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KAIST Develops a Multifunctional Structural Battery Capable of Energy Storage and Load Support
Structural batteries are used in industries such as eco-friendly, energy-based automobiles, mobility, and aerospace, and they must simultaneously meet the requirements of high energy density for energy storage and high load-bearing capacity. Conventional structural battery technology has struggled to enhance both functions concurrently. However, KAIST researchers have succeeded in developing foundational technology to address this issue.
< Photo 1. (From left) Professor Seong Su Kim, PhD candidates Sangyoon Bae and Su Hyun Lim of the Department of Mechanical Engineering >
< Photo 2. (From left) Professor Seong Su Kim and Master's Graduate Mohamad A. Raja of KAIST Department of Mechanical Engineering >
KAIST (represented by President Kwang Hyung Lee) announced on the 19th of November that Professor Seong Su Kim's team from the Department of Mechanical Engineering has developed a thin, uniform, high-density, multifunctional structural carbon fiber composite battery* capable of supporting loads, and that is free from fire risks while offering high energy density.
*Multifunctional structural batteries: Refers to the ability of each material in the composite to simultaneously serve as a load-bearing structure and an energy storage element.
Early structural batteries involved embedding commercial lithium-ion batteries into layered composite materials. These batteries suffered from low integration of their mechanical and electrochemical properties, leading to challenges in material processing, assembly, and design optimization, making commercialization difficult.
To overcome these challenges, Professor Kim's team explored the concept of "energy-storing composite materials," focusing on interface and curing properties, which are critical in traditional composite design. This led to the development of high-density multifunctional structural carbon fiber composite batteries that maximize multifunctionality.
The team analyzed the curing mechanisms of epoxy resin, known for its strong mechanical properties, combined with ionic liquid and carbonate electrolyte-based solid polymer electrolytes. By controlling temperature and pressure, they were able to optimize the curing process.
The newly developed structural battery was manufactured through vacuum compression molding, increasing the volume fraction of carbon fibers—serving as both electrodes and current collectors—by over 160% compared to previous carbon-fiber-based batteries.
This greatly increased the contact area between electrodes and electrolytes, resulting in a high-density structural battery with improved electrochemical performance. Furthermore, the team effectively controlled air bubbles within the structural battery during the curing process, simultaneously enhancing the battery's mechanical properties.
Professor Seong Su Kim, the lead researcher, explained, “We proposed a framework for designing solid polymer electrolytes, a core material for high-stiffness, ultra-thin structural batteries, from both material and structural perspectives. These material-based structural batteries can serve as internal components in cars, drones, airplanes, and robots, significantly extending their operating time with a single charge. This represents a foundational technology for next-generation multifunctional energy storage applications.”
< Figure 2. Supplementary cover of ACS Applied Materials & Interfaces >
Mohamad A. Raja, a master’s graduate of KAIST’s Department of Mechanical Engineering, participated as the first author of this research, which was published in the prestigious journal ACS Applied Materials & Interfaces on September 10. The paper was recognized for its excellence and selected as a supplementary cover article. (Paper title: “Thin, Uniform, and Highly Packed Multifunctional Structural Carbon Fiber Composite Battery Lamina Informed by Solid Polymer Electrolyte Cure Kinetics.” https://doi.org/10.1021/acsami.4c08698)
This research was supported by the National Research Foundation of Korea’s Mid-Career Researcher Program and the National Semiconductor Research Laboratory Development Program.
2024.11.27 View 93 -
KAIST Develops Technology for the Precise Diagnosis of Electric Vehicle Batteries Using Small Currents
Accurately diagnosing the state of electric vehicle (EV) batteries is essential for their efficient management and safe use. KAIST researchers have developed a new technology that can diagnose and monitor the state of batteries with high precision using only small amounts of current, which is expected to maximize the batteries’ long-term stability and efficiency.
KAIST (represented by President Kwang Hyung Lee) announced on the 17th of October that a research team led by Professors Kyeongha Kwon and Sang-Gug Lee from the School of Electrical Engineering had developed electrochemical impedance spectroscopy (EIS) technology that can be used to improve the stability and performance of high-capacity batteries in electric vehicles.
EIS is a powerful tool that measures the impedance* magnitude and changes in a battery, allowing the evaluation of battery efficiency and loss. It is considered an important tool for assessing the state of charge (SOC) and state of health (SOH) of batteries. Additionally, it can be used to identify thermal characteristics, chemical/physical changes, predict battery life, and determine the causes of failures. *Battery Impedance: A measure of the resistance to current flow within the battery that is used to assess battery performance and condition.
However, traditional EIS equipment is expensive and complex, making it difficult to install, operate, and maintain. Moreover, due to sensitivity and precision limitations, applying current disturbances of several amperes (A) to a battery can cause significant electrical stress, increasing the risk of battery failure or fire and making it difficult to use in practice.
< Figure 1. Flow chart for diagnosis and prevention of unexpected combustion via the use of the electrochemical impedance spectroscopy (EIS) for the batteries for electric vehicles. >
To address this, the KAIST research team developed and validated a low-current EIS system for diagnosing the condition and health of high-capacity EV batteries. This EIS system can precisely measure battery impedance with low current disturbances (10mA), minimizing thermal effects and safety issues during the measurement process.
In addition, the system minimizes bulky and costly components, making it easy to integrate into vehicles. The system was proven effective in identifying the electrochemical properties of batteries under various operating conditions, including different temperatures and SOC levels.
