Connecting the Dots to Find New Treatments for Bre..
Systems biologists uncovered new ways of cancer cell reprogramming to treat drug-resistant cancers < Professor Kwang-Hyun Cho and colleagues have developed a mathematical model and identified optimal targets reprogramming basal-like cancer cells into hormone therapy-responsive luminal-A cells by deciphering the complex molecular interactions within these cells through a systems biological approach. > Scientists at KAIST believe they may have found a way to reverse an aggressive, treatment-resistant type of breast cancer into a less dangerous kind that responds well to treatment. The study involved the use of mathematical models to untangle the complex genetic and molecular interactions that occur in the two types of breast cancer, but could be extended to find ways for treating many others. The study’s findings were published in the journal Cancer Research. Basal-like tumours are the most aggressive type of breast cancer, with the worst prognosis. Chemotherapy is the only available treatment option, but patients experience high recurrence rates. On the other hand, luminal-A breast cancer responds well to drugs that specifically target a receptor on their cell surfaces, called estrogen receptor alpha (ERα). KAIST systems biologist Kwang-Hyun Cho and colleagues analyzed the complex molecular and genetic interactions of basal-like and luminal-A breast cancers to find out if there might be a way to switch the former to the latter and give patients a better chance to respond to treatment. To do this, they accessed large amounts of cancer and patient data to understand which genes and molecules are involved in the two types. They then input this data into a mathematical model that represents genes, proteins and molecules as dots and the interactions between them as lines. The model can be used to conduct simulations and see how interactions change when certain genes are turned on or off. “There have been a tremendous number of studies trying to find therapeutic targets for treating basal-like breast cancer patients,” says Cho. “But clinical trials have failed due to the complex and dynamic nature of cancer. To overcome this issue, we looked at breast cancer cells as a complex network system and implemented a systems biological approach to unravel the underlying mechanisms that would allow us to reprogram basal-like into luminal-A breast cancer cells.” Using this approach, followed by experimental validation on real breast cancer cells, the team found that turning off two key gene regulators, called BCL11A and HDAC1/2, switched a basal-like cancer signalling pathway into a different one used by luminal-A cancer cells. The switch reprograms the cancer cells and makes them more responsive to drugs that target ERα receptors. However, further tests will be needed to confirm that this also works in animal models and eventually humans. “Our study demonstrates that the systems biological approach can be useful for identifying novel therapeutic targets,” says Cho. The researchers are now expanding its breast cancer network model to include all breast cancer subtypes. Their ultimate aim is to identify more drug targets and to understand the mechanisms that could drive drug-resistant cells to turn into drug-sensitive ones. This work was supported by the National Research Foundation of Korea, the Ministry of Science and ICT, Electronics and Telecommunications Research Institute, and the KAIST Grand Challenge 30 Project. -Publication Sea R. Choi, Chae Young Hwang, Jonghoon Lee, and Kwang-Hyun Cho, “Network Analysis Identifies Regulators of Basal-like Breast Cancer Reprogramming and Endocrine Therapy Vulnerability,” Cancer Research, November 30. (doi:10.1158/0008-5472.CAN-21-0621) -Profile Professor Kwang-Hyun Cho Laboratory for Systems Biology and Bio-Inspired Engineering Department of Bio and Brain Engineering KAIST
Deep Learning Framework to Enable Material Design ..
