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Based on the analysis of the importance of professional cluster construction by ecological theory, with the change of social demand for talents, this paper explores the practice of environmental chemical professional cluster construction in Pingdingshan University, including gradually perfecting teaching conditions and reforming teaching mode, breaking through the limitations of resources, integrating the boundaries of colleges and departments, integrating multiple resources, innovating systems and mechanisms, reconstructing professional clusters, deconstructing professional connotations, reorganizing curriculum systems, etc., in order to better build the ecological chain network of education in application-oriented colleges and universities, realize the deep integration of industry and education, train future-oriented interdisciplinary applied talents of new engineering, and realize the construction of characteristic professional cluster in application-oriented colleges. [ABSTRACT FROM AUTHOR]

Background: Depression is widespread global problem that not only severely impacts individuals’ physical and mental health but also imposes a heavy disease burden on nations and societies. The role of inflammation in the pathogenesis and pathophysiology of depression has received much attention, but the precise relationship between the two remains unclear. This study aims to investigate the correlation between depression and inflammation using a network medicine approach. Methods: We utilized a degree-preserving approach to identify the large connected component (LCC) of all depression-related proteins in the human interactome. The LCC was deemed as the disease module for depression. To measure the association between depression and other diseases, we calculated the overlap between these disease protein modules using the Sab algorithm. A smaller Sab value indicates a stronger association between diseases. Building on the results of this analysis, we further explored the correlation between inflammation and depression by conducting enrichment and pathway analyses of critical targets. Finally, we used a network proximity approach to calculate drugdisease proximity to predict the efficacy of drugs for the treatment of depression. We calculated and ranked the distances between depression disease modules and 6,100 drugs. The top-ranked drugs were selected to explore their potential for treating depression based on the hypothesis that their antidepressant effects are related to reducing inflammation. Results: In the human interactome, all depression-related proteins are clustered into a large connected component (LCC) consisting of 202 proteins and multiple small subgraphs. This indicates that depression-related proteins tend to form clusters within the same network. We used the 202 LCC proteins as the key disease module for depression. Next, we investigated the potential relationships between depression and 299 other diseases. Our analysis identified over 18 diseases that exhibited significant overlap with the depression module. Where SAB = −0.075 for the vascular disease and depressive disorders module, SAB = −0.070 for the gastrointestinal disease and depressive disorders module, and SAB = −0.062 for the endocrine system disease and depressive disorders module. The distance between them SAB < 0 implies that the pathogenesis of depression is likely to be related to the pathogenesis of its co-morbidities of depression and that potential therapeutic approaches may be derived from the disease treatment libraries of these co-morbidities. Further, considering that the inflammation is ubiquitous in some disease, we calculate the overlap between the collected inflammation module (236 proteins) and the depression module (202 proteins), finding that they are closely related (Sdi = −0.358) in the human protein interaction network. After enrichment and pathway analysis of key genes, we identified the HIF-1 signaling pathway, PI3K-Akt signaling pathway, Th17 cell differentiation, hepatitis B, and inflammatory bowel disease as key to the inflammatory response in depression. Finally, we calculated the Z-score to determine the proximity of 6,100 drugs to the depression disease module. Among the top three drugs identified by drug-disease proximity analysis were Perphenazine, Clomipramine, and Amitriptyline, all of which had a greater number of targets in the network associated with the depression disease module. Notably, these drugs have been shown to exert both anti-inflammatory and antidepressant effects, suggesting that they may modulate depression through an anti-inflammatory mechanism. These findings demonstrate a correlation between depression and inflammation at the network medicine level, which has important implications for future elucidation of the etiology of depression and improved treatment outcomes. Conclusion: Neuroimmune signaling pathways play an important role in the pathogenesis of depression, and many classes of antidepressants exhibiting antiinflammatory properties. The pathogenesis of depression is closely related to inflammation. [ABSTRACT FROM AUTHOR]

The quality and quantity of crop yields in agriculture primarily depend on the timing and precision of various implemented farming behaviors. Basins and hills dominate southwest China. Due to topographical constraints, the rate of agricultural mechanization in the region remains low, and agriculture remains primarily non-mechanized. The acquisition and recognition of information on farming behaviors play an important role in crop production. In this article, transfer learning was used in a current state-of-the-art 3DCNN-based behavior recognition model for farming behavior recognition and classification tasks. The focus was on fine-tuning and evaluating state-of-the-art 3D convolutional neural networks for farming behavior recognition. The evaluated architectures included Res3D, MC3, and R2+1D. The six common farming behaviors recognized include weeding, planting, harvesting, transplanting, fertilizing, and spraying. The accuracy of all models pretrained on Kinetics-400 after fine-tuning exceeded 90%, where MC3 had the best performance, with an accuracy of 0.9628, precision of 0.9647, sensitivity of 0.963, and specificity of 0.9925, which was slightly greater than the other two. MC3 was also the most lightweight of all models; its parameters were only 32.6% of Res3D and 36.7% of R2+1D. The experimental results demonstrated that the fine-tuned MC3 model offers high classification accuracy and effective recognition and classification of farming behaviors, which lays a good foundation for improved crop production. [ABSTRACT FROM AUTHOR]

