Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex-Based Differences in Autologous Cell Therapy Trials in Patients With Acute Myocardial Infarction: Subanalysis of the ACCRUE Database

Background: Sex-based differences are under-studied in cardiovascular trials as women are commonly underrepresented in dual sex studies, even though major sex-based differences in epidemiology, pathophysiology, and outcomes of cardiovascular disease have been reported. We examined sex-based differences in patient characteristics, outcome, and BM-CD34+ frequency of the ACCRUE (Meta-Analysis of Cell-based CaRdiac studies) database involving patients with acute myocardial infarction (AMI) randomized to autologous cell-based or control treatment. Methods: We compared baseline characteristics and 1-year follow-up clinical data: composite major adverse cardiac and cerebrovascular events (primary endpoint), and changes in left ventricular ejection fraction (LVEF), end-diastolic (EDV), and end-systolic volumes (ESV) (secondary efficacy endpoint) in women and men (N = 1,252; 81.4% men). Secondary safety endpoints included freedom from hard clinical endpoints. Results: In cell-treated groups, women but not men had a lower frequency of stroke, AMI, and mortality than controls. The frequency of BM-CD34+ cells was significantly correlated with baseline EDV and ESV and negatively correlated with baseline LVEF in both sexes; a left shift in regression curve in women indicated a smaller EDV and ESV was associated with higher BM-CD34+ cells in women. Conclusions: Sex differences were found in baseline cardiovascular risk factors and cardiac function and in outcome responses to cell therapy.
Comparison of Cortical Autonomic Network-Linked Sympathetic Excitation by Mueller Maneuvers and Breath-Holds in Subjects With and Without Obstructive Sleep Apnea

In healthy young volunteers, acquisition of blood oxygen level-dependent (BOLD) magnetic resonance (MR) and muscle sympathetic nerve (MSNA) signals during simulation of obstructive or central sleep apnea identified cortical cardiovascular autonomic regions in which the BOLD signal changed synchronously with acute noradrenergic excitation. In the present work, we tested the hypothesis that such Mueller maneuvers (MM) and breath-holds (BH) would elicit greater concomitant changes in mean efferent nerve firing and BOLD signal intensity in patients with moderate to severe obstructive sleep apnea (OSA) relative to age- and sex-matched individuals with no or only mild OSA (Apnea Hypopnea Index, AHI, <15 events/h). Forty-six participants, 24 with OSA [59 ± 8 years; AHI 31 ± 18 events/h (mean ± SD); seven women] and 22 without (58 ± 11 years; AHI 7 ± 4; nine women), performed a series of three MM and three BH, in randomly assigned order, twice: during continuous recording of MSNA from the right fibular nerve and, on a separate day, during T2∗-weighted echo planar functional MR imaging. MSNA at rest was greater in those with OSA (65 ± 19 vs. 48 ± 17 bursts per 100 heart beats; p < 0.01). MM and BH elicited similar heart rate, blood pressure, and MSNA responses in the two cohorts; group mean BOLD data were concordant, detecting no between-group differences in cortical autonomic region signal activities. The present findings do not support the concept that recurring episodes of cyclical apnea during sleep alter cortical or peripheral neural responsiveness to their simulation during wakefulness by volitional Mueller maneuvers or breath-holds.
Modeling the Spatial Dynamics of Soil Organic Carbon Using Remotely-Sensed Predictors in Fuzhou City, China

Assessing the spatial dynamics of soil organic carbon (SOC) is essential for carbon monitoring. Since variability of SOC is mainly attributed to biophysical land surface variables, integrating a compressive set of such indices may support the pursuit of an optimum set of predictor variables. Therefore, this study was aimed at predicting the spatial distribution of SOC in relation to remotely sensed variables and other covariates. Hence, the land surface variables were combined from remote sensing, topographic, and soil spectral sources. Moreover, the most influential variables for prediction were selected using the random forest (RF) and classification and regression tree (CART). The results indicated that the RF model has good prediction performance with corresponding R2 and root-mean-square error (RMSE) values of 0.96 and 0.91 mg·g−1, respectively. The distribution of SOC content showed variability across landforms (CV = 78.67%), land use (CV = 93%), and lithology (CV = 64.67%). Forestland had the highest SOC (13.60 mg·g−1) followed by agriculture (10.43 mg·g−1), urban (9.74 mg·g−1), and water body (4.55 mg·g−1) land uses. Furthermore, soils developed in bauxite and laterite lithology had the highest SOC content (14.69 mg·g−1). The SOC content was remarkably lower in soils developed in sandstones; however, the values obtained in soils from the rest of the lithologies could not be significantly differentiated. The mean SOC concentration was 11.70 mg·g−1, where the majority of soils in the study area were classified as highly humus and extremely humus. The soils with the highest SOC content (extremely humus) were distributed in the mountainous regions of the study area. The biophysical land surface indices, brightness removed vegetation indices, topographic indices, and soil spectral bands were the most influential predictors of SOC in the study area. The spatial variability of SOC may be influenced by landform, land use, and lithology of the study area. Remotely sensed predictors including land moisture, land surface temperature, and built-up indices added valuable information for the prediction of SOC. Hence, the land surface indices may provide new insights into SOC modeling in complex landscapes of warm subtropical urban regions.
Investigating the Effects of a Combined Spatial and Spectral Dimensionality Reduction Approach for Aerial Hyperspectral Target Detection Applications

