Miracle-dz/UniFAR-FTUs huggingface dataset:
Clonal clusters and virulence factors of methicillin-resistant Staphylococcus Aureus: Evidence for community-acquired methicillin-resistant Staphylococcus Aureus infiltration into hospital settings in Chennai, South India

Background and Objective: Staphylococcus aureus is one of the major pathogens of nosocomial infections as wells as community-acquired (CA) infections worldwide. So far, large-scale comprehensive molecular and epidemiological characterisation of S. aureus from very diverse settings has not been carried out in India. The objective of this study is to evaluate the molecular, epidemiological and virulence characteristics of S. aureus in both community and hospital settings in Chennai, southern India. Methods: S. aureus isolates were obtained from four different groups (a) healthy individuals from closed community settings, (b) inpatients from hospitals, (c) outpatients from hospitals, representing isolates of hospital–community interface and (d) HIV-infected patients to define isolates associated with the immunocompromised. Antibiotic susceptibility testing, multiplex polymerase chain reactions for detection of virulence and resistance determinants, molecular typing including Staphylococcal cassette chromosome mec (SCCmec) and agr typing, were carried out. Sequencing-based typing was done using spa and multilocus sequence typing (MLST) methods. Clonal complexes (CC) of hospital and CA methicillin-resistant S. aureus (MRSA) were identified and compared for virulence and resistance. Results and Conclusion: A total of 769 isolates of S. aureus isolates were studied. The prevalence of MRSA was found to be 7.17%, 81.67%, 58.33% and 22.85% for groups a, b, c and d, respectively. Of the four SCCmec types (I, III, IV and V) detected, SCCmec V was found to be predominant. Panton-Valentine leucocidin toxin genes were detected among MRSA isolates harbouring SCCmec IV and V. A total of 78 spa types were detected, t657 being the most prevalent. 13 MLST types belonging to 9 CC were detected. CC1 (ST-772, ST-1) and CC8 (ST238, ST368 and ST1208) were found to be predominant among MRSA. CA-MRSA isolates with SCCmec IV and V were isolated from all study groups including hospitalised patients and were found to be similar by molecular tools. This shows that CA MRSA has probably infiltrated into the hospital settings.
Studies on some major yield responsive genes in selected rice (Oryza species) cultivars grown in Nigeria using candidate gene SSR-based markers approach

This study was aimed at determining the presence of five major yield responsive genes shown to be among key determinants of rice yield in ten rice cultivars grown in Nigeria using candidate gene SSRs markers (Cg-SSRs). DNAs were extracted from young leaf samples using optimized cetyltrimethylammonium bromide (CTAB)-extraction method. An established gene sequences for high yielding rice Nipponbare cultivar for the five genes were retrieved from NCBI database and used to mine for SSRS using SSR identification tool. A pair of primer were designed manually from the nucleotide sequences flanking the selected SSRS for each gene. The designed primers were used to screen for the presence of the genes. Three pair of primers (for Gif1, Gn1a and Gw2) were able to amplify the targeted regions. The remaining two (for Gs3 and Dep1) did not amplify even the positive control. Two representative polymerase chain reaction (PCR)-products for the PCR-positive genes were sequenced and compared with established gene sequences of high yielding cultivars on NCBI database and their percentage sequence identities were determined. Phylogenetic trees (dendrograms) were generated using neighbor joining method. Overall, all the ten cultivars have both Gif1 and Gn1a genes while only eight cultivars have Gw2 gene. Gs3 and Dep1 need further optimization of PCR-conditions. Key words: Polymerase chain reaction, SSRs markers, primer.
File Type and Access Pattern Aware Buffer Cache Management for Rendering Systems

