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Pulmonary hypertension (PH) is a fatal disease characterized by elevated pulmonary pressures, progressive pulmonary vascular remodeling, and right heart failure [...].Pulmonary hypertension (PH) is a fatal disease characterized by elevated pulmonary pressures, progressive pulmonary vascular remodeling, and right heart failure [...].

Osteoarthritis (OA) is a painful, incurable disease affecting over 500 million people. Recent clinical trials of the nerve growth factor (NGF) inhibitors in OA patients have suggested adverse effects of NGF inhibition on joint structure. Here we report that nerve growth factor receptor (NGFR) is upregulated in skeletal cells during OA and plays an essential role in the remodeling and repair of osteoarthritic joints. Specifically, NGFR is expressed in osteochondral cells but not in skeletal progenitor cells and induced by TNFα to attenuate NF-κB activation, maintaining proper BMP-SMAD1 signaling and suppressing RANKL expression in mice. NGFR deficiency hyper-activates NF-κB in murine osteoarthritic joints, which impairs bone formation and enhances bone resorption as exemplified by a reduction in subchondral bone and osteophytes. In human OA cartilage, NGFR is also negatively associated with NF-κB activation. Together, this study suggests a role of NGFR in limiting inflammation for repair of diseased skeletal tissues. Osteoarthritis is a painful and debilitating condition. Here, the authors show that NGFR, a receptor for NGF, restricts NF-κB activation and its deficiency in skeletal cells impairs the remodeling and repair of osteoarthritic joints.

The motivation of this paper is to solve the problem of tourism majors’ lack of theoretical knowledge and professional ability by building a capacity training mechanism model based on grounded theory. The goal of the study is to optimize the ability training strategies of tourism undergraduates and improve their professional quality and competitiveness. The paper adopts the ability training model based on grounded theory, and combines with Back Propagation (BP) neural network for optimization and evaluation. By comparing the performance of different ability training mechanisms, this paper explores the best training strategies to provide support and guidance for the training of tourism undergraduates. Firstly, the employment background of the current market is studied and analyzed. By sorting out the relevant documents of grounded theory and combining with the current training strategies of tourism majors, the students’ personality characteristics and the basic principles of establishing models are integrated. The Back Propagation (BP) neural network is combined with the grounded theory. The data input of the student ability model is re-optimized. The undergraduate competency training mechanism model is constructed. The results show that when the number of iterations of the network model is 500.00, the evaluation accuracy of the competency training model based on BP neural network can reach 70.00%. At this time, the evaluation accuracy of competency training model based on content analysis method is only 55.00%. In addition, the results show that with the increase of model iterations, the recognition accuracy of the ability model based on grounded theory and the ability training mechanism of content analysis method is continuously improved. However, the ability evaluation model based on grounded theory has higher accuracy, and the accuracy of ability evaluation even exceeds 78.52% when the number of iterations of the network model is 600. Secondly, through the statistics and comparison of the grading results of students of different majors, it is found that the ability training mechanism based on grounded theory can improve the overall ability level of students more significantly. The research has important reference value for promoting the optimization and perfection of students’ training strategies.

Purpose To understand which environmental factors influence the distribution and ecological functions of bacteria in agricultural soil. Method A broad range of farmland soils was sampled from 206 locations in Jilin province, China. We used 16S rRNA gene-based Illumina HiSeq sequencing to estimated soil bacterial community structure and functions. Result The dominant taxa in terms of abundance were found to be, Actinobacteria, Acidobacteria, Gemmatimonadetes, Chloroflexi, and Proteobacteria. Bacterial communities were dominantly affected by soil pH, whereas soil organic carbon did not have a significant influence on bacterial communities. Soil pH was significantly positively correlated with bacterial operational taxonomic unit abundance and soil bacterial α-diversity (P<0.05) spatially rather than with soil nutrients. Bacterial functions were estimated using FAPROTAX, and the relative abundance of anaerobic and aerobic chemoheterotrophs, and nitrifying bacteria was 27.66%, 26.14%, and 6.87%, respectively, of the total bacterial community. Generally, the results indicate that soil pH is more important than nutrients in shaping bacterial communities in agricultural soils, including their ecological functions and biogeographic distribution.

