A team led by Rutgers University-New Brunswick engineers has developed a portable device capable of detecting rare genetic mutations from a single drop of blood.

The instrument, described in a study published in Communications Engineering of the Nature Publishing Group, was shown in lab experiments to quickly and accurately test for a genetic condition called hereditary transthyretin amyloidosis, which can cause heart problems.

Researchers from the School of Plant Sciences and Food Security at the Faculty of Life Sciences at Tel Aviv University have developed a genetic editing method tailored to crop plants, which has influenced various traits in tomato plants, including the taste and shape of the fruit.

A joint research team led by Prof. Yoonjae Choi of the Kim Jaechul Graduate School of AI and Prof. Hwajeong Hong of the Department of Industrial Design has successfully developed and deployed a Virtual Teaching Assistant (VTA) that provides personalized feedback to individual students even in large-scale classes.

This year marks the opening of the United Nations Decade of Action for Cryospheric Sciences, an international initiative focused on the rapid changes occurring in glaciers, snow cover, ice sheets, sea ice, and permafrost and their impacts on the planet. MBARI’s advanced technology will play a critical role in this effort, providing important data about the Arctic seafloor and the Southern Ocean. MBARI research is answering fundamental questions about polar environments and other components of the cryosphere. This information can help resource managers and policymakers make decisions about the future of this important ecosystem.

A study published by Associate Professor Yanfei Li’s team from Hunan Agricultural University in Frontiers of Agricultural Science and Engineering provides an innovative solution to this challenge (DOI: 10.15302/J-FASE-2025613).

Shuai Feng and Chunling Chen’s team from Shenyang Agricultural University, have developed a novel vegetation index—the Rice Blast Index (RBI)—using drone-based hyperspectral remote sensing technology, offering a solution to this challenge. The study has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024576).

A study led by Professor Hui Liu from Central South University, published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024599), introduces a hybrid deep learning model named “ICEEMDAN-LZC-BVMD-SSA-DELM”, which significantly enhances the prediction accuracy of soybean futures prices. By integrating multi-stage data preprocessing and intelligent optimization algorithms, the model addresses limitations of traditional methods in noise handling, parameter tuning, and generalization capabilities, offering new insights for risk management in agricultural financial markets.