Trevor van Mierlo, DBA, caps off a four-part exclusive series exploring the current and future landscape of digital behavioral health interventions with JMIR Publication's latest: “You Can’t Launch This: Trust as Infrastructure in Digital Behavioral Health”.

ASU scientists create the first complete, high-res global map of seagrass and reveal changes over a four-year period. The invaluable data will help us protect these underwater rainforests.

Researchers from EMBL, LUMC, and collaborators reanalysed nearly 6,800 gut microbiome profiles in colorectal cancer patients and controls.

One of the largest single-disease gut microbiome meta-analyses to date, the new study identified a robust colorectal cancer microbiome signature that was consistent across populations, sequencing methods, and age-of-onset groups. 

A machine-learning classifier could distinguish colorectal cancer from non-cancer microbiomes across datasets. 

The colorectal cancer microbiome signature was linked to lower dietary fibre intake and could be reduced through fibre-focused dietary interventions.

The National Science Foundation-funded Virtual Experience Research Accelerator (VERA), a platform created through a $5 million NSF award to UCF Pegasus Professor Gregory Welch, has reached two major milestones: launching its first large-scale remote study and awarding its first use grant. Led by UCF Research Associate Professor Gerd Bruder, the study will investigate cybersickness using remote participants in virtual reality environments, while researchers from San José State University will use the first VERA grant to explore how people engage with immersive technologies to improve learning experiences across education, cultural institutions and public engagement. The milestones mark a significant step toward expanding the scale, accessibility and impact of virtual and augmented reality research nationwide.

JMIR Publications Announces Expansion of Jisc Flat-Fee Unlimited Open Access Partnership, Adding Three New UK Institutions for 2026-2027

A tandem neural network capable of inferring key physical parameters of semiconductor materials from simple transistor measurements has been developed, as reported by researchers from Institute of Science Tokyo. While conventional approaches for this type of analysis require hours or even days, the proposed system produces results in under one millisecond with near-perfect accuracy.