Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and Characteristics of Borderline Intellectual Functioning in a Cohort of Patients With Polysubstance Use Disorder

Objective: To determine the prevalence and associated demographic and clinical features of borderline intellectual functioning (BIF) among individuals with polysubstance use disorder (pSUD). Methods: We applied a cross-sectional analytical design to data from the Norwegian STAYER study (n = 162), a cohort study of patients with a pSUD from the Stavanger University hospital catchment area. We used Wechsler Abbreviated Scale of Intelligence Full Scale IQ (FSIQ) to define BIF (FSIQ = 70–85) and non-BIF (FSIQ = >85) and collected demographic and clinical data using semi-structured interviews and self-reports on the Symptom Checklist 90-Revised (SCL-90-R) and the Satisfaction With Life Scale (SWLS). Results: The prevalence of BIF was 18% in the present study. The presence of BIF was associated with higher SCL-90-R GSI scores than in the non-BIF group. There were no significant differences between the BIF and non-BIF groups regarding age, gender, participation in meaningful daily activity, years of work experience, years of education, satisfaction with life, level of care, treatment attempts, age at substance-use onset, years of substance use, history of injecting drugs, or age of onset of injecting drugs. Conclusion: The present study confirmed a higher prevalence of BIF among patients with pSUD than expected from the distribution of IQ scores in a general population. Elevated SCL-90-R GSI scores suggested that BIF is associated with increased psychological distress in patients receiving treatment for pSUD. Further studies on this association, and its effect on treatment procedure and outcomes are strongly warranted.
Academic Integrity in Online Assessment: A Research Review

This paper provides a review of current research on academic integrity in higher education, with a focus on its application to assessment practices in online courses. Understanding the types and causes of academic dishonesty can inform the suite of methods that might be used to most effectively promote academic integrity. Thus, the paper first addresses the question of why students engage in academically dishonest behaviours. Then, a review of current methods to reduce academically dishonest behaviours is presented. Acknowledging the increasing use of online courses within the postsecondary curriculum, it is our hope that this review will aid instructors and administrators in their decision-making process regarding online evaluations and encourage future study that will form the foundation of evidence-based practices.
Salmonella spvC Gene Inhibits Autophagy of Host Cells and Suppresses NLRP3 as Well as NLRC4

Salmonella spvC gene, encoding a phosphothreonine lyase on host mitogen-activated protein kinases, facilitates systemic infection of Salmonella while the precise mechanisms remain elusive. Autophagy and pyroptosis dependent on the activation of inflammasomes, as parts of innate immune response, contribute to host defense against Salmonella infection. Recently, we reported that spvC could inhibit pyroptosis. To explore the effect of spvC on autophagy and the relationship between its function in pyroptosis and autophagy, infection models of macrophages J774A.1 and epithelial HeLa cells co-cultured with Salmonella Typhimurium wild type, spvC deletion, site-directed mutant which lacks phosphothreonine lyase activity, or complemented strain were established. The levels of LC3 turnover and Beclin 1 of J774A.1 cells were determined by western blot. Confocal laser scanning microscopy was used to visualize the autophagic flux after being transfected with mRFP-GFP-LC3 plasmid in HeLa cells. Results showed that SpvC inhibited autophagosome formation through its phosphothreonine lyase activity. Additionally, analysis of nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain-containing 3 (NLRP3) and NLR with CARD domain-containing 4 (NLRC4) in J774A.1 cells indicated that spvC decreased the protein levels of NLRP3 and NLRC4, which were significantly changed by autophagy inhibitor Bafilomycin A1. Together, our observations reveal a novel mechanism of spvC in Salmonella pathogenesis and host inflammatory response via inhibiting autophagy and NLRP3 as well as NLRC4. These pathways and their subversion by diverse pathogen virulence determinants are expected to throw light on the design of anti-infective agents.
A Comparison of Optimized Sentinel-2 Super-Resolution Methods Using Wald's Protocol and Bayesian Optimization

In the context of earth observation and remote sensing, super-resolution aims to enhance the resolution of a captured image by upscaling and enhancing its details. In recent years, numerous methods for super-resolution of Sentinel-2 (S2) multispectral images have been suggested. Most of those methods depend on various tuning parameters that affect how effective they are. This paper’s aim is twofold. Firstly, we propose to use Bayesian optimization at a reduced scale to select tuning parameters. Secondly, we choose tuning parameters for eight S2 super-resolution methods and compare them using real and synthetic data. While all the methods give good quantitative results, Area-To-Point Regression Kriging (ATPRK), Sentinel-2 Sharpening (S2Sharp), and Sentinel-2 Symmetric Skip Connection convolutional neural network (S2 SSC) perform markedly better on several datasets than the other methods tested in this paper.
The U-Shaped Association of Non-High-Density Lipoprotein Cholesterol Levels With All-Cause and Cardiovascular Mortality Among Patients With Hypertension

