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A KAIST research team unveils new path for dense photonic integration
Integrated optical semiconductor (hereinafter referred to as optical semiconductor) technology is a next-generation semiconductor technology for which many researches and investments are being made worldwide because it can make complex optical systems such as LiDAR and quantum sensors and computers into a single small chip. In the existing semiconductor technology, the key was how small it was to make it in units of 5 nanometers or 2 nanometers, but increasing the degree of integration in optical semiconductor devices can be said to be a key technology that determines performance, price, and energy efficiency.
KAIST (President Kwang-Hyung Lee) announced on the 19th that a research team led by Professor Sangsik Kim of the Department of Electrical and Electronic Engineering discovered a new optical coupling mechanism that can increase the degree of integration of optical semiconductor devices by more than 100 times.
The degree of the number of elements that can be configured per chip is called the degree of integration. However, it is very difficult to increase the degree of integration of optical semiconductor devices, because crosstalk occurs between photons between adjacent devices due to the wave nature of light.
In previous studies, it was possible to reduce crosstalk of light only in specific polarizations, but in this study, the research team developed a method to increase the degree of integration even under polarization conditions, which were previously considered impossible, by discovering a new light coupling mechanism.
This study, led by Professor Sangsik Kim as a corresponding author and conducted with students he taught at Texas Tech University, was published in the international journal 'Light: Science & Applications' [IF=20.257] on June 2nd. done. (Paper title: Anisotropic leaky-like perturbation with subwavelength gratings enables zero crosstalk).
Professor Sangsik Kim said, "The interesting thing about this study is that it paradoxically eliminated the confusion through leaky waves (light tends to spread sideways), which was previously thought to increase the crosstalk." He went on to add, “If the optical coupling method using the leaky wave revealed in this study is applied, it will be possible to develop various optical semiconductor devices that are smaller and that has less noise.”
Professor Sangsik Kim is a researcher recognized for his expertise and research in optical semiconductor integration. Through his previous research, he developed an all-dielectric metamaterial that can control the degree of light spreading laterally by patterning a semiconductor structure at a size smaller than the wavelength, and proved this through experiments to improve the degree of integration of optical semiconductors. These studies were reported in ‘Nature Communications’ (Vol. 9, Article 1893, 2018) and ‘Optica’ (Vol. 7, pp. 881-887, 2020). In recognition of these achievements, Professor Kim has received the NSF Career Award from the National Science Foundation (NSF) and the Young Scientist Award from the Association of Korean-American Scientists and Engineers.
Meanwhile, this research was carried out with the support from the New Research Project of Excellence of the National Research Foundation of Korea and and the National Science Foundation of the US.
< Figure 1. Illustration depicting light propagation without crosstalk in the waveguide array of the developed metamaterial-based optical semiconductor >
2023.06.21 View 4735 -
KAIST research team develops a forgery prevention technique using salmon DNA
The authenticity scandal that plagued the artwork “Beautiful Woman” by Kyung-ja Chun for 30 years shows how concerns about replicas can become a burden to artists, as most of them are not experts in the field of anti-counterfeiting. To solve this problem, artist-friendly physical unclonable functions (PUFs) based on optical techniques instead of electronic ones, which can be applied immediately onto artwork through brushstrokes are needed.
On May 23, a KAIST research team led by Professor Dong Ki Yoon in the Department of Chemistry revealed the development of a proprietary technology for security and certification using random patterns that occur during the self-assembly of soft materials.
With the development of the Internet of Things in recent years, various electronic devices and services can now be connected to the internet and carry out new innovative functions. However, counterfeiting technologies that infringe on individuals’ privacy have also entered the marketplace.
The technique developed by the research team involves random and spontaneous patterns that naturally occur during the self-assembly of two different types of soft materials, which can be used in the same way as human fingerprints for non-replicable security. This is very significant in that even non-experts in the field of security can construct anti-counterfeiting systems through simple actions like drawing a picture.
The team developed two unique methods. The first method uses liquid crystals. When liquid crystals become trapped in patterned substrates, they induce the symmetrical destruction of the structure and create a maze-like topology (Figure 1). The research team defined the pathways open to the right as 0 (blue), and those open to the left as 1 (red), and confirmed that the structure could be converted into a digital code composed of 0’s and 1’s that can serve as a type of fingerprint through object recognition using machine learning. This groundbreaking technique can be utilized by non-experts, as it does not require complex semiconductor patterns that are required by existing technology, and can be observed through the level of resolution of a smartphone camera. In particular, this technique can reconstruct information more easily than conventional methods that use semiconductor chips.
< Figure 1. Security technology using the maze made up of magnetically-assembled structures formed on a substrate patterned with liquid crystal materials. >
The second method uses DNA extracted from salmon. The DNA can be dissolved in water and applied with a brush to induce bulking instability, which forms random patterns similar to a zebra’s stripes. Here, the patterns create ridge endings and bifurcation, which are characteristics in fingerprints, and these can also be digitalized into 0’s and 1’s through machine learning. The research team applied conventional fingerprint recognition technology to this patterning technique and demonstrated its use as an artificial fingerprint. This method can be easily carried out using a brush, and the solution can be mixed into various colors and used as a new security ink.
