Expression of Distal-less, dachshund, and optomotor blind in Neanthes arenaceodentata (Annelida, Nereididae) does not support homology of appendage-forming mechanisms across the Bilateria

The similarity in the genetic regulation of arthropod and vertebrate appendage formation has been interpreted as the product of a plesiomorphic gene network that was primitively involved in bilaterian appendage development and co-opted to build appendages (in modern phyla) that are not historically related as structures. Data from lophotrochozoans are needed to clarify the pervasiveness of plesiomorphic appendage forming mechanisms. We assayed the expression of three arthropod and vertebrate limb gene orthologs, Distal-less (Dll), dachshund (dac), and optomotor blind (omb), in direct-developing juveniles of the polychaete Neanthes arenaceodentata. Parapodial Dll expression marks premorphogenetic notopodia and neuropodia, becoming restricted to the bases of notopodial cirri and to ventral portions of neuropodia. In outgrowing cephalic appendages, Dll activity is primarily restricted to proximal domains. Dll expression is also prominent in the brain. dac expression occurs in the brain, nerve cord ganglia, a pair of pharyngeal ganglia, presumed interneurons linking a pair of segmental nerves, and in newly differentiating mesoderm. Domains of omb expression include the brain, nerve cord ganglia, one pair of anterior cirri, presumed precursors of dorsal musculature, and the same pharyngeal ganglia and presumed interneurons that express dac. Contrary to their roles in outgrowing arthropod and vertebrate appendages, Dll, dac, and omb lack comparable expression in Neanthes appendages, implying independent evolution of annelid appendage development. We infer that parapodia and arthropodia are not structurally or mechanistically homologous (but their primordia might be), that Dll’s ancestral bilaterian function was in sensory and central nervous system differentiation, and that locomotory appendages possibly evolved from sensory outgrowths

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Super Responders: Predicting Expressive Language Gains Among Minimally Verbal Children with Autism Spectrum Disorder

Much research in autism spectrum disorders (ASD) has focused on the development of efficacious interventions to address the core deficits of ASD. However, the heterogeneous nature of ASD complicates the development of such interventions. With great heterogeneity in the expression of ASD’s core deficits, it is unlikely that there is a one size fits all intervention. It is important for researchers to understand for whom an intervention works. Advancements in data analytics, in particular machine learning, provide new methods to identify subgroups among a given population, and can potentially help to identify for whom intervention works best. Of particular interest are minimally verbal individuals. A targeted social communication intervention known as JASPER (Joint Attention, Symbolic Play, Engagement, and Regulation) has shown promise for improving language outcomes among minimally verbal children with ASD and may provide the context to examine the question of for whom an intervention benefits. This study aims to develop a model predicting expressive language gains among minimally verbal, preschool aged children with ASD that received a targeted social communication intervention. Classification and regression tree (CART) analysis was used to explore the relationship between child characteristics and gains in expressive language. Secondary data analysis was conducted on a sample of 99 minimally verbal, preschool age children with ASD, collected from participants across five previous intervention studies. Expressive language gains (outcome) were calculated using expressive language age equivalents from the Mullen Scales for Early Learning. Predictors for the analyses were taken from child demographics and behavioral assessments completed prior to intervention. The initial list of predictors included race, gender, ASD severity, visual reception age equivalent, fine motor age equivalent, joint attention gestures, requesting gestures, and play skills. Using expressive language age equivalent change scores, 47% (n = 47) of the sample were identified as “super responders,” children that exceeded expressive language gains typically expected through maturation. To predict responder status, all initial predictors were used to generate conditional inference forest, from which the most important variables would be chosen for the final model. Conditional inference results identified three variables to be fitted into the final model; play diversity, requesting gestures, and fine motor age equivalent. A final conditional inference tree was created, with play diversity being the only significant predictor of responder status. Participants with an entry play diversity score above 23 predicted super response while scores of 23 or below predicted slow response. The overall model accuracy was 67%, with a specificity of 55% and sensitivity of 78%. As a comparison, stepwise logistic regression was run, and play diversity was again the only significant predictor of responder status (χ2 (1) = 10.686, p = .001). Receiver operating characteristic curves were generated to compare model performance, and comparison of area under the curves for the two models showed no statistical difference (p = .82). Overall accuracy of the conditional inference tree was moderate, and performed similarly to the more traditional logistic regression analysis. However, the conditional inference tree provides a cutoff point that may provide clinical utility over the regression results. Both models identify play diversity as in important predictor of expressive language gains from JASPER, which is a play based social communication intervention. Additionally, our model appears to be more sensitive to identifying slow responders. The role of play diversity and expressive language gains in JASPER is discussed

