Morphometric and morphological evaluation of mastoid emissary canal using cone-beam computed tomography

Objectives: This study aimed to determine mastoid emissary canal’s (MEC) and mastoid foramen (MF) prevalence and morphometric characteristics on cone-beam computed tomography (CBCT) images to underline its clinical significance and discuss its surgical consequences. Methods: In the retrospective analysis, two oral and maxillofacial radiologists analyzed the CBCT images of 135 patients (270 sides). The biggest MF and MEC were measured in the images evaluated in MultiPlanar Reconstruction (MPR) views. The MF and MEC mean diameters were calculated. The mastoid foramina number was recorded. The prevalence of MF was studied according to gender and side of the patient. Results: The overall prevalence of MEC and MF was 119 (88.1%). The prevalence of MEC and MF is 55.5% in females and 44.5% in males. MEC and MF were identified as bilateral in 80 patients (67.20%) and unilateral in 39 patients (32.80%). The mean diameter of MF was 2.4 ± 0.9 mm. The mean height of MF was 2.3 ± 0.9. The mean diameter of the MEC was 2.1 ± 0.8, and the mean height of the MEC was 2.1 ± 0.8. There is a statistical difference between the genders (p = 0.043) in foramen diameter. Males had a significantly larger mean diameter of MF in comparison to females. Conclusion: MEC and MF must be evaluated thoroughly if the surgery is contemplated. Radiologists and surgeons should be aware of mastoid emissary canal morphology, variations, clinical relevance, and surgical consequences while operating in the suboccipital and mastoid areas to avoid unexpected and catastrophic complications. CBCT may be a reliable imaging diagnostic technique

Origin of the exchange bias training effects in magnetically coupled soft/hard synthetic bilayers at low temperature

WOS: 000347711600015Hysteresis loops of the nanoscale magnetic layer Co90Fe10 and Ni81Fe19 and bilayer Co90Fe10/Ni81Fe19 and Ni81Fe19/Co90Fe10 films were measured as a function of external dc magnetic Field and the thickness dependence of these Films were plotted as a function of temperature. Time evolution of the minor/middle/major hysteresis loops of 5/5 nm-thick Ni81Fe19/Co90Fe10 monolayer have been observed at 10 K. The spin valve, exchange bias training and Barkhausen effects for magnetic layer and bilayer films have been analysed at various temperatures, thicknesses and different orientations according to the substrate. The exchange-bias training effects have been observed only in positive magnetization region. Origin of the exchange-bias training effects and asymmetric hysteresis loops are related to the relaxation mechanism of a pinning layer in magnetically coupled soft/hard bilayers. (C) 2014 Elsevier B.V. All rights reservedResearch foundation of Nigde University [FEB2012/03]This study was supported by Research foundation of Nigde University (Grant no. FEB2012/03

5-Chloro-N-{4-oxo-2-[4-(trifluoromethyl)-phenyl]-1,3-thiazolidin-3-yl}-3-phenyl-1H- indole-2-carboxamide

In the title compound, C25H17ClF3N3O2S, the five-membered 1,3-thiazolidine ring adopts a twist conformation. The three F atoms of the CF3 group are disordered over two sets of sites with refined occupancies of 0.542 (18) and 0.458 (18). In the nine-membered 1H-indoline ring system, the 1H-pyrrole ring forms a dihedral angle of 4.7 (2)degrees with the benzene ring, while it is twisted at an angle of 46.5 (2)degrees with respect to the attached phenyl ring. The dihedral angle between the phenyl and trifluoromethyl-substituted benzene rings is 56.0 (2)degrees. In the crystal, N-H center dot center dot center dot O hydrogen bonds connect the molecules into a three-dimensional network. In addition, weak C-H center dot center dot center dot O hydrogen bonds and weak C-H center dot center dot center dot pi interactions are observed

