Avaliação e Ensino de Emoções com Crianças e Jovens Adultos com Autismo ou Síndrome de Down Mediado pela Família / Assessment and Teaching of Emotions with Children and Young Adults with Autism or Down Syndrome Mediated by Family / Evaluación y Enseñanza de las Emociones con Niños y Jóvenes con Autismo o Síndrome de Down Mediadas por la Familia

Resumo: Este estudo avaliou o reconhecimento (imitação, identidade e identificação) e a nomeação de estímulos emocionais de valência negativa (raiva e tristeza) e positiva (alegria e surpresa) em conjunto com a influência dos tipos de estímulos utilizados (social-feminino, social-masculino, familiar e emoji) em crianças e jovens adultos com autismo ou síndrome de Down, por meio de tarefas aplicadas pela família e mediadas por recursos tecnológicos durante a pandemia de covid-19. Participaram cinco crianças e dois jovens adultos com autismo e uma criança e dois jovens adultos com síndrome de Down. Foram implementadas tarefas de identidade, reconhecimento, nomeação e imitação, com estímulos faciais de função avaliativa (sem consequência diferencial) e de ensino (com consequência diferencial, uso de dicas e critério de aprendizagem), visando a emergência da nomeação emocional por meio do ensino das tarefas de reconhecimento. Os resultados da linha de base identificaram que, para os participantes que apresentaram menor tempo de resposta para o mesmo gênero, a diferença de tempo de resposta foi em média 57,28% menor. Em relação à valência emocional, 50% dos participantes apresentaram diferenças nos acertos, a depender da valência positiva e negativa, sendo que 66,66% apresentaram diferenças para o tempo de resposta a depender da valência emocional. Após o procedimento de ensino, os participantes mostraram maior número de acertos nas tarefas, independentemente do gênero de estímulo e valência emocional, criando ocasião para generalização da aprendizagem de reconhecimento e nomeação de emoções, além de consolidar a viabilidade de estratégias de ensino mediadas por recursos tecnológicos e aplicadas por familiares.

Abstract: This study evaluated the recognition (imitation, identity, and identification) and naming of negative (anger and sadness) and positive (joy and surprise) emotional stimuli alongside the influence of the types of stimuli (social-female, social-male, family, and emoji) in children and young adults with autism and Down syndrome, via tasks applied by the family and mediated by technological resources, during the COVID-19 pandemic. Five children and two young adults with autism and one child and two young adults with Down syndrome participated. Identity, recognition, naming, and imitation tasks were planned and implemented using facial stimuli with evaluative (without differential consequence) and teaching (with differential consequence, tips, and learning criteria) functions, aiming at the emergence of emotional naming from the recognition teaching tasks. The baseline results showed that, for participants who had a shorter response time for the same gender, the response time difference was on average 57.28% lower. Regarding the emotional valence, 50% of the participants showed differences in the correct answers, depending on the positive and negative valence, and 66.66% showed differences in the response time depending on the emotional valence. After the teaching procedure, the participants showed a greater number of correct answers in the tasks, regardless of the stimulus type and emotional valence, creating an opportunity for generalizing learning of emotion recognition and naming, in addition to consolidating the feasibility of teaching strategies mediated by technological resources and applied by family members.

Resumen: Este estudio evaluó el reconocimiento (imitación, identidad e identificación) y la denominación de estímulos emocionales negativos (enfado y tristeza) y positivos (alegría y sorpresa) y la influencia de los tipos de estímulos utilizados (social-femenino, social-masculino, familiar y emoji ) de niños y jóvenes con autismo o síndrome de Down, a través de tareas aplicadas por la familia, mediadas por recursos tecnológicos durante la pandemia de la covid-19. Participaron cinco niños y dos adultos jóvenes con autismo, y un niño y dos adultos jóvenes con síndrome de Down. Se planificaron e implementaron tareas de identidad, reconocimiento, nombramiento e imitación con estímulos faciales con función evaluativa (sin consecuencia diferencial) y enseñanza (con consecuencia diferencial, uso de ayudas y criterios de aprendizaje), buscando la emergencia del nombramiento emocional después de la enseñanza de tareas de reconocimiento. Los resultados de la línea de base identificaron que para los participantes que tenían un tiempo de respuesta más corto para el mismo género, la diferencia en el tiempo de respuesta fue un 57,28% menor. En cuanto a la valencia emocional, el 50% de los participantes mostraron diferencias en las respuestas correctas, en función de la valencia positiva y negativa, y el 66,66% tuvieron diferencias en el tiempo de respuesta, en función de la valencia emocional. Después del procedimiento de enseñanza, los participantes mostraron mayor número de aciertos en las tareas evaluadas, independientemente del tipo de estímulo o valencia emocional, lo que genera una oportunidad para la generalización del aprendizaje de reconocimiento y denominación de emociones, además de consolidar la viabilidad de estrategias de enseñanza mediadas por recursos tecnológicos y aplicadas por la familia.

