Miracle-dz/UniFAR-FTUs huggingface dataset:
C-band synthetic aperture radar (SAR) imagery for the classification of diverse cropping systems

ABSTRACT Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites – including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies – can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user’s and producer’s accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for individual crop types. These results have important operational implications for regions of the world dominated by cloudy conditions and the lack of adequate amounts of optical imagery to support satellite-based crop monitoring.
Correspondence on ‘The 2019 American College of Rheumatology/European League Against Rheumatism Classification Criteria for IgG4-Related Disease’

IgG4-related disease (IgG4-RD) is a multisystem fibroinflammatory disorder diagnosed and managed across a broad spectrum of specialities. The American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria for IgG4-RD, recently published in this journal,1 provide robust and validated measures for discrimination from disease mimics with excellent test performance. The classification criteria incorporates three domains: entry, exclusion and inclusion criteria, with numerically weighted inclusion criteria reaching a score of 20 points or more to support a diagnosis of IgG4-RD. We sought to evaluate these criteria in a real-world cohort of patients referred for discussion to our supraregional UK IgG4-RD multidisciplinary meeting (MDM) over a 3-year period from 2016 to 2019.2 We reviewed 165 patients discussed at our IgG4 MDM who had been classified as definite IgG4-RD, possible IgG4-RD or not IgG4-RD according to standard diagnostic criteria (HISORt for pancreas/biliary disease,3 Japanese Consensus Diagnostic Criteria for systemic disease,4 Consensus Statement on Pathology5) and MDM consensus opinion with prospective follow-up in a dedicated clinic. We applied the new ACR/EULAR classification criteria retrospectively to these cases to assess diagnostic …
Cross-Linked PIM-1 Membranes with Improved Stability to Aromatics

Composite membranes were prepared for nanofiltration of aromatic solvents. Cross-linking with AlCl3 was used to improve the stability of the PIM-1 selective layer in aromatic solvents like toluene, benzene and xylene. Nanofiltration performances of obtained membranes were tested with 4 different aromatic hydrocarbons and with 3 solvents from other classes of solvents. Obtained permeability for aromatic hydrocarbons was above 8,5 kg/m2·h·bar and retention of Remazol brilliant blue R dye with molecular mass 626 was up to 96 %. It was shown that permeability results correlated with Hansen solubility parameter and distance parameter between polymer and solvent. PIM-1 has higher permeability for non-polar hydrocarbons due to higher affinity between polymer and solvent.
Genomic Characterization of the Japanese Indigenous Wine Grape Vitis sp. cv. Koshu

Vitis sp. cv. Koshu is indigenous to Japan and used as a table and processing grape. It also constitutes an important grape cultivar in Japanese white wine making and is phylogenetically distinct from European grapes. To understand its genomic information, we compared its small and structural variations with those of the table grape cultivar “Thompson seedless” and European wine grape cultivar “Tannat” via a short-read-based resequencing approach. The Koshu genome exhibited high heterozygosity compared to these cultivars, with this characteristic being particularly prominent on chromosome 7. Furthermore, Koshu structural variation encompassed the most and largest extent of duplications and the fewest and smallest extent of deletions with regard to copy number variation and the fewest absence variations among the compared grape cultivars. Plant disease resistance related to cell death associated with hypersensitive response and environmental stress response, such as water deprivation, oxidative stress, and cell wall organization, was inferred through enrichment analysis of small and structural variations. Variant accumulation levels in Koshu indicated that phenylpropanoid, flavonoid, glutathione, and α-linolenic acid pathways were related to polyphenol and flavor components. Together, this Koshu genomic information provides a foundation for improving the quality of Koshu wine and may facilitate the use of Koshu as a genetic resource.
Muscle Fatigue Analysis With Optimized Complementary Ensemble Empirical Mode Decomposition and Multi-Scale Envelope Spectral Entropy

