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The dynamic response description of materials in high-speed impact fields is of practical significance to structural design and practical engineering application. In this paper, an electromagnetic impact (EI) loading process was proposed to acquire dynamic stress-strain relationships of 2A10 aluminum alloys. A modified Johnson-Cook (J-C) material model was obtained by combining with Quasi-static experiments and verified by numerical simulations. Comparing the J-C model obtained by a Split Hopkinson pressure bar, the simulative results about maximum deformation displacements showed the modified J-C model was more in line with actual experimental results. The accuracy under the discharge energy of 4 and 5 kJ was improved by 50% and 11%, respectively. In addition, electromagnetic impact loading characteristics and microstructure evolution of materials were studied. The discharge current with an attenuated sine wave caused that electromagnetic impact forces demonstrated a bimodal trend. The maximum impact velocities reached up to 4.7 m/s and 6.7 m/s under the discharge energy of 4 and 5 kJ, respectively (the maximum strain rates are 655.0 and 932.3 s−1, respectively). The high-speed impact effect led to the emergence of adiabatic shear bands (ASBs) during deformation microstructure evolution. Due to higher impact speed, the deformation concentration degree was more remarkable under the energy of 5 kJ. [ABSTRACT FROM AUTHOR]

Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases involving fibrosis may share common mechanisms and therapeutic targets, and therefore common intervention strategies and medicines may be applicable for these diseases. For this reason, deliberately introducing anti-fibrosis characteristics into predictive modeling may lead to more success in drug repositioning. In this study, anti-fibrosis knowledge base was first built by collecting data from multiple resources. Both structural and biological profiles were then derived from the knowledge base and used for constructing machine learning models including Structural Profile Prediction Model (SPPM) and Biological Profile Prediction Model (BPPM). Three external public data sets were employed for validation purpose and further exploration of potential repositioning drugs in wider chemical space. The resulting SPPM and BPPM models achieve area under the receiver operating characteristic curve (area under the curve) of 0.879 and 0.972 in the training set, and 0.814 and 0.874 in the testing set. Additionally, our results also demonstrate that substantial amount of multi-targeting natural products possess notable anti-fibrosis characteristics and might serve as encouraging candidates in fibrosis treatment and drug repositioning. To leverage our methodology and findings, we developed repositioning prediction platform, drug repositioning based on anti-fibrosis characteristic that is freely accessible via https://www.biosino.org/drafc. [ABSTRACT FROM AUTHOR]

Background: Histone deacetylase (HDAC) is a novel target for the treatment of cancer and it can be classified into three classes, i.e., classes I, II, and IV. The inhibitors selectively targeting individual HDAC have been proved to be the better candidate antitumor drugs. To screen selective HDAC inhibitors, several proteochemometric (PCM) models based on different combinations of three kinds of protein descriptors, two kinds of ligand descriptors and multiplication cross-terms were constructed in our study. Results: The results show that structure similarity descriptors are better than sequence similarity descriptors and geometry descriptors in the characterization of HDACs. Furthermore, the predictive ability was not improved by introducing the cross-terms in our models. Finally, a best PCM model based on protein structure similaritydescriptors and 32-dimensional general descriptors was derived (R2 = 0.9897, Qtest2 = 0.7542), which shows a powerful ability to screen selective HDAC inhibitors.Conclusions: Our best model not only predict the activities of inhibitors for each HDAC isoform, but also screen and distinguish class-selective inhibitors and even more isoform-selective inhibitors, thus it provides a potential way to discover or design novel candidate antitumor drugs with reduced side effect. [ABSTRACT FROM AUTHOR]