Researchers propose a deep neural network-based forward design space exploration using active transfer learning and data augmentation < Figure 1: Schematic of deep learning framework for material design space exploration. Schematic of gradual expansion of reliable prediction domain of DNN based on the addition of data generated from the hyper-heuristic genetic algorithm and active transfer learning. > A new study proposed a deep neural network-based forward design approach that enables an efficient search for superior materials far beyond the domain of the initial training set. This approach compensates for the weak predictive power of neural networks on an unseen domain through gradual updates of the neural network with active transfer learning and data augmentation methods. Professor Seungwha Ryu believes that this study will help address a variety of optimization problems that have an astronomical number of possible design configurations. For the grid composite optimization problem, the proposed framework was able to provide excellent designs close to the global optima, even with the addition of a very small dataset corresponding to less than 0.5％ of the initial training data-set size. This study was reported in npj Computational Materials last month. “We wanted to mitigate the limitation of the neural network, weak predictive power beyond the training set domain for the material or structure design,” said Professor Ryu from the Department of Mechanical Engineering. Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they cannot access data outside the range of training sets. Advanced generative models that were devised to overcome this limitation also suffer from weak predictive power for the unseen domain. Professor Ryu’s team, in collaboration with researchers from Professor Grace Gu’s group at UC Berkeley, devised a design method that simultaneously expands the domain using the strong predictive power of a deep neural network and searches for the optimal design by repetitively performing three key steps. First, it searches for few candidates with improved properties located close to the training set via genetic algorithms, by mixing superior designs within the training set. Then, it checks to see if the candidates really have improved properties, and expands the training set by duplicating the validated designs via a data augmentation method. Finally, they can expand the reliable prediction domain by updating the neural network with the new superior designs via transfer learning. Because the expansion proceeds along relatively narrow but correct routes toward the optimal design (depicted in the schematic of Fig. 1), the framework enables an efficient search. As a data-hungry method, a deep neural network model tends to have reliable predictive power only within and near the domain of the training set. When the optimal configuration of materials and structures lies far beyond the initial training set, which frequently is the case, neural network-based design methods suffer from weak predictive power and become inefficient. Researchers expect that the framework will be applicable for a wide range of optimization problems in other science and engineering disciplines with astronomically large design space, because it provides an efficient way of gradually expanding the reliable prediction domain toward the target design while avoiding the risk of being stuck in local minima. Especially, being a less-data-hungry method, design problems in which data generation is time-consuming and expensive will benefit most from this new framework. The research team is currently applying the optimization framework for the design task of metamaterial structures, segmented thermoelectric generators, and optimal sensor distributions. “From these sets of on-going studies, we expect to better recognize the pros and cons, and the potential of the suggested algorithm. Ultimately, we want to devise more efficient machine learning-based design approaches,” explained Professor Ryu.This study was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research Project. -Publication Yongtae Kim, Youngsoo, Charles Yang, Kundo Park, Grace X. Gu, and Seunghwa Ryu, “Deep learning framework for material design space exploration using active transfer learning and data augmentation,” npj Computational Materials (https://doi.org/10.1038/s41524-021-00609-2) -Profile Professor Seunghwa Ryu Mechanics & Materials Modeling Lab Department of Mechanical Engineering KAIST
A Mechanism Underlying Most Common Cause of Epilep..
An interdisciplinary study shows that neurons carrying somatic mutations in MTOR can lead to focal epileptogenesis via non-cell-autonomous hyperexcitability of nearby nonmutated neurons < Image 1: Neurons carrying somatic mutations in MTOR lead to focal epileptogenesis via non-cell autonomous hyperexcitability of nearby non-mutated neurons. (Left) Neurons with mTOR mutation (green) observed in a mouse brain section image. (Middle) Network model consisting of a small portion of mutated and a large portion of nearby non-mutated neurons. (Right) Mitigated hyperactivity of non-mutated neurons after the treatment of inhibitor of adenosine kinase. > During fetal development, cells should migrate to the outer edge of the brain to form critical connections for information transfer and regulation in the body. When even a few cells fail to move to the correct location, the neurons become disorganized and this results in focal cortical dysplasia. This condition is the most common cause of seizures that cannot be controlled with medication in children and the second most common cause in adults. Now, an interdisciplinary team studying neurogenetics, neural networks, and neurophysiology at KAIST has revealed how dysfunctions in even a small percentage of cells can cause disorder across the entire brain. They published their results on June 28 in Annals of Neurology. The work builds on a previous finding, also by a KAIST scientists, who found that focal cortical dysplasia was caused by mutations in the cells involved in mTOR, a pathway that regulates signaling between neurons in the brain. “Only 1 to 2％ of neurons carrying mutations in the mTOR signaling pathway that regulates cell signaling in the brain have been found to include seizures in animal models of focal cortical dysplasia,” said Professor Jong-Woo Sohn from the Department of Biological Sciences. “The main challenge of this study was to explain how nearby non-mutated neurons are hyperexcitable.” Initially, the researchers hypothesized that the