Harnessing the power of AI language models for taxonomy and systematics: a follow-up to "Can ChatGPT be leveraged for taxonomic investigations? Potential and limitations of a new technology" by Davinack (2023).

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Comparing Phylogenetic Trees Side by Side Through iPhyloC, a New Interactive Web-Based Framework.

Current frameworks of side-by-side phylogenetic trees comparison face two issues: (1) accepting mainly binary trees as input and (2) assuming input trees having identical or highly overlapping taxa. However, cladistic comparative studies often lead with multiple nontotally resolved trees with nonidentical sets of taxa. We tackle these issues in this study, presenting the iPhyloC, an interactive web-based framework for comparing phylogenetic trees side by side. iPhyloC supports automatic identification of the common taxa in the input trees, comparison options between them, intuitive design, high usability, scalability to large trees, and cross-platform support. iPhyloC was tested using different trees and a supertree depicting the phylogenetic relationships within the insect order Diptera as examples.

Laplacian Coordinates: Theory and Methods for Seeded Image Segmentation.

Seeded segmentation methods have gained a lot of attention due to their good performance in fragmenting complex images, easy usability and synergism with graph-based representations. These methods usually rely on sophisticated computational tools whose performance strongly depends on how good the training data reflect a sought image pattern. Moreover, poor adherence to the image contours, lack of unique solution, and high computational cost are other common issues present in most seeded segmentation methods. In this work we introduce Laplacian Coordinates, a quadratic energy minimization framework that tackles the issues above in an effective and mathematically sound manner. The proposed formulation builds upon graph Laplacian operators, quadratic energy functions, and fast minimization schemes to produce highly accurate segmentations. Moreover, the presented energy functions are not prone to local minima, i.e., the solution is guaranteed to be globally optimal, a trait not present in most image segmentation methods. Another key property is that the minimization procedure leads to a constrained sparse linear system of equations, enabling the segmentation of high-resolution images at interactive rates. The effectiveness of Laplacian Coordinates is attested by a comprehensive set of comparisons involving nine state-of-the-art methods and several benchmarks extensively used in the image segmentation literature.

Building combined MRP-matrices with BuM, an automated web-tool.

The most common methods for combining different phylogenetic trees with uneven but overlapping taxon sampling are the Matrix Representation with Parsimony (MRP) and consensus tree methods. Although straightforward, some steps of MRP are time-consuming and risky when manually performed, especially the preparation of the matrix representations from the original topologies, and the creation of the single matrix containing all the information of the individual trees. Here we present Building MRP-Matrices (BuM), a free online tool for generating a combined matrix, following Baum and Ragan coding scheme, from files containing phylogenetic trees in parenthetical format.

iTUPA: an online automated application to perform Topographic-Unit Parsimony Analysis.

SUMMARY: iTUPA is a free online application for automatizing the Topographic-Unit Parsimony Analysis (TUPA), which identifies areas of endemism based on topography. iTUPA generates species-occurrences matrices based on user-defined topographic units (TUs) and provides a parsimony analysis of the generated matrix. We tested iTUPA after a proposal of regionalization for the Brazilian Atlantic Forest. iTUPA can handle millions of species registers simultaneously and uses Google Earth high-definition maps to visually explore the endemism data. We believe iTUPA is a useful tool for further discussions on biodiversity conservation. AVAILABILITY AND IMPLEMENTATION: iTUPA is hosted on Google cloud and freely available at http://nuvem.ufabc.edu.br/itupa. iTUPA is implemented using R (version 3.5.1), with RStudio 1.1.453 used as the implementation IDE, Shiny 1.1.0 web framework, and Google Maps® API version 3.36.