Multimodal job interview simulator for training of autistic individuals.

Autistic individuals face difficulties in finding and maintaining employment, and studies have shown that the job interview is often a significant barrier to obtaining employment. Prior computer-based job interview training interventions for autistic individuals have been associated with better interview outcomes. These previous interventions, however, do not leverage the use of multimodal data that could give insight into the emotional underpinnings of autistic individuals' challenges in job interviews. In this article, the authors present the design of a novel multimodal job interview training platform called CIRVR that simulates job interviews through spoken interaction and collects eye gaze, facial expressions, and physiological responses of the participants to understand their stress response and their affective state. Results from a feasibility study with 23 autistic participants who interacted with CIRVR are presented. In addition, qualitative feedback was gathered from stakeholders on visualizations of data on CIRVR's visualization tool called the Dashboard. The data gathered indicate the potential of CIRVR along with the Dashboard to be used in the creation of individualized job interview training of autistic individuals.

Development of a Novel Telemedicine Tool to Reduce Disparities Related to the Identification of Preschool Children with Autism.

The wait for ASD evaluation dramatically increases with age, with wait times of a year or more common as children reach preschool. Even when appointments become available, families from traditionally underserved groups struggle to access care. Addressing care disparities requires designing identification tools and processes specifically for and with individuals most at-risk for health inequities. This work describes the development of a novel telemedicine-based ASD assessment tool, the TELE-ASD-PEDS-Preschool (TAP-Preschool). We applied machine learning models to a clinical data set of preschoolers with ASD and other developmental concerns (n = 914) to generate behavioral targets that best distinguish ASD and non-ASD features. We conducted focus groups with clinicians, early interventionists, and parents of children with ASD from traditionally underrepresented racial/ethnic and linguistic groups. Focus group themes and machine learning analyses were used to generate a play-based instrument with assessment tasks and scoring procedures based on the child's language (i.e., TAP-P Verbal, TAP-P Non-verbal). TAP-P procedures were piloted with 30 families. Use of the instrument in isolation (i.e., without history or collateral information) yielded accurate diagnostic classification in 63% of cases. Children with existing ASD diagnoses received higher TAP-P scores, relative to children with other developmental concerns. Clinician diagnostic accuracy and certainty were higher when confirming existing ASD diagnoses (80% agreement) than when ruling out ASD in children with other developmental concerns (30% agreement). Utilizing an equity approach to understand the functionality and impact of tele-assessment for preschool children has potential to transform the ASD evaluation process and improve care access.

Preterm Pigs Fed Donor Human Milk Have Greater Liver ß-Carotene Concentrations than Pigs Fed Infant Formula.

BACKGROUND: Milk carotenoids may support preterm infant health and neurodevelopment. Infants fed human milk often have higher blood and tissue carotenoid concentrations than infants fed carotenoid-containing infant formula (IF). Donor human milk (DHM) is a supplement to mother's own milk, used to support preterm infant nutrition. OBJECTIVES: We tested whether tissue and plasma ß-carotene concentrations would be higher in preterm pigs fed pasteurized DHM versus premature IF. METHODS: This is a secondary analysis of samples collected from a study of the effects of enteral diet composition on necrotizing enterocolitis incidence. Preterm pigs received partial enteral feeding of either DHM (n = 7) or premature IF (n = 7) from 2 to 7 d of age. The diets provided similar ß-carotene (32 nM), but DHM had higher lutein, zeaxanthin, and lycopene, whereas IF had higher total vitamin A. Plasma, liver, and jejunum carotenoid and vitamin A concentrations were measured by HPLC-PDA. Jejunal expression of 12 genes associated with carotenoid and lipid metabolism were measured. RESULTS: Liver ß-carotene concentrations were higher in DHM- than IF-fed piglets (23 ± 4 compared with 16 ± 2 µg/g, respectively, P = 0.0024), whereas plasma and jejunal ß-carotene concentrations were similar between diets. Liver vitamin A stores were higher in piglets fed IF than DHM (50.6 ± 10.1 compared with 30.9 ± 7.2 µg/g, respectively, P=0.0013); however, plasma vitamin A was similar between groups. Plasma, liver, and jejunum concentrations of lutein, zeaxanthin, and lycopene were higher with DHM than IF feeding. Relative to piglets fed DHM, jejunal low density lipoprotein receptor (Ldlr) expression was higher (61%, P = 0.018) and cluster determinant 36 (Cd36) expression (-27%, P = 0.034) was lower in IF-fed piglets. CONCLUSIONS: Preterm pigs fed DHM accumulate more liver ß-carotene than IF-fed pigs. Future studies should further investigate infant carotenoid bioactivity and bioavailability.

