research
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‘Urban Green Space Affects Citizens’ Happiness’
Study finds the relationship between green space, the economy, and happiness
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.
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.
-PublicationOh-Hyun Kwon, Inho Hong, Jeasurk Yang, Donghee Y. Wohn, Woo-Sung Jung, andMeeyoung Cha, 2021. Urban green space and happiness in developed countries. EPJ Data Science. DOI: https://doi.org/10.1140/epjds/s13688-021-00278-7
-ProfileProfessor Meeyoung ChaData Science Labhttps://ds.ibs.re.kr/
School of Computing
KAIST
2021.06.21 View 9263 -
Defining the Hund Physics Landscape of Two-Orbital Systems
Researchers identify exotic metals in unexpected quantum systems
Electrons are ubiquitous among atoms, subatomic tokens of energy that can independently change how a system behaves—but they also can change each other. An international research collaboration found that collectively measuring electrons revealed unique and unanticipated findings. The researchers published their results on May 17 in Physical Review Letters.
“It is not feasible to obtain the solution just by tracing the behavior of each individual electron,” said paper author Myung Joon Han, professor of physics at KAIST. “Instead, one should describe or track all the entangled electrons at once. This requires a clever way of treating this entanglement.”
Professor Han and the researchers used a recently developed “many-particle” theory to account for the entangled nature of electrons in solids, which approximates how electrons locally interact with one another to predict their global activity.
Through this approach, the researchers examined systems with two orbitals — the space in which electrons can inhabit. They found that the electrons locked into parallel arrangements within atom sites in solids. This phenomenon, known as Hund’s coupling, results in a Hund’s metal. This metallic phase, which can give rise to such properties as superconductivity, was thought only to exist in three-orbital systems.
“Our finding overturns a conventional viewpoint that at least three orbitals are needed for Hund’s metallicity to emerge,” Professor Han said, noting that two-orbital systems have not been a focus of attention for many physicists. “In addition to this finding of a Hund’s metal, we identified various metallic regimes that can naturally occur in generic, correlated electron materials.”
The researchers found four different correlated metals. One stems from the proximity to a Mott insulator, a state of a solid material that should be conductive but actually prevents conduction due to how the electrons interact. The other three metals form as electrons align their magnetic moments — or phases of producing a magnetic field — at various distances from the Mott insulator. Beyond identifying the metal phases, the researchers also suggested classification criteria to define each metal phase in other systems.
“This research will help scientists better characterize and understand the deeper nature of so-called ‘strongly correlated materials,’ in which the standard theory of solids breaks down due to the presence of strong Coulomb interactions between electrons,” Professor Han said, referring to the force with which the electrons attract or repel each other. These interactions are not typically present in solid materials but appear in materials with metallic phases.
The revelation of metals in two-orbital systems and the ability to determine whole system electron behavior could lead to even more discoveries, according to Professor Han.
“This will ultimately enable us to manipulate and control a variety of electron correlation phenomena,” Professor Han said.
Co-authors include Siheon Ryee from KAIST and Sangkook Choi from the Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory in the United States. Korea’s National Research Foundation and the U.S. Department of Energy’s (DOE) Office of Science, Basic Energy Sciences, supported this work.
-PublicationSiheon Ryee, Myung Joon Han, and SangKook Choi, 2021.Hund Physics Landscape of Two-Orbital Systems, Physical Review Letters, DOI: 10.1103/PhysRevLett.126.206401
-ProfileProfessor Myung Joon HanDepartment of PhysicsCollege of Natural ScienceKAIST
2021.06.17 View 6983 -
Biomimetic Resonant Acoustic Sensor Detecting Far-Distant Voices Accurately to Hit the Market
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).
-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
2021.06.14 View 8284 -
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
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 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 LeeMetabolic &Biomolecular Engineering National Research Laboratoryhttp://mbel.kaist.ac.kr
Department of Chemical and Biomolecular EngineeringKAIST
2021.06.09 View 8766 -
Ultrafast, on-Chip PCR Could Speed Up Diagnoses during Pandemics
A rapid point-of-care diagnostic plasmofluidic chip can deliver result in only 8 minutes
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.
