Miracle-dz/UniFAR-FTUs huggingface dataset:
Level of physical activity of health professionals in a district hospital in KwaZulu-Natal, South Africa

Background Health professionals have a role to play in the promotion of physical activity in order to prevent the ever-increasing burden of diseases associated with physical inactivity. Determination of the level of physical activity amongst health professionals managing patients presenting with various lifestyle-related conditions is most pertinent. Object The purpose of the present study was to ascertain the level of physical activity of health professionals at Estcourt Hospital in KwaZulu-Natal Province. Method A cross-sectional survey of 109 health professionals was conducted over a period of three consecutive weeks in 2012. The Global Physical Activity Questionnaire (GPAQ) and other related data such as socio-demographic characteristics was used to collect data. Data were analysed using descriptive and inferential statistics to determine relationships between variables. An analysis guide was used to determine the level of physical activity with reference to the GPAQ guidelines recommended by the Word Health Organization using the metabolic equivalent of task (MET)-minutes per week indicators. Results The overall level of physical activity was: 31% of participants were high, with MET-minutes/week ≥ 3000; 29% were moderate, with MET-minutes/week ≥ 600; and 40% were low, with MET-minutes/week < 600. Although black women predominantly reported low levels of physical activity, age was found to be significantly related to the level of physical activity (p = 0.000, r = −0.637). Conclusion An intervention to promote physical activity amongst health professionals is essential to promote healthy living.
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning

Extracting relations is critical for knowledge base completion and construction in which distant supervised methods are widely used to extract relational facts automatically with the existing knowledge bases. However, the automatically constructed datasets comprise amounts of low-quality sentences containing noisy words, which is neglected by current distant supervised methods resulting in unacceptable precisions. To mitigate this problem, we propose a novel word-level distant supervised approach for relation extraction. We first build Sub-Tree Parse(STP) to remove noisy words that are irrelevant to relations. Then we construct a neural network inputting the sub-tree while applying the entity-wise attention to identify the important semantic features of relational words in each instance. To make our model more robust against noisy words, we initialize our network with a priori knowledge learned from the relevant task of entity classification by transfer learning. We conduct extensive experiments using the corpora of New York Times(NYT) and Freebase. Experiments show that our approach is effective and improves the area of Precision/Recall(PR) from 0.35 to 0.39 over the state-of-the-art work.
Applying Code Vectors for Presenting Software Features in Machine Learning

Machine learning is a statistical approach to give machines the ability to learn with data, without being explicitly programmed. This approach is widely used in various areas with complex problems, such as image recognition, speech recognition, regression, classification, and prediction. The most important factor is how to express the characteristics of the data being analyzed. In this paper, we present code vectors as training data for presenting software features in software analysis, and give availability results for applying code vectors in machine learning approach for analyzing software through experiments with Java applications.
Inhibition of GluN2A NMDA receptors ameliorates synaptic plasticity deficits in the Fmr1−/y mouse model

Fragile X syndrome (FXS) is a genetic condition that is the most common form of inherited intellectual impairment and causes a range of neurodevelopmental complications including learning disabilities and intellectual disability and shares many characteristics with autism spectrum disorder (ASD). In the FXS mouse model, Fmr1−/y, impaired synaptic plasticity was restored by pharmacologically inhibiting GluN2A‐containing NMDA receptors but not GluN2B‐containing receptors. Similar results were obtained by crossing Fmr1−/y with GluN2A knock‐out (Grin2A−/−) mice. These results suggest that dampening the elevated levels of GluN2A‐containing NMDA receptors in Fmr1−/y mice has the potential to restore hyperexcitability of the neural circuitry to (a more) normal‐like level of brain activity.
Update of humanized animal disease models in studying Graft-versus-host disease

ABSTRACT Graft-versus-host disease (GVHD) is a severe adverse effect that results from bone marrow or peripheral blood cells transplantation and has a high rate of mortality. About 50% of the patients are accompanied with acute Graft-versus-Host Disease (aGVHD) after bone marrow cell transplantation and need systematic treatment. It has an important clinical significance to evaluate the prevention and treatment effects of GVHD. The stable and reliable approaches of humanized animal models are crucial for advancing on the study the biology of GVHD. Relative models transplanting the human immune cells into the mouse body can trigger immunoreaction similar to the humans. As it is a disease triggered by human immune cells, any intervention research prior to clinical treatment has more clinical interrelations compared with the general animal models. In this review, we update the current understanding on humanized animal disease models on studying Graft-versus-host disease and expect to provide more theoretical basis to further study on Graft-versus-host disease.
Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.
An empirical study on the sociality of tag selection on social bookmarking services

