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Powerful satellite will map changes on Earth in stunning detail — down to a centimetre

Retraction Note: Human fetal cerebellar cell atlas informs medulloblastoma origin and oncogenesis

LOMS.cz computational platform for high-throughput classical and combinatorial Judd-Ofelt analysis and rare-earth spectroscopy

Abstract We present LOMS.cz ( L uminescence, O ptical and M agneto-optical S oftware), an open-source computational platform that addresses the long-standing challenge of standardizing Judd-Ofelt (JO) calculations in rare-earth spectroscopy. Despite JO theory’s six-decade history as the fundamental framework for understanding $$4f\leftrightarrow 4f$$ transitions, the field lacks standardized computational methodologies for precise and reproducible parameter determination. LOMS integrates three key innovations: (1) automated computation of JO parameters, transition probabilities, branching ratios, and theoretical radiative lifetimes, (2) a dynamically expanding database of experimentally validated parameters enabling direct comparison between computed and empirical results, and (3) a novel Combinatorial JO (C-JO) analysis algorithm that systematically identifies optimal absorption band combinations to ensure reliable parameter extraction. As a proof-of-concept, we demonstrate how this computational framework enables rapid screening of spectroscopic parameters, allowing researchers to predict optical properties with enhanced reliability. By combining automated analysis with experimental validation through its integrated database, LOMS.cz establishes a standardized platform for accelerating the discovery and optimization of rare-earth-based photonic and optoelectronic materials.

Cell viability measured by cytotoxicity assay as a biomarker of chronic obstructive pulmonary disease exacerbation: a prospective cohort study

Abstract Acute severe exacerbation of chronic obstructive pulmonary disease (COPD) is related to high mortality; however, a robust blood biomarker for COPD exacerbation has not been established. Impaired clearance of apoptotic cells is a possible pathogenesis of COPD development. We evaluated the clinical utility of serum cell viability as a predictive biomarker for COPD exacerbation.Using serum from patients with stable COPD, cell viability was analyzed with a lactate dehydrogenase (LDH) assay. The patients were divided into low (optical density [OD] > 0.737) and high (OD ≤ 0.737) cell viability groups. Poisson regression analyses estimated the prognostic impact for COPD exacerbation, and a Cox proportional hazard model determined the impact on mortality. Among 162 patients, 47 were excluded due to follow-up loss within 1 year, asthma or combined interstitial lung disease diagnosis, and unsuitable cell viability measurements. The median follow-up duration was 6.3 years (range 0.7–11 years); 61 (53%) patients experienced at least one moderate or severe exacerbation, and 21 (19.7%) died. Patients in the low cell viability group were older, more likely to have poor quality of life and had a lower proportion of the non-exacerbator phenotype than those in the high cell viability group. The low cell viability group had a higher risk of moderate (incidence rate ratio [IRR], 1.58; p  = 0.049) and severe (IRR, 2.69; p  = 0.001) exacerbations and mortality (adjusted hazard ratio, 5.79; p  = 0.016).We identified that low cell viability, measured with a serum LDH cytotoxicity assay, was associated with severe COPD exacerbation and higher mortality in patients with COPD.

Comparative risk of retinal microvascular disorders in patients with gout initiating febuxostat versus allopurinol: a population-based cohort study

A preliminary study of lipid complex supplementation on fatty acid profile and blood antioxidant status after downhill running in athletes

Lava planets’ atmospheres give away what lies beneath

Predicting ‘sagittally unstable intertrochanteric fractures’ that require direct manipulation for reduction: a fracture morphology analysis

Enhancing image retrieval through optimal barcode representation

Butylated hydroxytoluene (BHT) induces zebrafish spinal cord defects and scoliosis by inhibiting the hedgehog pathway

A molecular cell atlas of mouse lemur, an emerging model primate

Abstract Mouse lemurs are the smallest and fastest reproducing primates, as well as one of the most abundant, and they are emerging as a model organism for primate biology, behaviour, health and conservation. Although much has been learnt about their ecology and phylogeny in Madagascar and their physiology, little is known about their cellular and molecular biology. Here we used droplet-based and plate-based single-cell RNA sequencing to create Tabula Microcebus, a transcriptomic atlas of 226,000 cells from 27 mouse lemur organs opportunistically obtained from four donors clinically and histologically characterized. Using computational cell clustering, integration and expert cell annotation, we define and biologically organize more than 750 lemur molecular cell types and their full gene expression profiles. This includes cognates of most classical human cell types, including stem and progenitor cells, and differentiating cells along the developmental trajectories of spermatogenesis, haematopoiesis and other adult tissues. We also describe dozens of previously unidentified or sparsely characterized cell types. We globally compare expression profiles to define the molecular relationships of cell types across the body, and explore primate cell and gene expression evolution by comparing lemur transcriptomes to those of human, mouse and macaque. This reveals cell-type-specific patterns of primate specialization and many cell types and genes for which the mouse lemur provides a better human model than mouse1. The atlas provides a cellular and molecular foundation for studying this model primate and establishes a general approach for characterizing other emerging model organisms.

