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BackgroundCurrently， the diagnosis of depressive disorder relies on symptomatology， which is greatly influenced by subjective factors such as clinicians' experience. Finding more accurate and reliable quantitative diagnostic methods is currently an urgent problem.ObjectiveTo explore the value of Fisher discriminant function based on inflammatory cytokines in the diagnosis of depressive disorder， so as to provide references for clinical diagnosis.MethodsA total of 136 patients diagnosed with depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） criteria， who underwent inpatient treatment at Hebei Mental Health Center from April 2020 to November 2020， were enrolled as study group. 67 healthy participants matched for age and gender， was recruited during the same period. Serum levels of inflammatory cytokine were measured using enzyme-linked immunosorbent assay（ELISA）. Fisher discriminant model was employed to establish a discriminant function for inflammatory cytokines exhibiting significant statistical differences between study group and control group， which was then validated.ResultsThe levels of pro-inflammatory cytokines interleukin-1β （IL-1β）， interleukin-6 （IL-6） ， interferon-γ （INF-γ） and tumor necrosis factor-α （TNF-α） were higher in the study group compared with control group， with statistically significant differences （U=9.115， 5.239， 4.431， 5.428， P

In order to solve the problems of difficult data integration, complex governance and low sharing efficiency, this paper proposes a scheme for construction of railway locomotive maintenance data integration and sharing platform. The author first realized the automatic and periodic data acquisition from different systems and saving of the same in a data warehouse through multi-source heterogeneous data acquisition technology, and then classified the data in the data warehouse for standardized management and forming service themes and thematic libraries. At the same time, the author also provided real-time and offline data sharing modes under security control, realizing data sharing in different scenarios. This platform, after being constructed in the field, saves more than 70% of the planned time for locomotive pre-scheduling and maintenance, and reduces the average downtime of locomotive maintenance by about 20 minutes. This shows the platform can help to promote multi-system data fusion and improve the informatization level, production efficiency and management ability of railway locomotive maintenance.

Objective: To improve the classification accuracy of strawberries. Methods: A method of strawberry classification based on improved CNN was proposed by improving CNN through mixing pool method. Firstly, through the combination of maximum pooling and average pooling techniques, a hybrid pooling method was obtained. Then, the hybrid pool method was used to improve the generalization ability of CNN model. After that, image data acquisition, image preprocessing and image feature extraction were carried out. Finally, sensitivity, specificity, accuracy, recall rate and F1 score were used to evaluate the effectiveness of the trained strawberry classification method. Results: The sensitivity, specificity, accuracy, recall rate and F1 score of the proposed method for strawberry classification in 16 pixel×16 pixel images reached 0.993, 0.993, 0.994, 0.992 and 0.991, respectively. Compared with the other five classification methods, the sensitivity, specificity, accuracy, recall rate and F1 score of the proposed method were improved by 3.44%, 3.96%, 4.26%, 3.92% and 4.08%, respectively. Conclusion: This method can achieve accurate classification of strawberries with different maturity, and is expected to provide technical support for the research and development of high-performance strawberry packaging robots and supermarket fruit automatic recognition machines.

To investigate unsaturated soil-water interaction at micro-scale, this study extends the numerical manifold method (NMM) by incorporating a soil-water coupling model considering specific capillary water distribution and capillary force calculation. The soil skeleton is constructed by a soil skeleton generation algorithm with random polygons. To more realistically capture the interaction between soil grains and capillary water, a capillary mechanics-based geometric algorithm is proposed to iteratively calculate the capillary water distribution. The capillary forces corresponding to the capillary water distribution are calculated based on the Young-Laplace equation. The proposed capillary water solving framework is first verified by reproducing the soil-water characteristic curve and the capillary water distribution of an ideal contact-disk model against analytical solutions. To further validate the ability of the capillary water solving framework to predict hydraulic behavior of the real soil, a laboratory test on the Toyoura sand is reproduced numerically. Then an ideal direct shear test is performed to further validate the two-way soil-water coupling procedure, in which a comparison between the numerical and analytical results regarding the shear strength and matric suction is presented. Finally, microscopic hydraulic and compression tests are conducted on two soil specimens with the same porosity and mean grain diameter but different uniformity coefficients. The results elucidate that the extended method is a potential tool to explore unsaturated soil behaviors at micro-scale.

In the field of rail transit, vehicle intelligent operation and maintenance system uses various big data technologies to realize real-time calculation and analysis of vehicle status data. With status monitoring and fault warning, the system guides the maintenance of key equipments, and effectively improves the work efficiency of vehicle operation and maintenance personnel. However, data flood occurs due to the instability of network communication environment between vehicle and ground system, resulting in performance bottleneck when big data computing framework uses the general configuration. This paper makes an in-depth analysis on the causes of the performance bottleneck, and proposes an optimization method based on the user-defined Kafka partition strategy and the optimization of Spark Streaming processing parameters. Data of different vehicles is written to different Kafka partitions, and the rate at which the Spark Executor reads data from Kafka is controlled. Actual project application results show that this method can effectively solve the problem of performance bottleneck when the processing rate of streaming data cannot keep up with the reading rate in the data flood scenario.

Introduction: Diffuse high-grade gliomas are the most common malignant adult neuroepithelial tumors in humans and a leading cause of cancer-related death worldwide. The advancement of high throughput transcriptome sequencing technology enables rapid and comprehensive acquisition of transcriptome data from target cells or tissues. This technology aids researchers in understanding and identifying critical therapeutic targets for the prognosis and treatment of diffuse high-grade glioma. Methods: Spatial transcriptomics was conducted on two cases of isocitrate dehydrogenase (IDH) wild-type diffuse high-grade glioma (Glio-IDH-wt) and two cases of IDH-mutant diffuse high-grade glioma (Glio-IDH-mut). Gene set enrichment analysis and clustering analysis were employed to pinpoint differentially expressed genes (DEGs) involved in the progression of diffuse high-grade gliomas. The spatial distribution of DEGs in the spatially defined regions of human glioma tissues was overlaid in the t-distributed stochastic neighbor embedding (t-SNE) plots. Results: We identified a total of 10,693 DEGs, with 5,677 upregulated and 5,016 downregulated, in spatially defined regions of diffuse high-grade gliomas. Specifically, SPP1 , IGFBP2 , CALD1 , and TMSB4X exhibited high expression in carcinoma regions of both Glio-IDH-wt and Glio-IDH-mut, and 3 upregulated DEGs (SMOC1 , APOE , and HIPK2) and 4 upregulated DEGs (PPP1CB , UBA52 , S100A6 , and CTSB) were only identified in tumor regions of Glio-IDH-wt and Glio-IDH-mut, respectively. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses revealed that upregulated DEGs were closely related to PI3K/Akt signaling pathway, virus infection, and cytokine-cytokine receptor interaction. Importantly, the expression of these DEGs was validated using GEPIA databases. Furthermore, the study identified spatial expression patterns of key regulatory genes, including those involved in protein post-translational modification and RNA binding protein-encoding genes, with spatially defined regions of diffuse high-grade glioma. Discussion: Spatial transcriptome analysis is one of the breakthroughs in the field of medical biotechnology as this can map the analytes such as RNA information in their physical location in tissue sections. Our findings illuminate previously unexplored spatial expression profiles of key biomarkers in diffuse high-grade glioma, offering novel insight for the development of therapeutic strategies in glioma. [ABSTRACT FROM AUTHOR]