Professor Kyeongha Kwon (the corresponding author) explained, “This system can be easily integrated into the battery management system (BMS) of electric vehicles and has demonstrated high measurement accuracy while significantly reducing the cost and complexity compared to traditional high-current EIS methods. It can contribute to battery diagnosis and performance improvements not only for electric vehicles but also for energy storage systems (ESS).”
This research, in which Young-Nam Lee, a doctoral student in the School of Electrical Engineering at KAIST participated as the first author, was published in the prestigious international journal IEEE Transactions on Industrial Electronics (top 2% in the field; IF 7.5) on September 5th. (Paper Title: Small-Perturbation Electrochemical Impedance Spectroscopy System With High Accuracy for High-Capacity Batteries in Electric Vehicles, Link: https://ieeexplore.ieee.org/document/10666864)
< Figure 2. Impedance measurement results of large-capacity batteries for electric vehicles. ZEW (commercial EW; MP10, Wonatech) versus ZMEAS (proposed system) >
This research was supported by the Basic Research Program of the National Research Foundation of Korea, the Next-Generation Intelligent Semiconductor Technology Development Program of the Korea Evaluation Institute of Industrial Technology, and the AI Semiconductor Graduate Program of the Institute of Information & Communications Technology Planning & Evaluation.
2024.10.17 View 1326 -
KAIST begins full-scale cooperation with Taiwan’s Formosa Group
< (From left) Senior Vice President for Planning and Budget Kyung-Soo Kim, and Professor Minee Choi of the Department of Brain and Cognitive Sciences of KAIST along with Chairman of Formosa Group Sandy Wang and KAIST President Kwang-Hyung Lee, and Dean Daesoo Kim of KAIST College of Life Science and Bioengineering >
KAIST is pursuing cooperation in the fields of advanced biotechnology and eco-friendly energy with Formosa Plastics Group, one of Taiwan's three largest companies.
To this end, Chairman Sandy Wang, a member of Formosa Group's standing committee and leader of the group's bio and eco-friendly energy sector, will visit KAIST on the 13th of this month. This is the first time that the owner of Formosa Group has made an official visit to KAIST.
Cooperation between the two institutions began last March when our university signed a memorandum of understanding on comprehensive exchange and cooperation with Ming Chi University of Science and Technology (明志科技大學), Chang Gung University(長庚大學), and Chang Gung Memorial Hospital(長庚記念醫院), three of many institutions established and supported by Formosa Group.
Based on this, Chairman Sandy Wang, who visits our university to promote more exchanges and cooperation, talked about ‘the education of children and corporate social return and practice of his father, Chairman Yung-Ching Wang,’ through a special lecture for the school leadership as a part of the Monthly Lecture on KAIST’s Leadership Innovation Day.
She then visited KAIST's research and engineering facilities related to Taiwan's future industries, such as advanced biotechnology and eco-friendly energy, and discussed global industry-academic cooperation plans. In the future, the two organizations plan to appoint adjunct professors and promote practical global cooperation, including joint student guidance and research cooperation. We plan to pursue effective mid- to long-term cooperation, such as conducting battery application research with the KAIST Next-Generation ESS Research Center and opening a graduate program specialized in stem cell and gene editing technology in connection with Chang Gung University and Chang Gung Memorial Hospital. The newly established cooperative relationship will also promote Formosa Group's investment and cooperation with KAIST's outstanding venture companies related to bio and eco-friendly energy to lay the foundation for innovative industrial cooperation between Taiwan and Korea.
President Kwang-Hyung Lee said, “The Formosa Group has a global network, so we regard it to be a key partner that will position KAIST’s bio and engineering technology in the global stages.” He also said, “With Chairman Sandy Wang’s visit, Taiwan is emerging as a global economic powerhouse,” and added, “We expect to continue our close cooperative relationship with the company.”
Formosa Group is a company founded by the late Chairman Yung-Ching Wang, the father of Chairman Sandy Wang. As the world's No. 1 plastic PVC producer, it is leading the core industries of Taiwan's economy, including semiconductors, steel, heavy industry, bio, and batteries. Chairman Yung-Ching Wang was respected by the Taiwanese people by setting an example of returning his wealth to society under the belief that the companies and assets he built ‘belonged to the people.’ Chang Gung University, Chang Gung Memorial Hospital, and Ming Chi University of Technology, which are pursuing cooperation with our university, were also established as part of the social contribution promoted by Chairman Yung-Ching Wang and are receiving financial support from Formosa Group.
2024.05.09 View 1561 -
'Jumping Genes' Found to Alter Human Colon Genomes, Offering Insights into Aging and Tumorigenesis
The Korea Advanced Institute of Science and Technology (KAIST) and their collaborators have conducted a groundbreaking study targeting 'jumping genes' in the entire genomes of the human large intestine. Published in Nature on May 18 2023, the research unveils the surprising activity of 'Long interspersed nuclear element-1 (L1),' a type of jumping gene previously thought to be mostly dormant in human genomes. The study shows that L1 genes can become activated and disrupt genomic functions throughout an individual's lifetime, particularly in the colorectal epithelium.
(Paper Title: Widespread somatic L1 retrotransposition in normal colorectal epithelium, https://www.nature.com/articles/s41586-023-06046-z)
With approximately 500,000 L1 jumping genes, accounting for 17% of the human genome, they have long been recognized for their contribution to the evolution of the human species by introducing 'disruptive innovation' to genome sequences. Until now, it was believed that most L1 elements had lost their ability to jump in normal tissues of modern humans. However, this study reveals that some L1 jumping genes can be widely activated in normal cells, leading to the accumulation of genomic mutations over an individual's lifetime. The rate of L1 jumping and resulting genomic changes vary among different cell types, with a notable concentration observed in aged colon epithelial cells. The study illustrates that every colonic epithelial cell experiences an L1 jumping event by the age of 40 on average.
