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Abstract Background Currently, there are many different findings on the relationship between physical activity and depression, and there may be differences between genders. This study therefore focused on gender differences to understand the relationship between physical activity behaviour and the risk of depression in married individuals. Methods 15607 married people in the China Family Panel Studies 2020 (CFPS 2020) were used to understand the relationship between physical activity and depression risk in different populations, and the chi-square test, Mann-Whitney U-test, and binary logistic regression were used to explore the relationship between physical activity and depression risk in the married population. Results 527 (6.64%) women were at high risk of depression and 365 (4.76%) men were at high risk of depression; physical activity was associated with the risk of depression in the married population, but after incorporating demographic and relevant cognitive variables, physical activity was negatively associated with the risk of depression in women (OR = 0.94, P 0.05). Conclusion Physical activity was directly related to the risk of depression in married women, but not in married men.

Abstract Leprosy and psoriasis rarely coexist, the specific molecular mechanisms underlying their mutual exclusion have not been extensively investigated. This study aimed to reveal the underlying mechanism responsible for the mutual exclusion between psoriasis and leprosy. We obtained leprosy and psoriasis data from ArrayExpress and GEO database. Differential expression analysis was conducted separately on the leprosy and psoriasis using DEseq2. Differentially expressed genes (DEGs) with opposite expression patterns in psoriasis and leprosy were identified, which could potentially involve in their mutual exclusion. Enrichment analysis was performed on these candidate mutually exclusive genes, and a protein–protein interaction (PPI) network was constructed to identify hub genes. The expression of these hub genes was further validated in an external dataset to obtain the critical mutually exclusive genes. Additionally, immune cell infiltration in psoriasis and leprosy was analyzed using single-sample gene set enrichment analysis (ssGSEA), and the correlation between critical mutually exclusive genes and immune cells was also examined. Finally, the expression pattern of critical mutually exclusive genes was evaluated in a single-cell transcriptome dataset. We identified 1098 DEGs in the leprosy dataset and 3839 DEGs in the psoriasis dataset. 48 candidate mutually exclusive genes were identified by taking the intersection. Enrichment analysis revealed that these genes were involved in cholesterol metabolism pathways. Through PPI network analysis, we identified APOE, CYP27A1, FADS1, and SOAT1 as hub genes. APOE, CYP27A1, and SOAT1 were subsequently validated as critical mutually exclusive genes on both internal and external datasets. Analysis of immune cell infiltration indicated higher abundance of 16 immune cell types in psoriasis and leprosy compared to normal controls. The abundance of 6 immune cell types in psoriasis and leprosy positively correlated with the expression levels of APOE and CYP27A1. Single-cell data analysis demonstrated that critical mutually exclusive genes were predominantly expressed in Schwann cells and fibroblasts. This study identified APOE, CYP27A1, and SOAT1 as critical mutually exclusive genes. Cholesterol metabolism pathway illustrated the possible mechanism of the inverse association of psoriasis and leprosy. The findings of this study provide a basis for identifying mechanisms and therapeutic targets for psoriasis.

Currently, in the production of cigarettes in the blade, charging export moisture control means is relatively single and can not effectively guarantee the excellent quality of cigarette filament. In this paper, first of all, the working principle of the tobacco blade charging machine is introduced, and the moisture of the tobacco leaf for the charging machine is dynamically analyzed, and the influence of the return air temperature control of the charging machine on the export moisture of the blade charging process is explored. Secondly, based on the traditional PID controller, an adaptive fuzzy PID controller is established by combining adaptive fuzzy rules, and then the stacked noise-reducing self-encoder in deep learning is combined with the adaptive fuzzy PID control to design the intelligent control structure of export moisture of leaf charging process. Finally, the effectiveness of export moisture intelligence control, process capability index, and the effect before and after application were analyzed in controlled experiments, respectively. The results show that the difference between the predicted value and the real value of blade export moisture in this paper’s method is only 0.5%, and the process capability index of this paper’s method is improved by 1.48 compared with the PID controller, and it can control the temperature of the return air of the charging machine in the range of 56.86℃~57.21℃. The intelligent control method of export moisture introduced by deep learning can accurately control the export moisture of the leaf dosing process, which effectively ensures the quality of tobacco filament making.

