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The size of brain ventricles is especially relevant in some brain diseases such as epilepsy, schizophrenia and other neurodegenerative diseases. Many studies have been conducted to assess the brain ventricles. To the best of our knowledge, however, there is no fourth, third and lateral ventricles volume study evaluating the efficiency and accuracy of point-counting and planimetry methods of the Cavalieri principle in the literature. In the current study, we estimated the volume of intracerebral ventricles in normal subjects using stereological methods. The volumes of fourth, third and lateral ventricle were estimated in 14 young Turkish volunteers (7 males and 7 females), aged between 18 and 36 years and free of any neurological symptoms and signs, using serial magnetic resonance imaging (MRI). Volumes of intracerebral ventricles were determined on MRIs using the point-counting and planimetry methods. The mean results of the point-counting method were 14.7 +/- 4.2, 8.7 +/- 3.0 and 131.8 +/- 33.1 mm(3) for the fourth, third and lateral ventricles, respectively. The mean results of the planimetry method were 15.4 +/- 3.4, 8.6 +/- 3.5 and 153.7 +/- 34.6 mm(3) for the fourth, third and lateral ventricles, respectively. Ventricle volumes obtained by the two different methods were not statistically different (p > 0.05) and they correlated well with each other. Good agreement was found between results obtained with the point-counting and planimetry techniques. The findings of the present study using stereological methods could provide data for the evaluation of normal and pathological volumes of intracerebral ventricles.

Bu tanımlayıcı araştırma, hipertansiyon hastalarının ilaç tedavisine uyum düzeyleri ve uyumu etkileyebilecek bireysel faktörleri incelemek amacıyla yapıldı.

This study aims to place EFL learners along an affective continuum via machine learning methods and present a new dataset about affective characteristics of EFL learners. In line with the purposes, written self-reports of 475 students from 5 different faculties in 3 universities in Turkey were collected and manually assigned by the researchers to one of the labels (positive, negative, or neutral). As a result, two combinations of the same dataset (AC-2 and AC-3) including different numbers of classes were used for the assessment of automatic classification approaches. Results revealed that automatic classification confirmed the manual classification to a great extent and machine learning methods could be used to classify EFL students along an affective continuum according to their affective characteristics. Maximum accuracy rate of automatic classification is 90.06% on AC-2 dataset including two classes. Similarly, on AC-3 dataset including three classes, maximum accuracy rate of classification is 71.79%. Last, the top-10 features/words obtained by feature selection methods are highly discriminative in terms of assessing student feelings for EFL learning. It could be stated that there is not an existing study in which feature selection methods and classifiers are used in the literature to automatically classify EFL learners’ feelings.

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