Miracle-dz/UniFAR-FTUs huggingface dataset:
Research Report Department of Statistics No . 2004 : 3 MODEL SELECTION FOR COGNITIVE SOCIAL STRUCTURES

Measurement accuracy is an inherent problem in social network analysis. The issue of actor accuracy in the reporting of their interactions with others, was raised by Bernard, Killworth and Sailer (e.g. Bernard et al., 1980) and provoked extensive debate. Krackhardt (1987) later introduced the concept of Cognitive Social Structures and several methods for aggregating different actor reports on the network into a single graph, with the aid of which for example the congruence of reports could be gaged. Often when this data collecting paradigm is used the interest is in correlating bias on the part of the perceivers with exogenous attributes of the perceivers (e.g. Bondonio, 1998; Casciaro, 1998; Casciaro et al., 1999). A statistical model for aggregating separate reports into a single consensus network, with the additional benefit of allowing estimates of actor accuracy to be obtained in the process, was proposed by Batchelder et al. (1997). Using an extension of this model and a Bayesian approach we are able to incorporate effects of known covariates and network effects on perceptional biases. In Koskinen (2002a) it was suggested that the conditional probability of reporting a tie as present when a tie is really present (or absent) be modeled using a probit link function. This is further elaborated here with a special focus on finding standard reference priors that enables model selection. The main obstacle is that the model is not fully identified, something which can not be solved in any obvious way through restrictions or highly informative priors. The proposed solution is to asses a posteriori which are the main determinants of identifying conditions. We present a procedure for choosing prior distributions and provide the necessary adjustments to the original sampling scheme.
MicroRNA ‐ 26 a induces osteosarcoma cell growth and metastasis via the Wnt / β ‐ catenin pathway

MicroRNAs (miRNAs/miRs) are a type of highly conserved, small non-coding RNA that are vital to the post-transcriptional regulation of gene expression via base pairing with target mRNA 3'-untranslated regions (3'-UTRs). Several studies have indicated that the abnormal expression of miRNAs occurs frequently in human osteosarcoma (OS). In the present study, the role of miR-26a in the progression and metastasis of OS was investigated using reverse transcription-quantitative polymerase chain reaction, a luciferase activity assay, cell viability assay, in vitro migration and invasion assays, transfection and western blot analysis. miR-26a was upregulated in OS tissues and cell lines, and the expression of miR-26a was indicated to affect the proliferation, migration and invasion of OS Saos-2 cells. At the molecular level, the results showed that glycogen synthase kinase-3β (GSK-3β) was identified as a target of miR‐26a, and the ectopic expression of miR-26a inhibited GSK-3β by directly binding to the 3'-UTR. Therefore, the expression of miR-26a was negatively correlated with GSK-3β in the OS tissues. These data suggest that miR‐26a is significant in the proliferation of human OS cells due to the direct regulation of Wnt/β-catenin signaling.
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We present an algorithm capable of making in real time image mosaics with enlarged field-of-view from the endoscopic video data stream. The algorithm is based on the method of Kourogi et al. [1] which we extend to the case of endoscopic masks. The algorithm finds the optimal affine transform between video frames and builds the enlarged field-of-view as an intervention-free side task. We apply our algorithm to endoscopic video sequences and compare it to the well-known image-mosaicing algorithm of Szeliski [2]. Our method turns out to be more robust, more than 3 times faster, having at the same time a 4 times smaller average motion estimation error: 0.19 pixel instead of 0.72 pixel between successive frames.
Title Analysis approach of SSO based on LDAE model

A linearized differential and algebraic equations (LDAE) model for both eigen-analysis and complex torque coefficient (CTC) calculation of power system subsynchronous oscillation (SSO) is proposed in this paper. The LDAE model for SSO study is derived at first. Generalized eigen-analysis and CTC calculation based on the LDAE model are presented thereafter. The effectiveness of the proposed LDAE model based SSO analysis approaches are verified through computer test results. The LDAEmodel-based SSO study approach suggested in this paper paves the way for SSO study with HVDC transmission and/or FACTS devices without eliminating their algebraic variables, which can obtain more valuable information.
Characterization of Staphylococcus spp . strains in milk from buffaloes with mastitis in Brazil : the need to identify to species level to avoid misidentification

Mastitis is an inflammation of the mammary gland that affects dairy cattle worldwide causing economic losses. Coagulase-negative staphylococci (CNS) are the predominant cause of this type of infection. We have recently showed that coagulase-positive staphylococci could be misidentified. So, the aim of this study was to characterize the Staphylococcus spp. strains initially classified as coagulase-negative Staphylococci, isolated from buffalo with subclinical mastitis. Milk of buffaloes with mastitis in herds was collected and 9 strains were identified as CNS by phenotypic tests. Molecular methodologies latter identified the strains as coagulase-negative Staphylococcus chromogenes (5), coagulase-positive Staphylococcus hyicus (2) and coagulase-positive Staphylococcus aureus (2). Our results strongly support the need to identify the isolates to a species level in order to avoid misidentification and to be aware of the classification using the coagulase test alone.
Prediction in the Era of Massive Datasets

