Long Noncoding RNA Expression Profiling During the Neuronal Differentiation of Glial Precursor Cells from Rat Dorsal Root Ganglia

Yunfei Dai,Wei Ma,Tong Zhang,Jinwei Yang,Chenghao Zang,Kuangpin Liu,Xianbin Wang,Jiawei Wang,Zhen Wu,Xingkui Zhang,Chunyan Li,Junjun Li,Xiangpeng Wang,Jianhui Guo,Liyan Li 한국생물공학회 2020 Biotechnology and Bioprocess Engineering Vol.25 No.3

Long noncoding RNAs (lncRNAs) play important roles in the process of cell fate determination. However, their function and expression profiles have not yet been systematically investigated during the transdifferentiation of glial precursor cells derived from dorsal root ganglia (DRG) in the peripheral nervous system. Our results demonstrated significant differences in gene architecture and expression among the three transcript types (lncRNA, mRNA, and TUCP). Distinct differences in transcript length, exon number, and ORF length were identified between lncRNAs and mRNAs after comparative analysis of their structure and sequence conservation. We found that the upregulated lncRNAs outnumbered the downregulated lncRNAs in glial precursor cells cultured with proBDNF antiserum compared with the levels in glial precursor cells cultured without proBDNF antiserum. By a series of GO and KEGG analyses, we found that the effects of some lncRNAs on their target genes in cis were related to nerve growth factor-induced cell cycle, cell phenotype change, and neuronal differentiation. The qRT-PCR verification results of lncRNAs ENSRNOT00000091991, ENSRNOT00000087717, and LNC_000429 were mostly consistent with the sequencing results. The candidate lncRNAs may be associated with the neuronal transdifferentiation of glial precursor cells. Our study provides the first evidence for a remarkably diverse pattern of lncRNA expression during neuronal differentiation of glial precursor cells from rat DRG, and also provides a resource for lncRNA studies in the field of cell differentiation.

Enhanced Li storage performance of ordered mesoporous MoO2 via tungsten doping.

Fang, Xiangpeng,Guo, Bingkun,Shi, Yifeng,Li, Bin,Hua, Chunxiu,Yao, Chaohua,Zhang, Yichi,Hu, Yong-Sheng,Wang, Zhaoxiang,Stucky, Galen D,Chen, Liquan RSC Pub 2012 Nanoscale Vol.4 No.5

<P>Ordered mesoporous tungsten-doped MoO(2) was synthesized by a nanocasting method. The Li storage performance of mesoporous MoO(2) is significantly improved by tungsten doping, which exhibits a reversible capacity of 700 mA h g(-1), better cycling and rate performance. This material combines the advantages of the high theoretical capacity of MoO(2) and the better electroactivity of WO(2).</P>

Mechanism of lithium storage in MoS2 and the feasibility of using Li2S/Mo nanocomposites as cathode materials for lithium-sulfur batteries.

Fang, Xiangpeng,Guo, Xianwei,Mao, Ya,Hua, Chunxiu,Shen, Lanyao,Hu, Yongsheng,Wang, Zhaoxiang,Wu, Feng,Chen, Liquan Wiley-VCH 2012 Chemistry, an Asian journal Vol.7 No.5

<P>The most-popular strategy to improve the cycling stability and rate performance of the sulfur electrode in lithium-sulfur (Li-S) batteries is to astrict the sulfur in a conducting medium by using complicated chemical/physical processing. Lithium sulfide (Li(2)S) has been proposed as an alternative electrode material to sulfur. However, for its application, it must meet challenges such as high instability in air together with all of the drawbacks of a sulfur-containing electrode. Herein, we report the feasibility of using Li(2)S, which was obtained by electrochemical conversion of commercial molybdenum disulfide (MoS(2)) into Li(2)S and metallic molybdenium (Mo) at low voltages, as a high-performance active material in Li-S batteries. Metallic Mo prevented the dissolution of lithium polysulfides into the electrolyte and enhanced the conductivity of the sulfide electrode. Therefore, the in situ electrochemically prepared Li(2)S/Mo composite exhibited both high cycling stability and high sulfur utilization.</P>

The Decoupled Active/Reactive Power Predictive Control of Quasi-Z-source Inverter for Distributed Generations

Dazhong Ma,Ke Cheng,Rui Wang,Sen Lin,Xiangpeng Xie 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.2

For the quasi-Z-source inverter (qZSI), capacitor voltage stability control, high performance of the inductor current reference tracking and fast response of the active/reactive power are key issues. Thus, a decoupled active/reactive power model predictive control (MPC) of the qZSI for distributed generations (DGs) is proposed to fulfill these requirements without additional control loops. Firstly, the digital observer is constructed to remove the utilization of the front voltage sensor and reduce the number of hardware equipment. Moreover, based on the advance determination of the system operation mode and the simplified cost function, the calculation complexity of the proposed MPC algorithm is simplified. Further, the proposed improved MPC method with the digital observer is proved to achieve the high accuracy and the zero prediction error, of which stability is demonstrated through Lyapunov stability criteria. Eventually, the proposed controller is compared with conventional MPC and PI controller in detail and its effectiveness is verified by both simulation and experimental results from a grid-connected qZSI.

Highly Ordered Mesoporous Crystalline MoSe₂ Material with Efficient Visible‐Light‐Driven Photocatalytic Activity and Enhanced Lithium Storage Performance

Shi, Yifeng,Hua, Chunxiu,Li, Bin,Fang, Xiangpeng,Yao, Chaohua,Zhang, Yichi,Hu, Yong‐,Sheng,Wang, Zhaoxiang,Chen, Liquan,Zhao, Dongyuan,Stucky, Galen D. WILEY‐VCH Verlag 2013 Advanced functional materials Vol.23 No.14

<P><B>Abstract</B></P><P>Highly ordered mesoporous crystalline MoSe<SUB>2</SUB> is synthesized using mesoporous silica SBA‐15 as a hard template via a nanocasting strategy. Selenium powder and phosphomolybdic acid (H<SUB>3</SUB>PMo<SUB>12</SUB>O<SUB>40</SUB>) are used as Se and Mo sources, respectively. The obtained products have a highly ordered hexagonal mesostructure and a rod‐like particle morphology, analogous to the mother template SBA‐15. The UV‐vis‐NIR spectrum of the material shows a strong light absorption throughout the entire visible wavelength region. The direct bandgap is estimated to be 1.37 eV. The high surface area MoSe<SUB>2</SUB> mesostructure shows remarkable photocatalytic activity for the degradation of rhodamine B, a model organic dye, in aqueous solution under visible light irradiation. In addition, the synthesized mesoporous MoSe<SUB>2</SUB> possess a reversible lithium storage capacity of 630 mAh g<SUP>−1</SUP> for at least 35 cycles without any notable decrease. The rate performance of mesoporous MoSe<SUB>2</SUB> is much better than that of analogously synthesized mesoporous MoS<SUB>2</SUB>, making it a promising anode for the lithium ion battery.</P>

Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors

Yang Jiejin,Chen Zeyang,Liu Weipeng,Wang Xiangpeng,Ma Shuai,Jin Feifei,Wang Xiaoying 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.3

Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834–0.877), specificity 67.5% (95% CI: 0.636–0.712), PPV 82.1% (95% CI: 0.797–0.843), NPV 73.0% (95% CI: 0.691–0.766), and AUC 0.771 (95% CI: 0.750–0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541–0.995), specificity 70.0% (95% CI: 0.354–0.919), PPV 75.0% (95% CI: 0.428–0.933), NPV 87.5% (95% CI: 0.467–0.993), and AUC 0.800 (95% CI: 0.563–0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.