Cyclic peptides offer a range of notable advantages, including potent antibacterial properties, high binding affinity and specificity to target molecules, and minimal toxicity, making them highly promising candidates for drug development. However, a comprehensive database that consolidates both synthetically derived and naturally occurring cyclic peptides is conspicuously absent. To address this void, we introduce CyclicPepedia (https://www.biosino.org/iMAC/cyclicpepedia/), a pioneering database that encompasses 8744 known cyclic peptides. This repository, structured as a composite knowledge network, offers a wealth of information encompassing various aspects of cyclic peptides, such as cyclic peptides' sources, categorizations, structural characteristics, pharmacokinetic profiles, physicochemical properties, patented drug applications, and a collection of crucial publications. Supported by a user-friendly knowledge retrieval system and calculation tools specifically designed for cyclic peptides, CyclicPepedia will be able to facilitate advancements in cyclic peptide drug development. [ABSTRACT FROM AUTHOR]

Background: Prior studies reported that 5 ~ 32% COVID-19 patients were critically ill, a situation that poses great challenge for the management of the patients and ICU resources. We aim to identify independent risk factors to serve as prediction markers for critical illness of SARS-CoV-2 infection.Methods: Fifty-two critical and 200 non-critical SARS-CoV-2 nucleic acid positive patients hospitalized in 15 hospitals outside Wuhan from January 19 to March 6, 2020 were enrolled in this study. Multivariable logistic regression and LASSO logistic regression were performed to identify independent risk factors for critical illness.Results: Age older than 60 years, dyspnea, respiratory rate > 24 breaths per min, leukocytosis > 9.5 × 109/L, neutrophilia > 6.3 × 109/L, lymphopenia < 1.1 × 109/L, neutrophil-to-lymphocyte ratio > 3.53, fibrinogen > 4 g/L, d-dimer > 0.55 μg/mL, blood urea nitrogen > 7.1 mM, elevated aspartate transaminase, elevated alanine aminotransferase, total bilirubin > 21 μM, and Sequential Organ Failure Assessment (SOFA) score ≥ 2 were identified as risk factors for critical illness. LASSO logistic regression identified the best combination of risk factors as SOFA score, age, dyspnea, and leukocytosis. The Area Under the Receiver-Operator Curve values for the risk factors in predicting critical illness were 0.921 for SOFA score, 0.776 for age, 0.764 for dyspnea, 0.658 for leukocytosis, and 0.960 for the combination of the four risk factors.Conclusions: Our findings advocate the use of risk factors SOFA score ≥ 2, age > 60, dyspnea and leukocytosis > 9.5 × 109/L on admission, alone or in combination, to determine the optimal management of the patients and health care resources. [ABSTRACT FROM AUTHOR]

Background: Observational studies have suggested the potential associations between atopic dermatitis (AD) and psychiatric disorders. However, the causal relationship between them remains uncertain. This study aimed to evaluate the potential bidirectional causal relationship between AD and psychiatric disorders, including autism spectrum disorder (ASD), major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), bipolar disorder (BD), anorexia nervosa (AN), Tourette syndrome (TS), schizophrenia, and anxiety. Methods: Bidirectional two-sample Mendelian randomization (MR) was employed to elucidate the causality between AD and psychiatric disorders, using summary statistics from the most comprehensive genome-wide association studies conducted on AD (Ncases = 60,653, Ncontrols = 804,329). Psychiatric disorders were derived from the Psychiatric Genomics Consortium and were independent of AD data sources. The MR analysis entailed the implementation of multiple methods, including the inverse variance weighted method, MR-Egger regression method, weighted median method, simple mode method, and weighted mode method. Results: Bidirectional two-sample MR analysis uncovered significant causal associations between AD and severe psychiatric disorders. Specifically, liability to AD was associated with increased risk of ADHD (OR = 1.116; 95% CI: [1.009, 1.234]; P = 0.033) and ASD (OR = 1.131; 95% CI: [1.023, 1.251]; P = 0.016). Additionally, evidence suggested that liability to ADHD (OR = 1.112; 95% CI: [1.094, 1.130]; P = 9.20e-40), liability to AN (OR = 1.1; 95% CI: [1.068, 1.134]; P = 4.45e-10) and liability to BD (OR = 1.067; 95% CI: [1.009, 1.128]; P = 0.023) were associated with an increased risk of AD. Only the causal association between AD and ASD was independent of the reverse effect bias. These causal associations were robust and not affected by biases of heterogeneity and horizontal pleiotropy. Conclusions: Our study emphasizes the significant causal association between AD and an increased risk of ASD, and also identifying BD and AN as risk factors for AD. [ABSTRACT FROM AUTHOR]

