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Autism Spectrum Disorder (ASD) is a neurodevelopmental condition often associated with delayed motor skills. The Motor Assessment Battery for Children – Second Edition (MABC-2) is a standardized motor assessment for identifying motor delays pertaining to ASD. It evaluates fine and gross motor tasks across three domains: Manual Dexterity, Aiming & Catching, and Balance. These tasks are categorized into three age bands: 3-6, 7-10, and 11-16. Virtual Reality (VR) has emerged as a promising intervention in the ASD realm. This study aimed to investigate the potential of VR to assist children with ASD in performing the gross motor skills (i.e., ball skills and balance) in the MABC-2. The children who participated in the study were attendees of a local Autism Summer Camp. Our research focused on adapting motor tasks for ages 7-10 (i.e., Age Band 2) to VR, as most campers fell in this age range. Within the VR environment, children could observe avatar demonstrations and practice motor skills in a highly immersive setting. The VR environment featured avatars demonstrating ball skills and balancing tasks. Developed with the Unity game engine, 3D software Blender, C# scripting, and mixed reality toolkits, this environment was tested on the Meta Quest 2 Oculus. The children's gross motor skill performance was scored before and after VR interactions. The test standard scores were categorized through a traffic-light scoring system comprising red, amber, and green zones. A standard score ≤4 is classified in the red zone, indicating a significant movement difficulty; a standard score >4 and ≤7 is classified in the amber zone, indicating a risk for movement difficulty; and a standard score >7 is classified in the green zone, indicating no movement difficulty detected. Following the VR intervention, we observed a notable improvement in the balance score (p < 0.05). Furthermore, using the Random Forest machine learning model, we analyzed a combined dataset of MABC-2 scores from 250 children across all age bands from the Autism Summer Camp in previous years and the MABC-2 scores from the 18 children in the present study. Our analysis revealed that Balance was crucial in classifying children with ASD with motor delays, with an importance score of 0.195, nearly double that of Manual Dexterity and Aiming & Catching. When the model was exclusively applied to the Balance component score, it achieved an impressive accuracy rate of 91% in identifying children with ASD. In summary, our findings underscore the promise of VR in enhancing balance among children with ASD. The Random Forest analysis reaffirmed the significant role of balance in identifying children with ASD. Given its precision in detecting children with ASD based on their balance performance, we anticipate the potential of future machine learning advancements in this field. Our research validates the effectiveness of a VR-based approach and emphasizes the significance of collaborative research in providing valuable support to the underserved ASD population. 

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data forE. coliand mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise. 

We report novel sterically-hindered ligands with strong σ-donation from the C3-indazole carbene center and flexible N-substitution with a 2,6-bis(diphenylmethyl)aryl group that extends beyond the metal center in non-classical N-heterocyclic carbenes.