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Deep Learning Helps Explore the Structural and Strategic Bases of Autism
Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person’s behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the “bible” of mental health diagnosis. However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult. But what if artificial intelligence (AI) could help? Deep learning, a type of AI, deploys artificial neural networks based on the human brain to recognize patterns in a way that is akin to, and in some cases can surpass, human ability. The technique, or rather suite of techniques, has enjoyed remarkable success in recent years in fields as diverse as voice recognition, translation, autonomous vehicles, and drug discovery. A group of researchers from KAIST in collaboration with the Yonsei University College of Medicine has applied these deep learning techniques to autism diagnosis. Their findings were published on August 14 in the journal IEEE Access. Magnetic resonance imaging (MRI) scans of brains of people known to have autism have been used by researchers and clinicians to try to identify structures of the brain they believed were associated with ASD. These researchers have achieved considerable success in identifying abnormal grey and white matter volume and irregularities in cerebral cortex activation and connections as being associated with the condition. These findings have subsequently been deployed in studies attempting more consistent diagnoses of patients than has been achieved via psychiatrist observations during counseling sessions. While such studies have reported high levels of diagnostic accuracy, the number of participants in these studies has been small, often under 50, and diagnostic performance drops markedly when applied to large sample sizes or on datasets that include people from a wide variety of populations and locations. “There was something as to what defines autism that human researchers and clinicians must have been overlooking,” said Keun-Ah Cheon, one of the two corresponding authors and a professor in Department of Child and Adolescent Psychiatry at Severance Hospital of the Yonsei University College of Medicine. “And humans poring over thousands of MRI scans won’t be able to pick up on what we’ve been missing,” she continued. “But we thought AI might be able to.” So the team applied five different categories of deep learning models to an open-source dataset of more than 1,000 MRI scans from the Autism Brain Imaging Data Exchange (ABIDE) initiative, which has collected brain imaging data from laboratories around the world, and to a smaller, but higher-resolution MRI image dataset (84 images) taken from the Child Psychiatric Clinic at Severance Hospital, Yonsei University College of Medicine. In both cases, the researchers used both structural MRIs (examining the anatomy of the brain) and functional MRIs (examining brain activity in different regions). The models allowed the team to explore the structural bases of ASD brain region by brain region, focusing in particular on many structures below the cerebral cortex, including the basal ganglia, which are involved in motor function (movement) as well as learning and memory. Crucially, these specific types of deep learning models also offered up possible explanations of how the AI had come up with its rationale for these findings. “Understanding the way that the AI has classified these brain structures and dynamics is extremely important,” said Sang Wan Lee, the other corresponding author and an associate professor at KAIST. “It’s no good if a doctor can tell a patient that the computer says they have autism, but not be able to say why the computer knows that.” The deep learning models were also able to describe how much a particular aspect contributed to ASD, an analysis tool that can assist psychiatric physicians during the diagnosis process to identify the severity of the autism. “Doctors should be able to use this to offer a personalized diagnosis for patients, including a prognosis of how the condition could develop,” Lee said. “Artificial intelligence is not going to put psychiatrists out of a job,” he explained. “But using AI as a tool should enable doctors to better understand and diagnose complex disorders than they could do on their own.” -Profile Professor Sang Wan Lee Department of Bio and Brain Engineering Laboratory for Brain and Machine Intelligence https://aibrain.kaist.ac.kr/ KAIST
Biomarker Predicts Who Will Have Severe COVID-19
- Airway cell analyses showing an activated immune axis could pinpoint the COVID-19 patients who will most benefit from targeted therapies.- KAIST researchers have identified key markers that could help pinpoint patients who are bound to get a severe reaction to COVID-19 infection. This would help doctors provide the right treatments at the right time, potentially saving lives. The findings were published in the journal Frontiers in Immunology on August 28. People’s immune systems react differently to infection with SARS-CoV-2, the virus that causes COVID-19, ranging from mild to severe, life-threatening responses. To understand the differences in responses, Professor Heung Kyu Lee and PhD candidate Jang Hyun Park from the Graduate School of Medical Science and Engineering at KAIST analysed ribonucleic acid (RNA) sequencing data extracted from individual airway cells of healthy controls and of mildly and severely ill patients with COVID-19. The data was available in a public database previously published by a group of Chinese researchers. “Our analyses identified an association between immune cells called neutrophils and special cell receptors that bind to the steroid hormone