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

Four “Lessons Learned” While Implementing a Multi-Site Caries Prevention Trial

As the number of dental-related randomized clinical trials (RCTs) increases, there is a need for literature to help investigators inexperienced in conducting RCTs design and implement studies. This commentary describes four “lessons learned,” or considerations important in the planning and initial implementation of RCTs in dentistry that to our knowledge have not been discussed in the general dental literature describing trial techniques. These considerations are 1) preparing or securing a thorough systematic review, 2) developing a comprehensive set of study documents, 3) designing and testing multiple recruitment strategies, and 4) employing a run-in period prior to enrollment. Attention to these considerations in the planning phases of a dental RCT can help ensure that the trial is clinically relevant while also maximizing the likelihood that its implementation will be successful

Design of the Prevention of Adult Caries Study (PACS): a randomized clinical trial assessing the effect of a chlorhexidine dental coating for the prevention of adult caries

Abstract Background Dental caries is one of the primary causes of tooth loss among adults. It is estimated to affect a majority of Americans aged 55 and older, with a disproportionately higher burden in disadvantaged populations. Although a number of treatments are currently in use for caries prevention in adults, evidence for their efficacy and effectiveness is limited. Methods/Design The Prevention of Adult Caries Study (PACS) is a multicenter, placebo-controlled, double-blind, randomized clinical trial of the efficacy of a chlorhexidine (10% w/v) dental coating in preventing adult caries. Participants (n = 983) were recruited from four different dental delivery systems serving four diverse communities, including one American Indian population, and were randomized to receive either chlorhexidine or a placebo treatment. The primary outcome is the net caries increment (including non-cavitated lesions) from baseline to 13 months of follow-up. A cost-effectiveness analysis also will be considered. Discussion This new dental treatment, if efficacious and approved for use by the Food and Drug Administration (FDA), would become a new in-office, anti-microbial agent for the prevention of adult caries in the United States. Trial Registration Number NCT0035787

Design of the Prevention of Adult Caries Study (PACS): A randomized clinical trial assessing the effect of a chlorhexidine dental coating for the prevention of adult caries

<p>Abstract</p> <p>Background</p> <p>Dental caries is one of the primary causes of tooth loss among adults. It is estimated to affect a majority of Americans aged 55 and older, with a disproportionately higher burden in disadvantaged populations. Although a number of treatments are currently in use for caries prevention in adults, evidence for their efficacy and effectiveness is limited.</p> <p>Methods/Design</p> <p>The Prevention of Adult Caries Study (PACS) is a multicenter, placebo-controlled, double-blind, randomized clinical trial of the efficacy of a chlorhexidine (10% w/v) dental coating in preventing adult caries. Participants (n = 983) were recruited from four different dental delivery systems serving four diverse communities, including one American Indian population, and were randomized to receive either chlorhexidine or a placebo treatment. The primary outcome is the net caries increment (including non-cavitated lesions) from baseline to 13 months of follow-up. A cost-effectiveness analysis also will be considered.</p> <p>Discussion</p> <p>This new dental treatment, if efficacious and approved for use by the Food and Drug Administration (FDA), would become a new in-office, anti-microbial agent for the prevention of adult caries in the United States.</p> <p>Trial Registration Number</p> <p>NCT00357877</p

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

Schizotypal Personality Disorder Or Prodromal Symptoms Of Schizophrenia?

