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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.”
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2020.10.15 View 9451 -
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.
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2020.10.15 View 10986 -
Big Ideas on Emerging Materials Explored at EMS
Renowned scholars and editors from academic journals joined the Emerging Materials e-Symposium (EMS) held at KAIST and shared the latest breakthroughs and big ideas in new material development last month. This e-symposium was organized by Professor Il-Doo Kim from the KAIST Department of Materials Sciences and Engineering over five days from September 21 through 25 via Zoom and YouTube. Professor Kim also serves as an associate editor of ACS Nano.
Esteemed scholars and editors of academic journals including ACS Nano, Nano Energy, and Energy Storage Materials made Zoom presentations in three main categories: 1) nanostructures for next-generation applications, 2) chemistry and biotechnology for applications in the fields of environment and industry, and 3) material innovation for technological applications.
During Session I, speakers including Professor John A. Rogers of Northwestern University and Professor Zhenan Bao of Stanford University led the session on Emerging Soft Electronics and 3D printing.
In later sessions, other globally recognized scholars gave talks titled Advanced Nanostructuring for Emerging Materials, Frontiers in Emerging Materials Research, Advanced Energy Materials and Functional Nanomaterials, and Latest Advances in Nanomaterials Research.
These included 2010 Nobel Prize laureate and professor at Manchester University Andre Geim, editor-in-chief of ACS Nano and professor at UCLA Paul S. Weiss, Professor Paul Alivisatos of UC Berkeley, Professor William Chueh of Stanford University, and Professor Mircea Dinca of MIT.
KAIST President Sung-Chul Shin, who is also a materials physicist, said in his opening address, “Innovation in materials science will become an important driving force to change our way of life. All the breakthroughs in materials have extended a new paradigm that has transformed our lives.”
“Creative research projects alongside global collaborators like all of you will allow the breakthroughs that will deliver us from these crises,” he added.
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2020.10.06 View 12322 -
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 9780 -
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
seungbum@kaist.ac.kr
http://mii.kaist.ac.kr/
Department of Materials Science and Engineering
KAIST
2020.09.17 View 11886 -
Life After COVID-19: Big Questions on Medical and Bio-Engineering
KAIST GSI forum explores big questions in the medical and bio-engineering revolution caused by the COVID-19 in fight against infectious diseases and life quality
On September 9, the Global Strategy Institute at KAIST will delve into innovative future strategies for the medical and bio-engineering sectors that have been disrupted by COVID-19. The forum will live stream via YouTube, KTV, and Naver TV from 9:00 am Korean time.
The online forum features a speaker lineup of world-renowned scholars who will discuss an array of bio-engineering technologies that will improve our quality of life and even extend our life span. This is the GSI’s third online forum since the first one in April that covered the socio-economic implications of the global pandemic and the second one in June focusing on the education sector.
In hosting the third round of the GSI Forum series, KAIST President Sung-Chul Shin stressed the power of science and technology saying, “In this world full of uncertainties, one thing for sure is that only the advancement of science and technology will deliver us from this crisis.” Korean Prime Minister Sye-Kyun Chung will also deliver a speech explaining the government’s response to COVID-19 and vaccine development strategies.
The President of the National Academy of Medicine in the US will share ideal policies to back up the bio-engineering and medical sectors and Futurist Thomas Frey from the Davinci Institute will present his distinct perspectives on our future lives after COVID-19. His thought-provoking insights on advancements in the bioengineering sector will examine whether humanity can put an end to infectious diseases and find new ways to lengthen our lives.
Two distinguished professors in the field of genetic engineering technology will share their latest breakthroughs. Professor George McDonald Church from Harvard Medical School who developed genome sequencing will deliver a keynote speech on how the advancement of gene editing and genome technology will overcome diseases and contribute to extending human life spans.
Professor Kwang-Soo Kim, a KAIST alumnus from Harvard Medical School who recently reported new discoveries for Parkinson’s disease treatment by reprogramming a patient’s own skin cells to replace cells in the brain, will introduce the latest clinical cell treatment technologies based on personalized therapeutics.
Senior Vice President and Chief Product Officer of Illumina Susan Tousi, a leading genome sequencing solution provider, will describe genome analysis technology and explore the potential for disease prevention.
KAIST medical scientist Jeong Ho Lee, who was the first to identify the causes of intractable epilepsies and has identified the genes responsible for several developmental brain disorders. Professor Jin-Hyung Lee from Stanford University and Dr. David B. Resnik from the National Institute of Environmental Health Science will also join the speaker lineup to discuss genetics-based personalized solutions to extend human life spans.
