Browse Articles

Haplotype-resolved genome assembly sheds light on the evolutionary history of autohexaploid Tripidium arundinaceum

Abstract Tripidium arundinaceum , a wild perennial grass with exceptional stress tolerance, has been tested in sugarcane breeding. Its complex polyploid genome has hindered understanding of its evolution and adaptive traits. Here, we present a high-quality, haplotype-resolved reference genome for autohexaploid T. arundinaceum Hainan92-105 (2 n  = 6 x  = 60). T. arundinaceum is likely originated from a diploid ancestor approximately 1.31 million years ago and contains abundant repetitive sequences from two major recent bursts of long terminal repeat retrotransposons following polyploidization. Phylogenetic analyses place Saccharum closer to Sorghum bicolor than to T. arundinaceum , indicating that the polyploidizations in these two lineages occurred independently. Population genomics analysis reveals southwestern China (Yunnan) as a diversity center for T. arundinaceum within China. Genotype–environment associations highlight temperature and precipitation as key local adaptation drivers mediated by stress-associated genes. This study provides a genomic resource for understanding polyploid evolution and climate adaptation in T. arundinaceum .

Enhancing molecular property prediction of transformer models with dual graph representation

Abstract Accurate prediction of molecular properties is central to advancing chemistry, materials science, and drug discovery. Machine learning on molecular graphs depends critically on representations that capture the topology and structure of molecules. Here we propose the dual graph transformer (DGT), a self-attention architecture that jointly models atom and bond graphs to achieve comprehensive molecular encodings. DGT fuses atom and bond features, graph topology and structure, and stereogeometric information within its self-attention module for an effective molecular representation. We benchmark DGT across a range of datasets for molecular property prediction, showing that it considerably outperforms the current state of the art. DGT demonstrates performance contributions from its dual graph representation, relative positional and structural encodings, and stereogeometric information incorporation while also offering interpretability at the molecular structural level. We envision DGT advancing molecular machine learning by improving both the prediction accuracy and interpretability of molecular properties.

Broadband Radiative Heat Transfer Suppression via Dispersion-Engineered Metasurfaces

Chronic suppression of a multidrug-resistant Pseudomonas aeruginosa in prosthetic joint infection using personalized bacteriophage treatment

Harnessing interfacial click polymerization using pyridinium-yne films as photochromic, radical generation and sensing platforms

Unifying network connectivity from geodesics to random walks via the random cluster model

Abstract Connectivity is a fundamental concept in network science, characterizing how interactions propagate through indirect pathways. While numerous connectivity metrics exist, such as shortest paths, effective resistance and minimum cut, each highlighting distinct structural features, their relationships remain largely fragmented. Here we show that these classical notions arise as limiting cases of a unified statistical-physics framework based on the random cluster (RC) model, which interprets connectivity as a principled synthesis of series and parallel transmission. By tuning its parameters, the RC model not only recovers classical connectivity measures but also extrapolates into unexplored regimes, leading to emergent notions of connectivity which yield practical tools for network learning tasks. In particular, RC connectivity naturally encodes the kinetics of growing paths, enhancing learning performance in dynamical settings such as epidemic spreading and neurodynamics. By linking structural, dynamical, and learning-based perspectives, RC connectivity establishes a general and interpretable foundation for the analysis of networked systems.

3D nanoprinting of metals by spatiotemporally confined hot electrons via multiple-electron excitations in nanocrystals

Development of high-precision automated dynamic plantar aesthesiometer (ADPA): a promising tool in pain research

Abstract Chronic pain is a severe burden affecting 20% of the population worldwide. To develop novel analgesics, in vivo preclinical assessment of the pain threshold is inevitable. Investigation of the nociception in rodents is still challenging, since most of the currently available methods are manually operated. So, the results highly depend on the experience of the examiner and can be significantly biased by subjective human factors. To improve this translational research paradigm, advanced tools are needed in this field. Therefore, the aim of the present study was to develop a new generation automated pain assessment device. In collaboration with Z-Elektronika Ltd., Pécs, Hungary we have designed and validated high-precision automated dynamic plantar aesthesiometer (ADPA) that is suitable for the assessment of mechanonociceptive threshold in rats and mice. It utilizes artificial intelligence (AI) to automatically recognize the animals investigated. The system’s software controls the mechanical stimulation of the hindpaws with simultaneous video recording of the nocifensive reaction and analysis of the pain thresholds. The main advantage of ADPA is the automated, computer-controlled induction and evaluation of the pain threshold, increasing the quality, comparability, reproducibility, and objectivity of the results. This device may significantly enhance the accuracy of pain assessment in animal models and contribute to improved preclinical pain research.

