Miracle-dz/UniFAR-FTUs huggingface dataset:
Scale development for measuring social capital in sports club

In this article, I develop a scale for measuring social capital scale in a sports club (SCSSC) to review the concept of social capital based on sports club members and construct social capital scale in a sports club context. This article employees the methodology of scale development, which involves three studies: Study 1, qualitative assessment for scale generation was insisted with an in-depth interview and focus group interview. To address scale development, two times quantitative surveys were conducted. Study 2, for scale refinement, used parallel analysis and exploratory factor analysis. Study 3, confirmatory factor analysis to confirm the essence of the SCSSC construct and its' dimensionality. In order to verify the hierarchical properties of the factor structure, higher-order factor analysis was implemented by dividing the above factors into a single factor and double factors. After comparing the two types of factors, results reflected a hierarchical relationship between the single factor and 5 sub-factors of social capital in sports clubs. Lastly, in order to verify the predictability of the deduced scale, satisfaction of life was used as a dependent variable to conduct a causality analysis in which results showed a statistical signification relationship.
Investigating the Potential Role of Serotonin-2B Receptor Antagonism in the Neuronal Actions of Adjunctive Aripiprazole

Introduction: Using serotonin-2B (5-HT2B) receptor knockout (KO) mice, previous research has demonstrated that 5-HT2B receptors contribute to the antidepressant-like response. Several serotonin-dopamine partial agonists (i.e. aripiprazole; previously known as atypical antipsychotics) that exhibit high affinity antagonism at the 5-HT2B receptor have been successfully used in combination with selective 5-HT reuptake inhibitors (SSRIs) in difficult to treat major depressive disorder. However, the exact contribution of the antagonistic action of aripiprazole on 5-HT2B receptors in the context of adjunct therapy is not known. Methods: In-vivo electrophysiological recordings of ventral tegmental area (VTA) dopamine (DA), prelimbic and infralimbic medial prefrontal cortical (mPFC) pyramidal and dorsal CA3 hippocampal pyramidal neurons were conducted in anaesthetized Sprague-Dawley rats. Results: Acute administration of the 5-HT2B receptor agonist BW723c86 (6 mg/kg, intravenously [i.v.]) decreased the firing and bursting activity of DA neurons, which was abolished by pre-administration of the selective 5-HT2B receptor antagonist RS127445 (2 mg/kg, subcutaneously [s.c.]). After two days, an SSRI-induced (escitalopram 10 mg/kg/day, s.c.) decrease in DA firing activity was rescued by co-administration of the selective 5-HT2B receptor antagonist LY266097 (0.6 mg/kg/day, intraperitoneally [i.p.]). Aripiprazole (2 mg/kg/day, s.c.) administered alone or in combination with escitalopram for 14 days increased mPFC neuron firing and bursting activity, however escitalopram alone did not. LY266097 alone or the addition of LY266097 for the last three days of a 14-day escitalopram treatment increased mPFC pyramidal neuron firing and bursting activity more so than escitalopram alone. Finally, acute ejection of BW723c86 via microiontophoresis impaired SSRI binding to the serotonin transporter (5-HTT) in dorsal hippocampal pyramidal neurons. Conclusions: 5-HT2B receptor blockade rescues an SSRI-mediated decrease in DA firing activity. The increase in mPFC pyramidal neuron firing and bursting activity mediated by aripiprazole and in combination with escitalopram may be, at least partly, moderated by 5-HT2B receptors expressed in this brain area. Lastly, 5-HT2B receptor agonism may impair SSRI binding to 5-HTT in the hippocampus. Altogether, these results strengthen the view that 5-HT2B receptor antagonism contributes to the therapeutic effect of aripiprazole.
Identification and assessment of security attacks and vulnerabilities, utilizing CVE, CWE and CAPEC

