Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Killer and Natural Killer T Cells as a Prognostic Factor for Rheumatoid Arthritis and Ankylosing Spondylitis

Object: We aimed to show the relationship between natural killer T and natural killer cells with the diseases activity and functional capacity, in the patient with rheumatoid arthritis and ankylosing spondylitis. Materials and Methods: All patients included in the study were evaluated to the disease activity and functional capacity and radiographic view and also; their’ blood count and C- reactive protein, erythrocyte sedimentation rate and rheumatoid factor and natural killer T and natural killer cells were measured, simultaneously. Results: It was shown to be the positive correlation between the disease activity with total Sharp score and functional capacity. Significant positive correlation was found the between BASDAI score with natural killer T cells. The ratio of natural killer T and natural killer cells was found higher, using of the methotrexate treatments with rheumatoid arthritis patients. No correlation was found the between percentage of natural killer T and natural killer cells with using of the methotrexate treatment, in the patients with ankylosing spondylitis. Conclusion: Determination of the levels of natural killer T and natural killer cells can contribute to estimation of the disease course and treatment response and the development of functional disorders, in the patients with rheumatoid arthritis and ankylosing spondylitis.
A Survey on Properties of Adaptive Boosting with Different Classifiers

: Adaboost or Adaptive boosting is a recently using boosting technique in machine learning. Generally boosting is used to improve the performance of various learning algorithms. It is mainly used in conjunction with different classifiers. This paper takes a survey based on the properties shown by adaboost with different base classifiers like, support vector machine, naive bayes, decision trees and decision stump. Boosting of these classifiers with adaboost shows difference in properties such as accuracy, error rate, performance etc. Adaptive boosting is widely used in various fields of data mining and image recognition . Now a days it is very helpful in the prediction and diagnosis of particular disease patterns with maximum degree of accuracy by converting a weak learner into a strong learner.
Choosing the Right Model for Distillation Processes in Packed Columns: Theory and Experiments

Packed distillation columns are usually designed on the basis of a sequential approach, in which first the Equilibrium Stage (EQ) model is used to quantify the separation effort before the required packing height is determined by HETP values. This approach is often sufficient for process design and proper sizing of the column. However, in certain cases the combination of the EQ model and HETP values results in an inappropriate column design. The design accuracy can be improved using a rate-based (RB) approach. Unfortunately, there is no reliable heuristic available to decide which modeling approach should be chosen. Therefore, the current study investigates the need for rate-based modeling in distillation processes by systematically evaluating the differences between EQ and RB composition trajectories. A criterion based on the Euclidean distance is proposed to evaluate the differences between the EQ and RB trajectories. Then, by means of a selectivity analysis basic criteria for model discrimination are derived and validated experimentally. Generally, for wide boiling mixtures the differences between the EQ and RB trajectories are the most distinct. The differences in the trajectories can be further increased or decreased by component specific mass transfer.
Modeling individual tree growth by fusing diameter tape and increment core data

Tree growth estimation is a challenging task as difficulties associated with data collection and inference often result in inaccurate estimates. Two main methods for tree growth estimation are diameter tape measurements and increment cores. The former involves repeatedly measuring tree diameters with a cloth or metal tape whose scale has been adjusted to give diameter readings directly. This approach has the advantage that diameters can be measured rapidly. However, because of the substantial error involved during tape measurements, negative diameter increments are often observed. Alternatively, annual radius increment data can be obtained by taking tree cores and averaging repeated measurements of the ring widths. Acquiring and analyzing tree cores is a time‐consuming process, and taking multiple cores may have adverse effects on tree health. Therefore, radius increment data are typically only available for a subset of trees within a stand. We offer a fusion of the data sources, which enables us to accommodate missingness and to borrow strength across individuals. It enables individual tree‐level inference as well as average or stand level inference. Our model recognizes that tree growth in a given year depends upon tree size at the start of the year as well as levels of appropriate covariates operating in that year. We apply our modeling to a fairly large dataset taken from two forest stands at Coweeta Hydrologic Laboratory in the southern Appalachians collected from 1991 to 2011. Copyright © 2014 John Wiley & Sons, Ltd.
Transition from accelerated to decelerated regimes in JT and CGHS cosmologies