The forum will also invite about 50 young scientists and medical researchers from around the world to participate in an online panel session. They will engage in a Q&A session and a discussion with the speakers.
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2020.09.04 View 8780 -
Professor Jaehyouk Choi, IT Young Engineer of the Year
Professor Jaehyouk Choi from the KAIST School of Electrical Engineering won the ‘IT Young Engineer Award’ for 2020. The award was co-presented by the Institute of Electrical and Electronics Engineers (IEEE) and the Institute of Electronics Engineers of Korea (IEIE), and sponsored by the Haedong Science and Culture Foundation.
The ‘IT Young Engineer Award’ selects only one mid-career scientist or engineer 40 years old or younger every year, who has made a great contribution to academic or technological advancements in the field of IT.
Professor Choi’s research topics include high-performance semiconductor circuit design for ultrahigh-speed communication systems including 5G communication. In particular, he is widely known for his field of the ‘ultra-low-noise, high-frequency signal generation circuit,’ key technology for next-generation wired and wireless communications, as well as for memory systems. He has published 64 papers in SCI journals and at international conferences, and applied for and registered 25 domestic and international patents.
Professor Choi is also an active member of the Technical Program Committee of international symposiums in the field of semiconductor circuits including the International Solid-State Circuits Conference (ISSCC) and the European Solid-State Circuit Conference (ESSCIRC). Beginning this year, he also serves as a distinguished lecturer at the IEEE Solid-State Circuit Society (SSCS).
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2020.08.20 View 9459 -
Deep Learning-Based Cough Recognition Model Helps Detect the Location of Coughing Sounds in Real Time
The Center for Noise and Vibration Control at KAIST announced that their coughing detection camera recognizes where coughing happens, visualizing the locations. The resulting cough recognition camera can track and record information about the person who coughed, their location, and the number of coughs on a real-time basis.
Professor Yong-Hwa Park from the Department of Mechanical Engineering developed a deep learning-based cough recognition model to classify a coughing sound in real time. The coughing event classification model is combined with a sound camera that visualizes their locations in public places. The research team said they achieved a best test accuracy of 87.4 %.
Professor Park said that it will be useful medical equipment during epidemics in public places such as schools, offices, and restaurants, and to constantly monitor patients’ conditions in a hospital room.
Fever and coughing are the most relevant respiratory disease symptoms, among which fever can be recognized remotely using thermal cameras. This new technology is expected to be very helpful for detecting epidemic transmissions in a non-contact way. The cough event classification model is combined with a sound camera that visualizes the cough event and indicates the location in the video image.
To develop a cough recognition model, a supervised learning was conducted with a convolutional neural network (CNN). The model performs binary classification with an input of a one-second sound profile feature, generating output to be either a cough event or something else.
In the training and evaluation, various datasets were collected from Audioset, DEMAND, ETSI, and TIMIT. Coughing and others sounds were extracted from Audioset, and the rest of the datasets were used as background noises for data augmentation so that this model could be generalized for various background noises in public places.
The dataset was augmented by mixing coughing sounds and other sounds from Audioset and background noises with the ratio of 0.15 to 0.75, then the overall volume was adjusted to 0.25 to 1.0 times to generalize the model for various distances.
The training and evaluation datasets were constructed by dividing the augmented dataset by 9:1, and the test dataset was recorded separately in a real office environment.
In the optimization procedure of the network model, training was conducted with various combinations of five acoustic features including spectrogram, Mel-scaled spectrogram and Mel-frequency cepstrum coefficients with seven optimizers. The performance of each combination was compared with the test dataset. The best test accuracy of 87.4% was achieved with Mel-scaled Spectrogram as the acoustic feature and ASGD as the optimizer.
The trained cough recognition model was combined with a sound camera. The sound camera is composed of a microphone array and a camera module. A beamforming process is applied to a collected set of acoustic data to find out the direction of incoming sound source. The integrated cough recognition model determines whether the sound is cough or not. If it is, the location of cough is visualized as a contour image with a ‘cough’ label at the location of the coughing sound source in a video image.
A pilot test of the cough recognition camera in an office environment shows that it successfully distinguishes cough events and other events even in a noisy environment. In addition, it can track the location of the person who coughed and count the number of coughs in real time. The performance will be improved further with additional training data obtained from other real environments such as hospitals and classrooms.
