Determination of Bioactive Compounds and Mineral Contents of Seedless Parts and Seeds of Grapes

In this study, phenolic compounds, minerals, total flavonoids, total phenolic contents and antioxidant activities of the seedless parts (pulp+skin) and seeds of table and wine grapes were determined. Also, the total oil, tocopherol contents and fatty acid composition of seed oils of table and wine grapes were investigated. The highest total phenolic content of the grape pulp was found in Trakya ilkeren (199.063mg/100 g), while total flavonoid and antioxidant activity of the pulp was determined at a high level in Red Globe (6.810 mg/g, 90.948%). Antioxidant activity, and the total phenolic and flavonoid contents of grape seeds varied between 86.688 and 90.974%, 421.563 and 490.625 mg GAE/100 g, and 90.595 and 145.595 mg/g respectively (p < 0.05). Generally, the main phenolic compounds of all grape pulps and seeds were gallic acid, 3,4- dihydroxybenzoic acid, (+)-catechin and 1,2-dihydroxybenzene. In addition, the oil contents of grape seeds ranged from 5.275 (Çavuş) to 13.881% (Çınarlı karası) (p < 0.05). The major fatty acids of grape seed oils were linoleic, oleic and palmitic acid. The seed oil of the Trakya ilkeren variety was rich in tocopherols in comparison with the other varieties. The major minerals of both the seedless parts and the seeds were determined as K, Ca, P, S, Mg

Forecasting of Turkey inflation with hybrid of feed forward and recurrent artifical neural networks

Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir. Öngörülerin doğru bir şekilde elde edilmesi daha doğru kararlara neden olacaktır. Enflasyon öngörüsü için literatürde çeşitli zaman serileri teknikleri kullanılmıştır. Son yıllarda zaman serisi öngörü probleminde esnek modelleme yeteneği nedeniyle, Yapay Sinir Ağları (YSA) tercih edilmektedir. Yapay sinir ağları doğrusal veya eğrisel belirli bir model kalıbı, durağanlık ve normal dağılım gibi ön koşullara ihtiyaç duymadığından herhangi bir zaman serisine kolaylıkla uygulanabilmektedir. Bu çalışmada Tüketici Fiyat Endeksi (TUFE) için ileri ve geri beslemeli yapay sinir ağları yaklaşımı kullanılarak öngörüler elde edilmiştir. Çözümlemede kullanılan YSA modellerinin öngörülerinin girdi olarak kullanıldığı, YSA’ya dayalı yeni bir melez yaklaşım önerilmiştir.Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI). A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data

Pancreatic ductal adenocarcinoma and chronic pancreatitis may be diagnosed by exhaled-breath profiles:a multicenter pilot study

Background: The diagnosis of pancreatic adenocarcinoma and chronic pancreatitis often rely on expensive and invasive diagnostic approaches, which are not always discriminative since patients with chronic pancreatitis and pancreatic adenocarcinoma may present with similar symptoms. Volatile organic compounds (VOCs) in expired breath, could be used as a non-invasive diagnostic biological marker for detection of pancreatic pathology. Detection and discrimination of pancreatic pathology with an electronic nose has not yet been reported. Purpose: The objective of this pilot study was to determine the diagnostic potential of an electronic nose to identify pancreatic adenocarcinoma and chronic pancreatitis by analyzing volatile organic compoundg (VOC) profiles in exhaled air. Patients and methods: In a multicenter study, the exhaled air of 56 chronic pancreatitis patients, 29 pancreatic adenocarcinoma patients, and 74 disease controls were analyzed using an electronic nose based on 3 metal oxide sensors (MOS). The measurements were evaluated utilizing an artificial neural network. Results: VOC profiles of chronic pancreatitis patients could be discriminated from disease controls with an accuracy of 0.87 (AUC 0.95, sensitivity 80%, specificity 92%). Also, VOC profiles of patients with pancreatic adenocarcinoma differed from disease controls with an accuracy of 0.83 (AUC 0.87, sensitivity 83%, specificity 82%). Discrimination between chronic pancreatitis and pancreatic adenocarcinoma showed an accuracy of 0.75 (AUC 0.83, sensitivity 83%, specificity 71%). Conclusion: An electronic nose may be a valuable diagnostic tool in diagnosis of pancreatic adenocarcinoma and chronic pancreatitis. The current study shows the potential of an electronic nose for discriminating between chronic pancreatitis, pancreatic adenocarcinoma and healthy controls. The results from this proof-of-concept study warrant external validation in larger cohorts