Data mining often involves processing massive, retrospective datasets to learn how to make predictions in the future. The researchers that compose the data mining community have approached the prediction problem in their own fashion and have produced a novel set of tools. This article discusses the complexity arising when tting prediction models to massive datasets, proposes a framework for algorithmic solutions based on bagging and boosting, and poses some ideas for future directions.
The Predictive Validity of the LSI-R on a Sample of Offenders Drawn from the Records of the Iowa Department of Corrections Data Management System

OFFENDER ASSESSMENT and classification have become common practice throughout correctional programming over the past two decades. In particular, one risk/ needs assessment tool, the Level of Service Inventory-Revised (LSI-R), has gained widespread popularity in correctional settings. While multiple studies have demonstrated the predictive validity of the LSIR in various correctional settings and populations, research continues to stress the importance of examining the predictive validity of the LSI-R (Andrews, 1982; Andrews & Bonta, 1995; Bonta & Andrews, 1993). As posited by Gottfredson and Moriarty (2006), examining the predictive validity of a risk/needs assessment is paramount since the samples used in the development of a risk tool should be representative of the population that the instrument is intended to be used upon. Further, they suggest that findings generated from similar samples should be cautiously interpreted due to the possibility of overestimating the tool’s validity in predicting recidivism. Given this warning, agencies should consider examining the predictive validity of their targeted populations in order to determine the instrument’s reliability and validity with their offender base. The current study attempts to demonstrate the utility of such a practice by examining the predictive validity of the LSI-R on a sample of probationers and parolees in Iowa.
A Comprehensive Quantitative Theory of the U . S . Wealth Distribution

This paper employs a benchmark heterogeneous-agent macroeconomic model to examine a number of plausible drivers of the rise in wealth inequality in the U.S. over the last forty years. We find that the significant drop in tax progressivity starting in the late 1970s is the most important driver of the increase in wealth inequality since then. The sharp observed increases in earnings inequality and the falling labor share over the recent decades fall far short of accounting for the data. The model can also account for the dynamics of wealth inequality over the period—in particular the observed U-shape— and here the observed variations in asset returns are key. Returns on assets matter because portfolios of households differ systematically both across and within wealth groups, a feature in our model that also helps us to match, quantitatively, a key long-run feature of wealth and earnings distributions: the former is much more highly concentrated than the latter.
Title The roles and training of primary care doctors : China , India , Brazil and South Africa

China, India, Brazil and South Africa contain 40% of the global population and are key emerging economies. All these countries have a policy commitment to universal health coverage with an emphasis on primary health care. The primary care doctor is a key part of the health workforce, and this article, which is based on two workshops at the 2014 Towards Unity For Health Conference in Fortaleza, Brazil, compares and reflects on the roles and training of primary care doctors in these four countries. Key themes to emerge were the need for the primary care doctor to function in support of a primary care team that provides community-orientated and first-contact care. This necessitates task-shifting and an openness to adapt one’s role in line with the needs of the team and community. Beyond clinical competence, the primary care doctor may need to be a change agent, critical thinker, capability builder, collaborator and community advocate. Postgraduate training is important as well as up-skilling the existing workforce. There is a tension between training doctors to be community-orientated versus filling the procedural skills gaps at the facility level. In training, there is a need to plan postgraduate education at scale and reform the system to provide suitable incentives for doctors to choose this as a career path. Exposure should start at the undergraduate level. Learning outcomes should be socially accountable to the needs of the country and local communities, and graduates should be person-centred comprehensive generalists.
Microeconomics ) Cloud Usage Patterns Or Cloud workloads Markov Chain Analysis Queueing Theory Cloud Service Metrics Cloud Market Segments Web Hosting Network VDI Database

However, the word “utility” is very ambiguous and often confusing. One of the primary reasons is that it has many different connotations [16]. The common sense of utility means “the usefulness of something, especially in a practical way. ” For example, the utility of database means to implement various processes or functions of the database, such as batch update, rebuild, recovery, backup, etc. Another sense of utility is quite close to the meaning of the usefulness that often refers to the state of being useful, which is to supply the basic infrastructure services to the general public. These services are offered by incumbent service providers, which are called “public utilities” or simply, “utilities. ” Buyya et al. [35] defined the infrastructure of “cloud computing” as the 5 utility. Another meaning of utility is the utilization rate, which is to measure effective usage of something.
Green synthesis of silver nanoparticles using stem extract of Berberis aristata and to study its characterization and antimicrobial activity

