CRF 2023/24 Collaborative Research Project Grant (CRPG) Proposals

Project Reference No. : C1018-23GF
Project Title : Controllable activation of anticancer prodrugs in vivo
Project Coordinator : Professor ZHU Guangyu
University : City University of Hong Kong

Layman Summary

Small-molecule chemotherapeutic agents have been extensively applied in the clinic in the battle against cancer. Conventional antineoplastic agents do not have controllable activation properties, leading to off-target effects that ultimately generate dose-limiting adverse side effects. In this collaborative project, the team aims to develop a series of anticancer prodrugs that can be controllably activated by exogenous stimuli that have been applied in clinical cancer therapeutics and diagnostics, namely, near-infrared (NIR)-I and NIR-II light, ultrasound, and X-ray. The prodrugs will be fully characterized, and their antitumor activity in vitro and in vivo will be assessed. Utilization of nano-drug delivery systems to improve tumor specificity and functionalization of prodrugs with immunostimulators to achieve chemo-immunotherapy are also planned. The success of this project will generate a class of anticancer prodrugs that can be controllably activated in vivo and that have clear action mechanisms from both chemical and biological aspects. These prodrugs may also have reduced drug resistance associated with the conventional parent drugs.

Project Reference No. : C1042-23GF
Project Title : Knowledge-Driven Digital Twin Networking for Autonomous Driving
Project Coordinator : Professor WANG Jianping
University : City University of Hong Kong

Layman Summary

Digital twins (DTs) have gained significant traction in recent years for their transformative impact on the autonomous vehicle industry. Interconnecting such digital twin silos will catalyze the integration of autonomous driving (AD) technology on roads. This collaborative project aims to build two foundational pillars of technology essential for the Autonomous Driving Digital Twin Network (AD-DTN). The first pillar concerns the development of technologies for knowledge discovery and acquisition, acting as a bridge between user needs and the offerings of the DTN overlay. The second focuses on knowledge-driven networking technologies, bridging the gap between the DTN overlay's needs and the capacity of the underlying network. Collectively, these solutions will establish a resilient AD-DTN overlay for the collaborative exchange of knowledge among various AD stakeholders. Developing these key enabling technologies will have a ripple effect on AD's research and industry sectors, influencing technology advancement, standardization, and governance.

Project Reference No. : C1045-23GF
Project Title : CILo: Cellular Indoor Localization
Project Coordinator : Professor WU Dapeng Oliver
University : City University of Hong Kong

Layman Summary

Localization aims to provide the coordinates of the position of a given device and provides a critical foundation for location-based services. Despite the ubiquitous service coverage of Global Positioning System (GPS) in outdoor scenarios, GPS fails to work indoors. To bridge this gap, numerous indoor localization techniques have been proposed. However, till now, there does not exist a single application (APP) solution with nationwide coverage for indoor localization.

To address these challenges, the team proposes CILo, a nationwide single-administrator-based indoor localization infrastructure that utilizes existing cellular networks only. Benefiting from the ubiquitously available cellular coverage of main service providers (e.g., China Mobile) and smartphones, CILo requires zero hardware deployment cost to achieve nationwide coverage localization with a single APP software. Specifically, CILo involves deep synergic collaboration over diverse research fields and has multiple novel aspects, including an electromagnetic metaverse (EMetaverse), a neuro-symbolic localization framework, and a transfer learning strategy.

Once completed, this project is expected to offer ubiquitous indoor localization services with a single APP software and further facilitate numerous critical services for Hong Kong and around the world.

Project Reference No. : C1055-23GF
Project Title : Fundamental Understanding on Stability for Halide Perovskite Electronics: from Materials Design to Device Engineering
Project Coordinator : Professor ZHU Zonglong
University : City University of Hong Kong

Layman Summary

Metal halide perovskites are promising materials for solar cells and other electronics due to their high absorption and tunable properties. However, their long-term stability is a concern for commercial use. This project aims to understand the degradation mechanisms of perovskites and develop strategies to stabilize them. Factors such as ion migration, material oxidation, and phase transitions can cause degradation. To investigate this, an advanced characterization system combining simulations and experimental techniques will be developed. The project also focuses on designing stable perovskite materials and interfacial layers. Low-dimensional perovskites will be synthesized with tailored crystallization processes, and stable interfaces will be created to protect the perovskite from external factors. Additionally, charge-transporting layers with strong binding capabilities will be developed. The project aims to advance the understanding and design of stable perovskite electronics, from studying mechanisms to developing reliable materials and devices.

Project Reference No. : C4001-23GF
Project Title : Curved π-Conjugated Systems: from Synthesis to Devices
Project Coordinator : Professor MIAO Qian
University : The Chinese University of Hong Kong

Layman Summary

Curved polycyclic aromatic molecules have attracted significant interest from various fields due to their unique structure and properties. Their curved π-systems deviate from the typical flat structure and offer opportunities to explore fundamental concepts in organic chemistry, such as aromaticity, conjugation, and strain. However, synthesizing highly strained curved polycyclic aromatics is a challenging task, which can drive the development of new methods and strategies in organic synthesis. These molecules not only play a crucial role in the precise synthesis of nanocarbons with unique shapes, but they also provide organic functional materials with special electronic and optical properties. In the solid state, they can also arrange themselves in specific patterns, which is not possible with flat π-molecules, leading to new organic electronic materials.

To advance the synthesis of curved polycyclic aromatic molecules, explore innovative organic semiconductor materials and develop high-performance electronic devices utilizing curved π-systems, the team proposes a collaborative research project. This project integrates diverse expertise in organic synthesis, catalysis, supramolecular chemistry, electrochemistry, scanning probe spectroscopy, electronic device engineering, and computational chemistry. Building upon previous research, the proposed investigation focuses on the following aspects:

1. Developing new ways to synthesize curved polycyclic aromatic molecules using transition metal-catalyzed cross-coupling reactions and Scholl reactions.

2. Designing and synthesizing structurally unique polycyclic aromatics, which can serve as crucial structural components for hypothetical carbon nanomaterials or provide a good opportunity to study unusual aromaticity.

3. Developing novel organic semiconductors based on curved π-systems. The team envisions that saddle-shaped π-molecules will interlock within a supramolecular nanosheet, leading to the emergence of a new class of supramolecular 2D materials.

4. Investigating the stereochemistry, electronic structures, and self-assemblies of the proposed curved π-systems using experimental techniques and computational methods.

5. Fabricating electronic devices utilizing the proposed electronic materials in the forms of monolayers, thin films, and single molecules.

Project Reference No. : C4004-23GF
Project Title : Spatially Resolved in Operando Temperature Sensing in Electrochemical Devices by Nanodiamond Quantum Sensors
Project Coordinator : Professor LI Quan
University : The Chinese University of Hong Kong

Layman Summary

Temperature is a critical parameter in operating electrochemical devices, as it serves as both a measure of device efficiency and an indicator of device stability. The evolving, inhomogeneous material architecture within the device and local variations in electrochemical activity leads to a time-dependent, spatial temperature variation at the nanoscale that can differ significantly from the spatially averaged temperature at the macroscale. Despite the crucial importance of accurately understanding device performance to develop materials and strategies for improving device efficiency and stability, information on spatially resolved temperature evolution is not readily available due to the lack of non-invasive, accurate nano-thermometry methods that can meet the requirements for sensitivity, spatial resolution, and temporal resolution within the reactive chemical environment of a working device.

In this work, the team proposes to develop sensing schemes to monitor the spatially resolved temperature evolution in working electrochemical devices using nanodiamond quantum sensors. The team will develop measurement interfaces for two representative device categories, namely, battery-like devices and electrocatalytic cells. With these new sensing interfaces and schemes, the team can investigate the thermal consequences of Li dendrite growth under abusive conditions in battery-like processes and the activity and stability evolution of CO2 electrolysis, both of which are important topics in energy sustainability and carbon neutrality. The advanced understanding of nanoscale temperature evolution in working devices will ultimately contribute to various strategy developments, ranging from electrocatalyst design to thermal management, to improvement of electrochemical device efficiency and stability.

Project Reference No. : C4008-23WF
Project Title : Targeting m6A Modification to Boost Chemotherapy and Immunotherapy Efficacy in Colorectal Cancer
Project Coordinator : Professor YU Jun
University : The Chinese University of Hong Kong

Layman Summary

Colorectal cancer (CRC) is the number one cancer in Hong Kong. The management of CRC remains challenging because of resistance to chemotherapy and immunotherapy, leading to poor prognosis. To address this unmet need, the team proposes to target aberrant RNA N6-methyladenosine (m6A) mRNA modification to improve therapy response in CRC. In the preliminary studies, the team has shown that m6A “writer” METTL3 and m6A “reader” YTHDF1 are critically involved in chemoresistance by boosting cancer stem cells (CSCs), a subgroup of cancer cells resilient to chemotherapy. Besides, METTL3 and YTHDF1 foster an immunosuppressive tumor microenvironment (TIME) to enable CRC to resist immunotherapy. Based on these findings, the team hypothesizes that targeting of METTL3 and YTHDF1 has the potential to potentiate efficacy of chemotherapy and immunotherapy in CRC, with the following objectives: (1) to investigate function and mechanism of m6A modification in resistance to chemotherapy and immunotherapy in CRC; (2) to test the efficacy of nanoparticles-delivered siRNA targeting METTL3 and YTHDF1 as adjuvants in CRC chemotherapy and immunotherapy; and (3) to decipher whether the expression of METTL3 and YTHDF1 predicts drug resistance in CRC. The team will apply a multi-disciplinary approach to clinically-relevant CRC models, including transgenic mice, humanized mice and patient-derived tumor organoids. This project will inform future therapeutic strategies for CRC.

Project Reference No. : C4014-23GF
Project Title : Molecular Mechanisms of Vacuole Biogenesis and Vacuole Fission in Plants
Project Coordinator : Professor JIANG Liwen
University : The Chinese University of Hong Kong

Layman Summary

The plant endomembrane system can selectively transport biological macromolecules to specific cellular compartments for their proper functions. It is composed of several functionally distinct membrane-bound organelles, including the endoplasmic reticulum (ER), Golgi apparatus, trans-Golgi network (TGN) or early endosome (EE), prevacuolar compartment (PVC) or multivesicular body (MVB), and the vacuole. Vacuoles in plants are the central organelles that play important roles in cellular homeostasis, growth & development, and environmental adaption.

