Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approachesdecision trees (DT) and random forest (RF)in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 x 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.open

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011-2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86-0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011-2013 and rebounded in 2014.open0

Detection of tropical overshooting cloud tops using himawari-8 imagery

Overshooting convective cloud Top (OT)-accompanied clouds can cause severe weather conditions, such as lightning, strong winds, and heavy rainfall. The distribution and behavior of OTs can affect regional and global climate systems. In this paper, we propose a new approach for OT detection by using machine learning methods with multiple infrared images and their derived features. Himawari-8 satellite images were used as the main input data, and binary detection (OT or nonOT) with class probability was the output of the machine learning models. Three machine learning techniques-random forest (RF), extremely randomized trees (ERT), and logistic regression (LR)-were used to develop OT classification models to distinguish OT from non-OT. The hindcast validation over the Southeast Asia andWest Pacific regions showed that RF performed best, resulting in a mean probabilities of detection (POD) of 77.06% and a mean false alarm ratio (FAR) of 36.13%. Brightness temperature at 11.2 ??m (Tb11) and its standard deviation (STD) in a 3 ?? 3 window size were identified as the most contributing variables for discriminating OT and nonOT classes. The proposed machine learning-based OT detection algorithms produced promising results comparable to or even better than the existing approaches, which are the infrared window (IRW)-texture and water vapor (WV) minus IRW brightness temperature difference (BTD) methods

Anti-stress effects of ginseng via down-regulation of tyrosine hydroxylase (TH) and dopamine β-hydroxylase (DBH) gene expression in immobilization-stressed rats and PC12 cells

Catecholamines are among the first molecules that displayed a kind of response to prolonged or repeated stress. It is well established that long-term stress leads to the induction of catecholamine biosynthetic enzymes such as tyrosine hydroxylase (TH) and dopamine β-hydroxylase (DBH) in adrenal medulla. The aim of the present study was to evaluate the effects of ginseng on TH and DBH mRNA expression. Repeated (2 h daily, 14 days) immobilization stress resulted in a significant increase of TH and DBH mRNA levels in rat adrenal medulla. However, ginseng treatment reversed the stress-induced increase of TH and DBH mRNA expression in the immobilization-stressed rats. Nicotine as a ligand of the nicotinic acetylcholine receptor (nAChR) in adrenal medulla stimulates catecholamine secretion and activates TH and DBH gene expression. Nicotine treatment increased mRNA levels of TH and DBH by 3.3- and 3.1-fold in PC12 cells. The ginseng total saponin exhibited a significant reversal in the nicotine-induced increase of TH and DBH mRNA expression, decreasing the mRNA levels of TH and DBH by 57.2% and 48.9%, respectively in PC12 cells. In conclusion, immobilization stress induced catecholamine biosynthetic enzymes gene expression, while ginseng appeared to restore homeostasis via suppression of TH and DBH gene expression. In part, the regulatory activity in the TH and DBH gene expression of ginseng may account for the anti-stress action produced by ginseng

Different Antimicrobial Susceptibility Testing Methods to Determine Vancomycin Susceptibility and MIC for Staphylococcus aureus with Reduced Vancomycin Susceptibility

The methods and results obtained using commercialized automation systems used for antimicrobial susceptibility testing are not entirely consistent. Therefore, we evaluated different antimicrobial susceptibility testing methods to determine vancomycin susceptibility and minimum inhibitory concentration (MIC) for Staphylococcus aureus with reduced vancomycin susceptibility (SA-RVS). A total of 128 clinical isolates of S. aureus were tested, including 99 isolates showing an MIC of ≥2 µg/mL using the VITEK2 system (VITEK2). Antimicrobial susceptibility tests were performed using the Sensititre system (Sensititre), Phoenix M50 system (Phoenix), and MicroScan WalkAway 96 Plus system (MicroScan). Vancomycin MICs were determined using the broth microdilution method (BMD) and Etest. Essential agreement and category agreement for each method were compared with BMD results as the reference method. The BMD and Etest showed complete essential agreement (100%). VITEK2, Sensititre, and Phoenix showed high essential agreement (>99%), while MicroScan showed the lowest essential agreement (92.2%). The MIC MICs determined via Etest, VITEK2, and MicroScan tended to be higher than that determined via BMD. When comparing BMD with Etest, the category agreement was 93.8% and minor errors were observed for eight isolates. VITEK2, Sensititre, and Phoenix showed category agreements of 96.1%, 96.1%, and 99.2%, respectively, while MicroScan showed the lowest category agreement of 85.2%. The determination of vancomycin susceptibility and MIC for S. aureus varied among the methods. Caution should be taken when interpreting RVS and intermediate results for S. aureus. For confirmation of SA-RVS results, it would be appropriate to test with BMD or a more reliable testing method