< Figure 2. Technology to produce security ink using DNA polymers extracted from salmon >
This new security technology developed by the research team uses only simple organic materials and requires basic manufacturing processes, making it possible to enhance security at a low cost. In addition, users can produce patterns in the shapes and sizes they want, and even if the patterns are made in the same way, their randomness makes each individual pattern different. This provides high levels of security and gives the technique enhanced marketability.
Professor Dong Ki Yoon said, “These studies have taken the randomness that naturally occurs during self-assembly to create non-replicable patterns that can act like human fingerprints.” He added, “These ideas will be the cornerstone of technology that applies the many randomities that exist in nature to security systems.”
The two studies were published in the journal Advanced Materials under the titles “1Planar Spin Glass with Topologically-Protected Mazes in the Liquid Crystal Targeting for Reconfigurable Micro Security Media” and “2Paintable Physical Unclonable Function Using DNA” on May 6 and 5, respectively.
Author Information: 1Geonhyeong Park, Yun-Seok Choi, S. Joon Kwon*, and Dong Ki Yoon*/ 2Soon Mo Park†, Geonhyeong Park†, Dong Ki Yoon*: †co-first authors, *corresponding author
This research was funded by the Center for Multiscale Chiral Architectures and supported by the Ministry of Science and ICT-Korea Research Foundation, BRIDGE Convergent Research and Development Program, the Running Together Project, and the Samsung Future Technology Development Program.
< Figure 1-1. A scene from the schematic animation of the process of Blues (0) and Reds (1) forming the PUF by exploring the maze. From "Planar Spin Glass with Topologically-Protected Mazes in the Liquid Crystal Targeting for Reconfigurable Micro Security Media" by Geonhyeong Park, Yun-Seok Choi, S. Joon Kwon, Dong Ki Yoon. https://doi.org/10.1002/adma.202303077 >
< Figure 2-1. A schematic diagram of the formation of digital fingerprints formed using the DNA ink. From "Paintable Physical Unclonable Function Using DNA" by Soon Mo Park, Geonhyeong Park, Dong Ki Yoon. https://doi.org/10.1002/adma.202302135 >
2023.06.08 View 4703 -
Researchers finds a way to reduce the overheating of semiconductor devices
The demand to shrink the size of semiconductors coupled with the problem of the heat generated at the hot spots of the devices not being effectively dispersed has negatively affected the reliability and durability of modern devices. Existing thermal management technologies have not been up to the task. Thus, the discovery of a new way of dispersing heat by using surface waves generated on the thin metal films over the substrate is an important breakthrough.
KAIST (President Kwang Hyung Lee) announced that Professor Bong Jae Lee's research team in the Department of Mechanical Engineering succeeded in measuring a newly observed transference of heat induced by 'surface plasmon polariton' (SPP) in a thin metal film deposited on a substrate for the first time in the world.
☞ Surface plasmon polariton (SPP) refers to a surface wave formed on the surface of a metal as a result of strong interaction between the electromagnetic field at the interface between the dielectric and the metal and the free electrons on the metal surface and similar collectively vibrating particles.
The research team utilized surface plasmon polaritons (SPP), which are surface waves generated at the metal-dielectric interface, to improve thermal diffusion in nanoscale thin metal films. Since this new heat transfer mode occurs when a thin film of metal is deposited on a substrate, it is highly usable in the device manufacturing process and has the advantage of being able to be manufactured over a large area. The research team showed that the thermal conductivity increased by about 25% due to surface waves generated over a 100-nm-thick titanium (Ti) film with a radius of about 3 cm.
KAIST Professor Bong Jae Lee, who led the research, said, "The significance of this research is that a new heat transfer mode using surface waves over a thin metal film deposited on a substrate with low processing difficulty was identified for the first time in the world. It can be applied as a nanoscale heat spreader to efficiently dissipate heat near the hot spots for easily overheatable semiconductor devices.”
The result has great implications for the development of high-performance semiconductor devices in the future in that it can be applied to rapidly dissipate heat on a nanoscale thin film. In particular, this new heat transfer mode identified by the research team is expected to solve the fundamental problem of thermal management in semiconductor devices as it enables even more effective heat transfer at nanoscale thickness while the thermal conductivity of the thin film usually decreases due to the boundary scattering effect.
This study was published online on April 26 in 'Physical Review Letters' and was selected as an Editors' Suggestion. The research was carried out with support from the Basic Research Laboratory Support Program of the National Research Foundation of Korea.
< Figure. Schematic diagram of the principle of measuring the thermal conductivity of thin Titanium (TI) films and the thermal conductivity of surface plasmon polariton measured on the Ti film >
2023.06.01 View 4842 -
KAIST debuts “DreamWaQer” - a quadrupedal robot that can walk in the dark
- The team led by Professor Hyun Myung of the School of Electrical Engineering developed “DreamWaQ”, a deep reinforcement learning-based walking robot control technology that can walk in an atypical environment without visual and/or tactile information
- Utilization of “DreamWaQ” technology can enable mass production of various types of “DreamWaQers”
- Expected to be used in exploration of atypical environment involving unique circumstances such as disasters by fire.
