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Purpose:The purpose of this study was to investigate the healing differences in between four different widely used suture materials in the oral surgery practice, including silk (Perma-Hand; Ethicon, INC., Somerville, NJ, USA), polypropylene (Prolene; Ethicon, INC., Somerville, NJ, USA), coated polyglactin 910 (Ethicon, INC., Somerville, NJ, USA). and polyglecaprone 25 (Ethicon, INC., Somerville, NJ, USA ). Materials and Methods: 20 male rats were randomly allocated into two groups depending on their sacrification days (post-operative 1st and the 7th days). Four longitudinal incision wounds, each 1cm in size, were created on the dorsum of each animal which were then primarily closed with four different types of sutures. Results: The effects of these suture materials on soft tissue healing were compared histopathologically, by means of density of the cells, necrosis, fibrosis, foreign body reaction, the presence of cells of acute and chronic infection. No statistically significant difference was observed between the groups regarding the density of the cells, necrosis, fibrosis, foreign body reaction, and the presence of the cells of acute & chronic infections. Of note, propylene showed slightly less tissue reaction among the other materials. Conclusion: The results of our study showed that there is no only one ideal suture material for surgical practice. The factors related to the patient, the type of the surgery and the quality of the tissue are important to decide an appropriate suture material.

Water is an essential source for earth. According to the United Nation’s report, every day 1800 children dies because of contaminated water. Dyes are one of the most common water pollutants. They are using in many areas like cosmetic, textile, pharmaceutical etc. Every year nearly 140,000 tons of dye releasing to the environment. Therefore, removal of dyes from water is an essential topic. There are many ways to remove dyes from water. However, studies showed that traditional methods are ineffective to removing pollutants from water. On the other hand, photocatalysis is a promising technology.In this study, Ag/AgCl and Ag/AgCl@GO photocatalysts were synthesized and their removal performances on Rhodamine B dye were investigated. In addition, the parameters affecting the removal performance were also studied.Characterization tests such as synthesized photocatalysts, XRD, BET, UV-Vis Analysis, TGA, SEM, TEM was carried out. According to the XRD results, the peak regions of the synthesized photocatalysts were similar to other studies in the literature. The synthesized photocatalysts were first studied under 3 different pH values (pH:3, pH:8, pH:11) using 10 ppm rhodamine b dye and 30 mg catalyst under UV light. According to the results, for both photocatalysts, their natural pH, namely pH:8, showed the best performance. Afterwards, experiments were carried out with different photocatalyst weights and it was observed that the removal performance did not change after 40 mg. Finally, different dye amounts were studied and it was observed that as the dye amount increased, its removal decreased. It has been observed that the addition of graphene oxide significantly improves the performance of the catalyst.

Histopathological examination is today’s gold standard for cancer diagnosis and grading. However, this task is time consuming and prone to errors as it requires detailed visual inspection and interpretation of a histopathological sample provided on a glass slide under a microscope by an expert pathologist. Low-cost and high-technology whole slide digital scanners produced in recent years have eliminated the disadvantages of physical glass slide samples by digitizing histopathological samples and relocating them to digital media. Digital pathology aims at alleviating the problems of traditional examination approaches by providing auxiliary computerized tools that quantitatively analyze digitized histopathological images.Traditional machine learning methods have proposed to extract handcrafted features from histopathological images and to use these features in the design of a classification or a segmentation algorithm. The performance of these methods mainly relies on the features that they use, and thus, their success strictly depends on the ability of these features to successfully quantify the histopathology domain. More recent studies have employed deep architectures to learn expressive and robust features directly from images avoiding complex feature extraction procedures of traditional approaches. Although deep learning methods perform well in many classification and segmentation problems, convolutional neural networks that they frequently make use of require annotated data for training and this makes it difficult to utilize unannotated data that cover the majority of the available data in the histopathology domain.This thesis addresses the challenges of traditional and deep learning approaches by incorporating unsupervised learning into classification and segmentation algorithms for feature extraction and training regularization purposes in the histopathology domain. As the first contribution of this thesis, the first study presents a new unsupervised feature extractor for effective representation and classification of histopathological tissue images. This study introduces a deep belief network to quantize the salient subregions, which are identified with domain-specific prior knowledge, by extracting a set of features directly learned on image data in an unsupervised way and uses the distribution of these quantizations for image representation and classification. As its second contribution, the second study proposes a new regularization method to train a fully convolutional network for semantic tissue segmentation in histopathological images. This study relies on the benefit of unsupervised learning, in the form of image reconstruction, for network training. To this end, it puts forward an idea of defining a new embedding, which is generated by superimposing an input image on its segmentation map, that allows uniting the main supervised task of semantic segmentation and an auxiliary unsupervised task of image reconstruction into a single one and proposes to learn this united task by a generative adversarial network. We compare our classification and segmentation methods with traditional machine learning methods and the state-of-the-art deep learning algorithms on various histopathological image datasets. Visual and quantitative results of our experiments demonstrate that the proposed methods are capable of learning robust features from histopathological images and provides more accurate results than their counterparts.

