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Malchow AK, Fandos G, Kormann UG, Grüebler MU, Kéry M, Hartig F, Zurell D. Fitting individual-based models of spatial population dynamics to long-term monitoring data. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2024; 34:e2966. [PMID: 38629509 DOI: 10.1002/eap.2966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/20/2023] [Indexed: 06/04/2024]
Abstract
Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.
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Affiliation(s)
| | - Guillermo Fandos
- Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Department of Biodiversity, Ecology and Evolution, Complutense University of Madrid, Madrid, Spain
| | - Urs G Kormann
- Swiss Ornithological Institute, Sempach, Switzerland
| | | | - Marc Kéry
- Swiss Ornithological Institute, Sempach, Switzerland
| | - Florian Hartig
- Theoretical Ecology, Faculty of Biology and Pre-Clinical Medicine, University of Regensburg, Regensburg, Germany
| | - Damaris Zurell
- Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany
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2
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Rincón-Barrado M, Villaverde T, Perez MF, Sanmartín I, Riina R. The sweet tabaiba or there and back again: phylogeographical history of the Macaronesian Euphorbia balsamifera. ANNALS OF BOTANY 2024; 133:883-904. [PMID: 38197716 PMCID: PMC11082519 DOI: 10.1093/aob/mcae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 03/01/2024] [Indexed: 01/11/2024]
Abstract
BACKGROUND AND AIMS Biogeographical relationships between the Canary Islands and north-west Africa are often explained by oceanic dispersal and geographical proximity. Sister-group relationships between Canarian and eastern African/Arabian taxa, the 'Rand Flora' pattern, are rare among plants and have been attributed to the extinction of north-western African populations. Euphorbia balsamifera is the only representative species of this pattern that is distributed in the Canary Islands and north-west Africa; it is also one of few species present in all seven islands. Previous studies placed African populations of E. balsamifera as sister to the Canarian populations, but this relationship was based on herbarium samples with highly degraded DNA. Here, we test the extinction hypothesis by sampling new continental populations; we also expand the Canarian sampling to examine the dynamics of island colonization and diversification. METHODS Using target enrichment with genome skimming, we reconstructed phylogenetic relationships within E. balsamifera and between this species and its disjunct relatives. A single nucleotide polymorphism dataset obtained from the target sequences was used to infer population genetic diversity patterns. We used convolutional neural networks to discriminate among alternative Canary Islands colonization scenarios. KEY RESULTS The results confirmed the Rand Flora sister-group relationship between western E. balsamifera and Euphorbia adenensis in the Eritreo-Arabian region and recovered an eastern-western geographical structure among E. balsamifera Canarian populations. Convolutional neural networks supported a scenario of east-to-west island colonization, followed by population extinctions in Lanzarote and Fuerteventura and recolonization from Tenerife and Gran Canaria; a signal of admixture between the eastern island and north-west African populations was recovered. CONCLUSIONS Our findings support the Surfing Syngameon Hypothesis for the colonization of the Canary Islands by E. balsamifera, but also a recent back-colonization to the continent. Populations of E. balsamifera from northwest Africa are not the remnants of an ancestral continental stock, but originated from migration events from Lanzarote and Fuerteventura. This is further evidence that oceanic archipelagos are not a sink for biodiversity, but may be a source of new genetic variability.
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Affiliation(s)
- Mario Rincón-Barrado
- Real Jardín Botánico (RJB), CSIC, Madrid, 28014, Spain
- Centro Nacional de Biotecnología (CNB), CSIC, Madrid, 28049, Spain
| | - Tamara Villaverde
- Universidad Rey Juan Carlos (URJC), Área de Biodiversidad y Conservación, Móstoles, 28933, Spain
| | - Manolo F Perez
- Institut de Systématique, Evolution, Biodiversité (ISYEB – URM 7205 CNRS), Muséum National d’Histoire Naturelle, SU, EPHE & UA, Paris, France
| | | | - Ricarda Riina
- Real Jardín Botánico (RJB), CSIC, Madrid, 28014, Spain
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Ciganda D, Lorenti A, Dommermuth L. Microfoundations of the weakening educational gradient in fertility. POPULATION STUDIES 2024:1-20. [PMID: 38700204 DOI: 10.1080/00324728.2024.2319031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 10/09/2023] [Indexed: 05/05/2024]
Abstract
The disappearance of the social gradient in fertility represents a paradigm shift that has called into question the validity of theories that predicted a decline in fertility with increased access to education and resources. Emerging theories have tried to explain this trend by highlighting a potential change in the fertility preferences of more educated couples. In this paper we add additional elements to this explanation. Using a computational modelling approach, we show that it is still possible to simulate the weakening social gradient in fertility, in the context of steady declines in family size preferences. Our results show that one of the key drivers of the change in the education-fertility relationship can be found in the transition to an increasingly regulated fertility regime. As the share of unplanned births decreases over time, the negative association between education and fertility weakens and the mechanisms that positively connect educational attainment with desired fertility become dominant.
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4
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Barton DL, Chang YR, Ducker W, Dobnikar J. Data-driven modelling makes quantitative predictions regarding bacteria surface motility. PLoS Comput Biol 2024; 20:e1012063. [PMID: 38743804 PMCID: PMC11125545 DOI: 10.1371/journal.pcbi.1012063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 05/24/2024] [Accepted: 04/09/2024] [Indexed: 05/16/2024] Open
Abstract
In this work, we quantitatively compare computer simulations and existing cell tracking data of P. aeruginosa surface motility in order to analyse the underlying motility mechanism. We present a three dimensional twitching motility model, that simulates the extension, retraction and surface association of individual Type IV Pili (TFP), and is informed by recent experimental observations of TFP. Sensitivity analysis is implemented to minimise the number of model parameters, and quantitative estimates for the remaining parameters are inferred from tracking data by approximate Bayesian computation. We argue that the motility mechanism is highly sensitive to experimental conditions. We predict a TFP retraction speed for the tracking data we study that is in a good agreement with experimental results obtained under very similar conditions. Furthermore, we examine whether estimates for biologically important parameters, whose direct experimental determination is challenging, can be inferred directly from tracking data. One example is the width of the distribution of TFP on the bacteria body. We predict that the TFP are broadly distributed over the bacteria pole in both walking and crawling motility types. Moreover, we identified specific configurations of TFP that lead to transitions between walking and crawling states.
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Affiliation(s)
- Daniel L. Barton
- CAS Key Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
| | - Yow-Ren Chang
- National Institute of Standards and Technology (NIST), 100 Bureau Dr, Gaithersburg, Maryland, United States of America
| | - William Ducker
- Department of Chemical Engineering and Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virgina, United States of America
| | - Jure Dobnikar
- CAS Key Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China
- Wenzhou Institute of the University of Chinese Academy of Sciences, Wenzhou, China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, China
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5
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Sandoval-Castellanos E, Hare AJ, Lin AT, Dimopoulos EA, Daly KG, Geiger S, Mullin VE, Wiechmann I, Mattiangeli V, Lühken G, Zinovieva NA, Zidarov P, Çakırlar C, Stoddart S, Orton D, Bulatović J, Mashkour M, Sauer EW, Horwitz LK, Horejs B, Atici L, Özkaya V, Mullville J, Parker Pearson M, Mainland I, Card N, Brown L, Sharples N, Griffiths D, Allen D, Arbuckle B, Abell JT, Duru G, Mentzer SM, Munro ND, Uzdurum M, Gülçur S, Buitenhuis H, Gladyr E, Stiner MC, Pöllath N, Özbaşaran M, Krebs S, Burger J, Frantz L, Medugorac I, Bradley DG, Peters J. Ancient mitogenomes from Pre-Pottery Neolithic Central Anatolia and the effects of a Late Neolithic bottleneck in sheep ( Ovis aries). SCIENCE ADVANCES 2024; 10:eadj0954. [PMID: 38608027 PMCID: PMC11014441 DOI: 10.1126/sciadv.adj0954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 03/11/2024] [Indexed: 04/14/2024]
Abstract
Occupied between ~10,300 and 9300 years ago, the Pre-Pottery Neolithic site of Aşıklı Höyük in Central Anatolia went through early phases of sheep domestication. Analysis of 629 mitochondrial genomes from this and numerous sites in Anatolia, southwest Asia, Europe, and Africa produced a phylogenetic tree with excessive coalescences (nodes) around the Neolithic, a potential signature of a domestication bottleneck. This is consistent with archeological evidence of sheep management at Aşıklı Höyük which transitioned from residential stabling to open pasturing over a millennium of site occupation. However, unexpectedly, we detected high genetic diversity throughout Aşıklı Höyük's occupation rather than a bottleneck. Instead, we detected a tenfold demographic bottleneck later in the Neolithic, which caused the fixation of mitochondrial haplogroup B in southwestern Anatolia. The mitochondrial genetic makeup that emerged was carried from the core region of early Neolithic sheep management into Europe and dominates the matrilineal diversity of both its ancient and the billion-strong modern sheep populations.
