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KAIST ISSS Research Session Captivates 150↑ International Scholars, Achieve Major Success
< Photo. Scholars gatheres for NRF Information Session at Chung Keun Mo Hall >
KAIST’s International Office, headed by Vice President Soyoung Kim, successfully organized the ‘NRF Information Session for International Scholars’ on September 11, 2024, in collaboration with the National Research Foundation of Korea (NRF).
The event was held at KAIST’s main campus to enourage the international scholar’s active participation in research projects and support their establishment of stable research environment and integration into Korea’s academic community by introducing NRF’s key research programs.
Divided into two main segments – science and engineering, and humanities and social sciences – the session attracted approximately 150 international faculty and researchers from 23 universities across the nation.
The event commenced with a keynote address by Vice President Soyoung Kim, followed by a presentation from Dr. Seol Min of the National Research Foundation, who highlighted basic research initiatives in the science and technology sector. Subsequently, Professor Daniel Martin from the Digital Humanities and Social Sciences Department and Professor Thomas Steinberger from the Department of Business and Technology Management presented practical research project support case studies, sharing invaluable insights gained from their domestic research experiences.
Following the information session, participants engaged in a networking event, where researchers involved in major R&D projects exchanged insights and discussed their ongoing research initiatives.
An international professor remarked, “My understanding of NRF’s research programs for international researchers has broadened considerably. I am now more inclined to actively participate in projects organized by NRF in the future.”
Vice President Kim expressed her aspiration that the event would address the challenges faced by researchers and offer essential support to those engaged in research projects. “We will stay attuned to the needs of the research community and work towards creating a more supportive research environment,” said the VP.
Meanwhile, KAIST hosts a distinguished faculty comprising 134 professors from 22 countries and 71 researchers representing 23 nations, all contributing to groundbreaking academic achievements. Additionally, KAIST is home to over 1,000 international students from more than 100 countries, actively pursuing their studies. This diverse composition of global talent reinforces KAIST's position as a leading international hub for research and education.
2024.09.13 View 1229 -
Deep Learning Framework to Enable Material Design in Unseen Domain
Researchers propose a deep neural network-based forward design space exploration using active transfer learning and data augmentation
A new study proposed a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods.
Professor Seungwha Ryu believes that this study will help address a variety of optimization problems that have an astronomical number of possible design configurations. For the grid composite optimization problem, the proposed framework was able to provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5% of the initial training data-set size. This study was reported in npj Computational Materials last month.
“We wanted to mitigate the limitation of the neural network, weak predictive power beyond the training set domain for the material or structure design,” said Professor Ryu from the Department of Mechanical Engineering.
Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome this limitation also suffer from weak predictive power for the unseen domain.
Professor Ryu’s team, in collaboration with researchers from Professor Grace Gu’s group at UC Berkeley, devised a design method that simultaneously expands the domain using the strong predictive power of a deep neural network and searches for the optimal design by repetitively performing three key steps.
First, it searches for few candidates with improved properties located close to the training set via genetic algorithms, by mixing superior designs within the training set. Then, it checks to see if the candidates really have improved properties, and expands the training set by duplicating the validated designs via a data augmentation method. Finally, they can expand the reliable prediction domain by updating the neural network with the new superior designs via transfer learning. Because the expansion proceeds along relatively narrow but correct routes toward the optimal design (depicted in the schematic of Fig. 1), the framework enables an efficient search.
As a data-hungry method, a deep neural network model tends to have reliable predictive power only within and near the domain of the training set. When the optimal configuration of materials and structures lies far beyond the initial training set, which frequently is the case, neural network-based design methods suffer from weak predictive power and become inefficient.
Researchers expect that the framework will be applicable for a wide range of optimization problems in other science and engineering disciplines with astronomically large design space, because it provides an efficient way of gradually expanding the reliable prediction domain toward the target design while avoiding the risk of being stuck in local minima. Especially, being a less-data-hungry method, design problems in which data generation is time-consuming and expensive will benefit most from this new framework.
The research team is currently applying the optimization framework for the design task of metamaterial structures, segmented thermoelectric generators, and optimal sensor distributions. “From these sets of on-going studies, we expect to better recognize the pros and cons, and the potential of the suggested algorithm. Ultimately, we want to devise more efficient machine learning-based design approaches,” explained Professor Ryu.This study was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research Project.
