Predicting suicide attempts among youth with a history of self-harm remains difficult. Researchers used surveys and activity tracker data from 1,321 United Kingdom adolescents with a history of self-harm to test predictive computer models.

Silva, M. S., Patwa, B., James, D., Lepage, M., Palaniyappan, L., & Orri, M. (2026). Predictors of suicide attempt in adolescents with a history of self-harm: a machine learning study combining psychosocial and actigraphy measures. Center for Open Science. https://doi.org/10.31234/osf.io/qhbde_v1   Lena Palaniyappan

Little is known about how food practices change when poor households experience a sudden increase in income. To investigate, researchers analyzed 79 open-ended interviews with recipients of a canceled basic income project in Ontario.

Halpenny, C., Power, E., Kirkpatrick, L., & Smith‐Carrier, T. (2026). A Seat at the (Dinner) Table: Food and Eating Practices in the Ontario Basic Income Pilot. Sociology of Health & Illness, 48(7). https://doi.org/10.1111/1467-9566.70261   Tracy Smith-Carrier

Many systems appear to function smoothly only because people stretch their roles and absorb strain to keep daily operations running.

Cristancho, S. M. (2026). Brittle threshold: When adaptive success is the warning sign. Medical Education. https://doi.org/10.1111/medu.70322   Sayra M Cristancho

Scientists poorly understand how bacterial diversity across vast northern peatlands affects the storage of carbon during global change. To investigate, researchers surveyed 171 moss-dominated peatlands and swapped peat samples between different regions in a large-scale experiment.

Jassey, V. E. J., Lafont Rapnouil, T., Le Geay, M., Walcker, R., Gandois, L., Barret, M., Fanin, N., Robroek, B. J. M., Lauga, B., Andrews, L. O., Greuza, A. B., Bakker, M. R., Bertrand, G., Birkwald, T., Borges, P. A. V., Branfireun, B., Brearley, F. Q., Brousseau, C., Bu, Z., ... Carrias, J. (2026). Tipping Points in Bacterial Richness Amplify Extracellular Enzyme Activities in Northern Peatlands. Global Change Biology, 32(9). https://doi.org/10.1111/gcb.71099   Zoë Lindo

Harmful algae blooms threaten water supplies, aquatic ecosystems, and human health, but existing monitoring tools face practical limitations. The researchers reviewed artificial intelligence applications and evaluated various computer learning methods used to detect and manage these blooms.

Mehdizadeh Allaf, M., Dehnavi, P., Erratt, K. J., Rego, L. W., & Peerhossaini, H. (2026). Artificial Intelligence-Augmented Approaches for Next-Generation Harmful Algal Bloom Management. Water, 18(18), 2342. https://doi.org/10.3390/w18182342   Hassan Peerhossaini