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Predicting bacterial transcriptional regulatory networks (TRNs) through computational methods is a core challenge in systems biology, and there is still a long way to go. Here we propose a powerful, general, and stable computational framework called PGBTR (Powerful and General Bacterial Transcriptional Regulatory networks inference method), which employs Convolutional Neural Networks (CNN) to predict bacterial transcriptional regulatory relationships from gene expression data and genomic information. PGBTR consists of two main components: the input generation step PDGD (Probability Distribution and Graph Distance) and the deep learning model CNNBTR (Convolutional Neural Networks for Bacterial Transcriptional Regulation inference). On the real Escherichia coli and Bacillus subtilis datasets, PGBTR outperforms other advanced supervised and unsupervised learning methods in terms of AUROC (Area Under the Receiver Operating Characteristic Curve), AUPR (Area Under Precision-Recall Curve), and F1-score. Moreover, PGBTR exhibits greater stability in identifying real transcriptional regulatory interactions compared to existing methods. PGBTR provides a new software tool for bacterial TRNs inference, and its core ideas can be further extended to other molecular network inference tasks and other biological problems using gene expression data.

The dynamic adaptation of bacteria to environmental changes is achieved through the coordinated expression of many genes, which constitutes a transcriptional regulatory network (TRN). Bradyrhizobium diazoefficiens USDA110 is an important model strain for the study of symbiotic nitrogen fixation (SNF), and its SNF ability largely depends on the TRN. In this study, independent component analysis was applied to 226 high-quality gene expression profiles of B. diazoefficiens USDA110 microarray datasets, from which 64 iModulons were identified. Using these iModulons and their condition-specific activity levels, we (1) provided new insights into the connection between the FixLJ-FixK2-FixK1 regulatory cascade and quorum sensing, (2) discovered the independence of the FixLJ-FixK2-FixK1 and NifA/RpoN regulatory cascades in response to oxygen, (3) identified the FixLJ-FixK2 cascade as a mediator connecting the FixK2-2 iModulon and the Phenylalanine iModulon, (4) described the differential activation of iModulons in B. diazoefficiens USDA110 under different environmental conditions, and (5) proposed a notion of active-TRN based on the changes in iModulon activity to better illustrate the relationship between gene regulation and environmental condition. In sum, this research offered an iModulon-based TRN for B. diazoefficiens USDA110, which formed a foundation for comprehensively understanding the intricate transcriptional regulation during SNF.

The disheveled, Egl-10, and pleckstrin (DEP) domain containing mammalian target of rapamycin (mTOR)-interacting protein (DEPTOR) is a binding protein containing mTOR complex 1 (mTORC1), mTOR complex 2 (mTORC2), and an endogenous mTOR inhibitor. DEPTOR shows abnormal expressions in numerous types of solid tumors. However, how DEP-TOR is expressed in esophageal squamous cell carcinoma (ESCC) remains elusive. The expression of DEPTOR in 220 cases of ESCC and non-cancerous adjacent tissues was detected by immunohistochemistry. DEPTOR levels in ESCC and paired normal tissue were quantified using reverse transcription-polymerase chain reaction and Western blot analysis to verify the immunohistochemical results. The relationship between DEPTOR expression and the clinicopathological features of ESCC was analyzed based on the results of immunohistochemistry. Finally, we analyzed the relationship between DEPTOR expression and the prognosis of patients with ESCC. Immunohistochemical staining showed that the expression rate of DEPTOR in ESCC tissues was significantly increased. DEPTOR mRNA and protein expression was significantly higher in ESCC tissues than in normal adjacent esophageal squamous tissues. High DEPTOR expression was significantly correlated with regional lymph node status in the TNM stage of patients with ESCC. Kaplan-Meier survival curves showed that the rate of overall survival was significantly lower in patients with high DEPTOR expression than in those with low DEPTOR expression. Additionally, high DEPTOR expression was an independent prognostic predictor for ESCC patients. High DEPTOR expression is an independent prognostic biomarker indicating a worse prognosis for patients with ESCC.

The dynamic adaptation of bacteria to environmental changes is achieved through the coordinated expression of many genes, which constitutes a transcriptional regulatory network (TRN). Bradyrhizobium diazoefficiens USDA110 is an important model strain for the study of symbiotic nitrogen fixation (SNF), and its SNF ability largely depends on the TRN. In this study, independent component analysis was applied to 226 high-quality gene expression profiles of B. diazoefficiens USDA110 microarray datasets, from which 64 iModulons were identified. Using these iModulons and their condition-specific activity levels, we (1) provided new insights into the connection between the FixLJ-FixK[sub.2]-FixK[sub.1] regulatory cascade and quorum sensing, (2) discovered the independence of the FixLJ-FixK[sub.2]-FixK[sub.1] and NifA/RpoN regulatory cascades in response to oxygen, (3) identified the FixLJ-FixK[sub.2] cascade as a mediator connecting the FixK[sub.2]-2 iModulon and the Phenylalanine iModulon, (4) described the differential activation of iModulons in B. diazoefficiens USDA110 under different environmental conditions, and (5) proposed a notion of active-TRN based on the changes in iModulon activity to better illustrate the relationship between gene regulation and environmental condition. In sum, this research offered an iModulon-based TRN for B. diazoefficiens USDA110, which formed a foundation for comprehensively understanding the intricate transcriptional regulation during SNF.

