Project Reference No. : C1002-24WF
Project Title : Long-term Rescuing Effects on Remembering and Memory Retention in Alzheimer’s Disease Mice with Self-synthesised CCK-B Receptor Agonists
Project Coordinator : Professor HE Jufang
University : City University of Hong Kong
Layman Summary
Alzheimer’s disease (AD) is the major cause of dementia. Anterograde amnesia, an inability to form new memories, is one major symptom of dementia. The entorhinal cortex (Ent) is the earliest brain atrophy site in AD patients. The team discovered that the Ent enables memory encoding in the neocortex by releasing cholecystokinin (CCK). CCK knockout mice show similar anterograde amnesia to AD mice, which are rescued by systemic administration of the CCK-B receptor agonist. In the team’s last CRF study, C1043-21G, the team synthesized and evaluated a series of new CCK-B receptor agonists and identified a leading compound as the drug candidate to treat dementia. In this proposed project, the team will thoroughly clarify the rescuing effects of the drug candidate on AD. The team will also investigate whether long-term treatment decelerates AD progression and whether it exhibits any side effects. The team aims to submit the investigational new drug application for the drug candidate and prepare it for clinical trials. The team looks forward to identifying a new AD drug to benefit dementia patients.
Project Reference No. : C1008-24GF
Project Title : Cell intrinsic and extrinsic effects of astrocytes lipid metabolism in spinal muscular atrophy
Project Coordinator : Professor Jessica Aijia LIU
University : City University of Hong Kong
Layman Summary
Spinal muscular atrophy (SMA) is a leading inherited cause of infant and childhood mortality, caused by mutations in survival motor neuron(SMN) 1. SMA leads to progressive degeneration of spinal motoneurons, resulting in decreased motor functions and early death. While there have been significant advancements with three licensed SMA therapies, SMN protein replacement is not curative, and many patients show poor responses, indicating the incomplete understanding of the disease mechanisms. Recent findings suggest SMA is a systemic disorder involving various extrinsic influence, such as astrocytes, contributing to MN degeneration. Astrocytes play crucial roles in the central nervous system, impacting synaptic function, axonal growth, and neuronal activity. Despite documented astrocyte abnormalities in SMA, their exact role and molecular mechanisms remain unclear.
The collaborative team aims to utilize advanced multi-omics techniques with clinical resources from SMA patients to investigate patient-specific neuromuscular organoids and astrocytes across different SMA types. By exploring cell-autonomous and non-cell autonomous mechanisms of astrocytes in SMA, the team aims to uncover their contributions to disease progression. Furthermore, using SMA mouse models, the team will assess the therapeutic potential of rectifying signaling disruptions in SMA astrocytes. This research seeks to unveil new signaling pathways in SMA pathogenesis, offering insights for enhanced therapeutics and care tailored to SMA patients in the future.
Project Reference No. : C1013-24GF
Project Title : Versatile omnidirectional mobile robots with diverse bioinspired motion modes: from theory to harsh water applications
Project Coordinator : Professor Xingjian JING
University : City University of Hong Kong
Layman Summary
Regularly inspecting underwater structures for health and damage, such as erosion, deformation, and cracks, is essential for public safety and structural integrity. However, the underwater environment often poses harsh or dangerous conditions for professional personnel. Advanced and deployable robotic systems offer potential solutions. Yet, most existing underwater robots, typically designed for deep-sea exploration, struggle with tasks in such challenging environments due to dynamic water flows, organic or plastic debris, sand, seaweed, confined spaces, and unpredictable obstacles. This groundbreaking project, fueled by a dynamic team of scholars from mechanical engineering, control, computer science, sensors, and signals, aims to create next-generation bio-inspired underwater robotic systems. These cutting-edge robots will feature innovative propulsion mechanisms and advanced modeling/control methods, ultimately delivering effective solutions for operations in the most demanding underwater environments.
Project Reference No. : C1017-24GF
Project Title : A Biorefining Approach to Converting Organic Solid Wastes into Multiple High Value-added Bioproducts
Project Coordinator : Professor Charles Chunbao XU
University : City University of Hong Kong
Layman Summary
According to Hong Kong Environmental Protection Department, approx. 1.2 million ton food waste (FW) and 80 kilo ton yard waste (YW), are generated annually in Hong Kong. However, the majority of these resources are disposed of as wastes at landfills or by incineration, causing not only fast-depleted landfill spaces, but also secondary pollutions. Therefore, it is urgent to develop new technologies for the harmless reduction, and resourceful treatment of organic wastes for bioenergy and high value-added bioproducts, in order to achieve the goal of zero landfill by 2035 & carbon neutrality by 2050 in Hong Kong. This proposed research project will develop an innovative biorefinery approach to converting FW & YW into multiple high value-added bioproducts, namely biobased nonisocyanate polyurethane resin, bio-carbon materials, green H2/CH4, and green chemicals. Thus, this project will contribute to the diversion of wastes from landfills, and reduction of greenhouse gases.
Project Reference No. : C1043-24GF
Project Title : Trustworthy Large Language Models: A Multifaceted Strategy for Truthfulness, Fairness, and Privacy Preservation
Project Coordinator : Professor ZHAO Xiangyu
University : City University of Hong Kong
Layman Summary
Large Language Models (LLMs) have become a key technology, transforming online services in areas like e-commerce, healthcare, and finance. The global market for LLMs is growing rapidly, but these models still face challenges that affect their reliability and trustworthiness. These include issues with providing false or biased information, treating certain groups unfairly, and potential privacy risks when handling sensitive data. This project aims to tackle these problems by developing new methods to improve the truthfulness, fairness, and privacy of LLMs. Researchers plan to create tools that help LLMs generate more accurate and unbiased responses, avoid harmful biases, and protect user privacy. The goal is to integrate these improvements into one unified system, creating LLMs that are more reliable and ethically sound. The results could revolutionize AI applications, boosting trust and making these models safer for use in critical sectors.
Project Reference No. : C1049-24GF
Project Title : Machine Learning for Reliable and Efficient Power System Operation Considering Renewable and Load Uncertainty
Project Coordinator : Professor Minghua Chen
University : City University of Hong Kong
Layman Summary
This project aims to improve how power grids incorporate renewable energy sources using machine learning as a principled approach. As more solar and wind power gets added to electrical grids, three key challenges emerge:
First, it's difficult to predict renewable generation and electricity load. The project will develop better forecasting tools using deep learning.
Second, grid operators must quickly decide how to dispatch controllable generations across the grid. Current methods are slow and complicated. The researchers plan to use neural network schemes to speed up these decisions.
Third, renewable generation can be erratic, which makes it harder to maintain stable grid frequency and voltage magnitude for renewable-rich grids. The team will employ reinforcement learning to discover stronger controllers to better stabilize the grid.
The solutions will be evaluated together with industry partners, including a research center of State Grid of China, using real-world data to verify their effectiveness.
Project Reference No. : C1073-24GF
Project Title : Advancing Shared Autonomy: Innovating Collaborative and Sensory Interfaces for Future Teleoperation Systems
Project Coordinator : Professor Xiaowei Luo
University : City University of Hong Kong
Layman Summary
The construction industry faces persistent challenges, including labor shortages and poor safety performance, underscoring the urgent need for technological advancements. Teleoperation, which allows machinery to be operated from a distance, offers the potential to revolutionize the industry by keeping workers away from hazardous environments. However, its adoption in construction faces significant challenges. This research project aims to address these challenges and develop next-generation teleoperation systems tailored for smart construction. Key objectives include designing a sensory transfer interface for efficient data transmission, developing a digital twin framework for intelligent perception and control, enhancing safety through smart shared control mechanism for tower crane teleoperation, investigating human-machine trust (HMT) factors to ensure effective collaboration between operators and machines, and developing a training system to empower non-expert operators with the skills for teleoperation. The project will collaborate with global partners, leveraging Hong Kong as a hub to promote the adoption of teleoperation across the construction industry. The successful implementation of such a teleoperation system will bring substantial benefits to worker well-being and the overall advancement of the construction sector.
Project Reference No. : C4003-24GF
Project Title : Prenatal Music Training to Enhance Postnatal Neurocognitive Development
Project Coordinator : Professor WONG Patrick Chun-man
University : The Chinese University of Hong Kong
Layman Summary
Fetuses can hear in the womb from as early as 20 weeks of gestation. Research has shown that prenatal music and spoken language experiences are related to better neural and behavioral outcomes at birth. These findings, though focused on outcomes shortly after birth, are consistent with a larger body of research showing positive effects of postnatal music training on children’s language and cognitive outcomes. The overarching aim of the project is to examine the effects of prenatal music training on infants’ neural outcomes at birth, which the team hypothesize is the basis of better language and cognitive outcomes measured up to two years of life. The team will conduct two randomized controlled trials with mothers and fetuses recruited from Hong Kong and Shenzhen. In Study 1, pregnant women whose fetuses are at average risk of developmental language and cognitive problems will be randomized into prenatal music training at two intensity levels or a passive reading control group. Training will be provided from 26 weeks of gestation. At birth, EEG neural encoding of speech will be measured from all neonates. Language and cognitive developmental outcomes will be measured at multiple time points after birth. In Study 2, fetuses who are at elevated likelihood of future neurodevelopmental conditions (e.g., due to atypical ultrasound findings, mothers with gestational diabetes mellitus, etc.) will go through similar training and testing protocols as Study 1. For both studies, the team expects a training-related enhancement of neural encoding of speech at birth, as well as better language and cognitive outcomes extended through at least the second year of life. Because some fetuses in Study 2 may be referred for fetal brain MRI, their fetal brain data, along with other baseline data, can be used to construct predictive models to forecast how much they may benefit from prenatal training, using causal forest and other machine learning methods. Prenatal music training is a simple procedure, yet it may have profound effects on children’s development. This well-sampled, prospective, and hypothesis-driven project may provide the necessary evidence to regularize such practice to enhance children’s development regardless of their baseline abilities. The opportunity to conduct the project in Hong Kong and a nearby Mainland city will broaden the generalizability and reach of the findings beyond Hong Kong.
Project Reference No. : C4013-24GF
Project Title : A Novel Neuro-Immune Axis MNT in Chronic Kidney Disease: From Molecular Mechanism to Clinical Implication
Project Coordinator : Professor TANG Patrick Ming-kuen
University : The Chinese University of Hong Kong
Layman Summary
Renal innervation is a common feature in chronic kidney disease (CKD) associated with dismal clinical outcomes. Better understanding the pathogenic innervation with single-cell resolution may discover new therapeutic strategies for the clinical CKD.
Recently, the team has reported a novel phenomenon "Macrophage to Neuron-like cell Transition (MNT)" contributing to de novo neurogenesis, its potential role in renal innervation is still yet unexplored. Interestingly, the team’s single-cell RNA-sequencing detected MNT in the patient biopsies that closely associated with the severity of CKD. More importantly, the team’s preliminary advanced bioinformatics and functional analysis discovered a direct role of MNT in the kidney pathogenesis, uncovering its clinical implication to the CKD patients.
By this multidisciplinary collaboration, the team can broaden the investigative scope of MNT from basic molecular mechanism to clinical implication in the context of CKD. Based on discoveries generated from the team’s synergism, the team will develop a precision MNT targeting approach for CKD that is experimentally evidenced by the pharmacological proof-of-concept using preclinical models. Thus, outcomes from this collaborative project will be highly innovative and clinically important for the scientific understanding and clinical treatment of CKD.
Project Reference No. : C4028-24GF
Project Title : Transforming Chinese Agriculture and Food Systems to Mitigate Reactive Nitrogen Emissions and their Contribution to Air Pollution and Climate Change
Project Coordinator : Professor TAI Amos Pui-kuen
University : The Chinese University of Hong Kong
Layman Summary
This research aims to assess how transforming the Chinese agriculture and food systems can mitigate nitrogen pollution and its associated adverse impacts on air quality, human health, ecosystems and climate in China. Growths in food production have historically enhanced food security and supported population growth, but also led to severe environmental consequences mainly due to inefficient cropland and livestock management. Agriculture has particularly become a major source of nitrogen compounds to the environment, which contribute significantly to air pollution and climate change and disrupt terrestrial and aquatic ecosystems. As the Chinese economy continues to grow and people’s diets continue to shift toward greater animal consumption, the conventional food supply faces the duo challenge of satisfying the population’s demands while ensuring environmental quality. Moreover, climate change itself may further threaten the food supply. Mitigating agricultural pollution and building climate resilience via food system transformation is thus urgently needed, but has thus far received much less attention than other major sources of environmental problems such as energy consumption and transportation.