The research, led by co-first authors Chang Hyun Nam (a graduate student at KAIST) and Dr. Jeonghwan Youk (former graduate student at KAIST and assistant clinical professor at Seoul National University Hospital), involved the analysis of whole-genome sequences from 899 single cells obtained from skin (fibroblasts), blood, and colon epithelial tissues collected from 28 individuals. The study uncovers the activation of L1 jumping genes in normal cells, resulting in the gradual accumulation of genomic mutations over time. Additionally, the team explored epigenomic (DNA methylation) sequences to understand the mechanism behind L1 jumping gene activation. They found that cells with activated L1 jumping genes exhibit epigenetic instability, suggesting the critical role of epigenetic changes in regulating L1 jumping gene activity. Most of these epigenomic instabilities were found to arise during the early stages of embryogenesis. The study provides valuable insights into the aging process and the development of diseases in human colorectal tissues.
"This study illustrates that genomic damage in normal cells is acquired not only through exposure to carcinogens but also through the activity of endogenous components whose impact was previously unclear. Genomes of apparently healthy aged cells, particularly in the colorectal epithelium, become mosaic due to the activity of L1 jumping genes," said Prof. Young Seok Ju at KAIST.
"We emphasize the essential and ongoing collaboration among researchers in clinical medicine and basic medical sciences," said Prof. Min Jung Kim of the Department of Surgery at Seoul National University Hospital. "This case highlights the critical role of systematically collected human tissues from clinical settings in unraveling the complex process of disease development in humans."
"I am delighted that the research team's advancements in single-cell genome technology have come to fruition. We will persistently strive to lead in single-cell genome technology," said Prof. Hyun Woo Kwon of the Department of Nuclear Medicine at Korea University School of Medicine.
The research team received support from the Research Leader Program and the Young Researcher Program of the National Research Foundation of Korea, a grant from the MD-PhD/Medical Scientist Training Program through the Korea Health Industry Development Institute, and the Suh Kyungbae Foundation.
< Figure 1. Experimental design of the study >
< Figure 2. Schematic diagram illustrating factors influencing the soL1R landscape. >
Genetic composition of rc-L1s is inherited from the parents. The methylation landscape of rc-L1 promoters is predominantly determined by global DNA demethylation, followed by remethylation processes in the developmental stages. Then, when an rc-L1 is promoter demethylated in a specific cell lineage, the source expresses L1 transcripts thus making possible the induction of soL1Rs.
2023.05.22 View 4598 -
KAIST’s unmanned racing car to race in the Indy Autonomous Challenge @ CES 2023 as the only contender representing Asia
- Professor David Hyunchul Shim of the School of Electrical Engineering, is at the Las Vegas Motor Speedway in Las Vegas, Nevada with his students of the Unmanned Systems Research Group (USRG), participating in the Indy Autonomous Challenge (IAC) @ CES as the only Asian team in the race.
Photo 1. Nine teams that competed at the first Indy Autonomous Challenge on October 23, 2021. (KAIST team is the right most team in the front row)
- The EE USRG team won the slot to race in the IAC @ CES 2023 rightly as the semifinals entree of the IAC @ CES 2022’ held in January of last year
- Through the partnership with Hyundai Motor Company, USRG received support to participate in the competition, and is to share the latest developments and trends of the technology with the company researchers
- With upgrades from last year, USRG is to race with a high-speed Indy racing car capable of driving up to 300 km/h and the technology developed in the process is to be used in further advancement of the high-speed autonomous vehicle technology of the future.
KAIST (President Kwang Hyung Lee) announced on the 5th that it will participate in the “Indy Autonomous Challenge (IAC) @ CES 2023”, an official event of the world's largest electronics and information technology exhibition held every year in Las Vegas, Nevada, of the United States from January 5th to 8th.
Photo 2. KAIST Racing Team participating in the Indy Autonomous Challenge @ CES 2023 (Team Leader: Sungwon Na, Team Members: Seongwoo Moon, Hyunwoo Nam, Chanhoe Ryu, Jaeyoung Kang)
“IAC @ CES 2023”, which is to be held at the Las Vegas Motor Speedway (LVMS) on January 7, seeks to advance technology developed as the result of last year's competition to share the results of such advanced high-speed autonomous vehicle technology with the public.
This competition is the 4th competition following the “Indy Autonomous Challenge (IAC)” held for the first time in Indianapolis, USA on October 23, 2021. At the IAC @ CES 2022 following the first IAC competition, the Unmmaned Systems Research Group (USRG) team led by Professor David Hyunchul Shim advanced to the semifinals out of a total of nine teams and won a spot to participate in CES 2023. As a result, the USRG comes into the challenge as the only Asian team to compete with other teams comprised of students and researchers of American and European backgrounds where the culture of motorsports is more deep-rooted.
For CES 2022, Professor David Hyunchul Shim’s research team was able to successfully develop a software that controlled the racing car to comply with the race flags and regulations while going up to 240 km/h all on its on.
Photo 3. KAIST Team’s vehicle on Las Vegas Motor Speedway during the IAC @ CES 2022
In the IAC @ CES 2023, the official racing vehicle AV-23, is a converted version of IL-15, the official racing car for Indy 500, fully automated while maintaining the optimal design for high-speed racing, and was upgraded from the last year’s competition taking up the highest speed up to 300 km/h.
