Miracle-dz/UniFAR-FTUs huggingface dataset:
The serotonin transporter gene-linked polymorphic region (5-HTTLPR) and the sleep-promoting effects of tryptophan: A randomized placebo-controlled crossover study

Background: The low-expressive short (S) allele of a functional polymorphism (5-HTTLPR) within the serotonin (5-hydroxytriptamine; 5-HT) transporter gene (SLC6A4) has been associated with a reduced functioning of the brain 5-HT system relative to the long (L) allele. As a consequence, the S-allele is found to predispose individuals to a higher risk of sleep quality reduction and clinical insomnia. Aims: The present study investigated whether subchronic pre-sleep tryptophan administration could compensate for this predisposition by improving sleep in 5-HTTLPR S-allele carriers. Methods: In a double-blind placebo-controlled crossover design a sample of homozygous 5-HTTLPR S-allele (n = 47) and L-allele (n = 51) carriers were assessed for subjective (sleep diary) and objective (actigraphy) sleep during a treatment protocol consisting of 1 week of placebo (1000 mg/day) and 1 week of tryptophan administration (1000 mg/day). Results: The results support the sleep-promoting effects of tryptophan. Tryptophan improved objective sleep efficiency and objective wake after sleep onset irrespective of allelic variation. There was a marginally significant improvement of subjective sleep quality in the 5-HTTLPR S-allele group but not in the L-allele group following tryptophan relative to placebo intake. In contrast, a significantly poorer sleep quality in the S-allele as opposed to the L-allele group in the placebo condition was not observed in the tryptophan condition. Conclusions: Tryptophan augmentation promises to be a valuable treatment strategy for sleep impairments related to genetic deficiencies in 5-HT functioning.
Heat‐stress Memory is Responsible for Acquired Thermotolerance in Bangia fuscopurpurea

The environmental stresses that sessile organisms experience usually fluctuate dramatically and are often recurrent. Terrestrial plants can acquire memory of exposure to sublethal heat stress to acquire thermotolerance and survive subsequent lethal high‐temperature stress; however, little is known concerning whether seaweeds acquire thermotolerance via heat‐stress memory. We have demonstrated that the red seaweed Bangia fuscopurpurea can indeed acquire memory of sublethal high‐temperature stress, resulting in the acquisition of thermotolerance that protects against subsequent lethal high‐temperature stress. Moreover, the maintenance of heat‐stress memory was associated with a slight increase in the saturation level of membrane fatty acids. This suggests that the modification of membrane fluidity via changes in membrane fatty acid composition is involved in the establishment and maintenance of heat‐stress memory in B. fuscopurpurea. These findings provide insights into the physiological survival and growth strategies of sessile red seaweeds to cope with recurrent changes in environmental conditions.
Design of Diverging Stacked Bar Charts for Likert Scales and Other Applications

Rating scales, such as Likert scales, are very common in marketing research, customer satisfaction studies, psychometrics, opinion surveys, population studies, and numerous other fields. We recommend diverging stacked bar charts as the primary graphical display technique for Likert and related scales. We also show other applications where diverging stacked bar charts are useful. Many examples of plots of Likert scales are given. We discuss the perceptual and programming issues in constructing these graphs. We present two implementations for diverging stacked bar charts. Most examples in this paper were drawn with the likert function included in the HH package in R. We also have a dashboard in Tableau.
Impacts of long‐term elevated atmospheric CO2 concentrations on communities of arbuscular mycorrhizal fungi

The ecological impacts of long‐term elevated atmospheric CO2 (eCO2) levels on soil microbiota remain largely unknown. This is particularly true for the arbuscular mycorrhizal (AM) fungi, which form mutualistic associations with over two‐thirds of terrestrial plant species and are entirely dependent on their plant hosts for carbon. Here, we use high‐resolution amplicon sequencing (Illumina, HiSeq) to quantify the response of AM fungal communities to the longest running (>15 years) free‐air carbon dioxide enrichment (FACE) experiment in the Northern Hemisphere (GiFACE); providing the first evaluation of these responses from old‐growth (>100 years) semi‐natural grasslands subjected to a 20% increase in atmospheric CO2. eCO2 significantly increased AM fungal richness but had a less‐pronounced impact on the composition of their communities. However, while broader changes in community composition were not observed, more subtle responses of specific AM fungal taxa were with populations both increasing and decreasing in abundance in response to eCO2. Most population‐level responses to eCO2 were not consistent through time, with a significant interaction between sampling time and eCO2 treatment being observed. This suggests that the temporal dynamics of AM fungal populations may be disturbed by anthropogenic stressors. As AM fungi are functionally differentiated, with different taxa providing different benefits to host plants, changes in population densities in response to eCO2 may significantly impact terrestrial plant communities and their productivity. Thus, predictions regarding future terrestrial ecosystems must consider changes both aboveground and belowground, but avoid relying on broad‐scale community‐level responses of soil microbes observed on single occasions.
Associations between Profiles of Self-Esteem and Achievement Goals and the Protection of Self-Worth in University Students

