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Regional drivers and contributors to changes in life expectancy across Asia
Multispectral image fusion via heterogeneous integration of broadband image sensor and sub‑Boltzmann Transistors on CMOS
Histidine-functionalized mixed-linker ZIF-8 for enhanced copper immobilization and efficient 1,2,3-triazole synthesis
Precise DNA base editing using AlphaFold3-based contact modelling
Lengthened autumn growing season contributes substantially to Earth’s greening
CaMKII-Mediated Phase Separation as a Driver of Synaptic Maturation
Synaptic maturation coordinates structural spine remodeling with the nanoscale organization of neurotransmitter receptors. While Ca 2+ /calmodulin-dependent protein kinase II (CaMKII) is a well-known structural and enzymatic scaffold at excitatory synapses, the mechanism linking its developmental expression to the maturation of the postsynaptic density (PSD) has remained unclear. Kuo et al. (2025) demonstrate that the developmental increase in CaMKII levels triggers liquid–liquid phase separation (LLPS) with the GluN2B subunit of NMDA receptors. This phase transition is necessary and sufficient to drive spine enlargement and the segregation of AMPA and NMDA receptor nanodomains. By identifying LLPS as the physical driver of this reorganization, the author provides a unified framework for how synapses acquire their mature architecture and highlights open questions about the functional impact of receptor nanodomains.
Advantages and challenges of training of inhalation technique with the Vitalograph AIM device
AI identifies interactions in CRISPR complexes to improve specificity of DNA editing
High-power hybrid commutated converter for carbon-neutral power transmission
Abstract To achieve carbon neutrality, we must overcome the challenges associated with the long-distance and large-capacity transmission of renewable energy. Direct-current transmission system has advantages in terms of high voltage and high controllability over the alternating-current transmission system. First-generation line-commutated-converter-based high-voltage direct-current systems risk commutation failures that threaten the safety of power grid, whereas second-generation voltage-source-converter systems entail higher carbon emissions, lower robustness, and higher power loss. We propose a next-generation high-power hybrid commutated converter that accesses renewable power, eliminating commutation failures through its hybrid operation modes of forced and recovery-enhanced commutation, with a hybrid device connection topology. We investigate the commutation principles and present the system design. Experimental results from a 120 kV/360 MW prototype demonstrate the efficacy of this high-power converter, which has been deployed in the Lingbao super-high-voltage direct-current project in Henan, China, and Mengxi ultra-high-voltage direct-current project in Inner Mongolia, China.
Thermodynamic Control of Facet Chemistry for Precise Solid-State Synthesis of Na Layered Cathodes
My Fifty Years Thinking about Emotional Consciousness in the Brain
This piece provides an overview of my fifty-year career as a neuroscientist. I spent much of this time trying to understand what emotions are and how they relate to human behavior and consciousness. I am pleased that my work on the amygdala helped bring emotion into the field. But late in the game I changed my views about the amygdala and emotions, causing some unrest in the field. Note that this is a personal, not an encyclopedic, overview of the neuroscience of emotion. In other words, this is my story about emotions and the brain, especially in relation to emotional consciousness.
Ultra-wideband printed monopole antenna with cardioid-shaped fractal geometry
High-linearity all-2D heterojunction-based neuromorphic image-sensing transistor array with a sandwiched tunneling layer for dynamic vision
Interpretable deep learning framework for secure clinical data analytics
Abstract The growing use of artificial intelligence in the healthcare sector has facilitated the emergence of sophisticated clinical data analytics, but the most critical issues pertain to data privacy, model interpretability, and model trustworthiness. These problems still pose a challenge to its real-world application. This study proposes an interpretation of a deep learning framework for secure clinical data analytics. This proposes a privacy-preserving distributed learning framework in combination with a naturally interpretable model structure. The suggested framework enables parallel training across several healthcare facilities without compromising raw patient information, thereby keeping the data confidential without adversely affecting the analysis. An idea-driven deep learning framework is also presented to produce clinically significant intermediate representations that enable clear, interpretable predictions. Moreover, a new explanation stability mechanism is established to maintain the consistency and reliability of model explanations in distributed environments. To promote clinical safety, the decision module includes uncertainty to enable the system to detect predictions with low confidence and defer them to experts. Massive tests using actual clinical data on patient conditions can be conducted to demonstrate that the framework presented is much more successful than other methods with respect to predictiveness, interpretability, explanation stability, fairness, and calibration. Furthermore, the framework demonstrates that privacy preservation, interpretability, explanation reliability, and clinical usability can be jointly optimized within a unified learning architecture. The findings indicate the effectiveness of the suggested strategy in providing secure, transparent, and reliable clinical decision support, rendering it an appropriate strategy to implement in contemporary healthcare systems.
