Fault-network geometry influences earthquake frictional behaviour.

Understanding the factors governing the stability of fault slip is a crucial problem in fault mechanics1-3. The importance of fault geometry and roughness on fault-slip behaviour has been highlighted in recent lab experiments4-7 and numerical models8-11, and emerging evidence suggests that large-scale complexities in fault networks have a vital role in the fault-rupture process12-18. Here we present a new perspective on fault creep by investigating the link between fault-network geometry and surface creep rates in California, USA. Our analysis reveals that fault groups exhibiting creeping behaviour show smaller misalignment in their fault-network geometry. The observation indicates that the surface fault traces of creeping regions tend to be simple, whereas locked regions tend to be more complex. We propose that the presence of complex fault-network geometries results in geometric locking that promotes stick-slip behaviour characterized by earthquakes, whereas simpler geometries facilitate smooth fault creep. Our findings challenge traditional hypotheses on the physical origins of fault creep explained primarily in terms of fault friction19-21 and demonstrate the potential for a new framework in which large-scale earthquake frictional behaviour is determined by a combination of geometric factors and rheological yielding properties.

A new species of the genus Proutia Tutt (Lepidoptera, Psychidae) from Korea, based on morphology and DNA barcodes.

Background: The genus Proutia Tutt, 1899 (Lepidoptera, Psychidae) comprises 14 species found throughout the world. In East Asia, three species, Proutiachinensis Hättenschwiler & Chao, 1990, P.maculatella Saigusa & Sugimoto, 2014 and P.nigra Saigusa & Sugimoto, 2014, are known from Korea, Japan and China. New information: Proutiacornucervae Roh & Lee, sp. nov. is newly recognised from Korea. In addition, Bruandellaniphonica (Hori) is transferred to genus Proutia. Male and genitalia of the species are described and DNA barcodes are provided.

Phenotype-based screening rediscovered benzopyran-embedded microtubule inhibitors as anti-neuroinflammatory agents by modulating the tubulin-p65 interaction.

Neuroinflammation is one of the critical processes implicated in central nervous system (CNS) diseases. Therefore, alleviating neuroinflammation has been highlighted as a therapeutic strategy for treating CNS disorders. However, the complexity of neuroinflammatory processes and poor drug transport to the brain are considerable hurdles to the efficient control of neuroinflammation using small-molecule therapeutics. Thus, there is a significant demand for new chemical entities (NCEs) targeting neuroinflammation. Herein, we rediscovered benzopyran-embedded tubulin inhibitor 1 as an anti-neuroinflammatory agent via phenotype-based screening. A competitive photoaffinity labeling study revealed that compound 1 binds to tubulin at the colchicine-binding site. Structure-activity relationship analysis of 1's analogs identified SB26019 as a lead compound with enhanced anti-neuroinflammatory efficacy. Mechanistic studies revealed that upregulation of the tubulin monomer was critical for the anti-neuroinflammatory activity of SB26019. We serendipitously found that the tubulin monomer recruits p65, inhibiting its translocation from the cytosol to the nucleus and blocking NF-κB-mediated inflammatory pathways. Further in vivo validation using a neuroinflammation mouse model demonstrated that SB26019 suppressed microglial activation by downregulating lba-1 and proinflammatory cytokines. Intraperitoneal administration of SB26019 showed its therapeutic potential as an NCE for successful anti-neuroinflammatory regulation. Along with the recent growing demands on tubulin modulators for treating various inflammatory diseases, our results suggest that colchicine-binding site-specific modulation of tubulins can be a potential strategy for preventing neuroinflammation and treating CNS diseases.

Discovery of E3 Ligase Ligands for Target Protein Degradation.

