中大研測菌機械人 又快又準

隨着新冠肺炎肆虐全球,快速檢測的需求飆升,香港中文大學(中大)研發創新微型機械人快速自動檢測系統「 QuickCAS」,第一代QuickCAS針對檢測難辨梭菌,由現存需時二至四小時,大幅縮短到15至30分鐘便可完成檢測,成本只是醫院傳統檢測的六分之一。團隊表示,至今實驗準繩度為100%,期望能加快對傳染病的診斷。系統將進行臨床試驗,期望明年在醫院投入試用。

Date: 
Wednesday, December 16, 2020
Media: 
大公報

中大研發15分鐘驗腸病毒 識別難辨梭菌 每次成本僅50元

在第四波新冠疫情下,醫護工作負擔沉重,如何減輕他們的工作量成為燃眉之急。香港中文大學醫學院及工程學院的團隊研發了一款微型機械人快速自動檢測系統「QuickCAS」,針對醫院內最常見的腸道感染病原體「難辨梭菌」(Clostridium Difficile),聲稱最快15分鐘完成檢測,成本僅約50元,是現時化學檢測方法成本的六分之一。

Date: 
Wednesday, December 16, 2020
Media: 
信報

CUHK Develops Novel Microrobotic Diagnostic System to Accurately Diagnose Infectious Pathogens with Full Automation and Low Cost

Date: 
2020-12-16
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CUHK has recently developed a fully automated, low cost and rapid microrobotic diagnostic system with comparable sensitivity and specificity to clinical detection methods. The research team is now studying the application of this microrobotic system for multiple pathogens including the COVID-19. This system has been developed by a collaborative research team led by Professor Li ZHANG, Associate Professor, Department of Mechanical and Automation Engineering, Professor Margaret IP, Professor, Department of Microbiology, Professor Joseph SUNG, Mok Hing Yiu Professor of Medicine and Director of the Institute of Digestive Disease, and Professor Sunny WONG, Associate Professor, Department of Medicine and Therapeutics.
 
Pathogen detention is indispensable for accurate diagnosis of diseases. As the pandemic of COVID-19 rages worldwide, the soaring demand for rapid testing has led to heavy workload for laboratory personnel on an unprecedented scale. Worse still, the longer the hospitalisation of patients, the higher the risk of patients being infected by pathogens that can be fatal. Hence, prompt clinical diagnosis is critical for patients showing signs of suspected infection. Under the dual pressure of pandemic and existing medical needs, shortages of medical manpower and resources including the laboratories may delay diagnosis, which can result in the suspension of other necessary medical procedures. Professor Margaret IP said, “With globalisation, the spread of infectious diseases is not restricted to geographical areas. To enhance the diagnosis of infections and control, testing using automatic rapid detection systems is the general trend.”
 
Professor Li ZHANG and his team have developed an innovative microrobotic detection system, integrating the novel fluorescent microrobots with an external magnetic actuation system to accurately detect specific pathogens in a short time. The microrobotic sensing probes are G. lucidum spores coated by a layer of iron oxide nanoparticles and functionalised with carbon dots. By analysing the changes in the fluorescence signal of the microrobots under green light excitation, the system can determine the presence of pathogen in patients’ samples. In addition, the system uses an external magnetic field to remotely actuate the microrobots, speeding up the fluorescence quenching and thus shortening the detection time.
 
The first generation of microrobotic detection system, “QuickCAS”, aims at detecting Clostridium difficile (C. diff), a common pathogen of nosocomial infection. The research team is now entering clinical trials, with the goal of testing in hospitals next year. In view of the pandemic disease, the team has worked closely with Professor Margaret IP in utilising the microrobotic detection system for the COVID-19 diagnosis. The development of multiple pathogens detection using the microrobotic detection system is underway, covering common pathogens such as Streptococcus pneumoniae, Salmonella, pathogenic Escherichia coli and Helicobacter pylori, and it is expected to benefit medical institutions worldwide.
 
