Determination of oxidative stress levels and some antioxidant enzyme activities in prostate cancer

In this study, the antioxidant enzyme activities such as (SOD, GSH, and CAT) and malondialdehyde (MDA) level which is the end product of lipid peroxidation, were determined from the serum samples taken from patients diagnosed with prostate cancer Van Yuzuncu Y?l University Medical Faculty of Educational Research and Training Hospital and ?stanbul Bagcilar Education Research Hospital. The SOD, GSH, and CAT activity of patient groups was found significantly lower than the healthy control group in patients with prostate cancer (p?相似文献   

Particle swarm optimization based Liu-type estimator

In this study, a new method for the estimation of the shrinkage and biasing parameters of Liu-type estimator is proposed. Because k is kept constant and d is optimized in Liu’s method, a (k, d) pair is not guaranteed to be the optimal point in terms of the mean square error of the parameters. The optimum (k, d) pair that minimizes the mean square error, which is a function of the parameters k and d, should be estimated through a simultaneous optimization process rather than through a two-stage process. In this study, by utilizing a different objective function, the parameters k and d are optimized simultaneously with the particle swarm optimization technique.     

Parental Support for Basic Psychological Needs and Happiness: The Importance of Sense of Uniqueness

Past empirical research relying on self-determination theory (SDT) has consistently shown that parental support of basic psychological needs (BPN) is associated with adolescent happiness. Yet, the specific mechanisms accounting for this link are still undetermined. The present study aimed to address this gap in the literature by testing a theoretical model proposing that adolescents’ satisfaction of BPN in life and sense of uniqueness mediate the association of parental support for BPN and happiness. The analyses relied on structural equation modeling and bootstrapping procedures and found support for the model. The theoretical implications of the model for SDT and applied considerations to improve the well-being of adolescents are discussed. Also, suggestions for future research that could further improve our understanding of the dynamic interplay between BPN, sense of uniqueness and happiness are presented.     

Run statistics in a sequence of arbitrarily dependent binary trials

Let {Z _i } _i≥1 be an arbitrary sequence of trials with two possible outcomes either success (1) or failure (0). General expressions for the exact distributions of runs, both success and failure, in Z ₁, . . . , Z _n are presented. Our method is based on the use of joint distribution of success and failure run lengths and unifies the results on distribution of runs. As a special case of our results we obtain the distributions of runs for various binary sequences. As illustrated in the paper the results enable us to derive the distribution of runs for binary trials arising in urn models.     

A new per-field classification method using mixture discriminant analysis

In this study, a new per-field classification method is proposed for supervised classification of remotely sensed multispectral image data of an agricultural area using Gaussian mixture discriminant analysis (MDA). For the proposed per-field classification method, multivariate Gaussian mixture models constructed for control and test fields can have fixed or different number of components and each component can have different or common covariance matrix structure. The discrimination function and the decision rule of this method are established according to the average Bhattacharyya distance and the minimum values of the average Bhattacharyya distances, respectively. The proposed per-field classification method is analyzed for different structures of a covariance matrix with fixed and different number of components. Also, we classify the remotely sensed multispectral image data using the per-pixel classification method based on Gaussian MDA.     

Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market

Artificial intelligence procedures such as artificial neural networks (ANNs), genetic algorithms and particle swarm optimization and other procedures such as fuzzy clustering have been successfully used in the various stages of different fuzzy time-series forecasting approaches. Fuzzy clustering, genetic algorithm and particle swarm optimization are generally used in the fuzzification stage, and this simplifies the applicability of this stage and makes the fuzzy time-series approach more systematic. ANNs have also been applied successfully in the fuzzy relationship determination stage. In this study, we propose a new hybrid fuzzy time-series approach in which fuzzy c-means clustering procedure is employed in the fuzzification stage and feed-forward neural networks are used in the fuzzy relationship determination stage. This study also includes an empirical analysis pertaining to the forecasting of Index 100 for the stocks and bonds exchange market of Istanbul.     

The Role of Progress Factors Explaining Inefficiencies in Transition Countries

This paper examines whether progress in transition has a significant effect on the economic efficiency for 24 transition countries from 1990 to 2006. It uses nine progress factors to analyze the role of the progress factors to explain inefficiencies. It also questions the effect of the transition countries that recently joined the European Union on efficiency. The results suggest that the average efficiency scores for EU-N10 are much higher than the average efficiency scores for SEE/CIS. The scores increase over time for both groups of transition countries. Reforms also contribute to efficiency in general.     

A Decision Support Tool for Predicting Patients at Risk of Readmission: A Comparison of Classification Trees,Logistic Regression,Generalized Additive Models,and Multivariate Adaptive Regression Splines

The number of emergency (or unplanned) readmissions in the United Kingdom National Health Service (NHS) has been rising for many years. This trend, which is possibly related to poor patient care, places financial pressures on hospitals and on national healthcare budgets. As a result, clinicians and key decision makers (e.g., managers and commissioners) are interested in predicting patients at high risk of readmission. Logistic regression is the most popular method of predicting patient‐specific probabilities. However, these studies have produced conflicting results with poor prediction accuracies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting emergency readmissions within 45 days after been discharged from hospital. We also examined the predictive ability of two other types of data‐driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 963 patients readmitted to hospitals with chronic obstructive pulmonary disease and asthma. We used repeated split‐sample validation: the data were divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using a number of performance measures, such as area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1,000 times—the initial data set was divided into derivation and validation samples 1,000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1,000 derivation samples was .928, while the mean ROC curve area of a logistic regression model was .924. Our study shows that logistic regression model and regression trees had performance comparable to that of more flexible, data‐driven models such as GAMs and MARS. Given that the models have produced excellent predictive accuracies, this could be a valuable decision support tool for clinicians (healthcare managers, policy makers, etc.) for informed decision making in the management of diseases, which ultimately contributes to improved measures for hospital performance management.