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9 results on '"Schuyler W. Liphardt"'

1. Reassortment Between Divergent Strains of Camp Ripley Virus (Hantaviridae) in the Northern Short-Tailed Shrew (Blarina brevicauda)

Author

Schuyler W. Liphardt, Hae Ji Kang, Satoru Arai, Se Hun Gu, Joseph A. Cook, and Richard Yanagihara

Subjects
Hantaviridae, host switching, reassortment, viral evolution, viral phylogenetics, Microbiology, QR1-502
Abstract

Genomic reassortment of segmented RNA virus strains is an important evolutionary mechanism that can generate novel viruses with profound effects on human and animal health, such as the H1N1 influenza pandemic in 2009 arising from reassortment of two swine influenza viruses. Reassortment is not restricted to influenza virus and has been shown to occur in members of the order Bunyavirales. The majority of reassortment events occurs between closely related lineages purportedly due to molecular constraints during viral packaging. In the original report of Camp Ripley virus (RPLV), a newfound hantavirus in the northern short-tailed shrew (Blarina brevicauda), phylogenetic incongruence between different genomic segments suggested reassortment. We have expanded sampling to include RPLV sequences amplified from archival tissues of 36 northern short-tailed shrews collected in 12 states (Arkansas, Iowa, Kansas, Maryland, Massachusetts, Michigan, Minnesota, New Hampshire, Ohio, Pennsylvania, Virginia, Wisconsin), and one southern short-tailed shrew (Blarina carolinensis) from Florida, within the United States. Using Bayesian phylogenetic analysis and Graph-incompatibility-based Reassortment Finder, we identified multiple instances of reassortment that spanned the Hantaviridae phylogenetic tree, including three highly divergent, co-circulating lineages of the M segment that have reassorted with a conserved L segment in multiple populations of B. brevicauda. In addition to identifying the first known mobatvirus-like M-segment sequences from a soricid host and only the second from a eulipotyphlan mammal, our results suggest that reassortment may be common between divergent virus strains and provide strong justification for expanded spatial, temporal, and taxonomic analyses of segmented viruses.

Published
2020
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2. Microbiomes From Biorepositories? 16S rRNA Bacterial Amplicon Sequencing of Archived and Contemporary Intestinal Samples of Wild Mammals (Eulipotyphla: Soricidae)

Author

Stephen E. Greiman, Joseph A. Cook, Timothy Odom, Katelyn Cranmer, Schuyler W. Liphardt, Damian M. Menning, Sarah A. Sonsthagen, and Sandra L. Talbot

Subjects
Museum-collections, microbiome, 16S rRNA, Sorex, contemporary, Evolution, QH359-425, Ecology, QH540-549.5
Abstract

Interest in gut microbial community composition has exploded recently as a result of the increasing ability to characterize these organisms and a growing understanding of their role in host fitness. New technologies, such as next generation amplicon (16S rRNA) sequencing, have enabled identification of bacterial communities from samples of diverse origin (e.g., fecal, skin, genital, environmental, etc.). Relatively little work, however, has explored the feasibility of utilizing historical samples (e.g., museum archived samples) of varying age, quality, and preservation type. Because natural history collections span multiple decades, these biorepositories have the potential to provide fundamental historical baselines to measure and better understand biodiversity on a changing planet. Utilizing even a small proportion of museum specimens could provide a means of sampling past microbial communities, allowing for direct comparison to contemporary communities and more complete understanding of dynamic shifts through time. We examined the feasibility of obtaining 16S rRNA amplicon microbiome data from whole gastrointestinal tracts (GIs) of shrews of varying age and preservation method, including 5 freshly collected shrew GIs immediately fixed in liquid nitrogen (LN2), 10 ten-year old shrew GIs frozen at −20°C (whole animal), and 10 shrews of varying ages (4 from 1968, 1 from 1980, 1 from 2001, 1 from 2004, 1 from 2007, 1 from 2011 and 2 from 2013) fixed and stored whole in 70% ethanol. Not surprisingly, results of 16S rDNA amplicon sequencing reveal significantly different bacterial communities between different preservation techniques and age of samples. Ten-year old frozen samples had bacterial communities most similar to freshly collected (LN2) samples, while the bacterial communities of both were significantly different from the 70% ethanol preserved samples of various ages. Amongst those preserved in 70% ethanol, age of samples also influenced bacterial community composition. Additionally, we compare results of OTU based and ASV based analyses. Looking ahead, field collectors and museums should develop and adopt best practices related to frozen preservation to ensure adequate material for future microbiome investigations.

