Identification of immune infiltration and cuproptosis-related molecular clusters in tuberculosis

BackgroundTuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) infection. Cuproptosis is a novel cell death mechanism correlated with various diseases. This study sought to elucidate the role of cuproptosis-related genes (CRGs) in TB.MethodsBased on the GSE83456 dataset, we analyzed the expression profiles of CRGs and immune cell infiltration in TB. Based on CRGs, the molecular clusters and related immune cell infiltration were explored using 92 TB samples. The Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was utilized to identify the co-expression modules and cluster-specific differentially expressed genes. Subsequently, the optimal machine learning model was determined by comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB). The predictive performance of the machine learning model was assessed by generating calibration curves and decision curve analysis and validated in an external dataset.Results11 CRGs were identified as differentially expressed cuproptosis genes. Significant differences in immune cells were observed in TB patients. Two cuproptosis-related molecular clusters expressed genes were identified. Distinct clusters were identified based on the differential expression of CRGs and immune cells. Besides, significant differences in biological functions and pathway activities were observed between the two clusters. A nomogram was generated to facilitate clinical implementation. Next, calibration curves were generated, and decision curve analysis was conducted to validate the accuracy of our model in predicting TB subtypes. XGB machine learning model yielded the best performance in distinguishing TB patients with different clusters. The top five genes from the XGB model were selected as predictor genes. The XGB model exhibited satisfactory performance during validation in an external dataset. Further analysis revealed that these five model-related genes were significantly associated with latent and active TB.ConclusionOur study provided hitherto undocumented evidence of the relationship between cuproptosis and TB and established an optimal machine learning model to evaluate the TB subtypes and latent and active TB patients

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

Discrete-time optimal control of double integrators and its application in maglev train

As an alternative to bang-bang control, a time optimal control (TOC) algorithm for discrete-time systems was first reported by Han (1). This algorithm not only acts as a noise-tolerant tracking differentiator (TD) to avoid setpoint jumps in control processes, but also has wide applications in the design of controllers and observers. However, determination of the real-time state position on the phase plane involves complex boundary transformations, which renders this algorithm impractical for some engineering applications. This paper proposes a methodology for discrete-time optimal control (DTOC) of double integrators with disturbances. The closed-form solution with lower computational burden can be easily extended to general second-order systems. Further, in consideration of the inevitable disturbances in the systems, a rigorous and full-convergence proof is presented for the proposed algorithm. The results show finite-time and fast convergence as well as provide the ultimate stable attraction regions for the system states. Examples and experiments are also presented to demonstrate the effectiveness of the proposed algorithm for solving a signal processing problem in a maglev train.Published versio

Th17: A New Participant in Gut Dysfunction in Mice Infected with Trichinella spiralis

Trichinella spiralis infection in rodents is a well-known model of intestinal inflammation associated with hypermotility. Our aim was to elucidate if Th17 cells were involved in the development of gastrointestinal hypermotility in this experimental model. Intestinal inflammation was observed by hematoxylin-eosin (HE) staining. Jejunal smooth muscle contractility was investigated in response to acetylcholine (Ach). The effects of IL-17 on jejunum smooth muscle contractility were explored. Flow cytometry was used to analyze the proportion of Th17 cells in jejunum. The levels of IL-17, IL-23, and TGF-β1 in jejunum were measured by Western blot. Our results showed that the inflammation in jejunum was severe at 2 weeks postinfection (PI), which was not discernible at 8 weeks PI. Jejunal smooth muscle contractility was increased at 2 weeks PI and kept higher at 12 weeks PI. The proportion of Th17 cells and the expression of IL-17 were upregulated in jejunum at 2 weeks PI and normalized at 8 weeks PI. When jejunual smooth muscle strips were cultured with IL-17, contractions elicited by Ach were enhanced in a concentration-dependent manner. Our data suggest that Th17 cells are increased during acute infection with Trichinella spiralis and IL-17 may contribute to jejunal muscle contractility in mice

Numerical Model Driving Multi-Domain Information Transfer Method for Bearing Fault Diagnosis

Given the complexity of the application scenarios of rolling bearing and the severe scarcity of fault samples, a solution to the issue of fault diagnosis under varying working conditions along with the absence of fault samples is required. A numerical model-driven cross-domain fault diagnosis method targeting variable working conditions is proposed based on the cross-Domain Nuisance Attribute Projection (cDNAP). Firstly, the simulation datasets consisting of multiple fault types under variable working conditions are constructed to solve the problem of incomplete fault samples. Secondly, the simulation datasets are expanded by means of generating adversarial network to ensure sufficient samples for subsequent model training. Finally, cDNAP is used to obtain the cross-domain simulation projection matrix, which eliminates the variance in the distribution of measured and simulated sample features under varying working conditions. The experimental results of cross-domain for variable working conditions show that the diagnostic accuracy reaches up to 99%. Compared with DANN, DSAN, and DAAN domain adversarial neural networks, the proposed method performs better in bearing fault diagnosis

Baicalin Attenuates Cardiac Dysfunction and Myocardial Remodeling in a Chronic Pressure-Overload Mice Model

Background/Aims: Baicalin has been shown to be effective for various animal models of cardiovascular diseases, such as pulmonary hypertension, atherosclerosis and myocardial ischaemic injury. However, whether baicalin plays a role in cardiac hypertrophy remains unknown. Here we investigated the protective effects of baicalin on cardiac hypertrophy induced by pressure overload and explored the potential mechanisms involved. Methods: C57BL/6J-mice were treated with baicalin or vehicle following transverse aortic constriction or Sham surgery for up to 8 weeks, and at different time points, cardiac function and heart size measurement and histological and biochemical examination were performed. Results: Mice under pressure overload exhibited cardiac dysfunction, high mortality, myocardial hypertrophy, increased apoptosis and fibrosis markers, and suppressed cardiac expression of PPARα and PPARβ/δ. However, oral administration of baicalin improved cardiac dysfunction, decreased mortality, and attenuated histological and biochemical changes described above. These protective effects of baicalin were associated with reduced heart and cardiomyocyte size, lower fetal genes expression, attenuated cardiac fibrosis, lower expression of profibrotic markers, and decreased apoptosis signals in heart tissue. Moreover, we found that baicalin induced PPARα and PPARβ/δ expression in vivo and in vitro. Subsequent experiments demonstrated that long-term baicalin treatment presented no obvious cardiac lipotoxicity. Conclusions: The present results demonstrated that baicalin attenuates pressure overload induced cardiac dysfunction and ventricular remodeling, which would be due to suppressed cardiac hypertrophy, fibrosis, apoptosis and metabolic abnormality