Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential dysfunction of inflammatory factors in patients with paroxysmal atrial fibrillation undergoing catheter radiofrequency ablation: a bioinformatics analysis

Paroxysmal atrial fibrillation (PAF) is one of the common arrhythmias in found adults. Radiofrequency catheter ablation (RFCA) is the main method to treat arrhythmias. However, the mechanism of dysfunction of inflammatory factors in paroxysmal atrial fibrillation during surgery has not been clearly elucidated in previous studies. Therefore, this study is based on the dysfunction module of paroxysmal atrial fibrillation mediated by multiple regulators in order to predict its potential dysfunction mechanisms. First, we combine differential analysis, coexpression analysis and enrichment analysis, and construct protein interaction networks, regulator prediction, and network connectivity analysis. Subsequently, the function and path network of the integrated modules are analyzed. Finally, we performed Pivot analysis of ncRNA and TF. Results A total of 804 differentially expressed and persistently dysfunctional genes and 12 co-expression modules were obtained. Importantly, we identified three different sets of gene modules in the protein interaction network (including the inflammation module, the expression disorder module and the synthesis module of surgical state diseases). They are mainly involved in protein polyubiquitination, post-translational protein modification and SCF-dependent proteasomal ubiquitin-dependent protein catabolic process and other functional pathways. Second, using crosstalk analysis, we found a complex relationship between module 3 and module 5, while there is a simple crosstalk relationships between other modules. In addition, 23 endogenous genes (such as CUL1, UBE2D2 and NEDD4) were selected through a network connectivity analysis. In total, 1716 ncRNAs (including FENDRR, microRNA-300 and microRNA-186-5p) and 136 transcription factors (including BRCA1, CEBPD and MYB) were also obtained. These crucial regulators might be related to the dysfunction mechanism of paroxysmal atrial fibrillation. Therefore, radiofrequency ablation for paroxysmal atrial fibrillation may provide a new research method for patients.
Green stability indicating UV- spectrophotometric techniques for estimation of tenofovir alafenamide in bulk form and dosage forms

Four simple, precise and sensitive UVspectrophotometric procedures were achieved for estimation of tenofovir alafenamide in the presence of its alkaline degradate. Dual wavelength was the first one which based on determination of the drug at 235.5 nm and 261.5 nm. First – derivative spectrophotometric method was the second one which the amplitude values were measured at 274 nm using Δλ of 8 nm and a scaling factor of 20. Third one was ratio difference which peak’s amplitudes ratio spectra difference of tenofovir alafenamide was measured between 261.5 nm and 252 nm using devisor of 10 μg mL-1 of its alkaline degradate. While the last method was a first derivative of ratio spectra using Δλ = 8 nm and a scaling factor = 10 to measure the amplitude at 275.6 nm. The linearity range was 5-35 μg mL–1 in all procedures. The suggested procedures were effectively utilized for the estimation of the cited drug in bulk form as well as its dosage forms. According to ICH guidelines, all procedures were validated. Moreover, Analytical eco-scale and Green Analytical Procedure Index (GAPI) were used to estimate the greenness of the suggested methods compared with a reported one as two assessment tools.
Production of polyhydroxyalkanoates (PHAs) by microorganism from contaminated soil, industrial wastewater and domestic wastewater

