Prof. TSUI Kwok-Wing Stephen
The Chinese University of Hong Kong, Hong Kong
TSUI Kwok-Wing Stephen is currently a Professor in the School of Biomedical Sciences, the Head of Division of Genomics and Bioinformatics and the Director of Hong Kong Bioinformatics Centre in the Chinese University of Hong Kong (CUHK). In 1995, he received his PhD degree in Biochemistry at CUHK. He was then appointed as an Assistant Professor in the Biochemistry Department in 1997 and promoted to the professorship in 2004. He was also a former member of the International HapMap Consortium and worked on the single nucleotide polymorphisms of human chromosome 3p. During the SARS outbreak in 2003, his team was one of the earliest teams that cracked the complete genome of the SARS-coronavirus. Totally, he has published more than 220 scientific papers in international journals, including Nature, NEJM, Lancet, PNAS, Circulation, JACI and Genome Biology. His major research interests are next generation sequencing, bioinformatics, human genetic diseases and molecular microbiology.
Speech Title: "The Genomes and Microbiomes of Dermatophagoides farinae and Dermatophagoides pteronyssinus Reveal a Broad Spectrum of Dust Mite Allergens"
Abstract: It is well known that house dust mites (HDMs) are predominant sources of inhalant allergens associated with allergic disease. Therefore, sequenced house dust mite (HDM) genomes would certainly advance our understanding of HDM allergens, a common cause of human allergies. To produce annotated Dermatophagoides (D.) farinae and D. pteronyssinus genomes, we developed a combined genomic-transcriptomic-proteomic approach for the elucidation of HDM allergens. High quality D. farinae and D. pteronyssinus genomes and transcriptomes were assembled with high-throughput DNA sequencing platforms including PacBio, Illumina HiSeq and ion torrent. The mite’s microbiome composition was at the same time determined and the predominant genus was validated immunohistochemically. Putative allergens were then evaluated with immunoblotting, immunosorbent assays, and skin prick tests. In this study, 79.79-Mb and 66.85-Mb genomes of D. farinae and D. pteronyssinus, respectively, was constructed. Moreover, the full gene structures of canonical allergens and non-canonical allergen homologues were produced. Using mass spectrometry analysis of D. farinae protein spots reactive to pooled sera from HDM-allergic patients, novel major allergens were found. In D. farinae, the predominant bacterial genus among 100 identified species was Enterobacter (63.4%), among them Enterobacter cloacae and Enterobacter hormaechei were most predominant. KEGG pathway analysis revealed a phototransduction pathway in D. farinae as well as thiamine and amino acid synthesis pathways suggestive of an endosymbiotic relationship between D. farinae and its microbiome. In summary, high quality HDM genomes produced from genomic, transcriptomic, and proteomic experiments revealed allergen genes and a diverse endosymbiotic microbiome, providing a tool for further identification and characterization of HDM allergens and development of diagnostics and immunotherapeutic vaccines.
Prof. Jean-Philippe Vert
Google Brain, France and MINES ParisTech, France
Jean-Philippe Vert is a research scientist at Google Brain, and adjunct researcher at MINES ParisTech. After a PhD in mathematics at ENS Paris in 2001 and a post-doc at Kyoto University, he held various academic positions at MINES ParisTech, Institut Curie, UC Berkeley and ENS Paris. His main research contributions are in machine learning and computational biology, in particular in cancer genomics and precisions medicine.
Speech Title: "Learning from Single-Cell Genomics Data"
Abstract: Single-cell genomics allows capture the diversity of individual cells at the molecular level, and has revolutionized our understanding of development processes or tumor heterogeneity. It also raises numerous modeling and computational challenges. In this talk I will present some approaches we developed for data normalization, gene network inference and integration of heterogeneous views from single-cell genomics data.
Prof. Kuo-Sheng Cheng
National Cheng Kung University, Taiwan
Prof. Kuo-Sheng Cheng received his B.Sc, M.Sc, and Ph.D degrees from Department of Electrical Engineering, National Cheng Kung University, Tainan, TAIWAN. He also received his M.Sc degree from Department of Biomedical Engineering, Rensselaer Polytechnic Institute, USA. Currently, he is a professor with the Department of Biomedical Engineering, National Cheng Kung University. He also is the Director of Department of Maintenance and Engineering, National Cheng Kung University Hospital and the Director of Engineering and Technology Promotion Center, which is financial supported by Ministry of Science and Technology, TAIWAN. He was the past President of the Biomedical Engineering Society of TAIWAN. His research interests includes medical image processing, electrical impedance imaging and biomedical instrumentation.
Speech Title: "An Integrated Analysis System for Cephalometric Applications"
Abstract: With the rapid advance of information and communication technologies, to develop the digital as well as smart dentistry becomes an important issue in oral medicine. In the procedures of conventional cephalometry, many steps rely on the manual processing such as the landmarking and superimposition. The automation of landmarking and superimposition are the first step in cephalometric analysis. Those points in cephalograms representing the anatomical structures of the skull are called landmarks, which are routinely analyzed for diagnosis and treatment planning. In this integrated analysis system, the image processing module was developed for locating the landmarks of X-ray cephalogram automatically. The image was divided into eight rectangular subimages that containing all the useful landmarks. A genetic algorithm combined with perceptron was proposed for feature subimage extraction. All the sugimages were enhanced in the preprocessing stage. The pyramid method was applied to reduce the resolution of image, and the edges were detected by the appropriate edge detectors or the best orientation edge detector. The curve of each edge was adjusted elastically with the pre-stored models. Positions of landmarks could be then located immediately and the associated parameters could also be computed for diagnosis. Secondly, the analysis of the spatial changes of the craniofacial structures for orthodontic treatment or surgery always relies on the superimposition of pre- and post-treatment cephalometric tracings. A computerized superimposition module was also developed for solving this problem. The feature curves were detected and traced for the cranial base using the best oriental edge detector and Hough transform, and for the mandibular using the Laplacian of Gaussian and grouping methods. The superimposition was automated following the clinically available procedures. From the experimental results, it was shown that the cephalometric analysis may be improved with its accuracy and processing time using this proposed integrated analysis system.
|Prof. Wen-Lian Hsu
Academia Sinica, Taiwan
|Prof. Kenta Nakai
The University of Tokyo, Japan
|Prof. Keimei Oh
Akita Prefectual University, Japan
|Prof. Manoj R. Tarambale
Marathwada Mitra Mandal’s College of Engineering, India
|Assoc. Prof. Siew Woh Choo
Xi'an Jiatong-Liverpool University, China
|Assoc. Prof. Riichi Kajiwara
Meiji University, Japan
Prof. Tatsuya Akutsu
Kyoto University, Japan
Distinguished Prof. Cuie Wen
RMIT University, Australia
Assoc. Prof. Hiroyuki Kudo
Meiji University, Japan
Assoc. Prof. Yuncang Li
RMIT University, Australia