-Publication
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
2021.06.08 View 8315 -
What Guides Habitual Seeking Behavior Explained
A new role of the ventral striatum explains habitual seeking behavior
Researchers have been investigating how the brain controls habitual seeking behaviors such as addiction. A recent study by Professor Sue-Hyun Lee from the Department of Bio and Brain Engineering revealed that a long-term value memory maintained in the ventral striatum in the brain is a neural basis of our habitual seeking behavior. This research was conducted in collaboration with the research team lead by Professor Hyoung F. Kim from Seoul National University. Given that addictive behavior is deemed a habitual one, this research provides new insights for developing therapeutic interventions for addiction.
Habitual seeking behavior involves strong stimulus responses, mostly rapid and automatic ones. The ventral striatum in the brain has been thought to be important for value learning and addictive behaviors. However, it was unclear if the ventral striatum processes and retains long-term memories that guide habitual seeking.
Professor Lee’s team reported a new role of the human ventral striatum where long-term memory of high-valued objects are retained as a single representation and may be used to evaluate visual stimuli automatically to guide habitual behavior.
“Our findings propose a role of the ventral striatum as a director that guides habitual behavior with the script of value information written in the past,” said Professor Lee.
The research team investigated whether learned values were retained in the ventral striatum while the subjects passively viewed previously learned objects in the absence of any immediate outcome. Neural responses in the ventral striatum during the incidental perception of learned objects were examined using fMRI and single-unit recording.
The study found significant value discrimination responses in the ventral striatum after learning and a retention period of several days. Moreover, the similarity of neural representations for good objects increased after learning, an outcome positively correlated with the habitual seeking response for good objects.
“These findings suggest that the ventral striatum plays a role in automatic evaluations of objects based on the neural representation of positive values retained since learning, to guide habitual seeking behaviors,” explained Professor Lee.
“We will fully investigate the function of different parts of the entire basal ganglia including the ventral striatum. We also expect that this understanding may lead to the development of better treatment for mental illnesses related to habitual behaviors or addiction problems.”
This study, supported by the National Research Foundation of Korea, was reported at Nature Communications (https://doi.org/10.1038/s41467-021-22335-5.)
-ProfileProfessor Sue-Hyun LeeDepartment of Bio and Brain EngineeringMemory and Cognition Laboratoryhttp://memory.kaist.ac.kr/lecture
KAIST
2021.06.03 View 8061 -
Identification of How Chemotherapy Drug Works Could Deliver Personalized Cancer Treatment
The chemotherapy drug decitabine is commonly used to treat patients with blood cancers, but its response rate is somewhat low. Researchers have now identified why this is the case, opening the door to more personalized cancer therapies for those with these types of cancers, and perhaps further afield.
Researchers have identified the genetic and molecular mechanisms within cells that make the chemotherapy drug decitabine—used to treat patients with myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) —work for some patients but not others. The findings should assist clinicians in developing more patient-specific treatment strategies.
The findings were published in the Proceedings of the National Academies of Science on March 30.
The chemotherapy drug decitabine, also known by its brand name Dacogen, works by modifying our DNA that in turn switches on genes that stop the cancer cells from growing and replicating. However, decitabine’s response rate is somewhat low (showing improvement in just 30-35% of patients), which leaves something of a mystery as to why it works well for some patients but not for others. To find out why this happens, researchers from the KAIST investigated the molecular mediators that are involved with regulating the effects of the drug.
Decitabine works to activate the production of endogenous retroviruses (ERVs), which in turn induces an immune response. ERVs are viruses that long ago inserted dormant copies of themselves into the human genome. Decitabine in essence, ‘reactivates’ these viral elements and produces double-stranded RNAs (dsRNAs) that the immune system views as a foreign body.
“However, the mechanisms involved in this process, in particular how production and transport of these ERV dsRNAs were regulated within the cell were understudied,” said corresponding author Yoosik Kim, professor in the Department of Chemical and Biomolecular Engineering at KAIST.
