The functional importance of Leu15 of human epidermal growth factor in receptor binding and activation

The biological importance of Leu15 of epidermal growth factor(EGF) is suggested by its conservation through evolution, itscritical location in the domain–domain interface of EGFand its close proximity to Arg41, a residue that is crucialfor receptor binding and activation. Mutagenesis of Leu15 ofhuman EGF (hEGF) was employed to examine the role of this residuein the ligand-receptor interaction. The relative receptor affinitiesof the hEGF variants, as determined by radioreceptor competitionassays, varied depending on the amino acid substitution. TheL15F, L15W and L15V hEGF analogues had receptor affinities 45,26 and 18% respectively of wild type hEGF. The L15A and L15Ranalogues displayed receptor affinities of only 2.4 and 1.6%relative to wild type hEGF. No binding of the L15E analoguewas detected. The relative agonist activities, as measured byreceptor tyrosine kinase stimulation assays, generally followeda similar trend. The L15F, L15W and L15V analogues stimulatedthe receptor kinase to a level (V_max) similar to that for wildtype hEGF. A striking difference was observed between the L15Aand L15R variants; although having similar binding affinities,the L15A mutant activated the receptor to only {small tilde}5%of the wild type V_max in contrast to 53% for the L15R mutant¹H-NMR analysis of the L15R and L15A mutants showed only minorstructural alterations that were not sufficient to account forthe dramatic losses in binding and agonist activities. The resultsindicate that both the size and hydrophobicity of the -branchedaliphatic side chain of Leu15 of hEGF are important in the formationof a catalytically active ligand–receptor complex.     

Optimising the T6 heat treatment for gravity cast AlSi7MgCu0.5 alloy V8 cylinder heads

Alternatives to the Standard T6 heat treatment that relied on a 5-h solution heat treatment at 490 °C were investigated in effort to reduce the heat treatment cycle time for the manufacture of AlSi7MgCu0.5 alloy V8 cylinder heads without sacrificing the mechanical properties while providing extra capacity in the heat treatment plant. The optimum T6 heat treatment process was identified to be a SHT at 545 °C for 1 h followed by ageing treatment at 180 °C for 3 h. Mechanical properties of the cylinder heads thus manufactured were markedly superior while the total heat treatment duration was reduced by 2.5 h with respect to the Standard heat treatment. This implied a capacity increase of %61 and rendered the undertaking of a major customer order possible.     

Activatable Photosensitizers: Agents for Selective Photodynamic Therapy

Recent developments in the design of bifunctional and activatable photosensitizers rejuvenate the aging field of photodynamic sensitization and photodynamic therapy. While systematic studies have uncovered new dyes that can serve as potential photosensitizers, the most promising results have come from studies aimed at gaining precise control over the location and rate of cytotoxic singlet oxygen generation. As a consequence, higher selectivities and efficiencies in photodynamic treatment protocols are now within reach. This feature article highlights the variety of approaches that have been pursued to improve photodynamic therapy and to transform simple photosensitizers into smarter theranostic agents.     

An optimum feature extraction method for texture classification

Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. In this paper a novel method, which is an intelligent system for texture classification is introduced. It used a combination of genetic algorithm, discrete wavelet transform and neural network for optimum feature extraction from texture images. An algorithm called the intelligent system, which processes the pattern recognition approximation, is developed. We tested the proposed method with several texture images. The overall success rate is about 95%.     

Forecasting of entropy production due to buoyant convection using support vector machines (SVM) in a partially cooled square cross-sectional room

Forecasting of entropy generation of laminar natural convection in a partially cooled square cross-sectional room has been performed using support vector machines (SVM). The two-dimensional room was modeled as floor heating story with a window. Values of temperature and velocities were obtained by solving governing equations of natural convection with finite difference technique and using these values entropy generation was calculated with its definition. Forecasting of entropy generation due to fluid friction irreversibility (FFI) and heat transfer irreversibility (HTI) were made with known values for unknown parameters using SVM. Thus, calculation time was extremely reduced and values were obtained even for non-convergence cases. It was observed that the SVM was a strong method to predict the entropy generation without computational fluid dynamic analysis for all cases.     

Air and oxy-fuel combustion kinetics of low rank lignites

The air and oxy-fuel combustion processes of two low-grade lignite coals were investigated by thermogravimetric analysis (TGA) method. Coals were provided from two different coal mines in the Aegean region of Turkey. Oxy-fuel combustion experiments were carried out with three different gas mixtures of 21% O₂–79% CO₂; 40% O₂–60% CO₂ and 50% O₂–50% CO₂ at 950 °C and heating rates of 10 °C/min, 20 °C/min and 40 °C/min. The kinetics of the oxy-fuel combustion of coals were studied by using four different methods namely, Coats-Redfern (model-fitting method), Friedman (FR), Flynn–Wall–Ozawa's (FWO) and Kissinger–Akahira–Sunose's (KAS) methods. The apparent activation energies of combustion process calculated by FWO method are slightly but systematically higher than that calculated by the KAS and FR methods for the oxy-fuel atmospheres. Combustion behavior of both coals in the oxy-fuel combustion environment could vary significantly, likely due to their characteristics such ash and volatile matter contents.     

Early prostate cancer diagnosis by using artificial neural networks and support vector machines

The aim of this study is to design a classifier based expert system for early diagnosis of the organ in constraint phase to reach informed decision making without biopsy by using some selected features. The other purpose is to investigate a relationship between BMI (body mass index), smoking factor, and prostate cancer. The data used in this study were collected from 300 men (100: prostate adenocarcinoma, 200: chronic prostatism or benign prostatic hyperplasia). Weight, height, BMI, PSA (prostate specific antigen), Free PSA, age, prostate volume, density, smoking, systolic, diastolic, pulse, and Gleason score features were used and independent sample t-test was applied for feature selection. In order to classify related data, we have used following classifiers; scaled conjugate gradient (SCG), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and Levenberg–Marquardt (LM) training algorithms of artificial neural networks (ANN) and linear, polynomial, and radial based kernel functions of support vector machine (SVM). It was determined that smoking is a factor increases the prostate cancer risk whereas BMI is not affected the prostate cancer. Since PSA, volume, density, and smoking features were to be statistically significant, they were chosen for classification. The proposed system was designed with polynomial based kernel function, which had the best performance (accuracy: 79%). In Turkish Family Health System, family physician to whom patients are applied firstly, would contribute to extract the risk map of illness and direct patients to correct treatments by using expert system such proposed.