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
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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
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http://www.hkcd.com/content/2020-12/10/content_1234931.html

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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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 至 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
Media LInk: 
https://is.gd/mygdky

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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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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https://is.gd/rlfqF0

中大與港大研發新法 檢測先天性巨結腸症致病基因

在香港，平均每3,500名新生嬰兒，便有一個患有「先天性巨結腸症」。患者從出生一刻開始就很少正常排便，患者通常有明顯腹部腫脹，他們需要進行外科手術切除腸道的「壞部分」，並重新連接消化系統的健康部分。即使進行了手術，部分患者仍然有腸道活動不足問題，甚至出現大便失禁和腸道感染。如果得不到適當治療，可導致死亡。

Date: 
Friday, December 4, 2020
Media: 
AM730
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https://is.gd/VmwdSH

CUHK and HKU Jointly Develop MARVEL Data Analysis Method to Detect Pathogenic Genes of Hirschsprung’s Disease

Date: 
2020-12-03
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Hirschsprung’s disease is one of the most common congenital diseases requiring an abdominal operation in the newborn period. Its strong heritability suggests that genetic factors play a key role in the development of the disease. To find out genetic variants that may cause the disease, researchers from CUHK and HKU jointly developed a novel data analysis method called MARVEL (Multiscale Analysis of Regulatory Variants on the Epigenomic Landscape). A scientific article describing the MARVEL method and the corresponding findings about Hirschsprung’s disease has recently been published in the journal Genome Research.
 
Most of us have experienced constipation before, which is caused by infrequent bowel movements. The situation can usually be improved within days by proper diet and physical exercise. A Hirschsprung patient has infrequent bowel movements starting from the day that he/she is born, and it can last his/her whole life if untreated, or even result in death. Typically characterised by a swollen belly, Hirschsprung patients need to go through a surgical operation to remove the “bad section” of the intestine and reconnect the healthy portions of the digestive system. Despite surgery, some patients continue to have infrequent bowel movements, or develop soiling and bowel infections. In Hong Kong, on average, there is a Hirschsprung case for every 3,500 newborns. 
Scientists have long known that many Hirschsprung patients have mutations around a gene called RET. This gene codes for a protein that is important for a type of cells involved in gut development and function. Mutating the gene is analogous to damaging the mould, which causes the products produced (the proteins) to be malfunctioning. There are also mutations that do not change the protein but instead alter the amount of it produced in the cells.
 
Notwithstanding, only less than a quarter of the Hirschsprung patients have RET mutations. For the remaining majority, the cause of the disease is expected to be more complex and involves other lesser known genes. Since there are millions of genetic variants in every person, and most of them have nothing to do with the disease, it is difficult to pinpoint the variants and the corresponding affected genes that are functionally related to the disease. To find out these genes systematically, a joint research project by CUHK and HKU was established in 2014. It required substantial collaboration among different professions: medical doctors collected samples; genome scientists deciphered the DNA; geneticists characterised DNA differences between patients and non-patients; computer scientists developed mathematical models to identify the most critical DNA differences; biologists experimentally tested the significance of these computational findings. 
MARVEL is one of the latest outcomes of the project. It is a computational data analysis method that has been used to discover many new genetic variants associated with Hirschsprung’s disease. Among its novelties, as compared to other methods, is its ability to consider the “convergent” effect of mutations; that the same functional impact can be produced by different mutations in different patients. 
 
Professor Kevin Yip, Associate Professor of the Department of Computer Science and Engineering at CUHK, said, “This project would not have been possible without the collaborative effort of people from diverse backgrounds. It is becoming a basic requirement for high impact biomedical projects.” 
 
Professor Yip is one of the three senior authors of the article in Genome Research. The other two are Professor Paul Tam and Professor Elly Ngan from the Department of Surgery at HKU. 
Professor Tam explained that the novel computational method is an important first step towards the application of precision medicine to this heterogeneous congenital disorder. In future, genome data will likely become routine in the medical care of Hirschsprung patients, and computation based genome analysis can aid in stratifying patients to receive “tailor -made” and more effective treatments. 
 
This project was funded by the Research Grants Council Theme-based Research Scheme (TRS). The project team will report its major research outcomes at the TRS Public Symposium on December 13th. Information about this symposium can be found at  https://www.ugc.edu.hk/eng/rgc/about/events/symposium/symposium20.html 
 
This article was originally published on CUHK Communications and Public Relations Office website.

 

 

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CUHK Receives Research Funding on Advancing Digital Biomarkers for Alzheimer's Disease

Date: 
2020-12-03
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A multi-disciplinary team of researchers from CUHK has been granted up to 5.6 million Hong Kong dollars to assist in developing machine learning technologies for advancing digital biomarkers for Alzheimer’s Disease. This will contribute to the efficiency and accuracy of early identification using the state-of-the-art sensing and artificial intelligent (AI) technologies. The project is funded by the Diagnostics Accelerator program of Alzheimer’s Drug Discovery Foundation (ADDF) and a matching fund from SenseTime Technologies (SenseTime). It is led by Professor Guoliang XING from the Department of Information Engineering, Faculty of Engineering at CUHK, joined by local engineering and medical experts.

