Browsing by Author "Kaya, Yilmaz"

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A 2,2-Dichloropropionic Acid-Degrading Novel Pseudomonas Fluorescence Strain Fatsa001: Isolation, Identification, and Characterization
(Taylor & Francis inc, 2025) Meral, Ulku Binboga; Edbeib, Mohamed Faraj; Kirkinci, Suleyman Faruk; Aksoy, Hasan Murat; Akman, Ayhan; Wahab, Roswanira Abdul; Kaya, Yilmaz
There are mounting concerns over the high concentrations of non-biogenic, toxic halogenated organic compounds being liberated into the ecosystem. Therefore, this study's isolation of a novel bacterium from a contaminated stream in Fatsa, Ordu, Turkey, adept in degrading 2,2-dichloropropionic (of 2,2-DCP) is a welcome endeavor. The ability of the bacterial isolate to utilize 2,2-DCP as the sole carbon and energy source was discovered when the bacterium was observed to grow well on liquid minimal media containing 20 mM of 2,2-DCP, showing a doubling time of 14.2 h. The following genetic and biochemical characterizations revealed that the 16S rRNA sequence of the fatsa001strain is identical (99%) to Pseudomonas fluorescence, after which the sequence was deposited in the NCBI GenBank as Pseudomonas sp. strain fatsa001 (MN098848). The halogen-degrading ability of the P. fluorescens fatsa001 bacterium was again confirmed by the PCR data, which showed the presence of a conserved group of amino acids from the group I dehalogenase gene. It worth mentioning here that this is the first report on a P. fluorescence bacterial strain with the ability to degrade toxic 2,2-DCP. The detoxification ability of this bacterium envisages its practicality as an in situ environmental bioremediation agent.
Agrobacterium- Mediated Transformation of Turkish Upland Rice (Oryza Sativa L.) for Dalapon Herbicide Tolerance
(Natl inst Science Communication-niscair, 2020) Kaya, Yilmaz; Aksoy, Hasan Murat; Edbeib, Mohamed Faraj; Wahab, Roswanira Abdul; Ozyigit, Ibrahim Ilker; Hamid, Azzmer Azzar Abdul; Aslan, Ali
Agrobacterium-mediated transformation of upland rice is established in few numbers of cultivars due to the high cultivar-specificity of regeneration from transformed explants. Further, dehalogenase E (dehE) gene had been characterized in Pseudomonas putida and it produces an enzyme that degrades dalapon. This study aimed to transform Turkish upland rice with the dehE herbicide resistant gene and addresses the challenges of transgenic rice recovery by identifying explant and transformation method. Constructed vector pCAMdehE carrying dehE gene was transferred into the rice shoot apex by Agrobacterium-mediated transformation. The transformed rice was analyzed for expression of the transgenes by polymerase chain reaction (PCR). Herbicide resistance leaf painting assay was carried out at different dalapon herbicide concentrations to the transgenic rice leaves. Transformation efficiency percentage (putative) was highest (32.66%) in 5 days old explants. PCR analysis resulted in the amplification of the dehE, T-DNA border endonuclease (virD2) and hygromycin phosphotransferase (hpt) genes from the transgenic rice. In addition, dehalogenase activity was proved with higher dalapon tolerance in the rice. Dalapon effects started to appear in the transformed rice at 180 mg/l, while in non-transformed ones at 60 mg/l concentration. The results showed that transformed plants have more tolerance to the herbicide than the non-transformed ones.
Analysis of Plant Protection Studies With Excess Zeros Using Zero-Inflated and Negative Binomial Hurdle Models
(Gazi Univ, 2010) Yesdlova, Abdullah; Kaya, Yilmaz; Kaki, Baris; Kasap, Ismail
In this study, the analysis of data with many zeros for plant protection area was carried out by using the models of Poisson Regression (PR), negative binomial (NB) regression, zero-inflated Poisson (ZIP) regression, zeroinflated negative binomial (ZINB) regression, and negative binomial hurdle (NBH) model. As zero-inflated observations are too much in the studies, done in the plant protection area; models considering zero-inflated observations are frequently required. Mites (Acari: Tetranychidae; Stigmaeidae), the basic material of this study, can reach to quite high amounts under convenient temperature (18-32 degrees C temperature). The fact that deviance obtained from PR model together with Pearson Chi-square and deviance goodness of statistics came about quite higher than the value of (1) represented that there was an overdispersion in data set. In the selection of appropriate regression model, Akaiki information criteria and Bayesian information criteria were used. At the end of these information criteria, ZINB regression was chosen as the best model. In ZINB model, the effects of Zetzellia mali, temperature, and periods were significant on the total P. ulmi number (p<0.01), while applying insecticide was insignificant (p>0.05).
