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The protein encoded by CUB and Sushi Multiple Domains 2 (CSMD2) is likely involved in regulating the complement cascade reaction of the immune system. However, current scientific evidence on the comprehensive roles of CSMD2 in pan-cancer is relatively scarce. Therefore, in this study, we explored the transcriptional level of CSMD2 in pan-caner using TCGA, GEO, and International Cancer Genome Consortium databases. Receiver operating characteristic curve analysis was used to investigate the diagnostic efficacy of CSMD2. The Kaplan-Meier Plotter and Oncolnc were used to investigate the correlation between CSMD2 expression and prognosis. Additionally, we analyzed the correlation between epigenetic methylation and CSMD2 expression in various cancers based on UALCAN, as well as, the correlation between CSMD2 and tumor mutational burden (TMB), microsatellite instability (MSI), and tumor neoantigen burden (TNB) in tumors. TIMER2.0 database was employed to investigate the correlation between CSMD2 and immune cells in the tumor microenvironment and immune checkpoints. Based on TISIDB, the correlation between CSMD2 and MHC molecules and immunostimulators was analyzed. Ultimately, we observed with a pan-cancer analysis that CSMD2 was upregulated in most tumors and had moderate to high diagnostic efficiency, and that high expression was closely associated with poor prognosis in patients with tumors. Moreover, hypermethylation of CSMD2 promoter and high levels of m6A methylation regulators were also observed in most cancers. CSMD2 expression was negatively correlated with TMB and MSI in stomach adenocarcinoma (STAD) and stomach and esophageal carcinoma (STES), as well as with tumor mutational burden, microsatellite instability, and TNB in head-neck squamous cell carcinoma (HNSC). In most cancers, CSMD2 might be associated with immune evasion or immunosuppression, as deficient anti-tumor immunity and upregulation of immune checkpoints were also observed in this study. In conclusion, CSMD2 could serve as a promising prognostic, diagnostic and immune biomarker in pan-cancer.

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Serine proteases play an important role in inflammation via PARs. However, little is known of expression levels of PARs on monocytes of allergic patients, and influence of serine proteases and PARs on TNF-α secretion from monocytes. Using quantitative real-time PCR (qPCR) and flowcytometry techniques, we observed that the expression level of PAR-2 in monocytes of patients with allergic rhinitis and asthma was increased by 42.9 and 38.2 %. It was found that trypsin, thrombin, and tryptase induced up to 200, 320, and 310 % increase in TNF-α release from monocytes at 16 h, respectively. PAR-1 agonist peptide, SFLLR-NH2, and PAR-2 agonist peptide tc-LIGRLO-NH2 provoked up to 210 and 240 % increase in release of TNF-α. Since SCH 79797, a PAR-1 antagonist, and PD98059, an inhibitor of ERK inhibited thrombin- and SFLLR-NH2-induced TNF-α release, the action of thrombin is most likely through a PAR-1- and ERK-mediated signaling mechanism. Similarly, because FSLLRN-NH2, an inhibitor of PAR-2 diminished tryptase- and tc-LIGRLO-NH2-induced TNF-α release, the action of tryptase appears PAR-2 dependent. Moreover, in vivo study showed that both recombinant cockroach major allergens Per a 1 and Per a 7 provoked upregulation of PAR-2 and PAR-1 expression on CD14+ cells in OVA-sensitized mouse peritoneum. In conclusion, increased expression of PAR-2 in monocytes of AR and asthma implicates that PAR-2 likely play a role in allergy. PAR-2- and PAR-1-mediated TNF-α release from monocytes suggests that these unique protease receptors are involved in the pathogenesis of inflammation.

Social networks have been widely used, providing people with great convenience but also yielding potential risk of privacy disclosure. To prevent attacks based on background information or query that may expose users' privacy, we propose a method to achieve k-anonymization for network graphs. The concept of similarity matrix and that of the distance between a vertex and a cluster are defined based on vertex connectivity. On this basis, we present a clustering-based graph partitioning algorithm to obtain the K-anonymized graph of a certain network graph. Simulation experiments are conducted to analyze and verify the effectiveness of our algorithm.

Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require human input, and are not adaptable to some application domains. This paper focuses directly on computing the similarity between very short texts of sentence length. It presents an algorithm that takes account of semantic information, structure information and word order information implied in the sentences. The semantic similarity of two sentences is calculated using information from a structured lexical database, How-net. The use of a lexical database enables our method to model human common sense knowledge. The proposed method can be used in a variety of applications that involve text knowledge representation and discovery. Experiments on two sets of selected sentence pairs demonstrate that the proposed method provides a similarity measure that shows higher accuracy than other methods.