glucocorticoid,” Professor Lee explained. “This finding could be used as a biomarker for predicting disease severity in patients and thus selecting a targeted therapy that can help treat them at an appropriate time,” he added. Severe illness in COVID-19 is associated with an exaggerated immune response that leads to excessive airway-damaging inflammation. This condition, known as acute respiratory distress syndrome (ARDS), accounts for 70% of deaths in fatal COVID-19 infections. Scientists already know that this excessive inflammation involves heightened neutrophil recruitment to the airways, but the detailed mechanisms of this reaction are still unclear. Lee and Park’s analyses found that a group of immune cells called myeloid cells produced excess amounts of neutrophil-recruiting chemicals in severely ill patients, including a cytokine called tumour necrosis factor (TNF) and a chemokine called CXCL8. Further RNA analyses of neutrophils in severely ill patients showed they were less able to recruit very important T cells needed for attacking the virus. At the same time, the neutrophils produced too many extracellular molecules that normally trap pathogens, but damage airway cells when produced in excess. The researchers additionally found that the airway cells in severely ill patients were not expressing enough glucocorticoid receptors. This was correlated with increased CXCL8 expression and neutrophil recruitment. Glucocorticoids, like the well-known drug dexamethasone, are anti-inflammatory agents that could play a role in treating COVID-19. However, using them in early or mild forms of the infection could suppress the necessary immune reactions to combat the virus. But if airway damage has already happened in more severe cases, glucocorticoid treatment would be ineffective. Knowing who to give this treatment to and when is really important. COVID-19 patients showing reduced glucocorticoid receptor expression, increased CXCL8 expression, and excess neutrophil recruitment to the airways could benefit from treatment with glucocorticoids to prevent airway damage. Further research is needed, however, to confirm the relationship between glucocorticoids and neutrophil inflammation at the protein level. “Our study could serve as a springboard towards more accurate and reliable COVID-19 treatments,” Professor Lee said. This work was supported by the National Research Foundation of Korea, and Mobile Clinic Module Project funded by KAIST. Figure. Low glucocorticoid receptor (GR) expression led to excessive inflammation and lung damage by neutrophils through enhancing the expression of CXCL8 and other cytokines. Image credit: Professor Heung Kyu Lee, KAIST. Created with Biorender.com. Image usage restrictions: News organizations may use or redistribute these figures and image, with proper attribution, as part of news coverage of this paper only. Publication: Jang Hyun Park, and Heung Kyu Lee. (2020). Re-analysis of Single Cell Transcriptome Reveals That the NR3C1-CXCL8-Neutrophil Axis Determines the Severity of COVID-19. Frontiers in Immunology, Available online at https://doi.org/10.3389/fimmu.2020.02145 Profile: Heung Kyu Lee Associate Professor firstname.lastname@example.org https://www.heungkyulee.kaist.ac.kr/ Laboratory of Host Defenses Graduate School of Medical Science and Engineering (GSMSE) The Center for Epidemic Preparedness at KAIST Institute http://kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea Profile: Jang Hyun Park PhD Candidate email@example.com GSMSE, KAIST (END)
Virtual Commencement Ceremony Honors the Class of 2020
The KAIST community gathered online to celebrate the 2020 graduating class. The blended ceremony conferred their hard-earned degrees on August 28. The belated celebration, which was postponed from February 21 due to the COVID-19 outbreak, honored the 2846 graduates with live streaming on YouTube beginning at 2:00 pm. The graduates include 721 PhDs and 1399 master’s degree holders. The government raised its social distancing guidelines to level two out of three on August 23 as the second wave of the virus hit the nation. Level two guidelines prohibit the gathering of more than 50 persons indoors or 100 persons outdoors. For the virtual ceremony, the Office of Student Affairs and Policy announced a list of 67 graduates who signed up to participate in the graduation ceremony. Graduates were divided into three groups to attend at three different places and watch the ceremony via Zoom. No family and friends of the graduates were allowed to participate at the campus. This year’s valedictorian, Kon-Yong Lee from the Department of Chemical and Biomolecular Engineering, received the Award of Minister of Science and Technology. Salutorian Hee-Kwang Roh from the Department of Chemistry received the Award of the KAIST Board of Trustees, while the recipient of the KAIST Presidential Award was Hong Jae-Min from the School of Computing. President Sung-Chul Shin, Chairman of the Board of Trustees Woo-Sik Kim, former Minister of Science and Technology and former Provost at KAIST Dr. KunMo Chung, and a very limited number of faculty and staff members officiated the commencement ceremony from the KAIST auditorium. President Shin in his commencement speech applauded the graduates’ hard work and dedication and delivered a very special congratulatory message to them. He encouraged the new graduates to be courageous enough to deal with these new challenges as well as future uncertainties, during the greatest transformation brought about by COVID-19. “Instead of following behind others as a fast follower, we should take the initiative and walk down new paths as a first mover.” He also stressed, “We can transform this crisis into an opportunity