Project Title: Aptamer: Molecular Insight & Translational Theranostics
Project Coordinator: Prof Aiping Lyu (HKBU)
Abstract
Aptamers are promising agents in both diagnostic and therapeutic applications. We are a collaborating team (Guangdong-Hong Kong-Macao Greater Bay Area Research Platform for Aptamer-based Translational Medicine and Drug Discovery (HKAP)) with top scientists in this field. We have established state-of-the-art technologies with research outcomes being published in high-impact journals (Cell, Science, Nature, Nature Medicine, Nature Communications, ACS Nano, PNAS). One of our aptamers for osteogenesis imperfecta therapy has been granted by US-FDA for Orphan Drug Designation (DRU-2019-6966).
Task 1: Aptamer selection optimization: Standard selection method SELEX is time-consuming with high failure rates. With rich experiences in SELEX and well-established automatic microfluidic system, we will develop a fully integrated SMART and high-throughput microfluidic platform for rapid, efficient and reliable selection of aptamers against circulating proteins. Selected aptamers against purified transmembrane proteins often fail to recognize targets in live cells. We will develop an innovative LOSS-GAIN cell-SELEX methodology incorporating into the microfluidics for selecting aptamers against transmembrane proteins on live cells.
Task 2: Aptamer molecular insight: The 3D structure of aptamer-target could not only help to understand the interaction mechanism, but also guide chemical modifications to improve affinity and activity of aptamers to facilitate applications. We have developed the aptamer LC07 against osteosarcoma cells and the aptamer XQ-2d against pancreatic cancer cells. PARP1 and CD71 were then identified as the target of LC07 and XQ-2d, respectively. We will determine the binding mode of LC07-PARP1 and XQ-2d-CD71, respectively, using our developed methodologies. Phosphorodithioate substitution will be performed on LC07 and XQ-2d with the guidance of the determined 3D structures, respectively, to enhance the induced-fit arrangement and binding affinity.
Task 3: Diagnostic aptamers: Cancer diagnosis at early stage is a major challenge in the clinic. We have established highly sensitive aptamer-based diagnostic devices for various diseases. We will develop an aptamer-mediated multiplexing diagnostic methodology by novel nucleic acid chemistries ideally for low-abundance biomarkers in latent pancreatic cancer.
Task 4: Therapeutic aptamer-drug conjugates: The highly specific aptamers is a promising strategy for targeted delivery of cytotoxic natural products. However, the low conjugating efficiency of aptamer to drug and the chemical instability of conjugating linker in liquid-phase reactions restrained their drugability. We will develop solid-phase aptamer-drug conjugating methodology to synthesize conjugates of pancreatic cancer-specific aptamer XQ-2d with cytotoxic anti-tumor natural products and examine their antitumor activities and toxicities, respectively. This project will promote molecular insight and translational theranostics of aptamers, allowing Hong Kong to become the world-leading centre for aptamer research.
Project Title: Towards Personalized and Innovative Treatment for Acute Myeloid Leukaemia
Project Coordinator: Prof Anskar Y.H. Leung (HKU)
Abstract
Acute myeloid leukaemia (AML) is one of the most lethal cancers worldwide. Recent advents of next generation sequencing (NGS) have shed light to its intra-tumoral heterogeneity at cellular level and the spectrum of mutations that co-exist in diverse combinations and evolve at relapse.
To translate these discoveries into novel therapeutic paradigms that can improve patient outcome, the proposal comprises 3 distinct programmes. First,
we will develop a comprehensive zebrafish programme that incorporates state-of-the-arts technologies with a focus to model cytogenetically normal (CN) AML carrying distinct mutation combinations. It will provide insights to the genetic underpinnings of AML subtypes and their therapeutic responses at high throughput. Second,
primary AML samples carrying specific mutation combinations as informed by zebrafish model will be tested in vitro and in vivo for drug sensitivity in a custom-made and validated AML culture platform and patient-derived xenograft mouse model. Third, cellular heterogeneity in AML, their differential gene expression in response to therapeutic agents being tested in clinical trial and the genetic modifier will be examined by
single-cell transcriptome and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 screen. Mechanistic and pharmacologic studies will be performed to inform design of clinical trials that can improve outcome.
