Jung, C. | bioRxiv Science

bioRxiv2018

Supercharging enables organized assembly of synthetic biomolecules

There are few methods for the assembly of defined protein oligomers and higher order structures that could serve as novel biomaterials. Using fluorescent proteins as a model system, we have engineered novel oligomerization states by combining oppositely supercharged variants. A well-defined, highly symmetrical 16-mer (two stacked, circular octamers) can be formed from alternating charged proteins; higher order structures then form in a hierarchical fashion from this discrete protomer. During SUpercharged PRotein Assembly (SuPrA), electrostatic attraction between oppositely charged variants drives interaction, while shape and patchy physicochemical interactions lead to spatial organization along specific interfaces, ultimately resulting in protein assemblies never before seen in nature.

bioengineering↗

bioRxiv2017

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↗