(lp5548
S'2008/12/10 [aheadofprint]'
p5549
asS'OID'
p5550
(lp5551
S'NLM: PMC2604923'
p5552
asS'MH'
p5553
(lp5554
S'Alleles'
p5555
aS'*Biological Evolution'
p5556
aS'Genes'
p5557
aS'*Models, Genetic'
p5558
aS'Recombination, Genetic'
p5559
aS'*Sex'
p5560
asS'EDAT'
p5561
S'2008/12/17 09:00'
p5562
sS'SO'
p5563
S'Proc Natl Acad Sci U S A. 2008 Dec 16;105(50):19803-8. doi: 10.1073/pnas.0803596105. Epub 2008 Dec 10.'
p5564
sS'SB'
p5565
S'IM'
p5566
sS'PMID'
p5567
S'19073912'
p5568
sS'PST'
p5569
S'ppublish'
p5570
stRp5571
ag2
(g3
g4
(dp5572
S'LID'
p5573
S'10.1371/journal.pntd.0000339 [doi]'
p5574
sS'STAT'
p5575
S'MEDLINE'
p5576
sS'DEP'
p5577
S'20081125'
p5578
sS'DA'
p5579
S'20081125'
p5580
sS'AID'
p5581
(lp5582
S'10.1371/journal.pntd.0000339 [doi]'
p5583
asS'CRDT'
p5584
(lp5585
S'2008/11/26 09:00'
p5586
asS'DP'
p5587
S'2008'
p5588
sS'AD'
p5589
S'Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield, United Kingdom. K.Hampson@Shef.ac.uk'
p5590
sS'OWN'
p5591
S'NLM'
p5592
sS'PT'
p5593
(lp5594
S'Journal Article'
p5595
aS'Research Support, N.I.H., Extramural'
p5596
aS"Research Support, Non-U.S. Gov't"
p5597
aS"Research Support, U.S. Gov't, Non-P.H.S."
p5598
asS'LA'
p5599
(lp5600
S'eng'
p5601
asS'FAU'
p5602
(lp5603
S'Hampson, Katie'
p5604
aS'Dobson, Andy'
p5605
aS'Kaare, Magai'
p5606
aS'Dushoff, Jonathan'
p5607
aS'Magoto, Matthias'
p5608
aS'Sindoya, Emmanuel'
p5609
aS'Cleaveland, Sarah'
p5610
asS'JT'
p5611
S'PLoS neglected tropical diseases'
p5612
sS'LR'
p5613
S'20151119'
p5614
sS'PG'
p5615
S'e339'
p5616
sS'TI'
p5617
S'Rabies exposures, post-exposure prophylaxis and deaths in a region of endemic canine rabies.'
p5618
sS'PL'
p5619
S'United States'
p5620
sS'TA'
p5621
S'PLoS Negl Trop Dis'
p5622
sS'JID'
p5623
S'101291488'
p5624
sS'AB'
p5625
S'BACKGROUND: Thousands of human deaths from rabies occur annually despite the availability of effective vaccines following exposure, and for disease control in the animal reservoir. Our aim was to assess risk factors associated with exposure and to determine why human deaths from endemic canine rabies still occur. METHODS AND FINDINGS: Contact tracing was used to gather data on rabies exposures, post-exposure prophylaxis (PEP) delivered and deaths in two rural districts in northwestern Tanzania from 2002 to 2006. Data on risk factors and the propensity to seek and complete courses of PEP was collected using questionnaires. Exposures varied from 6-141/100,000 per year. Risk of exposure to rabies was greater in an area with agropastoralist communities (and larger domestic dog populations) than an area with pastoralist communities. Children were at greater risk than adults of being exposed to rabies and of developing clinical signs. PEP dramatically reduced the risk of developing rabies (odds ratio [OR] 17.33, 95% confidence interval [CI] 6.39-60.83) and when PEP was not delivered the risks were higher in the pastoralist than the agro-pastoralist area (OR 6.12, 95% CI 2.60-14.58). Low socioeconomic class and distance to medical facilities lengthened delays before PEP delivery. Over 20% of rabies-exposed individuals did not seek medical treatment and were not documented in official records and <65% received PEP. Animal bite injury records were an accurate indicator of rabies exposure incidence. CONCLUSIONS: Insufficient knowledge about rabies dangers and prevention, particularly prompt PEP, but also wound management, was the main cause of rabies deaths. Education, particularly in poor and marginalized communities, but also for medical and veterinary workers, would prevent future deaths.'
p5626
sS'GR'
p5627
(lp5628
S'082715/Wellcome Trust/United Kingdom'
p5629
aS'Wellcome Trust/United Kingdom'
p5630
asS'IP'
p5631
S'11'
p5632
sS'IS'
p5633
S'1935-2735 (Electronic) 1935-2727 (Linking)'
p5634
sS'PMC'
p5635
S'PMC2582685'
p5636
sS'DCOM'
p5637
S'20100224'
p5638
sS'AU'
p5639
(lp5640
S'Hampson K'
p5641
aS'Dobson A'
p5642
aS'Kaare M'
p5643
aS'Dushoff J'
p5644
aS'Magoto M'
p5645
aS'Sindoya E'
p5646
aS'Cleaveland S'
p5647
asS'VI'
p5648
S'2'
sS'MHDA'
p5649
S'2010/02/25 06:00'
p5650
sS'PHST'
p5651
(lp5652
S'2008/03/04 [received]'
p5653
aS'2008/10/31 [accepted]'
p5654
aS'2008/11/25 [epublish]'
p5655
asS'OID'
p5656
(lp5657
S'NLM: PMC2582685'
p5658
asS'MH'
p5659
(lp5660
S'Adolescent'
p5661
aS'Adult'
p5662
aS'Africa/epidemiology'
p5663
aS'Aged'
p5664
aS'Animals'
p5665
aS'Animals, Domestic/parasitology'
p5666
aS'Asia/epidemiology'
p5667
aS'Bites and Stings/parasitology'
p5668
aS'Child'
p5669
aS'Child, Preschool'
p5670
aS'Dog Diseases/*parasitology'
p5671
aS'Dogs'
p5672
aS'Humans'
p5673
aS'Infant'
p5674
aS'Middle Aged'
p5675
aS'Rabies/epidemiology/mortality/*transmission/*veterinary'
p5676
aS'Risk Factors'
p5677
aS'Rural Population'
p5678
aS'Surveys and Questionnaires'
p5679
aS'Tanzania/epidemiology'
p5680
asS'EDAT'
p5681
S'2008/11/26 09:00'
p5682
sS'SO'
p5683
S'PLoS Negl Trop Dis. 2008;2(11):e339. doi: 10.1371/journal.pntd.0000339. Epub 2008 Nov 25.'
p5684
sS'SB'
p5685
S'IM'
p5686
sS'PMID'
p5687
S'19030223'
p5688
sS'PST'
p5689
S'ppublish'
p5690
stRp5691
ag2
(g3
g4
(dp5692
S'LID'
p5693
S'10.1086/592403 [doi]'
p5694
sS'STAT'
p5695
S'MEDLINE'
p5696
sS'AB'
p5697
S"The theory of insect population dynamics has shown that heterogeneity in natural-enemy attack rates is strongly stabilizing. We tested the usefulness of this theory for outbreaking insects, many of which are attacked by infectious pathogens. We measured heterogeneity among gypsy moth larvae in their risk of infection with a nucleopolyhedrovirus, which is effectively heterogeneity in the pathogen's attack rate. Our data show that heterogeneity in infection risk in this insect is so high that it leads to a stable equilibrium in the models, which is inconsistent with the outbreaks seen in North American gypsy moth populations. Our data further suggest that infection risk declines after epidemics, in turn suggesting that the model assumption of constant infection risk is incorrect. We therefore constructed an alternative model in which natural selection drives fluctuations in infection risk, leading to reductions after epidemics because of selection for resistance and increases after epidemics because of a cost of resistance. This model shows cycles even for high heterogeneity, and experiments confirm that infection risk is indeed heritable. The model is very general, and so we argue that natural selection for disease resistance may play a role in many insect outbreaks."
p5698
sS'JID'
p5699
S'2984688R'
p5700
sS'AD'
p5701
S'Department of Ecology and Evolution, University of Chicago, Chicago, Illinois 60637, USA.'
p5702
sS'JT'
p5703
S'The American naturalist'
p5704
sS'IP'
p5705
S'6'
sS'IS'
p5706
S'1537-5323 (Electronic) 0003-0147 (Linking)'
p5707
sS'DCOM'
p5708
S'20090126'
p5709
sS'DA'
p5710
S'20081112'
p5711
sS'AU'
p5712
(lp5713
S'Elderd BD'
p5714
aS'Dushoff J'
p5715
aS'Dwyer G'
p5716
asS'SB'
p5717
S'IM'
p5718
sS'AID'
p5719
(lp5720
S'10.1086/592403 [doi]'
p5721
asS'CRDT'
p5722
(lp5723
S'2008/11/04 09:00'
p5724
asS'VI'
p5725
S'172'
p5726
sS'DP'
p5727
S'2008 Dec'
p5728
sS'MHDA'
p5729
S'2009/01/27 09:00'
p5730
sS'OWN'
p5731
S'NLM'
p5732
sS'PT'
p5733
(lp5734
S'Comparative Study'
p5735
aS'Journal Article'
p5736
aS"Research Support, U.S. Gov't, Non-P.H.S."
p5737
asS'LA'
p5738
(lp5739
S'eng'
p5740
asS'MH'
p5741
(lp5742
S'Animals'
p5743
aS'Computer Simulation'
p5744
aS'Immunity, Innate/*genetics'
p5745
aS'*Models, Biological'
p5746
aS'Moths/genetics/*physiology/*virology'
p5747
aS'*Nucleopolyhedrovirus'
p5748
aS'Population Dynamics'
p5749
aS'Risk Factors'
p5750
aS'*Selection, Genetic'
p5751
asS'FAU'
p5752
(lp5753
S'Elderd, Bret D'
p5754
aS'Dushoff, Jonathan'
p5755
aS'Dwyer, Greg'
p5756
asS'EDAT'
p5757
S'2008/11/04 09:00'
p5758
sS'PST'
p5759
S'ppublish'
p5760
sS'LR'
p5761
S'20091119'
p5762
sS'PG'
p5763
S'829-42'
p5764
sS'TI'
p5765
S'Host-pathogen interactions, insect outbreaks, and natural selection for disease resistance.'
p5766
sS'SO'
p5767
S'Am Nat. 2008 Dec;172(6):829-42. doi: 10.1086/592403.'
p5768
sS'PMID'
p5769
S'18976065'
p5770
sS'PL'
p5771
S'United States'
p5772
sS'TA'
p5773
S'Am Nat'
p5774
stRp5775
ag2
(g3
g4
(dp5776
S'STAT'
p5777
S'Publisher'
p5778
sS'AB'
p5779
S'Knowledge of infection reservoir dynamics is critical for effective disease control, but identifying reservoirs of multi-host pathogens is challenging. Here, we synthesize several lines of evidence to investigate rabies reservoirs in complex carnivore communities of the Serengeti ecological region in northwest Tanzania, where the disease has been confirmed in 12 carnivore species.Long-term monitoring data suggest that rabies persists in high-density domestic dog Canis familiaris populations (> 11 dogs km(-2)) and occurs less frequently in lower-density (< 5 dogs km(-2)) populations and only sporadically in wild carnivores.Genetic data show that a single rabies virus variant belonging to the group of southern Africa canid-associated viruses (Africa 1b) circulates among a range of species, with no evidence of species-specific virus-host associations.Within-species transmission was more frequently inferred from high-resolution epidemiological data than between-species transmission. Incidence patterns indicate that spill-over of rabies from domestic dog populations sometimes initiates short-lived chains of transmission in other carnivores.Synthesis and applications. The balance of evidence suggests that the reservoir of rabies in the Serengeti ecosystem is a complex multi-host community where domestic dogs are the only population essential for persistence, although other carnivores contribute to the reservoir as non-maintenance populations. Control programmes that target domestic dog populations should therefore have the greatest impact on reducing the risk of infection in all other species including humans, livestock and endangered wildlife populations, but transmission in other species may increase the level of vaccination coverage in domestic dog populations necessary to eliminate rabies.'
p5780
sS'JID'
p5781
S'0055203'
p5782
sS'GR'
p5783
(lp5784
S'082715/Wellcome Trust/United Kingdom'
p5785
asS'JT'
p5786
S'The Journal of applied ecology'
p5787
sS'IP'
p5788
S'4'
sS'IS'
p5789
S'0021-8901 (Print) 0021-8901 (Linking)'
p5790
sS'PMC'
p5791
S'PMC3303133'
p5792
sS'MID'
p5793
(lp5794
S'UKMS40922'
p5795
asS'DA'
p5796
S'20120319'
p5797
sS'AU'
p5798
(lp5799
S'Lembo T'
p5800
aS'Hampson K'
p5801
aS'Haydon DT'
p5802
aS'Craft M'
p5803
aS'Dobson A'
p5804
aS'Dushoff J'
p5805
aS'Ernest E'
p5806
aS'Hoare R'
p5807
aS'Kaare M'
p5808
aS'Mlengeya T'
p5809
aS'Mentzel C'
p5810
aS'Cleaveland S'
p5811
asS'AID'
p5812
(lp5813
S'10.1111/j.1365-2664.2008.01468.x [doi]'
p5814
asS'FAU'
p5815
(lp5816
S'Lembo, Tiziana'
p5817
aS'Hampson, Katie'
p5818
aS'Haydon, Daniel T'
p5819
aS'Craft, Meggan'
p5820
aS'Dobson, Andy'
p5821
aS'Dushoff, Jonathan'
p5822
aS'Ernest, Eblate'
p5823
aS'Hoare, Richard'
p5824
aS'Kaare, Magai'
p5825
aS'Mlengeya, Titus'
p5826
aS'Mentzel, Christine'
p5827
aS'Cleaveland, Sarah'
p5828
asS'VI'
p5829
S'45'
p5830
sS'DP'
p5831
S'2008 Aug'
p5832
sS'AD'
p5833
S'Centre for Tropical Veterinary Medicine, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Veterinary Centre, Roslin, Midlothian EH25 9RG, UK.'
p5834
sS'MHDA'
p5835
S'2008/08/01 00:00'
p5836
sS'OWN'
p5837
S'NLM'
p5838
sS'PT'
p5839
(lp5840
S'JOURNAL ARTICLE'
p5841
asS'LA'
p5842
(lp5843
S'ENG'
p5844
asS'CRDT'
p5845
(lp5846
S'2012/03/20 06:00'
p5847
asS'EDAT'
p5848
S'2008/08/01 00:00'
p5849
sS'SO'
p5850
S'J Appl Ecol. 2008 Aug;45(4):1246-1257.'
p5851
sS'PG'
p5852
S'1246-1257'
p5853
sS'TI'
p5854
S'Exploring reservoir dynamics: a case study of rabies in the Serengeti ecosystem.'
p5855
sS'PMID'
p5856
S'22427710'
p5857
sS'PST'
p5858
S'ppublish'
p5859
sS'TA'
p5860
S'J Appl Ecol'
p5861
stRp5862
ag2
(g3
g4
(dp5863
S'LID'
p5864
S'10.1098/rspb.2008.0521 [doi]'
p5865
sS'STAT'
p5866
S'MEDLINE'
p5867
sS'JT'
p5868
S'Proceedings. Biological sciences / The Royal Society'
p5869
sS'DA'
p5870
S'20080919'
p5871
sS'AID'
p5872
(lp5873
S'M238761VL1348449 [pii]'
p5874
aS'10.1098/rspb.2008.0521 [doi]'
p5875
asS'CRDT'
p5876
(lp5877
S'2008/07/24 09:00'
p5878
asS'DP'
p5879
S'2008 Nov 7'
p5880
sS'EIN'
p5881
(lp5882
S'Proc Biol Sci. 2209 Aug 22;276(1669):3035. Plotkin, Joshua [corrected to Plotkin'
p5883
aS'Joshua B]'
p5884
asS'OWN'
p5885
S'NLM'
p5886
sS'PT'
p5887
(lp5888
S'Comparative Study'
p5889
aS'Journal Article'
p5890
aS"Research Support, Non-U.S. Gov't"
p5891
aS"Research Support, U.S. Gov't, Non-P.H.S."
p5892
asS'LA'
p5893
(lp5894
S'eng'
p5895
asS'FAU'
p5896
(lp5897
S'Kryazhimskiy, Sergey'
p5898
aS'Bazykin, Georgii A'
p5899
aS'Plotkin, Joshua B'
p5900
aS'Dushoff, Jonathan'
p5901
asS'LR'
p5902
S'20140903'
p5903
sS'PG'
p5904
S'2455-64'
p5905
sS'TI'
p5906
S'Directionality in the evolution of influenza A haemagglutinin.'
p5907
sS'RN'
p5908
(lp5909
S'0 (Hemagglutinins, Viral)'
p5910
asS'PL'
p5911
S'England'
p5912
sS'TA'
p5913
S'Proc Biol Sci'
p5914
sS'JID'
p5915
S'101245157'
p5916
sS'AB'
p5917
S"The evolution of haemagglutinin (HA), an important influenza virus antigen, has been the subject of intensive research for more than two decades. Many characteristics of HA's sequence evolution are captured by standard Markov chain substitution models. Such models assign equal fitness to all accessible amino acids at a site. We show, however, that such models strongly underestimate the number of homoplastic amino acid substitutions during the course of HA's evolution, i.e. substitutions that repeatedly give rise to the same amino acid at a site. We develop statistics to detect individual homoplastic events and find that they preferentially occur at positively selected epitopic sites. Our results suggest that the evolution of the influenza A HA, including evolution by positive selection, is strongly affected by the long-term site-specific preferences for individual amino acids."
p5918
sS'AD'
p5919
S'Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA. skr@sas.upenn.edu'
p5920
sS'IP'
p5921
S'1650'
p5922
sS'IS'
p5923
S'0962-8452 (Print) 0962-8452 (Linking)'
p5924
sS'PMC'
p5925
S'PMC2603193'
p5926
sS'DCOM'
p5927
S'20081117'
p5928
sS'AU'
p5929
(lp5930
S'Kryazhimskiy S'
p5931
aS'Bazykin GA'
p5932
aS'Plotkin JB'
p5933
aS'Dushoff J'
p5934
asS'VI'
p5935
S'275'
p5936
sS'MHDA'
p5937
S'2008/11/18 09:00'
p5938
sS'OID'
p5939
(lp5940
S'NLM: PMC2603193'
p5941
asS'MH'
p5942
(lp5943
S'Amino Acid Substitution/*genetics'
p5944
aS'Computational Biology'
p5945
aS'Computer Simulation'
p5946
aS'*Evolution, Molecular'
p5947
aS'Hemagglutinins, Viral/*genetics'
p5948
aS'Influenza A Virus, H3N2 Subtype/*genetics'
p5949
aS'*Models, Genetic'
p5950
aS'*Phylogeny'
p5951
asS'EDAT'
p5952
S'2008/07/24 09:00'
p5953
sS'SO'
p5954
S'Proc Biol Sci. 2008 Nov 7;275(1650):2455-64. doi: 10.1098/rspb.2008.0521.'
p5955
sS'SB'
p5956
S'IM'
p5957
sS'PMID'
p5958
S'18647721'
p5959
sS'PST'
p5960
S'ppublish'
p5961
stRp5962
ag2
(g3
g4
(dp5963
S'LID'
p5964
S'10.1128/JVI.02415-07 [doi]'
p5965
sS'STAT'
p5966
S'MEDLINE'
p5967
sS'DEP'
p5968
S'20080305'
p5969
sS'DA'
p5970
S'20080425'
p5971
sS'AID'
p5972
(lp5973
S'JVI.02415-07 [pii]'
p5974
aS'10.1128/JVI.02415-07 [doi]'
p5975
asS'CRDT'
p5976
(lp5977
S'2008/03/07 09:00'
p5978
asS'DP'
p5979
S'2008 May'
p5980
sS'AD'
p5981
S'Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey, USA. skr@sas.upenn.edu'
p5982
sS'OWN'
p5983
S'NLM'
p5984
sS'PT'
p5985
(lp5986
S'Journal Article'
p5987
aS'Research Support, N.I.H., Extramural'
p5988
aS"Research Support, Non-U.S. Gov't"
p5989
aS"Research Support, U.S. Gov't, Non-P.H.S."
p5990
asS'LA'
p5991
(lp5992
S'eng'
p5993
asS'FAU'
p5994
(lp5995
S'Kryazhimskiy, Sergey'
p5996
aS'Bazykin, Georgii A'
p5997
aS'Dushoff, Jonathan'
p5998
asS'JT'
p5999
S'Journal of virology'
p6000
sS'LR'
p6001
S'20140904'
p6002
sS'PG'
p6003
S'4938-45'
p6004
sS'TI'
p6005
S'Natural selection for nucleotide usage at synonymous and nonsynonymous sites in influenza A virus genes.'
p6006
sS'RN'
p6007
(lp6008
S'0 (Viral Proteins)'
p6009
asS'PL'
p6010
S'United States'
p6011
sS'TA'
p6012
S'J Virol'
p6013
sS'JID'
p6014
S'0113724'
p6015
sS'AB'
p6016
S'Influenza A virus is one of the best-studied viruses and a model organism for the study of molecular evolution; in particular, much research has focused on detecting natural selection on influenza virus proteins. Here, we study the dynamics of the synonymous and nonsynonymous nucleotide composition of influenza A virus genes. In several genes, the nucleotide frequencies at synonymous positions drift away from the equilibria predicted from the synonymous substitution matrices. We investigate possible reasons for this unexpected behavior by fitting several regression models. Relaxation toward a mutation-selection equilibrium following a host jump fails to explain the dynamics of the synonymous nucleotide composition, even if we allow for slow temporal changes in the substitution matrix. Instead, we find that deep internal branches of the phylogeny show distinct patterns of nucleotide substitution and that these branches strongly influence the dynamics of nucleotide composition, suggesting that the observed trends are at least in part a result of natural selection acting on synonymous sites. Moreover, we find that the dynamics of the nucleotide composition at synonymous and nonsynonymous sites are highly correlated, providing evidence that even nonsynonymous sites can be influenced by selection pressure for nucleotide composition.'