The identification and assessment of security vulnerabilities, as well as their linkage with potential security threats and attacks is a challenging task. Although the identification and assessment of software vulnerabilities used to depend only on the vendor side, the continuously increased complexity and interconnectivity of ICT systems has created the need for a unified classification and scoring of security vulnerabilities. Initiative on the matter was taken by the MITRE corporation as early as 1999 with the Common Vulnerabilities and Exposures (CVE), which was later on extended by the Common Weakness Enumeration (CWE) and the Common Attack Pattern Enumeration and Classification (CAPEC). Furthermore, NIST’s NVD is currently synchronized with the CVE list. Along with the entry list, NIST is using the Common Vulnerability Scoring System which provides a consistent way of calculating the severity of vulnerabilities based on certain key characteristics, thus enriching the existing entries. This information is being constantly used and updated, in order to remain relevant with current issues. Although there are existing links between CVEs and CWEs in NIST’s NVD database, there are no existing connections between CVEs and CAPEC. The main goal of this thesis is to develop a search engine in order to link specific security vulnerabilities (i.e. CVEs) with related attack patterns (i.e. CAPEC), using the abstract weaknesses enumerations (CWE) as a connecting dot. Such a search engine will assist security experts and system administrators to further understand threats that could compromise their IT assets and effectively mitigate the specific vulnerabilities that may trigger threats of high risk. Furthermore, an attempt is made to classify and present the CVE’s identified by a port scanning tool called Nmap, based on their CVSS scores on the mentioned lists.
Multi-Task Learning for Metaphor Detection with Graph Convolutional Neural Networks and Word Sense Disambiguation

The current deep learning works on metaphor detection have only considered this task independently, ignoring the useful knowledge from the related tasks and knowledge resources. In this work, we introduce two novel mechanisms to improve the performance of the deep learning models for metaphor detection. The first mechanism employs graph convolutional neural networks (GCN) with dependency parse trees to directly connect the words of interest with their important context words for metaphor detection. The GCN networks in this work also present a novel control mechanism to filter the learned representation vectors to retain the most important information for metaphor detection. The second mechanism, on the other hand, features a multi-task learning framework that exploits the similarity between word sense disambiguation and metaphor detection to transfer the knowledge between the two tasks. The extensive experiments demonstrate the effectiveness of the proposed techniques, yielding the state-of-the-art performance over several datasets.
Others-related motivation in decision making : computational and magnetoencephalographic correlates in humans

Humans are inherently social: most of human’s decisions are within a social context and depend on others. For more than a century, researchers explore aspects of social cognition. Aiming to understand human behavior in social contexts, neuro-economic researches showed that taking others into account involve complex brain computations that include all environmental and contextual factors. However, most of the work was made using money allocation tasks; mixing self-affecting and other-affecting rewards into the decision making process. The present work intended the understanding of the brain mechanisms underpinning the integration of others into the decision making process for decisions that include others and do not interfere with self-rewards.Taking advantage of mathematical models from the drift diffusion models framework, we conducted experiments investigating how others influence the mechanistic of perceptual decisions and their correlates in the human brain. We showed that taking rewards for others into account and being observed by others influence the drift rate of the decision variable. The drift rate is higher in audience than in secret and higher for self-rewards than for other-rewards. These results indicate that others are integrated into the accumulation process together with the evidence available for making a decision. At the brain level, we found difference between self and other decisions over the anterior temporal and centro-frontal cortices during decision making. This suggests that the beneficiary of a decision modifies sensory-motor transformation processes. In addition, self- and other-affecting difference showed difference over the medial frontal sensors after the decision making process, indicating a variation in the speed-accuracy tradeoff adjustment process
THE RISK OF TRANSFORMATION OF FOLLICULAR LYMPHOMA “TRANSFORMED” BY RITUXIMAB: THE ARISTOTLE STUDY PROMOTED BY THE EUROPEAN LYMPHOMA INSTITUTE