-Publication
Yongtae Kim, Youngsoo, Charles Yang, Kundo Park, Grace X. Gu, and Seunghwa Ryu, “Deep learning framework for material design space exploration using active transfer learning and data augmentation,” npj Computational Materials (https://doi.org/10.1038/s41524-021-00609-2)
-Profile
Professor Seunghwa Ryu
Mechanics & Materials Modeling Lab
Department of Mechanical Engineering
KAIST
2021.09.29 View 9411 -
Genomic Data Reveals New Insights into Human Embryonic Development
KAIST researchers have used whole-genome sequencing to track the development from a single fertilized-egg to a human body
Genomic scientists at KAIST have revealed new insights into the process of human embryonic development using large-scale, whole-genome sequencing of cells and tissues from adult humans. The study, published in Nature on Aug.25, is the first to analyse somatic mutations in normal tissue across multiple organs within and between humans.
An adult human body comprises trillions of cells of more than 200 types. How a human develops from a single fertilized egg to a fully grown adult is a fundamental question in biomedical science. Due to the ethical challenges of performing studies on human embryos, however, the details of this process remain largely unknown.
To overcome these issues, the research team took a different approach. They analysed genetic mutations in cells taken from adult human post-mortem tissue. Specifically, they identified mutations that occur spontaneously in early developmental cell divisions. These mutations, also called genomic scars, act like unique genetic fingerprints that can be used to trace the embryonic development process.
The study, which looked at 334 single-cell colonies and 379 tissue samples from seven recently deceased human body donors, is the largest single-cell, whole-genome analysis carried out to date. The researchers examined the genomic scars of each individual in order to reconstruct their early embryonic cellular dynamics.
The result revealed several key characteristics of the human embryonic development process. Firstly, mutation rates are higher in the first cell division, but then decrease to approximately one mutation per cell during later cell division. Secondly, early cells contributed unequally to the development of the embryo in all informative donors, for example, at the two-cell stage, one of the cells always left more progeny cells than the other. The ratio of this was different from person to person, implying that the process varies between individuals and is not fully deterministic.
The researchers were also able to deduce the timing of when cells begin to differentiate into individual organ-specific cells. They found that within three days of fertilization, embryonic cells began to be distributed asymmetrically into tissues for the left and right sides of the body, followed by differentiation into three germ layers, and then differentiation into specific tissues and organs.
“It is an impressive scientific achievement that, within 20 years of the completion of human genome project, genomic technology has advanced to the extent that we are now able to accurately identify mutations in a single-cell genome,” said Professor Young Seok Ju from the Graduate School of Medical Science and Engineering at KAIST. “This technology will enable us to track human embryogenesis at even higher resolutions in the future.”
The techniques used in this study could be used to improve our understanding of rare diseases caused by abnormalities in embryonic development, and to design new precision diagnostics and treatments for patients.
The research was completed in collaboration with Kyungpook National University Hospital, the Korea Institute of Science and Technology Information, Catholic University of Korea School of Medicine, Genome Insights Inc, and Immune Square Inc. This work was supported by the Suh Kyungbae Foundation, the Ministry of Health and Welfare of Korea, the National Research Foundastion of Korea.
-PublicationSeongyeol Park, Nanda Mali, Ryul Kim et al. ‘Clonal dynamics in early human embryogenesis inferred from somatic mutation’ Nature Online ahead of print, Aug. 25, 2021 (https://doi.org/10.1038/s41586-021-03786-8)
-ProfileProfessor Young Seok JuLab of Cancer Genomics (https://www.julab.kaist.ac.kr/)Graduate School of Medical Science and EngineeringKAIST
2021.08.31 View 7083 -
Repurposed Drugs Present New Strategy for Treating COVID-19
Virtual screening of 6,218 drugs and cell-based assays identifies best therapeutic medication candidates
A joint research group from KAIST and Institut Pasteur Korea has identified repurposed drugs for COVID-19 treatment through virtual screening and cell-based assays. The research team suggested the strategy for virtual screening with greatly reduced false positives by incorporating pre-docking filtering based on shape similarity and post-docking filtering based on interaction similarity. This strategy will help develop therapeutic medications for COVID-19 and other antiviral diseases more rapidly. This study was reported at the Proceedings of the National Academy of Sciences of the United States of America (PNAS).
