AI
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A Global Campaign of ‘Facts before Rumors’ on COVID-19 Launched
- A KAIST data scientist group responds to facts and rumors on COVID-19 for global awareness of the pandemic. -
Like the novel coronavirus, rumors have no borders. The world is fighting to contain the pandemic, but we also have to deal with the appalling spread of an infodemic that is as contagious as the virus. This infodemic, a pandemic of false information, is bringing chaos and extreme fear to the general public.
Professor Meeyoung Cha’s group at the School of Computing started a global campaign called ‘Facts before Rumors,’ to prevent the spread of false information from crossing borders. She explained, “We saw many rumors that had already been fact-checked long before in China and South Korea now begin to circulate in other countries, sometimes leading to detrimental results. We launched an official campaign, Facts before Rumors, to deliver COVID-19-related facts to countries where the number of cases is now increasing.” She released the first set of facts on March 26 via her Twitter account @nekozzang.
Professor Cha, a data scientist who has focused on detecting global fake news, is now part of the COVID-19 AI Task Force at the Global Strategy Institute at KAIST. She is also leading the Data Science Group at the Institute for Basic Science (IBS) as Chief Investigator.
Her research group worked in collaboration with the College of Nursing at Ewha Woman’s University to identify 15 claims about COVID-19 that circulated on social networks (SNS) and among the general public. The team fact-checked these claims based on information from the WHO and CDCs of Korea and the US. The research group is now working on translating the list of claims into Portuguese, Spanish, Persian, Chinese, Amharic, Hindi, and Vietnamese. Delivering facts before rumors, the team says, will help contain the disease and prevent any harm caused by misinformation.
The pandemic, which spread in China and South Korea before arriving in Europe and the US, is now moving into South America, Africa, and Southeast Asia. “We would like to play a part in preventing the further spread of the disease with the provision of only scientifically vetted, truthful facts,” said the team.
For this campaign, Professor Cha’s team investigated more than 200 rumored claims on COVID-19 in China during the early days of the pandemic. These claims spread in different levels: while some were only relevant locally or in larger regions of China, others propagated in Asia and are now spreading to countries that are currently most affected by the disease.
For example, the false claim which publicized that ‘Fireworks can help tame the virus in the air’ only spread in China. Other claims such as ‘Eating garlic helps people overcome the disease’ or ‘Gargling with salt water prevents the contraction of the disease,’ spread around the world even after being proved groundless.
The team noted, however, that the times at which these claims propagate are different from one country to another. “This opens up an opportunity to debunk rumors in some countries, even before they start to emerge,” said Professor Cha.
Kun-Woo Kim, a master’s candidate in the Department of Industrial Design who joined this campaign and designed the Facts before Rumors chart also expressed his hope that this campaign will help reduce the number of victims. He added, “I am very grateful to our scientists who quickly responded to the Fact Check in these challenging times.”
2020.03.27 View 12271 -
COVID-19 Map Shows How the Global Pandemic Moves
- A School of Computing team facilitated the data from COVID-19 to show the global spread of the virus. -
The COVID-19 map made by KAIST data scientists shows where and how the virus is spreading from China, reportedly the epicenter of the disease.
Professor Meeyoung Cha from the School of Computing and her group facilitated data based on the number of confirmed cases from January 22 to March 22 to analyze the trends of this global epidemic. The statistics include the number of confirmed cases, recoveries, and deaths across major continents based on the number of confirmed case data during that period.
The moving dot on the map strikingly shows how the confirmed cases are moving across the globe. According to their statistics, the centroid of the disease starts from near Wuhan in China and moved to Korea, then through the European region via Italy and Iran.
The data is collected by a graduate student from the School of Computing, Geng Sun, who started the process during the time he was quarantined since coming back from his home in China. An undergraduate colleague of Geng's, Gabriel Camilo Lima who made the map, is now working remotely from his home in Brazil since all undergraduate students were required to move out of the dormitory last week. The university closed all undergraduate housing and advised the undergraduate students to go back home in a preventive measure to stop the virus from spreading across the campus.
Gabriel said he calculated the centroid of all confirmed cases up to a given day. He explained, “I weighed each coordinate by the number of cases in that region and country and calculated an approximate center of gravity.”