mutated cells affected the number of excitatory and inhibitory synapses in all neurons, mutated or not. These neural gates can trigger or halt activity, respectively, in other neurons. Seizures are a result of extreme activity, called hyperexcitability. If the mutated cells upend the balance and result in more excitatory cells, the researchers thought, it made sense that the cells would be more susceptible to hyperexcitability and, as a result, seizures. “Contrary to our expectations, the synaptic input balance was not changed in either the mutated or non-mutated neurons,” said Professor Jeong Ho Lee from the Graduate School of Medical Science and Engineering. “We turned our attention to a protein overproduced by mutated neurons.” The protein is adenosine kinase, which lowers the concentration of adenosine. This naturally occurring compound is an anticonvulsant and works to relax vessels. In mice engineered to have focal cortical dysplasia, the researchers injected adenosine to replace the levels lowered by the protein. It worked and the neurons became less excitable. “We demonstrated that augmentation of adenosine signaling could attenuate the excitability of non-mutated neurons,” said Professor Se-Bum Paik from the Department of Bio and Brain Engineering. The effect on the non-mutated neurons was the surprising part, according to Paik. “The seizure-triggering hyperexcitability originated not in the mutation-carrying neurons, but instead in the nearby non-mutated neurons,” he said. The mutated neurons excreted more adenosine kinase, reducing the adenosine levels in the local environment of all the cells. With less adenosine, the non-mutated neurons became hyperexcitable, leading to seizures. “While we need further investigate into the relationship between the concentration of adenosine and the increased excitation of nearby neurons, our results support the medical use of drugs to activate adenosine signaling as a possible treatment pathway for focal cortical dysplasia,” Professor Lee said. The Suh Kyungbae Foundation, the Korea Health Technology Research and Development Project, the Ministry of Health & Welfare, and the National Research Foundation in Korea funded this work. -Publication: Koh, H.Y., Jang, J., Ju, S.H., Kim, R., Cho, G.-B., Kim, D.S., Sohn, J.-W., Paik, S.-B. and Lee, J.H. (2021), ‘Non–Cell Autonomous Epileptogenesis in Focal Cortical Dysplasia’ Annals of Neurology, 90: 285 299. (https://doi.org/10.1002/ana.26149) -Profile Professor Jeong Ho Lee Translational Neurogenetics Lab https://tnl.kaist.ac.kr/ Graduate School of Medical Science and Engineering KAIST Professor Se-Bum Paik Visual System and Neural Network Laboratory http://vs.kaist.ac.kr/ Department of Bio and Brain Engineering KAIST Professor Jong-Woo Sohn Laboratory for Neurophysiology, https://sites.google.com/site/sohnlab2014/home Department of Biological Sciences KAIST Dr. Hyun Yong Koh Translational Neurogenetics Lab Graduate School of Medical Science and Engineering KAIST Dr. Jaeson Jang Ph.D. Visual System and Neural Network Laboratory Department of Bio and Brain Engineering KAIST Sang Hyeon Ju M.D. Laboratory for Neurophysiology Department of Biological Sciences KAIST
Brain-Inspired Highly Scalable Neuromorphic Hardwa..
Neurons and synapses based on single transistor can dramatically reduce the hardware cost and accelerate the commercialization of neuromorphic hardware < Single transistor neurons and synapses fabricated using a standard silicon CMOS process. They are co-integrated on the same 8-inch wafer. > KAIST researchers fabricated a brain-inspired highly scalable neuromorphic hardware by co-integrating single transistor neurons and synapses. Using standard silicon complementary metal-oxide-semiconductor (CMOS) technology, the neuromorphic hardware is expected to reduce chip cost and simplify fabrication procedures. The research team led by Yang-Kyu Choi and Sung-Yool Choi produced a neurons and synapses based on single transistor for highly scalable neuromorphic hardware and showed the ability to recognize text and face images. This research was featured in Science Advances on August 4. Neuromorphic hardware has attracted a great deal of attention because of its artificial intelligence functions, but consuming ultra-low power of less than 20 watts by mimicking the human brain. To make neuromorphic hardware work, a neuron that generates a spike when integrating a certain signal, and a synapse remembering the connection between two neurons are necessary, just like the biological brain. However, since neurons and synapses constructed on digital or analog circuits occupy a large space, there is a limit in terms of hardware efficiency and costs. Since the human brain consists of about 1011 neurons and 1014 synapses, it is necessary to improve the hardware cost in order to apply it to mobile and IoT devices. To solve the problem, the research team mimicked the behavior of biological neurons and synapses with a single transistor, and co-integrated them onto an 8-inch wafer. The manufactured neuromorphic transistors have the same structure as the transistors for memory and logic that are currently mass-produced. In addition, the neuromorphic transistors proved for the first time that they can be implemented with a ‘Janus structure’ that functions as both neuron and synapse, just like coins have heads and tails. Professor Yang-Kyu Choi said that this work can dramatically reduce the hardware cost by replacing the neurons and synapses that were based on complex digital and analog circuits with a single transistor. "We have demonstrated that neurons and synapses can be implemented using a single transistor," said Joon-Kyu Han, the first author. "By co-integrating single transistor neurons and synapses on the same wafer using a standard CMOS process, the hardware cost of the neuromorphic hardware has been improved, which will accelerate the commercialization of neuromorphic hardware,” Han added.This research was supported by the National Research Foundation (NRF) and IC Design Education Center (IDEC). -Publication Joon-Kyu Han, Sung-Yool Choi, Yang-Kyu Choi, et al.“Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware,” Science Advances (DOI: 10.1126/sciadv.abg8836) -Profile Professor Yang-Kyu Choi Nano-Oriented Bio-Electronics Lab https://sites.google.com/view/nobelab/ School of Electrical Engineering KAIST Professor Sung-Yool Choi Molecular and Nano Device Laboratory https://www.mndl.kaist.ac.kr/ School of Electrical Engineering KAIST
3D Visualization and Quantification of Bioplastic ..