Published
2020
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3. Complex History of Codiversification and Host Switching of a Newfound Soricid-Borne Orthohantavirus in North America

Author

Schuyler W. Liphardt, Hae Ji Kang, Laurie J. Dizney, Luis A. Ruedas, Joseph A. Cook, and Richard Yanagihara

Subjects
hantaviridae, orthohantavirus, hantavirus, shrew, viral evolution, codiversification, Microbiology, QR1-502
Abstract

Orthohantaviruses are tightly linked to the ecology and evolutionary history of their mammalian hosts. We hypothesized that in regions with dramatic climate shifts throughout the Quaternary, orthohantavirus diversity and evolution are shaped by dynamic host responses to environmental change through processes such as host isolation, host switching, and reassortment. Jemez Springs virus (JMSV), an orthohantavirus harbored by the dusky shrew (Sorex monticola) and five close relatives distributed widely in western North America, was used to test this hypothesis. Total RNAs, extracted from liver or lung tissue from 164 shrews collected from western North America during 1983−2007, were analyzed for orthohantavirus RNA by reverse transcription polymerase chain reaction (RT-PCR). Phylogenies inferred from the L-, M-, and S-segment sequences of 30 JMSV strains were compared with host mitochondrial cytochrome b. Viral clades largely corresponded to host clades, which were primarily structured by geography and were consistent with hypothesized post-glacial expansion. Despite an overall congruence between host and viral gene phylogenies at deeper scales, phylogenetic signals were recovered that also suggested a complex pattern of host switching and at least one reassortment event in the evolutionary history of JMSV. A fundamental understanding of how orthohantaviruses respond to periods of host population expansion, contraction, and secondary host contact is the key to establishing a framework for both more comprehensive understanding of orthohantavirus evolutionary dynamics and broader insights into host−pathogen systems.

Published
2019
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4. An overview of the mammals of the Gila region, New Mexico

Author

Jason Malaney, Travis W. Perry, Joseph A. Cook, Schuyler W. Liphardt, Amanda K. Jones, and Jonathan L. Dunnum

Subjects
Neotoma albigula, Thomomys bottae, Geography, biology, Ecology, Notiosorex crawfordi, Range (biology), Perognathus, Animal Science and Zoology, Leptonycteris, Species richness, biology.organism_classification, Zapus
Abstract

A study of the mammals of the Gila region of New Mexico was conducted from 2012 through 2020, with 2,919 voucher specimens collected through fieldwork and collaborations with commercial trappers, in addition to data from camera traps, review of major holdings at 46 museums ( n = 12,505 georeferenced specimens), and literature review. Specimens cover a 170-year span, dating back to 1850 and were unevenly distributed spatially and temporally across the Gila region. Most areas were very poorly represented and when summed across all mammal species, ranged from 0.02 to 3.7 specimens per km 2 . The survey documented 108 species (104 now extant) for the region. High species richness, greater than that reported for 38 states in the United States, is likely due to the juxtaposition of multiple biomes in the Gila, including the Sonoran, Chihuahuan, and Great Basin deserts, the Rocky Mountains and Sierra Madre Occidental, and nearby “sky islands’’ of the Southwest. Two species, Leptonycteris yerbabuenae and Zapus luteus, are documented for the first time from the study area. Expansions of the known range of these species , and Sciurus arizonensis are described from specimen and camera data. Preliminary phylogeographic studies of four species ( Notiosorex crawfordi, Neotoma albigula, Perognathus flavus, and Thomomys bottae ) using the mitochondrial cytochrome- b gene reveal the dynamic biogeographic history of the region and reinforce how landscape complexity and climate change have jointly contributed to diversification and thus high mammalian diversity in the region.