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Affiliation(s)
- Edson Sandoval-Castellanos
- Population Genomics Group, Department of Veterinary Sciences, LMU Munich, 82152 Martinsried, Germany
- Institute of Palaeoanatomy, Domestication Research and the History of Veterinary Medicine, LMU Munich, 80539 Munich, Germany
| | - Andrew J. Hare
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 PN40, Ireland
| | - Audrey T. Lin
- The Palaeogenomics and Bio-archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, UK
- Department of Anthropology, National Museum of Natural History, Smithsonian Institution, Washington, DC, 20560 USA
| | - Evangelos A. Dimopoulos
- The Palaeogenomics and Bio-archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, UK
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Kevin G. Daly
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 PN40, Ireland
- School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Sheila Geiger
- Institute of Palaeoanatomy, Domestication Research and the History of Veterinary Medicine, LMU Munich, 80539 Munich, Germany
| | - Victoria E. Mullin
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 PN40, Ireland
| | - Ingrid Wiechmann
- Institute of Palaeoanatomy, Domestication Research and the History of Veterinary Medicine, LMU Munich, 80539 Munich, Germany
| | - Valeria Mattiangeli
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 PN40, Ireland
| | - Gesine Lühken
- Institute of Animal Breeding and Genetics, Justus Liebig University of Gießen, Ludwigstr. 21, 35390 Gießen, Germany
| | - Natalia A. Zinovieva
- L.K. Ernst Federal Research Centre for Animal Husbandry, Dubrovitsy, Podolsk, Moscow Region, Russia
| | - Petar Zidarov
- Institute of Prehistory, Early History and Medieval Archaeology, Tübingen University, Tübingen, Germany
| | - Canan Çakırlar
- Institute of Archaeology, University of Groningen, 9712 ER Groningen, Netherlands
| | - Simon Stoddart
- Magdalene College, University of Cambridge, Cambridge CB3 0AG, UK
| | - David Orton
- BioArCh, Department of Archaeology, University of York, York YO10 5NG, UK
| | - Jelena Bulatović
- Department of Historical Studies, University of Gothenburg, BOX 200, 40530 Gothenburg, Sweden
| | - Marjan Mashkour
- Unité Archéozoologie, Archéobotanique, Sociétés Pratiques et Environnements (AASPE), CNRS, Muséum National d’Histoire Naturelle, 75020 Paris, France
| | - Eberhard W. Sauer
- School of History, Classics and Archaeology, University of Edinburgh, Old Medical School, Teviot Place, Edinburgh EH8 9AG, UK
| | - Liora Kolska Horwitz
- National Natural History Collections, Faculty of Life Sciences, The Hebrew University, Jerusalem, Israel
| | - Barbara Horejs
- OeAI, Austrian Academy of Sciences and HEAS, University of Vienna, Vienna, Austria
| | - Levent Atici
- Department of Anthropology, University of Nevada, Las Vegas, NV 89154, USA
| | - Vecihi Özkaya
- Department of Archaeology, Dicle University, Diyarbakir, Türkiye
| | - Jacqui Mullville
- School of History, Archaeology and Religion, Cardiff University, Cardiff CF10 3EU, UK
| | | | - Ingrid Mainland
- The University of the Highlands and Islands Orkney, Kirkwall, UK
| | - Nick Card
- The University of the Highlands and Islands Orkney, Kirkwall, UK
| | | | - Niall Sharples
- School of History, Archaeology and Religion, Cardiff University, Cardiff CF10 3EU, UK
| | - David Griffiths
- University of Oxford, OUDCE, Rewley House, Oxford OX1 2JA, UK
| | - David Allen
- Hampshire Cultural Trust, Chilcomb House, Winchester, SO23 8RB, UK
| | - Benjamin Arbuckle
- Department of Anthropology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jordan T. Abell
- Department of Geosciences, University of Arizona, Tucson, AZ 85721, USA
| | - Güneş Duru
- Department of Archaeology, Mimar Sinan Fine Arts University, 34381 Şişli/İstanbul, Türkiye
| | - Susan M. Mentzer
- Senckenberg Centre for Human Evolution and Palaeoenvironment, Institute for Archaeological Sciences, Department of Geosciences, Tübingen University, 72074 Tübingen, Germany
| | - Natalie D. Munro
- Department of Anthropology, University of Connecticut, Storrs, CT 06269, USA
| | - Melis Uzdurum
- Department of Archaeology, Ondokuz Mayıs University, 55270 Atakum/Samsun, Türkiye
| | - Sevil Gülçur
- Prehistory Department, Faculty of Letters, Istanbul University, 34134 Istanbul, Türkiye
| | | | - Elena Gladyr
- L.K. Ernst Federal Research Centre for Animal Husbandry, Dubrovitsy, Podolsk, Moscow Region, Russia
| | - Mary C. Stiner
- School of Anthropology, University of Arizona, Tucson, AZ 85721, USA
| | - Nadja Pöllath
- Bavarian Natural History Collections, State Collection of Palaeoanatomy Munich, 80333 Munich, Germany
- ArchaeoBioCenter, LMU Munich, 80539 Munich, Germany
| | - Mihriban Özbaşaran
- Prehistory Department, Faculty of Letters, Istanbul University, 34134 Istanbul, Türkiye
| | - Stefan Krebs
- Laboratory for Functional Genome Analysis (LAFUGA), Gene Center, LMU Munich, Feodor-Lynen-Straße 25, 81377 Munich, Germany
| | - Joachim Burger
- Institute of Organismic and Molecular Evolution (iomE), Johannes Gutenberg University Mainz, 55128 Mainz, Germany
| | - Laurent Frantz
- Palaeogenomics Group, Institute of Palaeoanatomy, Domestication Research and the History of Veterinary Medicine, LMU Munich, 80539 Munich, Germany
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Ivica Medugorac
- Population Genomics Group, Department of Veterinary Sciences, LMU Munich, 82152 Martinsried, Germany
- ArchaeoBioCenter, LMU Munich, 80539 Munich, Germany
| | - Daniel G. Bradley
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin D02 PN40, Ireland
| | - Joris Peters
- Institute of Palaeoanatomy, Domestication Research and the History of Veterinary Medicine, LMU Munich, 80539 Munich, Germany
- Bavarian Natural History Collections, State Collection of Palaeoanatomy Munich, 80333 Munich, Germany
- ArchaeoBioCenter, LMU Munich, 80539 Munich, Germany
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6
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Engström J, Liu SY, Dinparastdjadid A, Simoiu C. Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework. ACCIDENT; ANALYSIS AND PREVENTION 2024; 198:107460. [PMID: 38295653 DOI: 10.1016/j.aap.2024.107460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 12/07/2023] [Accepted: 01/03/2024] [Indexed: 02/20/2024]
Abstract
There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. We present a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.
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Affiliation(s)
- Johan Engström
- Waymo LLC, 1600 Amphitheatre Parkway, Mountain View, 94043, CA, USA.
| | - Shu-Yuan Liu
- Waymo LLC, 1600 Amphitheatre Parkway, Mountain View, 94043, CA, USA
| | | | - Camelia Simoiu
- Waymo LLC, 1600 Amphitheatre Parkway, Mountain View, 94043, CA, USA
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7
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Ferreiro D, Branco C, Arenas M. Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation. Bioinformatics 2024; 40:btae096. [PMID: 38374231 PMCID: PMC10914458 DOI: 10.1093/bioinformatics/btae096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 01/15/2024] [Accepted: 02/16/2024] [Indexed: 02/21/2024] Open
Abstract
MOTIVATION The selection among substitution models of molecular evolution is fundamental for obtaining accurate phylogenetic inferences. At the protein level, evolutionary analyses are traditionally based on empirical substitution models but these models make unrealistic assumptions and are being surpassed by structurally constrained substitution (SCS) models. The SCS models often consider site-dependent evolution, a process that provides realism but complicates their implementation into likelihood functions that are commonly used for substitution model selection. RESULTS We present a method to perform selection among site-dependent SCS models, also among empirical and site-dependent SCS models, based on the approximate Bayesian computation (ABC) approach and its implementation into the computational framework ProteinModelerABC. The framework implements ABC with and without regression adjustments and includes diverse empirical and site-dependent SCS models of protein evolution. Using extensive simulated data, we found that it provides selection among SCS and empirical models with acceptable accuracy. As illustrative examples, we applied the framework to analyze a variety of protein families observing that SCS models fit them better than the corresponding best-fitting empirical substitution models. AVAILABILITY AND IMPLEMENTATION ProteinModelerABC is freely available from https://github.com/DavidFerreiro/ProteinModelerABC, can run in parallel and includes a graphical user interface. The framework is distributed with detailed documentation and ready-to-use examples.
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Affiliation(s)
- David Ferreiro
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain
| | - Catarina Branco
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain
| | - Miguel Arenas
- CINBIO, Universidade de Vigo, 36310 Vigo, Spain
- Department of Biochemistry, Genetics and Immunology, Universidade de Vigo, 36310 Vigo, Spain
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8
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Vollert SA, Drovandi C, Adams MP. Unlocking ensemble ecosystem modelling for large and complex networks. PLoS Comput Biol 2024; 20:e1011976. [PMID: 38483981 DOI: 10.1371/journal.pcbi.1011976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 03/26/2024] [Accepted: 03/07/2024] [Indexed: 03/27/2024] Open
Abstract
The potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models by forecasting populations with and without intervention. These model ensembles commonly assume stable coexistence of species in the absence of available data. However, existing ensemble-generation methods become computationally inefficient as the size of the ecosystem network increases, preventing larger networks from being studied. We present a novel sequential Monte Carlo sampling approach for ensemble generation that is orders of magnitude faster than existing approaches. We demonstrate that the methods produce equivalent parameter inferences, model predictions, and tightly constrained parameter combinations using a novel sensitivity analysis method. For one case study, we demonstrate a speed-up from 108 days to 6 hours, while maintaining equivalent ensembles. Additionally, we demonstrate how to identify the parameter combinations that strongly drive feasibility and stability, drawing ecological insight from the ensembles. Now, for the first time, larger and more realistic networks can be practically simulated and analysed.
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Affiliation(s)
- Sarah A Vollert
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Christopher Drovandi
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Matthew P Adams
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Chemical Engineering, The University of Queensland, St Lucia, Australia
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9
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Ellis J, Brown E, Colenutt C, Schley D, Gubbins S. Inferring transmission routes for foot-and-mouth disease virus within a cattle herd using approximate Bayesian computation. Epidemics 2024; 46:100740. [PMID: 38232411 DOI: 10.1016/j.epidem.2024.100740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 12/06/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024] Open
Abstract
To control an outbreak of an infectious disease it is essential to understand the different routes of transmission and how they contribute to the overall spread of the pathogen. With this information, policy makers can choose the most efficient methods of detection and control during an outbreak. Here we assess the contributions of direct contact and environmental contamination to the transmission of foot-and-mouth disease virus (FMDV) in a cattle herd using an individual-based model that includes both routes. Model parameters are inferred using approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC) applied to data from transmission experiments and the 2007 epidemic in Great Britain. This demonstrates that the parameters derived from transmission experiments are applicable to outbreaks in the field, at least for closely related strains. Under the assumptions made in the model we show that environmental transmission likely contributes a majority of infections within a herd during an outbreak, although there is a lot of variation between simulated outbreaks. The accumulation of environmental contamination not only causes infections within a farm, but also has the potential to spread between farms via fomites. We also demonstrate the importance and effectiveness of rapid detection of infected farms in reducing transmission between farms, whether via direct contact or the environment.
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Affiliation(s)
- John Ellis
- The Pirbright Institute, Pirbright, Surrey, UK.
| | - Emma Brown
- The Pirbright Institute, Pirbright, Surrey, UK
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10
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Alamoudi E, Reck F, Bundgaard N, Graw F, Brusch L, Hasenauer J, Schälte Y. A wall-time minimizing parallelization strategy for approximate Bayesian computation. PLoS One 2024; 19:e0294015. [PMID: 38386671 PMCID: PMC10883530 DOI: 10.1371/journal.pone.0294015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/24/2023] [Indexed: 02/24/2024] Open
Abstract
Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often necessary. However, the existing parallelization strategies leave computing resources unused at times and thus do not optimally leverage them yet. We present look-ahead scheduling, a wall-time minimizing parallelization strategy for ABC Sequential Monte Carlo algorithms, which avoids idle times of computing units by preemptive sampling of subsequent generations. This allows to utilize all available resources. The strategy can be integrated with e.g. adaptive distance function and summary statistic selection schemes, which is essential in practice. Our key contribution is the theoretical assessment of the strategy of preemptive sampling and the proof of unbiasedness. Complementary, we provide an implementation and evaluate the strategy on different problems and numbers of parallel cores, showing speed-ups of typically 10-20% and up to 50% compared to the best established approach, with some variability. Thus, the proposed strategy allows to improve the cost and run-time efficiency of ABC methods on high-performance infrastructure.
