Miracle-dz/UniFAR-FTUs huggingface dataset:
Acitretin Promotes the Differentiation of Myeloid-Derived Suppressor Cells in the Treatment of Psoriasis

Increased numbers of myeloid-derived suppressor cells (MDSCs) are involved in the development of psoriasis. Acitretin is used to treat psoriasis by regulating the proliferation and differentiation of keratinocytes, but little is known about the effect of acitretin on immune cells. Here, we reported that psoriasis patients had an expansion of MDSCs and monocytic-MDSCs (M-MDSCs) in peripheral blood and skin lesions. The number of MDSCs and M-MDSCs in peripheral blood correlated positively with disease severity. Acitretin could reduce the number of MDSCs and M-MDSCs in the peripheral blood of psoriasis patients as well as the spleen and skin lesions of IMQ-induced psoriasis-like model mice. Moreover, acitretin promoted the differentiation of MDSCs into macrophages, especially CD206+ M2 macrophages, and CD11c+MHC-II+ dendritic cells. Mechanically, acitretin dramatically increased the glutathione synthase (GSS) expression and glutathione (GSH) accumulation in MDSCs. Interruption of GSH synthesis abrogated the acitretin effect on MDSCs differentiation. Acitretin regulated GSS expression via activation of extracellular signal-regulated kinase 1/2. Thus, our data demonstrated a novel mechanism underlying the effects of acitretin on psoriasis by promoting MDSCs differentiation.
The Compensatory Protective Effects of Social Support at Work in Presenteeism During the Coronavirus Disease Pandemic

The present study investigated the lasting effects of sickness presenteeism on well-being and innovative job performance in the demanding Chinese work context compounded with the precarities of the post-pandemic business environment. Adopting the conservation of resources (COR) theory perspective, especially its proposition of compensation of resources, we incorporated social resources at work (supervisory support and collegial support) as joint moderators in the presenteeism–outcomes relationship. We employed a panel design in which all variables were measured twice with 6 months in between. Data were obtained from 323 Chinese employees working in diverse industries in Taiwan. We found that after controlling for the baseline level of well-being, presenteeism did not have a lasting effect on employees' exhaustion. However, presenteeism did have a negative lasting effect on employees' innovative behavior 6 months later. Moreover, we found a significant three-way interaction of presenteeism, supervisory support, and collegial support on employees' innovative job performance, after controlling for the baseline level of performance. Specifically, when working under illness, employees displayed the best innovative performance with high levels of both supervisory and collegial support, the worst performance with both support being low, and the intermediate when any one of the support being high. This can be taken as the preliminary evidence to support the COR proposition of resource caravans, showing that supervisory support and collegial support compensated for each other as critical resources in alleviating the impact of working under sickness on employees' innovative performance. Theoretical implications of the findings are discussed, taking into account the macro-cultural context of the East Asian Confucian societies. We also reflected on the managerial implications of the lasting damages of sickness presenteeism and benefits of mobilizing social resources on employees' well-being and performance.
Left Bundle Branch Pacing: Current Knowledge and Future Prospects

Cardiac pacing is an effective therapy for treating patients with bradycardia due to sinus node dysfunction or atrioventricular block. However, traditional right ventricular apical pacing (RVAP) causes electric and mechanical dyssynchrony, which is associated with increased risk for atrial arrhythmias and heart failure. Therefore, there is a need to develop a physiological pacing approach that activates the normal cardiac conduction and provides synchronized contraction of ventricles. Although His bundle pacing (HBP) has been widely used as a physiological pacing modality, it is limited by challenging implantation technique, unsatisfactory success rate in patients with wide QRS wave, high pacing capture threshold, and early battery depletion. Recently, the left bundle branch pacing (LBBP), defined as the capture of left bundle branch (LBB) via transventricular septal approach, has emerged as a newly physiological pacing modality. Results from early clinical studies have demonstrated LBBP's feasibility and safety, with rare complications and high success rate. Overall, this approach has been found to provide physiological pacing that guarantees electrical synchrony of the left ventricle with low pacing threshold. This was previously specifically characterized by narrow paced QRS duration, large R waves, fast synchronized left ventricular activation, and correction of left bundle branch block. Therefore, LBBP may be a potential alternative pacing modality for both RVAP and cardiac resynchronization therapy with HBP or biventricular pacing (BVP). However, the technique's widespread adaptation needs further validation to ascertain its safety and efficacy in randomized clinical trials. In this review, we discuss the current knowledge of LBBP.
Bone Erosions Detected by Ultrasound Are Prognostic for Clinical Arthritis Development in Patients With ACPA and Musculoskeletal Pain

