TRP channels – multifaceted biosensors
Date: 28 April 2010 (Wednesday)
Time: 12:30pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Yao Xiaoqiang, Professor, School of Biomedical Sciences, CUHK
Abstract: TRP (transient receptor potential) channels are cationic ion channels that function as cellular sensors to perceive and respond to many environmental stimuli including temperature, taste, mechanical pressure, osmolarity, pain and pheromones. In this talk, I will discuss possible role of TRP channels as flow sensor to detect the hymodynamic blood flow and as a pressure sensor to detect blood pressure. A number of methods used to study the channels will be discussed. These
include patch clamp, fluorescence Ca2+ imaging, fluorescence resonance energy transfer (FRET), blocking antibody targeted at pore-region of ion channels, and molecular technology. Both at molecular level, cellular level and whole animal levels will be discussed.
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Estrogens and Prostate Cancer
Date: 31 March 2010 (Wednesday)
Time: 12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Franky L. Chan, Professor and Chief, Cancer and Inflammation, School of Biomedical Sciences, CUHK
Abstract: Prostate cancer is a common cancer diagnosed in men, and a major health problem in many Western and also Asian countries. Its incidence rate has been rising rapidly in China and Hong Kong, likely due to increased aging populations and dietary consumption of animal fats. The growth of most clinical prostate cancers is slow and dependent on androgens. Thus, androgen-deprivation therapies and surgical treatment are effective. However, many prostate cancer patients under hormone-therapy will progress to a fatal androgen-independent and metastatic stage when hormone-therapy fails. Although human prostate cancer is generally considered as a mainly androgen-dependent cancer, cumulative evidences suggest that estrogens are also involved in the development and progression of prostate cancer. Paradoxically, estrogens have been traditionally used in the hormone treatment of advanced prostate cancer for over 60 years, largely based on its negative-feedback on the hypothalamus-pituitary-testis axis and achieving a final chemical-castration effect, and is still used in second-line hormone-therapy. The biological effects of estrogens on the prostate, and their roles in the prostate carcinogenesis are complex and could be mediated via their cognate receptors (estrogen receptors) (genomic effects) and the genotoxic estrogen metabolites (non-genomic effects). In this talk, the speaker will discuss on these issues. Besides, he will also discuss some of his recent findings that besides the estrogen-dependent estrogen receptor mediated-pathways, the prostatic epithelial and cancer cells also express another group of orphan nuclear receptors, estrogen-related receptors (ERRs), which are closely related to estrogen receptors and constitutively active independent of estrogens. His recent findings suggest that these ERRs perform certain growth-regulatory roles in prostate cancer and these orphan nuclear receptors could be potential therapeutic targets for this cancer.
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Solar Electricity and Solar Hydrogen
Date: 24 February 2010 (Wednesday)
Time: 12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Xiao Xu-dong, Department of Physics, The Chinese University of Hong Kong
Abstract: Energy and environment are critical issues for the sustainable development of our modern society. Renewable energy will play an increasingly important role in addressing our energy and environmental needs. In particular, solar electricity generated by photovoltaic technology will fulfill our demand on electricity and hydrogen produced through water splitting by photochemical technology may replace fossil fuel for transportation in the future. In this presentation, I will first describe how solar energy can be used to tackle the energy and environmental problems, and then share our research progress in Cu(InGa)Se2 thin film photovoltaic solar cells and CdSe/TiO2 photochemical cells.
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Biomass Ethanol: Being There
Date: 27 January 2010 (Wednesday)
Time: 12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Dr Wang Jun, Chief Scientific Officer, GeneHarbor (HK) Technologies Limited, Adjunct Professor, Department of Biochemistry (Science), CUHK
Abstract: The consensus that the existing petroleum reserve will not be able to meet the needs of global transportation and chemical industry in 40–50 years prompts the current urgent research for alternative energy. The production of bioethanol using cellulosic biomass including agricultural or forest wastes is recognized as one of the most realistic solutions to tthe energy shortage. Biomass ethanol is a safe and environmentally friendly fuel with less emission of carbon dioxide and sulphur -containing gas, alleviates the worsening global green house effect. The presentation will share with the audience various technological challenges of the project and the efforts that make biomass ethanol a commercially viable product.
