Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses.

Behçet's disease (BD) is a complex autoimmune disorder impacting several organ systems. Although the involvement of abdominal aortic aneurysm (AAA) in BD is rare, it can be associated with severe consequences. In the present study, we identified diagnostic biomarkers in patients with BD having AAA. Mendelian randomization (MR) analysis was initially used to explore the potential causal association between BD and AAA. The Limma package, WGCNA, PPI and machine learning algorithms were employed to identify potential diagnostic genes. A receiver operating characteristic curve (ROC) for the nomogram was constructed to ascertain the diagnostic value of AAA in patients with BD. Finally, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were conducted. The MR analysis indicated a suggestive association between BD and the risk of AAA (odds ratio [OR]: 1.0384, 95% confidence interval [CI]: 1.0081-1.0696, p = 0.0126). Three hub genes (CD247, CD2 and CCR7) were identified using the integrated bioinformatics analyses, which were subsequently utilised to construct a nomogram (area under the curve [AUC]: 0.982, 95% CI: 0.944-1.000). Finally, the immune cell infiltration assay revealed that dysregulation immune cells were positively correlated with the three hub genes. Our MR analyses revealed a higher susceptibility of patients with BD to AAA. We used a systematic approach to identify three potential hub genes (CD247, CD2 and CCR7) and developed a nomogram to assist in the diagnosis of AAA among patients with BD. In addition, immune cell infiltration analysis indicated the dysregulation in immune cell proportions.

Synthesis and Surface Strengthening Modification of Silica Aerogel from Fly Ash.

This study focuses on using activated fly ash to preparate silica aerogel by the acid solution-alkali leaching method and ambient pressure drying. Additionally, to improve the performance of silica aerogel, C6H16O3Si (KH-570) and CH3Si(CH3O)3 (MTMS) modifiers were used. Finally, this paper investigated the factors affecting the desilication rate of fly ash and analyzed the structure and performance of silica aerogel. The experimental results show that: (1) The factors affecting the desilication rate are ranked as follows: hydrochloric acid concentration > solid-liquid ratio > reaction temperature > reaction time. (2) KH-570 showed the best performance, and when the volume ratio of the silica solution to it was 10:1, the density of silica aerogel reached a minimum of 183 mg/cm3. (3) The optimal process conditions are a hydrochloric acid concentration of 20 wt%, a solid-liquid ratio of 1:4, a reaction time of two hours, and a reaction temperature of 100 °C. (4) The optimal performance parameters of silica aerogel were the thermal conductivity, specific surface area, pore volume, average pore size, and contact angle values, with 0.0421 W·(m·K)-1, 487.9 m2·g-1, 1.107 cm3·g-1, 9.075 nm, and 123°, respectively. This study not only achieves the high-value utilization of fly ash, but also facilitates the effective recovery and utilization of industrial waste.

Research on optimization of basic rail top bending prediction model.

Since the basic rail of the switch needs to have a certain bending angle when the train changes direction, top bending is an important link in the production process of the basic rail. The three-point pressure top bending method is simple, flexible and widely used. In this study, the traditional three-point pressure bending is optimized, the influence of the pick width in the model is considered, a corresponding rebound model is established, and the model is applied to the pressure bending process of the basic rail. The bilinear strengthening model of the material was used to construct the bending moment expressions at different positions during the top bending process, and the relationship between the load and bending deflection in the elastic stage and elastic-plastic stage was obtained. The final top bending prediction model was obtained by combining the load-deflection model in the bending stage and the rebound stage. The correctness of the theoretical mathematical model was verified by establishing finite element simulations, and the theoretical calculation results were compared with the experimental results. The results showed that the top bend prediction optimization model established in this study had high feasibility and met the machining accuracy requirements.

Identification of key biomarkers for predicting CAD progression in inflammatory bowel disease via machine-learning and bioinformatics strategies.