Paisley: Preliminary validation of a novel app-based e-Screener for ASD in children 18-36 months.

The purpose of this study was to assess the validity of an autism e-screener, Paisley, when utilized in a clinical research setting via a tablet application. The Paisley application used a series of play-based activities, all of which incorporated varying aspects of the ASD-PEDS. Participants included children (18-36 months; n = 198) referred for evaluation of autism spectrum disorder (ASD) and community providers (n = 66) with differing levels of familiarity with ASD. Community providers administered the Paisley application to children who then completed a comprehensive psychological evaluation. Based on comprehensive evaluation, 75% of children met diagnostic criteria for ASD. Paisley scores were significantly higher for children diagnosed with ASD (15.06) versus those not diagnosed (9.34). The newly determined cutoff ASD-PEDS cutoff score of 13 had significantly higher specificity and positive predictive value than the originally proposed cutoff of 11. Results support the use of Paisley by community providers to identify autism risk in toddlers. Limitations and strengths of the work, as well as opportunities for future clinical validation, are described.

Discordant Congenital Toxoplasmosis and Cytomegalovirus Infection in Dichorionic Diamniotic Twins.

BACKGROUND: Toxoplasmosis and cytomegalovirus (CMV) congenital infection present with similar clinical pictures. Both infections have long-term sequelae that can be mitigated by early detection and treatment. Coinfection is uncommonly reported. METHODS: Dichorionic diamniotic twins born at 35 weeks of gestation were investigated for congenital infections due to abnormalities on the antenatal scan at 31 weeks of gestation. Antenatal investigations were delayed due to late booking and delay in maternal investigations. In the neonatal period, they suffered discordant symptoms and were both investigated for Toxoplasma gondii infection. This diagnosis was confirmed in twin 2 but proved difficult in twin 1 who had a weakly positive polymerase chain reaction with inconclusive serology. Twin 1 was also diagnosed with congenital CMV, further complicating the clinical picture. Toxoplasmosis can cause long-term sequelae, and definitive diagnosis requires serology at 12 months of age; in view of this, treatment for congenital toxoplasmosis was initiated in both twins. Twin 1 was also treated for congenital CMV. RESULTS: Due to limitations in serological investigations in neonates, diagnosing congenital toxoplasmosis can be challenging, and initiating treatment may be warranted in suspected cases, given the risk of infective complications. Discordant presentations between twins are known in congenital toxoplasmosis and CMV, but coinfection has rarely been reported without concurrent immunocompromise. A high index of suspicion should be maintained in the twin of an infected neonate, and the possibility of multiple infections should be considered. Multidisciplinary working is crucial in reaching a diagnosis and treating appropriately.

Impact of major bleeding and thrombosis on 180-day survival in patients with severe COVID-19 supported with veno-venous extracorporeal membrane oxygenation in the United Kingdom: a multicentre observational study.

Bleeding and thrombosis are major complications in patients supported with extracorporeal membrane oxygenation (ECMO). In this multicentre observational study of 152 consecutive patients (≥18 years) with severe COVID-19 supported by veno-venous (VV) ECMO in four UK commissioned centres during the first wave of the COVID-19 pandemic (1 March to 31 May 2020), we assessed the incidence of major bleeding and thrombosis and their association with 180-day mortality. Median age (range) was 47 years (23-65) and 75% were male. Overall, the 180-day survival was 70·4% (107/152). The rate of major bleeding was 30·9% (47/152), of which intracranial bleeding (ICH) was 34% (16/47). There were 96 thrombotic events (63·1%) consisting of venous 44·7% [68/152 of which 66·2% were pulmonary embolism (PE)], arterial 18·6% (13/152) and ECMO circuit thrombosis 9·9% (15/152). In multivariate analysis, only raised lactate dehydrogenase (LDH) at the initiation of VV ECMO was associated with an increased risk of thrombosis [hazard ratio (HR) 1·92, 95% CI 1·21-3·03]. Major bleeding and ICH were associated with 3·87-fold (95% CI 2·10-7·23) and 5·97-fold [95% confidence interval (CI) 2·36-15·04] increased risk of mortality and PE with a 2·00-fold (95% CI1·09-3·56) risk of mortality. This highlights the difficult balancing act often encountered when managing coagulopathy in COVID-19 patients supported with ECMO.