Internet information technology has brought challenges to the integration of college and university Civics in student education and management work, and at the same time, it also provides an opportunity for the integration of information technology. In this paper, on the basis of two kinds of nodes of basic Petri nets, the library and change, we constructed the workflow of identification Petri nets oriented to the education and management of students in the Civic Government of colleges and universities. The workflow engine is designed as the center of gravity of the reference model, consisting of six basic elements of student management. In the information fusion workflow, a multi-source information fusion method based on CNN and LSTM is proposed to further integrate Civics into student education management. The response time of the student management action flow is shorter than the retrieval time based on the original mechanism, and the average response time is reduced by 57.4 ms when the workflow reaches 5000. The fusion of Civic Politics and education management in the context of three-whole-parenting optimizes the management work method.

Background: Hydrogen sulfide (H2S) has been identified as the third gaseous signaling molecule. Endogenous H2S plays a key role in the progression of various types of cancer. However, the effect of endogenous H2S on the growth of esophageal cancer (EC) remains unknown. Methods: In this study, three kinds of H2S-producing enzymes inhibitors, DL-propargylglycine (PAG, inhibitor of cystathionine-γ-lyase), aminooxyacetic acid (AOAA, inhibitor of cystathionine-β-synthase), and L-aspartic acid (L-Asp, inhibitor of 3-mercaptopyruvate sulfurtransferase) were used to determine the role of endogenous H2S in the growth of EC9706 and K450 human EC cells. Results: The results indicated that the combination (PAG+AOAA+L-Asp) group showed higher inhibitory effects on the viability, proliferation, migration, and invasion of EC cells than PAG, AOAA, and L-Asp group. Inhibition of endogenous H2S promoted apoptosis via activation of mitogen-activated protein kinase pathway in EC cells. Endogenous H2S suppression triggered pyroptosis of EC cells by activating reactive oxygen species-mediated nuclear factor-κB signaling pathway. In addition, the combine group showed its more powerful growth-inhibitory effect on the growth of human EC xenograft tumors in nude mice without obvious toxicity. Conclusion: Our results indicate that inhibition of endogenous H2S production can significantly inhibit human EC cell growth via promotion of apoptosis and pyroptosis. Endogenous H2S may be a promising therapeutic target in EC cells. Novel inhibitors for H2S-producing enzymes can be designed and developed for EC treatment.

Pancreatic lipase (PL) is a well-known key target for the prevention and treatment of obesity. Human carboxylesterase 1A (hCES1A) has become an important target for the treatment of hyperlipidaemia. Thus, the discovery of potent dual-target inhibitors based on PL and hCES1A hold great potential for the development of remedies for treating related metabolic diseases. In this study, a series of natural triterpenoids were collected and the inhibitory effects of these triterpenoids on PL and hCES1A were determined using fluorescence-based biochemical assays. It was found that oleanolic acid (OA) and ursolic acid (UA) have the excellent inhibitory effects against PL and hCES1A, and highly selectivity over hCES2A. Subsequently, a number of compounds based on the OA and UA skeletons were synthesised and evaluated. Structure–activity relationship (SAR) analysis of these compounds revealed that the acetyl group at the C-3 site of UA (compound 41) was very essential for both PL and hCES1A inhibition, with IC50 of 0.75 µM and 0.014 µM, respectively. In addition, compound 39 with 2-enol and 3-ketal moiety of OA also has strong inhibitory effects against both PL and hCES1A, with IC50 of 2.13 µM and 0.055 µM, respectively. Furthermore, compound 39 and 41 exhibited good selectivity over other human serine hydrolases including hCES2A, butyrylcholinesterase (BChE) and dipeptidyl peptidase IV (DPP-IV). Inhibitory kinetics and molecular docking studies demonstrated that both compounds 39 and 41 were effective mixed inhibitors of PL, while competitive inhibitors of hCES1A. Further investigations demonstrated that both compounds 39 and 41 could inhibit adipocyte adipogenesis induced by mouse preadipocytes. Collectively, we found two triterpenoid derivatives with strong inhibitory ability on both PL and hCES1A, which can be served as promising lead compounds for the development of more potent dual-target inhibitors targeting on PL and hCES1A.