by practicing the C3 values KAIST pursues: Challenging, Creating, and Caring.” As new alumni of Korea’s top science and technology university, he said, “Our graduates should focus on creating the world’s best, first, or only one in their research or their work.” However, he also pointed out the importance of a caring mind for others when working together. At the ceremony, KAIST conferred an honorary doctorate degree to Dr. Younghoon David Kim, CEO and Chairman of Daesung Group, in recognition of his lifetime dedication to making innovations in the energy industry. Daesung Group is a leading energy company in Korea which manufactures and supplies natural gas for industries and home users. Dr. Kim is committed to making efficient energy sources by advancing cutting-energy sciences and disruptive technologies. He has served as chairman of the World Energy Council since 2016. In his acceptance speech, Kim stressed the Grand Energy Transition as a new driving force in the future energy industry for maximizing energy efficiency. “Since energy is the most basic foundation for all industries, improvements in energy efficiency translate into benefits for all related industries in terms of its efficiency and productivity.” “The Grand Energy Transition is progressing widely and rapidly across the entire value chain of energy production, distribution, and consumption with decarbonization, decentralization, and digitalization serving as its driving force.” He went on, “We should regard energy efficiency not as the fifth fuel but the first primary fuel.” (END)
Before Eyes Open, They Get Ready to See
- Spontaneous retinal waves can generate long-range horizontal connectivity in visual cortex. - A KAIST research team’s computational simulations demonstrated that the waves of spontaneous neural activity in the retinas of still-closed eyes in mammals develop long-range horizontal connections in the visual cortex during early developmental stages. This new finding featured in the August 19 edition of Journal of Neuroscience as a cover article has resolved a long-standing puzzle for understanding visual neuroscience regarding the early organization of functional architectures in the mammalian visual cortex before eye-opening, especially the long-range horizontal connectivity known as “feature-specific” circuitry. To prepare the animal to see when its eyes open, neural circuits in the brain’s visual system must begin developing earlier. However, the proper development of many brain regions involved in vision generally requires sensory input through the eyes. In the primary visual cortex of the higher mammalian taxa, cortical neurons of similar functional tuning to a visual feature are linked together by long-range horizontal circuits that play a crucial role in visual information processing. Surprisingly, these long-range horizontal connections in the primary visual cortex of higher mammals emerge before the onset of sensory experience, and the mechanism underlying this phenomenon has remained elusive. To investigate this mechanism, a group of researchers led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering at KAIST implemented computational simulations of early visual pathways using data obtained from the retinal circuits in young animals before eye-opening, including cats, monkeys, and mice. From these simulations, the researchers found that spontaneous waves propagating in ON and OFF retinal mosaics can initialize the wiring of long-range horizontal connections by selectively co-activating cortical neurons of similar functional tuning, whereas equivalent random activities cannot induce such organizations. The simulations also showed that emerged long-range horizontal connections can induce the patterned cortical activities, matching the topography of underlying functional maps even in salt-and-pepper type organizations observed in rodents. This result implies that the model developed by Professor Paik and his group can provide a universal principle for the developmental mechanism of long-range horizontal connections in both higher mammals as well as rodents. Professor Paik said, “Our model provides a deeper understanding of how the functional architectures in the visual cortex can originate from the spatial organization of the periphery, without sensory experience during early developmental periods.” He continued, “We believe that our findings will be of great interest to scientists working in a wide range of fields such as neuroscience, vision science, and developmental biology.” This work was supported by the National Research Foundation of Korea (NRF). Undergraduate student Jinwoo Kim participated in this research project and presented the findings as the lead author as part of the Undergraduate Research Participation (URP) Program at KAIST. Figures and image credit: Professor Se-Bum Paik, KAIST Image usage restrictions: News organizations may use or redistribute these figures and image, with proper attribution, as part of news coverage of this paper only. Publication: Jinwoo Kim, Min Song, and Se-Bum Paik. (2020). Spontaneous retinal waves generate long-range horizontal connectivity in visual cortex. Journal of Neuroscience, Available online athttps://www.jneurosci.org/content/early/2020/07/17/JNEUROSCI.0649-20.2020 Profile: Se-Bum Paik Assistant Professor firstname.lastname@example.org http://vs.kaist.ac.kr/ VSNN Laboratory Department of Bio and Brain Engineering Program of Brain and Cognitive Engineering http://kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea Profile: Jinwoo Kim Undergraduate Student email@example.com Department of Bio and Brain Engineering, KAIST Profile: Min Song Ph.D. Candidate firstname.lastname@example.org Program of Brain and Cognitive Engineering, KAIST (END)
