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首页 > 商务会议 > 医疗医学会议 > MEDLIFE 2019第五届安康,医学与性命迷信国际会议(杭州) 更新时光:2019-11-08T10:46:01

MEDLIFE 2019第五届安康,医学与性命迷信国际会议(杭州)
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MEDLIFE 2019第五届安康,医学与性命迷信国际会议(杭州) 已截止报名

会议时光: 2019-12-16 08:00至 2019-12-18 18:00停止

会议所在: 杭州  具体地址会前告诉   周边酒店预订

主办单元: 上海来溪会务办事有限公司

发票类型:增值税通俗发票
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发票内容: 会议费 
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        会议引见

        会议内容 主办方引见


        MEDLIFE 2019第五届安康,医学与性命迷信国际会议(杭州)

        MEDLIFE 2019第五届安康,医学与性命迷信国际会议(杭州)宣扬图

        2019第五届安康,医学性命迷信国际会议为宽大从事安康,医学与性命迷信等相干范畴的研讨学者、专家供给交换平台。会议组委会诚邀全球相干范畴的学者、专家加入此次国际会议,就安康,医学与性命迷信为主题的相干热门成绩停止商量、交换,配合增进全球安康,医学与性命迷信的发展。

        2019第五届安康,医学与性命迷信国际会议将于2019年12月16-18日在中国杭州召开。杭州是浙江省的省会,中国七大古都之一。意大利观光家马可·波罗曾在他的纪行中赞叹:杭州是世界上最俏丽华贵的天城。杭州西湖位于杭州市西面,它以其娟秀的湖光山色和浩瀚的胜景事迹而著名中外,是中国有名的旅游胜地,被誉为“人世地狱”。另外,杭州也以“中国茶都”和“丝绸之府”著称。2022亚运会将在杭州举行,这让杭州成为中国第三个荣获亚运会举行权的城市。同时,中国最大的网络公司和世界第二大网络公司——阿里巴巴的总部,也座落于杭州。

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        会议日程

        (终究日程以会议现场为准)


        12月16日 11:00-17:00 报到
        12月17日 09:00-12:00 特邀佳宾演讲
        12:00-14:00 午饭
        14:00-17:30 行动申报
        18:30-20:00 晚宴
        12月18日 08:00-17:00 学术交换

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        会议佳宾

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        MEDLIFE 2019第五届安康,医学与性命迷信国际会议(杭州)

        Dr. Sarah Du, Associate Professor

        Department of Ocean and Mechanical Engineering, Department of Biological Sciences, Florida Atlantic University, Boca Raton, USA

        Biography: Dr. Sarah E Du earned a Ph.D. degree in Mechanical Engineering from Stevens Institute of Technology in 2011. She received her postdoctoral training in the Nanomechanics Lab at MIT between 2011 to 2014. After that, she joined Florida Atlantic University as an Assistant Professor in the Department of Ocean and Mechanical Engineering. Her research is focused on the cell biomechanics and biophysics under influences of human pathophysiological processes. She is also interested in development of microfluidics-based in vitro disease models and biosensors for the study of human placental malaria, sickle cell disease and nerve regeneration.

        Topic: Probing Fatigue Failure of Human Red Blood Cells in Health and Disease

        Abstract: Human red blood cells (RBCs) are subjected to dynamic and cyclic loads when they traverse through the cardiovascular system during their normal lifespan of approximately 90 to 120 days. Abnormal conditions such as cold storage and pathologies can affect their capability to withstand the cyclic stressing in blood circulation, leading to undermined biological functions and shortened lifespan. A quantitative measure of the dynamic behavior of RBCs under the cyclic loads, can elucidate the fatigue process of cell membranes during blood circulation. We established a new experimental approach based on amplitude modulated electrodeformation in a microfluidic platform. This approach allows generation of monotoic and cyclic loads in the forms of mathematically defined waveforms or arbitrary loading profiles for the study of fatigue failure in plasma membranes of cells. Deleterious effects of cyclic loads were quantified by the characteristic fatigue features and membrane biomechanics for both healthy and abnormal RBCs. The results showed strong correlations between cellular physiological state and fatigue life. This approach provides an accelerated lifespan testing for RBCs and can be extended to other cell types.

        MEDLIFE 2019第五届安康,医学与性命迷信国际会议(杭州)

        Dr. Fang Hu, Associate Professor

        College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, China

        Biography: Dr. Fang Hu received the Ph.D. degree in Complex Network from School of Computer, Central China Normal University, Wuhan, China. Dr. Hu acted as a postal-doctor researcher at University of West Florida, USA. She is currently an Associate Professor with the College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, China. She has published over 30 papers in SCI, EI journals, etc. She is the guest editor or reviewer for SCI journals. Her main research interests include complex networks, machine learning, optimization algorithms, and data modeling in various research fields.

        Topic: On Herb Compatibility Rule of Insomnia Based on Machine Learning Approaches

        Abstract: The herb compatibility rule of insomnia is studied using some machine learning approaches. The insomnia data set with 807 samples is extracted from the real-world Electronic Medical Records (EMRs). After cleaning and selecting the theme data referring to the prescriptions and their herbs, the herb network analysis model is constructed using complex network. Each herb node in network is trained to obtain the herb embeddings using the Skip-Gram model. After acquiring the digital vocabulary of herbs, the similarity among any two herb embeddings is calculated, and these herb embeddings are divided into seven communities using the Spectral Clustering (SC) algorithm. The experimental results shed light on that the methodologies can objectively and effectively discover the relationships among herbs and reveal the herb compatibility for clinical treatment research of insomnia.

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        参会指南

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        温馨提醒
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