“So to explain why decitabine works in some patients but not others, we investigated what these molecular mechanisms were,” added Kim.
To do so, the researchers used image-based RNA interference (RNAi) screening. This is a relatively new technique in which specific sequences within a genome are knocked out of action or “downregulated.” Large-scale screening, which can be performed in cultured cells or within live organisms, works to investigate the function of different genes. The KAIST researchers collaborated with the Institut Pasteur Korea to analyze the effect of downregulating genes that recognize ERV dsRNAs and could be involved in the cellular response to decitabine.
From these initial screening results, they performed an even more detailed downregulation screening analysis. Through the screening, they were able to identify two particular gene sequences involved in the production of an RNA-binding protein called Staufen1 and the production of a strand of RNA that does not in turn produce any proteins called TINCR that play a key regulatory role in response to the drug. Staufen1 binds directly to dsRNAs and stabilizes them in concert with the TINCR.
If a patient is not producing sufficient Staufen1 and TINCR, then the dsRNA viral mimics quickly degrade before the immune system can spot them. And, crucially for cancer therapy, this means that patients with lower expression (activation) of these sequences will show inferior response to decitabine. Indeed, the researchers confirmed that MDS/AML patients with low Staufen1 and TINCR expression did not benefit from decitabine therapy.
“We can now isolate patients who will not benefit from the therapy and direct them to a different type of therapy,” said first author Yongsuk Ku. “This serves as an important step toward developing a patient-specific treatment cancer strategy.”
As the researchers used patient samples taken from bone marrow, the next step will be to try to develop a testing method that can identify the problem from just blood samples, which are much easier to acquire from patients.
The team plans to investigate if the analysis can be extended to patients with solid tumors in addition to those with blood cancers.
-Profile
Professor Yoosik Kim
https://qcbio.kaist.ac.kr/
Department of Chemical and Biomolecular Engineering
KAIST
-Publication
Noncanonical immune response to the inhibition of DNA methylation by Staufen1 via stabilization of endogenous retrovirus RNAs, PNAS
2021.05.24 View 8290 -
Gut Hormone Triggers Craving for More Proteins
- Revelations from a fly study could improve our understanding of protein malnutrition in humans. -
A new study led by KAIST researchers using fruit flies reveals how protein deficiency in the diet triggers cross talk between the gut and brain to induce a desire to eat foods rich in proteins or essential amino acids. This finding reported in the May 5 issue of Nature can lead to a better understanding of malnutrition in humans.
“All organisms require a balanced intake of carbohydrates, proteins, and fats for their well being,” explained KAIST neuroscientist and professor Greg Seong-Bae Suh. “Taking in sufficient calories alone won’t do the job, as it can still lead to severe forms of malnutrition including kwashiorkor, if the diet does not include enough proteins,” he added.
Scientists already knew that inadequate protein intake in organisms causes a preferential choice of foods rich in proteins or essential amino acids but they didn’t know precisely how this happens. A group of researchers led by Professor Suh at KAIST and Professor Won-Jae Lee at Seoul National University (SNU) investigated this process in flies by examining the effects of different genes on food preference following protein deprivation.
The group found that protein deprivation triggered the release of a gut hormone called neuropeptide CNMamide (CNMa) from a specific population of enterocytes - the intestine lining cells. Until now, scientists have known that enterocytes release digestive enzymes into the intestine to help digest and absorb nutrients in the gut. “Our study showed that enterocytes have a more complex role than we previously thought,” said Professor Suh.
Enterocytes respond to protein deprivation by releasing CNMa that conveys the nutrient status in the gut to the CNMa receptors on nerve cells in the brain. This then triggers a desire to eat foods containing essential amino acids.
Interestingly, the KAIST-SNU team also found that the microbiome - Acetobacter bacteria - present in the gut produces amino acids that can compensate for mild protein deficit in the diet. This basal level of amino acids provided by the microbiome modifies CNMa release and tempers the flies’ compensatory desire to ingest more proteins.