The study aimed to identify the biomarkers for predicting coronary atherosclerotic lesions progression in patients with inflammatory bowel disease (IBD). Related transcriptome datasets were seized from Gene Expression Omnibus database. IBD-related modules were identified via Weighted Gene Co-expression Network Analysis. The 'Limma' was applied to screen differentially expressed genes between stable coronary artery disease (CAD) and acute myocardial infarction (AMI). Subsequently, we employed protein-protein interaction (PPI) network and three machine-learning strategies to further screen for candidate hub genes. Application of the receiver operating characteristics curve to quantitatively evaluate candidates to determine key diagnostic biomarkers, followed by a nomogram construction. Ultimately, we performed immune landscape analysis, single-gene GSEA and prediction of target-drugs. 3227 IBD-related module genes and 570 DEGs accounting for AMI were recognized. Intersection yielded 85 shared genes and mostly enriched in immune and inflammatory pathways. After filtering through PPI network and multi-machine learning algorithms, five candidate genes generated. Upon validation, CTSD, CEBPD, CYP27A1 were identified as key diagnostic biomarkers with a superior sensitivity and specificity (AUC > 0.8). Furthermore, all three genes were negatively correlated with CD4+ T cells and positively correlated with neutrophils. Single-gene GSEA highlighted the importance of pathogen invasion, metabolism, immune and inflammation responses during the pathogenesis of AMI. Ten target-drugs were predicted. The discovery of three peripheral blood biomarkers capable of predicting the risk of CAD proceeding into AMI in IBD patients. These identified biomarkers were negatively correlated with CD4+ T cells and positively correlated with neutrophils, indicating a latent therapeutic target.

Pyrolytic Modification of Heavy Coal Tar by Multi-Polymer Blending: Preparation of Ordered Carbonaceous Mesophase.

In order to achieve the high-value utilization of heavy tar for the production of enhanced-performance graphite foam carbon, the carbon mesophase was ready from the heavy component of low-temperature coal tar, and the coal tar was modified by styrene-butadiene-styrene (SBS), polyethylene (PE) and ethylene-vinyl-acetate (EVA) copolymers. The order degree of the carbonite mesophase was analyzed using a polarizing microscope test, Fourier transform infrared spectroscopy and X-ray diffraction to screen out the most suitable copolymer type and addition amount. Furthermore, the mechanism of modification by this copolymer was analyzed. The results showed that adding SBS, PE and EVA to coal tar would affect the order of carbonaceous mesophase; however, at an addition rate of 10.0 wt.%, the linear-structure SBS copolymer with a styrene/butadiene ratio (S/B) of 30/70 exhibited the optimal degree of ordering in the carbonaceous mesophase. Its foam carbon prepared by polymer modification is the only one that forms a graphitized structure, with d002 of 0.3430 nm, and the maximum values of Lc and La are 3.54 nm and 2.22 nm, respectively. This is because, under elevated pressure and high-temperature conditions, SBS underwent chain scission, releasing a more significant number of methyl and other free radicals that interacted with the coal tar constituents. As a result, it reduced the affinity density of heavy coal tar molecules, enhanced fluidity, promoted the stacking of condensed aromatic hydrocarbons and increased the content of soluble carbonaceous mesophase, ultimately leading to a more favorable alignment of the carbonaceous mesophase.

Systematical analysis of underlying markers associated with Marfan syndrome via integrated bioinformatics and machine learning strategies.

Marfan syndrome (MFS) is a hereditary disease with high mortality. This study aimed to explore peripheral blood potential markers and underlying mechanisms in MFS via a series bioinformatics and machine learning analysis. First, we downloaded two MFS datasets from the GEO database. A total of 215 differentially expressed genes (DEGs) and 78 differentially expressed miRNAs (DEMs) were identified via "Limma" package. 60 DEGs, mainly enriched in abnormal transportation of structure and energy substances, were selected after protein-protein interaction (PPI) network construction, of which 20 were chosen for machine learning after three algorithms (betweenness, closeness, and degree) filtration using Cytoscape. Four overlapping DEGs (ACTN1, CFTR, GCKR, LAMA3) were finally selected as the candidate markers based on three machine-learning approaches (Lasso, random forest, and support vector machine-recursive feature elimination). Furthermore, we collected peripheral blood from MFS patients and healthy control to validate the findings and the results showed that compared with the control, the expression of the four DEGs was all statistically different in MFS patients validated by qRT-PCR. Besides, the area under the receiver operating characteristics curve was greater than 0.8 for each DEG. Single-sample gene-set enrichment analysis showed that the four DEGs were strongly associated with inflammation and myogenesis pathway. Finally, we constructed the mRNA-miRNA network based on the intersection of DEMs and predicted miRNAs targeting DEGs. In conclusion, our study partially provided four potential markers for MFS pathogenesis.Communicated by Ramaswamy H. Sarma.

