Fusarium sacchari FsNis1 induces plant immunity.

Pokkah Boeng disease (PBD), caused by Fusarium sacchari, severely affects sugarcane yield and quality. Necrosis-inducing secreted protein 1 (Nis1) is a fungal secreted effector that induces necrotic lesions in plants. It interacts with host receptor-like kinases and inhibits their kinase activity. FsNis1 contains the Nis1 structure and triggered a pathogen-associated molecular pattern-triggered immune response in Nicotiana benthamiana, as reflected by causing reactive oxygen species production, callose accumulation, and the upregulated expression of defense response genes. Knockout of this gene in F. sacchari revealed a significant reduction in its pathogenicity, whereas the pathogenicity of the complementary mutant recovered to the wild-type levels, making this gene an important virulence factor for F. sacchari. In addition, the signal peptide of FsNis1 was required for the induction of cell death and PTI response in N. benthamiana. Thus, FsNis1 may not only be a key virulence factor for F. sacchari but may also induce defense responses in plants. These findings provide new insights into the function of Nis1 in host-pathogen interactions.

GRAND: GAN-based software runtime anomaly detection method using trace information.

Software runtime anomaly detection can detect manifestations (known as anomalies) caused by faults in complex systems before they lead to failure. Whereas most existing methods use external performance indicators, this study uses internal execution traces to reveal failures not only related to software performance issues but also functional errors. A neural network model called GRAND, which combines a variational autoencoder and a generative adversarial network, is proposed to mine anomalies in the execution trace. Cassandra, a widely used database system, was used as a representation to conduct the empirical study. The dataset was collected under a well-designed operational profile that contained 5180 time series, each containing more than ten million data points. GRAND achieved a higher detection performance than the other two SOTA baseline models, with a 99% F1-score compared with 93% and 87%. Ablation studies show that the workload information used in GRAND can determine whether the current internal status is consistent with the task, thus achieving a 16% improvement in the F1-score. The attention mechanism used for data fusion can achieve a 32% improvement in the F1-score.

Identification of the role of immune-related genes in the diagnosis of bipolar disorder with metabolic syndrome through machine learning and comprehensive bioinformatics analysis.

Background: Bipolar disorder and metabolic syndrome are both associated with the expression of immune disorders. The current study aims to find the effective diagnostic candidate genes for bipolar affective disorder with metabolic syndrome. Methods: A validation data set of bipolar disorder and metabolic syndrome was provided by the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were found utilizing the Limma package, followed by weighted gene co-expression network analysis (WGCNA). Further analyses were performed to identify the key immune-related center genes through function enrichment analysis, followed by machine learning-based techniques for the construction of protein-protein interaction (PPI) network and identification of the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). The receiver operating characteristic (ROC) curve was plotted to diagnose bipolar affective disorder with metabolic syndrome. To investigate the immune cell imbalance in bipolar disorder, the infiltration of the immune cells was developed. Results: There were 2,289 DEGs in bipolar disorder, and 691 module genes in metabolic syndrome were identified. The DEGs of bipolar disorder and metabolic syndrome module genes crossed into 129 genes, so a total of 5 candidate genes were finally selected through machine learning. The ROC curve results-based assessment of the diagnostic value was done. These results suggest that these candidate genes have high diagnostic value. Conclusion: Potential candidate genes for bipolar disorder with metabolic syndrome were found in 5 candidate genes (AP1G2, C1orf54, DMAC2L, RABEPK and ZFAND5), all of which have diagnostic significance.

Predictive model, miRNA-TF network, related subgroup identification and drug prediction of ischemic stroke complicated with mental disorders based on genes related to gut microbiome.