The research team was able to further clarify two signalling pathways that respond to protein loss from the diet and ultimately produce the CNMa hormone in these specific enterocytes.
The team said that further studies are still needed to understand how CNMa communicates with its receptors in the brain, and whether this happens by directly activating nerve cells that link the gut to the brain or by indirectly activating the brain through blood circulation. Their research could provide insights into the understanding of similar process in mammals including humans.
“We chose to investigate a simple organism, the fly, which would make it easier for us to identify and characterize key nutrient sensors. Because all organisms have cravings for needed nutrients, the nutrient sensors and their pathways we identified in flies would also be relevant to those in mammals. We believe that this research will greatly advance our understanding of the causes of metabolic disease and eating-related disorders,” Professor Suh added.
This work was supported by the Samsung Science and Technology Foundation (SSTF) and the National Research Foundation (NRF) of Korea.
Publication:
Kim, B., et al. (2021) Response of the Drosophila microbiome– gut–brain axis to amino acid deficit. Nature. Available online at https://doi.org/10.1038/s41586-021-03522-2
Profile:
Greg Seong-Bae Suh, Ph.D
Associate Professor
seongbaesuh@kaist.ac.krLab of Neural Interoception
https://www.suhlab-neuralinteroception.kaist.ac.kr/Department of Biological Sciences
https://bio.kaist.ac.kr/
Korea Advanced Institute of Science and Technology (KAIST)
https:/kaist.ac.kr/en/
Daejeon 34141, Korea
(END)
2021.05.17 View 6531 -
Observing Individual Atoms in 3D Nanomaterials and Their Surfaces
Atoms are the basic building blocks for all materials. To tailor functional properties, it is essential to accurately determine their atomic structures. KAIST researchers observed the 3D atomic structure of a nanoparticle at the atom level via neural network-assisted atomic electron tomography.
Using a platinum nanoparticle as a model system, a research team led by Professor Yongsoo Yang demonstrated that an atomicity-based deep learning approach can reliably identify the 3D surface atomic structure with a precision of 15 picometers (only about 1/3 of a hydrogen atom’s radius). The atomic displacement, strain, and facet analysis revealed that the surface atomic structure and strain are related to both the shape of the nanoparticle and the particle-substrate interface.
Combined with quantum mechanical calculations such as density functional theory, the ability to precisely identify surface atomic structure will serve as a powerful key for understanding catalytic performance and oxidation effect.
“We solved the problem of determining the 3D surface atomic structure of nanomaterials in a reliable manner. It has been difficult to accurately measure the surface atomic structures due to the ‘missing wedge problem’ in electron tomography, which arises from geometrical limitations, allowing only part of a full tomographic angular range to be measured. We resolved the problem using a deep learning-based approach,” explained Professor Yang.
The missing wedge problem results in elongation and ringing artifacts, negatively affecting the accuracy of the atomic structure determined from the tomogram, especially for identifying the surface structures. The missing wedge problem has been the main roadblock for the precise determination of the 3D surface atomic structures of nanomaterials.
The team used atomic electron tomography (AET), which is basically a very high-resolution CT scan for nanomaterials using transmission electron microscopes. AET allows individual atom level 3D atomic structural determination.
“The main idea behind this deep learning-based approach is atomicity—the fact that all matter is composed of atoms. This means that true atomic resolution electron tomogram should only contain sharp 3D atomic potentials convolved with the electron beam profile,” said Professor Yang.
“A deep neural network can be trained using simulated tomograms that suffer from missing wedges as inputs, and the ground truth 3D atomic volumes as targets. The trained deep learning network effectively augments the imperfect tomograms and removes the artifacts resulting from the missing wedge problem.”
The precision of 3D atomic structure can be enhanced by nearly 70% by applying the deep learning-based augmentation. The accuracy of surface atom identification was also significantly improved.
Structure-property relationships of functional nanomaterials, especially the ones that strongly depend on the surface structures, such as catalytic properties for fuel-cell applications, can now be revealed at one of the most fundamental scales: the atomic scale.
