Deep strain engineering of structures and functionalities of twisted bilayer Graphene
Hong Kong Principal Investigator: Prof Yang Lu (City University of Hong Kong)
Mainland Principal Investigator: Prof Zhuhua Zhang (Nanjing University of Aeronautics and Astronautics)
Graphene is a two-dimensional (2D) material, consists of a single layer of carbon atoms arranged in a hexagonal lattice. When such 2D lattices are stacked on top of each other and adhered by van der Waals (vdW) force, the resulting layered vdW structures offer a new paradigm for material design - their collective properties can be tuned by the vertical stacking sequence as well as by adding a mechanical twist, stretch, and hydrostatic pressure to the atomic structure. Simple twisting stacking of two layers of graphene can form a uniform and ordered Moiré superlattice, called twisted bilayer graphene (tBLG), which has led to the ground-breaking discovery of new physics (e.g., ‘magic-angle’ superconductivity). The recently achieved ‘deep elastic straining’ in low-dimensional nanomaterial systems shall endow tBLG (even other twisted 2D crystals) with unprecedented strain energy, which can lead to a competition between in-plane elastic energy of tBLG and vdW interfacial energy, further inducing stacking transition and topological defects (e.g., strain soliton) in strained tBLG. The dramatic change of atomic stacking structure under deep strain will change its intrinsic electronic structure and produce sophisticated mechano-electro-magnetic coupling. With the emerging opportunities of ‘deep strain engineering’ coupled with ‘twistronics’, this project aims to elucidate the synergistic mechanism of mechanical strain and superlattice twist in bilayer graphene, and providing an effective mechanical approach to modulate the physical and functional properties of tBLG (and other twisted 2D materials) for unprecedented nanoelectronics and nanoelectromechanical system (NEMS) applications.
In-situ scattering study of medium-range order evolution during phase transitions in glass-forming metallic liquids
Hong Kong Principal Investigator: Prof Xun-li Wang (City University of Hong Kong)
Mainland Principal Investigator: Prof Si Lan (Nanjing University of Science and Technology)
The performance of metallic glasses is known to be closely related to the structure and dynamics of the corresponding glass-forming metallic liquids, which exhibit abundant phase transformation behavior during cooling and heating. Therefore, studying the phase transformation kinetics of glass-forming metallic liquids can provide theoretical guidance for developing bulk metallic glasses and tuning their properties. This project proposes to conduct in-situ synchrotron/neutron scattering experiments in a containerless environment with the help of electrostatic levitation to explore the evolution of the cluster connection modes at different stages of liquid-state phase transformations. In particular, the project will focus on the development of medium-range order during the following phase transitions: 1) the polyamorphous phase transitions occurring at a temperature below the crystallization temperature but above the glass transition temperature; 2) liquid-liquid phase transition below the melting point but above the crystallization temperature; 3) Structure crossover well above melting point in glass-forming metallic liquids. Accordingly, the correlation between the medium-range order and the phase transformation will be established, thus decoding the phase transformation's underlying mechanism. The applicant has developed extensive and long-term collaboration with the mainland's collaborator in several areas, including amorphous phase transitions, medium-range order, scattering experiments and modeling, etc. Our joint project will deepen the understanding of the mystery of structure for glass-forming metallic liquid and guides the development of high-performance metallic glasses.
Lithium Metal Batteries with Long Life, High Energy Density and High Safety
Hong Kong Principal Investigator: Prof Guohua Chen (City University of Hong Kong)
Mainland Principal Investigator: Prof Baohua Li (Tsinghua Shenzhen International Graduate School)
Compared with the commercialized anode materials in batteries, lithium (Li) metal anode delivers higher energy density yet lower stability. It will react with electrolytes and causing severe loss of active materials and low Coulombic efficiency of batteries. Currently, strategies to augment Li metal anode are focusing on modification of current collector, adjustment of electrolyte and construction of artificial SEI film, while it is still challenging to let the Li metal batteries fulfilling the practical requirement. Among the reasons for this circumstance, the lack of knowledge on the basic physical and chemical process during the plating/stripping of Li metal is the dominating one. Therefore, in this program, we propose to disclose the structure evolution of Li metal and development of the SEI on top of Li metal by using various In-situ and Quasi In-situ methods. Further, the basic physical, chemical and electrochemical process such as the atomic structure, compositions and ion transportation mechanism of the interface between the Li metal and SEI film will be uncovered either. On top of these, the structure design during the plating process, regulation of the solid to liquid interface and construction of the solid to solid interface would be adopted to perfect the electrochemical performance of Li metal anode. The ultimate goal for this research is to disclose the evolution process of Li metal during the plating/stripping process, and improve the electrochemical performance of Li metal anode directionally, boosting the practical usage of high energy density Li metal batteries.
Synthesis of novel Conducting Metal Organic Frameworks for Selective Electrochemical Reduction of CO2 to C2+ Fuels
Hong Kong Principal Investigator: Dr Xunjin Zhu (Hong Kong Baptist University)
Mainland Principal Investigator: Dr Fuxiang Zhang (Dalian Institute of Chemical Physics)
Significant demands on low-carbon, zero-carbon and carbon-negative technologies are elicited to achieve the carbon peak and neutrality targets. Electrochemical reduction (ECR) of carbon dioxide (CO2) into value-added fuels and chemicals has been recognized as a promising pathway to diminish and utilize the superfluous CO2. However, due to CO2 is a relatively inert and stable triatomic molecule that exists at ambient temperatures and pressures. The electrochemical reduction of CO2 needs to be carried out at a higher negative potential with multiple protons coupled electrons transfer process to produce C1, C2, and C2+ products, which result in low selectivity and serious hydrogen evolution side reaction. Starting from the rational design of porphyrin molecular catalysts, this project intends to optimize the target molecular catalysts with high selectivity for reducing CO2 to C2+ products, such as ethanol and ethylene etc., through high-throughput screening. And then to construct conducting MOFs catalysts for heterogeneous catalysis by using the optimal porphyrin molecular catalysts as ligands. On this basis, in-situ synchrotron radiation and infrared/Raman coupling technology are planned to use for systematically studying the performance and structure relationship of catalysts, improving the understanding of structure-activity relationship, and then guiding synthesis of novel conducting MOF electrocatalysts that features high selectivity (>85%) for C2+ products of ECR.
Understanding Users' Deep Engagement and Deviant Behaviors in Metaverse through the Sociotechnical Perspective: A Mixed-Methods Research Approach
Hong Kong Principal Investigator: Prof Mei-Kwan (Christy) Cheung (Hong Kong Baptist University)
Mainland Principal Investigator: Dr Zhongyun Zhou (Tongji University)
Layman summary: (No more than 1 page)
The Metaverse, the digital reality where people work, play, and socialize, is expected to be the next-generation digital society. Understanding user engagement and platform safety are of great importance. In recent years, we have witnessed the emergence of a particularly brutal and insidious form of online deviant behaviors across digital platforms. The safety of the emerging Metaverse is expected to be at risk due to the massive growth in adoption and use. The project is to understand and identify the unique mechanism explaining in-depth use and deviant use in the Metaverse. In particular, the project aims to investigate the nature and formation of deviant behaviors in the Metaverse and identify technology-based prevention strategies. The project adopts mixed methods research approach by combining the technical and the social perspectives to give a holistic explanation of the synergistic mechanisms of user behaviors in the Metaverse. The project first develops a set of typologies of deviant behaviors; and explain deviant behaviors in the Metaverse and empirically tests the technology-based design for preventing deviant behaviors in the Metaverse. Guidelines and suggestions to stakeholders of the Metaverse for designing an engaging and healthy virtual space for their users will be provided based on the findings of our project.
Multi-scale Spatio-temporal Modeling, Learning, and Inference Methods for Complex Infectious Disease Systems
Hong Kong Principal Investigator: Prof Jiming Liu (Hong Kong Baptist University)
Mainland Principal Investigator: Prof Benyun Shi (Nanjing Tech University)
Understanding epidemiological characteristics and spatio-temporal transmission patterns of infectious diseases plays essential roles in developing effective and efficient disease intervention strategies. However, the epidemic dynamics are extraordinarily complex, as they are influenced by various impact factors at the macroscopic level and the molecular evolution of pathogens at the microscopic level. Moreover, due to underreporting and misreporting of cases, the transmission dynamics of many contagious diseases are only partially observable, making the common practice of disease intervention inefficient. Therefore, there is a pressing need to model, learn, and infer the underlying disease transmission by making full use of observational data from a variety of sources and over a wide range of scales.
First, human mobility and their demographic structure play critical roles in formulating social contact patterns, which directly affect the disease transmission across various spatio-temporal scales. Therefore, how to model the epidemic dynamics from heterogeneous observational data at varying granularities is the first challenge that we aim to address. Second, in addition to the direct impact factors, there are also indirect factors such as climate change and socio-economic status, whose impact on the epidemic has yet to be explored. Recently, extensive machine learning methods have been developed to characterize the dependencies between factors and epidemic dynamics in a purely data-driven manner. However, how to integrate disease-specific knowledge into the learning process to enhance the model accuracy and interpretability is seldom investigated. Third, the evolutionary analysis of viral pathogens can provide additional information for epidemic inference from genetic data. Therefore, how to simultaneously infer both epidemiological and evolutionary characteristics of viral pathogens remains great challenges.