Professor Park said, “In a pandemic situation like we are experiencing with COVID-19, a cough detection camera can contribute to the prevention and early detection of epidemics in public places. Especially when applied to a hospital room, the patient's condition can be tracked 24 hours a day and support more accurate diagnoses while reducing the effort of the medical staff."
This study was conducted in collaboration with SM Instruments Inc.
Profile: Yong-Hwa Park, Ph.D.
Associate Professor
yhpark@kaist.ac.kr
http://human.kaist.ac.kr/
Human-Machine Interaction Laboratory (HuMaN Lab.)
Department of Mechanical Engineering (ME)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr/en/
Daejeon 34141, Korea
Profile: Gyeong Tae Lee
PhD Candidate
hansaram@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Seong Hu Kim
PhD Candidate
tjdgnkim@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Hyeonuk Nam
PhD Candidate
frednam@kaist.ac.kr
HuMaN Lab., ME, KAIST
Profile: Young-Key Kim
CEO
sales@smins.co.kr
http://en.smins.co.kr/
SM Instruments Inc.
Daejeon 34109, Korea
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2020.08.13 View 13750 -
Sulfur-Containing Polymer Generates High Refractive Index and Transparency
Transparent polymer thin film with refractive index exceeding 1.9 to serve as new platform materials for high-end optical device applications
Researchers reported a novel technology enhancing the high transparency of refractive polymer film via a one-step vapor deposition process. The sulfur-containing polymer (SCP) film produced by Professor Sung Gap Im’s research team at KAIST’s Department of Chemical and Biomolecular Engineering has exhibited excellent environmental stability and chemical resistance, which is highly desirable for its application in long-term optical device applications. The high refractive index exceeding 1.9 while being fully transparent in the entire visible range will help expand the applications of optoelectronic devices.
The refractive index is a ratio of the speed of light in a vacuum to the phase velocity of light in a material, used as a measure of how much the path of light is bent when passing through a material. With the miniaturization of various optical parts used in mobile devices and imaging, demand has been rapidly growing for high refractive index transparent materials that induce more light refraction with a thin film.
As polymers have outstanding physical properties and can be easily processed in various forms, they are widely used in a variety of applications such as plastic eyeglass lenses. However, there have been very few polymers developed so far with a refractive index exceeding 1.75, and existing high refractive index polymers require costly materials and complicated manufacturing processes.
Above all, core technologies for producing such materials have been dominated by Japanese companies, causing long-standing challenges for Korean manufacturers. Securing a stable supply of high-performance, high refractive index materials is crucial for the production of optical devices that are lighter, more affordable, and can be freely manipulated.
The research team successfully manufactured a whole new polymer thin film material with a refractive index exceeding 1.9 and excellent transparency, using just a one-step chemical reaction. The SCP film showed outstanding optical transparency across the entire visible light region, presumably due to the uniformly dispersed, short-segment polysulfide chains, which is a distinct feature unachievable in polymerizations with molten sulfur.
The team focused on the fact that elemental sulfur is easily sublimated to produce a high refractive index polymer by polymerizing the vaporized sulfur with a variety of substances. This method suppresses the formation of overly long S-S chains while achieving outstanding thermal stability in high sulfur concentrations and generating transparent non-crystalline polymers across the entire visible spectrum.
Due to the characteristics of the vapor phase process, the high refractive index thin film can be coated not just on silicon wafers or glass substrates, but on a wide range of textured surfaces as well. We believe this thin film polymer is the first to have achieved an ultrahigh refractive index exceeding 1.9.
Professor Im said, “This high-performance polymer film can be created in a simple one-step manner, which is highly advantageous in the synthesis of SCPs with a high refractive index. This will serve as a platform material for future high-end optical device applications.”
This study, in collaboration with research teams from Seoul National University and Kyung Hee University, was reported in Science Advances. (Title: One-Step Vapor-Phase Synthesis of Transparent High-Refractive Index Sulfur-Containing Polymers）
This research was supported by the Ministry of Science and ICT’s Global Frontier Project (Center for Advanced Soft-Electronics), Leading Research Center Support Program (Wearable Platform Materials Technology Center), and Basic Science Research Program (Advanced Research Project).
2020.08.04 View 8141 -
Atomic Force Microscopy Reveals Nanoscale Dental Erosion from Beverages
KAIST researchers used atomic force microscopy to quantitatively evaluate how acidic and sugary drinks affect human tooth enamel at the nanoscale level. This novel approach is useful for measuring mechanical and morphological changes that occur over time during enamel erosion induced by beverages.