Target protein degradation has emerged as a promising strategy for the discovery of novel therapeutics during the last decade. Proteolysis-targeting chimera (PROTAC) harnesses a cellular ubiquitin-dependent proteolysis system for the efficient degradation of a protein of interest. PROTAC consists of a target protein ligand and an E3 ligase ligand so that it enables the target protein degradation owing to the induced proximity with ubiquitin ligases. Although a great number of PROTACs has been developed so far using previously reported ligands of proteins for their degradation, E3 ligase ligands have been mostly limited to either CRBN or VHL ligands. Those PROTACs showed their limitation due to the cell type specific expression of E3 ligases and recently reported resistance toward PROTACs with CRBN ligands or VHL ligands. To overcome these hurdles, the discovery of various E3 ligase ligands has been spotlighted to improve the current PROTAC technology. This review focuses on currently reported E3 ligase ligands and their application in the development of PROTACs.

Polymeric Microfluidic Devices Fabricated Using Epoxy Resin for Chemically Demanding and Day-Long Experiments.

Polydimethylsiloxane (PDMS) is a widely used material in laboratories for fabricating microfluidic devices with a rapid and reproducible prototypingability, owing to its inherent properties (e.g., flexibility, air permeability, and transparency). However, the PDMS channel is easily deformed under pressures applied to generate flows because of its elasticity, which can affect the robustness of experiments. In addition, air permeability of PDMS causes the pervaporation of water, and its porous structure absorbs oil and even small hydrophobic molecules, rendering it inappropriate for chemically demanding or day-long experiments. In this study, we develop a rapid and reproducible fabrication method for polymer-based rigid microfluidic devices, using epoxy resin that can overcome the limitations of PDMS channels, which are structurally and chemically robust. We first optimize a high-resolution fabrication protocol to achieve convenient and repeatable prototyping of polymeric devices via epoxy casting using PDMS soft molds. In addition, we compare the velocity changes in PDMS microchannels by tracking fluorescent particles in various flows (~133 µL/min) to demonstrate the structural robustness of the polymeric device. Furthermore, by comparing the adsorption of fluorescent hydrophobic chemicals and the pervaporation through channel walls, we demonstrate the excellent chemical resistance of the polymeric device and its suitability for day-long experiments. The rigid polymeric device can facilitate lab-on-chip research and enable various applications, such as high-performance liquid chromatography, anaerobic bacterial culture, and polymerase chain reaction, which require chemically or physically demanding experiments.

Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy.

Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements often cause problems that produce varying results. It raises concerns about the reproducibility of the results. Consequently, the manual classification of the SERS spectrum requires carefully controlled experimentation that further hinders the large-scale adaptation. Recent advances in machine learning offer decent opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are missing. Towards this end, we provide the SERS spectral benchmark dataset of lead(II) nitride (Pb(NO3)2) for a heavy metal ion detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. The proposed model can successfully identify the Pb(NO3)2 molecule from SERS measurements of independent test experiments. In particular, the proposed model shows an 84.6% balanced accuracy for the cross-batch testing task.

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network.

Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.

Seasonal distribution of Haemaphysalis longicornis (Acari: Ixodidae) and detection of SFTS virus in Gyeongbuk Province, Republic of Korea, 2018.

The seasonal distribution of hard ticks was investigated in 2018 in Gyeongbuk Province, Republic of Korea. Ticks were assayed for severe fever with thrombocytopenia syndrome virus (SFTSV). Ticks were collected monthly using CO2-baited traps from April to November in four habitats (grasslands, grave sites, hiking trails, and mixed forests). Based on morphological and molecular identification, Haemaphysalis longicornis was the most commonly collected species, followed by H. flava and Ixodes nipponensis. Ticks were more commonly collected in grassland habitats, followed by the grave sites, hiking trails, and mixed forests. Peak numbers of nymphs and adults of H. longicornis occurred in May and June, respectively, and Haemaphysalis larvae were collected from August to October. A total of 9/187 (4.8%) pools were positive for SFTSV between June and October in 2018. Phylogenetic analysis of partial fragments of the SFTSV obtained in this study showed that all positive virus samples clustered into genotype B.

Taxonomy of the coastal Thinobius Kiesenwetter (Coleoptera: Staphylinidae: Oxytelinae) in Korea.

