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Some studies have suggested the antihypertensive effects of statins, a class of lipid-lowering agents, particularly in patients with hypertension. However, the evidence for the role of statins in blood pressure (BP) lowering is controversial, and no meta-analysis of rosuvastatin therapy has been conducted to assess its BP-lowering effects. Therefore, the aim of this meta-analysis of randomized controlled trials (RCTs) was to investigate the effects of rosuvastatin on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in patients with hypertension. We systematically searched the electronic databases MEDLINE, EMBASE, and Cochrane Library to identify RCTs in which patients were assigned to groups of rosuvastatin plus antihypertensive agents vs. antihypertensive agents. The three authors independently selected the studies, extracted data, and assessed methodological quality. We included five RCTs in this meta-analysis with 288 patients treated with rosuvastatin and 219 patients without rosuvastatin. The mean DBP in the rosuvastatin group was significantly lower than that in the non-rosuvastatin group by −2.12 mmHg (95% confidence interval (CI) −3.72 to −0.52; P fixed-effects model = 0.009; I 2 = 0%, P heterogeneity = 0.97). Rosuvastatin treatment also lowered the mean SBP compared with the non-rosuvastatin treatment by −2.27 mmHg, but not significantly (95% CI − 4.75 to 0.25; P fixed-effects model = 0.08; I 2 = 0%, P heterogeneity = 0.82). In this study, we reviewed the antihypertensive effects of rosuvastatin in patients with hypertension and dyslipidemia. We demonstrated a modest significant reduction of DBP and a trend toward a lowered SBP in patients with hypertension with rosuvastatin therapy. Rosuvastatin could be beneficial to control hypertension and, consequently, contribute toward reducing the risk of cardiovascular events in patients with hypertension and dyslipidemia.

This paper presents an always-on Complementary Metal Oxide Semiconductor (CMOS) image sensor (CIS) using an analog convolutional neural network for image classification in mobile applications. To reduce the power consumption as well as the overall processing time, we propose analog convolution circuits for computing convolution, max-pooling, and correlated double sampling operations without operational transconductance amplifiers. In addition, we used the voltage-mode MAX circuit for max pooling in the analog domain. After the analog convolution processing, the image data were reduced by 99.58% and were converted to digital with a 4-bit single-slope analog-to-digital converter. After the conversion, images were classified by the fully connected processor, which is traditionally performed in the digital domain. The measurement results show that we achieved an 89.33% image classification accuracy. The prototype CIS was fabricated in a 0.11 μm 1-poly 4-metal CIS process with a standard 4T-active pixel sensor. The image resolution was 160 × 120, and the total power consumption of the proposed CIS was 1.12 mW with a 3.3 V supply voltage and a maximum frame rate of 120.

The performance and clinical implications of the deep learning aided algorithm using electrocardiogram of heart failure (HF) with reduced ejection fraction (DeepECG-HFrEF) were evaluated in patients with acute HF. The DeepECG-HFrEF algorithm was trained to identify left ventricular systolic dysfunction (LVSD), defined by an ejection fraction (EF) < 40%. Symptomatic HF patients admitted at Seoul National University Hospital between 2011 and 2014 were included. The performance of DeepECG-HFrEF was determined using the area under the receiver operating characteristic curve (AUC) values. The 5-year mortality according to DeepECG-HFrEF results was analyzed using the Kaplan–Meier method. A total of 690 patients contributing 18,449 ECGs were included with final 1291 ECGs eligible for the study (mean age 67.8 ± 14.4 years; men, 56%). HFrEF (+) identified an EF < 40% and HFrEF (−) identified EF ≥ 40%. The AUC value was 0.844 for identifying HFrEF among patients with acute symptomatic HF. Those classified as HFrEF (+) showed lower survival rates than HFrEF (−) (log-rank p  < 0.001). The DeepECG-HFrEF algorithm can discriminate HFrEF in a real-world HF cohort with acceptable performance. HFrEF (+) was associated with higher mortality rates. The DeepECG-HFrEF algorithm may help in identification of LVSD and of patients at risk of worse survival in resource-limited settings.

Temperature forecasting has been a consistent research topic owing to its significant effect on daily lives and various industries. However, it is an ever-challenging task because temperature is affected by various climate factors. Research on temperature forecasting has taken one of two directions: time-series analysis and machine learning algorithms. Recently, a large amount of high-frequent climate data have been well-stored and become available. In this study, we apply three types of neural networks, multilayer perceptron, recurrent, and convolutional, to daily average, minimum, and maximum temperature forecasting with higher-frequency input features than researchers used in previous studies. Applying these neural networks to the observed data from three locations with different climate characteristics, we show that prediction performance with highly frequent hourly input data is better than forecasting performance with less-frequent daily inputs. We observe that a convolutional neural network, which has been mostly employed for processing satellite images rather than numeric weather data for temperature forecasting, outperforms the other models. In addition, we combine state of the art weather forecasting techniques with the convolutional neural network and evaluate their effects on the temperature forecasting performances.