This year’s competition, will develop on last year’s head-to-head autonomous racing and take the form of the single elimination tournament to have the cars overtake the others without any restrictions on the driving course, which would have the team that constantly drives at the fastest speed will win the competition.
Photo 4. KAIST Team’s vehicle overtaking the Italian team, PoliMOVE’s vehicle during one of the race in the IAC @ CES 2022
Professor Shim's team further developed on the CES 2022 certified software to fine tune the external recognition mechanisms and is now focused on precise positioning and driving control technology that factors into maintaining stability even when driving at high speed.
Professor Shim's research team won the Autonomous Driving Competition hosted by Hyundai Motor Company in 2021. Starting with this CES 2023 competition, they signed a partnership contract with Hyundai to receive financial support to participate in the CES competition and share the latest developments and trends of autonomous driving technology with Hyundai Motor's research team.
During CES 2023, the research team will also participate in other events such as the exhibition by the KAIST racing team at the IAC’s official booth located in the West Hall.
Professor David Hyunchul Shim said, “With these competitions being held overseas, there were many difficulties having to keep coming back, but the students took part in it diligently, for which I am deeply grateful. Thanks to their efforts, we were able to continue in this competition, which will be a way to verify the autonomous driving technology that we developed ourselves over the past 13 years, and I highly appreciate that.”
“While high-speed autonomous driving technology is a technology that is not yet sought out in Korea, but it can be applied most effectively for long-distance travel in the Korea,” he went on to add. “It has huge advantages in that it does not require constructions for massive infrastructure that costs enormous amount of money such as high-speed rail or urban aviation and with our design, it is minimally affected by weather conditions.” he emphasized.
On a different note, the IAC @ CES 2023 is co-hosted by the Consumer Technology Association (CTA) and Energy Systems Network (ESN), the organizers of CES. Last year’s IAC winner, Technische Universität München of Germany, and MIT-PITT-RW, a team of Massachusetts Institute of Technology (Massachusetts), University of Pittsburgh (Pennsylvania), Rochester Institute of Technology (New York), University of Waterloo (Canada), with and the University of Waterloo, along with TII EuroRacing - University of Modena and Reggio Emilia (Italy), Technology Innovation Institute (United Arab Emirates), and five other teams are in the race for the win against KAIST.
Photo 5. KAIST Team’s vehicle on the track during the IAC @ CES 2022
The Indy Autonomous Challenge is scheduled to hold its fifth competition at the Monza track in Italy in June 2023 and the sixth competition at CES 2024.
2023.01.05 View 7149 -
'Fingerprint' Machine Learning Technique Identifies Different Bacteria in Seconds
A synergistic combination of surface-enhanced Raman spectroscopy and deep learning serves as an effective platform for separation-free detection of bacteria in arbitrary media
Bacterial identification can take hours and often longer, precious time when diagnosing infections and selecting appropriate treatments. There may be a quicker, more accurate process according to researchers at KAIST. By teaching a deep learning algorithm to identify the “fingerprint” spectra of the molecular components of various bacteria, the researchers could classify various bacteria in different media with accuracies of up to 98%.
Their results were made available online on Jan. 18 in Biosensors and Bioelectronics, ahead of publication in the journal’s April issue.
Bacteria-induced illnesses, those caused by direct bacterial infection or by exposure to bacterial toxins, can induce painful symptoms and even lead to death, so the rapid detection of bacteria is crucial to prevent the intake of contaminated foods and to diagnose infections from clinical samples, such as urine. “By using surface-enhanced Raman spectroscopy (SERS) analysis boosted with a newly proposed deep learning model, we demonstrated a markedly simple, fast, and effective route to classify the signals of two common bacteria and their resident media without any separation procedures,” said Professor Sungho Jo from the School of Computing.
Raman spectroscopy sends light through a sample to see how it scatters. The results reveal structural information about the sample — the spectral fingerprint — allowing researchers to identify its molecules. The surface-enhanced version places sample cells on noble metal nanostructures that help amplify the sample’s signals.
However, it is challenging to obtain consistent and clear spectra of bacteria due to numerous overlapping peak sources, such as proteins in cell walls. “Moreover, strong signals of surrounding media are also enhanced to overwhelm target signals, requiring time-consuming and tedious bacterial separation steps,” said Professor Yeon Sik Jung from the Department of Materials Science and Engineering.
To parse through the noisy signals, the researchers implemented an artificial intelligence method called deep learning that can hierarchically extract certain features of the spectral information to classify data. They specifically designed their model, named the dual-branch wide-kernel network (DualWKNet), to efficiently learn the correlation between spectral features. Such an ability is critical for analyzing one-dimensional spectral data, according to Professor Jo.
“Despite having interfering signals or noise from the media, which make the general shapes of different bacterial spectra and their residing media signals look similar, high classification accuracies of bacterial types and their media were achieved,” Professor Jo said, explaining that DualWKNet allowed the team to identify key peaks in each class that were almost indiscernible in individual spectra, enhancing the classification accuracies. “Ultimately, with the use of DualWKNet replacing the bacteria and media separation steps, our method dramatically reduces analysis time.”
The researchers plan to use their platform to study more bacteria and media types, using the information to build a training data library of various bacterial types in additional media to reduce the collection and detection times for new samples.
“We developed a meaningful universal platform for rapid bacterial detection with the collaboration between SERS and deep learning,” Professor Jo said. “We hope to extend the use of our deep learning-based SERS analysis platform to detect numerous types of bacteria in additional media that are important for food or clinical analysis, such as blood.”
