Browse Articles

A neuronal architecture underlying autonomic dysreflexia

Data navigation on the ENCODE portal

Abstract Spanning two decades, the collaborative ENCODE project aims to identify all the functional elements within human and mouse genomes. To best serve the scientific community, the comprehensive ENCODE data including results from 23,000+ functional genomics experiments, 800+ functional elements characterization experiments and 60,000+ results from integrative computational analyses are available on an open-access data-portal ( https://www.encodeproject.org/ ). The final phase of the project includes data from several novel assays aimed at characterization and validation of genomic elements. In addition to developing and maintaining the data portal, the Data Coordination Center (DCC) implemented and utilised uniform processing pipelines to generate uniformly processed data. Here we report recent updates to the data portal including a redesigned home page, an improved search interface, new custom-designed pages highlighting biologically related datasets and an enhanced cart interface for data visualisation plus user-friendly data download options. A summary of data generated using uniform processing pipelines is also provided.

Epidemiology, drug resistance, and molecular features of Klebsiella pneumoniae in Southwest Shandong, China

Efficient optimization accelerator framework for multi-state spin Ising problems

Abstract Ising Machines are emerging hardware architectures that efficiently solve NP-hard combinatorial optimization problems. Generally, combinatorial problems are transformed into quadratic unconstrained binary optimization (QUBO) form, but this transformation often complicates the solution landscape, degrading performance, especially for multi-state problems. To address this challenge, we model spin interactions as generalized boolean logic function to significantly reduce the exploration space. We demonstrate the effectiveness of our approach on graph coloring problem using probabilistic Ising solvers, achieving similar accuracy compared to state-of-the-art heuristics and machine learning algorithms. It also shows significant improvement over state-of-the-art QUBO-based Ising solvers, including probabilistic Ising and simulated bifurcation machines. We also design 1024-neuron all-to-all connected probabilistic Ising accelerator on FPGA with the proposed approach that shows $$\sim$$ ~ 10000 $$\times$$ × performance acceleration compared to GPU-based Tabucol heuristics and reducing physical neurons by 1.5 $$-$$ − 4 $$\times$$ × over baseline Ising frameworks. Thus, this work establishes superior efficiency, scalability and solution quality for multi-state optimization problems.

Prompt-dependent performance of multimodal AI model in oral diagnosis: a comprehensive analysis of accuracy, narrative quality, calibration, and latency versus human experts

Abstract Prompt design is a critical yet underexplored factor influencing the diagnostic performance of large language models (LLMs). Gemini Pro 2.5 shows promise in multimodal reasoning, but no prior study has systematically compared prompt structures in oral datasets against expert benchmarks. This study aimed to evaluate the diagnostic performance of a multimodal LLM (Gemini Pro 2.5) under different prompting strategies compared with oral medicine experts using prospective, histopathology-verified clinical vignettes. In a prospective, paired diagnostic accuracy study, Gemini pro 2.5 (a multimodal LLM) was evaluated under three prompting strategies: Direct (P-1), Chain-of-Thought (P-2), and Self-Reflection (P-3) on 300 oral lesion cases with histopathologic confirmation. Each prompt was applied to identical inputs and compared against diagnoses from board-certified oral medicine specialists. Accuracy, rubric-based narrative quality, probability calibration, and computational efficiency were assessed under STARD-AI guidelines. Human experts achieved the highest Top-1 accuracy (61%), but Chain-of-Thought prompting (P-2) led AI performance in Top-3 accuracy (82%) and produced the highest explanation quality (mean rubric score 8.49/10). No AI prompt matched human performance in low-difficulty cases. P-2 also showed the best calibration (Brier score 0.238) compared to P-1 and P-3. Resource-wise, Direct prompting was fastest, but longer outputs modestly improved Top-3 recall. Mixed-effects modeling confirmed that AI performance varied significantly by prompt structure, highlighting context-specific trade-offs. Prompt structure significantly affects the diagnostic performance and interpretability of AI-generated differentials in oral lesion diagnosis. While expert clinicians remain superior in straightforward cases, structured prompting, particularly Chain-of-Thought, may enhance AI reliability in complex diagnostic scenarios. These findings support the integration of prompt engineering into AI-assisted diagnostic tools to augment clinical decision-making in oral medicine.