The team has previously developed the whole-cell 3D Electron Tomography (ET) models with nanometer resolution of vacuole formation in both Arabidopsis root cortex cells and stomatal lineage cells, showing that MVB-MVB fusion contributes to the formation of small vacuoles (SVs) with intraluminal vesicles (ILVs) and subsequent SVs fusion leads to the central vacuole formation. The team’s recent study further demonstrated that the developing pollens is an excellent single-cell model for ET and cellular mechanistic analysis of vacuole dynamics, biogenesis, and function. Here the team proposes to study the molecular mechanisms of vacuole fission and transcriptional regulation of vacuole biogenesis in plants, with three objectives: (1) To illustrate mechanisms underlying vacuole fission during pollen development; (2) To understand mechanisms of transcriptional regulation of vacuole biogenesis; and (3) To develop advanced Cryo-FIB/CLEM/ET technologies to study vacuole dynamics and fission in their native state in plant cells.

Project Reference No. : C4042-23GF
Project Title : Intelligent Surgical Robotic Assistant with Multimodal-AI Perception and Interaction
Project Coordinator : Professor LI Zheng
University : The Chinese University of Hong Kong

Layman Summary

With the expanding of aging society, there is an increasing unmet need for clinical services. The shortage of clinical personnel resulted in a prolonged waiting time for surgery. Under pandemics, the situation is even worse. Besides, human errors particularly missing surgical items (MSI) in surgery could cause fatal consequences. One promising solution to this is to develop intelligent surgical robotic assistants (iSRAs) to support surgeons by leveraging advanced robotics and artificial intelligence technologies. To rival human assistants, three main challenges to be addressed are (1) human-robot co-working safety; (2) human-like surgeon-robot-interaction (SRI); and (3) surgical procedure safety and efficiency. In this project, the team aims to address these challenges and develop a framework for iSRAs by teaming up with leading experts in medical robotics, imaging AI, speech AI, and surgery from Hong Kong. Firstly, human-robot co-working safety will be addressed through robot design, sensing, motion planning, and control. Secondly, human-like SRI will be achieved by image-speech-robot multimodal AI-based surgeon intention perception for the first time. Gastrectomy (an operation to treat stomach cancer and obesity) image datasets and audio datasets will be created to train state-of-the-art deep-learning neural networks to perceive surgical instruments, organs/tissues, workflow, and voice commands. Thirdly, human errors will be reduced by AI-based intraoperative surveillance. Surgical items will be tracked with image AI to prevent MSI and improve surgery safety. In addition, surgical efficiency will be improved by smart robot functional design and assistive surgical task autonomy. To demonstrate this framework, two kinds of iSARs, including a flexible endoscope robot and a soft scrub nurse robot, will be developed. This framework will also be integrated into existing surgical robots and evaluated using cadaveric gastrectomy. It is expected that the collaboration will advance technologies in robotics, image AI, and speech AI as well as provide a paradigm for developing human-like iSRAs by synergizing these technologies. This could surely help to enhance Hong Kong’s position as an innovation hub both locally and internationally.

Project Reference No. : C4050-23GF
Project Title : Investigating Long-range Many-body Physics with Ultracold Dipolar Gases in Optical Lattices
Project Coordinator : Professor WANG Dajun
University : The Chinese University of Hong Kong

Layman Summary

The project explores the behavior of ultracold dipolar quantum gases of polar molecules and magnetic atoms in 1D and 2D optical lattices. The team will study how the long-range dipolar interactions between these particles affect their ground states and dynamics in lower dimensions. With the 1D systems, the team will investigate their equilibrium and non-equilibrium properties in the strongly interacting regime and examine the influence of dipolar interaction on the fermionization and thermalization processes. In particular, the team will construct novel lattice configurations and high-resolution detection systems to tell apart the effects resulting from intra-tube and inter-tube dipolar interactions and possibly their interplay. In the 2D systems, the team will study both quantum phases and dynamics, focusing on tunneling between adjacent layers in the presence of strong dipolar interaction and interlayer dipolar spin dynamics and relaxation. Finally, the team will study 2D dipolar superfluidity and the BKT mechanism with dipolar interaction. The work combines experimental techniques with theoretical knowledge to understand the unique properties of long-range interacting systems, which could improve the understanding of many-body physics and provide hints on designing new quantum materials.

Project Reference No. : C5004-23GF
Project Title : Towards future climate-resilient sea-crossing bridges via intelligent learning of long-term real monitoring data
Project Coordinator : Professor XIA Yong
University : The Hong Kong Polytechnic University

Layman Summary

Long-span sea-crossing bridges inevitably suffer from harsh corrosion environments, extreme loads, and operation deterioration. Increasing climate change is causing more extreme heatwaves, floods, and storms, aggravating the risks faced by bridges. However, the bridge engineering society is not ready in the design and maintenance of bridge structures for the adaptation to climate change. For example, the current bridge codes of temperature and wind loads in all countries/regions are based on past recorded data and do not consider the increasing climate change in recent decades. The project will study the effects of climate change on the safety and performance of long-span sea-crossing bridges during their design life, and to investigate the design and maintenance methodologies to make the bridges more resilient to climate change. The 1,377 m long main span Tsing Ma Bridge in Hong Kong will be used as the testbed. The bridge has been equipped with a structural health monitoring system since 1997, which is one of the earliest around the world. The long-term monitoring data collected from the bridge enable researchers to understand the bridge’s previous condition and predict its future performance under climate change, together with regional and global climate projections. The success of this project will improve the safety and resilience of sea-crossing bridges in a climate-altered future and reduce the life-cycle maintenance cost. Besides, updates to current bridge design codes in China and other countries that consider the impact of climate change will be suggested. The research methodologies are general and can be applied to sea-crossing bridges in other regions and extended to other types of civil infrastructure.

Project Reference No. : C5005-23WF
Project Title : High-resolution single-cell multi-omics: Joint profiling of multiple types of biomolecules in the same single cell
Project Coordinator : Professor YANG Mo
University : The Hong Kong Polytechnic University

Layman Summary

The current single-cell analysis techniques have entered the era of high-resolution and full-profile analysis of biomolecules using single-cell omics technique. However, the current single-cell omics technique is limited by the lack of joint single-cell multi-omics profiling and integrative analysis approach in the same single cell. Direct measurement of multi-components of the same single cell allows direct determination of genotype-phenotype linkages, reconstruction of lineage trees, and insight into cell phenotype and function. Moreover, there is lack of integrative analysis approaches of multiple biomolecules in single cells based on single-cell multi-omics molecular signatures. Although many single-cell omics experiments have been performed, computational methods for comprehensive analysis of single-cell multi-omics data to reveal the interplay of multi-omics and the complex regulatory network are just at the early stage.

With the multidisciplinary team of researchers from microfluidics, nanobiotechnology, proteomics, integrative genomics, cellular-oncology and clinical oncology, the team aims to take an integrative approach to address the main challenges for single-cell multi-omics. This project focuses on development of a single-cell multi-omics analysis microfluidic platform for joint profiling of 4 key cellular biomolecules including messenger RNAs (mRNAs) profiling for transcriptome analysis, mitochondrial DNA genotyping for metabolic activity analysis, chromosomal DNA profiling from accessible chromatins for genomic regulation analysis, and cell surface proteins for epitope analysis in the same single cells. A magnetic nanoparticle-based joint barcoding system will be developed for joint profiling 4 cellular components in the same single cell. The single-cell multi-omics analysis microfluidic platform integrated with magnetic nanoparticle-based joint barcoding system will be developed to establish the sequencing libraries of 4 cellular components with the same cells. Deep sequencing will then be performed and machine learning-based computational data analysis method will be developed for joint profiling of multi-omics and integrative analysis of correlation of the multi-components, the regulatory network and the overall comprehensive single-cell multi-omics map. Finally, the established single-cell multi-omics platform will be used to deeply study the effects of multi-parameters of tumor microenvironment on heterogeneity of breast cancer stem cells (CSCs) through single-cell multi-omics analysis. Success will help empower the new paradigm of single-cell multi-omics analysis, in turn enabling tumor precision therapy.

Project Reference No. : C5013-23GF
Project Title : Multi-sensor monitoring, geophysical interpretation and prediction of sea level rise in Hong Kong
Project Coordinator : Professor CHEN Jianli
University : The Hong Kong Polytechnic University

Layman Summary

Monitoring, understanding, and projecting sea level change is important for hazard preparedness, infrastructure development, ecosystem management, and the overall resiliency of the coastal regions, such as Hong Kong. Accurate quantification of sea level change is difficult due to the complicate nature of sea level change. A good understanding of the different driving forces of observed sea level rise is critical for understanding climate change and predicting future sea level rise.

The team proposes to carry out a comprehensive investigation of sea level rise in Hong Kong (HK) by integrating multi-sensor modern space geodetic measurements, tide gauge observations, geophysical model predictions, and numerical model simulations. This investigation will focus on 3 major objectives: (1) improved quantification of HK sea level change using local tide gauge stations measurements and satellite altimeter sea level observations. Integrating tide gauge and satellite altimeter data can help improve the quantification of HK sea level change and understand the limitations of different observational techniques; (2) geophysical interpretation of HK sea level change by considering major contributing factors, including vertical land motion which affects the interpretation of tide gauge observed sea level change, sea level change driven by water mass exchange between the ocean and land, and sea level change due to thermal expansion effect of the sea water; and (3) projections of potential future HK sea level rise by the end of this century 2100 based on advanced climate model projection and consideration of local sea level change patterns.

The team will study vertical land motion in the HK region using GPS and other satellite remote sensing observations. Mass component of sea level change in the HK region will be estimated using satellite gravity measurements with improved data processing techniques, and thermal expansion effect will be computed using ocean temperature and salinity data produced by advanced ocean general circulation models. In addition, the team will study potential impacts of extreme sea level rise due to storm surge during major tropical storms using advanced coastal hydrodynamic modeling, and results can be applied to flood risk assessments in the HK region. Outputs from this investigation can provide the scientific basis for the local governments to develop a sustainable environment and ecosystem, and help promote public awareness of sea level rise, climate change, and impacts on local, regional and world environments.