p6017
sS'GR'
p6018
(lp6019
S'P50 GM071508/GM/NIGMS NIH HHS/United States'
p6020
asS'IP'
p6021
S'10'
p6022
sS'IS'
p6023
S'1098-5514 (Electronic) 0022-538X (Linking)'
p6024
sS'PMC'
p6025
S'PMC2346729'
p6026
sS'DCOM'
p6027
S'20080513'
p6028
sS'AU'
p6029
(lp6030
S'Kryazhimskiy S'
p6031
aS'Bazykin GA'
p6032
aS'Dushoff J'
p6033
asS'VI'
p6034
S'82'
p6035
sS'MHDA'
p6036
S'2008/05/14 09:00'
p6037
sS'PHST'
p6038
(lp6039
S'2008/03/05 [aheadofprint]'
p6040
asS'OID'
p6041
(lp6042
S'NLM: PMC2346729'
p6043
asS'MH'
p6044
(lp6045
S'Cluster Analysis'
p6046
aS'Computational Biology'
p6047
aS'*Evolution, Molecular'
p6048
aS'*Genes, Viral'
p6049
aS'Humans'
p6050
aS'Influenza A virus/*genetics'
p6051
aS'Models, Statistical'
p6052
aS'*Mutation'
p6053
aS'Phylogeny'
p6054
aS'*Selection, Genetic'
p6055
aS'Viral Proteins/*genetics'
p6056
asS'EDAT'
p6057
S'2008/03/07 09:00'
p6058
sS'SO'
p6059
S'J Virol. 2008 May;82(10):4938-45. doi: 10.1128/JVI.02415-07. Epub 2008 Mar 5.'
p6060
sS'SB'
p6061
S'IM'
p6062
sS'PMID'
p6063
S'18321967'
p6064
sS'PST'
p6065
S'ppublish'
p6066
stRp6067
ag2
(g3
g4
(dp6068
S'LID'
p6069
S'10.1086/522937 [doi]'
p6070
sS'STAT'
p6071
S'MEDLINE'
p6072
sS'AB'
p6073
S'We use a two-species model of plant competition to explore the effect of intraspecific variation on community dynamics. The competitive ability ("performance") of each individual is assigned by an independent random draw from a species-specific probability distribution. If the density of individuals competing for open space is high (e.g., because fecundity is high), species with high maximum (or large variance in) performance are favored, while if density is low, species with high typical (e.g., mean) performance are favored. If there is an interspecific mean-variance performance trade-off, stable coexistence can occur across a limited range of intermediate densities, but the stabilizing effect of this trade-off appears to be weak. In the absence of this trade-off, one species is superior. In this case, intraspecific variation can blur interspecific differences (i.e., shift the dynamics toward what would be expected in the neutral case), but the strength of this effect diminishes as competitor density increases. If density is sufficiently high, the inferior species is driven to extinction just as rapidly as in the case where there is no overlap in performance between species. Intraspecific variation can facilitate coexistence, but this may be relatively unimportant in maintaining diversity in most real communities.'
p6074
sS'JID'
p6075
S'2984688R'
p6076
sS'AD'
p6077
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey 08544, USA. jwl@princeton.edu'
p6078
sS'JT'
p6079
S'The American naturalist'
p6080
sS'IP'
p6081
S'6'
sS'IS'
p6082
S'1537-5323 (Electronic) 0003-0147 (Linking)'
p6083
sS'DCOM'
p6084
S'20080124'
p6085
sS'DA'
p6086
S'20080103'
p6087
sS'AU'
p6088
(lp6089
S'Lichstein JW'
p6090
aS'Dushoff J'
p6091
aS'Levin SA'
p6092
aS'Pacala SW'
p6093
asS'AID'
p6094
(lp6095
S'10.1086/522937 [doi]'
p6096
asS'CRDT'
p6097
(lp6098
S'2008/01/04 09:00'
p6099
asS'VI'
p6100
S'170'
p6101
sS'PMID'
p6102
S'18171164'
p6103
sS'DP'
p6104
S'2007 Dec'
p6105
sS'MHDA'
p6106
S'2008/01/25 09:00'
p6107
sS'OWN'
p6108
S'NLM'
p6109
sS'PT'
p6110
(lp6111
S'Journal Article'
p6112
aS"Research Support, Non-U.S. Gov't"
p6113
aS"Research Support, U.S. Gov't, Non-P.H.S."
p6114
asS'LA'
p6115
(lp6116
S'eng'
p6117
asS'MH'
p6118
(lp6119
S'Anticipation, Genetic'
p6120
aS'Ecosystem'
p6121
aS'Models, Biological'
p6122
aS'Plant Lectins/*genetics'
p6123
aS'Population Dynamics'
p6124
aS'Seeds'
p6125
aS'Stochastic Processes'
p6126
asS'FAU'
p6127
(lp6128
S'Lichstein, Jeremy W'
p6129
aS'Dushoff, Jonathan'
p6130
aS'Levin, Simon A'
p6131
aS'Pacala, Stephen W'
p6132
asS'EDAT'
p6133
S'2008/01/04 09:00'
p6134
sS'PST'
p6135
S'ppublish'
p6136
sS'SO'
p6137
S'Am Nat. 2007 Dec;170(6):807-18. doi: 10.1086/522937.'
p6138
sS'PG'
p6139
S'807-18'
p6140
sS'TI'
p6141
S'Intraspecific variation and species coexistence.'
p6142
sS'SB'
p6143
S'IM'
p6144
sS'RN'
p6145
(lp6146
S'0 (Plant Lectins)'
p6147
asS'PL'
p6148
S'United States'
p6149
sS'TA'
p6150
S'Am Nat'
p6151
stRp6152
ag2
(g3
g4
(dp6153
S'STAT'
p6154
S'MEDLINE'
p6155
sS'AB'
p6156
S"One of the values of biodiversity is that it may provide 'biological insurance' for services currently rendered by domesticated species or technology. We used crop pollination as a model system, and investigated whether the loss of a domesticated pollinator (the honey bee) could be compensated for by native, wild bee species. We measured pollination provided to watermelon crops at 23 farms in New Jersey and Pennsylvania, USA, and used a simulation model to separate the pollen provided by honey bees and native bees. Simulation results predict that native bees alone provide sufficient pollination at > 90% of the farms studied. Furthermore, empirical total pollen deposition at flowers was strongly, significantly correlated with native bee visitation but not with honey bee visitation. The honey bee is currently undergoing extensive die-offs because of Colony Collapse Disorder. We predict that in our region native bees will buffer potential declines in agricultural production because of honey bee losses."
p6157
sS'JID'
p6158
S'101121949'
p6159
sS'AD'
p6160
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. rwinfree@princeton.edu'
p6161
sS'DEP'
p6162
S'20070917'
p6163
sS'IP'
p6164
S'11'
p6165
sS'IS'
p6166
S'1461-0248 (Electronic) 1461-023X (Linking)'
p6167
sS'DCOM'
p6168
S'20071108'
p6169
sS'DA'
p6170
S'20071003'
p6171
sS'AU'
p6172
(lp6173
S'Winfree R'
p6174
aS'Williams NM'
p6175
aS'Dushoff J'
p6176
aS'Kremen C'
p6177
asS'PST'
p6178
S'ppublish'
p6179
sS'AID'
p6180
(lp6181
S'ELE1110 [pii]'
p6182
aS'10.1111/j.1461-0248.2007.01110.x [doi]'
p6183
asS'CRDT'
p6184
(lp6185
S'2007/09/20 09:00'
p6186
asS'VI'
p6187
S'10'
p6188
sS'DP'
p6189
S'2007 Nov'
p6190
sS'MHDA'
p6191
S'2007/11/09 09:00'
p6192
sS'PHST'
p6193
(lp6194
S'2007/09/17 [aheadofprint]'
p6195
asS'OWN'
p6196
S'NLM'
p6197
sS'PT'
p6198
(lp6199
S'Journal Article'
p6200
aS"Research Support, Non-U.S. Gov't"
p6201
aS"Research Support, U.S. Gov't, Non-P.H.S."
p6202
asS'LA'
p6203
(lp6204
S'eng'
p6205
asS'MH'
p6206
(lp6207
S'Animals'
p6208
aS'Bees/*physiology'
p6209
aS'*Biodiversity'
p6210
aS'*Conservation of Natural Resources'
p6211
aS'Crops, Agricultural'
p6212
aS'*Ecosystem'
p6213
aS'Pollen/physiology'
p6214
aS'Population Dynamics'
p6215
asS'FAU'
p6216
(lp6217
S'Winfree, Rachael'
p6218
aS'Williams, Neal M'
p6219
aS'Dushoff, Jonathan'
p6220
aS'Kremen, Claire'
p6221
asS'EDAT'
p6222
S'2007/09/20 09:00'
p6223
sS'JT'
p6224
S'Ecology letters'
p6225
sS'SO'
p6226
S'Ecol Lett. 2007 Nov;10(11):1105-13. Epub 2007 Sep 17.'
p6227
sS'PG'
p6228
S'1105-13'
p6229
sS'TI'
p6230
S'Native bees provide insurance against ongoing honey bee losses.'
p6231
sS'SB'
p6232
S'IM'
p6233
sS'PMID'
p6234
S'17877737'
p6235
sS'PL'
p6236
S'England'
p6237
sS'TA'
p6238
S'Ecol Lett'
p6239
stRp6240
ag2
(g3
g4
(dp6241
S'STAT'
p6242
S'MEDLINE'
p6243
sS'DEP'
p6244
S'20070622'
p6245
sS'DA'
p6246
S'20070905'
p6247
sS'AID'
p6248
(lp6249
S'06-PLCB-RA-0467 [pii]'
p6250
aS'10.1371/journal.pcbi.0030159 [doi]'
p6251
asS'CRDT'
p6252
(lp6253
S'2007/08/22 09:00'
p6254
asS'DP'
p6255
S'2007 Aug'
p6256
sS'AD'
p6257
S'Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey, United States of America. skryazhi@princeton.edu'
p6258
sS'OWN'
p6259
S'NLM'
p6260
sS'PT'
p6261
(lp6262
S'Journal Article'
p6263
aS'Research Support, N.I.H., Extramural'
p6264
aS"Research Support, Non-U.S. Gov't"
p6265
aS"Research Support, U.S. Gov't, Non-P.H.S."
p6266
asS'LA'
p6267
(lp6268
S'eng'
p6269
asS'FAU'
p6270
(lp6271
S'Kryazhimskiy, Sergey'
p6272
aS'Dieckmann, Ulf'
p6273
aS'Levin, Simon A'
p6274
aS'Dushoff, Jonathan'
p6275
asS'JT'
p6276
S'PLoS computational biology'
p6277
sS'LR'
p6278
S'20140904'
p6279
sS'PG'
p6280
S'e159'
p6281
sS'TI'
p6282
S'On state-space reduction in multi-strain pathogen models, with an application to antigenic drift in influenza A.'
p6283
sS'RN'
p6284
(lp6285
S'0 (Antigens, Viral)'
p6286
asS'PL'
p6287
S'United States'
p6288
sS'TA'
p6289
S'PLoS Comput Biol'
p6290
sS'JID'
p6291
S'101238922'
p6292
sS'AB'
p6293
S'Many pathogens exist in phenotypically distinct strains that interact with each other through competition for hosts. General models that describe such multi-strain systems are extremely difficult to analyze because their state spaces are enormously large. Reduced models have been proposed, but so far all of them necessarily allow for coinfections and require that immunity be mediated solely by reduced infectivity, a potentially problematic assumption. Here, we suggest a new state-space reduction approach that allows immunity to be mediated by either reduced infectivity or reduced susceptibility and that can naturally be used for models with or without coinfections. Our approach utilizes the general framework of status-based models. The cornerstone of our method is the introduction of immunity variables, which describe multi-strain systems more naturally than the traditional tracking of susceptible and infected hosts. Models expressed in this way can be approximated in a natural way by a truncation method that is akin to moment closure, allowing us to sharply reduce the size of the state space, and thus to consider models with many strains in a tractable manner. Applying our method to the phenomenon of antigenic drift in influenza A, we propose a potentially general mechanism that could constrain viral evolution to a one-dimensional manifold in a two-dimensional trait space. Our framework broadens the class of multi-strain systems that can be adequately described by reduced models. It permits computational, and even analytical, investigation and thus serves as a useful tool for understanding the evolution and ecology of multi-strain pathogens.'
p6294
sS'GR'
p6295
(lp6296
S'P50 GM071508/GM/NIGMS NIH HHS/United States'
p6297
asS'IP'
p6298
S'8'
sS'IS'
p6299
S'1553-7358 (Electronic) 1553-734X (Linking)'
p6300
sS'PMC'
p6301
S'PMC1949840'
p6302
sS'DCOM'
p6303
S'20071018'
p6304
sS'AU'
p6305
(lp6306
S'Kryazhimskiy S'
p6307
aS'Dieckmann U'
p6308
aS'Levin SA'
p6309
aS'Dushoff J'
p6310
asS'VI'
p6311
S'3'
sS'MHDA'
p6312
S'2007/10/19 09:00'
p6313
sS'PHST'
p6314
(lp6315
S'2006/11/08 [received]'
p6316
aS'2007/06/22 [accepted]'
p6317
aS'2007/06/22 [aheadofprint]'
p6318
asS'OID'
p6319
(lp6320
S'NLM: PMC1949840'
p6321
asS'MH'
p6322
(lp6323
S'Antigenic Variation/*genetics'
p6324
aS'Antigens, Viral/*genetics/*immunology'
p6325
aS'*Biological Evolution'
p6326
aS'Computer Simulation'
p6327
aS'Genetic Drift'
p6328
aS'Genetic Variation/genetics'
p6329
aS'Influenza A virus/classification/*genetics/*immunology'
p6330
aS'*Models, Genetic'
p6331
aS'Models, Immunological'
p6332
aS'Models, Statistical'
p6333
aS'Species Specificity'
p6334
asS'EDAT'
p6335
S'2007/08/22 09:00'
p6336
sS'SO'
p6337
S'PLoS Comput Biol. 2007 Aug;3(8):e159. Epub 2007 Jun 22.'
p6338
sS'SB'
p6339
S'IM'
p6340
sS'PMID'
p6341
S'17708677'
p6342
sS'PST'
p6343
S'ppublish'
p6344
stRp6345
ag2
(g3
g4
(dp6346
S'STAT'
p6347
S'MEDLINE'
p6348
sS'DEP'
p6349
S'20070523'
p6350
sS'DA'
p6351
S'20070803'
p6352
sS'AID'
p6353
(lp6354
S'msm103 [pii]'
p6355
aS'10.1093/molbev/msm103 [doi]'
p6356
asS'CRDT'
p6357
(lp6358
S'2007/05/25 09:00'
p6359
asS'DP'
p6360
S'2007 Aug'
p6361
sS'AD'
p6362
S'National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.'
p6363
sS'OWN'
p6364
S'NLM'
p6365
sS'PT'
p6366
(lp6367
S'Journal Article'
p6368
aS'Research Support, N.I.H., Extramural'
p6369
asS'LA'
p6370
(lp6371
S'eng'
p6372
asS'FAU'
p6373
(lp6374
S'Simonsen, Lone'
p6375
aS'Viboud, Cecile'
p6376
aS'Grenfell, Bryan T'
p6377
aS'Dushoff, Jonathan'
p6378
aS'Jennings, Lance'
p6379
aS'Smit, Marita'
p6380
aS'Macken, Catherine'
p6381
aS'Hata, Mami'
p6382
aS'Gog, Julia'
p6383
aS'Miller, Mark A'
p6384
aS'Holmes, Edward C'
p6385
asS'JT'
p6386
S'Molecular biology and evolution'
p6387
sS'LR'
p6388
S'20131121'
p6389
sS'PG'
p6390
S'1811-20'
p6391
sS'TI'
p6392
S'The genesis and spread of reassortment human influenza A/H3N2 viruses conferring adamantane resistance.'
p6393
sS'RN'
p6394
(lp6395
S'0 (Antiviral Agents)'
p6396
aS'0 (M2 protein, Influenza A virus)'
p6397
aS'0 (Viral Matrix Proteins)'
p6398
aS'PJY633525U (Adamantane)'
p6399
asS'PL'
p6400
S'United States'
p6401
sS'TA'
p6402
S'Mol Biol Evol'
p6403
sS'JID'
p6404
S'8501455'
p6405
sS'AB'
p6406
S'A dramatic rise in the frequency of resistance to adamantane drugs by influenza A (H3N2) viruses has occurred in recent years -- from approximately 2% to approximately 90% in multiple countries worldwide-and associated with a single S31N amino acid replacement in the viral matrix M2 protein. To explore the emergence and spread of these adamantane resistant viruses we performed a phylogenetic analysis of recently sampled complete A/H3N2 genome sequences. Strikingly, all adamantane resistant viruses belonged to a single lineage (the "N-lineage") characterized by 17 amino acid replacements across the viral genome. Further, our analysis revealed that the genesis of the N-lineage was due to a 4+4 segment reassortment event involving 2 distinct lineages of influenza A/H3N2 virus. A subsequent study of hemagglutinin HA1 sequences suggested that the N-lineage was circulating widely in Asia during 2005, and then dominated the Northern hemisphere 2005-2006 season in Japan and the USA. Given the infrequent use of adamantane drugs in many countries, as well as the decades of use in the US associated with little drug resistance, we propose that the globally increasing frequency of adamantane resistance is more likely attributable to its interaction with fitness-enhancing mutations at other genomic sites rather than to direct drug selection pressure. This implies that adamantanes may not be useful for treatment and prophylaxis against influenza viruses in the long term. More generally, these findings illustrate that drug selection pressure is not the sole factor determining the evolution and maintenance of drug resistance in human pathogens.'
p6407
sS'GR'
p6408
(lp6409
S'P50 GM071508/GM/NIGMS NIH HHS/United States'
p6410
asS'IP'
p6411
S'8'
sS'IS'
p6412
S'0737-4038 (Print) 0737-4038 (Linking)'
p6413
sS'DCOM'
p6414
S'20071029'
p6415
sS'AU'
p6416
(lp6417
S'Simonsen L'
p6418
aS'Viboud C'
p6419
aS'Grenfell BT'
p6420
aS'Dushoff J'
p6421
aS'Jennings L'
p6422
aS'Smit M'
p6423
aS'Macken C'
p6424
aS'Hata M'
p6425
aS'Gog J'
p6426
aS'Miller MA'
p6427
aS'Holmes EC'
p6428
asS'VI'
p6429
S'24'
p6430
sS'MHDA'
p6431
S'2007/10/30 09:00'
p6432
sS'PHST'
p6433
(lp6434
S'2007/05/23 [aheadofprint]'
p6435
asS'MH'
p6436
(lp6437
S'Adamantane/*pharmacology'
p6438
aS'Amino Acid Substitution'
p6439
aS'Antiviral Agents/*pharmacology'
p6440
aS'Asia/epidemiology'
p6441
aS'Australia/epidemiology'
p6442
aS'Biological Assay'
p6443
aS'Drug Resistance, Viral/*genetics'
p6444
aS'Evolution, Molecular'
p6445
aS'Genetic Variation'
p6446
aS'Genome, Viral'
p6447
aS'Humans'
p6448
aS'Influenza A Virus, H3N2 Subtype/*drug effects/genetics'
p6449
aS'Influenza, Human/*drug therapy/epidemiology/virology'
p6450
aS'Japan/epidemiology'
p6451
aS'New Zealand/epidemiology'
p6452
aS'Phylogeny'
p6453
aS'United States/epidemiology'
p6454
aS'Viral Matrix Proteins/genetics'
p6455
asS'EDAT'
p6456
S'2007/05/25 09:00'
p6457
sS'SO'
p6458
S'Mol Biol Evol. 2007 Aug;24(8):1811-20. Epub 2007 May 23.'
p6459
sS'SB'
p6460
S'IM'
p6461
sS'PMID'
p6462
S'17522084'
p6463
sS'PST'
p6464
S'ppublish'
p6465
stRp6466
ag2
(g3
g4
(dp6467
S'STAT'
p6468
S'MEDLINE'
p6469
sS'JT'
p6470
S'PLoS medicine'
p6471
sS'DA'
p6472
S'20070530'
p6473
sS'AID'
p6474
(lp6475
S'06-PLME-RA-0863R2 [pii]'
p6476
aS'10.1371/journal.pmed.0040174 [doi]'
p6477
asS'CRDT'
p6478
(lp6479
S'2007/05/24 09:00'
p6480
asS'DP'
p6481
S'2007 May'
p6482
sS'OWN'
p6483
S'NLM'
p6484
sS'PT'
p6485
(lp6486
S'Journal Article'
p6487
asS'LA'
p6488
(lp6489
S'eng'
p6490
asS'FAU'
p6491
(lp6492
S'Dushoff, Jonathan'
p6493
aS'Plotkin, Joshua B'
p6494
aS'Viboud, Cecile'
p6495
aS'Simonsen, Lone'
p6496
aS'Miller, Mark'
p6497
aS'Loeb, Mark'
p6498
aS'Earn, David J D'
p6499
asS'LR'
p6500
S'20140904'
p6501
sS'PG'
p6502
S'e174'
p6503
sS'TI'
p6504
S'Vaccinating to protect a vulnerable subpopulation.'