Polyhydroxyalkanoates (PHAs) is a bio-polymer accumulated in a bacterial cell. PHA is a potential substitute in fuel industry, medical industry and material industry such as biofuel, surgical pins and latex. This work is to isolate PHA producing bacteria from sources, including contaminated soil, industrial wastewater and domestic wastewater in Bangkok Thailand. Total 250 strains screening for PHA production using Sudan black B, Nile blue A and Nile red staining methods. After, preliminary staining 9 isolates exhibiting high PHA production were confirm by High Pressure Liquid Chromatography (HPLC). The Strain KU G15 was selected for further analysis due to its high PHA production at 11.69 % by cell dry weight. Effect of carbon sources and nitrogen sources on growth and PHA production of strain KU G15 were investigated. Strain KU G15 was grown on mineral salt medium supplemented with different carbon sources (glycerol, palm oil, starch, glucose, sucrose, fructose, maltose, and lactose) and nitrogen sources (tryptone, peptone, yeast extract, (NH4)2SO4, (NH4)2HPO4, NaNO3 and KNO3 ) . The best result was obtained when using 30 g/l glycerol and 0.5 g/l KNO3 as carbon and nitrogen sources, respectively, demonstrating the accumulation of PHA up to 32.07 % and 34.87 %, respectively. The central composite design (CCD) was further applied to define the optimal production condition. Based on statistical analysis, maximal PHA production was reached using the optimal medium composition.
Determination of Cystein in Biology Solids by Electrochemical Methods with Gold Colloidal Particles

2 Abstract: The electrochemical behavior of cysteine at a graphite electrodes modified with gold nanoparticles was studied by cyclic voltammetry. We compared the results of determination of cysteine in the particulate, graphite and glasgraphite electrodes modified with gold nanoparticles and single crystals of gold. Specific feature of the electrooxidation of cystein at a graphite electrodes modified with gold nanoparticles and at a graphite electrodes modified from solution HAuCl . It was found that graphite electrodes modified with gold 4 nanoparticles have more electrocatalytic activity toward the oxidation of cysteine than single crystals of gold due to the increase in the number of atoms on the surface of the graphite electrode in comparison with single crystals of gold, which catalyze the oxidation process of cystein. The use of graphite electrode modified with gold nanoparticles reduces the detection limit for cystein under to 1•10 M. The method for determination of 12
Design and Test of the Genuine Leadership Outcomes Model in a Military Unit

The present research was carried out with the aim of designing and testing a model of genuine leadership outcomes, where genuine leadership construct was selected as an antecedent variable; the constructs of organizational citizenship behavior, psychological well-being, and job aspiration as outcome variables and ethical and trust atmosphere constructs as mediation variables. For this purpose, 200 personnel of a military unit were selected by simple random sampling. The instruments of this study were including questionnaires of genuine leadership, ethical work atmosphere, organizational trust, organizational citizenship behavior, psychological well-being, and job aspiration. The data were analyzed through structural equation modeling (SEM) and intermediate analysis. Modeling analyzes supported the structural equations considering the proposed model with the data. The results indicated a direct positive effect of genuine leadership on ethical job environment, organizational trust, organizational citizenship behaviors, and job aspiration, a direct positive effect of ethical work environment on organizational citizenship behavior and job aspiration, a direct positive effect of organizational trust on organizational citizenship behavior and job aspiration, and an indirect positive effect of genuine leadership on organizational citizenship behavior and job aspiration through the ethical work environment and organizational trust. Based on the findings of this study, it can be concluded that organization administrators should pay special attention to identify, evaluate, and promote authentic leadership in their organizations. Therefore, it is suggested that the organizations should recruit authentic leaders, and should encourage, support, and develop this type of leadership in order to increase the productivity of the organizational performance.
Biofilm Formation and its Correlation with Antimicrobial Resistance in Klebsiella pneumoniae