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Affiliation(s)
- Emad Alamoudi
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Felipe Reck
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Nils Bundgaard
- BioQuant—Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
| | - Frederik Graw
- BioQuant—Center for Quantitative Biology, Heidelberg University, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany
- Department of Medicine 5, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Lutz Brusch
- Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, Dresden, Germany
| | - Jan Hasenauer
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
- Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Yannik Schälte
- Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
- Helmholtz Zentrum München, Institute of Computational Biology, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
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11
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Wang X, Jenner AL, Salomone R, Warne DJ, Drovandi C. Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation. J Math Biol 2024; 88:28. [PMID: 38358410 PMCID: PMC10869399 DOI: 10.1007/s00285-024-02045-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/25/2023] [Accepted: 12/27/2023] [Indexed: 02/16/2024]
Abstract
Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo. To account for this phenomena, we extend the VCBM to capture the existence of two distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases of the biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.
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Affiliation(s)
- Xiaoyu Wang
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia.
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
| | - Robert Salomone
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
- School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia
| | - David J Warne
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
| | - Christopher Drovandi
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
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12
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Audzijonyte A, Delius GW, Stuart-Smith RD, Novaglio C, Edgar GJ, Barrett NS, Blanchard JL. Changes in sea floor productivity are crucial to understanding the impact of climate change in temperate coastal ecosystems according to a new size-based model. PLoS Biol 2023; 21:e3002392. [PMID: 38079442 PMCID: PMC10712853 DOI: 10.1371/journal.pbio.3002392] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 10/19/2023] [Indexed: 12/18/2023] Open
Abstract
The multifaceted effects of climate change on physical and biogeochemical processes are rapidly altering marine ecosystems but often are considered in isolation, leaving our understanding of interactions between these drivers of ecosystem change relatively poor. This is particularly true for shallow coastal ecosystems, which are fuelled by a combination of distinct pelagic and benthic energy pathways that may respond to climate change in fundamentally distinct ways. The fish production supported by these systems is likely to be impacted by climate change differently to those of offshore and shelf ecosystems, which have relatively simpler food webs and mostly lack benthic primary production sources. We developed a novel, multispecies size spectrum model for shallow coastal reefs, specifically designed to simulate potential interactive outcomes of changing benthic and pelagic energy inputs and temperatures and calculate the relative importance of these variables for the fish community. Our model, calibrated using field data from an extensive temperate reef monitoring program, predicts that changes in resource levels will have much stronger impacts on fish biomass and yields than changes driven by physiological responses to temperature. Under increased plankton abundance, species in all fish trophic groups were predicted to increase in biomass, average size, and yields. By contrast, changes in benthic resources produced variable responses across fish trophic groups. Increased benthic resources led to increasing benthivorous and piscivorous fish biomasses, yields, and mean body sizes, but biomass decreases among herbivore and planktivore species. When resource changes were combined with warming seas, physiological responses generally decreased species' biomass and yields. Our results suggest that understanding changes in benthic production and its implications for coastal fisheries should be a priority research area. Our modified size spectrum model provides a framework for further study of benthic and pelagic energy pathways that can be easily adapted to other ecosystems.
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Affiliation(s)
- Asta Audzijonyte
- Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
- Centre for Marine Socioecology, University of Tasmania, Hobart, Australia
| | - Gustav W. Delius
- Department of Mathematics, University of York, York, United Kingdom
| | - Rick D. Stuart-Smith
- Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
| | - Camilla Novaglio
- Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
- Centre for Marine Socioecology, University of Tasmania, Hobart, Australia
| | - Graham J. Edgar
- Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
| | - Neville S. Barrett
- Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
| | - Julia L. Blanchard
- Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
- Centre for Marine Socioecology, University of Tasmania, Hobart, Australia
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13
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Ong HG, Kim Y, Lee J, Kim B, Kang D, Jung E, Shin J, Kim Y. Approximate Bayesian computation and ecological niche models elucidate the demographic history and current fragmented population distribution of a Korean endemic shrub. Ecol Evol 2023; 13:e10792. [PMID: 38077507 PMCID: PMC10700048 DOI: 10.1002/ece3.10792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 09/15/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
Climatic fluctuations and geological events since the LGM are believed to have significantly impacted the population size, distribution, and mobility of many species that we observe today. In this paper, we determined the processes driving the phylogeographic structure of the Korean endemic white forsythia by combining the use of genome-wide SNPs and predicting paleoclimatic habitats during the LGM (21 kya), Early Holocene (10 kya), Mid-Holocene (6 kya), and Late Holocene (3 kya). Using a maximum of 1897 SNPs retrieved from 124 samples across nine wild populations, five environmental predictors, and the species' natural occurrence records, we aimed to infer the species' demographic history and reconstruct its possible paleodistributions with the use of approximate Bayesian computation and ecological niche models, respectively. Under this integrated framework, we found strong evidence for patterns of range shift and expansion, and population divergence events from the onset of the Holocene, resulting in the formation of its five distinct genetic units. The most highly supported model inferred that after the split of an ancestral population into the southern group and a larger central metapopulation lineage, the latter gave rise to the eastern and northern clusters, before finally dividing into two sub-central groups. While the use of molecular data allowed us to identify and refine the (phylo)genetic relationships of the species' lineages and populations, the use of ecological data helped us infer a past LGM refugium and the directions of post-glacial range dynamics. The time frames of these demographic events were shown to be congruent with climatic and geological events that affected the central Korean Peninsula during these periods. These findings gave us a better understanding of the consequences of past spatiotemporal factors that may have resulted in the current fragmented population distribution of this endangered plant.
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Affiliation(s)
| | - Yong‐In Kim
- On Biological Resource Research Institute (OBRRI)ChuncheonSouth Korea
| | - Jung‐Hoon Lee
- On Biological Resource Research Institute (OBRRI)ChuncheonSouth Korea
| | - Bo‐Yun Kim
- National Institute of Biological Resources (NIBR)IncheonSouth Korea
| | - Dae‐Hyun Kang
- Korea National Park Research InstituteWonjuSouth Korea
| | - Eui‐Kwon Jung
- Department of Life ScienceHallym UniversityChuncheonSouth Korea
| | - Jae‐Seo Shin
- Department of Life ScienceHallym UniversityChuncheonSouth Korea
| | - Young‐Dong Kim
- Multidisciplinary Genome InstituteHallym UniversityChuncheonSouth Korea
- Department of Life ScienceHallym UniversityChuncheonSouth Korea
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14
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Huang X, Athrey GN, Kaufman PE, Fredregill C, Slotman MA. Effective population size of Culex quinquefasciatus under insecticide-based vector management and following Hurricane Harvey in Harris County, Texas. Front Genet 2023; 14:1297271. [PMID: 38075683 PMCID: PMC10702589 DOI: 10.3389/fgene.2023.1297271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 10/24/2023] [Indexed: 02/12/2024] Open
Abstract
Introduction: Culex quinquefasciatus is a mosquito species of significant public health importance due to its ability to transmit multiple pathogens that can cause mosquito-borne diseases, such as West Nile fever and St. Louis encephalitis. In Harris County, Texas, Cx. quinquefasciatus is a common vector species and is subjected to insecticide-based management by the Harris County Public Health Department. However, insecticide resistance in mosquitoes has increased rapidly worldwide and raises concerns about maintaining the effectiveness of vector control approaches. This concern is highly relevant in Texas, with its humid subtropical climate along the Gulf Coast that provides suitable habitat for Cx. quinquefasciatus and other mosquito species that are known disease vectors. Therefore, there is an urgent and ongoing need to monitor the effectiveness of current vector control programs. Methods: In this study, we evaluated the impact of vector control approaches by estimating the effective population size of Cx. quinquefasciatus in Harris County. We applied Approximate Bayesian Computation to microsatellite data to estimate effective population size. We collected Cx. quinquefasciatus samples from two mosquito control operation areas; 415 and 802, during routine vector monitoring in 2016 and 2017. No county mosquito control operations were applied at area 415 in 2016 and 2017, whereas extensive adulticide spraying operations were in effect at area 802 during the summer of 2016. We collected data for eighteen microsatellite markers for 713 and 723 mosquitoes at eight timepoints from 2016 to 2017 in areas 415 and 802, respectively. We also investigated the impact of Hurricane Harvey's landfall in the Houston area in August of 2017 on Cx. quinquefasciatus population fluctuation. Results: We found that the bottleneck scenario was the most probable historical scenario describing the impact of the winter season at area 415 and area 802, with the highest posterior probability of 0.9167 and 0.4966, respectively. We also detected an expansion event following Hurricane Harvey at area 802, showing a 3.03-fold increase in 2017. Discussion: Although we did not detect significant effects of vector control interventions, we found considerable influences of the winter season and a major hurricane on the effective population size of Cx. quinquefasciatus. The fluctuations in effective population size in both areas showed a significant seasonal pattern. Additionally, the significant population expansion following Hurricane Harvey in 2017 supports the necessity for post-hurricane vector-control interventions.
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Affiliation(s)
- Xinyue Huang
- Department of Entomology, Texas A&M University, College Station, TX, United States
| | - Giridhar N. Athrey
- Department of Poultry Science, Texas A&M University, College Station, TX, United States
| | - Phillip E. Kaufman
- Department of Entomology, Texas A&M University, College Station, TX, United States
| | - Chris Fredregill
- Harris County Public Health, Mosquito & Vector Control Division, Houston, TX, United States
| | - Michel A. Slotman
- Department of Entomology, Texas A&M University, College Station, TX, United States
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15
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Fogg J, Allman ES, Ané C. PhyloCoalSimulations: A Simulator for Network Multispecies Coalescent Models, Including a New Extension for the Inheritance of Gene Flow. Syst Biol 2023; 72:1171-1179. [PMID: 37254872 DOI: 10.1093/sysbio/syad030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 05/03/2023] [Accepted: 05/15/2023] [Indexed: 06/01/2023] Open
Abstract
We consider the evolution of phylogenetic gene trees along phylogenetic species networks, according to the network multispecies coalescent process, and introduce a new network coalescent model with correlated inheritance of gene flow. This model generalizes two traditional versions of the network coalescent: with independent or common inheritance. At each reticulation, multiple lineages of a given locus are inherited from parental populations chosen at random, either independently across lineages or with positive correlation according to a Dirichlet process. This process may account for locus-specific probabilities of inheritance, for example. We implemented the simulation of gene trees under these network coalescent models in the Julia package PhyloCoalSimulations, which depends on PhyloNetworks and its powerful network manipulation tools. Input species phylogenies can be read in extended Newick format, either in numbers of generations or in coalescent units. Simulated gene trees can be written in Newick format, and in a way that preserves information about their embedding within the species network. This embedding can be used for downstream purposes, such as to simulate species-specific processes like rate variation across species, or for other scenarios as illustrated in this note. This package should be useful for simulation studies and simulation-based inference methods. The software is available open source with documentation and a tutorial at https://github.com/cecileane/PhyloCoalSimulations.jl.
