eCommons

 

CVM Research

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Research continues to be one of the major missions of the College of Veterinary Medicine at Cornell University. As such, its faculty, students, and research scientists produce materials such as posters and datasets that describe or are related to their basic, applied and translational research activities and accomplishments.

This collection includes research outputs that have been submitted voluntarily by faculty, postdoctoral fellows, graduate students, veterinary students, technical personnel, and other research associates. Open access is provided under Creative Commons licenses.

Further information about the College’s research activities can be found at http://www.vet.cornell.edu/research/

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Recent Submissions

Now showing 1 - 10 of 86
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    North American Wildlife Agency CWD Testing and Ancillary Data (2000 – 2022)
    Schuler, Krysten L.; Hanley, Brenda J.; Abbott, Rachel C.; Dayan, David B.; Hollingshead, Nicholas A.; Ballard, Jennifer R.; Middaugh, Christopher R.; Cunningham, Mark; Clemons, Bambi; Sayler, Katherine; Kelly, James D.; Killmaster, Charlie H.; Harms, Tyler M.; Ruden, Rachel M.; Caudell, Joe; Westrich, Michelle Benavidez; McCallen, Emily; Casey, Christine; O’Brien, Lindsey M.; Trudeau, Jonathan K.; Straka, Kelly; Stewart, Chad; Carstensen, Michelle; McKinley, William T.; Hynes, Kevin P.; Ableman, Ashley; Miller, Landon A.; Cook, Merril; Myers, Ryan; Shaw, Jonathan; Van de Berg, Sarah; Tonkovich, Michael J.; Grove, Daniel M.; Storm, Daniel J. (2024-05-06)
    The North American Wildlife Agency CWD Testing and Ancillary Data (2000-2022) dataset (“Dataset”) represents epidemiological, population, ecological, and anthropogenic data related to chronic wasting disease (CWD) surveillance in white-tailed deer (Odocoileus virginianus) in 16 US states (“Administrative Areas”) in North America. The data are summarized at the “Sub-Administrative Area” level. For most state wildlife agencies, counties or equivalent units within Administrative Areas serve as sub-administrative area. While the overall time-period represented by the Dataset spans the years 2000 through 2022, data availability varies by wildlife agency. Administrative Areas and associated wildlife agencies represented in the Dataset include Arkansas (Arkansas Game and Fish Commission), Florida (Florida Fish and Wildlife Conservation Commission), Georgia (Georgia Department of Natural Resources), Indiana (Indiana Department of Natural Resources), Iowa (Iowa Department of Natural Resources), Kentucky (Kentucky Department of Fish and Wildlife Resources), Maryland (Maryland Department of Natural Resources), Michigan (Michigan Department of Natural Resources), Minnesota (Minnesota Department of Natural Resources), Mississippi (Mississippi Department of Wildlife, Fisheries, and Parks), New York (New York State Department of Environmental Conservation), North Carolina (North Carolina Wildlife Resources Commission), Ohio (Ohio Department of Natural Resources), Tennessee (Tennessee Wildlife Resources Agency), Virginia (Virginia Department of Wildlife Resources), and Wisconsin (Wisconsin Department of Natural Resources). This dataset is intended for use in regional models to determine risk factors that can be used to predict locations of CWD incidence in white-tailed deer at the sub-administrative unit level. Version 2 (the current version) of the Dataset corrects errors present in Version 1 of the Dataset (Them et al. 2023). For Version 2, the original data sources were re-examined and re-processed to ensure the highest level of fidelity and accuracy possible. Data processing scripts were revised for improved precision and data quality and are available in a related repository (Hollingshead et al. 2024). All data and scripts were assessed for accuracy and consistency using a thorough QAQC process completed by internal and external collaborators.
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    North American Wildlife Agency CWD Testing and Ancillary Data (2000 – 2022) Scripts
    Hollingshead, Nicholas A.; Dayan, David B. (2024-05-03)
    These scripts have been used to generate the North American Wildlife Agency CWD Testing and Ancillary Data (2000 – 2022) (Version 2) (“Dataset”), representing epidemiological, population, ecological, and anthropogenic data related to chronic wasting disease (CWD) surveillance in white-tailed deer (Odocoileus virginianus) in 16 US states (“Administrative Areas”) in North America. The data are summarized by “Management Areas,” the geographic unit within the administrative area of a wildlife agency used for wildlife management, typically aligning with county or county-equivalent boundaries. The resulting dataset is intended for use in regional models to determine risk factors that can be used to predict locations of CWD incidence in white-tailed deer at the sub-administrative unit level. Version 2 of the dataset corrects errors present in Version 1 (https://doi.org/10.7298/7txw-2681). For Version 2, the original data sources were re-examined and re-processed to ensure the highest level of fidelity and accuracy possible. All data and scripts were assessed for accuracy and consistency by internal and external collaborators.