Background: Non-high-density lipoprotein cholesterol (non-HDL-C) is a valuable indicator in routine blood lipid tests, but the associations of non-HDL-C with mortality in hypertensive population still remain uncertain. Methods: In the National Health and Nutrition Examination Surveys from 1999 to 2014, participants having hypertension were included and grouped by non-HDL-C levels (<130, 130–159, 160–189, 190–219, and ≥220 mg/dl). Multivariate Cox regression was conducted for calculation of hazard ratios (HR) and 95% confidence interval (CI). To reveal the relationship between non-HDL-C and mortality, Kaplan–Meier survival curves, restricted cubic spline, linear regression, and subgroup analysis were also applied. Results: A total of 12,169 participants (47.52% males, mean age 57.27 ± 15.79 years) were included. During average follow-up of 92.5 months, 1,946 (15.99%) all-cause deaths and 422 (3.47%) cardiovascular deaths occurred. After adjusting for confounders, the association of non-HDL-C with mortality was detected as U-shaped. Threshold values were observed at 158 mg/dl for all-cause mortality and 190 mg/dl as to cardiovascular mortality. Below the threshold, every 10 mg/dl increment in non-HDL-C attributed to relatively low all-cause mortality significantly (HR = 0.94, 95% CI: 0.92–0.96). Above the threshold, non-HDL-C has significant positive associations with both all-cause (HR = 1.03, 95% CI: 1.01–1.05) and cardiovascular mortality (HR = 1.09, 95% CI: 1.05–1.14). For subgroups analysis, similar results were found among participants age <65 years old, non-white population, those were not taking lipid-lowering drugs, and subjects with body mass index (BMI) ≥25 kg/m2. Conclusion: The U-shaped association was detected between non-HDL-C and mortality among hypertensive population.
Modify the Accuracy of MODIS PWV in China: A Performance Comparison Using Random Forest, Generalized Regression Neural Network and Back-Propagation Neural Network

Water vapor plays an important role in climate change and water cycling, but there are few water vapor products with both high spatial resolution and high accuracy that effectively monitor the change of water vapor. The high precision Global Navigation Satellite System (GNSS) Precipitable Water Vapor (PWV) is often used to calibrate the high spatial resolution Moderate-resolution Imaging Spectroradiometer (MODIS) PWV to produce new PWV product with high accuracy and high spatial resolution. In addition, the machine learning method has a good performance in modifying the accuracy of MODIS PWV. However, the accuracy improvement of different machine learning methods and different modeling timescale is different. In this article, we use three machine learning methods, namely, the Random Forest (RF), Generalized Regression Neural Network (GRNN), and Back-propagation Neural Network (BPNN) to calibrate MODIS PWV in 2019, at annual and monthly timescales. We also use the Multiple Linear Regression (MLR) method for comparison. The root mean squares (RMSs) at the annual timescale with the three machine learning methods are 4.1 mm (BPNN), 3.3 mm (RF), and 3.9 mm (GRNN), and the average RMSs become 2.9 mm (BPNN), 2.8 mm (RF), and 2.5 mm (GRNN) at the monthly timescale. Those results are all better than the MLR method (5.0 mm at the annual timescale and 4.6 mm at the monthly timescale). When there is an obvious variation pattern in the training sample, the RF method can capture the pattern to achieve the best results since the RF achieves the best performance at the annual timescale. Dividing such samples into several sub-samples each having higher internal consistency could further improve the performance of machine learning methods, especially for the GRNN, since GRNN achieves the best performance at the monthly timescale, and the performance of those three machine learning methods at the monthly timescale is better than that of annual timescale. The spatial and temporal variation patterns of the RMS values are significantly weakened after the modeling by machine learning methods for both three methods.
Global Profiling of Phosphorylation Reveals the Barley Roots Response to Phosphorus Starvation and Resupply