Project Reference No. : C5016-23GF
Project Title : The roles of mechanically heterogeneous local niches within primary tumors in metastatic organotropism
Project Coordinator : Dr TAN Youhua
University : The Hong Kong Polytechnic University

Layman Summary

Metastasis leads to over 90% of cancer deaths. Notably, tumor cells preferentially metastasize to specific but not all organs, namely organotropism. Previous studies suggest that tumor cells with such ability arise in the primary site before dissemination, implicating the critical roles of primary tumor microenvironment in instructing the establishment of metastases in distal organs. Therefore, understanding the essential mechanisms underlying organotropism within primary tumors is crucial for the identification of potential targets to develop effective therapeutic strategies.

Except biochemical factors, the significance of primary tumor mechanics in metastasis has been increasingly appreciated. Most previous studies have assumed that primary tumors stiffen uniformly and have paid less attention to the substantial variations of local region mechanics that could be induced by intratumoral heterogeneity, a hallmark of cancer. How the mechanically heterogeneous primary tumor influences organotropism remains poorly understood. This project elucidates the critical roles of heterogeneous local region mechanics within primary tumors in organotropism, including: (1) the relationship between local region mechanics within primary tumors and organotropism; (2) the influence of local soft and stiff regions on organotropism; (3) the molecular mechanisms underlying local region mechanics-induced organotropism and the therapeutic effects; (4) clinical significance of mechanical regulation of organotropism. The team expects that local soft and stiff regions in primary breast tumors not only correlate with but also promote brain and bone metastasis through mechanotransduction-mediated HDAC3 and RUNX2 activation. This mechanical regulation is clinically relevant and the mechanical heterogeneity of primary tumors may be utilized to predict organotropism risk.

This project will demonstrate the mechanical regulation of metastatic organotropism through heterogeneous local region mechanics-mediated HDAC3 and RUNX2 signaling, and the therapeutic roles of these mechanisms in suppressing the acquisition of organotropism in the primary site and metastatic colonization in secondary sites. The distribution of local soft and stiff regions within primary tumors and the underlying mechanisms are clinically relevant to the metastatic risks of cancer patients in specific organs. This project is at the edge of a breakthrough that stresses the significance of heterogeneous primary tumor mechanics in organotropism and may lead the evolution of cancer therapy into a new paradigm that targets organotropism from the perspective of mechanobiology. The project outcomes will demonstrate that despite biochemical factors, mechanics also play critical roles in organ-specific metastasis, and will provide compelling evidence to support a new notion that cancer may be not only a genetic but also ‘mechanical’ disease.

Project Reference No. : C5032-23GF
Project Title : Heterogeneity-aware Collaborative Edge AI Acceleration
Project Coordinator : Professor CAO Jiannong
University : The Hong Kong Polytechnic University

Layman Summary

Recent years have witnessed the advances of edge AI, which refers to the training and inference of AI models near the users at the network edge. Edge AI is essential to support applications requiring short communication delays and high response speeds. For emerging advanced applications, such as autonomous vehicles, industrial IoT, and VR/AR, further research on edge AI is needed. This is because such applications demand ultra-low latency and support of distributed and collaborative edge computing, while existing edge AI approaches are inadequate to address the new challenges. Most existing works focus on edge AI acceleration on a single edge device. Only few works explore edge collaboration, but they do not consider the heterogeneity of different devices in the edge network.

This project investigates a systematic approach to supporting edge AI in a collaborative edge computing environment for advanced applications. The team proposes a framework with methods, algorithms, and mechanisms to explore heterogeneity and collaboration among edge devices for further acceleration of AI tasks while maintaining expected accuracy. There are many challenging issues to be addressed, including diversity of AI models, large scale and dynamicity of edge networks, and heterogeneity of edge devices. The team takes a cross-layer approach integrating hardware abstraction, resource management, task scheduling, and application execution throughout the hardware, software, and application layers.

More specifically, this project has the following research tasks: (1) develop a method to uniformly measure the computation capabilities of heterogeneous edge devices; (2) develop a hierarchical edge computing architecture to efficiently discover and manage the distributed resources over the large-scale edge network; (3) design resource-aware task scheduling algorithms to optimize the training and inference latency by jointly considering the coupled computation, networking and data resources, and the requirements of AI tasks; (4) design unified programming abstraction of AI tasks with mechanisms of automatic generation of optimized hardware-dedicated code for efficient task execution. To demonstrate the academic merit and practical impact, the team will develop a prototype system with an example application in autonomous vehicles.

The unique contributions of this project include (1) a cross-layer approach achieving software and hardware co-optimization; (2) cross-device resource management based on the abstraction of the distributed and heterogeneous edge resources; (3) intelligent partition and scheduling of edge AI tasks; (4) unified programming abstraction to enable heterogeneity-agnostic AI model development. This project is timely and will make original contributions to edge computing and AI technologies and benefit a wide range of applications.

Project Reference No. : C5033-23GF
Project Title : Improving the health and stability of roadside trees in compact urban development through novel road systems and tree root “training”
Project Coordinator : Professor WANG Yuhong
University : The Hong Kong Polytechnic University

Layman Summary

Many people live in compact cities such as Hong Kong. Trees offer numerous benefits to residents in compact cities especially amid the global climate change. Using urban heat as an example, a study of 293 European cities found that trees can cool land temperature by up to 8~12°C in Central Europe during hot weather extremes, and they cause an average land surface temperature reduction of 3.08°C during the summer season in Hong Kong.

Despite the benefits, there are tremendous challenges in planting trees in compact cities, where space competition is intense. Particularly, confinement by road infrastructure restricts root growth, affecting the health and stability of trees and creating fallen tree hazards. On the other hand, striving to breathe, tree roots lift and break numerous road pavements—a phenomenon called “root heave”. Therefore, city managers tend to reduce the number of trees or plant small and less intrusive ones in high-density areas. This also minimizes the benefits offered by trees.

This study aims to tackle the challenges of intensively growing roadside trees in compact cities. It consists of five major groups of objectives: (1) to develop novel structures and “breathable” materials to invite tree roots to grow into road structures without causing damages, (2) to “train” tree roots to grow toward desired locations and create healthy under-pavement growth environments, (3) to optimize the new pavement-tree systems for flood resilience, pollutant removal, and tree irrigation, (4) to prevent pavement damages caused by root heave and enhance tree stability through better root anchorage, (5) to synthesize the findings and develop workable roadside tree solutions for compact cities.

This project will integrate various engineering disciplines and biological science to solve a pressing problem for many cities. This study is anticipated to advance scientific and engineering knowledge on road infrastructure, environments, and plants (trees). More importantly, it will create genuine and profound impacts on sustainable development of compact cities by using limited lands to harbour a large number of healthy tree and improve the living environment.

Project Reference No. : C5044-23GF
Project Title : White Adipose Tissue (Fat) Dysfunction in Ageing and its Related Metabolic Diseases: New Insights and Therapeutic Potential
Project Coordinator : Dr CHENG King Yip
University : The Hong Kong Polytechnic University

Layman Summary

Hong Kong's elderly population is rapidly increasing, with over 1.3 million people aged 65 or above. This number is expected to double by 2038. Unfortunately, more than 30% of these older people have metabolic diseases such as type 2 diabetes and non-alcoholic fatty liver diseases. White adipose tissue (WAT), a type of fat tissue that controls systemic metabolism, is the first to decline as we age. It becomes inflamed, fibrosis and dysfunctional. Therefore, targeting WAT could be a potential strategy to tackle against ageing and its related metabolic diseases. The team has recently discovered several metabolic pathways and metabolites that are involved in WAT dysfunction in ageing. In this project, the team will investigate whether changing the metabolic pathways of fat cells can alleviate age-related metabolic diseases. The findings will provide new insights into how metabolism regulates WAT functions in ageing and identify new approaches to maintain whole-body metabolic balance by modifying fat cell function. This could be a new strategy to fight ageing and related metabolic disorders, a major health challenge affecting millions worldwide.

Project Reference No. : C5052-23GF
Project Title : Towards Next-generation Artificial Auditory System with Brain-inspired Technologies
Project Coordinator : Professor TAN Kay Chen
University : The Hong Kong Polytechnic University

Layman Summary

Hearing loss has emerged as a leading cause of disability, affecting over 20% of the global population and creating substantial societal and economic burdens. Confronted with the prevalence of age-related hearing loss coupled with the demographic shift towards aging populations in Hong Kong, there is an urgent need for transformative technologies in hearing healthcare and research.

This project aims to address this pressing need by developing novel hearing assistive devices (HADs) that can synergistically working with users to enhance or restore normal hearing abilities. Despite decades of development, HADs users are still facing significant challenges in complex acoustic environments with interfering speakers and background noise. Although advanced signal processing techniques exist to enhance received audio signals, it remains challenging to determine the sound source of interest that needs to be enhanced, especially when the sound source of interest is switching dynamically among multiple competing ones. To address this issue, this project aims to develop a real-time auditory attention decoding model capable of deciphering the sound source of interest from the brain signals recorded by ear-electrocorticography (ear-EEG) devices. Subsequently, leveraging the decoded auditory attention signals, this project will develop a target speaker extraction (TSE) model to extract the clean signal of interest from the noisy inputs. This neural-steered TSE system will substantially reduce the efforts required by HAD users to analyze complex acoustic scenes. Finally, this project will design and develop a binaural hearing aid based on this system, enabling users to flexibly and seamlessly switch between desired sound sources.

Furthermore, towards the goal of revolutionizing fundamental hearing research, this project seeks to develop an artificial auditory system that can well match to the human auditory system across the functional, structural, and mechanistic levels. This unprecedented system will be achieved by pooling research findings in auditory neuroscience and machine learning to incorporate brain-derived neural architectures, faithful neuronal and network dynamics, and biologically plausible neural plasticity. The resulting system will be made available as an open-source software library and shared among otolaryngologists and hearing researchers. This is anticipated to play a pivotal role in advancing the understanding of human hearing, generating and validating new hypotheses for complex hearing disorders, and predicting the optimal intervention timing and most effective treatment methods.

This project will foster collaboration between artificial intelligence and hearing research communities, placing Hong Kong at a leading position in pushing scientific advancement in hearing research and developing next-generation brain-inspired hearing technologies.