Enamel is the hard-white substance that forms the outer part of a tooth. It is the hardest substance in the human body, even stronger than bone. Its resilient surface is 96 percent mineral, the highest percentage of any body tissue, making it durable and damage-resistant. The enamel acts as a barrier to protect the soft inner layers of the tooth, but can become susceptible to degradation by acids and sugars.
Enamel erosion occurs when the tooth enamel is overexposed to excessive consumption of acidic and sugary food and drinks. The loss of enamel, if left untreated, can lead to various tooth conditions including stains, fractures, sensitivity, and translucence. Once tooth enamel is damaged, it cannot be brought back. Therefore, thorough studies on how enamel erosion starts and develops, especially at the initial stages, are of high scientific and clinical relevance for dental health maintenance.
A research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering at KAIST reported a new method of applying atomic force microscopy (AFM) techniques to study the nanoscale characterization of this early stage of enamel erosion. This study was introduced in the Journal of the Mechanical Behavior of Biomedical Materials (JMBBM) on June 29.
AFM is a very-high-resolution type of scanning probe microscopy (SPM), with demonstrated resolution on the order of fractions of a nanometer (nm) that is equal to one billionth of a meter. AFM generates images by scanning a small cantilever over the surface of a sample, and this can precisely measure the structure and mechanical properties of the sample, such as surface roughness and elastic modulus.
The co-lead authors of the study, Dr. Panpan Li and Dr. Chungik Oh, chose three commercially available popular beverages, Coca-Cola®, Sprite®, and Minute Maid® orange juice, and immersed tooth enamel in these drinks over time to analyze their impacts on human teeth and monitor the etching process on tooth enamel.
Five healthy human molars were obtained from volunteers between age 20 and 35 who visited the KAIST Clinic. After extraction, the teeth were preserved in distilled water before the experiment. The drinks were purchased and opened right before the immersion experiment, and the team utilized AFM to measure the surface topography and elastic modulus map.
The researchers observed that the surface roughness of the tooth enamel increased significantly as the immersion time increased, while the elastic modulus of the enamel surface decreased drastically. It was demonstrated that the enamel surface roughened five times more when it was immersed in beverages for 10 minutes, and that the elastic modulus of tooth enamel was five times lower after five minutes in the drinks.
Additionally, the research team found preferential etching in scratched tooth enamel. Brushing your teeth too hard and toothpastes with polishing particles that are advertised to remove dental biofilms can cause scratches on the enamel surface, which can be preferential sites for etching, the study revealed.
Professor Hong said, “Our study shows that AFM is a suitable technique to characterize variations in the morphology and mechanical properties of dental erosion quantitatively at the nanoscale level.”
This work was supported by the National Research Foundation (NRF), the Ministry of Science and ICT (MSIT), and the KUSTAR-KAIST Institute of Korea.
A dentist at the KAIST Clinic, Dr. Suebean Cho, Dr. Sangmin Shin from the Smile Well Dental, and Professor Kack-Kyun Kim at the Seoul National University School of Dentistry also collaborated in this project.
Publication:
Li, P., et al. (2020) ‘Nanoscale effects of beverages on enamel surface of human teeth: An atomic force microscopy study’. Journal of the Mechanical Behavior of Biomedical Materials (JMBBM), Volume 110. Article No. 103930. Available online at https://doi.org/10.1016/j.jmbbm.2020.103930
Profile: Seungbum Hong, Ph.D.
Associate Professor
seungbum@kaist.ac.kr
http://mii.kaist.ac.kr/
Materials Imaging and Integration (MII) Lab.
Department of Materials Science and Engineering (MSE)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr
Daejeon 34141, Korea
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2020.07.21 View 10639 -
COVID-19 Update: Fall Semester to Continue Offering Classes Online
KAIST announced that the university would continue online classes through the fall semester. However, the university will conduct additional in-person classes for upper-level undergraduate lab classes and some graduate courses where on-site interaction was deemed to be highly necessary. Some 600-level graduate courses at the Daejeon campus and graduate courses at the Seoul campus will carry out both in-person and online classes. The fall semester will start from August 31.
Provost and Executive Vice President Kwang Hyung Lee announced the fall semester plan in his letter to the entire student body on July 9. He said that the university decided to continue with online classes in consideration of the safety of KAIST community members and the current status of the COVID-19 spread. However, he said the new plan will help students choose class options between in-person and online classes.
“Although the number of classes with two versions is limited, we believe this will help many students continue learning without the sustained face-to-face contact that is inherent in residential education,” Provost Lee said.