IntroductionIn a previous phase, 12 draft definitions for clinically important worsening in axial spondyloarthritis (axSpA) were selected, of which 3 were based on absolute changes in Ankylosing Spondylitis Disease Activity Score (ASDAS)-CRP (ASDAS). The objective here was to select the best cut-off for ASDAS for clinically important worsening in axSpA for use in clinical trials and observational studies.MethodsAn international longitudinal prospective study evaluating stable patients with axSpA was conducted. Data necessary to calculate ASDAS were collected at two consecutive visits (spaced 7 days to 6 months). Sensitivity and specificity of the three cut-offs for change in ASDAS were tested against the patient’s subjective assessment of worsening as the external standard (ie, the patient reporting that he had worsened and felt a need for treatment intensification). Final selection was made by a consensus and voting procedure among Assessment of SpondyloArthritis International Society (ASAS) members.ResultsIn total, 1169 patients with axSpA were analysed: 64.8% were male and had a mean age of 41.7 (SD 12.4) years. At the second visit, 127 (10.9%) patients judged their situation as worsened.Sensitivity and specificity for an increase of at least 0.6, 0.9 and 1.1 ASDAS points to detect patient-reported worsening were 0.55 (Se) and 0.91 (Sp), 0.38 (Se) and 0.96 (Sp), and 0.33 (Se) and 0.98 (Sp), respectively. The ASAS consensus was to define clinically important worsening as an increase in ASDAS of at least 0.9 points.ConclusionThis data-driven ASAS consensus process resulted in an ASDAS-based cut-off value defining clinically important worsening in axSpA for use in trials.

目前，代谢综合征（MetS）在全世界范围的发病率逐年升高。MetS与前列腺癌的关系是当前的一个研究热点。然而，有关MetS与进展性前列腺癌的相关性研究仍较少。在本研究中，我们回顾性分析了1016名接受根治性前列腺切除术的前列腺癌患者的临床及病理资料，并使用Logistic回归模型分析了MetS与前列腺癌病理特征的相关性。我们发现，与不合并MetS的前列腺癌患者相比，合并MetS者其Gleason评分≥8的风险明显增加（OR = 1.670, 95% CI1.096–2.545, P=0.017），pT3–4 期前列腺癌的风险也增加了近1.5倍（OR = 1.583, 95% CI 1.106–2.266, P=0.012），合并MetS同时也是淋巴结转移的一个独立的预测因素（OR = 1.751, 95% CI 1.038–2.955,P=0.036）。此外，随着MetS成份数目的增加，Gleason评分≥8的风险也不断增加。本研究表明MetS与进展性前列腺癌之间存在显著的相关性。该结果仍需大规模前瞻性研究来证实。

Predicting bacterial transcriptional regulatory networks (TRNs) through computational methods is a core challenge in systems biology, and there is still a long way to go. Here we propose a powerful, general, and stable computational framework called PGBTR, which employs Convolutional Neural Networks (CNN) to predict bacterial transcriptional regulatory relationships from gene expression data and genomic information. PGBTR consists of two main components: the input generation step PDGD and the deep learning model CNNBTR. On the real Escherichia coli and Bacillus subtilis datasets, PGBTR outperforms other advanced supervised and unsupervised learning methods in terms of AUROC, AUPR, and F1-score. Moreover, PGBTR exhibits greater stability in identifying real transcriptional regulatory interactions compared to existing methods. PGBTR provides a new software tool for bacterial TRNs inference, and its core ideas can be further extended to other molecular network inference tasks and other biological problems using gene expression data.

Reconstruction of 3D structure models is of great importance for the study of chromosome function. In this paper, we present a novel reconstruction algorithm, called EVRC, which utilizes co-clustering coefficients and error-vector resultant for chromosome 3D structure reconstruction. To evaluate the effectiveness and accuracy of the EVRC algorithm, we applied it to simulation datasets and real human Hi-C datasets. The results show that the reconstructed structures have high similarity to the original/real structures, indicating the effectiveness and robustness of the EVRC algorithm. Furthermore, we applied the algorithm to the 3D conformation reconstruction of the wild-type and mutant Arabidopsis thaliana chromosomes and demonstrated the differences in structural characteristics between different chromosomes. We also accurately showed the conformational change in the centromere region of the mutant compared with the wild-type of Arabidopsis chromosome 1. Our EVRC algorithm is a valuable software tool for the field of chromatin structure reconstruction, and holds great promise for advancing our understanding on the chromosome functions.

The dynamic adaptation of bacteria to environmental changes is achieved through the coordinated expression of many genes, which constitutes a transcriptional regulatory network (TRN). Bradyrhizobium diazoefficiens USDA110 is an important model strain for the study of symbiotic nitrogen fixation (SNF), and its SNF ability largely depends on the TRN. In this study, independent component analysis was applied to 226 high-quality gene expression profiles of B. diazoefficiens USDA110 microarray datasets, from which 64 iModulons were identified. Using these iModulons and their condition-specific activity levels, we (1) provided new insights into the connection between the FixLJ-FixK2-FixK1 regulatory cascade and quorum sensing, (2) discovered the independence of the FixLJ-FixK2-FixK1 and NifA/RpoN regulatory cascades in response to oxygen, (3) identified the FixLJ-FixK2 cascade as a mediator connecting the FixK2-2 iModulon and the Phenylalanine iModulon, (4) described the differential activation of iModulons in B. diazoefficiens USDA110 under different environmental conditions, and (5) proposed a notion of active-TRN based on the changes in iModulon activity to better illustrate the relationship between gene regulation and environmental condition. In sum, this research offered an iModulon-based TRN for B. diazoefficiens USDA110, which formed a foundation for comprehensively understanding the intricate transcriptional regulation during SNF.

Norrin, also known as the Norrie disease protein or X-linked exudative vitreoretinopathy 2 protein, is a secreted retinal growth factor with angiogenic and neuroprotective properties. Mutations in the NPD gene, which encodes the Norrin protein, are associated with Norrie disease, familial exudative vitreoretinopathy （FEVR）, retinopathy of prematurity （ROP）, and other retinal hypovascularization diseases [1, 2].