Published
2021
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5. Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples

Author

John W. Terbot, Parul Johri, Schuyler W. Liphardt, Vivak Soni, Susanne P. Pfeifer, Brandon S. Cooper, Jeffrey M. Good, and Jeffrey D. Jensen

Subjects
Virology, Immunology, Genetics, Parasitology, Molecular Biology, Microbiology
Abstract

Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model—mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization—and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis.

Published
2023
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6. Leveraging natural history biorepositories as a global, decentralized, pathogen surveillance network

Author

Cody W. Thompson, M. Alejandra Camacho, Stephen E. Greiman, A. Townsend Peterson, Marcelo Weksler, Manuela Londoño-Gaviria, Bruce Struminger, John M. Bates, Nicté Ordóñez Garza, Carlos Carrion Bonilla, María Laura Martin, Isabel Constable, Eric P. Hoberg, Fernando Torres-Pérez, Enrique P. Lessa, Jocelyn P. Colella, Santiago F. Burneo, Holly L. Lutz, Joseph A. Cook, Camila C. Ribas, Elizabeth Losos, Guillermo D’Elía, Jonathan L. Dunnum, and Schuyler W. Liphardt

Subjects
0106 biological sciences, Epidemiology, Biosecurity, Wildlife, Pathology and Laboratory Medicine, Global Health, 01 natural sciences, Communicable Diseases, Emerging, Community Networks, Medical Conditions, Public health surveillance, Zoonoses, Global health, Medicine and Health Sciences, Public and Occupational Health, Public Health Surveillance, Biology (General), Biological Specimen Banks, 0303 health sciences, Communicable disease, Ecology, Geography, Eukaryota, Biodiversity, Biobank, Infectious Diseases, Public Health, Pathogens, Opinion, Infectious Disease Control, QH301-705.5, Immunology, Animals, Wild, Disaster Planning, Disease Surveillance, 010603 evolutionary biology, Microbiology, Zoonotic Pathogens, Risk Assessment, 03 medical and health sciences, Virology, Global network, Genetics, Animals, Humans, Molecular Biology, Environmental planning, Pandemics, 030304 developmental biology, Animal Pathogens, SARS-CoV-2, Ecology and Environmental Sciences, Organisms, Biology and Life Sciences, COVID-19, RC581-607, Biorepository, Emerging Infectious Diseases, Virtual community of practice, Medical Countermeasures, Infectious Disease Surveillance, Communicable Disease Control, Parasitology, Business, Immunologic diseases. Allergy, Zoology
Abstract

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic reveals a major gap in global biosecurity infrastructure: a lack of publicly available biological samples representative across space, time, and taxonomic diversity. The shortfall, in this case for vertebrates, prevents accurate and rapid identification and monitoring of emerging pathogens and their reservoir host(s) and precludes extended investigation of ecological, evolutionary, and environmental associations that lead to human infection or spillover. Natural history museum biorepositories form the backbone of a critically needed, decentralized, global network for zoonotic pathogen surveillance, yet this infrastructure remains marginally developed, underutilized, underfunded, and disconnected from public health initiatives. Proactive detection and mitigation for emerging infectious diseases (EIDs) requires expanded biodiversity infrastructure and training (particularly in biodiverse and lower income countries) and new communication pipelines that connect biorepositories and biomedical communities. To this end, we highlight a novel adaptation of Project ECHO’s virtual community of practice model: Museums and Emerging Pathogens in the Americas (MEPA). MEPA is a virtual network aimed at fostering communication, coordination, and collaborative problem-solving among pathogen researchers, public health officials, and biorepositories in the Americas. MEPA now acts as a model of effective international, interdisciplinary collaboration that can and should be replicated in other biodiversity hotspots. We encourage deposition of wildlife specimens and associated data with public biorepositories, regardless of original collection purpose, and urge biorepositories to embrace new specimen sources, types, and uses to maximize strategic growth and utility for EID research. Taxonomically, geographically, and temporally deep biorepository archives serve as the foundation of a proactive and increasingly predictive approach to zoonotic spillover, risk assessment, and threat mitigation.