Anti-citrullinated protein antibodies (ACPA) often precede onset of rheumatoid arthritis (RA) by years, and there is an urgent clinical need for predictors of arthritis development among such at-risk patients. This study assesses the prognostic value of ultrasound for arthritis development among ACPA-positive patients with musculoskeletal pain. We prospectively followed 82 ACPA-positive patients without clinical signs of arthritis at baseline. Ultrasound at baseline assessed synovial hypertrophy, inflammatory activity by power Doppler, and erosions in small joints of hands and feet. We applied Cox regression analyses to examine associations with clinical arthritis development during follow-up (median, 69 months; range, 24–90 months). We also compared the ultrasound findings among the patients to a control group of 100 blood donors without musculoskeletal pain. Clinical arthritis developed in 39/82 patients (48%) after a median of 6 months (range, 1–71 months). One or more ultrasound erosions occurred in 13/82 patients (16%), with none in control subjects (p < 0.001). Clinical arthritis development was more common among patients with baseline ultrasound erosions than those without (77 vs. 42%, p = 0.032), and remained significant in a multivariable Cox regression analysis that included previously described prognostic factors (HR 3.9, 95% CI 1.6–9.4, p = 0.003). Ultrasound-detected tenosynovitis was more frequent among the patients and associated with clinical arthritis development in a univariable analysis (HR 2.5, 95% CI 1.1–5.7, p = 0.031), but did not remain statistically significant in multivariable analysis. Thus, bone erosions detected by ultrasound are independent predictors of clinical arthritis development in an ACPA-positive at-risk population. Trial Registration: Regional Ethics Committee in Linköping, Sweden, Dnr M220-09. Registered 16 December 2009, https://etikprovningsmyndigheten.se/.
A Fear of COVID-19 and PTSD Symptoms in Pathological Personality: The Mediating Effect of Dissociation and Emotion Dysregulation

Background: The 2019 coronavirus disease (COVID-19) outbreak is currently putting a strain on the mental health resilience of the world's population. Specifically, it is likely to elicit an intense response to fear and to act as a risk factor for the onset of posttraumatic stress disorder (PTSD). Some individuals may be more at risk than others, with pathological personality variables being a potential candidate as a central vulnerability factor. In addition, the pathways that lead the pathological personality to PTSD and intense fear responses to COVID-19 are likely to be explained by poor emotion regulation capacities, as well as by dissociative mechanisms. Aims: This study aimed to shed light on vulnerability factors that may account for the onset of PTSD and intense responses of fear in response to COVID-19 outbreak and to test the mediating role of emotion dysregulation and dissociation proneness in these pathways. Methods: We used a longitudinal design of research administered to a sample of community individuals (N = 308; meanage = 35.31, SD = 13.91; 22.7% were male). Moreover, we used self-report questionnaires to measure pathological personality, emotion regulation capacities, dissociative proneness at the beginning of the lockdown, and PTSD symptoms and fear of COVID-19 at the end of the Italian lockdown (from March 9 to May 18, 2020). Structural equation modeling was used to test the hypotheses. Results: We found that pathological personality levels longitudinally predicted PTSD and fear of COVID-19 levels. Moreover, the associations between emotion dysregulation and dissociation were shown to significantly and totally mediate the relationship between pathological personality and PTSD, whereas no significant mediation effects were observed in relation to fear of COVID-19. Conclusions: Individuals with pathological personality traits may be more vulnerable to the onset of negative psychological consequences related to COVID-19 outbreak, such as PTSD symptomatology and fear levels. Emotion regulation capacities appear to be relevant targets of interventions for PTSD symptomatology. Future research should explore the mediating variables linking pathological personality to intense fear responses to COVID-19.
Nutrition and Obesity in the Pathogenesis of Youth-Onset Type 1 Diabetes and Its Complications

Since the 1980s, there has been a dramatic rise in the prevalence of overweight and obesity in pediatric populations, in large part driven by sedentary lifestyles and changing dietary patterns with more processed foods. In parallel with the rise in pediatric obesity in the general population, the prevalence of overweight and obesity has increased among children and adolescents with type 1 diabetes. Adiposity has been implicated in a variety of mechanisms both potentiating the risk for type 1 diabetes as well as exacerbating long-term complications, particularly cardiovascular disease. Treatment options targeting the unique needs of obese pediatric patients, both before and after diagnosis of type 1 diabetes, are limited. In this review, we discuss the history of the epidemiology of the obesity epidemic in the context of pediatric type 1 diabetes, highlight the possible role of obesity in type 1 diabetes pathogenesis and review the concept of “double diabetes”. The impact of obesity at and after diagnosis will be discussed, including noted differences in clinical and biochemical markers, lipid abnormalities, and long-term cardiovascular complications. Finally, we will review the existing literature on pharmacologic and nutritional interventions as potential treatment strategies for youth with coexisting type 1 diabetes and obesity.
Estimation of Surface NO2 Concentrations over Germany from TROPOMI Satellite Observations Using a Machine Learning Method