Researchers screened 6,218 drugs from a collection of FDA-approved drugs or those under clinical trial and identified 38 potential repurposed drugs for COVID-19 with this strategy. Among them, seven compounds inhibited SARS-CoV-2 replication in Vero cells. Three of these drugs, emodin, omipalisib, and tipifarnib, showed anti-SARS-CoV-2 activity in human lung cells, Calu-3.
Drug repurposing is a practical strategy for developing antiviral drugs in a short period of time, especially during a global pandemic. In many instances, drug repurposing starts with the virtual screening of approved drugs. However, the actual hit rate of virtual screening is low and most of the predicted drug candidates are false positives.
The research group developed effective filtering algorithms before and after the docking simulations to improve the hit rates. In the pre-docking filtering process, compounds with similar shapes to the known active compounds for each target protein were selected and used for docking simulations. In the post-docking filtering process, the chemicals identified through their docking simulations were evaluated considering the docking energy and the similarity of the protein-ligand interactions with the known active compounds.
The experimental results showed that the virtual screening strategy reached a high hit rate of 18.4%, leading to the identification of seven potential drugs out of the 38 drugs initially selected.
“We plan to conduct further preclinical trials for optimizing drug concentrations as one of the three candidates didn’t resolve the toxicity issues in preclinical trials,” said Woo Dae Jang, one of the researchers from KAIST.
“The most important part of this research is that we developed a platform technology that can rapidly identify novel compounds for COVID-19 treatment. If we use this technology, we will be able to quickly respond to new infectious diseases as well as variants of the coronavirus,” said Distinguished Professor Sang Yup Lee.
This work was supported by the KAIST Mobile Clinic Module Project funded by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF). The National Culture Collection for Pathogens in Korea provided the SARS-CoV-2 (NCCP43326).
-PublicationWoo Dae Jang, Sangeun Jeon, Seungtaek Kim, and Sang Yup Lee. Drugs repurposed for COVID-19 by virtual screening of 6,218 drugs and cell-based assay. Proc. Natl. Acad. Sci. U.S.A. (https://doi/org/10.1073/pnas.2024302118)
-ProfileDistinguished Professor Sang Yup LeeMetabolic &Biomolecular Engineering National Research Laboratoryhttp://mbel.kaist.ac.kr
Department of Chemical and Biomolecular EngineeringKAIST
2021.07.08 View 10742 -
Observing Individual Atoms in 3D Nanomaterials and Their Surfaces
Atoms are the basic building blocks for all materials. To tailor functional properties, it is essential to accurately determine their atomic structures. KAIST researchers observed the 3D atomic structure of a nanoparticle at the atom level via neural network-assisted atomic electron tomography.
Using a platinum nanoparticle as a model system, a research team led by Professor Yongsoo Yang demonstrated that an atomicity-based deep learning approach can reliably identify the 3D surface atomic structure with a precision of 15 picometers (only about 1/3 of a hydrogen atom’s radius). The atomic displacement, strain, and facet analysis revealed that the surface atomic structure and strain are related to both the shape of the nanoparticle and the particle-substrate interface.
Combined with quantum mechanical calculations such as density functional theory, the ability to precisely identify surface atomic structure will serve as a powerful key for understanding catalytic performance and oxidation effect.
“We solved the problem of determining the 3D surface atomic structure of nanomaterials in a reliable manner. It has been difficult to accurately measure the surface atomic structures due to the ‘missing wedge problem’ in electron tomography, which arises from geometrical limitations, allowing only part of a full tomographic angular range to be measured. We resolved the problem using a deep learning-based approach,” explained Professor Yang.
The missing wedge problem results in elongation and ringing artifacts, negatively affecting the accuracy of the atomic structure determined from the tomogram, especially for identifying the surface structures. The missing wedge problem has been the main roadblock for the precise determination of the 3D surface atomic structures of nanomaterials.
The team used atomic electron tomography (AET), which is basically a very high-resolution CT scan for nanomaterials using transmission electron microscopes. AET allows individual atom level 3D atomic structural determination.
“The main idea behind this deep learning-based approach is atomicity—the fact that all matter is composed of atoms. This means that true atomic resolution electron tomogram should only contain sharp 3D atomic potentials convolved with the electron beam profile,” said Professor Yang.