Published
2021

7. Variability in the analysis of a single neuroimaging dataset by many teams

Author

Richard C. Reynolds, Jeremy Hogeveen, Joseph T. McGuire, Vittorio Iacovella, Gregory R. Samanez-Larkin, Cemal Koba, Paolo Avesani, Mikella A. Green, Margaret L. Schlichting, Claudio Toro-Serey, Tristan Glatard, Phui Cheng Lim, Emily A. Yearling, Simon B. Eickhoff, Katherine L. Bottenhorn, Susanne Weis, Andrew Erhart, Doris Pischedda, Mauricio R. Delgado, William A. Cunningham, Robert Langner, Matthew B. Wall, Amr Eed, Jorge Moll, Jeffrey B. Dennison, Wouter D. Weeda, Annabel B. Losecaat Vermeer, Anthony C. Juliano, Felix Hoffstaedter, Julia A. Camilleri, Nadège Bault, Cristian Buc Calderon, Xu Zhang, Gustav Tinghög, Vuong Truong, Leah Bakst, Stephan Heunis, Bertrand Thirion, Colin F. Camerer, Bradley C. Love, Tom Verguts, Luca Cecchetti, Patricia A. Reuter-Lorenz, Camille Maumet, Gabrielle Herman, Giacomo Handjaras, Michalis Kassinopoulos, Anthony Romyn, Xiangzhen Kong, Michael Notter, Nina Lauharatanahirun, Claire Donnat, Norberto Malpica, Jelle J. Goeman, Kamalaker Dadi, Michael Mack, Joke Durnez, Alberto De Luca, Margaret A. Sheridan, Shruti Ray, Colin Hawco, Enrico Glerean, J. Paul Hamilton, Qiang Shen, Sebastian Bobadilla-Suarez, Andrea Leo, David Wisniewski, Ayse Ilkay Isik, Lei Zhang, Bharat B. Biswal, Rotem Botvinik-Nezer, Flora Li, Bronson Harry, Andrew Jahn, Elise Lesage, Jean M. Vettel, Marco Barilari, Adrian I. Onicas, Susan Holmes, Khoi Vo, Aahana Bajracharya, Matthew Hughes, Rui Yuan, Nuri Erkut Kucukboyaci, Remi Gau, Sangsuk Yoon, Hongmi Lee, Alexandru D. Iordan, John Thorp, Russell A. Poldrack, Anna Dreber, Joshua E. Zosky, Dylan M. Nielson, Adriana S. Méndez Leal, Erin W. Dickie, Roeland Hancock, Tiago Bortolini, Kaustubh R. Patil, Monica Y.C. Li, Kristin N. Meyer, Fu Shiguang, Kenny Skagerlund, Juan Carlos de la Torre, Jennifer A. Silvers, R. Alison Adcock, Gustav Nilsonne, Zachary J. Cole, Emilio Sanz-Morales, Charles P. Davis, Michael Joseph, Sangil Lee, Tom Johnstone, Peder M. Isager, Jonathan E. Peelle, Emiliano Ricciardi, Scott A. Huettel, G. Matthew Fricke, Leonardo Tozzi, Brice A. Kuhl, Sagana Vijayarajah, Alexander Bowring, Jeanette A. Mumford, Blazej M. Baczkowski, Shabnam Hakimi, Sebastian Kupek, Michael Kirchler, Yanina Prystauka, Niall W. Duncan, Anna van 't Veer, Emily G. Brudner, Jamil P. Bhanji, Sarah M. Tashjian, Senne Braem, Paolo Papale, Simon R. Steinkamp, Georgios D. Mitsis, Kenneth S. L. Yuen, Magnus Johannesson, David J. White, Alec Smith, Krzysztof J. Gorgolewski, Alexander Leemans, Robert W. Cox, Emanuele Olivetti, Luca Turella, Juergen Huber, Tom Schonberg, David V. Smith, Lysia Demetriou, Peter Sokol-Hessner, Juergen Dukart, Glad Mihai, Taylor Salo, Jean-Baptiste Poline, Angela R. Laird, Sergej A.E. Golowin, Sarah Genon, Adriana Galván, Claus Lamm, Joseph W. Kable, Roni Iwanir, Theo Marins, Matthew R. Johnson, Anais Rodriguez-Thompson, Benjamin Meyer, Stefan Czoschke, Loreen Tisdall, Douglas H. Schultz, Evan N. Lintz, Derek Beaton, Peer Herholz, Olivia Guest, Jenny R. Rieck, Laura Fontanesi, Ekaterina Dobryakova, Alexandre Pérez, Olivier Collignon, Xin Di, Ruud Berkers, Sheryl B. Ball, Carlos González-García, Schuyler W. Liphardt, Edna C. Cieslik, Roland G. Benoit, João F. Guassi Moreira, Helena Melero, Jaime J. Castrellon, Hayley R. Brooks, Chuan-Peng Hu, Felix Holzmeister, Cheryl L. Grady, Thomas E. Nichols, Alba Xifra-Porxas, Steven H. Tompson, Kelsey McDonald, Julia Beitner, Timothy R. Koscik, Tel Aviv University (TAU), Dartmouth College [Hanover], Leopold Franzens Universität Innsbruck - University of Innsbruck, California Institute of Technology (CALTECH), Stockholm School of Economics (SSE), University of Wisconsin-Madison, Duke University [Durham], Fondazione Bruno Kessler [Trento, Italy] (FBK), University of Trento [Trento], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Washington University in Saint Louis (WUSTL), Boston University [Boston] (BU), Virginia Tech [Blacksburg], Université Catholique de Louvain = Catholic University of