Project Reference No. : C5067-23GF
Project Title : Scalable Two-Dimensional Polymorphic Ferroelectrics Towards In-Memory Processing
Project Coordinator : Dr ZHAO Jiong
University : The Hong Kong Polytechnic University

Layman Summary

Current electronic devices have encountered grand challenges ─ How to scale down the device size further, upgrade the integration of input/output, memory and computing units, and reduce the energy consumption. Beyond current von Neumann systems, two-dimensional (2D) ferroelectric materials with miniaturized dimension, high speed and high sensitivity, and robust ferroic order with memory functionalities, are superior candidates for next-generation in-memory computing and sensing devices, which provide promising solutions to the aforementioned challenges. This project aims to clarify the major problems in 2D ferroelectric materials: (1) What are the ferroic orders and their physical origin in 2D limit? (2) How to control the phase transition and their correlation with the ferroelectric orders in 2D? Herein, a vibrant and collaborative team with complementary expertise is formed, to experimentally and theoretically investigate such problems. In this project, the team will focus on the polymorphic material systems such as 2D ferroelectric In2Se3, Bi3Se2O, MoTe2. The team will apply the in situ transmission electron microscopy (S/TEM), ultrafast optical spectroscopy and theoretical methods (DFT/MD/phase field) to understand the mechanisms of 2D ferroelectric orders and the correlated phase transitions down to the atomic (sub-angstrom) scale, and down to the femtosecond (fs) time scale. Meanwhile, the team will explore different growth methods and conditions using chemical vapor deposition (CVD) for the phase-controllable synthesis of continuous 2D ferroelectric films. Furthermore, new devices with these phase-transition coupled ferroics guided by the team’s in-depth understandings of the mechanisms will be demonstrated to develop the next-generation in-memory computing and in-memory sensing technologies. This project will establish a versatile material platform that will enable us to greatly enhance the device speed and reduce the energy consumption of electronic devices in the near future.

Project Reference No. : C6015-23GF
Project Title : Toward Efficient and Private Serverless Machine Learning Inference on GPU Clouds
Project Coordinator : Professor WANG Wei
University : The Hong Kong University of Science and Technology

Layman Summary

The remarkable advances in machine learning (ML) and its widespread adoption in various domains have fueled a surging demand for cloud-based ML inference services. However, the prevailing "serverful" cloud model used by existing inference services has led to numerous challenges. In this model, developers are required to rent virtual machines (VMs) equipped with GPUs and manually configure system-level parameters. Additionally, they must specify the number of VMs needed and dynamically scale them as the inference load changes. This serverful model not only imposes considerable configuration and management burdens but also leads to resource overprovisioning and GPU underutilization.

Severless computing offers a compelling cloud model for online inference services. In a serverless cloud, developers can publish ML models as inference functions and delegate resource provisioning and scaling responsibilities to the platform. Serverless computing is also economically appealing as developers only pay for the resources consumed when their functions are invoked to serve inference requests, eliminating the resource idling cost.

However, existing serverless computing platforms, such as AWS Lambda and Azure Functions, lack efficient support for GPUs, limiting their capability for high-performance ML inference. Furthermore, these platforms require developers to upload their function code and model files, potentially exposing sensitive intellectual property (IP) and raising security and privacy concerns.

This project aims to develop an efficient and private serverless computing platform for high-performance ML inference on GPU clouds, while safeguarding model confidentiality and IP. The platform will adopt a holistic design approach encompassing four key research tasks. First, the team will develop a shim GPU virtualization layer to enable efficient GPU sharing at a fine granularity among numerous inference functions, utilizing novel API redirection and pipelined model swapping techniques. Second, the team will explore intelligent request scheduling and load balancing algorithms that can well leverage high-speed GPU interconnects (e.g., NVLink and NVSwitch) to meet the latency service-level objectives (SLOs) for individual inference functions. Third, to address model confidentiality concerns, the team will develop novel privacy enhancing techniques to fortify both the model execution environments and the prediction results. Furthermore, the team will extend their system designs to support large language models (LLMs) and dynamic neural networks, which are emerging areas of interest. A prototype system will be developed for concept proof and performance evaluation in large-scale serverless cloud platforms operated by the team’s industry partners. The output of this project will push the boundaries of serverless computing and AI systems, enabling more efficient, secure, and cost-effective ML inference at scale.

Project Reference No. : C6022-23GF
Project Title : Fine particulate matter composition, oxidative potential, and population health in Hong Kong
Project Coordinator : Professor YU Jianzhen
University : The Hong Kong University of Science and Technology

Layman Summary

Numerous epidemiological studies have reported associations between fine particulate matter (PM2.5) exposure and adverse respiratory and cardiovascular health outcomes. However, few causative components of PM2.5 are identified with certainty and consistency across studies conducted in different locations. Increasing evidence suggests that oxidative potential (OP), which measures PM’s capacity to deplete antioxidants or to generate reactive oxygen species, is a plausible indicator of PM toxicity related to oxidative stress. The overall objective of this one-year project is to evaluate and preliminarily quantify the causal associations of distinct PM2.5 components and OP metrics with daily cardiopulmonary mortality and hospital admissions using Hong Kong as the study city. This project will first establish a database of comprehensive PM2.5 composition and five OP metrics in integrated daily samples collected consecutively for one-year in urban Hong Kong. Leveraging on this high time-resolution database, the project will then conduct a preliminary examination of the acute effects of distinct PM2.5 components, sources, and OP metrics on the daily fluctuation of cardiopulmonary mortality and emergency admission health outcomes in the general population of Hong Kong. Results from this project will facilitate the development of more cost-effective mitigation policies in protecting public health in Hong Kong.

Project Reference No. : C6029-23GF
Project Title : Scour protection for offshore wind turbine by structuralized cementing technology
Project Coordinator : Professor WANG Gang
University : The Hong Kong University of Science and Technology

Layman Summary

Offshore wind turbines, as a source of renewable energy, have seen rapid development around the world, especially in coastal areas in China in recent years. For near-shore wind turbines installed on monopile foundations, riprap is the most commonly used measure for scour protection. However, field observation shows that the existing design practice of the riprap system is inadequate. Significant seafloor scouring is still observed after riprap installation, and the system requires constant repairs, which significantly increases the maintenance cost. A more sustainable, cost-effective scour protection system is urgently needed.

This collaborative project aims at developing a new structurally cemented riprap system to improve scour protection for offshore wind turbines. Through an inter-disciplinary synergistic research effort, combining expertise from geotechnical and structural engineering, robotics, materials, and wind farm engineering, the following four research objectives will be achieved: (1) Improving scientific understanding of seabed scouring and the failure mechanism of scour protection through large flume tests, in-situ field data, and advanced computational methods; (2) Developing smart underwater sensing technology for faster and cost-effective seafloor scour detection and monitoring; (3) Innovating a structurally cemented riprap system and construction techniques to improve scour protection for offshore wind turbine foundations. The performance of the structurally cemented riprap system will be tested and optimized through large flume tests and advanced numerical modeling. In-situ field tests will be carried out at a fully operational, offshore wind farm site; (4) Enhancing sustainability, life-cycle performance and maintenance/repair strategies for the structurally cemented riprap system considering extreme weather conditions.

This project could generate unique laboratory/field data to advance the scientific understanding and develop smart, innovative, sustainable techniques for the design, construction and management of the new scour protection system for real-world application. The outcomes of this project will have great impact on sustainable development of offshore wind farms in Hong Kong, mainland and beyond.

Project Reference No. : C6044-23GF
Project Title : Construction skill transfer learning for smooth worker-robot collaboration in dynamic and uncertain workplaces
Project Coordinator : Professor YU Yantao
University : The Hong Kong University of Science and Technology

Layman Summary

The construction industry around the globe is riddled with unsatisfactory safety and productivity records. Moreover, the construction industry in Hong Kong has longstanding problems deriving from an aging workforce, where the average age of the registered workers is 47 years. As it is a labour-intensive industry, the team anticipates that the advent of robots will result in enormous industrial changes. Human-robot cooperation (HRC) could combine the flexibility and experience of humans with the strength and stamina of robots, relieving human workers of tedious tasks and boosting productivity. The great potential benefits have aroused growing interest in the construction industry, where tasks are both physically demanding and require dexterity. However, most HRC methods were developed for simple and repetitive assembly tasks in tidy and static workplaces and have yet to deal with worker-robot collaboration in complex and unstructured construction tasks. Enabling robots to work smoothly with construction workers is still an open challenge.

Human construction workers have an innate ability to cooperate with relative ease. Establishing human worker-inspired robotic cooperative intelligence may have the potential to improve the performance of robots when collaborating with human workers in construction tasks. Based on the observations above, this study seeks to investigate the transfer of human worker collaborative skills to enable a fluent and efficient worker-robot collaborative flow during construction tasks. This project will investigate the representation and transfer of perception, learning, reasoning, and cooperative skills from human workers to robots. Using onsite construction tasks as case studies, the project will: (1) investigate the key cues for human workers' assistance and decision-making from multimodal sensory data; (2) develop a knowledge- and data-driven digital twin of the tasks to transfer prior knowledge of construction tasks to robots; (3) model the supportive behaviours of human workers for robots generating a supportive strategy; and (4) transfer the cooperative skills to robots with different embodiments and tasks in different settings.

This project will advance understanding of the interaction between intelligent agents (human-human and human-robot) when collaborating in complex construction activities. This project will enhance knowledge of multimodal fusion-based environmental sensing, the mathematical modelling of long-horizon tasks, and the design of worker-robot collaboration workflows. The outcomes will facilitate smooth worker-robot collaboration and promote productivity in construction tasks where a collaborative robot proactively assists human workers. This project will yield new insights into human-robot collaboration in complex and dynamic environments, and can be generalized to other industries, such as agriculture and manufacturing.