The preprocessing of surface electromyography (sEMG) signals with complementary ensemble empirical mode decomposition (CEEMD) improves frequency identification precision and temporal resolution, and lays a good foundation for feature extraction. However, a mode-mixing problem often occurs when the CEEMD decomposes an sEMG signal that exhibits intermittency and contains components with a near-by spectrum into intrinsic mode functions (IMFs). This paper presents a method called optimized CEEMD (OCEEMD) to solve this problem. The method integrates the least-squares mutual information (LSMI) and the chaotic quantum particle swarm optimization (CQPSO) algorithm in signal decomposition. It uses the LSMI to calculate the correlation between IMFs so as to reduce mode mixing and uses the CQPSO to optimize the standard deviation of Gaussian white noise so as to improve iteration efficiency. Then, useful IMFs are selected and added to reconstruct a de-noised signal. Finally, considering that the IMFs contain abundant frequency and envelope information, this paper extracts the multi-scale envelope spectral entropy (MSESEn) from the reconstructed sEMG signal. Some original sEMG signals, which were collected from experiments, were used to validate the methods. Compared with the CEEMD and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the OCEEMD effectively suppresses mode mixing between IMFs with rapid iteration. Compared with approximate entropy (ApEn) and sample entropy (SampEn), the MSESEn clearly shows a declining tendency with time and is sensitive to muscle fatigue. This suggests a potential use of this approach for sEMG signal preprocessing and the analysis of muscle fatigue.
Activation of the ARCPOMC→MeA Projection Reduces Food Intake

Proopiomelanocortin (POMC) neurons in the arcuate nucleus of the hypothalamus (ARC) plays an essential role in the control of food intake and energy expenditure. Melanocortin-4 receptors (MC4Rs) are expressed in key areas that are implicated in regulating energy homeostasis. Although the importance of MC4Rs in the paraventricular hypothalamus (PVH) has been well documented, the role of MC4Rs in the medial amygdala (MeA) on feeding remains controversial. In this study, we specifically examine the role of a novel ARCPOMC→MeA neural circuit in the regulation of short-term food intake. To map a local melanocortinergic neural circuit, we use monosynaptic anterograde as well as retrograde viral tracers and perform double immunohistochemistry to determine the identity of the neurons receiving synaptic input from POMC neurons in the ARC. To investigate the role of the ARCPOMC→MeA projection on feeding, we optogenetically stimulate channelrhodopsin-2 (ChR2)-expressing POMC fibers in the MeA. Anterograde viral tracing studies reveal that ARC POMC neurons send axonal projections to estrogen receptor-α (ER-α)- and MC4R-expressing neurons in the MeA. Retrograde viral tracing experiments show that the neurons projecting to the MeA is located mainly in the lateral part of the ARC. Optogenetic stimulation of the ARCPOMC→MeA pathway reduces short-term food intake. This anorectic effect is blocked by treatment with the MC4R antagonist SHU9119. In addition to the melanocortinergic local circuits within the hypothalamus, this extrahypothalamic ARCPOMC→MeA neural circuit would play a role in regulating short-term food intake.
Consumers’ Purchase Intention of Organic Food via Social Media: The Perspectives of Task-Technology Fit and Post-acceptance Model

In the past, consumers were mainly informed about organic food by newspapers, magazines, and television advertisements. However, when consumers buy organic products in the market, they cannot get a complete information about the products from the appearance of the various products. In order to overcome this information asymmetry, social media has become an indispensable part of the promotion of organic food by providing a clear distinction between certified organic products and other types of products in the market. The purpose of this study is to demonstrate the antecedents and consequences of the influence of social media on the consumers’ selection of organic food, based on the post-acceptance model (PAM) and task-technology fit model. The empirical results indicated task characteristics and technology characteristics had the significant effects on confirmation of the expectations and perceived usefulness through the task-technology fit. Besides, the confirmation of expectations and perceived usefulness also influenced significantly the satisfaction and continuance intention, respectively. Finally, the results presented in this article would contribute to the practical and academic implications and recommendations on the promotion of organic food in the social media platform.
Identification and investigation of species diversity and richness of the Gastropoda in intertidal zone of Bushehr Port coastal area (the Persian Gulf waters)

This study aims to identify and determine species diversity and richness of the Gastropods of intertidal zone of Bushehr seaport coastal field and assess ecological status of the area using Welch index. Sampling was carried out at 6 stations in Bushehr seaport in winter and summer seasons during 2013-2014. Sampling bed sediments was done three times in each station and Gastropod samples were collected using Quadrate (25×25 cm) , then were preserved using 4 % buffered formalin. Then preserved samples were transported to the laboratory and were identified and counted by using valid identification keys. Furthermore, physicochemical properties of water including temperature, pH, Dissolved Oxygen (DO) and salinity were also measured in each station. According to the obtained results of this study, identified samples were totally 14 species from 12 families belonging to 7 orders. The most abundant identified species in studied stations belonged to Planaxis sulctus, Cerithidea cingulate, Anachis misera species. In this study species diversity was determined by using Shannon-weiner index, dominant species were investigated using Simpson index and species richness was calculated by using Margalof index. The results indicated that the most and the least Shannon and Margalof indexes were in cold and warm seasons respectively and Shannon and Margalof indexes showed significant differences between sampled stations and seasons (P˂0.05). Moreover, the results showed that the most and the least Simpson index was in cold and warm season respectively and Simpson index showed no significant differences between sampled stations and seasons (P˃0.05). Welch index was used in order to determine the ecological status of the regions in terms of the levels of pollution. Results of the status of water quality based on the mentioned indexes in sampled stations had average and rather low/weak pollution status.
Technical feasibility and acceptance of the Remote Monitoring Application in Psychiatry (ReMAP)

