Affect Expression During Social and Non-Social Contexts in Autistic Young Adults.

Autism spectrum disorder (ASD) is characterized by impairments in social affective engagement. The present study uses a mild social stressor task to add to inconclusive past literature concerning differences in affective expressivity between autistic young adults and non-autistic individuals from the general population (GP). Young adults (mean age = 21.

Clinical trial screening in gynecologic oncology: Defining the need and identifying best practices.

Background: Evidence is limited in gynecologic cancers on best practices for clinical trial screening, but the risk of ineffective screening processes and subsequent under-enrollment introduces significant cost to patient, healthcare systems, and scientific advancement. Absence of a defined screening process makes determination of who and when to screen potential patients inconsistent allowing inefficiency and potential introduction of biases. This is especially germane as generative artificial intelligence (AI), and electronic health record (EHR) integration is applied to trial screening.

A protocol for investigating long-term social discrimination memory: Evidence in female and male Long Evans rats.

Social discrimination, the investigation of a novel peer more so than a familiar peer, is used as a measure of social memory. There is much less research on long-term social memory than short-term social memory, and no long-term social memory research in female rats. The majority of long-term social discrimination research has relied on long familiarization session of an hour or more and involved juveniles as the stimulus peers.

Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs.

Rapid technological advances and growing participation from amateur naturalists have made countless images of insects in their natural habitats available on global web portals. Despite advances in automated species identification, traits like developmental stage or health remain underexplored or manually annotated, with limited focus on automating these features. As a proof-of-concept, we developed a computer vision model utilizing the YOLOv5 algorithm to accurately detect monarch butterfly caterpillars in photographs and classify them into their five developmental stages (instars).