Accordingly, in this project, we aim to develop a unified computational approach, which is composed of three interrelated components: (i) A multi-granularity spatio-temporal transmission model based on human mobility and demographic structure; (ii) A hybrid data-driven epidemic-informed machine learning method that integrates various direct and indirect factors with disease-specific knowledge; (iii) A phylodynamic inference method that unifies molecular evolution and epidemic dynamics in structured populations. We will evaluate the developed approach by carrying out case studies on two typical infectious diseases, COVID-19 and AIVs, as globally encountered.
In summary, this project will offer novel computational solutions to the fundamental challenges of heterogeneous data modelling, machine learning, and statistical inference, and provide practical tools for AI and data science-enabled disease intervention and control.
Robotic Capsule Endomicroscopy and Diagnosis of Gastric Intestinal Metaplasia
Hong Kong Principal Investigator: Prof Hongliang Ren (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Zhen Li (Qilu Hospital of Shandong University)
Gastric intestinal metaplasia (GIM) is recognized worldwide as a precancerous lesion of the gastric mucosa, and its early diagnosis and accurate evaluation are important for preventing gastric cancer. Most patients with GIM are asymptomatic or with non-specific digestive symptoms (such as abdominal distension, abdominal pain, heartburn, etc.). Thus, it is highly desirable to investigate a novel modality for GIM screening that can make an accurate diagnosis more efficiently and comfortably. Moreover, it is also essential to provide a more precise evaluation and standardized surveillance follow-up for patients with GIM of different severity. For this purpose, the main research tasks include:
1) Robotic OCT (optical coherence tomography) Capsule Endomicroscopy (ROCE) by magnetic navigation & control to achieve comprehensive observation of the gastric mucosa and precisely assess the extent and degree of GIM lesions;
2) An artificial intelligence (AI)-controlled OCT capsule endoscopy robot with in vivo tissue sampling function that can navigate to target lesion sites precisely for tissue collection, pathological diagnosis and molecular biology analysis;
3) AI identification models and quality control systems for specific anatomical sites of the stomach and GIM lesions. We will use deep learning algorithms to evaluate the reliability of ROCE and the accuracy of capsule-based fine tissue sampling;
4) Establishing animal models of GIM, to dynamically observe the effect of bile acid diet on GIM using OCT capsule robots, and to assess the molecular biological changes before and after the occurrence and alongside the progression of GIM using ROCE sampling, which will allow the exploration of potential biomarkers for GIM monitoring.
This project addresses the clinical difficulties of accurate screening and monitoring follow-up of asymptomatic people with intestinal metaplasia of the gastric mucosa. The innovative robotic capsule endoscopy based on OCT microscopic imaging will use artificial intelligence quality control and real-time targeted biopsy to achieve a minimally invasive, painless and accurate diagnosis and evaluation of GIM.
Study the Roles of Mitochondrial DNA Methylation in Regulating Nucleoid Phase Separation and Transcription Modulation
Hong Kong Principal Investigator: Prof Wai-yee Chan (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Xingguo Liu (Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences)
The mitochondrial nucleoid is a special sub-organelle responsible for mitochondrial DNA (mtDNA) storage, replication, and transcription. However, the mechanism of nucleoid assembly and function is still poorly understood. Our two groups have worked together for many years on the epigenetic regulation of mitochondria and have published three high-impact papers on this topic (Nature Cell Biology 2015, Cell Metabolism 2018, Nature Structural & Molecular Biology 2021). Recently, we proposed a novel model of nucleoid self-assembly and transcription modulation via phase separation (Nature Structural & Molecular Biology, 2021). The phase separation of mtDNA and TFAM drives nucleoid self-assembly, which further recruits transcription initiation, elongation, and terminational factors and promotes transcription after enriching the substrate for the transcription reaction.
In this proposal, we want to further study how mtDNA methylation regulates nucleoid assembly and transcription. We aim to identify the phase separation remodeling by mtDNA methylation in pluripotent stem cells (PSCs) and somatic cells such as cardiomyocytes and to reveal the mechanism through which mtDNA methylation regulates mitochondrial transcription via phase separation. This study seeks to reveal novel mechanisms of mitochondrial nucleoid structure and function, which will promote studies on cell fate and embryonic development.
Silicon Photonic Integrated Circuits Enabled Intelligent Multi-Dimensional Optical Signal Processing for High-Capacity Communication Systems
Hong Kong Principal Investigator: Prof Chester Ching-tat Shu (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Jian Wang (Huazhong University of Science and Technology)
To cope with the capacity bottleneck of optical communications, the space domain of light waves and multi-dimensional multiplexing fiber-optic communications have developed rapidly. Multi-dimensional multiplexing optical fiber transmission using optical vortex modes has been widely reported, but there is a lack of matched optical processing technology. Traditional optical processing is limited to a single dimension, and its integration and intelligence are limited. This project works on silicon photonic integrated circuits enabled intelligent multi-dimensional optical signal processing for high-capacity communication systems. Focusing on key scientific issues in basic theories, key devices and technology applications, the main research include: 1) study on the fiber-chip-fiber optical vortex coupling evolution mechanism; 2) study on the principle of broadband polarization diversity optical vortex (de)multiplexing; 3) design and fabrication of multi-dimensional silicon photonic integrated circuits; 4) characterization of multi-dimensional optical signal processing using silicon photonic integrated circuits; 5) multi-dimensional channel unscrambling by intelligent unsupervised learning; 6) multi-dimensional reconfigurable optical add/drop multiplexing and self-configuring optical switching. The Mainland side focuses on multi-dimensional optical processing with optical vortices, the Hong Kong side focuses on intelligent optical processing with probabilistic shaping signals, and they take complementary advantages. The Mainland side provides the support of optical vortex, reconfigurable optical add-drop multiplexing, and self-configuring optical switching, while the Hong Kong side provides the support of probabilistic shaping signals and unsupervised learning enabled channel unscrambling. The project will enable both sides to grasp skills of fabrication techniques of silicon photonic integrated circuits and intelligent multi-dimensional optical signal processing applications, and promote long-term co-operation.
Study of Very High Energy Gamma-Ray Radiation from the Sun Based on LHAASO Observation
Hong Kong Principal Investigator: Prof Chun-yu Ng (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Zhe Li (Particle Astrophysics Division / Institute of High Energy Physics, Chinese Academy of Science)
We propose to observe the Sun with the Large High Altitude Air Shower Observatory (LHAASO). Based on previous observations and theoretical understandings, the Sun emits gamma rays due to high-energy cosmic particles (cosmic rays) colliding with the solar atmosphere. However, this process is complicated by the complex and dynamic solar magnetic fields, and much is still not known about the detailed production process of solar gamma rays. There are no good explanations for the current solar gamma-ray observations.
LHAASO is one of the Chinese key national science and technology infrastructure facilities, just recently finished construction in 2021. LHAASO is the best solar gamma-ray observatory in the foreseeable future. Given that the Sun varies with time in the form of 11-year solar cycle (2022 is the peak of cycle 25), now is the best time to start observing the Sun to provide the best coverage for various stages of solar activities.
With LHAASO, we aim to expand Sun’s observation into the very high-energy regime, which will produce a picture of the Sun with the highest possible energy. We also aim to see a cutoff in the energy spectrum, which will reveal the characteristic magnetic fields that are responsible for the production of solar gamma rays. We aim to demonstrate the first direct link between solar magnetic fields and the gamma-ray data, providing an important perspective in solving the solar gamma-ray puzzle. This will also allow gamma rays to be a novel probe of the solar magnetic fields that have not been observed before. The improve understanding on solar magnetic fields could ultimately allow us to better understand solar dynamics and space weather, and thus to better safeguard our infrastructures from explosive solar activities.