Background: Over recent years smartphone-based monitoring has been recognized as a useful instrument in psychiatric research. Due to the phasic character of affective symptoms, mobile assessments of passive sensor data as well as active self-reported data via the participants smartphone might represent a cost-efficient and highly useful tool for prospective prediction of mood changes. Despite these promising opportunities, smartphone-based monitoring in psychiatry is still limited to pilot studies often focusing on a single disorder while large-scale, transdiagnostic studies are widely absent. Objectives: The present paper describes the functionality and development of the Remote Monitoring Application in Psychiatry (ReMAP). We aimed to investigate the technical feasibility, and the acceptance of the ReMAP app for the continuous assessment of affective symptoms among different patient groups. Methods: The ReMAP app was distributed among a sample of n=997 composed of healthy control participants and psychiatric patients. Continuous passive sensor data were assessed comprising acceleration, geolocation, as well as walking distance and steps. Further, participants optionally provided standardized self-reports on mood and sleep, as well as voice samples. Technical feasibility and acceptance were assessed based on the amount and frequency of transferred data events, as well as participation duration. Preliminary results are presented while data collection is ongoing. Results: Retention rates of 90.25% for the required minimum study duration of two weeks and 33.09% for one year respectively were achieved. On average, users participated for 150 days. An average of 51.83 passive events per day per participant was collected, with an average rate of 73.50% of days during participation containing passive events. An average of 34.59 active self-report events were transferred per participant, with a considerable range across participants (0-552 events). While clinical and non-clinical participant subgroups did not differ in participation duration, or in quantity or rate of passive or active data transfer, the rate of days with transferred passive data was considerably higher and less heterogeneous in iOS (mean=91.85%, SD=21.25) as compared to Android users (mean=63.04%, SD=35.09). Conclusions: The ReMAP app is technically feasible and generally well accepted and therefore represents a viable complementary tool for the continuous assessment of affective symptoms in large-scale transdiagnostic psychiatric studies. Future studies should account for the observed systematic differences between operating systems.
CPR: Collaborative Pairwise Ranking for Online List Recommendations

Classical approaches to recommendation systems like collaborative filtering learn a static model given the user historic interaction data. These approaches do not perform well in dynamic environments where the sets of users and items are continually changing. Users convey their preferences implicitly by providing feedback in the form of clicks, views and ratings, as they interact with the system. Utilizing this feedback in an online manner is crucial for building a good user experience. Contextual bandit algorithms provide a suitable framework for learning user preferences online by balancing the explore-exploit trade-off. Much of the bandit literature focuses on choosing one item, we extend these algorithms to recommend a list of actions by assuming a cascade click model. We provide an empirical study across different scenarios to showcase the benefits of collaborative online learning and exploration. Finally, we pro-pose a novel algorithm – Collaborative Pairwise Ranking (CPR), that uses pairwise differentiable gradient descent to perform online ranking collaboratively. We showcase that this approach outperforms state-of-the-art collaborative bandit approaches, especially in the presence of noisy feedback common in practical scenarios.
Incremental Graph of Sequential Interactions for Online Recommendation with Implicit Feedback

Recommender systems aim to recommend items to users based on their interests. Traditional models usually adopt batch processing. Considering that user feedback is generated continuously, it becomes desirable to design models that are capable of learning as data arrives. In this work, we propose an incremental graph of sequential user interactions using implicit feedback from a data stream, with the assumption that user behavior can be extracted from such sequence of interactions as time passes. The model was evaluated by recommending items with different strategies, and such strategies were compared with an incremental matrix factorization algorithm, using a prequential approach. Results highlight the potential of the proposed method, which obtained superior accuracy than the baseline with generally better update and recommendation times.
EEG Correlates of Learning From Speech Presented in Environmental Noise

