Dataset Information

Comparison of linear model and artificial neural network using antler beam diameter and length of white-tailed deer (Odocoileus virginianus) dataset.

ABSTRACT: Evaluation of harvest data remains one of the most important sources of information in the development of strategies to manage regional populations of white-tailed deer. While descriptive statistics and simple linear models are utilized extensively, the use of artificial neural networks for this type of data analyses is unexplored. Linear model was compared to Artificial Neural Networks (ANN) models with Levenberg-Marquardt (L-M), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) learning algorithms, to evaluate the relative accuracy in predicting antler beam diameter and length using age and dressed body weight in white-tailed deer. Data utilized for this study were obtained from male animals harvested by hunters between 1977-2009 at the Berry College Wildlife Management Area. Metrics for evaluating model performance indicated that linear and ANN models resulted in close match and good agreement between predicted and observed values and thus good performance for all models. However, metrics values of Mean Absolute Error and Root Mean Squared Error for linear model and the ANN-BR model indicated smaller error and lower deviation relative to the mean values of antler beam diameter and length in comparison to other ANN models, demonstrating better agreement of the predicted and observed values of antler beam diameter and length. ANN-SCG model resulted in the highest error within the models. Overall, metrics for evaluating model performance from the ANN model with BR learning algorithm and linear model indicated better agreement of the predicted and observed values of antler beam diameter and length. Results of this study suggest the use of ANN generated results that are comparable to Linear Models of harvest data to aid in the development of strategies to manage white-tailed deer.

SUBMITTER: Peters SO

PROVIDER: S-EPMC6386314 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Comparison of linear model and artificial neural network using antler beam diameter and length of white-tailed deer (Odocoileus virginianus) dataset.

Peters Sunday O SO Sinecen Mahmut M Gallagher George R GR Pebworth Lauren A LA Jacob Suleima S Hatfield Jason S JS Kizilkaya Kadir K

PloS one 20190222 2

Evaluation of harvest data remains one of the most important sources of information in the development of strategies to manage regional populations of white-tailed deer. While descriptive statistics and simple linear models are utilized extensively, the use of artificial neural networks for this type of data analyses is unexplored. Linear model was compared to Artificial Neural Networks (ANN) models with Levenberg-Marquardt (L-M), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) ...[more]

PMID: 30794631

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Project description:The origin of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing the global coronavirus disease 19 (COVID-19) pandemic, remains a mystery. Current evidence suggests a likely spillover into humans from an animal reservoir. Understanding the host range and identifying animal species that are susceptible to SARS-CoV-2 infection may help to elucidate the origin of the virus and the mechanisms underlying cross-species transmission to humans. Here we demonstrated that white-tailed deer (Odocoileus virginianus), an animal species in which the angiotensin converting enzyme 2 (ACE2) - the SARS-CoV-2 receptor - shares a high degree of similarity to humans, are highly susceptible to infection. Intranasal inoculation of deer fawns with SARS-CoV-2 resulted in established subclinical viral infection and shedding of infectious virus in nasal secretions. Notably, infected animals transmitted the virus to non-inoculated contact deer. Viral RNA was detected in multiple tissues 21 days post-inoculation (pi). All inoculated and indirect contact animals seroconverted and developed neutralizing antibodies as early as day 7 pi. The work provides important insights into the animal host range of SARS-CoV-2 and identifies white-tailed deer as a susceptible wild animal species to the virus.IMPORTANCEGiven the presumed zoonotic origin of SARS-CoV-2, the human-animal-environment interface of COVID-19 pandemic is an area of great scientific and public- and animal-health interest. Identification of animal species that are susceptible to infection by SARS-CoV-2 may help to elucidate the potential origin of the virus, identify potential reservoirs or intermediate hosts, and define the mechanisms underlying cross-species transmission to humans. Additionally, it may also provide information and help to prevent potential reverse zoonosis that could lead to the establishment of a new wildlife hosts. Our data show that upon intranasal inoculation, white-tailed deer became subclinically infected and shed infectious SARS-CoV-2 in nasal secretions and feces. Importantly, indirect contact animals were infected and shed infectious virus, indicating efficient SARS-CoV-2 transmission from inoculated animals. These findings support the inclusion of wild cervid species in investigations conducted to assess potential reservoirs or sources of SARS-CoV-2 of infection.

Dataset Information

Comparison of linear model and artificial neural network using antler beam diameter and length of white-tailed deer (Odocoileus virginianus) dataset.

Publications

Comparison of linear model and artificial neural network using antler beam diameter and length of white-tailed deer (Odocoileus virginianus) dataset.

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