To stabilize bromine during charging in zinc-bromide flow batteries, bromine-complexing agent is typically used as a supporting material in electrolyte. This paper describes the influences of the ...bromine-complexing agent on the electrochemical performances of the zinc deposition and dissolution process during charge and discharge. The surface morphologies before and after the zinc electrodissolution process are compared when using 1-Ethyl-1-methyl-pyrrolidinium bromide as a bromine-complexing agent in electrolyte, and the several measurements including surface chemical analysis are also performed in conjunction with the charge–discharge cell tests. Experimental results show that employing 1-Ethyl-1-methyl-pyrrolidinium bromide in electrolyte, through the formation of an electrostatic shield of 1-Ethyl-1-methyl-pyrrolidinium cations in-and-around the zinc dendrite during charging, provides powerful and effective effects yielding the uniformly flat formation of zinc as well as the prevention of zinc-dendrite growth. It also appears that the diffusion of 1-Ethyl-1-methyl-pyrrolidinium bromide on electrodeposited zinc produces not only two type morphologies of a melting-slurry agglomerate and a furrow ripple but also the change of chemical elemental composition, resulting in higher redox reaction reversibility and activity. Consequently, these results indicate that the propagation of zinc dendrite is mostly controlled by the diffusion rate of the 1-Ethyl-1-methyl-pyrrolidinium species on the zinc metallic surface.
Display omitted
•MEP∙Br in anolyte has a special effect on zinc deposition in ZnBr2 flow battery.•MEP cations lead to forming a electrostatic shield to prevent zinc dendrite growth.•MEP∙Br(C7H16NBr) provides a change in two-shape morphologies of electrodeposited zinc.•Overall discharge capacities are 35.77% higher than the pristine one.•Performance potentially leads to 16.65% higher current efficiency.
Aerosol Optical Depth (AOD) and Fine Mode Fraction (FMF) are important information for air quality research. Both are mainly obtained from satellite data based on a radiative transfer model, which ...requires heavy computation and has uncertainties. We proposed machine learning-based models to estimate AOD and FMF directly from Geostationary Ocean Color Imager (GOCI) reflectances over East Asia. Hourly AOD and FMF were estimated for 00–07 UTC at a spatial resolution of 6 km using the GOCI reflectances, their channel differences (with 30-day minimum reflectance), solar and satellite viewing geometry, meteorological data, geographical information, and the Day Of the Year (DOY) as input features. Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) machine learning approaches were applied and evaluated using random, spatial, and temporal 10-fold cross-validation with ground-based observation data. LightGBM (R2 = 0.89–0.93 and RMSE = 0.071–0.091 for AOD and R2 = 0.67–0.81 and RMSE = 0.079–0.105 for FMF) and RF (R2 = 0.88–0.92 and RMSE = 0.080–0.095 for AOD and R2 = 0.59–0.76 and RMSE = 0.092–0.118 for FMF) agreed well with the in-situ data. The machine learning models showed much smaller errors when compared to GOCI-based Yonsei aerosol retrieval and the Moderate Resolution Imaging Spectroradiometer Dark Target and Deep Blue algorithms. The Shapley Additive exPlanations values (SHAP)-based feature importance result revealed that the 412 nm band (i.e., ch01) contributed most in both AOD and FMF retrievals. Relative humidity and air temperature were also identified as important factors especially for FMF, which suggests that considering meteorological conditions helps improve AOD and FMF estimation. Besides, spatial distribution of AOD and FMF showed that using the channel difference features to indirectly consider surface reflectance was very helpful for AOD retrieval on bright surfaces.