In this project, the team therefore develops an integrated modeling and assessment platform that combines computer modeling, observations, and policy analysis tools to better estimate the spatiotemporal characteristics of reactive nitrogen emissions and their effects on air pollution and climate. The team comprehensively assesses the historical and future impacts of agricultural and food system changes on air quality, human health, crop yields and ecosystem productivity in China. The team also evaluates the effectiveness of various management strategies, including alternative farming practices for cropland and livestock systems, food consumption and dietary changes, and different land use policies, all along the backdrop of climate change. Within the national and provincial policy framework, the team further performs cost-and-benefit analyses for different transformation pathways, and identify the most sustainable strategies that align with policies in other sectors. This research provides important insights into the optimal agricultural and food system strategies for China that can balance multiple economic, social and environmental goals to ensure true sustainability.
Project Reference No. : C4038-24GF
Project Title : Development of Modular Microrobots and the Image-guided Intervention for Minimally Invasive Anti‐adhesion Treatment after Tubal Cannulation
Project Coordinator : Professor ZHANG Li
University : The Chinese University of Hong Kong
Layman Summary
Fallopian tube obstruction is a leading cause of female infertility (25 - 35%). Though hysteroscopic proximal tubal cannulation surpasses the conventional methods like microsurgical resection and anastomosis as it is less invasive, simpler to perform and has a satisfactory recannulation rate, one big challenge is that 67% of the opened tubes closed again in the nonpregnant patients after tubal cannulation, which is higher than that in the anastomosis group (20%). Such a low one-year patency rate of tubal cannulation diminishes its surgical and reproductive outcomes significantly.
Medical robotics has been developed for several decades, among which, micro-/nanorobotics has drawn lots of attention in research both in the fundamental aspects as well as their applications in minimally invasive intervention. As the characteristic dimensions of the robot or machines scaling down to the milli-/microscale or even smaller, they have high potential to navigate in hard-to-reach regions inside human body inaccessible to regular devices and may serve as microrobotic tools for in vivo applications such as health monitoring, early diagnosis, targeted therapy and minimally invasive medicine. Except for the primary challenges on the propulsion/actuation strategy at the small scales, several key challenges are yet to be extensively investigated for in vivo applications.
In this collaborative project, with the synergy between the researchers and medical doctors from CUHK and PolyU, the interdisciplinary team aim to develop a new minimally invasive therapeutic intervention to effectively reduce re-obstruction after fallopian tube cannulation. Magnetic modular microrobots consisting of heterogeneous modules (i.e. magnetic actuation (MA) module with strong magnetic force/torque for remote control and retrieval after delivery, and non-magnetic degradable functional (DF) module with high biodegradability for targeted therapy) will be developed. With the assistance of clinic hysteroscopy, ultrasound imaging and/or fluoroscopy, and magnetic control system, the newly proposed modular microrobot will be applied to realize localized cell/drug delivery and act as a physical barrier to the proximal part (lesion region) of fallopian tubes. The proposed modular microrobotic technology and the image-guided platform, for the first time, provide a promising medical robotic tool with a biodegradable module for localized and sustainable therapeutic effect to achieve prevention of re-obstruction of fallopian tubes and enhance the surgical outcomes of tubal cannulation. The proposed novel modular microrobots pave the way to a new antiadhesion treatment after tubal cannulation, which can greatly improve the therapeutic outcomes of tubal cannulation and reduce female infertility in Hong Kong, mainland China and the world.
Project Reference No. : C4042-24GF
Project Title : Targeting Metabolic Vulnerabilities to Overcome Chemotherapy and Immunotherapy Resistance in Colorectal Cancer
Project Coordinator : Professor WONG Chi-chun
University : The Chinese University of Hong Kong
Layman Summary
Colorectal cancer (CRC) is the number one cancer in Hong Kong with few therapeutic options. Management of CRC is challenging due to chemotherapy resistance and a lack of response to immune checkpoint blockade therapy. Cancer stem cells (CSCs) are a sub-population of highly tumorigenic cells associated with tumor initiation, metastasis and therapy resistance. In this proposal, the team proposes to target metabolic rewiring in CSCs to boost therapy responsiveness in CRC. In preliminary studies, the team has shown that a mitochondrial glutamate transporter (SLC25A22) is selectively enriched in CSCs from independent CRC patient cohorts. Corroborating this, functional studies demonstrated that SLC25A22 is essential for phenotypic effect of CSCs, including nonresponse to chemotherapy and immunotherapy. Based on these results, the team hypothesizes that CSC-specific metabolic rewiring drives therapy resistance and is a druggable target in CRC, with the following objectives: (1) to determine the function and mechanism of CSC-specific SLC25A22 in chemotherapy and immunotherapy resistance; (2) to assess whether targeting SLC25A22 boosts chemotherapy and immunotherapy efficacies; and (3) evaluate the translational value of SLC25A22 as a biomarker for therapy response. Collectively, this project will inform future therapeutic strategies for targeting CSCs to overcome therapy resistance in CRC.
Project Reference No. : C4052-24GF
Project Title : Brain Mechanism Underlying Experience-induced Modulation in Social Decision-making
Project Coordinator : Professor KE Ya
University : The Chinese University of Hong Kong
Layman Summary
Decision-making within dynamic social contexts is often influenced by the team’s previous experiences. Although the mechanisms behind social stimulus processing are well-documented, the way in which these processes inform subsequent social choices, particularly those influenced by prior social interactions, remains less clear. Understanding how past experiences recalibrate social strategies could shed light on the development of psychiatric conditions characterized by impaired social functioning in later stages of life. Presently, research is predominantly focused on human subjects, which limits the comprehension of the fundamental neurobiological underpinnings of socio-cognitive abilities, primarily due to the restricted spatiotemporal resolution and non-invasive nature of methods such as fMRI and EEG. Consequently, laboratory animals, whose brain architecture shares evolutionary similarities with humans, are invaluable for such research. The team’s pilot studies have shown that rodents display varying social decision-making behaviors, ranging from prosocial to antisocial actions. In this project, the team hypothesizes that social experiences alter a network-encoded social internal state of a subject that in turn affects how social stimuli are perceived and processed, resulting in an action that falls along a spectrum of actions from prosocial to antisocial.
Leveraging the team’s extensive expertise and proven success in unraveling neural circuits associated with diverse behaviors, the team aims to employ a rodent model to examine the effects of typical experiences— including social isolation, changes in social hierarchy, trauma, and competition—on social decision-making processes. The team’s research will 1) quantify the impact of social experiences on social decision-making, 2) identify the key brain areas and verify their roles in experience induced changes in decision-making, and 3) uncover the neural dynamics within the brain’s network throughout the experience and decision-making phases. The insights gained from this study promise to not only enhance the knowledge of the social brain but also potentially guide the development of rehabilitative strategies aimed at helping individuals recover from adverse social experiences.
Project Reference No. : C4057-24GF
Project Title : Advancing Photodynamic Therapy against Cancer through Bioorthogonal and Supramolecular Approaches
Project Coordinator : Professor NG Dennis Kee-pui
University : The Chinese University of Hong Kong
Layman Summary
As a promising treatment modality for cancer, photodynamic therapy (PDT) has received considerable attention due to its minimal invasiveness, low systemic toxicity, high spatiotemporal selectivity, and negligible drug resistance. It requires the excitation of a photosensitizing drug using light with an appropriate wavelength to produce reactive oxygen species (ROS) through interactions with the endogenous oxygen. Apart from the direct attack of cancer cells, leading to necrosis, apoptosis, and/or other forms of regulated cell death, these ROS can also disrupt the tumor vasculature and stimulate the host immune system. Despite these advantages, PDT still suffers from a number of limitations that hinder its clinical use. In particular, the low tumor selectivity and poor pharmacokinetics of most clinically used photosensitizers inevitably result in prolonged photosensitivity. Moreover, the PDT-induced antitumor immune response is generally weak, and the structure-activity relationship of photosensitizers in this aspect remains elusive. To address some of the major challenges of PDT for cancer treatment, the team proposes herein several initiatives using bioorthogonal and supramolecular chemistry as a versatile tool. The proposed studies include the design and synthesis of water-soluble and functionalizable boron dipyrromethenes (BODIPYs), conjugation of these photosensitizers with tumor-specific monoclonal antibodies through covalent linkage or host-guest interactions, and evaluation of their in vitro and in vivo tumor-targeting property and PDT efficacy. Attempts will also be made to confine the photosensitizers on the cancer cell membrane through different approaches with a view to inducing cell death through pyroptosis upon light irradiation, eventually leading to immunogenic cell death (ICD). This unique cell-death pathway and the extent of ICD induced under different conditions will be investigated systematically through a series of in vitro and in vivo experiments. In addition, the team also aims to develop novel far-red-absorbing BODIPY-based photosensitizers that can be specifically activated by click reactions and to use these bioorthogonally activatable photosensitizers for targeted elimination of cancer cells and cancer stem cells collectively. In this bioorthogonal "cocktail" PDT, multiple targets in tumors can be eliminated by a single activatable photosensitizer, which can greatly enhance the treatment efficacy. It is envisaged that, through a concerted effort of a team of chemists, biomedical scientists, and oncologists, these studies can circumvent some of the challenges of PDT and promote its clinical use against cancer.
Project Reference No. : C4075-24GF
Project Title : Tumor-homing Immunotherapeutic Phages for Efficiently Treating Hepatocellular Carcinoma
Project Coordinator : Professor MAO Chuanbin
University : The Chinese University of Hong Kong
Layman Summary
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. A common way cancer evades the immune system is through a mechanism involving two proteins: PD-L1 on cancer cells and PD-1 on immune cells called T cells. When these two proteins interact, T cells are “switched off” and cannot attack the cancer. One of current immunotherapy drugs, called PD-L1 blockers, aim to stop this interaction and help the immune system fight cancer. However, these drugs are ineffective for HCC because the tumors lack enough T cells to begin with — they aren’t "inflamed" with immune cells. To address this challenge, this team of bioengineers, biochemists, chemists, cancer biologists, and clinical oncologists proposes to develop a new type of immunotherapy using phages. The team will engineer two types of tumor-homing phages to work together: one blocks the PD-L1 protein on cancer cells, and the other inhibits an enzyme to make the tumor more inflamed with T cells. When both types of phages are injected into the bloodstream, the enzyme-targeting phages increase the number of T cells in the tumor, while the PD-L1-blocking phages prevent cancer cells from shutting down the T cells. This combined approach boosts the effectiveness of PD-L1 immunotherapy, making it a promising treatment for HCC. If successful, this project will provide a new strategy to treat cancers that typically resist current immunotherapies by turning “cold” tumors into “hot” ones that can be attacked by the immune system.
Project Reference No. : C5017-24GF
Project Title : Unraveling the mechanisms of targeted therapy resistance as a novel therapeutic strategy for hepatocellular carcinoma
Project Coordinator : Professor Lee Kin-wah
University : The Hong Kong Polytechnic University
Layman Summary
Hepatocellular carcinoma (HCC) is highly prevalent in Southeast Asia and Hong Kong. For patients with advanced-stage HCC, targeted molecular therapies and immune checkpoint inhibitors offer promising alternatives. The FDA approved the multiple-kinase inhibitors (MKIs) sorafenib in 2007 and lenvatinib in 2018 for treating unresectable advanced HCC. However, tumors rarely regress completely, and therapeutic effects are often temporary due to drug resistance. Recently, immune checkpoint inhibitors (ICIs) have shown promising results and durable clinical responses in some advanced HCC patients, although the overall response rate remains below 20%. Although some MKI, like bevacizumab, and ICI combinations, have been shown to achieve better therapeutic efficacy in recent years, there still lacks a rational basis for therapeutic combinations of MKIs with different ICIs, which hinders their therapeutic efficacy for HCC patients. Therefore, a better understanding of the molecular mechanisms underlying MKI and ICI resistance is urgently needed.