Project Reference No. : C6047-23GF
Project Title : High-Energy-Density All-Solid-State Lithium-Metal Batteries
Project Coordinator : Professor KIM Yoonseob
University : The Hong Kong University of Science and Technology

Layman Summary

Lithium (Li)-metal batteries (LMBs), holding the highest energy density, have great potential in the energy storage market. However, safety issues related to unstable Li anode and high reactivity of Li in all types of liquid solvents have severely impeded LMB development. As a breakthrough, solid electrolytes have received significant attention. These are promising if they can conduct Li+ rapidly in a reliable manner and can be integrated into full cells with minimum contact resistance. Here the team develops a new generation of solid electrolytes for LMBs. These solid electrolytes are called ionic covalent organic frameworks (iCOFs). iCOF solid electrolytes can transport Li+ rapidly and reliably, while showing high electrochemical/mechanical/chemical stability for battery applications. Since these iCOFs are emerging materials, the team conducts a multi-scale modeling, e.g., density functional theory and molecular dynamic simulations, to understand Li+ conduction pathways, etc. With these understandings, the team will fabricate LMB full cells to show a high reversible capacity to operate stably up to 800 cycles. These findings demonstrate the great potential of iCOFs for electrochemical energy storage devices. The successful completion of the proposed project will establish design guidelines for COF-based solid electrolytes for all-solid-state LMBs. Adopting all-solid-state rechargeable batteries will render electric vehicles more robust, safe, and affordable, ultimately leading to environmental and economic benefits both in Hong Kong and further afield.

Project Reference No. : C6053-23GF
Project Title : Study of Topological and Strongly Correlated Materials
Project Coordinator : Professor LAW Kam Tuen.
University : The Hong Kong University of Science and Technology

Layman Summary

The investigation into the electronic, magnetic, and optical properties of materials has been instrumental in advancing technology. Notably, semiconductor research has underpinned the development of integrated circuits, solar cells, lasers, and more. Moreover, the understanding of superconductors has led to their use in powerful electromagnets, ultra-sensitive magnetic sensors, and quantum computing circuits etc.

In recent years, it was realized that the so-called topological properties of a solid can be used to classify solid state materials. Just as a ball and a donut differ topologically due to the extra hole in the donut, a topologically nontrivial insulator varies from a trivial insulator. In a topological insulator, the bulk is insulating like ordinary insulators, but there are unique metallic states on the surface. Similarly, topological superconductors host special particles known as Majorana zero modes, which are absent in topologically trivial superconductors. These Majorana particles have significant potential for quantum computations. Hence, the study of topological insulators and superconductors has become a central topic in modern physics research over the past two decades.

The effects of electron-electron interactions, also known as correlation effects, can dramatically alter a material's properties. For instance, repulsive electron-electron interactions can drive a metal into a Mott insulator. Conversely, attractive electron interactions can make a metal superconducting.

In certain materials, such as the recently discovered twisted bilayer graphene and twisted transition metal dichalcogenides, both topology and correlation effects are crucial. This has led to the recent discovery of novel electronic phases in these moiré materials, such as the valley polarized quantum anomalous Hall state. A primary objective of this project is to examine materials in which topology and interaction effects are important.

Recently, it was discovered that many superconducting properties of twisted bilayer graphene dramatically deviate from the predictions of the renowned BCS theory. The team formulated a theory which that suggests the quantum metric of electrons plays a vital role in determining the properties of flat-band superconductors. Another goal of this project is to understand the impact of the quantum metric on the transport and superconducting properties of materials.

The team plans to study four topics concerning (1) the study of topological, quantum geometry and correlated effects in moiré materials; (2) the study of higher order nonlinear Hall effects; (3) the realization of superconducting diode effects; and (4) the realization of Majoranas. This project will substantially enhance the understanding of topological and correlated materials and lay the foundation for future device applications.

Project Reference No. : C7015-23GF
Project Title : A bioinformatics platform to decode causal effects of germline alterations in oral and oropharynx cancers
Project Coordinator : Professor WANG J.J.
University : The University of Hong Kong

Layman Summary

When a patient sees the doctor, the doctor will ask the patient’s family history, indicating heredity plays an important role in disease pathogenesis and progression. Germline mutations, which are inherited from both parents, are predisposed in our body, and can pass to our offspring. As we age, eat, and interact with the surrounding environment, certain parts of our genome will be mutated, leading to disease causing events, such as acquired DNA mutations, transcription dysregulation etc. This study addresses how inherited mutations affect these acquired events in oral and oropharynx cancers (OOC). The team aims to generate preliminary data to (1) develop novel computational methods to study how germline mutations lead to aberrant acquired somatic mutations; (2) develop multi-omics method to investigate how germline mutations cause transcriptomic gene dysregulation; (3) build computational tools, database, and software to help researchers in the oral and oropharynx cancers to speed up their biomedical discovery. By the end of this project, the team will build a team of bioinformaticians, surgerns, oncologists, statisticians, data scientists and cancer biologists to work on the OOC field, and develop the propotypes of bioinformatics, statistical methods that can better understand the causal effects of germline mutations, and the tools will benefit the cancer commmunity in the World.

Project Reference No. : C7035-23GF
Project Title : Integrative structural and chemical biology approaches to investigate pre-replicative complex assembly and its applications in human diseases
Project Coordinator : Professor ZHAI Y.
University : The University of Hong Kong

Layman Summary

All living organisms, from single-celled yeast to complex human beings, rely on cell division to propagate. During each cell division, accurate replication of the DNA genome is crucial to ensure that each daughter cell inherits an identical set of genetic information. Eukaryotic cells initiate DNA replication from many origins distributed along chromosomes to facilitate replication of their large genomes. Each initiation event is tightly regulated to ensure the integrity of the genome. Any defects in or mis-regulation of this process can result in genetic diseases, developmental abnormalities, and cancers. Therefore, elucidating the molecular basis of DNA replication initiation is essential for gaining critical insights into the pathogenesis of relevant diseases and identifying potential targets for their effective therapeutics.

Replication initiation starts with origin selection by origin recognition complex (ORC). In yeast, ORC recognizes specific DNA sequences to define sites for pre-replication complex (pre-RC) assembly, where MCM2-7 complexes are loaded into a head-to-head double hexamer (DH) encircling duplex DNA. This process is known as origin licensing. However, in human cells, no consensus DNA motif has been identified for ORC binding. The team and others have shown that this is largely due to the absence of a species-specific alpha-helix in human ORC4. Instead, local chromatin contexts dictate ORC recruitment to promote origin licensing. The team’s recent study has also revealed an unexpected initial open structure (IOS) of origin DNA melted by human MCM2-7. This IOS is essential for DH formation across the human genome but is not present in yeast. While the general principles of origin licensing obtained from yeast may apply to human, the detailed mechanisms regulating each step of this process could differ significantly between the two species. Despite significant progress in the yeast system, the understanding of this process in human cells remains limited. The roles of diverse licensing factors are not well understood at a molecular level. Therefore, this project aims to employ cutting-edge structural and chemical biology techniques to investigate how replication origins are licensed in human cells. The team also plans to develop and screen potent inhibitors against licensing factors, paving the way for anti-cancer drug development. The successful completion of this project is expected to advance the understanding of the regulation of replication initiation in the human system, ultimately benefiting human health.

Project Reference No. : C7046-23GF
Project Title : Integrating genetic, transcriptomic, and clinical studies for SLE to achieve personalized treatment of the disease based on predisposition, early manifestation, and molecular and cellular signatures
Project Coordinator : Professor LAU W.C.S.
University : The University of Hong Kong

Layman Summary

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by autoantibody production which leads to multi-organ inflammation, and hence often associated with higher morbidity and mortality in patients. It predominantly affects female with a higher prevalence in Asians than Caucasians. SLE patients often exhibit diverse clinical manifestations with unpredictable disease course and treatment response. The underlying cause, though not completely clear, is attributed to genetics, immune cell dysregulation and environmental factors. This study aims to dissect SLE disease heterogeneity using advanced genetics and transcriptomics platforms to detect genes and expression signatures associated with patients’ clinical manifestations. The team will genetically analyze 2,800 patients; and follow immune cell irregularity during 2-year disease course of 400 patients. These clinical and bioinformatics data will be used to build a model that predicts disease development and treatment response, thereby paving the way to achieve personalized treatment for SLE patients.

Project Reference No. : C7052-23GF
Project Title : Optogenetic Neuromodulation of Brain-wide Resting-state fMRI Networks and Functions
Project Coordinator : Professor WU E.X.
University : The University of Hong Kong

Layman Summary

One current overarching challenge in neuroscience aims to establish an integrated understanding of brain circuits and networks, particularly the interactions of neural populations across various temporal and spatial scales that give rise to higher-order functions and behavior. However, current basic brain research perturbing and examining brain circuits/networks still rarely employs the easily and highly measurable rsfMRI networks. This situation is due in part to the incomplete understanding of how specific rsfMRI networks drive normal brain functions and behaviors, as well as the neural bases of functional connectivity, as measured by rsfMRI. The team posits that rsfMRI and behavioral measurements with direct optogenetic modulation of two integral and distinct brain networks (i.e., thalamo-cortical and hippocampal-cortical) will reveal novel functional insights and advance the knowledge of how specific brain networks drive functions and behaviors. This knowledge will be essential to guide the team’s efforts to use neuromodulation interventions to diagnose, treat, and cure neurological diseases that feature dysfunctions at the circuit and system levels. New therapeutic strategies will also emerge from an integrated understanding of brain circuits/networks and their fundamental properties.

Project Reference No. : C7074-23GF
Project Title : Medium Entropy Alloy (MEA)-based Cubic Shell Lattice Metamaterials for Lightweight, Impact Resistance Applications
Project Coordinator : Professor LU Y.
University : The University of Hong Kong

Layman Summary

Lattice metamaterials are topologically ordered, three-dimensional (3D) architectures composed of repeating unit cells. Because of their lightweight, high-strength and multifunctional properties, mechanical metamaterials exhibit promising potential in engineering applications. In this interinstitutional collaborative research project, the concept of Design for AM (DfAM) will be developed to integrate the geometry, material, manufacture, and performance to explore the full potential of shell lattices capacities. The team will design a new family of cubic shell lattices and their enhancement through ribbing and corrugation. Advanced AM fabrication processes will be developed and employed for shell lattices from micro- to macro-scales, including the micro -selective laser melting (μSLM) method, along with process-driven medium entropy alloy (MEA) design strategy, targeting for their lightweight and high impact resistant. Multiscale in situ mechanical characterization methods will develop a comprehensive understanding on the multiscale MEA shell lattice metamaterials, including structural integrity and interface quality, microstructure, dynamical mechanical mechanisms. This project will contribute significantly to address both fundamental scientific and engineering problems of high-performance shell lattice metamaterials through this international multidisciplinary collaboration, to motivate the research community and industry to explore the new opportunities offered by DfAM and mechanical metamaterial concepts.