The high demands of academia and the fear of failure lead some university students to prioritize defending their personal worth through the use of complex strategies such as self-handicapping or defensive pessimism. Adopting a person-centered approach, this study established two objectives: First, to analyze the conformation of different motivational profiles based on the combination of self-esteem and achievement goals (learning, performance approach, and performance avoidance); and second, to determine if the identified profiles differ from one another in the use of self-handicapping and defensive pessimism. A total of 1028 university students participated in the research. Four motivational profiles were obtained: (a) High self-esteem, low learning goals, high performance approach goals, and high performance avoidance goals; (b) high self-esteem, high learning goals, low performance approach goals, and low performance avoidance goals; (c) low self-esteem, low learning goals, high performance approach goals, and high performance avoidance goals; and (d) low self-esteem, high learning goals, high performance approach goals, and medium performance avoidance goals. Profiles (c) and (d) were significantly related to self-handicapping and defensive pessimism, respectively. These results suggest that students with low self-esteem are more vulnerable to self-protection strategies. Additionally, under self-handicapping and defensive pessimism, the achievement goals are slightly different.
Predictability modulates the anticipation and perception of pain in both self and others

Abstract Predictability has been suggested to modulate both the anticipation and perception of self-pain. Considering the overlapping neural circuits between self-pain and other-pain perceptions, the present study investigated how the predictability of forthcoming pain modulates the anticipation and perception of self-pain and other-pain. We used a balanced, within-participant experimental design in which a visual cue indicating the recipient, intensity and predictability of an upcoming painful electrical stimulation was presented before its delivery. Subjective ratings and electroencephalography activities to the anticipation and perception of self-pain and other-pain were recorded and compared between certain and uncertain conditions. Results showed that predictability affected the perception of self-pain and other-pain in a similar manner such that the differences in behavioral ratings and event-related potentials to high-intensity and low-intensity pain were significantly reduced when the intensity was uncertain. The strengths of predictability-induced modulation of self-pain and other-pain perceptions were positively correlated with each other. Furthermore, predictability also modulated the anticipation of both self-pain and other-pain such that pre-stimulus high-frequency α-oscillation power at sensorimotor electrodes contralateral to the stimulation side was maximally suppressed when anticipating certain high-intensity pain. These findings demonstrate that predictability-induced modulation on pain anticipation and perception was similarly applied to both self-pain and other-pain.
Blockade of thrombospondin‐1 ameliorates high glucose‐induced peritoneal fibrosis through downregulation of TGF‐β1/Smad3 signaling pathway

Transforming growth factor‐β1 (TGF‐β1) is a profibrotic cytokine which induces mesothelial cell mesothelial‐to‐mesenchymal transition (MMT) and peritoneal fibrosis in patients receiving treatment of peritoneal dialysis. Because thrombospondin‐1 (TSP‐1) is able to activate latent TGF‐β1 in vivo, we investigated whether blockade of TSP‐1 could modulate mesothelial cell MMT and ameliorate peritoneal fibrosis.
The effect of Aronia consumption on lipid profile, blood pressure, and biomarkers of inflammation: A systematic review and meta‐analysis of randomized controlled trials

Plant derivatives, such as anthocyanin‐rich phytochemicals, have been reported to elicit a positive effect on lipid profile. Therefore, the aim of this study was to systematically review and meta‐analyze the effects of Aronia consumption on lipid profiles, blood pressure, and biomarkers of inflammation in randomized controlled trials. A systematic search was performed in PubMed/MEDLINE, Cochrane, and SCOPUS up to December 2018. Seven studies were identified and analyzed in this meta‐analysis. Our study found a significant increase in high‐density lipoprotein (HDL; weighted mean difference [WMD]: 1.48 mg/dl, 95% confidence interval, CI, [1.29, 1.68]) and diastolic blood pressure (WMD: 2.55 mmHg, 95% CI [0.63, 4.47]) following Aronia consumption. There was no significant effect on systolic blood pressure and C‐reactive protein, tumor necrosis factor, and interleukin‐1. Furthermore, subgroup analysis showed that cholesterol (WMD: −7.18, 95% CI [−13.90, −0.46]) and low‐density lipoprotein (LDL; WMD: −5.84, 95% CI [−6.91, −4.77]) decreased more significantly in interventions less than 10 weeks in duration. Dose–response analysis demonstrated a significant reduction in triglyceride levels when dose of Aronia was increased to 300 mg/day. In conclusion, the results demonstrated a significant increase in HDL and reduction in total cholesterol and LDL among patients supplementing with Aronia.
Preliminary Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study