Klebsiella pneumoniae (K. pneumoniae) is an opportunistic pathogen capable of causing a wide range of diseases in humans and animals. The increase, emergence, and spread of antimicrobial resistance among K. pneumoniae are the most important health problems worldwide. The production of biofilms by bacterial pathogens exacerbates the complexity of bacterial resistance and prolongs the treatment time. This study analyzed the possible relationship between antimicrobial resistance and biofilm formation in K. penumoniae isolated from different sources. Eleven K. pneumoniae isolates were recovered from 100 samples comprising 6 (12%) from chicken respiratory organs, 3 (12%) from meat products and 2 (8%) from milk products. All K. pneumonia isolates were resistant to ampicillin and amoxacillin-clavulinic acid (100%) followed by cefepime (72.72%), tetracycline, trimethoprime and trimethoprime/sulphamethaxole (54.54% each), while they were sensitive to imipenem (82 %) followed by aztreonam (55%) then amikacin and azithromycin (45% each). It is noteworthy that 10 (90.90%) K. pneumonia isolates were multidrug resistant (MDR) and their multiple antibiotic resistance (MAR) indices were far greater than 0.2 (0.8469.307). Of note, 81.81% of K. pneumonia isolates could produce biofilms, those categorized as strong (33.33%), moderate (22.22%) or weak (44.44%) biofilm producers, whereas 18.18% of the isolates were non-biofilm producers. Interestingly, resistance pattern of K. pneumoniae recovered from chicken source was higher than those from milk and meat products. Moreover, there is a non-significant (P ˃ 0.05) positive correlation (r= 0.38) between the antimicrobial resistance and biofilm formation in K. pneumoniae isolates recovered from animal sources . In conclusion, our results emphasized that biofilm formation may be an important factor that influences the antimicrobial resistance in K. pneumoniae, and strict measures of antimicrobial usage should be done in both animal husbandry and humans globally.
Cancer in Sri Lanka; trends, care and outcomes

Abstract Introduction The burden of cancer is increasing rapidly in Sri Lanka. However, limited data are available on incidence trends, diagnosis, treatment or outcomes of cancer in Sri Lanka. This oration encompasses a series of studies undertaken, Ÿ To describe the trends in incidence Ÿ To analyse stage at diagnosis, treatment and outcomes Ÿ To describe the long-term quality of life [QOL] after completion of treatment of cancer in Sri Lanka
Reduction of Leakage Current and Leakage Power during Write Operation Using Sleep Transistor in 6T, 8T, 10T SRAM

-The aim of this paper is to reduce the maximum leakage current and power consumption using sleep transistors. In total power dissipation, the major dissipated portion is due to leakage power . When compared to stack approach in SRAM cell sleep transistor gives more advantages. This is used for research purpose and to do the operations of 6T, 8T, 10T with sleep transistor and our enhanced work is to implement a 16*8 matrix with sleep transistor in 6T SRAM cell design . The SRAM cell with 16*8 sleep transistor shows better leakage reduction for real time applications. In this paper Analog environment virtuoso (Tanner) simulator is used for analysis of the power associated with CMOS SRAM cell .
Learning Human Intention for Taskable Agents

As AI systems are continuously developed and improved, they can be used for an increasing variety of tasks. At the same time, dependency on these systems grows, and it becomes more important for AI systems to perform their tasks as we intend them to do. In this study, the focus lies with agents that learn, given a task, how to perform this task as the human intended. The use of context-dependent task constraints is studied as an approximation to the human’s intention for how the task should be executed. A drone reconnaissance task was built using a new multi-agent simulator, called the Man-Agent Teaming Rapid Experimentation Simulator (MATRXS). In the pilot, a small number of participants taught an agent how they want a task to be completed in various contexts by specifying constraints. Machine learning models were able to effectively and efficiently learn the context-dependent constraints (XGBoost with average F1 score of 0.95, 128 data points) for each participant individually. Models trained without context input features scored significantly lower (average F1 score of 0.60), showing the context-dependency of human intention for agent tasking. Although the conclusiveness of the results is lower due to the small magnitude of the experiment, the results show this to be a promising approach for establishing meaningful human control over agents. Finally, lessons learned from this explorative study were summarized into a set of recommendations which indicate promising future research and how to scale up to an experiment of larger magnitude and complexity.
Synergistic Effects of Graphene Oxide and Vascular Endothelial Growth Factor Immobilized in Polycaprolactone Nanofiber as a Candidate for Diabetic Wound Healing

1. Dept. of Clinical Biochemistry, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2. Protein Research Center, Shahid Beheshti University, Tehran, Iran 3. Dept. of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran 4. Dept. of Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
An Overview of Statistical Methods for the Analysis of Compositional Data