Material Recognition via Heat Transfer Given Ambiguous Initial Conditions.

Humans and robots can recognize materials with distinct thermal effusivities by making physical contact and observing temperatures during heat transfer. This works well with room temperature materials, yet research has shown that contact with distinct materials can result in similar temperatures and confusion when one material is heated or cooled. To thoroughly investigate this form of ambiguity, we designed a psychophysical experiment in which a participant discriminates between two materials given initial conditions that result in similar temperatures (i.e., ambiguous initial conditions). In this article, we conducted a study with 32 human participants and a robot. Humans and the robot confused the materials. We also found that robots can overcome this ambiguity using two temperature sensors with different temperatures prior to contact. We support this conclusion based on a mathematical proof using a heat transfer model and empirical results in which a robot achieved 100% accuracy compared to 5% human accuracy. Our results also indicate that robots with a single temperature sensor can use subtle cues to outperform humans. Overall, our work provides insights into challenging conditions for material recognition via heat transfer, and suggests methods by which robots can overcome these challenges.

Toward Novel Tools for Autism Identification: Fusing Computational and Clinical Expertise.

Barriers to identifying autism spectrum disorder (ASD) in young children in a timely manner have led to calls for novel screening and assessment strategies. Combining computational methods with clinical expertise presents an opportunity for identifying patterns within large clinical datasets that can inform new assessment paradigms. The present study describes an analytic approach used to identify key features predictive of ASD in young children, drawn from large amounts of data from comprehensive diagnostic evaluations. A team of expert clinicians used these predictive features to design a set of assessment activities allowing for observation of these core behaviors. The resulting brief assessment underlies several novel approaches to the identification of ASD that are the focus of ongoing research.

Decoupling of spontaneous facial mimicry from emotion recognition in schizophrenia.

Past research indicates that spontaneous mimicry facilitates the decoding of others' emotions, leading to enhanced social perception and interpersonal rapport. Individuals with schizophrenia (SZ) show consistent deficits in emotion recognition and expression associated with poor social functioning. Given the prominence of blunted affect in schizophrenia, it is possible that spontaneous facial mimicry may also be impaired. However, studies assessing automatic facial mimicry in schizophrenia have yielded mixed results. It is therefore unknown whether emotion recognition deficits and impaired automatic facial mimicry are related in schizophrenia. SZ and demographically matched controls (CO) participated in a dynamic emotion recognition task. Electromyographic activity in muscles responsible for producing facial expressions was recorded during the task to assess spontaneous facial mimicry. SZ showed deficits in emotion identification compared to CO, but there was no group difference in the predictive power of spontaneous facial mimicry for avatar's expressed emotion. In CO, facial mimicry supported accurate emotion recognition, but it was decoupled in SZ. The finding of intact facial mimicry in SZ bears important clinical implications. For instance, clinicians might be able to improve the social functioning of patients by teaching them to pair specific patterns of facial muscle activation with distinct emotion words.

Brief Report: Evaluating the Utility of Varied Technological Agents to Elicit Social Attention from Children with Autism Spectrum Disorders.

Technological agents could be effective tools to be used in interventions for enhancing social orienting for some young children with ASD. We examined response to social bids in preschool children with ASD and typical development (TD) at a very early age (i.e., around 3 years) using social prompts presented by technological agents of various forms and human comparisons. Children with ASD demonstrated less response overall to social bids compared to TD controls, across agents or human. They responded more often to a simple humanoid robot and the simple avatar compared to the human. These results support the potential utilization of specific robotic and technological agents for harnessing and potentially increasing motivation to socially-relevant behaviors in some young children with ASD.

Feasibility of Automated Mobility Assessment of Older Adults via an Instrumented Cane.

This study explored the feasibility of automated characterization of functional mobility via an Instrumented Cane System (ICS) within an older adult sample of cane users. An off-the-shelf offset cane was instrumented with inertial, force, and ultrasound sensors for noninvasive data collection. Eighteen patients from a neurological out-patient rehabilitation clinic and nine independently mobile controls participated in standard clinical evaluations of mobility using the ICS while under the care of an attending physical therapist. Feasibility of the ICS was gauged through two studies. The first demonstrated the capability of the ICS to reliably collect meaningful usage metrics, and the second provided preliminary support for the discriminability of high and low falls risk from system-reported metrics. Specifically, the cane significantly differentiated patients and controls (p < 0.05), and a measure of the variation in rotational velocity was associated with total scores on the Functional Gait Assessment (partial r = 0.61, p < 0.01). These findings may ultimately serve to complement and even extend current clinical assessment practices.

