Physiological Reactions in the Therapist and Turn-Taking during Online Psychotherapy with Children and Adolescents with Autism Spectrum Disorder.

This study aims to analyze the relationship between the sociocognitive skills of a group of children and adolescents with autism spectrum disorder (ASD) at verbal level 1, the variability of the therapist's heart rate (HRV), and the conversational turn-taking during online psychotherapy sessions. Initially, we assessed the intelligence, narrative, and behavioral characteristics of the participants. We videotaped the online sessions and recorded the therapist's HRV via a smart wireless sensor. Finally, we analyzed the video sessions using an observation system and the therapist's HRV using the Poincaré technique. The results show that the patients' communicative intention was related to their narrative, intellectual and social competencies. Furthermore, the turn-taking between the therapist and the participant was associated with the patient's emotional and behavioral difficulties. On the other side, the therapist's heart rate variability (HRV) was related to the synchrony between the therapist and the participant with more significant stress on the therapist, when he shared and expanded the conversation with the patient, and when the patient broadened and shared the conversation with the therapist.

Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence.

The high infectivity of SARS-CoV-2 makes it essential to develop a rapid and accurate diagnostic test so that carriers can be isolated at an early stage. Viral RNA in nasopharyngeal samples by RT-PCR is currently considered the reference method although it is not recognized as a strong gold standard due to certain drawbacks. Here we develop a methodology combining the analysis of from human nasopharyngeal (NP) samples by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with the use of machine learning (ML). A total of 236 NP samples collected in two different viral transport media were analyzed with minimal sample preparation and the subsequent mass spectra data was used to build different ML models with two different techniques. The best model showed high performance in terms of accuracy, sensitivity and specificity, in all cases reaching values higher than 90%. Our results suggest that the analysis of NP samples by MALDI-TOF MS and ML is a simple, safe, fast and economic diagnostic test for COVID-19.

Temperature profile retrieval in axisymmetric combustion plumes using multilayer perceptron modeling and spectral feature selection in the infrared CO2 emission band.

In this work, a methodology based on the combined use of a multilayer perceptron model fed using selected spectral information is presented to invert the radiative transfer equation (RTE) and to recover the spatial temperature profile inside an axisymmetric flame. The spectral information is provided by the measurement of the infrared CO2 emission band in the 3-5 µm spectral region. A guided spectral feature selection was carried out using a joint criterion of principal component analysis and a priori physical knowledge of the radiative problem. After applying this guided feature selection, a subset of 17 wavenumbers was selected. The proposed methodology was applied over synthetic scenarios. Also, an experimental validation was carried out by measuring the spectral emission of the exhaust hot gas plume in a microjet engine with a Fourier transform-based spectroradiometer. Temperatures retrieved using the proposed methodology were compared with classical thermocouple measurements, showing a good agreement between them. Results obtained using the proposed methodology are very promising and can encourage the use of sensor systems based on the spectral measurement of the CO2 emission band in the 3-5 µm spectral window to monitor combustion processes in a nonintrusive way.

Recursive discriminant regression analysis to find homogeneous groups.

The main motivation of this paper is to propose a method to extract the output structure and find the input data manifold that best represents that output structure in a multivariate regression problem. A graph similarity viewpoint is used to develop an algorithm based on LDA, and to find out different output models which are learned as an input subspace. The main novelty of the algorithm is related with finding different structured groups and apply different models to fit better those structures. Finally, the proposed method is applied to a real remote sensing retrieval problem where we want to recover the physical parameters from a spectrum of energy.