The team has a proven track record of research cancer stem cell-driven drug resistance and has conducted numerous studies on MKI and ICI therapies, creating unique resources such as animal models and profiling data. Nonetheless, new research questions and avenues have emerged, partly due to previously unavailable technologies, requiring further investigation to fully understand MKI and ICI resistance in HCC. In this proposal, the team aims to utilize new technological breakthroughs and expertise contributed by the research team to exploit therapeutic resistance in HCC by addressing the above limitations through a multipronged approach consisting of three distinct but highly interconnected programs. Specifically, the team proposes to (1) dissect the mechanisms of MKI resistance using integrated single cell and spatial transcriptomic analyses, (2) systematically identify protein kinases critical for immune evasion and ICI resistance in HCC cells using CRISPR library screening technology, and (3) develop novel covalent ligands to combat MKI and ICI resistance by targeting the proteins identified in Objectives 1 and 2. The team believes that their findings will identify new target combinations and open new therapeutic avenues for the treatment of HCC.
Project Reference No. : C5026-24GF
Project Title : Data Storage with Proteins
Project Coordinator : Professor Yao Zhongping
University : The Hong Kong Polytechnic University
Layman Summary
Digital data are being increased faster than ever, and current data storage methods may not be able to keep up. New methods are needed to store large data in very small space and last for long time.
The team proposes using amino acid sequences for data storage. By assigning amino acids to different combinations of 0 and 1, the team can convert digital data into amino acid sequences for storage and then retrieve it back by sequencing of the amino acid sequences. Since 20 canonical amino acids and many more non-canonical ones are available, the amino acids sequences could have high storage capacity and can be optimized to be stable for long time. In a previous study, the team demonstrated the feasibility of storing data into 18-amino-acid-long peptides and retrieve them back by sequencing with liquid chromatography-tandem mass spectrometry (LC-MS/MS), while designing address codes and error-correction schemes to ensure the data order and integrity and developing a software for MS/MS sequencing of data-bearing peptides.
In this project, the team aims to develop a new method for data storage by using proteins. Proteins have longer amino acid sequences than peptides, so they have higher data storage efficiency. They can be expressed biologically instead of synthesized chemically, lowering the cost. Protein techniques can also be utilized to perform special functions such as random access and data encryption. However, it remains challenging to express and sequence these custom-designed data-bearing proteins, which will be addressed in this project. In the team’s preliminary study, the team successfully stored text files into designed proteins and expressed them using the bacteria E. coli, then retrieved them back using trypsin digestion following by LC-MS/MS analysis. By employing a sequence motif derived from collagen, a very stable protein, the team increased the yield of data-bearing proteins; and by employing the leucine zipper motif which forms coiled-coil structure, the team obtained data-bearing proteins with higher-order structures.
The implementation of this project will allow development of a new, high-capacity and long-term data storage method. It couples data storage with protein science and proteomics for the first time, creating new opportunities for these fields.
Project Reference No. : C5033-24GF
Project Title : Integrating Machine Learning, Behavioral Analysis and Multimodal Neuroimaging Techniques to Investigate the Comorbidity of Specific Learning Disabilities in Hong Kong
Project Coordinator : Professor Siok Wai Ting
University : The Hong Kong Polytechnic University
Layman Summary
Children with learning disabilities, such as developmental dyslexia (DD), dysgraphia, and dyscalculia, face significant challenges in reading, writing, sensory processing, mathematics, attention and executive functions, impacting their academic performance and overall well-being. These disabilities frequently co-occur with other neurodevelopmental disorders, such as developmental language disorder (DLD), complicating their identification and intervention. Failure to identify and treat them properly increases the risk of emotional distress, abuse and misconduct, making them significant public health concerns. While extensive research has explored the comorbidity of learning disabilities in Western countries, similar research is lacking in Chinese societies.
This project aims to explore DD's comorbidity with DLD, dysgraphia and dyscalculia by integrating multimodal neuroimaging and cognitive-behavioral data using machine learning. The team will recruit a minimum of 210 Cantonese-speaking primary school children in Hong Kong diagnosed primarily with DD, with and without comorbid conditions, and a control group of 210 or more typically developing (TD) children. A comprehensive set of behavioral assessments and multimodal neuroimaging methods will be employed to evaluate cognitive and neurocognitive abilities in children with DD and their comorbid conditions. By integrating this data into machine learning models, the team aims to classify participants, explore comorbidities, elucidate neurocognitive differences and understand comorbid patterns specific to the Chinese linguistic context, bridging the existing research gap. The findings will help develop accurate and simple assessment methods for detecting and categorizing learning disabilities. They will offer valuable insights into effective identification and remediation strategies, reveal the heterogeneity of these disorders, improve educational outcomes and support for affected individuals, and ultimately enhance the academic and socio-emotional well-being of children in Hong Kong and other Chinese-speaking communities.
Project Reference No. : C5047-24GF
Project Title : Seeing like Dragonflies: Optical-Fiber-based Artificial Compound Eyes for 3D Vision
Project Coordinator : Professor Zhang Xuming
University : The Hong Kong Polytechnic University
Layman Summary
The project “Seeing like Dragonflies: Optical-Fiber-based Artificial Compound Eyes for 3D Vision” aims to create a biomimetic curved artificial compound eye camera (ACEcam) device for robotics. Inspired by the natural compound eyes of arthropods, which feature an array of ommatidia on a curved surface, the ACEcam will provide a wide field of view (FOV), low distortion, rapid motion detection, and infinite depth of field.
The team’s ACEcam employs lensed polymer optical fibers as artificial ommatidia, arranged in 3D-printed housings to form a panoramic imaging system. Conical microlenses on the dome capture light from 180 degrees, with bundled fibers projecting images onto a flat imaging chip. Key innovations of this project include (1) the unique ACE structure design, (2) the use of conical-microlens optical fibers, (3) the batch fabrication technique for microlenses, and (4) a 3D vision system utilizing binocular ACEcams.
The project team includes Prof. Xuming Zhang from Hong Kong Polytechnic University (PolyU) as Project Coordinator, along with Co-Principal Investigators Prof. Wen Chen (PolyU) and Prof. Kenneth Kwan-Yee Wong (University of Hong Kong), and collaborator Shenzhen MileBot Robotics Co. Ltd. Their combined expertise spans micro-optics, optical imaging processing, and computer vision, positioning the ACEcam to achieve exceptional performance. The resulting 3D vision system will enhance real-time viewing for robots, drones, and unmanned vehicles, complementing high-resolution cameras with wider FOV and faster response times.
Project Reference No. : C5055-24GF
Project Title : Next-generation AI-XR Empowered Surgical Planning and Intraoperative Guidance System via Effective Fusion of Empirical Knowledge, Human Interaction, and Machine Inference
Project Coordinator : Professor Qin Jing Harry
University : The Hong Kong Polytechnic University
Layman Summary
Surgery is an integral and indivisible component of a healthcare system worldwide. Minimally invasive approaches have transformed all aspects of surgery, making patients have less trauma, experience fewer complications, and recover faster. However, they also make surgical procedures more complex and challenging owing to the limited range of view and operation, complicated surgical anatomy, unique psychomotor adaptations and sophisticated multidisciplinary management.
One of the most promising ways to meet these challenges is to leverage advanced artificial intelligence (AI) and extended reality (XR) techniques to construct AI-XR empowered surgical planning and intraoperative guidance systems, offering surgeons a realistic and interactive environment to make patient-specific precise planning before a surgery and providing them with real-time and effective guidance during the surgery. While AI and XR techniques have been applied to some computer-assisted intervention systems, we still face a lot of long-standing technical obstacles that prohibit them from being widely used in clinical practice, including insufficiency of training data, limited model generalizability and adaptation, unsatisfactory visualisation and registration for effective guidance. More importantly, most existing models failed to naturally and efficiently synergize clinical knowledge and human interaction with machine intelligence, which is a promising way to overcome above obstacles and improve the robustness and generalizability of the systems.
The central purpose of this project is to develop an AI-XR empowered next-generation surgical planning and intraoperative guidance system by comprehensively addressing above challenges. The investigation will focus on the transesophageal echocardiography (TEE)-guided catheter-based intervention, which is a kind of minimally invasive procedures for treating structural cardiac diseases (SCD). To achieve this, the team shall propose a set of novel medical image segmentation, visualization, detection, and registration techniques by effectively incorporating clinical/empirical knowledge and human interactions with machine inference. The team shall then integrate these techniques into a VR-based planning system and an AR-based intraoperative guidance system and extensively validate the algorithms and the system in clinical settings.
This project will ramp up the application of advanced information technologies, particularly AI and XR, to minimally invasive interventions, which is one of the most promising inter-disciplinary research directions in the coming decades. The deliverables will provide valuable knowledge and experience in developing next-generation computer-assisted surgical systems, and substantially advance the research frontier of this field. Particularly, the algorithms and systems can help surgeons optimize the workflow of TEE-guided catheter-based interventions and improve the surgical outcomes, benefitting the patients with structural heart diseases.
Project Reference No. : C5058-24GF
Project Title : Optimising Spinal Curvature Corrective Outcomes in Adolescent Idiopathic Scoliosis: An Investigation into Spinal Flexibility, Biomechanical Behaviour and Predictive Modelling
Project Coordinator : Professor Yip Yiu-wan
University : The Hong Kong Polytechnic University
Layman Summary
Adolescent Idiopathic Scoliosis (AIS) is a complex spinal deformity characterized by a three-dimensional curvature of the spine. Optimizing corrective outcomes for AIS remains a significant clinical challenge, necessitating a comprehensive understanding of spinal flexibility, biomechanical behaviour, and predictive modelling. This research aims to enhance curve correction outcomes by investigating these critical factors.
The primary objective of this research work is to explore spinal flexibility to enhance clinical decision-making strategies for correcting the spinal curvature of AIS patients. The team aims to assess segmental curve flexibility with magnetic resonance imaging (MRI), develop a finite element (FE) model of an AIS patient, and conduct a prospective randomised controlled trial (RCT) to compare the effectiveness of soft braces against traditional rigid braces. Additionally, the team seeks to develop a multidimensional predictive model based on deep learning to predict spinal curvature progression during in-brace treatment.
The methodology involves recruiting 100 AIS patients who are 10 to 16 years old with a Cobb angle that ranges from 10 to 40°. Using an MRI-compatible robotic device, the team will apply pressure to different spinal locations and monitor the response of the soft tissues and bones. The data collected will inform the development of a detailed FE model, which will be validated by using the EOS low-dose imaging system and pressure sensors. The RCT will involve two arms: one that compares soft braces with observation for a Cobb angle smaller than 25°, and the other compares soft braces to rigid braces for a Cobb angle between 25° and 40°. The primary outcome will be the change in the Cobb angle after 18 months, with secondary outcomes including immediate in-brace correction, brace failure rate, and quality of life (QoL). Finally, the team will develop a multidimensional predictive model that incorporates clinical, radiological, and FE modelling data to predict the progression of spinal curvature.
The impact of this research is multifaceted, with the potential to revolutionise AIS treatment by providing more bracing options that offer wear comfort, enhancing patient compliance, and improving the QoL of AIS patients. The predictive model will fill a significant research gap by offering a real-time tool for predicting curvature progression and informing brace design. Ultimately, this research will contribute to global efforts to manage AIS, reduce the burden on medical systems, and ensure equitable access to effective treatments.
Project Reference No. : C5078-24GF
Project Title : Ultrahigh-resolution optical vector analysis for broadband photonic devices
Project Coordinator : Professor Yu Changyuan
University : The Hong Kong Polytechnic University
Layman Summary
With data traffic doubling every two years, current communication technologies face growing challenges in efficiency and capacity. This project focuses on advancing photonic measurement tools and techniques, essential for next-generation optical communication systems. A key innovation is the development of an ultra-high-resolution Optical Vector Analyzer (OVA), capable of analyzing light signals with up to 50 kHz resolution—4,000 times finer than current methods—by integrating with a cutting-edge 67-GHz lightwave component analyzer (LCA). This advanced platform will characterize multi-dimensional (magnitude, phase, polarization) and multi-domain (optical, electrical, optoelectronic, electro-optic) frequency responses with unmatched precision.
The project team, equipped with world-class facilities and expertise, will develop critical technologies such as ultra-high-speed lithium niobate (LN) modulators, silicon-based modulators, metasurface-assisted orbital angular momentum (OAM) devices, and specialty optical fibers (SOFs), supported by detailed frequency-response analysis.