The acceptability and feasibility of a novel virtual reality based social skills training game for schizophrenia: Preliminary findings.

Social impairment is a core feature of schizophrenia that presents a major barrier toward recovery. Some of the psychotic symptoms are partly ameliorated by medication but the route to recovery is hampered by social impairments. Since existing social skills interventions tend to suffer from lack of availability, high-burden and low adherence, there is a dire need for an effective, alternative strategy. The present study examined the feasibility and acceptability of Multimodal Adaptive Social Intervention in Virtual Reality (MASI-VR) for improving social functioning and clinical outcomes in schizophrenia. Out of eighteen patients with schizophrenia who enrolled, seventeen participants completed the pre-treatment assessment and 10 sessions of MASI-VR, but one patient did not complete the post-treatment assessments. Therefore, the complete training plus pre- and post-treatment assessment data are available from sixteen participants. Clinical ratings of symptom severity were obtained at pre- and post-training. Retention rates were very high and training was rated as extremely satisfactory for the majority of participants. Participants exhibited a significant reduction in overall clinical symptoms, especially negative symptoms following 10 sessions of MASI-VR. These preliminary results support the feasibility and acceptability of a novel virtual reality social skills training program for individuals with schizophrenia.

A new measure of child vocal reciprocity in children with autism spectrum disorder.

Children's vocal development occurs in the context of reciprocal exchanges with a communication partner who models "speechlike" productions. We propose a new measure of child vocal reciprocity, which we define as the degree to which an adult vocal response increases the probability of an immediately following child vocal response. Vocal reciprocity is likely to be associated with the speechlikeness of vocal communication in young children with autism spectrum disorder (ASD). Two studies were conducted to test the utility of the new measure. The first used simulated vocal samples with randomly sequenced child and adult vocalizations to test the accuracy of the proposed index of child vocal reciprocity. The second was an empirical study of 21 children with ASD who were preverbal or in the early stages of language development. Daylong vocal samples collected in the natural environment were computer analyzed to derive the proposed index of child vocal reciprocity, which was highly stable when derived from two daylong vocal samples and was associated with speechlikeness of vocal communication. This association was significant even when controlling for chance probability of child vocalizations to adult vocal responses, probability of adult vocalizations, or probability of child vocalizations. A valid measure of children's vocal reciprocity might eventually improve our ability to predict which children are on track to develop useful speech and/or are most likely to respond to language intervention. A link to a free, publicly-available software program to derive the new measure of child vocal reciprocity is provided. Autism Res 2018, 11: 903-915. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Children and adults often engage in back-and-forth vocal exchanges. The extent to which they do so is believed to support children's early speech and language development. Two studies tested a new measure of child vocal reciprocity using computer-generated and real-life vocal samples of young children with autism collected in natural settings. The results provide initial evidence of accuracy, test-retest reliability, and validity of the new measure of child vocal reciprocity. A sound measure of children's vocal reciprocity might improve our ability to predict which children are on track to develop useful speech and/or are most likely to respond to language intervention. A free, publicly-available software program and manuals are provided.

Extraction of Emotional Information via Visual Scanning Patterns: A Feasibility Study of Participants with Schizophrenia and Neurotypical Individuals.

Emotion recognition impairment is a core feature of schizophrenia (SZ), present throughout all stages of this condition, and leads to poor social outcome. However, the underlying mechanisms that give rise to such deficits have not been elucidated and hence, it has been difficult to develop precisely targeted interventions. Evidence supports the use of methods designed to modify patterns of visual attention in individuals with SZ in order to effect meaningful improvements in social cognition. To date, however, attention-shaping systems have not fully utilized available technology (e.g., eye tracking) to achieve this goal. The current work consisted of the design and feasibility testing of a novel gaze-sensitive social skills intervention system called MASI-VR. Adults from an outpatient clinic with confirmed SZ diagnosis (n=10) and a comparison sample of neurotypical participants (n=10) were evaluated on measures of emotion recognition and visual attention at baseline assessment, and a pilot test of the intervention system was evaluated on the SZ sample following five training sessions over three weeks. Consistent with the literature, participants in the SZ group demonstrated lower recognition of faces showing medium intensity fear, spent more time deliberating about presented emotions, and had fewer fixations in comparison to neurotypical peers. Furthermore, participants in the SZ group showed significant improvement in the recognition of fearful faces post-training. Preliminary evidence supports the feasibility of a gaze-sensitive paradigm for use in assessment and training of emotion recognition and social attention in individuals with SZ, thus warranting further evaluation of the novel intervention.

EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention.

OBJECTIVE: To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. METHODS: Twenty adolescents with ASD participated in a six-session virtual reality driving simulator-based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist's rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins () were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. RESULTS: The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). CONCLUSION: Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. SIGNIFICANCE: The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention.

Cognitive Load Measurement in a Virtual Reality-based Driving System for Autism Intervention.

Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes-feature level fusion, decision level fusion and hybrid level fusion-were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization.

Are within-person Numerical Rating Scale (NRS) ratings of breathlessness 'on average' valid in advanced disease for patients and for patients' informal carers?

INTRODUCTION: The Numerical Rating Scale (NRS) is frequently used to assess patient-reported breathlessness in both a research and clinical context. A subgroup of patients report average breathlessness as worse than their worst breathlessness in the last 24 hours (paradoxical average). The Peak/End rule describes how the most extreme and current breathlessness influence reported average. This study seeks to highlight the existence of a subpopulation who give 'paradoxical averages using the NRS, to characterise this group and to investigate the explanatory relevance of the 'Peak/End' rule. METHODS: Data were collected within mixed method face-to-face interviews for three studies: the Living with Breathlessness Study and the two subprotocols of the Breathlessness Intervention Service phase III randomised controlled trial. Key variables from the three datasets were pooled (n=561), and cases where participants reported a paradoxical average (n=45) were identified. These were compared with non-cases and interview transcripts interrogated. NRS ratings of average breathlessness were assessed for fit to Peak/End rule. RESULTS: Patients in the paradoxical average group had higher Chronic Respiratory Questionnaire physical domain scores on average p=0.042). Peak/End rule analysis showed high positive correlation (Spearman's rho=0.756, p<0.001). CONCLUSIONS: The NRS requires further standardisation with reporting of question order and construction of scale used to enable informed interpretation. The application of the Peak/End rule demonstrates fallibility of NRS-Average as a construct as it is affected by current breathlessness. Measurement of breathlessness is important for both clinical management and research, but standardisation and transparency are required for meaningful results.

A Pilot Study Assessing Performance and Visual Attention of Teenagers with ASD in a Novel Adaptive Driving Simulator.

Individuals with Autism Spectrum Disorder (ASD), compared to typically-developed peers, may demonstrate behaviors that are counter to safe driving. The current work examines the use of a novel simulator in two separate studies. Study 1 demonstrates statistically significant performance differences between individuals with (N = 7) and without ASD (N = 7) with regards to the number of turning-related driving errors (p < 0.01). Study 2 shows that both the performance-based feedback group (N = 9) and combined performance- and gaze-sensitive feedback group (N = 8) achieved statistically significant reductions in driving errors following training (p < 0.05). These studies are the first to present results of fine-grained measures of visual attention of drivers and an adaptive driving intervention for individuals with ASD.

A Step towards EEG-based brain computer interface for autism intervention.

Autism Spectrum Disorder (ASD) is a prevalent and costly neurodevelopmental disorder. Individuals with ASD often have deficits in social communication skills as well as adaptive behavior skills related to daily activities. We have recently designed a novel virtual reality (VR) based driving simulator for driving skill training for individuals with ASD. In this paper, we explored the feasibility of detecting engagement level, emotional states, and mental workload during VR-based driving using EEG as a first step towards a potential EEG-based Brain Computer Interface (BCI) for assisting autism intervention. We used spectral features of EEG signals from a 14-channel EEG neuroheadset, together with therapist ratings of behavioral engagement, enjoyment, frustration, boredom, and difficulty to train a group of classification models. Seven classification methods were applied and compared including Bayes network, naïve Bayes, Support Vector Machine (SVM), multilayer perceptron, K-nearest neighbors (KNN), random forest, and J48. The classification results were promising, with over 80% accuracy in classifying engagement and mental workload, and over 75% accuracy in classifying emotional states. Such results may lead to an adaptive closed-loop VR-based skill training system for use in autism intervention.