Fly neurons carry a molecular ‘timestamp’ that encodes their birth order
A unified network systems approach uncovers a core program underlying T follicular helper cell differentiation
Comparative analysis of machine learning and regularized regression models for predicting cardiovascular outcomes in patients with type 2 diabetes after acute coronary syndrome
Abstract Identifying type 2 diabetes (T2D) patients at high cardiovascular risk following acute coronary syndrome (ACS) remains challenging despite treatment advances. We systematically compared machine learning and regularized regression methods for predicting cardiovascular outcomes using data from the EXAMINE trial (NCT00968708). Prediction models incorporating over 100 clinical and biomarker variables were developed for three endpoints: cardiovascular death, nonfatal myocardial infarction, or stroke; all-cause mortality; and cardiovascular death or heart failure hospitalization. Eleven algorithms were evaluated using area under the ROC curve (AUC) at 12 and 24 months, with tenfold stratified cross-validation. Regularized regression methods showed the most consistent and generalizable performance, with substantial overlap in confidence intervals between top-performing methods. At 24 months, among regularized models LASSO achieved the highest testing AUC for cardiovascular death or heart failure hospitalization (0.80, 95% CI 0.75–0.85) and all-cause mortality (0.75, 95% CI 0.69–0.81), while the best regularized model for the primary composite endpoint reached an AUC of 0.69 (95% CI 0.64–0.74); the strongest discrimination overall was observed for the 12-month heart failure endpoint (AUC up to 0.83). Adding the proteomic panel to clinical predictors improved or maintained discrimination across all six outcome-by-horizon strata (mean ΔAUC + 0.03). Complex algorithms including neural networks and random forests exhibited overfitting despite cross-validation. These findings indicate that regularized regression methods provide the most consistent and generalizable performance for cardiovascular risk prediction in T2D patients with recent ACS. For the heart failure hospitalization endpoint, the leading regularized model outperformed the best-tuned tree-ensemble method (XGBoost) significantly at 12 months (ΔAUC + 0.065, 95% CI + 0.031 to + 0.100, DeLong p < 0.001) and by a consistent but non-significant margin at 24 months (ΔAUC + 0.037, 95% CI − 0.007 to + 0.082, p = 0.10). The superior performance for heart failure events suggests current clinical and proteomic markers are particularly valuable for heart failure risk stratification in this population.
AI-redesigned starting points and outcomes enhance protein evolution
Abstract Engineered or laboratory-evolved proteins often have suboptimal stability, activity or specificity. Here we applied artificial intelligence (AI)-based protein sequence design to address challenges in experimental enzyme evolution. Using the model ProteinMPNN, we redesigned three distinct botulinum neurotoxin (BoNT) proteases, generating variants with improved stability and full catalytic efficiency 1 . We hypothesized that redesigned enzymes may be more mutationally robust than their wild-type (WT) counterparts, and therefore may serve as better starting points to evolve new function. We performed side-by-side phage-assisted continuous evolution campaigns initiated with AI-redesigned proteases or with the corresponding WT proteases 2 . Evolving three distinct redesigned enzymes as starting points consistently yielded proteases with higher activity than evolving WT proteases in the same selection. Across four evolution campaigns, redesign conferred robustness that unlocked access to otherwise inaccessible highly functional sequences, confirmed by the inability of redesign-evolved mutations to function in WT enzyme backgrounds. When redesign raises fitness in sequence space local to the starting point, redesigned starting points adapt at a faster rate. Finally, we evolved both WT and AI-redesigned BoNT/E protease to selectively cleave the therapeutically relevant protein ataxin-2. Proteases evolved from the redesigned starting point reached higher catalytic efficiency and stability while minimizing native substrate cleavage, achieving more than 79-fold greater selected specificity for ataxin-2 than the best-performing variant evolved from WT BoNT/E. This study establishes a practical workflow using AI-redesigned starting points to evolve enzymes with improved properties compared with those evolved from natural proteins, with broad implications for protein science.