Dairy products are susceptible to modifications in protein oxidation during heat processing, which can lead to changes in protein function, subsequently affecting intestinal health. Despite being a unique nutritional source, yak milk has not been thoroughly examined for the effects of its oxidized proteins on intestinal microbiota and metabolism. Hence, this study employed different heat treatment methods (low-temperature pasteurization, high-temperature pasteurization, and high-temperature sterilization) to induce oxidation in yak milk proteins. The study then assessed the degree of oxidation in these proteins and utilized mice as research subjects. Using metagenomics and metabolomics methods, this study examined the structure of intestinal microbial communities and metabolic products in mice consuming oxidized yak milk. The results showed a decrease in carbonyl and total thiol contents of yak milk proteins after different heat treatments, indicating that heat treatment causes oxidation in yak milk proteins. Metagenomic analysis of mouse intestinal microbiota revealed significant changes in 66 genera. In the high-temperature sterilization group (H), key differential genera included Verrucomicrobiales, Verrucomicrobiae, Akkermansiaceae, and 28 others. The high-temperature pasteurization group (M) mainly consisted of Latilactobacillus, Bacillus, and Romboutsia. The low-temperature pasteurization group (L) primarily comprised of Faecalibacterium, Chaetomium, Paenibacillaceae, Eggerthella, Sordariales, and 33 others. Functionally, compared to the control group (C), the H group upregulated translation and energy metabolism functions, the L group the M group significantly upregulated metabolism of other amino acids, translation, and cell replication and repair functions. Based on metabolomic analysis, differential changes in mouse metabolites could affect multiple metabolic pathways in the body. The most significantly affected metabolic pathways were phenylalanine metabolism, vitamin B6 metabolism, steroid hormone biosynthesis, and pantothenate and CoA biosynthesis. The changes were similar to the functional pathway analysis of mouse metagenomics, affecting amino acid and energy metabolism in mice. In summary, moderate oxidation of yak milk proteins exhibits a positive effect on mouse intestinal microbiota and metabolism. In conclusion, yak milk has a positive effect on mouse intestinal microflora and metabolism, and this study provides a scientific basis for optimizing dairy processing technology and further developing and applying yak milk.

The pentatricopeptide repeat (PPR) gene family is one of the largest gene families in land plants. However, current knowledge about the evolution of the PPR gene family remains largely limited. In this study, we performed a comparative genomic analysis of the PPR gene family in O. sativa and its wild progenitor, O. rufipogon, and outlined a comprehensive landscape of gene duplications. Our findings suggest that the majority of PPR genes originated from dispersed duplications. Although segmental duplications have only expanded approximately 11.30% and 13.57% of the PPR gene families in the O. sativa and O. rufipogon genomes, we interestingly obtained evidence that segmental duplication promotes the structural diversity of PPR genes through incomplete gene duplications. In the O. sativa and O. rufipogon genomes, 10 (~33.33%) and 22 pairs of gene duplications (~45.83%) had non-PPR paralogous genes through incomplete gene duplication. Segmental duplications leading to incomplete gene duplications might result in the acquisition of domains, thus promoting functional innovation and structural diversification of PPR genes. This study offers a unique perspective on the evolution of PPR gene structures and underscores the potential role of segmental duplications in PPR gene structural diversity.