Current chemical detection methods rely on the reaction between pathogen and biomolecular reagents. These bioreagents typically need to be refrigerated or frozen to preserve their structure and viability. On the other hand, the microrobots are stable for transportation and storage under room temperature. Professor Li ZHANG said, “As QuickCAS uses physical detection methods, the reagents do not require refrigeration. It successfully breaks through the pain points of current chemical detection methods. In the future, medical centres in remote and poor areas or small scale healthcare service providers will have the opportunity to provide accurate clinical diagnostic services.”
 
Additionally, the existing methods of C. diff detection take 2 to 4 hours, but QuickCAS only takes 15 to 30 minutes to complete, and the cost has been greatly reduced from approximately HK$300 to about HK$50 per test. The automated system can not only provide hospitals with timely diagnosis and treatment for patients, but also reduce the workload and the risk of infection of medical staff during laboratory tests. Moreover, infection controls can be implemented earlier to prevent infection outbreak. By simplifying the testing procedures, even junior laboratory technicians with only basic training can operate the system, alleviating the pressure from the current shortage of experienced laboratory technicians.
 
Demonstrating 20 innovative projects at the “InnoCarnival 2020”
 
CUHK will participate in the InnoCarnival 2020, organised by the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government, from 23 to 31 December on the virtual exhibition platform. Members of the public are welcome to visit CUHK’s virtual booth to learn more about the microrobotic detection system and see another 19 innovative projects. This year, to deepen the understanding of the CUHK projects, an online quiz competition will be launched during the InnoCarnival 2020, with fabulous prizes to be won.
 
Date: 23 to 31 December 2020
Time: 24 hours
Virtual exhibition website: http://innocarnival.hk
 
This article was originally published on CUHK Communications and Public Relations Office website.
 

The project team demonstrates the detection process of microrobotic detection system, “QuickCAS”.

 

The sample chip hold by Professor Li ZHANG (left) will be put into “QuickCAS” for auto analysis. The team is developing the multiple pathogens detection in the future, by inserting different types of sample chip into “QuickCAS”.

 

The first generation of microrobotic detection system, “QuickCAS”, aims at detecting Clostridium difficile (C. diff), a common pathogen of nosocomial infection.

 

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Media Release

Prof. Wong Kam Fai Elected Fellow of the Association for Computational Linguistics 2020

Date: 
2020-12-14
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Professor Wong Kam Fai, Associate Dean (External Affairs) of the Faculty of Engineering and Director of the Centre for Innovation and Technology), CUHK has been named Fellow of the Association for Computational Linguistics (ACL) in the class of 2020, in recognition of his significant contributions to social media processing, particularly in Chinese information retrieval, opinion mining, microblog processing and rumour detection.
 
Established in 2011, the ACL Fellows programme recognises its members whose contributions to the field of computational linguistics have been most extraordinary in terms of scientific and technical excellence, service to the association and the community and/or educational or outreach activities with broad impact.
 
Professor Wong is among the first researchers to investigate natural language processing (NLP) for microblogs, such as Twitter and Wechat. In today’s social media, the results of this timely research work are influential for future work in social media analysis. As a pioneer in the field, Professor Wong used different discourse integration techniques to identify contextual information in microblog repost trees, and proposed a novel summarisation system by effectively differentiating leader and follower messages on repost trees based on content-level structure information. He also introduced temporal features for microblog processing, especially for rumour detection. In his research paper “Detect Rumours Using Time Series of Social Context Information on Microblogging”, he adopted the change of word and emoji patterns over time for rumour detection. It became an open corpus for rumour detection, which has been widely used by the community, with over 400 citations globally over the past three years. In recent years, his work in rumour detection has been well published in ACL.
 