This study attempts to characterize users' sociality in tagging by clustering factors that users consider when selecting tags for their bookmarks. Twenty-three frequent users of social bookmarking services were invited, and Q method and factor analysis have been applied in the study. Each cluster of users has varied focuses on personal, content, situational, and social factors when tagging. The study explores the four distinct types of sociality in tagging by the orientation of tag selection and the complexity of affecting factors. The results show different tendencies of sociality in tagging emerge, i.e., tag for me, us, all, and mixed. As there is the shift from functionality towards sociality for the social tagging applications, the initial findings may benefit the future related research.
Vlogging Cancer: Predictors of Social Support in YouTube Cancer Vlogs

Social support research has demonstrated the physical and psychological benefits of social support for patients, but has done little to identify successful strategies for eliciting social support. However, contemporary digital channels offer new ways to examine this issue. In particular, this study explores vlogging as a coping strategy for cancer patients and a context in which to explore predictors of online social support. A content analysis of 69 YouTube vlogs and 869 associated comments was performed. A series of multilevel binomial logistic regression analyses revealed that narrative features that position the cancer patient as protagonist-including providing an explanation of the diagnosis experience, agentive problem solving, and positive reappraisal of the situation-were associated with receiving empathic support. In contrast, moralizing pleads for audience checkups decreased the likelihood of receiving empathic support. Findings contribute to an undertheorized body of research that also has translational value for patients, doctors, and designers of supportive online spaces who might eventually recommend narrative vlogging in clinical settings.
Geometrical methods for accurate forensic videogrammetry: Part I. Measuring with non-point features

In forensic applications of photogrammetric techniques any measurable information is a precious commodity. In this paper, we describe the development of a new geometrical approach to photogrammetric measurements. While classical photogrammetry requires knowledge of point measurements, in this approach we exploit other geometrical constraints that are often available in photo and video forensic evidence. In Part I paper, the first in the series, we introduce line constraints and demonstrate algorithms that combine point and line measurements.
Social–Emotional Expertise (SEE) Scale: Development and Initial Validation

Social–emotional expertise (SEE) represents a synthesis of specific cognitive abilities related to social interactions, and emphasizes the timing and synchrony of behaviors that contribute to overall social–emotional ability. As a step toward SEE construct validation, we conducted three experiments to develop a self-report measure that captured key elements of our conceptualization of SEE. In Experiment 1, we generated and tested 76 items for a measure of SEE. The resultant 25-item scale is reliable, test–retest: r(80) = . 82, p < . 001, and internally consistent (Cronbach’s α = .90). Experiments 2 and 3 examined the relationships between the SEE Scale and related constructs. Convergent constructs, such as emotional intelligence, r(885) = . 62, p < . 01, and social anxiety, r(885) = −. 59, p < . 01, and discriminant constructs, such as social desirability, r(885) = . 19, p < . 01, and self-monitoring, r(885) = . 28, p < . 01, were found to be related in the expected directions. Additionally, two factors were statistically identified: Adaptability and Expressivity. The items contributing to each factor describe the ability to successfully navigate social environments and the ability to successfully convey affect and ideas to other people, respectively. These factors correlate with related constructs in distinct and theoretically relevant ways.
Combining wearable physiological and inertial sensors with indoor user localization network to enhance activity recognition

Thanks to the pervasiveness of smart technologies, researchers could aggregate data and investigate user’s activities thus to deliver personalized home-care services. Activity recognition system have been widely developed, however some challenges still need to be addressed. This paper presents a system where information on body movement, vital signs and user indoor location are aggregated to improve the activity recognition. The system was tested in a realistic environment with a total of 3279 instances acquired from ten healthy users. These results encouraged the use of this approach in activity recognition applications, indeed, the overall accuracy values are satisfactory increased.
Downregulation of Krüppel-like factor 1 inhibits the metastasis and invasion of cervical cancer cells

