Browse Articles

Transient assembly of precision-tuned platinum-skin intermetallic catalysts for fuel cells

Highly efficient catalysts require precisely engineered intricate structures, yet conventional thermodynamically controlled syntheses often involve cumbersome procedures and limited structural precision. We report a nonequilibrium transient assembly strategy for the ultrafast synthesis of intricately structured nanocatalysts, including core-shell platinum (Pt)–skinned intermetallic nanocrystals exemplified by Pt@PtFe-i. By using a periodic thermal-pulse protocol to drive the continuous evolution of high-energy transient PtFe configurations, we achieved the synchronous assembly of a high-order PtFe intermetallic core and an atomic-layer-precise Pt skin. The Pt@PtFe-i catalyst exhibits coordination-dependent compressive strain within the Pt skin, creating a high density of highly active sites for the oxygen reduction reaction. The H 2 -air fuel cell with Pt@PtFe-i delivers a peak power of 1.25 watts per square centimeter at a cathode Pt loading of 0.1 milligrams per square centimeter, with a small peak power loss of 3.2% after 30,000 accelerated durability testing cycles.

An efficient and interpretable intrusion detection framework for software-defined networks with multi-class imbalanced data using genetic and GAN-based optimization

Antibodies sculpt adult brain circuits

Immune cells target hyperactive neurons to eliminate synaptic connections

ForNet: classification of critical forest acoustic events using discriminative CNN representations and ensemble learning

Abstract Various studies have been conducted in the field of sound event classification, with a specific emphasis on urban environments. However, there has been limited investigation in the realm of forest-oriented sound event classification. We introduce FSM5, a task-specific forest acoustic dataset curated from multiple publicly available sources to support forest sound event classification research, and propose a two-stage framework, ForNet, for effectively classifying critical sound events in forest. Initially, a Convolutional Neural Network (CNN) is used to extract discriminative audio embeddings. In the subsequent phase, ensemble classifiers such as XGBoost and Random Forest are employed for classification. The performance of MFCC, Log-Mel, and Mel spectrogram features is systematically evaluated, and the results indicate that MFCC and Log-Mel features significantly enhance classification performance. The findings indicate that combining handcrafted acoustic representations with CNN-derived embeddings yields improved performance compared to end-to-end CNN classification. The efficacy of deep CNN’s feature representation and the discriminative capability of shallow classifiers are evaluated. To showcase the reliability of ForNet, we have also conducted tests on the benchmark: Urbansound8k dataset. ForNet achieves an accuracy of 94% under 10-fold cross-validation on UrbanSound8K, outperforming several state-of-the-art methods, and attains an accuracy of 91.4% using 10-fold cross-validation on the FSM5 dataset.

USDA accelerates plan to close its flagship scientific campus

Agency says closing the research center will improve efficiency, but skeptics argue it will undercut research critical to farmers

Analysis and optimisation of the wastewater reinjection system at the Keshen gas field

A multi-agent system for automating scientific discovery

Ice as a geochemical reactor

Freezing redirects the fate of iron in Earth’s cryosphere

Performance of leading large language models in adhering to clinical guidelines for anaplastic thyroid cancer: a comparative study

Abstract Anaplastic thyroid cancer (ATC) is a rare, aggressive malignancy with poor prognosis. Adherence to guidelines from the National Comprehensive Cancer Network (NCCN), American Thyroid Association (ATA), and European Society for Medical Oncology (ESMO) is critical for optimal patient outcomes. As large language models (LLMs) increasingly enter clinical workflows, rigorous evaluation of their alignment with established guidelines is essential. We evaluated five leading LLMs for their ability to generate guideline-concordant responses to clinical questions about ATC. We conducted a comparative study in 2025 following TRIPOD-LLM (Transparent Reporting of a Multivariable Model for Individual Prognosis or Diagnosis, Large Language Models) guidelines. Seventy clinical questions of varying complexity were developed from ATA, NCCN, and ESMO guidelines. Three surgical oncology experts validated each question and subsequently evaluated responses from five LLMs: ChatGPT 4.1, ChatGPT 5, Gemini 2.5 Pro, Claude Sonnet 4, and DeepSeek R1. Each response was scored for relevancy, clarity, accuracy, and adequacy on a 5-point Likert scale. Inter-rater reliability was assessed using both intraclass correlation coefficients (ICC) and Gwet’s AC2 with ordinal weights. Model comparisons used the Kruskal-Wallis test with Dunn’s post-hoc analysis and Bonferroni correction. A pre-specified sensitivity analysis excluding the unblinded model (ChatGPT 5) was performed to confirm robustness. Significant performance differences emerged across all four metrics: accuracy ( p  = 0.007), adequacy ( p  = 0.003), clarity ( p  = 0.014), and relevance ( p  < 0.001). Gemini 2.5 Pro achieved the highest median accuracy (4.5), followed by DeepSeek R1 (4.4), while ChatGPT 4.1 scored lowest (4.0). ICC values ranged from 0.34 to 0.44 (poor to moderate), but Gwet’s AC2 yielded substantially higher estimates of 0.61 to 0.73 (moderate to substantial agreement), reflecting the impact of restricted score range on conventional reliability metrics. The sensitivity analysis excluding ChatGPT 5 confirmed the performance hierarchy among blinded models, with significance preserved or strengthened across all four metrics. Leading LLMs show variable capacity to align with ATC clinical guidelines. While top-performing models hold promise as supportive tools, their inconsistencies across domains and complexity levels preclude autonomous clinical use. These models should serve strictly as decision aids under expert supervision.