An odontogenic keratocyst is a unique cyst because of its locally aggressive behavior, high recurrence rate, and characteristic histologic appearance. In this article we present the case of a 22-year-old male patient with a large odontogenic keratocyst and describe his treatment with immediate dental implants.

Fast augmentation of large text collections in digital world makes inevitable to automatically extract short descriptions of those texts. Even if a lot of studies have been done on detecting hidden topics in text corpora, almost all models follow the bag-of-words assumption. This study presents a new unsupervised learning method that reveals topics in a text corpora and the topic distribution of each text in the corpora. The texts in the corpora are described by a generative graphical model, in which each sentence is generated by a single topic and the topics of consecutive sentences follow a hidden Markov chain. In contrast to bagof-words paradigm, the model assumes each sentence as a unit block and builds on a memory of topics slowly changing in a meaningful way as the text flows. The results are evaluated both qualitatively by examining topic keywords from particular text collections and quantitatively by means of perplexity, a measure of generalization of the model.

Purpose: Odontogenic cysts (OCs) are pathological lesions that include liquid or semi-liquid surrounded with epithelium. Radicular cysts' (RCs) and odontogenic keratocysts' (OKCs) are common odontogenic cysts of jaws, and may reach to a substantial size without symptoms for a long time. It is known that cytokines secreted during infection and inflammation regulate the immune response. This study aims to describe the relationship between bacteria as infection agents and the levels of interferon-gamma (IFN-γ) cytokine of innate and adaptive immune response. Materials and Methods: OC fluid samples with a history of infection were collected from a total of 39 OCs consisting 25 samples of odontogenic keratocysts (OKC) and 14 samples of radicular cysts (RC). Anaerobic bacteria detection was performed by a polymerase chain reaction (PCR) based on bacterial 16S rRNA genes. IFN-γ levels in OC fluids were determined using the luminex method. Results: No significant differences in IFN-γ levels and T cell type 1 cytokine responses were observed between the cystic fluid samples classified on the basis of age, gender, cyst-type, cyst-size, bacterial species and the number of bacterial species contained. The measured concentrations IFN-γ, which is a helper T cell type 1 cytokine were consistent with published data from experimental animal models, immunohistochemical studies, and molecular studies. Conclusion: Luminex method can detect the concentration of many different types of protein in a small sample volume and is suitable for determining the protein content of odontogenic cysts.

Söylem bağlamında dava; ferdi veya içtimai, bir Müslüman'ın siyasal ve/veya toplumsal nesnel ilişkilerini kuran, davranışsal ve düşünsel eylemlerini belirleyen geleneksel bir imgedir. Bu yönüyle dava Müslümanlar arasında bir bilinç durumunu ifade etmektedir. Saikleri ile ifade edilmek istenilen de bu imgeyi pekiştiren Müslüman toplumların tecrübelerine dayalı çok boyutlu tepkiler veya diğer bir ifadeyle olgulardır. Saikleri bağlamında dava söyleminin irdelenmesi ve açıklanması ikiye ayrılmaktadır. Klasik dönem dava söyleminin saikleri 19. ve 20. Yüzyıllarda yaşanan süreçlerin öncesini kapsamaktayken; modern dönem dava söyleminin saikleri ise bu yüzyıllar sonrasını kapsamaktadır. Dava söyleminin 20. Yüzyıldan itibaren uğradığı bu kırılma bağlamında dava söyleminin saikleri üç temel kategoriye ayrılabilmektedir. \"İslami diriliş\", \"İslami uyanış\" ve \"İslami örgütleniş\" kavramları dava söyleminin saikleri olarak modern dönemde siyasal ve toplumsal hareketlerin müteakip periyotlarını açıklamaya imkân vermektedir.