Risk Factors Associated With Colistin-Resistant Acinetobacter baumannii Infection

Acinetobacter baumannii is an important cause of healthcare-associated infections and is resistant to almost all antimicrobial agents, with strains recently reported to be resistant to colsitin. In this study, we aimed to identify the risk factors associated with colistin-resistant A. baumannii infections by comparing colistin-resistant and -susceptible A. baumannii isolates. We retrospectively reviewed the medical records of 51 and 100 cases in which colistin-resistant and -susceptible A. baumannii were isolated, respectively. Univariate analysis showed that compared with patients with colistin-sensitive infections, patients with colistin-resistant A. baumanni infections had a combined pulmonary disease (P = 0.017), were admitted to intensive care unit (P = 0.020), and had prior mechanical ventilation (P = 0.003), tracheostomy (P = 0.043), percutaneous drainage (P = 0.070), hemodialysis (P = 0.002); use of colistin (P = 0.000), carbapenem (P = 0.000), and teicoplanin (P = 0.004); and co-infection (P = 0.035). Multivariate analysis indicated that eight variables were related to the likelihood of colistin-resistant A. baumanni infections: use of teicoplanin (Odds ratio [OR]: 3.140, 95% confidence interval [CI]: 0.529–18.650), prior hemodialysis (OR: 2.722, 95% CI: 0.851–8.709), combined pulmonary disease (OR: 2.286, 95% CI: 0.998–5.283), prior use of carbapenem (OR: 0.199, 95% CI: 0.863–5.603), co-infection (OR: 1.706, 95% CI: 0.746–3.898), prior mechanical ventilation (OR: 1.614, 95% CI, 0.684–3.809), intensive care unit admission (OR: 1.387, 95% CI: 0.560–3.435), and prior tracheostomy (OR: 1.102, 95% CI: 0.344–3.527); however, no statistical differences were observed. Although colistin use could not be proven in multivariate analysis, the possibility of being a risk factor cannot be ruled out

Applying convolutional neural networks for detection of overshooting cloud tops with Himawari-8 satellite data

Overshooting tops (OTs) are one of the commonly observed phenomena in deep convective clouds over tropical regions. Convective clouds accompanied by OTs can cause severe weather conditions such as lightning, large hail, strong winds, and heavy rainfall more frequently, which influence ground and aviation operations. It may also affect global climate changes by transporting the tropospheric water vapor or greenhouse gases into the lower stratosphere when penetrating through the tropopause with strong updraft. Therefore, it is important to detect and monitor OTs, which will be helpful for related researches such as the relationship with severe weather events for better understanding of future weather conditions and the investigation of OT characteristics regarding global climate changes. Himawari-8 images were used as main input data to detect OTs using a convolutional neural network (CNN) for binary classification of OT and non-OT (i.e. anvil clouds and non convective clouds) classes. Input images were chopped into patches of a certain size and labeled either OT or non-OT. As OTs are clouds shaped like a dome protruding above cloud tops, CNN can identify OT regions by mimicking the human process with contextual information of pixels. The validation results show that CNN was successfully applied for the detection of OTs with 79.68% and 9.78% for a mean probability of detection (POD) and a mean false alarm ratio (FAR), respectively, which are comparable or even better than the performance of OT detection shown in the previous studies