AI
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E. coli Engineered to Grow on CO₂ and Formic Acid as Sole Carbon Sources
- An E. coli strain that can grow to a relatively high cell density solely on CO₂ and formic acid was developed by employing metabolic engineering. -
Most biorefinery processes have relied on the use of biomass as a raw material for the production of chemicals and materials. Even though the use of CO₂ as a carbon source in biorefineries is desirable, it has not been possible to make common microbial strains such as E. coli grow on CO₂.
Now, a metabolic engineering research group at KAIST has developed a strategy to grow an E. coli strain to higher cell density solely on CO₂ and formic acid. Formic acid is a one carbon carboxylic acid, and can be easily produced from CO₂ using a variety of methods. Since it is easier to store and transport than CO₂, formic acid can be considered a good liquid-form alternative of CO₂.
With support from the C1 Gas Refinery R&D Center and the Ministry of Science and ICT, a research team led by Distinguished Professor Sang Yup Lee stepped up their work to develop an engineered E. coli strain capable of growing up to 11-fold higher cell density than those previously reported, using CO₂ and formic acid as sole carbon sources. This work was published in Nature Microbiology on September 28.
Despite the recent reports by several research groups on the development of E. coli strains capable of growing on CO₂ and formic acid, the maximum cell growth remained too low (optical density of around 1) and thus the production of chemicals from CO₂ and formic acid has been far from realized.
The team previously reported the reconstruction of the tetrahydrofolate cycle and reverse glycine cleavage pathway to construct an engineered E. coli strain that can sustain growth on CO₂ and formic acid. To further enhance the growth, the research team introduced the previously designed synthetic CO₂ and formic acid assimilation pathway, and two formate dehydrogenases.
Metabolic fluxes were also fine-tuned, the gluconeogenic flux enhanced, and the levels of cytochrome bo3 and bd-I ubiquinol oxidase for ATP generation were optimized. This engineered E. coli strain was able to grow to a relatively high OD600 of 7~11, showing promise as a platform strain growing solely on CO₂ and formic acid.
Professor Lee said, “We engineered E. coli that can grow to a higher cell density only using CO₂ and formic acid. We think that this is an important step forward, but this is not the end. The engineered strain we developed still needs further engineering so that it can grow faster to a much higher density.”
Professor Lee’s team is continuing to develop such a strain. “In the future, we would be delighted to see the production of chemicals from an engineered E. coli strain using CO₂ and formic acid as sole carbon sources,” he added.
-Profile:Distinguished Professor Sang Yup Leehttp://mbel.kaist.ac.krDepartment of Chemical and Biomolecular EngineeringKAIST
2020.09.29 View 11006 -
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 11060 -
PhD Graduate Mekuria Teklemariam Inspired to Better Serve Ethiopia
Ethiopia’s Former Minister of Urban Development and Housing Mekuria Teklemariam became a KAIST alumnus, earning his PhD in the Global IT Technology Program (ITTP) last month. Dr. Telkemariam completed his degree summa cum laude in business administration in four years. He is the highest-ranking official among the ITTP Program recipients.
Dr. Teklemarian cited the ‘Saemaul Undong,’ also known as the New Community Movement as well as the strong infrastructure of IT industry as part of the driving forces behind Korea’s rapid economic success and this inspired him to choose KAIST as his academic destination.
The Global ITTP was launched in 2006 to educate elite public officials from diverse countries on information and communication technology. This program has played a vital role in transferring Korea’s advanced information and communications technology to many countries whose industries are in the budding stages.
Approximately 200 officials from over 50 countries have enrolled in the ITTP program, and the program has expanded to cover diverse areas of ICT and grown into a global network of ICT leaders abroad. The 2020 Class graduated five PhDs and five master’s degree holders.
Dr. Teklemariam plans to benchmark Korea to aid the development of Ethiopia when he returns home. “Korea is a country that has made remarkable progress in all areas including politics and economics in the last few decades, emerged from one of the poorest countries in the 1960s to be among the largest economies in the world today,” Dr. Telkemariam said.
“So I wanted to study what transformed Korea to make such a miraculous transformation academically for my country’s own development too,” he added, explaining his motivation to study in Korea. He also cited diverse IT education programs for the elderly as a Korean policy he would like to see applied in his country.
The 50-year-old former minister and incumbent urban affairs advisor to the prime minister of Ethiopia was elected to the country's parliament a decade ago, becoming the youngest member in Ethiopian history. He has led the economic development of Ethiopia in the areas of smart city development, land management, and housing development policies.
