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Engineered C. glutamicum Strain Capable of Producing High-Level Glutaric Acid from Glucose
An engineered C. glutamicum strain that can produce the world’s highest titer of glutaric acid was developed by employing systems metabolic engineering strategies A metabolic engineering research group at KAIST has developed an engineered Corynebacterium glutamicum strain capable of producing high-level glutaric acid without byproducts from glucose. This new strategy will be useful for developing engineered micro-organisms for the bio-based production of value-added chemicals. Glutaric acid, also known as pentanedioic acid, is a carboxylic acid that is widely used for various applications including the production of polyesters, polyamides, polyurethanes, glutaric anhydride, 1,5-pentanediol, and 5-hydroxyvaleric acid. Glutaric acid has been produced using various petroleum-based chemical methods, relying on non-renewable and toxic starting materials. Thus, various approaches have been taken to biologically produce glutaric acid from renewable resources. Previously, the development of the first glutaric acid producing Escherichia coli by introducing Pseudomonas putida genes was reported by a research group from KAIST, but the titer was low. Glutaric acid production by metabolically engineered Corynebacterium glutamicum has also been reported in several studies, but further improvements in glutaric acid production seemed possible since C. glutamicum has the capability of producing more than 130 g/L of L-lysine. A research group comprised of Taehee Han, Gi Bae Kim, and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering addressed this issue. Their research paper “Glutaric acid production by systems metabolic engineering of an L-lysine-overproducing Corynebacterium glutamicum” was published online in PNAS on November 16, 2020. This research reports the development of a metabolically engineered C. glutamicum strain capable of efficiently producing glutaric acid, starting from an L-lysine overproducer. The following novel strategies and approaches to achieve high-level glutaric acid production were employed. First, metabolic pathways in C. glutamicum were reconstituted for glutaric acid production by introducing P. putida genes. Then, multi-omics analyses including genome, transcriptome, and fluxome were conducted to understand the phenotype of the L-lysine overproducer strain. In addition to systematic understanding of the host strain, gene manipulation targets were predicted by omics analyses and applied for engineering C. glutamicum, which resulted in the development of an engineered strain capable of efficiently producing glutaric acid. Furthermore, the new glutaric acid exporter was discovered for the first time, which was used to further increase glutaric acid production through enhancing product excretion. Last but not least, culture conditions were optimized for high-level glutaric acid production. As a result, the final engineered strain was able to produce 105.3 g/L glutaric acid, the highest titer ever reported, in 69 hours by fed-batch fermentation. Professor Sang Yup Lee said, “It is meaningful that we were able to develop a highly efficient glutaric acid producer capable of producing glutaric acid at the world’s highest titer without any byproducts from renewable carbon sources. This will further accelerate the bio-based production of valuable chemicals in pharmaceutical/medical/chemical industries.” This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation and funded by the Ministry of Science and ICT. -Profile Distinguished Professor Sang Yup Lee firstname.lastname@example.org http://mbel.kaist.ac.kr Department of Chemical and Biomolecular Engineering KAIST
Chairman Soo-Young Lee Named Among the Heroes of Philanthropy in Asia
Chairman Soo-Young Lee from the KAIST Development Foundation was named one of 15 philanthropists who made the biggest donations in the Asia-Pacific region by Forbes Asia on November 11. The annual Heroes of Philanthropy list features the 15 the most generous individual philanthropists who are donating from their personal fortunes, not through companies. This year, the biggest philanthropies donated to make a difference in wide arrays of sectors such as Covid-19 relief to education and the arts. Chairman Lee donated totaling 68 billion KRW to KAIST in July. Her donation marked the largest donation KAIST has ever received. She is one of two Korean philanthropists that Forbes selected. Honorary Chairman of GS Caltex Dong-Soo Huh also made the list. Her donation will establish the Soo-Young Lee Science Education Foundation to support ‘the Singularity Professor program’ that KAIST is launching. She expressed confidence that her donation will fund KAIST researchers to make breakthroughs that will lead to a Nobel Prize. “Without the advancement of science and technology, Korea cannot be one of the top countries in the world. I believe KAIST can make it with our all supports,” she frequently said when asked why she selected KAIST for her donation. Chairman Lee previously made generous donations in 2012 and 2016 and said she plans to make another gift to KAIST in the very near future.
