Revitalizing the Space Shuttle's Thermal Protection System with Reverse Engineering and 3D Vision Technology

The Space Shuttle is protected by a Thermal Protection System (TPS) made of tens of thousands of individually shaped heat protection tile. With every flight, tiles are damaged on take-off and return to earth. After each mission, the heat tiles must be fixed or replaced depending on the level of damage. As part of the return to flight mission, the TPS requirements are more stringent, leading to a significant increase in heat tile replacements. The replacement operation requires scanning tile cavities, and in some cases the actual tiles. The 3D scan data is used to reverse engineer each tile into a precise CAD model, which in turn, is exported to a CAM system for the manufacture of the heat protection tile. Scanning is performed while other activities are going on in the shuttle processing facility. Many technicians work simultaneously on the space shuttle structure, which results in structural movements and vibrations. This paper will cover a portable, ultra-fast data acquisition approach used to scan surfaces in this unstable environment

Appreciating methodological complexity and integrating neurobiological perspectives to advance the science of resilience

Kalisch and colleagues identify several routes to a better understanding of mechanisms underlying resilience and highlight the need to integrate findings from neuroscience and animal learning. We argue that appreciating methodological complexity and integrating neurobiological perspectives will advance the science of resilience and ultimately help improve the lives of those exposed to stress and adversit

Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

Predicting non-response to multimodal day clinic treatment in severely impaired depressed patients: a machine learning approach

A considerable number of depressed patients do not respond to treatment. Accurate prediction of non-response to routine clinical care may help in treatment planning and improve results. A longitudinal sample of N = 239 depressed patients was assessed at admission to multi-modal day clinic treatment, after six weeks, and at discharge. First, patient’s treatment response was modelled by identifying longitudinal trajectories using the Hamilton Depression Rating Scale (HDRS-17). Then, individual items of the HDRS-17 at admission as well as individual patient characteristics were entered as predictors of response/non-response trajectories into the binary classification model (eXtremeGradient Boosting; XGBoost). The model was evaluated on a hold-out set and explained in human-interpretable form by SHapley Additive explanation (SHAP) values. The prediction model yielded a multi-class AUC = 0.80 in the hold-out set. The predictive power for the binary classification yielded an AUC = 0.83 (sensitivity = .80, specificity = .77). Most relevant predictors for non-response were insomnia symptoms, younger age, anxiety symptoms, depressed mood, being unemployed, suicidal ideation and somatic symptoms of depressive disorder. Non-responders to routine treatment for depression can be identified and screened for potential next-generation treatments. Such predictors may help personalize treatment and improve treatment response

Development of a Measure of Postpartum PTSD: The City Birth Trauma Scale

Post-traumatic stress disorder (PTSD) affects 4% of women after birth yet there are very few questionnaire measures of postpartum PTSD that have been validated in this population. In addition, none of the available questionnaires assess postpartum PTSD in accordance with criteria specified in the latest edition of the Diagnostic and Statistical Manual [DSM-5, (1)]. The City Birth Trauma Scale is a 29-item questionnaire developed to measure birth-related PTSD according to DSM-5 criteria of: stressor criteria (A), symptoms of re-experiencing (B), avoidance (C), negative cognitions and mood (D), and hyperarousal (E), as well as duration of symptoms (F), significant distress or impairment (E), and exclusion criteria or other causes (H). Two additional items from DSM-IV were also included on the basis of evidence suggesting they might be important in this population. The first was criterion A2 that women responded to events during birth with intense fear, helplessness or horror. The second was symptoms of emotional numbing. Items were first reviewed by researchers (n = 9) and postpartum women (n = 8) and revised accordingly. The questionnaire was then completed by 950 women recruited online. Results showed the City Birth Trauma Scale had excellent reliability (Cronbach's α = 0.92) and is easy to understand (Flesch reading score 64.17). Exploratory factor analysis found two factors which together accounted for 56% of the variance: (i) Birth-related symptoms (40.8% variance) and (ii) General symptoms (15.5% variance). PTSD symptoms were highly associated with distress, impaired functioning, and women reporting they wanted treatment (r = 0.50–0.61). Removing DSM-IV A2 criteria only increased births classified as traumatic by 2%. Adding the item on emotional numbing did not change the psychometric properties of the scale. These items were therefore removed. The City Birth Trauma Scale has good psychometric properties and the two symptom clusters identified are consistent with previous research on symptoms of postpartum PTSD. This scale therefore provides a promising measure of PTSD following childbirth that can be used in research and clinical practice. Future research should examine the scale's predictive validity using clinical interviews

Impact of Cannabis Use on Treatment Outcomes among Adults Receiving Cognitive-Behavioral Treatment for PTSD and Substance Use Disorders

Background: Research has demonstrated a strong link between trauma, posttraumatic stress disorder (PTSD) and substance use disorders (SUDs) in general and cannabis use disorders in particular. Yet, few studies have examined the impact of cannabis use on treatment outcomes for individuals with co-occurring PTSD and SUDs. Methods: Participants were 136 individuals who received cognitive-behavioral therapies for co-occurring PTSD and SUD. Multivariate regressions were utilized to examine the associations between baseline cannabis use and end-of-treatment outcomes. Multilevel linear growth models were fit to the data to examine the cross-lagged associations between weekly cannabis use and weekly PTSD symptom severity and primary substance use during treatment. Results: There were no significant positive nor negative associations between baseline cannabis use and end-of-treatment PTSD symptom severity and days of primary substance use. Cross-lagged models revealed that as cannabis use increased, subsequent primary substance use decreased and vice versa. Moreover, results revealed a crossover lagged effect, whereby higher cannabis use was associated with greater PTSD symptom severity early in treatment, but lower weekly PTSD symptom severity later in treatment. Conclusion: Cannabis use was not associated with adverse outcomes in end-of-treatment PTSD and primary substance use, suggesting independent pathways of change. The theoretical and clinical implications of the reciprocal associations between weekly cannabis use and subsequent PTSD and primary substance use symptoms during treatment are discussed