3D holographic microscopy leads to in-depth analysis of bacterial cells accumulating the bacterial bioplastic, polyhydroxyalkanoate (PHA) < Time-lapse movie of recombinant E. coli cells for 520 minutes. The red color indicates in vivo PHA granules. The movie shows PHA granule formation and distribution during the cell division process. > A research team at KAIST has observed how bioplastic granule is being accumulated in living bacteria cells through 3D holographic microscopy. Their 3D imaging and quantitative analysis of the bioplastic ‘polyhydroxyalkanoate’ (PHA) via optical diffraction tomography provides insights into biosynthesizing sustainable substitutes for petroleum-based plastics. The bio-degradable polyester polyhydroxyalkanoate (PHA) is being touted as an eco-friendly bioplastic to replace existing synthetic plastics. While carrying similar properties to general-purpose plastics such as polyethylene and polypropylene, PHA can be used in various industrial applications such as container packaging and disposable products. PHA is synthesized by numerous bacteria as an energy and carbon storage material under unbalanced growth conditions in the presence of excess carbon sources. PHA exists in the form of insoluble granules in the cytoplasm. Previous studies on investigating in vivo PHA granules have been performed by using fluorescence microscopy, transmission electron microscopy (TEM), and electron cryotomography. < Figure: Schematic process of 3D optical diffraction tomography for the bacterial cell accumulating bioplastic polyhydroxyalkanoate (PHA). A cell sample is illuminated at multiple sequential illumination angles (Left, Top). From the raw holograms recorded at individual angles (Left, Bottom), quantitative amplitude and phase information (Middle) is retrieved and the 3D refractive index distribution (Right, Top) is reconstructed. The 3D rendering image of the sample is then obtained (Right, Bottom). > These techniques have generally relied on the statistical analysis of multiple 2D snapshots of fixed cells or the short-time monitoring of the cells. For the TEM analysis, cells need to be fixed and sectioned, and thus the investigation of living cells was not possible. Fluorescence-based techniques require fluorescence labeling or dye staining. Thus, indirect imaging with the use of reporter proteins cannot show the native state of PHAs or cells, and invasive exogenous dyes can affect the physiology and viability of the cells. Therefore, it was difficult to fully understand the formation of PHA granules in cells due to the technical limitations, and thus several mechanism models based on the observations have been only proposed. The team of metabolic engineering researchers led by Distinguished Professor Sang Yup Lee and Physics Professor YongKeun Park, who established the startup Tomocube with his 3D holographic microscopy, reported the results of 3D quantitative label-free analysis of PHA granules in individual live bacterial cells by measuring the refractive index distributions using optical diffraction tomography. The formation and growth of PHA granules in the cells of Cupriavidus necator, the most-studied native PHA (specifically, poly(3-hydroxybutyrate), also known as PHB) producer, and recombinant Escherichia coli harboring C. necator PHB biosynthesis pathway were comparatively examined. From the reconstructed 3D refractive index distribution of the cells, the team succeeded in the 3D visualization and quantitative analysis of cells and intracellular PHA granules at a single-cell level. In particular, the team newly presented the concept of “in vivo PHA granule density.” Through the statistical analysis of hundreds of single cells accumulating PHA granules, the distinctive differences of density and localization of PHA granules in the two micro-organisms were found. Furthermore, the team identified the key protein that plays a major role in making the difference that enabled the characteristics of PHA granules in the recombinant E. coli to become similar to those of C. necator. The research team also presented 3D time-lapse movies showing the actual processes of PHA granule formation combined with cell growth and division. Movies showing the living cells synthesizing and accumulating PHA granules in their native state had never been reported before. Professor Lee said, “This study provides insights into the morphological and physical characteristics of in vivo PHA as well as the unique mechanisms of PHA granule formation that undergo the phase transition from soluble monomers into the insoluble polymer, followed by granule formation. Through this study, a deeper understanding of PHA granule formation within the bacterial cells is now possible, which has great significance in that a convergence study of biology and physics was achieved. This study will help develop various bioplastics production processes in the future.” This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (Grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) and the Bio & Medical Technology Development Program (Grant No. 2021M3A9I4022740) from the Ministry of Science and ICT (MSIT) through the National Research Foundation (NRF) of Korea to S.Y.L. This work was also supported by the KAIST Cross-Generation Collaborative Laboratory project. -Publication So Young Choi, Jeonghun Oh, JaeHwang Jung, YongKeun Park, and Sang Yup Lee. Three-dimensional label-free visualization and quantification of polyhydroxyalkanoates in individual bacterial cell in its native state. PNAS(https://doi.org./10.1073/pnas.2103956118) -Profile Distinguished Professor Sang Yup Lee Metabolic Engineering and Synthetic Biology http://mbel.kaist.ac.kr/ Department of Chemical and Biomolecular Engineering KAIST Endowed Chair Professor YongKeun Park Biomedical Optics Laboratory https://bmokaist.wordpress.com/ Department of Physics KAIST