Cervical cancer is one of the most common malignancies that seriously threatens women's health. Krüppel-like factors (KLFs) have been reported to be associated with the progression of cervical cancer. The role of KLF1 in cervical cancer, which still remains unclear, was investigated in the present study. The expression of KLF1 was detected in different cervical cell lines by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. Cell proliferation, metastasis and invasion were respectively detected by Cell Counting Kit-8, wound healing and transwell assays. Associated factor expression was also detected by RT-qPCR and western blotting. In addition, the phosphorylation levels of phosphatidylinositol-3-kinase (PI3K) and protein kinase B (Akt) were determined by western blot analysis. The results revealed that KLF1 expression was promoted in SiHa, Caski and C4-1 cervical cancer cells. However, KLF1 knockdown suppressed cell proliferation, metastasis and invasion in SiHa cervical cancer cells. KLF1 knockdown also inhibited the expressions of Ki67, metastasis-associated antigen 1 and matrix metalloproteinase (MMP)-2. KLF1 knockdown promoted the expressions of nonmetastatic clone 23 type 1 and tissue inhibitor of metalloproteinase-2, and the expression of MMP-9 was promoted slightly as well. In addition, KLF1 knockdown inhibited the PI3K/Akt signaling pathway. Hence, it was concluded that KLF1 promoted metastasis and invasion via the PI3K/Akt signaling pathway in cervical cancer cells.
Multi-Source Pointer Network for Product Title Summarization

In this paper, we study the product title summarization problem in E-commerce applications for display on mobile devices. Comparing with conventional sentence summarization, product title summarization has some extra and essential constraints. For example, factual errors or loss of the key information are intolerable for E-commerce applications. Therefore, we abstract two more constraints for product title summarization: (i) do not introduce irrelevant information; (ii) retain the key information (e.g., brand name and commodity name). To address these issues, we propose a novel multi-source pointer network by adding a new knowledge encoder for pointer network. The first constraint is handled by pointer mechanism. For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism. For evaluation, we build a large collection of real-world product titles along with human-written short titles. Experimental results demonstrate that our model significantly outperforms the other baselines. Finally, online deployment of our proposed model has yielded a significant business impact, as measured by the click-through rate.
The Learning Effectiveness Analysis of JAVA Programming with Automatic Grading System

With the development of science and technology, analysis of large data is used more and more widely, such as electronic commerce, biotechnology, retail sale business, finance, education are inseparable from our life. Big data can show its value with data mining, moreover, it plays an important role in the field of education. Most researches on the exploration of education data have implications in predicting students' learning effectiveness, which can predict students' mid-term grades, final grades and semester grades with the big data analysis. The exploration of education data has been used by many research to apply the existing predictive model, thus can predict students who failed the semester exam. When there are some students who have a tendency, teachers can early to care about their learning conditions, find out the reason why they are poor in the performance, which can improve the quality of student learning. This study extends JAVA automatic grading system that our laboratory developed, increases students' operation behaviors and forecasting analysis models on the system, cooperates with four algorithms like random forest, supporting vector machine, neural network, Naive Bayes to predict and analyze the student's drop point of semester grades. This study can predict semester grade with 77% accuracy. Furthermore, this study also makes behavior analysis based on the collected data to find out the behavior model for students with low grades. The result of the study analysis can help teachers to early tutor students and improve their quality of teaching. Moreover, it plays an important role in reference value.
A Market-Oriented Heuristic Algorithm for Scheduling Parallel Applications in Big Data Service Platform

Big Data analytics service platform delivers a new type of public cloud offerings, through which end users can outsource their job executions by using a group of professional Big Data processing services in a pay-per-use way. Different from other type of cloud services, parallel jobs dominate the domain of data processing services, whose execution time can be varied greatly with different runtime configurations, such as different degrees of parallelism. In such a market-oriented environment, scheduling jobs from end users efficiently to optimize the Big Data analytics service platform's revenue is a more challenging task. In this paper, we propose a market-oriented heuristic algorithm for scheduling parallel jobs in a Big Data analytics service platform with admission control to optimize the platform operator's revenue. The proposed scheduling heuristic takes into account not only the dynamic revenue gained from accomplishing a job within a specific runtime as well as the consumption of resources needed for running it to achieve this given runtime, but also the potential loss it causes to the system by running this job instead of other waiting jobs currently in the system. We also propose a collaborative filtering based approach to quickly and accurately predict the execution time of parallel jobs running in a Big Data analytics service platform. We have conducted extensive experiments and simulations based on workload data derived from the real-world data analytics service platform and parallel applications. We show that our scheduler can outperform the other scheduling algorithms used for comparison, which are based on classical heuristics from literature, thereby fully evaluating the effectiveness of our market-oriented heuristic scheduling algorithm.
Users' views on country-specific search engine results