In this work we discuss the possibility of positive-acceleration regimes, and their transition to decelerated regimes, in two-dimensional (2D) cosmological models. We use general relativity and the thermodynamics in a 2D space-time, where the gas is seen as the sources of the gravitational field. An early-Universe model is analyzed where the state equation of van der Waals is used, replacing the usual barotropic equation. We show that this substitution permits the simulation of a period of inflation, followed by a negative-acceleration era. The dynamical behavior of the system follows from the solution of the Jackiw-Teitelboim equations (JT equations) and the energy-momentum conservation laws. In a second stage we focus the Callan-Giddings-Harvey-Strominger model (CGHS model); here the transition from the inflationary period to the decelerated period is also present between the solutions, although this result depends strongly on the initial conditions used for the dilaton field. The temporal evolution of the cosmic-scale function, its acceleration, the energy density and the hydrostatic pressure are the physical quantities obtained through the analysis.
The polymorphism of bovine FIT gene and its associations with cattle (Bos taurus) growth traits

Fat-inducing transcript (FIT) are endoplasmic reticulum-resident membrane proteins that induce lipid droplet accumulation. It plays a crucial role in the fundamental process of storing fat. In this study, applying the PCR-SSCP and DNA sequencing methods, polymorphism of the FIT gene were detected. A total of 708 individuals from four Chinese cattle breeds were examined. The results showed that only P 5 locus had two SNPs, resulting in a synonymous mutation (NM_001103095: m.199G > T resulting in L124L) and a missense mutation (NM_001103095: m.434G > T resulting in V176L). The associations between polymorphic loci and selected growth traits of indigenous Nangyang cattle were analysed, and significant associations were found in body weight at the age of 12 months and mean daily live weight gain. The body weight at month 12 of life and mean daily live weight gain of individuals with genotype AA were by 3.75% and 4.88% higher than of those with genotype AB, respectively. Hence, it was suggested for the first time, that genotype AA could be regarded as molecular marker for superior body weight and daily live weight gain in Chinese Nanyang cattle.
Search For Gravitational Waves Through the Electromagnetic Faraday Rotation

A method is given which renders indirect detection of strong gravitational waves possible. This is based on the reflection (collision) of a linearly polarized electromagnetic shock wave from (with) a cross polarized impulsive and shock gravitational waves in accordance with the general theory of relativity. This highly nonlinear process induces a detectable Faraday rotation in the polarization vector of the electromagnetic field.
A regularized point process generalized linear model for assessing the connectivity in the cat motor cortex

Identification of multiple simultaneously recorded neural spike train recordings is an important task in understanding neuronal dependency, functional connectivity, and temporal causality in neural systems. An assessment of the functional connectivity in a group of ensemble cells was performed using a regularized point process generalized linear model (GLM) that incorporates temporal smoothness or contiguity of the solution. An efficient convex optimization algorithm was then developed for the regularized solution. The point process model was applied to an ensemble of neurons recorded from the cat motor cortex during a skilled reaching task. The implications of this analysis to the coding of skilled movement in primary motor cortex is discussed.
Cardiology Predictor : An aid to diagnosis of heart disease

Diagnosis of a heart disease is complex. Aspects such as clinical details, blood pressure, pulse rate, cholesterol level and blood test reports, and even patient‟s history, gender and age all are important when diagnosing cardiovascular disease. Interpretation of an Elector Cardiogram (ECG) is one of a key skill developed by cardiologists. Difficulties in analysing complicated ECG may lead to inaccurate diagnosis thus affecting the accuracy and quality with the diagnosis of cardiovascular disease especially among novice. “Cardiology Predictor” is a software system which is capable of assisting medical practitioners to diagnose cardiovascular diseases accurately. The inputs to the system would be ECG and other cardiac factors which would be processed to provide an output of the possible cardiovascular disease. To ensure accuracy and efficiency, ECG signals are used with digital signal processing for decrypting and feature extracting. Furthermore, artificial intelligence is used to diagnose cardiovascular diseases due to the complexity embedded into it. Therefore, an Artificial Neural Network was used to predict cardiovascular disease. The average success rate of the system was 85.6% based on the user evaluation. Domain experts such as cardiologists suggested that the system is most suitable for the emergency room where expert knowledge was not readily available.
Expression and characterization of rat UDP-N-acetylglucosamine: α-3-d-mannoside β-1,2-N-acetylglucosaminyltransferase I in Saccharomyces cerevisiae

