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.

A research team led by Professor Jie Zeng and Associate Researcher Han Yan from the University of Science and Technology of China, in collaboration with Professor Chao Ma from Hunan University, has developed a novel ceria-supported platinum bilayer cluster catalyst. This breakthrough material demonstrates exceptional catalytic activity and stability in alkene hydrosilylation reactions while achieving atomic-level precision structural identification of the catalyst.

A recent study published in National Science Review has developed a field wastewater pathogen detection technology named WATER NEWS. Through iterative optimization of CRISPR-based assays, this system ​eliminates​ all dependence on cold-chain storage and continuous power supply while preventing nucleic acid aerosol contamination, ​thus establishing​ a sustainable epidemic surveillance paradigm for the post-pandemic era.