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A vehicle trajectory prediction model that integrates spatial interaction and multiscale temporal features
The lateralized LC-NAergic system distinguishes vicarious versus direct fear in mice
The effect of MIND diet on sleep status, mental health, and serum level of BDNF in overweight/obese diabetic women with insomnia: a randomized controlled trial
Competitive antagonism of KAT7 crotonylation against acetylation affects procentriole formation and colorectal tumorigenesis
Bacterial lysates in modifying sIgA levels in the upper respiratory tract in COVID-19 patients
Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics
Abstract The structure of amorphous silicon has been studied for decades. The two main theories are based on a continuous random network and on a ‘paracrystalline’ model, respectively—the latter defined as showing localized structural order resembling the crystalline state whilst retaining an overall amorphous network. However, the extent of this local order has been unclear, and experimental data have led to conflicting interpretations. Here we show that signatures of paracrystallinity in an otherwise disordered network are indeed compatible with experimental observations for amorphous silicon. We use quantum-mechanically accurate, machine-learning-driven simulations to systematically sample the configurational space of quenched silicon, thereby allowing us to elucidate the boundary between amorphization and crystallization. We analyze our dataset using structural and local-energy descriptors to show that paracrystalline models are consistent with experiments in both regards. Our work provides a unified explanation for seemingly conflicting theories in one of the most widely studied amorphous networks.
Assessment of urban seismic social vulnerability based on game theory combination and TOPSIS model: a case study of Changchun City
Enantioselective construction of inherently chiral pillar[5]arenes via palladium-catalysed Suzuki–Miyaura cross-coupling
Exploring the acceptance of e-learning in health professions education in Iran based on the technology acceptance model (TAM)
Discrete and conserved inflammatory signatures drive thrombosis in different organs after Salmonella infection
Abstract Inflammation-induced thrombosis is a common consequence of bacterial infections, such as those caused by Salmonella Typhimurium (STm). The presentation of multi-organ thrombosis post-infection that develops and resolves with organ-specific kinetics raises significant challenges for its therapeutic control. Here, we identify specific inflammatory events driving thrombosis in the spleens and livers of STm-infected mice. IFN-γ or platelet expression of C-type lectin-like receptor CLEC-2, key drivers of thrombosis in liver, are dispensable for thrombosis in the spleen. Platelets, monocytes, and neutrophils are identified as core constituents of thrombi in both organs. Depleting either neutrophils or monocytic cells abrogates thrombus formation. Neutrophils and monocytes secrete TNF and blocking TNF diminishes both thrombosis and inflammation, which correlates with reduced endothelial expression of E-selectin and leukocyte infiltration. Moreover, inhibiting tissue factor and P-selectin glycoprotein ligand-1 pathways impairs thrombosis in both spleen and liver. Therefore, we identify organ-specific, and shared mechanisms driving thrombosis within a single infection. This may inform on tailoring treatments towards infection-induced inflammation, and single- or multi-organ thrombosis, based on the clinical need.