Even a toddler knows that plants need water. It’s perhaps the first thing we learn about these green lifeforms. But how plants budget this resource varies considerably. The kapok trees of the Amazon have adopted vastly different strategies than the switchgrass of the American plains. Unfortunately, it’s hard to directly measure which ones prevail in different ecosystem types and how they shift under changing conditions.

A study of migrants in Italy has shown how statistical modelling can help improve the identification of Neglected Tropical Disease (NTD) infections.

Research in PLOS Neglected Tropical Diseases focussed on soil-transmitted helminth (STH) infections using a case study of migrants in Italy’s Campania region.

STH is a type of worm infection caused by different species of roundworms with three types caused by A. lumbricoides, hookworms, and T. trichiura.

The data included 3,830 migrants from 64 countries, with over 87% male and with a median age of 27.

Industrial anomaly detection is crucial for maintaining quality control and reducing production errors, but traditional supervised models require extensive datasets. While embedding-based methods are promising for unsupervised anomaly detection, they are highly memory-intensive and unsuited to low-light conditions. In a new study, researchers developed a new unsupervised model that utilizes both well-lit and low-light images to achieve computationally efficient and memory-friendly industrial anomaly detection.