Nuclear receptors (NR) are a class of proteins that are responsible for sensing steroid and thyroid hormones and certain other molecules. In that case, NR have the ability to regulate the expression of specific genes and associated with various diseases, which make it essential drug targets. Approaches which can predict the inhibition ability of compounds for different NR target should be particularly helpful for drug development. In this study, proteochemometric modelling was introduced to analysis the bioactivity between chemical compounds and NR targets. Results illustrated the ability of our PCM model for high-throughput NR-inhibitor screening after evaluated on both internal (AUC > 0.870) and external (AUC > 0.746) validation set. Moreover, in-silico predicted bioactive compounds were clustered according to structure similarity and a series of representative molecular scaffolds can be derived for five major NR targets. Through scaffolds analysis, those essential bioactive scaffolds of different NR target can be detected and compared. Generally, the methods and molecular scaffolds proposed in this article can not only help the screening of potential therapeutic NR-inhibitors but also able to guide the future NR-related drug discovery. [ABSTRACT FROM AUTHOR]

The early history of life harbours many unresolved evolutionary questions, none more important than the genomic origin and cellular evolution of eukaryotes. An issue central to eukaryote origin concerns the position of eukaryotes in the tree of life and the relationship of the host lineage that acquired the mitochondrion some two billion years ago to lineages of modern-day archaea. Recent analyses indicate that the host lineage branches within the Archaea, prompting the search for novel archaeal lineages that can improve our understanding of the cellular evolution of eukaryotes. Here we give a brief review of the studies on Archaea, the tree of life and the cellular evolution of eukaryotes, which is followed by an overview of recent progress fueled by new genomic technologies and recent status of archaeal research in China. Future directions for the study of early evolution are considered. [ABSTRACT FROM AUTHOR]

Abstract: G protein-coupled receptors (GPCRs) are the most frequently addressed drug targets in the pharmaceutical industry. However, achieving highly safety and efficacy in designing of GPCR drugs is quite challenging since their primary amino acid sequences show fairly high homology. Systematic study on the interaction spectra of inhibitors with multiple human GPCRs will shed light on how to design the inhibitors for different diseases which are related to GPCRs. To reach this goal, several proteochemometric models were constructed based on different combinations of two protein descriptors, two ligand descriptors and one ligand–receptor cross-term by two kinds of statistical learning techniques. Our results show that support vector regression (SVR) performs better than Gaussian processes (GP) for most combinations of descriptors. The transmembrane (TM) identity descriptors have more powerful ability than the z-scale descriptors in the characterization of GPCRs. Furthermore, the performance of our PCM models was not improved by introducing the cross-terms. Finally, based on the TM Identity descriptors and 28-dimensional drug-like index, two best PCM models with GP and SVR (GP-S-DLI: R 2 =0.9345, Q 2 test =0.7441; SVR-S-DLI: R 2 =1.0000, Q 2 test =0.7423) were derived respectively. The area of ROC curve was 0.8940 when an external test set was used, which indicates that our PCM model obtained a powerful capability for predicting new interactions between GPCRs and ligands. Our results indicate that the derived best model has a high predictive ability for human GPCR–inhibitor interactions. It can be potentially used to discover novel multi-target or specific inhibitors of GPCRs with higher efficacy and fewer side effects. [Copyright &y& Elsevier]