A taxonomic study of the intertidal Thinobius Kiesenwetter in Korea is presented. Three species are recognized, two of which are identified as new species (Thinobius jejuensis Lee Ahn, sp. n. and Thinobius koreanus Lee Yoo, sp. n.) and Thinobius kuroshio (Sawada) is recorded for the first time in Korea. A key, descriptions, habitus photographs, and line drawings of diagnostic characters of these three species are provided to facilitate identification.

Sustainable Growth of Social Tourism: A Growth Mixture Modeling Approach Using Heterogeneous Travel Frequency Trajectories.

As travel activity has gained attention as one of the essential ways of understanding the sustainable growth of social tourism, a growing number of research projects have been conducted to elucidate the relationship between residents' travel quantity (frequency) and quality (experience) in both macro and micro perspectives. Yet, very little research has highlighted that travel opportunities are not equally available to residents, especially a longitudinal perspective. The current study classified domestic travelers into four distinct classes using four years of longitudinal data from 5054 Korean residents. Latent growth curve modeling (LGCM) and growth mixture modeling (GMM) were employed to find out (1) the optimal number of classes, (2) the longitudinal travel frequency trajectory of each class, and (3) the distinctive demographic and travel characteristics of the four classes. This study provides some practical implications for policymakers when optimizing available resources for sustainable travel opportunities to relevant target sub-populations. Furthermore, detailed step-by-step analytic tutorials are also introduced for the extended application of longitudinal latent variable analysis in the tourism and hospitality fields, providing additional insights for relevant stakeholders.

Development of a forward model for Bayesian analysis of a single crystal dispersion interferometer.

The Single Crystal Dispersion Interferometer (SCDI) is a newly developed dispersion interferometer (DI) system installed on KSTAR and has obtained the first data successfully in January 2020. Unlike conventional heterodyne DI systems, which use two nonlinear crystals, only one nonlinear crystal is used to eliminate the difficulty in overlapping the first and second harmonic beams, aligning and focusing the beams to a small aperture of the second nonlinear crystal, and resolving a problem of significant efforts to maintain the beam alignment to the second nonlinear crystal after a long beam transmission. The second nonlinear crystal is replaced by a frequency doubler, a simple electronic component. To infer a line integrated electron density with its associated uncertainty consistent with the measured data, we develop a forward model of the KSTAR SCDI that can be used as a likelihood within a Bayesian-based data analysis routine. The forward model consists of two main parts, which are an optical system and an electronics system, and it takes into account noises by modeling the mechanical vibrations and the electronic noises as Gaussian distributions, while the photon noise is modeled with a Poisson distribution. The developed forward model can be used for designing and improving the SCDI system.

The new single crystal dispersion interferometer installed on KSTAR and its first measurement.

Dispersion interferometers have been used to measure line integrated electron densities from many fusion devices. To optically suppress noise due to mechanical vibrations, a conventional dispersion interferometer typically uses two nonlinear crystals located before and after the plasma along the laser beam path. Due to the long beam path, it can be difficult to overlap the fundamental and second harmonic laser beams for a heterodyne dispersion interferometer and to focus the beams on the second nonlinear crystal located after the plasma, especially when the aperture of the nonlinear crystal is small, i.e., of the order of mm. To overcome such difficulties, a new concept of a heterodyne dispersion interferometer, a single crystal dispersion interferometer (SCDI), is developed and installed on KSTAR with the laser wavelength of 1064 nm. The concept and the optical setup of the KSTAR SCDI are discussed, as well as its first measurement during a shattered pellet injection that produces abrupt and large changes in the electron density. To demonstrate feasibility, the KSTAR SCDI measurements are also compared with those from the existing two-color interferometer.

Impacts of the Regional Greenhouse Gas Initiative (RGGI) on infant mortality: a quasi-experimental study in the USA, 2003-2014.