Project Reference No. : C7075-23WF
Project Title : University graduates' transitions to post-covid-19 workplaces: The impacts of the pandemic and adjusting to "new normal" work orders
Project Coordinator : Professor ZAYTS O.A.
University : The University of Hong Kong

Layman Summary

This project builds on the team’s previous work that examined the social, educational, and health impacts of the COVID-19 pandemic on university graduates’ transitions to the workforce in Hong Kong. This new project extends the previous project to post-COVID workplaces. Graduates are a vulnerable demographic group in the workforce post-pandemic, they are facing worsened mental health, changed workplace practices, and different employment opportunities. The project also examines new career opportunities in the Greater Bay Area (GBA). It will investigate how graduates could be best supported in different career choices across the GBA. To develop effective strategies to support graduates’ transitions, this project will draw on quantitative and qualitative interview and survey data from graduates, employers and university staff about the impacts of COVID-19 and its aftermath on graduates’ experiences. The project will combine insights from sociolinguistics, psychology, and other relevant disciplines. The project will also engage participants at every stage of the project, from design to implementation and dissemination. It will continue to develop Graduate Mindmap (graduatemindmap.com), a digital hub with support resources, such as core skills and competencies for post-COVID-19 workplaces and mental health resources.

Project Reference No. : C7096-23GF
Project Title : Programmable RNA Pseudouridylation for Premature Termination Codon Suppression as Gene Therapy for Cardiac Laminopathy
Project Coordinator : Professor TSE H.F.
University : The University of Hong Kong

Layman Summary

This study aims to develop a new therapy for treating a specific type of heart disease known as cardiac laminopathy, which is caused by a genetic mutation in the LMNA gene. Patients with cardiac laminopathy experience progressive heart failure and conduction disorders. Currently, there is no targeted therapy available for this disease.

This collaborative project team, consisting of Prof. Hung-Fat Tse and Prof. Chun-Ka Wong from the University of Hong Kong, and Prof. Chengqi Yi from Peking University, has designed a treatment method called RESTART. This gene therapy enables the body to produce corrected lamin protein by bypassing premature stop codons.

To study and model the disease, as well as test the safety and effectiveness of the RESTART therapy, the project team will utilize stem cells from patients with cardiac laminiopathy and mouse models. If successful, this method could be applied to treat other inherited diseases caused by similar genetic mutations. This project has the potential to pave the way for new treatment options for heart disease and other genetic conditions.

Project Reference No. : C7098-23GF
Project Title : Early Childhood Development in Hong Kong: A Longitudinal Study
Project Coordinator : Professor WEN Ming
University : The University of Hong Kong

Layman Summary

The sustained benefits of investing in early childhood development are well documented. Effective early childhood policies and programmes are central to nurturing a competitive, creative and healthy future workforce in Hong Kong as the city strives to become a knowledge-based economy and a hub for innovation. The government has stepped up its support for improving early childhood education in recent years, starting with the implementation of the kindergarten education scheme in the 2017/18 school year. To date, however, there is no comprehensive and representative large-scale database tracking the development of preschoolers in Hong Kong that can be used to guide early childhood research, policies, and programmes.

This project aims to develop and implement the first-ever city-representative early childhood longitudinal study in Hong Kong—a multidisciplinary endeavour integrating the expertise and experience of a team of scholars in complementary fields from several institutions to provide a comprehensive picture of children’s early development and the factors that influence it. The team will collaborate to (a) design and implement the Hong Kong Early Childhood Development Longitudinal Study (HK-ECDLS), which will collect information on the development of approximately 3,000 preschoolers (starting at age 3) from 100 kindergartens for 3 consecutive years, focusing on their holistic development, emotional and behavioural status, and physical health; (b) describe the children’s home environments, encompassing aspects such as the structure of their households, the dynamics of family relationships, the overall well-being of the family, the language environment within the family, as well as the learning opportunities and physical conditions; (c) analyse the effects of home environments on various developmental outcomes; and (d) design and execute an intervention study specifically targeting children from low-income families in Year 2.

In today’s era of evidence-based policymaking, this comprehensive longitudinal study focusing on preschoolers marks a significant milestone in the establishment of a robust database containing high-quality evidence on early childhood development in Hong Kong. By tracking children’s developmental trajectories during a critical life stage, this study will provide researchers and policymakers with a valuable opportunity to identify the contextual factors that contribute to unfavourable early developmental outcomes. Furthermore, it will also propose potential strategies for prevention and early intervention. The data and insights derived from this study will serve as a foundation for informing policies pertaining to child health and well-being, early childhood education, family dynamics, social welfare, and various other related areas.

Project Reference No. : C7101-23WF
Project Title : Attention-deficit hyperactive disorder Care in Children: Organized Research Data (ACCORD) Platform
Project Coordinator : Professor WONG I.C.K.
University : The University of Hong Kong

Layman Summary

Attention-Deficit/Hyperactivity Disorder (ADHD) is a condition that affects the brain's development and is a significant global public health issue. While there are treatments available for ADHD, it is still hard to provide effective care to everyone who needs it. If left untreated, ADHD can lead to serious problems, including difficulties in school, substance abuse, and even a higher chance of thinking about suicide or being mistreated as a child. To address these issues, the team has devised the ACCORD Programme, which aims to achieve the following objectives: (1) to assess the safety of ADHD medications in relation to adverse outcomes, namely suicide attempts and child abuse, using data extracted from electronic health records in Hong Kong, Taiwan, South Korea, New Zealand, the United Kingdom, and the United States with the team’s existing international big-data platform; (2) to study the economic costs associated with ADHD management throughout a person's life in Hong Kong; (3) to explore the feasibility of conducting a pragmatic randomised trial in Hong Kong to compare the clinical and cost-effectiveness of various ADHD medications in treatment-naive patients by conducting a qualitative study and a pilot study respectively. In general, this project aims to improve support for individuals with ADHD and their families worldwide, giving them more help at school, in their social life, and with their health.

CRF 2023/24 Collaborative Research Equipment Grant (CREG) Proposals

Project Reference No. : C1013-23EF
Project Title : High spatiotemporal resolution scanning electron microscope facility for multimodal dynamic imaging in materials science and physics
Project Coordinator : Professor ZHONG Xiaoyan
University : City University of Hong Kong

Layman Summary

A high spatiotemporal resolution scanning electron microscope (SEM) facility provides the various modes of imaging and spectroscopy to visualize dynamic responses of materials under external stimuli and understand the physical origin of dynamic functionality of materials in working conditions, which is crucial to optimize device performances in a wide range of industries and sectors including semiconductor, energy, automotive, chemicals, environment, and information technology. In this project, the team would like to set up the first high spatiotemporal resolution SEM facility in Hong Kong for time-resolved nanoscale imaging of carrier, exciton, and surface spin textures in multidisciplinary research fields, while the morphological, chemical, crystallographic, and structural information can be also accessed from the very same sample region by static SEM imaging and spectroscopy. The correlative information provides unique insight into the correlation between the physical origin and dynamic behavior of materials, which is in turn a prerequisite to improve device functionality.

Project Reference No. : C4049-23EF
Project Title : A Long-read and High Throughput DNA Sequencing Platform for Studying Genomic Landscapes and Metagenomics Involvement in Human Diseases
Project Coordinator : Professor TSUI Kwok-wing
University : The Chinese University of Hong Kong

Layman Summary

Over the last two decades, high-throughput DNA sequencing has revolutionized various aspects of biological and biomedical sciences. Currently, the most popular long-read sequencing (LRS) systems are provided by two biotechnology companies, Pacific Biosciences (PacBio) and Oxford Nanopore, each with their own competitive advantages. In late 2022, PacBio announced the launch of the PacBio Revio System, which will increase sequencing throughput by 15 times and lower sequencing costs by more than two folds. Incorporating the two powerful sequencing systems from PacBio and Oxford Nanopore in a timely manner will be an essential step in maintaining the competence of local universities and facilitating the continuous growth of biomedical research and the biotechnology industry in Hong Kong. In this Collaborative Research Fund Equipment Grant application, a multi-institutional team of capable local researchers will lead collaborative efforts to establish a shared platform catering to these two LRS systems. This shared platform will be open to all researchers in local institutions interested in using LRS for their research.

Project Reference No. : C7063-23EF
Project Title : Establishment of a high-speed fluorescence image-enabled cell sorter system for multidisciplinary life science research
Project Coordinator : Professor MA S.K.Y.
University : The University of Hong Kong

Layman Summary

Microscopy and fluorescence-activated cell sorting (FACS) are two of the most important tools used for single-cell phenotyping in basic and biomedical research. Microscopy provides high-resolution snapshots of cell morphology and the inner workings of cells, while FACS isolates thousands of cells per second using simple parameters such as cell size and the intensity of fluorescent protein labels. The information that could be acquired by FACS from each cell has greatly increased in the recent decade, allowing high-dimensional sorting using up to 40 cellular properties and sorting speeds of over 10,000 cells per second. Nonetheless, FACS is similar to looking at an artwork through tinted glass, as only simple morphological parameters can be used for separation. It is now known that a large part of cellular phenotypes is not associated with changes in protein abundance, but rather with spatial phenotypic changes, such as changes in the intracellular localization of proteins. The ability to sort and enrich cells with spatial phenotypes of interest will provide a deeper understanding of the causes and consequences of cellular morphology and protein localization and holds promise for identifying novel biomarkers and drug targets. Recent technologies are now combining imaging with cell sorting to enable the fast isolation of cells with microscopic phenotypes of interest, thereby bridging a long-standing gap in life sciences.