p6505
sS'RN'
p6506
(lp6507
S'0 (Influenza Vaccines)'
p6508
asS'PL'
p6509
S'United States'
p6510
sS'TA'
p6511
S'PLoS Med'
p6512
sS'JID'
p6513
S'101231360'
p6514
sS'AB'
p6515
S'BACKGROUND: Epidemic influenza causes serious mortality and morbidity in temperate countries each winter. Research suggests that schoolchildren are critical in the spread of influenza virus, while the elderly and the very young are most vulnerable to the disease. Under these conditions, it is unclear how best to focus prevention efforts in order to protect the population. Here we investigate the question of how to protect a population against a disease when one group is particularly effective at spreading disease and another group is more vulnerable to the effects of the disease. METHODS AND FINDINGS: We developed a simple mathematical model of an epidemic that includes assortative mixing between groups of hosts. We evaluate the impact of different vaccine allocation strategies across a wide range of parameter values. With this model we demonstrate that the optimal vaccination strategy is extremely sensitive to the assortativity of population mixing, as well as to the reproductive number of the disease in each group. Small differences in parameter values can change the best vaccination strategy from one focused on the most vulnerable individuals to one focused on the most transmissive individuals. CONCLUSIONS: Given the limited amount of information about relevant parameters, we suggest that changes in vaccination strategy, while potentially promising, should be approached with caution. In particular, we find that, while switching vaccine to more active groups may protect vulnerable groups in many cases, switching too much vaccine, or switching vaccine under slightly different conditions, may lead to large increases in disease in the vulnerable group. This outcome is more likely when vaccine limitation is stringent, when mixing is highly structured, or when transmission levels are high.'
p6516
sS'AD'
p6517
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America. dushoff@eno.princeton.edu'
p6518
sS'IP'
p6519
S'5'
sS'IS'
p6520
S'1549-1676 (Electronic) 1549-1277 (Linking)'
p6521
sS'PMC'
p6522
S'PMC1872043'
p6523
sS'DCOM'
p6524
S'20070928'
p6525
sS'AU'
p6526
(lp6527
S'Dushoff J'
p6528
aS'Plotkin JB'
p6529
aS'Viboud C'
p6530
aS'Simonsen L'
p6531
aS'Miller M'
p6532
aS'Loeb M'
p6533
aS'Earn DJ'
p6534
asS'VI'
p6535
S'4'
sS'MHDA'
p6536
S'2007/09/29 09:00'
p6537
sS'PHST'
p6538
(lp6539
S'2006/11/06 [received]'
p6540
aS'2007/03/19 [accepted]'
p6541
asS'OID'
p6542
(lp6543
S'NLM: PMC1872043'
p6544
asS'MH'
p6545
(lp6546
S'Disease Outbreaks/*prevention & control'
p6547
aS'Health Policy'
p6548
aS'Humans'
p6549
aS'*Influenza Vaccines'
p6550
aS'Influenza, Human/*prevention & control'
p6551
aS'*Models, Biological'
p6552
aS'Vaccination'
p6553
aS'*Vulnerable Populations'
p6554
asS'EDAT'
p6555
S'2007/05/24 09:00'
p6556
sS'SO'
p6557
S'PLoS Med. 2007 May;4(5):e174.'
p6558
sS'SB'
p6559
S'IM'
p6560
sS'PMID'
p6561
S'17518515'
p6562
sS'PST'
p6563
S'ppublish'
p6564
stRp6565
ag2
(g3
g4
(dp6566
S'STAT'
p6567
S'MEDLINE'
p6568
sS'DEP'
p6569
S'20070423'
p6570
sS'DA'
p6571
S'20070507'
p6572
sS'AID'
p6573
(lp6574
S'0609122104 [pii]'
p6575
aS'10.1073/pnas.0609122104 [doi]'
p6576
asS'CRDT'
p6577
(lp6578
S'2007/04/25 09:00'
p6579
asS'DP'
p6580
S'2007 May 1'
p6581
sS'OWN'
p6582
S'NLM'
p6583
sS'PT'
p6584
(lp6585
S'Journal Article'
p6586
aS'Research Support, N.I.H., Extramural'
p6587
aS"Research Support, Non-U.S. Gov't"
p6588
aS"Research Support, U.S. Gov't, Non-P.H.S."
p6589
asS'LA'
p6590
(lp6591
S'eng'
p6592
asS'FAU'
p6593
(lp6594
S'Hampson, Katie'
p6595
aS'Dushoff, Jonathan'
p6596
aS'Bingham, John'
p6597
aS'Bruckner, Gideon'
p6598
aS'Ali, Y H'
p6599
aS'Dobson, Andy'
p6600
asS'JT'
p6601
S'Proceedings of the National Academy of Sciences of the United States of America'
p6602
sS'LR'
p6603
S'20140907'
p6604
sS'PG'
p6605
S'7717-22'
p6606
sS'TI'
p6607
S'Synchronous cycles of domestic dog rabies in sub-Saharan Africa and the impact of control efforts.'
p6608
sS'RN'
p6609
(lp6610
S'0 (Viral Vaccines)'
p6611
asS'PL'
p6612
S'United States'
p6613
sS'TA'
p6614
S'Proc Natl Acad Sci U S A'
p6615
sS'JID'
p6616
S'7505876'
p6617
sS'AB'
p6618
S'Rabies is a fatal neurological pathogen that is a persistent problem throughout the developing world where it is spread primarily by domestic dogs. Although the disease has been extensively studied in wildlife populations in Europe and North America, the dynamics of rabies in domestic dog populations has been almost entirely neglected. Here, we demonstrate that rabies epidemics in southern and eastern Africa cycle with a period of 3-6 years and show significant synchrony across the region. The observed period is shorter than predictions based on epidemiological parameters for rabies in domestic dogs. We find evidence that rabies prevention measures, including vaccination, are affected by disease prevalence and show that a simple model with intervention responses can capture observed disease periodicity and host dynamics. We suggest that movement of infectious or latent animals combined with coordinated control responses may be important in coupling populations and generating synchrony at the continental scale. These findings have important implications for rabies prediction and control: large-scale synchrony and the importance of intervention responses suggest that control of canine rabies in Africa will require sustained efforts coordinated across political boundaries.'
p6619
sS'AD'
p6620
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. khampson@princeton.edu'
p6621
sS'IP'
p6622
S'18'
p6623
sS'IS'
p6624
S'0027-8424 (Print) 0027-8424 (Linking)'
p6625
sS'PMC'
p6626
S'PMC1863501'
p6627
sS'DCOM'
p6628
S'20070620'
p6629
sS'AU'
p6630
(lp6631
S'Hampson K'
p6632
aS'Dushoff J'
p6633
aS'Bingham J'
p6634
aS'Bruckner G'
p6635
aS'Ali YH'
p6636
aS'Dobson A'
p6637
asS'VI'
p6638
S'104'
p6639
sS'MHDA'
p6640
S'2007/06/21 09:00'
p6641
sS'PHST'
p6642
(lp6643
S'2007/04/23 [aheadofprint]'
p6644
asS'OID'
p6645
(lp6646
S'NLM: PMC1863501'
p6647
asS'MH'
p6648
(lp6649
S'Africa South of the Sahara/epidemiology'
p6650
aS'Animals'
p6651
aS'Animals, Domestic'
p6652
aS'Dog Diseases/*epidemiology/pathology/*prevention & control'
p6653
aS'Dogs'
p6654
aS'Incidence'
p6655
aS'Models, Biological'
p6656
aS'Population Density'
p6657
aS'Rabies/epidemiology/prevention & control/*veterinary'
p6658
aS'Time Factors'
p6659
aS'Viral Vaccines/administration & dosage/immunology'
p6660
asS'EDAT'
p6661
S'2007/04/25 09:00'
p6662
sS'SO'
p6663
S'Proc Natl Acad Sci U S A. 2007 May 1;104(18):7717-22. Epub 2007 Apr 23.'
p6664
sS'SB'
p6665
S'IM'
p6666
sS'PMID'
p6667
S'17452645'
p6668
sS'PST'
p6669
S'ppublish'
p6670
stRp6671
ag2
(g3
g4
(dp6672
S'STAT'
p6673
S'MEDLINE'
p6674
sS'DEP'
p6675
S'20070117'
p6676
sS'DA'
p6677
S'20070117'
p6678
sS'AID'
p6679
(lp6680
S'10.1371/journal.pone.0000165 [doi]'
p6681
asS'CRDT'
p6682
(lp6683
S'2007/01/18 09:00'
p6684
asS'DP'
p6685
S'2007'
p6686
sS'AD'
p6687
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America. pulliam@princeton.edu'
p6688
sS'OWN'
p6689
S'NLM'
p6690
sS'PT'
p6691
(lp6692
S'Journal Article'
p6693
aS'Research Support, N.I.H., Extramural'
p6694
aS"Research Support, Non-U.S. Gov't"
p6695
asS'LA'
p6696
(lp6697
S'eng'
p6698
asS'FAU'
p6699
(lp6700
S'Pulliam, Juliet R C'
p6701
aS'Dushoff, Jonathan G'
p6702
aS'Levin, Simon A'
p6703
aS'Dobson, Andrew P'
p6704
asS'JT'
p6705
S'PloS one'
p6706
sS'LR'
p6707
S'20140907'
p6708
sS'PG'
p6709
S'e165'
p6710
sS'TI'
p6711
S'Epidemic enhancement in partially immune populations.'
p6712
sS'RN'
p6713
(lp6714
S'0 (Vaccines)'
p6715
asS'PL'
p6716
S'United States'
p6717
sS'TA'
p6718
S'PLoS One'
p6719
sS'JID'
p6720
S'101285081'
p6721
sS'AB'
p6722
S'We observe that a pathogen introduced into a population containing individuals with acquired immunity can result in an epidemic longer in duration and/or larger in size than if the pathogen were introduced into a naive population. We call this phenomenon "epidemic enhancement," and use simple dynamical models to show that it is a realistic scenario within the parameter ranges of many common infectious diseases. This finding implies that repeated pathogen introduction or intermediate levels of vaccine coverage can lead to pathogen persistence in populations where extinction would otherwise be expected.'
p6723
sS'GR'
p6724
(lp6725
S'GM071508/GM/NIGMS NIH HHS/United States'
p6726
aS'R01-TW05869/TW/FIC NIH HHS/United States'
p6727
asS'IP'
p6728
S'1'
sS'IS'
p6729
S'1932-6203 (Electronic) 1932-6203 (Linking)'
p6730
sS'PMC'
p6731
S'PMC1769520'
p6732
sS'DCOM'
p6733
S'20100312'
p6734
sS'AU'
p6735
(lp6736
S'Pulliam JR'
p6737
aS'Dushoff JG'
p6738
aS'Levin SA'
p6739
aS'Dobson AP'
p6740
asS'GN'
p6741
(lp6742
S'NLM: Original DateCompleted: 20070723'
p6743
asS'VI'
p6744
S'2'
sS'MHDA'
p6745
S'2007/01/18 09:01'
p6746
sS'PHST'
p6747
(lp6748
S'2006/11/13 [received]'
p6749
aS'2006/12/11 [accepted]'
p6750
aS'2007/01/17 [aheadofprint]'
p6751
asS'OID'
p6752
(lp6753
S'NLM: PMC1769520'
p6754
asS'MH'
p6755
(lp6756
S'Adaptive Immunity/*immunology'
p6757
aS'Communicable Diseases/*epidemiology'
p6758
aS'Computer Simulation'
p6759
aS'*Disease Outbreaks'
p6760
aS'Host-Pathogen Interactions'
p6761
aS'Humans'
p6762
aS'Mathematics'
p6763
aS'*Models, Theoretical'
p6764
aS'*Population Dynamics'
p6765
aS'Stochastic Processes'
p6766
aS'Vaccines'
p6767
asS'EDAT'
p6768
S'2007/01/18 09:00'
p6769
sS'SO'
p6770
S'PLoS One. 2007 Jan 17;2(1):e165.'
p6771
sS'SB'
p6772
S'IM'
p6773
sS'PMID'
p6774
S'17225866'
p6775
sS'PST'
p6776
S'epublish'
p6777
stRp6778
ag2
(g3
g4
(dp6779
S'STAT'
p6780
S'MEDLINE'
p6781
sS'DEP'
p6782
S'20061212'
p6783
sS'DA'
p6784
S'20061220'
p6785
sS'AID'
p6786
(lp6787
S'0609484103 [pii]'
p6788
aS'10.1073/pnas.0609484103 [doi]'
p6789
asS'CRDT'
p6790
(lp6791
S'2006/12/14 09:00'
p6792
asS'DP'
p6793
S'2006 Dec 19'
p6794
sS'AD'
p6795
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.'
p6796
sS'OWN'
p6797
S'NLM'
p6798
sS'PT'
p6799
(lp6800
S'Comparative Study'
p6801
aS'Journal Article'
p6802
aS'Research Support, N.I.H., Extramural'
p6803
aS"Research Support, Non-U.S. Gov't"
p6804
aS"Research Support, U.S. Gov't, Non-P.H.S."
p6805
asS'LA'
p6806
(lp6807
S'eng'
p6808
asS'FAU'
p6809
(lp6810
S'Bazykin, Georgii A'
p6811
aS'Dushoff, Jonathan'
p6812
aS'Levin, Simon A'
p6813
aS'Kondrashov, Alexey S'
p6814
asS'JT'
p6815
S'Proceedings of the National Academy of Sciences of the United States of America'
p6816
sS'LR'
p6817
S'20140907'
p6818
sS'PG'
p6819
S'19396-401'
p6820
sS'TI'
p6821
S'Bursts of nonsynonymous substitutions in HIV-1 evolution reveal instances of positive selection at conservative protein sites.'
p6822
sS'RN'
p6823
(lp6824
S'0 (Viral Proteins)'
p6825
asS'PL'
p6826
S'United States'
p6827
sS'TA'
p6828
S'Proc Natl Acad Sci U S A'
p6829
sS'JID'
p6830
S'7505876'
p6831
sS'AB'
p6832
S'The fixation of a new allele can be driven by Darwinian positive selection or can be due to random genetic drift. Identifying instances of positive selection is a difficult task, because its impact is routinely obscured by the action of negative selection. The nature of the genetic code dictates that positive selection in favor of an amino acid replacement should often cause a burst of two or three nucleotide substitutions at a single codon site, because a large fraction of amino acid replacements cannot be achieved after just one nucleotide substitution. Here, we study pairs of successive nonsynonymous substitutions at one codon in the course of evolution of HIV-1 genes within HIV-1 populations inhabiting infected individuals. Such pairs are more numerous and more clumped than expected if different substitutions were independent and than what is observed for pairs of successive synonymous substitutions. Bursts of nonsynonymous substitutions in HIV-1 evolution cannot be explained by mutational biases and must, therefore, be due to positive selection. Both reversals, exact or imprecise, of fixed deleterious mutations and acquisitions of amino acids with new properties are responsible for the bursts. Temporal clumping is strongest at codon sites with a low overall rate of nonsynonymous evolution, implying that a substantial fraction of replacements of conservative amino acids are driven by positive selection. We identified many conservative sites of HIV-1 proteins that occasionally experience positive selection.'
p6833
sS'GR'
p6834
(lp6835
S'P50 GM071508/GM/NIGMS NIH HHS/United States'
p6836
asS'IP'
p6837
S'51'
p6838
sS'IS'
p6839
S'0027-8424 (Print) 0027-8424 (Linking)'
p6840
sS'PMC'
p6841
S'PMC1698441'
p6842
sS'DCOM'
p6843
S'20070302'
p6844
sS'AU'
p6845
(lp6846
S'Bazykin GA'
p6847
aS'Dushoff J'
p6848
aS'Levin SA'
p6849
aS'Kondrashov AS'
p6850
asS'VI'
p6851
S'103'
p6852
sS'MHDA'
p6853
S'2007/03/03 09:00'
p6854
sS'PHST'
p6855
(lp6856
S'2006/12/12 [aheadofprint]'
p6857
asS'OID'
p6858
(lp6859
S'NLM: PMC1698441'
p6860
asS'MH'
p6861
(lp6862
S'Amino Acid Substitution/*genetics'
p6863
aS'Computer Simulation'
p6864
aS'*Evolution, Molecular'
p6865
aS'HIV-1/*genetics'
p6866
aS'Models, Genetic'
p6867
aS'Models, Molecular'
p6868
aS'*Phylogeny'
p6869
aS'*Selection, Genetic'
p6870
aS'Viral Proteins/*genetics'
p6871
asS'EDAT'
p6872
S'2006/12/14 09:00'
p6873
sS'SO'
p6874
S'Proc Natl Acad Sci U S A. 2006 Dec 19;103(51):19396-401. Epub 2006 Dec 12.'
p6875
sS'SB'
p6876
S'IM'
p6877
sS'PMID'
p6878
S'17164328'
p6879
sS'PST'
p6880
S'ppublish'
p6881
stRp6882
ag2
(g3
g4
(dp6883
S'STAT'
p6884
S'MEDLINE'
p6885
sS'DEP'
p6886
S'20061014'
p6887
sS'DA'
p6888
S'20061114'
p6889
sS'AID'
p6890
(lp6891
S'10.1007/s00239-005-0233-x [doi]'
p6892
asS'CRDT'
p6893
(lp6894
S'2006/10/18 09:00'
p6895
asS'DP'
p6896
S'2006 Nov'
p6897
sS'OWN'
p6898
S'NLM'
p6899
sS'PT'
p6900
(lp6901
S'Journal Article'
p6902
aS"Research Support, Non-U.S. Gov't"
p6903
asS'LA'
p6904
(lp6905
S'eng'
p6906
asS'FAU'
p6907
(lp6908
S'Plotkin, Joshua B'
p6909
aS'Dushoff, Jonathan'
p6910
aS'Desai, Michael M'
p6911
aS'Fraser, Hunter B'
p6912
asS'JT'
p6913
S'Journal of molecular evolution'
p6914
sS'LR'
p6915
S'20091119'
p6916
sS'PG'
p6917
S'635-53'
p6918
sS'TI'
p6919
S'Codon usage and selection on proteins.'
p6920
sS'RN'
p6921
(lp6922
S'0 (Codon)'
p6923
aS'0 (Proteins)'
p6924
asS'PL'
p6925
S'Germany'
p6926
sS'TA'
p6927
S'J Mol Evol'
p6928
sS'JID'
p6929
S'0360051'
p6930
sS'AB'
p6931
S'Selection pressures on proteins are usually measured by comparing homologous nucleotide sequences (Zuckerkandl and Pauling 1965). Recently we introduced a novel method, termed volatility, to estimate selection pressures on proteins on the basis of their synonymous codon usage (Plotkin and Dushoff 2003; Plotkin et al. 2004). Here we provide a theoretical foundation for this approach. Under the Fisher-Wright model, we derive the expected frequencies of synonymous codons as a function of the strength of selection on amino acids, the mutation rate, and the effective population size. We analyze the conditions under which we can expect to draw inferences from biased codon usage, and we estimate the time scales required to establish and maintain such a signal. We find that synonymous codon usage can reliably distinguish between negative selection and neutrality only for organisms, such as some microbes, that experience large effective population sizes or periods of elevated mutation rates. The power of volatility to detect positive selection is also modest--requiring approximately 100 selected sites--but it depends less strongly on population size. We show that phenomena such as transient hyper-mutators can improve the power of volatility to detect selection, even when the neutral site heterozygosity is low. We also discuss several confounding factors, neglected by the Fisher-Wright model, that may limit the applicability of volatility in practice.'
p6932
sS'AD'
p6933
S'Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA. jplotkin@sas.upenn.edu'
p6934
sS'IP'
p6935
S'5'
sS'IS'
p6936
S'0022-2844 (Print) 0022-2844 (Linking)'
p6937
sS'DCOM'
p6938
S'20070402'
p6939
sS'AU'
p6940
(lp6941
S'Plotkin JB'
p6942
aS'Dushoff J'
p6943
aS'Desai MM'
p6944
aS'Fraser HB'
p6945
asS'VI'
p6946
S'63'
p6947
sS'MHDA'
p6948
S'2007/04/03 09:00'
p6949
sS'PHST'
p6950
(lp6951
S'2005/10/03 [received]'
p6952
aS'2006/06/12 [accepted]'
p6953
aS'2006/10/14 [aheadofprint]'
p6954
asS'MH'
p6955
(lp6956
S'Algorithms'
p6957
aS'Alleles'
p6958
aS'Codon/*genetics'
p6959
aS'Evolution, Molecular'
p6960
aS'Models, Genetic'
p6961
aS'Proteins/*genetics'
p6962
aS'*Selection, Genetic'
p6963
asS'EDAT'
p6964
S'2006/10/18 09:00'
p6965
sS'SO'
p6966
S'J Mol Evol. 2006 Nov;63(5):635-53. Epub 2006 Oct 14.'
p6967
sS'SB'
p6968
S'IM'
p6969
sS'PMID'
p6970
S'17043750'
p6971
sS'PST'
p6972
S'ppublish'
p6973
stRp6974
ag2
(g3
g4
(dp6975
S'STAT'
p6976
S'MEDLINE'
p6977
sS'AB'
p6978
S"For species in which group membership frequently changes, it has been a challenge to characterize variation in individual interactions and social structure. Quantifying this variation is necessary to test hypotheses about ecological determinants of social patterns and to make predictions about how group dynamics affect the development of cooperative relationships and transmission processes. Network models have recently become popular for analyzing individual contacts within a population context. We use network metrics to compare populations of Grevy's zebra (Equus grevyi) and onagers (Equus hemionus khur). These closely related equids, previously described as having the same social system, inhabit environments differing in the distribution of food, water, and predators. Grevy's zebra and onagers are one example of many sets of coarsely similar fission-fusion species and populations, observed elsewhere in other ungulates, primates, and cetaceans. Our analysis of the population association networks reveals contrasts consistent with their distinctive environments. Grevy's zebra individuals are more selective in their association choices. Grevy's zebra form stable cliques, while onager associations are more fluid. We find evidence that females associate assortatively by reproductive state in Grevy's zebra but not in onagers. The current approach demonstrates the utility of network metrics for identifying fine-grained variation among individuals and populations in association patterns. From our analysis, we can make testable predictions about behavioral mechanisms underlying social structure and its effects on transmission processes."
