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KAIST develops biocompatible adhesive applicable to hair transplants
Aside from being used as a new medical adhesive, the new material can be applied to developing a new method of hair transplants, which cannot be repeated multiple times using current method of implanting the wholly intact follicles into the skin. Medical adhesives are materials that can be applied to various uses such as wound healing, hemostasis, vascular anastomosis, and tissue engineering, and is expected to contribute greatly to the development of minimally invasive surgery and organ transplants. However, adhesives with high adhesion, low toxicity, and capable of decomposing in the body are rare. Adhesives based on natural proteins, such as fibrin and collagen, have high biocompatibility but insufficient adhesive strength. Synthetic polymer adhesives based on urethane or acrylic have greater adhesion but do not decompose well and may cause an inflammatory reaction in the body. A joint research team led by Professor Myungeun Seo and Professor Haeshin Lee from the KAIST Department of Chemistry developed a bio-friendly adhesive from biocompatible polymers using tannic acid, the source of astringency in wine. The research team focused on tannic acid, a natural polyphenolic product. Tannic acid is a polyphenol present in large amounts in fruit peels, nuts, and cacao. It has a high affinity and coating ability on other substances, and we sense the astringent taste in wine when tannic acid sticks to the surface of our tongue. When tannic acid is mixed with hydrophilic polymers, they form coacervates, or small droplets of jelly-like fluids that sink. If the polymers used are biocompatible, the mixture can be applied as a medical adhesive with low toxicity. However, coacervates are fundamentally fluid-like and cannot withstand large forces, which limits their adhesive capabilities. Thus, while research to utilize it as an adhesive has been actively discussed, a biodegradable material exhibiting strong adhesion due to its high shear strength has not yet been developed. The research team figured out a way to enhance adhesion by mixing two biocompatible FDA-approved polymers, polyethylene glycol (PEG) and polylactic acid (PLA). While PEG, which is used widely in eyedrops and cream, is hydrophilic, PLA, a well-known bioplastic derived from lactic acid, is insoluble in water. The team combined the two into a block copolymer, which forms hydrophilic PLA aggregates in water with PEG blocks surrounding them. A coacervate created by mixing the micelles and tannic acid would behave like a solid due to the hard PLA components, and show an elastic modulus improved by a thousand times compared to PEG, enabling it to withstand much greater force as an adhesive. Figure 1. (Above) Principle of biodegradable adhesive made by mixing poly(ethylene glycol)-poly(lactic acid) diblock copolymer and tannic acid in water. Yellow coacervate is precipitated through hydrogen bonding between the block copolymer micelles and tannic acid, and exhibits adhesion. After heat treatment, hydrogen bonds are rearranged to further improve adhesion. (Bottom) Adhesion comparison. Compared to using poly(ethylene glycol) polymer (d), it can support 10 times more weight when using block copolymer (e) and 60 times more weight after heat treatment (f). The indicated G' values represent the elastic modulus of the material. Furthermore, the research team observed that the material’s mechanical properties can be improved by over a hundred times through a heating and cooling process that is used to heat-treat metals. They also discovered that this is due to the enforced interactions between micelle and tannic acid arrays. The research team used the fact that the material shows minimal irritation to the skin and decomposes well in the body to demonstrate its possible application as an adhesive for hair transplantation through an animal experiment. Professor Haeshin Lee, who has pioneered various application fields including medical adhesives, hemostatic agents, and browning shampoo, focused on the adhesive capacities and low toxicity of polyphenols like tannic acid, and now looks forward to it improving the limitations of current hair transplant methods, which still involve follicle transfer and are difficult to be repeated multiple times. Figure 2. (a) Overview of a hair transplantation method using a biodegradable adhesive (right) compared to a conventional hair transplantation method (left) that transplants hair containing hair follicles. After applying an adhesive to the tip of the hair, it is fixed to the skin by implanting it through a subcutaneous injection, and repeated treatment is possible. (b) Initial animal test results. One day after 15 hair transplantation, 12 strands of hair remain. If you pull the 3 strands of hair, you can see that the whole body is pulled up, indicating that it is firmly implanted into the skin. All strands of hair applied without the new adhesive material fell off, and in the case of adhesive without heat treatment, the efficiency was 1/7. This research was conducted by first co-authors Dr. Jongmin Park (currently a senior researcher at the Korea Research Institute of Chemical Technology) from Professor Myeongeun Seo’s team and Dr. Eunsook Park from Professor Haeshin Lee’s team in the KAIST Department of Chemistry, and through joint research with the teams led by Professor Hyungjun Kim from the KAIST Department of Chemistry and Professor Siyoung Choi from the Department of Chemical and Biomolecular Engineering. The research was published online on August 22 in the international journal Au (JACS Au) under the title Biodegradable Block Copolymer-Tannic Acid Glue. This study was funded by the Support Research Under Protection Project of the National Research Foundation (NRF), Leading Research Center Support Project (Research Center for Multiscale Chiral Structure), Biodegradable Plastics Commercialization and Demonstration Project by the Ministry of Trade and Industry, and institutional funding from the Korea Research Institute of Chemical Technology.
