An Introduction to the Special Issue on Numerical Techniques Meet with OR - Part II

The special issue: "Numerical Techniques Meet with OR" of the Foundations of Computing and Decision Sciences consists of two parts which are of the main theme of numerical techniques and their applications in multi-disciplinary areas. The first part of this special issue was already collected in the FCDS Vol. 46, issue 1. In this second part of our special issue editorial, a description of the special issue presents numerical methods which can be used as alternative techniques for Scientific Computing and led Operational Research applications in many fields for further investigation

A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction

Heart disease is a general term for all of types of the disorders which is affects the heart. This research aims to compare several classification algorithms known as the C4.5 algorithm, Naïve Bayes, and Support Vector Machine. The algorithm is about to optimize of the heart disease predicting by applying Particle Swarm Optimization (PSO). Based on the test results, the accuracy value of the C4.5 algorithm is about 74.12% and Naïve Bayes algorithm accuracy value is about 85.26% and the last the Support Vector Machine algorithm is about 85.26%. From the three of algorithms above then continue to do an optimization by using Particle Swarm Optimization. The data is shown that Naïve Bayes algorithm with Particle Swarm Optimization has the highest value based on accuracy value of 86.30%, AUC of 0.895 and precision of 87.01%, while the highest recall value is Support Vector Machine algorithm with Particle Swarm Optimization of 96.00%. Based on the results of the research has been done, the algorithm is expected can be applied as an alternative for problem solving, especially in predicting of the heart disease

A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction

Heart disease is a general term for all of types of the disorders which is affects the heart. This research aims to compare several classification algorithms known as the C4.5 algorithm, Naïve Bayes, and Support Vector Machine. The algorithm is about to optimize of the heart disease predicting by applying Particle Swarm Optimization (PSO). Based on the test results, the accuracy value of the C4.5 algorithm is about 74.12% and Naïve Bayes algorithm accuracy value is about 85.26% and the last the Support Vector Machine algorithm is about 85.26%. From the three of algorithms above then continue to do an optimization by using Particle Swarm Optimization. The data is shown that Naïve Bayes algorithm with Particle Swarm Optimization has the highest value based on accuracy value of 86.30%, AUC of 0.895 and precision of 87.01%, while the highest recall value is Support Vector Machine algorithm with Particle Swarm Optimization of 96.00%. Based on the results of the research has been done, the algorithm is expected can be applied as an alternative for problem solving, especially in predicting of the heart disease

Use of social network sites among depressed adolescents

Social network sites (SNSs) are relatively new phenomena, and the relationship between SNSs and psychopathology remains unclear. The purpose of this study was to evaluate the type of SNSs depressed adolescents use and the incidence of depressive disclosure on SNSs among them. The study was designed to be cross-sectional. The sample consisted of 53 adolescents diagnosed with depressive disorder, as confirmed by K-SADS-PL, and 55 non-depressed adolescents. The Children's Depression Inventory, Social Anxiety Scale and Social Network Use Questionnaire were administered. The primary finding was that the amount of time spent on the Internet and on SNSs was significantly higher among depressed adolescents than non-depressed adolescents. Additionally, depressed adolescents reported significantly higher disclosure of anhedonia, worthlessness, guilt, loss of concentration, irritability and thoughts of suicide on SNSs. The intensity of the depression sharing was significantly higher in the depressed group. Depressed young people use social networks to express their symptoms. Adolescents' disclosure on social networks may be able to guide relatives, friends and mental health professionals

Comparison of microfluid sperm sorting chip and density gradient methods for use in intrauterine insemination cycles

Objective: To compare the effect of microfluiding sperm sorting chip and density gradient methods on ongoing pregnancy rates (PRs) of patients undergoing IUI

Loss-of-Function Mutations in PNPLA6

Context: Gordon Holmessyndrome (GHS) is characterized by cerebellar ataxia/atrophy and normosmic hypogonadotropic hypogonadism (nHH). The underlying pathophysiology of this combined neurodegeneration and nHH remains unknown. Objective: We aimed to provide insight in to the disease mechanism in GHS. Methods: We studied a cohort of six multiplex families with GHS through autozygosity mapping and whole exome sequencing. Results: We identified six patients from three independent families carrying loss-of-function mutations in PNPLA6, which encodes neuropathy target esterase (NTE), a lysophospholipase that maintains intracellular phospholipid homeostasis by converting lysophosphatidylcholine (LPC) to glycerophosphocholine. Wild-type PNPLA6, but not PNPLA6 bearing these mutations, rescued a well established Drosophila neurodegenerative phenotype caused by the absence of sws, the fly ortholog of mammalian PNPLA6. Inhibition of NTE activity in the LβT2 gonadotrope cell line diminished LH response to GnRH by reducing GnRH-stimulated LH exocytosis, without affecting GnRH receptor signaling or LHβ synthesis. Conclusion: These results suggest that NTE-dependent alteration of phosholipid homeostasis in GHS causes both neurodegeneration and impaired LH release from pituitary gonadotropes leading to nHH.</p

Therapeutic Targeting of AXL Receptor Tyrosine Kinase Inhibits Tumor Growth and Intraperitoneal Metastasis in Ovarian Cancer Models

Despite substantial improvements in the treatment strategies, ovarian cancer is still the most lethal gynecological malignancy. Identification of drug treatable therapeutic targets and their safe and effective targeting is critical to improve patient survival in ovarian cancer. AXL receptor tyrosine kinase (RTK) has been proposed to be an important therapeutic target for metastatic and advanced-stage human ovarian cancer. We found that AXL-RTK expression is associated with significantly shorter patient survival based on the The Cancer Genome Atlas patient database. To target AXL-RTK, we developed a chemically modified serum nuclease-stable AXL aptamer (AXL-APTAMER), and we evaluated its in vitro and in vivo antitumor activity using in vitro assays as well as two intraperitoneal animal models. AXL-aptamer treatment inhibited the phosphorylation and the activity of AXL, impaired the migration and invasion ability of ovarian cancer cells, and led to the inhibition of tumor growth and number of intraperitoneal metastatic nodules, which was associated with the inhibition of AXL activity and angiogenesis in tumors. When combined with paclitaxel, in vivo systemic (intravenous [i.v.]) administration of AXL-aptamer treatment markedly enhanced the antitumor efficacy of paclitaxel in mice. Taken together, our data indicate that AXL-aptamers successfully target in vivo AXL-RTK and inhibit its AXL activity and tumor growth and progression, representing a promising strategy for the treatment of ovarian cancer

Efficacy of regorafenib in the second-and third-line setting for patients with advanced hepatocellular carcinoma: A real life data of multicenter study from Turkey

Purpose: After failure of the first-line sorafenib treatment in advanced or metastatic stage hepatocellular carcinoma (HCC), regorafenib is one of the newly-approved targeted agents. We aimed to evaluate the efficacy of regorafenib in patients with advanced HCC treated in the secondor third-line setting