ABSTRACT
BACKGROUND: A fundamental understanding of live-cell dynamics is necessary in order to advance scientific techniques and personalized medicine. For this understanding to be possible, image processing techniques, probes, tracking algorithms and many other methodologies must be improved. Currently there are no large open-source datasets containing live-cell imaging to act as a standard for the community. As a result, researchers cannot evaluate their methodologies on an independent benchmark or leverage such a dataset to formulate scientific questions. FINDINGS: Here we present T-Time, the largest free and publicly available data set of T cell phase contrast imagery designed with the intention of furthering live-cell dynamics research. T-Time consists of over 40 GB of imagery data, and includes annotations derived from these images using a custom T cell identification and tracking algorithm. The data set contains 71 time-lapse sequences containing T cell movement and calcium release activated calcium channel activation, along with 50 time-lapse sequences of T cell activation and T reg interactions. The database includes a user-friendly web interface, summary information on the time-lapse images, and a mechanism for users to download tailored image datasets for their own research. T-Time is freely available on the web at http://ttime.mlatlab.org . CONCLUSIONS: T-Time is a novel data set of T cell images and associated metadata. It allows users to study T cell interaction and activation.
Subject(s)
Calcium Release Activated Calcium Channels/metabolism , Databases, Factual , T-Lymphocytes/metabolism , Time-Lapse Imaging/methods , Calcium/metabolism , Cell Communication , Cell Movement , Cell Tracking/methods , Cells, Cultured , Humans , Internet , Lymphocyte Activation , Microscopy, Fluorescence , T-Lymphocytes, Regulatory/metabolismABSTRACT
Ample research has shown that intensive applied behavior analysis (ABA) treatment produces robust outcomes for individuals with autism spectrum disorder (ASD); however, little is known about the relationship between treatment intensity and treatment outcomes. The current study was designed to evaluate this relationship. Participants included 726 children, ages 1.5 to 12 years old, receiving community-based behavioral intervention services. Results indicated a strong relationship between treatment intensity and mastery of learning objectives, where higher treatment intensity predicted greater progress. Specifically, 35% of the variance in mastery of learning objectives was accounted for by treatment hours using standard linear regression, and 60% of variance was accounted for using artificial neural networks. These results add to the existing support for higher intensity treatment for children with ASD.