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Affiliation(s)
- John Fogg
- Department of Statistics, University of Wisconsin - Madison, WI, 53706, USA
| | - Elizabeth S Allman
- Department of Mathematics and Statistics, University of Alaska - Fairbanks, AK, 99775, USA
| | - Cécile Ané
- Department of Statistics, University of Wisconsin - Madison, WI, 53706, USA
- Department of Botany, University of Wisconsin - Madison, WI, 53706, USA
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16
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Nanda P, Kirschner DE. Calibration methods to fit parameters within complex biological models. FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS 2023; 9:1256443. [PMID: 38222943 PMCID: PMC10785782 DOI: 10.3389/fams.2023.1256443] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.
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Affiliation(s)
- Pariksheet Nanda
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Denise E. Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
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17
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Nait Saada J, Tsangalidou Z, Stricker M, Palamara PF. Inference of Coalescence Times and Variant Ages Using Convolutional Neural Networks. Mol Biol Evol 2023; 40:msad211. [PMID: 37738175 PMCID: PMC10581698 DOI: 10.1093/molbev/msad211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 09/11/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023] Open
Abstract
Accurate inference of the time to the most recent common ancestor (TMRCA) between pairs of individuals and of the age of genomic variants is key in several population genetic analyses. We developed a likelihood-free approach, called CoalNN, which uses a convolutional neural network to predict pairwise TMRCAs and allele ages from sequencing or SNP array data. CoalNN is trained through simulation and can be adapted to varying parameters, such as demographic history, using transfer learning. Across several simulated scenarios, CoalNN matched or outperformed the accuracy of model-based approaches for pairwise TMRCA and allele age prediction. We applied CoalNN to settings for which model-based approaches are under-developed and performed analyses to gain insights into the set of features it uses to perform TMRCA prediction. We next used CoalNN to analyze 2,504 samples from 26 populations in the 1,000 Genome Project data set, inferring the age of ∼80 million variants. We observed substantial variation across populations and for variants predicted to be pathogenic, reflecting heterogeneous demographic histories and the action of negative selection. We used CoalNN's predicted allele ages to construct genome-wide annotations capturing the signature of past negative selection. We performed LD-score regression analysis of heritability using summary association statistics from 63 independent complex traits and diseases (average N=314k), observing increased annotation-specific effects on heritability compared to a previous allele age annotation. These results highlight the effectiveness of using likelihood-free, simulation-trained models to infer properties of gene genealogies in large genomic data sets.
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Affiliation(s)
| | | | | | - Pier Francesco Palamara
- Department of Statistics, University of Oxford, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
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18
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Jeger M, Hamelin F, Cunniffe N. Emerging Themes and Approaches in Plant Virus Epidemiology. PHYTOPATHOLOGY 2023; 113:1630-1646. [PMID: 36647183 DOI: 10.1094/phyto-10-22-0378-v] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Plant diseases caused by viruses share many common features with those caused by other pathogen taxa in terms of the host-pathogen interaction, but there are also distinctive features in epidemiology, most apparent where transmission is by vectors. Consequently, the host-virus-vector-environment interaction presents a continuing challenge in attempts to understand and predict the course of plant virus epidemics. Theoretical concepts, based on the underlying biology, can be expressed in mathematical models and tested through quantitative assessments of epidemics in the field; this remains a goal in understanding why plant virus epidemics occur and how they can be controlled. To this end, this review identifies recent emerging themes and approaches to fill in knowledge gaps in plant virus epidemiology. We review quantitative work on the impact of climatic fluctuations and change on plants, viruses, and vectors under different scenarios where impacts on the individual components of the plant-virus-vector interaction may vary disproportionately; there is a continuing, sometimes discordant, debate on host resistance and tolerance as plant defense mechanisms, including aspects of farmer behavior and attitudes toward disease management that may affect deployment in crops; disentangling host-virus-vector-environment interactions, as these contribute to temporal and spatial disease progress in field populations; computational techniques for estimating epidemiological parameters from field observations; and the use of optimal control analysis to assess disease control options. We end by proposing new challenges and questions in plant virus epidemiology.
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Affiliation(s)
- Mike Jeger
- Department of Life Sciences, Imperial College London, Silwood Park, U.K
| | - Fred Hamelin
- IGEPP INRAE, University of Rennes, Rennes, France
| | - Nik Cunniffe
- Department of Plant Sciences, University of Cambridge, Cambridge, U.K
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19
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Pantel JH, Becks L. Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics. Trends Ecol Evol 2023; 38:760-772. [PMID: 37437547 DOI: 10.1016/j.tree.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 07/14/2023]
Abstract
While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.
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Affiliation(s)
- Jelena H Pantel
- Ecological Modelling, Faculty of Biology, University of Duisburg-Essen, Universitätsstraße 2, 45117 Essen, Germany.
| | - Lutz Becks
- University of Konstanz, Aquatic Ecology and Evolution, Limnological Institute University of Konstanz Mainaustraße 252 78464, Konstanz/Egg, Germany
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20
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Booker WW, Ray DD, Schrider DR. This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks. Genetics 2023; 224:iyad063. [PMID: 37067864 PMCID: PMC10213497 DOI: 10.1093/genetics/iyad063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 02/23/2023] [Accepted: 04/05/2023] [Indexed: 04/18/2023] Open
Abstract
Numerous studies over the last decade have demonstrated the utility of machine learning methods when applied to population genetic tasks. More recent studies show the potential of deep-learning methods in particular, which allow researchers to approach problems without making prior assumptions about how the data should be summarized or manipulated, instead learning their own internal representation of the data in an attempt to maximize inferential accuracy. One type of deep neural network, called Generative Adversarial Networks (GANs), can even be used to generate new data, and this approach has been used to create individual artificial human genomes free from privacy concerns. In this study, we further explore the application of GANs in population genetics by designing and training a network to learn the statistical distribution of population genetic alignments (i.e. data sets consisting of sequences from an entire population sample) under several diverse evolutionary histories-the first GAN capable of performing this task. After testing multiple different neural network architectures, we report the results of a fully differentiable Deep-Convolutional Wasserstein GAN with gradient penalty that is capable of generating artificial examples of population genetic alignments that successfully mimic key aspects of the training data, including the site-frequency spectrum, differentiation between populations, and patterns of linkage disequilibrium. We demonstrate consistent training success across various evolutionary models, including models of panmictic and subdivided populations, populations at equilibrium and experiencing changes in size, and populations experiencing either no selection or positive selection of various strengths, all without the need for extensive hyperparameter tuning. Overall, our findings highlight the ability of GANs to learn and mimic population genetic data and suggest future areas where this work can be applied in population genetics research that we discuss herein.
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Affiliation(s)
- William W Booker
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514-2916, USA
| | - Dylan D Ray
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514-2916, USA
| | - Daniel R Schrider
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514-2916, USA
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21
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Benning JW, Faulkner A, Moeller DA. Rapid evolution during climate change: demographic and genetic constraints on adaptation to severe drought. Proc Biol Sci 2023; 290:20230336. [PMID: 37161337 PMCID: PMC10170215 DOI: 10.1098/rspb.2023.0336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 03/13/2023] [Indexed: 05/11/2023] Open
Abstract
Populations often vary in their evolutionary responses to a shared environmental perturbation. A key hurdle in building more predictive models of rapid evolution is understanding this variation-why do some populations and traits evolve while others do not? We combined long-term demographic and environmental data, estimates of quantitative genetic variance components, a resurrection experiment and individual-based evolutionary simulations to gain mechanistic insights into contrasting evolutionary responses to a severe multi-year drought. We examined five traits in two populations of a native California plant, Clarkia xantiana, at three time points over 7 years. Earlier flowering phenology evolved in only one of the two populations, though both populations experienced similar drought severity and demographic declines and were estimated to have considerable additive genetic variance for flowering phenology. Pairing demographic and experimental data with evolutionary simulations suggested that while seed banks in both populations probably constrained evolutionary responses, a stronger seed bank in the non-evolving population resulted in evolutionary stasis. Gene flow through time via germ banks may be an important, underappreciated control on rapid evolution in response to extreme environmental perturbations.
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Affiliation(s)
- John W. Benning
- Department of Botany, University of Wyoming, Laramie, WY 82071, USA
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55455, USA
| | - Alexai Faulkner
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55455, USA
| | - David A. Moeller
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55455, USA
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22
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Hirano T, Saito T, Ito S, Ye B, Linscott TM, Do VT, Dong Z, Chiba S. Phylogenomic analyses reveal incongruences between divergence times and fossil records of freshwater snails in East Asia. Mol Phylogenet Evol 2023; 182:107728. [PMID: 36804427 DOI: 10.1016/j.ympev.2023.107728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 01/26/2023] [Accepted: 02/05/2023] [Indexed: 02/17/2023]
Abstract
Fossils provide important insight into our understanding of phylogenetic history by serving as calibration points for divergence time estimation. However, uncertainties in the fossil record due to parallel evolution and convergent evolution can critically affect estimates of node ages. Here, we compare and contrast estimates of phylogenetic divergence with geologic and fossil history for two freshwater snail genera of the family Viviparidae in East Asia (Cipangopaludina and Margarya). Cipangopaludina species are commonly widely distributed species in East Asia, but extant Margarya species are endemic to the ancient lakes in Yunnan, China. According to some previous studies, parallel evolution or convergent evolution of shell morphology has occurred in the family several times which may affect divergence time estimation using fossil records. In this study, we used SNP data derived from ddRAD-seq loci to investigate population demographic history of both genera. Our results show a common pattern of lake endemic lineages diversifying from widely distributed lineages in the Miocene, and multiple colonization to a single ancient lake occurred in the Pleistocene. Our results indicate substantial incongruence among estimated phylogenomic divergence times, some fossil records, and formation ages of ancient lakes. These findings suggest some fossil records may be misidentified in these groups and highlight the need to carefully evaluate geological evidence and fossil records when using these for divergence time estimation.