Repurposed Drugs Present New Strategy for Treating..
Virtual screening of 6,218 drugs and cell-based assays identifies best therapeutic medication candidates < Figure: A schematic representation of computational drug repurposing strategy. Docking-based virtual screening can rapidly identify novel compounds for COVID-19 treatment among from the collection of approved and clinical trial drugs. > A joint research group from KAIST and Institut Pasteur Korea has identified repurposed drugs for COVID-19 treatment through virtual screening and cell-based assays. The research team suggested the strategy for virtual screening with greatly reduced false positives by incorporating pre-docking filtering based on shape similarity and post-docking filtering based on interaction similarity. This strategy will help develop therapeutic medications for COVID-19 and other antiviral diseases more rapidly. This study was reported at the Proceedings of the National Academy of Sciences of the United States of America (PNAS). Researchers screened 6,218 drugs from a collection of FDA-approved drugs or those under clinical trial and identified 38 potential repurposed drugs for COVID-19 with this strategy. Among them, seven compounds inhibited SARS-CoV-2 replication in Vero cells. Three of these drugs, emodin, omipalisib, and tipifarnib, showed anti-SARS-CoV-2 activity in human lung cells, Calu-3. Drug repurposing is a practical strategy for developing antiviral drugs in a short period of time, especially during a global pandemic. In many instances, drug repurposing starts with the virtual screening of approved drugs. However, the actual hit rate of virtual screening is low and most of the predicted drug candidates are false positives. The research group developed effective filtering algorithms before and after the docking simulations to improve the hit rates. In the pre-docking filtering process, compounds with similar shapes to the known active compounds for each target protein were selected and used for docking simulations. In the post-docking filtering process, the chemicals identified through their docking simulations were evaluated considering the docking energy and the similarity of the protein-ligand interactions with the known active compounds. The experimental results showed that the virtual screening strategy reached a high hit rate of 18.4％, leading to the identification of seven potential drugs out of the 38 drugs initially selected. “We plan to conduct further preclinical trials for optimizing drug concentrations as one of the three candidates didn’t resolve the toxicity issues in preclinical trials,” said Woo Dae Jang, one of the researchers from KAIST. “The most important part of this research is that we developed a platform technology that can rapidly identify novel compounds for COVID-19 treatment. If we use this technology, we will be able to quickly respond to new infectious diseases as well as variants of the coronavirus,” said Distinguished Professor Sang Yup Lee. This work was supported by the KAIST Mobile Clinic Module Project funded by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF). The National Culture Collection for Pathogens in Korea provided the SARS-CoV-2 (NCCP43326). -Publication Woo Dae Jang, Sangeun Jeon, Seungtaek Kim, and Sang Yup Lee. Drugs repurposed for COVID-19 by virtual screening of 6,218 drugs and cell-based assay. Proc. Natl. Acad. Sci. U.S.A. (https://doi/org/10.1073/pnas.2024302118) -Profile Distinguished Professor Sang Yup Lee Metabolic &Biomolecular Engineering National Research Laboratory http://mbel.kaist.ac.kr Department of Chemical and Biomolecular Engineering KAIST
Hydrogel-Based Flexible Brain-Machine Interface