Microscopy Approach Poised to Offer New Insights into Liver Diseases
Researchers have developed a new way to visualize the progression of nonalcoholic fatty liver disease (NAFLD) in mouse models of the disease. The new microscopy method provides a high-resolution 3D view that could lead to important new insights into NAFLD, a condition in which too much fat is stored in the liver. “It is estimated that a quarter of the adult global population has NAFLD, yet an effective treatment strategy has not been found,” said professor Pilhan Kim from the Graduate School of Medical Science and Engineering at KAIST. “NAFLD is associated with obesity and type 2 diabetes and can sometimes progress to liver failure in serious case.” In the Optical Society (OSA) journal Biomedical Optics Express, Professor Kim and colleagues reported their new imaging technique and showed that it can be used to observe how tiny droplets of fat, or lipids, accumulate in the liver cells of living mice over time. “It has been challenging to find a treatment strategy for NAFLD because most studies examine excised liver tissue that represents just one timepoint in disease progression,” said Professor Kim. “Our technique can capture details of lipid accumulation over time, providing a highly useful research tool for identifying the multiple parameters that likely contribute to the disease and could be targeted with treatment.” Capturing the dynamics of NAFLD in living mouse models of the disease requires the ability to observe quickly changing interactions of biological components in intact tissue in real-time. To accomplish this, the researchers developed a custom intravital confocal and two-photon microscopy system that acquires images of multiple fluorescent labels at video-rate with cellular resolution. “With video-rate imaging capability, the continuous movement of liver tissue in live mice due to breathing and heart beating could be tracked in real time and precisely compensated,” said Professor Kim. “This provided motion-artifact free high-resolution images of cellular and sub-cellular sized individual lipid droplets.” The key to fast imaging was a polygonal mirror that rotated at more than 240 miles per hour to provide extremely fast laser scanning. The researchers also incorporated four different lasers and four high-sensitivity optical detectors into the setup so that they could acquire multi-color images to capture different color fluorescent probes used to label the lipid droplets and microvasculature in the livers of live mice. “Our approach can capture real-time changes in cell behavior and morphology, vascular structure and function, and the spatiotemporal localization of biological components while directly visualizing of lipid droplet development in NAFLD progression,” said Professor Kim. “It also allows the analysis of the highly complex behaviors of various immune cells as NAFLD progresses.” The researchers demonstrated their approach by using it to observe the development and spatial distribution of lipid droplets in individual mice with NAFLD induced by a methionine and choline-deficient diet. Next, they plan to use it to study how the liver microenvironment changes during NAFLD progression by imaging the same mouse over time. They also want to use their microscope technique to visualize various immune cells and lipid droplets to better understand the complex liver microenvironment in NAFLD progression.
KAIST Technology Value Tops in Commercialization Market
KAIST became the first Korean university to achieve 10.183 billion KRW in annual technology royalties, and was also selected as an ‘Institution of Outstanding Patent Quality Management’ and an ‘Institution of Outstanding Public Patent Technology Transfer’ for 2020. KAIST earns its technology royalties through 56 technology transfer contracts. Following KAIST in the rankings were Seoul National University (SNU) in second place with 8.8 billion KRW from 87 contracts and Korea University (KU) in the third with 5.4 billion KRW from 133 contracts. The data shows the high value of KAIST-created technology in the market. The Korean Intellectual Property Office (KIPO) started to recognize the Institution of Outstanding Patent Quality Management this year to encourage profit-driven patent management at universities and public research institutes, and KAIST was selected as one of the four first recipients of this distinction. In addition, KAIST was selected as an Institution of Outstanding Public Patent Technology Transfer, a title given by KIPO to three universities and public research institutes this year with outstanding achievements in technology transfers and commercialization to encourage patent utilization. Director of the KAIST Institute of Technology Value Creation (ITVC) Professor Kyung-cheol Choi said that KAIST’s achievement in annual technology royalties and technology transfers and commercialization were prime examples of accelerating competitiveness in intellectual property through innovative R&D investment. In April, KAIST expanded and reorganized its Industry-Academia Collaboration Team into the ITVC to support technology transfers and commercialization. Specialized organizations such as the Intellectual Property and Technology Transfer Center and Industrial Liaison Center have been established under the ITVC, and industry experts have been recruited as special professors focusing on industry-academia collaborations to enhance its specialized functions. KAIST also operates an enterprise membership system and technology consulting system, aimed at sharing its outstanding intellectual property within domestic industries. In 2019, it secured a technology transfer