A systematic review and meta-analysis on sex disparities in the outcomes of fenestrated branched endovascular aortic aneurysm repair.

OBJECTIVE: To compare the risk of mortality and complications between male and female patients undergoing aortic aneurysm repair with fenestrated-branched endovascular aortic repair (FBEVAR). METHODS: The PubMed, Embase, and Scopus databases were searched systematically for observational studies in patients undergoing elective fenestrated branched endovascular repair for aortic aneurysm. The included studies compared outcomes of interest based on the sex of the patients. The pooled effect sizes were reported as odds ratio (OR) and weighted mean difference (WMD). STATA software was used for statistical analysis. RESULTS: The meta-analysis included nine studies. Compared with males, females had a higher risk of perioperative and in-hospital mortality (OR, 3.01; 95% confidence interval [CI], 2.01-4.53), mortality within 1 year postoperatively (OR, 1.79; 95% CI, 1.09-2.93) and mortality at more than 1 year postoperatively (OR, 1.31l 95% CI, 1.02-1.69). Female patients had significantly higher operative time (minutes) (WMD, 33.77; 95% CI, 12.01-55.52), length of hospital stay (days) (WMD, 2.29; 95% CI, 1.52-3.07), and the risk of major complications (OR, 2.93; 95% CI, 1.36-6.32) There was an increased risk of respiratory complications (OR, 1.70; 95% CI, 1.20-2.40), renal complications (OR, 2.68; 95% CI, 1.25-5.74), stroke (OR, 2.74; 95% CI, 1.44-5.22), sepsis (OR, 2.24; 95% CI, 1.23-4.09), and ischemic colitis (OR, 2.63; 95% CI, 1.48-4.68) in female patients, and they were less likely to be discharged home postoperatively (OR, 0.58; 95% CI, 0.43-0.77). CONCLUSIONS: In patients undergoing FBEVAR, female sex is associated with higher risk of mortality and complications. These findings suggest the need for careful supervision and management by multidisciplinary team in females undergoing FBEVAR.

Identification and validation of diagnostic biomarkers of coronary artery disease progression in type 1 diabetes via integrated computational and bioinformatics strategies.

OBJECTIVE: Our study aimed to identify early peripheral blood diagnostic biomarkers and elucidate the immune mechanisms of coronary artery disease (CAD) progression in patients with type 1 diabetes mellitus (T1DM). METHODS: Three transcriptome datasets were retrieved from the Gene Expression Omnibus (GEO) database. Gene modules associated with T1DM were selected with weighted gene co-expression network analysis. Differentially expressed genes (DEGs) between CAD and acute myocardial infarction (AMI) peripheral blood tissues were identified using limma. Candidate biomarkers were selected with functional enrichment analysis, node gene selection from a constructed protein-protein interaction (PPI) network, and 3 machine learning algorithms. Candidate expression was compared, and the receiver operating characteristic curve (ROC) and nomogram were constructed. Immune cell infiltration was assessed with the CIBERSORT algorithm. RESULTS: A total of 1283 genes comprising 2 modules were detected as the most associated with T1DM. In addition, 451 DEGs related to CAD progression were identified. Among them, 182 were common to both diseases and mainly enriched in immune and inflammatory response regulation. The PPI network yielded 30 top node genes, and 6 were selected using the 3 machine learning algorithms. Upon validation, 4 genes (TLR2, CLEC4D, IL1R2, and NLRC4) were recognized as diagnostic biomarkers with the area under the curve (AUC) > 0.7. All 4 genes were positively correlated with neutrophils in patients with AMI. CONCLUSION: We identified 4 peripheral blood biomarkers and provided a nomogram for early diagnosing CAD progression to AMI in patients with T1DM. The biomarkers were positively associated with neutrophils, indicating potential therapeutic targets.