While studying at KAIST, Dr. Telkemariam became the two-time winner of the Outstanding Collaborative Research Award presented by the KAIST Institute for IT Convergence through collaborative research with the National IT Industry Promotion Agency (NIPA) and the Science and Technology Policy Institute (STEPI).
In addition, his graduation thesis, "Differentiating mobile broadband policies across diffusion stages: A panel data analysis" was published in Telecommunications Policy.
President Sung-Chul Shin met with him during a luncheon meeting before he returned to home. During the meeting Dr. Telkemariam said, “I was impressed by the Korean people, who not only work hard to do their part wherever they are, but also put whatever they say into practice. I will apply and practice what I have learned from Korea and KAIST to Ethiopia.” President Shin responded, “We shall seek to find ways to cooperate that can be practically used to expand exchanges between the two countries.”
2020.09.21 View 7385 -
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 13416 -
Before Eyes Open, They Get Ready to See
- Spontaneous retinal waves can generate long-range horizontal connectivity in visual cortex. -
A KAIST research team’s computational simulations demonstrated that the waves of spontaneous neural activity in the retinas of still-closed eyes in mammals develop long-range horizontal connections in the visual cortex during early developmental stages.
This new finding featured in the August 19 edition of Journal of Neuroscience as a cover article has resolved a long-standing puzzle for understanding visual neuroscience regarding the early organization of functional architectures in the mammalian visual cortex before eye-opening, especially the long-range horizontal connectivity known as “feature-specific” circuitry.
To prepare the animal to see when its eyes open, neural circuits in the brain’s visual system must begin developing earlier. However, the proper development of many brain regions involved in vision generally requires sensory input through the eyes.
In the primary visual cortex of the higher mammalian taxa, cortical neurons of similar functional tuning to a visual feature are linked together by long-range horizontal circuits that play a crucial role in visual information processing.
Surprisingly, these long-range horizontal connections in the primary visual cortex of higher mammals emerge before the onset of sensory experience, and the mechanism underlying this phenomenon has remained elusive.
To investigate this mechanism, a group of researchers led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering at KAIST implemented computational simulations of early visual pathways using data obtained from the retinal circuits in young animals before eye-opening, including cats, monkeys, and mice.
From these simulations, the researchers found that spontaneous waves propagating in ON and OFF retinal mosaics can initialize the wiring of long-range horizontal connections by selectively co-activating cortical neurons of similar functional tuning, whereas equivalent random activities cannot induce such organizations.
The simulations also showed that emerged long-range horizontal connections can induce the patterned cortical activities, matching the topography of underlying functional maps even in salt-and-pepper type organizations observed in rodents. This result implies that the model developed by Professor Paik and his group can provide a universal principle for the developmental mechanism of long-range horizontal connections in both higher mammals as well as rodents.
Professor Paik said, “Our model provides a deeper understanding of how the functional architectures in the visual cortex can originate from the spatial organization of the periphery, without sensory experience during early developmental periods.”
He continued, “We believe that our findings will be of great interest to scientists working in a wide range of fields such as neuroscience, vision science, and developmental biology.”
This work was supported by the National Research Foundation of Korea (NRF). Undergraduate student Jinwoo Kim participated in this research project and presented the findings as the lead author as part of the Undergraduate Research Participation (URP) Program at KAIST.
Figures and image credit: Professor Se-Bum Paik, KAIST
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:
Jinwoo Kim, Min Song, and Se-Bum Paik. (2020). Spontaneous retinal waves generate long-range horizontal connectivity in visual cortex. Journal of Neuroscience, Available online athttps://www.jneurosci.org/content/early/2020/07/17/JNEUROSCI.0649-20.2020
Profile: Se-Bum Paik
Assistant Professor
sbpaik@kaist.ac.kr
http://vs.kaist.ac.kr/
VSNN Laboratory
Department of Bio and Brain Engineering
Program of Brain and Cognitive Engineering
http://kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea
Profile: Jinwoo Kim
Undergraduate Student
bugkjw@kaist.ac.kr
Department of Bio and Brain Engineering, KAIST
Profile: Min Song
Ph.D. Candidate
night@kaist.ac.kr
Program of Brain and Cognitive Engineering, KAIST
(END)
2020.08.25 View 12376 -
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 9314 -
‘SoundWear’ a Heads-Up Sound Augmentation Gadget Helps Expand Children’s Play Experience
In this digital era, there has been growing concern that children spend most of their playtime watching TV, playing computer games, and staring at mobile phones with ‘head-down’ posture even outdoors.
