Kaist

Natural Rainbow Colorants Microbially Produced​

Integrated strategies of systems metabolic engineering and membrane engineering led to the production of natural rainbow colorants comprising seven natural colorants from bacteria for the first time       < Systems metabolic engineering was employed to construct and optimize the metabolic pathways and membrane engineering was employed to increase the production of the target colorants, successfully producing the seven natural colorants covering the complete rainbow spectrum. >     A research group at KAIST has engineered bacterial strains capable of producing three carotenoids and four violacein derivatives, completing the seven colors in the rainbow spectrum. The research team integrated systems metabolic engineering and membrane engineering strategies for the production of seven natural rainbow colorants in engineered Escherichia coli strains. The strategies will be also useful for the efficient production of other industrially important natural products used in the food, pharmaceutical, and cosmetic industries.   Colorants are widely used in our lives and are directly related to human health when we eat food additives and wear cosmetics. However, most of these colorants are made from petroleum, causing unexpected side effects and health problems. Furthermore, they raise environmental concerns such as water pollution from dyeing fabric in the textiles industry. For these reasons, the demand for the production of natural colorants using microorganisms has increased, but could not be readily realized due to the high cost and low yield of the bioprocesses.   These challenges inspired the metabolic engineers at KAIST including researchers Dr. Dongsoo Yang and Dr. Seon Young Park, and Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering. The team reported the study entitled “Production of rainbow colorants by metabolically engineered Escherichia coli” in Advanced Science online on May 5. It was selected as the journal cover of the July 7 issue.   This research reports for the first time the production of rainbow colorants comprising three carotenoids and four violacein derivatives from glucose or glycerol via systems metabolic engineering and membrane engineering. The research group focused on the production of hydrophobic natural colorants useful for lipophilic food and dyeing garments. First, using systems metabolic engineering, which is an integrated technology to engineer the metabolism of a microorganism, three carotenoids comprising astaxanthin (red), -carotene (orange), and zeaxanthin (yellow), and four violacein derivatives comprising proviolacein (green), prodeoxyviolacein (blue), violacein (navy), and deoxyviolacein (purple) could be produced. Thus, the production of natural colorants covering the complete rainbow spectrum was achieved.   When hydrophobic colorants are produced from microorganisms, the colorants are accumulated inside the cell. As the accumulation capacity is limited, the hydrophobic colorants could not be produced with concentrations higher than the limit. In this regard, the researchers engineered the cell morphology and generated inner-membrane vesicles (spherical membranous structures) to increase the intracellular capacity for accumulating the natural colorants. To further promote production, the researchers generated outer-membrane vesicles to secrete the natural colorants, thus succeeding in efficiently producing all of seven rainbow colorants. It was even more impressive that the production of natural green and navy colorants was achieved for the first time.   “The production of the seven natural rainbow colorants that can replace the current petroleum-based synthetic colorants was achieved for the first time,” said Dr. Dongsoo Yang. He explained that another important point of the research is that integrated metabolic engineering strategies developed from this study can be generally applicable for the efficient production of other natural products useful as pharmaceuticals or nutraceuticals. “As maintaining good health in an aging society is becoming increasingly important, we expect that the technology and strategies developed here will play pivotal roles in producing other valuable natural products of medical or nutritional importance,” explained Distinguished Professor Sang Yup Lee.   This work was supported by the "Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ01550602)" Rural Development Administration, Republic of Korea.     -Publication:   Dongsoo Yang, Seon Young Park, and Sang Yup Lee. Production of rainbow colorants by metabolically engineered Escherichia coli. Advanced Science, 2100743.     -Profile   Distinguished Professor Sang Yup Lee Metabolic &Biomolecular Engineering National Research Laboratory http://mbel.kaist.ac.kr Department of Chemical and Biomolecular Engineering KAIST

Ultrafast, on-Chip PCR Could Speed Up Diagnoses du..

