October 21-24, 2018 - Bangkok, Thailand
Dr. Bernd Kuhn
Associate Professor, Okinawa Institute of Science and Technology Graduate University
Optical Neuroimaging Unit
Dr. Yingqiu Xie
Assistant Professor, Nazarbayev University
Title:Imaging neurons and their activity with two-photon microscopy in life animals
Time: Tuesday, October 23, 2018 (Tentative)
Abstract: One of the key questions in neuroscience is how behavior arises from neuronal activity. To find this connection it is necessary to know neuronal activity on a subcellular, cellular and network level while the animal is performing a behavioral task. Optical recording allows to this: Special fluorescent probes for different aspects of neuronal activity were developed to convert neuronal activity into an optical signal, and two-photon microscopy which allows to read out the optical signal from the scattering brain. The combination of these techniques have revolutionized brain imaging on a cellular scale. I will explain the principles of fluorescence probes and two-photon microscopy. I will show examples of functional imaging on different time scales. Starting from voltage imaging with happens on a sub-millisecond time scale, to imaging of calcium ion concentrations with transients on the 10-100 millisecond time scale, and finally to protein kinase A activity which changes on a 1-10 seconds time scale. I will also show examples of how these imaging experiments can be combined with behavioral tasks in virtual reality systems.
Title: Carbon nanodots as anti-prostate cancer agent, antibiotics and potential biosensors of ocean water
Time: Wednesday, October 24, 2018 (Tentative)
Abstract: Carbon nanodots are nanoscale carbon materials which have been widely studied in imaging and drug delivery. However, recent findings from our lab and others have shown the anti-cancer cell growth property of carbon nanodots. We previously found carbon nanodots can inhibit prostate cancer cell growth and migration with damaged actin cytoskeleton. Further we found carbon nanodots stimulate ARF to regulate AKT/mTOR/YAP pathway and induce elevation of DNA damage, ROS, γH2AX and DNA tails in prostate cancer cells. Most importantly, carbon nanodots inhibit PC3 cells drug resistance. Moreover, carbon nanodots suppress the tumor size and growth in vivo. In addition, carbon nanodots inhibit bacterial growth as potential antibiotics through targeting membrane reveled by AFM. Finally, carbon nanodots showed promising character of biosensor by regulating PH, or enhancing enzyme activity which can be used for detection of water pollution. Thus, our data suggest that as green nanomaterials, carbon nanodots have a great biomedical and environmental application potential.
Professor Paolo Soda
Computer Science at the Department of Engineering, University Campus Bio-Medico di Roma (UCBM), Italy.
Title: The challenge of imbalance learning in radiomics
Time: Wednesday, October 22, 2018 (Tentative)
Biography: Prof. Paolo Soda, PhD, is an Associate Professor in Computer Science at the Department of Engineering, University Campus Bio-Medico di Roma (UCBM), Italy. His research interests include pattern recognition, machine learning, big data analytics, and data mining applied to data, signal, 2D and 3D image and video processing and analysis. Practical applications of the research activities have impacted on the biomedical applications, with reference to computer-aided diagnosis and decision support systems. Prof. Paolo Soda has received six external grants from both government funding agencies and industry, totalizing over 500 thousand euros in external funding. He has published over 85 refereed papers in international journals and conference proceedings, being also co-author of two international patents.
Since June 2017 Paolo has served as elected chair of the IEEE Technical Committee on Computational Life Sciences (http://tccls.computer.org/). Since 2012, he has also served as associate editor of the proceedings of the annual international conference of the IEEE Engineering in Medicine & Biology Society, and since the same year he has been a member of the Steering Committee of the International Symposium on Computer-Based Medical Systems (CBMS). He was general co-chair of the 25th, 29th and 32nd CBMS editions in 2012, 2016, and 2019 respectively. In the last few years, Paolo Soda has also served as program and special tracks co-chair. From 2009 to 2012 he co-organized at CBMS special tracks on knowledge discovery and decision systems in biomedicine, and in 2012 he co-organized a contest on bioimage classification at the 21st International Conference on Pattern Recognition. He also currently serves as member of the program committee in several conferences. He was guest editor of Pattern Recognition (vol. 47(7), 2014) and Artificial Intelligence in Medicine (vol. 50(1), 2010).
Prof. Paolo Soda received his Master’s diploma and PhD in biomedical engineering from UCBM in 2004 and 2008, respectively, co-founding with his supervisor, Prof. Giulio Iannello, the Unit of Computer Systems and Bioinformatics. He continued as a postdoctoral researcher in 2009 at the Department of Engineering, UCBM, and as an assistant professor from 2010 to 2014 at the Department of Medicine, UCBM. In 2013 and 2015 he held a digital imaging class at the Technical Medical Superior School of Locarno, Switzerland; in 2014 he held a machine learning class at the faculty of Computer Science, Henan University, China, and in 2009 and 2012 he got European training grants to carry out scientific and teaching activities on machine learning and computer vision at the Polytech'Nice, Université de Nice-Sophia Antipolis, France, and at the Eindhoven University of Technology, The Netherlands.
Abstract: Radiomics, in a nutshell, would like to go beyond imaging for personalized and precision medicine in cancer. Indeed, it refers to the computation, analysis and selection of advanced quantitative imaging features with high throughput from standard-of-care medical images acquired using, for instance, CT, PET or MRI. The increasing adoption of electronic patient records as well as the diffused use of PACS have made available heterogeneous patient data, spanning different spatial and temporal scales, modalities, and functionalities. Radiomics is also evolving into radiogenomics that looks for correlation between cancer imaging features and gene expression. However, most of the available datasets show a certain degree of imbalance, which affects the training of most of the learners used. This usually reflects in unsatisfactory performances on the minority class that, in most of the cases, is the class of interest. For these reasons, algorithms originally introduced for imbalance learning in other domains are now starting to be used also in radiomics.
The challenge is of particular interest since using machine learning, image features, medical data and biological information, radiomics and radiogenomics are currently directed towards the development of personalized and precision medicine models that aim to provide diagnostic, prognostic or predictive value.
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