Over the years, Professor Wong has always been keen to transfer impactful research to commercial application. He co-founded Wisers Information Ltd in Hong Kong in early 1998 with his student, opening up a whole new era of newspaper digitisation, ahead of giants like Google and Baidu. In addition, he received the Second-class Award in Scientific and Technological Progress from the Ministry of Education, China (2017) based on his applied research and development work on the industrialisation of trilingual-based retrieval and understanding platforms on large scale social media. The system “SAMUL”, a toolkit for sentiment analysis for multi-language application developed by his team has contributed to software development kits for multilingual sentiment analysis, which includes extraction of credibility, content and mood of the message author. SAMUL can read Cantonese, Mandarin and English, and is eligible to be extended to other alphabetic languages. With SAMUL, his team received Silver Medal and Prize of the Ministry of Scientific Research and Innovation- Romania in the 47th International Exhibition of Inventions of Geneva in 2019.

Professor Wong Kam Fai

 

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SEEM
Media Release

中大與英國華威大學以T射線研發新技術 助治療各種皮膚病

香港中文大學今日(10日)表示,與英國華威大學的科學團隊合作,通過T射線技術精確檢測皮下結構與皮膚水份含量。該方法有助診斷及治療濕疹、銀屑病及皮膚癌等皮膚疾病。
 
中大工程學院電子工程系博士後研究員兼研究論文第一作者陳學權指,T射線對皮膚的水份含量十分敏感,然而科學團隊提出皮膚角質層的細胞結構,亦是影響T射線反射的重要因素;科學團隊設計的技術也能偵測此特性,從而提供有關皮膚的全面資訊,對準確診斷有很大幫助
Date: 
Friday, December 11, 2020
Media: 
點新聞

中大與英國華威大學研發新技術剖析皮膚狀態及結構

香港中文大學今日(10日)表示,工程學院與英國華威大學的科學團隊採用T射線技術,可精確地檢測皮下結構與皮膚水份含量;中大指新方法有助診斷及治療如如濕疹、銀屑病及皮膚癌的皮膚疾病,而該研究成果已發表在學術期刊Advanced Photonics Research。

Date: 
Friday, December 11, 2020
Media: 
香港商報

CUHK and University of Warwick Develop T-rays Technology To Analyse the Skin Conditions and Structure

Date: 
2020-12-10
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Scientists from the Faculty of Engineering at CUHK and the University of Warwick have developed a novel method for analysing the structure of skin using a type of radiation known as T-rays. It could build a more detailed picture of the structure of an area of skin and how hydrated it is than current methods allow, and help improve the diagnosis and treatment of skin conditions such as eczema, psoriasis and skin cancer. The finding has been reported in Advanced Photonics Research.
 
Terahertz (THz) radiation, or T-rays, sit in between infrared and Wi-Fi on the electromagnetic spectrum. T-rays can see through many common materials such as plastics, ceramics and clothes, making them potentially useful in non-invasive inspections. The low energy photons of T-rays are also non-ionizing, making them very safe in biological settings including security and medical screening.
 
Only the T-rays passing through the outer layers of skin (stratum corneum and epidermis) before being reflected back can be detected, as those travelling deeper are attenuated too much. Depending on the properties of the skin, those T-rays will be reflected back slightly differently. Scientists can then compare the properties of the light before and after it enters the skin.
 
There are limitations in standard THz reflection spectroscopy, and to overcome these the scientists behind this new research used ellipsometry instead, which involves focusing T-rays at multiple angles on the same area of skin.
 
In ordinary THz reflection imaging, the thickness and refractive index combine as one parameter. By taking measurements at multiple angles, the two can be separated. The team successfully demonstrated that using ellipsometry, they could accurately calculate the refractive index of skin, which determines how fast the ray travels through it, measured in two directions at a right angle. The difference between these refractive indices is termed birefringence – and this is the first time that the THz birefringence of human skin has been measured in vivo. These properties can provide valuable information on how much water is in the skin and enable the skin thickness to be calculated.
 
