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Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50–60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.

Long-term exposure to particulate matter (PM) with aerodynamic diameters < 10 (PM10) and 2.5 µm (PM2.5) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH3, SO2, primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 µg m−3 for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).

The inability to inspect metabolic activities within subcellular compartments has been a major barrier to our understanding of eukaryotic cell metabolism. Here, we describe a spatial-fluxomics approach for inferring metabolic fluxes in mitochondria and cytosol under physiological conditions, combining isotope tracing, rapid subcellular fractionation, LC-MS-based metabolomics, computational deconvolution, and metabolic network modeling. Applied to study reductive glutamine metabolism in cancer cells, shown to mediate fatty acid biosynthesis under hypoxia and defective mitochondria, we find a previously unappreciated role of reductive IDH1 as the sole net contributor of carbons to fatty acid biosynthesis under standard normoxic conditions in HeLa cells. In murine cells with defective SDH, we find that reductive biosynthesis of citrate in mitochondria is followed by a reversed CS activity, suggesting a new route for supporting pyrimidine biosynthesis. We expect this spatial-fluxomics approach to be a highly useful tool for elucidating the role of metabolic dysfunction in human disease. Measuring metabolic fluxes in cellular compartments is a challenge. Here, the authors introduce an approach to infer fluxes in mitochondria and cytosol, and find that IDH1 is the major producer of cytosolic citrate in HeLa cells and that in SDH- deficient cells citrate synthase functions in reverse.

Background: Syncope is a temporary loss of consciousness due to cerebral hypoperfusion associated with autonomic dysfunction. Vasovagal syncope (VVS) and postural orthostatic tachycardia syndrome (POTS) are the most common causes of syncope in adolescents.Purpose: Here we conducted a comparative analysis of VVS and POTS in adolescents using transcranial doppler (TCD) and autonomic function tests to identify the mechanisms underlying the occurrence of each.Methods: From August 2014 to July 2024, a tilt-table test was conducted on patients who presented with syncope or presyncope as the main symptom. Based on the head-up tilt test results, the patients were classified into the VVS or POTS groups and their medical records retrospectively analyzed.Results: The study included 137 patients: 100 (73%) in the VVS group and 37 (27%) in the POTS group. There were no significant intergroup differences in patient characteristics. In the TCD, the diastolic blood flow velocity during symptom onset was significantly lower in the VVS versus POTS group (18.40±7.14 cm/sec vs. 22.32±8.48 cm/sec, P=0.008). Additionally, the pulsatility index was higher in the VVS group (1.51±0.41 vs 1.22±0.37, P<0.005). There were no intergroup differences in autonomic function tests results or composite autonomic severity scores.Conclusion: The cerebral blood flow velocity during diastole differs between VVS and POTS, suggesting that it may be a determining factor in the pathogenesis of each.

A decrease in bone mineral density (BMD) is a systemic consequence of chronic obstructive pulmonary disease (COPD). Past reports have rarely examined any correlation between sarcopenia and BMD. We investigated the relationship cross-sectionally between the presence of sarcopenia and BMD reduction in COPD patients. COPD patients aged 50 or older with qualifying spirometry and dual-energy X-ray absorptiometry data were from participants in the Korean National Health and Nutrition Examination Surveys IV and V (2008-2011). There were 286 (33.3%) subjects in the sarcopenia group and 572 (66.7%) in the non-sarcopenia group. The sarcopenia group had lower T-scores than the non-sarcopenia group (femur: -0.73±0.88 vs. -0.18±0.97, p < 0.001; femur neck: -1.44±0.98 vs. -0.99±1.06, p < 0.001; lumbar: -1.38±1.36 vs. -0.84±1.38, p < 0.001). The prevalences of osteopenia and osteoporosis were 60.8% and 22.0%, respectively, in the sarcopenia group and 45.6% and 13.3% in the non-sarcopenia group (both p < 0.001). After adjusting for multiple variables, the presence of sarcopenia associated with increased the risk of osteopenia, osteoporosis, and a low BMD (OR = 3.227, 95% CI = 2.125-4.899, p < 0.001, OR = 6.952, 95% CI = 3.418-14.139, p < 0.001, and OR = 3.495, 95% CI = 2.315-5.278, p < 0.001, respectively). In a subgroup analysis, similar OR changes were confirmed in the high-body-weight group (n = 493) (OR = 2.248, 95% CI = 1.084-4.665, p = 0.030, OR = 4.621, 95% CI = 1.167-18.291, p = 0.029, and OR = 2.376, 95% CI = 1.158-4.877, p = 0.018, respectively). The presence of sarcopenia was associated with increased the risk for decreased BMD in COPD.