A rapid point-of-care diagnostic plasmofluidic chip can deliver result in only 8 minutes       < Professor Ki-Hun Chung(right) and PhD candidate Byoung-Hoon Kang pose with their vacuum-charged plasmofluidic PCR chip. >     Reverse transcription-polymerase chain reaction (RT-PCR) has been the gold standard for diagnosis during the COVID-19 pandemic. However, the PCR portion of the test requires bulky, expensive machines and takes about an hour to complete, making it difficult to quickly diagnose someone at a testing site. Now, researchers at KAIST have developed a plasmofluidic chip that can perform PCR in only about 8 minutes, which could speed up diagnoses during current and future pandemics.   The rapid diagnosis of COVID-19 and other highly contagious viral diseases is important for timely medical care, quarantining and contact tracing. Currently, RT-PCR uses enzymes to reverse transcribe tiny amounts of viral RNA to DNA, and then amplifies the DNA so that it can be detected by a fluorescent probe. It is the most sensitive and reliable diagnostic method.   But because the PCR portion of the test requires 30-40 cycles of heating and cooling in special machines, it takes about an hour to perform, and samples must typically be sent away to a lab, meaning that a patient usually has to wait a day or two to receive their diagnosis.   Professor Ki-Hun Jeong at the Department of Bio and Brain Engineering and his colleagues wanted to develop a plasmofluidic PCR chip that could quickly heat and cool miniscule volumes of liquids, allowing accurate point-of-care diagnoses in a fraction of the time. The research was reported in ACS Nano on May 19.   The researchers devised a postage stamp-sized polydimethylsiloxane chip with a microchamber array for the PCR reactions. When a drop of a sample is added to the chip, a vacuum pulls the liquid into the microchambers, which are positioned above glass nanopillars with gold nanoislands. Any microbubbles, which could interfere with the PCR reaction, diffuse out through an air-permeable wall. When a white LED is turned on beneath the chip, the gold nanoislands on the nanopillars quickly convert light to heat, and then rapidly cool when the light is switched off.   The researchers tested the device on a piece of DNA containing a SARS-CoV-2 gene, accomplishing 40 heating and cooling cycles and fluorescence detection in only 5 minutes, with an additional 3 minutes for sample loading. The amplification efficiency was 91％, whereas a comparable conventional PCR process has an efficiency of 98％. With the reverse transcriptase step added prior to sample loading, the entire testing time with the new method could take 10-13 minutes, as opposed to about an hour for typical RT-PCR testing. The new device could provide many opportunities for rapid point-of-care diagnostics during a pandemic, the researchers say.       < Vacuum-charged plasmofluidic PCR chip for real-time nanoplasmonic on-chip PCR (left) and ultrafast thermal cycling with amplification curve of plasmids expressing SARS-CoV-2 envelope protein (right). >     -Sources   Ultrafast and Real-Time Nanoplasmonic On-Chip Polymerase Chain Reaction for Rapid and Quantitative Molecular Diagnostics ACS Nano (https://doi.org/10.1021/acsnano.1c02154)     -Professor   Ki-Hun Jeong Biophotonics Laboratory https://biophotonics.kaist.ac.kr/ Department of Bio and Brain Engineeinrg KAIST

Dr. Won-Joon Lee from the ADD Wins the Jeong Hun C..

  < From left: KAIST PhD candidate Sok-Min Choi, Dr.Won-Joon Lee from ADD, Chong-Ho Park from Kongju National University High School, and Korea University >     Dr. Won-Joon Lee from the Agency for Defense Development (ADD) became the 17th Jeong Hun Cho Award recipient. KAIST PhD candidate Sok-Min Choi from the Department of Aerospace Engineering, Master’s-PhD combined course student Hyong-Won Choi from Korea University, and Chong-Ho Park from Kongju National University High School were also selected.   The award recognizes promising young scientists who makes significant achievements in the field of aerospace engineering in honor of Jeong Hun Cho, the former PhD candidate in the Department of Aerospace Engineering who died in a lab accident in May in 2003. Cho’s family endowed the award and scholarship to honor him. Three scholarship recipients from Cho’s alma mater, KAIST, Korea University, and Kongju National High School are selected every year.   Dr. Lee from the ADD has conducted research on shape design methods and radar absorbing structures for unmanned aerial vehicles, publishing more than 24 articles in SCI-level journals and 17 at academic conferences.   Dr. Lee was awarded 2.5 million KRW in prize money. the two students from KAIST and Korea University each received a 4 million KRW scholarship and Park received 3 million KRW.

T-GPS Processes a Graph with Trillion Edges on a S..

Trillion-scale graph processing simulation on a single computer presents a new concept of graph processing   A KAIST research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. Named as T-GPS (Trillion-scale Graph Processing Simulation) by the developer Professor Min-Soo Kim from the School of Computing at KAIST, it can process a graph with one trillion edges using a single computer.   Graphs are widely used to represent and analyze real-world objects in many domains such as social networks, business intelligence, biology, and neuroscience. As the number of graph applications increases rapidly, developing and testing new graph algorithms is becoming more important than ever before. Nowadays, many industrial applications require a graph algorithm to process a large-scale graph (e.g., one trillion edges). So, when developing and testing graph algorithms such for a large-scale graph, a synthetic graph is usually used instead of a real graph. This is because sharing and utilizing large-scale real graphs is very limited due to their being proprietary or being practically impossible to collect.   Conventionally, developing and testing graph algorithms is done via the following two-step approach: generating and storing a graph and executing an algorithm on the graph using a graph processing engine.         The first step generates a synthetic graph and stores it on disks. The synthetic graph is usually generated by either parameter-based generation methods or graph upscaling methods. The former extracts a small number of parameters that can capture some properties of a given real graph and generates the synthetic graph with the parameters. The latter upscales a given real graph to a larger one so as to preserve the properties of the original real graph as much as possible.   The second step loads the stored graph into the main memory of the graph processing engine such as Apache GraphX and executes a given graph algorithm on the engine. Since the size of the graph is too large to fit in the main memory of a single computer, the graph engine typically runs on a cluster of several tens or hundreds of computers. Therefore, the cost of the conventional two-step approach is very high.   The research team solved the problem of the conventional two-step approach. It does not generate and store a large-scale synthetic graph. Instead, it just loads the initial small real graph into main memory. Then, T-GPS processes a graph algorithm on the small real graph as if the large-scale synthetic graph that should be generated from the real graph exists in main memory. After the algorithm is done, T-GPS returns the exactly same result as the conventional two-step approach.   The key idea of T-GPS is generating only the part of the synthetic graph that the algorithm needs to access on the fly and modifying the graph processing engine to recognize the part generated on the fly as the part of the synthetic graph actually generated.   The research team showed that T-GPS can process a graph of 1 trillion edges using a single computer, while the conventional two-step approach can only process of a graph of 1 billion edges using a cluster of eleven computers of the same specification. Thus, T-GPS outperforms the conventional approach by 10,000 times in terms of computing resources. The team also showed that the speed of processing an algorithm in T-GPS is up to 43 times faster than the conventional approach. This is because T-GPS has no network communication overhead, while the conventional approach has a lot of communication overhead among computers.   Professor Kim believes that this work will have a large impact on the IT industry where almost every area utilizes graph data, adding, “T-GPS can significantly increase both the scale and efficiency of developing a new graph algorithm.”   This work was supported by the National Research Foundation (NRF) of Korea and Institute of Information & communications Technology Planning & Evaluation (IITP).     -Publication:   Park, H., et al. (2021) “Trillion-scale Graph Processing Simulation based on Top-Down Graph Upscaling,” IEEE ICDE 2021, Chania, Greece, Apr. 19-22, 2021. Available online at https://conferences.computer.org/icdepub   -Profile:   Min-Soo Kim Associate Professor http://infolab.kaist.ac.kr School of Computing KAIST