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 email@example.com 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)
Professor Kyu-Young Whang Donates Toward the 50th Anniversary Memorial Building
Distinguished Professor Kyu-Young Whang from the School of Computing made a gift of 100 million KRW toward the construction of the 50th Anniversary Memorial Building during a ceremony on November 3 at the Daejeon campus. "As a member of the first class of KAIST, I feel very delighted to play a part in the fundraising campaign for the 50th anniversary celebration. This is also a token of appreciation to my alma mater and I look forward to alumni and the KAIST community joining this campaign," said Professor Emeritus Whang. KAIST will name the Kyu-Young Whang and Jonghae Song Christian Seminar Room at the 50th Anniversary Memorial Building. The ground will be broken in 2022 for construction of the building.
'Mini-Lungs' Reveal Early Stages of SARS-CoV-2 Infection
Researchers in Korea and the UK have successfully grown miniature models of critical lung structures called alveoli, and used them to study how the coronavirus that causes COVID-19 infects the lungs. To date, there have been more than 40 million cases of COVID-19 and almost 1.13 million deaths worldwide. The main target tissues of SARS-CoV-2, the virus that causes COVID-19, especially in patients that develop pneumonia, appear to be alveoli – tiny air sacs in the lungs that take up the oxygen we breathe and exchange it with carbon dioxide to exhale. To better understand how SARS-CoV-2 infects the lungs and causes disease, a team of Professor Young Seok Ju from the Graduate School of Medical Science and Engineering at KAIST in collaboration with the Wellcome-MRC Cambridge Stem Cell Institute at the University of Cambridge turned to organoids – ‘mini-organs’ grown in three dimensions to mimic the behaviour of tissue and organs. The team used tissue donated to tissue banks at the Royal Papworth Hospital NHS Foundation Trust and Addenbrooke’s Hospital, Cambridge University NHS Foundations Trust, UK, and Seoul National University Hospital to extract a type of lung cell known as human lung alveolar type 2 cells. By reprogramming these cells back to their earlier ‘stem cell’ stage, they were able to grow self-organizing alveolar-like 3D structures that mimic the behaviour of key lung tissue. “The research community now has a powerful new platform to study precisely how the virus infects the lungs, as well as explore possible treatments,” said Professor Ju, co-senior author of the research. Dr. Joo-Hyeon Lee, another co-senior author at the Wellcome-MRC Cambridge Stem Cell Institute, said: “We still know surprisingly little about how SARS-CoV-2 infects the lungs and causes disease. Our approach has allowed us to grow 3D models of key lung tissue – in a sense, ‘mini-lungs’ – in the lab and study what happens when they become infected.” The team infected the organoids with a strain of SARS-CoV-2 taken from a patient in Korea who was diagnosed with COVID-19 on January 26 after traveling to Wuhan, China. Using a combination of fluorescence imaging and single cell genetic analysis, they were able to study how the cells responded to the virus. When the 3D models were exposed to SARS-CoV-2, the virus began to replicate rapidly, reaching full cellular infection just six hours after infection. Replication enables the virus to spread throughout the body, infecting other cells and tissue. Around the same time, the cells began to produce interferons – proteins that act as warning signals to neighbouring cells, telling them to activate their antiviral defences. After 48 hours, the interferons triggered the innate immune response – its first line of defence – and the cells started fighting back against infection. Sixty hours after infection, a subset of alveolar cells began to disintegrate, leading to cell death and damage to the lung tissue. Although the researchers observed changes to the lung cells within three days of infection, clinical symptoms of COVID-19 rarely occur so quickly and can sometimes take more than ten days after exposure to appear. The team say there are several possible reasons for this. It may take several days from the virus first infiltrating the upper respiratory tract to it reaching the alveoli. It may also require a substantial proportion of alveolar cells to be infected or for further interactions with immune cells resulting in inflammation before a patient displays symptoms. “Based on our model we can tackle many unanswered key questions, such as understanding genetic susceptibility to SARS-CoV-2, assessing relative infectivity of viral mutants, and revealing the damage processes of the virus in human alveolar cells,” said Professor Ju. “Most importantly, it provides the opportunity to develop and screen potential therapeutic agents against SARS-CoV-2 infection.” “We hope to use our technique to grow these 3D models from cells of patients who are particularly vulnerable to infection, such as the elderly or people with diseased lungs, and find out what happens to their tissue,” added Dr. Lee. The research was a collaboration involving scientists from KAIST, the University of Cambridge, Korea National Institute of Health, Institute for Basic Science (IBS), Seoul National University Hospital and Genome Insight in Korea. - ProfileProfessor Young Seok JuLaboratory of Cancer Genomics https://julab.kaist.ac.kr the Graduate School of Medical Science and EngineeringKAIST