With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) play crucial roles in multiple cancers, including hepatocellular carcinoma (HCC). However, the effects and molecular mechanisms of circ_LRIG3 in HCC remain barely unknown. qRT-PCR assay was employed to detect the levels of circ_LRIG3, LRIG3, miR-449a and ring finger protein 38 (RNF38). RNase R assay and Actinomycin D assay were performed to analyze the characteristics of circ_LRIG3. Colony formation assay and MTT assay were used to evaluate cell proliferation. Flow cytometry analysis and transwell assay were adopted for cell apoptosis and metastasis, respectively. Western blot assay was carried out for the protein levels of Ki67, Snail, E-cadherin, RNF38, Smad2/3 and p-Smad2/3. Murine xenograft model assay was used to explore the role of circ_LRIG3 in vivo. circ_LRIG3 expression was upregulated in HCC tissues and cells. Knockdown of circ_LRIG3 suppressed proliferation, migration and invasion and facilitated cell apoptosis in HCC cells in vitro and blocked tumor growth of HCC in vivo. RNF38 overexpression reversed the effects of circ_LRIG3 knockdown on the malignant behaviors of HCC cells. Moreover, circ_LRIG3 could sponge miR-449a to positively modulate RNF38 expression in HCC cells. circ_LRIG3 knockdown inhibited the progression of HCC cells by sponging miR-449a. In addition, circ_LRIG3 silencing might inhibit the Smad2/3 pathway. circ_LRIG3 facilitated HCC progression by modulation of miR-449a/LRIG3 axis, which might provide a novel method for HCC therapy. [ABSTRACT FROM AUTHOR]

Background: Non-obstructive azoospermia (NOA) is an important factor leading to male infertility and the genetic mechanism is not yet clear. It requires investigation to reveal its occurrence based on sequencing technology from the genetic level. Our previous genome wide association study (GWAS) using targeted high-throughput sequencing technology has identified suspected genetic variants including rs162036, rs161870, rs1677016R and rs1106042R associated with non-obstructive azoospermia (data not published). Methods: To further investigate the linkage between the four SNPs and the occurrence of NOA, 121 NOA patients and 256 controls were included. SNPs were detected by ligase detection reaction- polymerase chain reaction (LDRPCR). Association study between SNPs and NOA was analyzed. Results: As a result, we found no significant difference in all four alleles and genotypes frequencies in the SNPs between patients and controls (rs161870 p = 0.291; rs1677016R p = 0.264; rs161870 p = 0.291; rs1106042R p = 0.329). Conclusions: The four SNPs are not shown to be significantly related with NOA. Therefore, the underlying potential genetic markers to Northeast Chinese Han population remain unclear and need to be further clarified. [ABSTRACT FROM AUTHOR]

Motivation: In large-scale genetic association studies with tens of hundreds of single nucleotide polymorphisms (SNPs) genotyped, the traditional statistical framework of logistic regression using maximumlikelihood estimator (MLE) to infer the odds ratios of SNPsmay notwork appropriately. This is because a large number of odds ratios need to be estimated, and the MLEsmay be not stable when some of the SNPs are in high linkage disequilibrium. Under this situation, the P-value combination procedures seemto provide good alternatives as they are constructed on the basis of single-marker analysis. Results: The commonly used P-value combination methods (such as the Fisher’s combined test, the truncated product method, the truncated tail strength and the adaptive rank truncated product) may lose power when the significance level varies across SNPs. To tackle this problem, a group combined Pvaluemethod (GCP) is proposed,where the P-values are divided intomultiple groups and then are combined at the group level. With this strategy, the significance values are integrated at different levels, and the power is improved. Simulation shows that the GCP can effectively control the type I error rates and have additional power over the existingmethods—the power increase can be as high as over 50% under some situations. The proposed GCPmethod is applied to data from the Genetic AnalysisWorkshop 16. Among all the methods, only the GCP and ARTP can give the significance to identify a genomic region covering gene DSC3 being associatedwith rheumatoid arthritis, but theGCP provides smaller P-value. [ABSTRACT FROM AUTHOR]

By experiment we demonstrate an all-optical encryption/decryption scheme for nonreturn-to-zero differential phase-shift keying (NRZ-DPSK) signals at 10 Gbit/s using all-optical exclusive-OR (XOR) logic. The key bit stream is performed by a pseudorandom bit stream. The all-optical XOR logic is achieved by nondegenerate four-wave mixing (FWM) in a semiconductor optical amplifier (SOA), which allows high data rate operation and asymmetric optical powers of the two input bit streams. The gain dynamics and pattern effect associated with the SOA carrier lifetime are alleviated due to the constant envelope of the NRZ-DPSK signals. [ABSTRACT FROM AUTHOR]

A novel chaos-based selected mapping (SLM) scheme is proposed for physical layer security in OFDM-PONs, where a Hénon chaos map is employed to create a large key space for OFDM data encryption. The transmission performance is significantly improved due to the peak-to-average power ratio reduction in the SLM scheme. High quality 8.9 Gbit/s 16-QAM OFDM encrypted data transmission is demonstrated over a 20 km single-mode fibre. [ABSTRACT FROM AUTHOR]