Psoas muscle area and frailty in elderly trauma patients

Perception of being worse based on changes in pain and disability in people with low back pain

Simulating 135 years of post-hatchling sea turtle dispersal in a dynamic North Atlantic Ocean

Maternal and neonatal outcomes after bariatric surgery in pregnancy: a cohort study comparing women with and without prior bariatric surgery

Simulating tree responses to elevated CO2 and climate change in agroforestry system

From hybrid to hemiclone: the genetic basis and evolutionary significance of clonal reproduction in Pelophylax esculentus

Toothed soft pneumatic gripper for grasping irregular geometries

A lightweight heuristic for cost-efficient IaaS auto-scaling of small-scale web applications

Abstract Pay-per-use Infrastructure-as-a-Service (IaaS) makes web-application hosting affordable for small organisations, yet cost-efficient elasticity remains unsolved for deployments of two to eight virtual machine instances: enterprise auto-scalers demand weeks of traffic history and dozens of tuning parameters, while naive fixed-threshold policies react only after service degradation has begun. This paper proposes the Lightweight Adaptive Scheduling Heuristic (LASH), an O (1)-state two-phase algorithm that minimises hourly IaaS cost subject to a 200 ms P99 latency SLA. Phase 1 applies double exponential smoothing to forecast request rate one VM warm-up horizon ahead; phase 2 selects the minimum-cost instance count while a two-clause minimum-lifetime / billing-aware flag suppresses premature scale-in. LASH is evaluated against four competitive baselines (fixed-threshold, moving-average, recursive-least-squares regression, and AWS Target Tracking) in a trace-driven discrete-time simulation calibrated to AWS EC2 and Azure VM pricing, instance warm-up, and queueing behaviour, across six synthetic load profiles ( $$n = 10$$ seeded runs per cell; 600 simulated experiments) and, for the AWS EC2 configuration only, the real FIFA World Cup 1998 24-hour production trace ( $$n = 10$$ replays). In simulation, LASH dominates every baseline on cost across all six profiles and on P99 latency across all but the lowest-CoV profiles, where the regression forecaster $$\pi _\text {LR}$$ is competitive. The mean cost reduction versus the fixed-threshold baseline is 41.9 % (BCa 95 % CI [40.7 %, 43.1 %], quantifying simulator run-to-run variability rather than deployment uncertainty), with a 23.7 % P99 latency reduction and a 75.9 % SLA-violation reduction; against a CPU-target reactive policy modelled on AWS Target Tracking the cost reduction is 13.5 %. All improvements are statistically significant under the matched-block Friedman test ( $$p < 0.001$$ , Friedman $$\varepsilon ^{2} = 0.97$$ ) and a corroborating linear mixed-effects model on run-level data. As a simulation study, these results characterise expected behaviour under the modelling assumptions stated in the paper and are not a substitute for measurement on production infrastructure.

Better road conditions for everyone can be a co-benefit of road maintenance

Effects of straw return and nitrogen fertilizer application on photosynthesis, biomass, yield and economic benefits of kidney bean

Fracture mechanisms and normalized compressive response of a TPMS-based PLA-CF/silicone/graphene oxide interpenetrating phase composite

Patient-derived organoids as a predictive platform for drug sensitivity in bladder cancer

Abstract Bladder cancer (BC) exhibits high inter- and intra-patient heterogeneity, limiting the efficacy of standard treatments and underscoring the need for personalized therapeutic models. We established patient-derived organoids (PDOs) from 31 clinical BC specimens, achieving an 80.65% success rate. These organoids preserved distinct morphological subtypes: solid, hollow, and mixed and retained stable proliferative capacity. Histological and molecular analyses confirmed that PDOs recapitulated key features of the parental tumors, including a hybrid basal-luminal phenotype with elevated CD44, GATA3, and LGR5 expression, and peripheral localization of CK20 and Uroplakin 3 A (UPK3A), indicating structural polarity and urothelial differentiation. Functional drug screening of early-passage PDOs revealed heterogeneous responses to standard-of-care (SOC) agents, cisplatin and gemcitabine, as well as the EGFR/HER2 inhibitor lapatinib. Notably, lapatinib enhanced chemosensitivity in a dose-dependent manner, even at reduced concentrations of standard agents. The most effective combinatorial regimens significantly impaired organoid viability and architecture, suggesting a synergistic effect. Drug response variability across PDO lines correlated with patient-specific clinical features, including tumor grade and recurrence status. These results demonstrate that BC PDOs faithfully model tumor heterogeneity and offer a robust platform for individualized drug response profiling. Our findings support the utility of PDOs for preclinical drug evaluation and precision oncology, particularly in identifying effective combination therapies such as lapatinib-enhanced chemotherapy.