Browse Articles

Physical activity and anthropometric factors as predictors for postural stability in children

Abstract To determine whether anthropometric factors and different domains of physical activity are associated with postural stability outcomes in children. Ninety-five children aged 7–18 years were enrolled in a cross-sectional observational study. Exposures included demographic variables, BMI category (according to German percentiles), physical education (PE), and factor scores for physical activity domains derived from exploratory factor analysis. Postural stability was assessed using computerized dynamic posturography, including scores of Sensory Organization Test (SOT), Limits of Stability (LOS), and Motor Control Test (MCT). Primary hypotheses about BMI, height, sex, and PE grades were pre-specified, and factor-analytic activity scores were added as predictors. Among 95 participants (mean [SD] age, 13.0 [2.6] years; 44 [46%] male), an exploratory factor analysis of 12 self-reported activity indicators identified five domains: daily activity, cycling, walking, sports club activity, and leisure-time activity. These domains explained 72% of the variance. Age, height, PE, weight, daily activity, and leisure-time activity were associated with postural stability measures. Older age correlated with better directional control (β = 0.37; 95% CI, 0.03–0.71; P = .03) and higher SOT scores (β = 0.35; 95% CI, 0.01–0.68; P = .04). Lower PE grades were related to poorer directional control (β = −0.24; 95% CI, − 0.47 to − 0.02; P = .03). Overweight was linked to lower endpoint excursion (β = −0.65; 95% CI, − 1.27 to − 0.02; P = .04), and obesity to less favorable balance strategies (β = −0.62; 95% CI, − 1.08 to − 0.15; P = .009). Sports club participation moderated the association between obesity and MCT latency (β = −0.65; 95% CI, − 1.08 to − 0.22; P = .004). Model fit was modest (adjusted R² = 0.04–0.23). Postural stability correlated with school PE grades and several physical activity domains. Findings highlight complex biological-behavioral interactions, and the need for multidimensional assessment of motor, and balance competencies in youth.

Ice-sheet hydro-fracture not advanced inland by lower-elevation lake drainages in Kalaallit Nunaat

Abstract Drainage of supraglacial lakes via hydro-fracture is widely argued to be a mechanism for destabilization of grounded ice sheets under climate-warming scenarios because it may accelerate surface meltwater access to the ice-sheet bed. Progress in interrogating this hypothesis has been hindered by the lack of regional observations of hydro-fracture event occurrence, and the lack of observations of regional ice-sheet response to hydro-fracture events. Here, we remedy both deficiencies using a 22-station Global Navigation Satellite System array to discern inter-lake, hydro-fracture-event triggering potential between lakes spanning the mid-to-upper Greenland Ice Sheet ablation zone. In four separate instances, multiple lake hydro-fracture events occur close in time at similar elevations; meanwhile, strain rates across higher-elevation lake basins are unperturbed. Our findings support a simple model for the inland progression of surface-to-bed meltwater pathways beneath lakes: pathway initiation migrates alongside advancing surface melt, but is not accelerated by drainage activity at distant, lower-elevation lakes.

Ligand-Triggered Topology Switching Converts Transient Recognition into Durable Nanofibrillar Anchoring

Eco-friendly flower-like ZnO nanostructures for photocatalytic treatment of levofloxacin-contaminated wastewater

Abstract This work reports an environmentally benign strategy for producing zinc oxide (ZnO) nanoflowers from rubber fig ( Ficus elastica ) leaf extract and assesses their efficacy in sunlight-assisted degradation of levofloxacin. UV-visible spectrum displayed a clear absorption band at 373 nm, characteristic of ZnO nanoparticles. Field-emission scanning electron microscopic images showed flower-like nanostructures, and Fourier transform infrared spectra showed phytochemical functional moieties from the leaf extract that assist nanoparticle formation and stabilize the surface. X-ray diffraction patterns endorsed the crystallinity with a mean crystallite dimension of 44.86 nm and lattice parameters matching the hexagonal wurtzite structure. X-ray photoelectron spectroscopy further verified Zn 2+ through Zn 2p3∕2 and Zn 2p1∕2 peaks at 1021.32 and 1044.42 eV, a lattice oxygen signal at 529.9 eV, and minor C 1s contributions from residual phytochemicals, indicating high purity and a structurally stable lattice. Photoluminescence spectra showed a near-band-edge ultraviolet emission at 403 nm together with visible bands associated with defect-related states. Under natural sunlight, the ZnO nanoflowers consistently degraded levofloxacin, and the removal improved as the catalyst dose increased. The dataset follows a pseudo-first-order expression; the estimated rate constants span 0.0044 to 0.0056 min − 1 and R 2 > 0.99. The rise in performance at higher loadings aligns with a larger number of accessible surface sites and more efficient formation of reactive oxygen species. Collectively, ZnO nanoflowers synthesized by a green route show strong promise for the sustainable treatment of pharmaceutical wastewater.

4D metallic metamaterials for bone implants via biodegradation

Water-Induced Dynamic Structural Adaptivity of Zr-MOF for Holistic Metrics Optimization in Atmospheric Water Harvesting

Spatial distribution and habitat selection of common bottlenose dolphins (Tursiops truncatus) within two estuaries in southern coastal USA

Polyketide synthase-based controlled synthesis of polycyclopropanated fuel molecules

Abstract Reducing carbon emissions from aviation and long-distance transportation sectors requires the development of sustainable biofuels with suitable energy density, freezing point, and other physical properties. We previously demonstrated biological production of high energy polycyclopropanated fatty acids (POP-FAs, class I) using an iterative polyketide synthase (iPKS) pathway in a Streptomyces host. Here, we used a computational model of fuel properties to identify chain length and cyclopropanation control as critical steps to engineer this iPKS for biofuel applications. We next explored the natural diversity of POP biosynthesis by investigating homologous pathways. Then, by in vivo gene exchange, we determined cyclopropanase (CP) catalysis to be key for POP-FA engineering. Leveraging both natural and engineered pathway product diversity, we demonstrate targeted production of improved POP-FAs, namely shortened POP-FAs with predicted superior freezing point properties for aviation, as well as fully cyclopropane-saturated POP-FAs which should have superior energy-density. These precise and controllable modifications to POP-FA structure open the door for bioproduction of designer POP fuels.