Characterization of the Endoplasmic Reticulum-Mitochondria Contact Site in Plant Cells and its Function in Mediating Mitochondria Recycling
Hong Kong Principal Investigator: Dr Byung-ho Kang (The Chinese University of Hong Kong)
Mainland Principal Investigator: Dr Pengwei Wang (Huazhong Agricultural University)
Mitochondria play critical roles in generating ATP and cell signaling in eukaryotes. The electron transport chain and redox reactions in mitochondria expose them to constant oxidative damage. Eukaryotic cells are equipped with quality control systems to remove superfluous or damaged mitochondria from their healthy mitochondrial pools. Autophagy is a lysosome/vacuole-dependent degradation of intracellular materials, including unwanted organelles or pathogens, after capturing the cargos in double-membrane carriers called autophagosomes. Mitophagy is the autophagic recycling of mitochondria and was shown to operate in plant cells. Studies of yeast and mammalian cells revealed that the endoplasmic reticulum (ER) is the site for initiating the construction of autophagosomes and their growth. Mitophagy is also dependent on the ER-localized factors. Recent studies on plant autophagy support that the ER may serve as a hub for nucleating autophagy signaling and supply lipids for autophagosome membranes. Functional characterization of the ER in plant autophagy has been slow compared to that of yeast or mammalian systems. Almost nothing is known about the link between the ER and mitophagy in plants. Recently my group in CUHK has established an Arabidopsis system for dissecting the molecular mechanisms of plant mitophagy where we can damage mitochondria with uncouplers to trigger their autophagic elimination. As we can stimulate mitochondria recycling in a synchronous manner, it is possible to monitor Arabidopsis mitophagy with time-resolved microscopy analyses and biochemical assays. Taking advantage of the experimental system, we identified Friendly as a factor required for mitophagosome assembly. My collaborator, Dr. Wang’s group at the Huazhong Agricultural University, identified outer mitochondrial membrane proteins that interact with a family of ER membrane proteins localized at ER-organelle membrane contact sites. They also uncovered candidate proteins mediating ER dynamics for mitochondria biogenesis and recycling. Our preliminary data indicate that the mitochondria and ER proteins are involved in uncoupler-induced mitophagy and mitochondria turnover during metabolic switching. My group has a specialty in advanced electron microscopy and 3D cellular imaging. Dr. Wang’s group has studied ER-organelle contact sites and an excellent plant molecular biology setup. We will join forces under the proposed research project to investigate the ER-mitochondria contact sites (EMCSs) for mitophagy and mitochondria division. Components of EMCSs will be determined and their working mechanisms will be characterized. We expect to publish papers in top-notch journals, foster academic exchanges, and train future researchers to enhance agriculture research quality in Hong Kong and China.
Mathematical and Numerical Studies of Linear and Nonlinear Time-Harmonic Maxwell Systems with Singularities and Oscillations
Hong Kong Principal Investigator: Prof Jun Zou (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Huoyuan Duan (Wuhan University)
This three-year project aims to carry out a systematic mathematical and numerical study of time-harmonic Maxwell systems with singularities and oscillations, one of the most popular and important mathematical models that have been used in wide applications that involve scattering and propagation of electromagnetic waves. There are three general mathematical difficulties that make it challenging to construct stable and efficient numerical methods for solving these models, especially for nonlinear models with highly oscillatory solutions, and even more challenging to analyse the quantitative numerical stability and accuracies of these methods:
(1) the solutions of these models often have strong singularities or low global regularities, arising either from the corners or edges of the domains, or from the jumps of model parameters across the interface of different media, or from the nonlinearities in the models;
(2) the kernel space of the curl operators involved in the models is infinite;
(3) the resulting discrete systems or numerical solutions often have high indefiniteness or high oscillations because of high wavenumbers and nonlinearities in the models.
Edge element methods and adaptive methods have been proved to be very effective for standard Maxwell systems with singularities, but there are still no much studies of how to construct these methods and to quantitatively understand and analyse their convergence in the aforementioned three practically important scenarios. These are the major motivations of this project, to make a significant contribution in this direction. More specifically, this project will develop some stable and efficient edge element methods, especially some interior penalty edge element methods and adaptive methods based on a posteriori error estimate. A large number of numerical simulations will be conducted to illustrate and help improve these methods. Furthermore, a general mathematical theory will be developed to help analyze the stabilities, accuracies and optimal convergence of these methods, as well as to help understand how the high wavenumber, the nonlinearity and singularities affect the numerical stability and accuracies. The project is intended to cover a number of widely used nonlinear electromagnetic models.
The Role and Mechanism of Focal Adhesion Protein Kindlin-2 in Distraction Osteogenesis
Hong Kong Principal Investigator: Prof Gang Li (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Guozhi Xiao (Southern University of Science and Technology)
Distraction osteogenesis (DO) effectively stimulates the formation of new bone tissues through slow mechanical stretching, which has been widely applied in orthopedic surgery. However, the molecular mechanisms on how extracellular mechanical stretching signals translate into intracellular biochemical signals during the DO process and how these signals promote bone formation remain poorly defined. It is known that osteocytes buried in the bone matrices play an important role in mediating mechanical loading induced osteogenesis. Focal adhesions are a class of macromolecular assemblies that connect cells and extracellular matrices, and also play an important role in mediating mechanical signaling. We have demonstrated in our previous studies that the key focal adhesion protein Kindlin-2 regulates bone mass and bone remodeling through regulating bone formation and bone resorption processes through osteocytes. Importantly, we also demonstrated that deleting Kindlin-2 expression in osteocytes impairs the mechanical loading-stimulated bone formation. Based on these previous results, we hypothesize that DO may promote bone formation through upregulating Kindlin-2 in osteocytes. In this proposal, we will use our unique and well-established transgenic mouse models with Kindlin-2 overexpression or knockout, mouse and rat DO models to determine the roles and molecular mechanisms on Kindlin-2 regulating bone formation in DO. Results obtained from this study will lead to a better understanding of the biological mechanisms of DO, in particular the roles of Kindlin-2, and further development of new strategies for targeting Kindlin-2 to improve outcomes of DO clinical applications.
Grancalcin+ Immune Cells in Osteoarthritis: Cellular and Molecular Targets for Future Intervention
Hong Kong Principal Investigator: Prof Yangzi Jiang (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Changjun Li (Central South University)
Osteoarthritis (OA) is the most common form of arthritis that leads to chronic pain and disability, and it significantly decreases the patient’s quality of life. More than half of older adults suffer from this chronic inflammatory disease. Moreover, treatments available for OA are limited by the incomplete understanding of its pathology. The immune system plays crucial roles in OA progression, but the underlying mechanism remains to be clarified.
Recently, we discovered that a group of grancalcin-positive (GCA+) immune cells, mainly neutrophils and macrophages, accumulated in the bone marrow during ageing and secreted GCA that promoted skeletal ageing (Cell Metabolism, 2021). We here hypothesise that this newly identified immune cell population plays a significant role in OA development.
In this proposed project, we will investigate the role and mechanism of GCA+ immune cells in OA using comprehensive models, including gene and cell conditional knockout animals and human cartilage organoids. First, we will take advantage of conditional GCA gene knockout mice and mice with depletion of GCA+ cells to verify the effects of the GCA protein and GCA+ immune cells on OA; second, we will analyse the OA-related phenotype of chondrocyte- specific GCA receptor gene knockout mice and the network of GCA/GCA receptor/downstream molecules; finally, we will explore the strategy of intervening in GCA’s interaction with its downstream targets to alleviate OA in animal models and human chondroid organoids.
The research team combines strengths and the most advanced research platforms in the fields of immunology, metabolism study, and genetic animal models from Central South University (CSU, Xiangya hospital, Changsha) and in the fields of stem cells, tissue engineering, and OA pathology research from the Chinese University of Hong Kong (CUHK, Hong Kong). Our results will provide not only novel insights into OA pathology but also a new therapeutic target against which to develop effective interventions for OA treatment.
Unravelling Fusarium Wilt Resistant Gene(s) in Cavendish Banana and Exploring the Underlying Resistance Mechanisms
Hong Kong Principal Investigator: Prof Ting-fung Chan (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Guiming Deng (Guangdong Academy of Agricultural Sciences)
Banana is the fourth most important staple crop and the second-largest produced fruit worldwide. Currently, banana is cultivated in approximately 120 counties and is one of the most important plants. However, across the globe, banana cultivation is seriously threatened by the soil-borne fungus Fusarium oxysporum f. sp. cubense (Foc) and this pathogen causes Fusarium wilt (FW), a widely distributed disease across different banana-growing areas. Four variants of Foc are known, of which tropical race 4 (Foc-TR4) is the most harmful because it can infect most banana types and easily spread through infested soil, running water, and farm activities. Severe damage to the banana tree caused by Foc-TR4 infection can lead to whole-plant collapse. Although different strategies have been used to tackle FW, agronomic practices or management––such as using chemicals to eliminate Foc from the infested soil––are commonly used. However, these methods are not sustainable, and breeding of Foc-TR4-resistant banana accessions is thus the most promising, forward-looking solution. Selecting Foc-TR4-resistant bananas and deciphering their genetic content, particularly the resistance (R) gene content, can help identify Foc-TR4 R-genes for improving future crops. Nevertheless, naturally developing Foc-TR4-resistant bananas is challenging because most cultivated banana accessions are not resistant to Foc-TR4. A reference-grade genome assembly of autotriploid bananas (AAA) is currently absent owing to difficulties in assembling the genomes. After years of endeavours, our collaborators and banana breeding experts at the Guangdong Academy of Agricultural Sciences have successfully selected the ‘Zhongjiao 8 (ZJ-08)’ cultivar with high Foc-TR4 resistance. In this proposed study, therefore, we will attempt to apply the latest long-read sequencing and high-throughput chromosome conformation capture techniques and utilize our expert resources in optical genome mapping (the Principal Investigator previously received an RGC-Collaborative Research Fund for this technique) to assemble the genomes of ZJ-08 and ZJ-01 (a Foc-TR4-susceptible cultivar). The generation of two reference-quality genomes will lay the groundwork for identifying Foc-TR4 R-genes. Then, we will conduct the study at the population level to pinpoint the corresponding genetic loci and R-genes. We will further validate the candidate genes using CRISPR-Cas9 and transgenic technologies. This collaborative project will combine the expertise of the genomics and banana crop breeding teams to tackle one of the most destructive and cost-incurring crop pathogens. The reference-quality genomes produced will benefit the overall banana-related research.