Phosphorus (P) deficiency is a major threat to the crop production, and for understanding the response mechanism of plant roots, P stress may facilitate the development of crops with increased tolerance. Phosphorylation plays a critical role in the regulation of proteins for plant responses to biotic and abiotic stress; however, its functions in P starvation/resupply are largely unknown for barley (Hordeum vulgare) growth. Here, we performed a global review of phosphorylation in barley roots treated by P starvation/resupply. We identified 7,710 phosphorylation sites on 3,373 proteins, of which 76 types of conserved motifs were extracted from 10,428 phosphorylated peptides. Most phosphorylated proteins were located in the nucleus (36%) and chloroplast (32%). Compared with the control, 186 and 131 phosphorylated proteins under P starvation condition and 156 and 111 phosphorylated proteins under P resupply condition showed significant differences at 6 and 48 h, respectively. These proteins mainly participated in carbohydrate metabolism, phytohormones, signal transduction, cell wall stress, and oxidases stress. Moreover, the pathways of the ribosome, RNA binding, protein transport, and metal binding were significantly enriched under P starvation, and only two pathways of ribosome and RNA binding were greatly enriched under Pi resupply according to the protein–protein interaction analysis. The results suggested that the phosphorylation proteins might play important roles in the metabolic processes of barley roots in response to Pi deficiency/resupply. The data not only provide unique access to phosphorylation reprogramming of plant roots under deficiency/resupply but also demonstrate the close cooperation between these phosphorylation proteins and key metabolic functions.
Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT > FT > SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.
Galileo Second Generation: Two-Way Messaging for IoT

Internet of Things (IoT) applications are growing exponentially and are forecast to play an even more important role in Future Networks and Systems. However, it is widely accepted that the terrestrial network alone is not able to serve the requirements of the IoT market of a truly ubiquitous coverage. To this aim, several initiatives are currently addressing the inclusion of a satellite component into the telecommunication infrastructure to extend its coverage to those areas that are un- or under-served by the terrestrial networks. At the same time, European Space Agency (ESA) and its industry partners are designing the Galileo Second Generation (G2G) constellation, which is expected to be available by 2025.In this general context, our work proposes a two way communication service based on the new G2G constellation and using the Enhanced Spread Spectrum Aloha (E-SSA) air-interface, to address the IoT use cases in remote areas (e.g., cargo tracking, environmental monitoring). Thanks to their global coverage and fully operative ground infrastructure, Global Navigation Satellite Systems (GNSS) offer in fact a potential great advantage over the forthcoming satellite mega-constellations in serving IoT applications.
Sentinel-1 and 2 Time-Series for Vegetation Mapping Using Random Forest Classification: A Case Study of Northern Croatia

Land-cover (LC) mapping in a morphologically heterogeneous landscape area is a challenging task since various LC classes (e.g., crop types in agricultural areas) are spectrally similar. Most research is still mostly relying on optical satellite imagery for these tasks, whereas synthetic aperture radar (SAR) imagery is often neglected. Therefore, this research assessed the classification accuracy using the recent Sentinel-1 (S1) SAR and Sentinel-2 (S2) time-series data for LC mapping, especially vegetation classes. Additionally, ancillary data, such as texture features, spectral indices from S1 and S2, respectively, as well as digital elevation model (DEM), were used in different classification scenarios. Random Forest (RF) was used for classification tasks using a proposed hybrid reference dataset derived from European Land Use and Coverage Area Frame Survey (LUCAS), CORINE, and Land Parcel Identification Systems (LPIS) LC database. Based on the RF variable selection using Mean Decrease Accuracy (MDA), the combination of S1 and S2 data yielded the highest overall accuracy (OA) of 91.78%, with a total disagreement of 8.22%. The most pertinent features for vegetation mapping were GLCM Mean and Variance for S1, NDVI, along with Red and SWIR band for S2, whereas the digital elevation model produced major classification enhancement as an input feature. The results of this study demonstrated that the aforementioned approach (i.e., RF using a hybrid reference dataset) is well-suited for vegetation mapping using Sentinel imagery, which can be applied for large-scale LC classifications.
Evaluating the Performance of Lightning Data Assimilation from BLNET Observations in a 4DVAR-Based Weather Nowcasting Model for a High-Impact Weather over Beijing

