A generalized machine learning model for long-term coral reef monitoring in the Red Sea.

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems.

Adolescent use and co-use of tobacco and cannabis in California: The roles of local policy and density of tobacco, vape, and cannabis retailers around schools.

Adolescent tobacco use (particularly vaping) and co-use of cannabis and tobacco have increased, leading some jurisdictions to implement policies intended to reduce youth access to these products; however, their impacts remain unclear. We examine associations between local policy, density of tobacco, vape, and cannabis retailers around schools, and adolescent use and co-use of tobacco/vape and cannabis. We combined 2018 statewide California (US) data on: (a) jurisdiction-level policies related to tobacco and cannabis retail environments, (b) jurisdiction-level sociodemographic composition, (c) retailer locations (tobacco, vape, and cannabis shops), and (d) survey data on 534,176 middle and high school students (California Healthy Kids Survey).

A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election.

We capture the public sentiment towards candidates in the 2020 US Presidential Elections, by analyzing 7.6 million tweets sent out between October 31st and November 9th, 2020. We apply a novel approach to first identify tweets and user accounts in our database that were later deleted or suspended from Twitter.

Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The U.S. Body Project I.

Most body image studies assess only linear relations between predictors and outcome variables, relying on techniques such as multiple Linear Regression. These predictor variables are often validated multi-item measures that aggregate individual items into a single scale. The advent of machine learning has made it possible to apply Nonlinear Regression algorithms-such as Random Forest and Deep Neural Networks-to identify potentially complex linear and nonlinear connections between a multitude of predictors (e.

Evaluating the 0-10 Point Pain Scale on Adolescent Opioid Use in US Emergency Departments.

Objective: To evaluate trends in national emergency department (ED) adolescent opioid use in relation to reported pain scores.

Methods: A retrospective, cross-sectional analysis on National Hospital Ambulatory Medical Care Survey (NHAMCS) data was conducted on ED visits involving patients aged 11-21 from 2008-2017. Crude observational counts were extrapolated to weighted estimates matching total population counts.

Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study.

Background: Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking.

Objective: The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups.

Methods: Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed.

Racial and ethnic disparities in opioid use for adolescents at US emergency departments.

Background: Racial/ethnic disparities in the use of opioids to treat pain disorders have been previously reported in the emergency department (ED). Further research is needed to better evaluate the impact race/ethnicity may have on the use of opioids in adolescents for the management of pain disorders in the ED.

Methods: This was a cross-sectional study using data from the National Hospital Ambulatory Medical Care Survey from 2006 to 2016.

ML-MEDIC: A Preliminary Study of an Interactive Visual Analysis Tool Facilitating Clinical Applications of Machine Learning for Precision Medicine.

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration.

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

Background: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these diseases are well known, investigation of as-yet unidentified risk factors may improve risk assessment and patient adherence to prevention guidelines. We investigated the diet domain in FHS (Framingham Heart Study), CHS (Cardiovascular Heart Study), and the ARIC study (Atherosclerosis Risk in Communities) to identify potential lifestyle and behavioral factors associated with coronary heart disease, HF, and stroke.

Developing Employment Environments Where Individuals with ASD Thrive: Using Machine Learning to Explore Employer Policies and Practices.

An online survey instrument was developed to assess employers' perspectives on hiring job candidates with Autism Spectrum Disorder (ASD). The investigators used K-means clustering to categorize companies in clusters based on their hiring practices related to individuals with ASD. This methodology allowed the investigators to assess and compare the various factors of businesses that successfully hire employees with ASD versus those that do not.

Learning in the machine: To share or not to share?

Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary.

Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning.

Background And Objective: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes.

Exploring the relationship of digital information sources and medication adherence.

We present a retrospective analysis of data collected in the United States from the 2015 National Consumer Survey on the Medication Experience and Pharmacists' Role in order to model the relationship between health information sources and medication adherence and perception. Our results indicate that while the digital age has presented prescription users with many non-traditional alternatives for health information, the use of digital content has a significant negative correlation with pharmaceutical adherence and attitudes toward medication. These findings along with previous research suggest that in order to fully realize the potential benefits of the digital age in regards to patient health, positive patient-provider discussions regarding information found online, efforts to improve general health literacy and improvements in the quality and accuracy of the information found are key.

Trends in Opioid Use in Pediatric Patients in US Emergency Departments From 2006 to 2015.