Compositional data can be regarded as vectors representing the distribution of proportions over D categories of some unit. Because elements of a compositions denote proportions, they are non-negative and they add up to one. Therefore com¬ positions can be regarded as vectors in the simplex, a constrained sub-space of the real space. Aitchison (1986) showed that there are very specific problems with the statistical analysis of compositions observed on the simplex. In the first place, compositional vectors have a degenerate covariance structure. This leads to several problems in the interpretation of the covariance matrix of a composition. In the second place the assumption of a multivariate normal distribution for compositional vectors is doubtful. Therefore standard multivariate statistical methods can lead to distorted inferences. In this article, four statistical methods, more appropriate for the analysis of compositional data, are reviewed and discussed.
Modified HOG based on-road vehicle detection method

A new vehicle detection method based on modified histogram of oriented gradient (HOG) is introduced. Three variants of the modified HOG based feature are used to train linear and nonlinear support vector machine (SVM) classifiers. A comparison between classifiers based on proposed HOG variants and the conventional one is done. The classifiers are trained and tested on samples extracted from KITTI dataset. The comparison between the results show that the proposed method increased the discriminative power and improved the detection rates. Finally, the best performing classifiers in the comparison are used in detection of vehicles in image frames provided by KITTI dataset.
Finding optimal arbitrage opportunities using a quantum annealer

We present two formulations for finding optimal arbitrage opportunities as a quadratic unconstrained binary optimization problem, which can be solved using a quantum annealer. The formulations are based on finding the most profitable cycle in a graph in which the nodes are the assets and the edge weights are the conversion rates. The edge-based formulation is simpler, whereas the node-based formulation allows for the identification of specific optimal arbitrage strategies, while possibly requiring fewer variables. In addition, an alternative form is presented which allows the arbitrage opportunities that best balance profit and risk to be found, based on the trader's risk aversion. We discuss considerations for usage in practice. In particular, we suggest an application to illiquid assets and present an illustrative example.
Hydrogen electrified railways based shunt hybrid filter

This paper presents a proposal to keep the conventional railway system, by creating a secure external independent hydrogen station, with the integration of a parallel hybrid filter to isolate the propagation of harmonics, and the reduction of the current ripple. In modern applicactions hydrogen stations are one of the most important sources of sustainable energy; this has been considered one of the most significant sources of energy, as it has no pollution output. Most current research proposes to integrate the fuel cell into the railway, and this will make it face higher risks such as accidents and vibrations as well as the approach of the storage a system to different people. We research any aspect of the proposed method and we demonstrate the ffect of shunt hybrid filter (SHPF) uses in stopping the propagation of the harmonics to the hydrogen side.
COVID-19 prediction classifier model using hybrid algorithms in data mining

Increase of stored data in medical databases needs allocative tools to get access to data, data mining, discover knowledge and efficient use of data. Medical and treatment fields are two examples of data mining tools to analyze massive data and predictive modelling. In medical sciences, prediction and precise-quick detection of multiple diseases has to reduced exprense and also save people’s lives. Group based methods (Ensemble Methods) are approaches that use hybrid models to recover classification. Coronavirus (COVID-19) has killed many people around the world so far, and this could be a good reason to present a new model for diagnosing the disease using data mining algorithms. This research presents a hybrid model of basic data mining and hybrid algorithms according to information in medical and laboratory records of patients suffering Covid-19 in Emam-Reza (AS) hospital in Mashhad, Iran, to diagnose the sickness. The proposed method uses Ensemble base (hybrid) classifiers, where the general model can be used to provide diagnoses with higher precision rather than classifiers. To execute the proposed model, data mining tools including Rapid Miner 9.7 and Python 3.7 were used. This study used stacking classifiers composed of basic algorithms including simple baze, decision tree, K- nearest neighborhood backup vector machine for basic section and uses chaos jungle algorithm in stack section that has gained 86.5% accuracy for diagnosis of Covid-19.
The interaction between platinum films and silicon substrates: Effects of substrate bias during sputtering deposition

