No abstract

This paper reports on the design process of a web-based collaborative system for the production of multilingual health communication materials. The system is based on a workflow combining machine translation and human post-editing and has been designed for public health professionals who are bilingual domain experts but not necessarily trained translators. Our initial data gathering phase involved interviews and focus groups with local and regional public health departments. Based on the design recommendations extracted from the data, we implemented a web-based prototype collaborative translation management system. We further refined the system through an iterative design process that included informal user testing with multilingual participants. Future work will include usability studies with public health workers and the integration of additional collaborative features.

Protein structure information is essential to understand protein function. Computational methods to accurately predict protein structure from the sequence have primarily been evaluated on protein sequences representing full-length native proteins. Here, we demonstrate that top-performing structure prediction methods can accurately predict the partial structures of proteins encoded by sequences that contain approximately 50% or more of the full-length protein sequence. We hypothesize that structure prediction may be useful for predicting functions of proteins whose corresponding genes are mapped expressed sequence tags (ESTs) that encode partial-length amino acid sequences. Additionally, we identify a confidence score representing the quality of a predicted structure as a useful means of predicting the likelihood that an arbitrary polypeptide sequence represents a portion of a foldable protein sequence (“foldability”). This work has ramifications for the prediction of protein structure with limited or noisy sequence information, as well as genome annotation.