The motivation of this paper is to solve the problem of tourism majors' lack of theoretical knowledge and professional ability by building a capacity training mechanism model based on grounded theory. The goal of the study is to optimize the ability training strategies of tourism undergraduates and improve their professional quality and competitiveness. The paper adopts the ability training model based on grounded theory, and combines with Back Propagation (BP) neural network for optimization and evaluation. By comparing the performance of different ability training mechanisms, this paper explores the best training strategies to provide support and guidance for the training of tourism undergraduates. Firstly, the employment background of the current market is studied and analyzed. By sorting out the relevant documents of grounded theory and combining with the current training strategies of tourism majors, the students' personality characteristics and the basic principles of establishing models are integrated. The Back Propagation (BP) neural network is combined with the grounded theory. The data input of the student ability model is re-optimized. The undergraduate competency training mechanism model is constructed. The results show that when the number of iterations of the network model is 500.00, the evaluation accuracy of the competency training model based on BP neural network can reach 70.00%. At this time, the evaluation accuracy of competency training model based on content analysis method is only 55.00%. In addition, the results show that with the increase of model iterations, the recognition accuracy of the ability model based on grounded theory and the ability training mechanism of content analysis method is continuously improved. However, the ability evaluation model based on grounded theory has higher accuracy, and the accuracy of ability evaluation even exceeds 78.52% when the number of iterations of the network model is 600. Secondly, through the statistics and comparison of the grading results of students of different majors, it is found that the ability training mechanism based on grounded theory can improve the overall ability level of students more significantly. The research has important reference value for promoting the optimization and perfection of students' training strategies.

Multiple sclerosis (MS) is a chronic autoimmune disorder characterized by the infiltration of inflammatory cells and demyelination of nerves. Mitochondrial dysfunction has been implicated in the pathogenesis of MS, as studies have shown abnormalities in mitochondrial activities, metabolism, mitochondrial DNA (mtDNA) levels, and mitochondrial morphology in immune cells of individuals with MS. The presence of mitochondrial dysfunctions in immune cells contributes to immunological dysregulation and neurodegeneration in MS. This review provided a comprehensive overview of mitochondrial dysfunction in immune cells associated with MS, focusing on the potential consequences of mitochondrial metabolic reprogramming on immune function. Current challenges and future directions in the field of immune-metabolic MS and its potential as a therapeutic target were also discussed.

Background Anthocyanins, which have important biological functions and have a beneficial effect on human health, notably account for pigmentation in purple-fleshed sweet potato tuberous roots. Individual regulatory factors of anthocyanin biosynthesis have been identified; however, the regulatory network of anthocyanin biosynthesis in purple-fleshed sweet potato is unclear. Results We functionally determined that IbMYB340 cotransformed with IbbHLH2 in tobacco and strawberry receptacles induced anthocyanin accumulation, and the addition of IbNAC56a or IbNAC56b caused increased pigmentation. Furthermore, we confirmed the interaction of IbMYB340 with IbbHLH2 and IbNAC56a or IbNAC56b via yeast two-hybrid and firefly luciferase complementation assays; these proteins could form a MYB340-bHLH2-NAC56a or MYB340-bHLH2-NAC56b transcriptional complex to regulate anthocyanin biosynthesis by binding to the IbANS promoter rather than the IbUFGT promoter. Furthermore, it was found by a transient expression system in tobacco leaves that IbMYB44 could decrease anthocyanin accumulation. Moreover, the interaction of IbMYB44 with IbMYB340 and IbNAC56a or IbNAC56b was verified. This result suggested that IbMYB44 acts as a repressor of anthocyanin in sweet potato. Conclusions The repressor IbMYB44 affected anthocyanin biosynthesis by competitively inhibiting the IbMYB340 - IbbHLH2 - IbNAC56a or IbMYB340 - IbbHLH2 - IbNAC56b regulatory complex formation. Overall, the present study proposed a novel regulatory network whereby several vital TFs play key roles in regulating anthocyanin biosynthesis, and it provides strong insight into the potential mechanism underlying anthocyanin biosynthesis in sweet potato tuberous roots with purple color.