OBJECTIVES: The Regional Greenhouse Gas Initiative (RGGI) is the first mandatory market-based regulatory programme to limit regional carbon dioxide (CO2) emissions in the USA. Empirical evidence has shown that high concentrations of ambient air pollutants such as CO2 have been positively associated with an increased risk of morbidity (eg, respiratory conditions including asthma and lung cancer) and premature mortality. The purpose of this study was to examine the impacts of RGGI on death rates in infancy. DESIGN: A quasi-experimental difference-in-differences design. SETTING AND PARTICIPANTS: We estimated the impacts of RGGI on infant mortality from 2003 through 2014 in the USA (6 years before and after RGGI implementation). Our analytic models included state- and year-fixed effects in addition to a number of covariates. OUTCOME MEASURES: Death rates in infancy: neonatal mortality rates (NMRs), deaths under 28 days as well as infant mortality rates (IMRs), deaths under 1 year. RESULTS: Implementation of RGGI was associated with significant decreases in overall NMRs (a reduction of 0.41/1000 live births) and male NMRs (a reduction of 0.43/1000 live births). However, RGGI did not have a significant effect on female NMRs. Similarly, overall IMRs and male IMRs decreased significantly by 0.37/1000 live births and 0.61/1000 live births, respectively, after implementation of RGGI while female IMRs were not significantly affected by RGGI. CONCLUSIONS: RGGI was associated with decreases in overall infant mortality and boy mortality through reducing air pollutant concentrations. Of note, the impact of this environmental policy on infant girls was much smaller.

Effects of Injection Laryngoplasty with Hyaluronic Acid in Patients with Vocal Fold Paralysis.

OBJECTIVES: The purpose of this study was to explore the effects of injection laryngoplasty (IL) with hyaluronic acid in patients with vocal fold paralysis (VFP). METHODS: A total of 50 patients with VFP participated in this study. Pre- and post-IL assessments were performed, which included analyzing the sustained vowel /a/ phonation, and the patient reading 1 Korean sentence from the "Walk" passage that comprised 25 syllables in 10 words. To investigate the effect of IL on vocal fold function, acoustic analysis (acoustic voice quality index, cepstral peak prominence, maximum phonation time, speaking fundamental frequency) was conducted and auditory-perceptual (grade and overall severity), visual judgment (gap), and self-questionnaire (voice handicap index-10) assessments were performed. RESULTS: The patients with VFP showed statistically significant differences between pre-and post-IL assessments for acoustic and auditory-perception, visual judgment, and self-questionnaire assessments. CONCLUSION: The patients with VFP showed positive change in vocal fold function between pre- and post-IL measurements. The findings showed that IL with hyaluronic acid is an effective method to improve vocal fold function in patients with VFP.

Association between knee extensor strength and pulmonary function in the female elderly.

[Purpose] The purpose of this study is to study the impact of knee extensor strength, which is an index of the degree of physical activity on lung capacity in older women. [Subjects and Methods] Thirty-three older women who participated in the exercise program hosted by a senior citizen center and S University located in Busan. Force vital capacity (FVC) was measured with digital spirometry (Pony FX, COSMED Inc., Italy). Three rounds of measurements were taken for each participant, with one-minute rests between the tests. The means were computed for analysis. Knee extensor strength was measured with a hand-held dynamometer (HHD, MicroFET2®, Hogan Health Industries, Inc., UT, USA). Three rounds of measurements were taken for each knee extensor muscle, after which the mean of the measurements was taken for each side. The higher knee extensor muscle strength value was used for the analysis. [Results] Knee extensor muscle strength had significant positive correlations with Forced vital capacity (FVC), Forced expiratory volume in 1 second (FEV1), Peak expiratory flow (PEF), and Forced expiratory flow (FEF 25-75%). Association between knee extensor muscle strength and pulmonary function suggested that weakening of the elderly's knee extensor muscle strength has an adverse effect on pulmonary function, although the explanatory power was weak. [Conclusion] Knee extensor muscle weakness in elderly females restricts their physical activity and mobility, thereby serving as a key factor in decreasing their lung capacity.

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution.

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.

The change of lung capacity in elderly women caused by life span.