Schizotypal personality disorder shares some attenuated phenotypic features with schizophrenia, but represents an independent syndrome. In contrast, prodromal symptoms of schizophrenia represent early warning signs of the impending onset of schizophrenia. Although these constructs are intended to reflect independent syndromes, self-report instruments measuring these constructs assess similar symptoms. It does not appear that existing research has examined the relative discriminant validity of screening instruments for these syndromes. A sample of 998 young adults (68% female; 73% Caucasian), within the age of risk for schizophrenia (ages 18-34; mean 20.4 ± 2.2), met validity criteria after completing online versions of the Abbreviated Schizotypal Personality Questionnaire (SPQ-B) and the 24-item Abbreviated Youth Psychosis at Risk Questionnaire (Y-PARQ-B). Based on clinical cut-off scores used in previous research, 5.2% were [only] considered at heightened risk for psychosis (potentially prodromal), 3.4% had [only] schizotypal personality features, and 2.9% met criteria for both constructs (75% of individuals meeting cutoff for one measure did not meet criteria for the other). Males and younger participants scored significantly higher on both measures. The total scores from the SPQ-B and Y-PARQ-B showed a significant positive correlation (rs = .66, p \u3c .001, R2 = .43); however, 57% of the variance was not shared between the measures. Of the three SPQ-B subscales, Cognitive-Perceptual showed the strongest correlation with Y-PARQ-B. Results suggest that the SPQ-B and Y-PARQ-B have moderate discriminate validity between the overlapping, yet distinct, constructs of schizotypal personality and heightened risk of developing psychosis (potentially prodromal). © 2005 Elsevier B.V. All rights reserved

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

Difference in effectiveness of medication adherence intervention by health literacy level.

CONTEXT: There is little research investigating whether health information technologies, such as interactive voice recognition, are effective ways to deliver information to individuals with lower health literacy. OBJECTIVE: Determine the extent to which the impact of an interactive voice recognition-based intervention to improve medication adherence appeared to vary by participants\u27 health literacy level. DESIGN: Promoting Adherence to Improve Effectiveness of Cardiovascular Disease Therapies (PATIENT) was a randomized clinical trial designed to test the impact, compared with usual care, of 2 technology-based interventions that leveraged interactive voice recognition to promote medication adherence. A 14% subset of participants was sent a survey that included questions on health literacy. This exploratory analysis was limited to the 833 individuals who responded to the survey and provided data on health literacy. MAIN OUTCOME MEASURES: Adherence to statins and/or angiotensin-converting enzyme inhibitors and/or angiotensin II receptor blockers. RESULTS: Although intervention effects did not differ significantly by level of health literacy, the data were suggestive of differential intervention effects by health literacy level. CONCLUSIONS: The differences in intervention effects for high vs low health literacy in this exploratory analysis are consistent with the hypothesis that individuals with lower health literacy may derive greater benefit from this type of intervention compared with individuals with higher health literacy. Additional studies are needed to further explore this finding

Karol_etal_threegene_alignment.nex

Three plastid gene alignment for the Characeae including Lychnothamnus barbatus from the New World

Data from: First discovery of the charophycean green alga Lychnothamnus barbatus (Charophyceae) extant in the New World

Premise of the study: Although some species of Characeae, known as stoneworts, can be found on every continent except Antarctica, many species and some genera have limited geographic distributions. The genus Lychnothamnus, represented by a single extant species L. barbatus, was known only from scattered localities in Europe and Australasia until it was recently discovered in North America. Methods: Morphological identifications were made from specimens collected in Minnesota and Wisconsin, U.S.A. DNA sequences were obtained for three plastid-encoded genes (atpB, psbC, rbcL) from seven putative Lychnothamnus samples from two states in the U.S.A. Distribution and abundance were estimated in each lake using point intercept surveys where surveyors sampled aquatic vegetation. Key results: Fourteen lakes in Wisconsin and two lakes in Minnesota, U.S.A., were found to harbor Lychnothamnus barbatus. These represent the first report of this rare charophycean extant in the New World. The North American specimens matched the morphological description for L. barbatus and were compared directly with the neotype. Phylogenetic results using three plastid-encoded genes confirmed the identification placing New World samples with those from Europe and Australasia. Our phylogenetic analyses also confirmed the sister relationship between L. barbatus and Nitellopsis obtusa. Conclusions: Because this taxon is not known for aggressive invasiveness in its native range, it may have existed in heretofore-undiscovered native populations, although the possibility that it is a recent introduction cannot be eliminated. The potential for discovery of novel lineages of green algae in even well studied regions is apparently far from exhausted