The yeast Saccharomyces cerevisiae is a useful host for the production of heterologous proteins through the secretory pathway. However, because of the potential antigenicity of mannan-type sugar chains in humans, yeast cannot be used as a host for the production of glycoprotein therapeutics. To overcome this problem, we are trying to breed a yeast which can produce hybrid- or complex-type carbohydrates. UDP- N- acetylglucosamine:alpha-3-d-mannoside beta-1, 2- N- acetylglucosaminyltransferase I (GnT-I) is essential for the conversion of high mannose-type N- glycans to hybrid- and complex-type ones. As yeast lacks this enzyme, we have introduced the rat GnT-I cDNA into yeast cells. The transformed yeast cells expressed GnT-I activity in vitro. The expressed GnT-I was localized in all organella, including the endoplasmic reticulum (ER), Golgi apparatus, and vacuole, suggesting that the mammalian Golgi retention signal of GnT-I did not function in yeast cells. Analysis of the GnT-I gene product with a c-Myc epitope tag at the C-terminus elucidates that the N - terminal region of GnT-I, including the mammalian Golgi retention signal, should be removed in the yeast ER.
Extending customer order penetration concepts to engineering designs

Purpose - The customer order decoupling point (CODP) concept addresses the issue of customer engagement in the manufacturing process. This has traditionally been applied to material flows, but has more recently been applied to engineering activities. This later subject becomes of particular importance to companies operating in ‘engineer-to-order’ (ETO) supply chains, where each order is potentially unique. Existing conceptualisations of ETO are too generic for practical purposes, so there is a need to better understand order penetration in the context of engineering activities, especially design. Hence, we address the question ‘how do customer penetration concepts apply to engineering design activities? ’ Methodology - A collaborative form of inquiry is adopted, whereby academics and practitioners co-operated to develop a conceptual framework. Within this overarching research design, a focus group of senior practitioners and multiple case studies principally from complex civil and structural engineering as well as scientific equipment projects are used to explore the framework. Findings - The framework results in a classification of nine potential engineering subclasses, and insight is given into order penetration points, major uncertainties and enablers via the case studies. Focus group findings indicate that different managerial approaches are needed across subclasses. Implications –The findings give insight for companies that engage directly with customers on a one-to-one basis, outlining the extent of customer penetration in engineering activities, associated operational strategies and choices regarding the co-creation of products with customers. Care should be taken in generalising beyond the sectors addressed in the study. Originality - The paper refines the definition of the ETO concept, and gives a more complete understanding of customer penetration concepts. It provides a comprehensive reconceptualization of the ETO category, supported by exploratory empirical research.
Universitet Hagedorn Temperature of AdS 5 / CFT 4 via

Hagedorn Temperature of AdS5/CFT4 via Integrability Harmark, Troels; Wilhelm, Matthias Published in: Physical Review Letters DOI: 10.1103/PhysRevLett.120. 071605 Publication date: 2018 Document Version Publisher's PDF, also known as Version of record Citation for published version (APA): Harmark, T. , & Wilhelm, M. (2018). Hagedorn Temperature of AdS5/CFT4 via Integrability. Physical Review Letters, 120(7), [071605]. https://doi.org/10.1103/PhysRevLett.120. 071605
Impact of Ambiance Conditions on Customer Satisfaction in the Restaurant Industry ; Case Study of Debonairs Pizza Outlets in Botswana

This paper presents a study of the impact of ambient conditions on customer satisfaction at Debonairs Pizza outlets in Gaborone Botswana. Managers at Debonairs Pizza outlets are continually changing the design of restaurants, which are the colour, layout, lighting, scent, temperature and decor. However, the effect of this changed design is not fully understood whether it causes customer satisfaction or not. The objectives of the study was to establish the relationship between the scent in Debonairs Pizza outlets and customer satisfaction, to determine the relationship level between customer satisfaction and lighting at Debonairs Pizza outlets and lastly was to determine the relationship between temperature levels and customer satisfaction at Debonairs Pizza outlets.Casual research design was employed and simple random sampling was used during the study. A sample size of 81 customers was used. Data was obtained from three Debonairs Pizza outlets in Riverwalk, Main Mall and Pula Spar Mall in Gaborone, Botswana. The instrument for data collection was through personally administered closed ended questionnaires. Furthermore, SPSS software was used for regression analysis to analyse the relationship between dependent (customer satisfaction) and independent (ambient conditions) variable. Results indicated that there is a significant impact of ambient conditions on customer satisfaction at Debonairs Pizza outlets in Gaborone Botswana. Ambient scent was having a value of 0.805, lighting 0.771 and temperature 0.662 respectively. Hence it was recommended that Debonairs Pizza outlets managers should engage more into enhancing their ambient conditions as they cause customer satisfaction.
A Multiparameter Colloidal Titrations for the Determination of Cationic Polyelectrolytes