2022.10.07
View 6782
KAIST Research Team Proves How a Neurotransmitter may be the Key in Controlling Alzheimer’s Toxicity
With nearly 50 million dementia patients worldwide, and Alzheimers’s disease is the most common neurodegenerative disease. Its main symptom is the impairment of general cognitive abilities, including the ability to speak or to remember. The importance of finding a cure is widely understood with increasingly aging population and the life expectancy being ever-extended. However, even the cause of the grim disease is yet to be given a clear definition. A KAIST research team in the Department of Chemistry led by professor Mi Hee Lim took on a lead to discovered a new role for somatostatin, a protein-based neurotransmitter, in reducing the toxicity caused in the pathogenic mechanism taken towards development of Alzheimer’s disease. The study was published in the July issue of Nature Chemistry under the title, “Conformational and functional changes of the native neuropeptide somatostatin occur in the presence of copper and amyloid-β”. According to the amyloid hypothesis, the abnormal deposition of Aβ proteins causes death of neuronal cells. While Aβ agglomerations make up most of the aged plaques through fibrosis, in recent studies, high concentrations of transitional metal were found in the plaques from Alzheimer’s patients. This suggests a close interaction between metallic ions and Aβ, which accelerates the fibrosis of proteins. Copper in particular is a redox-activating transition metal that can produce large amounts of oxygen and cause serious oxidative stress on cell organelles. Aβ proteins and transition metals can closely interact with neurotransmitters at synapses, but the direct effects of such abnormalities on the structure and function of neurotransmitters are yet to be understood. Figure 1. Functional shift of somatostatin (SST) by factors in the pathogenesis of Alzheimer's disease. Figure 2. Somatostatin’s loss-of-function as neurotransmitter. a. Schematic diagram of SST auto-aggregation due to Alzheimer's pathological factors. b. SST’s aggregation by copper ions. c. Coordination-prediction structure and N-terminal folding of copper-SST. d. Inhibition of SST receptor binding specificity by metals. In their research, Professor Lim’s team discovered that when somatostatin, the protein-based neurotransmitter, is met with copper, Aβ, and metal-Aβ complexes, self-aggregates and ceases to perform its innate function of transmitting neural signals, but begins to attenuate the toxicity and agglomeration of metal-Aβ complexes. Figure 3. Gain-of-function of somatostatin (SST) in the dementia setting. a. Prediction of docking of SST and amyloid beta. b. SST making metal-amyloid beta aggregates into an amorphous form. c. Cytotoxic mitigation effect of SST. d. SST mitigating the interaction between amyloid beta protein with the cell membrane. This research, by Dr. Jiyeon Han et al. from the KAIST Department of Chemistry, revealed the coordination structure between copper and somatostatin at a molecular level through which it suggested the agglomeration mechanism, and discovered the effects of somatostatin on Aβ agglomeration path depending on the presence or absence of metals. The team has further confirmed somatostatin’s receptor binding, interactions with cell membranes, and effects on cell toxicity for the first time to receive international attention. Professor Mi Hee Lim said, “This research has great significance in having discovered a new role of neurotransmitters in the pathogenesis of Alzheimer’s disease.” “We expect this research to contribute to defining the pathogenic network of neurodegenerative diseases caused by aging, and to the development of future biomarkers and medicine,” she added. This research was conducted jointly by Professor Seung-Hee Lee’s team of KAIST Department of Biological Sciences, Professor Kiyoung Park’s Team of KAIST Department of Chemistry, and Professor Yulong Li’s team of Peking University. The research was funded by Basic Science Research Program of the National Research Foundation of Korea and KAIST. For more information about the research team, visit the website: https://sites.google.com/site/miheelimlab/1-professor-mi-hee-lim.