Background Social networking sites (SNSs) such as Twitter are widely used by diverse demographic populations. The amount of data within SNSs has created an efficient resource for real-time analysis. Thus, data from SNSs can be used effectively to track disease outbreaks and provide necessary warnings. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. Objective The objective of this study was to propose an efficient and accurate framework that uses data from SNSs to track disease outbreaks and provide early warnings, even for newest outbreaks, accurately. Methods We presented a framework of outbreak prediction that included 3 main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module used the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText (FT) and 6 conventional machine learning algorithms, were evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers were trained and tested using a prelabeled dataset of flu-related and unrelated Twitter postings. The selected text classifier was then used to classify over 8,400,000 tweet documents. The flu-related documents were then mapped on a weekly basis using a mapping module. Finally, the mapped results were passed together with historical Centers for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. Results The evaluation of flu tweet classification showed that FT, together with the extracted features, achieved accurate results with an F-measure value of 89.9% in addition to its efficiency. Therefore, FT was chosen to be the classification module to work together with the other modules in the proposed framework, including a regression-based estimator, for flu trend predictions. The estimator was evaluated using several regression models. Regression results show that the linear regression–based estimator achieved the highest accuracy results using the measure of Pearson correlation. Thus, the linear regression model was used for the module of weekly flu rate estimation. The prediction results were compared with the available recent data from CDC as the ground truth and showed a strong correlation of 96.29% . Conclusions The results demonstrated the efficiency and the accuracy of the proposed framework that can be used even for new outbreaks with new signs and symptoms. The classification results demonstrated that the FT-based framework improves the accuracy and the efficiency of flu disease surveillance systems that use unstructured data such as data from SNSs.
Chromosomal Localization of 18S-28S rDNA and (TTAGGG)n Sequences in Two South African Dormice of the Genus Graphiurus (Rodentia: Gliridae)

Classical cytogenetics and mapping of 18S-28S rDNA and (TTAGGG)n sequences by fluorescence in situ hybridization (FISH) was performed on Graphiurus platyops (GPL) and Graphiurus ocularis (GOC) metaphases with the aim to characterize the genomes. In both species, inverted DAPI karyotypes showed the same diploid number, 2n = 46, and hybridization of the (TTAGGG)n probe revealed interstitial telomeric sequences (ITSs) at the centromeres of almost all bi-armed chromosomes. FISH with the rDNA probe localized nucleolus organizer regions (NORs), at the terminal ends of the p arms of the subtelocentric pairs 16 and 17 in both species and detected additional signals on GPL8 and GOC18, 19, and 22. The species have similar karyotypes, but their chromosome pairs 18-22 differ in morphology; these are acrocentric in G. platyops, as also confirmed by C-banding, and subtelocentric in G. ocularis. These differences in pairs 18-22 were also highlighted by hybridization of the telomeric probe (TTAGGG)n, which showed the small p arms in G. ocularis enriched with ITSs. FISH of rDNA probes detected multiple NOR loci in G. ocularis, underlining the intense evolutionary dynamics related to these genes. Although the Graphiurus species analyzed have similar karyotypes, the results on the repetitive sequences indicate a complex pattern of genomic reorganization and evolution occurring in these phylogenetically close species.
Multi-task Self-Supervised Learning for Human Activity Detection

Deep learning methods are successfully used in applications pertaining to ubiquitous computing, pervasive intelligence, health, and well-being. Specifically, the area of human activity recognition (HAR) is primarily transformed by the convolutional and recurrent neural networks, thanks to their ability to learn semantic representations directly from raw input. However, in order to extract generalizable features massive amounts of well-curated data are required, which is a notoriously challenging task; hindered by privacy issues and annotation costs. Therefore, unsupervised representation learning (i.e., learning without manually labeling the instances) is of prime importance to leverage the vast amount of unlabeled data produced by smart devices. In this work, we propose a novel self-supervised technique for feature learning from sensory data that does not require access to any form of semantic labels, i.e., activity classes. We learn a multi-task temporal convolutional network to recognize transformations applied on an input signal. By exploiting these transformations, we demonstrate that simple auxiliary tasks of the binary classification result in a strong supervisory signal for extracting useful features for the down-stream task. We extensively evaluate the proposed approach on several publicly available datasets for smartphone-based HAR in unsupervised, semi-supervised and transfer learning settings. Our method achieves performance levels superior to or comparable with fully-supervised networks trained directly with activity labels, and it performs significantly better than unsupervised learning through autoencoders. Notably, for the semi-supervised case, the self-supervised features substantially boost the detection rate by attaining a kappa score between 0.7 - 0.8 with only 10 labeled examples per class. We get similar impressive performance even if the features are transferred from a different data source. Self-supervision drastically reduces the requirement of labeled activity data, effectively narrowing the gap between supervised and unsupervised techniques for learning meaningful representations. While this paper focuses on HAR as the application domain, the proposed approach is general and could be applied to a wide variety of problems in other areas.
Female Students' Future Engagement in STEM: Analyzing Factors from Students Themselves

