Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyze the correlation of hafiz qur'an and yaumiah worship toward the level of math anxiety

Anxiety towards mathematics is often experienced by students in the learning process,especially mathematics. High level of mathematical anxiety can affect the process and studentlearning outcomes. High levels of anxiety can make student learning outcomes low. This researchis aimed to analyze the relation of hafiz qur'an and yaumiah worship to the level of math anxiety.Subjects in this study are junior high school students, who in addition to learning he also doactivities memorization Al-Qur'an, other than that the students are also very maintainingyaumiahnya worship, such as wajib prayers and sunah, fasting sunah, read qur'an and zikir. Thisresearch are descriptive qualitative research. The researchers collected data throughquestionnaires of anxiety mathematics, sheet of memorization Al-Qur'an, and sheet of muttabaahyaumiah, then the researchers analyzed the data collected. The results are that: (1) Quantity ofhigh zikr can reduce anxiety in learning mathematics, to the level of medium learning anxiety,and (2) obligatory prayers are always done and on time can affect the level of anxiety studentsin doing math problems, to the level of medium anxiety.
Diversity and Inclusion in Esports Programs in Higher Education: Leading by Example at UCI

The﻿last﻿2﻿years﻿have﻿witnessed﻿a﻿tremendous﻿rise﻿in﻿esports﻿in﻿the﻿US﻿and,﻿with﻿it,﻿a﻿growing﻿concern﻿ about﻿the﻿lack﻿of﻿diversity﻿and﻿its﻿underlying﻿probable﻿cause:﻿toxicity﻿toward﻿women﻿and﻿minorities. ﻿ The﻿popularity﻿of﻿this﻿new﻿pastime﻿among﻿undergraduates﻿has﻿skyrocketed﻿and﻿club﻿leagues﻿are﻿quickly﻿ transitioning﻿into﻿collegiate﻿sports,﻿leaving﻿universities﻿to﻿rapidly﻿catch﻿up﻿with﻿student﻿demand﻿in﻿ order﻿to﻿attract﻿and﻿keep﻿a﻿technologically-adept﻿incoming﻿student﻿body. ﻿The﻿University﻿of﻿California,﻿ Irvine﻿has﻿become﻿a﻿leader﻿in﻿collegiate﻿esports﻿programs,﻿boasting﻿a﻿centrally﻿located,﻿dedicated﻿ esports﻿arena,﻿an﻿active﻿gaming﻿student﻿body﻿(72%),﻿and﻿undergraduate﻿scholarships. ﻿The﻿goal﻿is﻿to﻿ be﻿a﻿leader﻿not﻿merely﻿on﻿the﻿digital﻿field,﻿however. ﻿The﻿goal﻿is﻿to﻿also﻿live﻿up﻿to﻿the﻿long-standing﻿ commitment﻿to﻿diversity﻿and﻿inclusion﻿across﻿all﻿aspects﻿of﻿campus﻿life. ﻿In﻿this﻿article,﻿the﻿authors﻿ detail﻿the﻿strategy﻿for﻿accomplishing﻿this. ﻿As﻿university﻿esports﻿programs﻿emerge﻿nationwide,﻿so﻿too﻿ must﻿campus﻿policies﻿and﻿practices﻿that﻿ensure﻿a﻿welcoming﻿and﻿safe﻿environment﻿for﻿all﻿students. KEywoRDS Diversity, Esports, Higher Education, Inclusion, Policy International Journal of Gaming and Computer-Mediated Simulations Volume 10 • Issue 2 • April-June 2018
MicroRNA-376c-3p Facilitates Human Hepatocellular Carcinoma Progression via Repressing AT-Rich Interaction Domain 2