What Guides Habitual Seeking Behavior Explained

A new role of the ventral striatum explains habitual seeking behavior   Researchers have been investigating how the brain controls habitual seeking behaviors such as addiction. A recent study by Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering revealed that a long-term value memory maintained in the ventral striatum in the brain is a neural basis of our habitual seeking behavior. This research was conducted in collaboration with the research team lead by Professor Hyoung F. Kim from Seoul National University. Given that addictive behavior is deemed a habitual one, this research provides new insights for developing therapeutic interventions for addiction.   Habitual seeking behavior involves strong stimulus responses, mostly rapid and automatic ones. The ventral striatum in the brain has been thought to be important for value learning and addictive behaviors. However, it was unclear if the ventral striatum processes and retains long-term memories that guide habitual seeking.   Professor Lee’s team reported a new role of the human ventral striatum where long-term memory of high-valued objects are retained as a single representation and may be used to evaluate visual stimuli automatically to guide habitual behavior.       < The ventral striatum shows increased responses to high-valued objects (good objects) after habitual seeking training. >     “Our findings propose a role of the ventral striatum as a director that guides habitual behavior with the script of value information written in the past,” said Professor Lee.   The research team investigated whether learned values were retained in the ventral striatum while the subjects passively viewed previously learned objects in the absence of any immediate outcome. Neural responses in the ventral striatum during the incidental perception of learned objects were examined using fMRI and single-unit recording.   The study found significant value discrimination responses in the ventral striatum after learning and a retention period of several days. Moreover, the similarity of neural representations for good objects increased after learning, an outcome positively correlated with the habitual seeking response for good objects.   “These findings suggest that the ventral striatum plays a role in automatic evaluations of objects based on the neural representation of positive values retained since learning, to guide habitual seeking behaviors,” explained Professor Lee.   “We will fully investigate the function of different parts of the entire basal ganglia including the ventral striatum. We also expect that this understanding may lead to the development of better treatment for mental illnesses related to habitual behaviors or addiction problems.”   This study, supported by the National Research Foundation of Korea, was reported at Nature Communications (https://doi.org/10.1038/s41467-021-22335-5.)     -Profile   Professor Sue-Hyun Lee Department of Bio and Brain Engineering Memory and Cognition Laboratory http://memory.kaist.ac.kr/lecture KAIST

Microbial Production of a Natural Red Colorant Car..