Taesik Gong Named Google PhD Fellow
PhD candidate Taesik Gong from the School of Computing was named a 2020 Google PhD Fellow in the field of machine learning. The Google PhD Fellowship Program has recognized and supported outstanding graduate students in computer science and related fields since 2009. Gong is one of two Korean students chosen as the recipients of Google Fellowships this year. A total of 53 students across the world in 12 fields were awarded this fellowship. Gong’s research on condition-independent mobile sensing powered by machine learning earned him this year’s fellowship. He has published and presented his work through many conferences including ACM SenSys and ACM UbiComp, and has worked at Microsoft Research Asia and Nokia Bell Labs as a research intern. Gong was also the winner of the NAVER PhD Fellowship Award in 2018. (END)
Scientist of October: Professor Jungwon Kim
Professor Jungwon Kim from the Department of Mechanical Engineering was selected as the ‘Scientist of the Month’ for October 2020 by the Ministry of Science and ICT and the National Research Foundation of Korea. Professor Kim was recognized for his contributions to expanding the horizons of the basics of precision engineering through his research on multifunctional ultrahigh-speed, high-resolution sensors. He received 10 million KRW in prize money. Professor Kim was selected as the recipient of this award in celebration of “Measurement Day”, which commemorates October 26 as the day in which King Sejong the Great established a volume measurement system. Professor Kim discovered that the time difference between the pulse of light created by a laser and the pulse of the current produced by a light-emitting diode was as small as 100 attoseconds (10-16 seconds). He then developed a unique multifunctional ultrahigh-speed, high-resolution Time-of-Flight (TOF) sensor that could take measurements of multiple points at the same time by sampling electric light. The sensor, with a measurement speed of 100 megahertz (100 million vibrations per second), a resolution of 180 picometers (1/5.5 billion meters), and a dynamic range of 150 decibels, overcame the limitations of both existing TOF techniques and laser interferometric techniques at the same time. The results of this research were published in Nature Photonics on February 10, 2020. Professor Kim said, “I’d like to thank the graduate students who worked passionately with me, and KAIST for providing an environment in which I could fully focus on research. I am looking forward to the new and diverse applications in the field of machine manufacturing, such as studying the dynamic phenomena in microdevices, or taking ultraprecision measurement of shapes for advanced manufacturing.” (END)
Professor Won-Ki Cho Selected as the 2020 SUHF Young Investigator
Professor Won-Ki Cho from the Department of Biological Sciences was named one of three recipients of the 2020 Suh Kyung-Bae Science Foundation (SUHF) Young Investigator Award. The SUHF is a non-profit organization established in 2016 and funded by a personal donation of 300 billion KRW in shares from Chairman and CEO Kyung-Bae Suh of the Amorepacific Group. The primary purpose of the foundation is to serve as a platform to nurture and provide comprehensive long-term support for creative and passionate young Korean scientists committed to pursuing research in the field of life sciences. The SUHF selects three to five scientists through an open recruiting process every year and grants each scientist a maximum of 2.5 billion KRW over a period of up to five years. Since January this year, the foundation received 67 research proposals from scientists across the nation, especially from those who had less than five years of experience as professors, and selected the three recipients. Professor Cho proposed research on how to observe the interactions between nuclear structures and constantly-changing chromatin monomers in four dimensions through ultra-high-resolution imaging of single living cells. This proposal was recognized as one that could help us better understand the process of transcription regulation, which remains a long-standing question in biology. The other awards were given to Professor Soung-hun Roh of Seoul National University and Professor Joo-Hyeon Lee of the University of Cambridge. With these three new awardees, a total of 17 scientists have been named SUHF Young Investigators to date, and the funding to support these scientists now totals 42.5 billion KRW. Professor Inkyung Jung and Professor Ki-Jun Yoon from the Department of Biological Sciences, and Professor Young Seok Ju and Professor Jeong Ho Lee from the Graduate School of Medical Science and Engineering are the four previous winners from KAIST in the years 2017 through 2019. (END)
Fundraising for the 50th Anniversary Memorial Building Kicks Off
