Abstract: The disclosure relates to barcoded polymer nanoparticles for in vivo screening and for in vivo therapeutic delivery, and methods therefor. More particularly, the invention relates to polymer nanoparticles, such as reversible addition-fragmentation chain transfer (RAFT) polymer compositions, associated with polynucleotide barcodes, for therapeutic delivery, and for high throughput in vivo screening of drug delivery nanoparticles.

Abstract: The invention relates to barcoded nucleic acid nanostructure delivery compositions for in vivo screening for subsequent use in vivo therapeutic delivery, and methods therefor. More particularly, the invention relates to nucleic acid nanostructure delivery compositions, such as DNA origami structures, associated with barcodes for high throughput in vivo screening of the nucleic acid nanostructure delivery compositions for subsequent use in drug delivery, and methods therefor.

Abstract: Open Information Extraction (IE) systems extract relational tuples from text, without requiring a pre-specified vocabulary, by identifying relation phrases and associated arguments in arbitrary sentences. However, state-of-the-art Open IE systems such as REVERB and WOE share two important weaknesses—(1) they extract only relations that are mediated by verbs, and (2) they ignore context, thus extracting tuples that are not asserted as factual. This paper presents OLLIE, a substantially improved Open IE system that addresses both these limitations. First, OLLIE achieves high yield by extracting relations mediated by nouns, adjectives, and more. Second, a context-analysis step increases precision by including contextual information from the sentence in the extractions. OLLIE obtains 2.7 times the area under precision-yield curve (AUC) compared to REVERB and 1.9 times the AUC of WOEparse.

Abstract: A system for extracting relational tuples from sentences is provided. The system includes a bootstrapper, an open pattern learner, and a pattern matcher. The bootstrapper generates training data by, for each of a plurality of seed tuples, identifying sentences of a corpus that contains the words of the seed tuple. The open pattern learner learns, from the seed tuples and sentence pairs, open patterns that encode ways in which relational tuples may be expressed in a sentence, The pattern matcher matches the open patterns to a dependency parse of a sentence, identifies base nodes of the dependency parse for the arguments and relation for the relational tuple that the open pattern encodes, and expands the arguments and relation of the relational tuple.