progression. Twenty‐three genes reflecting both B‐cell biology and tumour microenvironment were retained to build a predictive model. The model identified a population at increased risk of progression in the training cohort (P < 0.001). The locked model was tested in the 3 independent validation cohorts. In the overall validation cohort, 26% of all patients (122/461) were identified as being at high risk of progression. The median PFS was 3.1 and 10.8 years in the high‐ and low‐risk groups, respectively (p < 0.001, Figure 1). The risk of lymphoma progression at 2 years was twice higher in the high‐risk group (38% versus 19%, P < 0.001). In a multivariate analysis, the score predicted PFS independently of anti‐CD20 maintenance treatment and of the FLIPI score (Hazard Ratio for the combined cohort, 2.30; 95% CI, 1.72‐3.08). In particular, the model stratified patients with high‐risk FLIPI into groups with markedly distinct outcome (median PFS of 2.1 vs 6.6 years; log‐rank test, p < 0.001). Additional unsupervised analyses of the training cohort expression data confirmed that both tumour cells and microenvironment features impacted FL prognosis. Namely, a centroblast‐associated signature had adverse prognostic significance, further strengthening the biological rationale of this model. Conclusion: Using the largest study evaluating molecular prognostic biomarkers in FL patients, we developed a robust 23‐gene expression–based predictor of PFS, applicable to routinely available FFPE biopsies from FL patients at diagnosis. In patients treated initially with rituximab‐chemotherapy, this model identifies patients with a high risk of early progression.
A Generalization of Rotational Invariance for Complex Fuzzy Operations

Complex fuzzy sets (CFSs) are a new extension of fuzzy sets, whose membership grades are vectors in the unit circle of the complex plane. Complex fuzzy operations are of high importance in both theoretical and applied studies of CFSs. The property of rotational invariance is highly pertinent to complex fuzzy operations. To enhance and extend the applicability of complex fuzzy operations, this article develops new types of rotational invariance for complex fuzzy operations. These rotational invariances are raised by different rotation operations of the arguments of complex fuzzy operations. After that, rotational invariance of several complex fuzzy operations is studied.
Printable and flexible graphene pH sensors utilising thin film melanin for physiological applications

The application of highly sensitive pH sensors manufactured in volume at low cost has great commercial interest due to an extensive array of potential applications. Such areas include industrial processing, biotechnology and medical diagnostics particularly in the development of point of care (POC) devices. A novel printable electrochemical pH sensor based on graphene and pigment melanin (PGM), was designed and produced by using a screen printing process that enables up scaling for potential commercial application. We demonstrate a highly sensitive pH sensor (62 mV pH−1 ± 7) over a pH range from 5 to 8, with high stability and superior performance when compared with a number of existing devices and making it suitable for physiological applications.
Natural rubber composites reinforced with Dregs residue from cellulose Kraft industry

The management and disposal of residues have become an industrial, economic, and environmental issue. Kraft process is widely employed in the production of pulp and, one of its characteristic is the almost complete recovery of the reagents used to obtain cellulose. However, this recovery process can generate residues such as Dregs. In order to recycle this residue, here we demonstrate for the first time the utilization of Dregs waste as filler in natural rubber (NR) composites in order to reinforce mechanical properties. Dregs waste was added to the NR in proportions of 10, 20, and 30 phr. Composites with 10 phr of Dregs showed the best result for abrasion resistance around 212.9 mm3. Meanwhile, NR composite with 20 phr of waste presented greater stress and strain at rupture, 12.44 MPa and 863%, respectively.
Microelectrode electrode array studies of spinal motor neurons