This project will enable breakthroughs in broad-band optical fiber communication technologies for beyond 5G and 6G communication, paving the way for faster, more efficient networks and solidifying Hong Kong’s role as a global leader in next-generation optical communication systems.
Project Reference No. : C5085-24GF
Project Title : Advancing Compound Hazard Resilience and Adaptation for Urban Building Community in a Changing Climate
Project Coordinator : Professor Dong You
University : The Hong Kong Polytechnic University
Layman Summary
Under global climate change, the compound tropical cyclone-heatwave (TC-HW) hazard has become increasingly apparent, causing significant damages and risks to coastal communities. A recent study has found that 70% of HWs co-occurred with TC activities in the past 60 years over the south-eastern coast of China. Hong Kong (HK), as one of the most densely developed and populated metropolises all over the world, is susceptible to increasing TC-HW threats under climate change. In addition to the structural damages induced by TC impacts, non-structural component damages, infrastructure system failure, and cascading adverse impacts by HW also threaten the public’s life and property safety. Moreover, HK’s unique urban environment formed by multiple high-rise buildings could cause unexpected wind turbulence and heat island phenomenon, amplifying TC-HW-induced losses and consequences. However, the structural and infrastructural failure mechanisms under compound TC-HW hazards in a changing climate have not yet been understood clearly.
To narrow these gaps, the proposed project aims to advance compound TC-HW hazard resilience and adaptation for urban building communities in a changing climate. Multiple high-risk communities in HK, representing city center, suburban region, and rural area, will be selected as testbeds of the project. The following innovative scientific problems will be studied. Future TC-HW projection models under climate change will be established based on historical monitored data recorded by Hong Kong Observation. Characteristics of wind turbulence effects in the complex urban environment will be investigated using artificial-intelligence-enhanced computational fluid dynamics models and verified by field monitoring data. A high-resolution numerical model will be developed to investigate urban microclimate during post-TC heatwave events. A new urban HW vulnerability model will be proposed in response to the growing threat from cascading impacts. On this basis, a community-level resilience/recovery framework will be developed by coupling multilayer structure and infrastructure networks. Robust design and adaptation strategies will be formulated for high-risk coastal communities. To facilitate decision-makers, managers, scientists, and engineers in this field, tools incorporating the developed data base, methods, numerical models, and analysis frameworks, namely Climate Adaptation and Risk Reduction for Resilient Infrastructure (CARD-RESIN), will be developed.
Overall, this project will focus on datasets, methods, software, tools, and insights essential to understanding, evaluating, and enhancing urban resilience against TC-HW compound events. Once completed, it will benefit industry managers, public officials, and policymakers a lot to prepare our coastal cities for future climate-related hazards and improve resilience, safety, and quality of life.
Project Reference No. : C6040-24GF
Project Title : Characterization and Molecular Mechanisms of IDH Mutant Mesenchymal Glioma: Implications for Diagnosis and Targeted Therapies
Project Coordinator : Professor WANG, Jiguang
University : The Hong Kong University of Science and Technology
Layman Summary
Refractory tumors exhibit remarkable plasticity that enables cancer cells to seamlessly transition between diverse cellular states while concurrently shaping the complex tumor microenvironment. Understanding the underlying interaction between tumor plasticity and the tumor microenvironment is crucial for elucidating the mechanisms behind tumor initialization, progression, and recurrence. IDH-mutant astrocytoma is an aggressive and plastic brain tumor where patients almost inevitably recur after standard treatments. Although the implementation of molecular diagnosis has improved patient management, treatment options for these patients remain limited. Recently, the team discovered a subtype of IDH mutant astrocytoma called IDH mutant mesenchymal (IDHmes) glioma from multiomics data integration. This subtype constitutes approximately 30% of all IDH mutant astrocytoma cases. It demonstrates unique cellular plasticity, distinct histopathological characteristics, and unfortunately, poor survival outcomes. The team hypothesizes that spatiotemporal single-cell level characterization of IDHmes glioma will provide insights into tumor plasticity and guide precise glioma medicine. Leveraging the team’s well-established collaboration, the team proposes to decipher the spatial architecture of IDHmes glioma and identify actionable cell–cell interactions and potential therapeutic targets for this particular subgroup. The successful completion of this project will yield valuable data resources for the glioma research community. It will contribute to our understanding of the biological and clinical aspects of IDH mutant glioma. Furthermore, this research endeavor has the potential to inform studies on other types of cancer.
Project Reference No. : C6041-24GF
Project Title : Phase transitions and surface properties in active colloidal solids
Project Coordinator : Professor HAN, Yilong
University : The Hong Kong University of Science and Technology
Layman Summary
Active matter composed of self-propelled particles is a new class of material that exhibits behavior distinct from conventional matter composed of atoms, molecules, or Brownian particles under equilibrium thermal motion. This emerging field has garnered significant interest in soft materials and statistical physics due to its potential applications in various scientific and engineering disciplines. Most studies on active matter have focused on collective motion in systems composed entirely of active particles. However, many real-world active matter and biological materials are made of mixtures of active and passive particles whose coupling is crucial and nontrivial. In this joint research, the team will study the poorly explored “semi-active” colloidal solids, including crystals, glasses, and gels.
Micron-sized colloids are powerful model systems for studying solid phases and phase transitions because their motions can be directly observed and tracked even inside the 3D bulk of dense fluids or solids. Such microscopic kinetics are not measurable in atomic systems. The mobility of the active particles is tunable by light intensity, whereas the size and attraction strength of the passive colloidal particles are tunable by temperature. These tunable active and passive colloids, together with the team’s powerful particle manipulation and sample measurement techniques, provide a versatile platform to study active matter. In this research, the team will focus on semi-active solids and their phase transitions, interface behaviors, and mechanical properties, which are closely related topics.
The team will fabricate high-quality semi-active crystals and study their melting and interplay between defects and active particle motion. The team will fabricate various types of semi-active glasses, and investigate vitrification, glass-to-crystal transition, and the seldom explored glass melting. The team will study the effect of local active components on gelation and rheological properties of semi-active gels. The particles will enable us to study the poorly explored solid surfaces at the microscopic scale. In addition, the team will fabricate new active and passive colloidal particles with various shapes and interactions and assemble them into novel phases. The team will look for any unique features that are absent in conventional passive solids. The team’s simulations will provide new theoretical insights for the experiments and offer guidance to identify interesting parameter regimes in the experiments.
This proposed interdisciplinary research, which spans statistical physics, fluid mechanics, chemical engineering, and materials science, is of fundamental importance to active matter and nonequilibrium phase transitions. The findings will illuminate practical applications, such as the fabrication of new active materials for robotics and biomedical engineering.
Project Reference No. : C6046-24GF
Project Title : Study of novel phases, properties and applications in spin-splitting antiferromagnetic systems
Project Coordinator : Professor Prof LIU, Junwei
University : The Hong Kong University of Science and Technology
Layman Summary
Modern technology is advancing quickly, and we need faster, more energy-efficient ways to handle huge amounts of information. Traditionally, ferromagnetic materials (FMs) have been used for long-term data storage because they can store information even when the power is off. However, FMs have a downside: they create stray magnetic fields that can interfere with nearby devices, especially when trying to make storage devices smaller and more compact. This limits how much data we can store in a small space.
On the other hand, antiferromagnetic materials (AFMs) do not have this problem. They do not produce stray fields, can operate very quickly, and are resistant to external magnetic fields. For a long time, AFMs were thought to be impractical because they do not have a detectable magnetic field, making it hard to read or control their internal state. But recent discoveries have changed this view. Scientists have found ways to manipulate AFMs using electric fields and have observed new effects, like spin and charge Hall effects, which were previously only seen in FMs. One exciting development is the discovery of spin-splitting AFMs, which combine the best features of AFMs, FMs, and non-magnetic materials. These materials have unique properties due to their crystal structure and crystal symmetry, allowing for new ways to control spin, charge, and other properties. This opens possibilities for creating advanced devices with multiple functions using just one material.
This project focuses on studying these spin-splitting AFMs in detail to unlock their potential for next-generation technology. The research is divided into three main goals:
1. Exploring new states and materials: The team will investigate the unique properties of spin-splitting AFMs and identify materials with special characteristics.
2. Studying new properties: Beyond their basic structure, the team will examine how these materials behave due to their unique spin patterns.
3. Developing control methods: The team will create ways to effectively manipulate these materials to harness their properties for practical applications.
To tackle these challenges, a team of experts from different fields — theory, materials science, experimental measurements, and device engineering — has come together. By combining their knowledge, the team aims to make breakthroughs in understanding these materials and develop innovative devices that take advantage of their unique features. This could lead to new technologies that are faster, more efficient, and capable of storing and processing information in ways we have not seen before.
Project Reference No. : C6049-24GF
Project Title : Closed-loop learning for motor rehabilitation based on integrated bidirectional Brain Machine Interface systems
Project Coordinator : Professor WANG, Yiwen
University : The Hong Kong University of Science and Technology
Layman Summary
Motor brain-machine interfaces (MBMIs) can help people with disabilities regain movement by directly connecting their brains to machines. This connection allows the devices to understand and translate brain signals into actions, like moving a prosthetic arm. The system uses smart algorithms to learn what the brain wants to do. Additionally, it can improve control by giving feedback to the brain, such as gentle electrical stimulation. Previous studies have shown that MBMIs work well in both animals and humans for tasks like controlling a computer cursor or a prosthetic arm. This research aims to create a new wireless system that can both record brain activity and stimulate the brain to assist with movement control. The focus will be on making the system adaptable during use and ensuring it is safe for medical applications. This system will improve motor rehabilitation and deepen our understanding of brain function.
Project Reference No. : C7002-24GF
Project Title : Carbon emissions from China’s inland waters and coastal ecosystems under global change
Project Coordinator : Professor L. Ran
University : The University of Hong Kong
Layman Summary
Aquatic ecosystems, including inland waters and coastal waters, are important components of the global carbon (C) cycle. Inland waters (e.g., rivers, reservoirs, and lakes) are significant sources of carbon emissions for the atmosphere whereas coastal ecosystems (e.g., mangroves, salt marshes, and tidal flats) are typically net carbon sinks. Globally, inland waters emit 2.3–3.9 Pg C per year into the atmosphere as carbon dioxide (CO2) and methane (CH4), which is comparable to that of the land sink of anthropogenic CO2 (1 Pg = 1 billion tons). Coastal ecosystems can offset part of those inland water carbon emissions. Meanwhile, lateral export of carbon from adjacent environments to coastal ecosystems can facilitate microbial respiration and carbon emissions, partially offsetting the carbon sequestration capacity of coastal ecosystems. Thus, carbon emissions from inland waters and carbon sink in coastal ecosystems together regulate global/regional carbon budgets. Yet, the environmental impacts and anthropogenic controls on carbon emissions and their response to global change remain poorly studied. Such knowledge gaps form barriers to projecting future carbon budgets at regional, national, and global scales and formulating effective strategies and measures for addressing climate change.
This project aims to conduct a comprehensive assessment of carbon emissions from Chinese inland waters and coastal ecosystems by combining field-based measurements and computer-based modelling. Particularly, field surveys will be conducted in representative aquatic ecosystems with contrasting settings of climate, hydrology, geology, and human disturbance. The team will quantitatively investigate the sources of CO2 and CH4 emissions from China’s aquatic ecosystems and examine the taxonomic composition of microbial community which governs the biogeochemical cycling of carbon and the resulting emissions. This project will also explore environmental and anthropogenic controls on aquatic carbon emissions and project carbon emissions from China’s aquatic ecosystems until the Year 2100 by developing machine learning-based models.
This is one of the first integrated research projects to investigate carbon emissions from both inland waters and coastal ecosystems over a large spatial scale. The team will critically analyze aquatic carbon cycling processes and related carbon emission fluxes and assess their biogeochemical implications in a rapidly changing world. The expected outcomes will deepen our understanding of carbon emissions from China’s aquatic ecosystems and shed light on quantification of regional and global carbon budgets, which will eventually contribute to the achievement of carbon neutrality targets of the Chinese central government and the Hong Kong SAR government.