“A deep neural network can be trained using simulated tomograms that suffer from missing wedges as inputs, and the ground truth 3D atomic volumes as targets. The trained deep learning network effectively augments the imperfect tomograms and removes the artifacts resulting from the missing wedge problem.”
The precision of 3D atomic structure can be enhanced by nearly 70% by applying the deep learning-based augmentation. The accuracy of surface atom identification was also significantly improved.
Structure-property relationships of functional nanomaterials, especially the ones that strongly depend on the surface structures, such as catalytic properties for fuel-cell applications, can now be revealed at one of the most fundamental scales: the atomic scale.
Professor Yang concluded, “We would like to fully map out the 3D atomic structure with higher precision and better elemental specificity. And not being limited to atomic structures, we aim to measure the physical, chemical, and functional properties of nanomaterials at the 3D atomic scale by further advancing electron tomography techniques.”
This research, reported at Nature Communications, was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research M3I3 Project.
-Publication
Juhyeok Lee, Chaehwa Jeong & Yongsoo Yang
“Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography”
Nature Communications
-Profile
Professor Yongsoo Yang
Department of Physics
Multi-Dimensional Atomic Imaging Lab (MDAIL)
http://mdail.kaist.ac.kr
KAIST
2021.05.12 View 9615 -
Singularity Professors Represent the Future of Research at KAIST
KAIST will launch a Singularity Professor track, which gives more freedom to researchers for pursuing their research goal. This more flexible and creative research environment institutionally supports researchers as they dive deeper into their research for a longer period of time without any strings attached.
The track was established in an effort to ensure more competitive researchers who can lead the way for new advances in science and technology. This innovative research initiative is part of KAIST’s expansive effort to envision and position itself to build global research competitiveness in the wake of its 50th anniversary in 2021 and beyond.
From this year, KAIST will select two to three research faculty for this special track with full-scale funding for 10 years. Singularity Professors will have their annual performance evaluations waived for 10 years. Instead, their research will be reviewed in their fifth year. The professors in this track will not participate in government-funded R&D projects and be fully funded by KAIST’s endowment.
In addition to those newly hired into this track, Singularity Professorships are opens to existing faculty members. The selection criteria are very simple but highly demanding: one who can pivot an existing academic paradigm or invent a new discipline by presenting a novel scientific theory.
KAIST recently hosted a briefing session for current faculty members and encouraged them to apply for the new track. As part of the selection criteria, the research topic’s innovativeness, feasibility, and appropriateness will be major factors for this track. Employment under this track will continue for up to 20 years. After receiving an evaluation of Very Satisfactory at the end of first ten-year contract, another ten years will be added.
President Sung-Chul Shin, who has pushed for this system since he took office in 2017, said during the briefing session, “It takes quite a long time to bear fruit in academics, especially in science. I am very delighted that KAIST is paving the way for building a longer-term research environment which allows full and longer commitments for research that the faculty is excited to try. That’s the first step to sow the seeds for bearing fruit in academics, especially in science.”
This is a paradigm shift to embrace transformation in a new era. The new institutional strategy supports the change from a fast follower to a first mover during these technologically turbulent times. Under its Global Singularity Research Projects initiative, KAIST already selected focus research topics in the most challenging as well as most creative fields of neuro-rehabilitation, new materials, and molecular optogenetics.
“Especially in the post-COVID era, we have a very clear mission for the world. Our knowledge should translate into global value that can benefit those suffering from this pandemic, and mitigate the inequity coming from the digital discrepancies,” President Shin added.
(END)
2020.07.21 View 7440 -
What Fuels a “Domino Effect” in Cancer Drug Resistance?
KAIST researchers have identified mechanisms that relay prior acquired resistance to the first-line chemotherapy to the second-line targeted therapy, fueling a “domino effect” in cancer drug resistance. Their study featured in the February 7 edition of Science Advances suggests a new strategy for improving the second-line setting of cancer treatment for patients who showed resistance to anti-cancer drugs.
Resistance to cancer drugs is often managed in the clinic by chemotherapy and targeted therapy. Unlike chemotherapy that works by repressing fast-proliferating cells, targeted therapy blocks a single oncogenic pathway to halt tumor growth. In many cases, targeted therapy is engaged as a maintenance therapy or employed in the second-line after front-line chemotherapy.
