Miracle-dz/UniFAR-FTUs huggingface dataset:
Lesbian, gay or bisexual identity as a risk factor for trauma and mental health problems in Northern Irish students and the protective role of social support

ABSTRACT Background: People identifying as lesbian, gay or bisexual (LGB) have been shown to experience more trauma and poorer mental health than their heterosexual counterparts, particularly in countries with discriminatory laws and policies. Northern Ireland is a post-conflict region with high rates of trauma and mental health problems, as well as significant levels of prejudice against the LGB community. To date, no studies in Northern Ireland have compared trauma exposure, social support and mental health status of LGB students to their heterosexual peers. Objective: The present study aimed to assess whether LGB status was associated with more trauma exposure and poorer mental health, and whether social support mediated these associations. Method: The sample was comprised of 1,116 university students. Eighty-nine percent (n = 993) identified as heterosexual and 11% (n = 123) identified as LGB. Path analysis was used to test the hypotheses. Results: LGB status was significantly associated with increased trauma exposure and with symptoms of PTSD, depression and anxiety, but not with problematic alcohol use. These associations were mediated by social support from family only. Conclusions: These results evidence vulnerabilities among Northern Irish students identifying as LGB in relation to trauma and mental health compared with their heterosexual peers. However, social support from family has the potential to mitigate risk. Educational initiatives should raise awareness of the importance of familial support for LGB youth, and those young people who lack family support should be considered an at-risk group, warranting particularly intensive targeting by relevant supports.
Candida biome of severe early childhood caries (S-ECC) and its cariogenic virulence traits

ABSTRACT The protected niche of deep-caries lesions is a distinctive ecosystem. We assessed the Candida biome and its cariogenic traits from dentin samples of 50 children with severe-early childhood caries (S-ECC). Asymptomatic, primary molars belonging to International Caries Detection and Assessment-ICDAS caries-code 5 and 6 were analyzed, and C. albicans (10-isolates), C. tropicalis (10), C. krusei (10), and C. glabrata (5) isolated from the lesions were then evaluated for their biofilm formation, acidogenicity, and the production of secreted hydrolases: hemolysins, phospholipase, proteinase and DNase. Candida were isolated from 14/43 ICDAS-5 lesions (32.5%) and 44/57 ICDAS-6 lesions (77.2%). Compared to, ICDAS-5, a significantly higher frequency of multi-species infestation was observed in ICDAS-6 lesions (p=0.001). All four candidal species (above) showed prolific biofilm growth, and an equal potency for tooth demineralization. A significant interspecies difference in the mean phospholipase, as well as proteinase activity was noted (p < 0.05), with C. albicans being the predominant hydrolase producer. Further, a positive correlation between phospholipase and proteinase activity of Candida-isolates was noted (r = 0.818, p < 0.001). Our data suggest that candidal mycobiota with their potent cariogenic traits may significantly contribute to the development and progression of S-ECC.
Surface reservoirs dominate dynamic gas-surface partitioning of many indoor air constituents

Large and labile surface reservoirs control air mixing ratios of many indoor species. Human health is affected by indoor air quality. One distinctive aspect of the indoor environment is its very large surface area that acts as a poorly characterized sink and source of gas-phase chemicals. In this work, air-surface interactions of 19 common indoor air contaminants with diverse properties and sources were monitored in a house using fast-response, on-line mass spectrometric and spectroscopic methods. Enhanced-ventilation experiments demonstrate that most of the contaminants reside in the surface reservoirs and not, as expected, in the gas phase. They participate in rapid air-surface partitioning that is much faster than air exchange. Phase distribution calculations are consistent with the observations when assuming simultaneous equilibria between air and large weakly polar and polar absorptive surface reservoirs, with acid-base dissociation in the polar reservoir. Chemical exposure assessments must account for the finding that contaminants that are fully volatile under outdoor air conditions instead behave as semivolatile compounds indoors.
Nomograms for the Prediction of Survival for Patients with Pediatric Adrenal Cancer after Surgery