Segmentation of brain magnetic resonance images (MRI) is crucial for the analysis of the human brain and diagnosis of various brain disorders. The drawbacks of time-consuming and error-prone manual delineation procedures are aimed to be alleviated by atlas-based and supervised machine learning methods where the former methods are computationally intense and the latter methods lack a sufficiently large number of labeled data. With this motivation, we propose CORPS, a semi-supervised segmentation framework built upon a novel atlas-based pseudo-labeling method and a 3D deep convolutional neural network (DCNN) for 3D brain MRI segmentation. In this work, we propose to generate expert-level pseudo-labels for unlabeled set of images in an order based on a local intensity-based similarity score to existing labeled set of images and using a novel atlas-based label fusion method. Then, we propose to train a 3D DCNN on the combination of expert and pseudo labeled images for binary segmentation of each anatomical structure. The binary segmentation approach is proposed to avoid the poor performance of multi-class segmentation methods on limited and imbalanced data. This also allows to employ a lightweight and efficient 3D DCNN in terms of the number of filters and reserve memory resources for training the binary networks on full-scale and full-resolution 3D MRI volumes instead of 2D/3D patches or 2D slices. Thus, the proposed framework can encapsulate the spatial contiguity in each dimension and enhance context-awareness. The experimental results demonstrate the superiority of the proposed framework over the baseline method both qualitatively and quantitatively without additional labeling cost for manual labeling.

Cemil Meriç (1916-1987) who has a exclusive writing style is an unique philosopher for political thought in Turkey. His writing style is so fascinating that it's difficult to be different from his writing style while writing about his ideas. Therefore, Meriç is still unique philosopher because of that Meriç's writing style is cause to yawn the academic writing style. Cemil Meriç's thoughts which are presented by Meriç discussing distance in between idea and actual are derived from his meaning world. Thence the method which is used by Meriç especially in ideas about political thought in Turkey appears because Meriç usually refers the historicity. And this method is not an analysis contrariwise it is a comparison. The question of what is not information is the basic approach in this method. İn this context Meriç examines cultural values that political thoughts are pullulated by these values, additionally Meriç never ignore the universal feature of thought. This article includes the liberty issue discoursed by Meriç. The relationship between subject and purpose is connected in search of new theme in the Meriç's ideas. İn this perspective the liberty that is subject of this article is examined in context textual an d discursive. İn this perspective, data acquiring and analysis technique of this article is discourse analysis on text that Meriç's books is fundamental source for acquiring data. Thus, all of the expressive entirety related with liberty carefully is compiled considering modernization process in Turkey.

This study is a result of research process based on empirical and practical findings. Research topic is to determine the social and economic profile of the Karamanoglu Mehmetbey University students. Especially as productivity of using sources which are consist of time and money is the main purpose. In this context, the appeared question is whether students use productivity their sources or not. Research data were obtained by survey technique. Social and economic features of the Karamanoglu Mehmetbey University students were determined after obtained demographic data. Thus, practical findings which are related social features have been examined on data. Additionally, the number of Karamanoglu Mehmetbey University student is 14.395 person. The number of surveyed students is 1.228 person. Depending on the data, empirical findings are consist of specific categories which are separated as gender, age, family situation, city where surveyed students not only born but also live and monetary situation. In the other context, categories of the practical findings are separated only time and money. SPPS (Statistical Package for the Social Science) was used to analyze the data and the findings exhibited on the figures and tables by using frequency and Chi-Square analysis. As a result that the factors which are relation with time and money used by Karamanoglu Mehmetbey University students were determined. The productivity of relationship between using time and money was evaluated as a positively and negatively aspect of students. In this context, the findings of this research show that time and money are used productivity by students are so important for their careers and social lives in the future.