L-MYC gene polymorphism and risk of thyroid cancer

L-myc gene polymorphism is a representative genetic trait responsible for an individual’s susceptibility to several cancers. However, there have been no reports concerning the association between thyroid cancer and L-myc gene polymorphism. Aim: To analyze the distribution of L-myc gene polymorphism in Turkish patients with thyroid disorders and thyroid cancers. Methods: We used a molecular genotyping method, polymerase chain reaction-based restriction fragment length polymorphism (PCR-RFLP). We studied 138 patients of whom 47 had multinodular goiter, 13 had follicular cancer and 69 had papillar cancer, in comparison with control group of 109 healthy individuals. Results: No significant difference in the distribution of genotypes was observed between thyroid patients and controls. Carrying SS or LS genotype revealed a 1.96-fold (95% CI 0.573–6.706) risk for the occurrence of follicular cancer when compared with controls, and 3.11-fold (95% CI 0.952–10.216), when compared with multinodular goiter patients (p = 0.04). Conclusion: We suggest that L-myc genotype profiling together with other susceptibility factors, may be useful in the screening for thyroid nodular malignancy.Для ряда опухолей человека показана корреляция между риском развития опухоли и определенным вариантом гена L-MYC. Данные о наличии такой связи при раке щитовидной железы к настоящему времени отсутствуют. Цель: проанализировать распределение полиморфных типов гена L-MYC в популяции больных с доброкачественными и злокачественными поражениями щитовидной железы, включая рак щитовидной железы, в Турции. Методы: для анализа полиморфизма гена L-MYC использован метод молекулярного генотипирования, в частности, метод определения полиморфизма длины рестрикционных фрагментов, основанный на полимеразной цепной реакции (PCR-RFLP). Определение проводили в лейкоцитах 138 больных, в том числе 48 больных с узловым зобом, 13 больных фолликулярным раком щитовидной железы и 69 больных папиллярным раком. Контрольную группу составляли 109 здоровых лиц. Результаты: статистически достоверных различий в распределении исследуемых генотипов у больных с патологией щитовидной железы и здоровых лиц не выявили. Показано, что относительный риск фолликулярного рака щитовидной железы у больных-носителей генотипа SS или LS составляет 1,96 по сравнению со здоровыми лицами (при 95% доверительном интервале от 0,573 до 6,706) и 3,11 по сравнению с больными с узловым зобом (при 95% доверительном интервале от 0,952 до 10,216) (р = 0,04). Выводы: по нашему предположению, определение профиля полиморфизма гена L-MYC с учетом других факторов, определяющих предрасположенность к развитию опухолей, может быть полезным при скрининге озлокачествления узелковых образований щитовидной железы

Accessory mitral valve tissue causing severe left ventricular outflow tract obstruction in a post-Senning patient with transposition of the great arteries

Accessory mitral valve tissue is a rare congenital anomaly associated with congenital cardiac defects and is usually detected in the first decade of life. We describe the case of an 18-year old post-Senning asymptomatic patient who was found to have accessory mitral valve tissue on transthoracic echocardiography producing severe left ventricular outflow tract obstruction

The effect of fermentation process on bioactive properties, essential oil composition and phenolic constituents of raw fresh and fermented sea fennel (Crithmum maritimum L.) leaves

