Autism-spectrum-disorders in adolescence and adulthood: is it possible to further optimize the diagnostic process using machine learning?

Introduction: Autism spectrum disorders (ASD) are serious and lifelong disorders that significantly impair the quality of life of those affected throughout the entire life span. Previous studies show that a diagnosis of high-functioning ASD often goes unrecognized until adolescence and adulthood. However, a correct diagnosis and precise differentiation between co-morbidity and differential diagnosis is of great importance for those affected and for the planning of appropriate psychosocial interventions. Diagnosing ASD in adolescence and adulthood is a complicated and time-consuming process, which requires the use of various standardized diagnostic tools and high clinical expertise. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than those comprising the full ADOS (Modules 1-3). Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. The present study investigates whether machine learning techniques can be used to identify a reduced subset of psychopathological characteristics from the ADOS Module 4 that can precisely differentiate between adolescents and adults with ASD and adolescents and adults with other clinical disorders. Methods: A sub-sample of the "ASD-Net" consortium consisting of 673 diagnosed cases from a clinical population was examined. The clinical diagnosis was based on the established international diagnostic “gold standard” of ASD. 57% of all cases received a diagnosis of ASD ("ASD": n=385, age 25.6 years, 74% male, IQ=104.7) and 43% did not receive a diagnosis of ASD but relevant differential diagnoses or no current psychiatric disorders ("non-ASD": n=288, age 26.8 years, 73% male, IQ=104.8). The data was analyzed using Support Vector Machine (SVM) to identify a subset of items from the ADOS module 4 that differentiate between the two classes (ASD vs. non-ASD). Results: We identified reduced subsets of 5 behavioral features for the whole sample as well as for each age subgroup (adolescents vs. adults) that showed good specificity and sensitivity. Our reduced subsets reached performance comparable to that of the full ADOS (consisting of 31 items) and depict a substantial reduction in the number of items that have to be coded. Conclusion: Although all items of the ADOS capture relevant behavioural concepts, the identified behavioural characteristics may include essential constructs that differentiate particularly well between individuals with ASD and individuals with other clinically complex presentations. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis.Einleitung: Autismus-Spektrum-Störungen (autism spectrum disorders, ASD) sind schwerwiegende und lebenslange Erkrankungen, die die Lebensqualität der Betroffenen in allen Lebensbereichen beeinträchtigen. Studien zeigen, dass die Diagnose einer hochfunktionalen ASD oftmals bis ins Jugend- und Erwachsenenalter unerkannt bleibt. Im fortgeschrittenen Alter ist eine Diagnosestellung häufig durch das Fehlen verlässlicher Aussagen über die frühe Entwicklung sowie durch Symptomüberlappungen verschiedener Störungsbilder mit ASD erschwert. Eine korrekte Diagnosestellung ist jedoch für die Betroffenen und für die Planung geeigneter psychosozialer Interventionen von hoher Bedeutung. Die Diagnosestellung einer ASD ist ein komplizierter und zeitaufwendiger Prozess, der den Einsatz verschiedener Diagnoseinstrumente erfordert. Eines der am häufigsten verwendeten Diagnoseinstrumente ist die Diagnostische Beobachtungsskala für Autistische Störungen (ADOS). Frühere Studien konnten mithilfe von Verfahren des maschinellen Lernens zeigen, dass die ASD-Klassifizierung bei Kindern (ADOS Module 1-3) mit wesentlich weniger Items erreicht werden kann als mit der gesamten ADOS. In der vorliegenden Studie wird diese Fragestellung auf das Jugend- und Erwachsenenalter (ADOS Modul 4) ausgeweitet. Es wird untersucht, ob mithilfe von Verfahren des maschinellen Lernens Merkmale aus der ADOS identifiziert werden können, die gut zwischen Jugendlichen und Erwachsenen mit ASD und mit anderen klinischen Störungsbildern differenzieren. Methodik: Untersucht wurden 673 diagnostizierte Fälle einer psychiatrischen Inanspruchnahme-Population. Die klinische Diagnosestellung erfolgte nach etabliertem Goldstandard. Bei 57% der Fälle lag eine ASD Diagnose vor („ASD“: n=385, Alter 25,6 Jahre, 74% männlich, IQ=104,7), bei 43% lagen relevante Differentialdiagnosen oder keine psychischen Störungen vor („non-ASD“: n=288, Alter 26,8 Jahre, 73% männlich, IQ=104,8). Die Daten wurden mittels Support Vector Machine (SVM) analysiert. Ergebnisse: Es ließen sich reduzierte Subsets bestehend aus 5 Verhaltensmerkmalen für die Gesamtstichprobe sowie für die Subgruppen (Jugendliche vs. Erwachsene) identifizieren, die eine gute Spezifität und Sensitivität aufwiesen. Die identifizierten Subsets stellen eine bedeutsame Reduzierung der zu kodierenden Items dar und wiesen dabei vergleichbar hohe Klassifizierungsleistungen auf wie die gesamte ADOS (bestehend aus 31 Items). Schlussfolgerung: Obwohl alle Items der ADOS relevante Verhaltenskonzepte umfassen, erfassen die identifizierten Items möglicherweise wesentliche Merkmale, die besonders gut zwischen Personen mit ASD und Personen mit anderen klinischen Störungsbildern differenzieren. Die Ergebnisse können einen Beitrag zur Verbesserung des ASD-Diagnoseprozesses bei Jugendlichen und Erwachsenen leisten, indem die identifizierten Items die Grundlage für die Entwicklung neuer Screening-Instrumente liefern und Kliniker*innen bei der schwierigen diagnostischen Entscheidung unterstützen

Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning

Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis

Rates of photosynthesis and transpiration of spring wheat and barley as influenced by fodder precrops and their cropping period

Perennial fodder cropping as compared to rotations including annual ploughing can increase the number of medium and large sized biopores in the subsoil. This can result in potentially facilitated root growth followed by increased accessibility of water in the subsoil. Additional plant-available water can enable stands to cope with dry periods in early summer, which will take place more often in future due to climate change. In this context we investigated whether crop species or cropping period of forage cropping influenced rates of photosynthesis and transpiration as indicators for water availability of spring wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.)

How Do Adults with Autism Spectrum Disorder Participate in the Labor Market? A German Multi-center Survey

International studies show disadvantages for adults with autism spectrum disorder (ASD) in the labor market. Data about their participation in the German labor market are scarce. The aim of this study was to examine the integration of adults with ASD in the German labor market in terms of education, employment and type of occupation by means of a cross-sectional-study, using a postal questionnaire. Findings show above average levels of education for adults with ASD compared to the general population of Germany and simultaneously, below average rates of employment and high rates of financial dependency. That indicates a poor integration of adults with ASD in the German labor market and emphasizes the need for vocational support policies for adults with ASD

Complementary and alternative medicine use in adults with autism spectrum disorder in Germany: results from a multi-center survey

Abstract Background Complementary and Alternative Medicine (CAM) is widely used both in the general population and for the treatment of somatic and psychiatric disorders. Studies on CAM use among patients with autism spectrum disorder (ASD) have so far only focused on children and adolescents. The aim of this study was to investigate patterns of CAM use among adults with ASD. Methods A questionnaire survey concerning current and lifetime use of CAM was distributed to adults with ASD between November 2015 and June 2016. Participants diagnosed by experienced clinicians using the current diagnostic gold standard were recruited from four ASD outpatient clinics in Germany. Questionnaire data was then linked to supplementary clinical data. Results The final sample consisted of 192 adults (response: 26.8%) with a mean age of 31.5 years (80% male; diagnoses: Asperger’s syndrome (58%), childhood autism (27%), atypical autism (12%)). 45% of the respondents stated that they were currently using or had used at least one CAM modality in their life. Among the participants with lifetime CAM use, almost half had used two or more different types of CAM. Alternative medical systems (e.g. homeopathy, acupuncture) were most frequently used, followed by mind-body interventions (e.g. yoga, biofeedback, animal assisted therapy). Overall, 20% of respondents stated that they would like to try at least one listed CAM modality in the future. Conclusions This is the first study on CAM use in adults with ASD, demonstrating considerable CAM use in this population. Given the popularity of CAM, patients should be informed about the effectiveness and potentially dangerous side effects of CAM treatments, as evidence for the majority of CAM methods in ASD is still limited

Integrating chytrid fungal parasites into plankton ecology: research gaps and needs

Chytridiomycota, often referred to as chytrids, can be virulent parasites with the potential to inflict mass mortalities on hosts, causing e.g. changes in phytoplankton size distributions and succession, and the delay or suppression of bloom events. Molecular environmental surveys have revealed an unexpectedly large diversity of chytrids across a wide range of aquatic ecosystems worldwide. As a result, scientific interest towards fungal parasites of phytoplankton has been gaining momentum in the past few years. Yet, we still know little about the ecology of chytrids, their life cycles, phylogeny, host specificity and range. Information on the contribution of chytrids to trophic interactions, as well as co‐evolutionary feedbacks of fungal parasitism on host populations is also limited. This paper synthesizes ideas stressing the multifaceted biological relevance of phytoplankton chytridiomycosis, resulting from discussions among an international team of chytrid researchers. It presents our view on the most pressing research needs for promoting the integration of chytrid fungi into aquatic ecology.UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Biologí

Megahertz single-particle imaging at the European XFEL

The emergence of high repetition-rate X-ray free-electron lasers (XFELs) powered by superconducting accelerator technology enables the measurement of significantly more experimental data per day than was previously possible. The European XFEL is expected to provide 27,000 pulses per second, over two orders of magnitude more than any other XFEL. The increased pulse rate is a key enabling factor for single-particle X-ray diffractive imaging, which relies on averaging the weak diffraction signal from single biological particles. Taking full advantage of this new capability requires that all experimental steps, from sample preparation and delivery to the acquisition of diffraction patterns, are compatible with the increased pulse repetition rate. Here, we show that single-particle imaging can be performed using X-ray pulses at megahertz repetition rates. The results obtained pave the way towards exploiting high repetition-rate X-ray free-electron lasers for single-particle imaging at their full repetition rate