The Potential of Accelerating Early Detection of Autism through Content Analysis of YouTube Videos

Abstract Autism is on the rise, with 1 in 88 children receiving a diagnosis in the United States, yet the process for diagnosis remains cumbersome and time consuming. Research has shown that home videos of children can help increase the accuracy of diagnosis. However the use of videos in the diagnostic process is uncommon. In the present study, we assessed the feasibility of applying a gold-standard diagnostic instrument to brief and unstructured home videos and tested whether video analysis can enable more rapid detection of the core features of autism outside of clinical environments. We collected 100 public videos from YouTube of children ages 1–15 with either a self-reported diagnosis of an ASD (N = 45) or not (N = 55). Four non-clinical raters independently scored all videos using one of the most widely adopted tools for behavioral diagnosis of autism, the Autism Diagnostic Observation Schedule-Generic (ADOS). The classification accuracy was 96.8%, with 94.1% sensitivity and 100% specificity, the inter-rater correlation for the behavioral domains on the ADOS was 0.88, and the diagnoses matched a trained clinician in all but 3 of 22 randomly selected video cases. Despite the diversity of videos and non-clinical raters, our results indicate that it is possible to achieve high classification accuracy, sensitivity, and specificity as well as clinically acceptable inter-rater reliability with nonclinical personnel. Our results also demonstrate the potential for video-based detection of autism in short, unstructured home videos and further suggests that at least a percentage of the effort associated with detection and monitoring of autism may be mobilized and moved outside of traditional clinical environments

Brown Planthopper (N. lugens Stal) Feeding Behaviour on Rice Germplasm as an Indicator of Resistance

BACKGROUND: The brown planthopper (BPH) Nilaparvata lugens (Stal) is a serious pest of rice in Asia. Development of novel control strategies can be facilitated by comparison of BPH feeding behaviour on varieties exhibiting natural genetic variation, and then elucidation of the underlying mechanisms of resistance. METHODOLOGY/PRINCIPAL FINDINGS: BPH feeding behaviour was compared on 12 rice varieties over a 12 h period using the electrical penetration graph (EPG) and honeydew clocks. Seven feeding behaviours (waveforms) were identified and could be classified into two phases. The first phase involved patterns of sieve element location including non penetration (NP), pathway (N1+N2+N3), xylem (N5) [21] and two new feeding waveforms, derailed stylet mechanics (N6) and cell penetration (N7). The second feeding phase consisted of salivation into the sieve element (N4-a) and sieve element sap ingestion (N4-b). Production of honeydew drops correlated with N4-b waveform patterns providing independent confirmation of this feeding behaviour. CONCLUSIONS/SIGNIFICANCE: Overall variation in feeding behaviour was highly correlated with previously published field resistance or susceptibility of the different rice varieties: BPH produced lower numbers of honeydew drops and had a shorter period of phloem feeding on resistant rice varieties, but there was no significant difference in the time to the first salivation (N4-b). These qualitative differences in behaviour suggest that resistance is caused by differences in sustained phloem ingestion, not by phloem location. Cluster analysis of the feeding and honeydew data split the 12 rice varieties into three groups: susceptible, moderately resistant and highly resistant. The screening methods that we have described uncover novel aspects of the resistance mechanism (or mechanisms) of rice to BPH and will in combination with molecular approaches allow identification and development of new control strategies

From 'publish or perish' to 'partner with patients or perish'

"The medical writing world is evolving rapidly, but a focus on patient partnerships provides writers with a ‘North Star’ – a strong, sound and stable guide for navigating their future"This article will help medical writers prepare to partner, ethically and effectively, with patients when working on publications, decentralised clinical trials, and diversity, equity, and inclusion initiatives.Full special edition of ICT 2024 medical writing here:https://www.calameo.com/read/00611338545666a5c40f0?page=33</p

The GapMap project: a mobile surveillance system to map diagnosed autism cases and gaps in autism services globally