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Knowledge Discovery and Management in Life Sciences
Date: 3 November 2009 (Tuesday)
Time: 4:30pm - 5:30pm
Venue: ERB LT, William M. W. Mong Engineering Building (Engineering Building Complex Phase 2)
Speaker: Dr. Fazel Famili, Knowledge Discovery (KD) Group, Institute for Information Technology National Research Council of Canada, Ottawa, Canada
Abstract: Knowledge discovery has emerged as a fundamental solution in understanding the real value of large amounts of data that we collect. Particular examples are related to life sciences, physical systems (e.g. sensor-based systems) and financial domain. Of the more complex of these examples is the life sciences domain where one tries to integrate and analyze large amounts of high-throughput genomics and proteomics data obtained from either single time point or time-series applications. Similar to many other domains, in life sciences, various methods have also been developed, and many data mining tools (commercial, non-commercial) have been introduced. These applications have all contributed to: (i) identification of certain genes or proteins and their functions, (ii) gene response analysis in biological studies, such in-vitro, in-vivo or x-vivo, research and (iii) understanding the molecular mechanism of certain species and their associated biological pathways. This wealth of newly discovered and existing knowledge has prompted a question: what is the best way to properly manage all discovered knowledge, when it is validated. This question has also been one of the motivations behind several data mining research projects that we have initiated in the KD group. Here, in addition to searching for patterns in genomics and proteomics data, we have been working to identify proper ways to represent, structure, and distribute all forms of knowledge, most preferably taking an AI approach. This talk consists of two parts. In part one, we provide an overview of knowledge discovery focusing on life sciences and describe the main motivations for developing and applying knowledge discovery methods to analyze complex biological data. We also briefly describe a few of our case studies where we have analyzed high throughput biological data using unsupervised or supervised machine learning techniques. These are cases in which real biological data sets (obtained from public or private sources) have been analyzed and studied for tasks such as gene function identification and gene response analysis. In part two of this talk, we describe how discovered and validated knowledge could be structured into knowledge bases where it can be integrated with other forms of knowledge, for dissemination to multiple users. We conclude our talk with some lessons learned and the research directions that we are currently pursuing.
Understanding the Complexity of Diabetes – A New Way of Looking at Old Data
Date: 27 May 2009 (Wednesday)
Time: 12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Juliana Chan Chung-ngor,Professor, Department of Medicine & Therapeutics, Director, Hong Kong Institute of Diabetes & Obesity, Prince of Wales Hospital, The Chinese University of Hong Kong
Abstract: According to the World Health Organization, 60% of all deaths are due to chronic diseases notably diabetes, heart disease, cancer, mental illnesses and respiratory diseases. A wide range of predisposing, precipitating and perpetuating factors, which can be genetic, environmental and lifestyle-related, interact in ways yet to be understood, to give rise to the multifaceted nature of these common and complex diseases. Using diabetes as an example, there are frequent but not invariable clustering of multiple risk factors including high blood pressure, obesity, abnormal lipids, inflammation, albuminuria (protein in urine) which interact in a multiplicative manner to give rise to heart disease, stroke, cancers and kidney failure. Since 1995, we have established a comprehensive diabetes registry as a quality improvement program to document all relevant clinical data including risk factors and complications in 8000 type 2 diabetic patients. In 2005, we censored their clinical outcomes including death and hospitalizations due to cancer, heart disease, kidney failure and stroke. Using various statistical techniques, we have developed a series of risk equations to predict the 5-year probability of these events with 70-90% sensitivity and specificity. In a subset of 1200 patients, we have genotyped over 100 SNPs of 60 proteins implicated in cardiovascular disease. We used conventional and novel analysis including structural equation modeling and decision trees to discover novel gene-gene interactions in diabetic cardio-renal complications. With advancing technology including high throughput genotyping and sequencing, unraveling the molecular architecture and its regulation and maintenance to predict biological behaviors and consequences in human is a distinct reality. However, to accomplish this challenging task, a cohesive strategy which cuts across all disciplines with inputs from clinicians, biologists, biochemists, bioinformatists, computational scientists, mathematicians, engineers and statisticians is needed. Given the large number of biobanks accompanied by comprehensive databases available in Hong Kong, the discovery of knowledge from these resources promises to provide new insights and solutions to benefit the growing number of people with chronic diseases, estimated to affect more than 1 billion of people in China.