A team of researchers led by Professor Yoosik Kim from the Department of Chemical and Biomolecular Engineering and the KAIST Institute for Health Science and Technology (KIHST) has discovered an unexpected resistance signature that occurs between chemotherapy and targeted therapy. The team further identified a set of integrated mechanisms that promotes this kind of sequential therapy resistance.
“There have been multiple clinical accounts reflecting that targeted therapies tend to be least successful in patients who have exhausted all standard treatments,” said the first author of the paper Mark Borris D. Aldonza. He continued, “These accounts ignited our hypothesis that failed responses to some chemotherapies might speed up the evolution of resistance to other drugs, particularly those with specific targets.”
Aldonza and his colleagues extracted large amounts of drug-resistance information from the open-source database the Genomics of Drug Sensitivity in Cancer (GDSC), which contains thousands of drug response data entries from various human cancer cell lines. Their big data analysis revealed that cancer cell lines resistant to chemotherapies classified as anti-mitotic drugs (AMDs), toxins that inhibit overacting cell division, are also resistant to a class of targeted therapies called epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs).
In all of the cancer types analyzed, more than 84 percent of those resistant to AMDs, representatively ‘paclitaxel’, were also resistant to at least nine EGFR-TKIs. In lung, pancreatic, and breast cancers where paclitaxel is often used as a first-line, standard-of-care regimen, greater than 92 percent showed resistance to EGFR-TKIs. Professor Kim said, “It is surprising to see that such collateral resistance can occur specifically between two chemically different classes of drugs.”
To figure out how failed responses to paclitaxel leads to resistance to EGFR-TKIs, the team validated co-resistance signatures that they found in the database by generating and analyzing a subset of slow-doubling, paclitaxel-resistant cancer models called ‘persisters’.
The results demonstrated that paclitaxel-resistant cancers remodel their stress response by first becoming more stem cell-like, evolving the ability to self-renew to adapt to more stressful conditions like drug exposures. More surprisingly, when the researchers characterized the metabolic state of the cells, EGFR-TKI persisters derived from paclitaxel-resistant cancer cells showed high dependencies to energy-producing processes such as glycolysis and glutaminolysis.
“We found that, without an energy stimulus like glucose, these cells transform to becoming more senescent, a characteristic of cells that have arrested cell division. However, this senescence is controlled by stem cell factors, which the paclitaxel-resistant cancers use to escape from this arrested state given a favorable condition to re-grow,” said Aldonza.
Professor Kim explained, “Before this research, there was no reason to expect that acquiring the cancer stem cell phenotype that dramatically leads to a cascade of changes in cellular states affecting metabolism and cell death is linked with drug-specific sequential resistance between two classes of therapies.”
He added, “The expansion of our work to other working models of drug resistance in a much more clinically-relevant setting, perhaps in clinical trials, will take on increasing importance, as sequential treatment strategies will continue to be adapted to various forms of anti-cancer therapy regimens.”
This study was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF-2016R1C1B2009886), and the KAIST Future Systems Healthcare Project (KAISTHEALTHCARE42) funded by the Korean Ministry of Science and ICT (MSIT). Undergraduate student Aldonza participated in this research project and presented the findings as the lead author as part of the Undergraduate Research Participation (URP) Program at KAIST.
< Figure 1. Schematic overview of the study. >
< Figure 2. Big data analysis revealing co-resistance signatures between classes of anti-cancer drugs. >
Publication:
Aldonza et al. (2020) Prior acquired resistance to paclitaxel relays diverse EGFR-targeted therapy persistence mechanisms. Science Advances, Vol. 6, No. 6, eaav7416. Available online at http://dx.doi.org/10.1126/sciadv.aav7416
Profile: Prof. Yoosik Kim, MA, PhD
ysyoosik@kaist.ac.kr
https://qcbio.kaist.ac.kr/
Assistant Professor
Bio Network Analysis Laboratory
Department of Chemical and Biomolecular Engineering
Korea Advanced Institute of Science and Technology (KAIST)
http://kaist.ac.kr
Daejeon, Republic of Korea
Profile: Mark Borris D. Aldonza
borris@kaist.ac.kr
Undergraduate Student
Department of Biological Sciences
Korea Advanced Institute of Science and Technology (KAIST)
http://kaist.ac.kr
Daejeon, Republic of Korea
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2020.02.10 View 11671 -
A System Controlling Road Active Noise to Hit the Road
The research team led by Professor Youngjin Park of the Department of Mechanical Engineering has developed a road noise active noise control (RANC) system to be commercialized in partnership with Hyundai Motor Group.