The National R&D Program, through a National Research Foundation of Korea grant funded by the Ministry of Science and ICT, supported this research.
-PublicationEojin Rho, Minjoon Kim, Seunghee H. Cho, Bongjae Choi, Hyungjoon Park, Hanhwi Jang, Yeon Sik Jung, Sungho Jo, “Separation-free bacterial identification in arbitrary media via deepneural network-based SERS analysis,” Biosensors and Bioelectronics online January 18, 2022 (doi.org/10.1016/j.bios.2022.113991)
-ProfileProfessor Yeon Sik JungDepartment of Materials Science and EngineeringKAIST
Professor Sungho JoSchool of ComputingKAIST
2022.03.04 View 19426 -
A Team of Three PhD Candidates Wins the Korea Semiconductor Design Contest
“We felt a sense of responsibility to help the nation advance its semiconductor design technology”
A CMOS (complementary metal-oxide semiconductor)-based “ultra-low noise signal chip” for 6G communications designed by three PhD candidates at the KAIST School of Electrical Engineering won the Presidential Award at the 22nd Korea Semiconductor Design Contest.
The winners are PhD candidates Sun-Eui Park, Yoon-Seo Cho, and Ju-Eun Bang from the Integrated Circuits and System Lab run by Professor Jaehyouk Choi. The contest, which is hosted by the Ministry of Trade, Industry and Energy and the Korea Semiconductors Industry Association, is one of the top national semiconductor design contests for college students.
Park said the team felt a sense of responsibility to help advance semiconductor design technology in Korea when deciding to participate the contest. The team expressed deep gratitude to Professor Choi for guiding their research on 6G communications.
“Our colleagues from other labs and seniors who already graduated helped us a great deal, so we owe them a lot,” explained Park. Cho added that their hard work finally got recognized and that acknowledgement pushes her to move forward with her research. Meanwhile, Bang said she is delighted to see that many people seem to be interested in her research topic.
Research for 6G is attempting to reach 1 tera bps (Tbps), 50 times faster than 5G communications with transmission speeds of up to 20 gigabytes. In general, the wider the communication frequency band, the higher the data transmission speed. Thus, the use of frequency bands above 100 gigahertz is essential for delivering high data transmission speeds for 6G communications.
However, it remains a big challenge to make a precise benchmark signal that can be used as a carrier wave in a high frequency band. Despite the advantages of CMOS’s ultra-small and low-power design, it still has limitations at high frequency bands and its operating frequency. Thus, it was difficult to achieve a frequency band above 100 gigahertz.
To overcome these challenges, the three students introduced ultra-low noise signal generation technology that can support high-order modulation technologies. This technology is expected to contribute to increasing the price competitiveness and density of 6G communication chips that will be used in the future.
5G just got started in 2020 and still has long way to go for full commercialization. Nevertheless, many researchers have started preparing for 6G technology, targeting 2030 since a new cellular communication appears in every other decade.
Professor Choi said, “Generating ultra-high frequency signals in bands above 100 GHz with highly accurate timing is one of the key technologies for implementing 6G communication hardware. Our research is significant for the development of the world’s first semiconductor chip that will use the CMOS process to achieve noise performance of less than 80fs in a frequency band above 100 GHz.”
The team members plan to work as circuit designers in Korean semiconductor companies after graduation. “We will continue to research the development of signal generators on the topic of award-winning 6G. We would like to continue our research on high-speed circuit designs such as ultra-fast analog-to-digital converters,” Park added.
2021.11.30 View 6888 -
The Dynamic Tracking of Tissue-Specific Secretory Proteins
Researchers develop a versatile and powerful tool for studying the spatiotemporal dynamics of secretory proteins, a valuable class of biomarkers and therapeutic targets
Researchers have presented a method for profiling tissue-specific secretory proteins in live mice. This method is expected to be applicable to various tissues or disease models for investigating biomarkers or therapeutic targets involved in disease progression. This research was reported in Nature Communications on September 1.
Secretory proteins released into the blood play essential roles in physiological systems. They are core mediators of interorgan communication, while serving as biomarkers and therapeutic targets.
Previous studies have analyzed conditioned media from culture models to identify cell type-specific secretory proteins, but these models often fail to fully recapitulate the intricacies of multi-organ systems and thus do not sufficiently reflect biological realities.
These limitations provided compelling motivation for the research team led by Jae Myoung Suh and his collaborators to develop techniques that could identify and resolve characteristics of tissue-specific secretory proteins along time and space dimensions.
For addressing this gap in the current methodology, the research team utilized proximity-labeling enzymes such as TurboID to label secretory proteins in endoplasmic reticulum lumen using biotin. Thereafter, the biotin-labeled secretory proteins were readily enriched through streptavidin affinity purification and could be identified through mass spectrometry.
To demonstrate its functionality in live mice, research team delivered TurboID to mouse livers via an adenovirus. After administering the biotin, only liver-derived secretory proteins were successfully detected in the plasma of the mice. Interestingly, the pattern of biotin-labeled proteins secreted from the liver was clearly distinctive from those of hepatocyte cell lines.
First author Kwang-eun Kim from the Graduate School of Medical Science and Engineering explained, “The proteins secreted by the liver were significantly different from the results of cell culture models. This data shows the limitations of cell culture models for secretory protein study, and this technique can overcome those limitations. It can be further used to discover biomarkers and therapeutic targets that can more fully reflect the physiological state.”
This work research was supported by the National Research Foundation of Korea, the KAIST Key Research Institutes Project (Interdisciplinary Research Group), and the Institute for Basic Science in Korea.