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Affiliation(s)
- Takahiro Hirano
- Center for Northeast Asian Studies, Tohoku University, Miyagi, Japan; Graduate School of Life Sciences, Tohoku University, Miyagi, Japan; Biology Program, Faculty of Science, University of the Ryukyus, Okinawa, Japan.
| | - Takumi Saito
- Center for Northeast Asian Studies, Tohoku University, Miyagi, Japan; Department of Botany and Zoology, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Shun Ito
- Center for Northeast Asian Studies, Tohoku University, Miyagi, Japan
| | - Bin Ye
- Center for Northeast Asian Studies, Tohoku University, Miyagi, Japan; Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - T Mason Linscott
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, USA
| | - Van Tu Do
- Institute of Ecology and Biological Resources, Vietnam Academy of Science and Technology, Ha Noi, Viet Nam; Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Ha Noi, Viet Nam
| | - Zhengzhong Dong
- Agricultural Experiment Station, Zhejiang University, Hangzhou, China
| | - Satoshi Chiba
- Center for Northeast Asian Studies, Tohoku University, Miyagi, Japan; Graduate School of Life Sciences, Tohoku University, Miyagi, Japan
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Johri P, Pfeifer SP, Jensen JD. Developing an evolutionary baseline model for humans: jointly inferring purifying selection with population history. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.11.536488. [PMID: 37090533 PMCID: PMC10120674 DOI: 10.1101/2023.04.11.536488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics - including quantifying the relative contributions of adaptive vs. non-adaptive processes - but it is also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection ( e.g., positive or balancing selection). Here, a baseline model was developed for a human population of West African ancestry, the Yoruba, comprising processes constantly operating on the genome ( i.e. , purifying and background selection, population size changes, recombination rate heterogeneity, and gene conversion). Specifically, to perform joint inference of selective effects with demography, an approximate Bayesian approach was employed that utilizes the decay of background selection effects around functional elements, taking into account genomic architecture. This approach inferred a recent 6-fold population growth together with a distribution of fitness effects that is skewed towards effectively neutral mutations. Importantly, these results further suggest that, while strong and/or frequent recurrent positive selection is inconsistent with observed data, weak to moderate positive selection is consistent but unidentifiable if rare.
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24
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Lu R, Zhu H, Wu X. Estimating mutation rates in a Markov branching process using approximate Bayesian computation. J Theor Biol 2023; 565:111467. [PMID: 36963627 DOI: 10.1016/j.jtbi.2023.111467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 02/15/2023] [Accepted: 03/15/2023] [Indexed: 03/26/2023]
Abstract
Estimating microbial mutation rates is an essential task in evolutionary biology, with wide range applications in related fields such as virology, epidemiology, clinic and public health, and antibiotic research. Significant progress has been made on this research since 1943 when Luria-Delbrück fluctuation analysis was first introduced. However, existing estimators of mutation rates are heavily reliant on model assumptions in fluctuation analysis, and become less applicable to real microbial experiments which deviate from the model assumptions. To overcome this difficulty, we propose to model fluctuation experimental data by a two-type Markov branching process (MBP) and use approximate Bayesian computation (ABC) to estimate the mutation probability parameters. Such an ABC-based mutation rate estimator is based on intensive simulations from the mutation process, thereby taking advantage of modern computing power. Most importantly, its likelihood-free feature allows more complex and realistic setups of the mutation process, especially when the distribution of the number of mutants cannot be easily derived. To further improve computation efficiency, we use a Gaussian process surrogate to substitute the simulator in the ABC algorithm, and call the resulting estimator GPS-ABC. Simulation studies show that, when used to estimate constant mutation rate in MBP, ABC-based estimators generally outperform traditional moment or likelihood-based estimators. When mutations occur in two stages, i.e., in MBP with a piece-wise constant mutation rate function, traditional mutation rate estimators become not applicable, yet GPS-ABC still achieves reasonable estimates. Finally, the proposed GPS-ABC estimator is used to analyze real fluctuation experimental datasets for studying drug resistance.
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Affiliation(s)
- Ruijin Lu
- Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States of America
| | - Hongxiao Zhu
- Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States of America
| | - Xiaowei Wu
- Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States of America.
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25
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Kaya S, Kabasakal B, Erdoğan A. Geographic Genetic Structure of Alectoris chukar in Türkiye: Post-LGM-Induced Hybridization and Human-Mediated Contaminations. BIOLOGY 2023; 12:biology12030401. [PMID: 36979093 PMCID: PMC10045126 DOI: 10.3390/biology12030401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 02/19/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023]
Abstract
Türkiye is considered an important evolutionary area for Chukar partridge (Alectoris chukar), since it is both a potential ancestral area and a diversification center for the species. Using 2 mitochondrial (Cty-b and D-loop) and 13 polymorphic microsatellite markers, we investigated the geographic genetic structure of A. chukar populations to determine how past climatic fluctuations and human activities have shaped the gene pool of this species in Türkiye. Our results indicate, firstly, that only A. chukar of the genus Alectoris is present in Türkiye (Anatolia and Thrace), with no natural or artificial gene flow from congenerics. Secondly, the geographic genetic structure of the species in Türkiye has been shaped by topographic heterogeneity, Pleistocene climatic fluctuations, and artificial transport by humans. Third, there appears to be three genetic clusters: Thracian, Eastern, and Western. Fourth, the post-LGM demographic expansion of the Eastern and Western populations has formed a hybrid zone in Central Anatolia (~8 kyBP). Fifth, the rate of China clade-B contamination in Türkiye is about 8% in mtDNA and about 12% in nuDNA, with the Southeastern Anatolian population having the highest contamination. Sixth, the Thracian population was the most genetically distinct, with the lowest genetic diversity and highest level of inbreeding and no China clad-B contamination. These results can contribute to the conservation regarding A. chukar populations, especially the Thracian population.
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Affiliation(s)
- Sarp Kaya
- First and Emergency Aid Programme, Department of Medical Services and Techniques, Vocational School of Burdur Health Services, Burdur Mehmet Akif Ersoy University, Burdur 15030, Turkey
| | - Bekir Kabasakal
- Department of Biology, Akdeniz University, Antalya 07058, Turkey
- Anesthesia Programme, Department of Medical Services and Techniques, Vocational School of Health Services, Antalya Bilim University, Antalya 07190, Turkey
- Correspondence:
| | - Ali Erdoğan
- Department of Biology, Akdeniz University, Antalya 07058, Turkey
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26
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Baey C, Smith HG, Rundlöf M, Olsson O, Clough Y, Sahlin U. Calibration of a bumble bee foraging model using Approximate Bayesian Computation. Ecol Modell 2023. [DOI: 10.1016/j.ecolmodel.2022.110251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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27
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Hashemi M, Vattikonda AN, Jha J, Sip V, Woodman MM, Bartolomei F, Jirsa VK. Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators. Neural Netw 2023; 163:178-194. [PMID: 37060871 DOI: 10.1016/j.neunet.2023.03.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 03/24/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models of abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders. However, the calculation of likelihood function at whole-brain scale is often intractable. Thus, likelihood-free algorithms are required to efficiently estimate the parameters pertaining to the hypothetical areas, ideally including the uncertainty. In this study, we introduce the simulation-based inference for the virtual epileptic patient model (SBI-VEP), enabling us to amortize the approximate posterior of the generative process from a low-dimensional representation of whole-brain epileptic patterns. The state-of-the-art deep learning algorithms for conditional density estimation are used to readily retrieve the statistical relationships between parameters and observations through a sequence of invertible transformations. We show that the SBI-VEP is able to efficiently estimate the posterior distribution of parameters linked to the extent of the epileptogenic and propagation zones from sparse intracranial electroencephalography recordings. The presented Bayesian methodology can deal with non-linear latent dynamics and parameter degeneracy, paving the way for fast and reliable inference on brain disorders from neuroimaging modalities.
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28
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Gaskell J, Campioni N, Morales JM, Husmeier D, Torney CJ. Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation. J R Soc Interface 2023; 20:20220676. [PMID: 36596456 PMCID: PMC9810425 DOI: 10.1098/rsif.2022.0676] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 12/06/2022] [Indexed: 01/05/2023] Open
Abstract
Inferring the underlying processes that drive collective behaviour in biological and social systems is a significant statistical and computational challenge. While simulation models have been successful in qualitatively capturing many of the phenomena observed in these systems in a variety of domains, formally fitting these models to data remains intractable. Recently, approximate Bayesian computation (ABC) has been shown to be an effective approach to inference if the likelihood function for a model is unavailable. However, a key difficulty in successfully implementing ABC lies with the design, selection and weighting of appropriate summary statistics, a challenge that is especially acute when modelling high dimensional complex systems. In this work, we combine a Gaussian process accelerated ABC method with the automatic learning of summary statistics via graph neural networks. Our approach bypasses the need to design a model-specific set of summary statistics for inference. Instead, we encode relational inductive biases into a neural network using a graph embedding and then extract summary statistics automatically from simulation data. To evaluate our framework, we use a model of collective animal movement as a test bed and compare our method to a standard summary statistics approach and a linear regression-based algorithm.
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Affiliation(s)
- Jennifer Gaskell
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8SQ, UK
| | - Nazareno Campioni
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8SQ, UK
| | - Juan M. Morales
- Grupo de Ecología Cuantitativa, INIBIOMA-CONICET, Universidad Nacional del Comahue, Bariloche, Argentina
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow G12 8SQ, UK
| | - Dirk Husmeier
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8SQ, UK
| | - Colin J. Torney
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8SQ, UK
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29
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Martin GM, Frazier DT, Robert CP. Approximating Bayes in the 21st Century. Stat Sci 2023. [DOI: 10.1214/22-sts875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Affiliation(s)
- Gael M. Martin
- Gael M. Martin is Professor, Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
| | - David T. Frazier
- David T. Frazier is Associate Professor, Department of Econometrics and Business Statistics, Monash University, Melbourne, Australia
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Jin H, Du W, Yin G. Approximate Bayesian computation design for phase I clinical trials. Stat Methods Med Res 2022; 31:2310-2322. [PMID: 36031856 PMCID: PMC9703391 DOI: 10.1177/09622802221122402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In the development of new cancer treatment, an essential step is to determine the maximum tolerated dose in a phase I clinical trial. In general, phase I trial designs can be classified as either model-based or algorithm-based approaches. Model-based phase I designs are typically more efficient by using all observed data, while there is a potential risk of model misspecification that may lead to unreliable dose assignment and incorrect maximum tolerated dose identification. In contrast, most of the algorithm-based designs are less efficient in using cumulative information, because they tend to focus on the observed data in the neighborhood of the current dose level for dose movement. To use the data more efficiently yet without any model assumption, we propose a novel approximate Bayesian computation approach to phase I trial design. Not only is the approximate Bayesian computation design free of any dose-toxicity curve assumption, but it can also aggregate all the available information accrued in the trial for dose assignment. Extensive simulation studies demonstrate its robustness and efficiency compared with other phase I trial designs. We apply the approximate Bayesian computation design to the MEK inhibitor selumetinib trial to demonstrate its satisfactory performance. The proposed design can be a useful addition to the family of phase I clinical trial designs due to its simplicity, efficiency and robustness.