Türk halk müziğinde Ege Türküleri ve Tolga Çandar'ın yeri

Ankara : İhsan Doğramacı Bilkent Üniversitesi İktisadi, İdari ve Sosyal Bilimler Fakültesi, Tarih Bölümü, 2018.This work is a student project of the Department of History, Faculty of Economics, Administrative and Social Sciences, İhsan Doğramacı Bilkent University.The History of Turkey course (HIST200) is a requirement for all Bilkent undergraduates. It is designed to encourage students to work in groups on projects concerning any topic of their choice that relates to the history of Turkey. It is designed as an interactive course with an emphasis on research and the objective of investigating events, chronologically short historical periods, as well as historic representations. Students from all departments prepare and present final projects for examination by a committee, with 10 projects chosen to receive awards.Includes bibliographical references (page 15).by Turaç Hakalmaz

Automatic Feature Segmentation in Dental Periapical Radiographs

While a large number of archived digital images make it easy for radiology to provide data for Artificial Intelligence (AI) evaluation; AI algorithms are more and more applied in detecting diseases. The aim of the study is to perform a diagnostic evaluation on periapical radiographs with an AI model based on Convoluted Neural Networks (CNNs). The dataset includes 1169 adult periapical radiographs, which were labelled in CranioCatch annotation software. Deep learning was performed using the U-Net model implemented with the PyTorch library. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown were 1, 1, and 1, respectively; sensitivity, precision and F1 score for dental pulp, were 0.97, 0.87 and 0.92, respectively; sensitivity, precision and F1 score for filling were 0.95, 0.95, and 0.95, respectively; sensitivity, precision and F1 score for the periapical lesion were 0.92, 0.85, and 0.88, respectively; sensitivity, precision and F1 score for root canal filling, were found to be 1, 0.96, and 0.98, respectively. The success of AI algorithms in evaluating periapical radiographs is encouraging and promising for their use in routine clinical processes as a clinical decision support system

Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images

This study aims to develop an algorithm for the automatic segmentation of the parotid gland on CT images of the head and neck using U-Net architecture and to evaluate the model’s performance. In this retrospective study, a total of 30 anonymized CT volumes of the head and neck were sliced into 931 axial images of the parotid glands. Ground truth labeling was performed with the CranioCatch Annotation Tool (CranioCatch, Eskisehir, Turkey) by two oral and maxillofacial radiologists. The images were resized to 512 × 512 and split into training (80%), validation (10%), and testing (10%) subgroups. A deep convolutional neural network model was developed using U-net architecture. The automatic segmentation performance was evaluated in terms of the F1-score, precision, sensitivity, and the Area Under Curve (AUC) statistics. The threshold for a successful segmentation was determined by the intersection of over 50% of the pixels with the ground truth. The F1-score, precision, and sensitivity of the AI model in segmenting the parotid glands in the axial CT slices were found to be 1. The AUC value was 0.96. This study has shown that it is possible to use AI models based on deep learning to automatically segment the parotid gland on axial CT images

Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images

This study aims to develop an algorithm for the automatic segmentation of the parotid gland on CT images of the head and neck using U-Net architecture and to evaluate the model’s performance. In this retrospective study, a total of 30 anonymized CT volumes of the head and neck were sliced into 931 axial images of the parotid glands. Ground truth labeling was performed with the CranioCatch Annotation Tool (CranioCatch, Eskisehir, Turkey) by two oral and maxillofacial radiologists. The images were resized to 512 × 512 and split into training (80%), validation (10%), and testing (10%) subgroups. A deep convolutional neural network model was developed using U-net architecture. The automatic segmentation performance was evaluated in terms of the F1-score, precision, sensitivity, and the Area Under Curve (AUC) statistics. The threshold for a successful segmentation was determined by the intersection of over 50% of the pixels with the ground truth. The F1-score, precision, and sensitivity of the AI model in segmenting the parotid glands in the axial CT slices were found to be 1. The AUC value was 0.96. This study has shown that it is possible to use AI models based on deep learning to automatically segment the parotid gland on axial CT images