Red Light-Gated Optogenetic Control of Calcium Signal for Cancer Immunotherapy
Hong Kong Principal Investigator: Prof Liting Duan (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Haifeng Ye (East China Normal University)
T cell-based immunotherapy has emerged as a powerful treatment option for cancer and brought hope to millions of cancer patients in the world. However, the clinical translation and therapeutic efficacy of T cell therapy are limited by modest anti-tumor activity and toxicities that may lead to severe side effects such as B cell aplasia and cytokine release syndrome.
Using a remote switch to precisely treat cancer is no longer science fiction but a dream coming true, thanks to the recent development of light-gated optogenetic tools. Here to address the current limitations of T cell therapy, we propose opto-immunotherapy that uses red light signals to instruct the cancer-killing activities of T cells in vivo for improved anti-tumor efficacy and decreased toxicity. This proposal is built on three stages. (1) We will develop a highly sensitive red light-gated optogenetic controller of Ca2+ signals (RedCa). Using red light as the switch, RedCa can remotely and non-invasively activate Ca2+ signaling with reversibility, high spatiotemporal resolution, tight controllability of signaling amplitudes, and deep-tissue penetration. (2) As Ca2+ signaling is of paramount importance to the regulation of T cell functions during the fight against cancers, we will utilize RedCa to control the cancer-killing activities of T cells. We will engineer primary T cells so that red light can enhance the cytotoxicity and anti-cancer cytokines/antibodies expression of T cells with spatiotemporal and dosage controllability. (3) By transferring the engineered primary T cells into mouse tumor models, we will use red light to precisely control the cancer-killing activities of T cells in vivo at the desired time, locations, and with desired dosages for effective, safe, and well-controllable precision cancer therapy.
Our project aims to generate multifaceted impacts. We expect that with excellent tissue penetration, spatiotemporal precision, tunability of activation levels, and fewer off-target effects, RedCa can be a powerful tool to facilitate the investigation of Ca2+ signaling in a myriad of biological processes in both physiological and pathological conditions. Moreover, our exploration of red light-gated T cell treatments with enhanced efficacy and safety can open up novel avenues to precision immunotherapy for different cancers.
This highly interdisciplinary project capitalizes on the complementary expertise of the Hong Kong team in constructing novel optogenetic control of Ca2+ signaling and the Mainland team in developing innovative gene-/cell-based immunotherapy. Our preliminary results have set a solid foundation for both teams to achieve the objectives.
Analysis of Plasma Epstein-Barr Virus (EBV) DNA and EBV Antibody for Early Detection of Nasopharyngeal Carcinoma and Protocol Optimisation
Hong Kong Principal Investigator: Dr Wai-kei Lam (The Chinese University of Hong Kong)
Mainland Principal Investigator: Prof Mingyuan Chen (Sun Yat-sen University)
Background: Nasopharyngeal carcinoma (NPC) is a prevalent cancer in the southern parts of China and it ranks among the ten most common types of male cancer in Hong Kong. Solid evidence has shown that NPC patients would enjoy better survival outcome if their diseases are discovered early. Unfortunately, the majority (~80%) of NPC patients present with advanced-stage disease with a 5-year overall survival rate of only 50%, while the 5-year overall survival rate of early-stage NPC is above 90%. There is a strong need for early detection of NPC given the high disease burden of NPC in endemic regions. The Hong Kong team has previously demonstrated that plasma Epstein-Barr virus (EBV) DNA testing could identify early-stage NPC, accompanied with a survival benefit. Together with serum EBV antibody, these two modalities have been validated for screening of NPC in separate studies. However, it would be difficult to directly compare the diagnostic performance results of the two methods due to the difference in the study population and design.
Objectives: This project aims to systematically evaluate the diagnostic performances of serum EBV antibody and plasma EBV DNA for screening of NPC through concurrent testing of both biomarkers in the same study cohort. Based on such knowledge, we aim to devise an optimal EBV biomarkers-based screening protocol in terms of diagnostic performance and cost-effectiveness.
Experimental plans: A prospective analysis of high-risk individuals defined by a positive family history of NPC is proposed. Study participants will be subject to concurrent plasma EBV DNA and EBV antibody testing for the purpose of NPC screening. The plasma EBV DNA testing protocol involves a sequential real-time polymerase chain reaction-based and next-generation sequencing-based analysis. The EBV antibody testing protocol targets 2 IgA-anti-EBV antibodies, i.e. EBNA1/IgA and VCA/ IgA. Participants who are defined as screen-positive by either plasma EBV DNA or EBV antibody will be referred for endoscopy for confirmation of NPC status.
Expected outcomes: This study will fill knowledge gaps in the diagnostic performance of plasma EBV DNA versus EBV antibody for screening of NPC. These data will be valuable for proposing an optimal NPC screening protocol in terms of cost-effectiveness.
Preliminary findings: Archived blood samples of 8 NPC and 10 non-NPC cases from another study cohort were analysed for both plasma EBV DNA and EBV antibody. The preliminary results reveal a difference in the diagnostic performance of the two biomarkers.
Stochastic Multiobjective Optimization and Applications
Hong Kong Principal Investigator: Prof Xiaojun Chen (The Hong Kong Polytechnic University)
Mainland Principal Investigator: Prof Xinmin Yang (Chongqing Normal University)
Stochastic multiobjective optimization models have been successfully used for data analytics, prediction and system control in science, engineering and economics. Many problems in machine learning and artificial intelligence are essentially complex mathematical optimization problems with multiple objectives. Mathematically, these problems have the difficulty of conflicts between different objectives and uncertainties in data and parameters. Moreover, in the era with big data where the dimensionality of models increases extremely rapidly, multiobjective optimization models usually have another difficulty of the high dimensionality. To overcome these difficulties, this project will develop innovative optimization theory and efficient algorithms for multiobjective optimization models. In theory, we will establish new optimality conditions, scalarization formulations, risk measures and the statistical robustness of stochastic multiobjective optimization. This new theory will be applied to systematically investigate the development of efficient algorithms for stochastic multiobjective optimization problems with applications. In particular, we will develop stochastic algorithms and splitting algorithms using advanced scalarization formulations and analyze the convergence and convergence rates of these algorithms for finding some optimal solutions or stationary points. Moreover, we will derive qualitative and quantitative convergence for the solutions obtained from stochastic algorithms and sample average approximations. We will use our new theoretical results and numerical algorithms to some data-driven applications including feature selection, model selection and predictive analysis in finance and economics with real data from banks and markets.
Mathematical Modeling and Analysis on the Predator-mediated Competitions and their Biological Consequences
Hong Kong Principal Investigator: Prof Zhian Wang (The Hong Kong Polytechnic University)
Mainland Principal Investigator: Prof Yuan Lou (Shanghai Jiao Tong University)
Predation and competition are two primary determinants of the structure and functioning of ecological systems for maintaining the biological diversity and balance. Although the importance of predator-mediated competitions has long been recognized in the biological literature, there are not theoretical works to qualitatively validate this important phenomenon and to verify which processes play major roles. The research projects in this proposal will come up with mathematical models along with rigorous analysis and numerical simulations to interpret some key biological observations reported in the literature. Our research results will not only provide theories to advance the understanding of the mechanism and complexity underlying the predator-mediated competitions but also suggest what measures can be taken to maintain the biodiversity and biological balance.
Tin-Based Metal Halide Perovskites for X-Ray Detectors
Hong Kong Principal Investigator: Prof Feng Yan (The Hong Kong Polytechnic University)
Mainland Principal Investigator: Prof Shihe Yang (Peking University)
X-ray detection has broad applications in medical imaging, crystallography, security inspection, non-destructive industrial radiography, and space exploration. Low-dose X-Ray detection is preferred in many applications, making the development of ultrasensitive X-Ray detectors in high demand. Metal halide perovskite materials have been found to be ideal candidate materials for next-generation X-Ray photodetectors thanks to their strong X-Ray absorption, low-temperature solution processability, convenient device fabrication and high carrier mobilities. Direct X-Ray detectors based on Pb-halide perovskites have been found to be much more sensitive than conventional X-Ray detectors. However, Pb-based perovskites are toxic and cannot be used in many scenarios. Hence, Sn-halide perovskites are excellent alternative materials for perovskite X-Ray detectors because they are less toxics and can show higher carrier mobilities than Pb-based counterparts. Herein, we plan to develop direct X-Ray detectors based on Sn-halide perovskites, which have not been reported until now.
The proposal is initiated on the base of the long-term collaboration between the two research groups on phototransistors and perovskite solar cells from 2011. Various strategies have been developed by us to improve the stability of Sn-based perovskites, elongate carrier lifetimes in the materials and optimize the device performance, which ensure the success of the proposed study. We will focus on the following objectives: (1) To realize Sn-halide perovskites with high air stability, high carrier mobility and suppressed ion migration by introducing additives and compositional engineering; (2) To prepare high-quality and thick Sn-based perovskite films by solution processes; (3) To fabricate perovskite photodetectors with rational device design for highly sensitive X-Ray detections; (4) To realize highly sensitive flexible X-Ray detectors that can conform to curved body surfaces.