Construction of an Algorithm for the Prediction of Diabetic Nephropathy using a Computational Approach
Date: 8 April 2009 (Wednesday)
Time: 12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Steven Tsui Kwok-wing, Professor, Department of Biochemistry (Medicine), The Chinese University of Hong Kong
Abstract: Diabetic nephropathy (DN) is a common and serious complication of diabetes, affecting roughly 60% of Asian diabetic patients. It is also a major cause of diabetes-related death in Asian population. Development of DN has been known to result from complex interactions between multiple genetic and environmental factors. Although association studies between single nucleotide polymorphisms (SNPs) of candidate gene and DN have been led to identification of numerous susceptibility loci, however, the information of single SNP alone is insufficient to evaluate the risk of multifactorial DN. It remains a significant challenge for clinical researchers to understand the latent cross-link amongst multiple genetic markers and effectively use combined information from genetic and clinical markers to predict the risk of DN. Since DN and cardiovascular complications share common risk factors including hypertension, dyslipidemia and obesity, we have genotyped 106 SNPs of 64 candidate genes implicated in cardiovascular complication through dysregulation of lipid and homocysteine metabolism, endothelial function, thrombosis and coagulation, inflammation, stress responses and natriuresis in a consecutive cohort of Chinese Type 2 diabetic patients. The aim of the present study was to investigate a set of predictors of DN amongst these SNPs by using a computational data mining approach.
Denoising of Fluorescence Microscopy Images
Date: 4 March 2009 (Wednesday)
Time: 12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Thierry Blu, Professor, Department of Electronic Engineering, The Chinese University of Hong Kong
Abstract: We propose a non-Bayesian denoising algorithm to reduce the Poisson noise that is typically dominant in fluorescence microscopy data. To process large datasets at a low computational cost, we use the unnormalized Haar wavelet transform. Thanks to some of its appealing properties, independent unbiased MSE estimates can be derived for each subband. Based on these Poisson unbiased MSE estimates, we then optimize linearly parameterized interscale thresholding. Correlations between adjacent images of the multidimensional data are accounted for through a sliding window approach. Experiments on simulated and real data show that the proposed solution is qualitatively similar to a state-of-the-art multiscale method, while being orders of magnitude faster.
Revisit an old problem in the development of artificial viruses – Complexation between DNA and PEI
Date: 18 February 2009 (Wednesday)
Time:12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Wu Chi, Professor of Chemistry, Department of Chemistry and Department of Physics, The Chinese University of Hong Kong
Abstract: After revisiting the captioned problem by using a combination of chemical synthesis and physical methods, we studied the dynamics of the complexation between branched polyethyleneimine (bPEI) and plasmid DNA (pDNA) and characterized the structure, size and surface charge of the resultant DNA/PEI complexes (polyplexes). As expected, in order to reach a high efficiency in gene transfection into cells it is necessary to use a higher N:P ratio and make the polyplexes positively charged. Our results reveal that it is those uncomplexed bPEI chains free in the solution mixture that plays a vitally important role in promoting the gene transfection, inspiring some new thinking of how to correlate in vitro and in vivo studies so that we can improve the in vivo transfection efficiency. Increasing the N:P ratio normally results in a higher cytotoxicity, which is a catch-22 problem. Recently, we found that a proper modification of bPEI can greatly reduce its cytotoxicity without any suffering in the transfection efficiency. In this lecture, we will show that our properly modified bPEI is even much more effective and less cytotoxic in the gene transfection than those commercially available lipoflexes. Our recent new development leads to a completely new direction in the development of non-viral vectors for molecular medicines, including gene transfection.