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

The giant tabular iceberg A68 broke away from the Larsen C Ice Shelf, Antarctic Peninsula, in July 2017. The evolution of A68 would have been affected by both the Larsen C Ice Shelf, the surrounding sea ice, and the nearby shallow seafloor. In this study, we analyze the initial evolution of iceberg A68A—the largest originating from A68—in terms of changes in its area, drift speed, rotation, and freeboard using Sentinel-1 synthetic aperture radar (SAR) images and CryoSat-2 SAR/Interferometric Radar Altimeter observations. The area of iceberg A68A sharply decreased in mid-August 2017 and mid-May 2018 via large calving events. In September 2018, its surface area increased, possibly due to its longitudinal stretching by melting of surrounding sea ice. The decrease in the area of A68A was only 2% over 1.5 years. A68A was relatively stationary until mid-July 2018, while it was surrounded by the Larsen C Ice Shelf front and a high concentration of sea ice, and when its movement was interrupted by the shallow seabed. The iceberg passed through a bay-shaped region in front of the Larsen C Ice Shelf after July 2018, showing a nearly circular motion with higher speed and greater rotation. Drift was mainly inherited from its rotation, because it was still located near the Bawden Ice Rise and could not pass through by the shallow seabed. The freeboard of iceberg A68A decreased at an average rate of −0.80 ± 0.29 m/year during February–November 2018, which could have been due to basal melting by warm seawater in the Antarctic summer and increasing relative velocity of iceberg and ocean currents in the winter of that year. The freeboard of the iceberg measured using CryoSat-2 could represent the returned signal from the snow surface on the iceberg. Based on this, the average rate of thickness change was estimated at −12.89 ± 3.34 m/year during the study period considering an average rate of snow accumulation of 0.82 ± 0.06 m/year predicted by reanalysis data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). The results of this study reveal the initial evolution mechanism of iceberg A68A, which cannot yet drift freely due to the surrounding terrain and sea ice.

The northern Bering Sea and the southern Chukchi Sea are undergoing rapid regional biophysical changes in connection with the recent extreme climate change in the Arctic. The ice concentration in 2018 was the lowest since observations began in the 1970s, due to the unusually warm southerly wind in winter, which continued in 2019. We analyzed the characteristics of spring phytoplankton biomass distribution under the extreme environmental conditions in 2018 and 2019. Our results show that higher phytoplankton biomass during late spring compared to the 18-year average was observed in the Bering Sea in both years. Their spatial distribution is closely related to the open water extent following winter-onset sea ice retreat in association with dramatic atmospheric conditions. However, despite a similar level of shortwave heat flux, the 2019 springtime biomass in the Chukchi Sea was lower than that in 2018, and was delayed to summer. We confirmed that this difference in bloom timing in the Chukchi Sea was due to changes in seawater properties, determined by a combination of northward oceanic heat flux modulation by the disturbance from more extensive sea ice in winter and higher surface net shortwave heat flux than usual.

Unmanned aerial vehicles (UAVs), also known as drones, are a cost-effective alternative to traditional surveying methods, and they can be used to collect geospatial data over inaccessible or hard-to-reach locations. UAV-integrated miniaturized remote sensing sensors such as hyperspectral and LiDAR sensors, which formerly operated on airborne and spaceborne platforms, have recently been developed. Their accuracies can still be guaranteed when incorporating pieces of equipment such as ground control points (GCPs) and field spectrometers. This study conducted three experiments for geometric and radiometric accuracy assessments of simultaneously acquired RGB, hyperspectral, and LiDAR data from a single mission. Our RGB and hyperspectral data generated orthorectified images based on direct georeferencing without any GCPs. Because of this, a base station is required for the post-processed Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) data. First, we compared the geometric accuracy of orthorectified RGB and hyperspectral images relative to the distance of the base station to determine which base station should be used. Second, point clouds could be generated from overlapped RGB images and a LiDAR sensor. We quantitatively and qualitatively compared RGB and LiDAR point clouds in this experiment. Lastly, we evaluated the radiometric quality of hyperspectral images, which is the most critical factor of the hyperspectral sensor, using reference spectra that was simultaneously measured by a field spectrometer. Consequently, the distance of the base station for post-processing the GNSS/IMU data was found to have no significant impact on the geometric accuracy, indicating that a dedicated base station is not always necessary. Our experimental results demonstrated geometric errors of less than two hyperspectral pixels without using GCPs, achieving a level of accuracy that is comparable to survey-level standards. Regarding the comparison of RGB- and LiDAR-based point clouds, RGB point clouds exhibited noise and lacked details; however, through the cleaning process, their vertical accuracy was found to be comparable with LiDAR’s accuracy. Although photogrammetry generated denser point clouds compared with LiDAR, the overall quality for extracting the elevation data greatly relies on factors such as the original image quality, including the image’s occlusions, shadows, and tie-points, for matching. Furthermore, the image spectra derived from hyperspectral data consistently demonstrated high radiometric quality without the need for in situ field spectrum information. This finding indicates that in situ field spectra are not always required to guarantee the radiometric quality of hyperspectral data, as long as well-calibrated targets are utilized.

Generation of high-speed laser pulses is essential for sustaining today’s global, hyper-connected society. One approach for achieving high spectral and temporal purity is to combine optical nonlinear materials with spectral filtering devices. In this work, a graphene-coated microresonator integrates a nonlinear material and a spectral filtering platform into a single device, creating a tunable GHz repetition rate mode-locked fiber laser. The graphene is directly synthesized on the non-planar surface of microresonator, resulting in a uniform, conformal coating with minimal optical loss in the device. The whispering gallery modes of the resonator filter the propagating modes, and the remaining modes from the interaction with graphene lock their relative phases to form short pulses at an elevated repetition rate relying on inter-modal spectral distance. Additionally, by leveraging the photo-thermal effect, all-optical tuning of the repetition rate is demonstrated. With optimized device parameters, repetition rates of 150 GHz and tuning of 6.1 GHz are achieved.