Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is a neurodegenerative disease that is caused by mutations in the gene. The product of this gene is a very large 520-kDa cytoplasmic protein, sacsin, with a ubiquitin-like (Ubl) domain at the N terminus followed by three large sacsin internal repeat (SIRPT) supradomains and C-terminal J and HEPN domains. The SIRPTs are predicted to contain Hsp90-like domains, suggesting a potential chaperone activity. In this work, we report the structures of the Hsp90-like Sr1 domain of SIRPT1 and the N-terminal Ubl domain determined at 1.55- and 2.1-Å resolutions, respectively. The Ubl domain crystallized as a swapped dimer that could be relevant in the context of full-length protein. The Sr1 domain displays the Bergerat protein fold with a characteristic nucleotide-binding pocket, although it binds nucleotides with very low affinity. The Sr1 structure reveals that ARSACS-causing missense mutations (R272H, R272C, and T201K) disrupt protein folding, most likely leading to sacsin degradation. This work lends structural support to the view of sacsin as a molecular chaperone and provides a framework for future studies of this protein.

Screening for the diagnosis of glaucoma through a fundus image can be determined by the optic cup to disc diameter ratio (CDR), which requires the segmentation of the cup and disc regions. In this paper, we propose two novel approaches, namely Parameter-Shared Branched Network (PSBN) and Weak Region of Interest Model-based segmentation (WRoIM) to identify disc and cup boundaries. Unlike the previous approaches, the proposed methods are trained endto-end through a single neural network architecture and use dynamic cropping instead of manual or traditional computer vision-based cropping. We are able to achieve similar performance as that of state-of-the-art approaches with less number of network parameters. Our experiments include comparison with different best known methods on publicly available Drishti-GS1 and RIM-ONE v3 datasets. With 7.8 × 10 6 parameters our approach achieves a Dice score of 0.96/0.89 for disc/cup segmentation on Drishti-GS1 data whereas the existing state-of-the-art approach uses 19.8 × 10 6 parameters to achieve a dice score of 0.97/0.89.

No abstract

We present a novel, unsupervised, and distance measure agnostic method for search space reduction in spell correction using neural character embeddings. The embeddings are learned by skip-gram word2vec training on sequences generated from dictionary words in a phonetic informationretentive manner. We report a very high performance in terms of both success rates and reduction of search space on the Birkbeck spelling error corpus. To the best of our knowledge, this is the first application of word2vec to spell correction.

No abstract