The Impact of Body Mass Index on Growth, Schooling, Productivity, and Savings: A Cross-Country Study

Gelir ve sağlık arasındaki ilişkiyi öne çıkan büyüme göstergeleri ve bilişsel yetenek aracılığıyla inceliyoruz. Bilişsel yetenek beslenme durumu ile temsil edilir. Beslenme durumu için proxy değişkeni BMI'dir. Bu ilişkiyi tahmin etmek için, bilişsel yetenekle güçlü bir şekilde ilişkili olan zaman tercih oranının kübik spesifikasyonunda indirgenmiş form denklemini kullanıyoruz. Kullanılan büyüme göstergeleri, kişi başına GSYİH, okullaşma, genel ve üretim üretkenlikleri ve tasarruflardır. Modellerimizi 1980-2009 dönemi için dengeli panel verilerle FE, GMM tahmin edicileri ve uzun fark OLS ve IV tahminlerini kullanarak tahmin ediyoruz. Tüm öne çıkan büyüme göstergeleri ile BMI arasındaki ilişkinin ters U şeklinde olduğu sonucuna varıyoruz. Başka bir deyişle, bilişsel yetenek, büyümeyi ve ekonomik gelişmeyi ancak sağlıklı bir durumda ilerletme konusunda önemli bir potansiyele sahiptir.We examine the relationship between wealth and health through prominent growth indicators and cognitive ability. Cognitive ability is represented by nutritional status. The proxy variable for nutritional status is BMI. We use the reduced form equation in the cubic specification of time preference rate, strongly related to cognitive ability, to estimate this relationship. The growth indicators utilized are GDP per capita, schooling, overall and manufacturing productivities, and savings. We estimate our models using the FE, GMM estimators, and long difference OLS and IV estimation through balanced panel data for the 1980-2009 period. We conclude that the relationship between all prominent growth indicators and BMI is inverse U-shaped. In other words, cognitive ability has a significant potential to progress growth and economic development only in a healthy status

Enrichment and isolation of anoxygenic phototrophic bacteria in Winogradsky column

The aim of the study is to isolate the anoxygenic photothophic bacteria from mud and soil obtained from Denizli Saraykoy and to determine bacteriochlorophyll content of the isolate. For this purpose samples were enriched under sunlight in Winogradsky column at room temperature for 2-3 months. Pure culture was obtained by decimal dilutions in Pfennig's medium and screened for maximum adsorption spectrum between 400-1100 nm. The absorption maxima of cell suspensions of isolated strains are 506-518, 806-810 and 854-865 nm.Anoksigenik fotosentetik bakterilerin wınogradsky kolonunda zenginleştirilmesi ve izolasyonu. Bu çalışmanın amacı Denizli Sarayköy’den alınan toprak ve çamur örneklerinden anoksigenik fototrofik bakteri izolasyonu ve saf kültür haline getirilen izolatın bakterioklorofil içeriğini belirlemektir. Bu amaçla örnekler, Winogradsky kolonunda, oda koşullarında 2-3 ay günışığında inkübe edilerek zenginleştirilmiştir. Phening’s ortamında ondalık seyreltmeler yapılarak saf kültür elde edilmiştir. Elde edilen izolatın 400-1100 nm dalga boyu aralığında adsorbsiyon spektrumları taranmış ve 506-518, 806-810 ve 854-865 nm.’de maksimum absorbans verdiği bulunmuştu

A Consistency-Based Feature Selection Method Allied with Linear SVMs for HIV-1 Protease Cleavage Site Prediction

<div><p>Background</p><p>Predicting type-1 Human Immunodeficiency Virus (HIV-1) protease cleavage site in protein molecules and determining its specificity is an important task which has attracted considerable attention in the research community. Achievements in this area are expected to result in effective drug design (especially for HIV-1 protease inhibitors) against this life-threatening virus. However, some drawbacks (like the shortage of the available training data and the high dimensionality of the feature space) turn this task into a difficult classification problem. Thus, various machine learning techniques, and specifically several classification methods have been proposed in order to increase the accuracy of the classification model. In addition, for several classification problems, which are characterized by having few samples and many features, selecting the most relevant features is a major factor for increasing classification accuracy.</p><p>Results</p><p>We propose for HIV-1 data a consistency-based feature selection approach in conjunction with recursive feature elimination of support vector machines (SVMs). We used various classifiers for evaluating the results obtained from the feature selection process. We further demonstrated the effectiveness of our proposed method by comparing it with a state-of-the-art feature selection method applied on HIV-1 data, and we evaluated the reported results based on attributes which have been selected from different combinations.</p><p>Conclusion</p><p>Applying feature selection on training data before realizing the classification task seems to be a reasonable data-mining process when working with types of data similar to HIV-1. On HIV-1 data, some feature selection or extraction operations in conjunction with different classifiers have been tested and noteworthy outcomes have been reported. These facts motivate for the work presented in this paper.</p><p>Software availability</p><p>The software is available at <a href="http://ozyer.etu.edu.tr/c-fs-svm.rar" target="_blank">http://ozyer.etu.edu.tr/c-fs-svm.rar</a>.</p><p>The software can be downloaded at <a href="http://esnag.etu.edu.tr/software/hiv_cleavage_site_prediction.rar" target="_blank">esnag.etu.edu.tr/software/hiv_cleavage_site_prediction.rar</a>; you will find a readme file which explains how to set the software in order to work.</p></div