Miracle-dz/UniFAR-FTUs huggingface dataset:
A Case for a New Satellite Mission for Remote Sensing of Night Lights

The physiology and behavior of most life at or near the Earth’s surface has evolved over billions of years to be attuned with our planet’s natural light–dark cycle of day and night. However, over a relatively short time span, humans have disrupted this natural cycle of illumination with the introduction and now widespread proliferation of artificial light at night (ALAN). Growing research in a broad range of fields, such as ecology, the environment, human health, public safety, economy, and society, increasingly shows that ALAN is taking a profound toll on our world. Much of our current understanding of light pollution comes from datasets generated by remote sensing, primarily from two missions, the Operational Linescan System (OLS) instrument of the now-declassified Defense Meteorological Satellite Program (DMSP) of the U.S. Department of Defense and its follow-on platform, the Day-Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board the Suomi National Polar-Orbiting Partnership satellite. Although they have both proved invaluable for ALAN research, sensing of nighttime lights was not the primary design objective for either the DMSP-OLS or VIIRS-DNB instruments; thus, they have some critical limitations. Being broadband sensors, both the DMSP-OLS and VIIRS-DNB instruments suffer from a lack of spectral information. Additionally, their spatial resolutions are too low for many ALAN research applications, though the VIIRS-DNB instrument is much improved over the DMSP-OLS in this regard, as well as in terms of dynamic range and quantization. Further, the very late local time of VIIRS-DNB observations potentially misses the true picture of ALAN. We reviewed both current literature and guiding advice from ALAN experts, aggregated from a diverse range of disciplines and Science Goals, to derive recommendations for a mission to expand knowledge of ALAN in areas that are not adequately addressed with currently existing orbital missions. We propose a stand-alone mission focused on understanding light pollution and its effects on our planet. Here we review the science cases and the subsequent mission recommendations for NITESat (Nighttime Imaging of Terrestrial Environments Satellite), a dedicated ALAN observing mission.
High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.
Fast Unsupervised Multi-Scale Characterization of Urban Landscapes Based on Earth Observation Data

Most remote sensing studies of urban areas focus on a single scale, using supervised methodologies and very few analyses focus on the “neighborhood” scale. The lack of multi-scale analysis, together with the scarcity of training and validation datasets in many countries lead us to propose a single fast unsupervised method for the characterization of urban areas. With the FOTOTEX algorithm, this paper introduces a texture-based method to characterize urban areas at three nested scales: macro-scale (urban footprint), meso-scale (“neighbourhoods”) and micro-scale (objects). FOTOTEX combines a Fast Fourier Transform and a Principal Component Analysis to convert texture into frequency signal. Several parameters were tested over Sentinel-2 and Pleiades imagery on Bouake and Brasilia. Results showed that a single Sentinel-2 image better assesses the urban footprint than the global products. Pleiades images allowed discriminating neighbourhoods and urban objects using texture, which is correlated with metrics such as building density, built-up and vegetation proportions. The best configurations for each scale of analysis were determined and recommendations provided to users. The open FOTOTEX algorithm demonstrated a strong potential to characterize the three nested scales of urban areas, especially when training and validation data are scarce, and computing resources limited.
Relacorilant, a Selective Glucocorticoid Receptor Modulator, Induces Clinical Improvements in Patients With Cushing Syndrome: Results From A Prospective, Open-Label Phase 2 Study

Introduction/Purpose Relacorilant is a selective glucocorticoid receptor modulator (SGRM) with no progesterone receptor activity. We evaluated the efficacy and safety of relacorilant in patients with endogenous Cushing syndrome (CS). Materials and Methods A single-arm, open-label, phase 2, dose-finding study with 2 dose groups (NCT02804750, https://clinicaltrials.gov/ct2/show/NCT02804750) was conducted at 19 sites in the U.S. and Europe. Low-dose relacorilant (100-200 mg/d; n = 17) was administered for 12 weeks or high-dose relacorilant (250-400 mg/d; n = 18) for 16 weeks; doses were up-titrated by 50 mg every 4 weeks. Outcome measures included proportion of patients with clinically meaningful changes in hypertension and/or hyperglycemia from baseline to last observed visit. For patients with hypertension, clinical response was defined as a ≥5-mmHg decrease in mean systolic or diastolic blood pressure, measured by a standardized and validated 24-h ABPM. For patients with hyperglycemia, clinical response was defined ad-hoc as ≥0.5% decrease in HbA1c, normalization or ≥50-mg/dL decrease in 2-h plasma glucose value on oral glucose tolerance test, or decrease in daily insulin (≥25%) or sulfonylurea dose (≥50%). Results 35 adults with CS and hypertension and/or hyperglycemia (impaired glucose tolerance or type 2 diabetes mellitus) were enrolled, of which 34 (24 women/10 men) received treatment and had postbaseline data. In the low-dose group, 5/12 patients (41.7%) with hypertension and 2/13 patients (15.4%) with hyperglycemia achieved response. In the high-dose group, 7/11 patients (63.6%) with hypertension and 6/12 patients (50%) with hyperglycemia achieved response. Common (≥20%) adverse events included back pain, headache, peripheral edema, nausea, pain at extremities, diarrhea, and dizziness. No drug-induced vaginal bleeding or hypokalemia occurred. Conclusions The SGRM relacorilant provided clinical benefit to patients with CS without undesirable antiprogesterone effects or drug-induced hypokalemia.
An Efficient Algorithm for Retrieving CO2 in the Atmosphere From Hyperspectral Measurements of Satellites: Application of NLS-4DVar Data Assimilation Method