2022.07.29
View 9429
Distinguished Professor Sukbok Chang Named the 2022 Ho-Am Laureate
Distinguished Professor Sukbok Chang from the Department of Chemistry was named the awardee of the Ho-Am Prize in the fields of chemistry and life sciences. The award has recognized the most distinguished scholars, individuals, and organizations in physics and mathematics, chemistry and life sciences, engineering, medicine, arts, and community service in honor of the late founder of Samsung Group Byong-Chul Lee, whose penname is Ho-Am. The awards ceremony will be held on May 31 and awardees will receive 300 million KRW in prize money. Professor Chang became the fourth KAIST Ho-Am laureate following Distinguished Professor Sang Yup Lee in engineering in 2014, Distinguished Professor Jun Ho Oh in engineering in 2016, and Distinguished Professor Gou Young Koh in medicine in 2018. Professor Chang is a renowned chemist who has made pioneering research in the area of transition metal catalysis for organic transformations. Professor Chang is also one of the Highly Cited Researchers who rank in the top 1% of citations by field and publication year in the Web of Science citation index. He has made the list seven years in a row from 2016. Professor Chang has developed a range of new and impactful C-H bond functionalization reactions. By using his approaches, value-added molecules can be readily produced from chemical feedstocks, representatively hydrocarbons and (hetero)arenes. His research team elucidated fundamental key mechanistic aspects in the course of the essential C-H bond activation process of unreactive starting materials. He was able to utilize the obtained mechanistic understanding for the subsequent catalyst design to develop more efficient and highly (stereo)selective catalytic reactions. Among the numerous contributions he made, the design of new mechanistic approaches toward metal nitrenoid transfers are of especially high impact to the chemical community. Indeed, a series of important transition metal catalyst systems were developed by Professor Chang to enable the direct and selective C-H amidation of unreactive organic compounds, thereby producing aminated compounds that have important applicability in synthetic, medicinal, and materials science. He has also pioneered in the area of asymmetric C-H amination chemistry by creatively devising various types of chiral transition metal catalyst systems, and his team proved for the first time that chiral lactam compounds can be obtained at an excellent level of stereoselectivity. Another significant contribution of Professor. Chang was the introduction of dioxazolones as a robust but highly reactive source of acyl nitrenoids for the catalytic C-H amidation reactions, and this reagent is now broadly utilized in synthetic chemistry worldwide. Professor Chang also leads a research group in the Center for Catalytic Hydrocarbon Functionalizations at the Institute for Basic Science.