Dr. Xuequan Chen, the study’s first author and post-doctoral fellow from the Department of Electronic Engineering at CUHK, said: “T-rays have been known to be sensitive to the hydration level of skin. However, we pointed out that the cellular structure of the stratum corneum also reacts to the THz reflections. Our technique enables this structure property to be sensitively probed, which provides comprehensive information about the skin and it is highly useful for skin diagnosis.”
 
Professor Emma Pickwell-MacPherson, from the Department of Electronic Engineering at CUHK and the Department of Physics at the University of Warwick, said: “Hydrated skin will have a different refractive index from dehydrated skin. If you’re trying to improve skincare products for people with conditions like eczema or psoriasis, we will potentially be able to make quantitative assessments of how the skin is improving with different products, or differentiate types of skin. For skin cancer patients, you could also use THz imaging to probe the skin before surgery is started, to get a better idea of how far a tumour has spread, especially for those unseen as they are beneath the skin surface.”

 
To test the novel method, the researchers had volunteers place their arm on the imaging window of their T-ray equipment for 30 minutes, after acclimatising to the ambient temperature and dryness of the laboratory. By holding their skin against the surface of the imaging window, they blocked water from escaping from their skin as perspiration, a process referred to as occlusion.
 
The researchers then made four measurements at right angles to each other every two minutes over half an hour, so they could monitor the effect of occlusion over time. Because T-rays are particularly sensitive to water, they could see a noticeable difference as water accumulated in the skin, suggesting that the method could show how effective a product is at keeping skin hydrated, for example. Further research will look at improving the instrumentation of the process and how it might work as a practical device.
 
Professor Pickwell-MacPherson said: “We do not have anything for clinical use that is accurate for measuring skin. Dermatologists need better quantitative tools to use, and use easily. If this works well, you could go into a clinic, put your arm on a scanner, your occlusion curve would be plotted and a suitable product for your skin could be recommended. We could get more tailored medicine and develop products for different skin responses. It could really fit in with the current focus on tailored medicine.”
 
This work was funded by the Research Grants Council of Hong Kong, the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation, and the Royal Society.
 
A full version of the paper can be found at: https://doi.org/10.1002/adpr.202000024

This figure shows the proposed double-prism ellipsometer design to fully resolve the terahertz birefringence of skin in vivo. The circular region behind shows the microscope image of skin.

 

(From left to right) Dr. Xuequan Chen, the study’s first author and post-doctoral fellow from the Department of Electronic Engineering at CUHK, and Professor Emma Pickwell-MacPherson from the Department of Electronic Engineering at CUHK and the Department of Physics at the University of Warwick.

 

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Media Release

新科技教育普及 推動中學 AI 認知

人工智能(AI)並非深不可測,任何人都有能力借此數學分析工具,製作造福人群的應用。本地教學界今年在應付遙距學習之餘,也開始解放 AI 相關的教學資源與人才培訓。

香港教育城行政總監鄭弼亮認為,總結與分享過去一年在新冠肺炎下的教學經驗與主題,他指目前本地有多方面人才從事「教育新常態」的研究,同時透露教育城網站在過去大半年時間,在包括閱讀、評估與教師培訓的平台用量,最多達以往的 4 5 倍,甚至 10 倍水平,可見在疫情間網上服務密度明顯提升。

 

Date: 
Tuesday, December 8, 2020
Media: 
E-Zone

Professor Raymond Yeung Receives IEEE 2021 Richard W. Hamming Medal

Date: 
2020-12-07
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Professor Yeung Wai-ho Raymond, Choh-Ming Li Professor of Information Engineering, and Co-Director of the Institute of Network Coding, The Chinese University of Hong Kong (CUHK), has been named the recipient of the IEEE 2021 Richard W. Hamming Medal for his fundamental contributions to information theory and pioneering network coding and its applications. This is one of the highest honours in electrical and electronics engineering. It is the first time this award has been won by an Asian researcher since its establishment in 1988. It is also the first time that home grown research in this area has been recognised at this level.  

