Ferroelectric polarization effect on hysteresis behaviors of single-walled carbon nanotube network field-effect transistors with lead zirconate-titanate gating

Yilin Sun,Dan Xie,Ruixuan Dai,Mengxing Sun,Weiwei Li,Tianling Ren 한국물리학회 2018 Current Applied Physics Vol.18 No.3

We report the fabrication of single-walled carbon nanotube (SWCNT) network transistors by ferroelectric Pb(Zr0.4Ti0.6)O3 (PZT) bottom-gating and investigate the polarization effects of PZT on the transport properties of the transistor device. Our devices exhibit typical p-channel transistor characteristics and a large hysteresis loop with high ON/OFF current ratio and large ON current as well as memory window (MW) measured up to 5.2 V. The origin of clockwise hysteresis is attributed to ferroelectric polarization modulated charge trapping/de-trapping process in the interface states between SWCNT networks and PZT. The retention time about 104s with two high stable current states preliminarily demonstrates great potential for future non-volatile memory applications based on such SWCNT/PZT hybrid systems.

CAB: Classifying Arrhythmias based on Imbalanced Sensor Data

( Yilin Wang ),( Le Sun ),( Sudha Subramani ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.7

Intelligently detecting anomalies in health sensor data streams (e.g., Electrocardiogram, ECG) can improve the development of E-health industry. The physiological signals of patients are collected through sensors. Timely diagnosis and treatment save medical resources, promote physical health, and reduce complications. However, it is difficult to automatically classify the ECG data, as the features of ECGs are difficult to extract. And the volume of labeled ECG data is limited, which affects the classification performance. In this paper, we propose a Generative Adversarial Network (GAN)-based deep learning framework (called CAB) for heart arrhythmia classification. CAB focuses on improving the detection accuracy based on a small number of labeled samples. It is trained based on the class-imbalance ECG data. Augmenting ECG data by a GAN model eliminates the impact of data scarcity. After data augmentation, CAB classifies the ECG data by using a Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM). Experiment results show a better performance of CAB compared with state-of-the-art methods. The overall classification accuracy of CAB is 99.71%. The F1-scores of classifying Normal beats (N), Supraventricular ectopic beats (S), Ventricular ectopic beats (V), Fusion beats (F) and Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively. Unclassifiable beats (Q) heartbeats are 99.86%, 97.66%, 99.05%, 98.57% and 99.88%, respectively.

Chondrogenic Differentiation and Three Dimensional Chondrogenesis of Human Adipose-Derived Stem Cells Induced by Engineered Cartilage-Derived Conditional Media

Hengyun Sun,Yu Liu,Ting Jiang,Xia Liu,Aijuan He,Jie Li,Wenjie Zhang,Wei Liu,Yilin Cao,Guangdong Zhou 한국조직공학과 재생의학회 2014 조직공학과 재생의학 Vol.11 No.1

Due to lack of optimal inductive protocols, how to effectively improve chondrogenesis of adipose-derived stem cells (ASCs) is still a great challenge. Our previous studies demonstrated that the culture media derived from chondrocyte-scaffold constructs (conditional media) contained various soluble chondrogenic factors and were effective for directing chondrogenic differentiation of bone marrow stem cells. Nevertheless, it remains unclear whether the conditional media can induce ASCs towards chondrogenic differentiation, especially for three-dimensional (3D) cartilage formation in a preshaped scaffold. In this study, it demonstrated that the conditional media derived from chondrocyte-scaffold constructs could promote ASCs to differentiate into chondrocyte-like cells, with similar expression of type II collagen to those induced by chondrogenic growth factors. Moreover, the expression level of chondrocyte-specific genes, such as SOX9, type II collagen, and COMP, was even higher in conditional medium group (CM) than that in optimized chondrogenic growth factor group (GF), indicating that the conditional media can serve as an effective inducer for chondrogenic differentiation of ASCs. Most importantly, the conditional media could also induce ASC-scaffold constructs to form 3D cartilage-like tissue with typical lacunae structures and positive expression of cartilage specific matrices, even higher contents of GAG and type II collagen were achieved in CM group compared to GF group. The current study establishes a simple, but stable, efficient, and economical method for directing 3D cartilage formation of ASCs, a strategy that may be more closely applicable for repairing cartilage defects.

Creative Illustration of Traditional Chinese Medicine

Qi Siyuan,Sun Yifei,Wang Yilin 한국콘텐츠학회 2023 한국콘텐츠학회 ICCC 논문집 Vol.2023 No.12

Creative Illustration of Traditional Chinese Medicine

Qi Siyuan,Sun Yifei,Wang Yilin 한국콘텐츠학회 2023 ICCC International Digital Design Invitation Exhib Vol.2023 No.12

Phage transcription activator RinA regulates Staphylococcus aureus virulence by governing sarA expression

Jiang Ming,Li Yilin,Sun Baolin,Xu Shiwen,Pan Ting,Li Yujie 한국유전학회 2023 Genes & Genomics Vol.45 No.2

Background Staphylococcus aureus is a major human pathogen, that can lead to various community- and hospital-acquired infections. RinA is a transcription activator of S. aureus phage φ 11 involved in phage packaging and virulence gene transfer. However, little is known about the molecular mechanism of RinA in the regulation of virulence. Objective We aimed to explore a novel contribution of RinA in the regulation of virulence and provide a new drug target in the treatment of S. aureus infections. Methods The specific functions of RinA in S. aureus were analyzed by the methods of growth curve, real-time quantitative PCR (RT-qPCR), subcellular localization, electrophoretic mobility shift assay (EMSA), infection model of Galleria mellonella larvae and the mouse subcutaneous abscess model. Results In this study, we demonstrated that RinA is a protein evenly distributed in the cytoplasm of S. aureus, and its deletion could cause the growth defects. RT-qPCR and EMSA determined that rinA could negatively regulate the expression of sarA by directly binding to its promoter, and vice versa. The Galleria mellonella larvae infection and mouse subcutaneous abscess models revealed that the rinA mutant strain exhibited obvious virulence defects. When sarA is knocked out, the virulence of S.aureus had no significantly changes whether rinA is knocked out or not. Conclusion Our fndings demonstrated that phage transcription activator RinA regulates S. aureus virulence by governing sarA expression.

Model-based Compensation and Pareto-optimal Trajectory Modification Method for Robotic Applications

Xiaoyan Chen,Qiuju Zhang,Yilin Sun 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.7

This study addresses the problem in accuracies of robot positioning and trajectory with compliance and geometric errors in robotic applications. A rigid–flexible coupling position error model of serial robot is presented to identify geometric and compliance error parameters simultaneously. On the basis of the error compensation model, the predicted position error can be corrected by the proposed hybrid error compensation method. Particular attention is paid to the deviation along the desired trajectory with respect to the corresponding updated trajectory, which is consecutively changing and cannot be corrected directly. A segmentation trajectory control method based on the Pareto-optimal with weighted-sum algorithm is proposed to solve the multi-objective optimisation problem in trajectory modification. The offline program optimiser integrates the proposed model-based compensation and trajectory modification method by MATLAB and VS software development platform. The method is developed to be an effective solution for the problem in absolute accuracies of positioning and trajectory with the experimental results achieved on a Staubli TX60L robot. Additional experiment is conducted with a Staubli RX160L robot to demonstrate the extensive feasibility and practical effectiveness of our approach for other industrial robots.

A ResNet based multiscale feature extraction for classifying multi-variate medical time series

Junke Zhu,Le Sun,Yilin Wang,Sudha Subramani,Dandan Peng,Shangwe Charmant Nicolas 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.5

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.