2022.04.06
View 5435
Mathematicians Identify a Key Source of Cell-to-Cell Variability in Cell Signaling
Systematic inferences identify a major source of heterogeneity in cell signaling dynamics Why do genetically identical cells respond differently to the same external stimuli, such as antibiotics? This long-standing mystery has been solved by KAIST and IBS mathematicians who have developed a new framework for analyzing cell responses to some stimuli. The team found that the cell-to-cell variability in antibiotic stress response increases as the effective length of the cell signaling pathway (i.e., the number of rate-limiting steps) increases. This finding could identify more effective chemotherapies to overcome the fractional killing of cancer cells caused by cell-to-cell variability. Cells in the human body contain signal transduction systems that respond to various external stimuli such as antibiotics and changes in osmotic pressure. When an external stimulus is detected, various biochemical reactions occur sequentially. This leads to the expression of relevant genes, allowing the cells to respond to the perturbed external environment. Furthermore, signal transduction leads to a drug response (e.g., antibiotic resistance genes are expressed when antibiotic drugs are given). However, even when the same external stimuli are detected, the responses of individual cells are greatly heterogeneous. This leads to the emergence of persister cells that are highly resistant to drugs. To identify potential sources of this cell-to cell variability, many studies have been conducted. However, most of the intermediate signal transduction reactions are unobservable with current experimental techniques. A group of researchers including Dae Wook Kim and Hyukpyo Hong and led by Professor Jae Kyoung Kim from the KAIST Department of Mathematical Sciences and IBS Biomedical Mathematics Group solved the mystery by exploiting queueing theory and Bayesian inference methodology. They proposed a queueing process that describes the signal transduction system in cells. Based on this, they developed Bayesian inference computational software using MBI (the Moment-based Bayesian Inference method). This enables the analysis of the signal transduction system without a direct observation of the intermediate steps. This study was published in Science Advances. By analyzing experimental data from Escherichia coli using MBI, the research team found that cell-to-cell variability increases as the number of rate-limiting steps in the signaling pathway increases. The rate-limiting steps denote the slowest steps (i.e., bottlenecks) in sequential biochemical reaction steps composing cell signaling pathways and thus dominates most of the signaling time. As the number of the rate-limiting steps increases, the intensity of the transduced signal becomes greatly heterogeneous even in a population of genetically identical cells. This finding is expected to provide a new paradigm for studying the heterogeneous antibiotic resistance of cells, which is a big challenge in cancer medicine. Professor Kim said, “As a mathematician, I am excited to help advance the understanding of cell-to-cell variability in response to external stimuli. I hope this finding facilitates the development of more effective chemotherapies.” This work was supported by the Samsung Science and Technology Foundation, the National Research Foundation of Korea, and the Institute for Basic Science. -Publication:Dae Wook Kim, Hyukpyo Hong, and Jae Kyoung Kim (2022) “Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: the rate-limiting step number,”Science Advances March 18, 2022 (DOI: 10.1126/sciadv.abl4598) -Profile:Professor Jae Kyoung Kimhttp://mathsci.kaist.ac.kr/~jaekkim jaekkim@kaist.ac.kr@umichkim on TwitterDepartment of Mathematical SciencesKAIST
2022.03.29
View 6609
Renault 5 EV and Canoo’s Pickup Truck Win the 2021 FMOTY Awards
KAIST Future Mobility of the Year Awards recognize the most innovative concept cars of the year The Renault 5 EV from France and a pickup truck from the US startup Canoo won the 2021 Future Mobility of the Year Awards (FMOTY) hosted by the Cho Chun Shik Graduate School of Green Transportation at KAIST. The awards ceremony was held at Renault Samsung Motors in Seoul on November 25. KAIST began the FMOTY in 2019 to advance future car technology and stimulate growth in the industry. The award recognizes the most innovative ideas for making the most futuristic concept car and improving the technological and social value of the industry. The awards ceremony was attended by KAIST President Kwang Hyung Lee, the dean of the Cho Chun Shik Graduate School of Green Transportation In Gwun Jang, CEO of Renault Samsung Motors Dominique Signora, and CEO of Canoo Tony Aquila. President Lee said, “The new world order will be impacted by new technology developers who envision the future. Their innovation and creative ideas will open a new world of sustainable future transportation.” Out of the 46 concept cars revealed at global motor exhibitions between last year and the first quarter of this year, models demonstrating transport technology useful for future society and innovative service were selected in the categories of passenger cars and commercial vehicles. Sixteen automotive journalists from 11 countries, including the chief editor of Car Magazine in Germany Georg Kacher and editorial director of BBC Top Gear Charlie Turner, participated as judges. This year’s award for the best concept car for a passenger vehicle went to an electric vehicle, the Renault 5 EV. The compact electric car was highly regarded for its practicality and environmental friendliness. A pickup truck by Canoo, an American EV manufacturing start-up, won the award in the commercial vehicle category. The pickup features an innovative design allowing for a variety of functions topped with a competitive price and it received overwhelming support from the judges. While Hyundai Motors swept both prizes at the awards last year and demonstrated the potential of Korean concept cars, Canoo’s win in the commercial vehicle section as a young American venture company brought attention to the changing dynamics in the automotive market. This shows that young EV start-ups can compete with existing car companies as the automotive paradigm is shifting from those with internal combustion engines to EVs. The awards organizers said that the Cho Chun Shik Graduate School of Green Transportation will continue to hold the FMOTY to lead the fast-changing global mobility market. For more information, please visit www.fmoty.org.