Target detection and classification is an important application of hyperspectral imaging in remote sensing. A wide range of algorithms for target detection in hyperspectral images have been developed in the last few decades. Given the nature of hyperspectral images, they exhibit large quantities of redundant information and are therefore compressible. Dimensionality reduction is an effective means of both compressing and denoising data. Although spectral dimensionality reduction is prevalent in hyperspectral target detection applications, the spatial redundancy of a scene is rarely exploited. By applying simple spatial masking techniques as a preprocessing step to disregard pixels of definite disinterest, the subsequent spectral dimensionality reduction process is simpler, less costly and more informative. This paper proposes a processing pipeline to compress hyperspectral images both spatially and spectrally before applying target detection algorithms to the resultant scene. The combination of several different spectral dimensionality reduction methods and target detection algorithms, within the proposed pipeline, are evaluated. We find that the Adaptive Cosine Estimator produces an improved F1 score and Matthews Correlation Coefficient when compared to unprocessed data. We also show that by using the proposed pipeline the data can be compressed by over 90% and target detection performance is maintained.
Understanding Consumers' Purchase Intentions in Social Commerce through Social Capital: Evidence from SEM and fsQCA

Social commerce has become a fiercely competitive environment. Understanding consumers’ purchase intentions can help social commerce platforms retain and attract more consumers. Social capital is one of the primary resources that plays a critical role in facilitating consumers’ purchase intentions in social commerce. Here, complex relationships between different dimensions of social capital are further clarified and its impact on consumers’ purchase intentions are discussed. Based on a survey of 302 social commerce users, this study utilizes an SEM and fsQCA approach to validate the effect of social capital on consumers’ purchase intentions. The SEM results reveal that the effect of structural social capital on consumers’ purchase intentions is fully mediated by relational and cognitive social capital. The fsQCA results confirm the significance of consumers’ social capital as determinants and provide the configurations that can lead to high purchase intentions. Though previous studies have discussed the factors influencing consumers’ purchase intentions, this study takes the first step toward enhancing the understanding of the configurations that link dimensions of social capital to consumers’ purchase intentions in s-commerce using fsQCA approach.
Impact of mobile stroke units

Since its first introduction in clinical practice in 2008, the concept of mobile stroke unit enabling prehospital stroke treatment has rapidly expanded worldwide. This review summarises current knowledge in this young field of stroke research, discussing topics such as benefits in reduction of delay before treatment, vascular imaging-based triage of patients with large-vessel occlusion in the field, differential blood pressure management or prehospital antagonisation of anticoagulants. However, before mobile stroke units can become routine, several questions remain to be answered. Current research, therefore, focuses on safety, long-term medical benefit, best setting and cost-efficiency as crucial determinants for the sustainability of this novel strategy of acute stroke management.
Monitoring of the Production Process of Graded Concrete Component Using Terrestrial Laser Scanning

Accepting the ecological necessity of a drastic reduction of resource consumption and greenhouse gas emissions in the building industry, the Institute for Lightweight Structures and Conceptual Design (ILEK) at the University of Stuttgart is developing graded concrete components with integrated concrete hollow spheres. These components weigh a fraction of usual conventional components while exhibiting the same performance. Throughout the production process of a component, the positions of the hollow spheres and the level of the fresh concrete have to be monitored with high accuracy and in close to real-time, so that the quality and structural performance of the component can be guaranteed. In this contribution, effective solutions of multiple sphere detection and concrete surface modeling based on the technology of terrestrial laser scanning (TLS) during the casting process are proposed and realized by the Institute of Engineering Geodesy (IIGS). A complete monitoring concept is presented to acquire the point cloud data fast and with high-quality. The data processing method for multiple sphere segmentation based on the efficient combination of region growing and random sample consensus (RANSAC) exhibits great performance on computational efficiency and robustness. The feasibility and reliability of the proposed methods are verified and evaluated by an experiment monitoring the production of an exemplary graded concrete component. Some suggestions to improve the monitoring performance and relevant future work are given as well.
Global infectious disease risks associated with occupational exposure among non-healthcare workers: a systematic review of the literature