-PublicationKwang-eun Kim, Isaac Park et al., “Dynamic tracking and identification of tissue-specific secretory proteins in the circulation of live mice,” Nature Communications on Sept.1,
2021(https://doi.org/10.1038/s41467-021-25546-y)
-ProfileProfessor Jae Myoung Suh Integrated Lab of Metabolism, Obesity and Diabetes Researchhttps://imodkaist.wixsite.com/home
Graduate School of Medical Science and Engineering College of Life Science and BioengineeringKAIST
2021.09.14 View 7421 -
Study of T Cells from COVID-19 Convalescents Guides Vaccine Strategies
Researchers confirm that most COVID-19 patients in their convalescent stage carry stem cell-like memory T cells for months
A KAIST immunology research team found that most convalescent patients of COVID-19 develop and maintain T cell memory for over 10 months regardless of the severity of their symptoms. In addition, memory T cells proliferate rapidly after encountering their cognate antigen and accomplish their multifunctional roles. This study provides new insights for effective vaccine strategies against COVID-19, considering the self-renewal capacity and multipotency of memory T cells.
COVID-19 is a disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. When patients recover from COVID-19, SARS-CoV-2-specific adaptive immune memory is developed. The adaptive immune system consists of two principal components: B cells that produce antibodies and T cells that eliminate infected cells. The current results suggest that the protective immune function of memory T cells will be implemented upon re-exposure to SARS-CoV-2.
Recently, the role of memory T cells against SARS-CoV-2 has been gaining attention as neutralizing antibodies wane after recovery. Although memory T cells cannot prevent the infection itself, they play a central role in preventing the severe progression of COVID-19. However, the longevity and functional maintenance of SARS-CoV-2-specific memory T cells remain unknown.
Professor Eui-Cheol Shin and his collaborators investigated the characteristics and functions of stem cell-like memory T cells, which are expected to play a crucial role in long-term immunity. Researchers analyzed the generation of stem cell-like memory T cells and multi-cytokine producing polyfunctional memory T cells, using cutting-edge immunological techniques.
This research is significant in that revealing the long-term immunity of COVID-19 convalescent patients provides an indicator regarding the long-term persistence of T cell immunity, one of the main goals of future vaccine development, as well as evaluating the long-term efficacy of currently available COVID-19 vaccines.
The research team is presently conducting a follow-up study to identify the memory T cell formation and functional characteristics of those who received COVID-19 vaccines, and to understand the immunological effect of COVID-19 vaccines by comparing the characteristics of memory T cells from vaccinated individuals with those of COVID-19 convalescent patients.
PhD candidate Jae Hyung Jung and Dr. Min-Seok Rha, a clinical fellow at Yonsei Severance Hospital, who led the study together explained, “Our analysis will enhance the understanding of COVID-19 immunity and establish an index for COVID-19 vaccine-induced memory T cells.”
“This study is the world’s longest longitudinal study on differentiation and functions of memory T cells among COVID-19 convalescent patients. The research on the temporal dynamics of immune responses has laid the groundwork for building a strategy for next-generation vaccine development,” Professor Shin added. This work was supported by the Samsung Science and Technology Foundation and KAIST, and was published in Nature Communications on June 30.
-Publication:
Jung, J.H., Rha, MS., Sa, M. et al. SARS-CoV-2-specific T cell memory is sustained in COVID-19 convalescent patients for 10 months with successful development of stem cell-like memory T cells. Nat Communications 12, 4043 (2021). https://doi.org/10.1038/s41467-021-24377-1
-Profile:
Professor Eui-Cheol Shin
Laboratory of Immunology & Infectious Diseases (http://liid.kaist.ac.kr/)
Graduate School of Medical Science and Engineering
KAIST
2021.07.05 View 10409 -
ACS Nano Special Edition Highlights Innovations at KAIST
- The collective intelligence and technological innovation of KAIST was highlighted with case studies including the Post-COVID-19 New Deal R&D Initiative Project. -
KAIST’s innovative academic achievements and R&D efforts for addressing the world’s greatest challenges such as the COVID-19 pandemic were featured in ACS Nano as part of its special virtual issue commemorating the 50th anniversary of KAIST. The issue consisted of 14 review articles contributed by KAIST faculty from five departments, including two from Professor Il-Doo Kim from the Department of Materials Science and Engineering, who serves as an associate editor of the ACS Nano.
ACS Nano, the leading international journal in nanoscience and nanotechnology, published a special virtual issue last month, titled ‘Celebrating 50 Years of KAIST: Collective Intelligence and Innovation for Confronting Contemporary Issues.’
This special virtual issue introduced KAIST’s vision of becoming a ‘global value-creative leading university’ and its progress toward this vision over the last 50 years. The issue explained how KAIST has served as the main hub for advanced scientific research and technological innovation in South Korea since its establishment in 1971, and how its faculty and over 69,000 graduates played a key role in propelling the nation’s rapid industrialization and economic development.
The issue also emphasized the need for KAIST to enhance global cooperation and the exchange of ideas in the years to come, especially during the post-COVID era intertwined with the Fourth Industrial Revolution (4IR). In this regard, the issue cited the first ‘KAIST Emerging Materials e-Symposium (EMS)’, which was held online for five days in September of last year with a global audience of over 10,000 participating live via Zoom and YouTube, as a successful example of what academic collaboration could look like in the post-COVID and 4IR eras.