Louvain (UCL), Plymouth University, Rotman Research Institute at the Baycrest Centre (RRI), University of Amsterdam [Amsterdam] (UvA), Goethe-University Frankfurt am Main, Rutgers University [Newark], Rutgers University System (Rutgers), New Jersey Institute of Technology [Newark] (NJIT), University of Electronic Science and Technology of China [Chengdu] (UESTC), University College of London [London] (UCL), D'Or Institute for Research and Education [Rio de Janeiro], Florida International University [Miami] (FIU), University of Oxford, Vrije Universiteit Brussel (VUB), Universiteit Gent = Ghent University (UGENT), University of Denver, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Jülich Research Centre, School for Advanced Studies Lucca (IMT), University of Nebraska–Lincoln, University of Nebraska System, National Institutes of Health [Bethesda] (NIH), University of Toronto, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Lancashire Teaching Hospital, University of Connecticut (UCONN), University Medical Center [Utrecht], Imperial College London, Temple University [Philadelphia], Pennsylvania Commonwealth System of Higher Education (PCSHE), Kessler Foundation [East Hanover], Stanford University, Taipei Medical University, TMU-ShuangHo Hospital [New Taipei City], Instituto de Neurociencias de Alicante, University of Basel (Unibas), The University of New Mexico [Albuquerque], Zhejiang University of Technology, University of California [Los Angeles] (UCLA), University of California (UC), Université catholique de Lille (UCL), Concordia University [Montreal], Aalto University, Leiden University Medical Center (LUMC), Universiteit Leiden, Linköping University (LIU), Western Sydney University, Centre for Addiction and Mental Health [Toronto] (CAMH), McGill University = Université McGill [Montréal, Canada], Eindhoven University of Technology [Eindhoven] (TU/e), Epilepsy Centre Kempenhaeghe [Heeze], Leibniz-Institut für Resilienzforschung [Mainz] (LIR), Swinburne University of Technology (Hawthorn campus), University of Michigan [Ann Arbor], University of Michigan System, Max Planck Institute for Empirical Aesthetics [Frankfurt am Main], University of Pennsylvania, Max Planck Institute for Psycholinguistics, Carver College of Medicine, University of Iowa, Rutgers New Jersey Medical School (NJMS), University of Oregon [Eugene], University of Vienna [Vienna], Johns Hopkins University (JHU), Nanjing University (NJU), Haskins Laboratories, The Alan Turing Institute, Universidad Rey Juan Carlos [Madrid] (URJC), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Technische Universität Dresden = Dresden University of Technology (TU Dresden), Karolinska Institutet [Stockholm], Stockholm University, Université de Lausanne = University of Lausanne (UNIL), Netherlands Institute for Neuroscience (NIN), Royal Netherlands Academy of Arts and Sciences (KNAW), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Humboldt University Of Berlin, Technical University of Berlin / Technische Universität Berlin (TU), University of California [Berkeley] (UC Berkeley), Columbia University [New York], Human Research and Engineering Directorate [Aberdeen Proving Ground], Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, University of California [Santa Barbara] (UC Santa Barbara), University of Dayton, Maumet, Camille, Technische Universität Berlin (TU), Leiden University, European Commission, Fundación 'la Caixa', Tel Aviv University [Tel Aviv], University of Innsbruck, University of Electronic Science and Technology of China (UESTC), University of Oxford [Oxford], Universiteit Gent = Ghent University [Belgium] (UGENT), University of Nebraska [Lincoln], Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, University of California, University of Pennsylvania [Philadelphia], Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Lausanne (UNIL), Humboldt-Universität zu Berlin, University of California [Berkeley], University of California [Santa Barbara] (UCSB), Signal Processing Systems, and Human Technology Interaction

