Folding and duplex formation in mixed sequence recognition-encoded m-phenylene ethynylene polymers

One pot oligomerisation reactions give access to families of oligomers that allow facile analysis of folding propensity and assessment of suitability for sequence-selective duplex formation.

Understanding the factors controlling the photo-oxidation of natural DNA by enantiomerically pure intercalating ruthenium polypyridyl complexes through TA/TRIR studies with polydeoxynucleotides and mixed sequence oligodeoxynucleotides

Efficient electron transfer requires the complex to be intercalated at a G-C base-pair. Identification of preferred intercalation sites is achieved by TRIR monitoring of the nucleobase vibrations before electron transfer.

Impact of non-nucleotidic bulges on recognition of mixed-sequence dsDNA by pyrene-functionalized Invader probes

Invader probes featuring non-nucleotidic bulges are energetically activated for highly specific recognition of complementary double-stranded DNA targets.

Head-to-head comparison of LNA, MPγPNA, INA and Invader probes targeting mixed-sequence double-stranded DNA

Double-stranded (ds) Invader and INA probes allow for efficient and specific recognition of mixed-sequence dsDNA targets, whereas recognition is less efficient and specific with single-stranded LNA-modified DNA strands and fully modified MPγPNAs.

Modeling Source Syntax and Semantics for Neural AMR Parsing

Sequence-to-sequence (seq2seq) approaches formalize Abstract Meaning Representation (AMR) parsing as a translation task from a source sentence to a target AMR graph. However, previous studies generally model a source sentence as a word sequence but ignore the inherent syntactic and semantic information in the sentence. In this paper, we propose two effective approaches to explicitly modeling source syntax and semantics into neural seq2seq AMR parsing. The first approach linearizes source syntactic and semantic structure into a mixed sequence of words, syntactic labels, and semantic labels, while in the second approach we propose a syntactic and semantic structure-aware encoding scheme through a self-attentive model to explicitly capture syntactic and semantic relations between words. Experimental results on an English benchmark dataset show that our two approaches achieve significant improvement of 3.1% and 3.4% F1 scores over a strong seq2seq baseline.

Exploring a glycosylation methodology for the synthesis of hydroxamate-modified alginate building blocks

Mixed sequence, C6-hydroxamate-modified alginate disaccharides are prepared using NIS/TMSOTf glycosylation.