🧬 biology

GcQCR7 Mediates Pathogenicity via Maintaining Cell Wall Integrity and Enhancing Oxidative Stress Tolerance in Glomerella cingulata

This study demonstrates that the mitochondrial subunit GcQCR7 is essential for *Glomerella cingulata* pathogenicity by maintaining cell wall integrity, energy metabolism, and oxidative stress tolerance, thereby identifying it as a novel target for managing Glomerella leaf spot in apple production.

Mingjuan Yang, Xinyue Cui, Shan Zhang, Fang Wang, Weichao Ren, Pingliang Li, Na Liu, Baohua Li, Chengying Jiang, Sen Lia (…)2026-07-01
🧬 biology

Mechanosensing at the endoplasmic reticulum by IRE1

This study identifies the endoplasmic reticulum as an autonomous mechanosensitive organelle where the transmembrane protein IRE1 directly senses membrane tension to trigger JNK signaling and enhance global protein synthesis, thereby linking mechanical forces to muscle adaptation independently of its canonical unfolded protein response role.

Hesso Farhan, Michaela Mayr, Luiz Garcia-Souza, Murphy McDermott, Utku Horzum, Yannick Frey, Margot Haun, Stephan Geley (…)2026-07-01
🧬 biology

Shotgun-NF: A reproducible Nextflow pipeline for end-to-end shotgun metagenomics analysis

Shotgun-NF is a portable, modular, and reproducible Nextflow pipeline that unifies a comprehensive suite of tools for end-to-end shotgun metagenomics analysis, from raw sequencing data to strain-level comparative insights, while ensuring execution consistency across diverse computing environments through containerization and automated provenance tracking.

Fereshteh Izadi, Sepideh Mofidifar, Emily A. Wasson, Carla S. Möller-Levet, André P. Gerber2026-07-01
🧬 biology

Predicting Microbiologically Influenced Corrosion Risk from Quorum Sensing Biofilm Community Features: A Random Forest-SHAP Approach

This study introduces a Random Forest-SHAP machine learning framework that successfully predicts microbiologically influenced corrosion risk by integrating novel quorum sensing biofilm community features with traditional environmental and microbial parameters, achieving an F1 score of 0.762 and demonstrating the predictive value of bacterial communication signals in corrosion assessment.

Bipul Bhattarai, Naina Maharajan, Sulav Dahal2026-07-01
🧬 biology

AI-Based Pipeline for Vascularization Detection in Histological Images; low-cost solution Using Moderate Computing Resources

This paper presents a low-cost, user-friendly AI pipeline that combines simple image processing with an interactive, self-learning similarity-search method to enable pathologists to rapidly and accurately detect vascularization in histological images using modest computing resources and minimal manual labeling.

Jan Žídek, Bretislav Lipový, Veronika Pavliňáková, Martin Knoz, Jakub Holoubek, David Izsák, Vladimir Váňa, Jan Herudek (…)2026-07-01
🧬 biology

Ecological Inference Is Structured Empirical Risk Minimization: Generalization Across Space, Nested Units, and Niche Support

This paper argues that the frequent failure of geographic models to generalize across sites stems from a fundamental estimand mismatch in nested survey data, demonstrating that ecological inference is structurally equivalent to empirical risk minimization under domain generalization and requiring population-level holdout validation rather than standard individual-level cross-validation to ensure reliable spatial transfer.

R. Craig Stillwell2026-07-01
🧬 biology

BioGPT-ClinVar: Parameter-Efficient Fine-Tuning of a Biomedical LLM for Genomic Variant Interpretation

This study presents BioGPT-ClinVar, a parameter-efficient fine-tuning framework using Low-Rank Adaptation (LoRA) to adapt the 150-million-parameter BioGPT model for genomic variant interpretation, demonstrating effective convergence on a curated ClinVar dataset while providing an open-source, reproducible pipeline for future clinical and surveillance applications.

Sepideh Moafi2026-07-01
🧬 biology

Trained quantum Kolmogorov--Arnold networks can dequantize, and a discrete-logarithm encoding need not: a measurement-based map of where quantum advantage can live

This paper demonstrates that while trained quantum Kolmogorov–Arnold networks are often classically simulable via low-bond-dimension tensor networks, genuine quantum advantage can be preserved and made trainable by employing discrete-logarithm encodings that embed number-theoretic hardness without succumbing to barren plateaus.

Hikaru Wakaura2026-07-01