How the human brain retains relevant vocal information while suppressing irrelevant sounds is one of the ongoing challenges in cognitive neuroscience. Knowledge of the underlying mechanisms of this ability can be used to identify whether a person is distracted during listening to a target speech, especially in a learning context. This paper investigates the neural correlates of learning from the speech presented in a noisy environment using an ecologically valid learning context and electroencephalography (EEG). To this end, the following listening tasks were performed while 64-channel EEG signals were recorded: (1) attentive listening to the lectures in background sound, (2) attentive listening to the background sound presented alone, and (3) inattentive listening to the background sound. For the first task, 13 lectures of 5 min in length embedded in different types of realistic background noise were presented to participants who were asked to focus on the lectures. As background noise, multi-talker babble, continuous highway, and fluctuating traffic sounds were used. After the second task, a written exam was taken to quantify the amount of information that participants have acquired and retained from the lectures. In addition to various power spectrum-based EEG features in different frequency bands, the peak frequency and long-range temporal correlations (LRTC) of alpha-band activity were estimated. To reduce these dimensions, a principal component analysis (PCA) was applied to the different listening conditions resulting in the feature combinations that discriminate most between listening conditions and persons. Linear mixed-effect modeling was used to explain the origin of extracted principal components, showing their dependence on listening condition and type of background sound. Following this unsupervised step, a supervised analysis was performed to explain the link between the exam results and the EEG principal component scores using both linear fixed and mixed-effect modeling. Results suggest that the ability to learn from the speech presented in environmental noise can be predicted by the several components over the specific brain regions better than by knowing the background noise type. These components were linked to deterioration in attention, speech envelope following, decreased focusing during listening, cognitive prediction error, and specific inhibition mechanisms.
A Review of the Challenge-Hindrance Stress Model: Recent Advances, Expanded Paradigms, and Recommendations for Future Research

Since its introduction approximately 20 years ago, the Challenge-Hindrance Stress Model (CHM) has been widely accepted both among academic and practitioner audiences. The model posits that workplace stressors can be grouped into two categories. Hindrance stressors will interfere with performance or goals, while challenge stressors contribute to performance opportunities. These two categories of stressors are theorized to exhibit differential relationships with strain, with hindrance stressors being more consistently linked to psychological, physical, or behavioral strain compared to challenge stressors. Despite the popularity of this model, recent evidence suggests that the proposed differential relationship hypothesis has not consistently held true for all types of strain. Thus, a reexamination or modification of this paradigm is clearly warranted. In the present review, we describe existing evidence surrounding the CHM and describe the rationale for a shifting paradigm. We outline recent advances in research using the CHM, such as novel moderators and mediators, the need to explicitly measure challenge and hindrance appraisal and differentiate between hindrance and threat appraisal, the dynamic nature of these appraisals over time, and the recognition that a single stressor could be appraised simultaneously as both a challenge and a hindrance. Finally, we provide recommendations and future research directions for scholars examining stress and stress management through a CHM lens, including recommendations related to study design, the measurement of stressors, the integration of CHM with other models of stress, and interventions for stress management.
Bullying and Substance Use in Early Adolescence: Investigating the Longitudinal and Reciprocal Effects Over 3 Years Using the Random Intercept Cross-Lagged Panel Model

Bullying and substance use among adolescents have been increasingly studied in the field of developmental psychology, but research to date has primarily investigated the cross-sectional relationship and, to a lesser extent, the long-term impact of bullying on substance use. Grounded in the General Theory of Crime, this study focused on the longitudinal reciprocal relationships between bullying and substance use (i.e., smoking and alcohol consumption) during early to mid-adolescence, which is a critical developmental phase. We aimed to investigate the hypothesis of a reinforcing downward spiral at the within-person level. Moreover, we intended to examine gender differences in the expected longitudinal relationships. Our analyses are based on self-report data for bullying and substance use collected from 1,495 adolescents (746 males; Mage at T1 = 12.42, SD = 0.58) at three waves between 2017 and 2019. We applied the random intercept cross-lagged panel model to separate within-person from between-person effects. At the between-person level, the mutual association between bullying and substance use, previously demonstrated in cross-sectional studies, was confirmed. At the within-person level, results provide evidence of a significant age-dependent change in bullying and substance use from 13 to 14 years old, where the significant increase in bullying could be attributed to females but not to males. We also found a gender-independent significant positive effect of bullying at 12 years old on substance use at 13 years, but not vice versa. Thus, the hypothesis of a reinforcing downward spiral, shown by significant positive reciprocal effects, did not find support. According to the General Theory of Crime, our findings underline that bullying can be considered a context-related factor inasmuch as it pushes adolescents to smoke and drink, which are both expressions of low levels of self-control, which need to be considered in intervention programs to effectively prevent unhealthy and risky behaviors in adolescence.