Measuring Joint Attention in Children with Autism Spectrum Disorder Through Structured and Unstructured Play

Joint attention, or the shared experience of an object or activity, is one of the earliest indicators of social interaction, and an important precursor to language. Skills used to coordinate joint attention often emerge within the first and second years of life. Research shows that children with an autism spectrum disorder (ASD) exhibit atypical development of joint attention skills compared to typically developing children. Considering the important role joint attention plays in language development, the accurate assessment of joint attention skills in children with ASD is critical for identifying deficits and designing early interventions. The commonly accepted gold standard for joint attention assessment is the Early Social Communication Scales (ESCS). However, researchers and clinicians may benefit from expanding their methods of joint attention assessment. Multiple observations across different constructs may improve the accuracy of assessing a child’s development of joint attention, and improve ecological validity. The current study aims to explore the validity of measuring joint attention within structured and unstructured play interactions by comparing rates of joint attention in these contexts with rates of joint attention in the ESCS. Using the same guidelines established by the ESCS, joint attention skills were coded from structured play assessments and unstructured caregiver child interactions administered to 28 young children with ASD, ages 2 to 5 years. Correlation analysis shows strong positive correlations between rates of child initiated joint attention in the structured (r = .67) and unstructured (r = .61) play when compared to the ESCS. Comparison of correlation coefficient rates of child initiated joint attention against rates of child initiated behavior regulation coded in the three measures provides evidence of convergent and discriminant validity. These findings suggest that structured and unstructured play assessments can be utilized as tools to measure child initiated joint attention, providing researchers with more opportunities to observe these skills in young children with ASD

Super Responders: Predicting Expressive Language Gains Among Minimally Verbal Children with Autism Spectrum Disorder

Much research in autism spectrum disorders (ASD) has focused on the development of efficacious interventions to address the core deficits of ASD. However, the heterogeneous nature of ASD complicates the development of such interventions. With great heterogeneity in the expression of ASD’s core deficits, it is unlikely that there is a one size fits all intervention. It is important for researchers to understand for whom an intervention works. Advancements in data analytics, in particular machine learning, provide new methods to identify subgroups among a given population, and can potentially help to identify for whom intervention works best. Of particular interest are minimally verbal individuals. A targeted social communication intervention known as JASPER (Joint Attention, Symbolic Play, Engagement, and Regulation) has shown promise for improving language outcomes among minimally verbal children with ASD and may provide the context to examine the question of for whom an intervention benefits. This study aims to develop a model predicting expressive language gains among minimally verbal, preschool aged children with ASD that received a targeted social communication intervention. Classification and regression tree (CART) analysis was used to explore the relationship between child characteristics and gains in expressive language. Secondary data analysis was conducted on a sample of 99 minimally verbal, preschool age children with ASD, collected from participants across five previous intervention studies. Expressive language gains (outcome) were calculated using expressive language age equivalents from the Mullen Scales for Early Learning. Predictors for the analyses were taken from child demographics and behavioral assessments completed prior to intervention. The initial list of predictors included race, gender, ASD severity, visual reception age equivalent, fine motor age equivalent, joint attention gestures, requesting gestures, and play skills. Using expressive language age equivalent change scores, 47% (n = 47) of the sample were identified as “super responders,” children that exceeded expressive language gains typically expected through maturation. To predict responder status, all initial predictors were used to generate conditional inference forest, from which the most important variables would be chosen for the final model. Conditional inference results identified three variables to be fitted into the final model; play diversity, requesting gestures, and fine motor age equivalent. A final conditional inference tree was created, with play diversity being the only significant predictor of responder status. Participants with an entry play diversity score above 23 predicted super response while scores of 23 or below predicted slow response. The overall model accuracy was 67%, with a specificity of 55% and sensitivity of 78%. As a comparison, stepwise logistic regression was run, and play diversity was again the only significant predictor of responder status (χ2 (1) = 10.686, p = .001). Receiver operating characteristic curves were generated to compare model performance, and comparison of area under the curves for the two models showed no statistical difference (p = .82). Overall accuracy of the conditional inference tree was moderate, and performed similarly to the more traditional logistic regression analysis. However, the conditional inference tree provides a cutoff point that may provide clinical utility over the regression results. Both models identify play diversity as in important predictor of expressive language gains from JASPER, which is a play based social communication intervention. Additionally, our model appears to be more sensitive to identifying slow responders. The role of play diversity and expressive language gains in JASPER is discussed

Super responders: Predicting language gains from JASPER among limited language children with autism spectrum disorder.