2021.11.26
View 5253
Industrial Liaison Program to Provide Comprehensive Consultation Services
The ILP’s one-stop solutions target all industrial sectors including conglomerates, small and medium-sized enterprises, venture companies, venture capital (VC) firms, and government-affiliated organizations. The Industrial Liaison Center at KAIST launched the Industrial Liaison Program (ILP) on September 28, an industry-academic cooperation project to provide comprehensive solutions to industry partners. The Industrial Liaison Center will recruit member companies for this service every year, targeting all industrial sectors including conglomerates, small and medium-sized enterprises, venture companies, venture capital (VC) firms, and government-affiliated organizations. The program plans to build a one-stop support system that can systematically share and use excellent resource information from KAIST’s research teams, R&D achievements, and infrastructure to provide member companies with much-needed services. More than 40 KAIST professors with abundant academic-industrial collaboration experience will participate in the program. Experts from various fields with different points of view and experiences will jointly provide solutions to ILP member companies. To actively participate in academic-industrial liaisons and joint consultations, KAIST assigned 10 professors from related fields as program directors. The program directors will come from four different fields including AI/robots (Professor Alice Oh, School from the School of Computing, Professor Young Jae Jang from the Department of Industrial & Systems Engineering, and Professor Yong-Hwa Park from Department of Mechanical Engineering), bio/medicine (Professor Daesoo Kim from Department of Biological Sciences and Professor YongKeun Park from Department of Physics), materials/electronics (Professor Sang Ouk Kim from the Department of Materials Science and Engineering and Professors Jun-Bo Yoon and Seonghwan Cho from the School of Electrical Engineering), and environment/energy (Professor Hee-Tak Kim from the Department of Biological Sciences and Professor Hoon Sohn from the Department of Civil and Environmental Engineering). The transdisciplinary board of consulting professors that will lead technology innovation is composed of 30 professors including Professor Min-Soo Kim (School of Computing, AI), Professor Chan Hyuk Kim (Department of Biological Sciences, medicine), Professor Hae-Won Park (Department of Mechanical Engineering, robots), Professor Changho Suh (School of Electrical Engineering, electronics), Professor Haeshin Lee (Department of Chemistry, bio), Professor Il-Doo Kim (Department of Materials Science and Engineering, materials), Professor HyeJin Kim (School of Business Technology and Management), and Professor Byoung Pil Kim (School of Business Technology and Management, technology law) The Head of the Industrial Liaison Center who is also in charge of the program, Professor Keon Jae Lee, said, “In a science and technology-oriented generation where technological supremacy determines national power, it is indispensable to build a new platform upon which innovative academic-industrial cooperation can be pushed forward in the fields of joint consultation, the development of academic-industrial projects, and the foundation of new industries. He added, “KAIST professors carry out world-class research in many different fields and faculty members can come together through the ILP to communicate with representatives from industry to improve their corporations’ global competitiveness and further contribute to our nation’s interests by cultivating strong small enterprises
2021.09.30
View 5051
Prof. Changho Suh Named the 2021 James L. Massey Awardee
Professor Changho Suh from the School of Electrical Engineering was named the recipient of the 2021 James L.Massey Award. The award recognizes outstanding achievement in research and teaching by young scholars in the information theory community. The award is named in honor of James L. Massey, who was an internationally acclaimed pioneer in digital communications and revered teacher and mentor to communications engineers. Professor Suh is a recipient of numerous awards, including the 2021 James L. Massey Research & Teaching Award for Young Scholars from the IEEE Information Theory Society, the 2019 AFOSR Grant, the 2019 Google Education Grant, the 2018 IEIE/IEEE Joint Award, the 2015 IEIE Haedong Young Engineer Award, the 