A novel and efficient inverse method, named Nonlinear least squares four-dimensional variational data Assimilation (NLS-4DVar)-based CO2 Retrieval Algorithm (NARA), is proposed for retrieving atmospheric CO2 from the satellite hyperspectral measurements, in which the NLS-4DVar method is used as the optimization method. As the NLS-4DVar method works independently of the tangent linear model and adjoint model, the time-consuming calculation of the weighting function matrix is unnecessary, and the computation complexity is tremendously reduced while maintaining the retrieval accuracy. This is extremely important for space-based CO2 retrievals with large data volumes. Observing system simulation experiments (OSSEs) over four different sites around the world showed that the NARA algorithm could retrieve X CO2 and CO2 profiles effectively. To further evaluate the NARA algorithm, it was used for real CO2 retrievals from target-mode observations of Orbiting Carbon Observatory-2 (OCO-2) over Lamont, Oklahoma, and Darwin, Australia. The results were compared with that of ground measurements of Total Carbon Column Observing Network (TCCON). The mean difference of X CO2 between NARA and TCCON over Lamont, from 180 observations, was −0.15 ppmv with a standard deviation (SD) of 0.76 ppmv. Over Darwin, the mean difference, from 180 observations (90 points over land and 90 points over the ocean), is −0.17 ppmv (SD: 1.26 ppmv). The preliminary results showed that the efficient NLS-4DVar-based algorithm could provide great help for satellite remote sensing of CO2, and it may be used as an operational procedure after further and extensive evaluations.
The Importance of Developmental Assets to Mental Health in Norwegian Youth

In the present study, we examined the importance of developmental assets to prolonged sadness (i.e., being sad most of the time or all the time for no reason in the last month) and suicide attempt. Cross–sectional data on items measuring developmental assets as well as prolonged sadness and suicide attempt were collected from high school students in Norway (N = 591, 55% girls). The findings from independent t–tests indicated that youth with poor mental health reported less developmental assets relative to their peers who did not report such problems. In logistic regression, asset categories, such as Positive identity and Personal assets, were significantly associated with poor mental health (especially prolonged sadness) after adjusting for other asset categories and demographic factors, such as age, sex, and parents' educational background. The influence of Empowerment and Family assets, which was significant when only the assets were assessed, was no longer significant when demographic variables were also considered. While more research on factors that can promote youth mental health is needed, our findings suggest that policies and programmes that ensure that youth have access to the necessary developmental resources and opportunities may also be empowering youth, enhancing their mental health, and consequently, facilitating their active involvement in their community.
Structure of the Region-Technology Network as a Driver for Technological Innovation

Agglomeration and spillovers are key phenomena of technological innovation, driving regional economic growth. Here, we investigate these phenomena through technological outputs of over 4,000 regions spanning 42 countries, by analyzing more than 30 years of patent data (approximately 2.7 million patents) from the European Patent Office. We construct a bipartite network—based on revealed comparative advantage—linking geographic regions with areas of technology and compare its properties to those of artificial networks using a series of randomization strategies, to uncover the patterns of regional diversity and technological ubiquity. Our results show that the technological outputs of regions create nested patterns similar to those of ecological networks. These patterns suggest that regions need to dominate various technologies first (those allegedly less sophisticated), creating a diverse knowledge base, before subsequently developing less ubiquitous (and perhaps more sophisticated) technologies as a consequence of complementary knowledge that facilitates innovation. Finally, we create a map—the Patent Space Network—showing the interactions between technologies according to their regional presence. This network reveals how technology across industries co-appear to form several explicit clusters, which may aid future works on predicting technological innovation due to agglomeration and spillovers.
Neural Kinesthetic Contribution to Motor Imagery of Body Parts: Tongue, Hands, and Feet

