PSYCHOLOGICAL AND PEDAGOGICAL SUPPORT FOR AN EXCEPTIONAL CHILD IN AN INCLUSIVE CLASS: COMPARISON OF WESTERN AND RUSSIAN REFLECTION

A comparative analysis of theoretical and conceptual ideas in the organization and further implementation of psychological and pedagogical support for an exceptional student in an inclusive educational process is carried out. Psychological and pedagogical methods for emphatic comfort initiation for each child in an inclusive educational environment are highlighted. Practical examples of such techniques are creating social success situations for an exceptional person in an inclusive group, introducing elements of creativity to solve possible issues. The principles of psychological and pedagogical support that contribute to the success of an exceptional child in an inclusive class are the following: resistance, cooperation between all participants, reliance on the potential of the student’s personality, and others. Pedagogical modifications that optimize the process of inclusive learning are the following: change of motives for inclusive education, consolidation of positive behavioral forms of communication in an inclusive group, and other modifications. The types of adaptability formed due to effective psychological and pedagogical support of an exceptional child in an inclusive environment are considered: epistemological, perceptual, sociocommunicative, and semiotic adaptation.

Paranormal Belief, Thinking Style and Delusion Formation: A Latent Profile Analysis of Within-Individual Variations in Experience-Based Paranormal Facets

This study examined the degree to which within-individual variations in paranormal experience were related to belief in the paranormal, preferential thinking style, and delusion formation. A sample of 956 non-clinical adults completed measures assessing experience-based paranormal indices (i.e., paranormal experience, paranormal practitioner visiting, and paranormal ability), paranormal belief, belief in science, proneness to reality testing deficits, and emotion-based reasoning. Latent profile analysis (LPA) combined the experience-based indices to produce six underlying groups. Inter-class comparison via multivariate analysis of variance (MANOVA) indicated that both breadth and intensity of experiential factors were associated with higher belief in in the paranormal, increased proneness to reality testing deficits, and greater emotion-based reasoning. Belief in science, however, was less susceptible to experiential variations. Further analysis of reality testing subscales revealed that experiential profiles influenced levels of intrapsychic activity in subtle and intricate ways, especially those indexing Auditory and Visual Hallucinations and Delusional Thinking. Collectively, identification of profiles and inter-class comparisons provided a sophisticated understanding of the relative contribution of experiential factors to differences in paranormal belief, belief in science, proneness to reality testing deficits, and emotion-based reasoning.

A combined test for feature selection on sparse metaproteomics data - alternative to missing value imputation

One of the difficulties encountered in the statistical analysis of metaproteomics data is the high proportion of missing values, which are usually treated by imputation. Nevertheless, imputation methods are based on restrictive assumptions regarding missingness mechanisms, namely "at random" or "not at random". To circumvent these limitations in the context of feature selection in a multi-class comparison, we propose a univariate selection method that combines a test of association between missingness and classes, and a test for difference of observed intensities between classes. This approach implicitly handles both missingness mechanisms. We performed a quantitative and qualitative comparison of our procedure with imputation-based feature selection methods on two experimental data sets. Whereas we observed similar performances in terms of prediction, the feature ranking from various imputation-based methods was strongly divergent. We showed that the combined test reaches a compromise by correlating reasonably with other methods.

A method for photo-identification of turtles of the Phrynops williamsi species

This paper presents a method for photo-identification of Phrynops williamsi turtles. This identification is performed using shape descriptors which computationally represent the horseshoe-shaped and circular-shaped bands that can be found on the ventral surface of the turtle's head. The input image is converted to grayscale, binarized, filtered with morphologic operations, segmented based on the contours of the object and the components are selected based on their geometric characteristics. With the extraction of these characteristics, the method calculates de Fourier Descriptors and create a unique identifier used to identify the turtle from the input image. Results show that the presented method has reached a success rate of 85.71% in intra-class comparison and 85.17% in inter-class comparison.

Machine Learning Models for Diagnosis of Cushing’s Syndrome Using Retrospective Data

ABSTRACTAccurate classification of Cushing’s Syndrome (CS) plays a critical role in providing early and correct diagnosis of CS that may facilitate treatment and improve patient outcomes. Diagnosis of CS is a complex process, which requires careful and concurrent interpretation of signs and symptoms, multiple biochemical test results, and findings of medical imaging by physicians with a high degree of specialty and knowledge to make correct judgments. In this article, we explore the state of the art machine learning algorithms to demonstrate their potential as a clinical decision support system to analyze and classify CS in order to facilitate the diagnosis, prognosis, and treatment of CS. Prominent algorithms are compared using nested cross-validation and various class comparison strategies including multiclass, one vs. all, and one vs. one binary classification. Our findings show that Random Forest (RF) algorithm is most suitable for the classification of CS. We demonstrate that the proposed approach can classify CS subjects with an average accuracy of 92% and an average F1 score of 91.5%, depending on the class comparison strategy and selected features. RF-based one vs. all binary classification model achieves sensitivity of 97.6%, precision of 91.1%, and specificity of 87.1% to discriminate CS from non-CS on the test dataset. RF-based multiclass classification model achieves average per class sensitivity of 91.8%, average per class specificity of 97.1%, and average per class precision of 92.1% to classify different subtypes of CS on the test dataset. Clinical performance evaluation suggests that the developed models can help improve physician’s judgment in diagnosing CS.

Universal Relation for Life-span Energy Consumption in Living Organisms: Insights for the origin of aging

Metabolic energy consumption has long been thought to play a major role in the aging process (1). Across species, a gram of tissue on average expends about the same amount of energy during life-span (2). Energy restriction has also been shown that increases maximum life-span (3) and retards age-associated changes (4). However, there are significant exceptions to a universal energy consumption during life-span, mainly coming from the inter-class comparison (5, 6). Here we present a unique relation for life-span energy consumption, valid for ∼300 species representing all classes of living organisms, from unicellular ones to the largest mammals. The relation has an average scatter of only 0.3 dex, with 95% of the organisms having departures less than a factor of π from the relation, despite the ∼20 orders of magnitude difference in body mass, reducing any possible inter-class variation in the relation to only a geometrical factor. This result can be interpreted as supporting evidence for the existence of an approximately constant total number Nr ∼ 108 of respiration cycles per lifetime for all organisms, effectively predetermining the extension of life by the basic energetics of respiration.