Purpose: To develop and validate a nomogram to postoperatively evaluate overall survival (OS) and cancer-specific survival (CSS) in patients with pediatric adrenal cancer. Methods: In total, 847 eligible patients diagnosed between 1988 and 2015 form the Surveillance Epidemiology, and End Results (SEER) database were enrolled in this study according to the specified inclusion and exclusion criteria. They were divided into a training set (n = 661) and a validation set (n = 186). Multivariate Cox proportional hazards regression algorithm was used to identify the independent predictors of OS and CSS in the training set, and develop the predicting models, which were presented two nomograms. The performance of the nomograms (discrimination, calibration and clinical usefulness) was assessed in the training set and validated in the validation set. Results: Based on the multivariate Cox proportional hazards regression analyses, three independent predictors including age at diagnosis, tumor size and M stage were identified for both OS and CSS. Then, an OS nomogram and a CSS nomogram were developed incorporating these three predictors, respectively. The OS nomogram showed good calibration and discrimination in the training set (C-index [95% CI], 0.744 [0.711-0.777]), which was confirmed in the validation set (C-index [95% CI], 0.746 [0.656-0.836]). Favorable calibration and discrimination of the CSS nomogram were also observed in the training set (C-index [95% CI], 0.749 [0.715-0.783]) and validation set (C-index [95% CI], 0.789 [0.710-0.868]). Moreover, the nomograms successfully distinguished patients with high risk of all-cause and cancer-specific mortality in all patients and in the stratified analyses. Decision curve analysis demonstrated the usefulness of the nomograms. Conclusion: The presented nomograms show favorable predictive accuracy for OS and CSS in patients with pediatric adrenal cancer after surgery. Further validation is warranted prior to clinical implementation.
Epstein barr virus encodes miRNAs to assist host immune escape

Epstein-barr virus (EBV) is a definite tumorigenic virus, which can form life-long latency in the host, which is difficult to be recognized and completely eliminated by the immune system. It is closely related to the occurrence and development of nasopharyngeal cancer, gastric cancer and various types of lymphoma. At present, a total of 44 Epstein-barr virus-encoded microRNAs (EBV miRNAs) have been found. In response to the immune system of the body, EBV miRNAs can inhibit the expression and presentation of viral antigens, inhibit immune activation and immunotoxicity, assisting host cells to escape from immunity, and providing conditions for further immortalized tumorigenesis of the host cells.
Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem

In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study.
Hailstorm Detection by Satellite Microwave Radiometers

Passive microwave measurements from satellites have been used to identify the signature of hail in intense thunderstorms. The scattering signal of hailstones is typically observed as a strong depression of upwelling brightness temperatures from the cloud to the satellite. Although the relation between scattering signal and hail diameter is often assumed linear, in this work a logistic model is used which seems to well approximate the complexity of the radiation extinction process by varying the hail cross-section. A novel probability-based method for hail detection originally conceived for AMSU-B/MHS and now extended to ATMS, GMI, and SSMIS, is presented. The measurements of AMSU-B/MHS were analyzed during selected hailstorms over Europe, South America and the US to quantify the extinction of radiation due to the hailstones and large ice aggregates. To this aim, a probabilistic growth model has been developed. The validation analysis based on 12-year surface hail observations over the US (NOAA official reports) collocated with AMSU-B overpasses have demonstrated the high performance of the hail detection method in distinguishing between moderate and severe hailstorms, fitting the seasonality of hail patterns. The flexibility of the method allowed its experimental application to other microwave radiometers equipped with MHS-like frequency channels revealing a high level of portability.
Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints

The development of the sharing economy has made carsharing the main future development model of car rental. Carsharing network investment is enormous, but the resource allocation is limited. Therefore, the reasonable location of the carsharing station is important to the development of carsharing companies. On the basis of the current status of carsharing development, this research considers multiple influencing factors of carsharing to meet the maximum user demand. Meanwhile, the constraint of the limited cost of the company is considered to establish a nonlinear integer programming model for station location of carsharing. A genetic algorithm is designed to solve the problem by analyzing the location model of the carsharing network. Finally, the results of a case study of Lanzhou, China show the effectiveness of the establishment and solution of the station location model.
Phenological Dynamics Characterization of Alignment Trees with Sentinel-2 Imagery: A Vegetation Indices Time Series Reconstruction Methodology Adapted to Urban Areas

