On the Collisional Damping of Giant Dipole Resonance

Collisional damping widths of giant dipole excitations are calculated in Thomas-Fermi approximation by employing the microscopic in-medium cross-sections of Li and Machleidt and the phenomenological Gogny force. The results obtained in both calculations compare well, but account for about 25-35% of the observed widths in $^{120}Sn$ and $^{208}Pb$ at finite temperatures.Comment: Latex, 13 pages, 4 figure

Modeling and numerical simulations of lignite char gasification with CO2: The effect of gasification parameters on internal transport phenomena

The purpose of this study is to develop an experimental data-based char particle gasification model in order to assess the effects of particle size, gasification temperature and char generation heating rate on global gasification parameters. Also, the effect of initial porosity is observed by performing parametrical numerical simulations. A continuum-based model is used to solve the gasification inside a char particle and within the external boundary layer. The intrinsic rate of CO2 gasification reaction is computed according to Langmuir-Hinshelwood (LH) mechanism. External mass transfer is modeled by Stefan-Maxwell relations, and Cylindrical Pore Interpolation Model (CPIM) is used for intra-particle molecular diffusion. In the model, all the effects due to particle internal structure changes are represented by a global conversion function, f(X) which is computed from local reaction rate values. In this study, f(X) is deduced from experimental results instead of phenomenological models almost impossible to validate. The best reproduction of the experimental gasification results is obtained for the function f(X) postulated as a summation of two Gaussian functions which represent the char particle random pore structures and their dynamics during gasification. Comparative simulation results show that the Gaussian for low conversion interval is shifted to even lower conversion values for higher gasification temperature and higher initial porosity. Thereby, the Gaussian function for low conversion rates (large particle sizes) is interpreted as representative of the diffusion-limited gasification regime in conjunction with the network of macropores and molecular diffusion rates. The modification of the pore structure due to char generation heating rates causes a shift of the second Gaussian towards higher conversion rates. It is therefore postulated that the second Gaussian function corresponds to the boundary layer diffusion-controlled regime related to available outer surface area of the particle

Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features

Emiroglu, Bulent Gursel/0000-0002-1656-6450; Cinarer, Gokalp/0000-0003-0818-6746; YURTTAKAL, Ahmet Hasim/0000-0001-5170-6466WOS:000580451100001Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I-II-III-IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II,n= 77; Grade III,n= 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification

Protective Role of Royal Jelly Against Radiation-Induced Oxidative Stress in Rats

The aim of this study was to investigate the effects of royal jelly against gamma-radiation induced oxidative damage in liver and lung tissue after total body irradiation. The adult male Sprague Dawley rats were randomly divided into six groups of sixteen animals each; group 1: control group (C), group 2: only irradiated rats (IR), group 3: irradiated rats with royal jelly administration at 25 mg/kg/day (IR+RJ25), group 4: irradiated rats with royal jelly administration at 50 mg/kg/day (IR+RJ50), group 5: only royal jelly administration at 25 mg/kg/day (RJ25), group 6: only royal jelly administration at 50 mg/kg/day (RJ50). Royal jelly (RJ) was administered at a dose of 25 and 50-mg/kg body weight, by gavage for 10 days prior to irradiation and 10 days after irradiation. On the tenth day of study, radiotherapy was applied to the whole-body by single fraction at a dose of 6 Gy. Half of rats were sacrificed at 24 hours and 10 days after irradiation under ether anesthesia. Blood samples were collected and analysed for alanine aminotransferase, aspartate aminotransferase, triygliceride, total cholesterol and gamma glutamyl transpeptidase levels. The lung and liver samples were stored for the measurement of malondialdehyde, glutathione peroxidase, superoxide dismutase and catalase activities. Rats exposed to whole-body irradiation induced a marked liver failure, characterized with a significant increase in serum AST, ALT, cholesterol and triglyceride concentrations, and also they had higher lung and liver MDA and lower GSH-Px, CAT and SOD (p<0.001). Administration of royal jelly resulted in a significant decreased in oxidative stress parameters and biochemical parameters, and certainly increased antioxidant activities. Furthermore, pre- and post-treatment with RJ was more effective than pre-treatment with RJ

A COMPARATIVE STUDY ON SEGMENTATION AND CLASSIFICATION IN BREAST MRI IMAGING

Cinarer, Gokalp/0000-0003-0818-6746; Erbay, Hasan/0000-0002-7555-541XWOS: 000455271800005Background: Breast cancer is the type of cancer that develops from cells in the breast tissue. The breast cancer is leading cancer in women. One in every eight to nine women has breast cancer at some point during their lifetime. Computer-Aided Diagnosis (CAD) Technology is getting more important to assist radiologists not only to detect breast cancer tumor but also to interpret lesioned regions. The CAD, as a second reader in the clinic, improves the classification of malignant and benign lesions. On the other hand, Magnetic Resonance Imaging (MRI) is a highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. In MRI image analysis, the segmentation images are important objective because accurate measurement of the delineation of the regions of interest (ROI) is critical for the breast cancer diagnosis and treatment. Herein, by using MRI scans, we propose a semi-automated CAD system prototype to assist radiologists in detecting breast cancer tumors and interpreting lesioned regions. The prototype, first, pre-processes the raw selected suspicious region to reduce the noises and to reveal the structure. Later, using Expectation Maximization (EM), the prototype segments the pre-processed region. After that, we use the Discrete Wavelet Transform (DWT) for providing efficient multi-resolution sub and decomposition of signals. Then Random Forest Algorithm is used for feature selection. Finally, Naive Bayes, Linear Discriminant Analysis and C4.5 Decision Tree Algorithms are used to classify the features of the ROI in the diagnosis analysis. We tested the prototype CAD on 105 patients, among them, 53 are benign and 52 malign. 80% of the images are allocated for training and 20% of images reserved for testing. The CAD classified 20 patients correctly in case of 5 fold cross-validation. Only one patient is misclassified. The computer-aided diagnosis system with the C4.5 has accuracy 95.24%. Furthermore, C4.5 classifies the breast cancer tumors better than Naive Bayes and Linear Discriminant Analysis. We tested the prototype CAD on 105 patients, among them, 53 are benign and 52 malign. The computer-aided diagnosis system with the C4.5 has accuracy 95.24%. Furthermore, C4.5 classifies the breast cancer tumors better than Naive Bayes and Linear Discriminant Analysis

