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Open access

Emir Šehović, Martin Zieger, Lemana Spahić, Damir Marjanović and Serkan Dogan

Abstract

The aim of this study is to provide an insight into Balkan populations’ genetic relations utilizing in silico analysis of Y-STR haplotypes and performing haplogroup predictions together with network analysis of the same haplotypes for visualization of the relations between chosen haplotypes and Balkan populations in general. The population dataset used in this study was obtained using 23, 17, 12, 9 and 7 Y-STR loci for 13 populations. The 13 populations include: Bosnia and Herzegovina (B&H), Croatia, Macedonia, Slovenia, Greece, Romany (Hungary), Hungary, Serbia, Montenegro, Albania, Kosovo, Romania and Bulgaria. The overall dataset contains a total of 2179 samples with 1878 different haplotypes. I2a was detected as the major haplogroup in four out of thirteen analysed Balkan populations. The four populations (B&H, Croatia, Montenegro and Serbia) which had I2a as the most prevalent haplogroup were all from the former Yugoslavian republic. The remaining two major populations from former Yugoslavia, Macedonia and Slovenia, had E1b1b and R1a haplogroups as the most prevalent, respectively. The populations with E1b1b haplogroup as the most prevalent one are Macedonian, Romanian, as well as Albanian populations from Kosovo and Albania. The I2a haplogroup cluster is more compact when compared to E1b1b and R1b haplogroup clusters, indicating a larger degree of homogeneity within the haplotypes that belong to the I2a haplogroup. Our study demonstrates that a combination of haplogroup prediction and network analysis represents an effective approach to utilize publicly available Y-STR datasets for population genetics.

Open access

Rukiye Nur Kaçmaz, Bülent Yılmaz, Mehmet Sait Dündar and Serkan Doğan

Abstract

Computer-aided detection is an integral part of medical image evaluation process because examination of each image takes a long time and generally experts’ do not have enough time for the elimination of images with motion artifact (blurred images). Computer-aided detection is required for both increasing accuracy rate and saving experts’ time. Large intestine does not have straight structure thus camera of the colonoscopy should be moved continuously to examine inside of the large intestine and this movement causes motion artifact on colonoscopy images. In this study, images were selected from open-source colonoscopy videos and obtained at Kayseri Training and Research Hospital. Totally 100 images were analyzed half of which were clear. Firstly, a modified version of histogram equalization was applied in the pre-processing step to all images in our dataset, and then, used Laplacian, wavelet transform (WT), and discrete cosine transform-based (DCT) approaches to extract features for the discrimination of images with no artifact (clear) and images with motion artifact. The Laplacian-based feature extraction method was used for the first time in the literature on colonoscopy images. The comparison between Laplacian-based features and previously used methods such as WT and DCT has been performed. In the classification phase of our study, support vector machines (SVM), linear discriminant analysis (LDA), and k nearest neighbors (k-NN) were used as the classifiers. The results showed that Laplacian-based features were more successful in the detection of images with motion artifact when compared to popular methods used in the literature. As a result, a combination of features extracted using already existing approaches (WT and DCT) and the Laplacian-based methods reached 85% accuracy levels with SVM classification approach