Project Reference No. : C7003-24GF
Project Title : Algorithmic Bias, Economic Efficiency, and Social Welfare
Project Coordinator : Professor Y. Wu
University : The University of Hong Kong
Layman Summary
Algorithmic efficiency is essential for the thriving of digital platforms and the AI-driven economy. However, with the growing public concerns about the detrimental and distributional impacts of algorithmic bias, there is an urgent need of building a regulatory framework under which information and AI technology is used for social good. In this proposed project, the team combines economic analysis, statistical methods, and computer science tools to analyze the economic and social impact of algorithmic decision-making and design social-welfare- enhancing algorithms in a wide range of economic and societal settings. Under this overarching goal, the team aims to achieve the intellectual, policy, and educational objectives through four work packages: (1) social media addiction and cognitive bias induced by recommendation algorithms, (2) decision bias in AI-human interactions, (3) the impact of platform promotion on grassroots innovation and consumer welfare, and (4) algorithmic design with social goals.
The key innovation of this project is the integration of economic analysis and algorithmic design. Unlike existing studies that typically focus on algorithmic bias caused by data, the team views AI as a self-serving agent intervening in digital consumption, expert decisions, market competition, and social norms. Algorithmic bias arises from self-interested decision making and reflects the fundamental tradeoff between private efficiency and social welfare. The team develops new analytical frameworks to elucidate this tradeoff in settings when consumer rationality is bounded, agency problems prevail, platform power dominates, and ethical concerns signify. Correspondingly, the team proposes ways to alleviate the detrimental impacts of algorithmic bias through content moderation, organizational design, platform regulation, and social engineering. The team applies their analysis and design to study digital addiction on social media, AI-human interactions in medicare management and legal decisions, innovations in content-creation industries, AI adoption in hiring, and risk control in Fintech.
This project will provide an analytical framework for regulatory applications with regards to social media, content-generative platforms, AI-based professional services, online labor markets, and Fintech. These applications are important for China as a leader of AI technology and the digital economy. They are particularly relevant to Hong Kong in its development towards a high-tech hub with international regulatory standards. The team will lead a AI-for-social- good initiative to inform policy makers and the public. Educationally, this project will set an example to promote interdisciplinary education in the social sciences, business studies, data science, and civil engineering.
Project Reference No. : C7011-24GF
Project Title : Physiological Amyloids as Physical Substrates Underlying Memory Persistence in Humans
Project Coordinator : Professor R. Hervas Millan
University : The University of Hong Kong
Layman Summary
The recurring failures in Alzheimer's pre-clinical and clinical trials underline the need for a deeper understanding of memory and the molecular alterations in Alzheimer's disease to develop effective treatments. A key unresolved question is how transient experiences become persistent memories. Memory studies have prioritized neuronal networks, identifying synapses as primary memory units. While encoding and maintaining memories involve lasting synaptic changes, the molecules behind these changes remain unclear.
Among them, a synaptic protein, cytoplasmic polyadenylation element-binding (CPEB), and its experience-dependent aggregation have been proposed as a physical substrate for long-term memories. Monomeric CPEB represses translation, while aggregated CPEB activates the translation of synaptic mRNAs encoding memory-related proteins. Hence, CPEB aggregation transforms transient memory into enduring memory by stabilizing altered synaptic function and new synapses.
Using cryogenic electron microscopy (cryo-EM) and functional assays, the team found that Drosophila CPEB adopts a translationally active amyloid form in the Drosophila brain, serving as a substrate for memory persistence. This raises critical questions: Can amyloids, usually considered harmful in neurodegenerative diseases, support memory in complex nervous systems like humans? How does the monomer-to-amyloid transition create a lasting alteration in synaptic function and memory?
This project aims to identify the biochemical substrates of long-lasting memories in humans by performing a high-resolution structural characterization of CPEB aggregates isolated from the human brain using cryo-EM and in situ within intact neurons using cryogenic electron tomography. Additionally, the team aims to determine the role of mammalian CPEB aggregation in synaptic protein translation and long-term memory formation, maintenance, and recall.
This research could provide the first evidence of functional amyloids linked to human memory, establish structural differences between functional and disease-related amyloids in humans, and precisely connect CPEB aggregation and activity with animals' memory formation or stabilization. This knowledge could form the foundation for investigating how pathological amyloids of Aβ42 or tau disrupt memory, aiding in the design of disease-related amyloid inhibitors and diagnostic tracers for amyloid-related diseases.
Project Reference No. : C7014-24GF
Project Title : Exploring Human Microbiota's Biosynthetic Potential for Antimicrobial Discovery
Project Coordinator : Professor Y. Li
University : The University of Hong Kong
Layman Summary
The escalating threat of multidrug-resistant superbugs demands urgent development of effective antimicrobials. This study focuses on the millions of tiny microbes in our bodies, collectively called the human microbiome, which holds immense potential for producing health-affecting substances. Among these substances, there are certain types of antimicrobial peptides that have caught researchers' attention because of their unique structure and ability to kill harmful microbes. These peptides are not only safe for us, but they also have a lower chance of causing bugs to become resistant to drugs.
This project is set out to find new germ-fighting peptides from the largely unexplored world of our body's own microbes. The study explores their potential using sophisticated techniques to study the genetic capabilities of these microbes and obtain these peptides synthetically in a lab. The next steps involve testing how well these germ-fighting peptides can prevent infection and understanding how they function. In brief, using a mix of techniques, including data mining, synthetic science, and biological activity testing, the team strives to unleash the potential of our body's microbes to produce these antimicrobial peptides.
Project Reference No. : C7015-24GF
Project Title : Study of Novel Phases of Matter with Unconventional Symmetries
Project Coordinator : Professor C. Wang
University : The University of Hong Kong
Layman Summary
Symmetry is a fundamental principle in physics. From the construction of the standard model in particle physics to the classification of crystal structures of matter, symmetry plays a crucial role. Recently, it has been realized that many physical phenomena extend beyond the explanation of conventional symmetries. Unconventional symmetries such as those in non-Hermitian systems and those characterized by category theory have become more and more important. In this project, the team will combine theoretical and experimental efforts to explore novel types of symmetries and study their consequences on various phases of matter. The team will perform theoretical study of stability of non-Hermitian gapless states of matter, experimental synthesis non-Abelian gauge fields and unconventional crystalline symmetries in photonic systems, and numerical and analytical investigation on the roles of emergent unconventional symmetries near quantum phase transitions. The team expects that the study will deepen the understanding of symmetries in many physical phenomena including topological phases of mater and quantum criticality.
Project Reference No. : C7030-24GF
Project Title : Mitigating Legal and Climate Risk in Asia Pacific Infrastructure Development
Project Coordinator : Professor S. Ali
University : The University of Hong Kong
Layman Summary
In the global Asia Pacific context of cross border infrastructure development, contracting across diverse legal systems with varying regulatory requirements and sustainability objectives presents new dimensions to risk. In particular, global infrastructure development, whether funded by China’s outbound financing initiatives or the Partnership for Global Infrastructure and Investment (“PGII”) face a common challenge of identifying sources of legal risk while aligning objectives with sustainable environmental and social governance (“ESG”) development objectives. While sustainability and carbon neutrality by 2060 have been articulated as regional environmental objectives, limited attention to infrastructure legal risk origination has left open questions as to how such risks will be addressed and sustainable objectives achieved. This project aims to develop a framework for assessing the sources of legal risk associated with infrastructure investment projects resulting in legal disputes. Understanding the sources and dynamics of legal risk will reduce the costs of risk identification and mitigation and contribute to advancing sustainable infrastructure development.
Project Reference No. : C7053-24GF
Project Title : The effect of pre-existing immunity on influenza infection and transmission
Project Coordinator : Professor H.L. Yen
University : The University of Hong Kong
Layman Summary
Humans are at risk of repeated infections by antigenically distinct influenza variants under epidemic or pandemic settings. Pre-existing immunity elicited by vaccinations and prior infections may confer protection by enhancing host resistance and increasing the pathogen load required to initiate infections. Pre-existing immunity to influenza is heterogenous between individuals, as immune memory is shaped by the first and subsequent infection and vaccination events, which can be confounded by demographic factors including age (frequency of exposure) and the birth year (antigenic variants encountered). Pending on the epitope similarities between a newly exposed strain and those shaped pre-existing immunity, humoral and cellular-mediated immunity may be recalled and confer cross-protection upon a new exposure to influenza. Previous observational or human challenge studies have identified multiple correlates of protection (CoP) associated with reduced viral shedding, symptom alleviation, and protection against infections. Understanding the relative significance of the multifaceted CoP and the potential synergy between them are critical for the development of better influenza vaccines. However, the heterogenous pre-existing immunity between individuals have posed the challenges in deciphering the relative significance of CoP. Animal models provide the benefit of investigating specific arms of immune response that protect against infection, but the readouts from the animal experiments tend to be qualitative than quantitative. The team hypothesizes that the level of host resistance, composed of multifaced CoP in pre-existing immunity, can be quantified by measuring the pathogen loads required for establishing infection during transmission. The team further hypothesizes that infections established in the resistant hosts with pre-existing immunity will lead to more rapid viral clearance and reduced onward transmission risk when compared to infections established in naïve hosts. With the use of barcoded influenza viruses, the team can quantify the pathogen load required in establishing infection and the subsequent viral load dynamics at high resolution in animal models with controlled pre-existing immunity. Using the time-dependent viral load kinetics as outcome, statistical models can be constructed using animal and human data to estimate the relative importance and synergy of different CoP in the pre-existing immunity. The results from the study will provide insights on the relative significance and potential synergies of CoP that reduce viral burden, which has implications for improving influenza vaccine design to increase host resistance and reduce onward transmission.
Project Reference No. : C7068-24GF
Project Title : Echoes of Urbanity: Coral Reef Health and Evolution in the Urbanized Waters of the South China Sea
Project Coordinator : Professor D.M. Baker
University : The University of Hong Kong
Layman Summary
Coral reefs, covering less than 0.1% of the ocean floor yet supporting 25% of marine species, are critical for over 300 million people in developing areas, providing ecosystem services valued at over US$375 billion annually. Protecting and restoring these biodiversity hotspots is essential for coastal cities' wellbeing. Urbanization is the chronic concentration of human populations in discrete areas, causing associated changes in land use and transitioning natural areas to a built environment. Urbanization is a major threat to our global ecology - for wildlife (biodiversity), ecosystem functioning, and their resulting ecosystem services (i.e. fisheries, coastal defense, etc.). Besides, rapid urbanization of coastal areas heavily impacts coral reef ecosystems. This leads to habitat fragmentation, reducing accessibility to both living areas and reproductive sites for species, and consequently causing a decline in biodiversity of coral reef ecosystems. Furthermore, eutrophication caused by an overabundance of sediments and nutrients from agricultural and urban runoff can limit suitable light and space for corals - favoring bloom forming algae and fouling invertebrates. This can further cause large-scale mortality of corals. However, current understanding of coral resilience to urbanization is constrained to the physiological and genetic mechanisms for acclimation to environmental stress - while the potential for adaptation and evolution is poorly understood. There's a significant gap in scientific data for effective management, especially in understanding coral reef responses to urbanization, coral evolutionary dynamics in urban coastal areas, and coral interactions with microbiomes under the pressures of human activity and climate change. The South China Sea lies within the Coral Triangle, a global hotspot of marine biodiversity, yet it faces major threats from human activities and climate change. This study will investigate the emerging field of marine urban ecology and evolution, with particular emphasis on the impact of urbanization on coral reef ecosystems. The team brings together extensive data analysis, advanced genomics methodologies, and ecological survey techniques to gain a comprehensive view of how urbanization influences coral reef ecosystems. The team will evaluate the health of coral reefs inhabiting urban coastal zones by analyzing coral cover, species richness, biodiversity, as well as coral reef ecosystem networks. The team will also learn how urbanization affects coral evolution by interpreting genetic diversity, adaptation, and gene flow. This research will provide crucial insights into coral reef resilience and inform effective management strategies for the South China Sea region.
Project Reference No. : C7085-24GF
Project Title : Disaster reconstruction of debris flow for rapid rescue of buried objects using differentiable physics-guided machine learning
Project Coordinator : Professor C.E. Choi
University : The University of Hong Kong
Layman Summary
The United Nations estimates that landslides kill more than 4,600 people and cause at least 20 billion USD in damage annually. Debris flows are soil-water mixtures that travel long distances at high velocities. The most significant hazard posed by debris flows is the displacement and burial of objects with victims trapped inside. Displaced objects make rescue efforts considerably more challenging. In 2006, rescuers in Guinsugon, Philippines, wasted the first six days excavating in the wrong location because the entire village was displaced 600 m downstream.