The success of the proposed research will not only lead to patentable technology for novel X-Ray detectors but also provide a deep understanding on the optoelectronic response of Sn-halide perovskite materials under X-Ray illumination. This novel X-Ray detector is low-cost, lightweight, miniaturized, mechanically flexible and easy to fabricate, which will definitely find many niche applications particularly in wearable electronics in the near future.
An Integrated System of Unmanned Aerial Vehicles and Unmanned Surface Vehicles for Smart Maritime Support in Guangdong-Hong Kong-Macao Greater Bay Area
Hong Kong Principal Investigator: Dr Wei Liu (The Hong Kong Polytechnic University)
Mainland Principal Investigator: Prof Yong Ma (Wuhan University of Technology)
This project aims to develop essential technologies and modelling tools for smart maritime support that optimally integrates unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), satellite navigation system and digital communication system, thereby enhancing the efficiency of maritime monitoring/surveillance, search and rescue. With the rapidly growing international trade, the surging marine traffic is imposing challenges in terms of maritime monitoring, safety and security, especially for economic hubs with complex open-water environment conditions such as the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Conventional maritime support measures, such as surveillance vessels and helicopters, consume tremendous financial and human resources and have limited flexibility. Emerging artificial intelligence technologies, USVs and UAVs bring new opportunities for maritime support operations. This project, by integrating emerging USV and UAV technologies, is particularly timely and relevant for accommodating the urgent and fast-growing demand for maritime support in GBA.
This project will advance modelling tools, algorithms and essential technologies for the smart maritime support platform that can optimally integrate the coordinated operation of USVs and UAVs. The smart maritime support platform to be developed consists of five major modules: multi-source maritime data management system, multi-dimensional maritime sensing system, robust maritime communication network, coordinated USV-UAV path-planning module, coordinated USV-UAV trajectory control module. The performance of the aforementioned tools to be developed will be evaluated by simulation studies based on local testbeds in mainland China and simulation environments of GBA.
The novelty of this project is three-fold: (i) it will develop an unmanned maritime support system with a robust sensing and communication network for GBA with complex environment conditions, which is able to enhance efficiency of maritime surveillance, searching and rescuing; (ii) it will establish an integrated USV-UAV system with coordinated path planning to overcome the deficiencies of standalone UAV and USV systems, which can accommodate complex and extreme scenarios; (iii) it will develop novel USV-UAV integrated trajectory control strategies (including precision landing of UAV on the paired USV) that accommodate stochastic external conditions. The expected outcomes of this project will provide both theoretical and technical support for the development of a smart maritime support platform.
This project will generate new knowledge on the automation in maritime support operation and management by incorporating the USV-UAV integrated system. The proposed coordinated path planning and joint autonomous control strategies for USVs and UAVs will support the decision-making for an efficient, safe and secure maritime system in GBA.
Time-sequence regenerative repair of atherosclerotic blood vessels with Janus cardiovascular stents
Hong Kong Principal Investigator: Dr Xin Zhao (The Hong Kong Polytechnic University)
Mainland Principal Investigator: Prof Zhilu Yang (Southern Medical University)
Stenting is prescribed as clinical treatment of cardiovascular diseases (CVDs), but the surgical procedure induces inevitable mechanical damage to the vascular site. This results in inflammation and poor vascular health, potentially leading to stent thrombosis and restenosis. Although the introduction of nitric oxide (NO, a key bioactive substance for the maintenance of vascular homeostasis) into stent coating can alleviate these complications, the existing hydrogen peroxide (H2O2) in the inflammatory environment will deplete NO, compromising the therapeutic efficiency of the stent coating. Moreover, the physiological microenvironment faced by the inner and outer walls of the stents has drastic "spatial" differences, where the inner wall contacts flowing blood and regenerative endothelial cells (ECs), while the outer wall attaches to the inflammatory and H2O2 generating vascular lesions. That is, a unitary design on both sides of the stent fails to achieve corresponding targeted treatment and this indistinguishable therapy may lead to large consumption of therapeutic molecules. Thus, a cardiovascular stent whose outer surface can scavenge H2O2 to alleviate inflammatory microenvironment while the inner surface can generate NO continuously to promote EC-mediated endothelialization on top of inhibiting thrombosis and restenosis is vastly beneficial in treating CVDs.
In this project, we propose a surface collaborative modification concept of Janus cardiovascular stents based on spatiotemporal differences in vascular lesion’s physiological microenvironment. Through femtosecond laser technology (FSL), we will etch the outer stent wall with vascular smooth muscle cell (VSMC)-alike micro-nano topological patterns and load the channels with antioxidant rosmarinic acid (RA). Direct contact of the outer stent with the attached vascular interface ensures a quick action of RA and scavenging of elevated inflammatory H2O2 on the inflammatory vascular lesion. Meanwhile, the inner surface of the stents will be covalently modified to form catecholmodified polyamine (CPAM) network grafted with copper-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA-Cu). This coating will sustainably catalyze the generation of endothelial bioactive agent NO from local S-nitrosothiols (RSNOs) for enhanced endothelialization and reduced thrombosis/restenosis. With both functional surfaces, the Janus stents will quickly scavenge the oxidative free radical H2O2 in the vascular lesions and continuously generate therapeutic NO from endogenous RSNOs to achieve “time-sequence regenerative repair” of diseased blood vessels. The resultant Janus stents will undergo (1) physical and chemical characterizations to examine the structure and function of the respective coatings, (2) in vitro and ex vivo assays for assessment of anti-inflammation and hemocompatibility of our Janus stents, and study of the underlying molecular mechanisms, and (3) in vivo model implantation to assess the clinical relevancy of the resultant Janus stents in terms of endothelialization and antithrombosis/restenosis.
Enhancing Digital Asset Security Based on the Blockchain Technology
Hong Kong Principal Investigator: Prof Bin Xiao (The Hong Kong Polytechnic University)
Mainland Principal Investigator: Prof Xiaotie Deng (Peking University)
The Digital asset is a kind of non-monetary assets in the form of data owned and controlled by individuals, enterprises, governments or various organizations. The scale of the digital economy exceeds 45 trillion yuan in China in 2022 according to the Chinese government, which is more than 4 times in 2012. The security of digital assets is always an important issue in their production, storage, transaction and applications. In July 2022, attackers have successfully hacked the Ali cloud and then demanded 10 Bitcoins (around $200,000) for selling over 23.88 TB data related to 1 billion Chinese citizens. Data security problems related to cloud and trading are not yet solved by current industrial and academic solutions. In this project, we study key technologies of blockchain-based digital asset security, to provide secure data search, trading, exchange and storage services.
The blockchain technology has the property of decentralization, data transparency and immutability, which can greatly enhance the digital asset security and privacy with cryptographic tools together. Thus, we conduct innovative research tasks in four aspects. (1) Secure cloud data search service: We design a novel order-revealing encryption scheme to handle the numerical search over encrypted data, ensuring secure, fair and privacy-preserving data search services between mutually untrusted clouds and data users. (2) Anti-attack digital asset trading system: We provide comprehensive security risk analysis from both technological and financial perspectives, and design layer-2 solutions to cost-effectively mitigate potential attacks. (3) Digital asset exchange traceability and privacy protection: We are the pioneer to use NFTs as on-chain credentials and provide zero-knowledge proofs, showing data exchange correctness and validity in their entire life cycle. (4) Reliable and secure decentralized digital asset storage: We convert digital assets into NFTs on side chains to make pioneering exploration to achieve cost-effective, reliable and scalable distributed data storage systems.
Our team’s previous work, an NFT trading mechanism, has been adopted by a blockchain startup Upshot One. This project will continuously advance digital asset security in several important fields, e.g., secure and privacy-preserving data search function design in untrusted environments, robust digital asset trading paradigm design to resist various attacks, novel data exchange authentication and traceability implementation, secure and scalable decentralized storage development. The project will generate long-term impact on digital asset security, and its results can be directly applied to industrial products, e.g., accelerating public data exchange and constructing practical blockchain-based data trading platforms.