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Affiliation(s)
- Huaqing Jin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
| | - Wenbin Du
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong,Guosheng Yin, Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong.
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31
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Hatami F, Chen S, Paul R, Thill JC. Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192315771. [PMID: 36497846 PMCID: PMC9736132 DOI: 10.3390/ijerph192315771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/12/2022] [Accepted: 11/24/2022] [Indexed: 05/09/2023]
Abstract
The global COVID-19 pandemic has taken a heavy toll on health, social, and economic costs since the end of 2019. Predicting the spread of a pandemic is essential to developing effective intervention policies. Since the beginning of this pandemic, many models have been developed to predict its pathways. However, the majority of these models assume homogeneous dynamics over the geographic space, while the pandemic exhibits substantial spatial heterogeneity. In addition, spatial interaction among territorial entities and variations in their magnitude impact the pandemic dynamics. In this study, we used a spatial extension of the SEIR-type epidemiological model to simulate and predict the 4-week number of COVID-19 cases in the Charlotte-Concord-Gastonia Metropolitan Statistical Area (MSA), USA. We incorporated a variety of covariates, including mobility, pharmaceutical, and non-pharmaceutical interventions, demographics, and weather data to improve the model's predictive performance. We predicted the number of COVID-19 cases for up to four weeks in the 10 counties of the studied MSA simultaneously over the time period 29 March 2020 to 13 March 2021, and compared the results with the reported number of cases using the root-mean-squared error (RMSE) metric. Our results highlight the importance of spatial heterogeneity and spatial interactions among locations in COVID-19 pandemic modeling.
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Affiliation(s)
- Faizeh Hatami
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Shi Chen
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Rajib Paul
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Jean-Claude Thill
- Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
- Correspondence:
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32
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Galvañ A, Boughalleb-M’Hamdi N, Benfradj N, Mannai S, Lázaro E, Vicent A. Climate suitability of the Mediterranean Basin for citrus black spot disease (Phyllosticta citricarpa) based on a generic infection model. Sci Rep 2022; 12:19876. [PMID: 36400797 PMCID: PMC9674692 DOI: 10.1038/s41598-022-22775-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 10/19/2022] [Indexed: 11/21/2022] Open
Abstract
Citrus black spot (CBS), caused by the fungus Phyllosticta citricarpa, is associated with serious yield and quality losses. The climate suitability of the Mediterranean Basin for CBS development has been long debated. However, CBS has been described in Tunisia. In this study, a generic model was used to simulate potential infections by ascospores and pycnidiospores together with a degree-day model to predict the onset of ascospore release. High-resolution climatic data were retrieved from the ERA5-Land dataset for the citrus-growing regions in the Mediterranean Basin and other locations where CBS is present. In general, the onset of ascospore release was predicted to occur late in spring, but there is no agreement on the adequacy of this empirical model for extrapolation to the Mediterranean Basin. The generic model indicated that infections by ascospores and pycnidiospores would be concentrated mainly in autumn, as well as in spring for pycnidiospores. In contrast to previous studies, the percentage of hours suitable for infection was higher for pycnidiospores than for ascospores. The values obtained with the generic infection model for Tunisia and several CBS-affected locations worldwide were similar to those for other citrus-growing regions in Europe and Northern Africa. These results support previous work indicating that the climate of the Mediterranean Basin is suitable for CBS development.
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Affiliation(s)
- Anaïs Galvañ
- grid.419276.f0000 0000 9605 0555Institut Valencià d’Investigacions Agràries (IVIA), Centre de Protecció Vegetal i Biotecnologia, 46113 Moncada, Valencia Spain
| | - Naima Boughalleb-M’Hamdi
- grid.7900.e0000 0001 2114 4570Department of Biological Sciences and Plant Protection, Institut Supérieur Agronomique de Chott Mariem, LR21AGR05, University of Sousse, Chott Mariem, Sousse, 4042 Tunisia
| | - Najwa Benfradj
- grid.7900.e0000 0001 2114 4570Department of Biological Sciences and Plant Protection, Institut Supérieur Agronomique de Chott Mariem, LR21AGR05, University of Sousse, Chott Mariem, Sousse, 4042 Tunisia
| | - Sabrine Mannai
- grid.7900.e0000 0001 2114 4570Department of Biological Sciences and Plant Protection, Institut Supérieur Agronomique de Chott Mariem, LR21AGR05, University of Sousse, Chott Mariem, Sousse, 4042 Tunisia
| | - Elena Lázaro
- grid.419276.f0000 0000 9605 0555Institut Valencià d’Investigacions Agràries (IVIA), Centre de Protecció Vegetal i Biotecnologia, 46113 Moncada, Valencia Spain
| | - Antonio Vicent
- grid.419276.f0000 0000 9605 0555Institut Valencià d’Investigacions Agràries (IVIA), Centre de Protecció Vegetal i Biotecnologia, 46113 Moncada, Valencia Spain
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33
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Moshe A, Wygoda E, Ecker N, Loewenthal G, Avram O, Israeli O, Hazkani-Covo E, Pe’er I, Pupko T. An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements. Mol Biol Evol 2022; 39:msac231. [PMID: 36282896 PMCID: PMC9692237 DOI: 10.1093/molbev/msac231] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024] Open
Abstract
The inference of genome rearrangement events has been extensively studied, as they play a major role in molecular evolution. However, probabilistic evolutionary models that explicitly imitate the evolutionary dynamics of such events, as well as methods to infer model parameters, are yet to be fully utilized. Here, we developed a probabilistic approach to infer genome rearrangement rate parameters using an Approximate Bayesian Computation (ABC) framework. We developed two genome rearrangement models, a basic model, which accounts for genomic changes in gene order, and a more sophisticated one which also accounts for changes in chromosome number. We characterized the ABC inference accuracy using simulations and applied our methodology to both prokaryotic and eukaryotic empirical datasets. Knowledge of genome-rearrangement rates can help elucidate their role in evolution as well as help simulate genomes with evolutionary dynamics that reflect empirical genomes.
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Affiliation(s)
- Asher Moshe
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Elya Wygoda
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Noa Ecker
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Gil Loewenthal
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Oren Avram
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Omer Israeli
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Einat Hazkani-Covo
- Department of Natural and Life Sciences, Open University of Israel, Ra'anana, Israel
| | - Itsik Pe’er
- Department of Computer Science, Columbia University, New York, USA
| | - Tal Pupko
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
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Probert WJM, Sauter R, Pickles M, Cori A, Bell-Mandla NF, Bwalya J, Abeler-Dörner L, Bock P, Donnell DJ, Floyd S, Macleod D, Piwowar-Manning E, Skalland T, Shanaube K, Wilson E, Yang B, Ayles H, Fidler S, Hayes RJ, Fraser C. Projected outcomes of universal testing and treatment in a generalised HIV epidemic in Zambia and South Africa (the HPTN 071 [PopART] trial): a modelling study. Lancet HIV 2022; 9:e771-e780. [PMID: 36332654 PMCID: PMC9646978 DOI: 10.1016/s2352-3018(22)00259-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 09/06/2022] [Accepted: 09/07/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND The long-term impact of universal home-based testing and treatment as part of universal testing and treatment (UTT) on HIV incidence is unknown. We made projections using a detailed individual-based model of the effect of the intervention delivered in the HPTN 071 (PopART) cluster-randomised trial. METHODS In this modelling study, we fitted an individual-based model to the HIV epidemic and HIV care cascade in 21 high prevalence communities in Zambia and South Africa that were part of the PopART cluster-randomised trial (intervention period Nov 1, 2013, to Dec 31, 2017). The model represents coverage of home-based testing and counselling by age and sex, delivered as part of the trial, antiretroviral therapy (ART) uptake, and any changes in national guidelines on ART eligibility. In PopART, communities were randomly assigned to one of three arms: arm A received the full PopART intervention for all individuals who tested positive for HIV, arm B received the intervention with ART provided in accordance with national guidelines, and arm C received standard of care. We fitted the model to trial data twice using Approximate Bayesian Computation, once before data unblinding and then again after data unblinding. We compared projections of intervention impact with observed effects, and for four different scenarios of UTT up to Jan 1, 2030 in the study communities. FINDINGS Compared with standard of care, a 51% (95% credible interval 40-60) reduction in HIV incidence is projected if the trial intervention (arms A and B combined) is continued from 2020 to 2030, over and above a declining trend in HIV incidence under standard of care. INTERPRETATION A widespread and continued commitment to UTT via home-based testing and counselling can have a substantial effect on HIV incidence in high prevalence communities. FUNDING National Institute of Allergy and Infectious Diseases, US President's Emergency Plan for AIDS Relief, International Initiative for Impact Evaluation, Bill & Melinda Gates Foundation, National Institute on Drug Abuse, and National Institute of Mental Health.
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Affiliation(s)
- William J M Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Rafael Sauter
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Michael Pickles
- Medical Research Council Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Anne Cori
- Medical Research Council Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Nomtha F Bell-Mandla
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | | | - Lucie Abeler-Dörner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Peter Bock
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | | | - Sian Floyd
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - David Macleod
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | - Ethan Wilson
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Blia Yang
- Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Helen Ayles
- Zambart, University of Zambia, Lusaka, Zambia; Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Sarah Fidler
- Department of Infectious Disease, Imperial College London, London, UK; NIHR Imperial Biomedical Research Centre, London, UK
| | - Richard J Hayes
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
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35
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Ciganda D, Todd N. Demographic models of the reproductive process: Past, interlude, and future. POPULATION STUDIES 2022; 76:495-513. [PMID: 34486942 DOI: 10.1080/00324728.2021.1959943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
After 30 years of active development, mechanistic models of the reproductive process nearly stopped attracting scholarly interest in the early 1980s. In the following decades, fertility research continued to thrive, relying on solid descriptive work and detailed analysis of micro-level data. The absence of systematic modelling efforts, however, has also made the field more fragmented, with empirical research, theory building, and forecasting advancing along largely disconnected channels. In this paper we outline some of the drivers of this process, from the popularization of user-friendly statistical software to the limitations of early family building models. We then describe a series of developments in computational modelling and statistical computing that can contribute to the emergence of a new generation of mechanistic models. Finally, we introduce a concrete example of this new kind of model, and show how they can be used to formulate and test theories coherently and make informed projections.