A team of Korean engineering researchers has developed a quadrupedal robot technology that can climb up and down the steps and moves without falling over in uneven environments such as tree roots without the help of visual or tactile sensors even in disastrous situations in which visual confirmation is impeded due to darkness or thick smoke from the flames.
KAIST (President Kwang Hyung Lee) announced on the 29th of March that Professor Hyun Myung's research team at the Urban Robotics Lab in the School of Electrical Engineering developed a walking robot control technology that enables robust 'blind locomotion' in various atypical environments.
< (From left) Prof. Hyun Myung, Doctoral Candidates I Made Aswin Nahrendra, Byeongho Yu, and Minho Oh. In the foreground is the DreamWaQer, a quadrupedal robot equipped with DreamWaQ technology. >
The KAIST research team developed "DreamWaQ" technology, which was named so as it enables walking robots to move about even in the dark, just as a person can walk without visual help fresh out of bed and going to the bathroom in the dark. With this technology installed atop any legged robots, it will be possible to create various types of "DreamWaQers".
Existing walking robot controllers are based on kinematics and/or dynamics models. This is expressed as a model-based control method. In particular, on atypical environments like the open, uneven fields, it is necessary to obtain the feature information of the terrain more quickly in order to maintain stability as it walks. However, it has been shown to depend heavily on the cognitive ability to survey the surrounding environment.
In contrast, the controller developed by Professor Hyun Myung's research team based on deep reinforcement learning (RL) methods can quickly calculate appropriate control commands for each motor of the walking robot through data of various environments obtained from the simulator. Whereas the existing controllers that learned from simulations required a separate re-orchestration to make it work with an actual robot, this controller developed by the research team is expected to be easily applied to various walking robots because it does not require an additional tuning process.
DreamWaQ, the controller developed by the research team, is largely composed of a context estimation network that estimates the ground and robot information and a policy network that computes control commands. The context-aided estimator network estimates the ground information implicitly and the robot’s status explicitly through inertial information and joint information. This information is fed into the policy network to be used to generate optimal control commands. Both networks are learned together in the simulation.
While the context-aided estimator network is learned through supervised learning, the policy network is learned through an actor-critic architecture, a deep RL methodology. The actor network can only implicitly infer surrounding terrain information. In the simulation, the surrounding terrain information is known, and the critic, or the value network, that has the exact terrain information evaluates the policy of the actor network.
This whole learning process takes only about an hour in a GPU-enabled PC, and the actual robot is equipped with only the network of learned actors. Without looking at the surrounding terrain, it goes through the process of imagining which environment is similar to one of the various environments learned in the simulation using only the inertial sensor (IMU) inside the robot and the measurement of joint angles. If it suddenly encounters an offset, such as a staircase, it will not know until its foot touches the step, but it will quickly draw up terrain information the moment its foot touches the surface. Then the control command suitable for the estimated terrain information is transmitted to each motor, enabling rapidly adapted walking.
The DreamWaQer robot walked not only in the laboratory environment, but also in an outdoor environment around the campus with many curbs and speed bumps, and over a field with many tree roots and gravel, demonstrating its abilities by overcoming a staircase with a difference of a height that is two-thirds of its body. In addition, regardless of the environment, the research team confirmed that it was capable of stable walking ranging from a slow speed of 0.3 m/s to a rather fast speed of 1.0 m/s.
The results of this study were produced by a student in doctorate course, I Made Aswin Nahrendra, as the first author, and his colleague Byeongho Yu as a co-author. It has been accepted to be presented at the upcoming IEEE International Conference on Robotics and Automation (ICRA) scheduled to be held in London at the end of May. (Paper title: DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning)
The videos of the walking robot DreamWaQer equipped with the developed DreamWaQ can be found at the address below.
Main Introduction: https://youtu.be/JC1_bnTxPiQ Experiment Sketches: https://youtu.be/mhUUZVbeDA0
Meanwhile, this research was carried out with the support from the Robot Industry Core Technology Development Program of the Ministry of Trade, Industry and Energy (MOTIE). (Task title: Development of Mobile Intelligence SW for Autonomous Navigation of Legged Robots in Dynamic and Atypical Environments for Real Application)
< Figure 1. Overview of DreamWaQ, a controller developed by this research team. This network consists of an estimator network that learns implicit and explicit estimates together, a policy network that acts as a controller, and a value network that provides guides to the policies during training. When implemented in a real robot, only the estimator and policy network are used. Both networks run in less than 1 ms on the robot's on-board computer. >
< Figure 2. Since the estimator can implicitly estimate the ground information as the foot touches the surface, it is possible to adapt quickly to rapidly changing ground conditions. >
< Figure 3. Results showing that even a small walking robot was able to overcome steps with height differences of about 20cm. >
2023.05.18 View 6877 -
KAIST gearing up to train physician-scientists and BT Professionals joining hands with Boston-based organizations
KAIST (President Kwang Hyung Lee) announced on the 29th that it has signed MOUs with Massachusetts General Hospital, a founding member of the Mass General Brigham health care system and a world-class research-oriented hospital, and Moderna, a biotechnology company that developed a COVID-19 vaccine at the Langham Hotel in Boston, MA, USA on the morning of April 28th (local time). The signing ceremony was attended by officials from each institution joined by others headed by Minister LEE Young of the Korean Ministry of SMEs and Startups (MSS), and Commissioner LEE Insil of the Korean Intellectual Property Office.