R3241 --> Background: Fusobacterium nucleatumis well known as an important pathogenic gut bacterium associated with colorectal cancer (CRC) and the distribution of F. nucleatum and other Fusobacterium species displayed a strong biogeography‐specific pattern. However, their capabilities for diagnosing CRC across multi‐populations remains unclear. Here we assessed the potential of these Fusobacterium species for CRC diagnosis using whole metagenomic sequencing (WMS) and 16S rRNA data. Design: Included in this study were published raw sequencing data, containing WMS data of 1202 subjects from seven countries and 16S rRNA sequencing data of 556 subjects from four countries. MetaPhlAn2 was used to obtain the Fusobacterium species abundance profiles of WMS data. 16S rRNA data were processed with QIIME2 platform. Random forest models were constructed to evaluate the efficiency of Fusobacterium species for CRC diagnosis. Results: Compared to western populations, we found that non‐nucleatum Fusobacterium species (F. mortiferum, F. ulcerans andF. varium) were more prevalent and abundant in Chinese populations. F. nucleatum was commonly detected in all cohorts across regional and ethnic variations. After adjustment for potential confounding factors, both WMS and 16S data showed that only F.nucleatum was significantly elevated in CRC patients in most populations. The diagnostic models constructed with all Fusobacterium taxa achieved an average area under the receiver operating characteristic curve (AUROC) score of 0.70 with the study cohorts. Similarly, the average AUROC based on 16S rRNA data was 0.74. Then we analyzed the contributions of each Fusobacterium species to the classification models. As expected, F. nucleatumcontributed most in all but the Italy cohort.F.nucleatumwas also the most important feature in models built with 16S rRNA data. Consequently, the models constructed withF. nucleatumperformed better than those with other single non‐nucleatum Fusobacterium species with an average AUROC of 0.64 (WMS) and 0.66 (16S rRNA). Conclusion: Our study validated that F. nucleatumcould act as the universal and reproducible microbial marker for CRC. It has been universally detected in different populations albeit being less abundant than other Fusobacterium species, and is likely one of the "keystone" species in the CRC pathogenesis. In contrast, non‐nucleatum Fusobacterium species were region‐specific and with relatively lower capability for diagnosing CRC. Keyworks: CRC, Fusobacterium nucleatum, prescreening, keystone [ABSTRACT FROM AUTHOR]

R3269 --> Background: Inflammatory bowel disease(IBD),including Crohn's Disease(CD) and ulcerative colitis (UC), is closely linked with altered gut microbiota.Roles of bacteria in IBD have been widely investigated, whereas other microbial kingdoms, fungi, archaea, virus, are less explored. Here, we aim to investigate the multi‐kingdom microbial alterations and their capabilities for IBD diagnosis. Methods: A total of 968 stool samples (292 CD, 305 UC and 371 controls) of untreated patients from iHMP were collected. Differential microorganisms were identified via wilcoxon test followed by adjustment for confounders. Inter‐kingdom co‐occurrence interactions analysis was performed by SparCC then microorganism modules were clustered by "MCODE". Subsequently, neural networks algorithm was used to build diagnostic models for CD or UC with single‐ or multi‐kingdom microbial features and the best panel of biomarkers was further identified by recursive feature elimination. Moreover, diagnostic models were validated in additional independent cohorts. Differential microbial pathways were analyzed to further explore functional alterations in CD and UC. Results: In CD and UC, the microbial compositional change was identified in different kingdoms. Compared to controls, we identified 41 bacteria, 37 fungi, 40 archaea and 50 viruses with differential abundance in CD. Similarly, 63 bacteria, 52 fungi, 58 archaea and 39 viruses with differential abundance were detected in UC. Moreover, inter‐kingdom interactions were more complex in IBD than controls, especially the interactions between fungal Aspergillusand BeAn 58058 virus. Notably, the diagnostic models constructed with multi‐kingdom microbial features performed better (AUC of 0.90 in CD and 0.91 in UC) than that with single kingdom microbial features. The diagnostic models also showed reliable accuracy in independent cohorts with an average AUC of 0.93 (CD) and 0.74 (UC), respectively. Among these biomarkers, bacterial Bacteroidesand fungalAspergillusare the main contributors to the distinguishing ability in both CD and UC diagnostic models. In addition, pathways related to lipid and terpenoids metabolism and immune response were altered in CD and UC, such as glycosphingolipid biosynthesis, carotenoid biosynthesis and IL‐17 signaling pathway. Conclusions: Our study investigated comprehensivepathogenic microbiota of IBD, which encompasses bacteria, fungi, virus and archaea. And the combination of multi‐kingdom microbial biomarkers may serve as effective tools for CD and UC diagnosis, which outperforms single kingdom biomarkers. The alterations of microbial inter‐kingdom interactions and cross‐kingdom functional changes provide new insight in understanding pathological mechanisms of IBD. [ABSTRACT FROM AUTHOR]

Background: Functional antibody genes are often assembled by VDJ recombination and then diversified by somatic hypermutation. Identifying the combination of sourcing germline genes is critical to understand the process of antibody maturation, which may facilitate the diagnostics and rapid generation of human monoclonal antibodies in therapeutics. Despite of successful efforts in V and J fragment assignment, method in D segment tracing remains weak for immunoglobulin heavy diversity (IGHD). Results: In this paper, we presented a D-sensitive mapping method called DSab-origin with accuracies around 90% in human monoclonal antibody data and average 95.8% in mouse data. Besides, DSab-origin achieved the best performance in holistic prediction of VDJ segments assignment comparing with other methods commonly used in simulation data. After that, an application example was explored on the antibody response based on a time-series antibody sequencing data after influenza vaccination. The result indicated that, despite the personal response among different donors, IGHV3–7 and IGHD4–17 were likely to be dominated gene segments in these three donors. Conclusions: This work filled in a computational gap in D segment assignment for VDJ germline gene identification in antibody research. And it offered an application example of DSab-origin for studying the antibody maturation process after influenza vaccination. [ABSTRACT FROM AUTHOR]

Motivation: Protein-binding sites prediction lays a foundation for functional annotation of protein and structure-based drug design. As the number of available protein structures increases, structural alignment based algorithm becomes the dominant approach for protein-binding sites prediction. However, the present algorithms underutilize the ever increasing numbers of three-dimensional protein-ligand complex structures (bound protein), and it could be improved on the process of alignment, selection of templates and clustering of template. Herein, we built so far the largest database of bound templates with stringent quality control. And on this basis, bSiteFinder as a protein-binding sites prediction server was developed. Results: By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server has been significantly improved. Further, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset. For 210 bound dataset, bSiteFinder achieved high accuracies up to 94.8 % (MCC 0.95). For another 48 bound/unbound dataset, bSiteFinder achieved high accuracies up to 93.8 % for bound proteins (MCC 0.95) and 85.4 % for unbound proteins (MCC 0.72). Our bSiteFinder server is freely available at , and the source code is provided at the methods page. Conclusion: An online bSiteFinder server is freely available at . Our work lays a foundation for functional annotation of protein and structure-based drug design. With ever increasing numbers of three-dimensional protein-ligand complex structures, our server should be more accurate and less time-consuming.[Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

R3041 --> 377.5 --> Background: The early detection of colorectal cancer (CRC), especially at precancerous adenoma stage, significantly reduces its incidence. Gut microbiome has become a promising non‐invasive tool for CRC screening, whereas the potential of microbial multidimensional signatures remains poorly understood. Here we performed a cross‐cohort analysis to evaluate the capability of microbial multidimensional biomarkers for adenoma detection. Design: Whole metagenome sequencing data of four public datasets including 183 adenoma patients and 439 healthy controls were reprocessed consistently to obtain taxonomic‐, functional‐ and single‐nucleotide variants (SNVs)‐profiling. With MMUPHin, differential multidimensional signatures were identified after adjusting confounders, based on which random forest (RF) models were constructed and then optimized by recursive feature elimination. Finally, internal validation and external validation with in‐house dataset (10 controls and 6 adenomas) and five resampled datasets were conducted to further assess the robustness of the best biomarker panel. Results: The integrated analysis identified 103 multi‐kingdom differential species between adenoma and control group, of which 15 optimal species were selected to construct a RF model achieving an AUC of 0.75. Meanwhile, the model constructed with 31 optimal biomarkers out of 386 differential KO genes reached an AUC of 0.74. Notably, the diagnostic model with 75 SNVs from 10 species showed superior accuracy (AUC = 0.85) with high specificity to adenoma. Co‐abundance analysis revealed intensive bacterial‐fungal associations in line with functional abnormalities related to microbial quorum sensing, purine and butanoate metabolism. Conclusion: Microbial SNV biomarkers outperform other biomarkers and display high specificity to adenoma, which may serve as a novel non‐invasive tool for early detection of CRC. Furthermore, multidimensional signatures provide potential therapeutic targets for adenoma. [ABSTRACT FROM AUTHOR]

Despite the high specificity between antigen and antibody binding, similar epitopes can be recognized or cross-neutralized by paratopes of antibody with different binding affinities. How to accurately characterize this slight variation which may or may not change the antigen-antibody binding affinity is a key issue in this area. In this report, by combining cylinder model with shell structure model, a new fingerprint was introduced to describe both the structural and physical-chemical features of the antigen and antibody protein. Furthermore, beside the description of individual protein, the specific epitope-paratope interaction fingerprint (EPIF) was developed to reflect the bond and the environment of the antigen-antibody interface. Finally, Proteochemometric Modeling of the antigen-antibody interaction was established and evaluated on 429 antigen-antibody complexes. By using only protein descriptors, our model achieved the best performance () among peers. Further, together with EPIF as a new cross-term, our model () can significantly outperform peers with multiplication of ligand and protein descriptors as a cross-term (). Results illustrated that: 1) our newly designed protein fingerprints and EPIF can better describe the antigen-antibody interaction; 2) EPIF is a better and specific cross-term in Proteochemometric Modeling for antigen-antibody interaction. The fingerprints designed in this study will provide assistance to the description of antigen-antibody binding, and in future, it may be valuable help for the high-throughput antibody screening. The algorithm is freely available on request. [ABSTRACT FROM AUTHOR]

Recipes (Qingre Jiedu (QJ), Wenyang Yiqi (WYYQ) and Huo Xue (HX)) in Qishen granules (QSG) are believed to synergistically exert cardio-protective effects. However, the underlying pattern of each decomposed recipe in QSG and their synergistic effects in the treatment of heart failure (HF) are not clear. The purpose of this study is to explore the biological contributions of decomposed recipes to therapeutic effects of QSG and reveal the pharmacological mechanism of QSG in treating HF. The therapeutic effects of QSG or its recipes on heart failure were examined in wet-lab at both transcription and phenotypic level using HF Sprague-Dawley rats. Sequencing and transcriptome analyses were performed using in silico approaches including identification of differentially expressed genes, pathway enrichment and protein-protein interaction network studies. Specially, an optimized in silico quantitative pathway analysis that maximally extracted gene expression information was developed to reveal differentially expressed pathways (DEPs) among various groups, and is publicly available as R package QPA on GitHub (https://github.com/github-gs/QPA). Finally, the HF-related genes predicted using DEP approach were validated by quantitative real-time polymerase chain reaction and western blot. Multiple key genes and the associated signaling pathways were shown to be highly relevant for the therapeutic effect of QSG. Decreased expression of Spp1 gene required for inflammatory signaling and profibrotic signaling were observed in failing hearts treated with QJ, WYYQ and HX. Decreased expression of Cx3cr1 gene required for inflammatory signaling was observed in failing hearts treated with WYYQ and HX. Decreased expression of Myc gene required for oxidative stress and Fgfr2 gene required for profibrotic signaling were observed in failing hearts treated with HX and WYYQ, respectively. Increased expression of Adcy1 gene required for cAMP-PKA signaling cascade was observed in failing hearts treated with WYYQ and HX. Our study suggests that QJ, WYYQ and HX recipes in QSG achieve synergistic and complementary therapeutic effects through alleviating inflammatory responses, attenuating ventricular remodeling and enhancing myocardial energy supply. Image 1 [ABSTRACT FROM AUTHOR]