Background: Patients with comorbid schizophrenia, depression, drug use, and multiple psychiatric diagnoses have a greater risk of carotid revascularization following stroke. The gut microbiome (GM) plays a crucial role in the attack of mental illness and IS, which may become an index for the diagnosis of IS. A genomic study of the genetic commonalities between SC and IS, as well as its mediated pathways and immune infiltration, will be conducted to determine how schizophrenia contributes to the high prevalence of IS. According to our study, this could be an indicator of ischemic stroke development. Methods: We selected two datasets of IS from the Gene Expression Omnibus (GEO), one for training and the other for the verification group. Five genes related to mental disorders and GM were extracted from Gene cards and other databases. Linear models for microarray data (Limma) analysis was utilized to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. It was also used to conduct machine learning exercises such as random forest and regression to identify the best candidate for immune-related central genes. Protein-protein interaction (PPI) network and artificial neural network (ANN) were established for verification. The receiver operating characteristic (ROC) curve was drawn for the diagnosis of IS, and the diagnostic model was verified by qRT-PCR. Further immune cell infiltration analysis was performed to study the IS immune cell imbalance. We also performed consensus clustering (CC) to analyze the expression of candidate models under different subtypes. Finally, miRNA, transcription factors (TFs), and drugs related to candidate genes were collected through the Network analyst online platform. Results: Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93-0.71) and the verification group (AUC 0.81, CI 0.90-0.72) had a good phenotype in the qRT-PCR test. And in verification group 2 we validated between the two groups with and without carotid-related ischemic cerebrovascular events (AUC 0.87, CI 1-0.64). Furthermore, we investigated cytokines in both GSEA and immune infiltration and verified cytokine-related responses by flow cytometry, particularly IL-6, which played an important role in IS occurrence and progression. Therefore, we speculate that mental illness may affect the development of IS in B cells and IL-6 in T cells. MiRNA (hsa-mir-129-2-3p, has-mir-335-5p, and has-mir-16-5p) and TFs (CREB1, FOXL1), which may be related to IS, were obtained. Conclusion: Through comprehensive analysis, a diagnostic prediction model with good effect was obtained. Both the training group (AUC 0.82, CI 0.93-0.71) and the verification group (AUC 0.81, CI 0.90-0.72) had a good phenotype in the qRT-PCR test. And in verification group 2 we validated between the two groups with and without carotid-related ischemic cerebrovascular events (AUC 0.87, CI 1-0.64). MiRNA (hsa-mir-129-2-3p, has-mir-335-5p, and has-mir-16-5p) and TFs (CREB1, FOXL1), which may be related to IS, were obtained.

Schizophrenia and cell senescence candidate genes screening, machine learning, diagnostic models, and drug prediction.

Background: Schizophrenia (SC) is one of the most common psychiatric diseases. Its potential pathogenic genes and effective treatment methods are still unclear. Cell senescence has been confirmed in mental diseases. A link exists between cellular senescence and immunity, and immune-related problems affect suicide rates in individuals suffering from schizophrenia. Therefore, the aims of this study were to identify candidate genes based on cell senescence that can affect the diagnosis and treatment of schizophrenia. Methods: Two data sets of schizophrenia were provided by the Gene Expression Omnibus (GEO) database, one was taken as training and the other as a validation group. The genes related to cell senescence were obtained from the CellAge database. DEGs were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). The function enrichment analysis was conducted, followed by machine learning-based identification for least absolute shrinking and selection operators (LASSO) regression. Random Forest were used to identify candidate immune-related central genes and establish artificial neural networks for verification of the candidate genes. The receiver operating characteristic curve (ROC curve) was used for the diagnosis of schizophrenia. Immune cell infiltrates were constructed to study immune cell dysregulation in schizophrenia, and relevant drugs with candidate genes were collected from the DrugBank database. Results: Thirteen co-expression modules were screened for schizophrenia, of which 124 were the most relevant genes.There were 23 intersected genes of schizophrenia (including DEGs and the cellular senescence-related genes), and through machine learning six candidate genes were finally screened out. The diagnostic value was evaluated using the ROC curve data. Based on these results it was confirmed that these candidate genes have high diagnostic value.Two drugs related to candidate genes, Fostamatinib and Ritodine, were collected from the DrugBanks database. Conclusion: Six potential candidate genes (SFN, KDM5B, MYLK, IRF3, IRF7, and ID1) had been identified, all of which had diagnostic significance. Fostamatinib might be a drug choice for patients with schizophrenia to develop immune thrombocytopenic purpura (ITP) after treatment, providing effective evidence for the pathogenesis and drug treatment of schizophrenia.

Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs.

Wireless sensor networks (WSNs) are widely used in various fields, and the reliability and performance of WSNs are critical for their applications. However, WSNs are vulnerable to jamming attacks, and the impact of movable jammers on WSNs' reliability and performance remains largely unexplored. This study aims to investigate the impact of movable jammers on WSNs and propose a comprehensive approach for modeling jammer-affected WSNs, comprising four parts. Firstly, agent-based modeling of sensor nodes, base stations, and jammers has been proposed. Secondly, a jamming-aware routing protocol (JRP) has been proposed to enable sensor nodes to weigh depth and jamming values when selecting relay nodes, thereby bypassing areas affected by jamming. The third and fourth parts involve simulation processes and parameter design for simulations. The simulation results show that the mobility of the jammer significantly affects WSNs' reliability and performance, and JRP effectively bypasses jammed areas and maintains network connectivity. Furthermore, the number and deployment location of jammers has a significant impact on WSNs' reliability and performance. These findings provide insights into the design of reliable and efficient WSNs under jamming attacks.

The forkhead box protein P3 gene rs3761548 promoter polymorphism confers a genetic contribution to the risk of preeclampsia: A systematic review and meta-analysis.

Various studies have investigated the risk of preeclampsia with the forkhead box protein P3 (FOXP3) gene rs2232365 and rs3761548 polymorphisms. However, the results remained contradictory. A comprehensive literature search was conducted using the Cochrane Library, PubMed, and Web of Science (up to Oct 11, 2021). Meta-analysis was carried out in the R language environment for statistical computing and graphics. A fixed-effect or random-effects model was used according to the statistical significance of heterogeneity among included studies. The pooled odds ratios and corresponding 95% confidence intervals were calculated to estimate the strength of the effect. For the rs2232365 polymorphism, statistical significance was detected neither in the overall population nor among the East Asian and West Asian subgroups. However, for rs3761548, the summarized statistics revealed a significant association between the C allele carriage and preeclampsia risk in the homozygote, heterozygote, and dominant models. The further stratified analysis found this effect might be specific to West-South Asian ethnic subgroups. To sum up, this meta-analysis showed that the FOXP3 rs3761548 polymorphism was significantly associated with preeclampsia susceptibility, and it had a deleterious effect especially in the West-South Asian population. In contrast, rs2232365 may serve as neither a protective nor a risk factor for preeclampsia onset.

Increasing prevalence of gestational diabetes mellitus when carrying the T variant allele of the MTHFR gene C677T polymorphism: a systematic review and meta-analysis.

PURPOSE: Previous epidemiological data linking the C677T and A1298C MTHFR polymorphisms to gestational diabetes risk have been mixed and controversial. Therefore, we conducted this meta-analysis to derive a more precise estimation of the relationship between MTHFR polymorphisms and this pregnancy disorder. METHODS: A systematic literature search for original epidemiological studies was performed in the CNKI, WanFang, Cochrane Library, PubMed, and Web of Science databases. R language-based programs were employed for all statistical analyses. Odds ratios and corresponding 95% confidence intervals were calculated to estimate the effects of the variant allele on gestational diabetes risk. RESULTS: A summary of the estimates for the C677T polymorphism showed that the exposure cohorts were prone to gestational diabetes by a greater magnitude than the control groups. Further subgroup analysis by ethnicity showed that the Asians carrying the variant T allele were more susceptible to this pregnancy disorder. However, the pathogenic effect was not evident in the non-Asian subgroup. For the A1298C polymorphism, no statistical significance could be detected. CONCLUSION: This meta-analysis suggests that the T allele of the MTHFR gene C677T polymorphism tends to increase gestational diabetes susceptibility, especially for Asians. However, the A1298C polymorphism is not associated with an increased risk of this crippling pregnancy disorder.

A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks.