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36
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Tominaga A, Yoshikawa N, Matsui M, Nagata N, Sato Y. The emergence of a cryptic lineage and cytonuclear discordance through past hybridization in the Japanese fire-bellied newt, Cynops pyrrhogaster (Amphibia: Urodela). Biol J Linn Soc Lond 2022. [DOI: 10.1093/biolinnean/blac120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Abstract
Discrepancies in geographic variation patterns between nuclear DNA and mitochondrial DNA (mtDNA) are the result of the complicated differentiation processes in organisms and are the key to understanding their true evolutionary processes. The genetic differentiation of the northern and Southern-Izu lineages of the Japanese newt, Cynops pyrrhogaster, was investigated through their single nucleotide polymorphism variations obtained via multiplexed ISSR genotyping by sequencing (MIG-seq). We found three genetic groups (Tohoku, N-Kanto and S-Kanto), that were not detected by mtDNA variations, in the northern lineage. N-Kanto has intermediate genetic characteristics between Tohoku and S-Kanto. The genetic groups are now moderately isolated from each other and have unique genetic characteristics. An estimation of the evolutionary history using the approximate Bayesian computation (ABC) approach suggested that Tohoku diverged from the common ancestor of S-Kanto and S-Izu. Then, S-Kanto and S-Izu split, and the recent hybridization between Tohoku and S-Kanto gave rise to N-Kanto. The origin of N-Kanto through the hybridization is relatively young and seems to be related to changes in the distributions of Tohoku and S-Kanto as a result of climatic oscillation in the Pleistocene. We conclude that the mitochondrial genome of S-Kanto was captured in Tohoku and that the original mitochondrial genome of Tohoku was entirely removed through hybridization.
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Affiliation(s)
- Atsushi Tominaga
- Faculty of Education, University of the Ryukyus , Senbaru 1, Nishihara, Okinawa 903-0213 , Japan
| | - Natsuhiko Yoshikawa
- National Museum of Nature and Science , 4-1-1 Amakubo, Tsukuba, Ibaraki 305 - 0005 , Japan
| | - Masafumi Matsui
- Graduate School of Human and Environmental Studies, Kyoto University , Yoshida Nihonmatsu-cho, Sakyo, Kyoto 606 - 8501 , Japan
| | - Nobuaki Nagata
- National Museum of Nature and Science , 4-1-1 Amakubo, Tsukuba, Ibaraki 305 - 0005 , Japan
| | - Yukuto Sato
- Faculty of Medicine, University of the Ryukyus , Uehara 207, Nishihara, Okinawa 903 - 0215 , Japan
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Hinch R, Panovska-Griffiths J, Probert WJM, Ferretti L, Wymant C, Di Lauro F, Baya N, Ghafari M, Abeler-Dörner L, Fraser C. Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022. [PMID: 35965459 DOI: 10.6084/m9.figshare.c.6067650] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- Robert Hinch
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jasmina Panovska-Griffiths
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Queen's College, and, University of Oxford, Oxford, UK
| | - William J M Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Francesco Di Lauro
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nikolas Baya
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mahan Ghafari
- Department of Zoology, University of Oxford, Oxford, UK
| | - Lucie Abeler-Dörner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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38
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Hinch R, Panovska-Griffiths J, Probert WJM, Ferretti L, Wymant C, Di Lauro F, Baya N, Ghafari M, Abeler-Dörner L, Fraser C. Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210304. [PMID: 35965459 PMCID: PMC9376717 DOI: 10.1098/rsta.2021.0304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 02/22/2022] [Indexed: 05/04/2023]
Abstract
The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- Robert Hinch
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jasmina Panovska-Griffiths
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Queen's College, University of Oxford, Oxford, UK
| | - William J. M. Probert
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Luca Ferretti
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Chris Wymant
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Francesco Di Lauro
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Nikolas Baya
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mahan Ghafari
- Department of Zoology, University of Oxford, Oxford, UK
| | - Lucie Abeler-Dörner
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | | | - Christophe Fraser
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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39
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Aushev A, Pesonen H, Heinonen M, Corander J, Kaski S. Likelihood-free inference with deep Gaussian processes. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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40
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Galvis JA, Corzo CA, Prada JM, Machado G. Modeling between-farm transmission dynamics of porcine epidemic diarrhea virus: Characterizing the dominant transmission routes. Prev Vet Med 2022; 208:105759. [PMID: 36155353 DOI: 10.1016/j.prevetmed.2022.105759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 09/06/2022] [Accepted: 09/13/2022] [Indexed: 10/31/2022]
Abstract
The role of transportation vehicles, pig movement between farms, proximity to infected premises, and feed deliveries has not been fully considered in the dissemination dynamics of porcine epidemic diarrhea virus (PEDV). This has limited efforts for disease prevention, control and elimination restricting the development of risk-based resource allocation to the most relevant modes of PEDV dissemination. Here, we modeled nine pathways of between-farm transmission represented by a contact network of pig movements between sites, farm-to-farm proximity (local transmission), four distinct contact networks of transportation vehicles (trucks that transport pigs from farm-to-farm and farm-to-markets, as well as trucks transporting feed and staff), the volume of animal by-products in feed diets (e.g., fat and meat-and-bone-meal) to reproduce PEDV transmission dynamics. The model was calibrated in space and time with weekly PEDV outbreaks. We investigated the model performance to identify outbreak locations and the contribution of each route in the dissemination of PEDV. The model estimated that 42.7% of the infections in sow farms were related to vehicles transporting feed, 34.5% of infected nurseries were associated with vehicles transporting pigs between farms, and for both farm types, local transmission or pig movements were the next most relevant transmission routes. On the other hand, finishers were most often (31.4%) infected via local transmission, followed by the vehicles transporting feed and pigs between farms. Feed ingredients did not significantly improve model calibration metrics, sensitivity, and specificity; therefore, it was considered to have a negligible contribution in the dissemination of PEDV. The proposed modeling framework provides an evaluation of PEDV transmission dynamics, ranking the most important routes of PEDV dissemination and granting the swine industry valuable information to focus efforts and resources on the most important transmission routes.
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Affiliation(s)
- Jason A Galvis
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA
| | - Cesar A Corzo
- Veterinary Population Medicine Department, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA
| | - Joaquín M Prada
- School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Gustavo Machado
- Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA.
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41
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Imfeld TS, Barker FK. Songbirds of the Americas show uniform morphological evolution despite heterogeneous diversification. J Evol Biol 2022; 35:1335-1351. [PMID: 36057939 DOI: 10.1111/jeb.14084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 06/17/2022] [Accepted: 07/19/2022] [Indexed: 11/30/2022]
Abstract
Studying the relationship between diversification and functional trait evolution among broadly co-occurring clades can shed light on interactions between ecology and evolutionary history. However, evidence from many studies is compromised because of their focus on overly broad geographic or narrow phylogenetic scales. We addressed these limitations by studying 46 independent, biogeographically delimited clades of songbirds that dispersed from the Eastern Hemisphere into the Americas and assessed (1) whether diversification has varied through time and/or among clades within this assemblage, (2) the extent of heterogeneity in clade-specific morphological trait disparity and (3) whether morphological disparity among these clades is consistent with a uniform diversification model. We found equivalent support for constant rates birth-death and density-dependent speciation processes, with notable outliers having significantly fewer or more species than expected given their age. We also found substantial variation in morphological disparity among these clades, but that variation was broadly consistent with uniform evolutionary rates, despite the existence of diversification outliers. These findings indicate relatively continuous, ongoing morphological diversification, arguing against conceptual models of adaptive radiation in these continental clades. Additionally, they suggest surprisingly consistent diversification among the majority of these clades, despite tremendous variance in colonization history, habitat valences and trophic specializations that exist among continental clades of birds.
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Affiliation(s)
- Tyler S Imfeld
- Department of Biology, Regis University, Denver, Colorado, USA
| | - F Keith Barker
- Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota, USA.,Bell Museum, University of Minnesota, St. Paul, Minnesota, USA
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42
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The fast invasion of Europe by the box tree moth: an additional example coupling multiple introduction events, bridgehead effects and admixture events. Biol Invasions 2022. [DOI: 10.1007/s10530-022-02887-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractIdentifying the invasion routes of non-native species is crucial to understanding invasions and customizing management strategies. The box tree moth, Cydalima perspectalis, is native to Asia and was recently accidentally introduced into Europe as a result of the ornamental plant trade. Over the last 15 years, it has spread across the continent and has reached the Caucasus and Iran. It is threatening Buxus trees in both urban areas and forests. To investigate the species’ invasion routes, native and invasive box tree moth populations were sampled, and moth’s genetic diversity and structure were compared using microsatellite markers. Our approximate Bayesian computation analyses strongly suggest that invasion pathways were complex. Primary introductions originating from eastern China probably occurred independently twice in Germany and once in the Netherlands. There were also possibly bridgehead effects, where at least three invasive populations may have served as sources for other invasive populations within Europe, with indication of admixture between the two primary invasive populations. The bridgehead populations were likely those in the countries that play a major role in the ornamental plant trade in Europe, notably Germany, the Netherlands, and Italy. All these invasion processes likely facilitated its fast expansion across Europe and illustrate the role played by the ornamental plant trade not only in the moth’s introduction from China but also in the species’ spread across Europe, leading to an invasion with a complex pattern.
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43
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Piccioni F, Casenave C, Baragatti M, Cloez B, Vinçon-Leite B. Calibration of a complex hydro-ecological model through approximate Bayesian computation and random forest combined with sensitivity analysis. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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44
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Chowdhury A, Egodawatta P. Automatic model calibration of combined hydrologic, hydraulic and stormwater quality models using approximate Bayesian computation. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2022; 86:321-332. [PMID: 35906910 DOI: 10.2166/wst.2022.207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A range of automatic model calibration techniques are used in water engineering practice. However, use of these techniques can be problematic due to the requirement of evaluating the likelihood function. This paper presents an innovative approach for overcoming this issue using a calibration framework developed based on Approximate Bayesian Computation (ABC) technique. Use of ABC in automatic model calibration was undertaken for a combined urban hydrologic, hydraulic and stormwater quality model. The simulated runoff hydrograph and total suspended solid (TSS) pollutograph were compared with observed data for multiple events from three different catchments, and found to be within 95% confidence intervals of the simulated results. The R programmed model was validated by comparing simulated flow with similar commercially available modeling software, MIKE URBAN output determined using mean value of parameters obtained from the calibration exercise, and performed well by satisfying statistical criteria's such as coefficient of determination (CD), root mean square error (RMSE) and maximum error (ME). The developed framework is useful for automatic calibration and uncertainty estimation using ABC approach in complex hydrologic, hydraulic and stormwater quality models with multi-input-output systems.