This study proposes a system that uses unsupervised learning to autonomously identify sensor data which suggest that a machine may soon fail. The system predicts three failure modes in the servo motor of an injection machine by learning multivariate data from heterogeneous sensors. The unsupervised learning model predicted failures with an average F1 score of 0.9958. A case study in an actual shop verified the system’s practical applicability. This shop is a factory that runs 27 injection machines of various tonnages. Results confirmed the ease of retraining the unsupervised learning model and demonstrated its portability. The use of an unsupervised learning model eliminates the difficulties and dependencies associated with data acquisition for supervised learning models. The case study indicated that the use of the proposed failure‐prediction program can reduce maintenance costs by up to $US 140,000/y. It can be applied to various machines across different industries.

The present study aims to investigate the immunomodulatory effects of essential oil from Chamaecyparis obtusa (EOCO) in an ovalbumin (OVA)-induced allergic rhinitis (AR) mouse model. BALB/c mice were intraperitoneally sensitized and stimulated with OVA. From day 22 to 35, 0.01% and 0.1% ECOC was intranasally administered 1 h before OVA stimulation. Nasal symptoms, as well as serum total and OVA-specific immunoglobulin (Ig) E levels, were measured. Interleukin (IL)-4, IL-10, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α levels in nasal lavage fluid (NLF) and their production by activated splenocytes were measured. Histological changes in the sinonasal mucosa were evaluated through hematoxylin and eosin and periodic acid-Schiff staining procedure. Th cytokines and their transcription factor mRNA expressions were determined using reverse-transcription polymerase chain reaction. Intranasal EOCO administration significantly suppressed allergic symptoms, OVA-specific IgE level, sinonasal mucosal inflammatory cell infiltration, and mucus-producing periodic acid-Schiff (PAS) positive cell count. EOCO also significantly inhibited IL-4, IL-10, and TNF-α levels in NLF and activated splenocytes. Th2 and Treg related cytokines and their transcription factors in sinonasal mucosa were significantly suppressed through intransal EOCO instillation. In conclusion, repetitive EOCO intranasal instillation showed anti-inflammatory and anti-allergic effects by suppressing nasal symptoms and inhibiting the production and expression of inflammatory mediators in the OVA-induced AR mouse model.

Demyelination leads to a loss of neurons, which results in, among other consequences, a severe reduction in locomotor function, and underlies several diseases in humans including multiple sclerosis and polyneuropathies. Considerable clinical progress has been made in counteracting demyelination. However, there remains a need for novel methods that reduce demyelination while concomitantly achieving remyelination, thus complementing the currently available tools to ameliorate demyelinating diseases. In this study, we used an established zebrafish demyelination model to test selected compounds, following a screening in cell culture experiments and in a mouse model of spinal cord injury that was aimed at identifying beneficial functions of the neural cell adhesion molecule L1. In comparison to mammalian nervous system disease models, the zebrafish allows testing of potentially promotive compounds more easily than what is possible in mammals. We found that our selected compounds tacrine and duloxetine significantly improved remyelination in the peripheral and central nervous system of transgenic zebrafish following pharmacologically induced demyelination. Given that both molecules are known to positively affect functions other than those related to L1 and in other disease contexts, we propose that their combined beneficial function raises hope for the use of these compounds in clinical settings.