[Purpose] Although lung capacity in the elderly is affected by age, little research has been performed studying decreasing lung capacity in relation to increasing life expectancy. The aim of this study was to examine the effects of increased life span on the lung capacity of women. [Subjects and Methods] The subjects of this study were 55 healthy elderly women over 60 years of age who were living in Busan. Subjects were classified in the following age categories: 60s, 70s and 80s. For the pulmonary function test, a spirometry (Pony FX, COSMED Inc., Italy) was used. The item for measurement of pulmonary function in elderly women was maximum-effort expiratory spirogram (MES). The pulmonary function test was performed 3 times, and its mean value was used for analysis. [Results] Among items of maximum-effort expiratory spirogram, a significant difference according to age was demonstrated in forced vital capacity, forced expiratory volume in 1 second, peak expiratory flow, maximum expiratory flow 75%, maximum expiratory flow 50%, and inspiratory capacity. [Conclusion] According to this study, lung capacity decreases remarkably as age increases. In conclusion, a continuous exercise program beginning at an early age is essential to prevent decrease in lung capacity as age progresses.

The effect of bridge exercise method on the strength of rectus abdominis muscle and the muscle activity of paraspinal muscles while doing treadmill walking with high heels.

[Purpose] The purpose of this research is to investigate the effect of the method of bridge exercise on the change of rectus abdominis muscle and the muscle activity of paraspinal muscles while doing treadmill walking with high heels. [Subjects and Methods] The subjects of this research are healthy female students consisting of 10 persons performing bridge exercises in a supine group, 10 persons performing bridge exercises in a prone group, and 10 persons in a control group while in S university in Busan. Bridge exercise in supine position is performed in hook lying position. Bridge exercise in prone position is plank exercise in prostrate position. To measure the strength of rectus abdominis muscle, maintaining times of the posture was used. To measure the muscle activity of paraspinal muscles, EMG (4D-MT & EMD-11, Relive, Korea) was used. [Results] The strength of rectus abdominis muscle of both bridge exercises in the supine group and bridge exercises in the prone group increases significantly after exercise. The muscle activity of paraspinal muscle such as thoracic parts and lumbar parts in bridge exercises in the prone group decreases statistically while walking on a treadmill with high heels. Muscle activity of thoracic parts paraspinal muscle and bridge exercises in the supine group decreased significantly. [Conclusion] According to this study, we noticed that bridge exercise in a prone position is desirable for women who prefer wearing high heels as a back pain prevention exercise method.

Terpenes from Forests and Human Health.

Forest bathing has beneficial effects on human health via showering of forest aerosols as well as physical relaxation. Terpenes that consist of multiple isoprene units are the largest class of organic compounds produced by various plants, and one of the major components of forest aerosols. Traditionally, terpene-containing plant oil has been used to treat various diseases without knowing the exact functions or the mechanisms of action of the individual bioactive compounds. This review categorizes various terpenes easily obtained from forests according to their anti-inflammatory, anti-tumorigenic, or neuroprotective activities. Moreover, potential action mechanisms of the individual terpenes and their effects on such processes, which are described in various in vivo and in vitro systems, are discussed. In conclusion, the studies that show the biological effectiveness of terpenes support the benefits of forest bathing and propose a potential use of terpenes as chemotherapeutic agents for treating various human diseases.

Pruning-Based Sparse Recovery for Electrocardiogram Reconstruction from Compressed Measurements.

Due to the necessity of the low-power implementation of newly-developed electrocardiogram (ECG) sensors, exact ECG data reconstruction from the compressed measurements has received much attention in recent years. Our interest lies in improving the compression ratio (CR), as well as the ECG reconstruction performance of the sparse signal recovery. To this end, we propose a sparse signal reconstruction method by pruning-based tree search, which attempts to choose the globally-optimal solution by minimizing the cost function. In order to achieve low complexity for the real-time implementation, we employ a novel pruning strategy to avoid exhaustive tree search. Through the restricted isometry property (RIP)-based analysis, we show that the exact recovery condition of our approach is more relaxed than any of the existing methods. Through the simulations, we demonstrate that the proposed approach outperforms the existing sparse recovery methods for ECG reconstruction.