Professor Yang concluded, “We would like to fully map out the 3D atomic structure with higher precision and better elemental specificity. And not being limited to atomic structures, we aim to measure the physical, chemical, and functional properties of nanomaterials at the 3D atomic scale by further advancing electron tomography techniques.”
This research, reported at Nature Communications, was funded by the National Research Foundation of Korea and the KAIST Global Singularity Research M3I3 Project.
-Publication
Juhyeok Lee, Chaehwa Jeong & Yongsoo Yang
“Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography”
Nature Communications
-Profile
Professor Yongsoo Yang
Department of Physics
Multi-Dimensional Atomic Imaging Lab (MDAIL)
http://mdail.kaist.ac.kr
KAIST
2021.05.12 View 9548 -
T-GPS Processes a Graph with Trillion Edges on a Single Computer
Trillion-scale graph processing simulation on a single computer presents a new concept of graph processing
A KAIST research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. Named as T-GPS (Trillion-scale Graph Processing Simulation) by the developer Professor Min-Soo Kim from the School of Computing at KAIST, it can process a graph with one trillion edges using a single computer.
Graphs are widely used to represent and analyze real-world objects in many domains such as social networks, business intelligence, biology, and neuroscience. As the number of graph applications increases rapidly, developing and testing new graph algorithms is becoming more important than ever before. Nowadays, many industrial applications require a graph algorithm to process a large-scale graph (e.g., one trillion edges). So, when developing and testing graph algorithms such for a large-scale graph, a synthetic graph is usually used instead of a real graph. This is because sharing and utilizing large-scale real graphs is very limited due to their being proprietary or being practically impossible to collect.
Conventionally, developing and testing graph algorithms is done via the following two-step approach: generating and storing a graph and executing an algorithm on the graph using a graph processing engine.
The first step generates a synthetic graph and stores it on disks. The synthetic graph is usually generated by either parameter-based generation methods or graph upscaling methods. The former extracts a small number of parameters that can capture some properties of a given real graph and generates the synthetic graph with the parameters. The latter upscales a given real graph to a larger one so as to preserve the properties of the original real graph as much as possible.
The second step loads the stored graph into the main memory of the graph processing engine such as Apache GraphX and executes a given graph algorithm on the engine. Since the size of the graph is too large to fit in the main memory of a single computer, the graph engine typically runs on a cluster of several tens or hundreds of computers. Therefore, the cost of the conventional two-step approach is very high.
The research team solved the problem of the conventional two-step approach. It does not generate and store a large-scale synthetic graph. Instead, it just loads the initial small real graph into main memory. Then, T-GPS processes a graph algorithm on the small real graph as if the large-scale synthetic graph that should be generated from the real graph exists in main memory. After the algorithm is done, T-GPS returns the exactly same result as the conventional two-step approach.
The key idea of T-GPS is generating only the part of the synthetic graph that the algorithm needs to access on the fly and modifying the graph processing engine to recognize the part generated on the fly as the part of the synthetic graph actually generated.
The research team showed that T-GPS can process a graph of 1 trillion edges using a single computer, while the conventional two-step approach can only process of a graph of 1 billion edges using a cluster of eleven computers of the same specification. Thus, T-GPS outperforms the conventional approach by 10,000 times in terms of computing resources. The team also showed that the speed of processing an algorithm in T-GPS is up to 43 times faster than the conventional approach. This is because T-GPS has no network communication overhead, while the conventional approach has a lot of communication overhead among computers.
Professor Kim believes that this work will have a large impact on the IT industry where almost every area utilizes graph data, adding, “T-GPS can significantly increase both the scale and efficiency of developing a new graph algorithm.”
This work was supported by the National Research Foundation (NRF) of Korea and Institute of Information & communications Technology Planning & Evaluation (IITP).