Plant-mediated synthesis of nanoparticles has been an emerging research area worldwide for its inherent features such as rapidity, simplicity, environment-friendly, and cost-effectiveness. The rapid and simple approach was used for the synthesis of silver nanoparticles (AgNPs) (AgNO3) using stem of Berberis aristata (Daruharidra) by biologically reducing AgNO3 with the aqueous extract of plant. The formation of AgNPs was indicated by the color change from slightly yellowish to yellowishbrown. Various techniques were used to characterize biosynthesized NPs by ultraviolet-visible, Fourier transform infrared, scanning electron microscopy, and transmission electron microscopy analysis. The biosynthesized AgNPs were also tested for antimicrobial activity against pathogenic bacteria such as Escherichia coli and Pseudomonas aeruginosa.
Diagnosis of Culture Negative Acute Bacterial Meningitis by Using A 16 S rDNA-Based Gene Sequencing Directly from CSF Samples

Background: Acute bacterial meningitis is an important medical emergency with high morbidity and mortality rate all over the world. The uses of 16S rDNA amplification and sequencing have been proposed as a strategy for diagnosis especially in culture negative cases. The aim of the study: Was to evaluate the diagnosis of culture negative acute bacterial meningitis by using 16S rDNA-based gene sequencing applied directly on CSF samples, in order to identify the causative bacterial species for the proper choice of subsequent antimicrobial therapy. Methodology: CSF samples were collected from 30 patients expected to have acute bacterial meningitis (15 with expected community acquired meningitis & 15 with expected VP shunt related meningitis) and from 7 patients expected to have viral meningitis. Blood samples were also collected from patients with expected community acquired meningitis for blood cultures. CSF samples were subjected to macroscopic, chemical, cytological and bacteriological examination. DNA extractions from CSF samples were done using the QIAamp mini kit. The extracted DNA was amplified and sequenced by using MicroSeq500 16S rDNA Bacterial Identification kit and cycle sequencing. The sequences were compared with those available in National Centre for Biotechnology Information (NCBI) by using Basic Local Alignment Search Tool (BLAST). Results: CSF culture was positive only in 16.7% while 16S rDNA was positive in 66.7% of cases. In culture negative cases, 16S rDNA was positive in 60% of cases. The detected bacterial species in expected VP related meningitis were E. coli, K. pneumoniae, S. epidermidis and P. aeruginosa and in expected community acquired meningitis were E. coli, K. pneumoniae, S. pneumoniae and S. aureus. The detection rate of 16S rDNA varies according to the combination of different diagnostic methods used for selection of patients, including clinical manifestations, CSF parameters and microbiological findings. In case diagnosed by gold standard methods, the sensitivity of 16S rDNA was 87.5% and the specificity was 100%. Conclusion Direct detection and sequencing of the 1 500 bp of 16S rDNA by using cycle sequencing can provide a specific, sensitive, early, rapid and non expensive method for improving the microbiological diagnosis of acute bacterial meningitis, especially after initiation of antimicrobial therapy and in culture negative cases. In spite of lacking bacterial isolation and sensitivity test, it could re-optimize the empirical use of antibiotics based on the identified bacterial species. [Esraa A. Mohamed, Mostafa S. Elrafei, Mona O. Ramadan, Magdy A. ElMahalawy and Eman A. Eisa. Diagnosis of Culture Negative Acute Bacterial Meningitis by Using A 16S rDNA Based Gene Sequencing Directly from CSF Samples. Life Sci J 2018;15(9):17-26]. ISSN: 1097-8135 (Print) / ISSN: 2372-613X (Online). http://www.lifesciencesite. com. 2. doi:10.7537/marslsj150918.02.
An Application of the Utaut Model to Understand the Behavior of Technology Usage (Case Study in Siaku Users at Udayana University)

: This research analyzed factors that affecting the behavior of technology usage by financial employees at Udayana University by using Unified Theory of Acceptance and Use of The Technology (UTAUT) model. The technology in this study is referred to Sistem Informasi Akuntansi dan Keuangan (SIAKU) that providing accounting and finance information, which has been inaugurated by Udayana University since 2014. Through the UTAUT model, the acceptance level of information systems by users can be analyzed in order to determine whether the existing information system is sufficiently feasible and ideal to meet the needs of users, and how the user’s expectancy of information systems. The population of this research is all financial employees that working at Udayana University. Research samples are determined by purposive sampling technique which producing 87 respondents as the sample. The data were collected using questionnaires. Analyzer used in this research is Partial Least Square (PLS). The results indicated that performance expectancy, effort expectancy, and social influence have positive effect on information system usage intention. Facilitating conditions and behavioral intention are positively affect the actual use of information systems.
Aeromonas culicicola sp . nov . , from the midgut of Culex quinquefasciatus