Alzheimer's Disease (AD) and related dementia is a growing health problem worldwide because of population aging. It affects 50 million people and there are more than 10 million new cases worldwide. It has been one of the most common chronic diseases for the aging population in Hong Kong, affecting one in every ten older adults. The number of confirmed cases is expected to rise from 115,000 in 2016 to around 240,000 cases in 2036.

Early identification of people at risk of developing AD and timely intervention to slow the onset and progression of AD are crucial, because disease modifying treatment for AD is not available at present. An accurate, easy-to-do test may enhance the effectiveness of screening and identification of AD and address the problem of denial of the illness. 

Recent years have witnessed significant progress in the Internet of Things (IoT) and smart sensing technologies, with many advanced sophisticated sensors making their way into personal and mobile devices. Many families today own more than a dozen  connected smart devices.  These devices can capture, on an unprecedented scale, the physiological, behavioral, lifestyle, and cognitive indicators, referred to as “digital biomarkers”, in natural living environments in a non-invasive manner.

Professor Bolei ZHOU from the Department of Information Engineering, CUHK, and a member of the research team, stated, “Machine learning technique allows us to conduct objective longitudinal monitoring and comparison of indicators, enabling the detection of minimal abnormalities in our nervous system, thus achieving early identification of and intervention in AD. However, the prevalence of sensors will lead to various issues such as privacy concerns and the “black box” nature of AI algorithms. These must be solved in order to interpret the digital markers and their links with disease pathophysiology.”

Comprising experts in AI and various medical specialties, the multi-disciplinary team aims to develop cutting-edge sensing and AI technologies to discover new digital biomarkers for early diagnosis of Alzheimer’s Disease.  The developed technologies will classify digital biomarkers for Activities of Daily Living (ADL), Behavioral and Psychological Symptoms of Dementia (BPSD), social interactions, motor function, and level of cognition. The team will also develop a real-time learning system which enables smart devices to collaboratively improve the accuracy of AI algorithms while keeping all the data on the device and hence preserving user privacy. In addition, the team will also propose interpretable machine learning algorithms to quantify the correlation of multi-modal digital biomarkers, and assist early detection, diagnosis and intervention. 

The new technologies from this project will lead to new approaches that can detect any immediate risks that require prompt attention or action. They will predict and identify individuals who have a higher chance of developing AD and dementia, introduce appropriate advice based on the specific needs of the individual, modify that according to the response of the prior suggestion or intervention, and provide feedback to the patients and caregivers so as to improve self-care management of the disease and stress adaptation. 

Professor Guoliang XING, the Principal Investigator (PI) of this project, stated, “This project will bring together top experts from engineering and medical fields to address one of the biggest challenges faced by our aging society. The sensing and AI technologies to be developed will empower doctors, caregivers and patients themselves to work together for early diagnosis of and intervention in AD. We believe this project will lead to important enabling technologies for the paradigm of “smart health”, the  vision of which is to transform today’s reactive hospital centred healthcare practice to proactive, individualised care and wellbeing.” 

Background of the project and research team

The project team includes Professor Guoliang XING as Principal Investigator (PI), and 5 Co-PIs: AI experts Professors Bolei ZHOU and Rosanna CHAN from the Department of Information Engineering, Professor Timothy KWOK from the Department of Medicine and Therapeutics, Dr. Allen LEE from the Department of Psychiatry, Faculty of Medicine at CUHK, and Professor Doris YU from the School of Nursing at the University of Hong Kong.

The major funding comes from the ADDF’s Diagnostics Accelerator, an initiative dedicated to developing reliable and affordable biomarker test for early diagnosis of Alzheimer’s disease. The ADDF was founded in 1998 and has awarded more than $150 million to fund over 626 programs for Alzheimer’s and related dementias in academic centers and biotechnology companies in 19 countries. To learn more, visit: www.alzdiscovery.org/ .

 

 

 

 

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中學生研發App及早識別學前讀寫障礙　下月參展創新科技嘉年華

本港中小學近年開始推行STEM教育，鼓勵及訓練學生實踐科學、數學等知識，研發新技術。有中學生就因為身邊同學有讀寫障礙，了解到本港讀寫障礙學童往往較遲被發現，錯失治療良機，因而研發能識別學前讀寫障礙兒童的應用程式及玩具，讓家長及學校可參考測試結果，及早求醫。

Date: 
Monday, November 30, 2020
Media: 
HK01
Media LInk: 
https://is.gd/srwLG3

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