The UN reports that for every 1 USD invested in risk reduction, 15 USD is saved in post-disaster recovery costs. However, there remains a dearth of knowledge and tools available for use in the most critical early stages of search and rescue operations. Existing understanding of the displacement and burial mechanisms of debris flows remains limited, existing methods for disaster reconstruction are time-consuming and existing prediction models for recovery do not explicitly simulate the physics between flow and objects.
This proposed project will be the first to reveal state-of-the-art knowledge and develop cutting-edge tools for rapidly recovering displaced and buried objects by debris flows. The tools will expedite rescue efforts, potentially saving lives and reducing the devastating consequences of debris flow disasters globally.
In this proposed project, one of the world’s largest experimental flumes will be used to model the scale-dependent behaviors of debris flow and produce unique evidence of how damaged objects are displaced and buried. Experimental results will then be used to validate and calibrate a GPU accelerated two-phase material point method and discrete element method coupling framework for debris flow simulation. The solver will be used to rapidly simulate scenarios beyond those modelled experimentally. The solver will then be equipped with differentiable modelling to enable rapid forward and backward simulations using limited post-disaster information for accurate object path prediction and terminal location of buried objects. Uncertainty analysis will be conducted to dynamically combine the uncertainties identified and quantified with field observations to speed up and improve predictions.
CRF 2024/25 Collaborative Research Equipment Grant (CREG) Proposals
Project Reference No. : C1041-24EF
Project Title : A high-performance mass spectrometry platform for next-generation proteomics and metabolomics
Project Coordinator : Professor ZHANG Liang
University : City University of Hong Kong
Layman Summary
This project aims to create a top-notch research center in Hong Kong to study various biological molecules in human body. The team needs the Astral mass spectrometer, a high-tech tool that uses advanced technologies to analyze complex biological samples quickly and accurately. Since 2023, this tool has been widely used in leading research institutes around the world, but it is not yet available in Hong Kong.
This funding will support the installation and operation of an Astral-MS in Hong Kong. It will greatly enhance the research capabilities, allowing us to study RNA, proteins, lipids, and metabolites in great detail. This will help us discover new drug targets and clinical biomarkers. The Astral-MS will be housed at City University of Hong Kong and will be available to researchers from across Hong Kong and beyond.
Project Reference No. : C4007-24EF
Project Title : Live Imaging of Early Embryonic Developmental Processes at Subcellular Level with a High-throughput Dual View Light Sheet Microscope
Project Coordinator : Professor CHAN David Yiu-leung
University : The Chinese University of Hong Kong
Layman Summary
Recent advances in human embryoid and organoid research are creating new opportunities to study specific organ types. However, currently available live-cell imaging microscopes still face various limitations. In this proposal, the team aims to acquire a next-generation live imaging system capable of revealing subcellular structures up to 300 μm deep using dual-light sheet objectives, while providing high throughput and supporting ultra-long incubation periods. The team anticipates that the LS-2 microscope will enable the discovery of many new developmental features in embryos, thereby enhancing our understanding of embryogenesis and ultimately assisting infertile couples worldwide.
Project Reference No. : C4043-24EF
Project Title : C-FIST: Establishing World's Premier Environmental Organic Carbon Fingerprint Imaging Science & Technology Laboratory in Hong Kong
Project Coordinator : Professor CHOW Alex Tat-shing
University : The Chinese University of Hong Kong
Layman Summary
Environmental organic carbon (EOC), such as in plastic, biosolids, soil, herbs, fuel oil, food waste, etc., is ubiquitous in our daily lives, influencing many ecological and engineering processes and impacting environmental and public health. EOC is a complex, heterogeneous, and dynamic mixture, comprising thousands of organic molecules from small identifiable compounds to large unknown macromolecules. Understanding their chemical composition and characteristics is essential information to advance Science, Technology, Engineering, and Mathematics (STEM) research and innovation including Carbon Neutrality, Traditional Chinese Medicine, and Environmental Sustainability. Despite many analytical techniques proposed to examine EOC, they require labor-intensive and time-consuming data collection processes. Importantly, these techniques only identify major components of EOC but cannot detect trace compounds embedded or minor molecular modifications due to degradation.
The collaborative project aims to establish a state-of-the-art user facility that will be equipped with a novel analytical instrument by coupling pyrolysis with comprehensive two-dimensional gas chromatography and high-resolution mass spectrometry (Py-GCxGC-HRMS). With greater separation power and better mass accuracy from the new instrument followed by an automated data pipeline and machine learning algorithm, this novel analytical platform is expected to analyze a variety of organic materials from diverse matrices. Once established, the C-FIST Laboratory will serve as a valuable resource in Hong Kong for researchers from various disciplines seeking to characterize EOC.
Project Reference No. : C5003-24EF
Project Title : A multifunctional time-space-energy-helicity resolved transient absorption microscopy imaging system for advanced materials and devices research
Project Coordinator : Professor Li Mingjie
University : The Hong Kong Polytechnic University
Layman Summary
This project is focused on developing a cutting-edge imaging system that will help researchers study new materials and devices in exciting ways. As technology advances, we are seeing the creation of high-performance materials that can generate, transport, and store energy and information. These materials are crucial for innovations in areas like solar cells, sensors, and quantum computing.
Currently, researchers use a technique called Transient Absorption Spectroscopy (TA) to understand how these materials behave at very fast timescales. However, this method has limitations when it comes to seeing fine details in the materials. To overcome this, the team are creating a new system called Transient Absorption Microscopy (TAM). This combines TA with advanced optical microscopy, allowing us to capture detailed images while also observing how energy and charges move within materials.
The new TAM facility will be the first of its kind in Hong Kong and will provide incredibly precise information about materials—down to billionths of a second and tiny spatial details. The goals of this project include: 1) Establishing a state-of-the-art imaging system to enhance research on advanced materials and devices. 2) Encouraging collaboration between researchers and industry experts in fields like solar energy and electronics. 3) Offering training for students and researchers, sharing their unique capabilities, and working with global partners. 4) Bridging the gap between academic research and industrial applications, particularly in semiconductors and optical materials.
By building this facility, the team aim to drive innovative research, support technological progress, and create educational opportunities. The team’s vision is to place Hong Kong at the forefront of scientific discovery, contributing valuable insights to the global scientific community.
Project Reference No. : C5057-24EF
Project Title : State-of-the-art Dynamic Nuclear Polarisation Enhanced Solid-State NMR Spectroscopy
Project Coordinator : Professor Yung Ka-fu
University : The Hong Kong Polytechnic University
Layman Summary
Dynamic Nuclear Polarisation Enhanced Solid-State NMR (DNP-SSNMR) spectroscopy is an extremely powerful and non-invasive spectroscopic technique that provides unparalleled insight into molecular structure, interactions, and dynamics. This advanced technique surpasses traditional SSNMR by offering exceptional sensitivity, high resolution, rapid data collection, and comprehensive detection of NMR-active nuclei. Its broad applicability makes it a cornerstone in the characterization of diverse materials, biomolecules, chemical processes, and pharmaceuticals, bridging the gap between cutting-edge research and practical industrial applications. There is no DNP-SSNMR instrument available in Hong Kong, a notable deficiency considering the sparse distribution of such facilities in the greater China region, with only two existing setups in Wuhan and Hefei province. Therefore, establishing a DNP-SSNMR facility in Hong Kong would not only fulfil the immediate needs of the local research community but also significantly enhance the scientific and technological prowess of the southern China region.
The potential of DNP-SSNMR to address critical global challenges is immense. In physical and material sciences, it is pivotal for advancing sustainable energy solutions by characterizing innovative materials for catalysts, batteries, and solar cells, and it plays a crucial role in the advancement of nanotechnology, pharmaceuticals, and supramolecular structures. Within life sciences, DNP-SSNMR is a driving force behind chemical and synthetic biology, modern diagnostics, and the personalized medicine movement, offering insights into disease-specific molecular markers. Furthermore, it elucidates the molecular workings of biological macromolecules in health and disease, aiding in the discovery of new therapeutic agents. As we navigate the big data era, DNP-SSNMR is set to tackle an array of scientific inquiries, from exploring novel material properties to studying complex biological systems.
The team proposes the establishment of a unique, state-of-the-art DNP-SSNMR facility in Hong Kong, designed to serve the entire Hong Kong and the Greater Bay Area scientific community, aiming to aid researchers in achieving high-quality and impactful research. This facility is expected to be in high demand, being the first in the region, and will initially support the key research areas identified by this core team. Besides universities, industrial bodies in Hong Kong and the Greater Bay Area will also be major beneficiaries of this facility, where many of their long-standing problems can be resolved.
Project Reference No. : C5074-24EF
Project Title : Development of A Colorimetric and Photometric Characterization Platform for Binocular VR/AR Headsets
Project Coordinator : Professor Wei Minchen
University : The Hong Kong Polytechnic University
Layman Summary
In today's world, about 80% of the information is obtained through the eyes. Various displays have become essential in our daily lives, and experts have worked hard on display design, engineering, and manufacturing. Virtual Reality (VR) and Augmented Reality (AR) headsets are emerging in recent years, offering immersive, interactive, and realistic experiences. Recent products from companies like Apple and Meta have generated significant interest.
Despite the excitement and substantial investments in VR and AR fields, the headsets are still not widely used. The main reason is that the visual experience is much poorer than conventional displays. Unlike conventional displays that we look at from a distance, VR/AR headsets place displays very close to our eyes, creating a completely different viewing condition. This proximity changes how we perceive brightness, color, and geometry, making it challenging to design and characterize headsets using traditional methods.
To address such an issue, this project aims to develop and build a new system to measure various photometric and colorimetric characteristics of these near-eye displays. The system will help improve VR/AR headsets by providing comprehensive and detailed measurements, analyses, and specifications that are directly related to users’ visual experience. For example, it will automatically measure and characterize the accuracy and uniformity of the displays, in terms of brightness and color, as perceived by users, as well as the image quality perceived by the users. These measurements are crucial because they directly impact the user's experience through product engineering and design.
The system will include advanced hardware and software, based on the research team’s long-term expertise and work. The hardware will have six workstations, with each designed to measure different aspects of the headset's performance. One of the most innovative components is the conoscope designed by the research team, which can capture images with a wide field of view and high resolution to ensure accurate and reliable measurements. The software will be developed by the team to perform comprehensive analyses based on the measurements, with the specifications to be developed by the team through psychophysical studies.
By linking the design and engineering results with user experiences, the system will provide valuable insights to both researchers and industry professionals, for better design, engineering, and manufacturing. These will ultimately lead to better products, providing more enjoyable and realistic visual experiences to users, making VR and AR technology more accessible and appealing to a broader audience.
CRF 2024/25 Young Collaborative Research Grant (YCRG) Proposals
Project Reference No. : C1002-24Y
Project Title : High-capacity Lithium-rich Cathode Materials for High-energy Lithium-ion Batteries
Project Coordinator : Professor LIU, Qi
University : City University of Hong Kong
Layman Summary
Lithium-ion batteries (LIBs) are leading power source for electric vehicles (EVs), but current cathode materials like ternary transition-metal oxides are nearing their capacity limits, hindering EV progress. Cobalt-free lithium-rich layered oxides (LLOs) offer a breakthrough with high capacities over 250 mAh·g⁻¹, driven by anionic redox chemistry and a high cut-off voltage of 3.7 V. However, LLOs face several intrinsic challenges such as the voltage decay, primarily due to structural instability. Recently, the team has successfully synthesized a new O2-type LLO with a TM-capped honeycomb structure which showed minimal voltage decay, inspiring further research. Building on this, the team aims to stabilize the structure and enhance the performance of the more promising O3-type LLOs through synergistic strategies of atomic-scale doping and surface modification. Accordingly, the goal is to develop a full-size battery with an optimized LLO cathode and advanced components, surpassing current battery technologies.