Importance: The use of opioids to treat pain in pediatric patients has been viewed as necessary; however, this practice has raised concerns regarding opioid abuse and the effects of opioid use. To effectively adjust policy regarding opioids in the pediatric population, prescribing patterns must be better understood.

Objective: To evaluate opioid prescribing patterns in US pediatric patients and factors associated with opioid prescribing.

Exploring Age-Related Metamemory Differences using Modified Brier Scores and Hierarchical Clustering.

Older adults (OAs) typically experience memory failures as they age. However, with some exceptions, studies of OAs' ability to assess their own memory functions-Metamemory (MM) - find little evidence that this function is susceptible to age-related decline. Our study examines OAs' and young adults' (YAs) MM performance and strategy use.

Remote sensing observation of annual dust cycles and possible causality of Kawasaki disease outbreaks in Japan.

Kawasaki disease (KD) is a rare vascular disease that, if left untreated, can result in irreparable cardiac damage in children. While the symptoms of KD are well-known, as are best practices for treatment, the etiology of the disease and the factors contributing to KD outbreaks remain puzzling to both medical practitioners and scientists alike. Recently, a fungus known as originating in the farmlands of China, has been blamed for outbreaks in China and Japan, with the hypothesis that it can be transported over long ranges via different wind mechanisms.

Evaluating Factors Impacting Medication Adherence Among Rural, Urban, and Suburban Populations.

Purpose: To evaluate differences in prescription medication adherence rates, as well as influencing factors, in rural and urban adults.

Methods: This is a retrospective analysis of the 2015 National Consumer Survey on the Medication Experience and Pharmacists' Role. A total of 26,173 participants completed the survey and provided usable data.

A Program Evaluation of Home and Center-Based Treatment for Autism Spectrum Disorder.

The present study aimed to retrospectively compare the relative rates of mastery of exemplars for individuals with ASD ( 313) who received home-based and center-based services. A between-group analysis found that participants mastered significantly more exemplars per hour when receiving center-based services than home-based services. Likewise, a paired-sample analysis found that participants who received both home and center-based services had mastered 100 % more per hour while at the center than at home.

T-Time: A data repository of T cell and calcium release-activated calcium channel activation imagery.

Background: A fundamental understanding of live-cell dynamics is necessary in order to advance scientific techniques and personalized medicine. For this understanding to be possible, image processing techniques, probes, tracking algorithms and many other methodologies must be improved. Currently there are no large open-source datasets containing live-cell imaging to act as a standard for the community.

AMP: A platform for managing and mining data in the treatment of Autism Spectrum Disorder.

We introduce AMP (Autism Management Platform), an integrated health care information system for capturing, analyzing, and managing data associated with the diagnosis and treatment of Autism Spectrum Disorder in children. AMP's mobile application simplifies the means by which parents, guardians, and clinicians can collect and share multimedia data with one another, facilitating communication and reducing data redundancy, while simplifying retrieval. Additionally, AMP provides an intelligent web interface and analytics platform which allow physicians and specialists to aggregate and mine patient data in real-time, as well as give relevant feedback to automatically learn data filtering preferences over time.

An Evaluation of the Impact of Supervision Intensity, Supervisor Qualifications, and Caseload on Outcomes in the Treatment of Autism Spectrum Disorder.

Ample research has shown the benefits of intensive applied behavior analysis (ABA) treatment for autism spectrum disorder (ASD); research that investigates the role of treatment supervision, however, is limited. The present study examined the relationship between mastery of learning objectives and supervision hours, supervisor credentials, years of experience, and caseload in a large sample of children with ASD ( = 638). These data were retrieved from a large archival database of children with ASD receiving community-based ABA services.

Intensity and Learning Outcomes in the Treatment of Children With Autism Spectrum Disorder.

Ample research has shown that intensive applied behavior analysis (ABA) treatment produces robust outcomes for individuals with autism spectrum disorder (ASD); however, little is known about the relationship between treatment intensity and treatment outcomes. The current study was designed to evaluate this relationship. Participants included 726 children, ages 1.

ChemDB update--full-text search and virtual chemical space.

Unlabelled: ChemDB is a chemical database containing nearly 5M commercially available small molecules, important for use as synthetic building blocks, probes in systems biology and as leads for the discovery of drugs and other useful compounds. The data is publicly available over the web for download and for targeted searches using a variety of powerful methods. The chemical data includes predicted or experimentally determined physicochemical properties, such as 3D structure, melting temperature and solubility.