A wireless sensor network (WSN) is a group of sensors connected with a wireless communications infrastructure designed to monitor and send collected data to the primary server. The WSN is the cornerstone of the Internet of Things (IoT) and Industry 4.0. Robustness is an essential characteristic of WSN that enables reliable functionalities to end customers. However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. This paper proposes a cross-domain agent-based model to analyze the connectivity robustness of a system in the malware propagation process. The agent characteristics and transition rules are also described in detail. To verify the practicality of the model, three scenarios based on different network topologies are proposed. Finally, the robustness of the scenarios and the topologies are discussed.

A Dynamic Anomaly Detection Approach Based on Permutation Entropy for Predicting Aging-Related Failures.

Software aging is a phenomenon referring to the performance degradation of a long-running software system. This phenomenon is an accumulative process during execution, which will gradually lead the system from a normal state to a failure-prone state. It is a crucial challenge for system reliability to predict the Aging-Related Failures (ARFs) accurately. In this paper, permutation entropy (PE) is modified to Multidimensional Multi-scale Permutation Entropy (MMPE) as a novel aging indicator to detect performance anomalies, since MMPE is sensitive to dynamic state changes. An experiment is set on the distributed database system Voldemort, and MMPE is calculated based on the collected performance metrics during execution. Finally, based on MMPE, a failure prediction model using the machine learning method to reveal the anomalies is presented, which can predict failures with high accuracy.

The roles of dipeptidyl peptidase-4 and its inhibitors in the regulation of airway epithelial-mesenchymal transition.

Objective: Dipeptidyl peptidase 4 (DPP4), also known as CD26, is a transmembrane glycoprotein with peptidase activity expressed on epithelial cells and some immune cells. It also occurs as a soluble form. Studies have revealed that the expression level of lymphocyte sCD26/sDPP4 was elevated in the asthmatic patients. Airway remodeling increases in asthma severity and these structural changes include, amongst others, the loss of epithelial integrity because of cell shedding, goblet cell hyperplasia, destruction of ciliated cells, and EMT. So we try to find whether sCD26/sDPP4 has a role in pathological/dysregulated transition from bronchial epithelial cells into fibroblasts cells in response to TGFß1 exposure in vitro. Therefore, our purpose in the present work was to identify the role of sCD26/sDPP4 in airway EMT regulation. Methods: The EMT cell model was established based on human 16HBE cells. The effects of sCD26/sDPP4 and its inhibitors on airway EMT and that of sCD26/sDPP4 on Th17/IL-17 and its role in airway EMT were investigated in vitro. Results: The mRNA and protein level of E-Cadherin decreased after the treatment of TGF-ß1 in 16HBE cells, while α-SMA was up-regulated. The level of E-Cadherin was significantly down-regulated after the sCD26/sDPP4 stimulation, and that of α-SMA was dramatically elevated. DPP4 inhibitors promoted the level of E-cadherin and inhibited that of α-SMA. Additionally, in the DPP4-treated IL-17 cells group, E-Cadherin was markedly down-regulated at the mRNA and protein level, while α-SMA was reversely up-regulated. Conclusion: The TGF-ß1-induced EMT of human bronchial epithelial cells could be promoted by sCD26/sDPP4. The suppression of EMT in human bronchial epithelial cells was achieved by DPP4 inhibitor, and the TGF-ß1-mediated EMT of human airway cells was promoted by the synergy of IL-17 and sCD26/sDPP4 in vitro.

Atrial natriuretic peptide inhibits epithelial-mesenchymal transition (EMT) of bronchial epithelial cells through cGMP/PKG signaling by targeting Smad3 in a murine model of allergic asthma.