Metabolic engineering and computer-simulated enzyme engineering led to the production of carminic acid, a natural red colorant, from bacteria for the first time       < Figure: A schematic biosynthetic pathway for the production of carminic acid from glucose. Biochemical reaction analysis and computer simulation-assisted enzyme engineering was employed to identify and improve the enzymes (DnrFP217K and GtCGTV93Q/Y193F) responsible for the latter two reactions. >     A research group at KAIST has engineered a bacterium capable of producing a natural red colorant, carminic acid, which is widely used for food and cosmetics. The research team reported the complete biosynthesis of carminic acid from glucose in engineered Escherichia coli. The strategies will be useful for the design and construction of biosynthetic pathways involving unknown enzymes and consequently the production of diverse industrially important natural products for the food, pharmaceutical, and cosmetic industries.   Carminic acid is a natural red colorant widely being used for products such as strawberry milk and lipstick. However, carminic acid has been produced by farming cochineals, a scale insect which only grows in the region around Peru and Canary Islands, followed by complicated multi-step purification processes. Moreover, carminic acid often contains protein contaminants that cause allergies so many people are unwilling to consume products made of insect-driven colorants. On that account, manufacturers around the world are using alternative red colorants despite the fact that carminic acid is one of the most stable natural red colorants.   These challenges inspired the metabolic engineering research group at KAIST to address this issue. Its members include postdoctoral researchers Dongsoo Yang and Woo Dae Jang, and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering. This study entitled “Production of carminic acid by metabolically engineered Escherichia coli” was published online in the Journal of the American Chemical Society (JACS) on April 2.   This research reports for the first time the development of a bacterial strain capable of producing carminic acid from glucose via metabolic engineering and computer simulation-assisted enzyme engineering. The research group optimized the type II polyketide synthase machinery to efficiently produce the precursor of carminic acid, flavokermesic acid.   Since the enzymes responsible for the remaining two reactions were neither discovered nor functional, biochemical reaction analysis was performed to identify enzymes that can convert flavokermesic acid into carminic acid. Then, homology modeling and docking simulations were performed to enhance the activities of the two identified enzymes. The team could confirm that the final engineered strain could produce carminic acid directly from glucose. The C-glucosyltransferase developed in this study was found to be generally applicable for other natural products as showcased by the successful production of an additional product, aloesin, which is found in aloe leaves.   “The most important part of this research is that unknown enzymes for the production of target natural products were identified and improved by biochemical reaction analyses and computer simulation-assisted enzyme engineering,” says Dr. Dongsoo Yang. He explained the development of a generally applicable C-glucosyltransferase is also useful since C-glucosylation is a relatively unexplored reaction in bacteria including Escherichia coli. Using the C-glucosyltransferase developed in this study, both carminic acid and aloesin were successfully produced from glucose.   “A sustainable and insect-free method of producing carminic acid was achieved for the first time in this study. Unknown or inefficient enzymes have always been a major problem in natural product biosynthesis, and here we suggest one effective solution for solving this problem. As maintaining good health in the aging society is becoming increasingly important, we expect that the technology and strategies developed here will play pivotal roles in producing other valuable natural products of medical or nutritional importance,” said Distinguished Professor Sang Yup Lee.   This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries of the Ministry of Science and ICT (MSIT) through the National Research Foundation (NRF) of Korea and the KAIST Cross-Generation Collaborative Lab project; Sang Yup Lee and Dongsoo Yang were also supported by Novo Nordisk Foundation in Denmark.     Publication:   Dongsoo Yang, Woo Dae Jang, and Sang Yup Lee. Production of carminic acid by metabolically engineered Escherichia coli. at the Journal of the American Chemical Society. https://doi.org.10.1021/jacs.0c12406   Profile: Sang Yup Lee, PhD Distinguished Professor leesy＠kaist.ac.kr http://mbel.kaist.ac.kr Metabolic &Biomolecular Engineering National Research Laboratory Department of Chemical and Biomolecular Engineering KAIST

Identification of How Chemotherapy Drug Works Coul..

  < Professor Yoosik Kim and PhD candidate Yongsuk Ku >     The chemotherapy drug decitabine is commonly used to treat patients with blood cancers, but its response rate is somewhat low. Researchers have now identified why this is the case, opening the door to more personalized cancer therapies for those with these types of cancers, and perhaps further afield.   Researchers have identified the genetic and molecular mechanisms within cells that make the chemotherapy drug decitabine—used to treat patients with myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) —work for some patients but not others. The findings should assist clinicians in developing more patient-specific treatment strategies.   The findings were published in the Proceedings of the National Academies of Science on March 30.   The chemotherapy drug decitabine, also known by its brand name Dacogen, works by modifying our DNA that in turn switches on genes that stop the cancer cells from growing and replicating. However, decitabine’s response rate is somewhat low (showing improvement in just 30-35％ of patients), which leaves something of a mystery as to why it works well for some patients but not for others. To find out why this happens, researchers from the KAIST investigated the molecular mediators that are involved with regulating the effects of the drug.   Decitabine works to activate the production of endogenous retroviruses (ERVs), which in turn induces an immune response. ERVs are viruses that long ago inserted dormant copies of themselves into the human genome. Decitabine in essence, ‘reactivates’ these viral elements and produces double-stranded RNAs (dsRNAs) that the immune system views as a foreign body.   “However, the mechanisms involved in this process, in particular how production and transport of these ERV dsRNAs were regulated within the cell were understudied,” said corresponding author Yoosik Kim, professor in the Department of Chemical and Biomolecular Engineering at KAIST.   “So to explain why decitabine works in some patients but not others, we investigated what these molecular mechanisms were,” added Kim.   To do so, the researchers used image-based RNA interference (RNAi) screening. This is a relatively new technique in which specific sequences within a genome are knocked out of action or “downregulated.” Large-scale screening, which can be performed in cultured cells or within live organisms, works to investigate the function of different genes. The KAIST researchers collaborated with the Institut Pasteur Korea to analyze the effect of downregulating genes that recognize ERV dsRNAs and could be involved in the cellular response to decitabine.       < Schematic diagram of the molecular mechanism of decitabine. Differences in immune responses in the body according to the expression of Staufen1 and TINCR. >     From these initial screening results, they performed an even more detailed downregulation screening analysis. Through the screening, they were able to identify two particular gene sequences involved in the production of an RNA-binding protein called Staufen1 and the production of a strand of RNA that does not in turn produce any proteins called TINCR that play a key regulatory role in response to the drug. Staufen1 binds directly to dsRNAs and stabilizes them in concert with the TINCR.   If a patient is not producing sufficient Staufen1 and TINCR, then the dsRNA viral mimics quickly degrade before the immune system can spot them. And, crucially for cancer therapy, this means that patients with lower expression (activation) of these sequences will show inferior response to decitabine. Indeed, the researchers confirmed that MDS/AML patients with low Staufen1 and TINCR expression did not benefit from decitabine therapy.   “We can now isolate patients who will not benefit from the therapy and direct them to a different type of therapy,” said first author Yongsuk Ku. “This serves as an important step toward developing a patient-specific treatment cancer strategy.”   As the researchers used patient samples taken from bone marrow, the next step will be to try to develop a testing method that can identify the problem from just blood samples, which are much easier to acquire from patients.   The team plans to investigate if the analysis can be extended to patients with solid tumors in addition to those with blood cancers.     -Profile Professor Yoosik Kim https://qcbio.kaist.ac.kr/ Department of Chemical and Biomolecular Engineering KAIST   -Publication Noncanonical immune response to the inhibition of DNA methylation by Staufen1 via stabilization of endogenous retrovirus RNAs, PNAS