“The Earth is round, so the shortest path from Asia to Europe is often through Russia. In early March, the center of gravity of new cases was moving from Asia to Europe. Therefore, the centroid is moving to the west and goes through Russia, even though Russia has not reported many cases,” he added.
Professor Cha, who is also responsible for the Data Science Group at the Institute for Basic Science (IBS) as the Chief Investigator, said their group will continue to update the map using public data at https://ds.ibs.re.kr/index.php/covid-19/.
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2020.03.27 View 11795 -
Ultrathin but Fully Packaged High-Resolution Camera
- Biologically inspired ultrathin arrayed camera captures super-resolution images. -
The unique structures of biological vision systems in nature inspired scientists to design ultracompact imaging systems. A research group led by Professor Ki-Hun Jeong have made an ultracompact camera that captures high-contrast and high-resolution images. Fully packaged with micro-optical elements such as inverted micro-lenses, multilayered pinhole arrays, and gap spacers on the image sensor, the camera boasts a total track length of 740 μm and a field of view of 73°.
Inspired by the eye structures of the paper wasp species Xenos peckii, the research team completely suppressed optical noise between micro-lenses while reducing camera thickness. The camera has successfully demonstrated high-contrast clear array images acquired from tiny micro lenses. To further enhance the image quality of the captured image, the team combined the arrayed images into one image through super-resolution imaging.
An insect’s compound eye has superior visual characteristics, such as a wide viewing angle, high motion sensitivity, and a large depth of field while maintaining a small volume of visual structure with a small focal length. Among them, the eyes of Xenos peckii and an endoparasite found on paper wasps have hundreds of photoreceptors in a single lens unlike conventional compound eyes. In particular, the eye structures of an adult Xenos peckii exhibit hundreds of photoreceptors on an individual eyelet and offer engineering inspiration for ultrathin cameras or imaging applications because they have higher visual acuity than other compound eyes.
For instance, Xenos peckii’s eye-inspired cameras provide a 50 times higher spatial resolution than those based on arthropod eyes. In addition, the effective image resolution of the Xenos peckii’s eye can be further improved using the image overlaps between neighboring eyelets. This unique structure offers higher visual resolution than other insect eyes.
The team achieved high-contrast and super-resolution imaging through a novel arrayed design of micro-optical elements comprising multilayered aperture arrays and inverted micro-lens arrays directly stacked over an image sensor. This optical component was integrated with a complementary metal oxide semiconductor image sensor.
This is first demonstration of super-resolution imaging which acquires a single integrated image with high contrast and high resolving power reconstructed from high-contrast array images. It is expected that this ultrathin arrayed camera can be applied for further developing mobile devices, advanced surveillance vehicles, and endoscopes.
Professor Jeong said, “This research has led to technological advances in imaging technology. We will continue to strive to make significant impacts on multidisciplinary research projects in the fields of microtechnology and nanotechnology, seeking inspiration from natural photonic structures.”
This work was featured in Light Science & Applications last month and was supported by the National Research Foundation (NRF) of and the Ministry of Health and Welfare (MOHW) of Korea.
Image credit: Professor Ki-Hun Jeong, 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:
Kisoo Kim, Kyung-Won Jang, Jae-Kwan Ryu, and Ki-Hun Jeong. (2020) “Biologically inspired ultrathin arrayed camera for high-contrast and high-resolution imaging”. Light Science & Applications. Volume 9. Article 28. Available online at https://doi.org/10.1038/s41377-020-0261-8
Profile:
Ki-Hun Jeong
Professor
kjeong@kaist.ac.kr
http://biophotonics.kaist.ac.kr/
Department of Bio and Brain Engineering
KAIST
Profile:
Kisoo Kim
Ph.D. Candidate
kisoo.kim1@kaist.ac.kr
http://biophotonics.kaist.ac.kr/
Department of Bio and Brain Engineering
KAIST
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2020.03.23 View 17490 -
3D Hierarchically Porous Nanostructured Catalyst Helps Efficiently Reduce CO2
- This new catalyst will bring CO2 one step closer to serving as a sustainable energy source. -
KAIST researchers developed a three-dimensional (3D) hierarchically porous nanostructured catalyst with carbon dioxide (CO2) to carbon monoxide (CO) conversion rate up to 3.96 times higher than that of conventional nanoporous gold catalysts. This new catalyst helps overcome the existing limitations of the mass transport that has been a major cause of decreases in the CO2 conversion rate, holding a strong promise for the large-scale and cost-effective electrochemical conversion of CO2 into useful chemicals.