2013 IEEE Communications Society Stephen O. Rice Prize, the 2011 David J. Sakrison Memorial Prize (the best dissertation award in UC Berkeley EECS), the 2009 IEEE ISIT Best Student Paper Award, the 2020 LINKGENESIS Best Teacher Award (the campus-wide Grand Prize in Teaching), and the four Departmental Teaching Awards (2013, 2019, 2020, 2021). Dr. Suh is an IEEE Information Theory Society Distinguished Lecturer, the General Chair of the Inaugural IEEE East Asian School of Information Theory, and a Member of the Young Korean Academy of Science and Technology. He is also an Associate Editor of Machine Learning for the IEEE Transactions on Information Theory, the Editor for the IEEE Information Theory Newsletter, a Column Editor for IEEE BITS the Information Theory Magazine, an Area Chair of NeurIPS 2021, and on the Senior Program Committee of IJCAI 2019–2021.
2021.07.27
View 5894
Professor Jung Receives the Hansong Science Award
Professor Yousung Jung of the Department of Chemical and Biomolecular Engineering has been selected as the recipient of the 5th Hansong Science Award in Chemistry. The award recognizes young and mid-career scholars who made outstanding achievement in physics, chemistry, and life sciences. Recipients receive 50 million KRW in prize money. Professor Jung was recognized for finding a new way to predict synthesis potentials when designing data-based materials and molecules through AI-powered inverse technology. Conventionally, new material discovery mainly relied on a method where the new materials were proposed by an expert’s intuition or experimental trial, then synthesized to measure the properties of the material before it was used. However, this method took a lot of time, which resulted in an inefficient discovery process. Professor Jung’s AI reverse design technology is reported to be more efficient for discovering new materials by finding crystal structures with desired properties using data and AI algorithms. "AI reverse design technology can accelerate the development of new materials and new drugs," Professor Jung said. "It can be used as an algorithm for future autonomous laboratories implemented by robots, algorithms, and data without human intervention," he added.
2021.07.13
View 5020
Ultrafast, on-Chip PCR Could Speed Up Diagnoses during Pandemics
A rapid point-of-care diagnostic plasmofluidic chip can deliver result in only 8 minutes Reverse transcription-polymerase chain reaction (RT-PCR) has been the gold standard for diagnosis during the COVID-19 pandemic. However, the PCR portion of the test requires bulky, expensive machines and takes about an hour to complete, making it difficult to quickly diagnose someone at a testing site. Now, researchers at KAIST have developed a plasmofluidic chip that can perform PCR in only about 8 minutes, which could speed up diagnoses during current and future pandemics. The rapid diagnosis of COVID-19 and other highly contagious viral diseases is important for timely medical care, quarantining and contact tracing. Currently, RT-PCR uses enzymes to reverse transcribe tiny amounts of viral RNA to DNA, and then amplifies the DNA so that it can be detected by a fluorescent probe. It is the most sensitive and reliable diagnostic method. But because the PCR portion of the test requires 30-40 cycles of heating and cooling in special machines, it takes about an hour to perform, and samples must typically be sent away to a lab, meaning that a patient usually has to wait a day or two to receive their diagnosis. Professor Ki-Hun Jeong at the Department of Bio and Brain Engineering and his colleagues wanted to develop a plasmofluidic PCR chip that could quickly heat and cool miniscule volumes of liquids, allowing accurate point-of-care diagnoses in a fraction of the time. The research was reported in ACS Nano on May 19. The researchers devised a postage stamp-sized polydimethylsiloxane chip with a microchamber array for the PCR reactions. When a drop of a sample is added to the chip, a vacuum pulls the liquid into the microchambers, which are positioned above glass nanopillars with gold nanoislands. Any microbubbles, which could interfere with the PCR reaction, diffuse out through an air-permeable wall. When a white LED is turned on beneath the chip, the gold nanoislands on the nanopillars quickly convert light to heat, and then rapidly cool when the light is switched off. The researchers tested the device on a piece of DNA containing a SARS-CoV-2 