Hepatocellular carcinoma (HCC), accounting for approximately 90% of liver cancer, is the most lethal malignant tumors in the world. Large amount of evidence indicate that microRNAs (miRNAs) contribute to the tumorigenesis and progression of HCC. Among them, miR-376c-3p was recently identified as a tumor-related miRNA and is up-regulated in HBV-related HCC. But, the clinical significance of miR-376c-3p and its biological function in HCC progression are still unclear. Here, we confirmed that miR-376c-3p expression level in HCC was markedly higher than that in noncancerous tissues. Up-regulation of miR-376c-3p was detected in four different HCC cell lines. High miR-376c-3p expression correlated with poor prognostic features, such as large tumor size and venous infiltration. Follow-up data indicated that high miR-376c-3p level evidently correlated with poor clinical outcomes of HCC patients. Moreover, knockdown of miR-376c-3p repressed HCC cell growth, migration and invasion in vitro. miR-376c-3p overexpression facilitated these malignant behaviors of Bel-7402 cells. Mechanistically, miR-376c-3p posttranscriptionally repressed ARID2 expression by directly interacting with its 3'-UTR. Furthermore, an obvious negative correlation between miR-376c-3p and ARID2 mRNA expression in HCC tissues was confirmed. Notably, miR-376c-3p knockdown suppressed HCC growth and metastasis in nude mice. Gain-of-function experiments showed that ARID2 inhibited cell growth and mobility of Hep3B cells. Subsequently, ARID2 knockdown rescued miR-376c-3p silencing attenuated Hep3B cell proliferation and mobility. Our results suggest that miR-376c-3p exerts an oncogenic role in HCC progression.
Designing e-commerce supply chains: a stochastic facility-location approach

e-Commerce activity has been increasing during recent years, and this trend is expected to continue in the near future. e-Commerce practices are subject to uncertainty conditions and high variability in customers’ demands. Considering these characteristics, we propose two facility–location models that represent alternative distribution policies in e-commerce (one based on outsourcing and another based on in-house distribution). These models take into account stochastic demands as well as more than one regular supplier per customer. Two methodologies are then introduced to solve these stochastic versions of the well-known capacitated facility–location problem. The first is a two-stage stochastic-programming approach that uses an exact solver. However, we show that this approach is not appropriate for tackle large-scale instances due to the computational effort required. Accordingly, we also introduce a “simheuristic” approach that is able to deal with large-scale instances in short computing times. An extensive set of benchmark instances contribute to illustrate the efficiency of our approach, as well as its potential utility in modern e-commerce practices.
Low-Overhead Feedback Topology Design for the K-User MIMO Interference Alignment

Since designing a feedback topology is a practical way to implement interference alignment at reduced cost of channel state information (CSI) feedback, six feedback topologies have been presented in prior works for a K-user multiple-input multiple-output interference channel. To fully reveal the potential benefits of the feedback topology in terms of the saving of CSI overhead, we propose a new feedback topology in this paper. By efficiently performing dimensionality-decreasing at the transmitter side and aligning interference signals at a subset of receivers, we show that the proposed feedback topology obtains substantial reduction of feedback cost over the existing six feedback designs under the same antenna configuration.
Neurobiological Determinants of Tobacco Smoking in Schizophrenia

Purpose of review: To provide an overview of the underlying neurobiology of tobacco smoking in schizophrenia, and implications for treatment of this comorbidity. Recent findings: Explanations for heavy tobacco smoking in schizophrenia include pro-cognitive effects of nicotine, and remediation of the underlying pathophysiology of schizophrenia. Nicotine may ameliorate neurochemical deficits through nicotine acetylcholine receptors (nAChRs) located on the dopamine, glutamate, and GABA neurons. Neurophysiological indices including electroencephalography, electromyography, and smooth pursuit eye movement (SPEM) paradigms may be biomarkers for underlying neuronal imbalances that contribute to the specific risk of tobacco smoking initiation, maintenance, and difficulty quitting within schizophrenia. Moreover, several social factors including socioeconomic factors and permissive smoking culture in mental health facilities, may contribute to the smoking behaviors (initiation, maintenance, and inability to quit smoking) within this disorder. Summary: Tobacco smoking may alleviate specific symptoms associated with schizophrenia. Understanding the neurobiological underpinnings and psychosocial determinants of this comorbidity may better explain these potential beneficial effects, while also providing important insights into effective treatments for smoking cessation.
Physical Health Problems in Psychosis: Is It Time to Consider the Views of Family Carers?

Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, 2 South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Beckenham, United Kingdom, Division of Psychology and Mental Health, University of Manchester, Manchester, United Kingdom, 4 Psychosis Research Unit, Greater Manchester Mental Health NHS Trust, Manchester, United Kingdom, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
Convex MR brain image reconstruction via non‐convex total variation minimization

Total variation (TV) regularization is a technique commonly utilized to promote sparsity of image in gradient domain. In this article, we address the problem of MR brain image reconstruction from highly undersampled Fourier measurements. We define the Moreau enhanced function of L1 norm, and introduce the minmax‐concave TV (MCTV) penalty as a regularization term for MR brain image reconstruction. MCTV strongly induces the sparsity in gradient domain, and fits the frame of fast algorithms (eg, ADMM) for solving optimization problems. Although MCTV is non‐convex, the cost function in each iteration step can maintain convexity by specifying the relative nonconvexity parameter properly. Experimental results demonstrate the superior performance of the proposed method in comparison with standard TV as well as non‐local TV minimization method, which suggests that MCTV may have promising applications in the field of neuroscience in the future.
Propofol Inhibits Cerebellar Parallel Fiber-Purkinje Cell Synaptic Transmission via Activation of Presynaptic GABAB Receptors in vitro in Mice

Propofol is a widely used intravenous sedative-hypnotic agent, which causes rapid and reliable loss of consciousness via activation of γ -aminobutyric acid A (GABAA) receptors. We previously found that propofol inhibited cerebellar Purkinje cells (PC) activity via both GABAA and glycine receptors in vivo in mice. We here examined the effect of propofol on the cerebellar parallel fiber (PF)-PC synaptic transmission in mouse cerebellar slices by whole-cell recording technique and pharmacological methods. We found that following blockade of GABAA and glycine receptors activity, propofol reversely decreased the amplitude of PF-PC excitatory postsynaptic currents (PF-PC EPSCs), and significantly increased paired-pulse ratio (PPR). The propofol-induced decrease in amplitude of PF-PC EPSCs was concentration-dependent. The half-inhibitory concentration (IC50) of propofol for inhibiting PF-PC EPSCs was 4.7 μM. Notably, the propofol-induced changes in amplitude and PPR of PF-PC EPSCs were abolished by GABAB receptor antagonist, saclofen (10 μM), but not blocked by N-methyl-D-aspartate receptor (NMDA) receptor antagonist, D-APV (50 μM). Application of the GABAB receptor agonist baclofen induced a decrease in amplitude and an increase in PPR of PF-PC EPSCs, as well masked the propofol-induced changes in PF-PC EPSCs. Moreover, the propofol-induced changes in amplitude and PPR of PF-PC EPSCs were abolished by a specific protein kinase A (PKA) inhibitor, KT5720. These results indicate that application of propofol facilitates presynaptic GABAB receptors, resulting in a depression of PF-PC synaptic transmission via PKA signaling pathway in mouse cerebellar cortex. The results suggest that the interaction with GABAB receptors may contribute to the general anesthetic action of propofol.
USL: A Domain-Specific Language for Precise Specification of Use Cases and Its Transformations

A use case model is often represented by a UML use case diagram and loosely structured textual descriptions. The use case model expressed in such a form contains ambiguous and imprecise parts. This prevents integrating it into model-driven approaches, where use case models are often taken as the source of transformations. In this paper, we introduce a domain-specific language named the Use case Specification Language (USL) to precisely specify use cases. We define the abstract syntax of USL using a metamodel together with OCL wellformedness rules and then provide a graphical concrete syntax for the usability goal. We also define a precise semantics for USL by mapping USL models to Labelled Transition Systems (LTSs). It opens a possibility to transform USL models to software artifacts such as test cases and design models. We focus on a transformation from a USL model to a template-based use case description in order to illustrate our method. A language evaluation of USL is also performed in this paper.
Approximating the solution to wave propagation using deep neural networks

Humans gain an implicit understanding of physical laws through observing and interacting with the world. Endowing an autonomous agent with an understanding of physical laws through experience and observation is seldom practical: we should seek alternatives. Fortunately, many of the laws of behaviour of the physical world can be derived from prior knowledge of dynamical systems, expressed through the use of partial differential equations. In this work, we suggest a neural network capable of understanding a specific physical phenomenon: wave propagation in a two-dimensional medium. We define `understanding' in this context as the ability to predict the future evolution of the spatial patterns of rendered wave amplitude from a relatively small set of initial observations. The inherent complexity of the wave equations -- together with the existence of reflections and interference -- makes the prediction problem non-trivial. A network capable of making approximate predictions also unlocks the opportunity to speed-up numerical simulations for wave propagation. To this aim, we created a novel dataset of simulated wave motion and built a predictive deep neural network comprising of three main blocks: an encoder, a propagator made by 3 LSTMs, and a decoder. Results show reasonable predictions for as long as 80 time steps into the future on a dataset not seen during training. Furthermore, the network is able to generalize to an initial condition that is qualitatively different from those seen during training.
Expression of a Recombinant High Affinity IgG Fc Receptor by Engineered NK Cells as a Docking Platform for Therapeutic mAbs to Target Cancer Cells