Rendering is the process of generating high-resolution images by software, which is widely used in animation, video games and visual effects in movies. Although rendering is a computation-intensive job, we observe that storage accesses may become another performance bottleneck in desktop-rendering systems. In this article, we present a new buffer cache management scheme specialized for rendering systems. Unlike general-purpose computing systems, rendering systems exhibit specific file access patterns, and we show that this results in significant performance degradation in the buffer cache system. To cope with this situation, we collect various file input/output (I/O) traces of rendering workloads and analyze their access patterns. The results of this analysis show that file I/Os in rendering processes consist of long loops for configuration, short loops for texture input, random reads for input, and single-writes for output. Based on this observation, we propose a new buffer cache management scheme for improving the storage performance of rendering systems. Experimental results show that the proposed scheme improves the storage I/O performance by an average of 19% and a maximum of 55% compared to the conventional buffer cache system.
A fully discrete finite element scheme for the Kelvin-Voigt model

In this paper, we study convergence of a fully discrete scheme for the two-dimensional nonstationary Kelvin-Voigt model. This scheme is based on a finite element approximation for space discretization and the Crank-Nicolson-type scheme for time discretization, which is a two step method. Moreover, we obtain error estimates of velocity and pressure. At last, the applicability and effectiveness of the present algorithm are illustrated by numerical experiments.
Factors Influencing Competitiveness of Small and Medium Enterprises (SMEs) in Nairobi County, Kenya

The purpose of this study was to examine factors influencing firm competitiveness among the Small and Medium Enterprises in Nairobi, Kenya. Specifically the study set out to examine the influence of strategic leadership, technology, resources and organization culture on competitiveness of Small and Medium Enterprises in Nairobi. The study adopted a descriptive research design. The study population comprised of SMEs in Nairobi which fall within the top 100 SMEs in Kenya. There are 79 SMEs from Nairobi County in the Top 100 SMEs in Kenya. A sample size of 25 SMEs which represented 30% of the target population was selected through stratified random sampling. The stratification was based on five business sector categories (real estate, supplies, services, distribution and manufacturing). The study utilized primary data which was collected from top management employees through the use of questionnaire. Data was analyzed using both descriptive and inferential statistics. Correlation analysis was employed to aid in establishing the nature and strength of the relationships between variables of interest. Correlation analysis results indicate that strategic leadership, adoption of relevant technology, resources availability and organization culture has a positive and significant relationship with firm competitiveness. The study concludes that sustainable competitive advantage of the firm stems from effective strategic leadership, adoption of technology, resources availability and effective organization culture. The study recommends that SMEs should embrace various competitive strategies to remain relevant in the market and achieve the required competitiveness. The SMEs managers should embrace strategic leadership practices, benchmark on best practices to ensure constant touch with their customers, embrace adoption of appropriate technology towards realizing higher levels of efficiency and effectiveness, ensure optimal utilization of resources, and cultivate a good balance between the organization culture and the organization processes so as to enhance competitive advantage. This study was confined to factors influencing organizational competitiveness of small and medium enterprises within Nairobi County. Other studies could be pursued in terms of expanding the geographical setting and taking recognition of other variables that could intervene and or moderate the investigated relationship.
REGIONALIZED DISCOUNT RATE TO EVALUATE RENEWABLE ENERGY PROJECTS IN COLOMBIA

In this paper we are interested in estimating the discount rate for electricity generation projects using renewable resources. More precisely we estimate the weighted average cost of capital (WACC). One of the most important feature of this research is that our WACC must be differentiated by regions, which is a challenging purpose taking into account that Colombia is a country with many regions that greatly differ among themselves. This goal requires estimating a risk parameter for each region, in this case for each state of the country, which would have the immediate effect of regionalizing the WACC for renewables non-conventional generation energy projects. it is worth noting that the emerging market bond index (EMBI+) is not a useful measurement of risk at regional level, so we have to look for a variables set in order to capture that risk. We propose to consider variables associated with corruption and violence levels in each state of the country in order to identify regional WACC.
Türkiyede Yetişen Phedimus stoloniferus Türünün Uçucu Kimyasal Bileşimi ve Biyoaktivite Değerlendirilmesi

