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Engineering tough blood clots for rapid haemostasis and enhanced regeneration
Genetic predictors of GLP1 receptor agonist weight loss and side effects
Post-swallowing voice-based aspiration screening in dysphagia using a deep learning approach: insights from audio segmentation
Evolutionary characterization of lung cancer metastasis
Abstract Limited understanding of the biological processes that govern metastatic dissemination hinders its prevention and treatment 1 . Here, using 501 longitudinally collected primary and metastatic tumour samples from 24 patients with non-small cell lung cancer (NSCLC) enrolled in the TRACERx lung study and PEACE autopsy programme, we infer tumour evolution from diagnosis to death. With DNA-sequencing data encompassing 70% of the metastases that were radiologically detected before death and paired multi-region sampled primary tumours, we show that the genomes of metastases diverge markedly from those of their ancestral primary tumour, with additional driver alterations and genome doubling events occurring after metastatic dissemination. In 62.5% of patients, multiple primary tumour subclones disseminated, each founding a distinct metastasis. These metastases served as sources of onward spread: more than half of the metastases sampled were seeded by other metastases. The duration that metastases existed in situ influenced their likelihood of seeding further metastases. Most metastatic migrations started and ended in the same anatomical cavity. The few subclones that exited the thorax to seed metastases disseminated widely and were enriched for somatic copy-number alterations, suggesting that chromosomal instability may facilitate extrathoracic spread. This spatial and temporal evolutionary analysis sheds light on the extent of metastatic diversity and seeding in advanced NSCLC—which tends to be underestimated in single metastasis biopsies—and identifies genomic and clinical mediators of metastatic progression.
Graphene scaffolds entrapped polyacrylonitrile electrospun nanofibrous separator for aqueous zinc-ion storage application
Can the ‘steroid Olympics’ show the sporting community how to support athletes better?
GLP-1R–GIPR–PPARα/γ/δ quintuple agonism corrects obesity and diabetes in mice
Abstract There are increasing numbers of effective drugs to improve obesity-linked metabolic dysfunction; GLP-1R–GIPR co-agonism is effective in the management of obesity and type 2 diabetes 1,2 , and lanifibranor—a nuclear-acting small-molecule triple agonist of PPARα, PPARγ and PPARδ—is in clinical phase 3 trials for the treatment of metabolic dysfunction-associated steatohepatitis 3 . Here, seeking to further improve the metabolic efficacy of GLP-1R–GIPR co-agonism, we report the development of a unimolecular quintuple agonist that combines the body weight-reducing and blood glucose-lowering effects of GLP-1R–GIPR co-agonism with the insulin-sensitizing and anti-inflammatory effects of lanifibranor via its targeted delivery into GLP-1R- and GIPR-expressing cells. In vitro, GLP-1–GIP–lanifibranor is indistinguishable from GLP-1–GIP in relation to incretin receptor signalling and shows equal stimulation of insulin secretion in isolated mouse islets. In vivo, however, GLP-1–GIP–lanifibranor outperforms GLP-1R–GIPR co-agonism and semaglutide, further decreasing body weight, food intake and hyperglycaemia in obese and insulin-resistant mice through synergistic incretin and PPAR action. The metabolic action of GLP-1–GIP–lanifibranor is blunted in mice with genetic or pharmacological inhibition of GLP-1R, GIPR or PPARδ and is absent in DIO double incretin receptor-knockout mice, collectively suggesting that GLP-1–GIP–lanifibranor has substantial therapeutic value in the treatment of obesity and diabetes.
Machine learning modeling of vegetation and limited two dimensional urban morphology effects on land surface temperature in Osaka using open data
Abstract Urban heat islands (UHI) significantly elevate land surface temperatures (LST) in high-density subtropical cities like Osaka, Japan, exacerbating energy demand, health risks, and climate vulnerability. This study investigates LST drivers using freely accessible Landsat 9 data (August 27, 2024) and OpenStreetMap (OSM) building footprints at 100 m resolution. Due to the absence of reliable 3D height data, we focus on vegetation indicators (mean NDVI and vegetation fraction) and basic 2D morphology (building coverage ratio [BCR] and building area density ratio [BADR], assuming uniform 10 m height) as a pragmatic open-data baseline. Vegetation metrics show weak positive associations with LST (Pearson r = 0.173 for NDVI_mean, 0.114 for VegFrac), while 2D morphology exhibits negligible links ( r ≈ 0.008). These results highlight the limited explanatory power of planimetric indicators alone in humid subtropical settings. Machine learning models (Random Forest [RF], XGBoost [XGB], Artificial Neural Network [ANN]) substantially outperformed multiple linear regression (R² = 0.045), with XGB achieving the highest performance (R² = 0.233, RMSE = 29.6 °C). NDVI_mean dominated feature importance (55.3%). Spatial predictions identified LST hotspots in central districts (high BCR, low vegetation), where partial dependence analysis suggests an indicative marginal LST reduction of ≈ 1.0–1.5 °C associated with a 10% point increase in VegFrac (model-derived statistical association, not causal; subject to considerable uncertainty due to the modest R² and excluded confounders). Results emphasize the need for multi-variable frameworks incorporating 3D morphology (e.g., sky view factor, height variation), landscape patterns, and meteorological factors to enhance predictive accuracy and inform targeted greening, ventilation corridors, and cool materials in Osaka’s urban planning. As a replicable, low-cost open-data baseline, this study offers practical insights for resource-constrained subtropical cities, contributing to Sustainable Development Goals (SDGs) 11 and 13.