p6979
sS'JID'
p6980
S'0150372'
p6981
sS'AD'
p6982
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. ssundare@princeton.edu'
p6983
sS'DEP'
p6984
S'20060909'
p6985
sS'IP'
p6986
S'1'
sS'IS'
p6987
S'0029-8549 (Print) 0029-8549 (Linking)'
p6988
sS'DCOM'
p6989
S'20070315'
p6990
sS'DA'
p6991
S'20070108'
p6992
sS'AU'
p6993
(lp6994
S'Sundaresan SR'
p6995
aS'Fischhoff IR'
p6996
aS'Dushoff J'
p6997
aS'Rubenstein DI'
p6998
asS'PST'
p6999
S'ppublish'
p7000
sS'AID'
p7001
(lp7002
S'10.1007/s00442-006-0553-6 [doi]'
p7003
asS'CRDT'
p7004
(lp7005
S'2006/09/12 09:00'
p7006
asS'VI'
p7007
S'151'
p7008
sS'DP'
p7009
S'2007 Feb'
p7010
sS'MHDA'
p7011
S'2007/03/16 09:00'
p7012
sS'PHST'
p7013
(lp7014
S'2006/02/18 [received]'
p7015
aS'2006/08/15 [accepted]'
p7016
aS'2006/09/09 [aheadofprint]'
p7017
asS'OWN'
p7018
S'NLM'
p7019
sS'PT'
p7020
(lp7021
S'Comparative Study'
p7022
aS'Journal Article'
p7023
aS"Research Support, Non-U.S. Gov't"
p7024
aS"Research Support, U.S. Gov't, Non-P.H.S."
p7025
asS'LA'
p7026
(lp7027
S'eng'
p7028
asS'MH'
p7029
(lp7030
S'Analysis of Variance'
p7031
aS'Animals'
p7032
aS'Behavior, Animal/*physiology'
p7033
aS'*Environment'
p7034
aS'Equidae/*physiology'
p7035
aS'Female'
p7036
aS'India'
p7037
aS'Kenya'
p7038
aS'Male'
p7039
aS'*Models, Theoretical'
p7040
aS'*Social Behavior'
p7041
aS'*Social Environment'
p7042
asS'FAU'
p7043
(lp7044
S'Sundaresan, Siva R'
p7045
aS'Fischhoff, Ilya R'
p7046
aS'Dushoff, Jonathan'
p7047
aS'Rubenstein, Daniel I'
p7048
asS'EDAT'
p7049
S'2006/09/12 09:00'
p7050
sS'JT'
p7051
S'Oecologia'
p7052
sS'SO'
p7053
S'Oecologia. 2007 Feb;151(1):140-9. Epub 2006 Sep 9.'
p7054
sS'PG'
p7055
S'140-9'
p7056
sS'TI'
p7057
S"Network metrics reveal differences in social organization between two fission-fusion species, Grevy's zebra and onager."
p7058
sS'SB'
p7059
S'IM'
p7060
sS'PMID'
p7061
S'16964497'
p7062
sS'PL'
p7063
S'Germany'
p7064
sS'TA'
p7065
S'Oecologia'
p7066
stRp7067
ag2
(g3
g4
(dp7068
S'STAT'
p7069
S'MEDLINE'
p7070
sS'DEP'
p7071
S'20060605'
p7072
sS'DA'
p7073
S'20060714'
p7074
sS'AID'
p7075
(lp7076
S'msl021 [pii]'
p7077
aS'10.1093/molbev/msl021 [doi]'
p7078
asS'CRDT'
p7079
(lp7080
S'2006/06/07 09:00'
p7081
asS'DP'
p7082
S'2006 Aug'
p7083
sS'OWN'
p7084
S'NLM'
p7085
sS'PT'
p7086
(lp7087
S'Comparative Study'
p7088
aS'Evaluation Studies'
p7089
aS'Letter'
p7090
aS'Research Support, N.I.H., Extramural'
p7091
aS"Research Support, Non-U.S. Gov't"
p7092
asS'LA'
p7093
(lp7094
S'eng'
p7095
asS'FAU'
p7096
(lp7097
S'Plotkin, Joshua B'
p7098
aS'Dushoff, Jonathan'
p7099
aS'Desai, Michael M'
p7100
aS'Fraser, Hunter B'
p7101
asS'JT'
p7102
S'Molecular biology and evolution'
p7103
sS'LR'
p7104
S'20091119'
p7105
sS'PG'
p7106
S'1457-9'
p7107
sS'TI'
p7108
S'Estimating selection pressures from limited comparative data.'
p7109
sS'RN'
p7110
(lp7111
S'0 (Codon)'
p7112
asS'PL'
p7113
S'United States'
p7114
sS'TA'
p7115
S'Mol Biol Evol'
p7116
sS'JID'
p7117
S'8501455'
p7118
sS'AB'
p7119
S'We recently introduced a novel method for estimating selection pressures on proteins, termed "volatility," which requires only a single genome sequence. Some criticisms that have been levied against this approach are valid, but many others are based on misconceptions of volatility, or they apply equally to comparative methods of estimating selection. Here, we introduce a simple regression technique for estimating selection pressures on all proteins in a genome, on the basis of limited comparative data. The regression technique does not depend on an underlying population-genetic mechanism. This new approach to estimating selection across a genome should be more powerful and more widely applicable than volatility itself.'
p7120
sS'GR'
p7121
(lp7122
S'P50-GM071508/GM/NIGMS NIH HHS/United States'
p7123
asS'IP'
p7124
S'8'
sS'IS'
p7125
S'0737-4038 (Print) 0737-4038 (Linking)'
p7126
sS'DCOM'
p7127
S'20061211'
p7128
sS'AU'
p7129
(lp7130
S'Plotkin JB'
p7131
aS'Dushoff J'
p7132
aS'Desai MM'
p7133
aS'Fraser HB'
p7134
asS'VI'
p7135
S'23'
p7136
sS'MHDA'
p7137
S'2006/12/12 09:00'
p7138
sS'PHST'
p7139
(lp7140
S'2006/06/05 [aheadofprint]'
p7141
asS'MH'
p7142
(lp7143
S'*Codon'
p7144
aS'*Evolution, Molecular'
p7145
aS'*Genome'
p7146
aS'Genome, Bacterial'
p7147
aS'Models, Genetic'
p7148
aS'Regression Analysis'
p7149
aS'Saccharomyces cerevisiae/*genetics'
p7150
aS'*Selection, Genetic'
p7151
asS'EDAT'
p7152
S'2006/06/07 09:00'
p7153
sS'SO'
p7154
S'Mol Biol Evol. 2006 Aug;23(8):1457-9. Epub 2006 Jun 5.'
p7155
sS'SB'
p7156
S'IM'
p7157
sS'PMID'
p7158
S'16754640'
p7159
sS'PST'
p7160
S'ppublish'
p7161
stRp7162
ag2
(g3
g4
(dp7163
S'STAT'
p7164
S'MEDLINE'
p7165
sS'JID'
p7166
S'8900488'
p7167
sS'JT'
p7168
S'BMJ (Clinical research ed.)'
p7169
sS'IP'
p7170
S'7534'
p7171
sS'IS'
p7172
S'1756-1833 (Electronic) 0959-535X (Linking)'
p7173
sS'PMC'
p7174
S'PMC1336810'
p7175
sS'DCOM'
p7176
S'20060203'
p7177
sS'DA'
p7178
S'20060120'
p7179
sS'AU'
p7180
(lp7181
S'Simonsen L'
p7182
aS'Taylor R'
p7183
aS'Viboud C'
p7184
aS'Dushoff J'
p7185
aS'Miller M'
p7186
asS'SB'
p7187
S'AIM IM'
p7188
sS'AID'
p7189
(lp7190
S'332/7534/177-a [pii]'
p7191
aS'10.1136/bmj.332.7534.177-a [doi]'
p7192
asS'CRDT'
p7193
(lp7194
S'2006/01/21 09:00'
p7195
asS'VI'
p7196
S'332'
p7197
sS'DP'
p7198
S'2006 Jan 21'
p7199
sS'MHDA'
p7200
S'2006/02/04 09:00'
p7201
sS'OWN'
p7202
S'NLM'
p7203
sS'PT'
p7204
(lp7205
S'Letter'
p7206
asS'LA'
p7207
(lp7208
S'eng'
p7209
asS'OID'
p7210
(lp7211
S'NLM: PMC1336810'
p7212
asS'MH'
p7213
(lp7214
S'Epidemiologic Methods'
p7215
aS'Humans'
p7216
aS'*Influenza A Virus, H3N2 Subtype'
p7217
aS'Influenza, Human/*mortality'
p7218
aS'United States/epidemiology'
p7219
asS'FAU'
p7220
(lp7221
S'Simonsen, Lone'
p7222
aS'Taylor, Robert'
p7223
aS'Viboud, Cecile'
p7224
aS'Dushoff, Jonathan'
p7225
aS'Miller, Mark'
p7226
asS'EDAT'
p7227
S'2006/01/21 09:00'
p7228
sS'PST'
p7229
S'ppublish'
p7230
sS'LR'
p7231
S'20140909'
p7232
sS'PG'
p7233
S'177-8'
p7234
sS'TI'
p7235
S'US flu mortality estimates are based on solid science.'
p7236
sS'SO'
p7237
S'BMJ. 2006 Jan 21;332(7534):177-8.'
p7238
sS'PMID'
p7239
S'16424502'
p7240
sS'PL'
p7241
S'England'
p7242
sS'TA'
p7243
S'BMJ'
p7244
stRp7245
ag2
(g3
g4
(dp7246
S'STAT'
p7247
S'MEDLINE'
p7248
sS'AB'
p7249
S'Influenza is an important cause of mortality in temperate countries, but there is substantial controversy as to the total direct and indirect mortality burden imposed by influenza viruses. The authors have extracted multiple-cause death data from public-use data files for the United States from 1979 to 2001. The current research reevaluates attribution of deaths to influenza, by use of an annualized regression approach: comparing measures of excess deaths with measures of influenza virus prevalence by subtype over entire influenza seasons and attributing deaths to influenza by a regression model. This approach is more conservative in its assumptions than is earlier work, which used weekly regression models, or models based on fitting baselines, but it produces results consistent with these other methods, supporting the conclusion that influenza is an important cause of seasonal excess deaths. The regression model attributes an annual average of 41,400 (95% confidence interval: 27,100, 55,700) deaths to influenza over the period 1979-2001. The study also uses regional death data to investigate the effects of cold weather on annualized excess deaths.'
p7250
sS'JID'
p7251
S'7910653'
p7252
sS'AD'
p7253
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. dushoff@eno.princeton.edu'
p7254
sS'DEP'
p7255
S'20051130'
p7256
sS'IP'
p7257
S'2'
sS'IS'
p7258
S'0002-9262 (Print) 0002-9262 (Linking)'
p7259
sS'DCOM'
p7260
S'20060223'
p7261
sS'DA'
p7262
S'20060106'
p7263
sS'AU'
p7264
(lp7265
S'Dushoff J'
p7266
aS'Plotkin JB'
p7267
aS'Viboud C'
p7268
aS'Earn DJ'
p7269
aS'Simonsen L'
p7270
asS'PST'
p7271
S'ppublish'
p7272
sS'AID'
p7273
(lp7274
S'kwj024 [pii]'
p7275
aS'10.1093/aje/kwj024 [doi]'
p7276
asS'CRDT'
p7277
(lp7278
S'2005/12/02 09:00'
p7279
asS'VI'
p7280
S'163'
p7281
sS'DP'
p7282
S'2006 Jan 15'
p7283
sS'MHDA'
p7284
S'2006/02/24 09:00'
p7285
sS'PHST'
p7286
(lp7287
S'2005/11/30 [aheadofprint]'
p7288
asS'OWN'
p7289
S'NLM'
p7290
sS'PT'
p7291
(lp7292
S'Journal Article'
p7293
asS'LA'
p7294
(lp7295
S'eng'
p7296
asS'MH'
p7297
(lp7298
S'Cause of Death'
p7299
aS'Humans'
p7300
aS'Influenza, Human/*mortality'
p7301
aS'Population Surveillance'
p7302
aS'Prevalence'
p7303
aS'Regression Analysis'
p7304
aS'Seasons'
p7305
aS'United States/epidemiology'
p7306
asS'FAU'
p7307
(lp7308
S'Dushoff, Jonathan'
p7309
aS'Plotkin, Joshua B'
p7310
aS'Viboud, Cecile'
p7311
aS'Earn, David J D'
p7312
aS'Simonsen, Lone'
p7313
asS'EDAT'
p7314
S'2005/12/02 09:00'
p7315
sS'JT'
p7316
S'American journal of epidemiology'
p7317
sS'SO'
p7318
S'Am J Epidemiol. 2006 Jan 15;163(2):181-7. Epub 2005 Nov 30.'
p7319
sS'PG'
p7320
S'181-7'
p7321
sS'TI'
p7322
S'Mortality due to influenza in the United States--an annualized regression approach using multiple-cause mortality data.'
p7323
sS'SB'
p7324
S'IM'
p7325
sS'PMID'
p7326
S'16319291'
p7327
sS'PL'
p7328
S'United States'
p7329
sS'TA'
p7330
S'Am J Epidemiol'
p7331
stRp7332
ag2
(g3
g4
(dp7333
S'STAT'
p7334
S'MEDLINE'
p7335
sS'JT'
p7336
S'Nature'
p7337
sS'MID'
p7338
(lp7339
S'UKMS35850'
p7340
asS'DA'
p7341
S'20051124'
p7342
sS'AID'
p7343
(lp7344
S'nature04024 [pii]'
p7345
aS'10.1038/nature04024 [doi]'
p7346
asS'CRDT'
p7347
(lp7348
S'2005/11/25 09:00'
p7349
asS'DP'
p7350
S'2005 Nov 24'
p7351
sS'AD'
p7352
S'Fogarty International Center, National Institutes of Health, Building 16, 16 Center Drive, Bethesda, Maryland 20892, USA. smitdave@helix.nih.gov'
p7353
sS'OWN'
p7354
S'NLM'
p7355
sS'PT'
p7356
(lp7357
S'Journal Article'
p7358
aS"Research Support, Non-U.S. Gov't"
p7359
aS"Research Support, U.S. Gov't, Non-P.H.S."
p7360
asS'LA'
p7361
(lp7362
S'eng'
p7363
asS'FAU'
p7364
(lp7365
S'Smith, D L'
p7366
aS'Dushoff, J'
p7367
aS'Snow, R W'
p7368
aS'Hay, S I'
p7369
asS'LR'
p7370
S'20140910'
p7371
sS'PG'
p7372
S'492-5'
p7373
sS'TI'
p7374
S'The entomological inoculation rate and Plasmodium falciparum infection in African children.'
p7375
sS'PL'
p7376
S'England'
p7377
sS'TA'
p7378
S'Nature'
p7379
sS'JID'
p7380
S'0410462'
p7381
sS'AB'
p7382
S'Malaria is an important cause of global morbidity and mortality. The fact that some people are bitten more often than others has a large effect on the relationship between risk factors and prevalence of vector-borne diseases. Here we develop a mathematical framework that allows us to estimate the heterogeneity of infection rates from the relationship between rates of infectious bites and community prevalence. We apply this framework to a large, published data set that combines malaria measurements from more than 90 communities. We find strong evidence that heterogeneous biting or heterogeneous susceptibility to infection are important and pervasive factors determining the prevalence of infection: 20% of people receive 80% of all infections. We also find that individual infections last about six months on average, per infectious bite, and children who clear infections are not immune to new infections. The results have important implications for public health interventions: the success of malaria control will depend heavily on whether efforts are targeted at those who are most at risk of infection.'
p7383
sS'GR'
p7384
(lp7385
S'058992/Wellcome Trust/United Kingdom'
p7386
aS'069045/Wellcome Trust/United Kingdom'
p7387
aS'Wellcome Trust/United Kingdom'
p7388
asS'IP'
p7389
S'7067'
p7390
sS'IS'
p7391
S'1476-4687 (Electronic) 0028-0836 (Linking)'
p7392
sS'PMC'
p7393
S'PMC3128496'
p7394
sS'DCOM'
p7395
S'20051215'
p7396
sS'AU'
p7397
(lp7398
S'Smith DL'
p7399
aS'Dushoff J'
p7400
aS'Snow RW'
p7401
aS'Hay SI'
p7402
asS'VI'
p7403
S'438'
p7404
sS'MHDA'
p7405
S'2005/12/16 09:00'
p7406
sS'PHST'
p7407
(lp7408
S'2005/05/26 [received]'
p7409
aS'2005/07/11 [accepted]'
p7410
asS'OID'
p7411
(lp7412
S'NLM: PMC3128496'
p7413
aS'NLM: UKMS35850'
p7414
asS'MH'
p7415
(lp7416
S'Adolescent'
p7417
aS'Africa/epidemiology'
p7418
aS'Age Distribution'
p7419
aS'Animals'
p7420
aS'Bites and Stings/epidemiology/parasitology'
p7421
aS'Child'
p7422
aS'Culicidae/parasitology/physiology'
p7423
aS'Disease Susceptibility'
p7424
aS'Humans'
p7425
aS'Insect Vectors/parasitology/physiology'
p7426
aS'Malaria, Falciparum/*epidemiology/immunology/parasitology/*transmission'
p7427
aS'*Models, Biological'
p7428
aS'Plasmodium falciparum/immunology/*physiology'
p7429
aS'Prevalence'
p7430
aS'Sensitivity and Specificity'
p7431
asS'EDAT'
p7432
S'2005/11/25 09:00'
p7433
sS'SO'
p7434
S'Nature. 2005 Nov 24;438(7067):492-5.'
p7435
sS'SB'
p7436
S'IM'
p7437
sS'PMID'
p7438
S'16306991'
p7439
sS'PST'
p7440
S'ppublish'
p7441
stRp7442
ag2
(g3
g4
(dp7443
S'STAT'
p7444
S'PubMed-not-MEDLINE'
p7445
sS'AD'
p7446
S'Dept. of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. dushoff@eno.princeton.edu'
p7447
sS'DEP'
p7448
S'20050721'
p7449
sS'VI'
p7450
S'2'
sS'IS'
p7451
S'1742-7622 (Electronic) 1742-7622 (Linking)'
p7452
sS'PMC'
p7453
S'PMC1196295'
p7454
sS'JID'
p7455
S'101232986'
p7456
sS'DA'
p7457
S'20050901'
p7458
sS'AU'
p7459
(lp7460
S'Dushoff J'
p7461
asS'PST'
p7462
S'epublish'
p7463
sS'AID'
p7464
(lp7465
S'1742-7622-2-7 [pii]'
p7466
aS'10.1186/1742-7622-2-7 [doi]'
p7467
asS'CRDT'
p7468
(lp7469
S'2005/07/27 09:00'
p7470
asS'DP'
p7471
S'2005 Jul 21'
p7472
sS'CON'
p7473
(lp7474
S'Emerg Themes Epidemiol. 2005 Jun 21;2:6. PMID: 15969758'
p7475
asS'MHDA'
p7476
S'2005/07/27 09:01'
p7477
sS'PHST'
p7478
(lp7479
S'2005/06/23 [received]'
p7480
aS'2005/07/21 [accepted]'
p7481
aS'2005/07/21 [aheadofprint]'
p7482
asS'OWN'
p7483
S'NLM'
p7484
sS'PT'
p7485
(lp7486
S'Comment'
p7487
aS'Journal Article'
p7488
asS'LA'
p7489
(lp7490
S'eng'
p7491
asS'OID'
p7492
(lp7493
S'NLM: PMC1196295'
p7494
asS'DCOM'
p7495
S'20051007'
p7496
sS'EDAT'
p7497
S'2005/07/27 09:00'
p7498
sS'JT'
p7499
S'Emerging themes in epidemiology'
p7500
sS'LR'
p7501
S'20091118'
p7502
sS'FAU'
p7503
(lp7504
S'Dushoff, Jonathan'
p7505
asS'TI'
p7506
S'Assessing influenza-related mortality: comment on Zucs et al.'
p7507
sS'SO'
p7508
S'Emerg Themes Epidemiol. 2005 Jul 21;2:7.'
p7509
sS'PMID'
p7510
S'16042766'
p7511
sS'TA'
p7512
S'Emerg Themes Epidemiol'
p7513
sS'PL'
p7514
S'England'
p7515
sS'PG'
p7516
S'7'
stRp7517
ag2
(g3
g4
(dp7518
S'STAT'
p7519
S'MEDLINE'
p7520
sS'DEP'
p7521
S'20050705'
p7522
sS'CIN'
p7523
(lp7524
S'PLoS Med. 2005 Nov;2(11):e383. PMID: 16288558'
p7525
asS'DA'
p7526
S'20050825'
p7527
sS'AID'
p7528
(lp7529
S'05-PLME-PF-0020 [pii]'
p7530
aS'10.1371/journal.pmed.0020232 [doi]'
p7531
asS'CRDT'
p7532
(lp7533
S'2005/06/30 09:00'
p7534
asS'DP'
p7535
S'2005 Aug'
p7536
sS'OWN'
p7537
S'NLM'
p7538
sS'PT'
p7539
(lp7540
S'Journal Article'
p7541
asS'LA'
p7542
(lp7543
S'eng'
p7544
asS'FAU'
p7545
(lp7546
S'Smith, David L'
p7547
aS'Dushoff, Jonathan'
p7548
aS'Morris, J Glenn'
p7549
asS'JT'
p7550
S'PLoS medicine'
p7551
sS'LR'
p7552
S'20151119'
p7553
sS'PG'
p7554
S'e232'
p7555
sS'TI'
p7556
S'Agricultural antibiotics and human health.'