Decoupling between carbon source and sink induced by responses of daily stem growth to water availability in subtropical urban forests.

Urban forests are anticipated to offer sustainable ecosystem services, necessitating a comprehensive understanding of the ways in which trees respond to environmental changes. This study monitored stem radius fluctuations in Cinnamomum camphora and Taxodium distichum var. imbricatum trees using high-resolution dendrometers at two sites, respectively. Gross primary production (GPP) was measured using eddy-covariance techniques and aggregated to daily sums. Hourly and daily stem radius fluctuations were estimated across both species, and the responses of stems to radiation (Rg), air temperature (Tair), vapor pressure deficit (VPD), and soil humidity (SoilH) were quantified using Bayesian linear models. The diel growth patterns of the monitored trees showed similar characteristics at the species level. Results revealed that trees growth occurred primarily at night, with the lowest hourly contribution to total growth and probability for growth occurring in the afternoon. Furthermore, the Bayesian models indicated that VPD was the most important driver of daily growth and growth probability. After considering the potential constraints imposed by VPD, a modified Gompertz equation showed good performance, with R2 ranging from 0.94 to 0.99 for the relationship between accumulative growth and time. Bayes-based model-independent data assimilation using advanced Markov chain Monte Carlo (MCMC) algorithms provided deeper insights into nonlinear model parameterization. Finally, the quantified relationship between GPP and stem daily growth revealed that the decoupling between carbon source and sink increased with VPD. These findings provided direct empirical evidence for VPD as a key driver of daily growth patterns and raise questions about carbon neutrality accounting under future climate change given the uncertainties induced by increased water stress limitations on carbon utilization.

Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis.

BACKGROUND: Uremia is one of the most challenging problems in medicine and an increasing public health issue worldwide. Patients with uremia suffer from accelerated atherosclerosis, and atherosclerosis progression may trigger plaque instability and clinical events. As a result, cardiovascular and cerebrovascular complications are more likely to occur. This study aimed to identify diagnostic biomarkers in uremic patients with unstable carotid plaques (USCPs). METHODS: Four microarray datasets (GSE37171, GSE41571, GSE163154, and GSE28829) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in uremia and USCP. Weighted gene co-expression network analysis (WGCNA) was used to determine the respective significant module genes associated with uremia and USCP. Moreover, a protein-protein interaction (PPI) network and three machine learning algorithms were applied to detect potential diagnostic genes. Subsequently, a nomogram and a receiver operating characteristic curve (ROC) were plotted to diagnose USCP with uremia. Finally, immune cell infiltrations were further analyzed. RESULTS: Using the Limma package and WGCNA, the intersection of 2795 uremia-related DEGs and 1127 USCP-related DEGs yielded 99 uremia-related DEGs in USCP. 20 genes were selected as candidate hub genes via PPI network construction. Based on the intersection of genes from the three machine learning algorithms, three hub genes (FGR, LCP1, and C5AR1) were identified and used to establish a nomogram that displayed a high diagnostic performance (AUC: 0.989, 95% CI 0.971-1.000). Dysregulated immune cell infiltrations were observed in USCP, showing positive correlations with the three hub genes. CONCLUSION: The current study systematically identified three candidate hub genes (FGR, LCP1, and C5AR1) and established a nomogram to assist in diagnosing USCP with uremia using various bioinformatic analyses and machine learning algorithms. Herein, the findings provide a foothold for future studies on potential diagnostic candidate genes for USCP in uremic patients. Additionally, immune cell infiltration analysis revealed that the dysregulated immune cell proportions were identified, and macrophages could have a critical role in USCP pathogenesis.

Effect of the timing of iliac vein stent implantation on catheter-directed thrombolysis in acute lower extremity deep venous thrombosis patients with severe iliac vein stenosis: a retrospective study.

OBJECTIVE: To investigate the effect of the timing of iliac vein stent implantation on catheter-directed thrombolysis (CDT) in acute lower extremity deep venous thrombosis (DVT) patients with severe iliac vein stenosis. METHODS: The clinical data of 66 patients with acute lower extremity DVT complicated with severe iliac vein stenosis from May 2017 to May 2020 were retrospectively analyzed. Patients were divided into two groups by timing of iliac vein stent implantation: group A (iliac vein stent implantation before CDT treatment) for 34 and group B (iliac vein stent implantation after CDT treatment) for 32. The detumescence rate of affected limb, the thrombus clearance rate, the thrombolytic efficiency, the complication rate, the hospitalization cost, the stent patency rate within 1 year, and the scores (venous clinical severity score, Villalta, and chronic venous insufficiency questionnaire (CIVIQ) score) at 1 year postoperatively were compared between the two groups. RESULTS: The thrombolytic efficiency of group A was higher than that of group B, while the incidence of complications and hospitalization expenses in group A were lower than those in group B. There was no statistical significance in the detumescence rate of affected limb, the thrombus clearance rate, the stent patency rate within 1 year, and the scores (VCSS, Villalta, and CIVIQ score) at 1 year postoperatively between the two groups. CONCLUSIONS: For acute lower extremity DVT patients with severe iliac vein stenosis, iliac vein stent implantation before CDT treatment can improve the thrombolytic efficiency, and reduce the incidence of complications and hospitalization costs.

In Vitro Hypoglycemic Diterpenoids from the Roots of Croton yunnanensis.

Fifteen compounds including nine new diterpenes were isolated from the roots of Croton yunnanensis. By HRESIMS, NMR, ECD data, and X-ray diffraction analysis, the new compounds were characterized as eight neo-clerodane diterpenes (compounds 1-8) and one 15,16-dinor-ent-pimarane diterpene (9). All diterpenes were assayed for their hypoglycemic activities. Compounds 1-4, 6, 7, and 10 promoted glucose uptake activity in insulin-resistant 3T3-L1 adipocytes. Compounds 1 and 6 showed insulin sensitizing activity, potentiating conspicuously their glucose uptake activity at a concentration of 20 µM when treated synergistically with low-concentration insulin at 1 nM.

Quorum sensing-based interactions among drugs, microbes, and diseases.

Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database ( http://www.qsdmd.lbci.net/ ), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning.

BACKGROUND: Systemic lupus erythematosus (SLE) has become a major public health problem over the years, and atherosclerosis (AS) is one of the main complications of SLE associated with serious cardiovascular consequences in this patient population. The present study aimed to identify potential biomarkers for SLE patients with AS. METHODS: Five microarray datasets (GSE50772, GSE81622, GSE100927, GSE28829, GSE37356) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS. Weighted gene coexpression network analysis (WGCNA) was used to identify significant module genes associated with SLE. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (Lasso, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest) were applied to identify hub genes. Subsequently, we generated a nomogram and receiver operating characteristic curve (ROC) for predicting the risk of AS in SLE patients. Finally, immune cell infiltrations were analyzed, and Consensus Cluster Analysis was conducted based on Single Sample Gene Set Enrichment Analysis (ssGSEA) scores. RESULTS: Five hub genes (SPI1, MMP9, C1QA, CX3CR1, and MNDA) were identified and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.900-0.981). Dysregulated immune cell infiltrations were found in AS, with positive correlations with the five hub genes. Consensus clustering showed that the optimal number of subtypes was 3. Compared to subtypes A and B, subtype C presented higher expression of the five hub genes, immune cell infiltration levels and immune checkpoint expression. CONCLUSION: Our study systematically identified five candidate hub genes (SPI1, MMP9, C1QA, CX3CR1, MNDA) and established a nomogram that could predict the risk of AS with SLE using various bioinformatic analyses and machine learning algorithms. Our findings provide the foothold for future studies on potential crucial genes for AS in SLE patients. Additionally, the dysregulated immune cell proportions and immune checkpoint expressions in AS with SLE were identified.

