Query: wang yihang liu xiaowei

Candidate papers:

abstract: A digital closed loop system design of a microelectromechanical systems (MEMS) disk resonator gyroscope (DRG) is proposed in this paper. Vibration models with non ideal factors are provided based on the structure characteristics and operation mode of the sensing element. The DRG operates in force balance mode with four control loops. A closed self excited loop realizes stable vibration amplitude on the basis of peak detection technology and phase control loop. Force to rebalance technology is employed for the closed sense loop. A high frequency carrier loaded on an anchor weakens the effect of parasitic capacitances coupling. The signal detected by the charge amplifier is demodulated and converted into a digital output for subsequent processing. Considering compatibility with digital circuits and output precision demands, a low passband sigma delta (SD) analog to digital converter (ADC) is implemented with a 111.8dB signal to noise ratio (SNR) The analog front end and digital closed self excited loop is manufactured with a standard 0.35 um complementary metal oxide semiconductor (CMOS) technology. The experimental results show a bias instability of 2.1 deg/h and a nonlinearity of 0.035% over the 400deg full scale range.
author_names: Yihang Wang

Q Fu

Yufeng Zhang

Wenbo Zhang

Dongliang Chen

Liang Yin

Xiao-wei Liu
title: A Digital Closed Loop Sense MEMS Disk Resonator Gyroscope Circuit Design Based on Integrated Analog Front end
venue: Sensors
year: 2020

abstract: The EM SD (electromechanical sigma delta) approach is a concise and efficient way to realize the digital interface for micro electromechanical systems (MEMS) accelerometers. However, including a fixed MEMS element makes the synthesizing of the EM SD loop an intricate problem. The loop parameters of EM SD can not be directly mapped from existing electrical SD modulator, and the synthesizing problem relies an experience dependent trail and error procedure. In this paper, we provide a new point of view to consider the EM SD loop. The EM SD loop is analyzed in detail from aspects of the signal loop, displacement modulation path and digital quantization loop. By taking a separate consideration of the signal loop and quantization noise loop, the design strategy is made clear and straightforward. On this basis, a discrete time PID (proportional integral differential) loop compensator is introduced which enhances the in band loop gain and suppresses the displacement modulation path, and hence, achieves better performance in system linearity and stability. A fifth order EM SD accelerometer system was designed and fabricated using 0.35 mm CMOS BCD technology. Based on proposed architecture and synthesizing procedure, the design effort was saved, and the in band performance, linearity and stability were improved. A noise floor of 1 mg/Hz, with a bandwidth 1 kHz and a dynamic range of 140 dB was achieved.
author_names: Dongliang Chen

Liang Yin

Q Fu

Wenbo Zhang

Yihang Wang

Guorui Zhang

Yufeng Zhang

Xiao-wei Liu
title: A Straightforward Approach for Synthesizing Electromechanical Sigma Delta MEMS Accelerometers
venue: Sensors
year: 2020

abstract: A digital output Disk Resonator Gyroscope (DRG) on chip temperature compensation method based on virtual sensor is proposed in this paper. DRG is a combination of solid wave gyroscope and MEMS gyro.
author_names: Yihang Wang

Xiao-wei Liu

Yufeng Zhang

Q Fu
title: A digital output MEMS DRG on chip temperature compensation method based on virtual sensor
year: 2020

abstract: Abstract The pandemic outbreak of coronavirus disease 2019 (COVID 19) is rapidly spreading all over the world. Reports from China showed that about 20% of patients developed severe disease, resulting in a fatality of 4% In the past two months, we clinical immunologists participated in multi rounds of MDT (multidiscipline team) discussion on the anti inflammation management of critical COVID 19 patients, with our colleagues dispatched from Chinese leading PUMC Hospital to Wuhan to admit and treat the most severe patients. Here, from the perspective of clinical immunologists, we will discuss the clinical and immunological characteristics of severe patients, and summarize the current evidence and share our experience in anti inflammation treatment, including glucocorticoids, IL 6 antagonist, JAK inhibitors and choloroquine/hydrocholoroquine, of patients with severe COVID 19 that may have an impaired immune system.
author_names: Wen Zhang

