Tongji University

About: Tongji University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Computer science & Population. The organization has 76116 authors who have published 81176 publications receiving 1248911 citations. The organization is also known as: Tongji & Tóngjì Dàxué.

Role of miR-1 and miR-133a in myocardial ischemic postconditioning

Abstract: Ischemic postconditioning (IPost) has aroused much attention since 2003 when it was firstly reported. The role of microRNAs (miRNAs or miRs) in IPost has rarely been reported. The present study was undertaken to investigate whether miRNAs were involved in the protective effect of IPost against myocardial ischemia-reperfusion (IR) injury and the probable mechanisms involved. Thirty SD rats weighing 250-300 g were equally randomized to three groups: Control group, where the rats were treated with thoracotomy only; IR group, where the rats were treated with ischemia for 60 min and reperfusion for 180 min; and IPost group, where the rats were treated with 3 cycles of transient IR just before reperfusion. The extent of myocardial infarction, LDH and CK activities were measured immediately after treatment. Myocardial apoptosis was detected by TUNEL assay. The myocardial tissue was collected after IR or IPost stimulation to evaluate the miRNAs expression level by miRNA-microarray and quantitative real-time RT-PCR. Real-time PCR was conducted to identify changes in mRNA expression of apoptosis-related genes such as Bcl-2, Bax and Caspase-9 (CASP9), and Western blot was used to compare the protein expression level of CASP9 in the three groups. The miRNA mimics and anti-miRNA oligonucleotides (AMO) were transferred into the cultured neonatal cardiomyocytes and myocardium before they were treated with IR. The effect of miRNAs on apoptosis was determined by flow cytometry and TUNEL assay. CASP9, as one of the candidate target of miR-133a, was compared during IR after the miR-133a mimic or AMO-133a was transferred into the myocardium. IPost reduced the IR-induced infarct size of the left ventricle, and decreased CK and LDH levels. TUNEL assay showed that myocardial apoptosis was attenuated by IPost compared with IR. MiRNA-microarray and RT-PCR showed that myocardial-specific miR-1 and miR-133a were down-regulated by IR, and up-regulated by IPost compared with IR. Furthermore, IPost up-regulated the mRNA expression of Bcl-2, down-regulated that of Bax and CASP9. Western blot showed that IPost also down-regulated the CASP9 protein expression compared with IR. The results of flow cytometry and TUNEL assay showed that up-regulation of miR-1 and miR-133a decreased apoptosis of cardiomyocytes. MiR-133a mimic down-regulated CASP9 protein expression and attenuated IR-induced apoptosis. MiRNAs are associated with the protective effect of IPost against myocardial IR injury. IPost can up-regulate miR-1 and miR-133a, and decrease apoptosis of cardiomyocyte. Myocardial-specific miR-1 and miR-133a may play an important role in IPost protection by regulating apoptosis-related genes. MiR-133a may attenuate apoptosis of myocardiocytes by targeting CASP9.

Associatively Segmenting Instances and Semantics in Point Clouds

Abstract: A 3D point cloud describes the real scene precisely and intuitively. To date how to segment diversified elements in such an informative 3D scene is rarely discussed. In this paper, we first introduce a simple and flexible framework to segment instances and semantics in point clouds simultaneously. Then, we propose two approaches which make the two tasks take advantage of each other, leading to a win-win situation. Specifically, we make instance segmentation benefit from semantic segmentation through learning semantic-aware point-level instance embedding. Meanwhile, semantic features of the points belonging to the same instance are fused together to make more accurate per-point semantic predictions. Our method largely outperforms the state-of-the-art method in 3D instance segmentation along with a significant improvement in 3D semantic segmentation. Code has been made available at: https://github.com/WXinlong/ASIS.

Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters

Abstract: We propose a stochastic multiscale method to quantify the correlated key-input parameters influencing the mechanical properties of polymer nanocomposites (PNCs). The variations of parameters at nano-, micro-, meso- and macro-scales are connected by a hierarchical multiscale approach. The first-order and total-effect sensitivity indices are determined first. The input parameters include the single-walled carbon nanotube (SWNT) length, the SWNT waviness, the agglomeration and volume fraction of SWNTs. Stochastic methods consistently predict that the key parameters for the Young’s modulus of the composite are the volume fraction followed by the averaged longitudinal modulus of equivalent fiber (EF), the SWNT length, and the averaged transverse modulus of the EF, respectively. The averaged longitudinal modulus of the EF is estimated to be the most important parameter with respect to the Poisson’s ratio followed by the volume fraction, the SWNT length, and the averaged transverse modulus of the EF, respectively. On the other hand, the agglomeration parameters have insignificant effect on both Young’s modulus and Poisson’s ratio compared to other parameters. The sensitivity analysis (SA) also reveals the correlation between the input parameters and its effect on the mechanical properties.