Plasma Jets Stabilize Water to Splash Less​

  < High-speed shadowgraph movie of water surface deformations induced by plasma impingement. >     A study by KAIST researchers revealed that an ionized gas jet blowing onto water, also known as a ‘plasma jet’, produces a more stable interaction with the water’s surface compared to a neutral gas jet. This finding reported in the April 1 issue of Nature will help improve the scientific understanding of plasma-liquid interactions and their practical applications in a wide range of industrial fields in which fluid control technology is used, including biomedical engineering, chemical production, and agriculture and food engineering.   Gas jets can create dimple-like depressions in liquid surfaces, and this phenomenon is familiar to anyone who has seen the cavity produced by blowing air through a straw directly above a cup of juice. As the speed of the gas jet increases, the cavity becomes unstable and starts bubbling and splashing.   “Understanding the physical properties of interactions between gases and liquids is crucial for many natural and industrial processes, such as the wind blowing over the surface of the ocean, or steelmaking methods that involve blowing oxygen over the top of molten iron,” explained Professor Wonho Choe, a physicist from KAIST and the corresponding author of the study.   However, despite its scientific and practical importance, little is known about how gas-blown liquid cavities become deformed and destabilized.   In this study, a group of KAIST physicists led by Professor Choe and the team’s collaborators from Chonbuk National University in Korea and the Jožef Stefan Institute in Slovenia investigated what happens when an ionized gas jet, also known as a ‘plasma jet’, is blown over water. A plasma jet is created by applying high voltage to a nozzle as gas flows through it, which causes the gas to be weakly ionized and acquire freely-moving charged particles.   The research team used an optical technique combined with high-speed imaging to observe the profiles of the water surface cavities created by both neutral helium gas jets and weakly ionized helium gas jets. They also developed a computational model to mathematically explain the mechanisms behind their experimental discovery.   The researchers demonstrated for the first time that an ionized gas jet has a stabilizing effect on the water’s surface. They found that certain forces exerted by the plasma jet make the water surface cavity more stable, meaning there is less bubbling and splashing compared to the cavity created by a neutral gas jet.   Specifically, the study showed that the plasma jet consists of pulsed waves of gas ionization propagating along the water’s surface so-called ‘plasma bullets’ that exert more force than a neutral gas jet, making the cavity deeper without becoming destabilized.   “This is the first time that this phenomenon has been reported, and our group considers this as a critical step forward in our understanding of how plasma jets interact with liquid surfaces. We next plan to expand this finding through more case studies that involve diverse plasma and liquid characteristics,” said Professor Choe.   This work was supported by KAIST as part of the High-Risk and High-Return Project, the National Research Foundation of Korea (NRF), and the Slovenian Research Agency (ARRS).       < Cavity formation at the water’s surface subjected to a neutral helium gas jet (left) and a weakly ionized helium gas jet (right). >     Image Credit: Professor Wonho Choe, KAIST   Usage Restrictions: News organizations may use or redistribute these materials, with proper attribution, as part of news coverage of this paper only.   Publication: Park, S., et al. (2021) Stabilization of liquid instabilities with ionized gas jets. Nature, Vol. No. 592, Issue No. 7852, pp. 49-53. Available online at https://doi.org/10.1038/s41586-021-03359-9     Profile:   Wonho Choe, Ph.D. Professor   wchoe＠kaist.ac.kr https://gdpl.kaist.ac.kr/ Gas Discharge Physics Laboratory (GDPL) Department of Nuclear and Quantum Engineering Department of Physics Impurity and Edge Plasma Research Center (IERC) http://kaist.ac.kr/en/ Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea   (END)

Centrifugal Multispun Nanofibers Put a New Spin on..