Age and gender distortion in online media and large language models

Kinetically-controlled intermediate-direct-pinning for homogeneous energy landscapes in quasi-two-dimensional perovskites for efficient and narrow blue emission

Abstract Quasi-two-dimensional perovskite structures hold great potential as active layers in blue perovskite light-emitting diodes. However, they face challenges of limited emission efficiency and broadened spectra due to phase inhomogeneity. Here, we report an intermediate-direct-pinning method to develop a uniform-phase quasi-two-dimensional structure with a homogeneous energy landscape. By forming a strong cation-π interaction complex, we stabilize a metastable intermediate phase with retarded crystallization toward low- n phases ( n  ≤ 3), followed by a direct pinning process favouring crystallization of medium- n phases ( n  = 4 and 5) without further broadening. Additionally, introducing a surface-anchoring ligand during the pinning process effectively suppresses non-radiative recombination. The resultant structure shows efficient sky-blue emission and narrow linewidth (108 meV). Devices fabricated with this structure reach a maximum external quantum efficiency of 22.5% at 489 nm, which is scalable to large-area (pixel size: 900 mm 2 ) and passive matrix devices (30 × 10 arrays, active area = 200 μm × 600 μm). These findings highlight the potential of perovskite light-emitting diodes for full-colour displays.

Multiple polygenic score approach in colorectal cancer risk prediction

Abstract Recent studies have demonstrated that for various diseases, incorporating polygenic risk scores (PRSs) for other traits and diseases into the PRS-based risk prediction model may improve predictive performance – known as Multiple Polygenic Score (MPS) approach. We aimed to examine whether the MPS approach improves colorectal cancer (CRC) risk prediction. We included 2,187 non-CRC PRSs from the polygenic Score (PGS) Catalog and used machine learning (ML) models to select the most predictive non-CRC PRSs, utilizing individual-level data from 31,257 CRC cases and 33,408 controls. An independent dataset from the Genetic Epidemiology Research in Adult Health and Aging (GERA) cohort (4,852 cases and 67,939 controls) was randomly split into subsets for model estimation and validation. The model combined MPS with two existing CRC-PRSs based on known loci and genome-wide genotyping. We then assessed model performance by calculating the area under the receiver operating curve (AUC) in the validation set and performed 1,000 bootstrapped iterations to evaluate AUC improvements. The ML model selected 337 non-CRC PRSs predictive of CRC risk. Adding MPS to the CRC-PRSs significantly improved AUC by 0.017 (95% CI: 0.011–0.022, p  < 0.0001) when combined with known-loci CRC-PRS, 0.005 (95% CI: 0.002–0.007, p  = 0.0005) with genome-wide CRC-PRS, and 0.004 (95% CI: 0.002–0.006, p  = 0.0005) with both the known loci and genome-wide CRC-PRSs. These findings demonstrate MPS’s potential to refine CRC risk prediction models and highlight opportunities for further advancements in risk prediction.

On anisotropy in cubic Cu2O photoelectrodes

Photoactivated conductive MOF thin film arrays on micro-LEDs for chemiresistive gas sensing

Abstract Electrically conductive metal-organic frameworks (cMOFs) are emerging as promising chemiresistors due to their diverse compositions, chemical properties, porosity, and room-temperature conductivity, enabling the design of energy-efficient devices. However, limited activation in this regime hinders sensitivity and reversibility. In this study, cMOF thin films are integrated onto a micro-LED (μLED) platform using a layer-by-layer method, enabling photoactivated gas sensing even at room-temperature. The systematic coating allows for precise tailoring of films (e.g., thickness and overlayer structures) based on the adsorption properties of each analyte (ethanol, trimethylamine, ammonia, nitrogen dioxide). The selected arrays are optimized by varying the wavelengths and intensities of μLED, enabling sensitive and reversible sensing through additional charge generation, while consuming ultra-low power (587 µW). Additionally, a deep learning algorithm achieves rapid gas recognition within tens of seconds, with 99.8% classification accuracy in concentration prediction. This work demonstrates the feasibility of the cMOF–μLED integrated sensor platform, paving the way for next-generation gas-sensing technologies

Evaluation of antioxidant, anticholinesterase and antiproliferative potential of Artemisia herba-alba by artificial intelligence-assisted extraction optimization

Automated navigation of condensate phase behavior with active machine learning

Abstract Biomolecular condensates are essential cellular structures formed via biomacromolecule phase separation. Synthetic condensates allow for systematic engineering and understanding of condensate formation mechanisms and to serve as cell-mimetic platforms. Phase diagrams give comprehensive insight into phase separation behavior, but their mapping is time-consuming and labor-intensive. Here, we present an automated platform for efficiently mapping multi-dimensional condensate phase diagrams. The automated platform incorporates a pipetting system for sample formulation and an autonomous confocal microscope for particle property analysis. Active machine learning is used for iterative model improvement by learning from previous results and steering subsequent experiments towards efficient exploration of the binodal. The versatility of the pipeline is demonstrated by showcasing its ability to rapidly explore the phase behavior of various polypeptides, producing detailed and reproducible multidimensional phase diagrams. The self-driven platform also quantifies key condensate properties such as particle size, count, and volume fraction, adding functional insights to phase diagrams.

