This study explores AI-generated writing adoption across consumer complaints, corporate press releases, job postings, and international organization communications, revealing widespread use following ...

This study explores AI-generated writing adoption across consumer complaints, corporate press releases, job postings, and international organization communications, revealing widespread use following ChatGPT’s release. Findings highlight significant generative AI integration by small firms, corporations, and global institutions, emphasizing its transformative impact on communication practices, business strategies, and public policy.

Visual quality assessment is crucial for both human perception and artificial vision systems. This study reveals how the human brain naturally perceives image quality through distinct neural pathways. Functional magnetic resonance imaging results show that while low-level visual areas are highly sensitive to distortions, higher-level regions compensate to maintain recognition, much like how AI systems might “fill in” missing details. These findings not only deepen our understanding of human vision but also provide biological inspiration for developing more robust image-processing algorithms.

The vast microbial “dark proteome” remains largely uncharacterized. Nelson et al. present LA4SR, a transformer framework that classifies algal sequences 10,000× faster than traditional methods with near-perfect recall. Trained on 77 million sequences including synthetic chimeras, the system can robustly classify unknown sequences with natural or scrambled start and stop information. They also developed an accompanying suite of explainability software to reveal biologically meaningful patterns linking amino acid patterns to evolutionary affiliations.

This study introduces a language-model-driven agent system that captures and applies laboratory knowledge to autonomously plan, execute, and analyze experiments. When the agent system was demonstrated on quantum computing tasks, the agents achieved performance on par with expert researchers. By enabling intelligent coordination and decision-making in complex lab settings, this approach paves the way for more flexible, scalable automation in experimental science.

LigUnity is a protein-ligand affinity foundation model, enabling both rapid virtual screening and precise hit-to-lead optimization. LigUnity learns a shared “language” for protein pockets and ligands, outperforming docking in screening and approaching FEP+ accuracy at far lower cost. It generalizes to new targets and new chemical scaffolds and achieves 106 times the speedup compared to Glide-SP, offering a practical foundation for early-stage drug discovery.

Sleep staging is vital for understanding sleep physiology and diagnosing sleep disorders, yet manual scoring is time-consuming and unscalable. The authors introduce AISleep, an unsupervised sleep staging algorithm based on feature-weighted kernel density estimation, using only a single EEG channel. AISleep outperforms SOTA unsupervised methods, generalizes better than supervised models, and uncovers age-related EEG feature decline impacting staging accuracy. Its lightweight design enables integration into portable devices, providing a practical, scalable solution for accurate and accessible home-based sleep monitoring.

This opinion piece discusses the Bioconductor project for open-source bioinformatics and the engineering concepts underlying its effectiveness to date. Since the inception of Bioconductor in 2002 with...

The authors present an integrated radiomics-deep learning-LLM agent framework for hepatocellular carcinoma (HCC) management. A multimodal fusion model accurately predicts key pathological features from MRI, while LLM agents generate personalized treatment strategies. Expert evaluation confirms strong clinical alignment. This work demonstrates the promise of AI-powered decision systems in precision oncology.

In this review, the authors explore the transformative impact of large language models (LLMs) on drug discovery and development. They detail how LLMs can potentially accelerate our understanding of disease mechanisms, facilitate de novo drug discovery, and optimize clinical trial processes, including patient matching and outcome prediction. They also assess the current maturity of LLM applications and outline future directions for integrating these advanced computational models into the pharmaceutical pipeline.

This review explains how artificial intelligence is reshaping in vitro fertilization, from forecasting embryo health to tailoring hormone therapy. By combining images, health records, and molecular clues, these tools promise safer, more effective fertility treatments for patients worldwide.

Biochemistry; Medical biochemistry; Omics; Proteomics

The rise of data-driven science has made research data management (RDM) essential, yet implementing it remains complex due to diverse disciplinary needs, limited tailored guidance, and gaps in training and knowledge among researchers. The RDMkit is a community-led, open resource that addresses these challenges by offering practical, domain-specific RDM guidance along with links to relevant tools, training, and knowledge for life sciences. Built on a reusable infrastructure, RDMkit supports the full data life cycle and enables organizations and communities to establish their own guidelines.

This perspective argues that artificial neural networks (ANNs), while effective as function approximators, omit biologically relevant structures, such as ion channels, that introduce intrinsic randomn...

This perspective argues that artificial neural networks (ANNs), while effective as function approximators, omit biologically relevant structures, such as ion channels, that introduce intrinsic randomness. Drawing on pragmatism and functionalism, Bikić, Kaspar, and Pernice propose that non-optimizing structures may be essential for developing robust intelligence and highlight the characteristics of current ANN architectures when compared to the biological complexity of real neurons.

Explainable AI (XAI) has emerged to address the “black box” problem in predictive modeling, offering transparency in AI decision making and helping to prevent critical failures in high-stakes environments. This review provides insight into the current state of XAI and the motivations for its uses, with a focus on bridging the gap between technical XAI developments and domain-specific applications in manufacturing and materials science.