Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards next generation diagnostics for tuberculosis: identification of novel molecular targets by large-scale comparative genomics

Tuberculosis remains one of the main causes of death worldwide. The long and cumbersome process of culturing Mycobacterium tuberculosis complex (MTBC) bacteria has encouraged the development of specific molecular tools for detecting the pathogen. Most of these tools aim to become novel tuberculosis diagnostics, and big efforts and resources are invested in their development, looking for the endorsement of the main public health agencies. Surprisingly, no study had been conducted where the vast amount of genomic data available is used to identify the best MTBC diagnostic markers. In this work, we use large-scale comparative genomics to provide a catalog of 30 characterized loci that are unique to the MTBC. Some of these genes could be targeted to assess the physiological status of the bacilli. Remarkably, none of the conventional MTBC markers is in our catalog. In addition, we develop a qPCR assay to accurately quantify MTBC DNA in clinical samples.
M-Learning Boost Students' Motivation in Learning Arabic Language Proficiency for Elementary Level

The language acquisition is one of the most important criteria that contribute to the measurement of language proficiency for students and the objectives of teaching of Arabic in Malaysia are currently based on the Standard Arabic Curriculum, which focuses on mastery of the four language skills. However, the inability to master the four language skills is still very significant. This occurs due to the fact that students only learn Arabic to pass the exam and that learning strategies applied to the students are less interactive and effective which led to demotivate the student to continue the learning process. Therefore, the primary purpose of this study is to examine the impact of mobile learning towards motivation in learning Arabic language proficiency. The research focuses on analysing the participants’ motivation regarding the education of Arabic language in particular, via mobile learning. The attention, relevance, confidence, and satisfaction (ARCS) model shaped the comprehension of the effect of education through mobile app towards the motivation of the respondents. A total of 273 participants in Malaysians Higher Institutions used mobile learning for Arabic language proficiency during teaching and learning process. After this, they will complete the motivation questionnaire. The results showed that attention, relevance, satisfaction, and confidence motivational factors had a significant impact on student’s motivation to learn Arabic through mobile app. Hence, this study contributes to the significant empirical results on the effectiveness of the integration of ARCS model in mobile app for learning Arabic language.
Role of FCBF Feature Selection in Educational Data Mining

The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the performance of students. Student performance prediction is not only important for the student but also helpful for academic organization to detect the causes of success and failures of students. Furthermore, the features selected through the students’ performance prediction models helps in developing action plans for academic welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance prediction model, where every feature is very important, as a neglection of any important feature can cause the wrong development of academic action plans. Moreover, the feature selection is a very important step in the development of student performance prediction models. There are different types of feature selection algorithms. In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step on the way to identifying the factors affecting the academic performance of the students. In this paper performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected well on a student dataset with greater no of features.
Mesoscopic simulation of three-dimensional pool boiling based on a phase-change cascaded lattice Boltzmann method

In this paper, a three-dimensional (3D) cascaded lattice Boltzmann method (CLBM) is implemented to simulate the liquid–vapor phase-change process. The multiphase flow field is solved by incorporating the pseudopotential multiphase model into an improved CLBM, the temperature field is solved by the finite difference method, and the two fields are coupled via a non-ideal equation of state. Through numerical simulations of several canonical problems, it is verified that the proposed phase-change CLBM is applicable for both the isothermal multiphase flow and the liquid–vapor phase-change process. Using the developed method, a complete 3D pool boiling process with up to hundreds of spontaneously generated bubbles is simulated, faithfully reproducing the nucleate boiling, transition boiling, and film boiling regimes. It is shown that the critical heat flux predicted by the 3D simulations agrees better with the established theories and correlation equations than that obtained by two-dimensional simulations. Furthermore, it is found that with the increase in the wall superheats, the bubble footprint area distribution changes from an exponential distribution to a power-law distribution, in agreement with experimental observations. In addition, insights into the instantaneous and time-averaged characteristics of the first two largest bubble footprints are obtained.
Political Legitimacy and Worldwide Terrorist Attacks, 1970-2017