Anti-tumor mAbs are the most widely used and characterized cancer immunotherapy agents. Despite having a significant impact on some malignancies, most cancer patients respond poorly or develop resistance to this therapy. A known mechanism of action of these therapeutic mAbs is antibody-dependent cell-mediated cytotoxicity (ADCC), which is a primarily effector function of NK cells. CD16A on human NK cells has an exclusive role in binding to tumor-bound IgG antibodies. Though CD16A is a potent activating receptor, it is a low affinity FcγR and its cell surface levels can be rapidly downregulated by a proteolytic process involving ADAM17 upon NK cell activation, which are likely to limit the efficacy of tumor-targeting therapeutic mAbs in the tumor environment. We sought to enhance NK cell binding to anti-tumor mAbs by engineering these cells with a recombinant FcγR consisting of the extracellular region of CD64, the highest affinity IgG Fc receptor expressed by leukocytes, and the transmembrane and cytoplasmic regions of CD16A. This novel recombinant FcγR (CD64/16A) was expressed in the human NK cell line NK92 and in induced pluripotent stem cells from which primary NK cells were derived. CD64/16A also lacked the ADAM17 cleavage region in CD16A and it was not rapidly downregulated in expression following NK cell activation during ADCC. CD64/16A on NK cells facilitated conjugation to antibody-treated tumor cells, ADCC, and cytokine production, demonstrating functional activity by its two components. Unlike NK cells expressing CD16A, CD64/16A captured soluble therapeutic mAbs and the modified NK cells mediated tumor cell killing. Hence, CD64/16A could potentially be used as a docking platform on engineered NK cells for therapeutic mAbs and IgG Fc chimeric proteins, allowing for switchable targeting elements, and a novel cancer cellular therapy.
White coat syndrome and its variations: differences and clinical impact

Hypertension is closely linked to increased cardiovascular risk and development of target organ damage (TOD). Therefore, proper clinical follow-up and treatment of hypertensive subjects are mandatory. A great number of individuals present a variation on blood pressure (BP) levels when they are assessed either in the office or in the out-of-office settings. This phenomenon is defined as white coat syndrome – a change in BP levels due to the presence of a physician or other health professional. In this context, the term “white coat syndrome” may refer to three important and different clinical conditions: 1) white coat hypertension, 2) white coat effect, and 3) masked hypertension. The development of TOD and the increased cardiovascular risk play different roles in these specific subgroups of white coat syndrome. Correct diagnose and clinical guidance are essential to improve the prognosis of these patients. The aim of this review was to elucidate contemporary aspects of these types of white coat syndrome on general and hypertensive population.
A Generalized Multivariate Approach for Possibilistic Fuzzy C-Means Clustering

Fuzzy c-Means (FCM) and Possibilistic c-Means (PCM) are the most popular algorithms of the fuzzy and possibilistic clustering approaches, respectively. A hybridization of these methods, called Possibilistic Fuzzy c-Means (PFCM), solves noise sensitivity defect of FCM and overcomes the coincident clusters problem of PCM. Although PFCM have shown good performance in cluster detection, it does not consider that different variables can produce different membership and possibility degrees and this can improve the clustering quality as it has been performed with the Multivariate Fuzzy c-Means (MFCM). Here, this work presents a generalized multivariate approach for possibilistic fuzzy c-means clustering. This approach gives a general form for the clustering criterion of the possibilistic fuzzy clustering with membership and possibility degrees different by cluster and variable and a weighted squared Euclidean distance in order to take into account the shape of clusters. Six multivariate clustering models (special cases) can be derivative from this general form and their properties are presented. Experiments with real and synthetic data sets validate the usefulness of the approach introduced in this paper using the special cases.
Impact of Sentiment Detection to Recognize Toxic and Subversive Online Comments

