Self-administered questionnaires enhance emotion estimation of individuals with autism spectrum disorders in a robotic interview setting.

Background: Robots offer many unique opportunities for helping individuals with autism spectrum disorders (ASD). Determining the optimal motion of robots when interacting with individuals with ASD is important for achieving more natural human-robot interactions and for exploiting the full potential of robotic interventions. Most prior studies have used supervised machine learning (ML) of user behavioral data to enable robot perception of affective states (i.e., arousal and valence) and engagement. It has previously been suggested that including personal demographic information in the identification of individuals with ASD is important for developing an automated system to perceive individual affective states and engagement. In this study, we hypothesized that assessing self-administered questionnaire data would contribute to the development of an automated estimation of the affective state and engagement when individuals with ASD are interviewed by an Android robot, which will be linked to implementing long-term interventions and maintaining the motivation of participants. Methods: Participants sat across a table from an android robot that played the role of the interviewer. Each participant underwent a mock job interview. Twenty-five participants with ASD (males 22, females 3, average chronological age = 22.8, average IQ = 94.04) completed the experiment. We collected multimodal data (i.e., audio, motion, gaze, and self-administered questionnaire data) to train a model to correctly classify the state of individuals with ASD when interviewed by an android robot. We demonstrated the technical feasibility of using ML to enable robot perception of affect and engagement of individuals with ASD based on multimodal data. Results: For arousal and engagement, the area under the curve (AUC) values of the model estimates and expert coding were relatively high. Overall, the AUC values of arousal, valence, and engagement were improved by including self-administered questionnaire data in the classification. Discussion: These findings support the hypothesis that assessing self-administered questionnaire data contributes to the development of an automated estimation of an individual's affective state and engagement. Given the efficacy of including self-administered questionnaire data, future studies should confirm the effectiveness of such long-term intervention with a robot to maintain participants' motivation based on the proposed method of emotion estimation.

Identification and characterization of endo-α-, exo-α-, and exo-ß-D-arabinofuranosidases degrading lipoarabinomannan and arabinogalactan of mycobacteria.

The cell walls of pathogenic and acidophilic bacteria, such as Mycobacterium tuberculosis and Mycobacterium leprae, contain lipoarabinomannan and arabinogalactan. These components are composed of D-arabinose, the enantiomer of the typical L-arabinose found in plants. The unique glycan structures of mycobacteria contribute to their ability to evade mammalian immune responses. In this study, we identified four enzymes (two GH183 endo-D-arabinanases, GH172 exo-α-D-arabinofuranosidase, and GH116 exo-ß-D-arabinofuranosidase) from Microbacterium arabinogalactanolyticum. These enzymes completely degraded the complex D-arabinan core structure of lipoarabinomannan and arabinogalactan in a concerted manner. Furthermore, through biochemical characterization using synthetic substrates and X-ray crystallography, we elucidated the mechanisms of substrate recognition and anomer-retaining hydrolysis for the α- and ß-D-arabinofuranosidic bonds in both endo- and exo-mode reactions. The discovery of these D-arabinan-degrading enzymes, along with the understanding of their structural basis for substrate specificity, provides valuable resources for investigating the intricate glycan architecture of mycobacterial cell wall polysaccharides and their contribution to pathogenicity.

A new group-based online job interview training program using computer graphics robots for individuals with autism spectrum disorders.

Introduction: Job interviews are a major barrier to employment for individuals with autism spectrum disorders (ASD). During the coronavirus pandemic, establishing online job interview training at home was indispensable. However, many hurdles prevent individuals with ASD from concentrating on online job interview training. To facilitate the acquisition of interview skills from home for individuals with ASD, we developed a group interview training program with a virtual conferencing system (GIT-VICS Program) that uses computer graphics (CG) robots. Methods: This study investigated the feasibility of the GIT-VICS Program in facilitating skill acquisition for face-to-face job interviews in pre-post measures. In the GIT-VICS Program, five participants were grouped and played the roles of interviewees (1), interviewers (2), and human resources (2). They alternately practiced each role in GIT-VICS Program sessions conducted over 8 or 9 days over three consecutive weeks. Before and after the GIT-VICS Program, the participants underwent a mock face-to-face job interview with two experienced human interviewers (MFH) to evaluate its effect. Results: Fourteen participants completed the trial procedures without experiencing any technological challenges or distress that would have led to the termination of the session. The GIT-VICS Program improved their job interview skills (verbal competence, nonverbal competence, and interview performance). Discussion: Given the promising results of this study and to draw clear conclusions about the efficacy of CG robots for mock online job interview training, future studies adding appropriate guidance for manner of job interview by experts are needed.