Long read sequencing reveals novel genomic and epigenomic alterations in repetitive regions of high grade serous ovarian cancer

Abstract Approximately half of high-grade serous ovarian carcinomas (HGSOCs) demonstrate homologous recombination deficiency (HRD) with characteristic genomic rearrangements. However, the impact of HRD on centromeres and transposable elements remains largely unexplored in HGSOC since conventional short-read sequencing is unable to interrogate these repetitive regions. We employed Oxford Nanopore long-read sequencing (LRS) to investigate genomic and epigenetic alterations in these regions. Pre-treatment archival cryopreserved tumor and matched blood samples were obtained for six patients with HGSOC. High-molecular-weight DNA was sequenced using Oxford Nanopore R10.4 flow cells and aligned to both the GRCh38 and the telomere-to-telomere T2T-CHM13 reference genome. Pathogenic gene mutations, allele-specific copy number variations, structural variants, and CpG methylation were analyzed. All six tumors had pathogenic TP53 mutations. Two carried germline BRCA1 mutations, while three showed CCNE1 amplifications. HRD scores and mutational signatures associated with HRD were elevated in the BRCA1 -mutated tumors. Centromeric regions were significantly hypomethylated in tumors and their methylation profiles distinctly separated HRD tumors from non-HRD tumors. LINE1 and ERV transposable elements showed marked hypomethylation in tumors without germline BRCA1 mutations. Chromosome arm-specific telomere lengths were significantly shortened in tumors. Allele-specific hypermethylation in the TERT hypermethylated oncological region was detected in three tumors. LRS uncovered HRD-related genomic and epigenomic alterations in previously inaccessible repetitive regions of HGSOC, including centromeric and transposable element hypomethylation. These findings highlight the potential of such abnormalities as novel biomarkers for HGSOC and warrant further application of the methods to larger cohorts in future studies.

Fast-switching dual-cathode electrochromic smart windows for year-round building energy savings

Rutin ameliorates sevoflurane-induced neurotoxicity by inhibiting microglial synaptic phagocytosis through the complement pathway

Reply to: Carbon implications of wood harvesting and forest management

Mitochondrial organization in the developing proximal tubule is controlled by LRRK2

Comparison of intubation techniques using standard geometric videolaryngoscope with bougie, hyperangulated videolaryngoscope, and videostylet in cadavers with only epiglottis visible

Polygenic viral factors enable efficient mosquito-borne transmission of African Zika virus

Silver-coated Zea mays L. nanocatalyst for efficient Azo dye photodegradation and antimicrobial applications

Abstract The increasing environmental issues and anticipated energy crisis highlight the urgent need for a cost-effective and efficient photocatalyst that responds to UV light for contaminant degradation. This work presents a novel approach to synthesizing Zea mays L. -loaded silver oxide nanoparticles (Ag 2 O) by chemically depositing a thin coating of Ag 2 O NPs onto the surface of Zea mays L. with two ratios (e.g., 5 & 10%) to form novel cost-effective core-shell Ag₂O/ Z nanostructures. To assess morphology, and elemental composition, the synthesized composite was examined using high-resolution transmission electron microscopy (HR-TEM) and scanning electron microscopy (SEM) together with energy dispersive X-ray (EDX) spectroscopy. The effectiveness of 10% Ag 2 O/Z as a catalyst and adsorbent was evaluated based on several criteria, including pH, beginning concentration of the target dye, and the amount of nanocomposite utilized. Significantly, 0.01 g of 10% Ag 2 O/Z attained a 95.0% elimination of MB at a solution pH of 9.0 after 120 min. Moreover, the Ag₂O NPs and Ag 2 O/Z core-shell nanostructures exhibited strong antibacterial effects, with the presence of Ag₂O NPs enhancing their antimicrobial properties and suggesting a synergistic effect with the Zea mays L. matrix against Klebsiella pneumoniae (ATCC:10031) , Staphylococcus aureus (ATCC:13565) , Bacillus subtilis (DSM:1088) , and Candida albicans (ATCC:10231) . In summary, Ag 2 O/Z core-shell nanostructure showed strong antibacterial activity against gram-positive, gram-negative, and fungal pathogens, as well as effective dye removal capabilities, making them a viable agent for industrial and environmental applications.