The interface is easy to insert into the body when dry, but behaves ‘stealthily’ inside the brain when wet < Figure 1. Schematic of Hydrogel Hybrid Brain-Machine Interfaces > Professor Seongjun Park’s research team and collaborators revealed a newly developed hydrogel-based flexible brain-machine interface. To study the structure of the brain or to identify and treat neurological diseases, it is crucial to develop an interface that can stimulate the brain and detect its signals in real time. However, existing neural interfaces are mechanically and chemically different from real brain tissue. This causes foreign body response and forms an insulating layer (glial scar) around the interface, which shortens its lifespan. To solve this problem, the research team developed a ‘brain-mimicking interface’ by inserting a custom-made multifunctional fiber bundle into the hydrogel body. The device is composed not only of an optical fiber that controls specific nerve cells with light in order to perform optogenetic procedures, but it also has an electrode bundle to read brain signals and a microfluidic channel to deliver drugs to the brain. The interface is easy to insert into the body when dry, as hydrogels become solid. But once in the body, the hydrogel will quickly absorb body fluids and resemble the properties of its surrounding tissues, thereby minimizing foreign body response. The research team applied the device on animal models, and showed that it was possible to detect neural signals for up to six months, which is far beyond what had been previously recorded. It was also possible to conduct long-term optogenetic and behavioral experiments on freely moving mice with a significant reduction in foreign body responses such as glial and immunological activation compared to existing devices. “This research is significant in that it was the first to utilize a hydrogel as part of a multifunctional neural interface probe, which increased its lifespan dramatically,” said Professor Park. “With our discovery, we look forward to advancements in research on neurological disorders like Alzheimer’s or Parkinson’s disease that require long-term observation.” The research was published in Nature Communications on June 8, 2021. (Title: Adaptive and multifunctional hydrogel hybrid probes for long-term sensing and modulation of neural activity) The study was conducted jointly with an MIT research team composed of Professor Polina Anikeeva, Professor Xuanhe Zhao, and Dr. Hyunwoo Yook. This research was supported by the National Research Foundation (NRF) grant for emerging research, Korea Medical Device Development Fund, KK-JRC Smart Project, KAIST Global Initiative Program, and Post-AI Project. < Figure 2. Design and Fabrication of Multifunctional Hydrogel Hybrid Probes > -Publication Park, S., Yuk, H., Zhao, R. et al. Adaptive and multifunctional hydrogel hybrid probes for long-term sensing and modulation of neural activity. Nat Commun 12, 3435 (2021). https://doi.org/10.1038/s41467-021-23802-9 -Profile Professor Seongjun Park Bio and Neural Interfaces Laboratory Department of Bio and Brain Engineering KAIST
Biomimetic Resonant Acoustic Sensor Detecting Far-..
A KAIST research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering has developed a bioinspired flexible piezoelectric acoustic sensor with multi-resonant ultrathin piezoelectric membrane mimicking the basilar membrane of the human cochlea. The flexible acoustic sensor has been miniaturized for embedding into smartphones and the first commercial prototype is ready for accurate and far-distant voice detection. In 2018, Professor Lee presented the first concept of a flexible piezoelectric acoustic sensor, inspired by the fact that humans can accurately detect far-distant voices using a multi-resonant trapezoidal membrane with 20,000 hair cells. However, previous acoustic sensors could not be integrated into commercial products like smartphones and AI speakers due to their large device size. In this work, the research team fabricated a mobile-sized acoustic sensor by adopting ultrathin piezoelectric membranes with high sensitivity. Simulation studies proved that the ultrathin polymer underneath inorganic piezoelectric thin film can broaden the resonant bandwidth to cover the entire voice frequency range using seven channels. Based on this theory, the research team successfully demonstrated the miniaturized acoustic sensor mounted in commercial smartphones and AI speakers for machine learning-based biometric authentication and voice processing. (Please refer to the explanatory movie KAIST Flexible Piezoelectric Mobile Acoustic Sensor). The resonant mobile acoustic sensor has superior sensitivity and multi-channel signals compared to conventional condenser microphones with a single channel, and it has shown highly accurate and far-distant speaker identification with a small amount of voice training data. The error rate of speaker identification was significantly reduced by 56％ (with 150 training datasets) and 75％ (with 2,800 training datasets) compared to that of a MEMS condenser device. Professor Lee said, “Recently, Google has been targeting the ‘Wolverine Project’ on far-distant voice separation from multi-users for next-generation AI user interfaces. I expect that our multi-channel resonant acoustic sensor with abundant voice information is the best fit for this application. Currently, the mass production process is on the verge of completion, so we hope that this will be used in our daily lives very soon.” Professor Lee also established a startup company called Fronics Inc., located both in Korea and U.S. (branch office) to commercialize this flexible acoustic sensor and is seeking collaborations with global AI companies. These research results entitled “Biomimetic and Flexible Piezoelectric Mobile Acoustic Sensors with Multi-Resonant Ultrathin Structures for Machine Learning Biometrics” were published in Science Advances in 2021 (7, eabe5683). < Figure: (a) Schematic illustration of the basilar membrane-inspired flexible piezoelectric mobile acoustic sensor (b) Real-time voice biometrics based on machine learning algorithms (c) The world’s first commercial production of a mobile-sized acoustic sensor. > -Publication “Biomimetic and flexible piezoelectric mobile acoustic sensors with multiresonant ultrathin structures for machine learning biometrics,” Science Advances (DOI: 10.1126/sciadv.abe5683) -Profile Professor Keon Jae Lee Department of Materials Science and Engineering Flexible and Nanobio Device Lab http://fand.kaist.ac.kr/ 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