Motor imagery (MI) is assimilated to a perception-action process, which is mentally represented. Although several models suggest that MI, and its equivalent motor execution, engage very similar brain areas, the mechanisms underlying MI and their associated components are still under investigation today. Using 22 Ag/AgCl EEG electrodes, 19 healthy participants (nine males and 10 females) with an average age of 25.8 years old (sd = 3.5 years) were required to imagine moving several parts of their body (i.e., first-person perspective) one by one: left and right hand, tongue, and feet. Network connectivity analysis based on graph theory, together with a correlational analysis, were performed on the data. The findings suggest evidence for motor and somesthetic neural synchronization and underline the role of the parietofrontal network for the tongue imagery task only. At both unilateral and bilateral cortical levels, only the tongue imagery task appears to be associated with motor and somatosensory representations, that is, kinesthetic representations, which might contribute to verbal actions. As such, the present findings suggest the idea that imagined tongue movements, involving segmentary kinesthetic actions, could be the prerequisite of language.
Optimization of Genomic Selection to Improve Disease Resistance in Two Marine Fishes, the European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata)

Disease outbreaks are a major threat to the aquaculture industry, and can be controlled by selective breeding. With the development of high-throughput genotyping technologies, genomic selection may become accessible even in minor species. Training population size and marker density are among the main drivers of the prediction accuracy, which both have a high impact on the cost of genomic selection. In this study, we assessed the impact of training population size as well as marker density on the prediction accuracy of disease resistance traits in European sea bass (Dicentrarchus labrax) and gilthead sea bream (Sparus aurata). We performed a challenge to nervous necrosis virus (NNV) in two sea bass cohorts, a challenge to Vibrio harveyi in one sea bass cohort and a challenge to Photobacterium damselae subsp. piscicida in one sea bream cohort. Challenged individuals were genotyped on 57K–60K SNP chips. Markers were sampled to design virtual SNP chips of 1K, 3K, 6K, and 10K markers. Similarly, challenged individuals were randomly sampled to vary training population size from 50 to 800 individuals. The accuracy of genomic-based (GBLUP model) and pedigree-based estimated breeding values (EBV) (PBLUP model) was computed for each training population size using Monte-Carlo cross-validation. Genomic-based breeding values were also computed using the virtual chips to study the effect of marker density. For resistance to Viral Nervous Necrosis (VNN), as one major QTL was detected, the opportunity of marker-assisted selection was investigated by adding a QTL effect in both genomic and pedigree prediction models. As training population size increased, accuracy increased to reach values in range of 0.51–0.65 for full density chips. The accuracy could still increase with more individuals in the training population as the accuracy plateau was not reached. When using only the 6K density chip, accuracy reached at least 90% of that obtained with the full density chip. Adding the QTL effect increased the accuracy of the PBLUP model to values higher than the GBLUP model without the QTL effect. This work sets a framework for the practical implementation of genomic selection to improve the resistance to major diseases in European sea bass and gilthead sea bream.
Light-Stress Response Mediated by the Transcription Factor KlMga2 in the Yeast Kluyveromyces lactis

In unicellular organisms like yeasts, which do not have specialized tissues for protection against environmental challenges, the presence of cellular mechanisms to respond and adapt to stress conditions is fundamental. In this work, we aimed to investigate the response to environmental light in Kluyveromyces lactis. Yeast lacks specialized light-sensing proteins; however, Saccharomyces cerevisiae has been reported to respond to light by increasing hydrogen peroxide level and triggering nuclear translocation of Msn2. This is a stress-sensitive transcription factor also present in K. lactis. To investigate light response in this yeast, we analyzed the different phenotypes generated by the deletion of the hypoxia responsive and lipid biosynthesis transcription factor KlMga2. Alterations in growth rate, mitochondrial functioning, ROS metabolism, and fatty acid biosynthesis provide evidence that light was a source of stress in K. lactis and that KlMga2 had a role in the light-stress response. The involvement of KlMsn2 and KlCrz1 in light stress was also explored, but the latter showed no function in this response.
Tissue Factor-Enriched Neutrophil Extracellular Traps Promote Immunothrombosis and Disease Progression in Sepsis-Induced Lung Injury