In addition, the “Science & Technology New Deal Project for COVID-19 Response,” a project conducted by KAIST with support from the Ministry of Science and ICT (MSIT) of South Korea, was also introduced as another excellent case of KAIST’s collective intelligence and technological innovation. The issue highlighted some key achievements from this project for overcoming the pandemic-driven crisis, such as: reusable anti-virus filters, negative-pressure ambulances for integrated patient transport and hospitalization, and movable and expandable negative-pressure ward modules.
“We hold our expectations high for the outstanding achievements and progress KAIST will have made by its centennial,” said Professor Kim on the background of curating the 14 review articles contributed by KAIST faculty from the fields of Materials Science and Engineering (MSE), Chemical and Biomolecular Engineering (CBE), Nuclear and Quantum Engineering (NQE), Electrical Engineering (EE), and Chemistry (Chem).
Review articles discussing emerging materials and their properties covered photonic carbon dots (Professor Chan Beum Park, MSE), single-atom and ensemble catalysts (Professor Hyunjoo Lee, CBE), and metal/metal oxide electrocatalysts (Professor Sung-Yoon Chung, MSE).
Review articles discussing materials processing covered 2D layered materials synthesis based on interlayer engineering (Professor Kibum Kang, MSE), eco-friendly methods for solar cell production (Professor Bumjoon J. Kim, CBE), an ex-solution process for the synthesis of highly stable catalysts (Professor WooChul Jung, MSE), and 3D light-patterning synthesis of ordered nanostructures (Professor Seokwoo Jeon, MSE, and Professor Dongchan Jang, NQE).
Review articles discussing advanced analysis techniques covered operando materials analyses (Professor Jeong Yeong Park, Chem), graphene liquid cell transmission electron microscopy (Professor Jong Min Yuk, MSE), and multiscale modeling and visualization of materials systems (Professor Seungbum Hong, MSE).
Review articles discussing practical state-of-the-art devices covered chemiresistive hydrogen sensors (Professor Il-Doo Kim, MSE), patient-friendly diagnostics and implantable treatment devices (Professor Steve Park, MSE), triboelectric nanogenerators (Professor Yang-Kyu Choi, EE), and next-generation lithium-air batteries (Professor Hye Ryung Byon, Chem, and Professor Il-Doo Kim, MSE).
In addition to Professor Il-Doo Kim, post-doctoral researcher Dr. Jaewan Ahn from the KAIST Applied Science Research Institute, Dean of the College of Engineering at KAIST Professor Choongsik Bae, and ACS Nano Editor-in-Chief Professor Paul S. Weiss from the University of California, Los Angeles also contributed to the publication of this ACS Nano special virtual issue.
The issue can be viewed and downloaded from the ACS Nano website at https://doi.org/10.1021/acsnano.1c01101.
Image credit: KAIST
Image usage restrictions: News organizations may use or redistribute this image,with proper attribution, as part of news coverage of this paper only.
Publication:
Ahn, J., et al. (2021) Celebrating 50 Years of KAIST: Collective Intelligence and Innovation for Confronting Contemporary Issues. ACS Nano 15(3): 1895-1907. Available online at https://doi.org/10.1021/acsnano.1c01101
Profile:
Il-Doo Kim, Ph.D
Chair Professor
idkim@kaist.ac.kr
http://advnano.kaist.ac.kr
Advanced Nanomaterials and Energy Lab.
Department of Materials Science and Engineering
Membrane Innovation Center for Anti-Virus and Air-Quality Control
https://kaist.ac.kr/
Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea
(END)
2021.03.05 View 25267 -
To Talk or Not to Talk: Smart Speaker Determines Optimal Timing to Talk
A KAIST research team has developed a new context-awareness technology that enables AI assistants to determine when to talk to their users based on user circumstances. This technology can contribute to developing advanced AI assistants that can offer pre-emptive services such as reminding users to take medication on time or modifying schedules based on the actual progress of planned tasks.
Unlike conventional AI assistants that used to act passively upon users’ commands, today’s AI assistants are evolving to provide more proactive services through self-reasoning of user circumstances. This opens up new opportunities for AI assistants to better support users in their daily lives. However, if AI assistants do not talk at the right time, they could rather interrupt their users instead of helping them.
The right time for talking is more difficult for AI assistants to determine than it appears. This is because the context can differ depending on the state of the user or the surrounding environment.
A group of researchers led by Professor Uichin Lee from the KAIST School of Computing identified key contextual factors in user circumstances that determine when the AI assistant should start, stop, or resume engaging in voice services in smart home environments. Their findings were published in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) in September.
The group conducted this study in collaboration with Professor Jae-Gil Lee’s group in the KAIST School of Computing, Professor Sangsu Lee’s group in the KAIST Department of Industrial Design, and Professor Auk Kim’s group at Kangwon National University.
After developing smart speakers equipped with AI assistant function for experimental use, the researchers installed them in the rooms of 40 students who live in double-occupancy campus dormitories and collected a total of 3,500 in-situ user response data records over a period of a week.
The smart speakers repeatedly asked the students a question, “Is now a good time to talk?” at random intervals or whenever a student’s movement was detected. Students answered with either “yes” or “no” and then explained why, describing what they had been doing before being questioned by the smart speakers.
Data analysis revealed that 47% of user responses were “no” indicating they did not want to be interrupted. The research team then created 19 home activity categories to cross-analyze the key contextual factors that determine opportune moments for AI assistants to talk, and classified these factors into ‘personal,’ ‘movement,’ and ‘social’ factors respectively.
Personal factors, for instance, include:
1. the degree of concentration on or engagement in activities,
2. the degree urgency and busyness,
3. the state of user’s mental or physical condition, and
4. the state of being able to talk or listen while multitasking.