KAIST started the fundraising campaign to construct the 50th Anniversary Memorial Building. This is one of the projects and events the 50th Anniversary Commemorative Committee established to celebrate the anniversary. The ground will be broken in 2022 after raising approximately 50 billion KRW through 2021. The five-story building will be the latest addition to the KAIST campus. To highlight the campus’s history, the new building will connect the N5 (Basic Experiment & Research) and N2 (Administration Branch) buildings, the first buildings on the main Daejeon campus after its main campus moved from Seoul in 1987. Currently, the College of Business remains at the Seoul campus. The 50th Anniversary Memorial Building will connect the two buildings in the shape of C, and represent KAIST’s C3 core value of Challenging, Creating, and Caring. The concept of this building was designed by Professor Sang-Min Bae from the Department of Industrial Design. The 50th Anniversary Commemorative Committee said the Memorial Building will reflect the spirit of its core values. The first floor will accommodate the auditorium and exhibition hall, showcasing the latest achievements in KAIST innovation and convergence research as well as alumni startups and companies. The second floor will be an education space for entrepreneurship and video studios. An area for delivering creative education platforms such as Education 4.0 will be prepared on the third floor. The fourth floor will be used for global leadership education. The fifth floor will house the KAIST Club, a lounge for alumni and the Global Strategy Institute. Co-Chair of the Fundraising & PR Sub-Committee of the KAIST 50th Anniversary Commemorative Committee and Former Vice President for Planning and Budget Seung-Bin Park and current Vice President for Planning and Budget Suchan Chae reiterated the importance of extending the infrastructure of the campus, saying that investments in the infrastructure will expand the university’s future growth potential. In a letter to kick off the fundraising efforts last month, they called for support from the entire KAIST community to help construct the new memorial building that will produce global talents and help young scientists make their dreams come true. To donate, click here
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
Biomarker Predicts Who Will Have Severe COVID-19
- Airway cell analyses showing an activated immune axis could pinpoint the COVID-19 patients who will most benefit from targeted therapies.- KAIST researchers have identified key markers that could help pinpoint patients who are bound to get a severe reaction to COVID-19 infection. This would help doctors provide the right treatments at the right time, potentially saving lives. The findings were published in the journal Frontiers in Immunology on August 28. People’s immune systems react differently to infection with SARS-CoV-2, the virus that causes COVID-19, ranging from mild to severe, life-threatening responses. To understand the differences in responses, Professor Heung Kyu Lee and PhD candidate Jang Hyun Park from the Graduate School of Medical Science and Engineering at KAIST analysed ribonucleic acid (RNA) sequencing data extracted from individual airway cells of healthy controls and of mildly and severely ill patients with COVID-19. The data was available in a public database previously published by a group of Chinese researchers. “Our analyses identified an association between immune cells called neutrophils and special cell receptors that bind to the steroid hormone glucocorticoid,” Professor Lee explained. “This finding could be used as a biomarker for predicting disease severity in patients and thus selecting a targeted therapy that can help treat them at an appropriate time,” he added. Severe illness in COVID-19 is associated with an exaggerated immune response that leads to excessive airway-damaging inflammation. This condition, known as acute respiratory distress syndrome (ARDS), accounts for 70% of deaths in fatal COVID-19 infections. Scientists already know that this excessive inflammation involves heightened neutrophil recruitment to the airways, but the detailed mechanisms of this reaction are still unclear. Lee and Park’s analyses found that a group of immune cells called myeloid cells produced excess amounts of neutrophil-recruiting chemicals in severely ill patients, including a cytokine called tumour necrosis factor (TNF) and a chemokine called CXCL8. Further RNA analyses of neutrophils in severely ill patients showed they were less able to recruit very important T cells needed for attacking the virus. At the same time, the neutrophils produced too many extracellular molecules that normally trap pathogens, but damage airway cells when produced in excess. The researchers additionally found that the airway cells in severely ill patients were not expressing enough glucocorticoid receptors. This was correlated with increased CXCL8 expression and neutrophil recruitment. Glucocorticoids, like the well-known drug dexamethasone, are anti-inflammatory agents that could play a role in treating COVID-19. However, using them in early or mild forms of the infection could suppress the necessary immune reactions to combat the virus. But if airway damage has already happened in more severe cases, glucocorticoid treatment would be ineffective. Knowing who to give this treatment to and when is really important. COVID-19 patients showing reduced glucocorticoid receptor expression, increased CXCL8 expression, and excess neutrophil recruitment to the airways could benefit from treatment with glucocorticoids to prevent airway damage. Further research is needed, however, to confirm the relationship between glucocorticoids and neutrophil inflammation at the protein level. “Our study could serve as a springboard towards more accurate and reliable COVID-19 treatments,” Professor Lee said. This work was supported by the National Research Foundation of Korea, and Mobile Clinic Module Project funded by KAIST. Figure. Low glucocorticoid receptor (GR) expression led to excessive