Yan Zhao

Feng-chun Zhang

Qian Wang

Taisheng Li

Zheng-yin Liu

Jinglan Wang

Yan Qin

Xuan Zhang

Xiaowei Yan

Xiaofeng Zeng

Shuyang Zhang
title: The use of anti inflammatory drugs in the treatment of people with severe coronavirus disease 2019 (COVID 19) The Perspectives of clinical immunologists from China
venue: Clinical Immunology
year: 2020

abstract: Coagulopathy in Critical Illness with Covid 19 The authors describe a 69 year old man with Covid 19 diagnosed in January 2020 in Wuhan, China, along with two other critically ill patients with Covid 19 who were also seen in the same intensive care unit. Coagulopathy and antiphospholipid antibodies were seen in all three patients.
author_names: Meng Xiao

Shulan Zhang

Peng Xia

Wei Cao

Xin Ding

Hongmin Zhang

Chun-yao Wang

Xuefeng Sun

Ran Tian

Wei Wu

Dong Wu

Jie Ma

Dong Zhang

Xiaowei Yan

Xiang Zhou

Jinglan Wang

Bin Du

Yan Qin

Peng Gao

Xuzhen Qin

Yingchun Xu

Wen Zhang

Yongzhe Li

Shuyang Zhang
title: Coagulopathy and Antiphospholipid Antibodies in Patients with Covid 19
venue: The New England journal of medicine
year: 2020

abstract: We perform a large scale RNA sequencing study to experimentally identify genes that are downregulated by 25 miRNAs. This RNA seq dataset is combined with public miRNA target binding data to systematically identify miRNA targeting features that are characteristic of both miRNA binding and target downregulation. By integrating these common features in a machine learning framework, we develop and validate an improved computational model for genome wide miRNA target prediction. All prediction data can be accessed at miRDB (http:/mirdb.org)
author_names: Weijun Liu

Xiaowei Wang
title: Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data
venue: Genome Biology
year: 2019

abstract: Electrochemical nitrogen reduction reaction (NRR) over nonprecious metal and single atom catalysts has received increasing attention as a sustainable strategy to synthesize ammonia. However, the atomic scale regulation of such active sites for NRR catalysis remains challenging because of the large distance between them, which significantly weakens their cooperation. Herein, the utilization of regular surface cavities with unique microenvironment on graphitic carbon nitride as "subnano reactors" to precisely confine multiple Fe and Cu atoms for NRR electrocatalysis is reported. The synergy of Fe and Cu atoms in such confined subnano space provides significantly enhanced NRR performance, with nearly doubles ammonia yield and 54% increased Faradic efficiency up to 34% comparing with the single metal counterparts. First principle simulation reveals this synergistic effect originates from the unique Fe Cu coordination, which effectively modifies the N2 absorption, improves electron transfer, and offers extra redox couples for NRR. This work thus provides new strategies of manipulating catalysts active centers at the sub nanometer scale.
author_names: Xiaowei Wang

Siyao Qiu

Jianmin Feng

Yueyu Tong

Feng Zhou

Qinye Li

Li Song

Shuangming Chen

Kuang-Hsu Wu

Panpan Su

Sheng Ye

Feng Hou

Shi Xue Dou

Hua Kun Liu

Gao Qing Max Lu

Chenghua Sun

Jian Liu

Ji Liang
title: Confined Fe Cu Clusters as Sub Nanometer Reactors for Efficiently Regulating the Electrochemical Nitrogen Reduction Reaction.
venue: Advanced materials
year: 2020

abstract: The COVID 19 outbreak continues to threaten the health and life of people worldwide. It is an immediate priority to develop and test a computer aided detection (CAD) scheme based on deep learning (DL) to automatically localize and differentiate COVID 19 from community acquired pneumonia (CAP) on chest X rays. Therefore, this study aims to develop and test an efficient and accurate deep learning scheme that assists radiologists in automatically recognizing and localizing COVID 19. A retrospective chest X ray image dataset was collected from open image data and the Xiangya Hospital, which was divided into a training group and a testing group. The proposed CAD framework is composed of two steps with DLs: the Discrimination DL and the Localization DL. The first DL was developed to extract lung features from chest X ray radiographs for COVID 19 discrimination and trained using 3,548 chest X ray radiographs. The second DL was trained with 406 pixel patches and applied to the recognized X ray radiographs to localize and assign them into the left lung, right lung or bipulmonary. X ray radiographs of CAP and healthy controls were enrolled to evaluate the robustness of the model. Compared to the radiologists' discrimination and localization results, the accuracy of COVID 19 discrimination using the Discrimination DL yielded 98.71% while the accuracy of localization using the Localization DL was 93.03% This work represents the feasibility of using a novel deep learning based CAD scheme to efficiently and accurately distinguish COVID 19 from CAP and detect localization with high accuracy and agreement with radiologists.
author_names: Zheng Wang