Immune-associated pivotal biomarkers identification and competing endogenous RNA network construction in post-operative atrial fibrillation by comprehensive bioinformatics and machine learning strategies.

Background: Atrial fibrillation (AF) is the most common arrhythmia. Previous studies mainly focused on identifying potential diagnostic biomarkers and treatment strategies for AF, while few studies concentrated on post-operative AF (POAF), particularly using bioinformatics analysis and machine learning algorithms. Therefore, our study aimed to identify immune-associated genes and provide the competing endogenous RNA (ceRNA) network for POAF. Methods: Three GSE datasets were downloaded from the GEO database, and we used a variety of bioinformatics strategies and machine learning algorithms to discover candidate hub genes. These techniques included identifying differentially expressed genes (DEGs) and circRNAs (DECs), building protein-protein interaction networks, selecting common genes, and filtering candidate hub genes via three machine learning algorithms. To assess the diagnostic value, we then created the nomogram and receiver operating curve (ROC). MiRNAs targeting DEGs and DECs were predicted using five tools and the competing endogenous RNA (ceRNA) network was built. Moreover, we performed the immune cell infiltration analysis to better elucidate the regulation of immune cells in POAF. Results: We identified 234 DEGs (82 up-regulated and 152 down-regulated) of POAF via Limma, 75 node genes were visualized via PPI network, which were mainly enriched in immune regulation. 15 common genes were selected using three CytoHubba algorithms. Following machine learning selection, the nomogram was created based on the four candidate hub genes. The area under curve (AUC) of the nomogram and individual gene were all over 0.75, showing the ideal diagnostic value. The dysregulation of macrophages may be critical in POAF pathogenesis. A novel circ_0007738 was discovered in POAF and the ceRNA network was eventually built. Conclusion: We identified four immune-associated candidate hub genes (C1QA, C1R, MET, and SDC4) for POAF diagnosis through the creation of a nomogram and evaluation of its diagnostic value. The modulation of macrophages and the ceRNA network may represent further therapy methods.

Extensive sharing of chloroplast haplotypes among East Asian Cerris oaks: The imprints of shared ancestral polymorphism and introgression.

Shared ancestral polymorphism and introgression are two main causes of chloroplast DNA (cpDNA) haplotype sharing among closely related angiosperms. In this study, we explored the roles of these two processes in shaping the phylogeographic patterns of East Asian Cerris oaks by examining the geographic distributions of randomly and locally distributed shared haplotypes, which coincide with the expectations of shared ancestry and introgression, respectively. We sequenced 1340 bp of non-coding cpDNA from Quercus acutissima (n = 418) and Q. chenii (n = 183) and compiled previously published sequence data of Q. variabilis (n = 439). The phylogenetic relationships among haplotypes were examined using a median-joining network. The geographic patterns of interspecifically shared haplotypes were assessed to test whether nearby populations have a higher degree of interspecific cpDNA sharing than distant ones. We identified a total of 27 haplotypes that were grouped into three non-species-specific lineages with overlapping distributions. Ancestral haplotypes were extensively shared and randomly distributed across populations of the three species. Some young haplotypes were locally shared in mountainous areas that may have been shared refugia. The local exchange of cpDNA resulted in an excess of similar haplotypes between nearby populations. Our study demonstrated that the haplotype sharing pattern among East Asian Cerris oaks reflected the imprints of both shared ancestral polymorphism and introgression. This pattern was also associated with the relatively stable climates and complex landscapes in East Asia, which not only allowed the long-term persistence of ancestral lineages but also connected the survived populations across refugia.

Identification and validation of differentially expressed chromatin regulators for diagnosis of aortic dissection using integrated bioinformatics analysis and machine-learning algorithms.