To counter such concerns, KAIST researchers designed a wearable bracelet using sound augmentation to leverage play benefits by employing digital technology. The research team also investigated how sound influences children’s play experiences according to their physical, social, and imaginative aspects.
Playing is a large part of enjoyable and rewarding lives, especially for children. Previously, a large part of children’s playtime used to take place outdoors, and playing outdoors has long been praised for playing an essential role in providing opportunities to perform physical activity, improve social skills, and boost imaginative thinking.
Motivated by these concerns, a KAIST research team led by Professor Woohun Lee and his researcher Jiwoo Hong from the Department of Industrial Design made use of sound augmentation, which is beneficial for motivating playful experiences by facilitating imagination and enhancing social awareness with its ambient and omnidirectional characteristics.
Despite the beneficial characteristics of sound augmentation, only a few studies have explored sound interaction as a technology to augment outdoor play due to its abstractness when conveying information in an open space outdoors. There is also a lack of empirical evidence regarding its effect on children's play experiences.
Professor Lee’s team designed and implemented an original bracelet-type wearable device called SoundWear. This device uses non-speech sound as a core digital feature for children to broaden their imaginations and improvise their outdoor games.
Children equipped with SoundWear were allowed to explore multiple sounds (i.e., everyday and instrumental sounds) on SoundPalette, pick a desired sound, generate the sound with a swinging movement, and transfer the sound between multiple devices for their outdoor play.
Both the quantitative and qualitative results of a user study indicated that augmenting playtime with everyday sounds triggered children’s imagination and resulted in distinct play behaviors, whereas instrumental sounds were transparently integrated with existing outdoor games while fully preserving play benefits in physical, social, and imaginative ways.
The team also found that the gestural interaction of SoundWear and the free sound choice on SoundPalette helped children to gain a sense of achievement and ownership toward sound. This led children to be physically and socially active while playing.
PhD candidate Hong said, “Our work can encourage the discussion on using digital technology that entails sound augmentation and gestural interactions for understanding and cultivating creative improvisations, social pretenses, and ownership of digital materials in digitally augmented play experiences.”
Professor Lee also envisioned that the findings being helpful to parents and educators saying, “I hope the verified effect of digital technology on children’s play informs parents and educators to help them make more informed decisions and incorporate the playful and creative usage of new media, such as mobile phones and smart toys, for young children.”
This research titled “SoundWear: Effect of Non-speech Sound Augmentation on the Outdoor Play Experience of Children” was presented at DIS 2020 (the ACM Conference on Designing Interactive Systems) taking place virtually in Eindhoven, Netherlands, from July 6 to 20. This work received an Honorable Mention Award for being in the top 5% of all the submissions to the conference.
Publication:
Hong, J., et al. (2020) ‘SoundWear: Effect of Non-speech Sound Augmentation on the Outdoor Play Experience of Children’. Proceedings of the 2020 ACM Designing Interactive Systems Conference (DIS'20), Pages 2201-2213. Available online at https://doi.org/10.1145/3357236.3395541
Profile: Professor Woohun Leewoohun.lee@kaist.ac.krhttp://wonderlab.kaist.ac.kr
Department of Industrial Design (ID) KAIST
2020.07.28 View 8487 -
KAIST Receives $57 Million Donation to Enhance Research
The largest amount since the opening of KAIST will fund ‘Singularity Professors’
KAIST Development Foundation Chairman Soo-Young Lee made a gift of real estate estimated at approximately $57 million on July 23. This is the largest donation KAIST has received since it was founded in 1971.
The fund will establish the “Soo-Young Lee Science Education Foundation” and the proceeds of the foundation will go to the “Singularity Professors” as necessary resources to help make discoveries and design new approaches to accelerate breakthroughs.
“KAIST should be the institute that will produce first Korean Nobel laureate in the field of science. I hope this fund will be utilized to enable Korea to stand out in this challenging time by accomplishing breakthroughs nobody has never imagined,” said Chairman Lee during the donation ceremony at KAIST’s campus in Daejeon.
This is Chairman Lee’s third donation following the $6.7 million donation in 2012 and the $830,000 donation in 2016. Chairman Lee began her career as a journalist in 1963. In 1981, she started her own business by launching Kwangwon Ranch and became a successful businesswoman. In 1988, Chairman Lee established the real estate company Kwangwon Industries. After receiving an honorary doctorate from KAIST in 2012, she has served as the chairman of the KAIST Development Foundation from 2013. Chairman Lee expressed her intention to make another donation to KAIST in the near future during the news conference.