Objectives Employees in non-healthcare occupations may be in several ways exposed to infectious agents. Improved knowledge about the risks is needed to identify opportunities to prevent work-related infectious diseases. The objective of the current study was to provide an updated overview of the published evidence on the exposure to pathogens among non-healthcare workers. Because of the recent SARS-CoV-2 outbreaks, we also aimed to gain more evidence about exposure to several respiratory tract pathogens. Methods Eligible studies were identified in MEDLINE, Embase and Cochrane between 2009 and 8 December 2020. The protocol was registered with International Prospective Register of Systematic Reviews (CRD42019107265). An additional quality assessment was applied according to the Equator network guidelines. Results The systematic literature search yielded 4620 papers of which 270 met the selection and quality criteria. Infectious disease risks were described in 37 occupational groups; 18 of them were not mentioned before. Armed forces (n=36 pathogens), livestock farm labourers (n=31), livestock/dairy producers (n=26), abattoir workers (n=22); animal carers and forestry workers (both n=16) seemed to have the highest risk. In total, 111 pathogen exposures were found. Many of these occupational groups (81.1%) were exposed to respiratory tract pathogens. Conclusion Many of these respiratory tract pathogens were readily transmitted where employees congregate (workplace risk factors), while worker risk factors seemed to be of increasing importance. By analysing existing knowledge of these risk factors, identifying new risks and susceptible risk groups, this review aimed to raise awareness of the issue and provide reliable information to establish more effective preventive measures.
Inverting COSMIC-2 Phase Data to Bending Angle and Refractivity Profiles Using the Full Spectrum Inversion Method

The radio occultation technique provides stable atmospheric measurements that can work as a benchmark for calibrating and validating satellite-sounding data. Launched on 25 June 2019, the Constellation Observing System for Meteorology, Ionosphere, and Climate 2 and Formosa Satellite Mission 7 (COSMIC-2/FORMOSAT-7) are expected to produce about 5000 high-quality RO observations daily over the tropics and subtropics. COSMIC-2 constellation consists of 6 Low Earth Orbit (LEO) satellites in 24° inclination orbits at 720 km altitude and distributed mainly between 45°N to 45°S. The COSMIC-2 observations have uniform temporal coverage between 30°N to 30°S. This paper presents an independent inversion algorithm to invert COSMIC-2 geometry and phase data to bending angle and refractivity. We also investigate the quality of Global Navigation Satellite System (GNSS) and LEO position vectors derived from the UCAR COSMIC Data Analysis and Archive Center (CDAAC). The GNSS and LEO position vectors are stable with LEO position variations < 1.4 mm/s. The signal-to-noise ratio (SNR) on the L1 band ranges from 300–2600 v/v with a mean of 1600 v/v. The inversion algorithm developed at NOAA Center for Satellite Applications and Research (STAR) uses the Full Spectrum Inversion (FSI) method to invert COSMIC-2 geometry and phase data to bending angle and refractivity profiles. The STAR COSMIC-2 bending angle and refractivity profiles are compared with in situ radiosonde, the current COSMIC-2 products derived from CDAAC, and the collocated European Center for Medium-Range Weather Forecasts (ECMWF) climate reanalysis data ERA5. The mean bias at 8–40 km altitude among the UCAR, ERA5, and STAR is <0.1% for both bending and refractivity, with a standard deviation in the range of 1.4–2.3 and 0.9–1.1% for bending angles refractivity, respectively. In the lowest 2 km, the RO bias relative to ERA-5 shows a strong latitudinal and SNR dependence.
Quantifying the Response of German Forests to Drought Events via Satellite Imagery

Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.
GOSAT CH4 Vertical Profiles over the Indian Subcontinent: Effect of a Priori and Averaging Kernels for Climate Applications