Investigation of Rainstorm−Storm Surge Joint Occurrence Pattern and Induced Flooding Risk Assessment in Coastal Cities within the Greater Bay Area (GBA)
Hong Kong Principal Investigator: Dr Huanfeng Duan (The Hong Kong Polytechnic University)
Mainland Principal Investigator: Prof Feifei Zheng (Zhejiang University)
The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) possesses an important strategic position in the overall development of China. However, due to global climate change and regional weather conditions, the coastal cities within the GBA are often subject to joint occurrences of coastal rainstorms and storm surges, which likely cause severe floods and hence threaten urban safety. To address this issue, the Chinese Central Government has released the “Outline Development Plan for the GBA” in 2019 and the “Water Resources Security Plan for the GBA” in 2021, highlighting the great necessity and importance to manage coastal floods, especially for those caused by co-occurrences of rainstorms and storm surges. Consequently, the prevention and mitigation of coastal floods caused by the joint occurrences of rainstorms and storm surges have become an urgent need for the GBA. Conditioned on the complementary research advantages of the Mainland and Hong Kong collaborative teams, this joint project aims to carry out research on investigating the patterns and characteristics of joint occurrences of coastal rainstorms and storm surges, as well as developing techniques to assess the coastal flooding risks. This research focuses on two key scientific research issues: (i) the underlying patterns of rainstorm-storm surge joint occurrences; and (ii) the physical mechanisms of flooding caused by such compound events in coastal cities within the GBA. To this end, this project intends to: (1) establish joint probability distribution functions (PDFs) between coastal rainstorms and storm surges to explore the spatiotemporal patterns of these compound events in the GBA; (2) build an effective hydrologic-hydraulic coupling model that can reveal the complex hydrodynamics and mechanism of rainfall-runoff-pipe flows-river flows-tide-surge interactions during flooding events within the GBA; and (3) develop the urban flooding risk assessment method in the context of compound events where rainstorms and storm surges occur simultaneously. The research results and achievements could provide scientific guidance and technical support for effective planning and management of coastal urban flooding in the GBA, as well as offer significant references for urban flooding mitigation and prevention in other coastal cities and regions around the world.
Mechanism and Optimization for Hospital Bed Sharing
Hong Kong Principal Investigator: Prof Hengqing Ye (The Hong Kong Polytechnic University)
Mainland Principal Investigator: Prof Guohua Wan (Shanghai Jiao Tong University)
The goal of the project is to study the efficient and effective modes for bed sharing in large hospitals, so as to improve their utilization and optimize the operational performance of bed management in hospitals. There are three issues to be addressed: To study the demand patterns, characteristics and the critical factors for bed sharing in hospitals; Through field study and game modelling, to design the mechanism for bed allocation, sharing and compensation, and to develop the operational modes for bed sharing in large hospitals; Based on the results from 1 and 2, to construct the estimation models for hospital bed demand and queueing network models for bed allocation, and design the solution policies and optimization algorithms for the network models.
Plant-based Sensing for Monitoring Moisture Condition in Earthen Landfill Covers: Proof-of-Concept Study
Hong Kong Principal Investigator: Prof Kwan Anthony Leung (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Prof Rui Chen (Harbin Institute of Technology)
Extreme rainfall has increased in frequency over recent decades, particularly in subtropical regions, such as the Greater Bay Area (GBA). Excess rainfall infiltration into landfill covers is a major cause of waste mass instability and overflow of leachate collection systems, leading to socioeconomic losses and environmental pollution. This issue demands the development of large-scale monitoring systems for the moisture conditions of landfill covers to enhance the ability of engineers to assess the water storage capability of landfill covers. Although installing networks of soil moisture or suction sensors across designated areas is a common practice, doing so on a city/region scale has huge financial implications and can lead to air pollution due to the potential leakage of landfill gas along the soil–sensor interface. Thus, a low-cost, environment-friendly and large-scale monitoring approach is urgently required to improve preparations for extreme precipitation events in the future.
This project is a proof-of-concept study on a novel idea, namely, plant-based sensing, i.e., the use of urban plants as soil suction sensors. We hypothesise that soil suction change can be predicted via remote sensing of the phenotypic responses of certain shrub species, including leaf surface temperature change (due to endothermic evaporation upon transpiration) and leaf spectral reflectance (due to the resemblance of chlorophyll pigments upon photosynthesis). To test this hypothesis, we will form a cross-disciplinary team that comprises a soil scientist, a geotechnical engineer, a plant scientist, and remote sensing experts to collaboratively perform experiments and modelling to investigate the involved soil–plant–atmosphere interaction.
Four tasks are proposed to determine the feasibility of this idea. Tasks A and B are glasshouse experiments designed to subject shrub species that are native to the GBA to controlled environmental stresses that are relevant to current and future climate change scenarios. The effects of abiotic stresses on plant phenotypic responses and plant-induced suction will be investigated in detail. To understand the theoretical relationships between plant phenotypic responses and soil suction, an integrated plant ecophysiological–hydraulic modelling framework with leaf-to-canopy upscaling capability will be derived and implemented in Task C. In Task D, full-scale field monitoring will be finally conducted in four designated sites within the GBA to evaluate the efficacy of our proposed plant-based sensing technique. Through real-time monitoring of leaf surface temperature and spectral reflectance and then inputting indices into plant models, soil suction will be predicted and verified from the measurements made by networks of tensiometers.
Design and application of data-driven real-time coordinated optimization and control for multi-phase batch processes
Hong Kong Principal Investigator: Prof Furong Gao (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Prof Dewei Li (Shanghai Jiao Tong University)
Batch process is a flexible manufacturing process that can adapt quickly to market changes. Most batch processes are multi-phase in nature, but their control is handled in a single-phase manner, in which the design of one phase of the process is treated independently of the tracking performance of the other phases. Furthermore, the set-point profiles and their optimizations are determined batch-by-batch without taking into account of the tracking performance or capabilities of the previous and subsequent phases. The separated designs of set-point profile setting and tracking control and the isolated treatment of phase strategy by ignoring intra-phase contributions to the final quality lead to poor control performance and a high rate of rejects. The purpose of this project is to remove these limitations by developing data-driven real-time coordinated optimization and control for multi-phase batch processes. To accomplish this, data-driven quality modeling and prediction methods, set-point profile real-time optimization methods, and online data-driven optimal control methods will be developed. The goal is to establish a comprehensive relationship between tracking control performance, set-point optimization and refinement, and inner- and intra-phase coordination for multi-phase batch processes to ensure quality and quality repeatability.
Emergent exotic phenomena in non-Hermitian and/or incommensurate optical lattices with spin-orbit couplings
Hong Kong Principal Investigator: Prof Gyu-Boong Jo (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Prof Xiong-Jun Liu (Peking University)
In the joint project, we aim to create and observe unconventional quantum states in engineered optical lattices with spin-orbit coupling. To this end, we will introduce dissipation into the atomic system and add incommensurate auxiliary lattice potential. Several exotic phenomena are expected to emerge in experiment, including critical phases and non-Hermitian topological phases.
MIMO-Empowered Ultra-Reliable and Low-Latency Communications with Cross-Layer Optimization
Hong Kong Principal Investigator: Prof Shenghui Song (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Prof Wei Chen (Tsinghua University)
The development of innovative applications, such as virtual reality, autonomous driving, and edge AI, put stringent requirements on wireless communications in terms of throughput, reliability, latency, and security. As a result, ultra-reliable and low-latency communication (URLLC) was proposed as one of the key specifications for 5G systems and has attracted much attention from both academia and industry. However, the design and analysis for URLLC over multiple-input multiple-output (MIMO) systems are still in their infancy. In this project, we will investigate the analysis and design of MIMO-empowered URLLC systems with cross-layer optimization. It will start from the physical layer and analyze the fundamental limits over general fading channels. Then, the cross-layer design that considers both physical channel and the packet arrival in data-link layer will be investigated. Finally, the joint communication and application design will be further considered to explore different trade-offs between throughput, reliability, and latency for diverse applications. The research proposed in this project will provide insightful understanding of MIMO systems regarding the fundamental limits in the physical layer, the cross-layer design between the physical and data link layers, and the task-oriented end-to-end analysis and design for different applications. The new knowledge and algorithms developed will be tested on a demonstration platform, and the data collected from the experiments will build a solid foundation for developing future MIMO–URLLC systems.
Investigation of the Carrier Dynamics of Three-dimensionally Assembled Metal Halide Perovskite Quantum Dots in Nanoporous Templates and Exploration of Large-scale High Performance Pixelated Light-emitting Diodes Array Toward Display Applications
Hong Kong Principal Investigator: Dr Zhiyong Fan (The Hong Kong University of Science and Technology)
Mainland Principal Investigator: Dr Haibo Zeng (Nanjing University of Science and Technology)
With extensive research on metal halide perovskite light-emitting didoes (PeLEDs) in the last decade, the performance of PeLEDs has experienced significant advancements, such as record external quantum efficiencies (EQEs) of over 28% and luminance (L) of over 470,000 cd m-2. However, fabricated by the traditional spin-coating method, the majority of PeLEDs reported hitherto have small device areas (<1 cm2), and their performance exponentially shrinks with the increase in the device area. Together with the short device operation lifetime, their practical applications in displays and lighting are hampered. Therefore, there is an echoing and urgent demand for new large-area and high-performance devices and a novel process that can lead to the formation of a light-emitting layer with high uniformity and low defect density across a large area while maintaining a balance between carrier injection and recombination.