The taxonomic position was examined of three isolates, MTCC 3249T, SH and SLH, from the midgut of female Culex quinquefasciatus and Aedes aegyptii mosquitoes. Numbers of cells of these isolates increased 2000-fold after a blood meal of the mosquitoes. 16S rRNA gene sequence analysis of the novel strains showed that they were highly homologous to strains of Aeromonas. DNA–DNA hybridization studies showed that DNA of strain MTCC 3249T was 96 and 88% similar to that of strains SH and SLH, respectively, and showed 54% relatedness to Aeromonas jandaei and 61% relatedness to Aeromonas sobria, which is below the cut-off value for species differentiation. The biochemical profiles of all three novel strains were identical. On the basis of a polyphasic approach using phenotypic analysis, 16S rRNA gene sequencing and DNA–DNA hybridization studies, a novel species is proposed for these isolates, Aeromonas culicicola sp. nov., with the type strain MTCC 3249T ( ̄NCIM 5147T). Isolation of A. culicicola from the midgut of mosquitoes might help to explain the origin of Aeromonas infections caused without exposure to contaminated water, soil or food.
Chapter 2 Cuff-Induced Neointimal Formation in Mouse Models

Ischemic heart failure caused by atherosclerosis is a major cause of death worldwide. Although remarkable technological advances have been made in the treatment of coronary heart disease, there is as yet no treatment that can sufficiently suppress the progression of atherosclerosis, including neointimal thickening. Therefore, a precise understanding of the mechanism of neointimal hyperplasia will provide the development of new technologies. Both ApoE-KO and LDLR-KO mice have been employed to generate other relevant mouse models of cardiovascular disease through breeding strategies. Although these mice are effective tools for the investigation of atherosclerosis, development of a progressive atherosclerotic lesion takes a long time, resulting in increase of both the costs and the space needed for the research. Thus, it is necessary to develop simpler tools that would allow easy evaluation of atherosclerosis in mouse models. In this review, we discuss our experience in generating mouse models of cuff-induced injury of the femoral artery and attempt to provide a better understanding of cuff-induced neointimal formation.
1 Polymorphisms in the LAC 12 gene explain lactose utilisation 1 variability in Kluyveromyces marxianus strains 2 3

1 Kluyveromyces marxianus is a safe yeast used in the food and biotechnology 2 sectors. One of the important traits that sets it apart from the familiar yeasts, 3 Saccharomyces cerevisiae, is its capacity to grow using lactose as a carbon 4 source. Like in its close relative, Kluyveromyces lactis, this requires lactose 5 transport via a permease and intracellular hydrolysis of the disaccharide. 6 Given the importance of the trait, it was intriguing that most, but not all, strains 7 of K. marxianus are reported to consume lactose efficiently. In this study, 8 primarily through heterologous expression in S. cerevisiae and K. marxianus, 9 it was established that a single gene, LAC12, is responsible for lactose uptake 10 in K. marxianus. Strains that failed to transport lactose showed variation in 13 11 amino acids in the Lac12p protein, rendering the protein non-functional for 12 lactose transport. Genome analysis showed that the LAC12 gene is present in 13 4 copies in the sub-telomeric regions of three different chromosomes but only 14 the ancestral LAC12 gene encodes a functional lactose transporter. Other 15 copies of LAC12 may be non-functional or have alternative substrates. The 16 analysis raises some interesting questions regarding the evolution of sugar 17 transporters in K. marxianus. 18 19
Investigating and Modeling Performance Scalability for Distributed Graph Analytics

Distributed systems for graph processing continue to attract attention in the literature, driven by the power of the underlying computational model and the availability of open-source software. Among the biggest barriers to achieving high resource efficiency are time and money-consuming system parameter tuning for cluster size. In this work, we undertake a comprehensive experimental study to characterize and then model the scaling behaviors of multiple graph analytics benchmarks in a production-grade distributed system, Apache Giraph. We show that our theoretical model can leverage existing domain knowledge in an interpretable fashion and accurately predict optimal choices even for previously unseen clusters. Our primary conclusions are that despite using randomized hash partitioning for work distribution, two factors dynamic algorithm behavior and graph dataset skew are the most important, respectively, for predicting scalability. Under the same regime, different numbers of partitions and memory optimizations have little impact on performance. Keywords-parallel and distributed systems; cloud computing; graph analytics; data analytics; big data; performance analysis; predictive modeling;