Feature analysis for classification of trace fluorescent labeled protein crystallization images

Abstract Background Large number of features are extracted from protein crystallization trial images to improve the accuracy of classifiers for predicting the presence of crystals or phases of the crystallization process. The excessive number of features and computationally intensive image processing methods to extract these features make utilization of automated classification tools on stand-alone computing systems inconvenient due to the required time to complete the classification tasks. Combinations of image feature sets, feature reduction and classification techniques for crystallization images benefiting from trace fluorescence labeling are investigated. Results Features are categorized into intensity, graph, histogram, texture, shape adaptive, and region features (using binarized images generated by Otsu’s, green percentile, and morphological thresholding). The effects of normalization, feature reduction with principle components analysis (PCA), and feature selection using random forest classifier are also analyzed. The time required to extract feature categories is computed and an estimated time of extraction is provided for feature category combinations. We have conducted around 8624 experiments (different combinations of feature categories, binarization methods, feature reduction/selection, normalization, and crystal categories). The best experimental results are obtained using combinations of intensity features, region features using Otsu’s thresholding, region features using green percentile G 90 thresholding, region features using green percentile G 99 thresholding, graph features, and histogram features. Using this feature set combination, 96% accuracy (without misclassifying crystals as non-crystals) was achieved for the first level of classification to determine presence of crystals. Since missing a crystal is not desired, our algorithm is adjusted to achieve a high sensitivity rate. In the second level classification, 74.2% accuracy for (5-class) crystal sub-category classification. Best classification rates were achieved using random forest classifier. Contributions The feature extraction and classification could be completed in about 2 s per image on a stand-alone computing system, which is suitable for real time analysis. These results enable research groups to select features according to their hardware setups for real-time analysis

Vision‐based trajectory tracking for mobile robots using Mirage pose estimation method

Unmanned vehicles are autonomous robotic systems that are fully or partially controlled by an operator remotely from a station. In the last two decades, massive amount of advancements have been observed regarding unmanned vehicles for both military and civilian purposes. Today majority of these vehicles require human guidance even for basic missions, thus, minimising the human intervention on such systems is one of the emerging research topics. To serve this purpose, this study proposes a new trajectory tracking algorithm using Mirage pose estimation method. Mirage employs target pixel errors in two‐dimensional image plane and analytically calculates the robot's pose in three‐dimensional Euclidean space. Therefore, complex computations are not needed and undesirable Euclidean trajectories are avoided since the vehicle's pose is directly controlled. Both simulations and real experiments were performed to verify the effectiveness of the method. The results show that the proposed method is a feasible alternative for vision‐based Euclidean trajectory tracking with high accuracy and low complexity

The prevalence of childhood psychopathology in Turkey: a cross-sectional multicenter nationwide study (EPICPAT-T).

Aim: The aim of this study was to determine the prevalence of childhood psychopathologies in Turkey

The prevalence of childhood psychopathology in Turkey: a cross-sectional multicenter nationwide study (EPICPAT-T)

Conclusion: This is the largest and most comprehensive epidemiological study to determine the prevalence of psychopathologies in children and adolescents in Turkey. Our results partly higher than, and partly comparable to previous national and international studies. It also contributes to the literature by determining the independent predictors of psychopathologies in this age group

Prevalence of Childhood Affective disorders in Turkey: An epidemiological study

Aim: To determine the prevalence of affective disorders in Turkey among a representative sample of Turkish population. Methods: This study was conducted as a part of the "The Epidemiology of Childhood Psychopathology in Turkey" (EPICPAT-T) Study, which was designed by the Turkish Association of Child and Adolescent Mental Health. The inclusion criterion was being a student between the second and fourth grades in the schools assigned as study centers. The assessment tools used were the K-SADS-PL, and a sociodemographic form that was designed by the authors. Impairment was assessed via a 3 point-Likert type scale independently rated by a parent and a teacher. Results: A total of 5842 participants were included in the analyses. The prevalence of affective disorders was 2.5 % without considering impairment and 1.6 % when impairment was taken into account. In our sample, the diagnosis of bipolar disorder was lacking, thus depressive disorders constituted all the cases. Among depressive disorders with impairment, major depressive disorder (MDD) (prevalence of 1.06%) was the most common, followed by dysthymia (prevalence of 0.2%), adjustment disorder with depressive features (prevalence of 0.17%), and depressive disorder-NOS (prevalence of 0.14%). There were no statistically significant gender differences for depression. Maternal psychopathology and paternal physical illness were predictors of affective disorders with pervasive impairment. Conclusion: MDD was the most common depressive disorder among Turkish children in this nationwide epidemiological study. This highlights the severe nature of depression and the importance of early interventions. Populations with maternal psychopathology and paternal physical illness may be the most appropriate targets for interventions to prevent and treat depression in children and adolescents