< Photo 1. Photo from the Signing of MOU between KAIST-Harvard University Massachusetts General Hospital and KAIST-Moderna >
Mass General is the first and largest teaching hospital of Harvard Medical School in Boston, USA, and it is one of the most innovative hospitals in the world being the alma mater of more than 13 Nobel Prize winners and the home of the Mass General Research Institute, the world’s largest hospital-based research program that utilizes an annual research budget of more than $1.3 billion.
KAIST signed a general agreement to explore research and academic exchange with Mass General in September of last year and this MOU is a part of its follow-ups.
Mass General works with Harvard and the Massachusetts Institute of Technology (MIT), as well as local hospitals, to support students learn the theories of medicine and engineering, and gain rich clinical research experience.
Through this MOU, KAIST will explore cooperation with an innovative ecosystem created through the convergence of medicine and engineering. In particular, KAIST’s goal is to develop a Korean-style training program and implement a differentiated educational program when establishing the science and technology-oriented medical school in the future by further strengthening the science and engineering part of the training including a curriculum on artificial intelligence (AI) and the likes there of.
Also, in order to foster innovative physician-scientists, KAIST plans to pursue cooperation to develop programs for exchange of academic and human resources including programs for student and research exchanges and a program for students of the science and technology-oriented medical school at KAIST to have a chance to take part in practical training at Mass General.
David F.M. Brown, MD, Mass General President, said, “The collaboration with KAIST has a wide range of potentials, including advice on training of physician-scientists, academic and human resource exchanges, and vitalization of joint research by faculty from both institutions. Through this agreement, we will be able to actively contribute to global cooperation and achieve mutual goals.”
Meanwhile, an MOU between KAIST and Moderna was also held on the same day. Its main focus is to foster medical experts in cooperation with KAIST Graduate School of Medical Science and Engineering (GSMSE), and plans to cooperate in various ways in the future, including collaborating for development of vaccine and new drugs, virus research, joint mRNA research, and facilitation of technology commercialization.
In over 10 years since its inception, Moderna has transformed from a research-stage company advancing programs in the field of messenger RNA (mRNA) to an enterprise with a diverse clinical portfolio of vaccines and therapeutics across seven modalities. The Company has 48 programs in development across 45 development candidates, of which 38 are currently in active clinical trials.
“We are grateful to have laid a foundation for collaboration to foster industry experts with the Korea Advanced Institute of Science and Technology, a leader of science and technology innovation in Korea,” said Arpa Garay, Chief Commercial Officer, Moderna. “Based on our leadership and expertise in developing innovative mRNA vaccines and therapeutics, we hope to contribute to educating and collaborating with professionals in the bio-health field of Korea.“
President Kwang Hyung Lee of KAIST, said, “We deem this occasion to be of grave significance to be able to work closely with Massachusetts General Hospital, one of the world's best research-oriented hospitals, and Moderna, one of the most influential biomedical companies.”
President Lee continued, "On the basis of the collaboration with the two institutions, we will be able to bring up qualified physician-scientists and global leaders of the biomedical business who will solve problems of human health and their progress will in turn, accelerate the national R&D efforts in general and diversify the industry."
2023.04.29 View 10670 -
Seanie Lee of KAIST Kim Jaechul Graduate School of AI, named the 2023 Apple Scholars in AI Machine Learning
Seanie Lee, a Ph.D. candidate at the Kim Jaechul Graduate School of AI, has been selected as one of the Apple Scholars in AI/ML PhD fellowship program recipients for 2023. Lee, advised by Sung Ju Hwang and Juho Lee, is a rising star in AI.
< Seanie Lee of KAIST Kim Jaechul Graduate School of AI >
The Apple Scholars in AI/ML PhD fellowship program, launched in 2020, aims to discover and support young researchers with a promising future in computer science. Each year, a handful of graduate students in related fields worldwide are selected for the program. For the following two years, the selected students are provided with financial support for research, international conference attendance, internship opportunities, and mentorship by an Apple engineer.
This year, 22 PhD students were selected from leading universities worldwide, including Johns Hopkins University, MIT, Stanford University, Imperial College London, Edinburgh University, Tsinghua University, HKUST, and Technion. Seanie Lee is the first Korean student to be selected for the program.
Lee’s research focuses on transfer learning, a subfield of AI that reuses pre-trained AI models on large datasets such as images or text corpora to train them for new purposes.
(*text corpus: a collection of text resources in computer-readable forms)
His work aims to improve the performance of transfer learning by developing new data augmentation methods that allow for more effective training using few training data samples and new regularization techniques that prevent the overfitting of large AI models to training data. He has published 11 papers, all of which were accepted to top-tier conferences such as the Annual Meeting of the Association for Computational Linguistics (ACL), International Conference on Learning Representations (ICLR), and Annual Conference on Neural Information Processing Systems (NeurIPS).
