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    张羿

    • 助理研究员
    • 性别:男
    • 毕业院校:悉尼科技大学
    • 学历:博士研究生毕业
    • 学位:工学博士学位
    • 在职信息:在岗
    • 所在单位:航空航天学院
    • 入职时间: 2014-04-07
    • 学科:生物医学工程
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    博士后学术沙龙系列活动

      
    发布时间:2018-06-10   点击次数:

    为搭建我校博士后之间的学术交流平台,促进学术水平提升,学校博士后管理办公室组织开展博士后学术沙龙活动。本次沙龙由我校博士后张琳、张羿和赖春友分享其研究成果,诚挚邀请感兴趣的师生参加。

      一、时 间:2016年10月19日(周三)9:30

      二、地 点:清水河校区经管楼宾诺咖啡

      三、主办单位:电子科技大学博士后管理办公室

      四、承办单位:生命科学与技术学院、医学院

             电子科技大学博士后联谊会

      五、活动安排:

      报告一:

      1、主题:家族性渗出性玻璃体视网膜病变的致病基因研究及治疗药物筛选

      2、主讲人:张琳  医学院博士后 

      3、交流内容:

      家族性渗出性玻璃体视网膜病变是一种严重的遗传性视网膜疾病。该病以视网膜血管发育不完整和新生血管形成为主要特征。该病的发病原因复杂,研究表明,遗传因素在该病的发病过程中起关键作用,目前已经发现至少5个基因的突变可导致FEVR:NDP, FZD4, LRP5,TSPAN12和ZNF408。然而,这些突变只能解释约50%的FEVR病例,且对FEVR发病的具体分子机制尚不清楚。因此,寻找新的致病突变或致病基因,并深入研究其致病机制,对FEVR的临床诊断和干预极其重要。FEVR作为一种进行性的视网膜疾病,适当的药物治疗有可能被用于阻止或减缓病情发展,然而,目前并没有针对FEVR的特异性治疗药物,治疗手段多停留在手术等物理疗法。因此,亟需寻找针对FEVR的治疗药物。本报告主要介绍关于致病基因及治疗药物的部分研究成果。

      4、主讲人简介:

      张琳,2013-2016就读于中国科学院成都生物研究所,药物化学专业,获理学博士学位,现在为电子科技大学医学院博士后,主要研究内容为眼科遗传病的致病基因及治疗。

      报告二:

      1、主题:A Noise-assisted Multivariate Empirical Mode Decomposition Based Approach to Multiple-channel Surface EMG

      2、主讲人:张羿 生命科学与技术学院博士后

      3、交流内容:

      Electromyography (EMG) is the collective electric manifestation during muscle contraction, and indicates the physiological response of a muscle’s motor units controlled by the nervous system. The surface EMG signals, originating in the muscle and then recorded by most of the electrophysiological measurement tools, was often contaminated by various types of noises or artifacts, e.g., power line interference, baseline wandering, electrocardiographic (ECG) artifacts, capacitive effects of the detection site, and the firing rate of motor units. Therefore, the identity of an actual EMG still remains difficult.

      Recently, several methods have been developed to analyze and de-noise the EMG signals. The conventional techniques based on Fourier analysis (e.g., IIR filters) were widely used for EMG-based filtering. However, Fourier analysis is purely based on the predefined basis functions, which not only reduces the noise but also attenuate the EMG signal. As an alternative to the usual Fourier transform method, wavelet analysis was also popularized due to its advantages of the time-frequency representation. The wavelet-based approaches can be also suboptimal because the pre-selected wavelet function was often not suitable for matching the natural property of EMGs. This is because the EMG signal is nonlinear and nonstationary which often leads to a drift of the frequency band, and those based on fixed bases are not well acceptable. Previous studies also introduced the Empirical Mode Decomposition (EMD) approach to handle EMG signals. Instead of other techniques proposed in the literature, EMD is a fully data-driven adaptive time-frequency analysis method, and offers no prior assumption in terms of the data processed during the EMD procedure.

      The EMD algorithm was first coined by Huang et al. and provided the most successful results for the decomposition and time-frequency analysis of nonstationary signals, particularly making it suitable for the analysis of EMGs. EMD is utilized as a sifting process that decomposes a signal into a finite set of Intrinsic Mode Functions (IMFs), which are amplitude- and/or frequency-modulated (AM/FM) components representing its inherent oscillatory modes. Adriano et al. first employed this technique for filtering EMG signals for background activity attenuation. However, the first version of EMD did not consider the appearance of mode-mixing. In order to alleviate this problem, the Ensemble EMD (EEMD) was then introduced as an adaptive dyadic filter bank. This method can effectively eliminate the mode-mixing and physically produce more unique time-frequency decomposition of the intrinsic oscillations. Literature shows that several studies have investigated the de-noising performance for EMG signals by using the EEMD algorithm. However, it is a single-channel based EMD algorithm, and cannot be directly applied into the multiple-channel EMG signal processing. Moreover, the EMD or EEMD algorithms cannot guarantee an equal number of IMFs in each channel when dealing with multiple-channel EMG signals, and may lead to subsequent EMG-based analyses physically meaningless. In this case, the multivariate extension of EMD (MEMD) and its noise-assisted analysis method, Noise-assisted Multivariate EMD (NA-MEMD) were developed recently to produce the same number of IMFs for all channels, facilitating direct multiple-channel modeling with the consideration of cross-channel interdependence.

      In this paper, we first introduce the compare multivariate EMD and its noise-assisted analysis methods to multichannel EMG signals, and quantitatively evaluate the performance of NA-MEMD with EEMD and MEMD by the number of IMFs across EMG channels, mode-alignment (common frequency scales in the same indexed IMFs across different channels), mode-mixing (a single IMF containing multiple scales and/or a single scale residing in multiple IMFs). The comparative results based on the experimental EMG data show that NA-MEMD achieved best decomposition performance for multiple-channel EMGs.

      4、主讲人简介:

      Dr. Yi Zhang received his B.E. from the Chongqing University of China in 2007, and held two M.E. degrees from the University of Technology Sydney in 2008 and the University of Sydney in 2009, respectively. In 2014, he completed his Ph.D degree from the University of Technology Sydney. Currently he is a lecturer in the school of Aeronautics and Astronautics, University of Electronic Science and Technology of China (UESTC), and is a postdoctoral fellow at UESTC in the key Laboratory for NeuroInformation, Ministry of Education of China. His research interests include acqusition of biomedical signals, modeling and regulation of biomedical systems. 

      报告三:

      1、主题:肝泡状棘球蚴病不同手术方式疗效及并发症的分析研究

      2、主讲人:赖春友  医学院博士后

      3、交流内容:

      泡状棘球蚴病世界范围内发病率极低,目前缺乏足够的医疗证据来制定完善的治疗方案。在肝泡状棘球蚴病治疗方面,一直存在争议,亟需进一步的深入研究来评价该病不同治疗方案的疗效。此次分享单中心肝泡状棘球蚴病不同手术方案的效果,并对肝泡状棘球蚴病术后短期并发症进行相关分析。

      4、主讲人简介:

      赖春友,2007-2015年就读于四川大学华西临床医学院-临床医学(八年制),获临床医学博士学位,现于电子科技大学医学院·四川省人民医院从事博士后研究工作,研究方向为肝胆外科、细胞移植。


                          电子科技大学博士后管理办公室

                                    2016年10月14日