An Observational Study With the Janssen Autism Knowledge Engine (JAKE((R))) in Individuals With Autism Spectrum Disorder

Objective: The Janssen Autism Knowledge Engine (JAKE(R)) is a clinical research outcomes assessment system developed to more sensitively measure treatment outcomes and identify subpopulations in autism spectrum disorder (ASD). Here we describe JAKE and present results from its digital phenotyping (My JAKE) and biosensor (JAKE Sense) components. Methods: An observational, non-interventional, prospective study of JAKE in children and adults with ASD was conducted at nine sites in the United States. Feedback on JAKE usability was obtained from caregivers. JAKE Sense included electroencephalography, eye tracking, electrocardiography, electrodermal activity, facial affect analysis, and actigraphy. Caregivers of individuals with ASD reported behaviors using My JAKE. Results from My JAKE and JAKE Sense were compared to traditional ASD symptom measures. Results: Individuals with ASD (N = 144) and a cohort of typically developing (TD) individuals (N = 41) participated in JAKE Sense. Most caregivers reported that overall use and utility of My JAKE was easy (69%, 74/108) or very easy (74%, 80/108). My JAKE could detect differences in ASD symptoms as measured by traditional methods. The majority of biosensors included in JAKE Sense captured sizable amounts of quality data (i.e., 93-100% of eye tracker, facial affect analysis, and electrocardiogram data was of good quality), demonstrated differences between TD and ASD individuals, and correlated with ASD symptom scales. No significant safety events were reported. Conclusions: My JAKE was viewed as easy or very easy to use by caregivers participating in research outside of a clinical study. My JAKE sensitively measured a broad range of ASD symptoms. JAKE Sense biosensors were well-tolerated. JAKE functioned well when used at clinical sites previously inexperienced with some of the technologies. Lessons from the study will optimize JAKE for use in clinical trials to assess ASD interventions. Additionally, because biosensors were able to detect features differentiating TD and ASD individuals, and also were correlated with standardized symptom scales, these measures could be explored as potential biomarkers for ASD and as endpoints in future clinical studies. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT02668991 identifier: NCT02668991

Relationship Between Sleep and Behavior in Autism Spectrum Disorder: Exploring the Impact of Sleep Variability.

Objective:The relationship between sleep (caregiver-reported and actigraphy-measured) and other caregiver-reported behaviors in children and adults with autism spectrum disorder (ASD) was examined, including the use of machine learning to identify sleep variables important in predicting anxiety in ASD. Methods:Caregivers of ASD (n = 144) and typically developing (TD) (n = 41) participants reported on sleep and other behaviors. ASD participants wore an actigraphy device at nighttime during an 8 or 10-week non-interventional study. Mean and variability of actigraphy measures for ASD participants in the week preceding midpoint and endpoint were calculated and compared with caregiver-reported and clinician-reported symptoms using a mixed effects model. An elastic-net model was developed to examine which sleep measures may drive prediction of anxiety. Results:Prevalence of caregiver-reported sleep difficulties in ASD was approximately 70% and correlated significantly (p < 0.05) with sleep efficiency measured by actigraphy. Mean and variability of actigraphy measures like sleep efficiency and number of awakenings were related significantly (p < 0.05) to ASD symptom severity, hyperactivity and anxiety. In the elastic net model, caregiver-reported sleep, and variability of sleep efficiency and awakenings were amongst the important predictors of anxiety. Conclusion:Caregivers report problems with sleep in the majority of children and adults with ASD. Reported problems and actigraphy measures of sleep, particularly variability, are related to parent reported behaviors. Measuring variability in sleep may prove useful in understanding the relationship between sleep problems and behavior in individuals with ASD. These findings may have implications for both intervention and monitoring outcomes in ASD

The down syndrome biomarker initiative (DSBI) pilot: proof of concept for deep phenotyping of Alzheimer’s disease biomarkers in down syndrome

