Antecedents of corporate sustainability performance in Turkey: The effects of ownership structure and board attributes on non-financial companies

The discourse of corporate sustainability performance (CSP) has created an increasing motivation for companies to improve their competitive advantage. This study examines the drivers leading to a high level of CSP within non-financial Turkish companies listed in the Borsa Istanbul Sustainability Index. Drawing on both stakeholder and agency theories, we formulate a set of hypotheses that link CSP with ownership structure, board diversity, and firm-specific characteristics. Based on logit and probit models, the empirical results tend to confirm the positive influence of foreign and institutional ownerships in shaping CSP and indicate that CSP is positively linked with board size and the proportion of independent board members. Further, the findings show that companies with a leading level of CSP have a lower return than companies with mediocre CSP based on a market-based measure, Tobin’s Q.Q1WOS:0005795008002252-s2.0-8509157100

Detection of Chronic Kidney Disease by Using Ensemble Classifiers

Chronic kidney disease is a major health problem that affect the lives of millions of people around the world and causes serious economical, social and medical problems. Chronic kidney disease can be detected with several automatic diagnosis systems. In this study, we apply Adaboost, Bagging and Random Subspaces ensemble learning algorithms for the diagnosis of chronic kidney diseases. Decision tree based classifiers are used in the decision stage. The classification performances are evaluated with kappa and accuracy criteria. Considering the performance analyses of the proposed systems, it is observed that ensemble learning classifiers provide better classification performance than individual classifiers

Chronic Kidney Disease Prediction with Reduced Individual Classifiers

Chronic kidney disease is a rising health problem and involves conditions that decrease the efficiency of renal functions and that damage the kidneys. Chronic kidney disease may be detected with several classification techniques, and these have been classified using various features and classifier combinations. In this study, we applied seven different classifiers (Naive Bayes, HoeffdingTree, RandomTree, REPTree, Random Subspaces, Adaboost, and IBk) for the diagnosis of chronic kidney disease. The classification performances are evaluated with five different performance metrics, i.e., accuracy, kappa, mean absolute error (MAE), root mean square error (RMSE), and F measures. Considering the classification performance analyses of these methods, six reduced features provide a better and more rapid classification performance. Seven individual classifiers are applied to the six features and the best results are obtained using individual random tree and IBk classifiers

Detection of Chronic Kidney Disease by Using Adaboost Ensemble Learning Approach

Chronic kidney disease can be detected with several automatic diagnosis systems. In this study, chronic kidney diseases are diagnosed with Adaboost ensemble learning algorithm. Decision tree based classifiers are used in the diagnosis. The classification performance are evaluated with kappa, mean absolute error (MAE), root mean squared error (RMSE) and area under curve (AUC) criterias. Considering the performance analyses, it is observed that adaboost ensemble learning algorithm provides better classification performance than individual classification

Morphologic Based Feature Extraction for Arrhythmia Beat Detection

Heart disease is one of the diseases which has highest mortality rate recently. Heart's electrical activity examination and interpretation are very important for the understanding of diseases. In this study, electrocardiogram signals are analyzed, then patient's healthy and arrhythmia beats are extracted. RR, QRS, Skewness and Linear Predictive Coding coefficients of the signals are considered for classification of the data. K-NN, Random SubSpaces, Naive Bayes and K-Star classifiers are used. The highest accuracy is obtained with the K-NN algorithm (98.32%). At the second stage of the K-NN algorithm, accuracy levels are examined by changing the 'k' parameter

Detection of proteomic alterations at different stages in a Huntington's disease mouse model via matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) imaging

Huntington's disease (HD) is a progressive and irreversible neurodegenerative disease leading to the inability to carry out daily activities and for which no cure exists. The underlying mechanisms of the disease have not been fully elucidated yet. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) allows the spatial information of proteins to be obtained upon the tissue sections without homogenisation. In this study, we aimed to examine proteomic alterations in the brain tissue of an HD mouse model with MALDI-MSI coupled to LC–MS/MS system. We used 3-, 6- and 12-month-old YAC128 mice representing pre-stage, mild stage and pathological stage of the HD and their non-transgenic littermates, respectively. The intensity levels of 89 proteins were found to be significantly different in YAC128 in comparison to their control mice in the pre-stage, 83 proteins in the mild stage, and 82 proteins in the pathological stage. Among them, Tau, EF2, HSP70, and NogoA proteins were validated with western blot analysis. In conclusion, the results of this study have provided remarkable new information about the spatial proteomic alterations in the HD mouse model, and we suggest that MALDI-MSI is an excellent technique for identifying such regional proteomic changes and could offer new perspectives in examining complex diseases