The classic work by Max Weber argued for the impact of political legitimacy on reducing conflict and violence within states. Based on Weber and more recent theorists we argue that governments whose legitimacy is undermined are more vulnerable to terrorist attacks. Using data on worldwide terrorist attacks from the Global Terrorism Database (GTD) and measures of good governance from the Varieties of Democracy Project (V-Dem), we find considerable (but not total) support for the impact of legitimacy on terrorist attacks. We perform fixed-effects negative binomial regressions of domestic terrorist attacks covering 131 countries from 1970-2017 to test the impact of four political legitimacy measures: accountability, efficiency, procedural and distributive fairness. Controlling for common economic, political and demographic measures, we find that from all legitimacy measures, accountability has the most robust influence: rates of domestic terrorist attacks are highest when governments are transitioning from highly unaccountable to highly accountable systems.
Adoption of Mobile ERP in Educational Environment

The objective of this research paper is to investigate the intention to use mobile ERP under the effect of computer self-efficacy and system security at a university setting. Mobile ERP, a business software that integrates core business functions into a single system, has been increasingly penetrating the ERP market with promising benefits like real-time data availability and sharing, greater productivity, and competitive advantage, but to the best of the author's knowledge, there is no study that has investigated its usage intention among a traditional-ERP organization. The updated DeLone and McLean IS success model with its three quality factors is employed in this study as a theoretical framework and extended with system security and computer self-efficacy to examine the adoption of mobile ERP as an emerging phenomenon. With a sample of 347 university students, SEM results suggest that service quality, system security, and computer self-efficacy are significant determinants of individuals' intention towards using mobile ERP.
Glutathionylation primes soluble GAPDH for late collapse into insoluble aggregates

Protein aggregation is a complex physiological process, primarily determined by stress-related factors revealing the hidden aggregation propensity of proteins that otherwise are fully soluble. Here we report a mechanism by which glycolytic glyceraldehyde-3-phosphate dehydrogenase of Arabidopsis thaliana (AtGAPC1) is primed to form insoluble aggregates by the glutathionylation of its catalytic cysteine (Cys149). Following a lag phase, glutathionylated AtGAPC1 initiates a self-aggregation process resulting in the formation of branched chains of globular particles made of partially misfolded and totally inactive proteins. GSH molecules within AtGAPC1 active sites are suggested to provide the initial destabilizing signal. The following removal of glutathione by the formation of an alternative disulfide bond between Cys149 and Cys153 reinforces the aggregation process. Besides acting as a protective mechanism against overoxidation, S-glutathionylation of AtGAPC1 triggers an unexpected aggregation pathway with completely different and still unexplored physiological implications.
Expression of Odorant Receptor Genes in Savannah Tsetse Fly Glossina morsitans morsitans Westwood, 1850 (Diptera: Glossinidae

Tsetse flies are the sole cyclical vectors of African trypanosomes with negative agricultural and economic impacts in sub-Saharan Africa, yet management of their populations has not been adequately addressed. Ecologically relevant molecules like odorant receptors and gustatory receptors were recently identified computationally. However, understanding the ecological behaviour of the tsetse remains limited because of absence to informative expression of the molecular data. This experimental study aimed to establish the expression levels of Glossina morsitans morsitans odorant receptor (OR) genes in the antennae and legs at different developmental stages of adults’ for sexes. Whole tissue mRNA was extracted from fresh samples of G. m. morsitans larvae, pupae, adults’ antennae and legs and used to synthesise cDNA. The Glossina morsitans morsitans Odorant Receptor genes expression levels were quantified from the cDNA relative amounts. Glossina morsitans morsitans protein sequences and Drosophila melanogaster orthologs in FlyBase and mosquitoes and other sequenced Glossina sequences from VectorBase were aligned for conserved sequences using MUSCLE alignment tool and their transmembrane domains ascertained with TMHMM tool. All the GmmORs had the 7-transmembrane domain, a hallmark feature for ORs, except GmmOR20. In adults, the OR genes were highly expressed in antennae than in the legs. GmmOR3 and GmmOR45 were dimorphically expressed in antennae in females and males respectively, while GmmOR26 and GmmOR20 were differentially expressed at high levels in female and male tsetse legs respectively. High expression OR genes in developmental stages, and particularly GmmOR28 in pupae, may suggest their involvement in development. The exclusive expression of GmmOR20 in larvae is indicative of the role this gene may play especially on feeding habits of the larvae. Higher expression of GmmOR15 in larvae also suggested that the gene product is critical in escape responses similar to confirmed role of its homolog in D. melanogaster, DmOR45. These results open avenues to determine functional roles of tsetse ORs by matching the receptors to their ecological odor ligands. This knowledge of ecologically active ORs will aid in designing effective tsetse control targets. Accordingly, the expressed OR genes established in this study should be subjected to functional studies to determine their specificity and, subsequently, their suitability to be used to be targeted in developing pest control measures.
A Scoping Review of the Effects of COVID-19 Medications on Pregnancy