“People matter most for advancing the world. KAIST has a very distinct mission to foster the brightest minds and will drive the nation to move forward. I have worked with KAIST for quite long time so that I have a strong belief that KAIST is the one that will not only make contributions to Korea but also to all humanity,” she explained.
“For example, about one-fourth of the R&D manpower at Samsung Electronics is from KAIST. In 2019, Samsung Electronics recorded a revenue of approximately $206 billion which accounted for about 16% of national GDP. KAIST is the one that fosters global talents who are working at global company such as Samsung and many others.”
KAIST President Sung-Chul Shin also expressed his deep respect for Chairman Lee’s decision, saying that the entire KAIST community will make every effort to keep up Chairman Lee’s noble idea encouraging KAIST to push forward and help realize KAIST’s role and mission.
(END)
2020.07.23 View 8681 -
Professor J.H. Lee Wins the Innovators in Science Award
Professor Jeong Ho Lee from the Graduate School of Medical Science and Engineering won the Early-Career Scientist Award of the 2020 Innovators in Science Award. The New York Academy of Sciences administers the award in partnership with Takeda Pharmaceutical Company.
The Innovators in Science Award grants two prizes of US $200,000 each year: one to an Early-Career Scientist and the other to a well-established Senior Scientist who have distinguished themselves for the creative thinking and impact of their rare disease research. The Senior Scientist Awardee is Dr. Adrian R. Krainer, at Cold Spring Harbor Laboratory whose research focused on the mechanisms and control of RNA splicing.
Prof. Lee is recognized for his research investigating genetic mutations in stem cells in the brain that result in rare developmental brain disorders. He was the first to identify the causes of intractable epilepsies and has identified the genes responsible for several developmental brain disorders, including focal cortical dysplasia, Joubert syndrome—a disorder characterized by an underdevelopment of the brainstem—and hemimegaloencephaly, which is the abnormal enlargement of one side of the brain.
“It is a great honor to be recognized by a jury of such globally respected scientists whom I greatly admire,” said Prof. Lee. “More importantly, this award validates research into brain somatic mutations as an important area of exploration to help patients suffering from devastating and untreatable neurological disorders.”
Prof. Lee also is the Director of the National Creative Research Initiative Center for Brain Somatic Mutations, and Co-founder and Chief Technology Officer of SoVarGen, a biopharmaceutical company aiming to discover novel therapeutics and diagnosis for intractable central nervous system (CNS) diseases caused by low-level somatic mutation.
The Innovators in Science Award is a limited submission competition in which research universities, academic institutions, government or non-profit institutions, or equivalent from around the globe with a well-established record of scientific excellence are invited to nominate their most promising Early-Career Scientists and their most outstanding Senior Scientists working in one of four selected therapeutic fields of neuroscience, gastroenterology, oncology, and regenerative medicine. The 2020 Winners will be honored at the virtual Innovators in Science Award Ceremony and Symposium in October 2020.
2020.07.09 View 9777 -
Quantum Classifiers with Tailored Quantum Kernel
Quantum information scientists have introduced a new method for machine learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.
The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.
Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.
More recently, the kernel method has been introduced in quantum machine learning with great success. The ability of quantum computers to efficiently access and manipulate data in the quantum feature space can open opportunities for quantum techniques to enhance various existing machine learning methods.
The idea of the classification algorithm with a nonlinear kernel is that given a quantum test state, the protocol calculates the weighted power sum of the fidelities of quantum data in quantum parallel via a swap-test circuit followed by two single-qubit measurements (see Figure 1). This requires only a small number of quantum data operations regardless of the size of data. The novelty of this approach lies in the fact that labeled training data can be densely packed into a quantum state and then compared to the test data.
The KAIST team, in collaboration with researchers from the University of KwaZulu-Natal (UKZN) in South Africa and Data Cybernetics in Germany, has further advanced the rapidly evolving field of quantum machine learning by introducing quantum classifiers with tailored quantum kernels.This study was reported at npj Quantum Information in May.
The input data is either represented by classical data via a quantum feature map or intrinsic quantum data, and the classification is based on the kernel function that measures the closeness of the test data to training data.
Dr. Daniel Park at KAIST, one of the lead authors of this research, said that the quantum kernel can be tailored systematically to an arbitrary power sum, which makes it an excellent candidate for real-world applications.