Brown adipose tissue (BAT) acts in mammals as a natural defense system against hypothermia, and its activation to a state of increased energy expenditure is believed to protect against the ...development of obesity. Even though the existence of BAT in adult humans has been widely appreciated, its cellular origin and molecular identity remain elusive largely because of high cellular heterogeneity within various adipose tissue depots. To understand the nature of adult human brown adipocytes at single cell resolution, we isolated clonally derived adipocytes from stromal vascular fractions of adult human BAT from two individuals and globally analyzed their molecular signatures. We used RNA sequencing followed by unbiased genome-wide expression analyses and found that a population of uncoupling protein 1 (UCP1)-positive human adipocytes possessed molecular signatures resembling those of a recruitable form of thermogenic adipocytes (that is, beige adipocytes). In addition, we identified molecular markers that were highly enriched in UCP1-positive human adipocytes, a set that included potassium channel K3 (KCNK3) and mitochondrial tumor suppressor 1 (MTUS1). Further, we functionally characterized these two markers using a loss-of-function approach and found that KCNK3 and MTUS1 were required for beige adipocyte differentiation and thermogenic function. The results of this study present new opportunities for human BAT research, such as facilitating cell-based disease modeling and unbiased screens for thermogenic regulators.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SBMB, UILJ, UKNU, UL, UM, UPUK
Abstract Statement of problem Conventional impression-making methods are being replaced by intraoral digital scanning. How long dental professionals take to master the new technologies is unknown. ...Purpose The purpose of this human subject study was to compare the experience curves of 2 intraoral scanners among dental hygienists and determine whether repeated scanning experience could change the scan time (ST). Material and methods A total of 29 dental hygienists with more than 3 years of working experience were recruited (group 1: 3-5 years; group 2: >6 years of clinical experience) to learn the iTero and Trios systems. All learners scanned the oral cavities of 4 human participants (participants A, B, C, and D) 10 times (T1-T10) throughout the learning sessions and the experimental dentoform model twice at the beginning and end of the 10 sessions. ST was measured, and changes in ST were compared between the 2 devices. Results The average ST for 10 sessions was greater with iTero than with Trios, but the decrease in the measured ST was greater for iTero than for Trios. Baseline and postexperience STs with iTero showed statistically significant differences, with a decrease in time related to the clinical experience levels of the dental hygienists (group 1: T2 and T4, P <.01; group 2: T2 and T5, P <.01). The experience curve with iTero was not influenced by the human participant’s intraoral characteristics, and greater ST was shown for participants B and C than for participants A and D with Trios. Conclusions Although the learning rate of iTero was rapid, the average ST for iTero was longer than Trios, and clinical experience levels influenced the operator’s ability to manipulate the device. In contrast, the learning rate of Trios was slow, and measured ST was shorter than iTero, and was not influenced by clinical experience.
Landfast sea ice (fast ice) is an important feature prevalent around the Antarctic coast, which is affected by climate change and energy exchanges with the atmosphere and ocean. This study proposed a ...method for detection of the West Antarctic fast ice using the Advanced Land Observing Satellite Phased Array L-band SAR (ALOS PALSAR) images. The algorithm has combined image segmentation, image correlation analysis, and machine learning techniques (i.e., random forest (RF), extremely randomized trees (ERT), and logistic regression (LR)). We used SAR images with a baseline of 5 days that are not in the same orbit but overlap each other as overlaps between swaths in adjacent orbits are often available in the polar regions. The underlying assumption for the proposed fast ice detection algorithm is that fast ice regions in SAR images with a time interval of 5 days are highly correlated. The object-based approach proposed in this study was well suited to high-resolution SAR images in deriving spatially homogeneous fast ice regions. The image segmentation results using the optimized parameters showed a distinct difference in the backscatter temporal evolution between fast ice and pack ice regions. Correlation and STD of backscattering coefficients were found to be the most significant variables for the object-based fast ice detection from two temporally separated images. In overall, the quantitative and qualitative evaluation demonstrated that the algorithm was an effective approach to detect fast ice with high accuracies. The models well detected various fast ice regions in the West Antarctica but misclassified some objects. The misclassifications occurred toward the edge of fast ice regions with relatively rapid changes in backscattering between both data acquisitions. On the other hand, few fast ice objects were misclassified as uniform backscattering over time occurred by chance on very small objects far from the coast. Very old multi-year fast ice regions with high backscattered signals were also a source for some misclassifications. This may be due to the sensitivity of L-band to snow structure to some extent and a thinner ice over the region with either ice growth (no deformation) or closing (slight deformation) between both images. Heavy snow load on the ice could be another error source for some misclassification as well. The approach allowed for the reliable detection of fast ice regions by using L-band SAR images with a small local incidence angle difference.
•A novel landfast sea ice detection approach was proposed over West Antarctica.•Landfast sea ice was detected using L-band SAR image pairs with a 5-day interval.•The approach combines image segmentation, object correlation and machine learning.•The proposed approach was evaluated using time series ALOS PALSAR data.