A Comparison of Hot Deck Imputation and Substitution Methods in the Estimation of Missing Data
(Gazi Univ, 2011) Yesilova, Abdullah; Kaya, Yilmaz; Almali, M. Nuri
It is of great importance to obtain data in an accurate and incomplete way for adequate conclusions to be drawn from investigations conducted. Due to various reasons, certain parts of an investigation might not be observed, and as a result of this, data might be missing and obtained incompletely. Missing value may not only be based on a single variable but also a multitude of variables. In this study, missing data in different proportions and belonging to more than a variable were produced. When data were considered within a context which is missing completely at random, Hot Deck imputation, random Hot Deck imputation and substitution methods (mean, median) were compared in the estimation of missing value. As a result of analysis, Hot Deck imputation method was found to be more effective in the estimation of missing value.
Detection of Parkinson's Disease by Shifted One Dimensional Local Binary Patterns From Gait
(Pergamon-elsevier Science Ltd, 2016) Ertugrul, Omer Faruk; Kaya, Yilmaz; Tekin, Ramazan; Almali, Mehmet Nuri
The Parkinson's disease (PD) is one of the most common diseases, especially in elderly people. Although the previous studies showed that the PD can be diagnosed by expert systems through its cardinal symptoms such as the tremor, muscular rigidity, disorders of movements and voice, it was reported that the presented approaches, which utilize simple motor tasks, were limited and lack of standardization. To achieve a standard approach in PD detection, an approach, which is built on shifted one-dimensional local binary patterns (Shifted 1D-LBP) and machine learning methods, was proposed. Shifted 1D-LBP is built on 1D-LBP, which is sensitive to local changes in a signal. In 1D-LBP the positions of neighbors around center data are constant and therefore, the number of patterns that can be exacted by it is limited. This drawback was solved by Shifted 1D-LBP by changeable positions of neighbors. In evaluation and validation stages, the Gait in Parkinson's Disease (gaitpdb) dataset, which consists of three gait datasets that were recorded in different tasks or experiment protocols, were employed. Statistical features were exacted from formed histograms of gait signals transformed by Shifted 1D-LBP. Whole features and selected features were classified by machine learning methods. Obtained results were compared with statistical features exacted from signals in both time and frequency domains and results reported in the literature. Achieved results showed that the proposed approach can be successfully employed in PD detection from gait. This work is not only an attempt to develop a PD detection method, but also a general-purpose approach that is based on detecting local changes in time ordered signals. (C) 2016 Elsevier Ltd. All rights reserved.
An Expert Classification System of Pollen of Onopordum Using a Rough Set Approach
(Elsevier, 2013) Kaya, Yilmaz; Pinar, S. Mesut; Erez, M. Emre; Fidan, Mehmet
Although pollen grains have a complicated 3-dimensional structure, they can be distinguished from one another by their specific and distinctive characteristics. Using microscopic differences between the pollen grains, it may be possible to identify them by family or even at the genus level. However for the identification of pollen grains at the taxon level, we require expert computer systems. For this purpose, we used 20 different pollen types, obtained from the genus Onopordum L (Asteraceae). For each pollen grain, 30 different images were photographed by microscope system and 11 different characteristic features (polar axis, equatorial axis, P/E ratio, colpus length, colpus weight, exine, intine, tectum, nexine, columellea, and echinae length) were measured for the analysis. The data set was formed from 600 samples, obtained from 20 different taxa, with 30 different images. The 440 samples were used for training and the remaining 160 samples were used for testing. The proposed method, a rough set-based expert system, has properly identified 145 of 160 pollen grains correctly. The success of the method for the identification of pollen grains was obtained at 90.625% (145/160). We can expect to achieve more efficient results with different genuses and families, considering the successful results in the same genus. Moreover, using computer-based systems in revision studies will lead us to more accurate and efficient results, and will identify which characters will be more effective for pollen identification. According to the literature, this is the first study for the identification and comparison of pollen of the same genus by using the measurements of distinctive characteristics with computer systems. (C) 2012 Elsevier B.V. All rights reserved.