Professor Rhee said that quantum forking, a technique that was invented by the team previously, makes it possible to start the protocol from scratch, even when all the labeled training data and the test data are independently encoded in separate qubits.
Professor Francesco Petruccione from UKZN explained, “The state fidelity of two quantum states includes the imaginary parts of the probability amplitudes, which enables use of the full quantum feature space.”
To demonstrate the usefulness of the classification protocol, Carsten Blank from Data Cybernetics implemented the classifier and compared classical simulations using the five-qubit IBM quantum computer that is freely available to public users via cloud service. “This is a promising sign that the field is progressing,” Blank noted.
Link to download the full-text paper:
https://www.nature.com/articles/s41534-020-0272-6
-Profile
Professor June-Koo Kevin Rhee
rhee.jk@kaist.ac.kr
Professor, School of Electrical Engineering
Director, ITRC of Quantum Computing for AIKAIST
Daniel Kyungdeock Parkkpark10@kaist.ac.krResearch Assistant ProfessorSchool of Electrical EngineeringKAIST
2020.07.07 View 11802 -
A New Strategy for the Optimal Electroreduction of CO2 to High-Value Products
-Researchers suggest that modulation of local CO2 concentration improves the selectivity, conversion rate, and electrode stability, and shed a new light on the electrochemical CO2 reduction technology for controlling emissions at a low cost.-
A KAIST research team presented three novel approaches for modulating local carbon dioxide (CO2) concentration in gas-diffusion electrode (GDE)-based flow electrolyzers. Their study also empirically demonstrated that providing a moderate local CO2 concentration is effective in promoting Carbon–Carbon (C–C) coupling reactions toward the production of multi-carbon molecules. This work, featured in the May 20th issue of Joule, serves as a rational guide to tune CO2 mass transport for the optimal production of valuable multi-carbon products.
Amid global efforts to reduce and recycle anthropogenic CO2 emissions, CO2 electrolysis holds great promise for converting CO2 into useful chemicals that were traditionally derived from fossil fuels. Many researches have been attempting to improve the selectivity of CO2 for commercially and industrially high-value multi-carbon products such as ethylene, ethanol, and 1-propanol, due to their high energy density and large market size.
In order to achieve the highly-selective conversion of CO2 into valuable multi-carbon products, past studies have focused on the design of catalysts and the tuning of local environment related to pH, cations, and molecular additives.
Conventional CO2 electrolytic systems relied heavily on an alkaline electrolyte that is often consumed in large quantities when reacting with CO2, and thus led to an increase in the operational costs. Moreover, the life span of a catalyst electrode was short, due to its inherent chemical reactivity.
In their recent study, a group of KAIST researchers led by Professor Jihun Oh from the Department of Materials Science and Engineering reported that the local CO2 concentration has been an overlooked factor that largely affects the selectivity toward multi-carbon products.
Professor Oh and his researchers Dr. Ying Chuan Tan, Hakhyeon Song, and Kelvin Berm Lee proposed that there is an intimate relation between local CO2 and multi-carbon product selectivity during electrochemical CO2 reduction reactions. The team employed the mass-transport modeling of a GDE-based flow electrolyzer that utilizes copper oxide (Cu2O) nanoparticles as model catalysts. They then identified and applied three approaches to modulate the local CO2 concentration within a GDE-based electrolytic system, including 1) controlling the catalyst layer structure, 2) CO2 feed concentration, and 3) feed flow rate.
Contrary to common intuition, the study showed that providing a maximum CO2 transport leads to suboptimal multi-carbon product faradaic efficiency. Instead, by restricting and providing a moderate local CO2 concentration, C–C coupling can be significantly enhanced.
The researchers demonstrated experimentally that the selectivity rate increased from 25.4% to 61.9%, and from 5.9% to 22.6% for the CO2 conversion rate. When a cheap milder near-neutral electrolyte was used, the stability of the CO2 electrolytic system improved to a great extent, allowing over 10 hours of steady selective production of multi-carbon products.
Dr. Tan, the lead author of the paper, said, “Our research clearly revealed that the optimization of the local CO2 concentration is the key to maximizing the efficiency of converting CO2 into high-value multi-carbon products.”
Professor Oh added, “This finding is expected to deliver new insights to the research community that variables affecting local CO2 concentration are also influential factors in the electrochemical CO2 reduction reaction performance. My colleagues and I hope that our study becomes a cornerstone for related technologies and their industrial applications.”
This work was supported by the Korean Ministry of Science and ICT (MSIT) Creative Materials Discovery Program.
