Knockdown of slincRAD leads to defective adipose development in vivo.

The development of adipose tissue is a precisely coordinated cellular process, in which both protein-coding and non-coding genes are involved. To characterize the in vivo function of a novel long non-coding RNA (lncRNAs), loss-of-function assays were performed with slincRAD knockdown mice. Down-regulation of slincRAD expression was found to impair the development of adipose tissue, leading to a slim phenotype for both of the male and female mice. Compared to normal adipocytes, slincRAD knockdown cells had defective differentiation features, such as smaller sizes and decreased lipid production. For elder mice, slincRAD knockdown led to abnormal glucose and lipid metabolism. Therefore, a physiologically important lncRNA was characterized in the development of adipose tissue.

Long non-coding RNA slincRAD functions in methylation regulation during the early stage of mouse adipogenesis.

Adipocyte differentiation is a coordinated cellular process, which involves a series of dynamic molecular events. Up-regulation of long noncoding RNA slincRAD expression was found to occur in the early differentiation stages of 3T3-L1 cell, prior to the regulation of major transcription factors. By interacting with DNMT1 in S phase, slincRAD guides this essentially epigenetic factor to mediate promoter methylation of a batch of cell cycle-related genes, including cyclin-dependent kinase inhibitor p21. The regulation promotes the growth-arrested cells to re-enter into cell cycle under hormone induction and thereby advances the process of differentiation to clonal expansion stage. The abolishment of the interaction between slincRAD and DNMT1 by slincRAD knockdown results in a defective epigenetic regulation and finally compromised adipogenesis. Collectively, our study characterizes the epigenetic regulation of lncRNA involved in the early stage of adipogenesis.

A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications.

Emotion recognition utilizing EEG signals has emerged as a pivotal component of human-computer interaction. In recent years, with the relentless advancement of deep learning techniques, using deep learning for analyzing EEG signals has assumed a prominent role in emotion recognition. Applying deep learning in the context of EEG-based emotion recognition carries profound practical implications. Although many model approaches and some review articles have scrutinized this domain, they have yet to undergo a comprehensive and precise classification and summarization process. The existing classifications are somewhat coarse, with insufficient attention given to the potential applications within this domain. Therefore, this article systematically classifies recent developments in EEG-based emotion recognition, providing researchers with a lucid understanding of this field's various trajectories and methodologies. Additionally, it elucidates why distinct directions necessitate distinct modeling approaches. In conclusion, this article synthesizes and dissects the practical significance of EEG signals in emotion recognition, emphasizing its promising avenues for future application.

Know Yourself: Physical and Psychological Self-Awareness With Lifelog.

Self-awareness is an essential concept in physiology and psychology. Accurate overall self-awareness benefits the development and well being of an individual. The previous research studies on self-awareness mainly collect and analyze data in the laboratory environment through questionnaires, user study, or field research study. However, these methods are usually not real-time and unavailable for daily life applications. Therefore, we propose a new direction of utilizing lifelog for self-awareness. Lifelog records about daily activities are used for analysis, prediction, and intervention on individual physical and psychological status, which can be automatically processed in real-time. With the help of lifelog, ordinary people are able to understand their condition more precisely, get effective personal advice about health, and even discover physical and mental abnormalities at an early stage. As the first step on using lifelog for self-awareness, we learn from the traditional machine learning problems, and summarize a schema on data collection, feature extraction, label tagging, and model learning in the lifelog scenario. The schema provides a flexible and privacy-protected method for lifelog applications. Following the schema, four topics were conducted: sleep quality prediction, personality detection, mood detection and prediction, and depression detection. Experiments on real datasets show encouraging results on these topics, revealing the significant relation between daily activity records and physical and psychological self-awareness. In the end, we discuss the experiment results and limitations in detail and propose an application, Lifelog Recorder, for multi-dimensional self-awareness lifelog data collection.

Autoantigen-targeting microRNAs in Sjögren's syndrome.

MicroRNAs are short endogenous non-coding RNAs that regulate gene expression in various physiological and pathological conditions. To characterize autoantigen-targeting microRNAs in Sjögren's syndrome (SS), a systematic study was carried out, in which a candidate microRNA set was first identified by bioinformatics analysis and literature search. Then, their gene silencing activities were evaluated with fusion reporter gene and endogenous targets, leading to the identification of three microRNAs: TRIM21-targeting miR-1207-5p, TRIM21-targeting miR-4695-3p, and La autoantigen-targeting miR-299-5p. Compared to healthy controls, downregulation of miR-1207-5p and miR-4695-3p expression was further revealed in the minor salivary glands of primary SS (pSS) patients. This, on the one hand, characterized two autoantigen-targeting microRNAs in Sjögren's syndrome and, on the other hand, suggested that downregulation of miR-1207-5p and miR-4695-3p expression may lead to increased TRIM21 levels in the minor salivary glands, which contributes to the development of Sjögren's syndrome.

MicroRNA profiling in Chinese patients with primary Sjögren syndrome reveals elevated miRNA-181a in peripheral blood mononuclear cells.

OBJECTIVE: Characterized by chronic inflammation, dysfunction of exocrine glands, and systemic autoimmunity, primary Sjögren syndrome (pSS) is a common autoimmune disease in elderly women. Our study was performed to explore the potential involvement of microRNA (miRNA) in Chinese patients with pSS. METHODS: Using microarrays, miRNA expression in peripheral blood mononuclear cells (PBMC) was profiled in 4 female patients with pSS and 3 healthy participants, followed by a large-scale study of 33 patients and 10 healthy individuals. Compared to the healthy participants, 202 miRNA were upregulated and 180 were downregulated in the patients with pSS. To confirm this finding, a set of regulated miRNA was further examined in a large patient group, using quantitative reverse transcriptase-PCR assays. RESULTS: MiR-181a was the miRNA that most profoundly differed between patients with pSS and healthy individuals; however, similar miRNA-181a expression profiles were found in groups with different disease phenotypes. Together, these observations suggested that an elevated miRNA-181a level is a general phenomenon in Chinese patients with pSS. CONCLUSION: In addition to the elevated miR-181a levels, our study led to the speculation that elevated miR-181a levels in the PBMC of these patients compromise the maturation of B cells, enabling them to recognize and attack autoantigens and resulting in disease phenotypes. In addition to the regulation of human miRNA, many virus-derived miRNA were unexpectedly upregulated in the patients with pSS, suggesting that viral infection of PBMC plays a role in this disease.