Abstract Although the number of autism diagnoses is on the rise, we have no evidence-based tracking of size and severity of gaps in access to autism-related resources, nor do we have methods to geographically triangulate the locations of the widest gaps in either the US or elsewhere across the globe. To combat these related issues of (1) mapping diagnosed cases of autism and (2) quantifying gaps in access to key intervention services, we have constructed a crowd-based mobile platform called “GapMap” ( http://gapmap.stanford.edu ) for real-time tracking of autism prevalence and autism-related resources that can be accessed from any mobile device with cellular or wireless connectivity. Now in beta, our aim is for this Android/iOS compatible mobile tool to simultaneously crowd-enroll the massive and growing community of families with autism to capture geographic, diagnostic, and resource usage information while automatically computing prevalence at granular geographical scales to yield a more complete and dynamic understanding of autism resource epidemiology

Development of flexible, free-standing, thin films for additive manufacturing and localized energy generation

Film energetics are becoming increasingly popular because a variety of technologies are driving a need for localized energy generation in a stable, safe and flexible form. Aluminum (Al) and molybdenum trioxide (MoO3) composites were mixed into a silicon binder and extruded using a blade casting technique to form flexible free-standing films ideal for localized energy generation. Since this material can be extruded onto a surface it is well suited to additive manufacturing applications. This study examines the influence of 0-35% by mass potassium perchlorate (KClO4) additive on the combustion behavior of these energetic films. Without KClO4 the film exhibits thermal instabilities that produce unsteady energy propagation upon reaction. All films were cast at a thickness of 1 mm with constant volume percent solids to ensure consistent rheological properties. The films were ignited and flame propagation was measured. The results show that as the mass percent KClO4 increased, the flame speed increased and peaked at 0.43 cm/s and 30 wt% KClO4. Thermochemical equilibrium simulations show that the heat of combustion increases with increasing KClO4 concentration up to a maximum at 20 wt% when the heat of combustion plateaus, indicating that the increased chemical energy liberated by the additional KClO4 promotes stable energy propagation. Differential scanning calorimeter and thermogravimetric analysis show that the silicone binder participates as a fuel and reacts with KClO4 adding energy to the reaction and promoting propagation

Mobile detection of autism through machine learning on home video: A development and prospective validation study.

BackgroundThe standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access to therapy. We hypothesize that the use of machine learning analysis on home video can speed the diagnosis without compromising accuracy. We have analyzed item-level records from 2 standard diagnostic instruments to construct machine learning classifiers optimized for sparsity, interpretability, and accuracy. In the present study, we prospectively test whether the features from these optimized models can be extracted by blinded nonexpert raters from 3-minute home videos of children with and without ASD to arrive at a rapid and accurate machine learning autism classification.Methods and findingsWe created a mobile web portal for video raters to assess 30 behavioral features (e.g., eye contact, social smile) that are used by 8 independent machine learning models for identifying ASD, each with >94% accuracy in cross-validation testing and subsequent independent validation from previous work. We then collected 116 short home videos of children with autism (mean age = 4 years 10 months, SD = 2 years 3 months) and 46 videos of typically developing children (mean age = 2 years 11 months, SD = 1 year 2 months). Three raters blind to the diagnosis independently measured each of the 30 features from the 8 models, with a median time to completion of 4 minutes. Although several models (consisting of alternating decision trees, support vector machine [SVM], logistic regression (LR), radial kernel, and linear SVM) performed well, a sparse 5-feature LR classifier (LR5) yielded the highest accuracy (area under the curve [AUC]: 92% [95% CI 88%-97%]) across all ages tested. We used a prospectively collected independent validation set of 66 videos (33 ASD and 33 non-ASD) and 3 independent rater measurements to validate the outcome, achieving lower but comparable accuracy (AUC: 89% [95% CI 81%-95%]). Finally, we applied LR to the 162-video-feature matrix to construct an 8-feature model, which achieved 0.93 AUC (95% CI 0.90-0.97) on the held-out test set and 0.86 on the validation set of 66 videos. Validation on children with an existing diagnosis limited the ability to generalize the performance to undiagnosed populations.ConclusionsThese results support the hypothesis that feature tagging of home videos for machine learning classification of autism can yield accurate outcomes in short time frames, using mobile devices. Further work will be needed to confirm that this approach can accelerate autism diagnosis at scale