As CO2 emissions increase and fossil fuels deplete globally, reducing and converting CO2 to clean energy electrochemically has attracted a great deal of attention as a promising technology. Especially due to the fact that the CO2 reduction reaction occurs competitively with hydrogen evolution reactions (HER) at similar redox potentials, the development of an efficient electrocatalyst for selective and robust CO2 reduction reactions has remained a key technological issue.
Gold (Au) is one of the most commonly used catalysts in CO2 reduction reactions, but the high cost and scarcity of Au pose obstacles for mass commercial applications. The development of nanostructures has been extensively studied as a potential approach to improving the selectivity for target products and maximizing the number of active stable sites, thus enhancing the energy efficiency.
However, the nanopores of the previously reported complex nanostructures were easily blocked by gaseous CO bubbles during aqueous reactions. The CO bubbles hindered mass transport of the reactants through the electrolyte, resulting in low CO2 conversion rates.
In the study published in the Proceedings of the National Academy of Sciences of the USA (PNAS) on March 4, a research group at KAIST led by Professor Seokwoo Jeon and Professor Jihun Oh from the Department of Materials Science and Engineering designed a 3D hierarchically porous Au nanostructure with two different sizes of macropores and nanopores. The team used proximity-field nanopatterning (PnP) and electroplating techniques that are effective for fabricating the 3D well-ordered nanostructures.
The proposed nanostructure, comprised of interconnected macroporous channels 200 to 300 nanometers (nm) wide and 10 nm nanopores, induces efficient mass transport through the interconnected macroporous channels as well as high selectivity by producing highly active stable sites from numerous nanopores.
As a result, its electrodes show a high CO selectivity of 85.8% at a low overpotential of 0.264 V and efficient mass activity that is up to 3.96 times higher than that of de-alloyed nanoporous Au electrodes.
“These results are expected to solve the problem of mass transfer in the field of similar electrochemical reactions and can be applied to a wide range of green energy applications for the efficient utilization of electrocatalysts,” said the researchers.
This work was supported by the National Research Foundation (NRF) of Korea.
Image credit: Professor Seokwoo Jeon and Professor Jihun Oh, 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:
Hyun et al. (2020) Hierarchically porous Au nanostructures with interconnected channels for efficient mass transport in electrocatalytic CO2 reduction. Proceedings of the National Academy of Sciences of the USA (PNAS). Available online at https://doi.org/10.1073/pnas.1918837117
Profile:
Seokwoo Jeon, PhD
Professor
jeon39@kaist.ac.kr
http://fdml.kaist.ac.kr
Department of Materials Science and Engineering (MSE)
https://www.kaist.ac.kr
Korea Advanced Institute of Science and Technology (KAIST)Daejeon, Republic of Korea
Profile:
Jihun Oh, PhD
Associate Professor
jihun.oh@kaist.ac.kr
http://les.kaist.ac.kr
Department of Materials Science and Engineering (MSE)
Department of Energy, Environment, Water and Sustainability (EEWS)
KAIST
Profile:
Gayea Hyun
PhD Candidate
cldywkd93@kaist.ac.kr
http://fdml.kaist.ac.kr
Flexible Devices and Metamaterials Laboratory (FDML)
Department of Materials Science and Engineering (MSE)
KAIST
Profile:
Jun Tae Song, PhD
Assistant Professor
song.juntae@cstf.kyushu-u.ac.jp
http://www.cstf.kyushu-u.ac.jp/~ishihara-lab/
Department of Applied Chemistry
https://www.kyushu-u.ac.jp
Kyushu UniversityFukuoka, Japan
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2020.03.13 View 16922 -
Professor Hojong Chang’s Research Team Wins ISIITA 2020 Best Paper Award
The paper written by Professor Hojong Chang’s research team from KAIST Institute for IT Convergence won the best paper award from the International Symposium on Innovation in Information Technology Application (ISIITA) 2020, held this month at Ton Duc Thang University in Vietnam.
ISIITA is a networking symposium where leading researchers from various fields including information and communications, biotechnology, and computer systems come together and share on the convergence of technology.