A Bayesian Data Integration Approach for Detecting Periodicity in Cell Cycle Gene Expression Profiles
Date: 21 January 2009 (Wednesday)
Time:12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Dr. Fan Xiaodan, Department of Statistics, The Chinese University of Hong Kong
Abstract: There is a growing interest in statistical methods for integrating multiple sources of information in an effort to improve statistical inference and gain deeper understanding of biological systems. In this talk we present a Bayesian meta-analysis approach for integrating multiple microarray time-series data sets to identify genes with periodic expression during the cell cycle from genome-wide microarray time series data. A hierarchical model was used for data integration. In order to facilitate an efficient Monte Carlo sampling from the joint posterior distribution, we develop a novel Metropolis-Hastings group move. A surprising finding from our integrated analysis is that about 40% or more of the genes in fission yeast are significantly periodically expressed, greatly enhancing the reported percentage of 10-15% in the current literature. It calls for a reconsideration of the periodically expressed gene detection problem. It also shows that the power and potential of model-based data integration is appealing.
Application of massively parallel genomic sequencing to noninvasive prenatal diagnosis
Date: 10 December 2008
Time:12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Rossa Chiu Wai-kwan, Centre for Research into Circulating Fetal Nucleic Acids, Li Ka Shing Institute of Health Sciences, Department of Chemical Pathology, The Chinese University of Hong Kong (Professor Chiu represents Professor Dennis Lo’s group to present their research work.)
Abstract: Chromosomal aneuploidy is the main reason why couples opt for prenatal diagnosis. Current methods for definitive genetic diagnosis rely on invasive procedures, such as chorionic villus sampling and amniocentesis, which are associated with a risk of fetal miscarriage. In 1997, our group reported the presence of fetal DNA in maternal plasma and offered new avenues for the development of non-invasive prenatal diagnosis. However, fetal DNA exists as a minor fraction among a high background of maternal DNA. Hence, quantitative perturbations caused by an aneuploid chromosome in the fetal genome, e.g. chromosome 21 for Down syndrome, to the overall representation of sequences from that chromosome in maternal plasma would be small. Even with highly precise single molecule counting methods such as digital PCR, a large number of DNA molecules and hence maternal plasma volume would need to be analyzed to achieve the necessary analytical precision. Recently, we reasoned that instead of using approaches which target specific gene loci, the use of a locus-independent method would greatly increase the number of target molecules from the aneuploid chromosome that could be analyzed within the same fixed volume of plasma. Hence, we used massively parallel genomic sequencing using the Illumina ‘Solexa?platform to quantify maternal plasma DNA sequences for the non-invasive prenatal detection of fetal trisomy 21. In this presentation, the potential of using massively parallel plasma DNA sequencing as a new approach for the noninvasive prenatal diagnosis of fetal chromosomal aneuploidies will be discussed. The seminar will be presented in English. Light Lunch will be provided. First-come, first-served.
Martingales of patterns
Date: 12 November 2008
Time:12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Robert Li Shuo-yen, Professor of Information Engineering, Department of Information Engineering, The Chinese University of Hong Kong
Abstract: This talk presents some probability knowledge in the daily language plus elementary terms. Toss a coin repeatedly until the pattern THTH appears in a run. The average waiting time is not 16. How about another pattern of length 4, say, HTHH? Not 16 either. What are the odds when the two patterns compete against each other? Well, be ready for a big surprise when you attend this talk.