Does AI work for us, or do we work for AI? <b>The Reverse Centaur’s Guide to Life After AI</b> <i>Cory Doctorow</i> MCD, 2026. 240 pp.

There is little enthusiasm for a technology that turns human users into “reverse centaurs,” argues a critic

Identification of vacuum OLTC faults using improved multimodal PKO-SVM

Accelerating scientific discovery with Co-Scientist

Abstract Scientific discovery is driven by scientists generating hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent artificial intelligence (AI) system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and previous scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system’s design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling, and (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. Although this is a general-purpose system, we focus the validation in three biomedical applications: drug repurposing; novel-target discovery 1 ; and explaining mechanisms of antimicrobial resistance 2 . Specifically, Co-Scientist helped to identify new drug-repurposing candidates and synergistic combination therapies for acute myeloid leukaemia that were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI-empowered scientists.

To eat or to breathe?

In highland mice, the responses to low oxygen and toxins compete for a shared molecular regulator

Farmer perceptions and adaptation to agricultural water scarcity in Kondagaon district, Chhattisgarh, rural India

Coffee is under threat: how scientists are fighting to save it from extinction

The CARM1 epigenetic enzyme inhibits cross-presenting dendritic cell function in cancer immunity

The cancer-immunity cycle requires cross-presenting type I conventional dendritic cells (cDC1s) that induce T cell–mediated immunity, but therapeutic strategies for enhancing intratumoral cDC1 function are currently inadequate. We found the epigenetic enzyme CARM1 (coactivator-associated arginine methyltransferase 1) to be a selective negative regulator of cancer antigen presentation by cDC1s but not cDC2s. Inactivation of the Carm1 gene promoted cDC1 antigen cross-presentation, activation, and accumulation in tumors, and a CARM1 inhibitor enhanced cDC1-mediated priming of T cells by means of a cancer neoantigen vaccine. CARM1 inhibition increased chromatin accessibility at BATF3-Jun and RelA sites that are critical for cDC1 function and activation. Transforming growth factor–β regulated Carm1 expression, which suggests that CARM1 inactivation enhanced intratumoral cDC1 function without altering cDC1 homeostasis. These studies identify CARM1 as a potential therapeutic target for enhancing the antitumor function of mouse and human cDC1s.

Strategies to reduce osmotic stress during cryopreservation of red blood cells when using trehalose

Abstract The aim of this study was to determine optimal conditions for cryopreserving red blood cells (RBCs) using trehalose. We assessed the extent of trehalose uptake by RBCs during incubation at 37 °C and following freezing-and-thawing. Additionally, we examined whether betaine could alleviate osmotic stress at various stages of the cryopreservation process, including trehalose loading, freezing, and return to isotonic conditions after thawing. Trehalose uptake during incubation at 37 °C was limited, whereas freezing-and-thawing RBCs in trehalose-containing solutions led to substantially higher intracellular trehalose concentrations. The greatest post-thaw survival was observed with 400 mM trehalose in combination with rapid cooling. When trehalose was combined with betaine, optimal cryosurvival was maintained at a total solute concentration of 400 mM, whereas supplementing 400 mM trehalose with membrane-permeating cryoprotectants such as glycerol or dimethyl sulfoxide (DMSO) further enhanced cell cryosurvival. Membrane permeability studies indicated that betaine acts as a non-permeating solute contradicting literature findings. Direct transfer of trehalose-loaded, cryopreserved RBCs to isotonic conditions after thawing resulted in hemolysis, but this could be reduced by using hypertonic washing solutions. In conclusion, no synergistic protective effect was observed from combining betaine with trehalose for RBC cryopreservation, whereas combinations of trehalose with membrane-permeating agents like glycerol or DMSO appear promising for improving RBC cryopreservation outcomes.

Vicinal disubstitution of alkyl C–X synthons via alkene radical cation generation

In organic chemistry, functionalization of two adjacent carbons often starts from alkenes or already disubstituted precursors. Herein, we report an exergonic activation mode that directly generates alkene radical cation intermediates from monofunctional C(sp 3 )–X handles through a photoredox-triggered hydrogen-atom abstraction (HAT) and spin-center shift (SCS) process. Computations show that electron delocalization and a network of hydrogen-bonding solvent molecules facilitate a concerted [HAT+SCS] mechanism. The catalytic platform was used to design a transfer of electrophilic reactivity (C–X) from one carbon to another, which we refer to as electrophilic shuttling. Thus, two nucleophiles can be used in the construction of 1,2-difunctionalization adducts from homobenzylic C–X synthons, delivering bisazole architectures and demonstrating compatibility with other nucleophile classes. A suite of transformations is developed that departs from conventional synthetic logic, for which alkyl C–X scaffolds are confined to single-site substitutions, now transforming them into nonintuitive precursors for building vicinal complexity.

Neural pattern stability within events is similar in young and older adults

Virome-wide ubiquitin ligase discovery reveals diverse mechanisms of immune evasion

Viruses are intracellular parasites that reprogram the host proteome to promote replication and evade immune recognition. We applied a virome-wide library of ~10,000 open reading frames to discover viral ubiquitin ligases, mapping their mechanisms of degradation and host substrates using targeted CRISPR screens and proteomics. These viral effectors could be classified as canonical ligases that mimic host E3s, hijackers that redirect host E3s, and non-canonical ligases that rewire Cullin-RING ligase machinery. These diverse strategies of virus-mediated degradation converged on immune-related substrates, including JAK1 and CUL1 β−TrCP , underscoring immune evasion as a major driver of viral ubiquitin ligase evolution. Our findings elucidate viral strategies for exploiting the ubiquitin–proteasome system with potential for therapeutic targeting.