KAIST researchers have developed a novel nanofiber production technique called ‘centrifugal multispinning’ that will open the door for the safe and cost-effective mass production of high-performance polymer nanofibers. This new technique, which has shown up to a 300 times higher nanofiber production rate per hour than that of the conventional electrospinning method, has many potential applications including the development of face mask filters for coronavirus protection.   Nanofibers make good face mask filters because their mechanical interactions with aerosol particles give them a greater ability to capture more than 90％ of harmful particles such as fine dust and virus-containing droplets.   The impact of the COVID-19 pandemic has further accelerated the growing demand in recent years for a better kind of face mask. A polymer nanofiber-based mask filter that can more effectively block harmful particles has also been in higher demand as the pandemic continues.   ‘Electrospinning’ has been a common process used to prepare fine and uniform polymer nanofibers, but in terms of safety, cost-effectiveness, and mass production, it has several drawbacks. The electrospinning method requires a high-voltage electric field and electrically conductive target, and this hinders the safe and cost-effective mass production of polymer nanofibers.   In response to this shortcoming, ‘centrifugal spinning’ that utilizes centrifugal force instead of high voltage to produce polymer nanofibers has been suggested as a safer and more cost-effective alternative to the electrospinning. Easy scalability is another advantage, as this technology only requires a rotating spinneret and a collector.   However, since the existing centrifugal force-based spinning technology employs only a single rotating spinneret, productivity is limited and not much higher than that of some advanced electrospinning technologies such as ‘multi-nozzle electrospinning’ and ‘nozzleless electrospinning.’ This problem persists even when the size of the spinneret is increased.   Inspired by these limitations, a research team led by Professor Do Hyun Kim from the Department of Chemical and Biomolecular Engineering at KAIST developed a centrifugal multispinning spinneret with mass-producibility, by sectioning a rotating spinneret into three sub-disks. This study was published as a front cover article of ACS Macro Letters, Volume 10, Issue 3 in March 2021.   Using this new centrifugal multispinning spinneret with three sub-disks, the lead author of the paper PhD candidate Byeong Eun Kwak and his fellow researchers Hyo Jeong Yoo and Eungjun Lee demonstrated the gram-scale production of various polymer nanofibers with a maximum production rate of up to 25 grams per hour, which is approximately 300 times higher than that of the conventional electrospinning system. The production rate of up to 25 grams of polymer nanofibers per hour corresponds to the production rate of about 30 face mask filters per day in a lab-scale manufacturing system.   By integrating the mass-produced polymer nanofibers into the form of a mask filter, the researchers were able to fabricate face masks that have comparable filtration performance with the KF80 and KF94 face masks that are currently available in the Korean market. The KF80 and KF94 masks have been approved by the Ministry of Food and Drug Safety of Korea to filter out at least 80％ and 94％ of harmful particles respectively.   “When our system is scaled up from the lab scale to an industrial scale, the large-scale production of centrifugal multispun polymer nanofibers will be made possible, and the cost of polymer nanofiber-based face mask filters will also be lowered dramatically,” Kwak explained.   This work was supported by the KAIST-funded Global Singularity Research Program for 2020.       < Figure. (A) Schematic illustration of the centrifugal multispinning polymer nanofiber production process. (B) The polymer nanofibers spun by the system. The increase of the number of sub-disk shows the proportional enhancement of the productivity. (C) Face masks and mask filters fabricated using mass-produced nanofibers (inset). >       < Image. Journal Cover >     Publication:   Byeong Eun Kwak, Hyo Jeong Yoo, Eungjun Lee, and Do Hyun Kim. (2021) Large-Scale Centrifugal Multispinning Production of Polymer Micro- and Nanofibers for Mask Filter Application with a Potential of Cospinning Mixed Multicomponent Fibers. ACS Macro Letters, Volume No. 10, Issue No. 3, pp. 382-388. Available online at https://doi.org/10.1021/acsmacrolett.0c00829     Profile:   Do Hyun Kim, Sc.D. Professor dohyun.kim＠kaist.edu http://procal.kaist.ac.kr/ Process Analysis Laboratory Department of Chemical and Biomolecular Engineering https:/kaist.ac.kr/en/ Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141, Korea     (END)

Acoustic Graphene Plasmons Study Paves Way for Opt..