p7557
sS'RN'
p7558
(lp7559
S'0 (Anti-Bacterial Agents)'
p7560
aS'0 (Food Additives)'
p7561
aS'0 (Glycopeptides)'
p7562
aS'6Q205EH1VU (Vancomycin)'
p7563
aS'WJ13O9MNTI (avoparcin)'
p7564
asS'PL'
p7565
S'United States'
p7566
sS'TA'
p7567
S'PLoS Med'
p7568
sS'JID'
p7569
S'101231360'
p7570
sS'AD'
p7571
S'Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA. smitdave@helix.nih.gov'
p7572
sS'IP'
p7573
S'8'
sS'IS'
p7574
S'1549-1676 (Electronic) 1549-1277 (Linking)'
p7575
sS'PMC'
p7576
S'PMC1167557'
p7577
sS'DCOM'
p7578
S'20060818'
p7579
sS'AU'
p7580
(lp7581
S'Smith DL'
p7582
aS'Dushoff J'
p7583
aS'Morris JG'
p7584
asS'VI'
p7585
S'2'
sS'MHDA'
p7586
S'2006/08/19 09:00'
p7587
sS'PHST'
p7588
(lp7589
S'2005/07/05 [aheadofprint]'
p7590
asS'OID'
p7591
(lp7592
S'NLM: PMC1167557'
p7593
asS'MH'
p7594
(lp7595
S'*Agriculture/methods'
p7596
aS'Animal Feed'
p7597
aS'Animals'
p7598
aS'Animals, Domestic/growth & development/microbiology'
p7599
aS'Anti-Bacterial Agents/*adverse effects'
p7600
aS'Community-Acquired Infections/microbiology/*transmission'
p7601
aS'*Disease Transmission, Infectious'
p7602
aS'Enterococcus/drug effects'
p7603
aS'Food Additives/*adverse effects'
p7604
aS'Glycopeptides/adverse effects'
p7605
aS'Humans'
p7606
aS'*Public Health'
p7607
aS'Vancomycin/adverse effects'
p7608
aS'Vancomycin Resistance'
p7609
aS'Zoonoses/microbiology/*transmission'
p7610
asS'EDAT'
p7611
S'2005/06/30 09:00'
p7612
sS'SO'
p7613
S'PLoS Med. 2005 Aug;2(8):e232. Epub 2005 Jul 5.'
p7614
sS'SB'
p7615
S'IM'
p7616
sS'PMID'
p7617
S'15984910'
p7618
sS'PST'
p7619
S'ppublish'
p7620
stRp7621
ag2
(g3
g4
(dp7622
S'STAT'
p7623
S'MEDLINE'
p7624
sS'AB'
p7625
S'Simple measures of habitat proximity made primarily on the basis of land cover are widely used in the ecological literature to infer habitat connectivity, or the potential for animal movement among resource patches. However, such indices rarely have been tested against observations of animal movement or against more detailed biological models. We developed a priori expectations as to the types of study systems and organisms for which various habitat proximity indices would be best suited. We then used data from three study systems and four species to test which, if any, of the indices were good predictors of population-level responses. Our a priori expectations about index performance were not upheld. The indices that consider both habitat area and distance from the focal patch were highly correlated with each other, suggesting that they do index similar quantities. However, none of the indices performed well in predicting population response variables. The results suggest that the pattern of habitat cover alone may be insufficient to predict the process of animal movement.'
p7626
sS'JID'
p7627
S'2984688R'
p7628
sS'AD'
p7629
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey 08544, USA. rwinfree@princeton.edu'
p7630
sS'DEP'
p7631
S'20050330'
p7632
sS'IP'
p7633
S'6'
sS'IS'
p7634
S'1537-5323 (Electronic) 0003-0147 (Linking)'
p7635
sS'DCOM'
p7636
S'20070228'
p7637
sS'DA'
p7638
S'20050606'
p7639
sS'AU'
p7640
(lp7641
S'Winfree R'
p7642
aS'Dushoff J'
p7643
aS'Crone EE'
p7644
aS'Schultz CB'
p7645
aS'Budny RV'
p7646
aS'Williams NM'
p7647
aS'Kremen C'
p7648
asS'PST'
p7649
S'ppublish'
p7650
sS'AID'
p7651
(lp7652
S'AN40353 [pii]'
p7653
aS'10.1086/430009 [doi]'
p7654
asS'CRDT'
p7655
(lp7656
S'2005/06/07 09:00'
p7657
asS'VI'
p7658
S'165'
p7659
sS'DP'
p7660
S'2005 Jun'
p7661
sS'MHDA'
p7662
S'2007/03/01 09:00'
p7663
sS'PHST'
p7664
(lp7665
S'2004/03/18 [received]'
p7666
aS'2005/02/25 [accepted]'
p7667
aS'2005/03/30 [aheadofprint]'
p7668
asS'OWN'
p7669
S'NLM'
p7670
sS'PT'
p7671
(lp7672
S'Journal Article'
p7673
aS"Research Support, Non-U.S. Gov't"
p7674
asS'LA'
p7675
(lp7676
S'eng'
p7677
asS'MH'
p7678
(lp7679
S'*Animal Migration'
p7680
aS'Animals'
p7681
aS'Arvicolinae'
p7682
aS'Bees'
p7683
aS'Butterflies'
p7684
aS'*Ecosystem'
p7685
aS'Models, Biological'
p7686
aS'Population Dynamics'
p7687
asS'FAU'
p7688
(lp7689
S'Winfree, Rachael'
p7690
aS'Dushoff, Jonathan'
p7691
aS'Crone, Elizabeth E'
p7692
aS'Schultz, Cheryl B'
p7693
aS'Budny, Robert V'
p7694
aS'Williams, Neal M'
p7695
aS'Kremen, Claire'
p7696
asS'EDAT'
p7697
S'2005/06/07 09:00'
p7698
sS'JT'
p7699
S'The American naturalist'
p7700
sS'SO'
p7701
S'Am Nat. 2005 Jun;165(6):707-17. Epub 2005 Mar 30.'
p7702
sS'PG'
p7703
S'707-17'
p7704
sS'TI'
p7705
S'Testing simple indices of habitat proximity.'
p7706
sS'SB'
p7707
S'IM'
p7708
sS'PMID'
p7709
S'15937750'
p7710
sS'PL'
p7711
S'United States'
p7712
sS'TA'
p7713
S'Am Nat'
p7714
stRp7715
ag2
(g3
g4
(dp7716
S'STAT'
p7717
S'MEDLINE'
p7718
sS'DEP'
p7719
S'20041119'
p7720
sS'DA'
p7721
S'20041201'
p7722
sS'AID'
p7723
(lp7724
S'0407293101 [pii]'
p7725
aS'10.1073/pnas.0407293101 [doi]'
p7726
asS'CRDT'
p7727
(lp7728
S'2004/11/24 09:00'
p7729
asS'DP'
p7730
S'2004 Nov 30'
p7731
sS'AD'
p7732
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA. dushoff@eno.princeton.edu'
p7733
sS'OWN'
p7734
S'NLM'
p7735
sS'PT'
p7736
(lp7737
S'Journal Article'
p7738
aS"Research Support, Non-U.S. Gov't"
p7739
aS"Research Support, U.S. Gov't, P.H.S."
p7740
asS'LA'
p7741
(lp7742
S'eng'
p7743
asS'FAU'
p7744
(lp7745
S'Dushoff, Jonathan'
p7746
aS'Plotkin, Joshua B'
p7747
aS'Levin, Simon A'
p7748
aS'Earn, David J D'
p7749
asS'JT'
p7750
S'Proceedings of the National Academy of Sciences of the United States of America'
p7751
sS'LR'
p7752
S'20140608'
p7753
sS'PG'
p7754
S'16915-6'
p7755
sS'TI'
p7756
S'Dynamical resonance can account for seasonality of influenza epidemics.'
p7757
sS'PL'
p7758
S'United States'
p7759
sS'TA'
p7760
S'Proc Natl Acad Sci U S A'
p7761
sS'JID'
p7762
S'7505876'
p7763
sS'AB'
p7764
S"Influenza incidence exhibits strong seasonal fluctuations in temperate regions throughout the world, concentrating the mortality and morbidity burden of the disease into a few months each year. The cause of influenza's seasonality has remained elusive. Here we show that the large oscillations in incidence may be caused by undetectably small seasonal changes in the influenza transmission rate that are amplified by dynamical resonance."
p7765
sS'GR'
p7766
(lp7767
S'1-R01-GM60729-01/GM/NIGMS NIH HHS/United States'
p7768
asS'IP'
p7769
S'48'
p7770
sS'IS'
p7771
S'0027-8424 (Print) 0027-8424 (Linking)'
p7772
sS'PMC'
p7773
S'PMC534740'
p7774
sS'DCOM'
p7775
S'20050111'
p7776
sS'AU'
p7777
(lp7778
S'Dushoff J'
p7779
aS'Plotkin JB'
p7780
aS'Levin SA'
p7781
aS'Earn DJ'
p7782
asS'VI'
p7783
S'101'
p7784
sS'MHDA'
p7785
S'2005/01/12 09:00'
p7786
sS'PHST'
p7787
(lp7788
S'2004/11/19 [aheadofprint]'
p7789
asS'OID'
p7790
(lp7791
S'NLM: PMC534740'
p7792
asS'MH'
p7793
(lp7794
S'*Disease Outbreaks'
p7795
aS'Humans'
p7796
aS'Influenza, Human/*epidemiology'
p7797
aS'*Seasons'
p7798
asS'EDAT'
p7799
S'2004/11/24 09:00'
p7800
sS'SO'
p7801
S'Proc Natl Acad Sci U S A. 2004 Nov 30;101(48):16915-6. Epub 2004 Nov 19.'
p7802
sS'SB'
p7803
S'IM'
p7804
sS'PMID'
p7805
S'15557003'
p7806
sS'PST'
p7807
S'ppublish'
p7808
stRp7809
ag2
(g3
g4
(dp7810
S'STAT'
p7811
S'MEDLINE'
p7812
sS'DEP'
p7813
S'20041026'
p7814
sS'DA'
p7815
S'20041119'
p7816
sS'AID'
p7817
(lp7818
S'10.1371/journal.pbio.0020368 [doi]'
p7819
asS'CRDT'
p7820
(lp7821
S'2004/10/29 09:00'
p7822
asS'DP'
p7823
S'2004 Nov'
p7824
sS'AD'
p7825
S'Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA. smitdave@mail.nih.gov <smitdave@mail.nih.gov>'
p7826
sS'OWN'
p7827
S'NLM'
p7828
sS'PT'
p7829
(lp7830
S'Journal Article'
p7831
asS'LA'
p7832
(lp7833
S'eng'
p7834
asS'FAU'
p7835
(lp7836
S'Smith, David L'
p7837
aS'Dushoff, Jonathan'
p7838
aS'McKenzie, F Ellis'
p7839
asS'JT'
p7840
S'PLoS biology'
p7841
sS'LR'
p7842
S'20150325'
p7843
sS'PG'
p7844
S'e368'
p7845
sS'TI'
p7846
S'The risk of a mosquito-borne infection in a heterogeneous environment.'
p7847
sS'PL'
p7848
S'United States'
p7849
sS'TA'
p7850
S'PLoS Biol'
p7851
sS'JID'
p7852
S'101183755'
p7853
sS'AB'
p7854
S'A common assumption about malaria, dengue, and other mosquito-borne infections is that the two main components of the risk of human infection--the rate at which people are bitten (human biting rate) and the proportion of mosquitoes that are infectious--are positively correlated. In fact, these two risk factors are generated by different processes and may be negatively correlated across space and time in heterogeneous environments. Uneven distribution of blood-meal hosts and larval habitat creates a spatial mosaic of demograPhic sources and sinks. Moreover, mosquito populations fluctuate temporally, forced by environmental variables such as rainfall, temperature, and humidity. These sources of spatial and temporal heterogeneity in the distribution of mosquito populations generate variability in the human biting rate, in the proportion of mosquitoes that are infectious, and in the risk of human infection. To understand how heterogeneity affects the epidemiology of mosquito-borne infections, we developed a set of simple models that incorporate heterogeneity in a stepwise fashion. These models predict that the human biting rate is highest shortly after the mosquito densities peak, near breeding sites where adult mosquitoes emerge, and around the edges of areas where humans are aggregated. In contrast, the proportion of mosquitoes that are infectious reflects the age structure of mosquito populations; it peaks where old mosquitoes are found, far from mosquito breeding habitat, and when mosquito population density is declining. Finally, we show that estimates for the average risk of infection that are based on the average entomological inoculation rate are strongly biased in heterogeneous environments.'
p7855
sS'GR'
p7856
(lp7857
S'Z99 TW999999/Intramural NIH HHS/United States'
p7858
asS'IP'
p7859
S'11'
p7860
sS'IS'
p7861
S'1545-7885 (Electronic) 1544-9173 (Linking)'
p7862
sS'PMC'
p7863
S'PMC524252'
p7864
sS'DCOM'
p7865
S'20060203'
p7866
sS'AU'
p7867
(lp7868
S'Smith DL'
p7869
aS'Dushoff J'
p7870
aS'McKenzie FE'
p7871
asS'VI'
p7872
S'2'
sS'MHDA'
p7873
S'2006/02/04 09:00'
p7874
sS'PHST'
p7875
(lp7876
S'2004/01/06 [received]'
p7877
aS'2004/08/24 [accepted]'
p7878
aS'2004/10/26 [aheadofprint]'
p7879
asS'OID'
p7880
(lp7881
S'NLM: PMC524252'
p7882
asS'MH'
p7883
(lp7884
S'Animals'
p7885
aS'Communicable Diseases/*epidemiology/*parasitology/transmission'
p7886
aS'Culicidae/*parasitology'
p7887
aS'Environment'
p7888
aS'Humans'
p7889
aS'Insect Bites and Stings/*epidemiology'
p7890
aS'*Insect Vectors'
p7891
aS'Larva'
p7892
aS'Models, Theoretical'
p7893
aS'Population Density'
p7894
aS'Population Dynamics'
p7895
aS'Risk Factors'
p7896
aS'Seasons'
p7897
aS'Sensitivity and Specificity'
p7898
aS'Temperature'
p7899
aS'Time Factors'
p7900
aS'Weather'
p7901
asS'EDAT'
p7902
S'2004/10/29 09:00'
p7903
sS'SO'
p7904
S'PLoS Biol. 2004 Nov;2(11):e368. Epub 2004 Oct 26.'
p7905
sS'SB'
p7906
S'IM'
p7907
sS'PMID'
p7908
S'15510228'
p7909
sS'PST'
p7910
S'ppublish'
p7911
stRp7912
ag2
(g3
g4
(dp7913
S'STAT'
p7914
S'MEDLINE'
p7915
sS'DEP'
p7916
S'20040730'
p7917
sS'DA'
p7918
S'20040826'
p7919
sS'AID'
p7920
(lp7921
S'10.1186/1475-2875-3-29 [doi]'
p7922
aS'1475-2875-3-29 [pii]'
p7923
asS'CRDT'
p7924
(lp7925
S'2004/08/03 05:00'
p7926
asS'DP'
p7927
S'2004 Jul 30'
p7928
sS'AD'
p7929
S'Fogarty International Center, National Institutes of Health, 16 Center Drive, Bethesda MD 20892, USA. depinayj@mail.nih.gov'
p7930
sS'OWN'
p7931
S'NLM'
p7932
sS'PT'
p7933
(lp7934
S'Journal Article'
p7935
asS'LA'
p7936
(lp7937
S'eng'
p7938
asS'DCOM'
p7939
S'20041026'
p7940
sS'JT'
p7941
S'Malaria journal'
p7942
sS'LR'
p7943
S'20150325'
p7944
sS'FAU'
p7945
(lp7946
S'Depinay, Jean-Marc O'
p7947
aS'Mbogo, Charles M'
p7948
aS'Killeen, Gerry'
p7949
aS'Knols, Bart'
p7950
aS'Beier, John'
p7951
aS'Carlson, John'
p7952
aS'Dushoff, Jonathan'
p7953
aS'Billingsley, Peter'
p7954
aS'Mwambi, Henry'
p7955
aS'Githure, John'
p7956
aS'Toure, Abdoulaye M'
p7957
aS'McKenzie, F Ellis'
p7958
asS'TI'
p7959
S'A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission.'
p7960
sS'PL'
p7961
S'England'
p7962
sS'PG'
p7963
S'29'
p7964
sS'JID'
p7965
S'101139802'
p7966
sS'AB'
p7967
S'BACKGROUND: Malaria is one of the oldest and deadliest infectious diseases in humans. Many mathematical models of malaria have been developed during the past century, and applied to potential interventions. However, malaria remains uncontrolled and is increasing in many areas, as are vector and parasite resistance to insecticides and drugs. METHODS: This study presents a simulation model of African malaria vectors. This individual-based model incorporates current knowledge of the mechanisms underlying Anopheles population dynamics and their relations to the environment. One of its main strengths is that it is based on both biological and environmental variables. RESULTS: The model made it possible to structure existing knowledge, assembled in a comprehensive review of the literature, and also pointed out important aspects of basic Anopheles biology about which knowledge is lacking. One simulation showed several patterns similar to those seen in the field, and made it possible to examine different analyses and hypotheses for these patterns; sensitivity analyses on temperature, moisture, predation and preliminary investigations of nutrient competition were also conducted. CONCLUSIONS: Although based on some mathematical formulae and parameters, this new tool has been developed in order to be as explicit as possible, transparent in use, close to reality and amenable to direct use by field workers. It allows a better understanding of the mechanisms underlying Anopheles population dynamics in general and also a better understanding of the dynamics in specific local geographic environments. It points out many important areas for new investigations that will be critical to effective, efficient, sustainable interventions.'
p7968
sS'GR'
p7969
(lp7970
S'Z99 TW999999/Intramural NIH HHS/United States'
p7971
asS'VI'
p7972
S'3'
sS'IS'
p7973
S'1475-2875 (Electronic) 1475-2875 (Linking)'
p7974
sS'PMC'
p7975
S'PMC514565'
p7976
sS'AU'
p7977
(lp7978
S'Depinay JM'
p7979
aS'Mbogo CM'
p7980
aS'Killeen G'
p7981
aS'Knols B'
p7982
aS'Beier J'
p7983
aS'Carlson J'
p7984
aS'Dushoff J'
p7985
aS'Billingsley P'
p7986
aS'Mwambi H'
p7987
aS'Githure J'
p7988
aS'Toure AM'
p7989
aS'McKenzie FE'
p7990
asS'MHDA'
p7991
S'2004/10/27 09:00'
p7992
sS'PHST'
p7993
(lp7994
S'2003/12/15 [received]'
p7995
aS'2004/07/30 [accepted]'
p7996
aS'2004/07/30 [aheadofprint]'
p7997
asS'OID'
p7998
(lp7999
S'NLM: PMC514565'
p8000
asS'MH'
p8001
(lp8002
S'Animal Nutritional Physiological Phenomena'
p8003
aS'Animals'
p8004
aS'Anopheles/growth & development/*physiology'
p8005
aS'Computer Simulation'
p8006
aS'Feeding Behavior'
p8007
aS'Female'
p8008
aS'Fertility'
p8009
aS'Fresh Water'
p8010
aS'Humans'
p8011
aS'Insect Vectors/growth & development/*physiology'
p8012
aS'Life Cycle Stages'
p8013
aS'Malaria/*transmission'
p8014
aS'*Models, Biological'
p8015
aS'Movement'
p8016
aS'Oviposition'
p8017
aS'Population Density'
p8018
aS'Population Dynamics'
p8019
aS'Temperature'
p8020
asS'EDAT'
p8021
S'2004/08/03 05:00'
p8022
sS'SO'
p8023
S'Malar J. 2004 Jul 30;3:29.'
p8024
sS'SB'
p8025
S'IM'
p8026
sS'PMID'
p8027
S'15285781'
p8028
sS'TA'
p8029
S'Malar J'
p8030
sS'PST'
p8031
S'epublish'
p8032
stRp8033
ag2
(g3
g4
(dp8034
S'STAT'
p8035
S'MEDLINE'
p8036
sS'JT'
p8037
S'Nature'
p8038
sS'CIN'
p8039
(lp8040
S'Nature. 2004 Jul 15;430(6997):299-300. PMID: 15254519'
p8041
asS'DA'
p8042
S'20040715'
p8043
sS'AID'
p8044
(lp8045
S'10.1038/nature02569 [doi]'
p8046
aS'nature02569 [pii]'
p8047
asS'CRDT'
p8048
(lp8049
S'2004/07/16 05:00'
p8050
asS'DP'
p8051
S'2004 Jul 15'
p8052
sS'OWN'
p8053
S'NLM'
p8054
sS'PT'
p8055
(lp8056
S'Journal Article'
p8057
aS"Research Support, Non-U.S. Gov't"
p8058
aS"Research Support, U.S. Gov't, Non-P.H.S."
p8059
asS'LA'
p8060
(lp8061
S'eng'
p8062
asS'FAU'
p8063
(lp8064
S'Dwyer, Greg'
p8065
aS'Dushoff, Jonathan'
p8066
aS'Yee, Susan Harrell'
p8067
asS'LR'
p8068
S'20071115'
p8069
sS'PG'
p8070
S'341-5'
p8071
sS'TI'
p8072
S'The combined effects of pathogens and predators on insect outbreaks.'