In this research, volatile organic compounds (VOCs) in the essential oil (EO), SPME and SPME of n-hexane extract of Phedimus stoloniferus (Crassulaceae) grown in Turkey were determined by GC-FID/MS. Major chemical class for the volatile compounds in the EO of P. stoloniferus is carboxylic acids (50.2%). SPME and SPME of the hexane extracts of P. stoloniferus revealed aromatic compounds (51.9% and 65.9%, respectively) as the main constituents. n-Hexadecanoic acid (34.0%), 1-methyl-4-ethylbenzene (20.2%), and mesitylene (28.8%) were the major compounds in the EO, SPME and SPME of hexane extract obtained from P. stoloniferus, respectively. Antioxidant activity (CUPRAC assay) and tyrosinase inhibitions of the essential oil and n-hexane, methanol, and water extracts obtained from P. stoloniferus were also studied. While the antioxidant activity was determined to be highest in methanol extract (TEAC: 1.23±0.04), tyrosinase enzyme inhibition was highest in the aqueous extract (IC50: 0.168±0.018) of P. stoloniferus. KeywordsCrassulaceae, Phedimus stoloniferus, SPME, GC-FID/MS, Antioxidant, Tyrosinase Inhibition BŞEÜ Fen Bilimleri Dergisi 6(2), 585-593, 2019 BSEU Journal of Science DOI: 10.35193/bseufbd.633654 e-ISSN: 2458-7575 (http://dergipark.gov.tr/bseufbd)
Frequency-magnitude of rockfall events for hazard analysis; a comparison of data from LiDAR scanning with traditional methods of reporting

Railways in Western Canada are exposed to rockfall hazards that can be understood and managed through the use of a rockfall event database, which includes information on rockfall magnitude and frequency. Traditional rockfall inventories collected by railway personnel are often incomplete or lacking in volume information. In this study, LiDAR change detection is used to understand variations in rockfall magnitude, mechanism, and source zone by rock type and rock mass quality for an area of track along the Thompson River valley near Lytton, BC. Results are compared to traditional inventories to understand how each may contribute to establishing a complete rockfall inventory and characterization for this section of rockfall vulnerable railway track. RÉSUMÉ Les chemins de fer de l’ouest du Canada sont exposés aux chutes de pierres. Ces évènements peuvent être mieux compris et mieux gérés grâce à l’utilisation d’une base de données incluant l’ampleur et la fréquence de ces chutes de pierres. Les inventaires traditionnels de chutes de pierres recueillis par le personnel des compagnies de chemins de fer sont souvent incomplets ou manquent d’information sur le volume de pierres. Dans cette étude, la détection des changements par LiDAR est utilisée pour comprendre la grosseur, les mécanismes et les zones sources des effondrements le long du chemin de fer de la vallée de la rivière Thompson près de Lytton, BC. Les résultats obtenus sont comparés aux inventaires traditionnels pour comprendre comment chaque chute de pierres peut contribuer à l’instauration d’un inventaire complet pour cette section de chemin de fer vulnérable.
The detection of microbes in Martian mudstone analogues using laser ablation ionization mass spectrometry at high spatial resolution