This article presents a novel methodology for the characterization of tree vegetation phenology, based on vegetation indices time series reconstruction and adapted to urban areas. The methodology is based on a pixel by pixel curve fitting classification, together with a subsequent Savitzky–Golay filtering of raw phenological curves from pixels classified as vegetation. Moreover, the new method is conceived to face specificities of urban environments such as: the high heterogeneity of impervious/natural elements, the 3D structure of the city inducing shadows, the restricted spatial extent of individual tree crowns and the strong biodiversity of urban vegetation. Three vegetation indices have been studied: Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index 1 (NDRE1), which are mainly linked to chlorophyll content and leaf density and Normalized Burn Ratio (NBR) mostly correlated to water content and leaf density. The methodology has been designed to allow the analysis of annual and intra-annual vegetation phenological dynamics. Then, different annual and intra-annual criteria for phenology characterization are proposed and criticized. To show the applicability of the methodology, this article focuses on Sentinel-2 (S-2) imagery covering 2018 and the study of groups of London planes in an alignment structure in the French city of Toulouse. Results showed that the new method allows the ability to 1) describe the heterogeneity of phenologies from London planes exposed to different environmental conditions (urban canyons, proximity with a source of water) and 2) to detect intra-annual phenological dynamics linked to changes in meteorological conditions.
Precipitation and Minimum Temperature are Primary Climatic Controls of Alpine Grassland Autumn Phenology on the Qinghai-Tibet Plateau

Autumn phenology is a crucial indicator for identifying the alpine grassland growing season’s end date on the Qinghai-Tibet Plateau (QTP), which intensely controls biogeochemical cycles in this ecosystem. Although autumn phenology is thought to be mainly influenced by the preseason temperature, precipitation, and insolation in alpine grasslands, the relative contributions of these climatic factors on the QTP remain uncertain. To quantify the impacts of climatic factors on autumn phenology, we built stepwise linear regression models for 91 meteorological stations on the QTP using in situ herb brown-off dates, remotely sensed autumn phenological metrics, and a multi-factor climate dataset during an optimum length period. The results show that autumn precipitation has the most extensive influence on interannual variation in alpine grassland autumn phenology. On average, a 10 mm increase in autumn precipitation during the optimum length period may lead to a delay of 0.2 to 4 days in the middle senescence date (P < 0.05) across the alpine grasslands. The daily minimum air temperature is the second most important controlling factor, namely, a 1 °C increase in the mean autumn minimum temperature during the optimum length period may induce a delay of 1.6 to 9.3 days in the middle senescence date (P < 0.05) across the alpine grasslands. Sunshine duration is the third extensive controlling factor. However, its influence is spatially limited. Moreover, the relative humidity and wind speed also have strong influences at a few stations. Further analysis indicates that the autumn phenology at stations with less autumn precipitation is more sensitive to precipitation variation than at stations with more autumn precipitation. This implies that autumn drought in arid regions would intensely accelerate the leaf senescence of alpine grasslands. This study suggests that precipitation should be considered for improving process-based autumn phenology models in QTP alpine grasslands.
Scattering Feature Set Optimization and Polarimetric SAR Classification Using Object-Oriented RF-SFS Algorithm in Coastal Wetlands

The utilization of advanced remote sensing methods to monitor the coastal wetlands is essential for conservation and sustainable development. With multiple polarimetric channels, the polarimetric synthetic aperture radar (PolSAR) is increasingly employed in land cover classification and information extraction, as it has more scattering information than regular SAR images. Polarimetric decomposition is often used to extract scattering information from polarimetric SAR. However, distinguishing all land cover types using only one polarimetric decomposition in complex ecological environments such as coastal wetlands is not easy, and thus integration of multiple decomposition algorithms is an effective means of land cover classification. More than 20 decompositions were used in this research to extract polarimetric scattering features. Furthermore, a new algorithm combining random forest (RF) with sequential forward selection (SFS) was applied, in which the importance values of all polarimetric features can be evaluated quantitatively, and the polarimetric feature set can be optimized. The experiments were conducted in the Jiangsu coastal wetlands, which are located in eastern China. This research demonstrated that the classification accuracies were improved relative to regular decision tree methods, and the process of polarimetric scattering feature set optimization was intuitive. Furthermore, the scattering matrix elements and scattering features derived from H / α , Yamaguchi3, VanZyl3, and Krogager decompositions were determined to be very supportive of land cover identification in the Jiangsu coastal wetlands.
Structure-Aware Convolution for 3D Point Cloud Classification and Segmentation