COVID-19 is a pandemic disease caused by the SARS-CoV-2 which began to appear around in December 2019 in Wuhan, China and spread globally in the last few months. Currently, there is no specific treatment for SARS-CoV-2 which forced clinicians to use old drugs, chosen for their efficacy against similar viruses or their in vitro activity. The majority of information comes from small case series and single center reports which showed that COVID-19 infection in pregnant women can lead to intrauterine growth restriction, premature labor and spontaneous abortion. So, in the view of the urgency of COVID-19 pandemic and the uncertainties about its management during pregnancy, we aimed to provide a literature review on the effectiveness and safety of available medications for COVID-19 in pregnant women. Here, our overview may provide useful information for physicians to choose the best available medications for treatment a pregnant case with COVID-19. inflammatory protein1 alpha, monocyte chemoattractant protein1, granulocyte-colony stimulating factor, interferon gamma inducible
A novel intelligent detection schema of series arc fault in photovoltaic (PV) system based convolutional neural network

Series Arc Fault (SAF) can be defined as the failure that occurs between any electrical contact and any electrical circuitry. However, it considered one of the common failures that affect the operation of the PV system and causes serious problems such as fires and electrical shock hazards. Several reasons increase the possibility of this type of failures such as incorrect installation, irregular maintenance, and some environmental effects. This paper presents a new intelligent and accurate detection method of SAF in the PV system. In this method, Convolutional neural networks (CNN) which is a discriminative (supervised) deep learning algorithm used for the process of fault detection. In normal cases, the signal consists of DC component, inverter component and noise of Network. In the case of SAF, a new component will add to the signal; therefore, CNN used to discriminate against the new component to accurately detect the SAF. PSCAD is used to generate the Arc fault model; Performance evaluation and the results of the proposed method implemented using Python. The achieved accuracy of the proposed detection method is 98.9%.
The Analysis of Critical Thinking Trends in Primary School Teacher Candidates

– This study aims to determine the level of critical thinking tendencies (CTT) for primary school teacher candidates. This research is a descriptive study with a total of 118 research subjects at Primary School Education Program at the faculty of teacher training and education Universitas Mataram which were randomly selected. CTT data were collected through a modified CCT instrument consisting of seven indicators, namely the inquisitiveness, the open-mindedness, the system, the analysis, the truth-seeking, self-confidence, and the maturity of judgment. Data were analyzed descriptively and the results showed that the average score of CCT level of the student was in the positive category. Furthermore, based on each indicator, it is found that from the seven indicators, the maturity of judgment indicator is at the ambivalent level, while the other indicators are in the positive category. Thus, it can be concluded that the level of critical thinking tendencies of prospective primary school teacher candidates is in the positive category.
How do the Hospital Services in Katsina State Nigeria Use PerformanceManagement in Order to Manage the Delivery of Emergency Services

Introduction: High quality performance and timely intervention is of utmost priority in medical emergency management. Performance management in the healthcare settings ensures that highest level performance is achieved through active management that aligns organizational activities with its set objectives and policies. Objective: To understand how and to what extent the Katsina state Hospital Services is using performance management to ensure efficiency and effectiveness in the delivery of emergency medical services to the population. Study Design: A crosssectional survey was done using quantitative methods. Paper based questionnaire was used to obtain responses from different stakeholders in emergency services delivery in order to get variety of opinions about performance management of emergency services delivery. Principal Findings: Analysis of the results have shown that the managers of Katsina state Hospital Services Management Board have knowledge about performance management however its processes are inadequately implemented in managing the delivery of emergency services which lead to moderate level performance indicators. Conclusion: Performance management processes are relevant to the performance of emergency services delivery system. Adequate knowledge of these processes is needed by managers to implement effective emergency services delivery desired by the populace.
The Deterrent Effect of Whistleblowing on Insider Trading