Ying Xiao

Yong Li

Jie Zhang

Fanggen Lu

Muzhou Hou

Xiaowei Liu
title: Automatically discriminating and localizing COVID 19 from community acquired pneumonia on chest X rays
venue: Pattern Recognition
year: 2020

abstract: A reconfigurable neural network vision sensor is proposed by using gate tunable photoresponse of van der Waals heterostructures. Early processing of visual information takes place in the human retina. Mimicking neurobiological structures and functionalities of the retina provides a promising pathway to achieving vision sensor with highly efficient image processing. Here, we demonstrate a prototype vision sensor that operates via the gate tunable positive and negative photoresponses of the van der Waals (vdW) vertical heterostructures. The sensor emulates not only the neurobiological functionalities of bipolar cells and photoreceptors but also the unique connectivity between bipolar cells and photoreceptors. By tuning gate voltage for each pixel, we achieve reconfigurable vision sensor for simultaneous image sensing and processing. Furthermore, our prototype vision sensor itself can be trained to classify the input images by updating the gate voltages applied individually to each pixel in the sensor. Our work indicates that vdW vertical heterostructures offer a promising platform for the development of neural network vision sensor.
author_names: Chen-Yu Wang

Shi-Jun Liang

Shuang Wang

Pengfei Wang

Zhu'an Li

Zhongrui Wang

Anyuan Gao

Chen Pan

Chuan Liu

Jiaokun Liu

Huafeng Yang

Xiaowei Liu

Wenhao Song

Cong Wang

Bin Cheng

Xiaomu Wang

Kunji Chen

Zhenlin Wang

Kenji Watanabe

Takashi Taniguchi

J Joshua Yang

Feng Miao
title: Gate tunable van der Waals heterostructure for reconfigurable neural network vision sensor
venue: Science Advances
year: 2020

abstract: Significance Viruses pose a great threat to animal and plant health worldwide. Whereas most plant viruses only replicate in plant hosts, some also replicate in their animal (insect) vector. A detailed knowledge of host expansion will give a better understanding of virus evolution, and identification of virus and host components involved in this process can lead to new strategies to combat virus spread. Here, we reveal that a plant DNA virus has evolved to induce and recruit insect DNA synthesis machinery to support its replication in vector salivary glands. Our study sheds light on the understanding of TYLCV whitefly interactions and provides insights into how a plant virus may evolve to infect and replicate in an insect vector. Whereas most of the arthropod borne animal viruses replicate in their vectors, this is less common for plant viruses. So far, only some plant RNA viruses have been demonstrated to replicate in insect vectors and plant hosts. How plant viruses evolved to replicate in the animal kingdom remains largely unknown. Geminiviruses comprise a large family of plant infecting, single stranded DNA viruses that cause serious crop losses worldwide. Here, we report evidence and insight into the replication of the geminivirus tomato yellow leaf curl virus (TYLCV) in the whitefly (Bemisia tabaci) vector and that replication is mainly in the salivary glands. We found that TYLCV induces DNA synthesis machinery, proliferating cell nuclear antigen (PCNA) and DNA polymerase d (Pold) to establish a replication competent environment in whiteflies. TYLCV replication associated protein (Rep) interacts with whitefly PCNA, which recruits DNA Pold for virus replication. In contrast, another geminivirus, papaya leaf curl China virus (PaLCuCNV) does not replicate in the whitefly vector. PaLCuCNV does not induce DNA synthesis machinery, and the Rep does not interact with whitefly PCNA. Our findings reveal important mechanisms by which a plant DNA virus replicates across the kingdom barrier in an insect and may help to explain the global spread of this devastating pathogen.
author_names: Ya-Zhou He

Yu-Meng Wang

Tian-Yan Yin

Elvira Fiallo-Olive

Yinquan Liu

Linda Hanley-Bowdoin

Xiaowei Wang
title: A plant DNA virus replicates in the salivary glands of its insect vector via recruitment of host DNA synthesis machinery
venue: Proceedings of the National Academy of Sciences
year: 2020