Semantic feature learning on 3D point clouds is quite challenging because of their irregular and unordered data structure. In this paper, we propose a novel structure-aware convolution (SAC) to generalize deep learning on regular grids to irregular 3D point clouds. Similar to the template-matching process of convolution on 2D images, the key of our SAC is to match the point clouds’ neighborhoods with a series of 3D kernels, where each kernel can be regarded as a “geometric template” formed by a set of learnable 3D points. Thus, the interested geometric structures of the input point clouds can be activated by the corresponding kernels. To verify the effectiveness of the proposed SAC, we embedded it into three recently developed point cloud deep learning networks (PointNet, PointNet++, and KCNet) as a lightweight module, and evaluated its performance on both classification and segmentation tasks. Experimental results show that, benefiting from the geometric structure learning capability of our SAC, all these back-end networks achieved better classification and segmentation performance (e.g., +2.77% mean accuracy for classification and +4.99% mean intersection over union (IoU) for segmentation) with few additional parameters. Furthermore, results also demonstrate that the proposed SAC is helpful in improving the robustness of networks with the constraints of geometric structures.
Remotely Sensed Land Surface Temperature-Based Water Stress Index for Wetland Habitats

Despite covering only 2–6% of land, wetland ecosystems play an important role at the local and global scale. They provide various ecosystem services (carbon dioxide sequestration, pollution removal, water retention, climate regulation, etc.) as long as they are in good condition. By definition, wetlands are rich in water ecosystems. However, ongoing climate change with an ambiguous balance of rain in a temperate climate zone leads to drought conditions. Such periods interfere with the natural processes occurring on wetlands and restrain the normal functioning of wetland ecosystems. Persisting unfavorable water conditions lead to irreversible changes in wetland habitats. Hence, the monitoring of habitat changes caused by an insufficient amount of water (plant water stress) is necessary. Unfortunately, due to the specific conditions of wetlands, monitoring them by both traditional and remote sensing techniques is challenging, and research on wetland water stress has been insufficient. This paper describes the adaptation of the thermal water stress index, also known as the crop water stress index (CWSI), for wetlands. This index is calculated based on land surface temperature and meteorological parameters (temperature and vapor pressure deficit—VPD). In this study, an unmanned aerial system (UAS) was used to measure land surface temperature. Performance of the CWSI was confirmed by the high correlation with field measurements of a fraction of absorbed photosynthetically active radiation (R = −0.70) and soil moisture (R = −0.62). Comparison of the crop water stress index with meteorological drought indices showed that the first phase of drought (meteorological drought) cannot be detected with this index. This study confirms the potential of using the CWSI as a water stress indicator in wetland ecosystems.
Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.
Real-time Reconstruction of Surface Velocities from Satellite Observations in the Alboran Sea

Surface currents in the Alboran Sea are characterized by a very fast evolution that is not well captured by altimetric maps due to sampling limitations. On the contrary, satellite infrared measurements provide high resolution synoptic images of the ocean at high temporal rate, allowing to capture the evolution of the flow. The capability of Surface Quasi-Geostrophic (SQG) dynamics to retrieve surface currents from thermal images was evaluated by comparing resulting velocities with in situ observations provided by surface drifters. A difficulty encountered comes from the lack of information about ocean salinity. We propose to exploit the strong relationship between salinity and temperature to identify water masses with distinctive salinity in satellite images and use this information to correct buoyancy. Once corrected, our results show that the SQG approach can retrieve ocean currents slightly better to that of near-real-time currents derived from altimetry in general, but much better in areas badly sampled by altimeters such as the area to the east of the Strait of Gibraltar. Although this area is far from the geostrophic equilibrium, the results show that the good sampling of infrared radiometers allows at least retrieving the direction of ocean currents in this area. The proposed approach can be used in other areas of the ocean for which water masses with distinctive salinity can be identified from satellite observations.