Publication:
Park, H., et al. (2021) “Trillion-scale Graph Processing Simulation based on Top-Down Graph Upscaling,” Presented at the IEEE ICDE 2021 (April 19-22, 2021, Chania, Greece)
Profile:
Min-Soo Kim
Associate Professor
minsoo.k@kaist.ac.kr
http://infolab.kaist.ac.kr
School of Computing
KAIST
2021.05.06 View 6548 -
Centrifugal Multispun Nanofibers Put a New Spin on COVID-19 Masks
KAIST researchers have developed a novel nanofiber production technique called ‘centrifugal multispinning’ that will open the door for the safe and cost-effective mass production of high-performance polymer nanofibers. This new technique, which has shown up to a 300 times higher nanofiber production rate per hour than that of the conventional electrospinning method, has many potential applications including the development of face mask filters for coronavirus protection.
Nanofibers make good face mask filters because their mechanical interactions with aerosol particles give them a greater ability to capture more than 90% of harmful particles such as fine dust and virus-containing droplets.
The impact of the COVID-19 pandemic has further accelerated the growing demand in recent years for a better kind of face mask. A polymer nanofiber-based mask filter that can more effectively block harmful particles has also been in higher demand as the pandemic continues.
‘Electrospinning’ has been a common process used to prepare fine and uniform polymer nanofibers, but in terms of safety, cost-effectiveness, and mass production, it has several drawbacks. The electrospinning method requires a high-voltage electric field and electrically conductive target, and this hinders the safe and cost-effective mass production of polymer nanofibers.
In response to this shortcoming, ‘centrifugal spinning’ that utilizes centrifugal force instead of high voltage to produce polymer nanofibers has been suggested as a safer and more cost-effective alternative to the electrospinning. Easy scalability is another advantage, as this technology only requires a rotating spinneret and a collector.
However, since the existing centrifugal force-based spinning technology employs only a single rotating spinneret, productivity is limited and not much higher than that of some advanced electrospinning technologies such as ‘multi-nozzle electrospinning’ and ‘nozzleless electrospinning.’ This problem persists even when the size of the spinneret is increased.
Inspired by these limitations, a research team led by Professor Do Hyun Kim from the Department of Chemical and Biomolecular Engineering at KAIST developed a centrifugal multispinning spinneret with mass-producibility, by sectioning a rotating spinneret into three sub-disks. This study was published as a front cover article of ACS Macro Letters, Volume 10, Issue 3 in March 2021.
Using this new centrifugal multispinning spinneret with three sub-disks, the lead author of the paper PhD candidate Byeong Eun Kwak and his fellow researchers Hyo Jeong Yoo and Eungjun Lee demonstrated the gram-scale production of various polymer nanofibers with a maximum production rate of up to 25 grams per hour, which is approximately 300 times higher than that of the conventional electrospinning system. The production rate of up to 25 grams of polymer nanofibers per hour corresponds to the production rate of about 30 face mask filters per day in a lab-scale manufacturing system.
By integrating the mass-produced polymer nanofibers into the form of a mask filter, the researchers were able to fabricate face masks that have comparable filtration performance with the KF80 and KF94 face masks that are currently available in the Korean market. The KF80 and KF94 masks have been approved by the Ministry of Food and Drug Safety of Korea to filter out at least 80% and 94% of harmful particles respectively.
“When our system is scaled up from the lab scale to an industrial scale, the large-scale production of centrifugal multispun polymer nanofibers will be made possible, and the cost of polymer nanofiber-based face mask filters will also be lowered dramatically,” Kwak explained.
This work was supported by the KAIST-funded Global Singularity Research Program for 2020.
Publication:
Byeong Eun Kwak, Hyo Jeong Yoo, Eungjun Lee, and Do Hyun Kim. (2021) Large-Scale Centrifugal Multispinning Production of Polymer Micro- and Nanofibers for Mask Filter Application with a Potential of Cospinning Mixed Multicomponent Fibers. ACS Macro Letters, Volume No. 10, Issue No. 3, pp. 382-388. Available online at https://doi.org/10.1021/acsmacrolett.0c00829
Profile:
Do Hyun Kim, Sc.D.
Professor
dohyun.kim@kaist.edu
http://procal.kaist.ac.kr/
Process Analysis Laboratory
Department of Chemical and Biomolecular Engineering
https:/kaist.ac.kr/en/
Korea Advanced Institute of Science and Technology (KAIST)Daejeon 34141, Korea
(END)
2021.04.12 View 10810 -
Microbial Production of a Natural Red Colorant Carminic Acid
Metabolic engineering and computer-simulated enzyme engineering led to the production of carminic acid, a natural red colorant, from bacteria for the first time
A research group at KAIST has engineered a bacterium capable of producing a natural red colorant, carminic acid, which is widely used for food and cosmetics. The research team reported the complete biosynthesis of carminic acid from glucose in engineered Escherichia coli. The strategies will be useful for the design and construction of biosynthetic pathways involving unknown enzymes and consequently the production of diverse industrially important natural products for the food, pharmaceutical, and cosmetic industries.
Carminic acid is a natural red colorant widely being used for products such as strawberry milk and lipstick. However, carminic acid has been produced by farming cochineals, a scale insect which only grows in the region around Peru and Canary Islands, followed by complicated multi-step purification processes. Moreover, carminic acid often contains protein contaminants that cause allergies so many people are unwilling to consume products made of insect-driven colorants. On that account, manufacturers around the world are using alternative red colorants despite the fact that carminic acid is one of the most stable natural red colorants.
These challenges inspired the metabolic engineering research group at KAIST to address this issue. Its members include postdoctoral researchers Dongsoo Yang and Woo Dae Jang, and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering. This study entitled “Production of carminic acid by metabolically engineered Escherichia coli” was published online in the Journal of the American Chemical Society (JACS) on April 2.
This research reports for the first time the development of a bacterial strain capable of producing carminic acid from glucose via metabolic engineering and computer simulation-assisted enzyme engineering. The research group optimized the type II polyketide synthase machinery to efficiently produce the precursor of carminic acid, flavokermesic acid.
Since the enzymes responsible for the remaining two reactions were neither discovered nor functional, biochemical reaction analysis was performed to identify enzymes that can convert flavokermesic acid into carminic acid. Then, homology modeling and docking simulations were performed to enhance the activities of the two identified enzymes. The team could confirm that the final engineered strain could produce carminic acid directly from glucose. The C-glucosyltransferase developed in this study was found to be generally applicable for other natural products as showcased by the successful production of an additional product, aloesin, which is found in aloe leaves.
“The most important part of this research is that unknown enzymes for the production of target natural products were identified and improved by biochemical reaction analyses and computer simulation-assisted enzyme engineering,” says Dr. Dongsoo Yang. He explained the development of a generally applicable C-glucosyltransferase is also useful since C-glucosylation is a relatively unexplored reaction in bacteria including Escherichia coli. Using the C-glucosyltransferase developed in this study, both carminic acid and aloesin were successfully produced from glucose.
“A sustainable and insect-free method of producing carminic acid was achieved for the first time in this study. Unknown or inefficient enzymes have always been a major problem in natural product biosynthesis, and here we suggest one effective solution for solving this problem. As maintaining good health in the aging society is becoming increasingly important, we expect that the technology and strategies developed here will play pivotal roles in producing other valuable natural products of medical or nutritional importance,” said Distinguished Professor Sang Yup Lee.
This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries of the Ministry of Science and ICT (MSIT) through the National Research Foundation (NRF) of Korea and the KAIST Cross-Generation Collaborative Lab project; Sang Yup Lee and Dongsoo Yang were also supported by Novo Nordisk Foundation in Denmark.
Publication:
Dongsoo Yang, Woo Dae Jang, and Sang Yup Lee. Production of carminic acid by metabolically engineered Escherichia coli. at the Journal of the American Chemical Society. https://doi.org.10.1021/jacs.0c12406
Profile:
Sang Yup Lee, PhD
Distinguished Professor
leesy@kaist.ac.kr
http://mbel.kaist.ac.kr
Metabolic &Biomolecular Engineering National Research Laboratory
Department of Chemical and Biomolecular Engineering
KAIST
2021.04.06 View 10267