p8073
sS'PL'
p8074
S'England'
p8075
sS'TA'
p8076
S'Nature'
p8077
sS'JID'
p8078
S'0410462'
p8079
sS'AB'
p8080
S'The economic damage caused by episodic outbreaks of forest-defoliating insects has spurred much research, yet why such outbreaks occur remains unclear. Theoretical biologists argue that outbreaks are driven by specialist pathogens or parasitoids, because host-pathogen and host-parasitoid models show large-amplitude, long-period cycles resembling time series of outbreaks. Field biologists counter that outbreaks occur when generalist predators fail, because predation in low-density defoliator populations is usually high enough to prevent outbreaks. Neither explanation is sufficient, however, because the time between outbreaks in the data is far more variable than in host-pathogen and host-parasitoid models, and far shorter than in generalist-predator models. Here we show that insect outbreaks can be explained by a model that includes both a generalist predator and a specialist pathogen. In this host-pathogen-predator model, stochasticity causes defoliator densities to fluctuate erratically between an equilibrium maintained by the predator, and cycles driven by the pathogen. Outbreaks in this model occur at long but irregular intervals, matching the data. Our results suggest that explanations of insect outbreaks must go beyond classical models to consider interactions among multiple species.'
p8081
sS'AD'
p8082
S'Department of Ecology and Evolution, 1101 E 57th Street, University of Chicago, Chicago, Illinois 60637-1573, USA. gdwyer@uchicago.edu'
p8083
sS'IP'
p8084
S'6997'
p8085
sS'IS'
p8086
S'1476-4687 (Electronic) 0028-0836 (Linking)'
p8087
sS'DCOM'
p8088
S'20040803'
p8089
sS'AU'
p8090
(lp8091
S'Dwyer G'
p8092
aS'Dushoff J'
p8093
aS'Yee SH'
p8094
asS'VI'
p8095
S'430'
p8096
sS'MHDA'
p8097
S'2004/08/04 05:00'
p8098
sS'PHST'
p8099
(lp8100
S'2003/12/10 [received]'
p8101
aS'2004/04/13 [accepted]'
p8102
asS'MH'
p8103
(lp8104
S'Animals'
p8105
aS'*Food Chain'
p8106
aS'Host-Parasite Interactions'
p8107
aS'Insects/*parasitology/*physiology'
p8108
aS'*Models, Biological'
p8109
aS'Population Dynamics'
p8110
aS'Predatory Behavior/*physiology'
p8111
aS'Species Specificity'
p8112
aS'Stochastic Processes'
p8113
aS'Time Factors'
p8114
asS'EDAT'
p8115
S'2004/07/16 05:00'
p8116
sS'SO'
p8117
S'Nature. 2004 Jul 15;430(6997):341-5.'
p8118
sS'SB'
p8119
S'IM'
p8120
sS'PMID'
p8121
S'15254536'
p8122
sS'PST'
p8123
S'ppublish'
p8124
stRp8125
ag2
(g3
g4
(dp8126
S'STAT'
p8127
S'MEDLINE'
p8128
sS'JT'
p8129
S'Nature'
p8130
sS'CIN'
p8131
(lp8132
S'Nature. 2005 Jan 20;433(7023):E5-6; discussion E7-8. PMID: 15662370'
p8133
aS'Nature. 2005 Jan 20;433(7023):E6-7; discussion E7-8. PMID: 15662371'
p8134
aS'Nature. 2005 Jan 20;433(7023):E6; discussion E7-8. PMID: 15662372'
p8135
asS'DA'
p8136
S'20040430'
p8137
sS'AID'
p8138
(lp8139
S'10.1038/nature02458 [doi]'
p8140
aS'nature02458 [pii]'
p8141
asS'CRDT'
p8142
(lp8143
S'2004/05/01 05:00'
p8144
asS'DP'
p8145
S'2004 Apr 29'
p8146
sS'OWN'
p8147
S'NLM'
p8148
sS'PT'
p8149
(lp8150
S'Journal Article'
p8151
asS'LA'
p8152
(lp8153
S'eng'
p8154
asS'FAU'
p8155
(lp8156
S'Plotkin, Joshua B'
p8157
aS'Dushoff, Jonathan'
p8158
aS'Fraser, Hunter B'
p8159
asS'LR'
p8160
S'20091119'
p8161
sS'PG'
p8162
S'942-5'
p8163
sS'TI'
p8164
S'Detecting selection using a single genome sequence of M. tuberculosis and P. falciparum.'
p8165
sS'RN'
p8166
(lp8167
S'0 (Bacterial Proteins)'
p8168
aS'0 (Codon)'
p8169
aS'0 (Protozoan Proteins)'
p8170
asS'PL'
p8171
S'England'
p8172
sS'TA'
p8173
S'Nature'
p8174
sS'JID'
p8175
S'0410462'
p8176
sS'AB'
p8177
S"Selective pressures on proteins are usually measured by comparing nucleotide sequences. Here we introduce a method to detect selection on the basis of a single genome sequence. We catalogue the relative strength of selection on each gene in the entire genomes of Mycobacterium tuberculosis and Plasmodium falciparum. Our analysis confirms that most antigens are under strong selection for amino-acid substitutions, particularly the PE/PPE family of putative surface proteins in M. tuberculosis and the EMP1 family of cytoadhering surface proteins in P. falciparum. We also identify many uncharacterized proteins that are under strong selection in each pathogen. We provide a genome-wide analysis of natural selection acting on different stages of an organism's life cycle: genes expressed in the ring stage of P. falciparum are under stronger positive selection than those expressed in other stages of the parasite's life cycle. Our method of estimating selective pressures requires far fewer data than comparative sequence analysis, and it measures selection across an entire genome; the method can readily be applied to a large range of sequenced organisms."
p8178
sS'AD'
p8179
S'Harvard Society of Fellows and Bauer Center for Genomics Research, 7 Divinity Avenue, Cambridge, Massachusetts 02138, USA. jplotkin@fas.harvard.edu'
p8180
sS'IP'
p8181
S'6986'
p8182
sS'IS'
p8183
S'1476-4687 (Electronic) 0028-0836 (Linking)'
p8184
sS'DCOM'
p8185
S'20040521'
p8186
sS'AU'
p8187
(lp8188
S'Plotkin JB'
p8189
aS'Dushoff J'
p8190
aS'Fraser HB'
p8191
asS'VI'
p8192
S'428'
p8193
sS'MHDA'
p8194
S'2004/05/22 05:00'
p8195
sS'PHST'
p8196
(lp8197
S'2003/09/22 [received]'
p8198
aS'2004/03/02 [accepted]'
p8199
asS'MH'
p8200
(lp8201
S'Animals'
p8202
aS'Bacterial Proteins/genetics'
p8203
aS'Codon/genetics'
p8204
aS'Evolution, Molecular'
p8205
aS'Gene Expression Regulation'
p8206
aS'Genes, Bacterial/*genetics'
p8207
aS'Genes, Protozoan/*genetics'
p8208
aS'*Genome, Bacterial'
p8209
aS'*Genome, Protozoan'
p8210
aS'Mutation/genetics'
p8211
aS'Mycobacterium tuberculosis/*genetics'
p8212
aS'Plasmodium falciparum/*genetics/growth & development'
p8213
aS'Protozoan Proteins/genetics'
p8214
aS'*Selection, Genetic'
p8215
asS'EDAT'
p8216
S'2004/05/01 05:00'
p8217
sS'SO'
p8218
S'Nature. 2004 Apr 29;428(6986):942-5.'
p8219
sS'SB'
p8220
S'IM'
p8221
sS'PMID'
p8222
S'15118727'
p8223
sS'PST'
p8224
S'ppublish'
p8225
stRp8226
ag2
(g3
g4
(dp8227
S'STAT'
p8228
S'MEDLINE'
p8229
sS'JID'
p8230
S'0404511'
p8231
sS'AD'
p8232
S'Department of Ecology and, Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA. dushoff@eno.princeton.edu'
p8233
sS'JT'
p8234
S'Science (New York, N.Y.)'
p8235
sS'IP'
p8236
S'5671'
p8237
sS'IS'
p8238
S'1095-9203 (Electronic) 0036-8075 (Linking)'
p8239
sS'DCOM'
p8240
S'20040520'
p8241
sS'DA'
p8242
S'20040430'
p8243
sS'AU'
p8244
(lp8245
S'Dushoff J'
p8246
asS'SB'
p8247
S'IM'
p8248
sS'AID'
p8249
(lp8250
S'10.1126/science.1095317 [doi]'
p8251
aS'304/5671/684c [pii]'
p8252
asS'CRDT'
p8253
(lp8254
S'2004/05/01 05:00'
p8255
asS'VI'
p8256
S'304'
p8257
sS'DP'
p8258
S'2004 Apr 30'
p8259
sS'CON'
p8260
(lp8261
S'Science. 2003 Sep 12;301(5639):1525-6. PMID: 12970563'
p8262
asS'MHDA'
p8263
S'2004/05/21 05:00'
p8264
sS'OWN'
p8265
S'NLM'
p8266
sS'PT'
p8267
(lp8268
S'Comment'
p8269
aS'Journal Article'
p8270
aS"Research Support, Non-U.S. Gov't"
p8271
asS'LA'
p8272
(lp8273
S'eng'
p8274
asS'MH'
p8275
(lp8276
S'Animals'
p8277
aS'*Ecosystem'
p8278
aS'*Environment'
p8279
aS'Population Dynamics'
p8280
aS'Probability'
p8281
aS'Time Factors'
p8282
asS'FAU'
p8283
(lp8284
S'Dushoff, Jonathan'
p8285
asS'EDAT'
p8286
S'2004/05/01 05:00'
p8287
sS'PST'
p8288
S'ppublish'
p8289
sS'LR'
p8290
S'20070319'
p8291
sS'PG'
p8292
S'684; author reply 684'
p8293
sS'TI'
p8294
S'Comment on "Metapopulation persistence with age-dependent disturbance or succession".'
p8295
sS'SO'
p8296
S'Science. 2004 Apr 30;304(5671):684; author reply 684.'
p8297
sS'PMID'
p8298
S'15118146'
p8299
sS'PL'
p8300
S'United States'
p8301
sS'TA'
p8302
S'Science'
p8303
stRp8304
ag2
(g3
g4
(dp8305
S'STAT'
p8306
S'MEDLINE'
p8307
sS'DEP'
p8308
S'20040225'
p8309
sS'DA'
p8310
S'20040310'
p8311
sS'AID'
p8312
(lp8313
S'10.1073/pnas.0400456101 [doi]'
p8314
aS'0400456101 [pii]'
p8315
asS'CRDT'
p8316
(lp8317
S'2004/02/27 05:00'
p8318
asS'DP'
p8319
S'2004 Mar 9'
p8320
sS'EIN'
p8321
(lp8322
S'Proc Natl Acad Sci U S A. 2004 Apr 13;101(15):5696'
p8323
asS'OWN'
p8324
S'NLM'
p8325
sS'PT'
p8326
(lp8327
S'Journal Article'
p8328
aS"Research Support, U.S. Gov't, Non-P.H.S."
p8329
aS"Research Support, U.S. Gov't, P.H.S."
p8330
asS'LA'
p8331
(lp8332
S'eng'
p8333
asS'FAU'
p8334
(lp8335
S'Smith, David L'
p8336
aS'Dushoff, Jonathan'
p8337
aS'Perencevich, Eli N'
p8338
aS'Harris, Anthony D'
p8339
aS'Levin, Simon A'
p8340
asS'JT'
p8341
S'Proceedings of the National Academy of Sciences of the United States of America'
p8342
sS'LR'
p8343
S'20140609'
p8344
sS'PG'
p8345
S'3709-14'
p8346
sS'TI'
p8347
S'Persistent colonization and the spread of antibiotic resistance in nosocomial pathogens: resistance is a regional problem.'
p8348
sS'PL'
p8349
S'United States'
p8350
sS'TA'
p8351
S'Proc Natl Acad Sci U S A'
p8352
sS'JID'
p8353
S'7505876'
p8354
sS'AB'
p8355
S'Infections with antibiotic-resistant bacteria (ARB) in hospitalized patients are becoming increasingly frequent despite extensive infection-control efforts. Infections with ARB are most common in the intensive care units of tertiary-care hospitals, but the underlying cause of the increases may be a steady increase in the number of asymptomatic carriers entering hospitals. Carriers may shed ARB for years but remain undetected, transmitting ARB to others as they move among hospitals, long-term care facilities, and the community. We apply structured population models to explore the dynamics of ARB, addressing the following questions: (i) What is the relationship between the proportion of carriers admitted to a hospital, transmission, and the risk of infection with ARB? (ii) How do frequently hospitalized patients contribute to epidemics of ARB? (iii) How do transmission in the community, long-term care facilities, and hospitals interact to determine the proportion of the population that is carrying ARB? We offer an explanation for why ARB epidemics have fast and slow phases and why resistance may continue to increase despite infection-control efforts. To successfully manage ARB at tertiary-care hospitals, regional coordination of infection control may be necessary, including tracking asymptomatic carriers through health-care systems.'
p8356
sS'AD'
p8357
S'Fogarty International Center, National Institutes of Health, Bethesda, MD 20892-2220, USA. smitdave@helix.nih.gov'
p8358
sS'IP'
p8359
S'10'
p8360
sS'IS'
p8361
S'0027-8424 (Print) 0027-8424 (Linking)'
p8362
sS'PMC'
p8363
S'PMC373527'
p8364
sS'DCOM'
p8365
S'20040505'
p8366
sS'AU'
p8367
(lp8368
S'Smith DL'
p8369
aS'Dushoff J'
p8370
aS'Perencevich EN'
p8371
aS'Harris AD'
p8372
aS'Levin SA'
p8373
asS'VI'
p8374
S'101'
p8375
sS'MHDA'
p8376
S'2004/05/07 05:00'
p8377
sS'PHST'
p8378
(lp8379
S'2004/02/25 [aheadofprint]'
p8380
asS'OID'
p8381
(lp8382
S'NLM: PMC373527'
p8383
asS'MH'
p8384
(lp8385
S'Bacterial Infections/drug therapy/epidemiology/*microbiology/*transmission'
p8386
aS'Cross Infection/drug therapy/epidemiology/*microbiology/*transmission'
p8387
aS'*Drug Resistance, Bacterial'
p8388
aS'Humans'
p8389
aS'Models, Biological'
p8390
asS'EDAT'
p8391
S'2004/02/27 05:00'
p8392
sS'SO'
p8393
S'Proc Natl Acad Sci U S A. 2004 Mar 9;101(10):3709-14. Epub 2004 Feb 25.'
p8394
sS'SB'
p8395
S'IM'
p8396
sS'PMID'
p8397
S'14985511'
p8398
sS'PST'
p8399
S'ppublish'
p8400
stRp8401
ag2
(g3
g4
(dp8402
S'STAT'
p8403
S'MEDLINE'
p8404
sS'AB'
p8405
S'The evolution of the etiological agents of disease presents one of the greatest challenges for their control, and makes essential complementing standard epidemiological investigations with broader approaches that allow for evolutionary change. Given the stunning genetic diversity that is possible for many such agents, such as the influenza virus, it is impossible to represent all of the diversity manifest at the level of amino acid sequences. We show that drift-variant influenza strains naturally cluster into groups which are associated with functionally important epitopic regions. Dominant clusters typically replace each other every 2-5 years, and this feature is fundamental to the development of vaccination strategies. We furthermore show that stochastic fluctuations can greatly magnify small interference effects among strains, or even among subtypes, leading for example to competitive exclusion in situations where such effects would be unexpected based on the usual deterministic models. We suggest that this effect might be involved in the explanations of some persistent empirical anomalies.'
p8406
sS'GR'
p8407
(lp8408
S'1-R01-GM60729-01/GM/NIGMS NIH HHS/United States'
p8409
asS'JT'
p8410
S'Mathematical biosciences'
p8411
sS'VI'
p8412
S'188'
p8413
sS'IS'
p8414
S'0025-5564 (Print) 0025-5564 (Linking)'
p8415
sS'JID'
p8416
S'0103146'
p8417
sS'DA'
p8418
S'20040209'
p8419
sS'AU'
p8420
(lp8421
S'Levin SA'
p8422
aS'Dushoff J'
p8423
aS'Plotkin JB'
p8424
asS'SB'
p8425
S'IM'
p8426
sS'AID'
p8427
(lp8428
S'10.1016/j.mbs.2003.08.010 [doi]'
p8429
aS'S0025556403001810 [pii]'
p8430
asS'CRDT'
p8431
(lp8432
S'2004/02/10 05:00'
p8433
asS'DP'
p8434
S'2004 Mar-Apr'
p8435
sS'AD'
p8436
S'Department of Ecology and Evolutionary Biology, Princeton University, 203 Eno Hall, Princeton, NJ 08544 1003, USA. simon@eno.princeton.edu'
p8437
sS'MHDA'
p8438
S'2004/05/07 05:00'
p8439
sS'PHST'
p8440
(lp8441
S'2002/12/17 [received]'
p8442
aS'2003/08/13 [revised]'
p8443
aS'2003/08/13 [accepted]'
p8444
asS'OWN'
p8445
S'NLM'
p8446
sS'PT'
p8447
(lp8448
S'Journal Article'
p8449
aS"Research Support, Non-U.S. Gov't"
p8450
aS"Research Support, U.S. Gov't, Non-P.H.S."
p8451
aS"Research Support, U.S. Gov't, P.H.S."
p8452
asS'LA'
p8453
(lp8454
S'eng'
p8455
asS'MH'
p8456
(lp8457
S'Animals'
p8458
aS'Evolution, Molecular'
p8459
aS'Humans'
p8460
aS'Influenza A virus/*genetics'
p8461
aS'*Models, Biological'
p8462
aS'Orthomyxoviridae Infections/*epidemiology/*virology'
p8463
aS'Stochastic Processes'
p8464
asS'DCOM'
p8465
S'20040506'
p8466
sS'EDAT'
p8467
S'2004/02/10 05:00'
p8468
sS'PST'
p8469
S'ppublish'
p8470
sS'LR'
p8471
S'20091111'
p8472
sS'FAU'
p8473
(lp8474
S'Levin, Simon A'
p8475
aS'Dushoff, Jonathan'
p8476
aS'Plotkin, Joshua B'
p8477
asS'TI'
p8478
S'Evolution and persistence of influenza A and other diseases.'
p8479
sS'SO'
p8480
S'Math Biosci. 2004 Mar-Apr;188:17-28.'
p8481
sS'PMID'
p8482
S'14766091'
p8483
sS'TA'
p8484
S'Math Biosci'
p8485
sS'PL'
p8486
S'United States'
p8487
sS'PG'
p8488
S'17-28'
p8489
stRp8490
ag2
(g3
g4
(dp8491
S'STAT'
p8492
S'MEDLINE'
p8493
sS'DEP'
p8494
S'20030514'
p8495
sS'DA'
p8496
S'20030611'
p8497
sS'AID'
p8498
(lp8499
S'10.1073/pnas.1132114100 [doi]'
p8500
aS'1132114100 [pii]'
p8501
asS'CRDT'
p8502
(lp8503
S'2003/05/16 05:00'
p8504
asS'DP'
p8505
S'2003 Jun 10'
p8506
sS'AD'
p8507
S'Institute for Advanced Study, Olden Lane, Princeton, NJ 08540, USA.'
p8508
sS'OWN'
p8509
S'NLM'
p8510
sS'PT'
p8511
(lp8512
S'Journal Article'
p8513
aS"Research Support, Non-U.S. Gov't"
p8514
aS"Research Support, U.S. Gov't, Non-P.H.S."
p8515
aS"Research Support, U.S. Gov't, P.H.S."
p8516
asS'LA'
p8517
(lp8518
S'eng'
p8519
asS'FAU'
p8520
(lp8521
S'Plotkin, Joshua B'
p8522
aS'Dushoff, Jonathan'
p8523
asS'JT'
p8524
S'Proceedings of the National Academy of Sciences of the United States of America'
p8525
sS'LR'
p8526
S'20140611'
p8527
sS'PG'
p8528
S'7152-7'
p8529
sS'TI'
p8530
S'Codon bias and frequency-dependent selection on the hemagglutinin epitopes of influenza A virus.'
p8531
sS'RN'
p8532
(lp8533
S'0 (Codon)'
p8534
aS'0 (Epitopes)'
p8535
aS'0 (Hemagglutinin Glycoproteins, Influenza Virus)'
p8536
asS'PL'
p8537
S'United States'
p8538
sS'TA'
p8539
S'Proc Natl Acad Sci U S A'
p8540
sS'JID'
p8541
S'7505876'
p8542
sS'AB'
p8543
S"Although the surface proteins of human influenza A virus evolve rapidly and continually produce antigenic variants, the internal viral genes acquire mutations very gradually. In this paper, we analyze the sequence evolution of three influenza A genes over the past two decades. We study codon usage as a discriminating signature of gene- and even residue-specific diversifying and purifying selection. Nonrandom codon choice can increase or decrease the effective local substitution rate. We demonstrate that the codons of hemagglutinin, particularly those in the antibody-combining regions, are significantly biased toward substitutional point mutations relative to the codons of other influenza virus genes. We discuss the evolutionary interpretation and implications of these biases for hemagglutinin's antigenic evolution. We also introduce information-theoretic methods that use sequence data to detect regions of recent positive selection and potential protein conformational changes."