Why leukaemia? Leukaemia has long been the
foundational paradigm for new concepts in cancer biology and innovation in therapeutic targeting. AML is a devastating disease for which emerging tools and growing genomics platform can be exploited.
Why us? The team has established the aforementioned laboratory platforms. Moreover,
≥ 2000 blood and/or marrow samples with AML have been cryopreserved, forming one of the most comprehensive archives in the international AML communities. These expertise and resources
have put us on the international map of leukaemia research in areas of epigenetic regulation of tumour suppressors; leukaemogenesis, clonal evolution during targeted therapy, mechanisms of drug resistance, novel therapeutic strategies, clinical prognostication and biomarker development.
Preliminary data have proven the feasibility of each project and enabled an integrated plan to be formulated.
Contingencies will be developed to deal with potentially unexpected findings.
Impact. This program will generate unique information of translational value and catapult HK as an
international hub of excellence in research and treatment of blood cancers.
Project Title: Assess Antibiotic Resistome Flows from Pollution Hotspots to Environments and Explore the Control Strategies
Project Coordinator: Prof Tong Zhang (HKU)
Abstract
Antibiotic resistance (AR) is a global crisis of public health, which together with climate change, water stress and environmental damage, has been regarded as a grand challenge facing humanity today. To tackle this pressing issue, the holistic 'One Health' concept has now been recognized as a critical framework for drivers of AR development in humans, animals and environments. With its large number and diversity of bacteria as well as their complex interactions, the environment alone has been recognized as the single largest reservoir of antibiotic resistance genes (ARGs) – the genetic entities conferring resistance against antibiotics.
Prioritization of control strategies of ARGs in the environment is in its infancy as the emergence and dynamics of ARGs remain poorly understood. In alignment with global strategies, regional differences in ARGs background and environmental management reinforce the need for local coordinated efforts to tackle ARGs. Due to its unique socioeconomic structure, Hong Kong (HK) has been experiencing a significant public health threat from AR. In the recent 'Strategy and Action Plan on Antimicrobial Resistance 2017-2022', the government of HK has committed to manage AR from a 'One Health' perspective, where the environment has been explicitly included.
In this project, the world's top scientists in environmental science and engineering will team up with leading researchers in molecular biology, microbial ecology, public health, medicine, and computer science to conduct an integrated research program on the environmental ARGs in HK. The ultimate goal is to mitigate the burden of AR that citizens are exposed to. We will perform unprecedented systematic surveillance of ARGs profile across environments in HK. The resulting local map of ARGs will assist in developing a risk assessment framework, which will identify critical control points that should be prioritized to tackle ARGs in the environment. These will facilitate the development of advanced technologies for ARGs removal at critical control sites.
The proposed research will not only address fundamental questions regarding ARGs in environment, but also form the basis of local AR management under 'One Health' concept. The first local concerted effort will largely benefit academia, the public and government in combatting ARGs in environment collectively. In the long-term, the project will substantially ease health and environmental burden, contributing to the sustainable development of HK. Together with efforts in relevant sectors of human and animal health, it will contribute to the goal of United Nations for securing the future from antibiotic resistance.
Project Title: Wireless Power Transfer: The Next Stage
Project Coordinator: Prof Ron Shu-yuen Hui (HKU)
Abstract
Increasing global demand on wireless power transfer (WPT) technologies is linked to increasing environmental impacts. With over 500 company members, the Wireless Power Consortium launched the world's first wireless charging standard "Qi" for portable electronics such as mobile phones. Recently, the Society of Automobile Engineers also set up the SAE J2954 for wireless charging of electric vehicles. These standards are still evolving because of demands for increasing charging power and number of applications.