Background Patients with sepsis may progress to acute respiratory distress syndrome (ARDS). Evidence of neutrophil extracellular traps (NETs) in sepsis-induced lung injury has been reported. However, the role of circulating NETs in the progression and thrombotic tendency of sepsis-induced lung injury remains elusive. The aim of this study was to investigate the role of tissue factor-enriched NETs in the progression and immunothrombosis of sepsis-induced lung injury. Methods Human blood samples and an animal model of sepsis-induced lung injury were used to detect and evaluate NET formation in ARDS patients. Immunofluorescence imaging, ELISA, Western blotting, and qPCR were performed to evaluate in vitro NET formation and tissue factor (TF) delivery ability. DNase, an anti-TF antibody, and thrombin inhibitors were applied to evaluate the contribution of thrombin to TF-enriched NET formation and the contribution of TF-enriched NETs to immunothrombosis in ARDS patients. Results Significantly increased levels of TF-enriched NETs were observed in ARDS patients and mice. Blockade of NETs in ARDS mice alleviated disease progression, indicating a reduced lung wet/dry ratio and PaO2 level. In vitro data demonstrated that thrombin-activated platelets were responsible for increased NET formation and related TF exposure and subsequent immunothrombosis in ARDS patients. Conclusion The interaction of thrombin-activated platelets with PMNs in ARDS patients results in local NET formation and delivery of active TF. The notion that NETs represent a mechanism by which PMNs release thrombogenic signals during thrombosis may offer novel therapeutic targets.
Development of a Methodological Quality Criteria List for Observational Studies: The Observational Study Quality Evaluation

Background: Existing study quality and risk of bias lists for observational studies have important disadvantages. For this reason, a comprehensive widely applicable quality assessment tool for observational studies was developed. Methods: Criteria from three quality lists were merged into a new quality assessment tool: the observational study quality evaluation (OSQE). OSQE consists of a cohort, case–control, and cross-sectional version. Results: The OSQE cohort, the OSQE case–control, and the OSQE cross-sectional version include all items applicable to that type of study, for example, the representativeness of the study population, the validity of the independent and dependent variables, and the statistical methods used. Before scoring the OSQE, the rater is asked to define how to score items, in detail. A study can obtain a star for each item. Each item also has a veto cell. This cell can be checked when poor quality with respect to that specific item results in a low quality of the study despite stars on other items. Although stars add to a sum score, the comment field is the most important part of the OSQE. Conclusion: The OSQE presented in the current article provides a short, comprehensive, and widely applicable list to assess study quality and therewith risk of bias.
Effects of Choice Restriction on Accuracy and User Experience in an Internet-Based Geopolitical Forecasting Task

Large-scale geopolitical forecasting tournaments have emerged in recent years as effective testbeds for conducting research into novel forecasting tools and methods. A challenge of such tournaments involves the distribution of forecasting load across forecasters, since there are often more forecasting questions than an individual forecaster can answer. Intelligent load distribution, or triage, may therefore be helpful in ensuring that all questions have sufficient numbers of forecasts to benefit from crowd-based aggregation and that individual forecasters are matched to the questions for which they are best suited. A possible downside of triage, however, is that it restricts the choices of forecasters, potentially degrading motivation and accuracy. In two studies involving pools of novice forecasters recruited online, we examined the impact of limiting forecaster choice on forecasters’ accuracy and subjective experience, including motivation. In Study 1, we tested the impact of restricted choice by comparing the forecasting accuracy and subjective experience of users who perceived they did or did not have choice in the questions they forecasted. In Study 2, we further tested the impact of restricted choice by providing users with different menu sizes of questions from which to choose. In both studies, we found no evidence that limiting forecaster choice adversely affected forecasting accuracy or subjective experience. This suggests that in large-scale forecasting tournaments, it may be possible to implement choice-limiting triage strategies without sacrificing individual accuracy and motivation.
Fast and Fine Location of Total Lightning from Low Frequency Signals Based on Deep-Learning Encoding Features

Lightning location provides an important means for the study of lightning discharge process and thunderstorms activity. The fine positioning capability of total lightning based on low-frequency signals has been improved in many aspects, but most of them are based on post waveform processing, and the positioning speed is slow. In this study, artificial intelligence technology is introduced for the first time to lightning positioning, based on low-frequency electric-field detection array (LFEDA). A new method based on deep-learning encoding features matching is also proposed, which provides a means for fast and fine location of total lightning. Compared to other LFEDA positioning methods, the new method greatly improves the matching efficiency, up to more than 50%, thereby considerably improving the positioning speed. Moreover, the new algorithm has greater fine-positioning and anti-interference abilities, and maintains high-quality positioning under low signal-to-noise ratio conditions. The positioning efficiency for return strokes of triggered lightning was 99.17%, and the standard deviation of the positioning accuracy in the X and Y directions was approximately 70 m.