Background: Osteosarcoma (OS) is the most common primary bone tumors diagnosed in children and adolescents. Recent studies have shown a prognostic role of DNA methylation in various cancers, including OS.The aim of this study was to identify the aberrantly methylated genes that are prognostically relevant in OS. Methods:The differentially expressed mRNAs, miRNAs and methylated genes (DEGs, DEMs and DMGs respectively) were screened from various GEO databases, and the potential target genes of the DEMs were predicted by the RNA22 program. The protein-protein interaction (PPI) networks were constructed using the STRING database and visualized by Cytoscape software. The functional enrichment and survival analyses of the screened genes was performed using the R software.Results: Forty-seven downregulated hypermethylated genes and three upregulated hypomethylated genes were identified that were enriched in cell activation, migration and proliferation functions, and were involved in cancer-related pathways like JAK-STAT and PI3K-AKT. Eight downregulated hypermethylated tumor suppressor genes (TSGs) were identified among the screened genes based on the TSGene database.These hub genes are likely involved in OS genesis, progression and metastasis, and are potential prognostic biomarkers and therapeutic targets.Conclusions: TSGs including PYCARD, STAT5A, CXCL12 and CXCL14 were aberrantly methylated in OS, and are potential prognostic biomarkers and therapeutic targets. Our findings provide new insights into the role of methylation in OS progression.

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The nucleic acid test is still the standard assessment for the diagnosis of coronavirus disease 2019 (COVID-19), which is caused by human infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In addition to supporting the confirmation of disease cases, serological assays are used for the analysis of antibody status and epidemiological surveys. In this study, a single Western blot strip (WBS) coated with multiple Escherichia coli ( E. coli )-expressed SARS-CoV-2 antigens was developed for comprehensive studies of antibody profiles in COVID-19 patient sera. The levels of specific antibodies directed to SARS-CoV-2 spike (S), S2, and nucleocapsid (N) proteins were gradually increased with the same tendency as the disease progressed after hospitalization. The signal readouts of S, S2, and N revealed by the multi-antigen-coated WBS (mWBS)-based serological assay (mWBS assay) also demonstrated a positive correlation with the SARS-CoV-2 neutralizing potency of the sera measured by the plaque reduction neutralization test (PRNT) assays. Surprisingly, the detection signals against the unstructured receptor-binding domain (RBD) purified from E. coli inclusion bodies were not observed, although the COVID-19 patient sera exhibited strong neutralizing potency in the PRNT assays, suggesting that the RBD-specific antibodies in patient sera mostly recognize the conformational epitopes. Furthermore, the mWBS assay identified a unique and major antigenic epitope at the residues 1148, 1149, 1152, 1155, and 1156 located within the 1127–1167 fragment of the S2 subunit, which was specifically recognized by the COVID-19 patient serum. The mWBS assay can be finished within 14–16 min by using the automatic platform of Western blotting by thin-film direct coating with suction (TDCS WB). Collectively, the mWBS assay can be applied for the analysis of antibody responses, prediction of the protective antibody status, and identification of the specific epitope. Key points • A Western blot strip (WBS) coated with multiple SARS-CoV-2 antigens was developed for the serological assay. • The multi-antigen-coated WBS (mWBS) can be utilized for the simultaneous detection of antibody responses to multiple SARS-CoV-2 antigens. • The mWBS-based serological assay (mWBS assay) identified a unique epitope recognized by the COVID-19 patient serum.

Recently, users who search on the web are targeting to more complex tasks due to the explosive growth of web usage. To accomplish a complex task, users may need to obtain information of various entities. For example, a user who wants to travel to Beijing, should book a flight, reserve a hotel room, and survey a Beijing map. A complex task thus needs to submit several queries in order to seeking each of entities. Understanding complex tasks can allow a search engine to suggest related entities and help users explicitly assign their ongoing tasks.

Nowadays, due to the explosive growth of web content and usage, users deal with their complex search tasks by web search engines. However, conventional search engines consider a search query corresponding only to a simple search task. In order to accomplish a complex search task, which consists of multiple subtask search goals, users usually have to issue a series of queries. For example, the complex search task “travel to Dubai” may involve several subtask search goals, including reserving hotel room, surveying Dubai landmarks, booking flights, and so forth. Therefore, a user can efficiently accomplish his or her complex search task if search engines can predict the complex search task with a variety of subtask search goals. In this work, we propose a complex search task model (CSTM) to deal with this problem. The CSTM first groups queries into complex search task clusters, and then generates subtask search goals from each complex search task cluster. To raise the performance of CSTM, we exploit four web resources including community question answering, query logs, search engine result pages, and clicked pages. Experimental results show that our CSTM is effective in identifying the comprehensive subtask search goals of a complex search task.