This TRS program will develop (i) Wireless Charging Technologies with higher efficiency for Electric Vehicles (EVs), portable electronics and emerging applications, and (ii) Wireless Charging Infrastructure with technologies to stabilize the power grid fed with intermittent renewable energy. This project will accelerate the adoption of electric vehicles to replace combustion ones and the adoption of renewable energy sources to replace fossil fuels, leading to a complementary solution to reduce greenhouse emission and combat climate change.
Project Title: Financial Technology, Stability, and Inclusion
Project Coordinator: Prof Chen Lin (HKU)
Abstract
FinTech has witnessed a spectacular growth in the aftermath of the 2008 financial crisis. While the industry has taken innovative approaches to promote financial inclusion by serving traditionally under-banked businesses and people, it is also exposed to nonnegligible risk that can threaten the stability of financial market. Hence, a key challenge facing the industry is to assess and manage financial risks properly while extending financial services to a wider spectrum of firms (especially small and medium enterprises SMEs) and consumers. Since SMEs and consumers possess little "hard information" (e.g., financial statements, collaterals, credit history), it makes it a grand challenge – how to evaluate and finance them adequately. This is particularly the case as SMEs and consumers play a crucial role in economic development. According to the State Council of China and HKTID, SMEs contribute to more than 80% (46%) of employment and 90% (98%) of business units in mainland China (Hong Kong). Household consumption is also a key driver of economic growth, accounting for 60% of GDP (World Bank, 2018). Therefore, it is imperative to search for the solutions. In this research, we aim to develop a scientific framework on credit risk assessment and management for small businesses and consumers, lay out a formal foundation for smart contract / mechanism design, provide a platform for conducting program evaluation on the real economic and social impact of FinTech, form and test macro theories/models of financial risk featured with FinTech development, and provide policy recommendations for financial stability and inclusion. The framework is featured with a comprehensive guidance on (a) "high-frequency, high-dimensions, high-coverage" data collection, such as capturing granular digital footprints and networks of firms and consumers, (b) feature extraction based on behavioral consistency theories (Cronqvist et al., 2012), such as extracting behavioral traits (e.g., risk attitude, self-efficacy) of small business owners and consumers from investment, consumption and their behaviors through social networks and ownership chains, (c) transformation of such "soft information" into "hard information", (d) design, selection and training of credit models that (i) exploit the predictive power of digital footprints, networks and behavioral traits versus traditional financial metrics, and (ii) dynamically adapt to business cycles, and (e) interpretation and application of model outcomes for financial institutions and policy makers. To achieve these objectives, we will convene a team of experts in finance, economics, law, data science and engineering, collaborate with top financial institutions (e.g., the largest FinTech company), and form an advisory board of world leading economists (e.g., Nobel Laureates) and important policy makers (e.g., IMF Deputy MD overseeing FinTech). The extensive network of the team with global and regional institutions (e.g., IMF, World Bank, BIS, ECB, FSB, Fed, PBC, HKMA, HKFSDC, HK Competition Commission, etc.) will help disseminate the research outputs. The deliverables will help Hong Kong transit from a traditional International Financial Centre to a FinTech hub.
Project Title: A High-performance Distributed Machine Learning Framework for Graph-based Streaming Data with Smart City Applications
Project Coordinator: Prof Kai Chen (HKUST)
Abstract
Many smart city applications can be modeled as graphs, where nodes represent different entities/locations such as buildings, road intersections, districts, etc., and links consist of roads connecting these nodes. In the big data era, each node or link may involve continuous data streams. Advanced machine learning techniques are instrumental in mining inherent patterns in such dynamic data streams for making predictions. Computational efficiency and prediction accuracy are fundamental requirements for a machine learning framework to effectively support smart city applications such as transportation optimization, urban planning, and crowd sensing. However, none of the existing machine learning frameworks can achieve both computational efficiency and prediction accuracy for a large graph of streaming data. The underlying grand challenges include data scarcity, algorithm limitation, and computing power inefficiency.