CRxK dataset: a multi-view surveillance video dataset for re-enacted crimes in Korea

Mouse lemur cell atlas informs primate genes, physiology and disease

Abstract Mouse lemurs (Microcebus spp.) are an emerging primate model organism, but their genetics, cellular and molecular biology remain largely unexplored. In an accompanying paper1, we performed large-scale single-cell RNA sequencing of 27 organs from mouse lemurs. We identified more than 750 molecular cell types, characterized their transcriptomic profiles and provided insight into primate evolution of cell types. Here we use the generated atlas to characterize mouse lemur genes, physiology, disease and mutations. We uncover thousands of previously unidentified lemur genes and hundreds of thousands of new splice junctions including over 85,000 primate splice junctions missing in mice. We systematically explore the lemur immune system by comparing global expression profiles of key immune genes in health and disease, and by mapping immune cell development, trafficking and activation. We characterize primate-specific and lemur-specific physiology and disease, including molecular features of the immune program, lemur adipocytes and metastatic endometrial cancer that resembles the human malignancy. We present expression patterns of more than 400 primate genes missing in mice, many with similar expression patterns to humans and some implicated in human disease. Finally, we provide an experimental framework for reverse genetic analysis by identifying naturally occurring nonsense mutations in three primate immune genes missing in mice and by analysing their transcriptional phenotypes. This work establishes a foundation for molecular and genetic analyses of mouse lemurs and prioritizes primate genes, isoforms, physiology and disease for future study.

Astrocyte morphogenesis requires self-recognition

A comparative study of manifold learning methods for scRNA-seq with a trajectory-aware metric

The role of metabolism in shaping enzyme structures over 400 million years

Abstract Advances in deep learning and AlphaFold2 have enabled the large-scale prediction of protein structures across species, opening avenues for studying protein function and evolution 1 . Here we analyse 11,269 predicted and experimentally determined enzyme structures that catalyse 361 metabolic reactions across 225 pathways to investigate metabolic evolution over 400 million years in the Saccharomycotina subphylum 2 . By linking sequence divergence in structurally conserved regions to a variety of metabolic properties of the enzymes, we reveal that metabolism shapes structural evolution across multiple scales, from species-wide metabolic specialization to network organization and the molecular properties of the enzymes. Although positively selected residues are distributed across various structural elements, enzyme evolution is constrained by reaction mechanisms, interactions with metal ions and inhibitors, metabolic flux variability and biosynthetic cost. Our findings uncover hierarchical patterns of structural evolution, in which structural context dictates amino acid substitution rates, with surface residues evolving most rapidly and small-molecule-binding sites evolving under selective constraints without cost optimization. By integrating structural biology with evolutionary genomics, we establish a model in which enzyme evolution is intrinsically governed by catalytic function and shaped by metabolic niche, network architecture, cost and molecular interactions.

The effect of coadministration of D156844 and AR42 (REC-2282) on the survival and motor phenotype of mice with spinal muscular atrophy

Sparse transformer and multipath decision tree: a novel approach for efficient brain tumor classification

Synthesis and characterization of carbon based polymer composites reinforced with MWCNTs and graphite in PVDF matrix

Prediction of success of slings in female stress incontinence, statistical and AI modeling

Abstract Studies on predicting the outcome of sling surgery are limited. Most depend on analysis of multiple confounding factors using regression models. However, their prediction results are limited. In this study, we tested a statistical regression model and an AI model for the prediction of the outcome of mid-urethral sling. Data were collected from 151 patients who underwent MUS surgery in our center from 2002 to 2022 and confounding factors that affect the outcome of the surgery at a minimum of one year. The study was divided into two phases. Phase I included the construction of a prediction model using binomial logistic regression. In phase II, we applied AI techniques (Artificial neural network (ANN) and Support Vector Machines (SVM) trying to obtain better predictions. Phase I: The logistic regression model predicted the outcome of surgery with overall accuracy of 90.7% and positive predictive value of 61.5% [X 2 (11) = 46.24, P < 0.001]. Phase II: The data of the patients were entered as 10 features; 9 were predictors and the 10 th was the output. The output comprised 18 cases designated as ‘failure’ and 133 as ‘success’ output. The best model performance-wise was the (SVM) with 92% accuracy and 96% F1-score, which meets the industrial standards for predictive models. However, ANN produced 90% accuracy and 94% F1-score. However, our sample size is small. Prediction of the outcome of MUS surgery was achieved using different modalities with the best prediction of the outcome obtained by SVM method. This is helpful in future counseling of women undergoing sling surgery, whatever its type as to what to expect after surgery.