In this paper we report the results of a user study that compared different country-specific search results of Google and Live search. The users were Israelis and the search results came from six sources: Google Israel, Google.com, Google UK, Live Search Israel, Live Search US and Live Search UK. The users evaluated the results of nine pre-selected queries, created their own preferred ranking and picked the best ranking from the six sources. The results indicate that the group of users in this study liked most the local Google interface, i.e. Google succeeded in it its country-specific customization of search results. Live.com was much less successful in this aspect. However search engines are highly dynamic, thus these findings have to be viewed cautiously. The main contribution of the paper is a two-phase methodology for comparing and evaluating search results from different sources.
Antidermatophytic Activity and Skin Retention of Clotrimazole Microemulsion and Microemulsion-Based Gel in Comparison to Conventional Cream

Aim: Antifungal activity, skin permeation and skin retention of water-in-oil microemulsion (ME) and microemulsion-based gel (MBG) containing clotrimazole (CTZ) were evaluated in comparison to a conventional CTZ cream. Methods: CTZ-ME and CTZ-MBG containing 1% w/w of CTZ were produced. Antifungal activity against Trichophyton mentagrophytes was assessed by the agar diffusion method. Pig skin was used in the in vitro penetration study using modified Franz diffusion cells. Drug amounts which permeated into the receptor fluid, retained in the skin membrane and remained in the donor compartment were analyzed by a validated HPLC technique. Results: CTZ-ME and CTZ-MBG exhibited inhibition zones against T. mentagrophytes whereas the conventional cream did not reveal any inhibition zone in the assay. While no CTZ was detected in the receptor fluid up to 24 h following the in vitro penetration study from all tested formulations, the amount of CTZ retained in the skin membrane when applying CTZ-ME and CTZ-MBG was remarkably higher than that when applying the cream. Conclusion: Results revealed the capacity of ME and MBG in improving skin bioavailability of CTZ while reducing the risk of systemic side effects. Thereby, ME and MBG could increase the efficacy of CTZ for dermatophytosis treatment in comparison to conventional cream.
Glucolipotoxicity-Inhibited miR-299-5p Regulates Pancreatic β-Cell Function and Survival

Inhibition of microRNAs (miRNAs) essential for pancreatic β-cell biology (e.g., miR-375) results in β-cell failure and diabetes in rodent models. Whether the downregulation of miRNAs in pancreatic islets is involved in the development of human type 2 diabetes remains unclear. Here, with the use of an miRNA microarray, we identified a set of miRNAs that were differentially expressed in healthy human islets under glucolipotoxic conditions. A downregulated miRNA, miR-299-5p, was preferentially studied because its inhibition causes dramatic β-cell dysfunction and apoptosis. Proteomic profiling and bioinformatics methods identified four target genes, including a Trp53 effector, Perp, that were further confirmed by luciferase reporter assays. We narrowed down the effector of miR-299-5p downregulation to PERP owing to its upregulation in islets from diabetic rodents. Indeed, Perp inhibition prevented the β-cell impairment caused by either miR-299-5p reduction or glucolipotoxicity. Additional investigations confirmed the modulatory effect of PERP on insulin secretion. Collectively, miR-299-5p appears to be an essential regulator of β-cell biology, and its downregulation links PERP enhancement to β-cell dysfunction and apoptosis in glucolipotoxic settings. Our work demonstrates a novel mechanism of glucolipotoxicity-induced β-cell failure mediated through miR-299-5p downregulation.
CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data

CoDDA (Copula-based Distribution Driven Analysis) is a flexible framework for large-scale multivariate datasets. A common strategy to deal with large-scale scientific simulation data is to partition the simulation domain and create statistical data summaries. Instead of storing the high-resolution raw data from the simulation, storing the compact statistical data summaries results in reduced storage overhead and alleviated I/O bottleneck. Such summaries, often represented in the form of statistical probability distributions, can serve various post-hoc analysis and visualization tasks. However, for multivariate simulation data using standard multivariate distributions for creating data summaries is not feasible. They are either storage inefficient or are computationally expensive to be estimated in simulation time (in situ) for large number of variables. In this work, using copula functions, we propose a flexible multivariate distribution-based data modeling and analysis framework that offers significant data reduction and can be used in an in situ environment. The framework also facilitates in storing the associated spatial information along with the multivariate distributions in an efficient representation. Using the proposed multivariate data summaries, we perform various multivariate post-hoc analyses like query-driven visualization and sampling-based visualization. We evaluate our proposed method on multiple real-world multivariate scientific datasets. To demonstrate the efficacy of our framework in an in situ environment, we apply it on a large-scale flow simulation.