Identification of Onopordum Pollen Using the Extreme Learning Machine, a Type of Artificial Neural Network
(Taylor & Francis inc, 2014) Kaya, Yilmaz; Pinar, S. Mesut; Erez, M. Emre; Fidan, Mehmet; Riding, James B.
Pollen grains are complex three-dimensional structures, and are identified using specific distinctive morphological characteristics. An efficient automatic system for the accurate and rapid identification of pollen grains would significantly enhance the consistency, objectivity, speed and perhaps accuracy of pollen analysis. This study describes the development and testing of an expert system for the identification of pollen grains based on their respective morphologies. The extreme learning machine (ELM) is a type of artificial neural network, and has been used for automatic pollen identification. To test the equipment and the method, pollen grains from 10 species of Onopordum (a thistle genus) from Turkey were used. In total, 30 different images were acquired for each of the 10 species studied. The images were then used to measure 11 morphological parameters; these were the colpus length, the colpus width, the equatorial axis (E), the polar axis (P), the P/E ratio, the columellae length, the echinae length, and the thicknesses of the exine, intine, nexine and tectum. Pollen recognition was performed using the ELM for the 50-50%, 70-30% and 80-20% training-test partitions of the overall dataset. The classification accuracies of these three training-test partitions of were 84.67%, 91.11% and 95.00%, respectively. Therefore, the ELM exhibited a very high success rate for identifying the pollen types considered here. The use of computer-based systems for pollen recognition has great potential in all areas of palynology for the accurate and rapid accumulation of data.
Investigation of the Effects of Physico-Chemical Environmental Conditions on Population Fluctuations of Notonecta Viridis Delcourt, 1909 (Hemiptera: Notonectidae) in Van Lake by Using Zero-Inflated Generalized Poisson Regression
(Entomological Soc Turkey, Ege Univ, 2011) Yesilova, Abdullah; Ozgokce, Mehmet Salih; Atlihan, Remzi; Karaca, Ismail; Ozgokce, Fevzi; Yildiz, Sukran; Kaya, Yilmaz
In ecological studies, it is a common situation occured that population density of species extremly increases or decreases in certain periods depending on many abiotic and biotic factors. Because of ecologial factors that cause high level fluctuation in population density, It is possible to get zero individual at samplings, and on the other hand, differences between maximum and minimum values obtained in different samplings intervals can be very high. Because this type of data based on counting does not show normal distribution, and shape of the distribution is skewed to the right because of the abundance of zero, using the Zero-inflated Poisson regression method (ZIGP) is required. This study was carried out to obtain information on effects of physico-chemical environmental conditions on population fluctuation of Notonecta viridis. Samplings were conducted with monthly periods along the coastal band of Van Lake in 2005-2006. Samples were taken from 20 sampling places where have three different characters as stream entrances, settlements and natural coastlines. Reults were analysed by using ZIGP regression model. According to results, Effect of sampling intervals and sampling stations on population densities of Notonecta viridis were important. On the other hand, HCO3 had negative effect on population densities in zero-inflated model while it had possitive effect on population densities in other two models. It was determined that Fe effected the species populations in the negative way in the mean regression model, and Cl and Mg effected it in possitive way in the overdispersion regression. In the result, it was deductived that Notonecta viridis was found excessive numbers or none in some sampling stations because of the pysico-chemical structures of water.
Modeling Insect-Egg Data With Excess Zeros Using Zero-Inflated Regression Models
(Hacettepe Univ, Fac Sci, 2010) Yesilova, Abdullah; Kaydan, M. Bora; Kaya, Yilmaz
As zero-inflated observations occur very often in studies on plant protection, models taking into account zero-inflated observations are frequently required. Especially, zero-inflated observations occur in large numbers for insects whose post-oviposition period lasts long, or that generally lay their eggs during the first clays of the oviposition period. For the data used in this study, 1114 (43.84%) of the 2541 observations were zero. In the selection of an appropriate regression model, zero-inflated negative binomial regression was chosen as the best model. In all regression models, the day of laying and the three different hosts were seen to have a significant effect on daily egg numbers (p < 0.01).