In-person classes conducted in the fall semester will also be provided online for students who are not available for in-person classes. Students may choose the type of the classes they prefer according to their situation, among only the courses that will offer two versions. Professors will decide if they will conduct two versions of their classes. The Office of Academic Affairs is collecting the professors’ applications for conducting both versions until July 24.
KAIST offered real-time online classes and pre-recorded KLMS (KAIST Learning Management System) classes during the spring semester with a very limited number of in-person lab classes for graduate courses and these two versions of online class will continue for fall semester.
Provost Lee asked the students who will take the in-person classes to strictly observe all precaution measures as the university will do its best to abide by the government guidelines against the Covid-19 in preparation for the fall semester.
“We will continue to make appropriate and safe accommodations for them,” said Provost Lee.
Those who need to reside in on-campus dormitories are required to be approved for moving. The applications will open after all the in-person class schedules are fixed next month. However, students who were approved for staying in the dormitories last semester can move in without additional approval procedures for the fall semester.
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2020.07.10 View 7138 -
X-ray Scattering Shines Light on Protein Folding
- Multiple forms of a non-functional, unfolded protein follow different pathways and timelines to reach its folded, functional state, a study reveals. -
KAIST researchers have used an X-ray method to track how proteins fold, which could improve computer simulations of this process, with implications for understanding diseases and improving drug discovery. Their findings were reported in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on June 30.
When proteins are translated from their DNA codes, they quickly transform from a non-functional, unfolded state into their folded, functional state. Problems in folding can lead to diseases like Alzheimer’s and Parkinson’s.
“Protein folding is one of the most important biological processes, as it forms the functioning 3D protein structure,” explained the physical chemist Hyotcherl Ihee of the Department of Chemistry at KAIST. Dr. Tae Wu Kim, the lead author of this research from Ihee’s group, added, “Understanding the mechanisms of protein folding is important, and could pave the way for disease study and drug development.”
Ihee’s team developed an approach using an X-ray scattering technique to uncover how the protein cytochrome c folds from its initial unfolded state. This protein is composed of a chain of 104 amino acids with an iron-containing heme molecule. It is often used for protein folding studies.
The researchers placed the protein in a solution and shined ultraviolet light on it. This process provides electrons to cytochrome c, reducing the iron within it from the ferric to the ferrous form, which initiates folding. As this was happening, the researchers beamed X-rays at very short intervals onto the sample. The X-rays scattered off all the atomic pairs in the sample and a detector continuously recorded the X-ray scattering patterns. The X-ray scattering patterns provided direct information regarding the 3D protein structure and the changes made in these patterns over time showed real-time motion of the protein during the folding process.
The team found cytochrome c proteins initially exist in a wide variety of unfolded states. Once the folding process is triggered, they stop by a group of intermediates within 31.6 microseconds, and then those intermediates follow different pathways with different folding times to reach an energetically stable folded state.
“We don’t know if this diversity in folding paths can be generalized to other proteins,” Ihee confessed. He continued, “However, we believe that our approach can be used to study other protein folding systems.”
Ihee hopes this approach can improve the accuracy of models that simulate protein interactions by including information on their unstructured states. These simulations are important as they can help identify barriers to proper folding and predict a protein’s folded state given its amino acid sequence. Ultimately, the models could help clarify how some diseases develop and how drugs interact with various protein structures.
Ihee’s group collaborated with Professor Young Min Rhee at the KAIST Department of Chemistry, and this work was supported by the National Research Foundation of Korea (NRF) and the Institute for Basic Science (IBS).
Figure. The scientists found that non-functional unfolded forms of the protein cytochrome c follow different pathways and timelines to reach a stable functional folded state.
Publications:
Kim, T. W., et al. (2020) ‘Protein folding from heterogeneous unfolded state revealed by time-resolved X-ray solution scattering’. PNAS. Volume 117. Issue 26. Page 14996-15005. Available online at https://doi.org/10.1073/pnas.1913442117
Profile: Hyotcherl Ihee, Ph.D.
Professor
hyotcherl.ihee@kaist.ac.kr
http://time.kaist.ac.kr/
Ihee Laboratory
Department of Chemistry
KAIST
https://www.kaist.ac.kr
Daejeon 34141, Korea
Profile: Young Min Rhee, Ph.D.
Professor
ymrhee@kaist.ac.kr
http://singlet.kaist.ac.kr
Rhee Research Group
Department of Chemistry
KAIST
https://www.kaist.ac.kr
Daejeon 34141, Korea
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2020.07.09 View 12540