A comprehensive review published in Science China Life Sciences by a collaborative team led by Prof. Wenjie Shu (Bioinformatics Center of AMMS) et al. highlights that Protein Foundation Models (pFMs) have emerged as game-changers in life science.

These AI tools, trained on large-scale datasets, can predict protein characteristics and design new proteins with desired functions. This review explores the progress, uses, challenges, and future of pFMs. It looks at the diverse data—from genetic sequences to 3D structures and functional information—that these models learn from. It covers key AI methods and highlights real-world impacts in research, protein design, and medicine. The article also discusses major challenges, including data scarcity and the complexity of validating model outputs. Looking ahead, the review highlights promising developments, such as modeling protein interactions and building virtual cell systems, which have the potential to enpower the next generation of bioengineering. This comprehensive overview serves as both a valuable resource for computational researchers and a strategic reference for scientists using these tools in related fields.

Recently, Dr. Yuese Ning and colleagues at the Institute of Plant Protection, Chinese Academy of Agricultural Sciences (IPPCAAS) published a paper in Science Bulletin entitled “Pyramiding basal immunity and effector-triggered immunity to achieve broad-spectrum disease resistance in rice”. This research successfully developed broad-spectrum disease-resistant rice by pyramiding plant basal immunity and resistance (R) genes, providing robust support for the application of different resistance genes in rice and other crops.

To overcome the lack of wavelength-selective extraction in existing on-chip metasurfaces, Chinese scientists developed a novel approach by leveraging a nonlocal on-chip design based on symmetry-broken quasi-bound states in the continuum (q-BICs) physics, enabling precise wavelength-selective extraction and color routing of guided waves. Beyond free-space spatial-multiplexing schemes, these on-chip cascaded-multiplexing architectures achieve a significant improvement in the energy utilization efficiency, offering a new pathway for high-efficiency spectral control and routing on chip-integrated metadevices.

This study develops a spatial "source-flow-use" accounting framework to quantify the supply and allocation of high-quality water resources (HQWRs) from ecological conservation areas. Using a national park case study, it reveals significant allocation mismatches, with agriculture consuming over one-third of HQWRs while higher-value sectors receive minimal shares. Billions of cubic meters of HQWRs remain underutilized. The framework offers a replicable metric for instituting ecological compensation, differentiated pricing, and improved governance by valuing pristine water at its source, thereby integrating protected-area ecosystem services into regional water management.

This study develops a "Sustainable Water Space" network model to analyze the synergistic relationships among 53 Sustainable Development Goals (SDGs) indicators in the Yellow River Basin from 2015 to 2022. It reveals a stable four-cluster structure and identifies key water-related indicators—such as water use per unit GDP—that have evolved into critical "bridges" linking socioeconomic and water systems. The analysis further categorizes regions by complexity and eigenvector centrality, proposing differentiated policy strategies, such as focusing on residential wastewater reduction in high-complexity areas and industrial pollution control in low-complexity ones. The framework offers a systematic tool for guiding coordinated water management and sustainable development in the basin.