An objective of the Dodd-Frank Act was to improve the detection of financial fraud, including insider trading, through the creation of the whistle-blower bounty program at the SEC. This program provides for substantial financial rewards and strong anti-retaliation provisions to protect whistle-blowers. I test the effectiveness of the whistle-blower program in reducing informed trading by corporate insiders. I isolate the effect of the program from that of other concurrent reforms by identifying insiders of firms that are more sensitive to whistle-blowing allegations. For a sample of firms that lobbied against the whistle-blower provisions of the Act, I find that the profitability of insider purchases significantly reduces post Dodd-Frank relative to that of other insiders. The reduction in abnormal profits is economically meaningful, in the range of 0.05% to 0.10% daily return. I find similar results for insiders of firms with weak internal whistle-blower programs who are likely to be more sensitive to the new regulation, and for insiders of firms perceived by market participants as benefiting from the additional oversight provided by the program. I analyze whether the whistle-blower program was effective in reducing informed insider sales by examining pre-earnings announcement and pre-M&A settings when insider transactions are more likely to be information-driven. I find that, post Dodd-Frank, insiders are less likely to sell before events that are perceived negatively by investors. The legal literature suggests that insider trading behavior is more difficult to expose and prove than corporate-level fraud. In contrast with the beliefs of the program critics, I find that whistle-blowers are effective deterrents of insider trading, suggesting that they are a valuable resource for unravelling this hard-to-detect illegal activity.
Sensorimotor functional connectivity in unilateral cerebral palsy: influence of corticospinal tract wiring pattern and clinical correlates

In children with unilateral cerebral palsy (uCP), the corticospinal tract (CST) wiring patterns may differ (contralateral, ipsilateral or bilateral), partially determining motor deficits. However, the impact of such CST wiring on functional connectivity remains unknown. Here, we explored differences in functional connectivity of the resting-state sensorimotor network in 26 uCP with periventricular white matter lesions (mean age (SD): 12.87m (±4.5), CST wiring: 9 contralateral, 9 ipsilateral, 6 bilateral) compared to 60 healthy controls (mean age (SD): 14.54 (±4.8)), and between CST wiring patterns. Functional connectivity from each M1 to three bilateral sensorimotor regions of interest (primary sensory cortex, dorsal and ventral premotor cortex) and the supplementary motor area was compared between groups (healthy controls vs. uCP; and healthy controls vs. each CST wiring group). Results from the seed-to-voxel analyses from bilateral M1 were compared between groups. Additionally, relations with upper limb motor deficits were explored. Aberrant sensorimotor functional connectivity seemed to be CST-dependent rather than specific from all the uCP population: in the dominant hemisphere, the contralateral CST group showed increased connectivity between M1 and premotor cortices, whereas the bilateral CST group showed higher connectivity between M1 and somatosensory association areas. These results suggest that functional connectivity of the sensorimotor network is CST wiring-dependent, although the impact on upper limb function remains unclear.
An OpenCL 3D FFT for Molecular Dynamics Distributed Across Multiple FPGAs.

3D FFTs are used to accelerate MD electrostatic forces computations but are difficult to parallelize due to communications requirements. We present a distributed OpenCL 3D FFT implementation on Intel Stratix 10 FPGAs for grids up to {\boldmath $128^3$}. We use FPGA hardware features such as HBM2 memory and multiple 100 Gbps links to provide scalable memory accesses and communications. Our implementation outperforms GPUs for smaller FFTs, even without distribution. For {\boldmath$32^3$} we achieve 4.4 microseconds on a single FPGA, similar to Anton 1 on 512 nodes. For 8 parallel pipelines (hardware limited), we reach the same performance both locally and distributed, showing that communications are not limiting the performance. Our FFT implementation is designed to be part of the electrostatic force pipeline of a scalable MD engine.
Generative and Discriminative Learning for Distorted Image Restoration

Liquify is a common technique for image editing, which can be used for image distortion. Due to the uncertainty in the distortion variation, restoring distorted images caused by liquify filter is a challenging task. To edit images in an efficient way, distorted images are expected to be restored automatically. This paper aims at the distorted image restoration, which is characterized by seeking the appropriate warping and completion of a distorted image. Existing methods focus on the hardware assistance or the geometric principle to solve the specific regular deformation caused by natural phenomena, but they cannot handle the irregularity and uncertainty of artificial distortion in this task. To address this issue, we propose a novel generative and discriminative learning method based on deep neural networks, which can learn various reconstruction mappings and represent complex and high-dimensional data. This method decomposes the task into a rectification stage and a refinement stage. The first stage generative network predicts the mapping from the distorted images to the rectified ones. The second stage generative network then further optimizes the perceptual quality. Since there is no available dataset or benchmark to explore this task, we create a Distorted Face Dataset (DFD) by forward distortion mapping based on CelebA dataset. Extensive experimental evaluation on the proposed benchmark and the application demonstrates that our method is an effective way for distorted image restoration.