By addressing the above challenges, in this project, we will develop a new machine learning framework for large-scale graph-based streaming data with smart city applications. Specifically, this project will make four main contributions: (1) A new deep learning methodology for graph-based streaming data: existing deep learning algorithms such as CNNs (convolutional neural networks) are mostly designed for regular structures such as image grids, which are ineffective for irregular graphs with highly dynamic data streams. We will design new deep learning algorithms to effectively learn from irregular city graphs with streaming data. (2) A new transfer learning framework for inter-city knowledge sharing: since Hong Kong is lacking the necessary data in large volumes, we will develop inter-city transfer learning algorithms to transfer knowledge learned from other cities with rich data sources to our Hong Kong model. As no two cities are the same, transferable and non-transferable knowledge must be identified and separated. To this end, we will propose new domain adaptation and adversarial neural network techniques. (3) A high-performance distributed AI computing architecture to support the above deep learning and transfer learning over large graph streaming data. In particular, efficient RDMA (remote direct memory access) technique will be adopted to achieve high-throughput, low-latency communications among computing nodes in large AI clusters in order to improve the overall cluster computing efficiency. (4) In collaboration with the Hong Kong Transport Department and the Hong Kong Observatory, we will apply the proposed machine learning framework to optimize the transportation system for Hong Kong. In particular, we will first implement an AI-driven taxi dispatching system for Hong Kong based on the taxi scheduling data we have collected. Then, with the first milestone, we will expand our machine learning platform to optimize for the entire transportation network in Hong Kong, including the buses, MTR, ferries, and so on.
Project Title: Intelligent Robotics for Elderly Assistance in Hong Kong
Project Coordinator: Prof Wenping Wang (HKU)
Abstract
Hong Kong is facing a significant societal challenge – a rapidly aging society. The proportion of the population aged 65 and over in Hong Kong increased from 16.6% in 2005 to 20.1% in 2020. The number of people aged 65 or older worldwide is estimated to reach 1.6 billion by 2050. In terms of quality of life, a major difficulty that many older people experience is severe limitation in mobility and manipulability in their daily life, resulting in tremendous social and economic challenges. Hence, we propose to develop innovative intelligent robotics systems to improve mobility and manipulability, prevent falls, enhance independence, and improve the quality of life of older adults. In particular, we propose a User-Centric Co-creation (UC³) approach to developing novel intelligent wearable robots to enhance mobility and manipulability. The UC³ approach will start with a psycho-social study to identify the individual needs of older adults for achieving mobility and manipulability, which then leads to determining kinesiology-based design parameters for personalized wearable robots. The robots will be developed based on novel hybrid soft/rigid structures integrated with intelligent sensors, distributed actuators, and cooperative control methods. The robotic devices will be tested with elderly users in a user-centric environment for evaluation and continuous improvement. We have conducted preliminary studies of the proposed approach. The results of the preliminary studies have clearly shown the feasibility as well as the novelty of the proposed approach. It can be stated confidently that our multidisciplinary team of experts in engineering, gerontology and medicine will be able to work with the elderly community and potential users to successfully deliver the project objectives. Furthermore, an Impact Committee, consisting of leaders in Hong Kong's elder community, elderly care organizations and related industries, has been proposed. It will advise and facilitate the research team to ensure the maximum impact of the research results and successful technology transfer. Commercialization efforts will be embedded in every phase of the project to ensure that the results will both benefit the elderly community and contribute to the economic development of Hong Kong. The outcomes of this project will place Hong Kong at the frontier of global robotics research and technology, provide critical technology to transform the elderly care services in Hong Kong, and create opportunities for training the next generation of scientists and engineers in robotics technology in Hong Kong.