“Being selected as one of the Apple Scholars in AI/ML PhD fellowship program is a great motivation for me,” said Lee. “So far, AI research has been largely focused on computer vision and natural language processing, but I want to push the boundaries now and use modern tools of AI to solve problems in natural science, like physics.”
2023.04.20 View 4817 -
KAIST Team Develops Highly-Sensitive Wearable Piezoelectric Blood Pressure Sensor for Continuous Health Monitoring
- A collaborative research team led by KAIST Professor Keon Jae Lee verifies the accuracy of the highly-sensitive sensor through clinical trials
- Commercialization of the watch and patch-type sensor is in progress
A KAIST research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering and the College of Medicine of the Catholic University of Korea has developed a highly sensitive, wearable piezoelectric blood pressure sensor.
Blood pressure is a critical indicator for assessing general health and predicting stroke or heart failure. In particular, cardiovascular disease is the leading cause of global death, therefore, periodic measurement of blood pressure is crucial for personal healthcare.
Recently, there has been a growing interest in healthcare devices for continuous blood pressure monitoring. Although smart watches using LED-based photoplethysmography (PPG) technology have been on market, these devices have been limited by the accuracy constraints of optical sensors, making it hard to meet the international standards of automatic sphygmomanometers.
Professor Lee’s team has developed the wearable piezoelectric blood pressure sensor by transferring a highly sensitive, inorganic piezoelectric membrane from bulk sapphire substrates to flexible substrates. Ultrathin piezoelectric sensors with a thickness of several micrometers (one hundredth of the human hair) exhibit conformal contact with the skin to successfully collect accurate blood pressure from the subtle pulsation of the blood vessels.
Clinical trial at the St. Mary’s Hospital of the Catholic University validated the accuracy of blood pressure sensor at par with international standard with errors within ±5 mmHg and a standard deviation under 8 mmHg for both systolic and diastolic blood pressure. In addition, the research team successfully embedded the sensor on a watch-type product to enable continuous monitoring of blood pressure.
Prof. Keon Jae Lee said, “Major target of our healthcare devices is hypertensive patients for their daily medical check-up. We plan to develop a comfortable patch-type sensor to monitor blood pressure during sleep and have a start-up company commercialize these watch and patch-type products soon.”
This result titled “Clinical validation of wearable piezoelectric blood pressure sensor for health monitoring” was published in the online issue of Advanced Materials on March 24th, 2023. (DOI: 10.1002/adma.202301627)
Figure 1. Schematic illustration of the overall concept for a wearable piezoelectric blood pressure sensor (WPBPS).
Figure 2. Wearable piezoelectric blood pressure sensor (WPBPS) mounted on a watch (a) Schematic design of the WPBPS-embedded wristwatch. (b) Block diagram of the wireless communication circuit, which filters, amplifies, and transmits wireless data to portable devices. (c) Pulse waveforms transmitted from the wristwatch to the portable device by the wireless communication circuit. The inset shows a photograph of monitoring a user’s beat-to-beat pulses and their corresponding BP values in real time using the developed WPBPS-mounted wristwatch.
2023.04.17 View 5397 -
KAIST leads AI-based analysis on drug-drug interactions involving Paxlovid
KAIST (President Kwang Hyung Lee) announced on the 16th that an advanced AI-based drug interaction prediction technology developed by the Distinguished Professor Sang Yup Lee's research team in the Department of Biochemical Engineering that analyzed the interaction between the PaxlovidTM ingredients that are used as COVID-19 treatment and other prescription drugs was published as a thesis. This paper was published in the online edition of 「Proceedings of the National Academy of Sciences of America」 (PNAS), an internationally renowned academic journal, on the 13th of March.
* Thesis Title: Computational prediction of interactions between Paxlovid and prescription drugs (Authored by Yeji Kim (KAIST, co-first author), Jae Yong Ryu (Duksung Women's University, co-first author), Hyun Uk Kim (KAIST, co-first author), and Sang Yup Lee (KAIST, corresponding author))
In this study, the research team developed DeepDDI2, an advanced version of DeepDDI, an AI-based drug interaction prediction model they developed in 2018. DeepDDI2 is able to compute for and process a total of 113 drug-drug interaction (DDI) types, more than the 86 DDI types covered by the existing DeepDDI.
The research team used DeepDDI2 to predict possible interactions between the ingredients (ritonavir, nirmatrelvir) of Paxlovid*, a COVID-19 treatment, and other prescription drugs. The research team said that while among COVID-19 patients, high-risk patients with chronic diseases such as high blood pressure and diabetes are likely to be taking other drugs, drug-drug interactions and adverse drug reactions for Paxlovid have not been sufficiently analyzed, yet. This study was pursued in light of seeing how continued usage of the drug may lead to serious and unwanted complications.
* Paxlovid: Paxlovid is a COVID-19 treatment developed by Pfizer, an American pharmaceutical company, and received emergency use approval (EUA) from the US Food and Drug Administration (FDA) in December 2021.
The research team used DeepDDI2 to predict how Paxrovid's components, ritonavir and nirmatrelvir, would interact with 2,248 prescription drugs. As a result of the prediction, ritonavir was predicted to interact with 1,403 prescription drugs and nirmatrelvir with 673 drugs.