800-804The influence of fermentation on antioxidant activity, total phenol, total flavonoid and phenolic compounds of sea fennel and also volatile compounds of sea fennel essential oil was investigated and compared with fresh samples. Antioxidant activity, total fenolic and flavonoid contents decresed from 89.79 to 63.13%; from 259.58 to 77.92 mg/100 g; from 2114.67 to 390.50 mg/100 g, respectively. Twenty-six and thirty-three components of sea fennel oils were identified in raw and fermented sea fennel, accounting to about 99.99% and 99.44% of the total oil, respectively. The raw and fermented sea fennel leaves contained 22.31 and 1.32% sabinene, 12.08% and 7.45% limonene, 10.30% and 11.61% β-phellandrene, 8.59% and 9.17% (Z)-β-ocimene, 7.08% and 3.55% α-pinene, 28.36% and 42.05% γ-terpinene, 2.57% and 8.64% terpinene-4-ol, respectively. Dominant phenolic compounds were (+)-catechin, gallic acid, 3,4-dihydroxybenzoic acid and p-coumaric acid. Generally, all of the phenolic compounds reduced the effect of microorganisms during,. However, essential oil contents of sea fennel were not effected from fermentation process

Synthesis of Carboxymethyl Starch for increasing drilling mud quality in drilling oil and gas wells

This paper describes the impact of carboxymethyl starch preparation conditions on physicochemical properties of polysaccharide reagent, widely used as fluid loss reducing agent in drilling mud. Variation of the main parameters of carboxymethylation is researched in the experiment. The following conditions such as temperature and reaction time, amount of water, as well as ratio of NaOH to monochloracetic acid define the characteristics of carboxymethyl starch. The degree of substitution is defined for polysaccharides, as well as the characteristics of samples have been studied by infrared spectroscopy. Rheological characteristics and fluid loss indicator have been investigated to study the impact of the reagents on drilling mud quality

Türkiye’de Enflasyonun İleri ve Geri Beslemeli Yapay Sinir Ağlarının Melez Yaklaşımı ile Öngörüsü

Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI). A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data.Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir. Öngörülerin doğru bir şekilde elde edilmesi daha doğru kararlara neden olacaktır. Enflasyon öngörüsü için literatürde çeşitli zaman serileri teknikleri kullanılmıştır. Son yıllarda zaman serisi öngörü probleminde esnek modelleme yeteneği nedeniyle, Yapay Sinir Ağları (YSA) tercih edilmektedir. Yapay sinir ağları doğrusal veya eğrisel belirli bir model kalıbı, durağanlık ve normal dağılım gibi ön koşullara ihtiyaç duymadığından herhangi bir zaman serisine kolaylıkla uygulanabilmektedir. Bu çalışmada Tüketici Fiyat Endeksi (TUFE) için ileri ve geri beslemeli yapay sinir ağları yaklaşımı kullanılarak öngörüler elde edilmiştir. Çözümlemede kullanılan YSA modellerinin öngörülerinin girdi olarak kullanıldığı, YSA’ya dayalı yeni bir melez yaklaşım önerilmiştir

Türkiye’de Enflasyonun İleri ve Geri Beslemeli Yapay Sinir Ağlarının Melez Yaklaşımı ile Öngörüsü

Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI). A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data.Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir. Öngörülerin doğru bir şekilde elde edilmesi daha doğru kararlara neden olacaktır. Enflasyon öngörüsü için literatürde çeşitli zaman serileri teknikleri kullanılmıştır. Son yıllarda zaman serisi öngörü probleminde esnek modelleme yeteneği nedeniyle, Yapay Sinir Ağları (YSA) tercih edilmektedir. Yapay sinir ağları doğrusal veya eğrisel belirli bir model kalıbı, durağanlık ve normal dağılım gibi ön koşullara ihtiyaç duymadığından herhangi bir zaman serisine kolaylıkla uygulanabilmektedir. Bu çalışmada Tüketici Fiyat Endeksi (TUFE) için ileri ve geri beslemeli yapay sinir ağları yaklaşımı kullanılarak öngörüler elde edilmiştir. Çözümlemede kullanılan YSA modellerinin öngörülerinin girdi olarak kullanıldığı, YSA’ya dayalı yeni bir melez yaklaşım önerilmiştir