Researchers from the Chinese Academy of Sciences review the disconnect between academic breakthroughs and industrial mass production of Flexible Transparent Conductive Films (FTCFs), proposing an application-driven framework and Manufacturing Readiness Level (MRL) assessment to accelerate commercialization.

A new study published in Big Earth Data develops explainable Random Forest models to predict stream total nitrogen (TN) and total phosphorus (TP) concentrations using upstream catchment characteristics derived from openly accessible environmental datasets. The results show that the models provide robust predictions for TN, while reduced and spatial covariates help mitigate overfitting and improve model interpretability, demonstrating the potential of explainable machine learning for regional nutrient monitoring.

Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy with variable responses to radiotherapy. Identifying reliable biomarkers to predict treatment response remains an urgent clinical need. This study investigated secretory leukocyte peptidase inhibitor (SLPI) as a dynamic biomarker for radiotherapy response in ESCC. We found that SLPI expression correlated with treatment outcomes and may serve as a prognostic indicator for ESCC patients undergoing radiotherapy. These findings could help guide personalized treatment decisions and improve outcomes for ESCC patients.

A key challenge in cancer precision oncology is the limited ability of genomic analyses to accurately predict changes in protein expression or function, even though proteins serve as the main targets of numerous modern therapies. Bridging this gap necessitates precise quantification of proteins and their post-translational modifications (PTMs). Recent advances in mass spectrometry (MS)-based proteomics now enable large-scale, quantitative characterization of proteins and PTMs in tumor tissues. To link genomic aberrations to cancer phenotypes, the emerging field of proteogenomics integrates proteomic data, including PTMs, with genomic, epigenomic, and transcriptomic information. This comprehensive approach offers a deeper understanding of cancer biology at multiple levels. This review highlights recent advancements in MS-based proteomics, key discoveries in cancer proteogenomics, and the transformative potential of this field in decoding the complexities of cancer across diverse dimensions.

Among 241,576 UK Biobank participants, residential greenspace was associated differently with incident ocular and adnexal disease subtypes. Higher exposure was associated with lower incidence of lens and, borderline, choroid and retina disorders, but associations differed for eyelid, lacrimal system, orbit, and conjunctiva disorders.

The study highlights the importance of outcome-specific environmental-health assessment. It cannot establish that greenspace causes or prevents disease, and mechanisms and generalizability remain to be tested.

Ancient single-celled red algae that thrive in scalding, acidic waters are emerging as far more than specialized survivors.

Research by Rutgers evolutionary biologist Debashish Bhattacharya and his collaborators show that the organisms, known as Cyanidiophyceae , form the foundation of entire microbial ecosystems, providing new insights into how complex life may have evolved and adapted to some of Earth’s harshest environments.

An innovative formulation based on natural compounds could become a sustainable alternative for controlling moniliasis, one of the most devastating diseases affecting cocoa in Latin America. A new study shows that emulsions combining fengycin—a lipopeptide produced by bacteria of the genus Bacillus—with essential oils can significantly inhibit the growth of the fungus Moniliophthora roreri, responsible for losses that can exceed 70–80% of the harvest.