Erythrocyte potassium and glutathione polymorphism determination in Saanen x Malta crossbred goats

This research is aimed at determining the erythrocyte potassium and glutathione polymorphisms and also to identify the relationship among the various blood parameters in  Saanen x Malta crossbred goat raised in Turkey. The allele gene frequencies of KH and KL associated with the potassium concentration were calculated as 0.94 and 0.06, respectively. The differences between the mean values of low and high potassium concentrations in erythrocyte were statistically significant (P < 0.01). In addition, there were some significant relationships between erythrocyte potassium types and some blood parameters such as whole blood sodium and potassium concentrations, erythrocyte sodium and potassium concentrations and total monovalent cation concentration in erythrocyte (P < 0.05). The allele gene frequencies of GSHH and GSHh related with the glutathione concentration were calculated as 0.43 and 0.57, respectively. The difference between the mean values of low and high glutathione erythrocyte concentrations were also statistically significant (P < 0.01). Finally, the significant correlation coefficient between erythrocyte sodium and potassium concentrations was observed in this study (P < 0.05).Key words: Erythrocyte potassium, glutathione, blood polymorphism, Saanen, Malta goat

Extraction of Saponins from Soapnut (Sapindus Mukorossi) and Their Antimicrobial Properties

In this study optimization of extraction conditions for saponin from Sapindus mukorossi was investigated. Results showed that polarity of the extraction solvent affects the yield percentage of the extraction process. Best yield percentage was obtained as 78.1 % at 1:10 solid-liquid ratio in aqueous ethanol solution (50% v/v). The antimicrobial properties of extracts containing saponins were investigated for different microorganisms. Minimum inhibition concentrations of extract were obtained against Escherichia coli, Staphylococcus aureus and Candida albicans. Minimum inhibition concentrations (MIC) of saponin extract ranged between 12.5 mg/mL to 25 mg/mL.nbs

Projections Onto Convex Sets (POCS) Based Optimization by Lifting

Two new optimization techniques based on projections onto convex space (POCS) framework for solving convex and some non-convex optimization problems are presented. The dimension of the minimization problem is lifted by one and sets corresponding to the cost function are defined. If the cost function is a convex function in R^N the corresponding set is a convex set in R^(N+1). The iterative optimization approach starts with an arbitrary initial estimate in R^(N+1) and an orthogonal projection is performed onto one of the sets in a sequential manner at each step of the optimization problem. The method provides globally optimal solutions in total-variation, filtered variation, l1, and entropic cost functions. It is also experimentally observed that cost functions based on lp, p<1 can be handled by using the supporting hyperplane concept

Compressive sensing using the modified entropy functional

Cataloged from PDF version of article.In most compressive sensing problems, 1 norm is used during the signal reconstruction process. In this article, a modified version of the entropy functional is proposed to approximate the 1 norm. The proposed modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregman’s row-action method for compressive sensing applications. Simulation examples with both 1D signals and images are presented. © 2013 Elsevier Inc. All rights reserved

Fairness-aware Optimal Graph Filter Design

Graphs are mathematical tools that can be used to represent complex real-world interconnected systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has attracted significant attention recently. However, it has been demonstrated that ML over graphs amplifies the already existing bias towards certain under-represented groups in various decision-making problems due to the information aggregation over biased graph structures. Faced with this challenge, here we take a fresh look at the problem of bias mitigation in graph-based learning by borrowing insights from graph signal processing. Our idea is to introduce predesigned graph filters within an ML pipeline to reduce a novel unsupervised bias measure, namely the correlation between sensitive attributes and the underlying graph connectivity. We show that the optimal design of said filters can be cast as a convex problem in the graph spectral domain. We also formulate a linear programming (LP) problem informed by a theoretical bias analysis, which attains a closed-form solution and leads to a more efficient fairness-aware graph filter. Finally, for a design whose degrees of freedom are independent of the input graph size, we minimize the bias metric over the family of polynomial graph convolutional filters. Our optimal filter designs offer complementary strengths to explore favorable fairness-utility-complexity tradeoffs. For performance evaluation, we conduct extensive and reproducible node classification experiments over real-world networks. Our results show that the proposed framework leads to better fairness measures together with similar utility compared to state-of-the-art fairness-aware baselines.Comment: 12 pages, 3 figures, 9 tables. arXiv admin note: text overlap with arXiv:2303.1145

Entropy-Functional-Based Online Adaptive Decision Fusion Framework with Application to Wildfire Detection in Video

Cataloged from PDF version of article.In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented

Prevalence and distribution of the fabella: a radiographic study in Turkish subjects

Background: The purpose of this study is to examine the plain knee radiographs in Turkish subjects in order to determine the prevalence of the fabella and analyse the differences between age, gender, laterality and its symmetry pattern. Materials and methods: Bilateral antero-posterior and lateral knee radiographs of 500 patients (250 male and 250 female subjects, 1000 knee radiographs) were randomly selected from the clinical database and retrospectively evaluated. Data on patient age, gender, and knee laterality (right–left) were evaluated from hospital records. The differences between the sesamoid bones at a particular location and the side, sex and age groups were analysed. Results: The overall prevalence of the fabella (unilateral or bilateral) was 22.8% (114 subjects). The fabella was present unilaterally in 38 (7.6%) subjects, while it was present bilaterally in 76 (15.2%) subjects. The prevalence of the fabella was similar between the body sides. The prevalence of the fabella was also similar between genders (unilateral or bilateral cases) and age groups. Conclusions: We examined the prevalence, symmetry pattern, age and gender differences in Turkish population. It is the first study performed on Turkish population with the largest sample in current literature. Prevalence of fabella is found to be 22.8% which is quite similar with other Caucasian ethnic populations

Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions

Software testing identifies defects in software products with varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. A subsequent thoroughbred systematic review was conducted on the final selected studies to answer eleven main research questions, which uncovers approaches that speak to the aforementioned germane areas of interest. The results indicate that while the machine learning approach is ubiquitous for predicting software defect severity, germane techniques central to better predictive analytics are infrequent in literature. This study is concluded by summarizing prominent study trends in a mind map to stimulate future research in the software engineering industry.publishedVersio