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Finding new high-energy-density materials with desired properties has been intensely-pursued in recent decades. However, the contradictory relationship between high energy and low mechanical sensitivity makes the innovation of insensitive high-energy-density materials an enormous challenge. Here, we show how a materials genome approach can be used to accelerate the discovery of new insensitive high-energy explosives by identification of “genetic” features, rapid molecular design, and screening, as well as experimental synthesis of a target molecule, 2,4,6-triamino-5-nitropyrimidine-1,3-dioxide. This as-synthesized energetic compound exhibits a graphite-like layered crystal structure with a high measured density of 1.95 g cm −3 , high thermal decomposition temperature of 284 °C, high detonation velocity of 9169 m s −1 , and extremely low mechanical sensitivities (impact sensitivity, >60 J and friction sensitivity, >360 N). Besides the considered system of six-member aromatic and hetero-aromatic rings, this materials genome approach can also be applicable to the development of new high-performing energetic materials. The synthesis of explosive materials that are stable, highly dense, and have low sensitivity to external stimuli is a challenge. Here, the authors use a genomic approach to accelerate the discovery of insensitive high explosive molecules with good detonation and low sensitivity properties.

Pancreatitis-associated sepsis (PAS) caused by carbapenem-resistant bacteria poses significant clinical challenges. The objective of this research was to examine the microbial and metabolic profiles of individuals with carbapenem-resistant Acinetobacter baumannii (CRAB) and Escherichia coli (CREC) infections using integrated microbiomics and metabolomics approaches. Peripheral blood samples from 11 PAS patients (8 CRAB, 3 CREC) were analyzed using 16S rDNA gene sequencing and untargeted metabolomics via LC-MS. Microbial diversity, community structure, and differential metabolites were examined between CRAB and CREC groups. CRAB patients exhibited higher microbial diversity compared to CREC patients. p-Proteobacteria, p-Firmicutes, and p-Cyanobacteria predominated in both patient groups. Significant differences in microbial composition were observed, with p-Proteobacteria more abundant in CRAB and p-Cyanobacteria in CREC samples. g-Enhydrobacter and s-Moraxella osloensis were the biomarkers, significantly higher in CREC patients. Metabolomic analysis revealed 328 differential metabolites between groups, with the majority being downregulated in CRAB. The main categories of identified differential metabolites were amino acids and their derivatives. These differential metabolites were closely related to various metabolic pathways. The most significant metabolic difference between the two patient groups was the level of triglycerides. R-2 Methanandamide and 13-(β-D-glucosyloxy) docosanoic acid showed the highest correlation with g-Enhydrobacter and s-Moraxella osloensis. In PAS patients, s-Moraxella osloensis is a biomarker distinguishing CRAB and CREC infections, correlating with R-2 Methanandamide and 13-(β-D-glucosyloxy) docosanoic acid.

Sweet potato ( Ipomoea batatas [L.] Lam) is an important food crop, an excellent fodder crop, and a new type of industrial raw material crop. The lack of genomic resources could affect the process of industrialization of sweet potato. Few detailed reports have been completed on the mitochondrial genome of sweet potato. In this research, we sequenced and assembled the mitochondrial genome of sweet potato and investigated its substructure. The mitochondrial genome of sweet potato is 270,304 bp with 23 unique core genes and 12 variable genes. We detected 279 pairs of repeat sequences and found that three pairs of direct repeats could mediate the homologous recombination into four independent circular molecules. We identified 70 SSRs in the whole mitochondrial genome of sweet potato. The longest dispersed repeat in mitochondrial genome was a palindromic repeat with a length of 915 bp. The homologous fragments between the chloroplast and mitochondrial genome account for 7.35% of the mitochondrial genome. We also predicted 597 RNA editing sites and found that the rps 3 gene was edited 54 times, which occurred most frequently. This study further demonstrates the existence of multiple conformations in sweet potato mitochondrial genomes and provides a theoretical basis for the evolution of higher plants and cytoplasmic male sterility breeding.

•A review of the adoption of generative adversarial networks in clinical neuroimaging.•We focus on GAN's applications in modeling disease effects of neurologic diseases.•We discuss the pitfalls of current studies and provide future perspectives. Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases. [Display omitted]

Over the tropics, a robust statistical relationship between outgoing longwave radiation ( R ) and precipitation ( P ) is observed, linked to the cloud-radiative effect (CRE). To quantify this R – P relation, we define the CRE parameter, which exhibits significant disparities across global climate models (GCMs), with most overestimating it relative to the observation. Given the strong correlation between the CRE parameter and both historical and future extreme precipitation, an emergent constraint on the hydrological cycle projection is constructed. It lowers the fractional increase in the 99.9th percentile of tropical precipitation by the end of the 21st century from 29% to 24% under the high-emission warming scenario, with a 58% reduction in uncertainty. Overall, GCMs tend to underestimate the intensity of tropical extreme precipitation while overestimating its fractional increase. These findings provide valuable insights for model evaluation, improvement, and climate adaptation strategies.

ObjectivesRapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone.MethodsA consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student’s t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram.ResultsEighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1–3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits.ConclusionsBased on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19.Key Points• Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished.• A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.

