First Report of the Emerging Pathogen Kodamaea ohmeri in Honduras.

Kodamaea ohmeri is an environmental yeast considered a rare emerging pathogen. In clinical settings, the correct identification of this yeast is relevant because some isolates are associated with resistance to antifungals. There is a lack of available data regarding the geographical distribution, virulence, and drug resistance profile of K. ohmeri. To contribute to the knowledge of this yeast, this study aimed to describe in depth three isolates of K. ohmeri associated with fungemia in Honduras. The identification of the isolates was carried out by sequencing the ribosomal ITS region. In addition, the susceptibility profile to antifungals was determined, and some properties associated with virulence were evaluated (exoenzyme production, biofilm formation, cell adhesion, and invasion). The isolates showed strong protease, phospholipase, and hemolysin activity, in addition to being biofilm producers. Adherence and invasion capacity were evident in the HeLa and Raw 264.7 cell lines, respectively. This study expands the understanding of the underlying biological traits associated with virulence in K. ohmeri, and it is the first report of the detection and identification of K. ohmeri in Honduras as a cause of human infection.

A network approach to the investigation of childhood irritability: probing frustration using social stimuli.

BACKGROUND: Self-regulation in early childhood develops within a social context. Variations in such development can be attributed to inter-individual behavioral differences, which can be captured both as facets of temperament and across a normal:abnormal dimensional spectrum. With increasing emphasis on irritability as a robust early-life transdiagnostic indicator of broad psychopathological risk, linkage to neural mechanisms is imperative. Currently, there is inconsistency in the identification of neural circuits that underlie irritability in children, especially in social contexts. This study aimed to address this gap by utilizing a functional magnetic resonance imaging (fMRI) paradigm to investigate pediatric anger/frustration using social stimuli. METHODS: Seventy-three children (M = 6 years, SD = 0.565) were recruited from a larger longitudinal study on irritability development. Caregivers completed questionnaires assessing irritable temperament and clinical symptoms of irritability. Children participated in a frustration task during fMRI scanning that was designed to induce frustration through loss of a desired prize to an animated character. Data were analyzed using both general linear modeling (GLM) and independent components analysis (ICA) and examined from the temperament and clinical perspectives. RESULTS: ICA results uncovered an overarching network structure above and beyond what was revealed by traditional GLM analyses. Results showed that greater temperamental irritability was associated with significantly diminished spatial extent of activation and low-frequency power in a network comprised of the posterior superior temporal sulcus (pSTS) and the precuneus (p < .05, FDR-corrected). However, greater severity along the spectrum of clinical expression of irritability was associated with significantly increased extent and intensity of spatial activation as well as low- and high-frequency neural signal power in the right caudate (p < .05, FDR-corrected). CONCLUSIONS: Our findings point to specific neural circuitry underlying pediatric irritability in the context of frustration using social stimuli. Results suggest that a deliberate focus on the construction of network-based neurodevelopmental profiles and social interaction along the normal:abnormal irritability spectrum is warranted to further identify comprehensive transdiagnostic substrates of the irritability.

ARGUS: Visualization of AI-Assisted Task Guidance in AR.

The concept of augmented reality (AR) assistants has captured the human imagination for decades, becoming a staple of modern science fiction. To pursue this goal, it is necessary to develop artificial intelligence (AI)-based methods that simultaneously perceive the 3D environment, reason about physical tasks, and model the performer, all in real-time. Within this framework, a wide variety of sensors are needed to generate data across different modalities, such as audio, video, depth, speech, and time-of-flight. The required sensors are typically part of the AR headset, providing performer sensing and interaction through visual, audio, and haptic feedback. AI assistants not only record the performer as they perform activities, but also require machine learning (ML) models to understand and assist the performer as they interact with the physical world. Therefore, developing such assistants is a challenging task. We propose ARGUS, a visual analytics system to support the development of intelligent AR assistants. Our system was designed as part of a multi-year-long collaboration between visualization researchers and ML and AR experts. This co-design process has led to advances in the visualization of ML in AR. Our system allows for online visualization of object, action, and step detection as well as offline analysis of previously recorded AR sessions. It visualizes not only the multimodal sensor data streams but also the output of the ML models. This allows developers to gain insights into the performer activities as well as the ML models, helping them troubleshoot, improve, and fine-tune the components of the AR assistant.

PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines.

In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to generate end-to-end ML pipelines. While these techniques facilitate the creation of models, given their black-box nature, the complexity of the underlying algorithms, and the large number of pipelines they derive, they are difficult for developers to debug. It is also challenging for machine learning experts to select an AutoML system that is well suited for a given problem. In this paper, we present the Pipeline Profiler, an interactive visualization tool that allows the exploration and comparison of the solution space of machine learning (ML) pipelines produced by AutoML systems. PipelineProfiler is integrated with Jupyter Notebook and can be combined with common data science tools to enable a rich set of analyses of the ML pipelines, providing users a better understanding of the algorithms that generated them as well as insights into how they can be improved. We demonstrate the utility of our tool through use cases where PipelineProfiler is used to better understand and improve a real-world AutoML system. Furthermore, we validate our approach by presenting a detailed analysis of a think-aloud experiment with six data scientists who develop and evaluate AutoML tools.

Estrategia de orientación psicopedagógica para la prevención del síndrome de Burnout Laboral / Psychopedagogical Strategy for preventing Burnout Syndrome at workplace

RESUMEN Introducción: el síndrome de Burnout consiste en una respuesta prolongada del organismo ante los factores estresantes, emocionales e interpersonales que se presentan en el trabajo e incluye fatiga crónica, ineficacia y negación de lo ocurrido. Se suele presentar en aquellas situaciones laborales en las que los excesivos niveles de exigencia ya se han vuelto un hábito inconsciente e incluso socialmente valorado. Objetivo: elaborar una estrategia de orientación educativa a los profesores desde la función orientadora del psicopedagogo, para la prevención del Síndrome de Burnout Laboral. Métodos: se realizó un estudio descriptivo, prospectivo y cualitativo mediante los métodos teóricos y empíricos, como: histórico-lógico, análisis-síntesis, encuestas, entrevistas, observación, técnica grupal e inventario para la evaluación del Burnout. Resultados: los resultados permitieron precisar las manifestaciones de este síndrome y sus insuficiencias. En la muestra estudiada se comprobó que de 19 profesionales se manifiestan en 15 (79%), en alguna medida indicadores de esta afectación, como síntomas de cansancio emocional y la baja realización personal. Conclusiones: la estrategia de orientación aplicada contribuye con la preparación del psicopedagogo en la prevención del Síndrome de Burnout Laboral y perfecciona el proceso docente educativo, proporciona mayor estabilidad psicológica y favorece la unidad de influencias educativas.

ABSTRACT Introduction: Burnout syndrome is provoqued by a prolonged response of the organism to stress, emotional and interpersonal factors such as chronic fatigue, inefficiency and refutation. It is usually present in those workers with ridiculous levels of tasks have already become an unconscious and social habit. Objective: to elaborate an educational guidance strategy for teachers, according to their function of Psychopedagogue for Burnout syndrome prevention. Methods: a descriptive, prospective and qualitative study was carried out using theoretical and empirical methods such as: historical-logical, analysis-synthesis, surveys, interviews, observation, group technique and Burnout syndrome records. Results: there were insufficiencies. From the total sample of 19 professionals, some indicators revealed 15 (79%) of participants with emotional fatigue and low self-realization highlighted symptoms. Conclusions: the strategy contributes to prepare the psychopedagogue on Burnout syndrome prevention and improves the educational process. It also provides greater psychological stability and favors educational influences unity.