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    Surveillance Optimization Project for Chronic Wasting Disease dataset for Virginia, US, 2001- 2021
    Virginia Department of Wildlife Resources (2024)
    This dataset contains four files containing data from the Virginia Department of Wildlife Resources shared with the Cornell Wildlife Health Lab (CWHL) at Cornell University for the purpose of the Surveillance Optimization Project for Chronic Wasting Disease (SOP4CWD). Professionals at the source facility have provided written permission for professionals at the CWHL to post this open data to this persistent eCommons repository.
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    Data from: Evolutionary genomic analyses of canine E. coli infections identifies a relic capsular locus associated with resistance to multiple classes of antimicrobial drugs
    Ceres, Kristina; Zehr, Jordan D.; Murrell, Chloe; Millet, Jean K.; Sun, Qi; McQueary, Holly C.; Horton, Alanna; Cazer, Casey; Sams, Kelly; Reboul, Guillaume; Andreopoulos, William B.; Mitchell, Patrick K.; Anderson Renee; Franklin-Guild, Rebecca; Cronk, Brittany D.; Stanhope, Bryce J.; Burbick, Claire R.; Wolking, Rebecca; Peak, Laura; Zhang, Yan; McDowall, Rebeccah; Krishnamurthy, Aparna; Slavic, Durda; Sekhon, Prabhjot Kaur; Tyson, Gregory H.; Ceric, Olgica; Stanhope, Michael J.; Goodman, Laura B. (2024)
    These files contain data that support hypotheses presented in Ceres et. al. Evolutionary genomic analyses of canine E. coli infections identifies a relic capsular locus associated with resistance to multiple classes of antimicrobial drugs. In Ceres et. al. we found: Escherichia coli is the leading cause of death attributed to antimicrobial resistance (AMR) worldwide, and the known AMR mechanisms involve a range of functional proteins. Here we employed a pan-GWAS approach on over 1,000 E. coli isolates from sick dogs collected across the US and Canada and identified a strong statistical association of AMR, involving a range of antibiotics, to a group 1 capsular (CPS) gene cluster. This cluster included genes under relaxed selection pressure, had several loci missing, and pseudogenes for other key loci. It is widespread in E. coli and Klebsiella infections across multiple host species. Earlier studies demonstrated that the octameric CPS polysaccharide export protein Wza can transmit macrolide antibiotics into the E. coli periplasm. We suggest the CPS in question, and its highly divergent Wza, functions as an antibiotic trap, preventing drug penetration. We also highlight the high diversity of lineages circulating in dogs across all regions studied, overlap with human lineages, and regional prevalence of resistance to multiple drug classes.
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    Data from: Environmental and ecological signals predict periods of nutritional stress for Eastern Australian flying fox populations
    Eby, Peggy; Lagergren, John; Ruiz-Aravena, Manuel; Becker, Daniel J.; Madden, Wyatt; Ruytenberg, Lib; Hoegh, Andrew; Han, Barbara; Peel, Alison J.; Jacobson, Daniel; Plowright, Raina K. (2024-01-30)
    Food availability determines where animals use space across a landscape and therefore affects the risk of encounters that lead to zoonotic spillover. This relationship is evident in Australian flying foxes (Pteropus spp; fruit bats), where acute food shortages precede clusters of Hendra virus spillovers. Using explainable artificial intelligence, we predicted months of food shortages from climatological and ecological covariates (1996-2022) in eastern Australia. Overall accuracy in predicting months of low food availability on a test set from 2018 up to 2022 reached 93.33% and 92.59% based on climatological and bat-level features, respectively. Seasonality and Oceanic El Niño Index were the most important environmental features, while the number of bats in rescue centers and their body weights were the most important bat-level features. These models support predictive signals up to nine months in advance, facilitating action to mitigate spillover risk. This dataset supports this research and conclusions.
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    White Matter Atlas of the Domestic Canine Brain
    Inglis, Fiona M.; Taylor, Paul; Barry, Erica F.; Pascalau, Raluca; Voss, Henning; Johnson, Philippa J. (2024-01-23)
    These files contain a White Matter Atlas of the Domestic Canine Brain. This atlas is generated from diffusion tensor imaging and T1-weighted data collected from 30 mesaticephalic or dolicocephalic clinically and neurologically healthy canines. The final atlas includes a population average template generated from T1-weighted data, whole brain white matter priors created using manual segementation and white matter tracts generated from a population average diffusion dataset tractogram.
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    Data from: Low-dose sugammadex reverses moderate rocuronium-induced neuromuscular block in horses
    Martin-Flores, Manuel; Sakai, Daniel; Araos, Joaquin; Campoy, Luis (2024-01-19)