In this project, we propose to assemble perovskite quantum dots (QDs), which are promising candidates for high-performance PeLEDs, in three-dimensional porous alumina membranes (PAMs) to produce large-area, high-performance and stable PeLEDs. We will firstly develop a rational fabrication process aiming at assembling perovskite QDs in PAMs across a large area (> 4-inch wafer-scale) with high uniformity. We will then investigate the light extraction mechanisms in the QD/PAM assembly and the carrier dynamics of perovskite QDs in this unique structure. Subsequently, the uniformity/stability and large-area coverage traits brought forth by the PAM, in conjunction with the high-performance metrics of perovskite QDs, will be utilized to achieve a high external quantum efficiency of ~15%, luminance over 5,000 cd m−2, and half-lifetime of over 50 h on a 4-inch wafer-scale (7,850 mm2) device. Ultimately, we propose to explore pixelating our large-area PeLEDs to achieve 1,000 ppi to demonstrate their potential in high-resolution display applications.
This project will lead to the development of novel PeLEDs with large area and high performance, and pave the way for the practical applications of PeLEDs as an alternative candidate for next-generation displays and lighting systems by superseding current mainstream display technologies, such as OLEDs and QLEDs.
Novel ceramic-supported sub-nanoporous membranes for wastewater treatment under harsh environments: separation performances and mechanisms
Hong Kong Principal Investigator: Prof Chuyang Tang (The University of Hong Kong)
Mainland Principal Investigator: Prof Yingchao Dong (Dalian University of Technology)
Conventional membrane-based technologies face key bottlenecks such as insufficient stability and severe membrane fouling when treating challenging wastewaters involving extreme environmental conditions. Leveraging on the research consortium’s strong track records and prior collaborations, the proposed collaborative project will focus on the rational design and fabrication of highly robust ceramic-supported novel sub-nanoporous separation membranes to enable highly efficient treatment of wastewaters under representative harsh conditions. The research team will first tailor the structure and properties of highly stable ceramic membrane substrates and interlayers, and the underpinning microscale mechanism that regulates surface physicochemical properties will be revealed. The team will then rationally construct stable novel sub-nanoporous separation layers made of polyamide and inorganic materials onto ceramic substrates. The structure-property relationship for these membranes will be systematically studied. Detailed investigations will be performed to understand the separation performance, long-term stability and anti-fouling performance of the specially tailored novel membranes in extreme environments. These novel membranes will be benchmarked against commercial and other state-of-the-art membranes through systematic performance tests using both synthetic and representative real wastewaters. Advanced characterization techniques and simulations will be employed to reveal the microscopic mass transfer and separation mechanisms in extreme environments. Fouling models based on molecular-level interactions and interaction energy will be engaged to reveal the qualitative and quantitative contributions of different fouling mechanisms. The proposed project will address the key fundamental scientific issues and practical aspects for membrane-based wastewater treatment in extreme environments. It is expected to provide important new scientific and technical guidelines for environmental applications of membrane separation technology.
High-Resolution, Printed Perovskite Light-Emitting Diodes
Hong Kong Principal Investigator: Dr Jitae Kim (The University of Hong Kong)
Mainland Principal Investigator: Prof Mingjian Yuan (Nankai University)
The manufacture of light-emitting diodes is a core task in displays and hybrid perovskites can offer great innovations due to high-color-purity luminescence and low-cost solution processability. This project aims to develop a new printing method for high-resolution, high-brightness perovskite light-emitting diodes. The strategy is to combine (i) the compositional/crystal engineering of perovskites and (ii) the development of electrohydrodynamic nanoprinting. We expect the outcome of this project to open a high-precision and cost-effective manufacturing routes for new display devices, enhancing the international competitiveness of the display industry.
Multifunctional polymer composites and bio-adaptive nerve conduits for peripheral nerve regeneration and repair
Hong Kong Principal Investigator: Prof Min Wang (The University of Hong Kong)
Mainland Principal Investigator: Prof Xuemin Du (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences)
Tissue repair and functional restoration of peripheral nerves with large-gap defects is one of the major challenges in regenerative medicine. Using autologous grafts or transplanted tissues to treat peripheral nerve injuries has been the gold standard for treatments but suffers from inherent drawbacks such as limited sources of donor nerves and damages at the donor sites. Therefore, great efforts have been made to develop bioartificial nerve grafts (“conduits”) to guide peripheral nerve repair or regeneration. However, given the difficulty and complexity of peripheral nerve repair or regeneration, existing nerve conduits have limitations and cannot meet the requirements for multiscale adaptions at the conduit-tissue interface, i.e., synergistic regulation of the behavior and functions of multiple nerve cells at the cellular level and adaptive bio-integration at the tissue level, resulting in less-than-desirable nerve tissue repair and even sometimes new damages during surgery. To address the challenges, this project is proposed to develop bio-adaptive nerve conduits with multifunctional polymer composite interfaces, which will be constructed by using alginate with grafting N-hydrosuccinimide esters, chitosan hydrogels, electroactive poly(vinylidene fluoride), nerve growth factor (NGF), basic fibroblast growth factor (bFGF), and near infrared (NIR)-responsive photothermal black phosphorous nanosheets. The bio-adaptive nerve conduits will exhibit multi-features/functions, including anisotropic topography, neurogenic factor gradient through controlled release of NGF and bFGF, NIR-controlled electrical stimulation, programmable shape changes, and wet adhesion to nerve tissues that is achieved through robust intermolecular interactions between the conduit and local nerve tissue. They will realize both the synergistic regulation of multiple nerve cell types (neurons, Schwann cells, etc.) by different cues and adaptive bio-integration with nerve tissues. This project will develop methodologies and techniques for constructing multifunctional polymer composite interfaces for bio-adaptive nerve conduits, optimize the conformal, stable, and sutureless bio-integration of these conduits with peripheral nerve tissues, reveal the synergistic regulation effects of multifunctional interfaces of the conduits on the behavior and functions of different nerve cell types, and promote tissue repair and functional restoration of injured peripheral nerves with large-gap defects. The breakthroughs from the proposed research will provide new insights and strategies for addressing the clinical challenge of severe peripheral nerve injury management.
Experimental investigation on the chiral topological nanophotonics integrated with semiconductor moiré superlattices
Hong Kong Principal Investigator: Prof Xiang Zhang (The University of Hong Kong)
Mainland Principal Investigator: Prof Xiaoze Liu (Wuhan University)
Topological photonics, inspired by the discovery of topological theory in condensed-matter systems, offers novel mechanisms of optical manipulation and rich properties of light propagation for optical information technology. Based on particle interactions during light-matter coupling process, topological photonics could even go beyond the framework of condensed-matter topological theory with electron interactions. This also becomes a long-sought goal for the frontier research of topological photonics. However, the experimental investigations have so far been limited to the topological photonic models without considering the particle interactions.
In this proposal, chiral topological photonic nanostructures integrated with semiconductor moiré superlattices will be taken as a major experimental platform to investigate fundamental mechanisms and related applications of topological photonics with strong interactions induced by the bosonic particles, i.e., moiré excitons. The proposed study can open the avenue towards new topological physics with bosonic interactions that are otherwise unavailable in electronic topological insulators. Typical topics include (but are not limited to) topological solitons, topological Bose-Einstein polariton condensates, and topological quantum simulators. Achieving this goal will also lead to many applications for next-generation photonic integrated circuits (PICs) in both classical and quantum regimes such as unidirectional waveguides, chiral polariton lasers, chiral quantum logic gates. This proposal will have far-reaching significance to both fundamental research and cutting-edge applications for optical information technologies.
Spatial sorting induced synthetic pattern formation in bacterial range expansion
Hong Kong Principal Investigator: Prof Jiandong Huang (The University of Hong Kong)
Mainland Principal Investigator: Prof Xiongfei Fu (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences)
How living organisms develop various patterns and body structures is a fundamental question in biology. This question attracts many biologists, physicists, and computational scientists, such as Allan Turing, to investigate theories of self-generated life forms through system instabilities. Traditional theories often considered the process of bio-pattern formation under fixed boundaries to mimic the bio-pattern emerged in early embryonic development. However, as the cell mass starts to grow, the biological structure will expand in space, which introduces more complex instabilities that may result in new principles of self-generated bio-pattern formation.
Other than the classical theoretical approach, recent advances in synthetic biology have bred a new build-to-learn methodology to investigate the bio-pattern formation by designing and constructing gene circuits to control cell behavior. This build-to-learn approach promotes the development of theories of bio-pattern formation, through experimental synthesis of bio-patterns under rational design.
Using this approach, we together with our mainland collaborators have successfully reconstructed stripe patterns during range expansion with engineered bacteria. Later, we expanded the stripe patterns with two synthetic interacting strains. From these activities of bio-patterns synthesis, we learned that the dynamics of the leading front of range expansion plays a central role in the biopattern formation. Moreover, in recent works, we have revealed that cells in the leading front are spatially sorted according to various diversities. Based on these discoveries, we wonder whether the spatial sorting mechanism in the leading front can be applied to the design of bio-patterns, and how to experimentally synthesize new bio-patterns with this principle.
To explore this question, we will develop new theory of the spatial sorting mechanism during range expansion and design bio-patterns with various cell behaviors such as motility, growth, cell–cell interactions, and phage infections. Following the theoretical guide, we will design synthetic gene circuits to realize new bio-patterns. Successful completion of this projects will provide new insights into self-generation of bio-patterns and will also advance our ability to design and synthesize complex biological systems.