Antibody‐mediated rejection (AMR), also known as B‐cell–mediated or humoral rejection, is a significant complication after kidney transplantation that carries a poor prognosis. Although fewer than ...10% of kidney transplant patients experience AMR, as many as 30% of these patients experience graft loss as a consequence. Although AMR is mediated by antibodies against an allograft and results in histologic changes in allograft vasculature that differ from cellular rejection, it has not been recognized as a separate disease process until recently. With an improved understanding about the importance of the development of antibodies against allografts as well as complement activation, significant advances have occurred in the treatment of AMR. The standard of care for AMR includes plasmapheresis and intravenous immunoglobulin that remove and neutralize antibodies, respectively. Agents targeting B cells (rituximab and alemtuzumab), plasma cells (bortezomib), and the complement system (eculizumab) have also been used successfully to treat AMR in kidney transplant recipients. However, the high cost of these medications, their use for unlabeled indications, and a lack of prospective studies evaluating their efficacy and safety limit the routine use of these agents in the treatment of AMR in kidney transplant recipients.
Overshooting tops (OTs) play a crucial role in carrying tropospheric water vapor to the lower stratosphere. They are closely related to climate change as well as local severe weather conditions, such ...as lightning, hail, and air turbulence, which implies the importance of their detection and monitoring. While many studies have proposed threshold-based detection models using the spatial characteristics of OTs, they have shown varied performance depending on the seasonality and study areas. In this study, we propose a pre-trained feature-aggregated convolutional neural network approach for OT detection and monitoring. The proposed approach was evaluated using multi-channel data from Geo-Kompsat-2A Advanced Meteorological Imager (GK2A AMI) over East Asia. The fusion of a visible channel and multi-infrared channels enabled the proposed model to consider both physical and spatial characteristics of OTs. Six schemes were evaluated according to two types of data pre-processing methods and three types of deep learning model architectures. The best-performed scheme yielded a probability of detection (POD) of 92.1%, a false alarm ratio (FAR) of 21.5%, and a critical success index (CSI) of 0.7. The results were significantly improved when compared to those of the existing CNN-based OT detection model (POD increase by 4.8% and FAR decrease by 29.4%).
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 purpose of this study was to compare the precision of three-dimensional (3D) images acquired using iTero® (Align Technology Inc., San Jose, CA, USA) and Trios® (3Shape Dental Systems, Copenhagen, ...Denmark) digital intraoral scanners, and to evaluate the effects of the severity of tooth irregularities and scanning sequence on precision.
Dental arch models were fabricated with differing degrees of tooth irregularity and divided into 2 groups based on scanning sequence. To assess their precision, images were superimposed and an optimized superimposition algorithm was employed to measure any 3D deviation. The t-test, paired t-test, and one-way ANOVA were performed (p < 0.05) for statistical analysis.
The iTero® and Trios® systems showed no statistically significant difference in precision among models with differing degrees of tooth irregularity. However, there were statistically significant differences in the precision of the 2 scanners when the starting points of scanning were different. The iTero® scanner (mean deviation, 29.84 ± 12.08 µm) proved to be less precise than the Trios® scanner (22.17 ± 4.47 µm).
The precision of 3D images differed according to the degree of tooth irregularity, scanning sequence, and scanner type. However, from a clinical standpoint, both scanners were highly accurate regardless of the degree of tooth irregularity.
Aerosols are a critical component of the climate system and a risk to human health. Here, the lockdown response to the coronavirus outbreak is used to analyse effects of dramatic reduction in ...anthropogenic aerosol sources on satellite-retrieved aerosol optical depth (AOD). A machine learning model is applied to estimate daily AOD during the initial lockdown in China in early 2020. The model uses information on aerosol climatology, geography and meteorological conditions, and explains 69% of the day-to-day AOD variability. A comparison of model-expected and observed AOD shows that no clear, systematic decrease in AOD is apparent during the lockdown in China. During March 2020, regional AOD is observed to be significantly lower than expected by the machine learning model in some coastal regions of the North China Plains and extending to the Korean peninsula. While this may possibly indicate a small lockdown effect on regional AOD, and potentially pointing trans-boundary effects of the lockdown measures, due to uncertainties associated with the method and the limited sample sizes, this AOD decrease cannot be unequivocally attributed to reduced anthropogenic emissions. Climatologically expected AOD is compared to a weather-adjusted expectation of AOD, indicating that meteorological influences have acted to significantly increase AOD during this time, in agreement with recent literature. The findings highlight the complexity of aerosol variability and the challenges of observation-based attribution of columnar aerosol changes.
PDF