The presence of toxic content has become a major problem for many online communities. Moderators try to limit this problem by implementing more and more refined comment filters, but toxic users are constantly finding new ways to circumvent them. Our hypothesis is that while modifying toxic content and keywords to fool filters can be easy, hiding sentiment is harder. In this paper, we explore various aspects of sentiment detection and their correlation to toxicity, and use our results to implement a toxicity detection tool. We then test how adding the sentiment information helps detect toxicity in three different real-world datasets, and incorporate subversion to these datasets to simulate a user trying to circumvent the system. Our results show sentiment information has a positive impact on toxicity detection against a subversive user.
Chronic Kidney Disease Severity Is Associated With Selective Expansion of a Distinctive Intermediate Monocyte Subpopulation

Chronic kidney disease (CKD) affects 11–13% of the world's population and greatly increases risk of atherosclerotic cardiovascular disease (ASCVD) and death. It is characterized by systemic inflammation and disturbances in the blood leukocytes that remain incompletely understood. In particular, abnormalities in the numbers and relative proportions of the three major monocyte subsets—classical, intermediate, and non-classical—are described in CKD and end-stage renal disease. In this study, we characterized absolute numbers of blood leukocyte subtypes in adults with renal function varying from normal to advanced CKD. The primary aim was to identify monocyte subpopulations that associated most closely with current estimated glomerular filtration rate (eGFR) and subsequent rate of eGFR decline. Leucocyte and monocyte populations were enumerated by multi-color flow cytometry of whole blood and peripheral blood mononuclear cell (PBMC) samples from adults with CKD stage 1–5 (n = 154) and healthy adults (n = 33). Multiple-linear regression analyses were performed to identify associations between numbers of leucocyte and monocyte populations and clinical characteristics including eGFR and rate of eGFR decline with adjustment for age and gender. In whole blood, total monocyte and neutrophil, but not lymphocyte, numbers were higher in adults with CKD 1-5 compared to no CKD and were significantly associated with current eGFR even following correction for age. In PBMC, classical and intermediate monocyte numbers were higher in CKD 1-5 but only intermediate monocyte numbers were significantly associated with current eGFR in an age-corrected analysis. When intermediate monocytes were further sub-divided into those with mid- and high-level expression of class II MHC (HLA-DRmid and HLA-DRhi intermediate monocytes) it was found that only DRhi intermediate monocytes were increased in number in CKD 1-5 compared to no CKD and were significantly associated with eGFR independently of age among the total (No CKD + CKD 1-5) study cohort as well as those with established CKD (CKD 1-5 only). Furthermore, blood number of DRhi intermediate monocytes alone proved to be significantly associated with subsequent rate of renal functional decline. Together, our data confirm neutrophil and monocyte subset dysregulation in CKD and identify a distinct subpopulation of intermediate monocytes that is associated with higher rate of loss of kidney function.
Mild and severe childhood depression: differences and implications for prevention programs in the school setting

Purpose The study had two goals as follows: 1) to identify the prevalence of children with mild and severe depression (MD and SD), exploring sex differences; and 2) to determine possible significant differences in adaptive and clinical variables between children with different levels of depression (absence of depression, MD, and SD). Materials and methods We used a sample of 420 participants aged 7–10 years (53.3% boys) enrolled in third and fourth grade of primary education, from schools in the Basque Country (Spain). The Children’s Depression Scale (CDS, self-assessment) was administered as well as six more assessment instruments. Results It was found that 84.2% of children had no depression, 10% had MD, and 5.8% had SD. Compared with children with depressive symptoms, participants without depression had significantly: 1) lower levels of maladjustment, stress, emotional symptoms, internalizing problems, and behavioral problems; and 2) higher levels of personal adjustment, resilience, social skills, and self-concept. In addition, children with MD had significantly lower levels of clinical maladjustment, emotional symptoms, and stress than children with SD. However, no differences were found in school maladjustment, internalizing problems, behavioral problems, personal adjustment, resilience, social skills, and self-concept between children with MD and SD. Conclusion The discussion emphasizes the importance of identifying children with MD and implementing universal prevention programs from an early age.