Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

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 trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths

Results from the Xylitol for Adult Caries Trial (X-ACT)

Although caries is prevalent in adults, few preventive therapies have been tested in adult populations. This randomized clinical trial evaluated the effectiveness of xylitol lozenges in preventing caries in elevated caries-risk adults

Infusing Sodium Bicarbonate Suppresses Hydrogen Peroxide Accumulation and Superoxide Dismutase Activity in Hypoxic-Reoxygenated Newborn Piglets

The effectiveness of sodium bicarbonate (SB) has recently been questioned although it is often used to correct metabolic acidosis of neonates. The aim of the present study was to examine its effect on hemodynamic changes and hydrogen peroxide (H(2)O(2)) generation in the resuscitation of hypoxic newborn animals with severe acidosis.Newborn piglets were block-randomized into a sham-operated control group without hypoxia (n = 6) and two hypoxia-reoxygenation groups (2 h normocapnic alveolar hypoxia followed by 4 h room-air reoxygenation, n = 8/group). At 10 min after reoxygenation, piglets were given either i.v. SB (2 mEq/kg), or saline (hypoxia-reoxygenation controls) in a blinded, randomized fashion. Hemodynamic data and blood gas were collected at specific time points and cerebral cortical H(2)O(2) production was continuously monitored throughout experimental period. Plasma superoxide dismutase and catalase and brain tissue glutathione, superoxide dismutase, catalase, nitrotyrosine and lactate levels were assayed.Two hours of normocapnic alveolar hypoxia caused cardiogenic shock with metabolic acidosis (PH: 6.99 ± 0.07, HCO(3)(-): 8.5 ± 1.6 mmol/L). Upon resuscitation, systemic hemodynamics immediately recovered and then gradually deteriorated with normalization of acid-base imbalance over 4 h of reoxygenation. SB administration significantly enhanced the recovery of both pH and HCO(3-) recovery within the first hour of reoxygenation but did not cause any significant effect in the acid-base at 4 h of reoxygenation and the temporal hemodynamic changes. SB administration significantly suppressed the increase in H(2)O(2) accumulation in the brain with inhibition of superoxide dismutase, but not catalase, activity during hypoxia-reoxygenation as compared to those of saline-treated controls.Despite enhancing the normalization of acid-base imbalance, SB administration during resuscitation did not provide any beneficial effects on hemodynamic recovery in asphyxiated newborn piglets. SB treatment also reduced the H(2)O(2) accumulation in the cerebral cortex without significant effects on oxidative stress markers presumably by suppressing superoxide dismutase but not catalase activity

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

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 trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths