PCMD: A multilevel comparison database of intra- and cross-species metabolic profiling in 530 plant species.

Comparative metabolomics plays a crucial role in investigating gene function, exploring metabolite evolution, and accelerating crop genetic improvement. However, a systematic platform for comparing intra- and cross-species metabolites is currently lacking. Here, we report the Plant Comparative Metabolome Database (PCMD; http://yanglab.hzau.edu.cn/PCMD), a multilevel comparison database based on predicted metabolic profiles in 530 plant species. PCMD serves as a platform for comparing metabolite characteristics at various levels, including species, metabolites, pathways, and biological taxonomy. The database also provides a series of user-friendly online tools, such as Species-comparison, Metabolites-enrichment, and ID conversion, enabling users to perform comparisons and enrichment analyses of metabolites across different species. In addition, PCMD establishes a unified system based on existing metabolite-related databases by standardizing metabolite numbering. PCMD is the most species-rich comparative plant metabolomics database currently available, and a case study demonstrated its capability to provide new insights into understanding plant metabolic diversity.

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity.

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR-epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR-epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.

A small molecule compound targeting hemagglutinin inhibits influenza A virus and exhibits broad-spectrum antiviral activity.

Influenza A virus (IAV) is a widespread pathogen that poses a significant threat to human health, causing pandemics with high mortality and pathogenicity. Given the emergence of increasingly drug-resistant strains of IAV, currently available antiviral drugs have been reported to be inadequate to meet clinical demands. Therefore, continuous exploration of safe, effective and broad-spectrum antiviral medications is urgently required. Here, we found that the small molecule compound J1 exhibited low toxicity both in vitro and in vivo. Moreover, J1 exhibits broad-spectrum antiviral activity against enveloped viruses, including IAV, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human coronavirus OC43 (HCoV-OC43), herpes simplex virus type 1 (HSV-1) and HSV-2. In this study, we explored the inhibitory effects and mechanism of action of J1 on IAV in vivo and in vitro. The results showed that J1 inhibited infection by IAV strains, including H1N1, H7N9, H5N1 and H3N2, as well as by oseltamivir-resistant strains. Mechanistic studies have shown that J1 blocks IAV infection mainly through specific interactions with the influenza virus hemagglutinin HA2 subunit, thereby blocking membrane fusion. BALB/c mice were used to establish a model of acute lung injury (ALI) induced by IAV. Treatment with J1 increased survival rates and reduced viral titers, lung index and lung inflammatory damage in virus-infected mice. In conclusion, J1 possesses significant anti-IAV effects in vitro and in vivo, providing insights into the development of broad-spectrum antivirals against future pandemics.

The relationship between self-oriented perfectionism and exercise participation: based on the dualistic model of passion.

Background: During the critical formative years of college, active participation in sports not only helps to alleviate stress, but also promotes the development of healthy habits. Although the multifaceted benefits of exercise have been widely recognized, there is a relative dearth of research on the relationship between personality traits, particularly college students' self-oriented perfectionism (SOP), and exercise participation. Methods: A questionnaire survey of 374 college students was conducted using the snowball sampling method. SPSS 26.0 and Mplus 8.3 were employed in this study to analyze the correlations between the variables, and on this basis, the effect of SOP on exercise participation was examined. The study also used 5,000 bootstrap samples and a 95% bias-corrected confidence interval to test the significance of the mediating effects. Results: Correlation analysis showed that SOP was positively correlated with exercise participation. Harmonious passion and obsessive passion were positively correlated with SOP, and exercise participation. Further, the results of structural equation analysis revealed that SOP increased exercise participation. Harmonious passion and obsessive passion positively mediated the effect between SOP and exercise participation, respectively. Conclusion: This study provides new perspectives to better understand college students' exercise participation, emphasizing the importance of SOP and its influence on exercise participation through harmonious and obsessive passions. These findings have important implications for the development of effective exercise promotion strategies.

B-cell hub genes play a cardiovascular pathogenic role of in childhood obesity and Kawasaki disease as revealed by transcriptomics-based analyses.

The study aims to explore the central genes that Kawasaki disease (KD) and Obesity (OB) may jointly contribute to coronary artery disease. Investigating single-cell datasets (GSE168732 and GSE163830) from a comprehensive gene expression database, we identified characteristic immune cell subpopulations in KD and OB. B cells emerged as the common immune cell characteristic subgroup in both conditions. Subsequently, we analyzed RNA sequencing datasets (GSE18606 and GSE87493) to identify genes associated with B-cell subpopulations in KD and OB. Lastly, a genome-wide association study and Mendelian randomization were conducted to substantiate the causal impact of these core genes on myocardial infarction. Quantitative real-time PCR (qRT-PCR) to validate the expression levels of hub genes in KD and OB. The overlapping characteristic genes of B cell clusters in both KD and OB yielded 70 shared characteristic genes. PPI analysis led to the discovery of eleven key genes that significantly contribute to the crosstalk. Employing receiver operating characteristic analysis, we evaluated the specificity and sensitivity of these core genes and scored them using Cytoscape software. The inverse variance weighting analysis suggested an association between TNFRSF17 and myocardial infarction risk, with an odds ratio of 0.9995 (95% CI = 0.9990-1.0000, p = 0.049). By employing a single-cell combined transcriptome data analysis, we successfully pinpointed central genes associated with both KD and OB. The implications of these findings extend to shedding light on the increased risk of coronary artery disease resulting from the co-occurrence of OB and KD.

The effect of goethite aging on Cd adsorption: Constraints of mineral condensation and surface site density.

Iron (hydr)oxides, as natural geosorbents, play a crucial role in retaining toxic heavy metals, and their aging process greatly influences heavy metals distributions and migration in soil systems. However, limited attention has been given to the interaction between heavy metals and crystalline-aged goethite. In this study, we investigated the sorption behavior and sorption mechanism of cadmium (Cd) with freshly synthesized or aged goethite. We quantified the total Cd sorption load, as well as the proportion of Cd with different sorption strengths on minerals. It has been found that in different aged goethite samples, approximately 71.3-84.7 % of Cd is strongly bound (bidentate inner-sphere complexes) and 16.0 % to 26.4 % of Cd is weakly bound (electrostatic adsorption and partially through monodentate inner-sphere complexes) by goethite. This observation is consistent with the distribution characteristics of Cd species fitted by the charge distribution and multisite surface complexation model. Additionally, the total Cd load and strongly bound Cd content on goethite aged at pH 7.5 decreased with extended aging time. Upon combining the mineral characterization analysis and surface hydroxyl density calculation, we found that the morphology transformation and the deterioration in sorption ability on goethite results from a condensation process through a surface hydroxyl oxolation reaction on the {110} facet between adjacent goethite crystals during the aging process at pH 7.5. This condensation process causes goethite to lose many hydroxyl sites, which is the dominant reason for the decrease in inner-sphere complexed Cd. The amount of weakly bound Cd decreases slightly with aging, because the decrease in inner-sphere complexed Cd is not conducive to balancing the positively charged mineral surface, resulting in a slight reduction in the amount of Cd adsorbed through electrostatic attractions.

One-pot complexation of phytic acid and polyethyleneimine on cellulosic microfibers towards insulative and flame-resistant foam.

Flame resistance is required for the deployment of bio-based materials, especially those forming cellular structures that endow thermal insulation. This study proposes a one-pot strategy to prepare cellular lignocellulosic composites with excellent flame resistance. Lignocellulosic microfibers were used as the substrate onto which a flame-retardant complex consisting of P-containing phytic acid (PA) and N-containing polyethyleneimine (PEI) was formed. Following the prediction of ab initio molecular dynamics simulation, PA and PEI are integrated onto MF-CTMP following a single-step complexation assembly triggered by pH effects. The PA-PEI modified MF-CTMP can be readily transformed into a composite solid foam by dewatering a wet foam followed by oven drying. At the expense of a slightly reduced thermal insulation (thermal conductivity increase from 33.6 ±â€¯0.6 to 40.0 ±â€¯0.6 mW/(m·K)) the presence of PA-PEI complexes significantly improved the mechanical performance of the foam and uniquely endows it with flame resistance. Compared to unmodified MF-CTMP foams, the composite foams showed significant improvement in the Young's, specific compression, and flexural moduli (increased by 13.5, 5.5, and 7.3 folds, respectively), a high oxygen index (up to 40.8 %) and self-extinguishing effects. The results suggest the suitability of the introduced lignocellulosic foam as an alternative to traditional synthetic polymer-based counterparts as well as inorganic matter for insulation, particularly relevant to the building sector.

Making Full Use of TMSCF3: Deoxygenative Trifluoromethylation/Silylation of Amides.

As one of the most powerful trifluoromethylation reagents, (trifluoromethyl)trimethylsilane (TMSCF3) has been widely used for the synthesis of fluorine-containing molecules. However, to the best of our knowledge, the simultaneous incorporation of both TMS- and CF3- groups of this reagent onto the same carbon of the products has not been realized. Herein, we report an unprecedented SmI2/Sm promoted deoxygenative difunctionalization of amides with TMSCF3, in which both silyl and trifluoromethyl groups are incorporated into the final product, yielding α-silyl-α-trifluoromethyl amines with high efficiency. Notably, the silyl group could be further transformed into other functional groups, providing a new method for the synthesis of α-quaternary α-CF3-amines.

An Adaptively Weighted Averaging Method for Regional Time Series Extraction of fMRI-based Brain Decoding.

Brain decoding that classifies cognitive states using the functional fluctuations of the brain can provide insightful information for understanding the brain mechanisms of cognitive functions. Among the common procedures of decoding the brain cognitive states with functional magnetic resonance imaging (fMRI), extracting the time series of each brain region after brain parcellation traditionally averages across the voxels within a brain region. This neglects the spatial information among the voxels and the requirement of extracting information for the downstream tasks. In this study, we propose to use a fully connected neural network that is jointly trained with the brain decoder to perform an adaptively weighted average across the voxels within each brain region. We perform extensive evaluations by cognitive state decoding, manifold learning, and interpretability analysis on the Human Connectome Project (HCP) dataset. The performance comparison of the cognitive state decoding presents an accuracy increase of up to 5% and stable accuracy improvement under different time window sizes, resampling sizes, and training data sizes. The results of manifold learning show that our method presents a considerable separability among cognitive states and basically excludes subject-specific information. The interpretability analysis shows that our method can identify reasonable brain regions corresponding to each cognitive state. Our study would aid the improvement of the basic pipeline of fMRI processing.

A few-shot identification method for stochastic dynamical systems based on residual multipeaks adaptive sampling.

Neural networks are popular data-driven modeling tools that come with high data collection costs. This paper proposes a residual-based multipeaks adaptive sampling (RMAS) algorithm, which can reduce the demand for a large number of samples in the identification of stochastic dynamical systems. Compared to classical residual-based sampling algorithms, the RMAS algorithm achieves higher system identification accuracy without relying on any hyperparameters. Subsequently, combining the RMAS algorithm and neural network, a few-shot identification (FSI) method for stochastic dynamical systems is proposed, which is applied to the identification of a vegetation biomass change model and the Rayleigh-Van der Pol impact vibration model. We show that the RMAS algorithm modifies residual-based sampling algorithms and, in particular, reduces the system identification error by 76% with the same sample sizes. Moreover, the surrogate model accurately predicts the first escape probability density function and the P bifurcation behavior in the systems, with the error of less than 1.59×10-2. Finally, the robustness of the FSI method is validated.

Effects of super-enhancers in cancer metastasis: mechanisms and therapeutic targets.

Metastasis remains the principal cause of cancer-related lethality despite advancements in cancer treatment. Dysfunctional epigenetic alterations are crucial in the metastatic cascade. Among these, super-enhancers (SEs), emerging as new epigenetic regulators, consist of large clusters of regulatory elements that drive the high-level expression of genes essential for the oncogenic process, upon which cancer cells develop a profound dependency. These SE-driven oncogenes play an important role in regulating various facets of metastasis, including the promotion of tumor proliferation in primary and distal metastatic organs, facilitating cellular migration and invasion into the vasculature, triggering epithelial-mesenchymal transition, enhancing cancer stem cell-like properties, circumventing immune detection, and adapting to the heterogeneity of metastatic niches. This heavy reliance on SE-mediated transcription delineates a vulnerable target for therapeutic intervention in cancer cells. In this article, we review current insights into the characteristics, identification methodologies, formation, and activation mechanisms of SEs. We also elaborate the oncogenic roles and regulatory functions of SEs in the context of cancer metastasis. Ultimately, we discuss the potential of SEs as novel therapeutic targets and their implications in clinical oncology, offering insights into future directions for innovative cancer treatment strategies.

Marital status impacts survival of stage I non-small-cell lung cancer: a propensity-score matching analysis.

Aim: This population-based analysis aimed to explore the associations among marital status, prognosis and treatment of stage I non-small-cell lung cancer. Materials & methods: The propensity score matching (PSM), logistic regression and Cox proportional hazards model were used in this study. Results: A total of 13,937 patients were included. After PSM, 10579 patients were co-insured. The married were more likely to receive surgical treatment compared with the unmarried patients (OR: 1.841, p < 0.001), and patients who underwent surgery also tended to have better survival (HR: 0.293, p < 0.001). Conclusion: Compared with unmarried patients, a married group with stage I NSCLC had timely treatment and more satisfactory survival. This study highlights the importance of prompt help and care for unmarried patients.

Hierarchical supramolecules composed of starch-based nanocluster aggregates with light-responsive mechanical strain for remotely rapid and precise actuation.

Hierarchical supramolecular systems, characterized by nanoscale sensitivity and macroscopic tangible changes, offer promising perspectives for the design of remotely controllable, rapid, and precise actuation materials, serving as a potential substitution for non-intelligent and complex actuation switches. Herein, we reported on the disassembly of orderly and rigid starch helical covalent structures, and their subsequent reassembly into a hierarchical supramolecular gel composed of nanocluster aggregates, integrating supramolecular interactions of three different scales. The incorporation of photo-sensitive FeIIITA, a complex of trivalent iron ions and tannic acid, significantly enhances the photo-responsive strain capacity of the hierarchical supramolecular gel. The supramolecular gel exhibits its features in a rapid light-responsive rate of hardness and viscosity, enabling the actuation of objects within 22 s under light exposure when employed as a remote actuation switch. Meanwhile, this actuation mechanism of the hierarchical supramolecular gel also has a promising perspective in precise control, identifying and actuating one of the two objects in distances of 0.8 mm even smaller scales. Our work provides a reliable reference for replacing complex actuation switches with intelligent materials for remote, rapid, and accurate actuation, and offers valuable insights for actuation in harsh and vacuum outdoor environments.

Revealing brain cell-stratified causality through dissecting causal variants according to their cell-type-specific effects on gene expression.

The human brain has been implicated in the pathogenesis of several complex diseases. Taking advantage of single-cell techniques, genome-wide association studies (GWAS) have taken it a step further and revealed brain cell-type-specific functions for disease loci. However, genetic causal associations inferred by Mendelian randomization (MR) studies usually include all instrumental variables from GWAS, which hampers the understanding of cell-specific causality. Here, we developed an analytical framework, Cell-Stratified MR (csMR), to investigate cell-stratified causality through colocalizing GWAS signals with single-cell eQTL from different brain cells. By applying to obesity-related traits, our results demonstrate the cell-type-specific effects of GWAS variants on gene expression, and indicate the benefits of csMR to identify cell-type-specific causal effect that is often hidden from bulk analyses. We also found csMR valuable to reveal distinct causal pathways between different obesity indicators. These findings suggest the value of our approach to prioritize target cells for extending genetic causation studies.

A bibliometric analysis of endoplasmic reticulum stress and atherosclerosis.

The mechanisms underlying the occurrence and development of atherosclerosis (AS) are diverse, among which endoplasmic reticulum stress (ERS) is an important mechanism that should not be overlooked. However, up to now, there has been no bibliometric study on the relationship between ERS and AS. To understand the research progress in ERS and AS, this paper conducted a statistical analysis of publications in this field using bibliometrics. A total of 1,035 records were retrieved from the Web of Science Core Collection. CiteSpace, VOSviewer, and the R package "bibliometric" were used to analyze the spatiotemporal distribution, countries, authors, institutions, journals, references, and keywords of the literature, and to present the basic information of this field through visualized maps, as well as determine the collaboration relationships among researchers in this field. This field has gradually developed and stabilized over the past 20 years. The current research hotspots in this field mainly include the relationship between ERS and AS-related cells, the mechanisms by which ERS promotes AS, related diseases, and associated cytokines, etc. Vascular calcification, endothelial dysfunction, NLRP3 inflammasome, and heart failure represent the frontier research in this field and are becoming new research hotspots. It is hoped that this study will provide new insights for research and clinical work in the field of ERS and AS.

Glycolysis in the tumor microenvironment: a driver of cancer progression and a promising therapeutic target.

Even with sufficient oxygen, tumor cells use glycolysis to obtain the energy and macromolecules they require to multiply, once thought to be a characteristic of tumor cells known as the "Warburg effect". In fact, throughout the process of carcinogenesis, immune cells and stromal cells, two major cellular constituents of the tumor microenvironment (TME), also undergo thorough metabolic reprogramming, which is typified by increased glycolysis. In this review, we provide a full-scale review of the glycolytic remodeling of several types of TME cells and show how these TME cells behave in the acidic milieu created by glucose shortage and lactate accumulation as a result of increased tumor glycolysis. Notably, we provide an overview of putative targets and inhibitors of glycolysis along with the viability of using glycolysis inhibitors in combination with immunotherapy and chemotherapy. Understanding the glycolytic situations in diverse cells within the tumor immunological milieu will aid in the creation of subsequent treatment plans.

Biomass-Derived Carbon Utilization for Electrochemical Energy Enhancement in Lithium-Ion Batteries.

Cathodes made of LiFePO4 (LFP) offer numerous benefits including being non-toxic, eco-friendly, and affordable. The distinctive olivine structure of LFP cathodes contributes to their electrochemical stability. Nonetheless, this structure is also the cause of their low ionic and electronic conductivity. To enhance these limitations, an uncomplicated approach has been effectively employed. A straightforward solid-state synthesis technique is used to apply a coating of biomass from potato peels to the LFP cathode, boosting its electrochemical capabilities. Potato peels contain pyridinic and pyrrolic nitrogen, which are conducive to ionic and electronic movement and facilitate pathways for lithium-ion and electron transfer, thus elevating electrochemical performance. When coated with nitrogen-doped carbon derived from potato peel biomass (PPNC@LFP), the LFP cathode demonstrates an improved discharge capacity of 150.39 mAh g-1 at a 0.1 C-rate and 112.83 mAh g-1 at a 1.0 C-rate, in contrast to the uncoated LFP which shows capacities of 141.34 mAh g-1 and 97.72 mAh g-1 at the same rates, respectively.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

Research situation, hot spots, and global trends of melasma therapy: Bibliometric insights and visual analysis from 2000 to 2023.

BACKGROUND: Melasma is a prevalent pigmented disease, yet its pathogenesis remains unclear, posing challenges for effective treatment. Bibliometric analysis, a novel approach to literature research, offers the opportunity to evaluate research trends through qualitative and quantitative methods. This study utilizes bibliometric methods to analyze the existing literature on melasma treatment, examining influential publications, institutions, countries, and authors through statistical analysis. METHODS: In order to retrieve manuscripts related to the topic of melasma treatment, we conducted a search using the search formula: (TS = (melasma or Chloasma or "mask of pregnancy")) AND TS = (treatment or therapy). We searched through the Web of Science Core Collection database, covering publications from 2000 to 2023. VOSviewer, CiteSpace and the Bibliometric online site (https://bibliometric.com/app) were used to conduct this bibliometric analysis. Our analysis focused on various factors including publications, authors co-authorship, institutions, countries, citation analysis, keywords co-occurrence, references co-citation and journal co-citation. RESULTS: A total of 943 articles and 200 reviews were published between 2000 and 2023, accumulating a total of 8628 citations. The average number of citations per item was 18.85, and the average number of citations per year was 292.69. The most prolific author, Sungeun Chang, contributed a total of 9 articles. Cario University emerged as the top research institution. The United States led in terms of article publications with a count of 276. In the past 5 years, the research trends in this field have primarily focused on tranexamic acid and epidermal melasma, as indicated by the burst analysis of publications and keywords. CONCLUSIONS: The United States continues to lead in terms of institutions and research output. The current emphasis is on the meticulous implementation of tranexamic acid and laser therapy. It is crucial to foster enhanced collaboration among countries, institutions, and authors to facilitate improved research.