Background Synovitis, among the most common signs of early-stage osteoarthritis (OA), is mainly mediated by fibroblast-like synoviocytes (FLSs). Cartilage destruction creates chondrocyte lysates (CLs) that activate synovial inflammation. A comprehensive understanding of chondrocyte–FLS communication might offer novel, specific therapeutic targets for treating synovitis and OA. Hence, we sought to uncover the specific role of CLs in OA-FLSs and synovitis. Methods Isolated CLs were cocultured with FLSs to test whether they could stimulate synovial inflammation. A model of medial meniscus destabilization was prepared in C57BL/6 mice and NLRP3 knockout mice, and adeno-associated virus overexpressing Caveolin-1 (CAV1) was intra-articularly injected for 8 weeks once a week after dissection of the medial meniscus (DMM). Proteins expressed in FLSs with and without CL coculture were screened using liquid chromatography-tandem mass spectrometry to identify CL-specific regulators of NLRP3 inflammasome-mediated pyroptosis. Results CLs were engulfed by FLSs, which aggravated inflammatory cytokine release and NLRP3 inflammasome-mediated FLS pyroptosis. NLRP3 expression was significantly upregulated in human OA-FLSs and FLSs cocultured with CLs, while CAV1 was downregulated. CAV1 overexpression reversed the inflammatory phenotype in FLSs and simultaneously rescued pyroptosis in CL-pre-treated FLSs. Both synovial hyperplasia and inflammatory infiltration in C57BL/6 mice with DMM surgery were alleviated after intra-articular AAV-CAV1 injection. Moreover, the CL-specific protein LIM-containing lipoma preferred partner (LPP) markedly exacerbated FLS pyroptosis and inflammation. Conclusions CLs were endocytosed by FLSs through CAV1, and the CL-specific protein LPP stimulated NLRP3 inflammasome-mediated pyroptosis and synovitis by inhibiting CAV1 expression. Our findings offer a novel therapeutic target for treating synovitis.

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10 −8 ). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine . The biological basis of brain aging is not well understood, but it has implications for human health. Here, the authors explore the genetic basis of human brain aging, finding genetic variants, genes and potential causal relationships with disease.

Primary brain calcification (PBC) is a rare neurodegenerative disease featured by bilateral brain calcifications and exhibiting high phenotypic and genetic heterogeneity. The clinical manifestations mainly include movement disorders, cognitive deficits, and neuropsychiatric symptoms. In this study, a novel heterozygous nonsense variant, c.1669C > T [p.(Gln557*)], in the solute carrier family 20 member 2 gene ( SLC20A2 ), encoding type III sodium-dependent inorganic phosphate transporter 2 (PiT2), was identified in a Han-Chinese family with PBC using whole exome sequencing and Sanger sequencing. Bioinformatics analysis predicted the variant’s deleterious effect. The cellular function impacts of the p.Gln557* variant and three other common PBC-related SLC20A2 variants, p.Ser113*, p.Ala585Thr, and p.Ser601Trp, were subsequently revealed. Subcellular localization analysis showed that the PiT2-Q557*, A585T, and S601W mutants mainly distributed in the plasma membrane and cytosol, whereas the PiT2-S113* mutant showed a diffuse distribution throughout the cells. All four investigated variants significantly impaired cellular inorganic phosphate transport activity. The protein mislocalization-inducing p.Ser113* variant likely causes haploinsufficiency, while the p.Gln557*, p.Ala585Thr, and p.Ser601Trp variants may lead to full or partial loss of function, or exert a dominant-negative effect. Cells expressing PiT2 mutants all exhibited inhibited proliferative and migratory activities, along with enhanced apoptosis. These SLC20A2 variants probably impair critical cellular functions, potentially providing an explanation for the neurological symptoms observed in PBC patients. These findings further broaden SLC20A2 variant spectrum and provide valuable mechanistic insights into the pathogenesis of SLC20A2 -associated PBC.

Background Most of Camellia oleifera forests have low fruit yield and poor oil quality that are largely associated with soil fertility. Soil physical and chemical properties interact with each other affecting soil fertility and C. oleifera growing under different soil conditions produced different yield and oil composition. Three main soil types were studied, and redundancy, correlation, and double-screening stepwise regression analysis were used for exploring the relationships between C. oleifera nutrients uptake and soil physical and chemical properties, shedding light on the transport law of nutrient elements from root, leaves, and kernel, and affecting the regulation of fruit yield and oil composition. Results In the present study, available soil elements content of C. oleifera forest were mainly regulated by water content, pH value, and total N, P and Fe contents. Seven elements (N, P, K, Mg, Cu, Mn and C) were key for kernel’s growth and development, with N, P, K, Cu and Mn contents determining 74.0% the yield traits. The transport characteristics of these nutrients from root, leaves to the kernel had synergistic and antagonistic effects. Increasing oil production and unsaturated fatty acid content can be accomplished in two ways: one through increasing N, P, Mg, and Zn contents of leaves by applying corresponding N, P, Mg, Zn foliar fertilizers, while the other through maintaining proper soil moisture content by applying Zn fertilizer in the surface layer and Mg and Ca fertilizer in deep gully. Conclusion Soil type controlled nutrient absorption by soil pH, water content and total N, P and Fe content. There were synergistic and antagonistic effects on the inter-organ transport of nutrient elements, ultimately affecting N, P, K, Cu and Mn contents in kernel, which determined the yield and oil composition of C. oleifera.