    These files contain data supporting all results reported in M Martin-Flores et al, EVJ-OA-23-200.R1 - Low-dose sugammadex reverses moderate rocuronium-induced neuromuscular block in horses. Fourteen adult horses undergoing different procedures were anesthetized with detomidine and isoflurane. All horses received NMB with rocuronium 0.3 mg/kg IV. Neuromuscular function was measured with acceleromyographic train-of-four (TOF) ratio. Recovery occurred spontaneously in five horses weighing [median (range)] 548 (413 – 594) kg and was enhanced with sugammadex 200 mg (total dose) in nine horses [433 (362 – 515)] kg. Recovery time from moderate NMB to a TOF ratio 1.0, and total duration of NMB were compared between groups. The dose of sugammadex was 0.46 (0.39 – 0.55) mg/kg. The recovery period lasted 21 (17 – 39) minutes for spontaneous and 4 (3 – 7) minutes for sugammadex. Total duration of NMB was 58 (41 – 70) minutes for spontaneous and 36 (21 – 43) for sugammadex (both p ≤ 0.003). There were no instances of recurarization.
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    Chronic Wasting Disease Surveillance Optimization Software (n x 2)
    Hanley, Brenda J.; Mitchell, Corey I.; Them, Cara E.; Walter, W. David; Walsh, Daniel P.,; Jennelle, Christopher S.; Hollingshead, Nicholas A.; Abbott, Rachel C.; Kelly, James D.; Grove, Daniel M.; Williams, David; Christensen, Sonja A.; Ahmed, Md Sohel; Booth, James G.; Guinness, Joseph; Gagne, Roderick B.; DiSalvo, Andrew R.; Fleegle, Jeannine T.; Rosenberry, Christopher S.; Miller, Landon A.; Schuler, Krysten L. (2023-09-22)
    The Chronic Wasting Disease Surveillance Optimization Software (n x 2) computes sampling recommendations for state, tribal, or provincial wildlife management agencies when the goal of the disease surveillance program is to detect chronic wasting disease (CWD) in white-tailed deer (Odocoileus virginianus). Driven by a combinatorial optimization algorithm, the Software pinpoints the number of surveillance points that should be evaluated in each county (or administrative area) to maximize the return-on-investment of sampling to the agency, while staying within the predetermined annual CWD surveillance budget. Agency representatives parameterize their Software with their total annual budget, weightings for specific management objectives, a summary of the historical sampling data, per-deer sampling costs, benefits of first and early detections, risk of spread, and benefits of an ad hoc sampling strategy. Outputs include the set of counties that should be sampled in the upcoming surveillance season. We designed the Software for use in Tennessee, US, but included directions for other states or provinces.
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    Wildlife Disease Hazard Software Version 2
    Them, Cara E.; Hanley, Brenda J.; Mitchell, Corey I.; Abbott, Rachel C.; Hollingshead, Nicholas A.; Schuler, Krysten L. (2023-08-21)
    The Wildlife Disease Hazard Software Version 2 depicts US counties in which anthropogenic activities enhance the risk of CWD introduction and pinpoints how cervid demographic parameters influence the spread of CWD. We packaged the software using examples from the states of Tennessee and New York. We included for each example state the (redacted) hazard and (redacted) demographic data from white-tailed deer (Odocoileus virginianus). The software can be adapted for use in other geographical entities such as province or Tribal nation. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
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    Raptor Health V4: Software to assess the population-scale impact of mortality in raptors
    Hanley, Brenda; Dhondt, André; Forzán, María; Bunting, Elizabeth M.; Pokras, Mark A.; Hynes, Kevin P.; Dominquez-Villegas, Ernesto; Schuler, Krysten L. (2023-08-18)
    The Raptor Health V4 software allows wildlife professionals to use a mathematical model and 17-sequential years of nesting or breeding adult count data to examine the localized population-scale impacts to raptor populations arising from one or more source of observed mortality. The mathematical model is the closed-system Lefkovitch matrix, where closed-system is defined to be no gains or losses to the population from dispersal. The software allows the user to input demographic parameters as well as time series data (counts of nesting pairs and counts of mortalities) to produce comparative demographic quantities between a population with and without the source of mortality, including the annual and bi-annual abundances of hatchlings, non-breeders, and breeders, growth rates (long-term, transient, cumulative, and stochastic), stable stage distributions, reproductive values, and dominant elasticities. The software automatically generates non-parametric (Kruskal-Wallis) statistical tests to compare differences in median growth rates between populations with and without the source of mortality, as well as quantifies the bias introduced through the use of the algorithm as an estimator. Default values in the software depict parameters of bald eagles (Haliaeetus leucocephalus) and eagle nesting and mortality data collected by biologists at the New York State Department of Environmental Conservation (NYSDEC).