Co-cultures are a traditional method for studying the cellular properties of cell to cell interactions among different cell types. How network properties in these multicellular synthetic systems vary from monocultures are of particular interest. Understanding the changes in the functional output of these in vitro spiking neural networks can provide new insights into in vivo systems and how to develop biological system models that better reflect physiological conditions something of paramount importance to the progress of synthetic biology. Culture models of spinal motor neurons have been customarily studied as a monoculture, and the overwhelming consensus is that in culture they are different in nature from their in vivo counterparts. I studied the electrophysiological properties of spinal ventral horn networks cocultured with myocytes or astrocytes using a 64 channel microelectrode array system to record extracellular voltage measurements. When compared over a period of 40 days, significant differences were found between coculture types in metrics of spiking, bursting, and network bursting. Myocyte cocultures, when compared with simple ventral horn cultures, showed significant decreases in spikes, spike amplitudes, spike energy, number of units in network burst, and an increase in interspike interval. Astrocyte cocultures, when compared with simple ventral horn cultures showed decreases in sorted units, burst duration, mean interspike interval, and network burst
Sub-Inhibitory Concentrations of Sodium Houttuyfonate in Combination with Erythromycin Inhibit Biofilm Formation and Expression of IcaA in Staphylococcus epidermidis

Background: Staphylococcus epidermidis is an important opportunistic pathogenic bacterium causing infections on implanted medical devices by bioﬁlm formation. Sodium houttuyfonate (SH), which is the active modiﬁed compound of Houttuynia cordata is commonly applied for treating chronic bronchitis, purulent skin infections, and respiratory tract infections in China. Objectives: We aimed to investigate the in vitro eﬀects of SH in combination with erythromycin on the production of IcaA and expression of icaA to shed the light on the mechanism of SH and erythromycin in inhibiting bioﬁlm formation of S. epidermidis . Methods: The morphology of the bioﬁlm of S. epidermidis was examined under a scanning electron microscope. Furthermore, inhibitory eﬀects of SH and erythromycin on the production of IcaA and the expression of icaA were detected by Western blotting and quantitative reverse transcription PCR, respectively. Results: First, the morphological results indicated that combing SH and erythromycin at sub-MIC concentrations can eﬀectively inhibit bioﬁlm formation of S. epidermidis . Furthermore, our presented results showed that combing SH and erythromycin at sub-MIC concentration can signiﬁcantly downregulate the expression of icaA and repress the protein production of IcaA at irreversible attachment and maturation stages of bioﬁlm formation of S. epidermidis . Conclusions: Therefore, our ﬁndings suggest that the potent antibioﬁlm activity of SH in combination with erythromycin against S. epidermidis may be partially due to inactivation of icaA .
Eulerian video magnification: a review

Many important subtle changes in the environment are invisible to the naked human eyes. These subtle changes occur because of colour variations, such as blood flow in a human face that leads to face colour change, or motion variations, such as vena movement under human skin and vibration of buildings. The human eye requires optical microscopes to detect these variations. Alternatively, new technologies, such as high-speed imagery and computer processing, can be used to detect these variations. These computerised microscopes depend on computation rather than optical amplification to amplify subtle colour and motion changes in videos. The most popular technique to achieve computation-based microscope is the Eulerian video magnification (EVM). However, several challenges in EVM still need to be solved to meet the requirements of real time and video quality. This paper presents a comprehensive study of EVM methods and reviews the related literature. The strengths and drawbacks of existing works are discussed, and the important research fields and challenges in the area of EVM are concluded.
Sentiment Analysis of Movie Review using Naïve Bayes Method with Gini Index Feature Selection

In movie reviews, there is information that determines whether the movie is good or bad. Sentiment analysis is used to process information to determine the polarity of the sentence. With unstructured reviews and a lot of data attributes so that it requires much time and computational capabilities that become a problem in the classification process. To process a lot of data selection features becomes a solution to reduce dimensions so it accelerate the classification process and reduce the occurrence of misclassification. The first Gini Index Text feature selection used to classify documents and successfully enhanced the classifier performance. Multinomial Naive Bayes (MNNB) is a popular classifier used for document classification however, will the Gini Index Text feature selection able to improve MNNB classification performance. Therefore in this study the author aims to use the Gini Index Text (GIT) for text feature selection with MNNB classifier to classify movie review into positive and negative classes. The data used is IMDB dataset that contains reviews in English sentences, the data will be divided into two parts, training data is 90% and data testing is 10%. The test results prove that the Gini index as a selection feature can increase accuracy where accuracy without feature selection is 56% and with feature selection of 59.54% with an increase of 3.54%.
Meta-analysis for Non-Common Events: Rabies