Antioxidant potentials and anticholinesterase activities of methanolic and aqueous extracts of three endemic Centaurea L. species

WOS: 000317536900038PubMed: 23357566The methanol and aqueous extracts of three endemic Centaurea species (C. polypodiifolia var. pseudobehen, C pyrrhoblephara and C antalyense) were investigated for their antioxidant and cholinesterase inhibitory activities. The antioxidant activities of these extracts were evaluated by in vitro models including, phosphomolybdenum assay, free radical scavenging assays (DPPH and ABTS), beta-carotene/linoleic acid test system, metal chelating assay, FRAP assay, ferric and cupric reducing power. Cholinesterase inhibitory activities were examined using Ellman's calorimetric method. Total phenol, flavonoid, and saponin contents were also measured. Among the six Centaurea extracts evaluated, the highest antioxidant abilities were obtained from C polypodiifolia var. pseudobehen. Methanolic extracts from C polypodiifolia var. pseudobehen and C antalyense had a noticeable inhibition towards AChE and BChE. These findings suggest that Centaurea species could be an anticholinesterase agent and antioxidant resource in some industries, such as food, pharmacology, and cosmetics. (C) 2013 Elsevier Ltd. All rights reserved.Selcuk University Scientific Research Foundation (BAP); [11401066]This study was supported financially as a project (11401066). The authors thank Selcuk University Scientific Research Foundation (BAP) for providing financial support for this study. The authors also want to thank Dr. Harun Simsek for proofreading the present manuscript

A new morphological approach for removing acid dye from leather waste water: Preparation and characterization of metal-chelated spherical particulated membranes (SPMs)

WOS: 000350087900030PubMed ID: 25585142In this study, p(HEMA-GMA) poly(hydroxyethyl methacrylate-co-glycidyl methacrylate) spherical particulated membranes (SPMs) were produced by UV-photopolymerization and the synthesized SPMs were coupled with iminodiacetic acid (IDA). Finally the novel SPMs were chelated with Cr(III) ions as ligand and used for removing acid black 210 dye. Characterizations of the metal-chelated SPMs were made by SEM, FTIR and swelling test. The water absorption capacities and acid dye adsorption properties of the SPMs were investigated and the results were 245.0, 50.0, 55.0 and 51.9% for p(HEMA), p(HEMA-GMA), p(HEMA-GMA)-IDA and p(HEMA-GMA)-IDA-Cr(III) SPMs respectively. Adsorption properties of the p(HEMA-GMA)-IDA-Cr(III) SPMs were investigated under different conditions such as different initial dye concentrations and pH. The optimum pH was observed at 43 and the maximum adsorption capacity was determined as 885.14 mg/g at about 8000 ppm initial dye concentration. The concentrations of the dyes were determined using a UV/Vis Spectrophotometer at a wavelength of 435 nm. Reusability of p(HEMA-GMA)-IDA-Cr(III) SPMs was also shown for five adsorption-desorption cycles without considerable decrease in its adsorption capacity. Finally, the results showed that the metal-chelated p(HEMA-GMA)-IDA SPMs were effective sorbent systems removing acid dye from leather waste water. (C) 2014 Elsevier Ltd. All rights reserved

Synthesis and Biological Evaluation of New Quinoline-Based Thiazolyl Hydrazone Derivatives as Potent Antifungal and Anticancer Agents

WOS: 000423791300010Background: In medicinal chemistry, thiazoles have gained great importance in anti-fungal and anticancer drug design and development. Objectives: The aim of this study was to synthesize new quinoline-based thiazolyl hydrazone derivatives and evaluate their anticandidal and anticancer effects. Methods: New thiazolyl hydrazone derivatives were evaluated for their anticandidal effects using disc diffusion method. Ames MPF assay was carried out to determine the genotoxicity of the most effective antifungal derivative. MTT assay was also performed to assess the cytotoxic effects of the compounds on A549 human lung adenocarcinoma, HepG2 human hepatocellular carcinoma, MCF7 human breast adenocarcinoma and NIH/3T3 mouse embryonic fibroblast (healthy) cell lines. Results: 4-(4-Fluorophenyl)-2-(2-((quinolin-4-yl) methylene) hydrazinyl) thiazole (4) showed antifungal activity against Candida albicans and Candida krusei in the concentration of 1 mg/mL. In MTT and Ames MPF tests, it was determined that compound 4 did not show cytotoxic and genotoxic effects. MTT assay indicated that 4-(naphthalen-2-yl)-2-(2-((quinolin-4-yl) methylene) hydrazinyl) thiazole (10) showed more selective anticancer activity than cisplatin against A549 and MCF-7 cell lines. Besides, 4-(4-chlorophenyl)-2-(2-((quinolin-4-yl) methylene) hydrazinyl) thiazole (5) exhibited more selective anticancer activity than cisplatin against HepG2 cell line. Conclusion: Due to their high selectivity index, these compounds are considered as candidate compounds to participate in further research