inflammation and lung damage by neutrophils through enhancing the expression of CXCL8 and other cytokines. Image credit: Professor Heung Kyu Lee, KAIST. Created with Biorender.com. Image usage restrictions: News organizations may use or redistribute these figures and image, with proper attribution, as part of news coverage of this paper only. -Publication: Jang Hyun Park, and Heung Kyu Lee. (2020). Re-analysis of Single Cell Transcriptome Reveals That the NR3C1-CXCL8-Neutrophil Axis Determines the Severity of COVID-19. Frontiers in Immunology, Available online at https://doi.org/10.3389/fimmu.2020.02145 -Profile: Heung Kyu Lee Associate Professor firstname.lastname@example.org https://www.heungkyulee.kaist.ac.kr/ Laboratory of Host Defenses Graduate School of Medical Science and Engineering (GSMSE) The Center for Epidemic Preparedness at KAIST Institute http://kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea Profile: Jang Hyun Park PhD Candidate email@example.com GSMSE, KAIST
Sturdy Fabric-Based Piezoelectric Energy Harvester Takes Us One Step Closer to Wearable Electronics
KAIST researchers presented a highly flexible but sturdy wearable piezoelectric harvester using the simple and easy fabrication process of hot pressing and tape casting. This energy harvester, which has record high interfacial adhesion strength, will take us one step closer to being able to manufacture embedded wearable electronics. A research team led by Professor Seungbum Hong said that the novelty of this result lies in its simplicity, applicability, durability, and its new characterization of wearable electronic devices. Wearable devices are increasingly being used in a wide array of applications from small electronics to embedded devices such as sensors, actuators, displays, and energy harvesters. Despite their many advantages, high costs and complex fabrication processes remained challenges for reaching commercialization. In addition, their durability was frequently questioned. To address these issues, Professor Hong’s team developed a new fabrication process and analysis technology for testing the mechanical properties of affordable wearable devices. For this process, the research team used a hot pressing and tape casting procedure to connect the fabric structures of polyester and a polymer film. Hot pressing has usually been used when making batteries and fuel cells due to its high adhesiveness. Above all, the process takes only two to three minutes. The newly developed fabrication process will enable the direct application of a device into general garments using hot pressing just as graphic patches can be attached to garments using a heat press. In particular, when the polymer film is hot pressed onto a fabric below its crystallization temperature, it transforms into an amorphous state. In this state, it compactly attaches to the concave surface of the fabric and infiltrates into the gaps between the transverse wefts and longitudinal warps. These features result in high interfacial adhesion strength. For this reason, hot pressing has the potential to reduce the cost of fabrication through the direct application of fabric-based wearable devices to common garments. In addition to the conventional durability test of bending cycles, the newly introduced surface and interfacial cutting analysis system proved the high mechanical durability of the fabric-based wearable device by measuring the high interfacial adhesion strength between the fabric and the polymer film. Professor Hong said the study lays a new foundation for the manufacturing process and analysis of wearable devices using fabrics and polymers. He added that his team first used the surface and interfacial cutting analysis system (SAICAS) in the field of wearable electronics to test the mechanical properties of polymer-based wearable devices. Their surface and interfacial cutting analysis system is more precise than conventional methods (peel test, tape test, and microstretch test) because it qualitatively and quantitatively measures the adhesion strength. Professor Hong explained, “This study could enable the commercialization of highly durable wearable devices based on the analysis of their interfacial adhesion strength. Our study lays a new foundation for the manufacturing process and analysis of other devices using fabrics and polymers. We look forward to fabric-based wearable electronics hitting the market very soon.” The results of this study were registered as a domestic patent in Korea last year, and published in Nano Energy this month. This study has been conducted through collaboration with Professor Yong Min Lee in the Department of Energy Science and Engineering at DGIST, Professor Kwangsoo No in the Department of Materials Science and Engineering at KAIST, and Professor Seunghwa Ryu in the Department of Mechanical Engineering at KAIST. This study was supported by the High-Risk High-Return Project and the Global Singularity Research Project at KAIST, the National Research Foundation, and the Ministry of Science and ICT in Korea. -Publication: Jaegyu Kim, Seoungwoo Byun, Sangryun Lee, Jeongjae Ryu, Seongwoo Cho, Chungik Oh, Hongjun Kim, Kwangsoo No, Seunghwa Ryu, Yong Min Lee, Seungbum Hong*, Nano Energy 75 (2020), 104992. https://doi.org/10.1016/j.nanoen.2020.104992 -Profile: Professor Seungbum Hong firstname.lastname@example.org http://mii.kaist.ac.kr/ Department of Materials Science and Engineering KAIST
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