On December 11, Hyundai Motor Group announced the successful development of the RANC system, which significantly reduces the road noise flowing into cars. The carmaker has completed the domestic and American patent applications for the location of sensors and the signal selection method, the core technology of RANC.
RANC is a technology for reducing road noise during driving. This system consists of an acceleration sensor, digital signal processor (the control computer to analyze sound signals), microphone, amplifier, and audio system. To make the system as simple as possible, the audio system utilizes the original audio system embedded in the car instead of a separate system.
The acceleration sensor first calculates the vibration from the road into the car. The location of the sensor is important for accurately identifying the vibration path. The research team was able to find the optimal sensor location through a number of tests.
The System Dynamics and Applied Control Laboratory of Professor Park researched ways to significantly reduce road noise with Hyundai Motor Group for four years from 1993 as a G7 national project and published the results in international journals. In 2002, the researchers published an article titled “Noise Quietens Driving” in Nature, where they announced the first success in reducing road noise in actual cars. The achievement did not lead to commercialization, however, due to the lack of auxiliary technologies at the time, digital amplifiers and DSP for cars for example, and pricing issues.
Since 2013, Professor Park’s research team has participated in one technology transfer and eight university-industry projects. Based on these efforts, the team was able to successfully develop the RANC system with domestic technology in partnership with Hyundai’s NVH Research Lab (Research Fellow, Dr. Gangdeok Lee; Ph.D. in aviation engineering, 1996), Optomech (Founder, Professor Gyeongsu Kim; Ph.D. in mechanical engineering, 1999), ARE (CEO Hyeonseok Kim; Ph.D. in mechanical engineering, 1998), WeAcom, and BurnYoung.
Professor Park’s team led the project by performing theory-based research during the commercialization stage in collaboration with Hyundai Motor Group.
For the commercialization of the RANC system, Hyundai Motor Group is planning to collaborate with the global car audio company Harman to increase the degree of completion and apply the RANC system to the GV 80, the first SUV model of the Genesis brand.
“I am very delighted as an engineer to see the research I worked on from my early days at KAIST be commercialized after 20 years,” noted Professor Park. “I am thrilled to make a contribution to such commercialization with my students in my lab.”
2019.12.27 View 9784 -
Accurate Detection of Low-Level Somatic Mutation in Intractable Epilepsy
KAIST medical scientists have developed an advanced method for perfectly detecting low-level somatic mutation in patients with intractable epilepsy. Their study showed that deep sequencing replicates of major focal epilepsy genes accurately and efficiently identified low-level somatic mutations in intractable epilepsy.
According to the study, their diagnostic method could increase the accuracy up to 100%, unlike the conventional sequencing analysis, which stands at about 30% accuracy. This work was published in Acta Neuropathologica.
Epilepsy is a neurological disorder common in children. Approximately one third of child patients are diagnosed with intractable epilepsy despite adequate anti-epileptic medication treatment.
Somatic mutations in mTOR pathway genes, SLC35A2, and BRAF are the major genetic causes of intractable epilepsies. A clinical trial to target Focal Cortical Dysplasia type II (FCDII), the mTOR inhibitor is underway at Severance Hospital, their collaborator in Seoul, Korea. However, it is difficult to detect such somatic mutations causing intractable epilepsy because their mutational burden is less than 5%, which is similar to the level of sequencing artifacts. In the clinical field, this has remained a standing challenge for the genetic diagnosis of somatic mutations in intractable epilepsy.
Professor Jeong Ho Lee’s team at the Graduate School of Medical Science and Engineering analyzed paired brain and peripheral tissues from 232 intractable epilepsy patients with various brain pathologies at Severance Hospital using deep sequencing and extracted the major focal epilepsy genes.
They narrowed down target genes to eight major focal epilepsy genes, eliminating almost all of the false positive calls using deep targeted sequencing. As a result, the advanced method robustly increased the accuracy and enabled them to detect low-level somatic mutations in unmatched Formalin Fixed Paraffin Embedded (FFPE) brain samples, the most clinically relevant samples.