p8544
sS'GR'
p8545
(lp8546
S'1-R01-GM60729-01/GM/NIGMS NIH HHS/United States'
p8547
asS'IP'
p8548
S'12'
p8549
sS'IS'
p8550
S'0027-8424 (Print) 0027-8424 (Linking)'
p8551
sS'PMC'
p8552
S'PMC165845'
p8553
sS'DCOM'
p8554
S'20030722'
p8555
sS'AU'
p8556
(lp8557
S'Plotkin JB'
p8558
aS'Dushoff J'
p8559
asS'VI'
p8560
S'100'
p8561
sS'MHDA'
p8562
S'2003/07/23 05:00'
p8563
sS'PHST'
p8564
(lp8565
S'2003/05/14 [aheadofprint]'
p8566
asS'OID'
p8567
(lp8568
S'NLM: PMC165845'
p8569
asS'MH'
p8570
(lp8571
S'Codon/genetics'
p8572
aS'Epitopes/chemistry/genetics'
p8573
aS'Evolution, Molecular'
p8574
aS'Genes, Viral'
p8575
aS'Genetic Variation'
p8576
aS'Hemagglutinin Glycoproteins, Influenza Virus/chemistry/*genetics'
p8577
aS'Humans'
p8578
aS'Influenza A virus/*genetics/*immunology'
p8579
aS'Information Theory'
p8580
aS'Models, Molecular'
p8581
aS'Point Mutation'
p8582
aS'Protein Conformation'
p8583
aS'Selection, Genetic'
p8584
asS'EDAT'
p8585
S'2003/05/16 05:00'
p8586
sS'SO'
p8587
S'Proc Natl Acad Sci U S A. 2003 Jun 10;100(12):7152-7. Epub 2003 May 14.'
p8588
sS'SB'
p8589
S'IM'
p8590
sS'PMID'
p8591
S'12748378'
p8592
sS'PST'
p8593
S'ppublish'
p8594
stRp8595
ag2
(g3
g4
(dp8596
S'STAT'
p8597
S'MEDLINE'
p8598
sS'AB'
p8599
S'The hierarchical competition model has been a useful tool in investigating the mechanisms of coexistence between competing species, and thus for understanding the foundations of biodiversity. Here we show that the geometric picture of community assemblage found by Nowak and May for the constant-fecundity version of this model can be extended to a whole family of tradeoffs between fecundity and mortality. In this picture, the proportion of the remaining space used by a species can be related to the amount of free space (in aspect space) behind the "competitive shadow" of the adjacent superior competitor, and in turn to the size of the competitive shadow cast by the species itself. We show that this geometric model is scale invariant in the rescaled aspect space and use this fact to investigate the limits to diversity and explore how communities assemble under this model.'
p8600
sS'AD'
p8601
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. jdushoff@princeton,edu'
p8602
sS'JT'
p8603
S'Theoretical population biology'
p8604
sS'IP'
p8605
S'4'
sS'IS'
p8606
S'0040-5809 (Print) 0040-5809 (Linking)'
p8607
sS'JID'
p8608
S'0256422'
p8609
sS'DA'
p8610
S'20021112'
p8611
sS'DCOM'
p8612
S'20030303'
p8613
sS'AU'
p8614
(lp8615
S'Dushoff J'
p8616
aS'Worden L'
p8617
aS'Keymer J'
p8618
aS'Levin S'
p8619
asS'SB'
p8620
S'IM'
p8621
sS'AID'
p8622
(lp8623
S'S0040580902000060 [pii]'
p8624
asS'CRDT'
p8625
(lp8626
S'2002/11/13 04:00'
p8627
asS'VI'
p8628
S'62'
p8629
sS'DP'
p8630
S'2002 Dec'
p8631
sS'MHDA'
p8632
S'2003/03/04 04:00'
p8633
sS'OWN'
p8634
S'NLM'
p8635
sS'PT'
p8636
(lp8637
S'Journal Article'
p8638
aS"Research Support, Non-U.S. Gov't"
p8639
aS"Research Support, U.S. Gov't, Non-P.H.S."
p8640
asS'LA'
p8641
(lp8642
S'eng'
p8643
asS'CI'
p8644
(lp8645
S'Copyright 2002 Elsevier Science (USA)'
p8646
asS'MH'
p8647
(lp8648
S'Animals'
p8649
aS'*Competitive Behavior'
p8650
aS'*Ecosystem'
p8651
aS'Hierarchy, Social'
p8652
aS'*Models, Biological'
p8653
aS'*Population Density'
p8654
aS'Species Specificity'
p8655
asS'FAU'
p8656
(lp8657
S'Dushoff, Jonathan'
p8658
aS'Worden, Lee'
p8659
aS'Keymer, Juan'
p8660
aS'Levin, Simon'
p8661
asS'EDAT'
p8662
S'2002/11/13 04:00'
p8663
sS'PST'
p8664
S'ppublish'
p8665
sS'LR'
p8666
S'20061115'
p8667
sS'PG'
p8668
S'329-38'
p8669
sS'TI'
p8670
S'Metapopulations, community assembly, and scale invariance in aspect space.'
p8671
sS'SO'
p8672
S'Theor Popul Biol. 2002 Dec;62(4):329-38.'
p8673
sS'PMID'
p8674
S'12427456'
p8675
sS'PL'
p8676
S'United States'
p8677
sS'TA'
p8678
S'Theor Popul Biol'
p8679
stRp8680
ag2
(g3
g4
(dp8681
S'STAT'
p8682
S'MEDLINE'
p8683
sS'DEP'
p8684
S'20020423'
p8685
sS'DA'
p8686
S'20020501'
p8687
sS'AID'
p8688
(lp8689
S'10.1073/pnas.082110799 [doi]'
p8690
aS'082110799 [pii]'
p8691
asS'CRDT'
p8692
(lp8693
S'2002/04/25 10:00'
p8694
asS'DP'
p8695
S'2002 Apr 30'
p8696
sS'AD'
p8697
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA. plotkin@ias.edu'
p8698
sS'OWN'
p8699
S'NLM'
p8700
sS'PT'
p8701
(lp8702
S'Journal Article'
p8703
aS"Research Support, Non-U.S. Gov't"
p8704
aS"Research Support, U.S. Gov't, Non-P.H.S."
p8705
aS"Research Support, U.S. Gov't, P.H.S."
p8706
asS'LA'
p8707
(lp8708
S'eng'
p8709
asS'FAU'
p8710
(lp8711
S'Plotkin, Joshua B'
p8712
aS'Dushoff, Jonathan'
p8713
aS'Levin, Simon A'
p8714
asS'JT'
p8715
S'Proceedings of the National Academy of Sciences of the United States of America'
p8716
sS'LR'
p8717
S'20140612'
p8718
sS'PG'
p8719
S'6263-8'
p8720
sS'TI'
p8721
S'Hemagglutinin sequence clusters and the antigenic evolution of influenza A virus.'
p8722
sS'RN'
p8723
(lp8724
S'0 (Antibodies)'
p8725
aS'0 (Hemagglutinins)'
p8726
aS'0 (Influenza Vaccines)'
p8727
aS'0 (Vaccines)'
p8728
asS'PL'
p8729
S'United States'
p8730
sS'TA'
p8731
S'Proc Natl Acad Sci U S A'
p8732
sS'JID'
p8733
S'7505876'
p8734
sS'AB'
p8735
S'Continual mutations to the hemagglutinin (HA) gene of influenza A virus generate novel antigenic strains that cause annual epidemics. Using a database of 560 viral RNA sequences, we study the structure and tempo of HA evolution over the past two decades. We detect a critical length scale, in amino acid space, at which HA sequences aggregate into clusters, or swarms. We investigate the spatio-temporal distribution of viral swarms and compare it to the time series of the influenza vaccines recommended by the World Health Organization. We introduce a method for predicting future dominant HA amino acid sequences and discuss its potential relevance to vaccine choice. We also investigate the relationship between cluster structure and the primary antibody-combining regions of the HA protein.'
p8736
sS'GR'
p8737
(lp8738
S'1-R01-GM60729-01/GM/NIGMS NIH HHS/United States'
p8739
asS'IP'
p8740
S'9'
sS'IS'
p8741
S'0027-8424 (Print) 0027-8424 (Linking)'
p8742
sS'PMC'
p8743
S'PMC122937'
p8744
sS'DCOM'
p8745
S'20020611'
p8746
sS'AU'
p8747
(lp8748
S'Plotkin JB'
p8749
aS'Dushoff J'
p8750
aS'Levin SA'
p8751
asS'VI'
p8752
S'99'
p8753
sS'MHDA'
p8754
S'2002/06/12 10:01'
p8755
sS'PHST'
p8756
(lp8757
S'2002/04/23 [aheadofprint]'
p8758
asS'OID'
p8759
(lp8760
S'NLM: PMC122937'
p8761
asS'MH'
p8762
(lp8763
S'Antibodies/metabolism'
p8764
aS'Binding Sites'
p8765
aS'Databases as Topic'
p8766
aS'Genotype'
p8767
aS'Hemagglutinins/*genetics'
p8768
aS'Influenza A virus/*genetics'
p8769
aS'Influenza Vaccines/genetics'
p8770
aS'Models, Genetic'
p8771
aS'Multigene Family'
p8772
aS'*Mutation'
p8773
aS'Time Factors'
p8774
aS'Vaccines/*genetics'
p8775
asS'EDAT'
p8776
S'2002/04/25 10:00'
p8777
sS'SO'
p8778
S'Proc Natl Acad Sci U S A. 2002 Apr 30;99(9):6263-8. Epub 2002 Apr 23.'
p8779
sS'SB'
p8780
S'IM'
p8781
sS'PMID'
p8782
S'11972025'
p8783
sS'PST'
p8784
S'ppublish'
p8785
stRp8786
ag2
(g3
g4
(dp8787
S'STAT'
p8788
S'MEDLINE'
p8789
sS'DEP'
p8790
S'20010904'
p8791
sS'CIN'
p8792
(lp8793
S'Proc Natl Acad Sci U S A. 2001 Sep 25;98(20):10979-80. PMID: 11572952'
p8794
asS'DA'
p8795
S'20010926'
p8796
sS'AID'
p8797
(lp8798
S'10.1073/pnas.171315998 [doi]'
p8799
aS'171315998 [pii]'
p8800
asS'CRDT'
p8801
(lp8802
S'2001/09/06 10:00'
p8803
asS'DP'
p8804
S'2001 Sep 25'
p8805
sS'OWN'
p8806
S'NLM'
p8807
sS'PT'
p8808
(lp8809
S'Journal Article'
p8810
aS"Research Support, Non-U.S. Gov't"
p8811
aS"Research Support, U.S. Gov't, Non-P.H.S."
p8812
asS'LA'
p8813
(lp8814
S'eng'
p8815
asS'FAU'
p8816
(lp8817
S'Norberg, J'
p8818
aS'Swaney, D P'
p8819
aS'Dushoff, J'
p8820
aS'Lin, J'
p8821
aS'Casagrandi, R'
p8822
aS'Levin, S A'
p8823
asS'JT'
p8824
S'Proceedings of the National Academy of Sciences of the United States of America'
p8825
sS'LR'
p8826
S'20140613'
p8827
sS'PG'
p8828
S'11376-81'
p8829
sS'TI'
p8830
S'Phenotypic diversity and ecosystem functioning in changing environments: a theoretical framework.'
p8831
sS'PL'
p8832
S'United States'
p8833
sS'TA'
p8834
S'Proc Natl Acad Sci U S A'
p8835
sS'JID'
p8836
S'7505876'
p8837
sS'AB'
p8838
S'Biodiversity plays a vital role for ecosystem functioning in a changing environment. Yet theoretical approaches that incorporate diversity into classical ecosystem theory do not provide a general dynamic theory based on mechanistic principles. In this paper, we suggest that approaches developed for quantitative genetics can be extended to ecosystem functioning by modeling the means and variances of phenotypes within a group of species. We present a framework that suggests that phenotypic variance within functional groups is linearly related to their ability to respond to environmental changes. As a result, the long-term productivity for a group of species with high phenotypic variance may be higher than for the best single species, even though high phenotypic variance decreases productivity in the short term, because suboptimal species are present. In addition, we find that in the case of accelerating environmental change, species succession in a changing environment may become discontinuous. Our work suggests that this phenomenon is related to diversity as well as to the environmental disturbance regime, both of which are affected by anthropogenic activities. By introducing new techniques for modeling the aggregate behavior of groups of species, the present approach may provide a new avenue for ecosystem analysis.'
p8839
sS'AD'
p8840
S'Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA. jon.norberg@ecology.su.se'
p8841
sS'IP'
p8842
S'20'
p8843
sS'IS'
p8844
S'0027-8424 (Print) 0027-8424 (Linking)'
p8845
sS'PMC'
p8846
S'PMC58737'
p8847
sS'DCOM'
p8848
S'20011204'
p8849
sS'AU'
p8850
(lp8851
S'Norberg J'
p8852
aS'Swaney DP'
p8853
aS'Dushoff J'
p8854
aS'Lin J'
p8855
aS'Casagrandi R'
p8856
aS'Levin SA'
p8857
asS'VI'
p8858
S'98'
p8859
sS'MHDA'
p8860
S'2002/01/05 10:01'
p8861
sS'PHST'
p8862
(lp8863
S'2001/09/04 [aheadofprint]'
p8864
asS'OID'
p8865
(lp8866
S'NLM: PMC58737'
p8867
asS'MH'
p8868
(lp8869
S'Biomass'
p8870
aS'*Ecosystem'
p8871
aS'Environment'
p8872
aS'Mathematics'
p8873
aS'*Models, Biological'
p8874
aS'*Phenotype'
p8875
asS'EDAT'
p8876
S'2001/09/06 10:00'
p8877
sS'SO'
p8878
S'Proc Natl Acad Sci U S A. 2001 Sep 25;98(20):11376-81. Epub 2001 Sep 4.'
p8879
sS'SB'
p8880
S'IM'
p8881
sS'PMID'
p8882
S'11535803'
p8883
sS'PST'
p8884
S'ppublish'
p8885
stRp8886
ag2
(g3
g4
(dp8887
S'STAT'
p8888
S'Publisher'
p8889
sS'AB'
p8890
S'Models of outbreaks in forest-defoliating insects are typically built from a priori considerations and tested only with long time series of abundances. We instead present a model built from experimental data on the gypsy moth and its nuclear polyhedrosis virus, which has been extensively tested with epidemic data. These data have identified key details of the gypsy moth-virus interaction that are missing from earlier models, including seasonality in host reproduction, delays between host infection and death, and heterogeneity among hosts in their susceptibility to the virus. Allowing for these details produces models in which annual epidemics are followed by bouts of reproduction among surviving hosts and leads to quite different conclusions than earlier models. First, these models suggest that pathogen-driven outbreaks in forest defoliators occur partly because newly hatched insect larvae have higher average susceptibility than do older larvae. Second, the models show that a combination of seasonality and delays between infection and death can lead to unstable cycles in the absence of a stabilizing mechanism; these cycles, however, are stabilized by the levels of heterogeneity in susceptibility that we have observed in our experimental data. Moreover, our experimental estimates of virus transmission rates and levels of heterogeneity in susceptibility in gypsy moth populations give model dynamics that closely approximate the dynamics of real gypsy moth populations. Although we built our models from data for gypsy moth, our models are, nevertheless, quite general. Our conclusions are therefore likely to be true, not just for other defoliator-pathogen interactions, but for many host-pathogen interactions in which seasonality plays an important role. Our models thus give qualitative insight into the dynamics of host-pathogen interactions, while providing a quantitative interpretation of our gypsy moth-virus data.'
p8891
sS'JT'
p8892
S'The American naturalist'
p8893
sS'IP'
p8894
S'2'
sS'IS'
p8895
S'1537-5323 (Electronic) 0003-0147 (Linking)'
p8896
sS'JID'
p8897
S'2984688R'
p8898
sS'DA'
p8899
S'20000616'
p8900
sS'AU'
p8901
(lp8902
S'Dwyer G'
p8903
aS'Dushoff J'
p8904
aS'Elkinton JS'
p8905
aS'Levin SA'
p8906
asS'AID'
p8907
(lp8908
S'AN990200 [pii]'
p8909
aS'10.1086/303379 [doi]'
p8910
asS'CRDT'
p8911
(lp8912
S'2000/06/17 00:00'
p8913
asS'VI'
p8914
S'156'
p8915
sS'DP'
p8916
S'2000 Aug'
p8917
sS'MHDA'
p8918
S'2000/06/17'
p8919
sS'PHST'
p8920
(lp8921
S'1999/07/12 [received]'
p8922
aS'2000/03/07 [accepted]'
p8923
asS'OWN'
p8924
S'NLM'
p8925
sS'PT'
p8926
(lp8927
S'JOURNAL ARTICLE'
p8928
asS'LA'
p8929
(lp8930
S'ENG'
p8931
asS'FAU'
p8932
(lp8933
S'Dwyer'
p8934
aS'Dushoff'
p8935
aS'Elkinton'
p8936
aS'Levin'
p8937
asS'EDAT'
p8938
S'2000/06/17'
p8939
sS'SO'
p8940
S'Am Nat. 2000 Aug;156(2):105-120.'
p8941
sS'PG'
p8942
S'105-120'
p8943
sS'TI'
p8944
S'Pathogen-Driven Outbreaks in Forest Defoliators Revisited: Building Models from Experimental Data.'
p8945
sS'PMID'
p8946
S'10856195'
p8947
sS'PST'
p8948
S'ppublish'
p8949
sS'TA'
p8950
S'Am Nat'
p8951
stRp8952
ag2
(g3
g4
(dp8953
S'STAT'
p8954
S'MEDLINE'
p8955
sS'AB'
p8956
S'The stochastic logistic model is the simplest model that combines individual-level demography with density dependence. It explicitly or implicitly underlies many models of biodiversity of competing species, as well as non-spatial or metapopulation models of persistence of individual species. The model has also been used to study persistence in simple disease models. The stochastic logistic model has direct relevance for questions of limiting similarity in ecological systems. This paper uses a biased random walk heuristic to derive a scaling relationship for the persistence of a population under this model, and discusses its implications for models of biodiversity and persistence. Time to extinction of a species under the stochastic logistic model is approximated by the exponential of the scaling quantity U=(R-1)(2) N/R(R+1), where N is the habitat size and R is the basic reproductive number.'
p8957
sS'AD'
p8958
S'Institute of Physics, Academia Sinica, Nankang 105, Taipei, Taiwan.'
p8959
sS'JT'
p8960
S'Theoretical population biology'
p8961
sS'IP'
p8962
S'1'
sS'IS'
p8963
S'0040-5809 (Print) 0040-5809 (Linking)'
p8964
sS'JID'
p8965
S'0256422'
p8966
sS'DA'
p8967
S'20000413'
p8968
sS'DCOM'
p8969
S'20000413'
p8970
sS'AU'
p8971
(lp8972
S'Dushoff J'
p8973
asS'SB'
p8974
S'IM'
p8975
sS'AID'
p8976
(lp8977
S'10.1006/tpbi.1999.1434 [doi]'
p8978
aS'S0040-5809(99)91434-X [pii]'
p8979
asS'CRDT'
p8980
(lp8981
S'2000/03/10 09:00'
p8982
asS'VI'
p8983
S'57'
p8984
sS'DP'
p8985
S'2000 Feb'
p8986
sS'MHDA'
p8987
S'2000/04/15 09:00'
p8988
sS'OWN'
p8989
S'NLM'
p8990
sS'PT'
p8991
(lp8992
S'Journal Article'
p8993
asS'LA'
p8994
(lp8995
S'eng'
p8996
asS'CI'
p8997
(lp8998
S'Copyright 2000 Academic Press.'
p8999
asS'MH'
p9000
(lp9001
S'Animals'
p9002
aS'Bias (Epidemiology)'
p9003
aS'Competitive Behavior/*physiology'
p9004
aS'*Demography'
p9005
aS'*Ecosystem'
p9006
aS'Finite Element Analysis'
p9007
aS'*Logistic Models'
p9008
aS'*Population Density'
p9009
aS'Reproduction'
p9010
aS'Selection, Genetic'
p9011
aS'Species Specificity'
p9012
aS'*Stochastic Processes'
p9013
aS'Time Factors'
p9014
asS'FAU'
p9015
(lp9016
S'Dushoff, J'
p9017
asS'EDAT'
p9018
S'2000/03/10 09:00'
p9019
sS'PST'
p9020
S'ppublish'
p9021
sS'LR'
p9022
S'20091119'
p9023
sS'PG'
p9024
S'59-65'
p9025
sS'TI'
p9026
S'Carrying capacity and demographic stochasticity: scaling behavior of the stochastic logistic model.'
p9027
sS'SO'
p9028
S'Theor Popul Biol. 2000 Feb;57(1):59-65.'
p9029
sS'PMID'
p9030
S'10708629'
p9031
sS'PL'
p9032
S'UNITED STATES'
p9033
sS'TA'
p9034
S'Theor Popul Biol'
p9035
stRp9036
ag2
(g3
g4
(dp9037
S'STAT'
p9038
S'MEDLINE'
p9039
sS'AB'
p9040
S'This paper investigates the possibility of understanding the effects of host heterogeneity on disease levels through the use of moment approximations. The approach is to avoid assumptions about the distribution of mixing rates (or other parameters) in the population, by treating the low-order moments of the distribution as estimable parameters. This approach, while approximate, can greatly reduce the number of parameters needed to explore the effects of population heterogeneity on disease dynamics. This makes the approach useful for both inference and prediction, and also for gaining insight into the qualitative effects of heterogeneity on the spread of disease. This paper focuses on populations with variations in mixing rate and random mixing. It is shown that moment-based approximations can provide good quantitative estimates of disease dynamics, as well as aiding in qualitative under- standing, over a respectable range of parameters. It is hoped that this approach will provide a useful complement to more traditional box models of heterogeneity.'
p9041
sS'AD'
p9042
S'Institute of Physics, Academia Sinica, Nankang 11529, Taipei, Taiwan.'