commercialization fund of 1.2 billion KRW available for three years under KIPO’s Intellectual Property Profit Reinvestment Support Program (formerly the Korean Patent Gap Fund Creation Project). This program was introduced to bridge the gap between the technology developed in universities and the level of technology required by industry. Under the program, bold investments are made in early-stage technologies at the research paper or experiment phase. The program encourages enterprises to take active steps for the transfer of technologies by demonstrating their commercial potential through prototype production, testing and certification, and standard patent filing. KAIST is currently funding approximately 20 new technologies under this program as of July 2020. KAIST’s outstanding intellectual property management has also received international recognition, with its selection as Asia’s leading institution in university R&D intellectual property at the Intellectual Property Business Congress (IPBC) Asia 2019 held in Tokyo, Japan last October. (END)
Deep Learning-Based Cough Recognition Model Helps Detect the Location of Coughing Sounds in Real Time
The Center for Noise and Vibration Control at KAIST announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis. Professor Yong-Hwa Park from the Department of Mechanical Engineering developed a deep learning-based cough recognition model to classify a coughing sound in real time. The coughing event classification model is combined with a sound camera that visualizes their locations in public places. The research team said they achieved a best test accuracy of 87.4 %. Professor Park said that it will be useful medical equipment during epidemics in public places such as schools, offices, and restaurants, and to constantly monitor patients’ conditions in a hospital room. Fever and coughing are the most relevant respiratory disease symptoms, among which fever can be recognized remotely using thermal cameras. This new technology is expected to be very helpful for detecting epidemic transmissions in a non-contact way. The cough event classification model is combined with a sound camera that visualizes the cough event and indicates the location in the video image. To develop a cough recognition model, a supervised learning was conducted with a convolutional neural network (CNN). The model performs binary classification with an input of a one-second sound profile feature, generating output to be either a cough event or something else. In the training and evaluation, various datasets were collected from Audioset, DEMAND, ETSI, and TIMIT. Coughing and others sounds were extracted from Audioset, and the rest of the datasets were used as background noises for data augmentation so that this model could be generalized for various background noises in public places. The dataset was augmented by mixing coughing sounds and other sounds from Audioset and background noises with the ratio of 0.15 to 0.75, then the overall volume was adjusted to 0.25 to 1.0 times to generalize the model for various distances. The training and evaluation datasets were constructed by dividing the augmented dataset by 9:1, and the test dataset was recorded separately in a real office environment. In the optimization procedure of the network model, training was conducted with various combinations of five acoustic features including spectrogram, Mel-scaled spectrogram and Mel-frequency cepstrum coefficients with seven optimizers. The performance of each combination was compared with the test dataset. The best test accuracy of 87.4% was achieved with Mel-scaled Spectrogram as the acoustic feature and ASGD as the optimizer. The trained cough recognition model was combined with a sound camera. The sound camera is composed of a microphone array and a camera module. A beamforming process is applied to a collected set of acoustic data to find out the direction of incoming sound source. The integrated cough recognition model determines whether the sound is cough or not. If it is, the location of cough is visualized as a contour image with a ‘cough’ label at the location of the coughing sound source in a video image. A pilot test of the cough recognition camera in an office environment shows that it successfully distinguishes cough events and other events even in a noisy environment. In addition, it can track the location of the person who coughed and count the number of coughs in real time. The performance will be improved further with additional training data obtained from other real environments such as hospitals and classrooms. Professor Park said, “In a pandemic situation like we are experiencing with COVID-19, a cough detection camera can contribute to the prevention and early detection of epidemics in public places. Especially when applied to a hospital room, the patient's condition can be tracked 24 hours a day and support more accurate diagnoses while reducing the effort of the medical staff." This study was conducted in collaboration with SM Instruments Inc. Profile: Yong-Hwa Park, Ph.D. Associate Professor email@example.com http://human.kaist.ac.kr/ Human-Machine Interaction Laboratory (HuMaN Lab.) Department of Mechanical Engineering (ME) Korea Advanced Institute of Science and Technology (KAIST) https://www.kaist.ac.kr/en/ Daejeon 34141, Korea Profile: Gyeong Tae Lee PhD Candidate firstname.lastname@example.org HuMaN Lab., ME, KAIST Profile: Seong Hu Kim PhD Candidate email@example.com HuMaN Lab., ME, KAIST Profile: Hyeonuk Nam PhD Candidate firstname.lastname@example.org HuMaN Lab., ME, KAIST Profile: Young-Key Kim CEO email@example.com http://en.smins.co.kr/ SM Instruments Inc. Daejeon 34109, Korea (END)
