Exosomal miR-93-5p regulated the progression of osteoarthritis by targeting ADAMTS9.

Osteoarthritis (OA) is a type of common degenerative joint disorder, in which adipose mesenchymal stem cells (ADSCs) and the secreted exosomes play an important role. The purpose of this study was to investigate the role and mechanism of exosomes derived from ADSCs (ADSC-exos) in OA. The gradient of IL-1ß concentration was designed to construct the articular chondrocyte model of arthritic mice. The expression of miR-93-5p and ADAMTS9 in articular chondrocytes was detected by reverse transcription quantitative polymerase chain reaction. Dual luciferase reporter gene assay was performed to verify the interaction between them. Monodansylcadaverine staining was used to visualize the autophagosome formation and cell apoptosis was analyzed by flow cytometry. ADSC-exos were authenticated by transmission electron microscope and western blot assay. miR-93-5p was found to be downregulated in IL-1ß-treated articular chondrocytes compared with OA cartilage while ADAMTS9 was upregulated, which was identified as a direct target gene of miR-93-5p. Silencing of ADAMTS9 attenuated the effects of miR-93-5p. Exosomal miR-93-5p can reduce the release of inflammatory factors in mouse arthritis cell models. This study first described the mechanism under that ADSC-exos inhibited inflammation and alleviated OA through the innovative targets miR-93-5p/ADAMTS9 signal axis. This provided a new method for the treatment of OA.

A Characterization of Maximally Entangled Two-Qubit States.

As already known by Rana's result, all eigenvalues of any partial-transposed bipartite state fall within the closed interval [-12,1]. In this note, we study a family of bipartite quantum states where the minimal eigenvalues of partial-transposed states are -12. For a two-qubit system, we find that the minimal eigenvalue of its partial-transposed state is -12 if and only if such a two-qubit state is maximally entangled. However this result does not hold in general for a two-qudit system when the dimensions of the underlying space are larger than two.

Computer Audition for Fighting the SARS-CoV-2 Corona Crisis-Introducing the Multitask Speech Corpus for COVID-19.

Computer audition (CA) has experienced a fast development in the past decades by leveraging advanced signal processing and machine learning techniques. In particular, for its noninvasive and ubiquitous character by nature, CA-based applications in healthcare have increasingly attracted attention in recent years. During the tough time of the global crisis caused by the coronavirus disease 2019 (COVID-19), scientists and engineers in data science have collaborated to think of novel ways in prevention, diagnosis, treatment, tracking, and management of this global pandemic. On the one hand, we have witnessed the power of 5G, Internet of Things, big data, computer vision, and artificial intelligence in applications of epidemiology modeling, drug and/or vaccine finding and designing, fast CT screening, and quarantine management. On the other hand, relevant studies in exploring the capacity of CA are extremely lacking and underestimated. To this end, we propose a novel multitask speech corpus for COVID-19 research usage. We collected 51 confirmed COVID-19 patients' in-the-wild speech data in Wuhan city, China. We define three main tasks in this corpus, i.e., three-category classification tasks for evaluating the physical and/or mental status of patients, i.e., sleep quality, fatigue, and anxiety. The benchmarks are given by using both classic machine learning methods and state-of-the-art deep learning techniques. We believe this study and corpus cannot only facilitate the ongoing research on using data science to fight against COVID-19, but also the monitoring of contagious diseases for general purpose.