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Affiliation(s)
- Anupam Chowdhury
- Department of Civil Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh E-mail:
| | - Prasanna Egodawatta
- Science and Engineering Faculty, Queensland University of Technology (QUT), GPO Box 2434, Brisbane 4001, Queensland, Australia
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45
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Gallet R, Di Mattia J, Ravel S, Zeddam JL, Vitalis R, Michalakis Y, Blanc S. Gene Copy Number Variations at the Within-Host Population Level Modulate Gene Expression in a Multipartite Virus. Virus Evol 2022; 8:veac058. [PMID: 35799884 PMCID: PMC9255600 DOI: 10.1093/ve/veac058] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/02/2022] [Accepted: 06/21/2022] [Indexed: 11/12/2022] Open
Abstract
Multipartite viruses have a segmented genome, with each segment encapsidated separately. In all multipartite virus species for which the question has been addressed, the distinct segments reproducibly accumulate at a specific and host-dependent relative frequency, defined as the ‘genome formula’. Here, we test the hypothesis that the multipartite genome organization facilitates the regulation of gene expression via changes of the genome formula and thus via gene copy number variations. In a first experiment, the faba bean necrotic stunt virus (FBNSV), whose genome is composed of eight DNA segments each encoding a single gene, was inoculated into faba bean or alfalfa host plants, and the relative concentrations of the DNA segments and their corresponding messenger RNAs (mRNAs) were monitored. In each of the two host species, our analysis consistently showed that the genome formula variations modulate gene expression, the concentration of each genome segment linearly and positively correlating to that of its cognate mRNA but not of the others. In a second experiment, twenty parallel FBNSV lines were transferred from faba bean to alfalfa plants. Upon host switching, the transcription rate of some genome segments changes, but the genome formula is modified in a way that compensates for these changes and maintains a similar ratio between the various viral mRNAs. Interestingly, a deep-sequencing analysis of these twenty FBNSV lineages demonstrated that the host-related genome formula shift operates independently of DNA-segment sequence mutation. Together, our results indicate that nanoviruses are plastic genetic systems, able to transiently adjust gene expression at the population level in changing environments, by modulating the copy number but not the sequence of each of their genes.
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Affiliation(s)
- Romain Gallet
- PHIM, Univ Montpellier, INRAE, CIRAD, IRD, Institut Agro, Montpellier, France
- CBGP, Univ Montpellier, INRAE, CIRAD, IRD, Institut Agro, Montpellier, France
| | - Jérémy Di Mattia
- PHIM, Univ Montpellier, INRAE, CIRAD, IRD, Institut Agro, Montpellier, France
| | - Sébastien Ravel
- PHIM, Univ Montpellier, INRAE, CIRAD, IRD, Institut Agro, Montpellier, France
| | - Jean-Louis Zeddam
- PHIM, Univ Montpellier, INRAE, CIRAD, IRD, Institut Agro, Montpellier, France
| | - Renaud Vitalis
- CBGP, Univ Montpellier, INRAE, CIRAD, IRD, Institut Agro, Montpellier, France
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Berec L, Smyčka J, Levínský R, Hromádková E, Šoltés M, Šlerka J, Tuček V, Trnka J, Šmíd M, Zajíček M, Diviák T, Neruda R, Vidnerová P. Delays, Masks, the Elderly, and Schools: First Covid-19 Wave in the Czech Republic. Bull Math Biol 2022; 84:75. [PMID: 35726074 PMCID: PMC9208712 DOI: 10.1007/s11538-022-01031-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 05/16/2022] [Indexed: 11/29/2022]
Abstract
Running across the globe for nearly 2 years, the Covid-19 pandemic keeps demonstrating its strength. Despite a lot of understanding, uncertainty regarding the efficiency of interventions still persists. We developed an age-structured epidemic model parameterized with epidemiological and sociological data for the first Covid-19 wave in the Czech Republic and found that (1) starting the spring 2020 lockdown 4 days earlier might prevent half of the confirmed cases by the end of lockdown period, (2) personal protective measures such as face masks appear more effective than just a realized reduction in social contacts, (3) the strategy of sheltering just the elderly is not at all effective, and (4) leaving schools open is a risky strategy. Despite vaccination programs, evidence-based choice and timing of non-pharmaceutical interventions remains an effective weapon against the Covid-19 pandemic.
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Affiliation(s)
- Luděk Berec
- Department of Mathematics, Centre for Mathematical Biology, Faculty of Science, University of South Bohemia, Branišovská 1760, 37005, České Budějovice, Czech Republic. .,Czech Academy of Sciences, Biology Centre, Institute of Entomology, Branišovská 31, 37005, České Budějovice, Czech Republic. .,Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.
| | - Jan Smyčka
- Center for Theoretical Studies, Husova 4, 11000, Prague 1, Czech Republic
| | - René Levínský
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,CERGE-EI, Politických vězňů 7, 11121, Prague 1, Czech Republic
| | - Eva Hromádková
- CERGE-EI, Politických vězňů 7, 11121, Prague 1, Czech Republic
| | - Michal Šoltés
- CERGE-EI, Politických vězňů 7, 11121, Prague 1, Czech Republic
| | - Josef Šlerka
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,New Media Studies, Faculty of Arts, Charles University, Na Příkopě 29, 11000, Prague 1, Czech Republic
| | - Vít Tuček
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Department of Mathematics, University of Zagreb, Bijenička 30, 10000, Zagreb, Croatia
| | - Jan Trnka
- Department of Biochemistry, Cell and Molecular Biology, Third Faculty of Medicine, Charles University, Ruská 87, 100 00, Prague 10, Czech Republic
| | - Martin Šmíd
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200, Prague 8, Czech Republic
| | - Milan Zajíček
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Information Theory and Automation, Pod Vodárenskou věží 4, 18200, Prague 8, Czech Republic
| | - Tomáš Diviák
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Department of Criminology, School of Social Sciences, University of Manchester, Oxford Rd, Manchester, UK
| | - Roman Neruda
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200, Prague 8, Czech Republic
| | - Petra Vidnerová
- Centre for Modelling of Biological and Social Processes, Na břehu 497/15, 19000, Prague 9, Czech Republic.,Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou věží 2, 18200, Prague 8, Czech Republic
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47
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Alcala N, Rosenberg NA. Mathematical constraints on FST: multiallelic markers in arbitrarily many populations. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200414. [PMID: 35430885 PMCID: PMC9014193 DOI: 10.1098/rstb.2020.0414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/23/2021] [Indexed: 11/12/2022] Open
Abstract
Interpretations of values of the FST measure of genetic differentiation rely on an understanding of its mathematical constraints. Previously, it has been shown that FST values computed from a biallelic locus in a set of multiple populations and FST values computed from a multiallelic locus in a pair of populations are mathematically constrained as a function of the frequency of the allele that is most frequent across populations. We generalize from these cases to report here the mathematical constraint on FST given the frequency M of the most frequent allele at a multiallelic locus in a set of multiple populations. Using coalescent simulations of an island model of migration with an infinitely-many-alleles mutation model, we argue that the joint distribution of FST and M helps in disentangling the separate influences of mutation and migration on FST. Finally, we show that our results explain a puzzling pattern of microsatellite differentiation: the lower FST in an interspecific comparison between humans and chimpanzees than in the comparison of chimpanzee populations. We discuss the implications of our results for the use of FST. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.
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Affiliation(s)
- Nicolas Alcala
- Rare Cancers Genomics Team (RCG), Genetic Epidemiology Branch (GEM), International Agency for Research on Cancer/World Health Organization, Lyon 69008, France
| | - Noah A. Rosenberg
- Department of Biology, Stanford University, Stanford, CA 94305-5020, USA
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48
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Oyanedel R, Gelcich S, Mathieu E, Milner-Gulland EJ. A dynamic simulation model to support reduction in illegal trade within legal wildlife markets. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2022; 36:e13814. [PMID: 34342038 DOI: 10.1111/cobi.13814] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 07/13/2021] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
Sustainable wildlife trade is critical for biodiversity conservation, livelihoods, and food security. Regulatory frameworks are needed to secure these diverse benefits of sustainable wildlife trade. However, regulations limiting trade can backfire, sparking illegal trade if demand is not met by legal trade alone. Assessing how regulations affect wildlife market participants' incentives is key to controlling illegal trade. Although much research has assessed how incentives at both the harvester and consumer ends of markets are affected by regulations, little has been done to understand the incentives of traders (i.e., intermediaries). We built a dynamic simulation model to support reduction in illegal wildlife trade within legal markets by focusing on incentives traders face to trade legal or illegal products. We used an Approximate Bayesian Computation approach to infer illegal trading dynamics and parameters that might be unknown (e.g., price of illegal products). We showcased the utility of the approach with a small-scale fishery case study in Chile, where we disentangled within-year dynamics of legal and illegal trading and found that the majority (∼77%) of traded fish is illegal. We utilized the model to assess the effect of policy interventions to improve the fishery's sustainability and explore the trade-offs between ecological, economic, and social goals. Scenario simulations showed that even significant increases (over 200%) in parameters proxying for policy interventions enabled only moderate improvements in ecological and social sustainability of the fishery at substantial economic cost. These results expose how unbalanced trader incentives are toward trading illegal over legal products in this fishery. Our model provides a novel tool for promoting sustainable wildlife trade in data-limited settings, which explicitly considers traders as critical players in wildlife markets. Sustainable wildlife trade requires incentivizing legal over illegal wildlife trade and consideration of the social, ecological, and economic impacts of interventions.
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Affiliation(s)
- Rodrigo Oyanedel
- Interdisciplinary Centre for Conservation Science, Department of Zoology, University of Oxford, Oxford, UK
| | - Stefan Gelcich
- Instituto Milenio en Socio-Ecología Costera (SECOS), Santiago, Chile
- Center of Applied Ecology and Sustainability, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Emile Mathieu
- Department of Statistics, University of Oxford, Oxford, UK
| | - E J Milner-Gulland
- Interdisciplinary Centre for Conservation Science, Department of Zoology, University of Oxford, Oxford, UK
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Martinez K, Brown G, Pankavich S. Spatially-heterogeneous embedded stochastic SEIR models for the 2014–2016 Ebola outbreak in West Africa. Spat Spatiotemporal Epidemiol 2022; 41:100505. [DOI: 10.1016/j.sste.2022.100505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 12/03/2021] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
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50
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Vlasta T, Münzbergová Z. Genetic variation in lowland and mountain populations of Tofieldia calyculata and their ability to survive within low levels of genetic diversity. CONSERV GENET 2022. [DOI: 10.1007/s10592-022-01439-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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