ABSTRACTIn the context of global warming and the increasing water shortage in dry areas, it is necessary to find a sustainable pathway for utilization of land resources (ULR) to solve these problems. For this purpose, using socioeconomic and environmental indicators from 2006 to 2018 and taking the Tarim Basin, China as an example, this study employed an integrated approach composed of the indicator evaluation system (IES) and the System Dynamics (SD) model to evaluate the possibility of sustainable ULR in the basin. During the simulation, three scenarios namely traditional development (TD), economic priority (EP) and harmonious development (HD) were designed, and different thresholds were set for each indicator of potential ULR in each sub-region to predict their future development status. The IES was established to derive the sustainable utilization degree (SUD) of ULR. The results show that the SUD in Tarim in 2030 is likely be 0.60 (TD), 0.62 (EP) and 0.68 (HD), indicating that only in HD scenario is it possible to find pathway to solve water scarcity problem and achieve sustainable ULR. This study provides a supportive idea for decision-making on sustainable ULR in Tarim and also an approach for similar sustainability assessment in other dry areas.

Land use/cover change (LUCC) detection and modeling play an important role in global environmental change research, in particular, policy-making to mitigate climate change, support land spatial planning, and achieve sustainable development. For the time being, a couple of hybrid models, such as cellular automata–Markov (CM), logistic–cellular automata-Markov (LCM), multicriteria evaluation (MCE), and multicriteria evaluation–cellular automata–Markov (MCM), are available. However, their disadvantages lie in either dependence on expert knowledge, ignoring the constraining factors, or without consideration of driving factors. For this purpose, we proposed in this paper a new hybrid model, the logistic–multicriteria evaluation–cellular automata–Markov (LMCM) model, that uses the fully standardized logistic regression coefficients as impact weights of the driving factors to represent their importance on each land use type in order to avoid these defects but is able to better predict the future land use pattern with higher accuracy taking Hefei, China as a study area. Based on field investigation, Landsat images dated 2010, 2015, and 2020, together with digital elevation model (DEM) data, were harnessed for land use/cover (LUC) mapping using a supervised classification approach, which was achieved with high overall accuracy (AC) and reliability (AC > 95%). LUC changes in the periods 2010–2015 and 2015–2020 were hence detected using a post-classification differencing approach. Based on the LUC patterns of the study area in 2010 and 2015, the one of 2020 was simulated by the LMCM, CM, LCM, and MCM models under the same conditions and then compared with the classified LUC map of 2020. The results show that the LMCM model performs better than the other three models with a higher simulation accuracy, i.e., 1.72–5.4%, 2.14–6.63%, and 2.78–9.33% higher than the CM, LCM, and MCM models, respectively. For this reason, we used the LMCM model to simulate and predict the LUC pattern of the study area in 2025. It is expected that the results of the simulation may provide scientific support for spatial planning of territory in Hefei, and the LMCM model can be applied to other areas in China and the world for similar purposes.

Traffic flow information is an important basis for intelligent transportation systems and urban computing.Traffic flow data is a new type of time series data.Due to the data collection method and the influence of external complex factors,the phenomenon of data loss is common and unavoidable.How to effectively mine the spatial-temporal characteristics of traffic flow data and the correlation between the data becomes the key to improve the missing data completion accuracy.Traditional statistical methods cannot meet the increasingly complex data requirements,and the application of deep learning promotes the development of missing data completion methods to higher accuracy.The article deeply analyzes the spatial-temporal characteristics of traffic flow,makes assumptions about the missing traffic flow,and proposes a UMAtNet (U-net with Multi-view Attention Mechanisms) traffic flow complement model.The model fuses closeness,trend and period time data with spatial data,and adopts diffe-rent data correlation measurement methods to fuse a multi-view attention mechanism,which can optimize the impact of the model on the spatial correlation of missing data.In order to verify the model,we use the open source data set of Beijing traffic data in the experiment,and analyzes in detail the influence of each part of the model and the loss function on the completion accuracy.The experimental results show that the fusion of UMAtNet and corresponding components can further improve the completion accuracy.