- The first images of mid-infrared optical waves compressed 1,000 times captured using a highly sensitive scattering-type scanning near-field optical microscope. -       < Post-doc Researcher Sergey G. Menabde (Left) and Professor Min Seok Jang (Right) >     KAIST researchers and their collaborators at home and abroad have successfully demonstrated a new methodology for direct near-field optical imaging of acoustic graphene plasmon fields. This strategy will provide a breakthrough for the practical applications of acoustic graphene plasmon platforms in next-generation, high-performance, graphene-based optoelectronic devices with enhanced light-matter interactions and lower propagation loss.   It was recently demonstrated that ‘graphene plasmons’ – collective oscillations of free electrons in graphene coupled to electromagnetic waves of light – can be used to trap and compress optical waves inside a very thin dielectric layer separating graphene from a metallic sheet. In such a configuration, graphene’s conduction electrons are “reflected” in the metal, so when the light waves “push” the electrons in graphene, their image charges in metal also start to oscillate. This new type of collective electronic oscillation mode is called ‘acoustic graphene plasmon (AGP)’.   The existence of AGP could previously be observed only via indirect methods such as far-field infrared spectroscopy and photocurrent mapping. This indirect observation was the price that researchers had to pay for the strong compression of optical waves inside nanometer-thin structures. It was believed that the intensity of electromagnetic fields outside the device was insufficient for direct near-field optical imaging of AGP.   Challenged by these limitations, three research groups combined their efforts to bring together a unique experimental technique using advanced nanofabrication methods. Their findings were published in Nature Communications on February 19.   A KAIST research team led by Professor Min Seok Jang from the School of Electrical Engineering used a highly sensitive scattering-type scanning near-field optical microscope (s-SNOM) to directly measure the optical fields of the AGP waves propagating in a nanometer-thin waveguide, visualizing thousand-fold compression of mid-infrared light for the first time.   Professor Jang and a post-doc researcher in his group, Sergey G. Menabde, successfully obtained direct images of AGP waves by taking advantage of their rapidly decaying yet always present electric field above graphene. They showed that AGPs are detectable even when most of their energy is flowing inside the dielectric below the graphene.   This became possible due to the ultra-smooth surfaces inside the nano-waveguides where plasmonic waves can propagate at longer distances. The AGP mode probed by the researchers was up to 2.3 times more confined and exhibited a 1.4 times higher figure of merit in terms of the normalized propagation length compared to the graphene surface plasmon under similar conditions.   These ultra-smooth nanostructures of the waveguides used in the experiment were created using a template-stripping method by Professor Sang-Hyun Oh and a post-doc researcher, In-Ho Lee, from the Department of Electrical and Computer Engineering at the University of Minnesota.   Professor Young Hee Lee and his researchers at the Center for Integrated Nanostructure Physics (CINAP) of the Institute of Basic Science (IBS) at Sungkyunkwan University synthesized the graphene with a monocrystalline structure, and this high-quality, large-area graphene enabled low-loss plasmonic propagation.   The chemical and physical properties of many important organic molecules can be detected and evaluated by their absorption signatures in the mid-infrared spectrum. However, conventional detection methods require a large number of molecules for successful detection, whereas the ultra-compressed AGP fields can provide strong light-matter interactions at the microscopic level, thus significantly improving the detection sensitivity down to a single molecule.   Furthermore, the study conducted by Professor Jang and the team demonstrated that the mid-infrared AGPs are inherently less sensitive to losses in graphene due to their fields being mostly confined within the dielectric. The research team’s reported results suggest that AGPs could become a promising platform for electrically tunable graphene-based optoelectronic devices that typically suffer from higher absorption rates in graphene such as metasurfaces, optical switches, photovoltaics, and other optoelectronic applications operating at infrared frequencies.   Professor Jang said, “Our research revealed that the ultra-compressed electromagnetic fields of acoustic graphene plasmons can be directly accessed through near-field optical microscopy methods. I hope this realization will motivate other researchers to apply AGPs to various problems where strong light-matter interactions and lower propagation loss are needed.”   This research was primarily funded by the Samsung Research Funding & Incubation Center of Samsung Electronics. The National Research Foundation of Korea (NRF), the U.S. National Science Foundation (NSF), Samsung Global Research Outreach (GRO) Program, and Institute for Basic Science of Korea (IBS) also supported the work.       < Figure. Laser-illuminated nano-tip excites the acoustic graphene plasmon in the layer between the graphene and the gold/alumina. >     Publication:   Menabde, S. G., et al. (2021) Real-space imaging of acoustic plasmons in large-area graphene grown by chemical vapor deposition. Nature Communications 12, Article No. 938. Available online at https://doi.org/10.1038/s41467-021-21193-5       Profile:   Min Seok Jang, MS, PhD   Associate Professor jang.minseok＠kaist.ac.kr http://jlab.kaist.ac.kr/ Min Seok Jang Research Group School of Electrical Engineering   http://kaist.ac.kr/en/ Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea     (END)

A Biological Strategy Reveals How Efficient Brain ..

- A KAIST team’s mathematical modelling shows that the topographic tiling of cortical maps originates from bottom-up projections from the periphery. -   Researchers have explained how the regularly structured topographic maps in the visual cortex of the brain could arise spontaneously to efficiently process visual information. This research provides a new framework for understanding functional architectures in the visual cortex during early developmental stages.   A KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering has demonstrated that the orthogonal organization of retinal mosaics in the periphery is mirrored onto the primary visual cortex and initiates the clustered topography of higher visual areas in the brain.   This new finding provides advanced insights into the mechanisms underlying a biological strategy of brain circuitry for the efficient tiling of sensory modules. The study was published in Cell Reports on January 5.   In higher mammals, the primary visual cortex is organized into various functional maps for neural tuning such as ocular dominance, orientation selectivity, and spatial frequency selectivity. Correlations between the topographies of different maps have been observed, implying their systematic organizations for the efficient tiling of sensory modules across cortical areas.   These observations have suggested that a common principle for developing individual functional maps may exist. However, it has remained unclear how such topographical organizations could arise spontaneously in the primary visual cortex of various species.   The research team found that the orthogonal organization in the primary visual cortex of the brain originates from the spatial organization in bottom-up feedforward projections. The team showed that an orthogonal relationship among sensory modules already exists in the retinal mosaics, and that this is mirrored onto the primary visual cortex to initiate the clustered topography.   By analyzing the retinal ganglion cell mosaics data in cats and monkeys, the researchers found that the structure of ON-OFF feedforward afferents is organized into a topographic tiling, analogous to the orthogonal intersection of cortical tuning maps.   Furthermore, the team’s analysis of previously published data collected on cats also showed that the ocular dominance, orientation selectivity, and spatial frequency selectivity in the primary visual cortex are correlated with the spatial profiles of the retinal inputs, implying that efficient tiling of cortical domains can originate from the regularly structured retinal patterns.   Professor Paik said, “Our study suggests that the structure of the periphery with simple feedforward wiring can provide the basis for a mechanism by which the early visual circuitry is assembled.”   He continued, “This is the first report that spatially organized retinal inputs from the periphery provide a common blueprint for multi-modal sensory modules in the visual cortex during the early developmental stages. Our findings would make a significant impact on our understanding the developmental strategy of brain circuitry for efficient sensory information processing.”   This work was supported by the National Research Foundation of Korea (NRF).       < Figure 1. The image depicts the retinal origin of functional maps of neural tuning in visual cortex. >       < Figure 2. The image depicts the orthogonal intersection of cortical tuning maps that are initiated by the topographic tiling of retinal ganglion cell mosaics. >       < Figure 3. The regularly structured retinal circuits provide a blueprint of the clustered topography of multiple tuning maps in the primary visual cortex. >       Image credit: Professor Se-Bum Paik, KAIST   Image usage restrictions: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only.     Publication: Song, M, et al. (2021) Projection of orthogonal tiling from the retina to the visual cortex. Cell Reports 34, 108581. Available online at https://doi.org/10.1016/j.celrep.2020.108581     Profile: Se-Bum Paik, Ph.D 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: Min Song Ph.D. Candidate night＠kaist.ac.kr Program of Brain and Cognitive Engineering   Profile: Jaeson Jang, Ph.D. Researcher jaesonjang＠kaist.ac.kr Department of Bio and Brain Engineering, KAIST     (END)

DeepTFactor Predicts Transcription Factors​

A deep learning-based tool predicts transcription factors using protein sequences as inputs   A joint research team from KAIST and UCSD has developed a deep neural network named DeepTFactor that predicts transcription factors from protein sequences. DeepTFactor will serve as a useful tool for understanding the regulatory systems of organisms, accelerating the use of deep learning for solving biological problems.   A transcription factor is a protein that specifically binds to DNA sequences to control the transcription initiation. Analyzing transcriptional regulation enables the understanding of how organisms control gene expression in response to genetic or environmental changes. In this regard, finding the transcription factor of an organism is the first step in the analysis of the transcriptional regulatory system of an organism.   Previously, transcription factors have been predicted by analyzing sequence homology with already characterized transcription factors or by data-driven approaches such as machine learning. Conventional machine learning models require a rigorous feature selection process that relies on domain expertise such as calculating the physicochemical properties of molecules or analyzing the homology of biological sequences. Meanwhile, deep learning can inherently learn latent features for the specific task.   A joint research team comprised of Ph.D. candidate Gi Bae Kim and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at KAIST, and Ye Gao and Professor Bernhard O. Palsson of the Department of Biochemical Engineering at UCSD reported a deep learning-based tool for the prediction of transcription factors. Their research paper “DeepTFactor: A deep learning-based tool for the prediction of transcription factors” was published online in PNAS.   Their article reports the development of DeepTFactor, a deep learning-based tool that predicts whether a given protein sequence is a transcription factor using three parallel convolutional neural networks. The joint research team predicted 332 transcription factors of Escherichia coli K-12 MG1655 using DeepTFactor and the performance of DeepTFactor by experimentally confirming the genome-wide binding sites of three predicted transcription factors (YqhC, YiaU, and YahB).   The joint research team further used a saliency method to understand the reasoning process of DeepTFactor. The researchers confirmed that even though information on the DNA binding domains of the transcription factor was not explicitly given the training process, DeepTFactor implicitly learned and used them for prediction. Unlike previous transcription factor prediction tools that were developed only for protein sequences of specific organisms, DeepTFactor is expected to be used in the analysis of the transcription systems of all organisms at a high level of performance.   Distinguished Professor Sang Yup Lee said, “DeepTFactor can be used to discover unknown transcription factors from numerous protein sequences that have not yet been characterized. It is expected that DeepTFactor will serve as an important tool for analyzing the regulatory systems of organisms of interest.”   This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT through the National Research Foundation of Korea.       < Figure: The network architecture of DeepTFactor. An input protein sequence is processed using three parallel subnetworks. >     -Publication   Gi Bae Kim, Ye Gao, Bernhard O. Palsson, and Sang Yup Lee. DeepTFactor: A deep learning-based tool for the prediction of transcription factors. (https://doi.org/10.1073/pnas202117118)   -Profile   Distinguished Professor Sang Yup Lee leesy＠kaist.ac.kr Metabolic &Biomolecular Engineering National Research Laboratory http://mbel.kaist.ac.kr Department of Chemical and Biomolecular Engineering KAIST