The Land Institute and HudsonAlpha Institute for Biotechnology have assembled the first chromosome-scale, haplotype-phased genomes for Silphium integrifolium (rosinweed, silflower) and Silphium perfoliatum (cup plant, silphie). The findings are published in the journal Nature Communications. This landmark research promises to expand and accelerate the development of more climate-resilient, sustainable perennial crops for food, fiber, forage, and medicines.

Researchers release free future climate files for 5,000+ European locations, ready to plug into standard building simulation tools. The data allows us to design buildings for the future European climate.

The 4-week individualized, dose-controlled HD-tDCS combined with foot core exercise intervention improved sensorimotor function, reduced cortical activation during dorsi-plantarflexion tasks, and enhanced functional connectivity (FC) within related brain regions in individuals with chronic ankle instability. Lower cortical activation and higher increased related FC are associated with better ankle force sense. These findings indicated that this kind of combined intervention—simultaneously targeting cerebral cortex and peripheral ankle dysfunction—offers greater benefits than traditional functional training alone.

This study provides the first comprehensive estimation of the lifetime risk (LTR) of digestive system cancers in China using the Global Burden of Disease (GBD) 2023 data. With a 11.36% LTR for incidence and 8.28% for mortality, the findings highlight a profound public health challenge. Significant male-to-female and regional disparities—particularly the high burden in East China—underscore the urgent need for geographically tailored screening programs and primary prevention to mitigate this escalating health threat.

A joint research team from China explored a new paradigm integrating physical models with deep learning, proposing “McLDI-INR: Mask-constraint Lensless Dynamic Imaging by Dual-Domain Collaborative Implicit Neural Representation.” This framework overcomes lensless imaging’s conventional reliance on multiple acquisitions, strong prior assumptions, and high-precision physical models in dynamic scenarios. It enables lensless imaging systems to recover clearer, higher-resolution images of moving samples, paving the way for more flexible and practical lensless imaging applications.