Sulfur-Containing Polymer Generates High Refractive Index and Transparency
Transparent polymer thin film with refractive index exceeding 1.9 to serve as new platform materials for high-end optical device applications Researchers reported a novel technology enhancing the high transparency of refractive polymer film via a one-step vapor deposition process. The sulfur-containing polymer (SCP) film produced by Professor Sung Gap Im’s research team at KAIST’s Department of Chemical and Biomolecular Engineering has exhibited excellent environmental stability and chemical resistance, which is highly desirable for its application in long-term optical device applications. The high refractive index exceeding 1.9 while being fully transparent in the entire visible range will help expand the applications of optoelectronic devices. The refractive index is a ratio of the speed of light in a vacuum to the phase velocity of light in a material, used as a measure of how much the path of light is bent when passing through a material. With the miniaturization of various optical parts used in mobile devices and imaging, demand has been rapidly growing for high refractive index transparent materials that induce more light refraction with a thin film. As polymers have outstanding physical properties and can be easily processed in various forms, they are widely used in a variety of applications such as plastic eyeglass lenses. However, there have been very few polymers developed so far with a refractive index exceeding 1.75, and existing high refractive index polymers require costly materials and complicated manufacturing processes. Above all, core technologies for producing such materials have been dominated by Japanese companies, causing long-standing challenges for Korean manufacturers. Securing a stable supply of high-performance, high refractive index materials is crucial for the production of optical devices that are lighter, more affordable, and can be freely manipulated. The research team successfully manufactured a whole new polymer thin film material with a refractive index exceeding 1.9 and excellent transparency, using just a one-step chemical reaction. The SCP film showed outstanding optical transparency across the entire visible light region, presumably due to the uniformly dispersed, short-segment polysulfide chains, which is a distinct feature unachievable in polymerizations with molten sulfur. The team focused on the fact that elemental sulfur is easily sublimated to produce a high refractive index polymer by polymerizing the vaporized sulfur with a variety of substances. This method suppresses the formation of overly long S-S chains while achieving outstanding thermal stability in high sulfur concentrations and generating transparent non-crystalline polymers across the entire visible spectrum. Due to the characteristics of the vapor phase process, the high refractive index thin film can be coated not just on silicon wafers or glass substrates, but on a wide range of textured surfaces as well. We believe this thin film polymer is the first to have achieved an ultrahigh refractive index exceeding 1.9. Professor Im said, “This high-performance polymer film can be created in a simple one-step manner, which is highly advantageous in the synthesis of SCPs with a high refractive index. This will serve as a platform material for future high-end optical device applications.” This study, in collaboration with research teams from Seoul National University and Kyung Hee University, was reported in Science Advances. (Title: One-Step Vapor-Phase Synthesis of Transparent High-Refractive Index Sulfur-Containing Polymers） This research was supported by the Ministry of Science and ICT’s Global Frontier Project (Center for Advanced Soft-Electronics), Leading Research Center Support Program (Wearable Platform Materials Technology Center), and Basic Science Research Program (Advanced Research Project).
Atomic Force Microscopy Reveals Nanoscale Dental Erosion from Beverages
KAIST researchers used atomic force microscopy to quantitatively evaluate how acidic and sugary drinks affect human tooth enamel at the nanoscale level. This novel approach is useful for measuring mechanical and morphological changes that occur over time during enamel erosion induced by beverages. Enamel is the hard-white substance that forms the outer part of a tooth. It is the hardest substance in the human body, even stronger than bone. Its resilient surface is 96 percent mineral, the highest percentage of any body tissue, making it durable and damage-resistant. The enamel acts as a barrier to protect the soft inner layers of the tooth, but can become susceptible to degradation by acids and sugars. Enamel erosion occurs when the tooth enamel is overexposed to excessive consumption of acidic and sugary food and drinks. The loss of enamel, if left untreated, can lead to various tooth conditions including stains, fractures, sensitivity, and translucence. Once tooth enamel is damaged, it cannot be brought back. Therefore, thorough studies on how enamel erosion starts and develops, especially at the initial stages, are of high scientific and clinical relevance for dental health maintenance. A research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering at KAIST reported a new method of applying atomic force microscopy (AFM) techniques to study the nanoscale characterization of this early stage of enamel erosion. This study was introduced in the Journal of the Mechanical Behavior of Biomedical Materials (JMBBM) on June 29. AFM is a very-high-resolution type of scanning probe microscopy (SPM), with demonstrated resolution on the order of fractions of a nanometer (nm) that is equal to one billionth of a meter. AFM generates images by