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Ultrawide-temperature-stable high-entropy relaxor ferroelectrics for energy-efficient capacitors
Adoption of contract vegetable marketing in Ethiopia, the case of Enebsie Sar Midir District in the Amhara Region
Future changes in severe hail across Europe, including regional emergence of warm-type thunderstorms
Abstract Hail is a major threat to agriculture, properties, and people, yet little is known about changes to hail with anthropogenic warming. Here, we use pan-European convection-permitting simulations, and a contemporary hail proxy benefiting from simulated thunderstorm features, and show that the potential for severe hail decreases under RCP8.5, except potentially for very large hail. This is despite an increase in the number of convective storms producing many small ice particles functioning as hail embryos. The decrease in severe hail potential is partly due to hail forming at higher altitudes as the atmosphere warms, which impacts both the updraft strength in the hail growth layer and the extent to which hail melts before reaching the surface. Our results contradict those from coarser resolution models which typically project future increases in hail frequency, estimated using environmental proxies. However, we find that future warm seasons feature a warmer thunderstorm type akin to hail-producing storms found in the tropics, where the largest hailstones can still reach the surface as evidenced from observations. In the future, these storms are most frequent over southern Europe, leading to regional increases in severe hail frequency. We conclude that society may need to be prepared for (infrequent but) more impactful hail in a future warmer world.
Butin regulates the NLRP3/CASP-1/GSDMD pyroptosis axis to inhibit proliferation and pyroptosis in human fibroblast-like synoviocytes of rheumatoid arthritis
Genomic insights into the 2020 mass die-off event among African elephants
Decentralized resource allocation in UAV communication networks through reward based multi agent learning
V-ATPase-dependent induction of selective autophagy
Explainable artificial intelligence-based cyber resilience in internet of things networks using hybrid deep learning with improved chimp optimization algorithm
Unravelling the drivers of marine biodiversity across the Phanerozoic
Abstract Understanding the potential drivers of spatial-temporal patterns in biodiversity has been a central tenet in biogeography and palaeontology for decades. More than 30 hypotheses have been proposed, including null models and theories based on environmental controls, energy, area, speciation/extinction dynamics, time, habitat features, ecological niches and biotic interactions. Yet, no consensus has been reached, and question remains whether a primary cause explains temporal trends and spatial patterns in biodiversity such as the latitudinal biodiversity gradient. Here we combine a macroecological model with global climate simulations to show that the niche-environment interaction may explain changes in global marine biodiversity and associated large-scale spatial patterns during the Phanerozoic (last 541 million years). We show that the niche-environment interaction imposed both a species carrying capacity and spatial constraints on marine biodiversity that defined the latitudinal biodiversity gradient. Although our model suggests that climate modulated the niche-environment interaction, hence spatial biodiversity patterns, it also demonstrates that palaeogeographical evolution imposed changes in shallow-water area, continental fragmentation and the location of landmasses relative to climatic belts and may have constituted the fundamental driver of changes in global marine biodiversity at the geological time scale. Therefore, several mechanisms interacted to balance the niche-environment interaction and drove the trajectory of marine biodiversity during the Phanerozoic.
Integrative analysis of molecular mechanisms in prostate cancer via single-cell RNA sequencing and weighted gene co-expression network analysis
Nonreciprocal quantum synchronization
Improved 3D reconstruction pipeline for enhancing the quality of 3D model generation from multi-view images
Direct chiroptical correlation of dissymmetric crystal morphologies
Abstract The expression of molecular dissymmetry in crystal form has been studied since Pasteur correlated macroscopic and microscopic chirality by measuring the optical activity of solutions of enantiomorphous tartrate salt crystals. Here, we show a direct correlation between enantiomorphous metal-organic framework (MOF) crystals and the chirality of the molecular structure. The geometry of the habits is correlated with single-crystal optical activity along the accessible low-symmetry directions. Weak X-ray scattering from small crystals was consistent with a hexagonal, enantiomorphous space group. However, the heterochirality of the mirror image forms could not be established with X-rays, necessitating a different approach. The optical circular birefringence of the enantiomorphs as evidenced by chiroptical imaging with a complete polarimetric microscope, was used to correlate optical and morphological chirality.
Probable respiratory sarcopenia and 9-year mortality in community-dwelling older adults: the first longitudinal evidence from the CHARLS
High-performance gas-phase chiral enantiomer detectors based on chiral-induced spin selectivity effect
Toxicological insights into the non-target effects of ornidazole using the Allium cepa bioassay system
Accurate prediction of gene deletion phenotypes with Flux Cone Learning
Abstract Understanding the impact of gene deletions is crucial for biological discovery, biomedicine, and biotechnology. Due to the complexity of genome-wide deletion screens, there is growing interest in computational methods that leverage existing screening data for predictive modeling. Here, we present Flux Cone Learning, a general framework designed to predict the effects of metabolic gene deletions on cellular phenotypes. Using Monte Carlo sampling and supervised learning, our approach identifies correlations between the geometry of the metabolic space and experimental fitness scores from deletion screens. Flux Cone Learning delivers best-in-class accuracy for prediction of metabolic gene essentiality in organisms of varied complexity (Escherichia coli, Saccharomyces cerevisiae, Chinese Hamster Ovary cells), outperforming the gold standard predictions of Flux Balance Analysis. We demonstrate the versatility of our approach by training a predictor of small molecule production using data from a large deletion screen. Flux Cone Learning can be applied to many organisms and phenotypes, without the need to encode cellular objectives as an optimization task. Our work offers a broadly applicable tool for phenotypic prediction and lays the groundwork for building metabolic foundation models across the kingdom of life.