Meta-analysis of common events is readily available when a literature search is carried out for use in a meta-analysis. Usually, the fixed and random effect models work well with the common events. Non-common events are associated with effect estimates that are undefined or unstable when data is needed to carry out a meta-analysis. The application of the single mean imputation technique provided a complete data set in the meta-analysis of four studies that encountered loss of data. Data was sourced via Google scholar, jstor website, MEDLINE, Infectious Diseases Journal, Veterinary Medicine Journal, Public Health Journal and Cochrane database. Twelve studies out of sixteen had complete data and were meta-analyzed, resulting in a summary effect of 4.77 and a 95% confidence interval of (3.34, 6.81), I-squared = 26.7% with p-value = 0.182. After tackling missing values of the four studies using the MATLAB software for imputation of missing values, another meta-analysis was carried out with the 16 studies, the resulting summary effect was 5.4 and 95% confidence interval of (3.36, 8.67), I-squared=66.9%, p-value<0.001. Sequential sensitivity analyses and publication bias tests led to the exclusion of 2 studies. The final meta-analysis consisted of 14 studies and resulted in a summary effect of 5.69, 95% confidence interval of (3.51, 9.23) in favour of mortality, I-squared=69.9%, p-value<0.001. The result of the meta-analysis involving 14 studies was accepted over that of 12 studies going by the P-value significance (Table 3). Protective measures should be used by the individuals who are exposed to rabies by slaughtering or consumption of infected species because rabies is extremely fatal.
Gauging Human Performance with an Automated Aid in Low Prevalence Conditions

Zinn, Cara M. M.S. , Department of Psychology, Wright State University, 2019. Gauging Human Performance with an Automated Aid in Low Prevalence Conditions. When receiving assistance from an automated aid, human operators do not necessarily perform better than without the automated aid. The current work explored the impact of integrating the automated aid with the task information in low prevalence conditions. Specifically, this work compares displays where the automated aid was integrated with task information in general or with more meaningful task information. Subjects performed a speeded judgment task with the assistance of an automated aid, varying in display type, difficulty, and prevalence. Results indicated that there was no effect of display type or prevalence on human temporal performance, and that the effect of low target prevalence on miss rates weakened in the context of an automated aid. Automated aids could be used in real world contexts to alleviate the effects of low target or target prevalence. Designers should consider the potential utility of automated aids for low prevalence tasks in real world applications.
The Implementation of the New Environmental Protection Law and Audit Fees

This paper mainly studies the impact of the implementation of the new Environmental Protection Law on the change of audit fees of heavy polluting enterprises and non-heavy polluting enterprises. Based on the listed companies in China as samples from year 2012 to 2016 (excluding year 2014), I use new Environmental Protection Law this “natural experiment”, which came into effect on January 1, 2015, and divide the whole samples into the experimental group and control group, according to the list of heavy pollution industry from the Environmental Information Disclosure Guidelines for Listed Companies (draft) issued by ministry of environmental protection in 2010. This passage tries to test whether the new Environmental Protection Law has impacted on audit fees. Furthermore, this paper finds that after the implementation of the new Environmental Protection Law, the audit fees of heavy polluting enterprises increase significantly, and this phenomenon is more significant in regions with good legal environment and strong environmental regulations. The contribution of this paper is to expand the academic research on the influence factor of audit fee, connect the influence of macroscopic law on the behavior of certified public accountants. At the same time, this paper expands the research scope of the intervention of laws and regulations, adding certified public accountants and finds the disciplinary function of audit fees.