Birds get a bad rap: why we should look up to our feathered friends
A digital twin-driven computation and analysis framework for low-altitude airspace
Mitigating errors in satellite solar irradiation using a sequential empirical-ANN model for four cities across Central and Northern Pakistan
Introgression of QTL hotspot regions enhances grain yield and maize lethal necrosis resistance in elite maize lines
Abstract Maize lethal necrosis (MLN) poses a severe threat to maize production in eastern and southern Africa, causing significant yield losses. In this study, marker-assisted backcross introgression (MABI) was used to introgress major-effect MLN resistance Quantitative Trait Loci (QTL). These QTLs located on chromosomes 3 and 6, were introgressed into 14 MLN-susceptible CIMMYT maize lines. Ten Kompetitive Alelle Specific PCR (KASP) SNP markers closely linked to three validated QTL-hotspot regions were applied for foreground selection, with at least two hotspots polymorphic across all donor–recipient combinations. Foreground and background selection enabled fast tracking of MLN resistance alleles and recovery of near-recurrent parent genomes. The resulting BC₄F 3 introgressed lines exhibited markedly reduced MLN severity under artificial inoculation, with several lines showing a 50% reduction relative to their recurrent parents. Testcrosses of these lines demonstrated yield advantages of 2–4 t/ha under MLN pressure compared with original parental lines, while maintaining comparable performance under optimum conditions. Notably, introgressed derivatives of CML312, CML539, and CZL052 displayed both enhanced MLN resistance and superior yield performance, with CZL052-derived testcrosses achieving nearly two-fold yield gains under severe MLN stress. Importantly, equivalence trials confirmed that MLN resistance was improved without compromising resistance to gray leaf spot, turcicum leaf blight, or common rust. These findings validate the effectiveness of QTL-based conversion for enhancing MLN resistance in elite breeding lines and demonstrate the potential of these improved lines as robust parental sources for developing MLN-resilient hybrids adapted to eastern and southern Africa.
Research on optimization of power grid load forecasting models based on deep learning
Population-scale repeat expansions elucidate disease risk and brain atrophy
Bridging laboratory findings and artificial intelligence for the design of TlInTe2 crystals
Criminals are made, not born: how when you live shapes whether you will break the law
Comparative analysis of advanced constraint-handling in quantum PSO with differential mutation for optimal power flow
Abstract Optimal Power Flow (OPF) is a highly nonlinear and constrained optimization problem that seeks optimal operating conditions while ensuring secure and efficient power system operation. Although metaheuristic algorithms have demonstrated strong global search capability for OPF, their performance is often limited by ineffective constraint handling. This paper presents a systematic investigation of three advanced constraint-handling (CH) techniques, Epsilon (ε) constraint (ECO), Superiority of Feasible Solutions (SFS), and Stochastic Ranking (SRA), when integrated into a unified Quantum-behaved Particle Swarm Optimization with Differential Mutation (QPSODM) framework. The proposed approaches are evaluated on IEEE 30, 57, and 118 bus test systems under multiple OPF objectives, including fuel cost, emission, voltage deviation, and power loss, while considering practical modeling features such as valve-point loading and multi-fuel generation. Statistical significance is assessed using the Wilcoxon signed-rank test complemented by effect size analysis. Numerical results indicate that QPSODM–ECO consistently achieves superior feasibility and convergence behavior, yielding up to 3.8% cost reduction and significantly lower constraint violations compared to SFS and SRA in large-scale systems. SFS exhibits comparable performance in several cases, whereas SRA shows inferior robustness under tight constraints. These findings confirm that the ε-constraint strategy is particularly well suited to quantum-behaved swarm dynamics and highlight the critical role of constraint-handling mechanisms in advanced OPF solvers.