Using the prediction results, the research team proposed alternative drugs with the same mechanism but low drug interaction potential for prescription drugs with high adverse drug events (ADEs). Accordingly, 124 alternative drugs that could reduce the possible adverse DDI with ritonavir and 239 alternative drugs for nirmatrelvir were identified.
Through this research achievement, it became possible to use an deep learning technology to accurately predict drug-drug interactions (DDIs), and this is expected to play an important role in the digital healthcare, precision medicine and pharmaceutical industries by providing useful information in the process of developing new drugs and making prescriptions.
Distinguished Professor Sang Yup Lee said, "The results of this study are meaningful at times like when we would have to resort to using drugs that are developed in a hurry in the face of an urgent situations like the COVID-19 pandemic, that it is now possible to identify and take necessary actions against adverse drug reactions caused by drug-drug interactions very quickly.”
This research was carried out with the support of the KAIST New-Deal Project for COVID-19 Science and Technology and the Bio·Medical Technology Development Project supported by the Ministry of Science and ICT.
Figure 1. Results of drug interaction prediction between Paxlovid ingredients and representative approved drugs using DeepDDI2
2023.03.16 View 5395 -
The cause of disability in aged brain meningeal membranes identified
Due to the increase in average age, studies on changes in the brain following general aging process without serious brain diseases have also become an issue that requires in-depth studies. Regarding aging research, as aging progresses, ‘sugar’ accumulates in the body, and the accumulated sugar becomes a causative agent for various diseases such as aging-related inflammation and vascular disease. In the end, “surplus” sugar molecules attach to various proteins in the body and interfere with their functions.
KAIST (President Kwang Hyung Lee), a joint research team of Professor Pilnam Kim and Professor Yong Jeong of the Department of Bio and Brain Engineering, revealed on the 15th that it was confirmed that the function of being the “front line of defense” for the cerebrocortex of the brain meninges, the layers of membranes that surrounds the brain, is hindered when 'sugar' begins to build up on them as aging progresses.
Professor Kim's research team confirmed excessive accumulation of sugar molecules in the meninges of the elderly and also confirmed that sugar accumulation occurs mouse models in accordance with certain age levels. The meninges are thin membranes that surround the brain and exist at the boundary between the cerebrospinal fluid and the cortex and play an important role in protecting the brain. In this study, it was revealed that the dysfunction of these brain membranes caused by aging is induced by 'excess' sugar in the brain. In particular, as the meningeal membrane becomes thinner and stickier due to aging, a new paradigm has been provided for the discovery of the principle of the decrease in material exchange between the cerebrospinal fluid and the cerebral cortex.
This research was conducted by the Ph.D. candidate Hyo Min Kim and Dr. Shinheun Kim as the co-first authors to be published online on February 28th in the international journal, Aging Cell. (Paper Title: Glycation-mediated tissue-level remodeling of brain meningeal membrane by aging)
The meninges, which are in direct contact with the cerebrospinal fluid, are mainly composed of collagen, an extracellular matrix (ECM) protein, and are composed of fibroblasts, which are cells that produce this protein. The cells that come in contact with collagen proteins that are attached with sugar have a low collagen production function, while the meningeal membrane continuously thins and collapses as the expression of collagen degrading enzymes increases.
Studies on the relationship between excess sugar molecules accumulation in the brain due to continued sugar intake and the degeneration of neurons and brain diseases have been continuously conducted. However, this study was the first to identify meningeal degeneration and dysfunction caused by glucose accumulation with the focus on the meninges itself, and the results are expected to present new ideas for research into approach towards discoveries of new treatments for brain disease.
Researcher Hyomin Kim, the first author, introduced the research results as “an interesting study that identified changes in the barriers of the brain due to aging through a convergent approach, starting from the human brain and utilizing an animal model with a biomimetic meningeal model”.
Professor Pilnam Kim's research team is conducting research and development to remove sugar that accumulated throughout the human body, including the meninges. Advanced glycation end products, which are waste products formed when proteins and sugars meet in the human body, are partially removed by macrophages. However, glycated products bound to extracellular matrix proteins such as collagen are difficult to remove naturally. Through the KAIST-Ceragem Research Center, this research team is developing a healthcare medical device to remove 'sugar residue' in the body.
This study was carried out with the National Research Foundation of Korea's collective research support.
Figure 1. Schematic diagram of proposed mechanism showing aging‐related ECM remodeling through meningeal fibroblasts on the brain leptomeninges. Meningeal fibroblasts in the young brain showed dynamic COL1A1 synthetic and COL1‐interactive function on the collagen membrane. They showed ITGB1‐mediated adhesion on the COL1‐composed leptomeningeal membrane and induction of COL1A1 synthesis for maintaining the collagen membrane. With aging, meningeal fibroblasts showed depletion of COL1A1 synthetic function and altered cell–matrix interaction.