The detection and identification of biosignatures on planetary bodies such as Mars is extremely challenging. Current knowledge from space exploration missions suggests that a suite of complementary instruments is required for a successful identification of past or present life. For future exploration missions, new and innovative instrumentation capable for high spatial resolution chemical (elemental and isotope) analysis of solids with improved measurement capabilities is of considerable interest because a multitude of potential signatures of extinct or extant life have dimensions on the micrometre scale. The aim of this study is to extend the current measurement capabilities of a miniature laser ablation ionisation mass spectrometer designed for space exploration missions to detect signatures of microbial life. In total, fourteen Martian mudstone analogue samples were investigated regarding their elemental composition. Half of the samples were artificially inoculated with a low number density of microbes and half were used as abiotic controls. The samples were treated in a number of ways. Some were cultured anaerobically and some aerobically; some abiotic samples were incubated with water and some remained dry. Some of the samples were exposed to a large dose of γ-radiation and some were left un-irradiated. While no significant elemental differences were observed between the applied sample treatments, the instrument showed the capability to detect biogenic element signatures of the inoculated microbes by monitoring biologically relevant elements, such as hydrogen, carbon, sulphur, iron, etc. When an enrichment in carbon was measured in the samples but no simultaneous increase in other biologically relevant elements was detected, it suggests carbon-grain inclusions; when the enrichment was in carbon and in bio-relevant elements, it suggests the presences of microbes. This study presents first results on the detection of biogenic element patterns of microbial life using a miniature LIMS system designed for space exploration missions.
Tyrosine kinase inhibitors vandetanib , lenvatinib and cabozantinib 1 modulate oxidation of an anticancer agent ellipticine catalyzed by 2 cytochromes P 450 in vitro 3

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THE EFFECT OF ENERGY CRYPTOS ON EFFICIENT PORTFOLIOS OF KEY ENERGY LISTED COMPANIES IN THE S&P COMPOSITE 1500 ENERGY INDEX

This paper investigates if energy block chain based crypto currencies can help diversify equity portfolios consisting primarily of leading energy companies of the US S&P Composite 1500 energy index. Key contributions are in terms of assessing the importance of energy cryptos as alternative investments in portfolio management, and whether different volatility models such as autoregressive moving average – Generalized Autoregressive Heteroskedasticity (ARMA-GARCH) and machine learning (ML) can help investors make better investment decisions. The methodology utilizes the traditional Markowitz mean-variance framework to obtain optimized portfolio combinations. Volatility measures, derived from the Cornish-Fisher adjusted variance, ARMA family classes and ML models are used to compare efficient portfolios. The study also analyses the effect of adding cryptos to equity portfolios with non-positive excess returns. Different models are assessed using the Sharpe performance measure. Daily data is used, spanning from November 21, 2017 to January 31, 2019. Findings suggest that energy based cryptos do not have a significant impact on energy equity portfolios, despite the use of different risk measures. This is attributable to the relatively poor performance of energy cryptos which did not contribute in improving the excess return per unit of risk of efficient portfolios based on the leading US energy stocks.
Quantitative LC-MS/MS method for nivolumab in human serum using IgG purification and immobilized tryptic digestion

A liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) method is a novel approach for the quantification of therapeutic monoclonal antibody. However, this method has severe ionization suppression and time-consuming sample preparation. In the present study, we developed a simple and rapid LC-MS/MS method for quantifying serum nivolumab with good analytical performance using immunoglobulin G (IgG) purification and immobilized tryptic digestion. Surrogate peptide derived from nivolumab was identified by a hybrid quadrupole-Orbitrap mass spectrometer. IgG purification and immobilized tryptic digestion were completed within 25 minutes. The chromatographic separation was completed in 10 minutes with stepwise-gradient elution. Chromatographic peaks interfering with the surrogate peptide and its stable isotope-labeled peptide as an internal standard were not observed from serum digests. The dynamic range of the calibration curve was 2–200 μg mL−1. The intra- and inter-assay accuracies and imprecisions were 92.2–104.5% and less than 10.0%, respectively. Serum nivolumab concentrations ranged from 12–112 μg mL−1 in 14 cancer patients. The measured concentrations in the LC-MS/MS method were strongly correlated with those in the ELISA method (r = 0.92, P < 0.01). In conclusion, a simple and rapid LC-MS/MS method for quantifying serum nivolumab using immobilized tryptic digestion coupled to IgG purification was found to be acceptable for clinical settings.
A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism

Power transmission networks play an important role in smart girds. Fast and accurate faulty-equipment identification is critical for fault diagnosis of power systems; however, it is rather difficult due to uncertain and incomplete fault alarm messages in fault events. This paper proposes a new fault diagnosis method of transmission networks in the framework of membrane computing. We first propose a class of spiking neural P systems with self-updating rules (srSNPS) considering biological apoptosis mechanism and its self-updating matrix reasoning algorithm. The srSNPS, for the first time, effectively unitizes the attribute reduction ability of rough sets and the apoptosis mechanism of biological neurons in a P system, where the apoptosis algorithm for condition neurons is devised to delete redundant information in fault messages. This simplifies the complexity of the srSNPS model and allows us to deal with the uncertainty and incompleteness of fault information in an objective way without using historical statistics and expertise. Then, the srSNPS-based fault diagnosis method is proposed. It is composed of the transmission network partition, the SNPS model establishment, the pulse value correction and computing, and the protection device behavior evaluation, where the first two components can be finished before failures to save diagnosis time. Finally, case studies based on the IEEE 14- and IEEE 118-bus systems verify the effectiveness and superiority of the proposed method.
Convergence of a finite element method for degenerate two-phase flow in porous media

A finite element method with mass-lumping and flux upwinding, is formulated for solving the immiscible two-phase flow problem in porous media. The method approximates directly the wetting phase pressure and saturation, which are the primary unknowns. The discrete saturation satisfies a maximum principle. Theoretical convergence is proved via a compactness argument. The proof is convoluted because of the degeneracy of the phase mobilities and the unboundedness of the capillary pressure.
CHAPTER-3 THRESHOLDING APPROACHES FOR RSI CLASSIFICATION 3 . 1

Thresholding is one of the simplest method of remote sensing image classifications. Thresholding is a non-linear operation that converts a gray scale image into a binary image where the two levels are assigned to pixels that are below or above the specified threshold value. It is however extreme more efficient to use in the remote sensing image. Thresholding often provides an easy and convenient way to perform this classification on the basis of the different intensities or colours in the foreground and background regions of an image. Threshold operation provides several methods for finding the optimal threshold value for a given remote sensing image. Thresholding algorithms are successively applied on the input image to yield better results [1]. The classification process is more robust, the threshold should be automatically selected by the system. The feature about the objects, the application and the environment should be used to choose the threshold automatically:
Experimental Study on Software Fault Prediction Using Machine Learning Model

Faults are the leading cause of time consuming and cost wasting during software life cycle. Predicting faults in early stage improves the quality and reliability of the system and also reduces cost for software development. Many researches proved that software metrics are effective elements for software fault prediction. In addition, many machine learning techniques have been developed for software fault prediction. It is important to determine which set of metrics are effective for predicting fault by using machine learning techniques. In this paper, we conduct an experimental study to evaluate the performance of seven popular techniques including Logistic Regression, K-nearest Neighbors, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine and Multilayer Perceptron using software metrics from Promise repository dataset usage. Our experiment is performed on both method-level and class-level datasets. The experimental results show that Support Vector Machine archives a higher performance in class-level datasets and Multilayer Perception produces a better accuracy in method-level datasets among seven techniques above.
Parameterized Optimization in Uncertain Graphs - A Survey and Some Results

We present a detailed survey of results and two new results on graphical models of uncertainty and associated optimization problems. We focus on two well-studied models, namely, the Random Failure (RF) model and the Linear Reliability Ordering (LRO) model. We present an FPT algorithm parameterized by the product of treewidth and max-degree for maximizing expected coverage in an uncertain graph under the RF model. We then consider the problem of finding the maximal core in a graph, which is known to be polynomial time solvable. We show that the Probabilistic-Core problem is polynomial time solvable in uncertain graphs under the LRO model. On the other hand, under the RF model, we show that the Probabilistic-Core problem is W[1]-hard for the parameter d, where d is the minimum degree of the core. We then design an FPT algorithm for the parameter treewidth.