Asymmetric Decarboxylative Protonation and Deuteration of Cyanoacetic Acids Using an Organometallic Proton Shuttle

PSCSE: prompt-based contrastive learning with sample filtering for unsupervised sentence embedding

Abstract Unsupervised sentence representation learning is critical in natural language processing. Recently, contrastive learning methods achieved remarkable performance by optimizing the alignment and uniformity of embedding spaces. Nevertheless, the dominant methods mainly focus on the data augmentation for the positive samples but ignore the sampling approach for the negative samples. Most methods rely on the random in-batch sampling approach for the generation of the negative samples. This approach could result in false negatives and create sampling biases for the model’s discriminative ability. To address this problem, we propose a new method prompt-based contrastive learning with sample filtering for unsupervised sentence embedding (PSCSE). In the proposed model, we used the synthesized hard negatives generated by the “NOT”-style prompts (e.g., “This sentence: [X] does not mean [MASK]”) to optimize the uniformity of the learned representations. In addition, we used the auxiliary encoder for the sample filtering approach to address the false negatives. We conducted experiments on the semantic textual similarity dataset and achieved remarkable performance by surpassing the dominant methods SimCSE, E-SimCSE, and PromptBERT by up to 1.0 points in the average Spearman’s correlation score.

Cuproptosis inducers mediate cold lethality via SLCR-46.1 in C. elegans

Triphenylene-Derived Polyimide Covalent Organic Frameworks for Efficient Photosynthesis of Hydrogen Peroxide

Persistent loss of membrane-associated MUC4 reprograms physiological functions in corneal epithelial cells via MAPK-dependent cascade

Muscle mitochondria, function, mass, and quality of life in prostate cancer during androgen deprivation therapy

Abstract Prostate cancer (PCa) negatively impacts muscle mass, physical function, and patient-reported outcomes (PROs), while androgen deprivation therapy (ADT) exacerbates these effects. Mitochondria are important for muscle function but their role in PCa patients undergoing ADT is not well-established. Our study characterizes the relationship between muscle mass, strength, endurance, PROs, and mitochondria in PCa patients over six months of ADT. Prior to ADT, higher mitochondrial function and endurance are associated with better PROs; whereas higher appendicular lean mass (ALM) correlate with worse PROs. Greater baseline VO 2 peak and mitochondrial function predict smaller declines in ALM, muscle function, and PROs. Proteomics analysis indicates mitochondrial dysfunction and upregulation of extracellular matrix organization, inflammation and coagulation-related pathways with ADT. Our findings suggest that mitochondrial function plays a role in muscle endurance and PROs in PCa. These data may help select patients and outcomes for clinical trials. Future studies should test whether targeting mitochondria can improve physical function and PROs in PCa.

Relation-aware context aggregation framework for sparse knowledge graph completion

Single-nucleus analysis reveals human-specific oligodendrocyte polarization and conserved neuronal responses after severe traumatic brain injury

Synergistic application of nano-silicon and Streptomyces albogriseolus enhances maize salinity tolerance by modifying soil silicon fractions and ionic homeostasis

Explainable time-series forecasting with sampling-free SHAP for Transformers

Abstract Time-series forecasting is essential for planning and decision-making across domains, and model explainability is critical for fostering user trust and satisfying transparency requirements. We introduce SHAPformer, an accurate, fast and explainable time-series forecasting model based on the Transformer architecture and Shapley Additive Explanations (SHAP). SHAPformer leverages attention manipulation to make predictions using feature subsets, thereby eliminating the need for sampling from background data required by established SHAP algorithms. As a result, it produces exact explanations in less than one second, achieving speedups of 50–1000 × compared to PermutationSHAP. On synthetic data with known ground-truth explanations, SHAPformer generates explanations that are true to the data. When applied to electrical load data and electricity price data, it achieves competitive predictive performance while providing meaningful local and global insights, including the identification of the past target as the key predictor and the detection of distinct load forecasting behavior during the Christmas period.

In Situ Light-Induced Degradation of Hybrid Perovskites by NMR Spectroscopy

Neural correlates of appetitive extinction learning: an fMRI study with actively participating pigeons

Abstract Extinction learning is an important learning process that enables adaptive and flexible behavior. Human neuroimaging studies show that the neural basis of extinction learning consists of a neural network that includes the hippocampus, amygdala, and subcomponents of the prefrontal cortex, but also extends beyond them. The limitations of applying fMRI to actively participating animals have so far restricted the identification of the entire extinction network in non-human animals. Here, we present the first fMRI study of extinction in awake and actively participating pigeons, using a Go/NoGo operant paradigm with a water reward. Our study revealed an extensive and largely left hemispheric telencephalic network of sensory, limbic, executive, and motor areas that slowly ceased to be active during the process of extinction learning. We propose that the beginning of extinction ignites a neuronal updating of the associated consequences of own actions within a large telencephalic neural network until a new association is established which competes with the previously acquired operant response.