Figure 2. Representative rat meningeal images observed in the study. Compared to young rats, it was confirmed that type 1 collagen (COL1) decreased along with the accumulation of glycated end products (AGE) in the brain membrane of aged rats, and the activity of integrin beta 1 (ITGB1), a representative receptor corresponding to cell-collagen interaction. Instead, it was observed that the activity of discoidin domain receptor 2 (DDR2), one of the tyrosine kinases, increased.
Figure 3. Substance flux through the brain membrane decreases with aging. It was confirmed that the degree of adsorption of fluorescent substances contained in cerebrospinal fluid (CSF) to the brain membrane increased and the degree of entry into the periphery of the cerebral blood vessels decreased in the aged rats. In this study, only the influx into the brain was confirmed during the entry and exit of substances, but the degree of outflow will also be confirmed through future studies.
2023.03.15 View 4882 -
KAIST team develops smart immune system that can pin down on malignant tumors
A joint research team led by Professor Jung Kyoon Choi of the KAIST Department of Bio and Brain Engineering and Professor Jong-Eun Park of the KAIST Graduate School of Medical Science and Engineering (GSMSE) announced the development of the key technologies to treat cancers using smart immune cells designed based on AI and big data analysis. This technology is expected to be a next-generation immunotherapy that allows precision targeting of tumor cells by having the chimeric antigen receptors (CARs) operate through a logical circuit. Professor Hee Jung An of CHA Bundang Medical Center and Professor Hae-Ock Lee of the Catholic University of Korea also participated in this research to contribute joint effort.
Professor Jung Kyoon Choi’s team built a gene expression database from millions of cells, and used this to successfully develop and verify a deep-learning algorithm that could detect the differences in gene expression patterns between tumor cells and normal cells through a logical circuit. CAR immune cells that were fitted with the logic circuits discovered through this methodology could distinguish between tumorous and normal cells as a computer would, and therefore showed potentials to strike only on tumor cells accurately without causing unwanted side effects.
This research, conducted by co-first authors Dr. Joonha Kwon of the KAIST Department of Bio and Brain Engineering and Ph.D. candidate Junho Kang of KAIST GSMSE, was published by Nature Biotechnology on February 16, under the title Single-cell mapping of combinatorial target antigens for CAR switches using logic gates.
An area in cancer research where the most attempts and advances have been made in recent years is immunotherapy. This field of treatment, which utilizes the patient’s own immune system in order to overcome cancer, has several methods including immune checkpoint inhibitors, cancer vaccines and cellular treatments. Immune cells like CAR-T or CAR-NK equipped with chimera antigen receptors, in particular, can recognize cancer antigens and directly destroy cancer cells.
Starting with its success in blood cancer treatment, scientists have been trying to expand the application of CAR cell therapy to treat solid cancer. But there have been difficulties to develop CAR cells with effective killing abilities against solid cancer cells with minimized side effects. Accordingly, in recent years, the development of smarter CAR engineering technologies, i.e., computational logic gates such as AND, OR, and NOT, to effectively target cancer cells has been underway.
At this point in time, the research team built a large-scale database for cancer and normal cells to discover the exact genes that are expressed only from cancer cells at a single-cell level. The team followed this up by developing an AI algorithm that could search for a combination of genes that best distinguishes cancer cells from normal cells. This algorithm, in particular, has been used to find a logic circuit that can specifically target cancer cells through cell-level simulations of all gene combinations. CAR-T cells equipped with logic circuits discovered through this methodology are expected to distinguish cancerous cells from normal cells like computers, thereby minimizing side effects and maximizing the effects of chemotherapy.
Dr. Joonha Kwon, who is the first author of this paper, said, “this research suggests a new method that hasn’t been tried before. What’s particularly noteworthy is the process in which we found the optimal CAR cell circuit through simulations of millions of individual tumors and normal cells.” He added, “This is an innovative technology that can apply AI and computer logic circuits to immune cell engineering. It would contribute greatly to expanding CAR therapy, which is being successfully used for blood cancer, to solid cancers as well.”
This research was funded by the Original Technology Development Project and Research Program for Next Generation Applied Omic of the Korea Research Foundation.
Figure 1. A schematic diagram of manufacturing and administration process of CAR therapy and of cancer cell-specific dual targeting using CAR.
Figure 2. Deep learning (convolutional neural networks, CNNs) algorithm for selection of dual targets based on gene combination (left) and algorithm for calculating expressing cell fractions by gene combination according to logical circuit (right).
2023.03.09 View 6366 -
KAIST researchers develops a tech to enable production of ultrahigh-resolution LED with sub-micrometer scale pixels
Ultrahigh-resolution displays are an essential element for developing next-generation electronic products such as virtual reality (VR), augmented reality (AR), and smart watches, and can be applied not only to head-mounted displays, but also to smart glasses and smart lenses. The technology developed through this research is expected to be used to make such next-generation ultrahigh-resolution displays and other various sub-micro optoelectronic devices.
KAIST (President Kwang Hyung Lee) announced on the 22nd that Professor Yong-Hoon Cho's research team of KAIST Department of Physics developed the core technology for an ultrahigh resolution light-emitting diode (LED) display that can realize 0.5 micron-scale pixels smaller than 1/100 of the average hair thickness (about 100 microns) using focused ion beams.