Professor Chang’s team won the best paper award at this year’s symposium with its paper, “A Study of Single Photon Counting System for Quantitative Analysis of Luminescence”. The awarded paper discusses the realization of a signal processing system for silicon photomultipliers.
The silicon photomultiplier is the core of a urinalysis technique that tests for sodium and potassium in the body using simple chemical reactions. If our bodily sodium and potassium levels exceed a certain amount, it can lead to high blood pressure, cardiovascular problems, and kidney damage.
Through this research, the team has developed a core technique that quantifies the sodium and potassium discharged in the urine. When the reagent is injected into the urine, a very small amount of light is emitted as a result of the chemical reaction. However, if there is a large amount of sodium and potassium, they interrupt the reaction and reduce the emission. The key to this measurement technique is digitizing the strength of this very fine emission of light. Professor Chang’s team developed a system that uses a photomultiplier to measure the chemiluminescence.
Professor Chang said, “I look forward for this signal processing system greatly helping to prevent diseases caused by the excessive consumption of sodium and potassium through quick and easy detection.”
Researcher Byunghun Han who carried out the central research for the system design added, “We are planning to focus on miniaturizing the developed technique, so that anyone can carry our device around like a cellphone.”
The research was supported by the Ministry of Science and ICT.
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2020.02.27 View 9967 -
New Insights into How the Human Brain Solves Complex Decision-Making Problems
A new study on meta reinforcement learning algorithms helps us understand how the human brain learns to adapt to complexity and uncertainty when learning and making decisions. A research team, led by Professor Sang Wan Lee at KAIST jointly with John O’Doherty at Caltech, succeeded in discovering both a computational and neural mechanism for human meta reinforcement learning, opening up the possibility of porting key elements of human intelligence into artificial intelligence algorithms. This study provides a glimpse into how it might ultimately use computational models to reverse engineer human reinforcement learning.
This work was published on Dec 16, 2019 in the journal Nature Communications. The title of the paper is “Task complexity interacts with state-space uncertainty in the arbitration between model-based and model-free learning.”
Human reinforcement learning is an inherently complex and dynamic process, involving goal setting, strategy choice, action selection, strategy modification, cognitive resource allocation etc. This a very challenging problem for humans to solve owing to the rapidly changing and multifaced environment in which humans have to operate. To make matters worse, humans often need to often rapidly make important decisions even before getting the opportunity to collect a lot of information, unlike the case when using deep learning methods to model learning and decision-making in artificial intelligence applications.
In order to solve this problem, the research team used a technique called 'reinforcement learning theory-based experiment design' to optimize the three variables of the two-stage Markov decision task - goal, task complexity, and task uncertainty. This experimental design technique allowed the team not only to control confounding factors, but also to create a situation similar to that which occurs in actual human problem solving.
Secondly, the team used a technique called ‘model-based neuroimaging analysis.’ Based on the acquired behavior and fMRI data, more than 100 different types of meta reinforcement learning algorithms were pitted against each other to find a computational model that can explain both behavioral and neural data. Thirdly, for the sake of a more rigorous verification, the team applied an analytical method called ‘parameter recovery analysis,’ which involves high-precision behavioral profiling of both human subjects and computational models.
In this way, the team was able to accurately identify a computational model of meta reinforcement learning, ensuring not only that the model’s apparent behavior is similar to that of humans, but also that the model solves the problem in the same way as humans do.
The team found that people tended to increase planning-based reinforcement learning (called model-based control), in response to increasing task complexity. However, they resorted to a simpler, more resource efficient strategy called model-free control, when both uncertainty and task complexity were high. This suggests that both the task uncertainty and the task complexity interact during the meta control of reinforcement learning. Computational fMRI analyses revealed that task complexity interacts with neural representations of the reliability of the learning strategies in the inferior prefrontal cortex.
These findings significantly advance understanding of the nature of the computations being implemented in the inferior prefrontal cortex during meta reinforcement learning as well as providing insight into the more general question of how the brain resolves uncertainty and complexity in a dynamically changing environment. Identifying the key computational variables that drive prefrontal meta reinforcement learning, can also inform understanding of how this process might be vulnerable to break down in certain psychiatric disorders such as depression and OCD. Furthermore, gaining a computational understanding of how this process can sometimes lead to increased model-free control, can provide insights into how under some situations task performance might break down under conditions of high cognitive load.
