Impact of community-based technology training with low-income older adults.

Objectives: Compared with younger and middle-aged adults, older adults are less likely to adopt new computer technology, potentially limiting access to healthcare and many other important resources available online. This limitation could impact cognitive abilities, well-being, and mental health outcomes of older adults. The aims of the present study were to increase access to online county and healthcare resources, while also assessing the impact of technology access on cognitive functioning and multiple well-being domains.

Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study.

Background: Sepsis is a life-threatening condition that can rapidly lead to organ damage and death. Existing risk scores predict outcomes for patients who have already become acutely ill.

Objective: We aimed to develop a model for identifying patients at risk of getting sepsis within 2 years in order to support the reduction of sepsis morbidity and mortality.

Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon.

This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program ('Translator'). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses.

Machine Learning of Spatiotemporal Bursting Behavior in Developing Neural Networks.

As with other modern sciences (and their computational counterparts), neuroscience experiments can now produce data that, in terms of both quantity and complexity, challenge our interpretative abilities. It is relatively common to be faced with datasets containing many millions of neural spikes collected from tens of thousands of neurons. Traditional data analysis methods can, in a relatively straightforward manner, identify large-scale features in such data (e.