Commonly, pixelation of ultrahigh-resolution LED displays usually relies on the etching method that physically cuts the area around the pixel, but as the pixel becomes smaller due to the occurrence of various defects around it, leading to side-effects of having leakage of current increased and light-emission efficiency decreased. In addition, various complex processes such as patterning for pixelation and post-processing for prevention of leakage current are required.
Professor Yong-Hoon Cho's research team developed a technology that can create pixels down to the size of a microscale without the complicated pre- and post-processing using a focused ion beam. This method has the advantage of being able to freely set the shape of the emitting pixel without causing any structural deformation on the material surface by controlling the intensity of the focused ion beam.
The focused ion beam technology has been widely used for ultrahigh-magnification imaging and nanostructure fabrication in fields such as materials engineering and biology. However, when a focused ion beam is used on a light emitting body such as an LED, light emission of a portion hit by the beam and a surrounding area rapidly decreases, which has been a barrier to fabricating a nano-scale light emitting structure. Upon facing this issue, Professor Cho's research team began the research on the idea that if they turned things around to use these problematic phenomena, they can be used in ultra-fine pixelation method on a sub-micron scale.
The research team used a focused ion beam whose intensity was softened to the extent that the surface was not shaved, and found that not only the light-emission rapidly decreased in the area hit by the focused ion beam, but also the local resistance greatly increased. As a result, while the surface of the LED is kept flat, the portion hit by the focused ion beam is optically and electrically isolated, enabling pixelation for independent operation.
Professor Yong-Hoon Cho, who led the research, said, “We have newly developed a technology that can create sub-micron-scale pixels without complicated processes using a focused ion beam, which will be a base technology that can be applied to next-generation ultrahigh-resolution displays and nano-photoelectronic devices.”
This research in which the Master's student Ji-Hwan Moon and the Ph.D. student Baul Kim of KAIST Department of Physics participated as co-first authors, was carried out with the support of the National Research Foundation of Korea's Support Program for Mid-Career Researchers and the Institute of Information and Communications Technology Planning and Evaluation. It was published online in 'Advanced Materials' on February 13, and was also selected as the internal cover of the next offline edition. (Title: Electrically Driven Sub-Micron Light-Emitting Diode Arrays Using Maskless and Etching-Free Pixelation)
Figure 1. Schematic diagram of the technology for ultrahigh density sub-micron-sized pixels through He focused ion beam (FIB) irradiation on an LED device
Figure 2. Ultra-high-density pixelation technology of micro light-emitting diodes (μLED) through He focused ion beam (FIB) irradiation
Figure 3. Rectangular pixels of different sizes (surface structure picture and luminescence picture) realized by a focused ion beam. Luminescence pictures of pixel arrays ranging in size from 20 µm x 20 µm to 0.5 µm x 0.5 µm, with surface flatness maintained.
2023.03.08 View 4969 -
KAIST researchers discovers the neural circuit that reacts to alarm clock
KAIST (President Kwang Hyung Lee) announced on the 20th that a research team led by Professor Daesoo Kim of the Department of Brain and Cognitive Sciences and Dr. Jeongjin Kim 's team from the Korea Institute of Science and Technology (KIST) have identified the principle of awakening animals by responding to sounds even while sleeping.
Sleep is a very important physiological process that organizes brain activity and maintains health. During sleep, the function of sensory nerves is blocked, so the ability to detect danger in the proximity is reduced. However, many animals detect approaching predators and respond even while sleeping. Scientists thought that animals ready for danger by alternating between deep sleep and light sleep.
A research team led by Professor Daesoo Kim at KAIST discovered that animals have neural circuits that respond to sounds even during deep sleep. While awake, the medial geniculate thalamus responds to sounds, but during deep sleep, or Non-REM sleep, the Mediodorsal thalamus responds to sounds to wake up the brain.
As a result of the study, when the rats fell into deep sleep, the nerves of the medial geniculate thalamus were also sleeping, but the nerves of mediodorsal thalamus were awake and responded immediately to sounds. In addition, it was observed that when mediodorsal thalamus was inhibited, the rats could not wake up even when a sound was heard, and when the mediodorsal thalamus was stimulated, the rats woke up within a few seconds without sound.
This is the first study to show that sleep and wakefulness can transmit auditory signals through different neural circuits, and was reported in the international journal, Current Biology on February 7, and was highlighted by Nature. (https://www.nature.com/articles/d41586-023-00354-0)
Professor Daesoo Kim explained, “The findings of this study can used in developing digital healthcare technologies to be used to improve understanding of disorders of senses and wakefulness seen in various brain diseases and to control the senses in the future.”
This research was carried out with the support from the National Research Foundation of Korea's Mid-Career Research Foundation Program.
Figure 1. Traditionally, sound signals were thought to be propagated from the auditory nerve to the auditory thalamus. However, while in slow-wave sleep, the auditory nerve sends sound signals to the mediodorsal thalamic neurons via the brainstem nerve to induce arousal in the brain.
Figure 2. GRIK4 dorsomedial nerve in response to sound stimulation. The awakening effect is induced as the activity of the GRIK4 dorsal medial nerve increases based on the time when sound stimulation is given.
2023.03.03 View 3971