Professor Lee said, “This study will be of enormous interest to researchers in both the artificial intelligence and human/computer interaction fields since this holds significant potential for applying core insights gleaned into how human intelligence works with AI algorithms.”
This work was funded by the National Institute on Drug Abuse, the National Research Foundation of Korea, the Ministry of Science and ICT, Samsung Research Funding Center of Samsung Electronics.
Figure 1 (modified from the figures of the original paper doi:10.1038/s41467-019-13632-1). Computations implemented in the inferior prefrontal cortex during meta reinforcement learning. (A) Computational model of human prefrontal meta reinforcement learning (left) and the brain areas whose neural activity patterns are explained by the latent variables of the model. (B) Examples of behavioral profiles. Shown on the left is choice bias for different goal types and on the right is choice optimality for task complexity and uncertainty. (C) Parameter recoverability analysis. Compared are the effect of task uncertainty (left) and task complexity (right) on choice optimality.
-Profile
Professor Sang Wan Lee
sangwan@kaist.ac.kr
Department of Bio and Brain Engineering
Director, KAIST Center for Neuroscience-inspired AI
KAIST Institute for Artificial Intelligence (http://aibrain.kaist.ac.kr)
KAIST Institute for Health, Science, and Technology
KAIST (https://www.kaist.ac.kr)
2020.01.31 View 5907 -
Professor Yim Decorated with the Chongjo Order of Merit
Professor Yong-Taek Yim from the Department of Mechanical Engineering was awarded the highest order of merit, the “Chongjo Keunjong Medal,” bestowed to public officials by the government in celebration of Invention Day on May 27.
Professor Yim was recognized for his innovative achievements to increase royalty income by introducing an IP-based management system at the Korean Institute of Machinery & Materials. He served as the president of KIMM for three years from 2014.
His idea led to new approaches to help explore diverse revenue creating sources such as dividend earnings and share sales, apart from simply relying on technology transfer fees. His efforts to disseminate the in-house R&D results also led to the foundation of six tech-based startups and spinoffs, which generated 11.2 billion KRW in sales. He also helped set up three spinoffs abroad.
Professor Yim said, “I pushed employee invention as a new value creator at KIMM. I thank each and every researcher and staff member at KIMM who worked so hard to create such an innovative IP-based R&D environment.”
2019.05.28 View 6952 -
KAIST Unveils the Hidden Control Architecture of Brain Networks
(Professor Kwang-Hyun Cho and his team)
A KAIST research team identified the intrinsic control architecture of brain networks. The control properties will contribute to providing a fundamental basis for the exogenous control of brain networks and, therefore, has broad implications in cognitive and clinical neuroscience.
Although efficiency and robustness are often regarded as having a trade-off relationship, the human brain usually exhibits both attributes when it performs complex cognitive functions. Such optimality must be rooted in a specific coordinated control of interconnected brain regions, but the understanding of the intrinsic control architecture of brain networks is lacking.
Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering and his team investigated the intrinsic control architecture of brain networks. They employed an interdisciplinary approach that spans connectomics, neuroscience, control engineering, network science, and systems biology to examine the structural brain networks of various species and compared them with the control architecture of other biological networks, as well as man-made ones, such as social, infrastructural and technological networks.
In particular, the team reconstructed the structural brain networks of 100 healthy human adults by performing brain parcellation and tractography with structural and diffusion imaging data obtained from the Human Connectome Project database of the US National Institutes of Health.
The team developed a framework for analyzing the control architecture of brain networks based on the minimum dominating set (MDSet), which refers to a minimal subset of nodes (MD-nodes) that control the remaining nodes with a one-step direct interaction. MD-nodes play a crucial role in various complex networks including biomolecular networks, but they have not been investigated in brain networks.
By exploring and comparing the structural principles underlying the composition of MDSets of various complex networks, the team delineated their distinct control architectures. Interestingly, the team found that the proportion of MDSets in brain networks is remarkably small compared to those of other complex networks. This finding implies that brain networks may have been optimized for minimizing the cost required for controlling networks. Furthermore, the team found that the MDSets of brain networks are not solely determined by the degree of nodes, but rather strategically placed to form a particular control architecture.