gene, accomplishing 40 heating and cooling cycles and fluorescence detection in only 5 minutes, with an additional 3 minutes for sample loading. The amplification efficiency was 91%, whereas a comparable conventional PCR process has an efficiency of 98%. With the reverse transcriptase step added prior to sample loading, the entire testing time with the new method could take 10-13 minutes, as opposed to about an hour for typical RT-PCR testing. The new device could provide many opportunities for rapid point-of-care diagnostics during a pandemic, the researchers say. -Publication Ultrafast and Real-Time Nanoplasmonic On-Chip Polymerase Chain Reaction for Rapid and Quantitative Molecular Diagnostics ACS Nano (https://doi.org/10.1021/acsnano.1c02154) -Professor Ki-Hun Jeong Biophotonics Laboratory https://biophotonics.kaist.ac.kr/ Department of Bio and Brain Engineeinrg KAIST
2021.06.08
View 7684
KAIST Teams Up with Yozma Group to Nurture Startups
KAIST has joined hands with Israeli venture capital investor Yozma Group to help campus-based startups grow and build success. The two signed a memorandum of understanding (MOU) on joint technology value creation initiatives at the signing ceremony that was held at KAIST’s main campus in Daejeon on April 8. Under the MOU, Yozma Group will make investments and implement acceleration programs for startups established by KAIST professors, graduates, and students, as well as those invested in by the university. Yozma Group already launched a $70 million fund to help grow companies in Korea and Israel. Yozma Group will use the fund as well as its global acceleration know-how and network of over 400 R&D centers across Israel to help promising KAIST startups enter overseas markets. Moreover, Yozma Group also plans to discover and support KAIST startups that need technology from the Weizmann Institute of Science, Israel’s leading multidisciplinary basic research institution in natural and exact sciences. KAIST is also in talks to locate Yozma Group’s branch office on the university’s campus to ensure seamless collaborations. KAIST President Kwang Hyung Lee explained to Yozma Group’s Founder and Chairman Yigal Erlich and Head of Asia Pacific Won-Jae Lee at the MOU signing ceremony that “startup and technology commercialization are the crucial areas where KAIST will make innovations.” “Cooperation with Yozma Group will help KAIST startups transform their ideas and technologies into real businesses and build a global presence,” he added. Yozma Group started as Yozma Fund, created in conjunction with the Israeli government in 1993 to support the globalization of Israeli startups and to foster the growth of Israel’s venture capital industry. The Fund, which was privatized in 1998, has supported 97 Israeli tech ventures joining the Nasdaq, leading Israel to become a global innovation hub that has the third-most companies listed on the Nasdaq. (END)
2021.04.20
View 5852
A Self-Made Couple in Their 90s Donates to KAIST
A self-made elderly couple in their 90s made a 20 billion KRW donation to KAIST on March 13. Chairman of Samsung Brush Sung-Hwan Chang and his wife Ha-Ok Ahn gave away their two properties valued at 20 billion in Nonhyon-dong in Seoul to KAIST during a ceremony on March 13 in Seoul. Chairman Chang, 92, made a huge fortune starting his business manufacturing cosmetic brushes. Building two factories in China, he expanded his business to export to high-end cosmetic companies. Chairman Chang, a native of North Korea, is a refugee who fled his hometown with his sister at age 18 during the Korean War. He said remembering his mother who was left behind in North Korea was the most painful thing. “We always wanted to help out people in need when we would earn enough money. We were inspired by our friends at our retirement community who made a donation to KAIST several years ago. We believe this is the right time to make this decision,” said Chairman Chang. The couple lives in same retirement community, a famous place for many successful businessmen and wealthy retired figures, located in Yongin, Kyonggi-do with Chairmen Beang-Ho Kim, Chun-Shik Cho, and Chang-Keun Son. With their gift, KAIST established Kim Beang-Ho & Kim Sam-Youl ITC Building as well as the Cho Chun-Shik Graduate School of Green Transportation. The four senior couples’ donations amount to 76.1 billion KRW. “It would be the most meaningful way if we could invest in KAIST for the country’s future,” said Chairman Chang. “I