Publication:
Tan, Y. C et al. (2020) ‘Modulating Local CO2 Concentration as a General Strategy for Enhancing C−C Coupling in CO2 Electroreduction’, Joule, Vol. 4, Issue 5, pp. 1104-1120. Available online at https://doi.org/10.1016/j.joule.2020.03.013
Profile: Jihun Oh, PhD
Associate Professor
jihun.oh@kaist.ac.kr
http://les.kaist.ac.kr/
Laboratory for Energy and Sustainability (LE&S)
Department of Materials Science and Engineering (MSE)
Korea Advanced Institute of Science and Technology (KAIST)
https://www.kaist.ac.kr
Daejeon 34141, Republic of Korea
Profile: Ying Chuan Tan, PhD
tanyc@kaist.ac.kr
LE&S, MSE, KAIST
Profile: Hakhyeon Song, PhD Candidate
hyeon0401@kaist.ac.kr
LE&S, MSE, KAIST
Profile: Kelvin Berm Lee, M.S. Candidate
kbl9105@kaist.ac.kr
LE&S, MSE, KAIST
(END)
2020.06.03 View 11906 -
Universal Virus Detection Platform to Expedite Viral Diagnosis
Reactive polymer-based tester pre-screens dsRNAs of a wide range of viruses without their genome sequences
The prompt, precise, and massive detection of a virus is the key to combat infectious diseases such as Covid-19. A new viral diagnostic strategy using reactive polymer-grafted, double-stranded RNAs will serve as a pre-screening tester for a wide range of viruses with enhanced sensitivity.
Currently, the most widely using viral detection methodology is polymerase chain reaction (PCR) diagnosis, which amplifies and detects a piece of the viral genome. Prior knowledge of the relevant primer nucleic acids of the virus is quintessential for this test.
The detection platform developed by KAIST researchers identifies viral activities without amplifying specific nucleic acid targets. The research team, co-led by Professor Sheng Li and Professor Yoosik Kim from the Department of Chemical and Biomolecular Engineering, constructed a universal virus detection platform by utilizing the distinct features of the PPFPA-grafted surface and double-stranded RNAs.
The key principle of this platform is utilizing the distinct feature of reactive polymer-grafted surfaces, which serve as a versatile platform for the immobilization of functional molecules. These activated surfaces can be used in a wide range of applications including separation, delivery, and detection. As long double-stranded RNAs are common byproducts of viral transcription and replication, these PPFPA-grafted surfaces can detect the presence of different kinds of viruses without prior knowledge of their genomic sequences.
“We employed the PPFPA-grafted silicon surface to develop a universal virus detection platform by immobilizing antibodies that recognize double-stranded RNAs,” said Professor Kim.
To increase detection sensitivity, the research team devised two-step detection process analogues to sandwich enzyme-linked immunosorbent assay where the bound double-stranded RNAs are then visualized using fluorophore-tagged antibodies that also recognize the RNAs’ double-stranded secondary structure.
By utilizing the developed platform, long double-stranded RNAs can be detected and visualized from an RNA mixture as well as from total cell lysates, which contain a mixture of various abundant contaminants such as DNAs and proteins.
The research team successfully detected elevated levels of hepatitis C and A viruses with this tool.
“This new technology allows us to take on virus detection from a new perspective. By targeting a common biomarker, viral double-stranded RNAs, we can develop a pre-screening platform that can quickly differentiate infected populations from non-infected ones,” said Professor Li.
“This detection platform provides new perspectives for diagnosing infectious diseases. This will provide fast and accurate diagnoses for an infected population and prevent the influx of massive outbreaks,” said Professor Kim.
This work is featured in Biomacromolecules. This work was supported by the Agency for Defense Development (Grant UD170039ID), the Ministry of Science and ICT (NRF-2017R1D1A1B03034660, NRF-2019R1C1C1006672), and the KAIST Future Systems Healthcare Project from the Ministry of Science and ICT (KAISTHEALTHCARE42).
Profile:-Professor Yoosik KimDepartment of Chemical and Biomolecular Engineeringhttps://qcbio.kaist.ac.kr
KAIST-Professor Sheng LiDepartment of Chemical and Biomolecular Engineeringhttps://bcpolymer.kaist.ac.kr
KAIST
Publication:Ku et al., 2020. Reactive Polymer Targeting dsRNA as Universal Virus Detection Platform with Enhanced Sensitivity. Biomacromolecules (https://doi.org/10.1021/acs.biomac.0c00379).
2020.06.01 View 18496