Consequently, the team revealed the hidden control architecture of brain networks, namely, the distributed and overlapping control architecture that is distinct from other complex networks. The team found that such a particular control architecture brings about robustness against targeted attacks (i.e., preferential attacks on high-degree nodes) which might be a fundamental basis of robust brain functions against preferential damage of high-degree nodes (i.e., brain regions).
Moreover, the team found that the particular control architecture of brain networks also enables high efficiency in switching from one network state, defined by a set of node activities, to another – a capability that is crucial for traversing diverse cognitive states.
Professor Cho said, “This study is the first attempt to make a quantitative comparison between brain networks and other real-world complex networks. Understanding of intrinsic control architecture underlying brain networks may enable the development of optimal interventions for therapeutic purposes or cognitive enhancement.”
This research, led by Byeongwook Lee, Uiryong Kang and Hongjun Chang, was published in iScience (10.1016/j.isci.2019.02.017) on March 29, 2019.
Figure 1. Schematic of identification of control architecture of brain networks.
Figure 2. Identified control architectures of brain networks and other real-world complex networks.
2019.04.23 View 38362 -
Wafer-Scale Multilayer Fabrication of Silk Fibroin-Based Microelectronics
A KAIST research team developed a novel fabrication method for the multilayer processing of silk-based microelectronics. This technology for creating a biodegradable silk fibroin film allows microfabrication with polymer or metal structures manufactured from photolithography. It can be a key technology in the implementation of silk fibroin-based biodegradable electronic devices or localized drug delivery through silk fibroin patterns.
Silk fibroins are biocompatible, biodegradable, transparent, and flexible, which makes them excellent candidates for implantable biomedical devices, and they have also been used as biodegradable films and functional microstructures in biomedical applications. However, conventional microfabrication processes require strong etching solutions and solvents to modify the structure of silk fibroins.
To prevent the silk fibroin from being damaged during the process, Professor Hyunjoo J. Lee from the School of Electrical Engineering and her team came up with a novel process, named aluminum hard mask on silk fibroin (AMoS), which is capable of micropatterning multiple layers composed of both fibroin and inorganic materials, such as metal and dielectrics with high-precision microscale alignment. The AMoS process can make silk fibroin patterns on devices, or make patterns on silk fibroin thin films with other materials by using photolithography, which is a core technology in the current microfabrication process.
The team successfully cultured primary neurons on the processed silk fibroin micro-patterns, and confirmed that silk fibroin has excellent biocompatibility before and after the fabrication process and that it also can be applied to implanted biological devices.
Through this technology, the team realized the multilayer micropatterning of fibroin films on a silk fibroin substrate and fabricated a biodegradable microelectric circuit consisting of resistors and silk fibroin dielectric capacitors in a silicon wafer with large areas.
They also used this technology to position the micro-pattern of the silk fibroin thin film closer to the flexible polymer-based brain electrode, and confirmed the dye molecules mounted on the silk fibroin were transferred successfully from the micropatterns.
Professor Lee said, “This technology facilitates wafer-scale, large-area processing of sensitive materials. We expect it to be applied to a wide range of biomedical devices in the future. Using the silk fibroin with micro-patterned brain electrodes can open up many new possibilities in research on brain circuits by mounting drugs that restrict or promote brain cell activities.”
This research, in collaboration with Dr. Nakwon Choi from KIST and led by PhD candidate Geon Kook, was published in ACS AMI (10.1021/acsami.8b13170) on January 16, 2019.
Figure 1. The cover page of ACS AMI
Figure 2. Fibroin microstructures and metal patterns on a fibroin produced by using the AMoS mask.
Figure 3. Biocompatibility assessment of the AMoS Process. Top: Schematics image of a) fibroin-coated silicon b) fibroin-pattered silicon and c) gold-patterned fibroin. Bottom: Representative confocal microscopy images of live (green) and dead (red) primary cortical neurons cultured on the substrates.
2019.03.15 View 22334 -
1g-Ultrasound System for the Brain Stimulation of a Freely-moving Mouse
A KAIST research team developed a light-weight capacitive micromachined ultrasonic transducer (CMUT) and succeeded in the ultrasound brain stimulation of a freely-moving mouse. With this lightweight and compact system, researchers can conduct a versatile set of in vivo experiments.