Professor Raymond Yeung is a world-renowned expert in information theory and a co-founder of the field of network coding. In the early 1990s, he was invited to participate in a project of NASA’s Jet Propulsion Laboratory for salvaging the malfunctioning Galileo spacecraft. The 25-bit synchronization marker he designed was used for transmitting back to Earth the images of Jupitar and its satellites taken by the spacecraft. Since 2010, he has been a Co-Director of the Institute of Network Coding at CUHK, the largest engineering research project ever funded in Hong Kong, with the total budget exceeding HKD100M.

Information inequalities are an indispensable tool for proving theorems in information theory. For a long time, Shannon-type inequalities were all the known information inequalities. In the mid-1990s, Professor Yeung contemplated the existence of non-Shannon-type inequalities and discovered with his collaborator the first such inequality which is now known as the Zhang-Yeung inequality. This groundbreaking work proves the incompleteness of Shannon-type inequalities. In addition to information theory, this finding has significant implications in different fields in information sciences, mathematics, and physics.

Professor Yeung also co-founded the field of network coding. In the late-1990s, he proposed the concept of network coding that revolutionised network communication. In the past, information was transmitted in a network very much like commodity flow, with the intermediate nodes relaying data packets passively. Simply speaking, with network coding, by applying coding to data packets inside the network, more information can be transmitted through the network. In practice, this means people can download data faster, watch video streaming with less delay, and communicate more securely on the Internet.

Professor Yeung has co-founded n-hop technologies, a startup company in Hong Kong Science Park that focuses on BATS, a network coding technology that solves the longstanding problem of packet loss in wireless multi-hop communication. Currently they are applying this technology to provide WiFi service at country parks that are not well covered by the cellular network. With this service, hikers can use mobile devices for navigation, accessing weather information, uploading pictures to social media, and even emergency calls. Professor Yeung has also co-founded CU Coding, another startup company that focuses on network coding data storage and physical-layer network coding.

Professor Yeung is the author of two textbooks on information theory and network coding that have been adopted by over 100 universities around the world. The paper he co-authored that founded the field of network coding has received close to 10,000 citations on Google Scholar. In 2014, he offered the first online course in information theory that attracted 25,000 students. Since then, the course has been offered regularly on Coursera and other platforms.

Professor Yeung has received numerous awards for his research contributions, including the 2018 ACM SIGMOBILE Test-of-Time Paper Award and the 2016 IEEE Eric E. Sumner Award. He is a Fellow of the Hong Kong Institution of Engineers, the Hong Kong Academy of Engineering Sciences, and IEEE. He holds 10 patents on BATS codes.

 

About IEEE Richard W. Hamming Medal

IEEE Medals are the highest awards presented by the IEEE. The IEEE Richard W. Hamming Medal, established in 1986, is named in honor of Dr. Richard W. Hamming, who had a central role in the development of computer and computing science, and whose many significant contributions in the area of information science include his error-correcting codes. It is awarded to an individual or team for exceptional contributions to information sciences, systems, and technology.

 

 

Professor Yeung Wai-ho Raymond

 

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Media Release

香港高校研究用人工智能科技助診斷阿茲海默症

香港中文大學(中大)的跨學科研究團隊3日公佈,近日獲資助約560萬港元展開阿兹海默症數碼生物標記的研發工作,透過尖端的傳感及人工智能技術,提升及早診斷阿茲海默症的效率及準確度。
全球人口老化問題嚴重,阿茲海默症和相關的認知障礙症患者亦隨之而增加。世界衛生組織統計顯示,全球有5千萬人患上認知障礙症,每年新增確診個案更高達1千萬。在香港,每10名年長人士便有一個罹患認知障礙症;到2036年,患者數目估計較2016年的11萬5千人,大幅增加兩倍多至約24萬人
Date: 
Friday, December 4, 2020
Media: 
中通社

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