p9043
sS'JT'
p9044
S'Theoretical population biology'
p9045
sS'IP'
p9046
S'3'
sS'IS'
p9047
S'0040-5809 (Print) 0040-5809 (Linking)'
p9048
sS'JID'
p9049
S'0256422'
p9050
sS'DA'
p9051
S'20000208'
p9052
sS'DCOM'
p9053
S'20000208'
p9054
sS'AU'
p9055
(lp9056
S'Dushoff J'
p9057
asS'SB'
p9058
S'IM'
p9059
sS'AID'
p9060
(lp9061
S'10.1006/tpbi.1999.1428 [doi]'
p9062
aS'S0040-5809(99)91428-4 [pii]'
p9063
asS'CRDT'
p9064
(lp9065
S'1999/12/23 00:00'
p9066
asS'VI'
p9067
S'56'
p9068
sS'DP'
p9069
S'1999 Dec'
p9070
sS'MHDA'
p9071
S'1999/12/23 00:01'
p9072
sS'OWN'
p9073
S'NLM'
p9074
sS'PT'
p9075
(lp9076
S'Journal Article'
p9077
aS"Research Support, Non-U.S. Gov't"
p9078
aS"Research Support, U.S. Gov't, Non-P.H.S."
p9079
asS'LA'
p9080
(lp9081
S'eng'
p9082
asS'CI'
p9083
(lp9084
S'Copyright 1999 Academic Press.'
p9085
asS'MH'
p9086
(lp9087
S'Communicable Diseases/*transmission'
p9088
aS'Disease Susceptibility'
p9089
aS'Endemic Diseases/*statistics & numerical data'
p9090
aS'Humans'
p9091
aS'*Models, Biological'
p9092
aS'*Population Dynamics'
p9093
aS'Sexual Behavior'
p9094
aS'Sexually Transmitted Diseases/transmission'
p9095
asS'FAU'
p9096
(lp9097
S'Dushoff, J'
p9098
asS'EDAT'
p9099
S'1999/12/23'
p9100
sS'PST'
p9101
S'ppublish'
p9102
sS'LR'
p9103
S'20061115'
p9104
sS'PG'
p9105
S'325-35'
p9106
sS'TI'
p9107
S'Host heterogeneity and disease endemicity: a moment-based approach.'
p9108
sS'SO'
p9109
S'Theor Popul Biol. 1999 Dec;56(3):325-35.'
p9110
sS'PMID'
p9111
S'10607525'
p9112
sS'PL'
p9113
S'UNITED STATES'
p9114
sS'TA'
p9115
S'Theor Popul Biol'
p9116
stRp9117
ag2
(g3
g4
(dp9118
S'STAT'
p9119
S'MEDLINE'
p9120
sS'AB'
p9121
S'In sessile organisms such as plants, interactions occur locally so that important ecological aspects like frequency dependence are manifest within local neighborhoods. Using probabilistic cellular automata models, we investigated how local frequency-dependent competition influenced whether two species could coexist. Individuals of the two species were randomly placed on a grid and allowed to interact according to local frequency-dependent rules. For four different frequency-dependent scenarios, the results indicated that over a broad parameter range the two species could coexist. Comparisons between explicit spatial simulations and the mean-field approximation indicate that coexistence occurs over a broader region in the explicit spatial simulation.'
p9122
sS'AD'
p9123
S'Department of Botany, University of Vermont, Burlington, Vermont 05405, USA. jmolofsk@zoo.uvm.edu'
p9124
sS'JT'
p9125
S'Theoretical population biology'
p9126
sS'IP'
p9127
S'3'
sS'IS'
p9128
S'0040-5809 (Print) 0040-5809 (Linking)'
p9129
sS'JID'
p9130
S'0256422'
p9131
sS'DA'
p9132
S'19990708'
p9133
sS'DCOM'
p9134
S'19990708'
p9135
sS'AU'
p9136
(lp9137
S'Molofsky J'
p9138
aS'Durrett R'
p9139
aS'Dushoff J'
p9140
aS'Griffeath D'
p9141
aS'Levin S'
p9142
asS'SB'
p9143
S'IM'
p9144
sS'AID'
p9145
(lp9146
S'10.1006/tpbi.1998.1404 [doi]'
p9147
aS'S0040-5809(98)91404-6 [pii]'
p9148
asS'CRDT'
p9149
(lp9150
S'1999/06/15 00:00'
p9151
asS'VI'
p9152
S'55'
p9153
sS'DP'
p9154
S'1999 Jun'
p9155
sS'MHDA'
p9156
S'1999/06/15 00:01'
p9157
sS'OWN'
p9158
S'NLM'
p9159
sS'PT'
p9160
(lp9161
S'Journal Article'
p9162
aS"Research Support, Non-U.S. Gov't"
p9163
aS"Research Support, U.S. Gov't, Non-P.H.S."
p9164
asS'LA'
p9165
(lp9166
S'eng'
p9167
asS'CI'
p9168
(lp9169
S'Copyright 1999 Academic Press.'
p9170
asS'MH'
p9171
(lp9172
S'*Ecosystem'
p9173
aS'Gene Frequency/*genetics'
p9174
aS'Genotype'
p9175
aS'*Models, Genetic'
p9176
aS'Nonlinear Dynamics'
p9177
aS'*Plant Development'
p9178
aS'Plants/*genetics'
p9179
aS'Population Density'
p9180
aS'Probability'
p9181
aS'Random Allocation'
p9182
aS'Species Specificity'
p9183
aS'Stochastic Processes'
p9184
asS'FAU'
p9185
(lp9186
S'Molofsky, J'
p9187
aS'Durrett, R'
p9188
aS'Dushoff, J'
p9189
aS'Griffeath, D'
p9190
aS'Levin, S'
p9191
asS'EDAT'
p9192
S'1999/06/15'
p9193
sS'PST'
p9194
S'ppublish'
p9195
sS'LR'
p9196
S'20121115'
p9197
sS'PG'
p9198
S'270-82'
p9199
sS'TI'
p9200
S'Local frequency dependence and global coexistence.'
p9201
sS'SO'
p9202
S'Theor Popul Biol. 1999 Jun;55(3):270-82.'
p9203
sS'PMID'
p9204
S'10366552'
p9205
sS'PL'
p9206
S'UNITED STATES'
p9207
sS'TA'
p9208
S'Theor Popul Biol'
p9209
stRp9210
ag2
(g3
g4
(dp9211
S'STAT'
p9212
S'MEDLINE'
p9213
sS'JID'
p9214
S'7502105'
p9215
sS'AD'
p9216
S'Department of Ecology and Evolutionary Biology, Princeton University, NJ 08542, USA.'
p9217
sS'JT'
p9218
S'Journal of mathematical biology'
p9219
sS'IP'
p9220
S'3'
sS'IS'
p9221
S'0303-6812 (Print) 0303-6812 (Linking)'
p9222
sS'DCOM'
p9223
S'19980504'
p9224
sS'DA'
p9225
S'19980504'
p9226
sS'AU'
p9227
(lp9228
S'Dushoff J'
p9229
aS'Huang W'
p9230
aS'Castillo-Chavez C'
p9231
asS'SB'
p9232
S'IM X'
p9233
sS'CRDT'
p9234
(lp9235
S'1998/04/07 00:00'
p9236
asS'VI'
p9237
S'36'
p9238
sS'DP'
p9239
S'1998 Feb'
p9240
sS'MHDA'
p9241
S'1998/04/07 00:01'
p9242
sS'OWN'
p9243
S'NLM'
p9244
sS'PT'
p9245
(lp9246
S'Journal Article'
p9247
aS"Research Support, Non-U.S. Gov't"
p9248
asS'LA'
p9249
(lp9250
S'eng'
p9251
asS'MH'
p9252
(lp9253
S'Acquired Immunodeficiency Syndrome/epidemiology/transmission'
p9254
aS'Disease Transmission, Infectious'
p9255
aS'Humans'
p9256
aS'Incidence'
p9257
aS'Infection/*epidemiology/transmission'
p9258
aS'*Models, Theoretical'
p9259
aS'*Mortality'
p9260
aS'Multivariate Analysis'
p9261
aS'Prevalence'
p9262
asS'FAU'
p9263
(lp9264
S'Dushoff, J'
p9265
aS'Huang, W'
p9266
aS'Castillo-Chavez, C'
p9267
asS'EDAT'
p9268
S'1998/04/07'
p9269
sS'PST'
p9270
S'ppublish'
p9271
sS'LR'
p9272
S'20081121'
p9273
sS'PG'
p9274
S'227-48'
p9275
sS'TI'
p9276
S'Backwards bifurcations and catastrophe in simple models of fatal diseases.'
p9277
sS'SO'
p9278
S'J Math Biol. 1998 Feb;36(3):227-48.'
p9279
sS'PMID'
p9280
S'9528119'
p9281
sS'PL'
p9282
S'GERMANY'
p9283
sS'TA'
p9284
S'J Math Biol'
p9285
stRp9286
ag2
(g3
g4
(dp9287
S'STAT'
p9288
S'MEDLINE'
p9289
sS'AB'
p9290
S'Many diseases show important interactions between epidemiology and immunology. Both models and data suggest that epidemiologically controlled variables, like frequency and intensity of exposure, can affect immunological outcomes in a wide variety of diseases. Conversely, the results of the immunological "battle" between host and parasite determine the ability of the parasite to spread. I present a simple model with two possible states of infection, which assumes that higher exposure to infection is correlated with likelihood of acquiring a more severe infection. For some parameter values, this model leads to simultaneous stability of the disease-free equilibrium and an endemic equilibrium, implying that the disease might be able to persist in a population that it could not invade. I also derive a simple and interpretable sufficient condition for multiple stable states. The "cartoon" model presented here shows that interaction between epidemiology and immunology can have important effects on the invasion and persistence of diseases. In particular, it raises the possibility that this mechanism can lead to mathematical "catastrophe" and to long-term cycles in disease prevalence.'
p9291
sS'JID'
p9292
S'0376342'
p9293
sS'AD'
p9294
S'Department of Ecology and Evolutionary Biology, Princeton University, NJ 08544, USA.'
p9295
sS'JT'
p9296
S'Journal of theoretical biology'
p9297
sS'IP'
p9298
S'3'
sS'IS'
p9299
S'0022-5193 (Print) 0022-5193 (Linking)'
p9300
sS'DCOM'
p9301
S'19960920'
p9302
sS'DA'
p9303
S'19960920'
p9304
sS'AU'
p9305
(lp9306
S'Dushoff J'
p9307
asS'SB'
p9308
S'IM'
p9309
sS'AID'
p9310
(lp9311
S'S0022-5193(96)90094-6 [pii]'
p9312
aS'10.1006/jtbi.1996.0094 [doi]'
p9313
asS'CRDT'
p9314
(lp9315
S'1996/06/07 00:00'
p9316
asS'VI'
p9317
S'180'
p9318
sS'DP'
p9319
S'1996 Jun 7'
p9320
sS'MHDA'
p9321
S'1996/06/07 00:01'
p9322
sS'OWN'
p9323
S'NLM'
p9324
sS'PT'
p9325
(lp9326
S'Journal Article'
p9327
asS'LA'
p9328
(lp9329
S'eng'
p9330
asS'MH'
p9331
(lp9332
S'*Epidemiology'
p9333
aS'Humans'
p9334
aS'*Models, Immunological'
p9335
aS'*Models, Statistical'
p9336
aS'Parasitic Diseases/*transmission'
p9337
aS'Prevalence'
p9338
asS'FAU'
p9339
(lp9340
S'Dushoff, J'
p9341
asS'EDAT'
p9342
S'1996/06/07'
p9343
sS'PST'
p9344
S'ppublish'
p9345
sS'LR'
p9346
S'20041117'
p9347
sS'PG'
p9348
S'181-7'
p9349
sS'TI'
p9350
S'Incorporating immunological ideas in epidemiological models.'
p9351
sS'SO'
p9352
S'J Theor Biol. 1996 Jun 7;180(3):181-7.'
p9353
sS'PMID'
p9354
S'8759527'
p9355
sS'PL'
p9356
S'ENGLAND'
p9357
sS'TA'
p9358
S'J Theor Biol'
p9359
stRp9360
ag2
(g3
g4
(dp9361
S'STAT'
p9362
S'MEDLINE'
p9363
sS'AB'
p9364
S'The incorporation of population heterogeneity is a central issue in theoretical biology, and it has received considerable attention in epidemiology recently. This paper presents general conclusions and interpretations about the effects of heterogeneity, with and without positive assortative mating, on the ability of a disease to establish itself. We show that the invasion of a disease into a population with random mixing is determined by an average of reproductive numbers for each subgroup, weighted by the total amount of mixing activity of the subgroup. In particular, if the mixing rate is constant across the population, invasion occurs if and only if the average reproductive number for the population exceeds 1. In the case of "preferred mixing," one can find a critical number for each subgroup such that invasion occurs if and only if a suitably defined average over subgroups exceeds 1.'
p9365
sS'JID'
p9366
S'0103146'
p9367
sS'AD'
p9368
S'Department of Ecology and Evolutionary Biology, Princeton University, New Jersey, USA.'
p9369
sS'JT'
p9370
S'Mathematical biosciences'
p9371
sS'IP'
p9372
S'1-2'
p9373
sS'IS'
p9374
S'0025-5564 (Print) 0025-5564 (Linking)'
p9375
sS'DCOM'
p9376
S'19950811'
p9377
sS'DA'
p9378
S'19950811'
p9379
sS'AU'
p9380
(lp9381
S'Dushoff J'
p9382
aS'Levin S'
p9383
asS'SB'
p9384
S'IM'
p9385
sS'AID'
p9386
(lp9387
S'0025-5564(94)00065-8 [pii]'
p9388
asS'CRDT'
p9389
(lp9390
S'1995/07/01 00:00'
p9391
asS'VI'
p9392
S'128'
p9393
sS'DP'
p9394
S'1995 Jul-Aug'
p9395
sS'MHDA'
p9396
S'1995/07/01 00:01'
p9397
sS'OWN'
p9398
S'NLM'
p9399
sS'PT'
p9400
(lp9401
S'Journal Article'
p9402
aS"Research Support, U.S. Gov't, Non-P.H.S."
p9403
asS'LA'
p9404
(lp9405
S'eng'
p9406
asS'MH'
p9407
(lp9408
S'*Epidemiologic Methods'
p9409
aS'*Epidemiology'
p9410
aS'Humans'
p9411
aS'*Models, Theoretical'
p9412
aS'*Population'
p9413
asS'FAU'
p9414
(lp9415
S'Dushoff, J'
p9416
aS'Levin, S'
p9417
asS'EDAT'
p9418
S'1995/07/01'
p9419
sS'PST'
p9420
S'ppublish'
p9421
sS'LR'
p9422
S'20091111'
p9423
sS'PG'
p9424
S'25-40'
p9425
sS'TI'
p9426
S'The effects of population heterogeneity on disease invasion.'
p9427
sS'SO'
p9428
S'Math Biosci. 1995 Jul-Aug;128(1-2):25-40.'
p9429
sS'PMID'
p9430
S'7606137'
p9431
sS'PL'
p9432
S'UNITED STATES'
p9433
sS'TA'
p9434
S'Math Biosci'
p9435
stRp9436
ag2
(g3
g4
(dp9437
S'STAT'
p9438
S'MEDLINE'
p9439
sS'JID'
p9440
S'1306050'
p9441
sS'JT'
p9442
S'Plastic and reconstructive surgery'
p9443
sS'IP'
p9444
S'6'
sS'IS'
p9445
S'0032-1052 (Print) 0032-1052 (Linking)'
p9446
sS'DCOM'
p9447
S'19730717'
p9448
sS'DA'
p9449
S'19730717'
p9450
sS'AU'
p9451
(lp9452
S'Dushoff IM'
p9453
asS'SB'
p9454
S'AIM IM'
p9455
sS'CRDT'
p9456
(lp9457
S'1973/06/01 00:00'
p9458
asS'VI'
p9459
S'51'
p9460
sS'DP'
p9461
S'1973 Jun'
p9462
sS'MHDA'
p9463
S'1973/06/01 00:01'
p9464
sS'OWN'
p9465
S'NLM'
p9466
sS'PT'
p9467
(lp9468
S'Journal Article'
p9469
asS'LA'
p9470
(lp9471
S'eng'
p9472
asS'MH'
p9473
(lp9474
S'*Anesthesia, Local'
p9475
aS'Arm/blood supply'
p9476
aS'Female'
p9477
aS'Hand/*surgery'
p9478
aS'Humans'
p9479
aS'Male'
p9480
aS'Methods'
p9481
aS'*Tourniquets'
p9482
aS'*Wrist'
p9483
asS'FAU'
p9484
(lp9485
S'Dushoff, I M'
p9486
asS'EDAT'
p9487
S'1973/06/01'
p9488
sS'PST'
p9489
S'ppublish'
p9490
sS'LR'
p9491
S'20150624'
p9492
sS'PG'
p9493
S'685-6'
p9494
sS'TI'
p9495
S'Hand surgery under wrist block and local infiltration anesthesia, using an upper arm tourniquet.'
p9496
sS'SO'
p9497
S'Plast Reconstr Surg. 1973 Jun;51(6):685-6.'
p9498
sS'PMID'
p9499
S'4705325'
p9500
sS'PL'
p9501
S'UNITED STATES'
p9502
sS'TA'
p9503
S'Plast Reconstr Surg'
p9504
stRp9505
ag2
(g3
g4
(dp9506
S'STAT'
p9507
S'MEDLINE'
p9508
sS'OID'
p9509
(lp9510
S'CLML: 5834:10334:552'
p9511
asS'JID'
p9512
S'101243645'
p9513
sS'JT'
p9514
S'Plastic and reconstructive surgery and the transplantation bulletin'
p9515
sS'IP'
p9516
S'1'
sS'IS'
p9517
S'0096-8501 (Print) 0096-8501 (Linking)'
p9518
sS'DCOM'
p9519
S'20000701'
p9520
sS'DA'
p9521
S'19581201'
p9522
sS'AU'
p9523
(lp9524
S'RANDALL P'
p9525
aS'DUSHOFF IM'
p9526
asS'SB'
p9527
S'OM'
p9528
sS'CRDT'
p9529
(lp9530
S'1958/01/01 00:00'
p9531
asS'VI'
p9532
S'21'
p9533
sS'DP'
p9534
S'1958 Jan'
p9535
sS'MHDA'
p9536
S'1958/01/01 00:01'
p9537
sS'OWN'
p9538
S'NLM'
p9539
sS'PT'
p9540
(lp9541
S'Journal Article'
p9542
asS'LA'
p9543
(lp9544
S'eng'
p9545
asS'OTO'
p9546
(lp9547
S'NLM'
p9548
asS'MH'
p9549
(lp9550
S'*Allografts'
p9551
aS'Animals'
p9552
aS'Mice'
p9553
aS'*Skin'
p9554
aS'*Skin Transplantation'
p9555
asS'FAU'
p9556
(lp9557
S'RANDALL, P'
p9558
aS'DUSHOFF, I M'
p9559
asS'EDAT'
p9560
S'1958/01/01'
p9561
sS'PST'
p9562
S'ppublish'
p9563
sS'LR'
p9564
S'20070925'
p9565
sS'PG'
p9566
S'24-41'
p9567
sS'TI'
p9568
S'Skin cycles and other physiological variables of rodent skin; the effect of the skin cycle on skin homograft survival in the mouse.'
p9569
sS'SO'
p9570
S'Plast Reconstr Surg Transplant Bull. 1958 Jan;21(1):24-41.'
p9571
sS'PMID'
p9572
S'13517958'
p9573
sS'OT'
p9574
(lp9575
S'*SKIN TRANSPLANTATION/experimental'
p9576
asS'PL'
p9577
S'Not Available'
p9578
sS'TA'
p9579
S'Plast Reconstr Surg Transplant Bull'
p9580
stRp9581
ag2
(g3
g4
(dp9582
S'STAT'
p9583
S'MEDLINE'
p9584
sS'OID'
p9585
(lp9586
S'CLML: 5732:31665'
p9587
asS'JT'
p9588
S'Surgical forum'
p9589
sS'VI'
p9590
S'7'
sS'IS'
p9591
S'0071-8041 (Print) 0071-8041 (Linking)'
p9592
sS'JID'
p9593
S'0337723'
p9594
sS'DA'
p9595
S'19571201'
p9596
sS'OT'
p9597
(lp9598
S'*SKIN TRANSPLANTATION/experimental'
p9599
asS'AU'
p9600
(lp9601
S'DUSHOFF IM'
p9602
aS'RANDALL P'
p9603
asS'CRDT'
p9604
(lp9605
S'1957/01/01 00:00'
p9606
asS'DP'
p9607
S'1957'
p9608
sS'MHDA'
p9609
S'1957/01/01 00:01'
p9610
sS'OWN'
p9611
S'NLM'
p9612
sS'PT'
p9613
(lp9614
S'Journal Article'
p9615
asS'LA'
p9616
(lp9617
S'eng'
p9618
asS'OTO'
p9619
(lp9620
S'NLM'
p9621
asS'MH'
p9622
(lp9623
S'*Allografts'
p9624
aS'Animals'
p9625
aS'Mice'
p9626
aS'*Skin'
p9627
aS'*Skin Transplantation'
p9628
asS'DCOM'
p9629
S'20020501'
p9630
sS'EDAT'
p9631
S'1957/01/01'
p9632
sS'PST'
p9633
S'ppublish'
p9634
sS'SO'
p9635
S'Surg Forum. 1957;7:631-6.'
p9636
sS'FAU'
p9637
(lp9638
S'DUSHOFF, I M'
p9639
aS'RANDALL, P'
p9640
asS'TI'
p9641
S'The effect of skin cycles on the survival time of skin homografts in mice.'
p9642
sS'SB'
p9643
S'OM'
p9644
sS'PMID'
p9645
S'13433461'
p9646
sS'TA'
p9647
S'Surg Forum'
p9648
sS'PL'
p9649
S'Not Available'
p9650
sS'PG'
p9651
S'631-6'
p9652
stRp9653
a.