Conventional methods for stimulating a specific brain region, such as deep brain stimulation (DBS) and optogenetics technology, are highly invasive because they have to insert probes into a target brain, which makes them difficult to use for clinical application. While transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (TES) are noninvasive, they have a wide range of stimulation and problems with in-depth stimulation, which makes them problematic for target-specific treatment.
Therefore, noninvasive and focused ultrasound stimulation technology is gaining a great deal of attention as a next-generation brain stimulation alternative. Since it is delivered noninvasively, it can be applied safely in humans as well as animal experiments. Focused ultrasound stimulation is more advantageous than conventional methods in terms of providing both local and deep stimulation.
Animal behavior experiments are essential for brain stimulation research; however, ultrasonic brain stimulation technology is currently in the early stages of development. So far, only research outcomes with fixed anesthetized mice have been studied because of the heavy ultrasonic device.
Professor Hyunjoo J. Lee from the School of Electrical Engineering and her team reported a technology that can provide ultrasound stimulation to the brain of a freely-moving mouse through a microminiaturized ultrasound device.
The team studied miniaturization and ultra-lightweight CMUTs through microelectromechanical systems (MEMS) technology and designed a device suitable for behavior experiments. The device weighing less than 1g (around 0.05% of the mouse’s weight) has the center frequency, size, focal length, and ultrasonic intensity to fit a mouse’s dimensions.
To evaluate the performance of the ultrasonic device, the team stimulated the motor cortex of the mouse brain and observed the movement reaction of its forefoot. They also measured the electromyography (EMG) of the trapezius.
As a result, the team confirmed that their ultrasonic device can deliver ultrasound to a depth of 3-4mm in the mouse brain and stimulate an area of the mouse brain that represents 25% of its total size.
Based on this research, the team is investigating the effects of ultrasound on sleep by stimulating the brain of sleeping mice.
Professor Lee said, “Going beyond experimenting on fixed anesthetized mice, this research succeeded in the brain stimulation of a freely-moving mouse. We are planning to study mice with diseases, such as Parkinson’s disease, dementia, depression, and epilepsy. I believe that this basic research can contribute to treating human brain-related diseases through ultrasound brain stimulation.
This research, led by Masters candidates Hyunggug Kim and Seongyeon Kim, was published in Brain Stimulation (10.1016/j.brs.2018.11.007) on November 17, 2018.
Figure 1. The miniature transducer for the transcranial ultrasound of a freely-moving mouse
Figure 2. Its structure and simulated 2D beam profile in the axial ad radial directions
2019.03.13 View 9323 -
Noninvasive Light-Sensitive Recombinase for Deep Brain Genetic Manipulation
A KAIST team presented a noninvasive light-sensitive photoactivatable recombinase suitable for genetic manipulation in vivo. The highly light-sensitive property of photoactivatable Flp recombinase will be ideal for controlling genetic manipulation in deep mouse brain regions by illumination with a noninvasive light-emitting diode. This easy-to-use optogenetic module made by Professor Won Do Heo and his team will provide a side-effect free and expandable genetic manipulation tool for neuroscience research.
Spatiotemporal control of gene expression has been acclaimed as a valuable strategy for identifying functions of genes with complex neural circuits. Studies of complex brain functions require highly sophisticated and robust technologies that enable specific labeling and rapid genetic modification in live animals. A number of approaches for controlling the activity of proteins or expression of genes in a spatiotemporal manner using light, small molecules, hormones, and peptides have been developed for manipulating intact circuits or functions.
Among them, recombination-employing, chemically inducible systems are the most commonly used in vivo gene-modification systems. Other approaches include selective or conditional Cre-activation systems within subsets of green fluorescent protein-expressing cells or dual-promoter-driven intersectional populations of cells.
However, these methods are limited by the considerable time and effort required to establish knock-in mouse lines and by constraints on spatiotemporal control, which relies on a limited set of available genetic promoters and transgenic mouse resources.
Beyond these constraints, optogenetic approaches allow the activity of genetically defined neurons in the mouse brain to be controlled with high spatiotemporal resolution. However, an optogenetic module for gene-manipulation capable of revealing the spatiotemporal functions of specific target genes in the mouse brain has remained a challenge.
