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Tran, C.

Publications and source records attributed to Tran, C..

2 recordsLinked to original sources

Live imaging of Aiptasia larvae, a model system for studying coral bleaching, using a simple microfluidic device

Coral reefs, and their associated diverse ecosystems, are of enormous ecological importance. In recent years, coral health has been severely impacted by environmental stressors brought on by human activity and climate change, threatening the extinction of several major reef ecosystems. Reef damage is mediated by a process called coral bleaching where corals, sea anemones, and other cnidarians lose their photosynthetic algal symbionts (genus Symbiodinium) upon stress induction, resulting in drastically decreased host energy harvest and, ultimately, coral death. The mechanism by which this critical cnidarian-algal symbiosis is lost remains poorly understood. Here, we report Traptasia, a simple microfluidic device with multiple traps designed to isolate and image individual live larvae of Aiptasia, a sea anemone model organism, and their algal symbionts over extended time courses. Aiptasia larvae are ~100 m in length, deformable, and highly motile, posing particular challenges for long-term imaging. Using a trap design optimized via fluid flow simulations and polymer bead loading tests, we trapped Aiptasia larvae containing algal symbionts and demonstrated stable imaging for >10 hours. We visualized algal migration within Aiptasia larvae and observed algal expulsion under an environmental stressor. To our knowledge, this device is the first to enable live imaging of cnidarian larvae and their algal symbionts and, in further implementation, could provide important insights into the cellular mechanisms of coral bleaching under different environmental stressors. The device is simple to use, requires minimal external equipment and no specialized training to operate, and can easily be adapted to study a variety of large, motile organisms.

ecology

The Anatomical Tracings of Lesions After Stroke (ATLAS) Dataset - Release 1.1

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.

neuroscience