Early intervention can provide a great benefit for children with autism spectrum disorder (ASD). However, no single intervention is effective for all children. Even when an intervention is effective overall, individual child response varies. Some children make incredible progress, and others make slow or no progress. Therefore, it is important that the field move towards developing methods to personalize intervention. Operationalizing meaningful change and predicting intervention response are critical steps in designing systematic and personalized early intervention. The present research used improvement in expressive language to group children that received a targeted social communication early intervention, Joint Attention, Symbolic Play, Engagement, and Regulation (JASPER), into super responders and slow responders. Using baseline data from traditional standardized assessments of cognition and behavioral data from validated experimental measures of play and social communication, we used conditional inference tree models to predict responder status. From a sample of 99 preschool age, limited language children with ASD, play diversity was the most significant predictor of responder status. Children that played functionally with a wider variety of toys had increased odds of being a super responder to JASPER. A combination of lower play diversity and impairments in fine motor abilities increased the odds of children being slow responders to JASPER. Results from the present study can inform future efforts to individualize intervention and systematic approaches to augmenting treatment in real time. LAY SUMMARY: To help us answer the question of for whom an intervention works best, we examined 99 children, age three to five, who qualified as being limited spoken language communicators, and received a targeted intervention for social communication and language. We used child characteristics before intervention to predict which children would improve their language the most and found that the ability to play appropriately with a wider variety of toys predicted the best improvements in expressive language. These findings will help better inform future work to individualize intervention based on the unique needs of each child

Educators apply new teaching strategies despite initial attributions of autistic students controllability of their behaviors.

Autistic children are less likely to be jointly engaged with a play partner than nonautistic children, negatively impacting social communication development. Promoting joint engagement during play can be an important target for educators of autistic students, but educator perceptions of autistic students may affect their interactions with students. This secondary data analysis investigated educator perceptions of the behaviors of their autistic students, their relationship on educator behavior, and their relationship on the implementation of an intervention promoting joint engagement. Participants included 66 autistic preschool students, and twelve educators from six preschools. Schools were randomized to educator training or a waitlist. Before training, educators rated their students controllability over autism related behaviors. To observe educator behavior, they were filmed playing for ten minutes with students, before and after receiving training. Ratings of controllability were positively correlated with cognitive scores, and negatively correlated with ADOS (Autism Diagnostic Observation Schedule) comparison scores. Furthermore, educator ratings of controllability predicted joint engagement strategies used by educators during play. Educators tended to use strategies promoting joint engagement for students perceived as more able to control their autism spectrum disorder behavior. Among educators that received JASPER (Joint Attention, Symbolic Play, Engagement, and Regulation) training, ratings of controllability did not predict changes in strategy scores after training. Educators were able to learn and implement new joint engagement strategies despite their initial perceptions

Developmental screening and early intervention in a childcare setting for young children at risk for autism and other developmental delays: A feasibility trial

Efforts to decrease disparity in diagnosis and treatment for under-resourced children with developmental delays, such as autism spectrum disorder, have led to increased interest in developing programs in community settings. One potential setting that has already demonstrated feasibility in conducting universal screening is the childcare setting. The current study conducted developmental screening in a total of 116 children ages 16-80 months of age in an urban low-income community childcare center. Parents of 20 children who screened positive were enrolled in the intervention phase of the study, where children received a staff-delivered targeted early intervention or a waitlist control condition. Given the small and imbalanced sample sizes, confidence intervals from mixed effect models were used to measure changes across time for each group. Of the children who received treatment, there was an average increase in child initiated joint engagement, symbolic play, and language use. This study provides initial feasibility data for the implementation of a screening and early intervention program to service a predominantly low-resource and ethnically diverse population within the childcare system in a large metropolitan city. Autism Res 2019, 12: 1423-1433. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Identifying and delivering treatment services for young children with developmental delays, such as autism spectrum disorder, may be most successful in community settings, especially for those children from under-resourced areas. This study found preliminary evidence that the childcare setting is a good place to conduct screening and deliver early interventions for children at risk for autism and other developmental delays