The speed of cell sorting, in combination with super-fast imaging, enables screens that require an image-based readout (e.g., nuclear translocation and asymmetric partitioning). This will further permit a large range of other functional assays of rare cell populations with distinct morphological or spatial characteristics. High-throughput fluorescence image-based cell sorting is a real game-changer in the field of biomedical research, and it is exciting to see the many possibilities that can be studied in cellular systems in a wide range of fields. In this project, active researchers from various universities across Hong Kong were teamed up. Together, the team aims to use the high-throughput image-based cell sorting system to investigate a wide range of research topics, including cancer immunology and cancer stem cell biology, molecular trafficking and nanoparticles, stem cell and regenerative medicine biology, CRISPR-enabled biology, synthetic biology, biomedical engineering, and cardiovascular research, studied across various cell models and tissue types. The establishment of this spectral flow cytometer sorter with a sort-capable image analysis system will be the first of its kind in Hong Kong and will greatly strengthen the research competitiveness of this region and enable researchers to explore the next frontier of biomedical sciences.

CRF 2023/24 Young Collaborative Research Grant (YCRG) Proposals

Project Reference No. : C1002-23Y
Project Title : Electrification and Decarbonization: Multi-port Wireless Dock and Charge for Waterborne Transportation
Project Coordinator : Professor JIANG Chaoqiang
University : City University of Hong Kong

Layman Summary

It is reported that one heavy fuel oil cruise ship emits as much carbon dioxide (CO2) as 70,000 cars, as much nitrogen oxide as 2 million cars, and as much fine dust and carcinogenic particles as 2.5 million cars. As a result, the electric ship is attracting significant attention due to the energy crisis and environmental issues. With regard to zero-emission and decarbonization, the fully battery-based type is much more demanded by short-distance ferries, tugs, and inland ships, especially in Hong Kong. Due to the tight schedule and the short docking time, high-power recharging is required. However, cable charging will cause the problems of long labor operation time, leakage current and corrosion in a saline environment, and electric shock risk then not safe enough. Thus, the multi-port wireless dock-and-charge system proposed in this project can well solve these problems and make the system more reliable and more intelligent.

Project Reference No. : C1003-23Y
Project Title : Electrochemical Lithium Intercalation & Exfoliation for Mass Production of 2D Transition Metal Dichalcogenides and Their Application for Water Purification
Project Coordinator : Professor ZENG Zhiyuan
University : City University of Hong Kong

Layman Summary

Hong Kong lacks natural lakes, rivers, and underground water resources and hence mostly imports its freshwater. This water shortage problem could potentially be solved by filtering seawater and waste water, using membrane separation technology to remove salt and other contaminants. The membrane material is key to filtering efficiency, water safety, cost-effectiveness, and sustainability. However, current commercial membranes are generally expensive and have poor stability and recyclability. Therefore, low-cost and recyclable water purification membranes are urgently needed. This proposed project aims to develop a reusable nanolaminate membrane based on transition metal dichalcogenides, which will have applications not only in personal portable or indoor water purifiers, but also in industrial water purification plants, providing more choice for wastewater treatment in Hong Kong. The membranes will equip Hong Kong and other regions with a self-sufficient means for seawater and wastewater filtration as a sustainable source of potable water.

Project Reference No. : C2001-23Y
Project Title : Perovskite opto-ionics for in-sensor computing
Project Coordinator : Professor ZHOU Yuanyuan
University : The Hong Kong University of Science and Technology

Layman Summary

In-sensor computing has rapidly emerged as a promising avenue towards building next-generation intelligent systems that can find enormous uses in the sensor nodes in the future IoTs. But the fundamental device unit – (opto-)electronic memristors, being developed to date, is still limited in controllability and biomimicry. This collaborative project proposes to explore emerging (opto-)ionics in metal halide perovskites, and to exploit the uniqueness and versatility of these materials to develop memristors with low device-to-device and cycle-to-cycle variations for more reliable in-sensor computing.

Ordinary memristors face significant challenges for being applied to in-sensor computing due to programming stochasticity, unstable response to electrical stimulation, and insufficient response to light. These issues are rooted in the fact that memristive layers are metal oxides and chalcogenides in low-crystallinity states. They respond to electrical simulation with redox reactions and ion migrations in electrolytes with random or disorder structures, resulting in limited controllability and biomimicry. Perovskites are a new class of ionic semiconductors which exhibit a high-level coupling of photonic, electronic, and ionic processes, potentially delivering multilevel biomimetic behavior involving the interplay with light. Furthermore, the versatility of the perovskite family has set a foundation for achieving a wide range of controllable properties, which can be further exploited by forming perovskite composites between two or more distinct compositions to create new bulk properties. This unleashes the high potential of perovskite memristors.

By assembling all key expertise of areas, this project aims to establish an interdisciplinary research program centered at the frontier science and engineering of perovskite memristors. A bottom-up approach will be adopted to reveal new sciences across the atomic to device/system scales. Highly interrelated tasks will be performed with the following three objectives: (i) fundamental investigation of emerging (opto-)ionics underpinning the memristive behavior in perovskites; (ii) nano-engineering of perovskite film structures for achieving high-performance memristors; (iii) development of a perovskite-based in-sensor reservoir computer to enable edge learning. The PC and co-PIs will collaborate closely in a close loop towards the attainment of project objectives.

The outcome of this project will generate impacts not only for developing a new prototypical in-sensor computing technology that finds vital use in edge scenarios in the practical world, but more importantly for creating an uncharted territory of materials and device sciences with overarching impacts on the progress of various energy and electronic devices.

Project Reference No. : C2003-23Y
Project Title : Federated Graph Management and Querying: Subgraphs, Keywords, and Privacy
Project Coordinator : Dr. HUANG Xin
University : Hong Kong Baptist University

Layman Summary

Data is crucial for decision-making across various domains, but it faces challenges such as explosive growth, complicated integration, and privacy sensitivity. However, isolated data repositories owned by independent organizations are incompatible with each other, which prevents collaborative data analytics. On the other hand, many entities perform actions with others, which are usually modeled as graphs, such as social networks, financial networks, and biomedical networks. This project targets the problem of federated graph data analytics, which performs graph query processing over multiple parties of graph data whose owners agree to collaboratively tackle graph data silos under a privacy protection mechanism. It aims to develop efficient federated algorithms for subgraph search and counting while enhancing them through differential privacy protection. This collaborative project is expected to generate new techniques and theories for federated graph analytics systems, which can benefit the industry and society in graph databases and analytics.

Project Reference No. : C2004-23Y
Project Title : Improved Metabolic Network Reconstruction and Metabolite Profiles Prediction using Complete and Strain-Resolved Microbial Genomes
Project Coordinator : Dr. ZHANG Lu
University : Hong Kong Baptist University

Layman Summary

The human gut microbiome, a community with the highest microbial density in the body, is composed of thousands of microbial species mixed in varying proportions. Microbial genomes play a crucial role in producing microbial metabolites, as they contain all the genetic information necessary for synthesizing them. This project aims to advance the understanding of the human gut microbiome, particularly how its metabolites influence health and diseases. The project will utilize cutting-edge long-read metagenomic sequencing technology to generate complete and strain-resolved metagenome-assembled genomes. This strategy addresses the limitations of short-read sequencing, which often results in fragmented and unphased microbial genomes. The high-quality genomes enable us to develop supplicated deep genomic language models for microbial taxonomic annotation and gene function prediction based on a huge number of publicly available microbial genomes. In addition, an open-source pipeline will be established to reconstruct metabolic networks and predict metabolite profiles. These computational models will be evaluated in a cross-sectional study of diarrhea-predominant irritable bowel syndrome (IBS-D). Through an in-depth examination of metagenomic and metabolomic data, the project team intends to discover new biomarkers linked to IBS-D. The team believes the success of this project has the potential to establish a reliable framework to interpret the elusive world of microbial dark matter. This could also pave the way for designing personalized treatment approaches for IBS-D patients.

Project Reference No. : C4001-23Y
Project Title : A Predictive Personalization Approach to Enhance Foreign Language Learning and Teaching
Project Coordinator : Professor FENG Gangyi
University : The Chinese University of Hong Kong

Layman Summary

Language learning is critical in today's interconnected world, offering many advantages across professional, personal, and societal spheres. However, learning a new language as an adult presents substantial challenges, with varying learning difficulties and outcomes across learners. Contemporary foreign language training programs often fail to take into account the unique needs and profiles of learners, resulting in suboptimal learning processes and outcomes. This project aims to develop an outcome-prediction-guided personalized approach to enhance foreign language learning and teaching. The team constructs and refines prediction models capable of forecasting individual learners’ future classroom-based foreign language learning performances. The team designs a customized language remediation protocol based on learners’ predictive outcomes and profiles, then assess its effectiveness on students who are identified by the model as potentially having difficulty in foreign language learning. This project plans to collect multi-site longitudinal learner data from Hong Kong and the Mainland, incorporating more heterogeneous samples to improve and validate the team’s existing prediction models, thereby enhancing model prediction and generalizability to unseen samples. The prediction models strive to identify learners who may face difficulties in foreign language learning and reveal their prospective profiles for designing effective personalized instruction. The success of personalized instruction depends on identifying the specific language components and processes that individual learners struggle with and providing timely, effective remediation based on their cognitive, motivational, and language learning profiles. The team aims to create a prediction-based personalized language training approach, which has the potential to improve language learning outcomes, understand the driving factors of learning success, influence educational policies, and find practical applications in classrooms.

Project Reference No. : C4003-23Y
Project Title : Uncovering the Whole Spectrum of Genetic Variations Underlying Schizophrenia and its Prognosis: A Whole-genome Sequencing (WGS) Study and Prediction Modeling with Machine Learning Approaches
Project Coordinator : Dr. SO Hon-Cheong
University : The Chinese University of Hong Kong

Layman Summary

Schizophrenia and psychotic disorders affect 1-3% of the population worldwide, and is a leading cause of disability. While the heritability is high, its causes and genetic basis remains poorly understood, and novel treatments are lacking. Genomic research is among the most promising approaches for uncovering the pathophysiology of schizophrenia and psychotic disorders. It may also translate into clinical benefits, for example in risk prediction, disease subtyping and drug discovery.

This study aims to look at all the genetic variations including rare and common variants that may increase the risk of schizophrenia in Chinese people. The team will use whole genome sequencing which looks at a person's entire DNA code. The team hopes to find genetic variations that can help predict who may be at higher risk of schizophrenia, in order to help with diagnosis and to enable more timely treatment.