In this paper, we present the estimation of surface NO2 concentrations over Germany using a machine learning approach. TROPOMI satellite observations of tropospheric NO2 vertical column densities (VCDs) and several meteorological parameters are used to train the neural network model for the prediction of surface NO2 concentrations. The neural network model is validated against ground-based in situ air quality monitoring network measurements and regional chemical transport model (CTM) simulations. Neural network estimation of surface NO2 concentrations show good agreement with in situ monitor data with Pearson correlation coefficient (R) of 0.80. The results also show that the machine learning approach is performing better than regional CTM simulations in predicting surface NO2 concentrations. We also performed a sensitivity analysis for each input parameter of the neural network model. The validated neural network model is then used to estimate surface NO2 concentrations over Germany from 2018 to 2020. Estimated surface NO2 concentrations are used to investigate the spatio-temporal characteristics, such as seasonal and weekly variations of NO2 in Germany. The estimated surface NO2 concentrations provide comprehensive information of NO2 spatial distribution which is very useful for exposure estimation. We estimated the annual average NO2 exposure for 2018, 2019 and 2020 is 15.53, 15.24 and 13.27 µµg/m3, respectively. While the annual average NO2 concentration of 2018, 2019 and 2020 is only 12.79, 12.60 and 11.15 µµg/m3. In addition, we used the surface NO2 data set to investigate the impacts of the coronavirus disease 2019 (COVID-19) pandemic on ambient NO2 levels in Germany. In general, 10–30% lower surface NO2 concentrations are observed in 2020 compared to 2018 and 2019, indicating the significant impacts of a series of restriction measures to reduce the spread of the virus.
Multi-Source EO for Dynamic Wetland Mapping and Monitoring in the Great Lakes Basin

Wetland managers, citizens and government leaders are observing rapid changes in coastal wetlands and associated habitats around the Great Lakes Basin due to human activity and climate variability. SAR and optical satellite sensors offer cost effective management tools that can be used to monitor wetlands over time, covering large areas like the Great Lakes and providing information to those making management and policy decisions. In this paper we describe ongoing efforts to monitor dynamic changes in wetland vegetation, surface water extent, and water level change. Included are assessments of simulated Radarsat Constellation Mission data to determine feasibility of continued monitoring into the future. Results show that integration of data from multiple sensors is most effective for monitoring coastal wetlands in the Great Lakes region. While products developed using methods described in this article provide valuable management tools, more effort is needed to reach the goal of establishing a dynamic, near-real-time, remote sensing-based monitoring program for the basin.
Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies

Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany; sina.keller@kit.edu (S.K.); stefan.hinz@kit.edu (S.H.) * Correspondence: philipp.maier@kit.edu; Tel. : +49-721-608-44131 † These authors contributed equally to this work.
On the Spectral and Polarimetric Signatures of a Bright Scatterer before and after Hardware Replacement

A previous study has used the stable and peculiar echoes backscattered by a single “bright scatterer” (BS) during five winter days to characterize the hardware of C-band, the dual-polarization radar located at Monte Lema (1625 m altitude) in Southern Switzerland. The BS is the 90 m tall metallic tower on Cimetta (1633 m altitude, 18 km range). In this note, the statistics of the echoes from the BS were derived from other ten dry days with normal propagation conditions in winter 2015 and January 2019. The study confirms that spectral signatures, such as spectrum width, wideband noise and Doppler velocity, were persistently stable. Regarding the polarimetric signatures, the large values (with small dispersion) of the copolar correlation coefficient between horizontal and vertical polarization were also confirmed: the average value was 0.9961 (0.9982) in winter 2015 (January 2019); the daily standard deviations were very small, ranging from 0.0007 to 0.0030. The dispersion of the differential phase shift was also confirmed to be quite small: the daily standard deviation ranged from a minimum of 2.5° to a maximum of 5.3°. Radar reflectivities in both polarizations were typically around 80 dBz and were confirmed to be among the largest values observed in the surveillance volume of the Monte Lema radar. Finally, another recent 5-day data set from January 2020 was analyzed after the replacement of the radar calibration unit that includes low noise amplifiers: these five days show poorer characteristics of the polarimetric signatures and a few outliers affecting the spectral signatures. It was shown that the “historical” polarimetric and spectral signatures of a bright scatterer could represent a benchmark for an in-depth comparison after hardware replacements.
Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study

The accurate monitoring and measurement of sea ice thickness (SIT) is crucial for understanding climate change and preventing economic losses caused by sea ice disasters near coastal regions. In this study, a new method is developed to retrieve the SIT in Liaodong Bay (LDB) based on the Rayleigh-corrected reflectance from Geostationary Ocean Color Imager (GOCI) images in the winters of 2012 and 2013. Compared with previously developed SIT retrieval methods (e.g., the method based on the thermodynamic principle of sea ice) using remote sensing data, our method has significant advantages with respect to the inversion accuracy (achieving retrieval skill scores as high as 0.86) and spatiotemporal resolution. Moreover, there is no significant increase in the computational cost with this method, which makes the method suitable for operational SIT retrieval in the global ocean.
SSCNN-S: A Spectral-Spatial Convolution Neural Network with Siamese Architecture for Change Detection