We examined methane (CH4) variability over different regions of India and the surrounding oceans derived from thermal infrared (TIR) band observations (TIR CH4) by the Thermal and Near-infrared Sensor for carbon Observation—Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) for the period 2009–2014. This study attempts to understand the sensitivity of the vertical profile retrievals at different layers of the troposphere and lower stratosphere, on the basis of the averaging kernel (AK) functions and a priori assumptions, as applied to the simulated concentrations by the MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM). We stress that this is of particular importance when the satellite-derived products are analyzed using different ACTMs other than those used as retrieved a priori. A comparison of modeled and retrieved CH4 vertical profiles shows that the GOSAT/TANSO-FTS TIR instrument has sufficient sensitivity to provide critical information about the transport of CH4 from the top of the boundary layer to the upper troposphere. The mean mismatch between TIR CH4 and model is within 50 ppb, except for the altitude range above 150 hPa, where the sensitivity of TIR CH4 observations becomes very low. Convolved model profiles with TIR CH4 AK reduces the mismatch to less than the retrieval uncertainty. Distinct seasonal variations of CH4 have been observed near the atmospheric boundary layer (800 hPa), free troposphere (500 hPa), and upper troposphere (300 hPa) over the northern and southern regions of India, corresponding to the southwest monsoon (July–September) and post-monsoon (October–December) seasons. Analysis of the transport and emission contributions to CH4 suggests that the CH4 seasonal cycle over the Indian subcontinent is governed by both the heterogeneous distributions of surface emissions and the influence of the global monsoon divergent wind circulations. The major contrast between monsoon, and pre- and post-monsoon profiles of CH4 over Indian regions are noticed near the boundary layer heights, which is mainly caused by seasonal change in local emission strength with a peak during summer due to increased emissions from the paddy fields and wetlands. A strong difference between seasons in the middle and upper troposphere is caused by convective transport of the emission signals from the surface and redistribution in the monsoon anticyclone of upper troposphere. TIR CH4 observations provide additional information on CH4 in the region compared to what is known from in situ data and total-column (XCH4) measurements. Based on two emission sensitivity simulations compared to TIR CH4 observations, we suggest that the emissions of CH4 from the India region were 51.2 ± 4.6 Tg year−1 during the period 2009–2014. Our results suggest that improvements in the a priori profile shape in the upper troposphere and lower stratosphere (UT/LS) region would help better interpretation of CH4 cycling in the earth’s environment.
Single Image Super-Resolution Restoration of TGO CaSSIS Colour Images: Demonstration with Perseverance Rover Landing Site and Mars Science Targets

The ExoMars Trace Gas Orbiter (TGO)’s Colour and Stereo Surface Imaging System (CaSSIS) provides multi-spectral optical imagery at 4–5 m/pixel spatial resolution. Improving the spatial resolution of CaSSIS images would allow greater amounts of scientific information to be extracted. In this work, we propose a novel Multi-scale Adaptive weighted Residual Super-resolution Generative Adversarial Network (MARSGAN) for single-image super-resolution restoration of TGO CaSSIS images, and demonstrate how this provides an effective resolution enhancement factor of about 3 times. We demonstrate with qualitative and quantitative assessments of CaSSIS SRR results over the Mars2020 Perseverance rover’s landing site. We also show examples of similar SRR performance over 8 science test sites mainly selected for being covered by HiRISE at higher resolution for comparison, which include many features unique to the Martian surface. Application of MARSGAN will allow high resolution colour imagery from CaSSIS to be obtained over extensive areas of Mars beyond what has been possible to obtain to date from HiRISE.
Distorted Vehicle Detection and Distance Estimation by Metric Learning-Based SSD

Object detection and distance estimation based on videos are important issues in advanced driver-sssistant system (ADAS). In practice, fisheye cameras are widely used to capture images with a large field of view, which will produce distorted image frames. But most of the object detection algorithms were designed for the nonfisheye camera videos without distortion, which is not suitable for the application of ADAS since one always expects the panorama stitching and object detection system should share one set of cameras. The research of vehicle detection based on fisheye cameras is relatively rare. In this paper, vehicle detection and distance estimation based on fisheye cameras are studied. First, a multi-scale partition preprocessing is proposed, which can enlarge the size of small targets to improve the detection accuracy of small targets. Second, parameters learned from the public datasets without distortion is transferred to our fisheye video dataset. Then metric learning-based single shot multibox detector (MLSSD) is proposed to improve the accuracy of distorted vehicle detection. Combining metric learning and SSD network, MLSSDcansignificantlyreducethemissingandfalsedetectionrates.Moreover,ascalableoverlappingpartitionpoolingmethod isproposedtoexploretherelationsamongtheadjacentfeaturesinafeaturemap.Finally,thedistancebetweenthedrivingvehicle andvehiclesaroundthisvehicleisestimatedbasedontheobjectdetectionresultsbythemethodofmarkerpoints.Experimental resultsshowthatourproposedMLSSDnetworksignificantlyoutperformsothernetworksfordistortedobjectdetection.