Background Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer related death in the world with a five-year survival rate of less than 5%. Not all PDAC are the same, because there exist intra-tumoral heterogeneity between PDAC, which poses a great challenge to personalized treatments for PDAC. Methods To dissect the molecular heterogeneity of PDAC, we performed a retrospective meta-analysis on whole transcriptome data from more than 1200 PDAC patients. Subtypes were identified based on non-negative matrix factorization (NMF) biclustering method. We used the gene set enrichment analysis (GSEA) and survival analysis to conduct the molecular and clinical characterization of the identified subtypes, respectively. Results Six molecular and clinical distinct subtypes of PDAC: L1-L6, are identified and grouped into tumor-specific (L1, L2 and L6) and stroma-specific subtypes (L3, L4 and L5). For tumor-specific subtypes, L1 (~ 22%) has enriched carbohydrate metabolism-related gene sets and has intermediate survival. L2 (~ 22%) has the worst clinical outcomes, and is enriched for cell proliferation-related gene sets. About 23% patients can be classified into L6, which leads to intermediate survival and is enriched for lipid and protein metabolism-related gene sets. Stroma-specific subtypes may contain high non-epithelial contents such as collagen, immune and islet cells, respectively. For instance, L3 (~ 12%) has poor survival and is enriched for collagen-associated gene sets. L4 (~ 14%) is enriched for various immune-related gene sets and has relatively good survival. And L5 (~ 7%) has good clinical outcomes and is enriched for neurotransmitter and insulin secretion related gene sets. In the meantime, we identified 160 subtype-specific markers and built a deep learning-based classifier for PDAC. We also applied our classification system on validation datasets and observed much similar molecular and clinical characteristics between subtypes. Conclusions Our study is the largest cohort of PDAC gene expression profiles investigated so far, which greatly increased the statistical power and provided more robust results. We identified six molecular and clinical distinct subtypes to describe a more complete picture of the PDAC heterogeneity. The 160 subtype-specific markers and a deep learning based classification system may be used to better stratify PDAC patients for personalized treatments.

Sea surface temperature (SST) is vital for climate monitoring and extreme weather forecasting. Existing global SST datasets are typically provided at daily to seasonal resolutions, while hourly data remain limited to regional scales. Polar-orbiting satellites offer global coverage but low temporal resolution, providing only 1–2 observations per day. Geostationary satellites provide high temporal resolution but cover only part of the region. These limitations create a gap in the availability of global, hourly SST data. To address this, we propose a Triple-Collocation (TC)-based fusion algorithm for generating accurate global hourly SST data through multi-source integration. The method includes data preprocessing (quality control and linear interpolation), merging five geostationary SST datasets into two global sets by priority, applying TC fusion to three polar-orbiting datasets, and finally combining all sources via multi-source TC fusion. Results show improved temporal resolution and increased spatial coverage to 32%. The fused dataset achieves high accuracy, with a daily mean Bias below 0.0427 °C, RMSE about 0.5938 °C to 0.6965 °C, and R2 exceeding 0.9879. These outcomes demonstrate the method’s reliability and its potential for supporting climate and environmental research.

Background Sepsis-associated encephalopathy (SAE) increases the mortality of septic patients, but its mechanism remains unclear. The present study aimed to investigate the roles of T lymphocytes, proBDNF, and their interaction in the pathogenesis of SAE. Methods Fear conditioning tests were conducted for cognitive assessment in the lipopolysaccharide (LPS, 5 mg kg −1 )-induced septic mice. Meninges and peripheral blood were harvested for flow cytometry or qPCR. FTY720 and monoclonal anti-proBDNF antibody (McAb-proB) were used to investigate the effect of lymphocyte depletion and blocking proBDNF on the impaired cognitive functions in the septic mice. Results In the septic mice, cognitive function was impaired, the percentage of CD4 + T cells were decreased in the meninges ( P = 0.0021) and circulation ( P = 0.0222), and pro-inflammatory cytokines were upregulated, but the anti-inflammatory cytokines interleukin (IL)-4 ( P < 0.0001) and IL-13 ( P = 0.0350) were downregulated in the meninges. Lymphocyte depletion by intragastrically treated FTY720 (1 mg kg −1 ) for 1 week ameliorated LPS-induced learning deficit. In addition, proBDNF was increased in the meningeal ( P = 0.0042) and peripheral ( P = 0.0090) CD4 + T cells. Intraperitoneal injection of McAb-proB (100 μg) before LPS treatment significantly alleviated cognitive dysfunction, inhibited the downregulation of meningeal ( P = 0.0264) and peripheral ( P = 0.0080) CD4 + T cells, and normalized the gene expression of cytokines in the meninges. However, intra-cerebroventricular McAb-proB injection (1 μg) did not have such effect. Finally, exogenous proBDNF downregulated the percentage of CD4 + T cells in cultured splenocytes from septic mice ( P = 0.0021). Conclusion Upregulated proBDNF in immune system promoted the pathogenesis of SAE through downregulating the circulating CD4 + T cells, limiting its infiltration into the meninges and perturbing the meningeal pro-/anti-inflammatory homeostasis.