The Beijing Broadband Lightning Network (BLNET) was successfully set up in North China and had yielded a considerable detection capability of total lightning (intracloud and cloud to ground) over the regions with complex underlying (plains, mountains, and oceans). This study set up a basic framework for the operational application of assimilating total lightning activities from BLNET and assesses the potential benefits in cloud-scale, very short-term forecast (nowcasting) by modulating the vertical velocity using the 4DVar technique. Nowcast statistics aggregated over 11 cycles show that the nowcasting performances with the assimilation of BLNET lightning datasets outperform RAD and the assimilation of GLD360 (Global Lightning) datasets. The assimilation of BLNET data improves the model's dynamical states in the analysis by enhancing the convergence and updraft in and near the convective system. To better implement of assimilating real-time lightning data, this study also conducts sensitivity experiments to investigate the impact of the horizontal length scale of a distance-weighted interpolation, binning time intervals, and different vertical profile or distance weights prior to the DA. The results indicate that the best forecast performance for assimilating BLNET lightning datasets is obtained in a 4DVar cycle when the lightning accumulation interval is 3 min, the radius of horizontal interpolation is 5 × 5, and the statistically vertical velocity profile and the distance weights obtained from cumulus cloud.
Glycerol Carbonate Synthesis by Hierarchically Structured Catalysts: Catalytic Activity and Characterization

The surge in biodiesel production in recent years has resulted in enhanced research interest in the conversion of glycerol to other valuable chemicals such as glycerol carbonate (GLC). In the present study, the catalytic transesterification of glycerol with dimethyl carbonate (DMC) for the production of GLC was studied with calcium–lanthanum mixed-oxide catalysts at different Ca/La molar ratios. These catalysts were synthesized using an exo- and endotemplating method. The physicochemical characteristics of the catalysts were determined using powder X-ray diffraction (XRD), N2 sorption, scanning electron microscopy (SEM), and temperature-programmed desorption (TPD) of CO2 and NH3. The transesterification of glycerol was found to be highly dependent on the basicity of the catalysts. The catalyst with a Ca/La molar ratio of 3 (3CaLa) showed the highest glycerol conversion and GLC yield. Optimization of the reaction parameters and reusability of the catalyst were studied with the 3CaLa catalyst in terms of gl.
Real-Time Parameter Estimation of a Dual-Pol Radar Rain Rate Estimator Using the Extended Kalman Filter

The extended Kalman filter is an extended version of the Kalman filter for a non-linear problem. This study applies this extended Kalman filter to the real-time estimation of the parameters of the dual-pol radar rain rate estimator. The estimated parameters are also compared with those based on the least squares method. As an application example, this study considers four storm events observed by the Beaslesan radar in Korea. The findings derived include, first, that the parameters of the radar rain rate estimator obtained by the extended Kalman filter are totally different from those by the least squares method. In fact, the parameters obtained by the extended Kalman filter are found to be more reasonable, and are similar to those reported in previous studies. Second, the estimated rain rates based on the parameters obtained by the extended Kalman filter are found to be similar to those observed on the ground. Even though the parameters estimated by applying the least squares method are quite different from previous studies as well as those based on the extended Kalman filter, the resulting radar rain rate is found to be quite similar to that based on the extended Kalman filter. In conclusion, the extended Kalman filter can be a reliable method for real-time estimation of the parameters of the dual-pol radar rain rate estimator. The resulting rain rate is also found to be of sufficiently high quality to be applicable for other purposes, such as various flood warning systems.
Multitemporal Landslide Inventory and Activity Analysis by Means of Aerial Photogrammetry and LiDAR Techniques in an Area of Southern Spain

This paper deals with the use of aerial photogrammetry and LiDAR techniques to analyze landslide activity over a long time span—just over 32 years. The data correspond to several aerial surveys (1984, 1996, 2001, 2005, 2009, 2010, 2011, 2013 and 2016) covering an area of about 50 km2 along highway A-44, near Jaén (Southern Spain). An ad hoc combined photogrammetric and LiDAR aerial survey of 2010 was established as the reference flight. This flight was processed by means of direct orientation methods and iterative adjustments between both data sets. Meanwhile, historical flights available in public geographical data servers were oriented by transferring ground control points from the reference flight. Then, digital surface models (DSMs) and orthophotographs were generated, as well as the corresponding differential models (DoDs), which, after the application of filters and taking into account the estimated uncertainty of ± 1 m, allowed us to identify true changes on the ground surface. This analysis, complemented by photointerpretation, led us to obtain a landslide multitemporal inventory in the study area that was analyzed in order to characterize the landslide type, morphology and activity. Three basic typologies were identified: rock falls–collapses, slides and flows. These types present different morphometric properties (area, perimeter and height interval) and are associated with different conditions (height, slope, orientation and lithology). Moreover, a set of monitoring areas, common for the different flights, was also used to analyze the activity throughout the study period. Thus, some more active periods were identified (2009–2010, 2010–2011, 2011–2013 and 1996–2001) among other less active ones (1984–1996, 2001–2005, 2005–2009 and 2013–2016), which are related to rainy events and dry years, respectively.