Computational biologists consider patterns over the DNA alphabet {A, T, G, C} in stead of coin tossing. Financial engineers deal with patterns of patterns of ups and downs. In ad hoc networks ofwireless communications, some protocols use binary patterns of listen/talk for node discovery.
Martingale, in layman’s term, means fair gamble. Often, commonly used tools, such as markov chains, can derive special cases of certain problems through long computation, while martingale yields the general result with almost no computation. Moreover, the general result usually offers more transparent insight. A good example is the ‘team gambling” concept that first appeared in the “Martingale of patterns paper” [Ann. Prob., 1980].
RNAi, microRNA and functional genomics
Date: 22 October 2008
Time:12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Chen Yangchao,Department of Medicine and Therapeutics, The Chinese University of Hong Kong (Professor Chen represents Professor Kung Hsiang-fu’s group to present their research work.)
Abstract: The gene knockdown technique of RNA interference (RNAi) has revolutionized the study of functional genomics, the discovery of drug targets and even the treatment of human diseases. A lot of genes have been identified to be dys-regulated in cancers by large-scale screening experiments. RNAi has remarkably accelerated the delineation of the exact functions of each gene in disease progression. In this presentation, we briefly describe our work on RNAi and its application on functional genomics. We developed an inducible RNAi system for mammalian cells for the study of genes essential for cell growth. We developed a lentiviral RNAi system and combined this platform with proteomics approaches for gene functional analysis and identification of drug targets. We are also studying the function of microRNAs implicated in carcinogenesis. The application of siRNA as potential therapeutic agents for the treatment of human diseases will also be discussed.
Inferring transcription networks in plant stress response
Date: 14 May 2008 (Wednesday)
Time:12:30 pm – 1:30 pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Diane Guo Dianjing, Assistant Professor, Department of Biology, CUHK
Abstract: Bayesian Networks have been praised for being a powerful tool for performing probabilistic inference of molecular networks. However, they do have limitations that impede their application to complex biological systems, mainly because Bayesian Networks assume a simple attribute-value representation. Here we propose a novel probabilistic graphical model framework which integrates information from different biological data sources for gene function inference and regulatory network discovery. The approach is based on the assumption that genes involved in the same cellular process tend to have similar expression pattern and regulated by the same sets of regulatory proteins at transcription level. By applying our algorithm to publicly available gene expression profiling dataset on Arabidopsis thaliana, we provide functional predictions for stress-relevant genes and infer transcription network underlying plant stress response.
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Recent Progress in Phylogenetic Combinatorics
Date: 30 April 2008 (Wednesday)
Time:12:30 pm – 2:00 pm
Venue: L2, Science Centre
Speaker: Professor Andreas Dress, Director, Chinese Academy of Sciences--Max Planck Institute, Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences
Abstract: Phylogenetic combinatorics deals with the combinatorial aspects of phylogenetic-tree reconstruction. A starting point was the observation that, given a finite collection X of taxa, there is a close relationship between phylogenetic X-trees, certain metrics D defined on X, and weighted compatible systems of X-splits. In my lecture, I will focus on some rather new developments within this context relating to block decomposition and virtual cut points of metric spaces reported in [1] to [4] that allow to canonically decompose any given finite metric space into a sum of pairwise compatible block metrics thus providing a far-reaching generalization of the result referred to above.