Background: Aortic dissection (AD) is a life-threatening disease. Chromatin regulators (CRs) are indispensable epigenetic regulators. We aimed to identify differentially expressed chromatin regulators (DECRs) for AD diagnosis. Methods: We downloaded the GSE52093 and GSE190635 datasets from the Gene Expression Omnibus database. Following the merging and processing of datasets, bioinformatics analysis was applied to select candidate DECRs for AD diagnosis: CRs exertion; DECR identification using the "Limma" package; analyses of enrichment of function and signaling pathways; construction of protein-protein interaction (PPI) networks; application of machine-learning algorithms; evaluation of receiver operating characteristic (ROC) curves. GSE98770 served as the validation dataset to filter DECRs. Moreover, we collected peripheral-blood samples to further validate expression of DECRs by real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Finally, a nomogram was built for clinical use. Results: A total of 841 CRs were extracted from the merged dataset. Analyses of functional enrichment of 23 DECRs identified using Limma showed that DECRs were enriched mainly in epigenetic-regulation processes. From the PPI network, 17 DECRs were selected as node DECRs. After machine-learning calculations, eight DECRs were chosen from the intersection of 13 DECRs identified using support vector machine recursive feature elimination (SVM-RFE) and the top-10 DECRs selected using random forest. DECR expression between the control group and AD group were considerably different. Moreover, the area under the ROC curve (AUC) of each DECR was >0.75, and four DECRs (tumor protein 53 (TP53), chromobox protein homolog 7 (CBX7), Janus kinase 2 (JAK2) and cyclin-dependent kinase 5 (CDK5)) were selected as candidate biomarkers after validation using the external dataset and clinical samples. Furthermore, a nomogram with robust diagnostic value was established (AUC = 0.960). Conclusion: TP53, CBX7, JAK2, and CDK5 might serve as diagnostic DECRs for AD diagnosis. These DECRs were enriched predominantly in regulating epigenetic processes.

Machine learning aided construction of the quorum sensing communication network for human gut microbiota.

Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database ( http://www.qshgm.lbci.net/ ) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases.

Tigliane Diterpenoids with Larvicidal, Antifungal, and α-Glucosidase Inhibitory Activities from Croton damayeshu.

Thirty-five tigliane diterpenoids and two ent-kaurane diterpenoids were isolated from the leaves of Croton damayeshu, and, among them, compounds 1-10 were characterized as new tigliane diterpenoids. The structures of compounds 1-10 were determined by analysis of their HRESIMS, NMR, and ECD data and by chemical methods. The isolates were assayed for their larvicidal, antifungal, and α-glucosidase inhibitory activities, and compounds 8-10 were found to possess larvicidal activities against Plutella xylostella with LC50 values of 0.19, 0.16, and 0.26 µM, respectively, comparable to the LC50 of 0.14 µM for the positive control, flubendiamide.

The role of biodiversity in mitigating the effects of nutrient limitation and short-term rotations in plantations of subtropical China.

Species diversity plays an essential role in enhancing ecosystem functions (EF) in both natural and plantation forests. However, we do not fully understand whether species diversity could maintain the sustainability of EFs in multiple-rotation plantations. Here, we hypothesized that tree species mixtures could mitigate declines in EFs along successive rotations, but could not maintain ecosystem multifunctionality. To test our hypothesis, we examined the effects of species diversity on four EFs, i.e., aboveground biomass (AGB), soil available nitrogen (SAN) and phosphorus (SAP), and soil organic matter (SOM), based on pure model simulation in plantations of subtropical China. The model fusion framework was set up by the integration of the process-based FORECAST and Multivariate Diversity-Interactions models. In the simulation, four local typical plantation tree species (two conifers, one evergreen broadleaf, and one deciduous N-fixing broadleaf) were selected and combined to form four monoculture and 11 mixture stands, and for each stand, the simulation was made for four 25-year rotations. The results showed that all the four EFs declined with the progress of rotations in both monoculture and mixtures, and the declining range was larger in monoculture than in mixtures in each rotation. Particularly, SAP significantly decreased while AGB, SAN, and SOM increased with diversity evenness from 0 (monoculture) to 1 (four species being equal abundant in the mixture). Overall, SAP and AGB displayed higher sensitivity to the disturbance of successive rotations compared with SAN and SOM. These results suggest that mixing species could not maintain EFs along with successive rotations because it could not alleviate SAP deficiencies in the soils resulted from the disturbances of silvicultural measures.