Bad mathematics performance is a crucial barrier impeding women's entry into more prestigious occupations. Issues. Thus, this paper aims to employ the latest statistics to explore the current state of gender differences in students' maths-related attitudes. Two factors from students themselves ---students' preferences and confidence in maths would be analyzed. The research results show that males tend to have more positive attitudes towards maths in all three aspects. The research in this paper is of great significance to how to promote students to Engagement in STEM.
SOTagRec: A Combined Tag Recommendation Approach for Stack Overflow

Stack Overflow is one of the most popular online programming question and answer websites for developers around the world. Generally, developers need to provide tags for their posting. High-quality tags are expected to facilitate correct classification and efficient search. Unfortunately, tagging process is distributed and uncoordinated due to developers' understanding of their postings, English skills and preferences. Automatic tag recommendation becomes increasingly important for these information sites. In this paper, we propose SOTagRec, a novel tag recommendation approach combing convolutional neural network model and collaborative filtering method. By learning historical postings and their tags from existing information, SOTagRec can accurately infer tags for new postings. We have evaluated SOTagRec on Stackoverflow and compare with the state-of-the-art methods. Experiments Results show that SOTagRec achieves 81.7% and 88.7% respectively for Recall@5 and Recall@10, which outperforms the previous relevant methods.
Drinks & Crowds

The design of computational methods to recognize alcohol intake is a relevant problem in ubiquitous computing. While mobile crowdsensing and social media analytics are two current approaches to characterize alcohol consumption in everyday life, the question of how they can be integrated, to examine their relative value as informative of the drinking phenomenon and to exploit their complementarity towards the classification of drinking-related attributes, remains as an open issue. In this paper, we present a comparative study based on five years of Instagram data about alcohol consumption and a 200+ person crowdsensing campaign collected in the same country (Switzerland). Our contributions are two-fold. First, we conduct data analyses that uncover temporal, spatial, and social contextual patterns of alcohol consumption on weekend nights as represented by both crowdsensing and social media. This comparative analysis provides a contextual snapshot of the alcohol drinking practices of urban youth dwellers. Second, we use a machine learning framework to classify individual drinking events according to alcohol and non-alcohol categories, using images features and contextual cues from individual and joint data sources. Our best performing models give an accuracy of 82.3% on alcohol category classification (against a baseline of 48.5%) and 90% on alcohol/non-alcohol classification (against a baseline of 65.9%) using a fusion of image features and contextual cues in this task. Our work uncovers important patterns in drinking behaviour across these two datasets and the results of study are promising towards developing systems that use machine learning for self-monitoring of alcohol consumption.
QoS-based scheduling of workflows on global grids

Grid computing has emerged as a global cyber-infrastructure for the next-generation of e-Science applications by integrating large-scale, distributed and heterogeneous resources. Scientific communities are utilizing Grids to share, manage and process large data sets. In order to support complex scientific experiments, distributed resources such as computational devices, data, applications, and scientific instruments need to be orchestrated while managing the application workflow operations within Grid environments. This thesis investigates properties of Grid workflow management systems, presents a workflow engine and algorithms for mapping scientific workflow applications to Grid resources based on specified QoS (Quality of Service) constraints. To address the field of Grid computing of workflow application scheduling, the thesis has made the following contributions: • proposed a taxonomy of workflow management systems for Grid computing. • developed a workflow engine which leverages tuple spaces to provide event-based execution management. • developed deadline and budget distribution strategies based on the workload and dependency of tasks. • developed algorithms for scheduling workflows with QoS constraints using genetic algorithms. • leveraged multi-objective evolutionary algorithms (MOEAs) for workflow execution planning to generate a set of trade-off alternative scheduling solutions.
Energy Fraud Detection in Advanced Metering Infrastructure AMI

The Smart Grid advanced metering infrastructure (AMI) is one of the key components of the smart grid. It provides a two-way communication network between smart meters and utility systems, offering interactive services for managing billing and energy consumption. However, connecting the smart grid distributed elements, also introduces new vectors for fraud and cyberattacks. In this paper, we propose a novel approach for detecting energy fraud in AMI. The proposed approach leverages the predictability property of the consumption data by applying artificial neural network to profile normal energy consumption enabling the detection of fraudulent behavior. The evaluation of the proposed system on a real publicly available dataset of smart meter consumption data shows a high performance.