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Disease Gene Hunting in the “post”-genomic era
Date: 9 April 2008 (Wednesday)
Time: 12:30pm - 1:30pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker:Professor Nelson Tang Leung-sang, Professor, Department of Chemical Pathology, Faculty of Medicine, CUHK; Principal Investigator, Laboratory for Genetics of Disease Susceptibility; Li Ka Shing Institute of Health Sciences, Faculty of Medicine, CUHK
Abstract: It is commonly recognized that familial disease inherited in a Mendelian fashion only accounts for a minority of patients with common diseases. For example, only 5% of all breast cancer patients were carrying a mutation in one of the BRCA genes. On the other hand, the reminding 95% of breast cancer patients developed the disease under the influence of both environmental and other genetic risk factors (Tang et al 1999). Recently, we witness the completion of the international HapMap project, of which The Chinese University of Hong Kong is a participating institute. The HapMap project and genome wide association studies provide huge amount of population genetic data that not only facilitate but also revolutionize genetic study of common diseases. New developments in population genetics and genetic statistics also contribute, in a timely manner, to the post-genomic era, when huge amount of data, in various ways, “overload” conventional statistical tools. New concepts emerge in genetic study of common diseases, like population linkage disequilibrium, haploblock and tagging SNPs. We showed the sampling error in determining linkage disequilibrium with HapMap samples and present a resampling method to determine the confidence interval for such parameters (Tang et al 2005). But this is only one of the many unsolved bioinformatic and data analysis issues that we have to tackle at the moment.
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Discovery of structures and processes from biological data -- an outsider point of view
Date: 12 March 2008 (Wednesday)
Time: 12:30pm - 1:30pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Leung Yee, Professor of Geography, Department of Geography & Resource Management
Abstract: Basic concepts of knowledge discovery in data will be discussed in this talk. Methods for the discovery of structures and processes hidden in data will be examined from the non-technical point of view. In particular, a vision-based method for the uncovering of natural structures in data will be introduced. A regression-class decomposition method for the unraveling of structures in mixture data will be discussed. Neural and evolutionary computation methods and other relevant frameworks will also be introduced for the plausible analysis of biological data. The discussion will be substantiated by some examples in human genome and evolution research.
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Pre-Clinical Study of New Drug Candidate LC978 for the Therapy of Human Leukemia, Sickle Cell Anemia and Beta-thalassemia
Date: 13 February 2008 (Wednesday)
Time: 12:30pm - 1:30pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Fung Ming-chiu, Department of Biology
Abstract: Leukemia, sickle cell anemia and thalassemias are among the most prevalent serious blood disorders affecting human populations and represent a major health burden worldwide. However, the search for future treatments aimed at either inhibition of cancer cell proliferation or reduction of globin chain imbalance has focused on the pharmacologic genetic manipulation. Previously, a rational strategy was established by discovering new lead compound from traditional Chinese herbal medicines (TCHM). One leading compound, namely LC978, was identified and purified from the TCHM. The preliminary in vitro and in vivo pharmacological assessment of LC978 indicates its predominant performance comparing with current clinical drugs, including Doxorubicin, Nocodazole and Hydroxyurea. In this project, we have determined the chemical structure of LC978, compared the potency of its derivatives, and studied the in vivo effect in mouse model. We have collaborated with Dr. Li Chi-kong of Pediatric Department for the ex vivo study, and with Prof. Leung Ping-chung and Fung Kwok Pui of ICTCM in the clinical study of the crude extract of the herb. The molecular mechanisms of anti-leukemia and human gamma globin gene expression are now under investigation.
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A Paradigm of Collaborative Research in Integrative Medicine
Date: 9 January 2008 (Wednesday)
Time: 12:30pm - 1:30pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Che Chun-tao, School of Chinese Medicine; Professor Joseph Sung Jao-yiu, Department of Medicine and Therapeutic
Abstract: Modern studies on traditional medicine and medicinal plants require a multidisciplinary approach, interfacing the traditional knowledge, modern sciences, as well as clinical expertise. To illustrate this approach, the scenario of studying a multi-item herbal prescription for the treatment of irritable bowel syndrome (IBS) is described in this presentation. The project is part of an international collaboration involving researchers based in Hong Kong, United States and Australia, and consists of the following aspects of investigations: (1) Documentation and analysis of traditional and modern literature on ethnomedical, biological, chemical, toxicological, and clinical data; (2) Quality standardization of individual herbs as well as the combined medicinal formula; (3) Biological evaluation using in vitro and in vivo bioassay models; (4) Preclinical toxicology of the herbal formula including acute/chronic toxicity and mutagenicity evaluations; (5) Clinical trial on the efficacy of the herbal mixture on patients suffering from IBS, and (6) Experimental acupuncture on an animal model.