In this paper, a spectral-spatial convolution neural network with Siamese architecture (SSCNN-S) for hyperspectral image (HSI) change detection (CD) is proposed. First, tensors are extracted in two HSIs recorded at different time points separately and tensor pairs are constructed. The tensor pairs are then incorporated into the spectral-spatial network to obtain two spectral-spatial vectors. Thereafter, the Euclidean distances of the two spectral-spatial vectors are calculated to represent the similarity of the tensor pairs. We use a Siamese network based on contrastive loss to train and optimize the network so that the Euclidean distance output by the network describes the similarity of tensor pairs as accurately as possible. Finally, the values obtained by inputting all tensor pairs into the trained model are used to judge whether a pixel belongs to the change area. SSCNN-S aims to transform the problem of HSI CD into a problem of similarity measurement for tensor pairs by introducing the Siamese network. The network used to extract tensor features in SSCNN-S combines spectral and spatial information to reduce the impact of noise on CD. Additionally, a useful four-test scoring method is proposed to improve the experimental efficiency instead of taking the mean value from multiple measurements. Experiments on real data sets have demonstrated the validity of the SSCNN-S method.
A Lidar-Based 3-D Photosynthetically Active Radiation Model Reveals the Spatiotemporal Variations of Forest Sunlit and Shaded Leaves

Accurately identifying sunlit and shaded leaves using process-based ecological models can improve the simulation accuracy of forest photosynthetic rates and potential carbon sequestration capacity. However, it is still challenging to characterize their three dimensional (3-D) spatiotemporal distributions due to the complex structure. In this study, we developed a light detection and ranging (lidar)-based approach to map the spatiotemporal distribution patterns of photosynthetically active radiation (PAR) and sunlit and shaded leaves within forest canopies. By using both terrestrial laser scanning (TLS) and unmanned aerial vehicle-based lidar system (UAV-LS), we analyzed the influences of different scanning geometries and associated point densities on the separation of sunlit and shaded leaves. Moreover, we further investigated the effects of woody materials and penumbra sizes on identifying sunlit and shaded leaves by separating the foliage and woody materials and estimating the penumbras of sunlit leaves. Our results showed that: (1) The proposed lidar-based PAR model could well capture the variations of field-based pyranometer measurements using fused point data by combining UAV-LS and TLS data (mean R-square = 0.88, mean root mean square error (RMSE) = 155.5 μmol·m−2·s−1, p < 0.01). The separate UAV-LS and TLS-based fractions of sunlit leaves were averagely overestimated by 34.3% and 21.6% when compared to the fused point data due to their different coverages and comprehensiveness. (2) The woody materials showed different effects on sunlit leaf fraction estimations for forest overstory and understory due to the variations of solar zenith angle and tree spatial distribution patterns. The most noticeable differences (i.e., −36.4%) between the sunlit leaf fraction before and after removing woody materials were observed around noon, with a small solar zenith angle and low-density forest stand. (3) The penumbra effects were seen to increase the sunlit leaf fraction in the lower canopy by introducing direct solar radiation, and it should be considered when using 3-D structural information from lidar to identify sunlit and shaded leaves.
Complex Analysis of the Efficiency of Difference Reflectance Indices on the Basis of 400-700 nm Wavelengths for Revealing the Influences of Water Shortage and Heating on Plant Seedlings

A drought, which can be often accompanied by increased temperature, is a key adverse factor for agricultural plants. Remote sensing of early plant changes under water shortage is a prospective way to improve plant cultivation; in particular, the sensing can be based on measurement of difference reflectance indices (RIs). We complexly analyzed the efficiency of RIs based on 400–700 nm wavelengths for revealing the influences of water shortage and short-term heating on plant seedlings. We measured spectra of reflected light in leaves of pea, wheat, and pumpkin under control and stress conditions. All possible RIs in the 400–700 nm range were calculated, significances of differences between experimental and control indices were estimated, and heatmaps of the significances were constructed. It was shown that the water shortage (pea seedlings) changed absolute values of large quantity of calculated RIs. Absolute values of some RIs were significantly changed for 1–5 or 2–5 days of the water shortage; they were strongly correlated to the potential quantum yield of photosystem II and relative water content in leaves. In contrast, the short-term heating (pea, wheat, and pumpkin seedlings) mainly influenced light-induced changes in RIs. Our results show new RIs, which are potentially sensitive to the action of stressors.