The Geostationary Environment Monitoring Spectrometer (GEMS) on board GEO-KOMPSAT-2B was launched in February 2020 and has been monitoring atmospheric chemical compositions over Asia. We present the first evaluation of the operational GEMS formaldehyde (HCHO) vertical column densities (VCDs) during and after the in-orbit test (IOT) period (August–October 2020) by comparing them with the products from the TROPOspheric Monitoring Instrument (TROPOMI) and Fourier-transform infrared (FTIR) and multi-axis differential optical absorption spectroscopy (MAX-DOAS) instruments. During the IOT, the GEMS HCHO VCDs reproduced the observed spatial pattern of TROPOMI VCDs over the entire domain (r= 0.62) with high biases (10 %–16 %). We found that the agreement between GEMS and TROPOMI was substantially higher in Northeast Asia (r= 0.90), encompassing the Korean Peninsula and east China. GEMS HCHO VCDs captured the seasonal variation in HCHO, primarily driven by biogenic emissions and photochemical activities, but showed larger variations than those of TROPOMI over coastal regions (Kuala Lumpur, Singapore, Shanghai, and Busan). In addition, GEMS HCHO VCDs showed consistent hourly variations with MAX-DOAS (r= 0.77) and FTIR (r= 0.86) but were 30–40 % lower than ground-based observations. Different vertical sensitivities of GEMS and ground-based instruments caused these biases. Utilizing the averaging kernel smoothing method reduces the low biases by approximately 10 % to 15 % (normalized mean bias (NMB): −47.4 % to −31.5 % and −38.6 % to −26.7 % for MAX-DOAS and FTIR, respectively). The remaining discrepancies are due to multiple factors, including spatial collocation and different instrumental sensitivities, requiring further investigation using inter-comparable datasets.

Aerosol optical properties have been provided by the Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary-Earth-orbit (GEO) satellite instrument designed for air quality monitoring. This study describes improvements made to the GEMS aerosol retrieval (AERAOD) algorithm, including spectral binning, surface reflectance estimation, cloud masking, and post-processing, along with validation results. These enhancements aim to provide more accurate and reliable aerosol-monitoring results for Asia. The adoption of spectral binning in the lookup table (LUT) approach reduces random errors and enhances the stability of satellite measurements. In addition, we introduced a new high-resolution database for surface reflectance estimation based on the minimum-reflectance method, which was adapted to the GEMS pixel resolution. Monthly background aerosol optical depth (BAOD) values were used to estimate hourly GEMS surface reflectance consistently. Advanced cloud-removal techniques have been implemented to significantly improve the effectiveness of cloud detection and enhance aerosol retrieval quality. An innovative post-processing correction method based on machine learning has been introduced to address artificial diurnal biases in aerosol optical depth (AOD) observations. In this study, we investigated selected aerosol events, highlighting the capability of GEMS in monitoring and providing insights into hourly aerosol optical properties during various atmospheric events. The performance of the GEMS AERAOD products was validated against the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data for the period from November 2021 to October 2022. GEMS AOD at 443 nm demonstrated a strong correlation with AERONET AOD at 443 nm (R = 0.792). However, it exhibited biased patterns, including the underestimation of high AOD values and overestimation of low-AOD conditions. Different aerosol types (highly absorbing fine aerosols, dust aerosols, and non-absorbing aerosols) exhibited distinct validation results. The retrievals of GEMS single-scattering albedo (SSA) at 443 nm agreed well with the AERONET SSA at 440 nm within reasonable error ranges, with variations observed among aerosol types. For GEMS AOD at 443 nm exceeding 0.4 (1.0), 42.76 % (56.61 %) and 67.25 % (85.70 %) of GEMS SSA data points fell within the ±0.03 and ±0.05 error bounds, respectively. Model-enforced post-processing correction improved GEMS AOD and SSA performance, thereby reducing the diurnal variation in the biases. The validation of the retrievals of GEMS aerosol layer height (ALH) against the CALIOP data demonstrates good agreement, with a mean bias of −0.225 km and 55.29 % (71.70 %) of data points falling within ±1 km (1.5 km).