Development of novel anti-coronavirus drugs targeting the multi-domain SARS-CoV-2 NSP3 protein
Hong Kong Principal Investigator: Dr Shuofeng Yuan (The University of Hong Kong)
Mainland Principal Investigator: Prof Sheng Cui (Chinese Academy of Medical Sciences)
As the COVID-19 pandemic threatens is a long-term problem and vaccines do not promise complete and lasting protection, antiviral molecules will remain an important line of defense. Therefore, “vaccine + drug” strategy may hold the only promise to end the Pandemic. We hypothesize that combinational therapy targeting multiple functional domains of the SARS-CoV-2 non-structural protein 3 (NSP3) would achieve antiviral synergy and less prone of drug resistance.
Based on our exciting preliminary data achieved, we will perform structural and pharmacokinetic modification of the selected papain-like protease (PLpro) inhibitor F0213 for enhanced potency and bioavailability. We will employ the state-of-the-art fragment-based drug design to fundamentally discover a novel inhibitor that targets to the SARS‐unique domain (SUD) of NSP3. Finally, we will explore the best regimen that exhibiting antiviral synergy between the selected inhibitors. They are safe, potent and can be taken as a pill, which enrich the arsenal on the prescriptions for treating COVID-19.
Adaptive potential and ecological resilience of benthic keystone species to environmental extremes and compound events
Hong Kong Principal Investigator: Dr Juan Diego Gaitan-Espitia (The University of Hong Kong)
Mainland Principal Investigator: Prof Hongsheng Yang ( Institute of Oceanology, Chinese Academy of Sciences)
Extreme weather and climate events can be lethal for most marine organisms. These anomalous events are rapidly increasing in frequency, intensity and duration at a global scale, causing devasting ecological impacts on coastal ecosystems. Marine heatwaves, ocean deoxygenation and acidification are extreme events that can occur both individually and in parallel or in close sequence (compound events). These events are becoming major threats for the diversity and resilience of marine life, the services it provides for human wellbeing, as well as for the long-term social and economic development (e.g., aquaculture, fisheries). Just during the last decade, the East Asia region has experienced several unprecedented anomalous extreme events that have triggered substantial changes in the functionality of benthic ecosystems, altering their productivity and trophic webs through the reconfiguration of benthic communities due to mass mortalities of keystone species. Although extreme hypoxia, warming and acidification events can alter both the structure and function of benthic communities, these effects are expected to differ across biogeographic regions depending in part on the degree of natural environmental variability. Benthic organisms in temperate and sub-tropical regions are generally predicted to cope better with extreme and compound events as they experience marked natural variability in sea surface temperature, pH, and O2 availability mainly due to seasonal dynamics (i.e., Climate Variability Hypothesis). Tropical benthic species, on the other hand, are predicted to be particularly sensitive to extreme events as they are considered more narrowly endemic in both geographic and climatic space. Although we have gain better understanding of the socio-economic and ecological impacts of extreme events in the region, we still lack the empirical information required to inform scientists and stakeholders about the ecological resilience and adaptive potential of marine biodiversity to extreme events. By integrating different approaches in comparative physiology (Obj 1), transcriptomics (Obj 2), quantitative-genetics (Obj 3) and molecular ecology (Obj 4), this NSFC-RGC project will develop a mechanistic understanding of the molecular, functional and ecological processes underpinning population and community responses to extreme events, their tolerances, resilience and adaptive potential across biogeographic scales. Through this novel information we would be able to provide a scientific framework, data and tools to conservation managers, stakeholders and policymakers, that allow them to develop more effective response strategies for biodiversity and human society, for conservation and restoration interventions, and to achieve sustainable development of ecosystem services in a world of extreme environmental events and climate change.
Ant invasions in China: multiple dimensions of impacts on diversity and ecosystem functioning along a latitudinal gradient
Hong Kong Principal Investigator: Dr Benoit Guénard (The University of Hong Kong)
Mainland Principal Investigator: Prof Yijuan Xu (South China Agricultural University)
Biological invasions are considered the second largest threat to biodiversity globally and generate numerous issues to the economy and public health within our societies. Surprisingly in Asia, research on biological invasions remain in its infancy despite the high level of invasions observed.
This is especially true for insect invasions within China which have been limited to a few regions and on species like the Red Imported Fire Ant, Solenopsis invicta. Yet, for decades, several other invasive ant species have spread widely in SE China and may now represent a threat for local and regional biodiversity.
Advancements in ecological approaches and techniques in the recent years have allowed to understand multiple dimensions of biodiversity beyond the traditional taxonomic approach. These innovative approaches using functional and phylogenetic diversity grant new insights into the roles and ecological characteristics of species within ecosystems as well as their evolutionary relationships. Most importantly, they also provide a novel framework for the study of mechanisms operating during invasions and may ultimately offer specific predictions about the alteration of species communities and of the associated ecosystems functions.
Yet, the impacts of invasive species likely differ between regions in function of the climate and the diversity of the local fauna encountered. For instance, the latitudinal diversity gradient, one of the most general patterns in ecology in which species diversity tend to increase with decreasing latitudes, may provide a good opportunity to study and understand the large-scale variations of biological invasions impacts on multiple dimensions of biodiversity.
In this project, using a 2000 km long transect ranging from temperate to subtropical regions in China we will study the impacts of four invasive ant species on different dimensions of diversity and associated ecosystem functions; namely decomposition and seed dispersal. We posit that effects of invasions will differ along the latitudinal gradient, with stronger effects encountered in cooler and species-poor areas. Moreover, with the continuous introductions of new exotic species into new regions, several invasive species can now co-exist within a given area. We predict that additive effects of multiple invasions will impact native communities more strongly than singular invasions. Finally, we test how the changes on ecosystem functions are driven by the body size of invasive species and by the exclusion of particular ranges of body-size in native species.
Altogether, those results will provide a better understanding on the ecological impacts of multiple ant invasions within China, and beyond, while informing how and where conservation programs should be focusing on.
Attosecond Coincidence Streaking spectroscopy probes photoemission dynamics
Hong Kong Principal Investigator: Dr Tran Trung Luu (The University of Hong Kong)
Mainland Principal Investigator: Dr Xiaochun Gong (East China Normal University)
Pursuing the understanding of fundamental principles of nature has always been the major goal of Physics. To understand a given physical phenomenon, it is important to observe it first. The most fundamental principles can only be founded and verified at the limits of the observation. By pushing the limits of the observation, we aim to not only provide a pure and comprehensive picture of dynamics of complicated systems, but also to hopefully test the limits of the fundamental physical laws. Historically, such studies provided the foundation for creation of new physical principles. Thanks to the development over the last three decades, it has been possible to simultaneously measure the motion of charged particles that were released from an atom/molecule that was driven or pumped by a flash of light. In this type of measurement, conservation laws play an important role in extraction of the full motion of charged particles. In addition, there was a limit on how fast one can observe such event. This limit is introduced by the involved flashes of light and until now the light involved in such experiment was not the absolute state-of-the-art light source. Development along generation of short flashes of light has achieved great results, especially over the last two decades. Due to the complexity of each technique, simultaneous measurement of motion of charged particles and generation and characterization of extremely short light pulse, the two techniques have never been merged. In this proposal, we would like to combine the two excellent techniques utilizing the harmonized expertise from both groups. Each group has obtained extensive experience and expertise for one of these two techniques. In this project, we aim to extract information on reaction and motion of charged particles at the fastest possible time scale. The project will shed light on the concerted motion of electrons at a peculiar region of energy. The project will provide information on the liberation of one or two electrons and how do their liberation stack up to each other and if the electrostatic/Coulomb force would play any role in their concerted motion. In addition, the project will give us hints on a physical process that could help us to link very fast motion of charged particles released in gas or solid phase. Finally, our project would pave the road for similar studies with much better fidelity than what is currently possible using state-of-the-art technologies.
Machine Learning in Decision Rule Design
Hong Kong Principal Investigator: Dr Ye Luo (The University of Hong Kong)
Mainland Principal Investigator: Prof Chunrong Ai (The Chinese University of Hong Kong (Shenzhen))
In the modern digital economy, the firms are constantly making decisions and learning from data. This project studies how to learn and design optimal decision rule in dynamic environment. The project consists of three important challenges in data analytics: first, real time decision needs to be made in the digital economy; second, learning and decision making are mutually involved; third, there could be endogeneity problems in business data, which causes biases in learning and therefore, suboptimal decision rules. The goal of the project is to solve these challenges. In particular, five specific topics will be covered: (1) optimal decision rule design with counter-factual analysis. Counter-factual analysis is the basic idea to resolve endogeneity in data. The main challenge is to estimate counter-factual using existing sample of data; (2) rather than collecting all data before analysis, decision maker faces both learning and decision making with counter-factual analysis when streaming data comes in; (3) optimal decision rule design with social interactions. The main challenge is to estimate network effect and incorporate such effect into the counter-factual analysis; (4) optimal decision rule with both social interactions and learning when streaming data comes in; (5) case studies, including applications such as e-commerce recommendation, housing rental pricing, etc. The output of this project can serve as important tools to solve many key challenges in the digital economy today, both theoretically and empirically.