Project Reference No. : C1003-24Y
Project Title : High-performance and Long-life Anion Exchange Membrane Water Electrolysis: From Material Design to Cell Innovation
Project Coordinator : Professor WANG, Jian
University : City University of Hong Kong
Layman Summary
Hydrogen is a promising green fuel to tackle climate change for the UN’s Sustainable Development Goals. For green hydrogen production, anion exchange membrane water electrolysis (AEMWE), splitting water into hydrogen and oxygen using electricity from renewable sources, is the leading next-generation technology. However, the energy efficiency and operation stability of AEMWE are currently unsatisfactory, owing to poor performance of catalysts that improve the efficiency of hydrogen production, low-conductivity and unstable membrane that transport ions between electrodes, and suboptimal cell design configurations. The intricate interplay among those multiple components within the AEMWE makes the optimization of this technology tremendously challenging. To address it, this project integrates the necessary interdisciplinary expertise including energy, material science, chemistry, and mechanical engineering to develop high-performance and long-life AEMWE, by innovating catalysts, membranes, and cell configurations. The team’s joint efforts will develop a rational method to significantly boost the AEMWE performance.
Project Reference No. : C2005-24Y
Project Title : Towards Trustworthy Foundation Models under Imperfect Scenarios
Project Coordinator : Dr. HAN Bo
University : Hong Kong Baptist University
Layman Summary
We have entered a new age of artificial intelligence, since Foundation Models (FMs) like ChatGPT and Sora emerge as pivotal tools with great capabilities in a broad range of domains and tasks. However, the deployment of FMs has surfaced critical concerns, particularly in robustness, safety, fairness, and reliability. In social science, while FMs offer advanced analysis of extensive qualitative data sets, they also face the problem of ensuring robustness against data anomalies and fairness in representation. In medical sciences, FMs promise a revolution through their ability to process large-scale medical datasets, yet they must do so with utmost safety and reliability to prevent harmful outcomes. Therefore, this project introduces solutions to the issues of FMs by developing trustworthy FMs. Specifically, trustworthy FMs will address the four grand challenges, including robustness against noisy inputs, safety against adversarial prompts, fairness against biased training data, and reliability against insufficient knowledge. Moreover, by developing advanced and targeted solutions, this project aims to bolster the functionality and dependability of trustworthy FMs, particularly within the critical spheres of social and medical sciences, thereby facilitating their responsible and beneficial integration into these fields. In summary, this collaborative project is expected to address the four grand challenges and construct trustworthy FMs, which can be further deployed to broader scientific and industrial applications.
Project Reference No. : C4002-24Y
Project Title : Clinical and Mechanistic Investigation of the Cross Reactivity of Antibodies against Emerging Viral Variants to Facilitate Vaccine Design
Project Coordinator : Professor CHEUNG Peter Pak-hang
University : The Chinese University of Hong Kong
Layman Summary
Rapidly evolving viruses pose a constant threat, hindering traditional vaccine effectiveness. To combat this, the team is creating advanced computer models simulating antigen-antibody interactions at the epitope level. This "virtual lab" will pinpoint broadly cross-reactive antibodies, crucial for designing next-generation vaccines that offer wider protection against viral mutations. These computational models will be validated with real-world clinical samples from diverse patient groups. By comparing model predictions with actual antibody responses, this models will be robust, clinically relevant, and accurately reflect human immune responses, bridging the gap between theory and practice. To achieve lasting broad protection, the team will track antibody evolution within living systems following vaccination which will reveal how broadly reactive vaccine candidates drive antibody maturation, providing essential insights to optimize vaccine design for sustained, adaptable immunity against future viral variants.
Project Reference No. : C4004-24Y
Project Title : 3D Photonic-electronic Neural Network Enabling Versatile and Large-scale AI Computing
Project Coordinator : Professor HUANG Chaoran
University : The Chinese University of Hong Kong
Layman Summary
Conventional integrated circuits (ICs) struggle to meet the escalating demands of artificial intelligence (AI). OpenAI estimates that these demands have been growing 100 times every two years, outpacing Moore’s Law by 50 times. To surpass Moore’s Law, neuromorphic computing has emerged. Unlike traditional processors solving problems in a sequential manner, neuromorphic computing executes in a highly parallel manner, leading to substantial speed and energy efficiency improvement. However, scaling up components in neuromorphic hardware remains a challenge. As a result, most neuromorphic hardware is limited to basic benchmark demonstrations, hindering its application to real-world AI challenges.
This project aims to develop a practical pathway to realize large-scale neuromorphic computing systems, not only to surpass digital electronics in speed and energy efficiency, but also capable of handling a large number of parameters to close the performance gap with large-scale AI models. To achieve this, the project proposes a 3D photonic-electronic neuromorphic computing system, leveraging a novel device optical metasurface and its numerous spatial modes. Optical metasurfaces, comprising sub-wavelength meta-atoms on a 2D plane, offer unmatched parallelism, processing tens of millions of weights in one operation with zero power consumption. This preliminary study integrating over 40 million meta-atoms on a metasurface chip showed a single-layer metasurface can provide matched performance of a 50-layer convolutional NN (ResNet 50), while reducing computing time and energy consumption by over 1000 times compared to GPU.
Despite their advantages, optical metasurfaces are challenged by fabrication imperfections and limited programmability. To overcome these challenges, this project will innovate a new computing framework and architecture suitable for optical metasurface- based NN to scale to arbitrary widths, depths, and complexity. These innovations will enable large-scale AI models while being immune to fabrication and implementation errors. Furthermore, the project will expand the system to realize a multi-skilled AI system capable of parallel processing multi-modal AI tasks. This expansion will leverage multi- dimensional optical multiplexing without adding design and device complexity. Finally, the team expects this system will be capable of delivering high-performance solutions to real- world AI challenges through its unprecedented scale. The team will demonstrate the practical application and impact of the proposed system in multidisciplinary fields, including acceleration the analysis of whole slide images (WSIs) for cancer detection and large-scale scientific simulations for smart grids. The successful accomplishment of this project will deliver a large-scale, low-power and high-performance neuromorphic computing system, fueling a variety of future disruptive AI technologies.
Project Reference No. : C4005-24Y
Project Title : Base Editing as an Intervention Strategy for Neurodevelopmental Disorders: Investigating Pathophysiological Mechanisms and Therapeutic Applications
Project Coordinator : Professor IP Jacque Pak-kan
University : The Chinese University of Hong Kong
Layman Summary
Neurodevelopmental disorders (NDDs) affect a significant proportion of children worldwide, with a prevalence of 5-10%. Despite differences in the causal gene, different NDDs share comorbidities, including intellectual disability, autistic features, sensory and motor abnormalities. In the absence of promising drug candidates, the field is shifting its focus on gene therapy as a potential cure. However, whether gene therapy can be reliably applied to correct neurodevelopmental defects remains unclear. Therefore, in this proposal, the team assembled a multidisciplinary team to evaluate the feasibility and efficacy of a base editing system as a potentially translatable treatment strategy for NDDs. The team’s initial genetic analyses have identified that over 40% of pathogenic variants in high-confidence NDD-related genes can be precisely corrected using this strategy, highlighting the potential impact of this technology for NDDs.
Project Reference No. : C5001-24Y
Project Title : Organic/two-dimensional materials heterostructure based memristor array for flexible retinomorphic system
Project Coordinator : Professor Han Suting
University : The Hong Kong Polytechnic University
Layman Summary
This project focuses on developing a new type of artificial vision system inspired by the human eye, called a retinomorphic system. The human eye is incredibly efficient, with a wide field of view, low power usage, and the ability to process visual information before it even reaches the brain. By mimicking these features, scientists aim to create a flexible, dome-shaped artificial retina using advanced materials called organic/2D heterostructure memristors. These materials are flexible, cost-effective, and compatible with biological systems, making them ideal for creating artificial retinas that can see without distortion.
The goal is to build a system with a 180°×180° field of view and high resolution, which uses very little power. This could revolutionize machine vision, allowing robots and autonomous vehicles to see and process visual information more like humans do. It could improve manufacturing automation, enhance navigation for self-driving cars, and lead to more advanced surveillance systems.
Additionally, this project could advance neuromorphic computing, which seeks to replicate the brain's neural processing. By doing so, it could lead to more efficient and powerful computers that process information similarly to biological systems. The research will also contribute to fields like materials science, electronics, and neuroscience, providing valuable learning opportunities for students and researchers interested in cutting-edge technology and interdisciplinary studies.
Project Reference No. : C5002-24Y
Project Title : Design of ultrastrong, ductile, and thermally stable nanocrystalline dual-phase alloys via coherent nano-honeycomb architectures
Project Coordinator : Professor Jiao Zengbao
University : The Hong Kong Polytechnic University
Layman Summary
In many industries, such as aerospace, transportation, and energy, there is a significant demand for advanced materials that are incredibly strong, ductile, and stable at both room and elevated temperatures. Nanocrystalline materials, which have nanosized grains, are particularly interesting because they can be very strong and resistant to wear. However, these materials tend to be brittle at room temperature and unstable at high temperatures, limiting their use in practical applications. This project aims to overcome these challenges by developing a new type of nanocrystalline alloys with a honeycomb-like architecture at the nanoscale. The goal of this research is to design and test these innovative alloys to understand their atomic-scale structure, stability, and mechanical properties under different conditions. Scientifically, this project will reveal the fundamental mechanisms that dictate the formation, deformation, and thermal stability of these materials across different temperatures. Technically, this project could lead to the development of a new class of materials that are not only strong but also ductile and thermally stable for a wide range of applications. The findings of this project are expected to provide valuable insights into understanding and enhancing the mechanical properties and thermal stability of nanostructured materials. This could pave the way for solving the longstanding issues of brittleness and instability in nanocrystalline alloys, enabling the design of advanced materials with exceptional strength, ductility, and thermal stability. Ultimately, this would contribute to technological progress and support industrial growth in Hong Kong.
Project Reference No. : C5004-24Y
Project Title : AI-empowered wind field simulation for sustainable urban microclimate design
Project Coordinator : Professor You Ruoyu
University : The Hong Kong Polytechnic University
Layman Summary
The world is struggling with climate change, and Hong Kong is facing enormous challenges as a high-density city. Urban environments can become intolerable due to heatwaves amplified by the Urban Heat Island (UHI) effect. Previous research has shown that while a city’s climate cannot change, we can create localized outdoor cooling hubs. We can optimize thermal and wind comfort and minimize thermal stress risk through early-stage design and planning of urban microclimate, and the design process requires sophisticated urban wind simulation. Thus, there is a need for an efficient and accurate wind field simulation tool that is accessible for urban planners. The team will develop an AI-enhanced turbulence model to enhance the accuracy of the current Computational Fluid Dynamics (CFD) simulation while maintaining similar computing cost. The team plans to employ the developed AI-enhanced turbulence model to train an end-to-end neural network to serve as a surrogate of CFD for sustainable urban microclimate design. At last, the team will demonstrate the use of this AI-empowered wind field simulation tool for air ventilation assessment and urban ventilation corridor plan in Hong Kong.
Project Reference No. : C6001-24Y
Project Title : Marine emissions of volatile organic compounds in the Greater Bay Area: interactions with air quality and climate change
Project Coordinator : Professor GU, Dasa
University : The Hong Kong University of Science and Technology
Layman Summary
Volatile organic compounds (VOCs) that come from the ocean play a key role in forming ozone and aerosols in coastal areas, especially when there are increases in emissions during phytoplankton blooms. Some of these VOCs, like halocarbons, are considered greenhouse gases and ozone-depleting substances, posing significant threats to the environment, such as climate change and loss of ozone in the atmosphere. However, there is a lack of measurement data and a complicated production process, which leads to large differences in estimates of marine VOCs. This highlights the urgent need for more research on marine VOCs and how they affect air quality and climate change.
This project’s aim is to create a framework to study marine VOCs in the coastal waters of the Greater Bay Area (GBA) using various technologies. The project team will gather data from ground, sea, and air platforms to measure marine VOCs using a "top-down" approach. Additionally, satellite observations will be used to estimate marine VOC emissions using a "bottom-up" approach. The project team will also study how different phytoplankton species produce VOCs to understand their sensitivities. Through computer modeling, the project team will explore how marine VOCs relate to air quality and climate change and assess the uncertainties in the findings. This collaborative project brings together experts in fields like atmospheric chemistry, marine ecology, and numerical modeling. The expected results are vital for monitoring and improving the coastal environment in Hong Kong and the GBA. Moreover, this project can have broader applications, contributing to regional, national, and global efforts toward sustainable development.