Professor Lee conducted this study in collaboration with Professor Dong Suk Kim and Hoon-Chul Kang at Severance Hospital of Yonsei University. He said, “This advanced method of genetic analysis will improve overall patient care by providing more comprehensive genetic counseling and informing decisions on alternative treatments.”
Professor Lee has investigated low-level somatic mutations arising in the brain for a decade. He is developing innovative diagnostics and therapeutics for untreatable brain disorders including intractable epilepsy and glioblastoma at a tech-startup called SoVarGen. “All of the technologies we used during the research were transferred to the company. This research gave us very good momentum to reach the next phase of our startup,” he remarked.
The work was supported by grants from the Suh Kyungbae Foundation, a National Research Foundation of Korea grant funded by the Ministry of Science and ICT, the Korean Health Technology R&D Project from the Ministry of Health & Welfare, and the Netherlands Organization for Health Research and Development.
(Figure: Landscape of somatic and germline mutations identified in intractable epilepsy patients. a Signaling pathways for all of the mutated genes identified in this study. Bold: somatic mutation, Regular: germline mutation. b The distribution of variant allelic frequencies (VAFs) of identified somatic mutations. c The detecting rate and types of identified mutations according to histopathology. Yellow: somatic mutations, green: two-hit mutations, grey: germline mutations.)
2019.08.14 View 27002 -
'Flying Drones for Rescue'
(Video Credit: ⓒNASA JPL)
< Team USRG and Professor Shim (second from the right) >
Having recently won the AI R&D Grand Challenge Competition in Korea, Team USRG (Unmanned System Research Group) led by Professor Hyunchul Shim from the School of Electrical Engineering is all geared up to take on their next challenges: the ‘Defense Advanced Research Projects Agency Subterranean Challenge (DARPA SubT Challenge)’ and ‘Lockheed Martin’s AlphaPilot Challenge’ next month.
Team USRG won the obstacle course race in the ‘2019 AI R&D Grand Challenge Competition’ on July 12. They managed to successfully dominate the challenging category of ‘control intelligence.’ Having to complete the obstacle course race solely using AI systems without any connection to the internet made it difficult for most of the eight participating teams to pass the third section of the race, and only Team USRG passed the long pipeline course during their attempt in the main event. They also demonstrated, after the main event, that their drone can navigate all of the checkpoints including landing on the “H” mark using deep learning.
Their drone flew through polls and pipes, and escaped from windows and mazes against strong winds, amid cheers and groans from the crowd gathered at the Korea Exhibition Center (KINTEX) in Goyang, Korea. The team was awarded three million KRW in prize money, and received a research grant worth six hundred million KRW from the Ministry of Science and ICT (MSIT).
“Being ranked first in the race for which we were never given a chance for a test flight means a lot to our team. Considering that we had no information on the exact size of the course in advance, this is a startling result,” said Professor Shim. “We will carry out further research with this funding, and compete once again with the improved AI and drone technology in the 2020 competition,” he added.
The AI R&D Grand Challenge Competition, which was first started in 2017, has been designed to promote AI research and development and expand its application to addressing high-risk technical challenges with significant socio-economic impact.
This year’s competition presented participants with a task where they had to develop AI software technology for drones to navigate themselves autonomously during complex disaster relief operations such as aid delivery.
Each team participated in one of the four tracks of the competition, and their drones were evaluated based on the criteria for each track. The divisions were broken up into intelligent context-awareness, intelligent character recognition, auditory intelligence, and control intelligence.
Team USRG’s technological prowess has been already well acclaimed among international peer groups. Teamed up with NASA JPL, Caltech, and MIT, they will compete in the subterranean mission during the ‘DARPA SubT Challenge’.
Team CoSTAR, as its name stands for, is working together to build ‘Collaborative SubTerranean Autonomous Resilient Robots.’ Professor Shim emphasized the role KAIST plays in Team CoSTAR as a leader in drone technology.
“I think when our drone technology will be added to our peers’ AI and robotics, Team CoSTAR will bring out unsurpassable synergy in completing the subterrestrial and planetary applications. I would like to follow the footprint of Hubo, the winning champion of the 2015 DARPA Robotics Challenge and even extend it to subterranean exploration,” he said.