In the study published at Nature Communication on Jan. 18, the team featured photoactivatable Flp recombinase by searching out split sites of Flp recombinase that were not previously identified, being capable of reconstitution to be active. The team validated the highly light-sensitive, efficient performance of photoactivatable Flp recombinase through precise light targeting by showing transgene expression within anatomically confined mouse brain regions.
The concept of local genetic labeling presented here suggests a new approach for genetically identifying subpopulations of cells defined by the spatial and temporal characteristics of light delivery. To date, an optogenetic module for gene-manipulation capable of revealing spatiotemporal functions of specific target genes in the mouse brain has remained out of reach and no such light-inducible Flp system has been developed. Accordingly, the team sought to develop a photoactivatable Flp recombinase that takes full advantage of the high spatiotemporal control offered by light stimulation.
This activation through noninvasive light illumination deep inside the brain is advantageous in that it avoids chemical or optic fiber implantation-mediated side effects, such as off-target cytotoxicity or physical lesions that might influence animal physiology or behaviors. The technique provides expandable utilities for transgene expression systems upon Flp recombinase activity in vivo, by designing a viral vector for minimal leaky expression influenced by viral nascent promoters.
The team demonstrated the utility of PA-Flp as a noninvasive in vivo optogenetic manipulation tool for use in the mouse brain, even applicable for deep brain structures as it can reach the hippocampus or medial septum using external LED light illumination.
The study is the result of five years of research by Professor Heo, who has led the bio-imaging and optogenetics fields by developing his own bio-imaging and optogenetics technologies. “It will be a great advantage to control specific gene expression desired by LEDs with little physical and chemical stimulation that can affect the physiological phenomenon in living animals,” he explained.
2019.01.22 View 7017 -
KAIST Presents Innovations at CES 2019
Ten of the most innovative technologies spun off from KAIST made a debut at the Consumer Electronics Show (CES) 2019, the world’s largest consumer electronics and IT exhibition being held in Las Vegas from January 8 to 11. The KAIST booth at the CES featured technologies made by KAIST research teams and five startup companies including LiBEST, Memslux, and Green Power. In particular, the KAIST Alumni Association invited 33 aspiring alumni entrepreneurs selected from the KAIST Startup Competition to the show.
At the exhibition, KAIST is presenting innovations in the fields of AI and Bio-IT convergence for the Fourth Industrial Revolution. These include real-time upscaling from Full HD to 4K UHD using AI deep learning-based convolutional neural networks (Professor Munchurl Kim, School of Electrical Engineering) and an AI conversation agent that responds to user’s emotions (Professor Soo-Young Lee, School of Electrical Engineering).
Other technologies include optimal drug target identification by cancer cell type through drug response prediction to be used in personalized cancer treatments (Professor Kwang-Hyun Cho, Department of Bio and Brain Engineering), a nanofiber-based color changing gas sensor with greater sensitivity than conventional paper-based color changing sensors (Professor Il-Doo Kim, Department of Materials Science and Engineering), and functional near-infrared spectroscopy (fNIRS) for brain imaging and muscle fatigue measurement (Professor Hyeonmin Bae, School of Electrical Engineering).
The KAIST booth also features startups founded by KAIST alumni including LiBEST with a flexible lithium polymer secondary cell optimized for smart wearable devices and Rempus with a high-performance lithium ion cell packaging technology for outstanding safety, high capacity, long life, and fast charging.
Green Power and Smart Radar Systems are also joining the booth with a highly efficient and eco-friendly wireless charging system for electrical cars, and a 4D image radar sensor that detects 3D images and speed in real time for applications in self-driving cars, drones, and security systems respectively.
Faculty-founded startup Memslux (CEO Jun-Bo Yoon, School of Electrical Engineering) is presenting a transparent surface light source solution for next-generation display devices.
Associate Vice President of Office of University-Industry Cooperation Kyung Cheol Choi said, “I believe that universities should play a role in connecting technological innovations to business startups for creating value at a global level. In that sense, it is a great opportunity to present innovative technologies from KAIST and promote outstanding KAIST startups at CES 2019. Hopefully, this experience will lead to joint R&D, investment, cooperation, and international technology transfer contracts with leading companies from around the world.”
Here are the five key technologies presented by KAIST at CES 2019.
2019.01.10 View 8128