Project Reference No. : C6002-24Y
Project Title : Living Building Material-Based Extra-Terrestrial Construction Technology for Resource-Starved and Extremely Harsh Environments
Project Coordinator : Professor QIU, Jishen
University : The Hong Kong University of Science and Technology
Layman Summary
The Moon and Mars have zero resource but extreme environmental conditions including freezing temperature, (quasi-)vacuum, and decreased gravity. In preparation for future Lunar and Martian bases, many concept extra-terrestrial construction (ETC) technologies have been proposed. They share a similar approach of additively manufacturing full-scale structures by processing regolith in situ with solar light, laser, or microwave. While these technologies address challenges in resource, environment, and automation, they are infeasible energy-wise. Even in the most optimistic scenario, where power generators with record-high specific power (kW/kg) are shipped to site using the largest rocket, it would still take months to build a full-scale shelter.
The team proposes to study a distinct ETC technology that will also be applicable for Antarctic construction. This technology is centered around living building materials (LBMs)—a new class of load-bearing composites consisting of inert particles bound together by living microbes and biosynthetic hydrogels. Compared with regolith-based materials, LBMs can be formed via spontaneous H2O phase change and biopolymer cross-linking under low temperatures and low air pressure. They can achieve extraordinary energy and material efficiencies simultaneously via genetic engineering. While PC and Co-PIs have demonstrated the manufacturability and mechanical performance of LBM structures in a Mars-like environment, this technology would still face multiple challenges in true extra-terrestrial environments, which will be addressed in this project. First, the team will develop methodologies of biologically synthesizing adhesive protein-based sol as the binder of living building materials (LBMs) and study the effect of anti-freeze agents on the rheological and mechanical behaviors of biosynthetic hydrosols at freezing temperature. Second, the team will upgrade an existing 3D-printing technology by integrating real-time temperature monitor for printing the new biosynthetic hydrosols and thus study the feasibility of printing LBM at freezing temperature. Third, the team will develop a machine learning (ML)-based methodology to predict the internal structure of LBM formed at freezing temperature and/or reduced air pressure, and study the feasibility of integrating the ML module into multi-scale CFD-DEM model for optimizing the mechanical properties of LBM. Fourth, the team will study the effect of extremely low temperature and air pressure on the living functions of LBM and thus demonstrate the feasibility of re-generating after serving in a extra-terrestrial environment.
Project Reference No. : C6003-24Y
Project Title : Intelligent and Agile Integrated Circuit Design Methodologies Based on Circuit Foundation Models
Project Coordinator : Professor XIE, Zhiyao
University : The Hong Kong University of Science and Technology
Layman Summary
Integrated circuit (IC) is the foundation of our modern information society. However, the ever-increasing complexity of ICs has led to skyrocketing IC design costs, estimated to surpass US$500 million for an IC design at the latest technology node. To address the challenge, in recent years, artificial intelligence (AI)-assisted circuit design methodologies have proven highly promising in improving IC design efficiency and reducing design costs. In this collaborative project, the team will develop new AI solutions named circuit foundation models (CFMs) to support an agile chip design process. CFMs are novel foundation AI models customized to capture many unique properties of circuits. Compared with traditional task-specific supervised AI solutions, CFMs aim to essentially boost AI solutions’ understanding of circuits and generalize across various types of digital IC design applications. Specifically, the team will develop multimodal circuit encoder AI models to capture circuit structure and semantic information, as well as large language model (LLM)-based decoder AI models to automatically generate IC design solutions. CFMs will also provide a natural language interface to the circuit design process. By scaling up the model size and amount of training data, the team’s open-source CFMs are expected to serve as a general foundation, which supports different circuit design teams to build their own fine-tuned AI solutions. In summary, CFMs will promote a shift of the IC design paradigm, from the manual and iterative flow to an intelligent and agile flow based on a customized AI foundation.
Project Reference No. : C7001-24Y
Project Title : Multimodality biopsychosocial evaluation and non-pharmacological interventions for cognitive function decline in patients with treatment-resistant schizophrenia-spectrum disorders
Project Coordinator : Professor K.W. Chan
University : The University of Hong Kong
Layman Summary
Schizophrenia is a severe and chronic psychiatric illness with diverse long-term outcomes. Despite the advancement in interventions, more recent longer-term follow up studies suggested a limited improvement of longer-term functional recovery of patients. Cognitive function deficits may be the main culprits to that. About 15-30% of patients with schizophrenia are not responding to standard antipsychotic medications and are considered as treatment resistant schizophrenia (TRS), who have poorer functional outcomes and cognitive functions. There is still a limited understanding on the longitudinal cognitive function changes of TRS patients, its possible mechanisms and effective interventions. Emergent evidence suggesting the neurodegenerative process may be one of the key explanations to the development of schizophrenia and its outcomes. Furthermore, recent findings of overlapping genetic vulnerability of schizophrenia and Alzheimer’s disorders as well as the possible role of microbiome-gut-brain axis in cognitive functions of different populations suggest a complex interplay of biopsychosocial factors contributing to the neurodegenerative process in patients. The team proposed four interrelated studies focusing patients with treatment resistant schizophrenia to elucidate the mechanisms of cognitive function deficits and its accelerated decline, and develop effective interventional strategies. Study 1 is a 12-year follow up study of a previous nested case-control study to explore the longitudinal changes of cognitive functions of patients with TRS and patients who responded to treatment, utilizing clinical, cognitive function and brain MRI assessments. Patients with TRS are likely to have accelerated cognitive function decline and greater deviation of brain volume from normal brain aging trajectories. Study 2 is a randomised control trial to examine the effect of aerobic exercise alone, tDCS alone and aerobic exercise+tDCS on the improvement of the cognitive function in patients with TRS. The possible role of gut microbiome and brain function in relation to these effects will also be explored. Study 3 will study the role of the microbiota- gut-brain axis in the cognitive function differences between TRS and treatment response schizophrenia, possible accelerated cognitive decline in TRS patients, and the effect of interventions in study 2 on cognition in TRS patients. Study 4 will study the genetic vulnerability or predisposition to schizophrenia, Alzheimer’s disease (AD) and intelligence as well as longitudinal psychosocial exposure on the cognitive deficits in patients with schizophrenia and accelerated cognitive decline in patients with TRS. These results would be crucial in understanding the nature of neurodegenerative processes of patients, and guide future service development to improve patient’s long-term outcomes.
Project Reference No. : C7003-24Y
Project Title : Heterogenous in-memory computing hardware technologies for fast and efficient genomic analysis
Project Coordinator : Professor Can Li
University : The University of Hong Kong
Layman Summary
Precision medicine benefits from rapid genetic diagnosis, influencing clinical decisions, improving prognosis, and reducing costs. This has prompted US, UK, China, Saudi Arabia, and others to launch national genome projects. Pathogen detection plays a crucial role in healthcare and policymaking, with applications ranging from the identification of antimicrobial resistance genes to the tracking of the SARS-CoV-2 virus through environmental samples. These tasks leverage advancements in sequencing technologies that have significantly sped up DNA and RNA sequencing.
However, the surge in sequencing data processing needs has outpaced hardware development, a problem magnified by recent developments in single-cell analysis. Consequently, there is a pressing need to develop key technologies capable of analyzing massive amounts of sequence data, which face clear memory bandwidth and energy bottlenecks. Therefore, this project aims to develop disruptive hardware technologies that process data directly in its memory for genomic analysis. In-memory computing technologies have been extensively researched recently for accelerating AI algorithms and big data analysis. However, these operations are often not the only bottlenecks in genomic analysis performance. This requires developing technologies that involve cross-layer expertise from biomedical algorithms to non-von Neumann computing architecture and emerging devices.
This multidisciplinary team aims to develop various in-memory computing paradigms and technologies for genomic analysis. On the hardware side, the team will use RRAM for key in-memory operations in genomic analysis, not only for multiplication, but also for different distances and probabilities calculation within the device. The team will build the prototype system with back-end integrated optimized devices. On the algorithm side, algorithms that are better suited for the hardware will be developed, for example directly processing the raw signal data from the sequencer, making full use of the analog features of the memory devices. The final RP4 will demonstrate applications like virus variant identification, single-cell, and spatial transcriptomics analysis, and provide a benchmark that demonstrates orders of magnitude improvement over the current approaches with the same or improved accuracy.
This team, with a proven track record, along with industry advisors on DNA/RNA sequencing and in-memory computing, possesses a deep understanding of the required technologies. The team uses RRAM technology as a testbed for the technology, but the developed method can be transferred to other memory technologies. The successful development and integration of the proposed technologies will pave the way for a hardware acceleration solution for sequence data analytics, which will be embraced by biologists and doctors, expediting diagnosis, research, and technological innovation.
Project Reference No. : C7004-24Y
Project Title : Boron and Nitrogen (BN) Doped Helicenes with High Performance Circularly Polarized Luminescence: New Materials Synthesis, Fundamental Mechanism, and Devices Engineering.
Project Coordinator : Professor J. Liu
University : The University of Hong Kong
Layman Summary
Organic light-emitting diodes (OLEDs) have been regarded as the most promising candidates for next-generation lighting and display technology owing to the perfect color, lower energy consumption, and higher efficiency. However, the contrast-enhancing circular polarizer in OLED displays leads to ~50% internal light loss, in which this can be overcome by using organic chiral materials in circularly polarized OLEDs (CP-OLEDs). Although significant efforts have been dedicated to the development of chiral organic molecules for CP-OLEDs in the past few years, these molecules exhibited small luminescence dissymmetry factors (glum) values and therefore demonstrated poor device performances. Accordingly, the development of chiral organic materials with high photoluminescence quantum yields (PLQYs) and large glum values has remained an unsolved scientific challenge that limits the performance of CP-OLED devices.
In this collaborative project, the team targets to achieve rational development of a new class of chiral pi-functional materials, namely heli(aminoborane)s (HABs), which is pioneered by this team at HKU recently. The team’s early results show these unconventional chiral molecules exhibit outstanding circularly polarized luminescence (CPL), with one of the largest glum values reported to date. The team’s target is to establish a comprehensive understanding of the fundamental mechanism for CPL in this new material class, which will in turn enable us to design and synthesize novel molecules with even better CPL performance. Finally, the team will explore their applications in CP-OLEDs. As the key achievements, the team expects to establish novel solution-based materials chemistry, delineation of reliable structure-property-performance relationships for applications of these resultant unconventional BN-doped π-conjugated helical materials.
Project Reference No. : C7005-24Y
Project Title : Sleep and Circadian Rhythm as a Transdiagnostic Mechanism in Attention Deficit Hyperactivity Disorder (ADHD)
Project Coordinator : Professor S.X. Li
University : The University of Hong Kong
Layman Summary
Attention deficit hyperactivity disorder (ADHD) is a common condition among young people, but it may present and affect individuals in different ways, making it challenging to develop effective treatments. One important factor that may contribute to the complexity of ADHD is sleep. Many young people with ADHD experience sleep problems, which can exacerbate their ADHD symptoms and increase the risk of other mental health issues. The interaction between ADHD, sleep, and circadian rhythms is complex and may particularly impact one’s arousal level, which is essential for understanding the manifestation of ADHD. Individuals with ADHD often struggle to regulate their arousal levels, making it difficult for them to consistently achieve optimal arousal states. These difficulties may be further exacerbated by sleep problems and circadian rhythm disturbances, impairing their ability to maintain ideal arousal states. Nevertheless, the specific mechanisms of how sleep and circadian disturbances affect ADHD, particularly on arousal systems, are not well understood.
This project aims to address the research gaps by utilizing advanced techniques, including machine learning and comprehensive assessments of neurobiological, environmental, behavioural, and psychological factors, to enhance our understanding of the relationship between sleep, circadian rhythm, and ADHD. The goal is to uncover the underlying mechanisms of sleep and circadian issues in ADHD and identify different subtypes of ADHD. Additionally, this project will evaluate the effects of sleep- and circadian-focused intervention in adolescents with ADHD, with a specific focus on the arousal system. In the long run, this research could potentially lead to improved, more personalized treatments for ADHD. The findings and treatment approaches developed through this project could be implemented in real-world clinical settings to enhance care and clinical services for young people with ADHD.