These next generation autonomous subsurface explorers are now all optimizing the physical AI robot systems developed by Team CoSTAR. They will test their systems in more realistic field environments August 15 through 22 in Pittsburgh, USA. They have already received funding from DARPA for participating.
Team CoSTAR will compete in three consecutive yearly events starting this year, and the last event, planned for 2021, will put the team to the final test with courses that incorporate diverse challenges from all three events. Two million USD will be awarded to the winner after the final event, with additional prizes of up to 200,000 USD for self-funded teams.
Team USRG also ranked third in the recent Hyundai Motor Company’s ‘Autonomous Vehicle Competition’ and another challenge is on the horizon: Lockheed Martin’s ‘AlphaPilot Challenge’. In this event, the teams will be flying their drones through a series of racing gates, trying to beat the best human pilot. The challenge is hosted by Lockheed Martin, the world’s largest military contractor and the maker of the famed F-22 and F-35 stealth fighters, with the goal of stimulating the development of autonomous drones. Team USRG was selected from out of more than 400 teams from around the world and is preparing for a series of races this fall, beginning from the end of August.
Professor Shim said, “It is not easy to perform in a series of competitions in just a few months, but my students are smart, hardworking, and highly motivated. These events indeed demand a lot, but they really challenge the researchers to come up with technologies that work in the real world. This is the way robotics really should be.”
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2019.07.26 View 10117 -
CLKIP Bearing Fruit in China
The Chongqing Liangjiang KAIST International Program (CLKIP) is rapidly gaining steam in China. CLKIP, an educational program operated in Chongqing internationally by KAIST since 2015, offers two majors, Electronic Information Engineering and Computer Science and Technology, applying the same curriculum as at KAIST.
To operate the program, KAIST assigns professors from the School of Electrical Engineering and the School of Computing to the program every year. They are in charge of one-third of the major courses, and transfer KAIST’s educational curriculum and know-how.
A total of 13 professors from Chongqing University of Technology (CQUT) have received or are receiving training on advanced education methodologies and technical know-how, including an on and offline integrated learning program, called Education 4.0 and large-scale internet open learning.As CLKIP is gaining in popularity, the number of students for its undergraduate courses keeps increasing, from 66 in 2015 to 172 in 2016 and 200 students in 2017, achieving the student volume for enrollment annually.
CLKIP selected seven exchange undergraduate students and five dual-degree students this fall, and they are currently studying in KAIST for either one semester or one full year.
CLKIP is located in Chongqing, one of the major direct-controlled municipalities and a focal point for notable government projects. The Korea-China industrial zone is also located in this area.
Considering its location, CLKIP is more than just an international programs for educational cooperation. The program will provide opportunities to cooperate with Korean enterprises including Hyundai, SK Hynix, LG Chem and Hankook Tire. While cooperating in research and development as well as technical assistance, KAIST hopes that these enterprises will play a bridging role for KAIST alumni entering the Chinese market.
President Sung-Chul Shin said, “The success of CLKIP shows that KAIST programs for fostering future manpower and developing cutting-edge technologies do work in other countries. Based on this case, KAST will put more effort into transferring our innovative education systems abroad. We are also pushing ahead to establish a joint institute between KAIST and CQUT by 2018, which will become a foundation for facilitating the entry of KAIST’s cutting-edge technologies into the Chinese market.”
“KAIST aims to become an entrepreneurial university that creates value through technology commercialization. In this sense, KAIST plans to transfer advanced technologies to domestic and international companies located in the Liangjiang district,” he added.
2017.12.12 View 9384 -
IDKAIST Graduation Show, Interative and Innovative Works
Undergraduate students from the Department of Industrial Design at KAIST opened up their graduation show in the Industrial Design Building for eight days in KAIST from November 10 and another four days in Coex, Seoul from December 7.
The students showcased their creative and novel works in the exhibition. Some designs successfully showed change concepts such as for mixing straws. There were also several projects designed to meet individual demand, such as a customized shoe-making application and personal makeup colorings.
Since the establishment of its undergraduate program in 1983, the department has held a graduation show to demonstrate four years of the students’ academic work and research performance to KAIST members, externals specialists, and the public.
Professor Daniel Saakes, who is in charge of the show, said, “Please come by the show and support the 28 students for their hard work. This year, students’ projects are more socially-oriented through applications and social media, making them easily approachable for consumers.”
2017.11.13 View 5878