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Full-Text Articles in Medicine and Health Sciences
Improving The Accessibility And Transferability Of Machine Learning Algorithms For Identification Of Animals In Camera Trap Images: Mlwic2, Michael A. Tabak, Mohammad S. Norouzzadeh, David W. Wolfson, Erica J. Newton, Raoul K. Boughton, Jacob S. Ivan, Eric Odell, Eric S. Newkirk, Reesa Y. Conrey, Jennifer Stenglein, Fabiola Iannarilli, John Erb, Ryan K. Brook, Amy J. Davis, Jesse Lewis, Daniel P. Walsh, James C. Beasley, Kurt C. Vercauteren, Jeff Clune, Ryan S. Miller
Improving The Accessibility And Transferability Of Machine Learning Algorithms For Identification Of Animals In Camera Trap Images: Mlwic2, Michael A. Tabak, Mohammad S. Norouzzadeh, David W. Wolfson, Erica J. Newton, Raoul K. Boughton, Jacob S. Ivan, Eric Odell, Eric S. Newkirk, Reesa Y. Conrey, Jennifer Stenglein, Fabiola Iannarilli, John Erb, Ryan K. Brook, Amy J. Davis, Jesse Lewis, Daniel P. Walsh, James C. Beasley, Kurt C. Vercauteren, Jeff Clune, Ryan S. Miller
USDA Wildlife Services: Staff Publications
Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera …
Coproid Predicts The Source Of Coprolites And Paleofeces Using Microbiome Composition And Host Dna Content, Maxime Borry, Bryan Cordova, Angela Perri, Marsha Wibowo, Tanvi Prasad Honap, Jada Ko, Kate Britton, Linus Girdland-Flink, Robert C. Power, Ingelise Stuijts, Domingo C. Salazar-García, Courtney Hofman, Richard Hagan, Thérèse Samdapawindé Kagoné, Nicolas Meda, Helene Carabin, David Jacobson, Karl Reinhard, Cecil Lewis, Aleksandar Kostic, Choongwon Jeong, Alexander Herbig, Alexander Hübner, Christina Warinner
Coproid Predicts The Source Of Coprolites And Paleofeces Using Microbiome Composition And Host Dna Content, Maxime Borry, Bryan Cordova, Angela Perri, Marsha Wibowo, Tanvi Prasad Honap, Jada Ko, Kate Britton, Linus Girdland-Flink, Robert C. Power, Ingelise Stuijts, Domingo C. Salazar-García, Courtney Hofman, Richard Hagan, Thérèse Samdapawindé Kagoné, Nicolas Meda, Helene Carabin, David Jacobson, Karl Reinhard, Cecil Lewis, Aleksandar Kostic, Choongwon Jeong, Alexander Herbig, Alexander Hübner, Christina Warinner
Karl Reinhard Publications
Shotgun metagenomics applied to archaeological feces (paleofeces) can bring new insights into the composition and functions of human and animal gut microbiota from the past. However, paleofeces often undergo physical distortions in archaeological sediments, making their source species difficult to identify on the basis of fecal morphology or microscopic features alone. Here we present a reproducible and scalable pipeline using both host and microbial DNA to infer the host source of fecal material. We apply this pipeline to newly sequenced archaeological specimens and show that we are able to distinguish morphologically similar human and canine paleofeces, as well as non-fecal …