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| 1 | The recommendations of Chinese Parkinson’s disease and movement disorder society consensus on therapeutic management of Parkinson’s disease显示文摘Background:Parkinson’s disease(PD)is a chronic,progressive and debilitating disease,which affects over 2.5 million people in China.PD is characterized clinically by resting tremor,muscular rigidity,bradykinesia and postural instability.As the disease progresses,additional complications can arise such as non-motor and neurobehavioral symptoms.Pharmacological treatment and surgical intervention for PD have been implemented in China.Until 10 years ago,there was lack of standardization for the management of PD in different regions and among different physicians,leading to different treatment levels in different regions and different physicians.Since then,the Chinese Parkinson’s Disease and Movement Disorder Society have published three versions of guidelines for the management of PD in China,in 2006,2009 and 2014,respectively.Correspondingly,the overall level of treatment for PD in China improved.Objectives:To update the treatment guidelines based on current foreign and domestic practice guidelines and clinical evidence,and to improve the treatment options available to physicians in the management of PD.Summary:A variety of treatment recommendations in the treatment guidelines have been proposed,including physical activity and disease-modifying medication,which should be initiated at the early-stage of the disease.The principles of dosage titration should be followed to avoid acute adverse reactions to the drugs,to achieve a satisfactory clinical effect with a low dose and to reduce the incidence of long-term motor complications.Moreover,different treatment strategies should be considered at different stages of the disease.Importantly,treatment guidelines and personalized treatments should be valued equally.A set of treatment recommendations has been developed to assist physicians to improve and optimize clinical outcomes for patients with PD in China. | Shengdi Chen Piu Chan Shenggang Sun Haibo Chen Baorong Zhang Weidong Le Chunfeng Liu Guoguang Peng Beisha Tang Lijuan Wang Yan Cheng Ming Shao Zhenguo Liu Zhenfu Wang Xiaochun Chen Mingwei Wang Xinhua Wan Huifang Shang Yiming Liu Pingyi Xu Jian Wang Tao Feng Xianwen Chen Xingyue Hu Anmu Xie Qin Xiao | 2016 | Translational Neurodegeneration2016，5，1： | 23 |
| 2 | GenCLiP:asoftware program for clustering gene lists by literatureprofiling and constructing geneco-occurrence networks relatedtocustom keywords显示文摘 | Zhongxi Huang Huiyong Tian Zhenfu Hu | 2008 | BMC Bioinformatics2008，13，9： | 1 |
| 3 | An Efficient Sleep Spindle Detection Algorithm Based on MP and LSBoost显示文摘Sleep spindles are an electroencephalogram(EEG)biomarker of non-rapid eye movement(NREM)sleep and have important implications for clinical diagnosis and prognosis.However,it is challenging to accurately detect sleep spindles due to the complexity of the human brain and the uncertainty of neural mechanisms.To improve the reliability and objectivity of sleep spindle detection and to compensate for the limitations of manual annotation,this study proposes a new automatic detection algorithm based on Matching Pursuit(MP)and Least Squares Boosting(LSBoost),where the automatic sleep spindle detection algorithm can help reduce the visual annotation workload of sleep clinicians.Specifically,MP is a time-frequency analysis method suitable for extracting spindle wave characteristics,which can accurately locate spindle waves on a time-frequency plane.LSBoost is an ensemble learning classification method to deal with unbalanced data.Initially,theMP method is used to search for EEG segments that are possible spindle waves from the filtered raw EEG data.Then,the designed feature segments are thrown into the LSBoost classifier to further identify the real spindles from all candidates and output the final results.The proposed method is verified on the common public dataset DREAMS.The experiment results showthat the sensitivity and F1-scores based on the sample-based assessments achieve 68.2% and 55.4%,respectively.Furthermore,the Recall and F1-score based on the event assessments are 83.8% and 70.8%,respectively.These results show that the proposed algorithm is robust to the subject changes in the DREAMS dataset.In addition,it improves the quality of sleep spindle detection,which is expected to assist the manual marking of experts. | Fei Wang Li Li Yinxing Wan Zhuorong Li Lixian Luo Bangshun Hu Jiahui Pan Zhenfu Wen Haiyun Huang | 2023 | Computers, Materials & Continua2023，76，8： | 0 |
| 4 | Studies of Structure Evolution of Nanostructured Hf_(11)Ni_(89) Alloy显示文摘A nanostructured Hf11Ni89 ribbon sample was prepared by melt-spinning. It was found that the as-quenched sample is composed of a major HfNi5 compound nanophase and an interfacial magnetic Ni(Hf) solid solution phase. The structure evolution of the sample was studied by using X-ray difFraction (XRD), transmission electron microscopy (TEM), difFerential scanning calorimetry (DSC), resistivity and magnetothermal analysis. Upon heating, a second precipitation process of the Ni(Hf) phase prior to grain growth wa5 detected by means of both structural analysis and physical property measurements. The measurement results are discussed based on the relationship between microstructure and physical properties. | Zhenfu DONG and Lianmao PENG(Beijing Lab. of Electron Microscopy, Institute of Physics and Center for Condensed Matter Physics,Chinese Academy of Sciences, P.O.Box 2724, Beijing 100080, China)Ke LU and Zhuangqi HU(State Key Lab. for RSA, Institute of Meta | 1998 | Journal of Materials Science & Technology1998，14，3： | 0 |
| 5 | State Estimation of Vehicle’s Dynamic Stability Based on the Nonlinear Kalman Filter显示文摘An accurate estimation of a vehicle’s state of motion is the basis of dynamic stability control.Two different nonlinear Kalman filters are adopted for the estimation of the vehicle’s lateral/rollover stability state.First,the overall structure of the state estimation with four inputs and four outputs is introduced.After determining tire-cornering stiffness using a recursive leastsquares(RLS)method,the equations of state and of observation for the nonlinear Kalman filter are established based on a vehicle model with four degrees of freedom including planar and rollover dynamics.Then,the specific steps of real-time state estimation using the extended Kalman filter(EKF)and unscented Kalman filter(UKF)are both given.In a co-simulation,we find that the RLS algorithm estimates tire-cornering stiffness accurately and quickly,and the UKF improves the effect of state estimation compared with EKF.In addition,the UKF is verified against data from vehicle tests.The results show the proposed method is reliable and practical in estimating vehicle states. | Xiaofei Pei Xu Hu Wei Liu Zhenfu Chen Bo Yang | 2018 | Automotive Innovation2018，1，3： | 0 |
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