‘Urban Green Space Affects Citizens’ Happiness’
Study finds the relationship between green space, the economy, and happiness < Figure 1. (a) The map of urban green space and happiness in 60 developed countries. The size and color of circles represent the level of happiness and urban green space in a country, respectively. The markers are placed on the most populated cities of each country. (b) Urban green space is measured by the UGS in four world cities. The green areas indicate the adjusted NDVI per capita (i.e., UGS) for every 10m by 10m pixel. > A recent study revealed that as a city becomes more economically developed, its citizens’ happiness becomes more directly related to the area of urban green space. A joint research project by Professor Meeyoung Cha of the School of Computing and her collaborators studied the relationship between green space and citizen happiness by analyzing big data from satellite images of 60 different countries. Urban green space, including parks, gardens, and riversides not only provides aesthetic pleasure, but also positively affects our health by promoting physical activity and social interactions. Most of the previous research attempting to verify the correlation between urban green space and citizen happiness was based on few developed countries. Therefore, it was difficult to identify whether the positive effects of green space are global, or merely phenomena that depended on the economic state of the country. There have also been limitations in data collection, as it is difficult to visit each location or carry out investigations on a large scale based on aerial photographs. The research team used data collected by Sentinel-2, a high-resolution satellite operated by the European Space Agency (ESA) to investigate 90 green spaces from 60 different countries around the world. The subjects of analysis were cities with the highest population densities (cities that contain at least 10％ of the national population), and the images were obtained during the summer of each region for clarity. Images from the northern hemisphere were obtained between June and September of 2018, and those from the southern hemisphere were obtained between December of 2017 and February of 2018. The areas of urban green space were then quantified and crossed with data from the World Happiness Report and GDP by country reported by the United Nations in 2018. Using these data, the relationships between green space, the economy, and citizen happiness were analyzed. The results showed that in all cities, citizen happiness was positively correlated with the area of urban green space regardless of the country’s economic state. However, out of the 60 countries studied, the happiness index of the bottom 30 by GDP showed a stronger correlation with economic growth. In countries whose gross national income (GDP per capita) was higher than 38,000 USD, the area of green space acted as a more important factor affecting happiness than economic growth. Data from Seoul was analyzed to represent South Korea, and showed an increased happiness index with increased green areas compared to the past. < Figure 2. The relations of (a) log-GDP and happiness, and (b) urban green space (i.e., UGS) and happiness across 60 developed countries. The top 30 and the lowest 30 countries ranked by GDP are sized by the population size and colored by red and black. The dotted lines are the linear fit for each GDP group. (c) Changes of coefficients between urban green space and happiness for different sets of GDP rank with increasing window size from top 10 to 60. (d) The rank correlations between UGS and happiness for the groups of countries in the increasing GDP rank order. > The authors point out their work has several policy-level implications. First, public green space should be made accessible to urban dwellers to enhance social support. If public safety in urban parks is not guaranteed, its positive role in social support and happiness may diminish. Also, the meaning of public safety may change; for example, ensuring biological safety will be a priority in keeping urban parks accessible during the COVID-19 pandemic. Second, urban planning for public green space is needed for both developed and developing countries. As it is challenging or nearly impossible to secure land for green space after the area is developed, urban planning for parks and green space should be considered in developing economies where new cities and