Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading

Additive manufacturing has been recognized as an industrial technological revolution for manufacturing, which allows fabrication of materials with complex three-dimensional (3D) structures directly from computer-aided design models. The mechanical properties of interpenetrating phase composites (IPCs), especially response to dynamic loading, highly depend on their 3D structures. In general, for each specified structural design, it could take hours or days to perform either finite element analysis (FEA) or experiments to test the mechanical response of IPCs to a given dynamic load. To accelerate the physics-based prediction of mechanical properties of IPCs for various structural designs, we employ a deep neural operator (DNO) to learn the transient response of IPCs under dynamic loading as surrogate of physics-based FEA models. We consider a 3D IPC beam formed by two metals with a ratio of Young's modulus of 2.7, wherein random blocks of constituent materials are used to demonstrate the generality and robustness of the DNO model. To obtain FEA results of IPC properties, 5,000 random time-dependent strain loads generated by a Gaussian process kennel are applied to the 3D IPC beam, and the reaction forces and stress fields inside the IPC beam under various loading are collected. Subsequently, the DNO model is trained using an incremental learning method with sequence-to-sequence training implemented in JAX, leading to a 100X speedup compared to widely used vanilla deep operator network models. After an offline training, the DNO model can act as surrogate of physics-based FEA to predict the transient mechanical response in terms of reaction force and stress distribution of the IPCs to various strain loads in one second at an accuracy of 98%. Also, the learned operator is able to provide extended prediction of the IPC beam subject to longer random strain loads at a reasonably well accuracy.Comment: 23 pages, 14 figure

Multi-omics analysis explores the effect of chronic exercise on liver metabolic reprogramming in mice

Background: The effect of exercise on human metabolism is obvious. However, the effect of chronic exercise on liver metabolism in mice is less well described.Methods: The healthy adult mice running for 6 weeks as exercise model and sedentary mice as control were used to perform transcriptomic, proteomic, acetyl-proteomics, and metabolomics analysis. In addition, correlation analysis between transcriptome and proteome, and proteome and metabolome was conducted as well.Results: In total, 88 mRNAs and 25 proteins were differentially regulated by chronic exercise. In particular, two proteins (Cyp4a10 and Cyp4a14) showed consistent trends (upregulated) at transcription and protein levels. KEGG enrichment analysis indicated that Cyp4a10 and Cyp4a14 are mainly involved in fatty acid degradation, retinol metabolism, arachidonic acid metabolism and PPAR signaling pathway. For acetyl-proteomics analysis, 185 differentially acetylated proteins and 207 differentially acetylated sites were identified. Then, 693 metabolites in positive mode and 537 metabolites in negative mode were identified, which were involved in metabolic pathways such as fatty acid metabolism, citrate cycle and glycolysis/gluconeogenesis.Conclusion: Based on the results of transcriptomic, proteomics, acetyl-proteomics and metabolomics analysis, chronic moderate intensity exercise has certain effects on liver metabolism and protein synthesis in mice. Chronic moderate intensity exercise may participate in liver energy metabolism by influencing the expression of Cyp4a14, Cyp4a10, arachidonic acid and acetyl coenzyme A and regulating fatty acid degradation, arachidonic acid metabolism, fatty acyl metabolism and subsequent acetylation

Frailty and Anticoagulant Therapy in Patients Aged 65 Years or Older with Atrial Fibrillation

Background: Elderly adults with atrial fibrillation (AF) are at increased risk of frailty and thromboembolic complications. However, studies on the prevalence of frailty in AF patients and data on the relationship between frailty and the use of anticoagulants are limited. Methods: We conducted a cross-sectional study involving 500 participants. Patients aged 65 years or older were consecutively selected from the Chinese Atrial Fibrillation Registry study. The patient’s frailty status was assessed with use of the Canadian Study of Health and Aging Clinical Frailty Scale. We assessed the prevalence of and factors associated with frailty, and how frailty affects anticoagulant therapy. Results: In 500 elderly adults with AF (age 75.2±6.7 years; 51.6% female), 201 patients (40.2%) were frail. The prevalence of frailty was higher in females (P=0.002) and increased with age and CHA 2 DS 2 -VASc score (P for trend less than 0.001 for both). The factors associated with frailty were a history of heart failure (odds ratio [OR] 2.40, 95% confidence interval [CI] 1.39–4.14), female sex (OR 2.09, 95% CI 1.27–3.43), and advanced age (OR 1.13, 95% CI 1.09–1.17). Frail patients were significantly less likely to have ever been prescribed anticoagulants compared with nonfrail patients (81.7 vs. 54.9%, P<0.001). Conclusions: Frailty is prevalent in elderly adults with AF, especially in females, those of advanced age, and those with heart failure. Frailty status has a significant impact on prescription of anticoagulants for high-risk AF patients

Triticeae crop genome biology: an endless frontier

Triticeae, the wheatgrass tribe, includes several major cereal crops and their wild relatives. Major crops within the Triticeae are wheat, barley, rye, and oat, which are important for human consumption, animal feed, and rangeland protection. Species within this tribe are known for their large genomes and complex genetic histories. Powered by recent advances in sequencing technology, researchers worldwide have made progress in elucidating the genomes of Triticeae crops. In addition to assemblies of high-quality reference genomes, pan-genome studies have just started to capture the genomic diversities of these species, shedding light on our understanding of the genetic basis of domestication and environmental adaptation of Triticeae crops. In this review, we focus on recent signs of progress in genome sequencing, pan-genome analyses, and resequencing analysis of Triticeae crops. We also propose future research avenues in Triticeae crop genomes, including identifying genome structure variations, the association of genomic regions with desired traits, mining functions of the non-coding area, introgression of high-quality genes from wild Triticeae resources, genome editing, and integration of genomic resources

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Correlation of Internal Exposure Levels of Polycyclic Aromatic Hydrocarbons to Methylation of Imprinting Genes of Sperm DNA

Human exposure to polycyclic aromatic hydrocarbons (PAHs) results in adverse health implications. However, the specific impact of paternal preconception PAHs exposure has not been fully studied. In this study, a total of 219 men aged 24&ndash;53 were recruited and an investigation was conducted using a questionnaire requesting information about age, occupation, education, family history, lifestyle, and dietary preferences. Urine and semen samples were examined for the levels of the hydroxyl metabolites of PAHs (OH-PAHs) using ultra-high-performance liquid chromatography&ndash;tandem mass spectrometry and sperm DNA methylation by pyrosequencing. The results from the correlation analysis using seven OH-PAHs and the average methylation levels of the imprinting genes H19, PEG3, and MEG3 indicated that 1-OHPH is positively correlated with H19/PEG3 methylation levels. We further examined the correlation between each OH-PAH and the methylation levels at the individual CpGs. The results showed 1-OHPH is specifically correlated with CpG4 and CpG6 of the imprinted gene H19, CpG1 and CpG2 of PEG3, and CpG2 of MEG3; whereas 1-OHP is positively correlated with PEG3 at CpG1. Multivariate regression model analysis confirmed that 1-OHPH and 1-OHP are independent risk factors for the methylation of H19. These data show that sperm DNA imprinting genes are sensitive to adverse environmental perturbations