scanning a small cantilever over the surface of a sample, and this can precisely measure the structure and mechanical properties of the sample, such as surface roughness and elastic modulus. The co-lead authors of the study, Dr. Panpan Li and Dr. Chungik Oh, chose three commercially available popular beverages, Coca-Cola®, Sprite®, and Minute Maid® orange juice, and immersed tooth enamel in these drinks over time to analyze their impacts on human teeth and monitor the etching process on tooth enamel. Five healthy human molars were obtained from volunteers between age 20 and 35 who visited the KAIST Clinic. After extraction, the teeth were preserved in distilled water before the experiment. The drinks were purchased and opened right before the immersion experiment, and the team utilized AFM to measure the surface topography and elastic modulus map. The researchers observed that the surface roughness of the tooth enamel increased significantly as the immersion time increased, while the elastic modulus of the enamel surface decreased drastically. It was demonstrated that the enamel surface roughened five times more when it was immersed in beverages for 10 minutes, and that the elastic modulus of tooth enamel was five times lower after five minutes in the drinks. Additionally, the research team found preferential etching in scratched tooth enamel. Brushing your teeth too hard and toothpastes with polishing particles that are advertised to remove dental biofilms can cause scratches on the enamel surface, which can be preferential sites for etching, the study revealed. Professor Hong said, “Our study shows that AFM is a suitable technique to characterize variations in the morphology and mechanical properties of dental erosion quantitatively at the nanoscale level.” This work was supported by the National Research Foundation (NRF), the Ministry of Science and ICT (MSIT), and the KUSTAR-KAIST Institute of Korea. A dentist at the KAIST Clinic, Dr. Suebean Cho, Dr. Sangmin Shin from the Smile Well Dental, and Professor Kack-Kyun Kim at the Seoul National University School of Dentistry also collaborated in this project. Publication: Li, P., et al. (2020) ‘Nanoscale effects of beverages on enamel surface of human teeth: An atomic force microscopy study’. Journal of the Mechanical Behavior of Biomedical Materials (JMBBM), Volume 110. Article No. 103930. Available online at https://doi.org/10.1016/j.jmbbm.2020.103930 Profile: Seungbum Hong, Ph.D. Associate Professor firstname.lastname@example.org http://mii.kaist.ac.kr/ Materials Imaging and Integration (MII) Lab. Department of Materials Science and Engineering (MSE) Korea Advanced Institute of Science and Technology (KAIST) https://www.kaist.ac.kr Daejeon 34141, Korea (END)
Study Finds Interferon Triggers Inflammation in Severe COVID-19
KAIST medical scientists and their colleagues confirmed that the type I interferon response plays a pivotal role in exacerbating inflammation in severe COVID-19 cases. Severe COVID-19 has been shown to be caused by a hyper-inflammatory response. Particularly, inflammatory cytokines secreted by classical monocytes and macrophages are believed to play a crucial role in the severe progression of COVID-19. A new single-cell RNA sequencing analysis of more than 59,000 cells from three different patient cohorts provided a detailed look at patients’ immune responses in severe cases of COVID-19. The results suggest that patients with severe cases of COVID-19 experience increased regulation of the type I interferon (IFN-I) inflammation-triggering pathway, a signature that the researchers also observed in patients hospitalized with severe cases of influenza. Their findings suggest that anti-inflammatory treatment strategies for COVID-19 should also be aimed toward the IFN-I signaling pathway, in addition to targeting inflammatory molecules such as TNF, IL-1, and IL-6, which have been associated with COVID-19. The research team under Professor Eui-Cheol Shin from the Graduate School of Medical Science and Engineering sequenced the RNA from a total of 59,572 blood cells obtained from four healthy donors, eight patients with mild or severe COVID-19, and five patients with severe influenza. By comparison, patients with severe cases of influenza showed increased expression of various IFN-stimulated genes, but did not experience TNF/IL-1 responses as seen in COVID-19 patients. Unlike the flu cohort, patients in the severe COVID-19 cohort exhibited the IFN-I signature concurrently with TNF/IL-1-driven inflammation – a combination also not seen in patients with milder cases of COVID-19. Their result, along with past mouse studies that highlight how the timing of IFN-I expression is critical to determining the outcome of SARS, support targeting IFN-I as a potential treatment strategy for severe COVID-19. Professor Shin said, “This research provides insights for designing therapeutic options for COVID-19 by investigating very closely how the immune cells of COVDI-19 patients develop. We will continue to conduct research on novel therapeutic immune mechanisms and target therapeutic anti-inflammatory medication to improve the survival of severe COVID-19 patients.” This study, conducted in collaboration with Severance Hospital at Yonsei University, Asan Medical Center, and Chungbuk National University, was featured in Science Immunology on July 10. This work was funded by Samsung Science and Technology Foundation and SUHF Fellowship. -PublicationScience Immunology 10 Jul 2020:Vol. 5, Issue 49, eabd1554DOI: 10.1126/sciimmunol.abd1554 -ProfileProfessorEui-Cheol ShinGraduate School of Medical Science and EngineeringLaboratory of Immunology & Infectious Diseases (http://liid.kaist.ac.kr/)email@example.comKAIST
