Motor Assessment With the STEGA iPad App to Measure Handwriting in Children.

Importance: Handwriting and the fine motor control (hand and fingers) underlying it are key indicators of numerous motor disorders, especially among children. However, current assessment methods are expensive, slow, and subjective, leading to a lack of knowledge about the relationship between handwriting and motor control.

Objective: To develop and validate the iPad precision drawing app Standardized Tracing Evaluation and Grapheme Assessment (STEGA) to enable rapid quantitative assessment of fine motor control and handwriting.

High-resolution HLA typing by long reads from the R10.3 Oxford nanopore flow cells.

Nanopore sequencing has been investigated as a rapid and cost-efficient option for HLA typing in recent years. Despite the lower raw read accuracy, encouraging typing accuracy has been reported, and long reads from the platform offer additional benefits of the improved phasing of distant variants. The newly released R10.

Measuring Activities of Daily Living in Stroke Patients with Motion Machine Learning Algorithms: A Pilot Study.

Measuring activities of daily living (ADLs) using wearable technologies may offer higher precision and granularity than the current clinical assessments for patients after stroke. This study aimed to develop and determine the accuracy of detecting different ADLs using machine-learning (ML) algorithms and wearable sensors. Eleven post-stroke patients participated in this pilot study at an ADL Simulation Lab across two study visits.