BackgroundPsoriasis is a common inflammatory skin disease that has a great impact on patients’ physical and mental health. However, the causes and underlying molecular mechanisms of psoriasis are still largely unknown.MethodsThe expression profiles of genes from psoriatic lesion samples and skin samples from healthy controls were integrated via the sva software package, and differentially expressed genes (DEGs) between psoriasis and healthy skin were screened by the limma package. Furthermore, GO and KEGG pathway enrichments for the DEGs were performed using the Clusterprofiler package. Protein–protein interaction (PPI) networks for the DEGs were then constructed to identify hub genes. scGESA analysis was performed on a single-cell RNA sequencing dataset via irGSEA. In order to find the cytokines correlated with the hub genes expression, single cell weighted gene co-expression network analyses (scWGCNA) were utilized to build a gene co-expression network. Furthermore, the featured genes of psoriasis found in suprabasal keratinocytes were intersected with hub genes. We then analyzed the expression of the intersection genes and cytokines in the integrated dataset. After that, we used other datasets to reveal the changes in the intersection genes’ expression levels during biological therapy. The relationship between intersection genes and PASI scores was also explored.ResultsWe identified 148 DEGs between psoriatic and healthy samples. GO and KEGG pathway enrichment analysis suggested that DEGs are mainly involved in the defense response to other organisms. The PPI network showed that 11 antiviral proteins (AVPs) were hub genes. scGSEA analysis in the single-cell transcriptome dataset showed that those hub genes are highly expressed in keratinocytes, especially in suprabasal keratinocytes. ISG15, MX1, IFI44L, and IFI27 were the characteristic genes of psoriasis in suprabasal keratinocytes. scWGCNA showed that three cytokines—IL36G, MIF, and IL17RA—were co-expressed in the turquoise module. Only interleukin-36 gamma (IL36G) was positively correlated with AVPs in the integrated dataset. IL36G and AVPs were found co-expressed in a substantial number of suprabasal keratinocytes. Furthermore, we found that the expression levels of IL36G and the 4 AVPs showed positive correlation with PASI score in patients with psoriasis, and that these levels decreased significantly during treatment with biological therapies, but not with methotrexate.ConclusionIL36G and antiviral proteins may be closely related with the pathogenesis of psoriasis, and they may represent new candidate molecular markers for the occurrence and severity of psoriasis.

Abstract Although water‐soluble graphene quantum dots (GQDs) have shown various promising bio‐applications due to their intriguing optical and chemical properties, the large heterogeneity in compositions, sizes, and shapes of these GQDs hampers the better understanding of their structure‐properties correlation and further uses in terms of large‐scale manufacturing practices and safety concerns. It is shown here that a water‐soluble atomically‐precise GQD (WAGQD‐C96) is synthesized and exhibits a deep‐red emission and excellent sonodynamic sensitization. By decorating sterically hindered water‐soluble functional groups, WAGQD‐C96 can be monodispersed in water without further aggregation. The deep‐red emission of WAGQD‐C96 facilitates the tracking of its bio‐process, showing a good cell‐uptake and long‐time retention in tumor tissue. Compared to traditional molecular sonosensitizers, WAGQD‐C96 generates superior reactive oxygen species and demonstrates excellent tumor inhibition potency as an anti‐cancer sonosensitizer in in vivo studies. A good biosafety of WAGQD‐C96 is validated in both in vitro and in vivo assays.

Abstract Background Antiphospholipid syndrome (APS) is a non-inflammatory autoimmune disorder induced by antiphospholipid antibodies, which occurs exceedingly rarely in pediatric population and even more rarely reported in HIV positive children. Case summary A case of 11 years old boy had a sudden onset of swelling in his left lower leg along with pain which were worsening gradually. Initially, topical ointment was applied for 1 month which were ineffective in reducing pain and swelling. Instead, the symptoms were aggravated and suddenly spread to the proximal thigh, accompanied by dyskinesia of left lower leg. Both color doppler ultrasonography and vascular CT scan of left lower leg revealed deep venous thrombosis. His serum anti-phospholipid antibodies (aPLs) were tested positive. He was a known case of HIV virological failure with substantial HIV viral load (VL) despite receiving regular antiretroviral therapy (ART). His symptoms improved after giving aggressive antithrombotic and high dose corticosteroid treatments. Conclusion When pediatric patients develop thrombotic disease, APS also needs to be ruled out. The autoantibodies levels should be routinely tested to look for recurrent thrombosis in children with HIV/AIDS.