COVID-19 Update: Fall Semester to Continue Offering Classes Online
KAIST announced that the university would continue online classes through the fall semester. However, the university will conduct additional in-person classes for upper-level undergraduate lab classes and some graduate courses where on-site interaction was deemed to be highly necessary. Some 600-level graduate courses at the Daejeon campus and graduate courses at the Seoul campus will carry out both in-person and online classes. The fall semester will start from August 31. Provost and Executive Vice President Kwang Hyung Lee announced the fall semester plan in his letter to the entire student body on July 9. He said that the university decided to continue with online classes in consideration of the safety of KAIST community members and the current status of the COVID-19 spread. However, he said the new plan will help students choose class options between in-person and online classes. “Although the number of classes with two versions is limited, we believe this will help many students continue learning without the sustained face-to-face contact that is inherent in residential education,” Provost Lee said. In-person classes conducted in the fall semester will also be provided online for students who are not available for in-person classes. Students may choose the type of the classes they prefer according to their situation, among only the courses that will offer two versions. Professors will decide if they will conduct two versions of their classes. The Office of Academic Affairs is collecting the professors’ applications for conducting both versions until July 24. KAIST offered real-time online classes and pre-recorded KLMS (KAIST Learning Management System) classes during the spring semester with a very limited number of in-person lab classes for graduate courses and these two versions of online class will continue for fall semester. Provost Lee asked the students who will take the in-person classes to strictly observe all precaution measures as the university will do its best to abide by the government guidelines against the Covid-19 in preparation for the fall semester. “We will continue to make appropriate and safe accommodations for them,” said Provost Lee. Those who need to reside in on-campus dormitories are required to be approved for moving. The applications will open after all the in-person class schedules are fixed next month. However, students who were approved for staying in the dormitories last semester can move in without additional approval procedures for the fall semester. (END)
Quantum Classifiers with Tailored Quantum Kernel
Quantum information scientists have introduced a new method for machine learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology. The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space. Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data. More recently, the kernel method has been introduced in quantum machine learning with great success. The ability of quantum computers to efficiently access and manipulate data in the quantum feature space can open opportunities for quantum techniques to enhance various existing machine learning methods. The idea of the classification algorithm with a nonlinear kernel is that given a quantum test state, the protocol calculates the weighted power sum of the fidelities of quantum data in quantum parallel via a swap-test circuit followed by two single-qubit measurements (see Figure 1). This requires only a small number of quantum data operations regardless of the size of data. The novelty of this approach lies in the fact that labeled training data can be densely packed into a quantum state and then compared to the test data. The KAIST team, in collaboration with researchers from the University of KwaZulu-Natal (UKZN) in South Africa and Data Cybernetics in Germany, has further advanced the rapidly evolving field of quantum machine learning by introducing quantum classifiers with tailored quantum kernels.This study was reported at npj Quantum Information in May. The input data is either represented by classical data via a quantum feature map or intrinsic quantum data, and the classification is based on the kernel function that measures the closeness of the test data to training data. Dr. Daniel Park at KAIST, one of the lead authors of this research, said that the quantum kernel can be tailored systematically to an arbitrary power sum, which makes it an excellent candidate for real-world applications. Professor Rhee said that quantum forking, a technique that was invented by the team previously, makes it possible to start the protocol from scratch, even when all the labeled training data and the test data are independently encoded in separate qubits. Professor Francesco Petruccione from UKZN explained, “The state fidelity of two quantum states includes the imaginary parts of the probability amplitudes, which enables use of the full quantum feature space.” To demonstrate the usefulness of the classification protocol, Carsten Blank from Data Cybernetics implemented the classifier and compared classical simulations using the five-qubit IBM quantum computer that is freely available to public users via cloud service. “This is a promising sign that the field is progressing,” Blank noted. Link to download the full-text paper: https://www.nature.com/articles/s41534-020-0272-6 -Profile Professor June-Koo Kevin Rhee firstname.lastname@example.org Professor, School of Electrical Engineering Director, ITRC of Quantum Computing for AIKAIST Daniel Kyungdeock Parkkpark10@kaist.ac.krResearch Assistant ProfessorSchool of Electrical EngineeringKAIST
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