Subjects
Data Analysis, Male, Scientific community, Computer science, Decision, Datasets as Topic, Variation (game tree), Field (computer science), 0302 clinical medicine, Multidisciplinary approach, Data Science/methods, Models, Meta-Analysis as Topic, 0303 health sciences, Multidisciplinary, medicine.diagnostic_test, Brain, Magnetic Resonance Imaging, Research Personnel, Datasets as Topic/statistics & numerical data, Neurological, Female, ddc:500, Research Personnel/organization & administration, Models, Neurological, BF, Article, 03 medical and health sciences, Neuroimaging, Functional neuroimaging, Human behaviour, medicine, Humans, 030304 developmental biology, Statistical hypothesis testing, Flexibility (engineering), [SCCO.NEUR]Cognitive science/Neuroscience, Functional Neuroimaging, [SCCO.NEUR] Cognitive science/Neuroscience, Data Science, Reproducibility of Results, Brain/diagnostic imaging, Pipeline (software), Data science, Workflow, Logistic Models, Functional magnetic resonance imaging, Decision making, 030217 neurology & neurosurgery, Software
Abstract

Botvinik-Nezer, Rotem et al., Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed., European Union’s Horizon 2020 research and innovation programme (https://ec.europa.eu/programmes/horizon2020/en) under the Marie Skłodowska-Curie grant agreement no. 665501. A. Eed was supported by a predoctoral fellowship La Caixa-Severo Ochoa from Obra Social La Caixa. S.B.E. was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 785907 (HBP SGA2).

Published
2020
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8. The Beringian Coevolution Project: holistic collections of mammals and associated parasites reveal novel perspectives on evolutionary and environmental change in the North

Author

John M. Kinsella, Robert Mulders, Kayce C. Bell, Jason L. Malaney, Schuyler W. Liphardt, Andrew G. Hope, Jackson S. Whitman, Anson V. Koehler, Joseph A. Cook, Bryan S. McLean, Eric Waltari, Heather M. Toman, Suzanne Carrière, Batsaikhan Nyamsuren, Dianna Krejsa, Donavan J. Jackson, Thomas S. Jung, Albina Tsvetkova, S. O. MacDonald, Jon Martin, Genevieve M. S. Haas, Heikki Henttonen, Ralph P. Eckerlin, Jocelyn P. Colella, Susan J. Kutz, Kurt E. Galbreath, Vadim B. Fedorov, Voitto Haukisalmi, Stephen E. Greiman, Arseny A. Makarikov, Sandra L. Talbot, Eric P. Hoberg, Mariel L. Campbell, Jonathan L. Dunnum, Natalie G. Dawson, and Vasyl V. Tkach