SIMON: A Digital Protocol to Monitor and Predict Suicidal Ideation

Each year, more than 800,000 persons die by suicide, making it a leading cause of death worldwide. Recent innovations in information and communication technology may offer new opportunities in suicide prevention in individuals, hereby potentially reducing this number. In our project, we design digital indices based on both self-reports and passive mobile sensing and test their ability to predict suicidal ideation, a major predictor for suicide, and psychiatric hospital readmission in high-risk individuals: psychiatric patients after discharge who were admitted in the context of suicidal ideation or a suicidal attempt, or expressed suicidal ideations during their intake. Specifically, two smartphone applications -one for self-reports (SIMON-SELF) and one for passive mobile sensing (SIMON-SENSE)- are installed on participants' smartphones. SIMON-SELF uses a text-based chatbot, called Simon, to guide participants along the study protocol and to ask participants questions about suicidal ideation and relevant other psychological variables five times a day. These self-report data are collected for four consecutive weeks after study participants are discharged from the hospital. SIMON-SENSE collects behavioral variables -such as physical activity, location, and social connectedness- parallel to the first application. We aim to include 100 patients over 12 months to test whether (1) implementation of the digital protocol in such a high-risk population is feasible, and (2) if suicidal ideation and psychiatric hospital readmission can be predicted using a combination of psychological indices and passive sensor information. To this end, a predictive algorithm for suicidal ideation and psychiatric hospital readmission using various learning algorithms (e.g., random forest and support vector machines) and multilevel models will be constructed. Data collected on the basis of psychological theory and digital phenotyping may, in the future and based on our results, help reach vulnerable individuals early and provide links to just-in-time and cost-effective interventions or establish prompt mental health service contact. The current effort may thus lead to saving lives and significantly reduce economic impact by decreasing inpatient treatment and days lost to inability

Ontologies, Mental Disorders and Prototypes

As it emerged from philosophical analyses and cognitive research, most concepts exhibit typicality effects, and resist to the efforts of defining them in terms of necessary and sufficient conditions. This holds also in the case of many medical concepts. This is a problem for the design of computer science ontologies, since knowledge representation formalisms commonly adopted in this field do not allow for the representation of concepts in terms of typical traits. However, the need of representing concepts in terms of typical traits concerns almost every domain of real world knowledge, including medical domains. In particular, in this article we take into account the domain of mental disorders, starting from the DSM-5 descriptions of some specific mental disorders. On this respect, we favor a hybrid approach to the representation of psychiatric concepts, in which ontology oriented formalisms are combined to a geometric representation of knowledge based on conceptual spaces

Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric Inpatients: Proof-of-Concept Study

BACKGROUND Multiple symptoms of suicide risk have been assessed based on visual and auditory information, including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment. OBJECTIVE We aimed to examine measurements extracted from video interviews using open-source deep learning algorithms to quantify facial, vocal, and movement behaviors in relation to suicide risk severity in recently admitted patients following a suicide attempt. METHODS We utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open-source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide risk with the Beck Scale for Suicide Ideation controlling for age and sex, using multiple linear regression. RESULTS Suicide severity was associated with multiple visual and auditory markers, including speech prevalence (β=-0.68, P=.02, r$^{2}$=0.40), overall expressivity (β=-0.46, P=.10, r$^{2}$=0.27), and head movement measured as head pitch variability (β=-1.24, P=.006, r$^{2}$=0.48) and head yaw variability (β=-0.54, P=.06, r$^{2}$=0.32). CONCLUSIONS Digital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and linear associations with the severity of suicidal ideation

Heterogeneity in psychiatric diagnostic classification

The theory and practice of psychiatric diagnosis are central yet contentious. This paper examines the heterogeneous nature of categories within the DSM-5, how this heterogeneity is expressed across diagnostic criteria, and its consequences for clinicians, clients, and the diagnostic model. Selected chapters of the DSM-5 were thematically analysed: schizophrenia spectrum and other psychotic disorders; bipolar and related disorders; depressive disorders; anxiety disorders; and trauma- and stressor-related disorders. Themes identified heterogeneity in specific diagnostic criteria, including symptom comparators, duration of difficulties, indicators of severity, and perspective used to assess difficulties. Wider variations across diagnostic categories examined symptom overlap across categories, and the role of trauma. Pragmatic criteria and difficulties that recur across multiple diagnostic categories offer flexibility for the clinician, but undermine the model of discrete categories of disorder. This nevertheless has implications for the way cause is conceptualised, such as implying that trauma affects only a limited number of diagnoses despite increasing evidence to the contrary. Individual experiences and specific causal pathways within diagnostic categories may also be obscured. A pragmatic approach to psychiatric assessment, allowing for recognition of individual experience, may therefore be a more effective way of understanding distress than maintaining commitment to a disingenuous categorical system