Background: Atrial natriuretic peptide (ANP) inhibits TGF-ß1-induced epithelial-mesenchymal transition (EMT) in human airway cells. We aim to explore the role and mechanism of ANP on EMT of bronchial epithelial cells from murine model of allergic asthma in vitro. Methods: Murine model of allergic asthma was established with BALB/c mice using ovalbumin (OVA). Bronchial epithelial cells were isolated from OVA-exposed mice, and then were cocultured with TGF-ß1, ANP, natriuretic peptide receptor A antagonist, cGMP analog, cGMP inhibitor or/and protein kinase G (PKG) inhibitor, respectively. We assessed expressions of E-Cadherin, α-SMA, cGMP, Smad3 and p-Smad3 in the murine cells before and after Smad3 silence. Results: Compared with bronchial epithelial cells from controls and OVA-exposed mice without additional stimulation, the mRNA and protein expressions of E-Cadherin were decreased but α-SMA expressions were increased in cells with TGF-ß1 stimulation from OVA-exposed mice in vitro. That could be reversed by ANP. The effect of ANP could be mimicked by the cGMP analog, which could be reversed by cGMP or PKG inhibitor. Moreover, the phosphorylated Smad3 expression was consistent with that of α-SMA. When Smad3 was silenced, Smad3 was mostly expressed in cytoplasm. In contrast, it is mostly expressed in nucleus of non-silenced cells during EMT. Conclusions: In a murine model of allergic asthma, ANP could inhibit TGF-ß1-induced EMT of bronchial epithelial cells through cGMP/PKG signaling, targeting TGF-ß1/Smad3 via attenuating phosphorylation of Smad3 in vitro, which may provide potential of ANP in treating allergic asthma with airway remodeling.

Association between carotid plaque and Parkinson's disease.

BACKGROUND: Epidemiological studies show that patients with Parkinson's disease (PD) are prone to have a reduced incidence of ischemic cerebrovascular disease. Previous studies show the correlation between PD and the lipids serum levels. The PD,s patients are found with a reduced serum level of triglyceride and low-density lipoprotein cholesterol (LDL-C); thus, the level of serum uric acid (UA) is closely related to the occurrence and development of PD. Patients with low serum UA levels have a higher chance of developing PD than the ones who do not. However, the relationship between carotid plaques and PD is still unknown. METHODS: Our study was based on 68 patients with PD (known as the PD group) and 81 people without PD (known as the control group). Patients in the PD group were of the same age and gender. Both groups were recorded and analyzed for UA, LDL-C, and carotid plaques or intima-media thickness (IMT). The PD group was then divided into three subgroups: the stable plaque group, the unstable plaque group, and the non-plaque group. RESULTS: In the present study, the PD group showed a significantly lower level of UA and LDL-C than the control group (P<0.01); somehow there were no statistically significant differences in the IMT and plaque incidence between the two groups (P>0.05). There were also no significant differences (P>0.05) in both the LDL-C and UA levels in all subgroups, but there was a close relation in both age and duration of disease to IMT. According to the Hoehn and Yahr staging scale, serum levels of LDL-C were inversely correlated in PD patients, while UA was related to the duration of the disease. CONCLUSIONS: Our study suggested that there were no differences in carotid artery arteriosclerosis plaque and IMT, but the PD progress was indeed correlated with IMT. Meanwhile, LDL-C and UA had different priorities in H&Y and disease progression.

A single nucleotide polymorphism in the promoter of the LOXL1 gene and its relationship to pelvic organ prolapse and preterm premature rupture of membranes.

Pelvic organ prolapse and preterm premature rupture of membranes, the 2 conditions which have in common weakening of the tensile strength of tissues, are thought to be caused, in part, by abnormal extracellular matrix synthesis and/or catabolism. We identified a new single nucleotide polymorphism (NT_010194(LOXL1):g.45008784A>C) in the promoter of the LOXL1 gene, which is essential for elastin synthesis. Promoter studies showed that the minor "C'' allele had significantly greater activity than the major "A'' allele. Case-control studies examined the association of the alleles of this single nucleotide polymorphism with pelvic organ prolapse and preterm premature rupture of membranes. When comparing allele frequencies and genotypes in pelvic organ prolapse cases versus controls, no significant associations were found. A case-control study conducted in African American neonates also found no significant associations between the promoter alleles and preterm premature rupture of membranes. We conclude that a functional single nucleotide polymorphism exists in the promoter region of the LOXL1 gene. Association studies suggest that the promoter single nucleotide polymorphism does not contribute significantly to risk of pelvic organ prolapse or preterm premature rupture of membranes.