Subjects
geographic and host colonization, museum specimen archives, 0106 biological sciences, 0301 basic medicine, ecological perturbation, Environmental change, Ecology, Environmental engineering, beringia, bioinformatics, TA170-171, 010603 evolutionary biology, 01 natural sciences, Field (geography), Environmental sciences, 03 medical and health sciences, climate change, 030104 developmental biology, Geography, arctic, General Earth and Planetary Sciences, GE1-350, General Agricultural and Biological Sciences, Coevolution, General Environmental Science
Abstract

The Beringian Coevolution Project (BCP), a field program underway in the high northern latitudes since 1999, has focused on building key scientific infrastructure for integrated specimen-based studies on mammals and their associated parasites. BCP has contributed new insights across temporal and spatial scales into how ancient climate and environmental change have shaped faunas, emphasizing processes of assembly, persistence, and diversification across the vast Beringian region. BCP collections also represent baseline records of biotic diversity from across the northern high latitudes at a time of accelerated environmental change. These specimens and associated data form an unmatched resource for identifying hidden diversity, interpreting past responses to climate oscillations, documenting contemporary conditions, and anticipating outcomes for complex biological systems in a regime of ecological perturbation. Because of its dual focus on hosts and parasites, the BCP record also provides a foundation for comparative analyses that can document the effects of dynamic change on the geographic distribution, transmission dynamics, and emergence of pathogens. By using specific examples from carnivores, eulipotyphlans, lagomorphs, rodents, ungulates, and their associated parasites, we demonstrate how broad, integrated field collections provide permanent infrastructure that informs policy decisions regarding human impact and the effect of climate change on natural populations.

Published
2017
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9. Leveraging natural history biorepositories as a global, decentralized, pathogen surveillance network.

Author

Jocelyn P Colella, John Bates, Santiago F Burneo, M Alejandra Camacho, Carlos Carrion Bonilla, Isabel Constable, Guillermo D'Elía, Jonathan L Dunnum, Stephen Greiman, Eric P Hoberg, Enrique Lessa, Schuyler W Liphardt, Manuela Londoño-Gaviria, Elizabeth Losos, Holly L Lutz, Nicté Ordóñez Garza, A Townsend Peterson, María Laura Martin, Camila C Ribas, Bruce Struminger, Fernando Torres-Pérez, Cody W Thompson, Marcelo Weksler, and Joseph A Cook

Subjects
Immunologic diseases. Allergy, RC581-607, Biology (General), QH301-705.5
Abstract

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic reveals a major gap in global biosecurity infrastructure: a lack of publicly available biological samples representative across space, time, and taxonomic diversity. The shortfall, in this case for vertebrates, prevents accurate and rapid identification and monitoring of emerging pathogens and their reservoir host(s) and precludes extended investigation of ecological, evolutionary, and environmental associations that lead to human infection or spillover. Natural history museum biorepositories form the backbone of a critically needed, decentralized, global network for zoonotic pathogen surveillance, yet this infrastructure remains marginally developed, underutilized, underfunded, and disconnected from public health initiatives. Proactive detection and mitigation for emerging infectious diseases (EIDs) requires expanded biodiversity infrastructure and training (particularly in biodiverse and lower income countries) and new communication pipelines that connect biorepositories and biomedical communities. To this end, we highlight a novel adaptation of Project ECHO's virtual community of practice model: Museums and Emerging Pathogens in the Americas (MEPA). MEPA is a virtual network aimed at fostering communication, coordination, and collaborative problem-solving among pathogen researchers, public health officials, and biorepositories in the Americas. MEPA now acts as a model of effective international, interdisciplinary collaboration that can and should be replicated in other biodiversity hotspots. We encourage deposition of wildlife specimens and associated data with public biorepositories, regardless of original collection purpose, and urge biorepositories to embrace new specimen sources, types, and uses to maximize strategic growth and utility for EID research. Taxonomically, geographically, and temporally deep biorepository archives serve as the foundation of a proactive and increasingly predictive approach to zoonotic spillover, risk assessment, and threat mitigation.

Published
2021
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