To all accounts, this is a unique project owing to its inter-cultural, international, and inter-disciplinary nature, and the characteristics of interfacing between traditional and modern sciences. Multidisciplinary collaboration is considered a most desirable and effective approach for modern study of Chinese medicine, in particular towards the ultimate goal of integrative medicine.
(This project is supported by an International Collaborative Research Center grant of the National Center for Complementary and Alternative Medicine, National Institutes of Health, USA. The presenting authors acknowledge their collaborators in this project for their partnership and friendship: Professors Brian Berman, David Yew, Wai Keung Leung, Justin Wu, Lixing Lao, Harry Fong, Alan Bensoussan, and other colleagues who participate in the project.)
Surface Plasmon Resonance Photonic Biosensors
Date: 12 December 2007 (Wednesday)
Time: 12:30pm - 1:30pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Aaron Ho Ho-pui, Department of Electronic Engineering
Abstract: Surface plasmon resonance (SPR) has been known to be an effective approach for label-free detection of binding between bio-molecular species. Several companies have been very successful in turning SPR into commercial equipment products for the R&D community. With the recent expansion of nanophotonics and bio-related research activities, we have been exploring new ideas in SPR photonics biosensors. We shall first report a highly sensitive surface plasmon resonance (SPR) biosensor design based on the measurement of differential phase between the s- and p-polarization. Unlike conventional SPR systems, in which the SPR data is measured through varying the angle of incidence, our phase-sensitive measurement approach does not require any change in angle of incidence. This makes this technique well suited for imaging applications, which also leads to parallel sensing of multiple analytes. Within this presentation, several novel ideas on achieving very high resolution in phase detection will be addressed.
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Surface Scientists Meeting Biomedical Researchers
Date: 7 November 2007 (Wednesday)
Time: 12:30pm - 1:30pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Professor Leo Lau, Professor of Materials Science, CUHK, Director, Surface Science Western
Professor, Department of Physics & Astronomy and Department of Chemistry, UWO
Abstract: I will talk about the development of new analytical techniques to accelerate the development and applications of diabetes and autism spectrum disorders (ASD) research in Canada. For this, my team will develop new methods in secondary ion mass spectrometry (SIMS) and atomic force microscopy (AFM), to address the common technical hurdles of detecting and imaging cellular/intracellular compositional and microstructural changes in diabetes and ASD research. For example, islet transplantation promises to cure Type 1 diabetes (T1D) which currently affects some 200,000 Canadians and costs the Canadian healthcare system a recorded amount of $1.3 billion in 2002 and a projected amount of $1.6 billion in 2010. However, the development of this technology requires the transfection of a compound to label islets for tracking them in vivo after transplantation, and the approval of clinical trials requires clarification of the toxicity of the labeling agent. The proposed sensitive and quantitative SIMS methods can detect and locate the agent and the 3-D AFM methods can give additional information on this and the induced microstructural changes in a transfected islet cell. As such, the proposed research can accelerate the curing of T1D. In addition, these techniques will also be applied to study compositional and microstructural changes in rat brain tissues/cells induced by the infusion of propanoic acid (PPA) which causes rats to show ASD-like behavior. Emerging experimental evidence has indicated that environmental factors such as PPA present in the food chain or produced in human bodies can trigger ASD, a neuro-developmental abnormality which affects 1/150 children. The proposed ASD work on the rat model can enhance our understanding of ASD and lead to the development of technologies to suppress ASD in humans.
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