The reactions between platinum and silicon, both during platinum deposition at elevated temperature and during a thermal annealing process, have been investigated using x-ray diffraction, cross-sectional transmission electron microscopy, and x-ray photoelectron spectroscopy. It has been found that sputtering deposition of platinum on a silicon substrate at 200 °C results in the formation of PtSi at the Pt–Si interface. But the reaction cannot fully proceed at this temperature for a platinum film with a thickness of 35 nm. Further annealing at 450 °C causes the platinum film to transform to PtSi completely. A substrate bias of −90 V during sputtering deposition leads to the formation of platinum films with larger columnar grains, instead of finer grains as being formed without substrate bias. In such a case, oxygen diffusion toward the interface was enhanced through the boundaries of these columnar grains, and this results in an accumulation of oxygen and oxide formation at the interface. As a result, the .
AlphaB3 – Expert Advisor using Artiﬁcial Neural Networks and Genetic Algorithms to Predict Stock Market Trends

The comprehension of the relation between stock market’s nature and a country’s economy is an essential part of the components of any ﬁnancial decision-making system. This paper describes the steps to build an Expert Ad-visor (EA) named AlphaB3, specialized in stock trading in brazilian ﬁnancial market. AlphaB3 uses neural networks and genetic algorithms to buy or sell ﬁ-nancial assets dynamically based on the stock value variation. According to the evaluations of AlphaB3, when a neural network is trained with sufﬁcient data, the created mechanism guides the investor to buy or sell with a higher proﬁt than if it uses an EA based on an alternative rule.
AN ANALYSIS OF MOBILE LEARNING ACCEPTANCE BY COLLEGE STUDENTS

Mobile technologies, applications and ease of Internet access have significantly increased the convenience of accessing information and bridging the digital divide. This research focuses technology acceptance, proposes an extended model and investigates the determinants associated with college students’ acceptance of mobile learning. The behavioral intention to use mobile learning by students is a critical success factor of mobile learning implementation process. In this context, it’s essential to enlighten the factors that affect college students’ intention to use mobile learning. This research is based on related technology acceptance literature and the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The purpose of this study is to examine the indicators of the behavioral intention to use mobile learning by college students and to explore differences according to various demographics. The results indicate that performance expectancy, facilitating conditions and social influence were all significant factors of behavioral intention to use mobile learning. Effort expectancy and personal propensity to learning were not found to be significant predictor variables. This paper provides useful information about the triggers of mobile learning acceptance in order to take precautions for students who have difficulty adopting mobile learning.
Measuring the risk of lending to small and medium-sized enterprises in the Republic of Serbia in light of modern banking regulations

Small and Medium-sized Enterprises (SMEs) represent the most efficient segment of the economy in almost all countries of the world. These companies usually face limited access to the favorable sources of financing in both the money market and the capital market, especially in terms of the conditions and price of their use. It is precisely the difficult financing that is the biggest obstacle to the growth and development of SMEs. As SMEs usually have no access to organized capital markets, bank financing often remains the only alternative. Banks’ biggest problems when granting loans to SMEs are related to MEASURING THE RISK OF LENDING TO SMALL AND MEDIUM-SIZED ENTERPRISES IN THE REPUBLIC OF SERBIA IN LIGHT OF MODERN BANKING REGULATIONS
Facilitators and Barriers faced by Mobile Payment Fintechs in the Brazilian Context

Fintechs have created a new global financial reality, providing access to services that were previously provided only by banks. This article aims to identifyfacilitators and barriers faced by mobile payment fintechs for their entry and development in the Brazilian context. The research method was a single casestudy, whose unit of analysis was the mobile payment fintech sector in the Brazilian context, involving nine companies in this industry. As a result, weidentified that several factors act as facilitators to the entry and development of fintechs, such as convenience and focus of their solutions, innovation in the use of technology, collaboration, and partnerships between fintechs. However, several barriers are faced in the emergence and development of these companies, such as regulatory issues, investment needs, difficulties in finding partners, and conflicts of interest with major players in the financial market. These results offer inputs to fintech entrepreneurs and suggest directions to regulators and public policymakers.