In water treatment processes and conditioning drinking water, PEs are widely used; however, their environmental impact is still doubtful, since residual concentrations increase organic matter content and represents a potential health hazard. This paper demonstrates a multiparametric study of two colloidal titration methods: spectrophotometric and zeta potential end point detection. The first one was optimized to guarantee the accuracy of cationic commercial PE quantification. It includes the indicator dose optimization using analytical criteria for competing equilibria, a calibration curve for two ranges of CPE concentration (1 - 5 ppm and 5 - 100 ppm) and the interference study of flocculant and Sn in the CPE quantification. The second method provides a physicochemical validation of the electric surface phenomena occurring during the colloidal titration and the end point detection. As an additional contribution the zeta potential titration was discussed and proposed as an alternative method for quantifying CPE when the sample is metal free.
Title Ability of HIV-1 Nef to downregulate CD 4 and HLA class I differs among viral subtypes Permalink

Background: The highly genetically diverse HIV-1 group M subtypes may differ in their biological properties. Nef is an important mediator of viral pathogenicity; however, to date, a comprehensive inter-subtype comparison of Nef in vitro function has not been undertaken. Here, we investigate two of Nef’s most well-characterized activities, CD4 and HLA class I downregulation, for clones obtained from 360 chronic patients infected with HIV-1 subtypes A, B, C or D. Results: Single HIV-1 plasma RNA Nef clones were obtained from N=360 antiretroviral-naïve, chronically infected patients from Africa and North America: 96 (subtype A), 93 (B), 85 (C), and 86 (D). Nef clones were expressed by transfection in an immortalized CD4+ T-cell line. CD4 and HLA class I surface levels were assessed by flow cytometry. Nef expression was verified by Western blot. Subset analyses and multivariable linear regression were used to adjust for differences in age, sex and clinical parameters between cohorts. Consensus HIV-1 subtype B and C Nef sequences were synthesized and functionally assessed. Exploratory sequence analyses were performed to identify potential genotypic correlates of Nef function. Subtype B Nef clones displayed marginally greater CD4 downregulation activity (p = 0.03) and markedly greater HLA class I downregulation activity (p < 0.0001) than clones from other subtypes. Subtype C Nefs displayed the lowest in vitro functionality. Inter-subtype differences in HLA class I downregulation remained statistically significant after controlling for differences in age, sex, and clinical parameters (p < 0.0001). The synthesized consensus subtype B Nef showed higher activities compared to consensus C Nef, which was most pronounced in cells expressing lower protein levels. Nef clones exhibited substantial inter-subtype diversity: cohort consensus residues differed at 25% of codons, while a similar proportion of codons exhibited substantial inter-subtype differences in major variant frequency. These amino acids, along with others identified in intra-subtype analyses, represent candidates for mediating inter-subtype differences in Nef function. Conclusions: Results support a functional hierarchy of subtype B > A/D > C for Nef-mediated CD4 and HLA class I downregulation. The mechanisms underlying these differences and their relevance to HIV-1 pathogenicity merit further investigation.
Tackling the motivation to monitor: success and sustainability of a participatory monitoring program

Monitoring of species and their ecosystem attributes is a fundamental requirement in applied ecology and conservation. However, landscape scale monitoring requires an immense effort and commitment, especially when species have a wide distribution or are migratory in nature. Participatory monitoring, whereby local communities are engaged, is increasingly being proposed to address landscape scale monitoring. Its implementation is met with many challenges related to finances, motivation of the local people, lack of trained manpower, and nondirect legal use of the species in question. It is of interest to determine what makes a participatory monitoring program interesting for locals to ensure their long term engagement. Using the unique 26-year program of hunters' observations of moose (Alces alces) in Sweden as a case study, we present the evolution of this highly successful participatory monitoring program and show that tackling the motivation to monitor, early involvement of local NGOs, social activities revolving around use of the resource, the biology and economic value of the species, and technical and practical aspects related to the monitoring, together create a successful participatory monitoring program. When users benefit directly from the resource, participate in conservation/ management decision making, socialize with other participants, and get rewards for their commitment and effective monitoring, participatory monitoring schemes can then become rewarding and sustainable.
Source Code Author Attribution Using Author’s Programming Style and Code Smells

Source code is an intellectual property and using it without author’s permission is a violation of property right. Source code authorship attribution is vital for dealing with software theft, copyright issues and piracies. Characterizing author’s signature for identifying their footprints is the core task of authorship attribution. Different aspects of source code have been considered for characterizing signatures including author’s coding style and programming structure, etc. The objective of this research is to explore another trait of authors’ coding behavior for personifying their footprints. The main question that we want to address is that “can code smells are useful for characterizing authors’ signatures? A machine learning based methodology is described not only to address the question but also for designing a system. Two different aspects of source code are considered for its representation into features: author’s style and code smells. The author’s style related feature representation is used as baseline. Results have shown that code smell can improves the authorship attribution.