Proteomic alterations in the cerebellum and hippocampus in an Alzheimer's disease mouse model: alleviating effect of palmatine

Alzheimer's disease (AD) is one of the most prevalent diseases that lead to memory deficiencies, severe behavioral abnormalities, and ultimately death. The need for more appropriate treatment of AD continues, and remains a sought-after goal. Previous studies showed palmatine (PAL), an isoquinoline alkaloid, might have the potential for combating AD because of its in vitro and in vivo activities. In this study, we aimed to assess PAL's therapeutic potential and gain insights into the working mechanism on protein level in the AD mouse model brain, for the first time. To this end, PAL was administered to 12-month-old 5xFAD mice at two doses after its successful isolation from the Siberian barberry shrub. PAL (10 mg/kg) showed statistically significant improvement in the memory and learning phase on the Morris water maze test. The PAL's ability to pass through the blood-brain barrier was verified via Multiple Reaction Monitoring (MRM). Label-free proteomics analysis revealed PAL administration led to changes most prominently in the cerebellum, followed by the hippocampus, but none in the cortex. Most of the differentially expressed proteins in PAL compared to the 5xFAD control group (ALZ) were the opposite of those in ALZ in comparison to healthy Alzheimer's littermates (ALM) group. HS105, HS12A, and RL12 were detected as hub proteins in the cerebellum. Collectively, here we present PAL as a potential therapeutic candidate owing to its alleviating effect in 5xFAD mice on not only cognitive impairment but also proteomes in the cerebellum and hippocampus

CdZnTe BULK-CRYSTAL GROWTH AND SURFACE PROCESSING TECHNOLOGY AT METU-CGL

The study of Cd1-xZnxTe (Cadmium Zinc Telluride) bulk-crystal growth and surface processing technology at the Middle East Technical University (METU) began in 2012. The initial R&D efforts were started with the growing of CdZnTe ingots up to a size of 15 mm in diameter in a three-zone vertical Bridgman furnace located in a limited laboratory area of 15 m2. Following promising development in terms of single crystal yield and the crystal growth process, a new vertical gradient freeze (VGF) multi-zone furnace setup was designed and developed to accommodate the production of 60 mm diameter CdZnTe ingots. The entire furnace setup is located in a newly founded 90 m2 laboratory named the METU Crystal Growth Laboratory (METU-CGL) in 2013. The laboratory is fully dedicated to the CdZnTe material growth and surface processing technology. Currently, METU-CGL is capable of producing 60 mm diameter CdZnTe ingots with one large grain and a few small grains. CdZnTe material is continuously grown in order to serve as either a substrate material (Cd0.96Zn0.04Te) for infrared detectors or an active material (Cd0.90Zn0.10Te) for X-ray/Gamma-ray detectors. As a typical yield, 2-3 oriented wafers per radial slice are retrieved from the grown ingots. The target wafer dimensions are 20 mm x 20 mm; however, larger or smaller crystals can be obtained based on the application of interest. The crystalline quality of the produced crystals is way below 50 arcsec of FWHM (Full width at half maximum) values from the DCRC (Double crystal rocking curves) measurements and the EPD (Etch-pit density) values are typically mid-104/cm2. Infrared (IR) transmission of the home-grown CdZnTe crystals is exceeding 60% and stays constant within 2-20 µm wavelength interval showing that the crystals have low density of inclusions and precipitates. Not only limited to CdZnTe bulk growth technology, the METU-CGL is also capable of slicing and surface processing technologies including optimized lapping, rough mechanical polishing, and performing final chemo-mechanical polishing steps with extreme care regarding surface roughness and subsurface damage. Achievable surface roughness values of produced wafers are well below 0.5 nm (Rrms). Various state-of-the-art characterization techniques including HRTEM (High-resolution transmission electron microscopy) and APT (Atom probe tomography) were conducted to study nanoscale defects in CdZnTe as a material property. This paper reviews many aspects of CdZnTe bulk-growth, surface finishing, and characterization technologies at METU-CGL as well as the laboratory infrastructure itself

PTK-7 Expression in Gastric Cancer: A Prognostic Determinant

Background: Protein tyrosine kinase-7, a regulatory protein in the Wnt signaling pathway, was highly overexpressed in various cancer types and assumed to be related to prognosis