suburban areas are rapidly expanding. Third, recent climate changes can present substantial difficulty in sustaining urban green space. Extreme events such as wildﬁres, ﬂoods, droughts, and cold waves could endanger urban forests while global warming could conversely accelerate tree growth in cities due to the urban heat island effect. Thus, more attention must be paid to predict climate changes and discovering their impact on the maintenance of urban green space. “There has recently been an increase in the number of studies using big data from satellite images to solve social conundrums,” said Professor Cha. “The tool developed for this investigation can also be used to quantify the area of aquatic environments like lakes and the seaside, and it will now be possible to analyze the relationship between citizen happiness and aquatic environments in future studies,” she added. Professor Woo Sung Jung from POSTECH and Professor Donghee Wohn from the New Jersey Institute of Technology also joined this research. It was reported in the online issue of EPJ Data Science on May 30. -Publication Oh-Hyun Kwon, Inho Hong, Jeasurk Yang, Donghee Y. Wohn, Woo-Sung Jung, and Meeyoung Cha, 2021. Urban green space and happiness in developed countries. EPJ Data Science. DOI: https://doi.org/10.1140/epjds/s13688-021-00278-7 -Profile Professor Meeyoung Cha Data Science Lab https://ds.ibs.re.kr/ School of Computing 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
Wearable Device to Monitor Sweat in Real Time
An on-skin platform for the wireless monitoring of flow rate, cumulative loss, and temperature of sweat in real time < Sweat as a source of biomarkers. A wireless electronic patch (left) measures sweat’s volumetric flow and cumulative loss. It can be combined with the microfluidic system (right) that provides pH measurements and determines the concentration of chloride, creatine, and glucose in a user’s sweat. These indicators could be used to diagnose cystic fibrosis, diabetes, kidney dysfunction, and metabolic alkalosis. > An electronic patch can monitor your sweating and check your health status. Even more, the soft microfluidic device that adheres to the surface of the skin, captures, stores, and performs biomarker analysis of sweat as it is released through the eccrine glands. This wearable and wireless electronic device developed by Professor Kyeongha Kwon and her collaborators is a digital and wireless platform that could help track the so-called ‘filling process’ of sweat without having to visually examine the device. The platform was integrated with microfluidic systems to analyze the sweat’s components. To monitor the sweat release rate in real time, the researchers created a ‘thermal flow sensing module.’ They designed a sophisticated microfluidic channel to allow the collected sweat to flow through a narrow passage and a heat source was placed on the outer surface of the channel to induce a heat exchange between the sweat and the heated channel. As a result, the researchers could develop a wireless electronic patch that can measure the temperature difference in a specific location upstream and downstream of the heat source with an electronic circuit and convert it into a digital signal to measure the sweat release rate in real time. The patch accurately measured the perspiration rate in the range of 0-5 microliters/minute (μl/min), which was considered physiologically significant. The sensor can measure the flow of sweat directly and then use the information it collected to quantify total sweat loss. Moreover, the device features advanced microfluidic systems and colorimetric chemical reagents to gather pH measurements and determine the concentration of chloride, creatinine, and glucose in a user's sweat. Professor Kwon said that these indicators could be used to diagnose various diseases related with sweating such as cystic fibrosis, diabetes, kidney dysfunction, and metabolic alkalosis. “As the sweat flowing in the microfluidic channel is completely separated from the electronic circuit, the new patch overcame the shortcomings of existing flow rate measuring devices, which were vulnerable to corrosion and aging,” she explained. The patch can be easily attached to the skin with flexible circuit board printing technology and silicone sealing technology. It has an additional sensor that detects changes in skin temperature. Using a smartphone app, a user can check the data measured by the wearable patch in real time. Professor Kwon added, “This patch can be widely used for personal hydration strategies, the detection of dehydration symptoms, and other health management purposes. It can also be used in a systematic drug delivery system, such as for measuring the blood flow rate in blood vessels near the skin’s surface or measuring a drug’s release rate in real time to calculate the exact dosage.” -Publication Kyeongha Kwon, Jong Uk Kim, John A. Rogers, et al. “An on-skin platform for wireless monitoring of flow rate, cumulative loss and temperature of sweat in real time.” Nature Electronics (doi.org/10.1038/s41928-021-00556-2) -Profile Professor Kyeongha Kwon School of Electrical Engineering KAIST