Lifelong Text-Audio Sentiment Analysis learning.

Sentiment analysis refers to the mining of textual context, which is conducted with the aim of identifying and extracting subjective opinions in textual materials. However, most existing methods neglect other important modalities, e.g., the audio modality, which can provide intrinsic complementary knowledge for sentiment analysis. Furthermore, much work on sentiment analysis cannot continuously learn new sentiment analysis tasks or discover potential correlations among distinct modalities. To address these concerns, we propose a novel Lifelong Text-Audio Sentiment Analysis (LTASA) model to continuously learn text-audio sentiment analysis tasks, which effectively explores intrinsic semantic relationships from both intra-modality and inter-modality perspectives. More specifically, a modality-specific knowledge dictionary is developed for each modality to obtain shared intra-modality representations among various text-audio sentiment analysis tasks. Additionally, based on information dependence between text and audio knowledge dictionaries, a complementarity-aware subspace is developed to capture the latent nonlinear inter-modality complementary knowledge. To sequentially learn text-audio sentiment analysis tasks, a new online multi-task optimization pipeline is designed. Finally, we verify our model on three common datasets to show its superiority. Compared with some baseline representative methods, the capability of the LTASA model is significantly boosted in terms of five measurement indicators.

Combined Analysis of RNA Sequence and Microarray Data Reveals a Competing Endogenous RNA Network as Novel Prognostic Markers in Malignant Pleural Mesothelioma.

Malignant pleural mesothelioma (MPM) is a highly aggressive cancer with short survival time. Unbalanced competing endogenous RNAs (ceRNAs) have been shown to participate in the tumor pathogenesis and served as biomarkers for the clinical prognosis. However, the comprehensive analyses of the ceRNA network in the prognosis of MPM are still rarely reported. In this study, we obtained the transcriptome data of the MPM and the normal samples from TCGA, EGA, and GEO databases and identified the differentially expressed (DE) mRNAs, lncRNAs, and miRNAs. The functions of the prognostic genes and the overlapped DEmRNAs were further annotated by the multiple enrichment analyses. Then, the targeting relationships among lncRNA-miRNA and miRNA-mRNA were predicted and calculated, and a prognostic ceRNA regulatory network was established. We included the prognostic 73 mRNAs and 13 miRNAs and 26 lncRNAs into the ceRNA network. Moreover, 33 mRNAs, three miRNAs, and seven lncRNAs were finally associated with prognosis, and a model including seven mRNAs, two lincRNAs, and some clinical factors was finally established and validated by two independent cohorts, where CDK6 and SGMS1-AS1 were significant to be independent prognostic factors. In addition, the identified co-expressed modules associated with the prognosis were overrepresented in the ceRNA network. Multiple enrichment analyses showed the important roles of the extracellular matrix components and cell division dysfunction in the invasion of MPM potentially. In summary, the prognostic ceRNA network of MPM was established and analyzed for the first time and these findings shed light on the function of ceRNAs and revealed the potential prognostic and therapeutic biomarkers of MPM.

Systemic immune-inflammation index and incident cardiovascular diseases among middle-aged and elderly Chinese adults: The Dongfeng-Tongji cohort study.

BACKGROUND AND AIMS: Systemic immune-inflammation index (SII) has been recently investigated as a novel inflammatory and prognostic marker. SII may be used as an indicator reflecting the progressive inflammatory process in atherosclerosis, although its link to incident cardiovascular disease (CVD) has not been examined in previous studies. This study aims to prospectively assess the association of SII with incident CVD and its main subtypes in Chinese adults. METHODS: Using data from the Dongfeng-Tongji cohort study, 13,929 middle-aged and older adults with a mean age of 62.56 years (range 35-91 years), who were free of CVD and cancer, were included for analysis. The baseline study was conducted in Shiyan city, Hubei province from 2008 to 2009. The SII was calculated as platelet count (/L) × neutrophil count (/L)/lymphocyte count (/L). Cox regression models were used to examine the associations of SII with incident CVD, including stroke and coronary heart disease (CHD). RESULTS: Over a median 8.28 years (maximum 8.98 years) of follow-up, 3386 total CVD cases, including 801 stroke cases and 2585 total CHD cases, were identified. In multivariable Cox regression analyses, higher levels of log-transformed SII were significantly associated with total stroke (HR 1.224, 95% CI 1.065-1.407) and ischemic stroke (HR 1.234, 95% CI 1.055-1.442). For those participants with the highest quartiles of SII versus the lowest quartiles of SII, the HRs were 1.358 (95% CI 1.112-1.658) for total stroke, 1.302 (95% CI 1.041-1.629) for ischemic stroke, and 1.600 (95% CI 1.029-2.490) for hemorrhagic stroke. CONCLUSIONS: SII may serve as a useful marker to elucidate the role of the interaction of thrombocytosis, inflammation, and immunity in the development of cerebrovascular diseases in the middle-aged and elderly population.

Association of Gut Microbiota during Early Pregnancy with Risk of Incident Gestational Diabetes Mellitus.

AIMS: We aimed to assess the association between gut bacterial biomarkers during early pregnancy and subsequent risk of gestational diabetes mellitus (GDM) in Chinese pregnant women. METHODS: Within the Tongji-Shuangliu Birth Cohort study, we conducted a nested case-control study among 201 incident GDM cases and 201 matched controls. Fecal samples were collected during early pregnancy (at 6-15 weeks), and GDM was diagnosed at 24 to 28 weeks of pregnancy. Community DNA isolated from fecal samples and V3-V4 region of 16S rRNA gene amplicon libraries were sequenced. RESULTS: In GDM cases versus controls, Rothia, Actinomyces, Bifidobacterium, Adlercreutzia, and Coriobacteriaceae and Lachnospiraceae spp. were significantly reduced, while Enterobacteriaceae, Ruminococcaceae spp., and Veillonellaceae were overrepresented. In addition, the abundance of Staphylococcus relative to Clostridium, Roseburia, and Coriobacteriaceae as reference microorganisms were positively correlated with fasting blood glucose, 1-hour and 2-hour postprandial glucose levels. Adding microbial taxa to the base GDM prediction model with conventional risk factors increased the C-statistic significantly (P < 0.001) from 0.69 to 0.75. CONCLUSIONS: Gut microbiota during early pregnancy was associated with subsequent risk of GDM. Several beneficial and commensal gut microorganisms showed inverse relations with incident GDM, while opportunistic pathogenic members were related to higher risk of incident GDM and positively correlated with glucose levels on OGTT.

A Bibliometric Insight of Genetic Factors in ASD: Emerging Trends and New Developments.

Autism spectrum disorder (ASD) cases have increased rapidly in recent decades, which is associated with various genetic abnormalities. To provide a better understanding of the genetic factors in ASD, we assessed the global scientific output of the related studies. A total of 2944 studies published between 1997 and 2018 were included by systematic retrieval from the Web of Science (WoS) database, whose scientific landscapes were drawn and the tendencies and research frontiers were explored through bibliometric methods. The United States has been acting as a leading explorer of the field worldwide in recent years. The rapid development of high-throughput technologies and bioinformatics transferred the research method from the traditional classic method to a big data-based pipeline. As a consequence, the focused research area and tendency were also changed, as the contribution of de novo mutations in ASD has been a research hotspot in the past several years and probably will remain one into the near future, which is consistent with the current opinions of the major etiology of ASD. Therefore, more attention and financial support should be paid to the deciphering of the de novo mutations in ASD. Meanwhile, the effective cooperation of multi-research centers and scientists in different fields should be advocated in the next step of scientific research undertaken.