To gain further knowledge on the preclinical phase of AD, we sought to characterize cognitive performance, volumetric MRI, amyloid PET, FDG PET, retinal amyloid, and plasma biomarkers in a cohort of non-demented adults with Down Syndrome (DS). The goal of the Down Syndrome Biomarker Initiative (DSBI) pilot is to test feasibility of this approach for future multicenter studies. We enrolled 12 non-demented participants with DS between the ages of 30-60 years old. Participants underwent extensive cognitive testing, volumetric MRI, amyloid PET 18F-florbetapir, 18F-fluorodeoxyglucose (18F-FDG) PET, and retinal amyloid imaging. In addition, plasma beta-amyloid species were measured and ApoE genotyping was performed. Consistent with previous autopsy studies, subjects demonstrated amyloid PET positivity reflecting fibrillar amyloid plaque deposition. Results from our multimodal analysis also suggest greater hippocampal atrophy with amyloid load. Additionally, we identified an inverse relationship between amyloid load and regional glucose metabolism. Cognitive and functional measures did not correlate with amyloid load in DS but did correlate with regional FDG PET measures. Retinal amyloid imaging demonstrated presence of plaques. Biomarkers of AD can be readily studied in adults with DS as in other preclinical AD populations. Importantly, all subjects in this feasibility study were able to complete all test procedures. The data indicate that a large, multicenter longitudinal study is feasible to better understand the trajectories of AD biomarkers in this enriched population. This trial is registered with ClinicalTrials.gov, number NCT02141971

A Radial Age Gradient in the Geometrically Thick Disk of the Milky Way

In the Milky Way, the thick disk can be defined using individual stellar abundances, kinematics, or age, or geometrically, as stars high above the midplane. In nearby galaxies, where only a geometric definition can be used, thick disks appear to have large radial scale lengths, and their red colors suggest that they are uniformly old. The Milky Way's geometrically thick disk is also radially extended, but it is far from chemically uniform: α-enhanced stars are confined within the inner Galaxy. In simulated galaxies, where old stars are centrally concentrated, geometrically thick disks are radially extended, too. Younger stellar populations flare in the simulated disks' outer regions, bringing those stars high above the midplane. The resulting geometrically thick disks therefore show a radial age gradient, from old in their central regions to younger in their outskirts. Based on our age estimates for a large sample of giant stars in the APOGEE survey, we can now test this scenario for the Milky Way. We find that the geometrically defined thick disk in the Milky Way has indeed a strong radial age gradient: the median age for red clump stars goes from ~9 Gyr in the inner disk to 5 Gyr in the outer disk. We propose that at least some nearby galaxies could also have thick disks that are not uniformly old, and that geometrically thick disks might be complex structures resulting from different formation mechanisms in their inner and outer parts

The formation of nuclear stellar discs in bar-induced gas inflows

The role of gas in the mass assembly at the nuclei of galaxies is still subject to some uncertainty. Stellar nuclear discs bridge the gap between the large-scale galaxy and the central massive objects that reside there. Using a high resolution simulation of a galaxy forming out of gas cooling and settling into a disc, we study the formation and properties of nuclear discs. Gas, driven to the centre by a bar, settles into a rotating star-forming nuclear disc (ND). This ND is thinner, younger, kinematically cooler, and more metal-rich than the surrounding bar. The ND is elliptical and orthogonal to the bar. The complex kinematics in the region of the ND are a result of the superposition of older stars streaming along the bar and younger stars circulating within the ND. The signature of the ND is therefore subtle in the kinematics. Instead the ND stands out clearly in metallicity and age maps. We compare the model to the density and kinematics of real galaxies with NDs finding qualitative similarities. Our results suggest that gas dissipation is very important for forming nuclear structures

Comparisons of electron fluxes measured in the crustal fields at Mars by the MGS magnetometer/electron reflectometer instrument with a B field–dependent transport code

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95296/1/jgre1782.pd

Shear thickening regimes of dense non-Brownian suspensions

We propose a unifying rheological framework for dense suspensions of non-Brownian spheres, predicting the onsets of particle friction and particle inertia as distinct shear thickening mechanisms, while capturing quasistatic and soft particle rheology at high volume fractions and shear rates respectively. Discrete element method simulations that take suitable account of hydrodynamic and particle-contact interactions corroborate the model predictions, demonstrating both mechanisms of shear thickening, and showing that they can occur concurrently with carefully selected particle surface properties under certain flow conditions. Microstructural transitions associated with frictional shear thickening are presented. We find very distinctive divergences of both the microstructural and dynamic variables with respect to volume fraction in the thickened and non-thickened states