talked a lot with Chairman Kim on how KAIST utilizes its donations and have developed a strong belief in the future of KAIST.” Chairman and Mrs. Chang already toured the campus several times at the invitation of President Kwang-Hyung Lee and President Lee himself presented the vision of KAIST to the couple. The couple also attended President Lee’s inauguration ceremony on March 8. President Lee thanked the couple for their donation, saying “I take my hat off to Chairman Chang and his wife for their generous donation that was amassed over their lifetime. They lived very fiscally responsible lives. We will efficiently utilize this fund for educating future global talents." (END)
2021.03.15
View 6629
DeepTFactor Predicts Transcription Factors
A deep learning-based tool predicts transcription factors using protein sequences as inputs A joint research team from KAIST and UCSD has developed a deep neural network named DeepTFactor that predicts transcription factors from protein sequences. DeepTFactor will serve as a useful tool for understanding the regulatory systems of organisms, accelerating the use of deep learning for solving biological problems. A transcription factor is a protein that specifically binds to DNA sequences to control the transcription initiation. Analyzing transcriptional regulation enables the understanding of how organisms control gene expression in response to genetic or environmental changes. In this regard, finding the transcription factor of an organism is the first step in the analysis of the transcriptional regulatory system of an organism. Previously, transcription factors have been predicted by analyzing sequence homology with already characterized transcription factors or by data-driven approaches such as machine learning. Conventional machine learning models require a rigorous feature selection process that relies on domain expertise such as calculating the physicochemical properties of molecules or analyzing the homology of biological sequences. Meanwhile, deep learning can inherently learn latent features for the specific task. A joint research team comprised of Ph.D. candidate Gi Bae Kim and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at KAIST, and Ye Gao and Professor Bernhard O. Palsson of the Department of Biochemical Engineering at UCSD reported a deep learning-based tool for the prediction of transcription factors. Their research paper “DeepTFactor: A deep learning-based tool for the prediction of transcription factors” was published online in PNAS. Their article reports the development of DeepTFactor, a deep learning-based tool that predicts whether a given protein sequence is a transcription factor using three parallel convolutional neural networks. The joint research team predicted 332 transcription factors of Escherichia coli K-12 MG1655 using DeepTFactor and the performance of DeepTFactor by experimentally confirming the genome-wide binding sites of three predicted transcription factors (YqhC, YiaU, and YahB). The joint research team further used a saliency method to understand the reasoning process of DeepTFactor. The researchers confirmed that even though information on the DNA binding domains of the transcription factor was not explicitly given the training process, DeepTFactor implicitly learned and used them for prediction. Unlike previous transcription factor prediction tools that were developed only for protein sequences of specific organisms, DeepTFactor is expected to be used in the analysis of the transcription systems of all organisms at a high level of performance. Distinguished Professor Sang Yup Lee said, “DeepTFactor can be used to discover unknown transcription factors from numerous protein sequences that have not yet been characterized. It is expected that DeepTFactor will serve as an important tool for analyzing the regulatory systems of organisms of interest.” This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT through the National Research Foundation of Korea. -Publication Gi Bae Kim, Ye Gao, Bernhard O. Palsson, and Sang Yup Lee. DeepTFactor: A deep learning-based tool for the prediction of transcription factors. (https://doi.org/10.1073/pnas202117118) -Profile Distinguished Professor Sang Yup Lee leesy@kaist.ac.kr Metabolic &Biomolecular Engineering National Research Laboratory http://mbel.kaist.ac.kr Department of Chemical and Biomolecular Engineering KAIST
2021.01.05
View 7266
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