While users were busy concentrating on studying, tired, or drying hair, they found it difficult to engage in conversational interactions with the smart speakers.
Some representative movement factors include departure, entrance, and physical activity transitions. Interestingly, in movement scenarios, the team found that the communication range was an important factor. Departure is an outbound movement from the smart speaker, and entrance is an inbound movement. Users were much more available during inbound movement scenarios as opposed to outbound movement scenarios.
In general, smart speakers are located in a shared place at home, such as a living room, where multiple family members gather at the same time. In Professor Lee’s group’s experiment, almost half of the in-situ user responses were collected when both roommates were present. The group found social presence also influenced interruptibility. Roommates often wanted to minimize possible interpersonal conflicts, such as disturbing their roommates' sleep or work.
Narae Cha, the lead author of this study, explained, “By considering personal, movement, and social factors, we can envision a smart speaker that can intelligently manage the timing of conversations with users.”
She believes that this work lays the foundation for the future of AI assistants, adding, “Multi-modal sensory data can be used for context sensing, and this context information will help smart speakers proactively determine when it is a good time to start, stop, or resume conversations with their users.”
This work was supported by the National Research Foundation (NRF) of Korea.
Publication:
Cha, N, et al. (2020) “Hello There! Is Now a Good Time to Talk?”: Opportune Moments for Proactive Interactions with Smart Speakers. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Vol. 4, No. 3, Article No. 74, pp. 1-28. Available online at https://doi.org/10.1145/3411810
Link to Introductory Video:
https://youtu.be/AA8CTi2hEf0
Profile:
Uichin Lee
Associate Professor
uclee@kaist.ac.kr
http://ic.kaist.ac.kr
Interactive Computing Lab.
School of Computing
https://www.kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea
(END)
2020.11.05 View 10216 -
Deep Learning Helps Explore the Structural and Strategic Bases of Autism
Psychiatrists typically diagnose autism spectrum disorders (ASD) by observing a person’s behavior and by leaning on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), widely considered the “bible” of mental health diagnosis.
However, there are substantial differences amongst individuals on the spectrum and a great deal remains unknown by science about the causes of autism, or even what autism is. As a result, an accurate diagnosis of ASD and a prognosis prediction for patients can be extremely difficult.
But what if artificial intelligence (AI) could help? Deep learning, a type of AI, deploys artificial neural networks based on the human brain to recognize patterns in a way that is akin to, and in some cases can surpass, human ability. The technique, or rather suite of techniques, has enjoyed remarkable success in recent years in fields as diverse as voice recognition, translation, autonomous vehicles, and drug discovery.
A group of researchers from KAIST in collaboration with the Yonsei University College of Medicine has applied these deep learning techniques to autism diagnosis. Their findings were published on August 14 in the journal IEEE Access.
Magnetic resonance imaging (MRI) scans of brains of people known to have autism have been used by researchers and clinicians to try to identify structures of the brain they believed were associated with ASD. These researchers have achieved considerable success in identifying abnormal grey and white matter volume and irregularities in cerebral cortex activation and connections as being associated with the condition.
These findings have subsequently been deployed in studies attempting more consistent diagnoses of patients than has been achieved via psychiatrist observations during counseling sessions. While such studies have reported high levels of diagnostic accuracy, the number of participants in these studies has been small, often under 50, and diagnostic performance drops markedly when applied to large sample sizes or on datasets that include people from a wide variety of populations and locations.
“There was something as to what defines autism that human researchers and clinicians must have been overlooking,” said Keun-Ah Cheon, one of the two corresponding authors and a professor in Department of Child and Adolescent Psychiatry at Severance Hospital of the Yonsei University College of Medicine.
“And humans poring over thousands of MRI scans won’t be able to pick up on what we’ve been missing,” she continued. “But we thought AI might be able to.”
So the team applied five different categories of deep learning models to an open-source dataset of more than 1,000 MRI scans from the Autism Brain Imaging Data Exchange (ABIDE) initiative, which has collected brain imaging data from laboratories around the world, and to a smaller, but higher-resolution MRI image dataset (84 images) taken from the Child Psychiatric Clinic at Severance Hospital, Yonsei University College of Medicine. In both cases, the researchers used both structural MRIs (examining the anatomy of the brain) and functional MRIs (examining brain activity in different regions).
The models allowed the team to explore the structural bases of ASD brain region by brain region, focusing in particular on many structures below the cerebral cortex, including the basal ganglia, which are involved in motor function (movement) as well as learning and memory.
Crucially, these specific types of deep learning models also offered up possible explanations of how the AI had come up with its rationale for these findings.
“Understanding the way that the AI has classified these brain structures and dynamics is extremely important,” said Sang Wan Lee, the other corresponding author and an associate professor at KAIST. “It’s no good if a doctor can tell a patient that the computer says they have autism, but not be able to say why the computer knows that.”
The deep learning models were also able to describe how much a particular aspect contributed to ASD, an analysis tool that can assist psychiatric physicians during the diagnosis process to identify the severity of the autism.
“Doctors should be able to use this to offer a personalized diagnosis for patients, including a prognosis of how the condition could develop,” Lee said.
“Artificial intelligence is not going to put psychiatrists out of a job,” he explained. “But using AI as a tool should enable doctors to better understand and diagnose complex disorders than they could do on their own.”
-ProfileProfessor Sang Wan LeeDepartment of Bio and Brain EngineeringLaboratory for Brain and Machine Intelligence https://aibrain.kaist.ac.kr/
KAIST
2020.09.23 View 9772