The study involves looking at genetic profiles of people with schizophrenia, depression and bipolar disorder to see what is similar or different across these conditions. The team members will develop new statistical methods to group patients based on genetic and clinical features over time to better understand subtypes of these disorders. The study will also involve building machine learning models that use both clinical information and genetic data to predict outcomes of schizophrenia.

In summary, this study uses whole genome sequencing to further the understanding of the genetics of schizophrenia and related psychiatric disorders in Chinese populations to improve diagnosis, treatment and risk prediction.

Project Reference No. : C4004-23Y
Project Title : Intercepting the Mitochondrial-to-Nuclear Communication for Treatment of Vascular Inflammation and Atherosclerosis
Project Coordinator : Professor TIAN Xiaoyu
University : The Chinese University of Hong Kong

Layman Summary

Atherosclerosis, is the major underlying cause of cardiovascular disease (CVD), the leading cause of death worldwide. Atherosclerotic CVD management includes lipid-lowering drugs, cardiovascular risk factor management, and some emerging anti-inflammatory therapies. Currently, the non-invasive treatment options particularly for reverting plaque growth, is still very limited. During atherosclerosis, various stress signals, such as hyperlipidemia and inflammatory cytokines, trigger mitochondrial dysfunction. Emerging evidence has revealed that mitochondrial dysfunction specifically triggers the integrated stress response in the nucleus through mitochondrial proteases. The persistent over-activation of this type of mitochondrial-to-nuclear communication is likely to be harmful which might be pathological during atherosclerosis, which is unknown at the moment.

In this project, the team will first use transgenic mice with selective ablation, and endothelial cells with genome editing, to interrogate the key molecules of the mito-nuclear communication pathway, for investigating the cellular and molecular mechanisms during atherosclerosis and vascular inflammation. Next, the team will apply an atherosclerotic plaque-targeting nanoparticle to deliver therapeutic oligonucleotides to inhibit the key molecules involved in mito-nuclear communication, and examine their bio-distribution, intracellular processing, and therapeutic efficacy, in order to demonstrate the feasibility of this approach targeting mito-nuclear signaling pathway to treat atherosclerosis. In addition, the team will also examine the effect of phenolic metabolites, which are derived from dietary polyphenols, to target the mito-nuclear signaling for reverting atherosclerosis in plaque-bearing mice and further study the cellular and molecular mechanisms of cardiovascular-protective phenolic metabolites. With this project, the team will emphasize the role and function mitochondria-to-nuclear communication and signal transduction in vascular cells during atherosclerosis. These new targets will provide possibilities for therapeutic developments to not only atherosclerotic cardiovascular disease, but also aging and other chronic diseases.

Project Reference No. : C5001-23Y
Project Title : The Role of the Human Frontopolar Cortex in Complex Decision Making: Neural Network Modelling, Aging, and Enhancement
Project Coordinator : Dr. CHAU Ka-hung Bolton
University : The Hong Kong Polytechnic University

Layman Summary

Why are we capable of making highly complex decisions? What features in the human brain enable complex decision making? Why does this capacity decline as we age, and are there ways to forestall this decline? Despite rapid advancements in cognitive neuroscience, these questions remain unresolved. One reason is that most classical studies typically focused on simple decisions that were usually straightforward to quantify, parameterize, and control. However, this focus overlooks the human brain’s strength in complex decision making.

This proposal tackles some grand challenges in cognitive neuroscience. These include investigating the strength of human in making complex decisions, reverse engineering the underlying brain mechanisms, investigating age-related decline in these mechanisms, and enhancing these mechanisms using brain stimulation. To achieve these, a series of studies will be conducted by employing multiple state-of-the-art techniques in cognitive neuroscience, including brain imaging, brain stimulation, and artificial intelligence.

In addition to theoretical contributions, this project will provide insights into the challenges the aging population faces. This project will also contribute to the development of brain-based approaches to help understand and alleviate these decision-making challenges.

Project Reference No. : C5002-23Y
Project Title : Sensing in 6G Cellular Networks
Project Coordinator : Dr. LIU Liang
University : The Hong Kong Polytechnic University

Layman Summary

The sixth-generation (6G) cellular network will be commercialized in 2030s. Compared to the previous generations which solely provided communication services, an unprecedentedly new feature in 6G network will be the capacity to perform integrated sensing and communication (ISAC). Specifically, base stations in 6G network will emit signals not only to convey information to communication users, but also to sense the environment with ultra-high range/angle resolutions, thanks to the wide bandwidth at mmWave band and the large antenna array brought by massive multiple-input multiple-output (MIMO). Such 6G-enabled ISAC technologies will empower a wide range of applications such as smart transportation and smart factory that require both precise sensing and high-quality communication services.

Since the communication function in cellular network is already mature, this project will focus on the cutting-edge area of 6G-based sensing to pave the way for future 6G ISAC network. First, the team will characterize the fundamental performance limits of 6G-based sensing according to the unique properties of 6G communication signals. Second, the team will propose practical signal processing techniques to image static targets and track moving targets via 6G signals. Specifically, the team will consider two cases: (i) a single base station emits signals for independent sensing; and (ii) multiple base stations collaboratively perform networked sensing, where powerful data fusion algorithms will be developed to optimally utilize the sensing data captured by various base stations. Third, the team will build prototypes to verify the feasibility and effectiveness of the 6G-based sensing techniques. The team’s ultimate goal is to transform the world’s largest wireless communication network into the world’s largest sensing network, which is able to realize radar functionalities using 6G signals and hardware.

Project Reference No. : C6001-23Y
Project Title : Living Active Protein Materials for Axon Regeneration
Project Coordinator : Professor SUN Fei
University : The Hong Kong University of Science and Technology

Layman Summary

When the central nervous system (CNS) is injured, it is difficult for axons to regenerate. Traditional methods for promoting axon growth involve designing materials that act as scaffolds to deliver biomolecules and cells that can promote growth. However, these materials are not very effective because the CNS is a complex dynamic system that is out of equilibrium. The team proposes a synthetic biology approach that involves creating living active protein materials with out-of-equilibrium thermodynamics to promote CNS axon regeneration. These materials will be made up of various molecular and cellular components that will work together to promote axon growth. This project involves researchers from different areas and has the potential to transform the field and lead to the development of new treatments for CNS injuries.

Project Reference No. : C7001-23Y
Project Title : Accessing Molecular Complexity and Functionality from Active Methylene Compounds via Asymmetric Catalysis
Project Coordinator : Professor HUANG Z.
University : The University of Hong Kong

Layman Summary

Organic synthesis often recruits active methylene compounds (AMCs) to build complex structures, as these chemical feedstocks are produced in more than ten thousand tons per year. While synthetic routes originating from these compounds have found wide application in manufacturing fine chemicals such as vitamins, agrochemicals, and cosmetics, the majority of these terminal products are structurally flat and non-enantioenriched. Here, the team aims to unleash the potential of AMCs in preparing scaffolds of higher three-dimensionality stereoselectively via asymmetric catalysis. With synergistic collaboration across Hong Kong, diverse transition metal, organic, and heterogeneous catalysts will be designed, examined, and optimized to enable asymmetric transformation of the ester, acid, and nitrile motifs in AMCs. The success of this project is expected to provide expeditious and modular approaches towards functionalized molecules with significance across academia research and industrial manufacturing, such as chiral amines, nitriles, and amino acids.

Project Reference No. : C7002-23Y
Project Title : Development of non-nucleoside inhibitors of SARS-CoV-2 RdRp-nsp7-nsp8 replication complex
Project Coordinator : Professor YUAN S.
University : The University of Hong Kong

Layman Summary

The RNA-dependent RNA polymerase (RdRp) is one of the key druggable targets for coronaviruses (CoVs) due to its essential role in viral replication, high degree of sequence and structural conservation and the lack of homologues in human cells. Possible development approaches include nucleoside and non-nucleoside inhibitors (NNI). Due to the high binding specificity, structural diversity and metabolic stability of NNIs, the team believes the development of such group of inhibitors has the potential to produce more potent and effective antiviral agents. This proposal balances a hit compound asset ML-18, with the highly innovative fully functionalized fragment (FFF) screen approach to identify novel lead NNIs. Of note, antivirals derived from current proposal employ distinct mode of action from those clinically prescribed, which complement existing anti-COVID strategy (remdesivir, molnupiravir) and may constitute a ‘cocktail’ regimen with higher efficacy while less concern of resistance and side effect.

Project Reference No. : C7005-23Y
Project Title : Fundamental studies of organic mixed conductors for body-centric wearable applications
Project Coordinator : Professor ZHANG S.
University : The University of Hong Kong

Layman Summary

Wearable research has reached a new level due to its potential to enable the transition from hospital-centric to human-centric healthcare. Electrodes are key components of wearable biosensors that determine sensing quality at the bottom. However, when placed closer to the human body, significant mismatches arise: the human body is organic, soft, and conducts ions, whereas electrodes are inorganic, hard, and conduct electrons. These mismatches lead to poor signal quality at the body-electrode interface.

Organic mixed ion-electron conductors (OMIECs) are emerging as a new class of materials that can enable seamless signal communication between research tools with biological tissues. However, research in this field is still in its infancy, with much unknown knowledge to be explored. For example, their synthesis, development, and fabrication remain challenging due to the difficulty of combining inherently and mutually exclusive properties together, such as high mixed conductivity, stretchability, and viscoelasticity. Besides, the applications at the biological interface require these materials to work in harsh environments with excellent biocompatibility with tissues. Systematic research is needed here to provide feasible materials solutions.

This collaborative proposal aims to bridge the knowledge gap between OMIECs materials and practical wearable applications. The following aims are set. Aim 1: To synthesize a series of organic mixed ion-electron conductors (OMIECs) and investigate the dependency of ionic, electronic, and ion-electron coupling properties on material structures and processing conditions; Aim 2: To investigate the mechanical and fatigue properties of the OMIECs, revealing the effect of strain on ionic, electronic, and ion-electron coupling properties; Aim 3: To investigate the dependency of device performance on OMIEC properties and to optimize the performance for practical wearable applications.

The proposed project will generate new knowledge in materials science. The deliverables will contribute to the advancement of high-performance electrode technologies for body-centric wearable healthcare applications, therefore benefiting various sectors of society.