The Potential Roles of Long Noncoding RNAs (lncRNA) in Glioblastoma Development

Long noncoding RNA (lncRNA) may contribute to the initiation and progression of tumor. In this study, we first systematically compared lncRNA and mRNA expression between glioblastoma and paired normal brain tissues using microarray data. We found 27 lncRNA and 82 mRNA significantly upregulated in glioblastoma, as well as 198 lncRNA and 285 mRNA significantly downregulated in glioblastoma. We identified 138 coexpressed lncRNA–mRNA pairs from these differentially expressed lncRNA and genes. Subsequent pathway analysis of the lncRNA-paired genes indicated that EphrinB–EPHB, p75-mediated signaling, TNFα/NF-κB, and ErbB2/ErbB3 signaling pathways might be altered in glioblastoma. Specifically, lncRNA RAMP2-AS1 had significant decrease of expression in glioblastoma tissues and showed coexpressional relationship with NOTCH3, an important tumor promoter in many neoplastic diseases. Our follow up experiment indicated that (i) an overexpression of RAMP2-AS1 reduced glioblastoma cell proliferation in vitro and also reduced glioblastoma xenograft tumors in vivo; (ii) NOTCH3 and RAMP2-AS1 coexpression rescued the inhibitory action of RAMP2-AS1 in glioblastoma cells; and (iii) RNA pull-down assay revealed a direct interaction of RAMP2-AS1 with DHC10, which may consequently inhibit, as we hypothesize, the expression of NOTCH3 and its downstream signaling molecule HES1 in glioblastoma. Taken together, our data revealed that lncRNA expression profile in glioblastoma tissue was significantly altered; and RAMP2-AS1 might play a tumor suppressive role in glioblastoma through an indirect inhibition of NOTCH3. Our results provided some insights into understanding the key roles of lncRNA–mRNA coregulation in human glioblastoma and the mechanisms responsible for glioblastoma progression and pathogenesis. Mol Cancer Ther; 15(12); 2977–86. ©2016 AACR

Biphenyl-bridged 6-(1-aryliminoethyl)-2-iminopyridyl-cobalt complexes: synthesis, characterization and ethylene polymerization behavior

A series of biphenyl-bridged 6-(1-aryliminoethyl)-2-iminopyridine derivatives reacted with cobalt dichloride in dichloromethane/ethanol to afford the corresponding binuclear cobalt complexes. The cobalt complexes were characterized by FT-IR spectroscopy and elemental analysis, and the structure of a representative complex was confirmed by single-crystal X-ray diffraction. Upon activation with either MAO or MMAO, these cobalt complexes performed with high activities of up to 1.2 × 10⁷ g (mol of Co)⁻¹ h⁻¹ in ethylene polymerization, which represents one of the most active cobalt-based catalytic systems in ethylene reactivity. These biphenyl-bridged bis(imino)pyridylcobalt precatalysts exhibited higher activities than did their mononuclear bis(imino)pyridylcobalt precatalyst counterparts, and more importantly, the binuclear precatalysts revealed a better thermal stability and longer lifetimes. The polyethylenes obtained were characterized by GPC, DSC, and high-temperature NMR spectroscopy and mostly possessed unimodal and highly linear features

Current-driven skyrmionium in a frustrated magnetic system

Magnetic skyrmionium can be used as a nanometer-scale non-volatile information carrier, which shows no skyrmion Hall effect due to its special structure carrying zero topological charge. Here, we report the static and dynamic properties of an isolated nanoscale skyrmionium in a frustrated magnetic monolayer, where the skyrmionium is stabilized by competing interactions. The frustrated skyrmionium has a size of about $10$ nm, which can be further reduced by tuning perpendicular magnetic anisotropy or magnetic field. It is found that the nanoscale skyrmionium driven by the damping-like spin-orbit torque shows directional motion with a favored Bloch-type helicity. A small driving current or magnetic field can lead to the transformation of an unstable N\'eel-type skyrmionium to a metastable Bloch-type skyrmionium. A large driving current may result in the distortion and collapse of the Bloch-type skyrmionium. Our results are useful for the understanding of frustrated skyrmionium physics, which also provide guidelines for the design of spintronic devices based on topological spin textures.Comment: 5 pages, 5 figure

Ranolazine recruits muscle microvasculature and enhances insulin action in rats: Ranolazine, microvasculature and insulin action

Ranolazine, an anti-anginal compound, has been shown to significantly improve glycaemic control in large-scale clinical trials, and short-term ranolazine treatment is associated with an improvement in myocardial blood flow. As microvascular perfusion plays critical roles in insulin delivery and action, we aimed to determine if ranolazine could improve muscle microvascular blood flow, thereby increasing muscle insulin delivery and glucose use. Overnight-fasted, anaesthetized Sprague-Dawley rats were used to determine the effects of ranolazine on microvascular recruitment using contrast-enhanced ultrasound, insulin action with euglycaemic hyperinsulinaemic clamp, and muscle insulin uptake using 125I-insulin. Ranolazine's effects on endothelial nitric oxide synthase (eNOS) phosphorylation, cAMP generation and endothelial insulin uptake were determined in cultured endothelial cells. Ranolazine-induced myographical changes in tension were determined in isolated distal saphenous artery. Ranolazine at therapeutically effective dose significantly recruited muscle microvasculature by increasing muscle microvascular blood volume (∼2-fold, P < 0.05) and increased insulin-mediated whole body glucose disposal (∼30%, P= 0.02). These were associated with an increased insulin delivery into the muscle (P < 0.04). In cultured endothelial cells, ranolazine increased eNOS phosphorylation and cAMP production without affecting endothelial insulin uptake. In ex vivo studies, ranolazine exerted a potent vasodilatatory effect on phenylephrine pre-constricted arterial rings, which was partially abolished by endothelium denudement. In conclusion, ranolazine treatment vasodilatates pre-capillary arterioles and increases microvascular perfusion, which are partially mediated by endothelium, leading to expanded microvascular endothelial surface area available for nutrient and hormone exchanges and resulting in increased muscle delivery and action of insulin. Whether these actions contribute to improved glycaemic control in patients with insulin resistance warrants further investigation

Multimodal educational data fusion for students' mental health detection

Mental health issues can lead to serious consequences like depression, self-mutilation, and worse, especially for university students who are not physically and mentally mature. Not all students with poor mental health are aware of their situation and actively seek help. Proactive detection of mental problems is a critical step in addressing this issue. However, accurate detections are hard to achieve due to the inherent complexity and heterogeneity of unstructured multi-modal data generated by campus life. Against this background, we propose a detection framework for detecting students' mental health, named CASTLE (educational data fusion for mental health detection). Three parts are involved in this framework. First, we utilize representation learning to fuse data on social life, academic performance, and physical appearance. An algorithm, named MOON (multi-view social network embedding), is proposed to represent students' social life in a comprehensive way by fusing students' heterogeneous social relations effectively. Second, a synthetic minority oversampling technique algorithm (SMOTE) is applied to the label imbalance issue. Finally, a DNN (deep neural network) model is utilized for the final detection. The extensive results demonstrate the promising performance of the proposed methods in comparison to an extensive range of state-of-the-art baselines. © 2013 IEEE

TOSNet : a topic-based optimal subnetwork identification in academic networks

Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic- aware subnetwork identification is essential to discover potential researchers on particular research topics and provide qualitywork. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved