Global health should be trans-disciplinary. [PDF]
Lawrence RJ.
europepmc +1 more source
Plant Genetic Engineering: Technological Pathways, Application Scenarios, and Future Directions
This review maps the fast‐evolving landscape of plant genetic engineering, linking enabling platforms with trait‐focused applications in architecture optimization, stress resilience, yield improvement, and quality enhancement. It highlights how genome editing, transgenic strategies, and emerging multi‐gene approaches reshape breeding pipelines, while ...
Peilin Wang +4 more
wiley +1 more source
Commentary: Psychology's Questionable Research Fundamentals (QRFs): key problems in quantitative psychology and psychological measurement beyond Questionable Research Practices (QRPs). [PDF]
Staller MS, Koerner S.
europepmc +1 more source
Protein complexes like KIBRA‐PKMζ are crucial for maintaining memories, forming month‐long protein traces in memory‐tagged neurons, but conventional RNA‐seq analysis fails to detect their transcript changes, leaving memory molecules undetected in the shadows of abundantly‐expressed genes.
Jiyeon Han +10 more
wiley +1 more source
CENTRA: knowledge-based gene contextuality graphs reveal functional master regulators by centrality and fractality. [PDF]
Hause F +9 more
europepmc +1 more source
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju +5 more
wiley +1 more source
Value landscapes in interdisciplinary and transdisciplinary research and assessment: exploring indeterminacies and disconnects. [PDF]
Schaltegger AS, Vienni-Baptista B.
europepmc +1 more source
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
Statistical Signatures of Quantum Contextuality. [PDF]
Hofmann HF.
europepmc +1 more source

