In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Q: Redirecting the splicing machinery through the hybridization of high affinity, RNase H-incompetent oligonucleotide analogs such as phosphoramidate morpholino oligonucleotides (PMO) might lead to important clinical applications. Chemical conjugation of PMO to arginine-rich cell penetrating peptides (CPP) such as (R-Ahx-R) 4 (with Ahx standing for 6-aminohexanoic acid) leads to sequencespecific splicing correction in the absence of endosomolytic agents in cell culture at variance with most conventional CPPs. Importantly, (R-Ahx-R) 4 -PMO conjugates are effective in mouse models of various viral infections and Duchenne muscular dystrophy. Unfortunately, active doses in some applications might be close to cytotoxic ones thus presenting challenge for systemic administration of the conjugates in those clinical settings. Structureactivity relationship studies have thus been undertaken to unravel CPP structural features important for the efficient nuclear delivery of the conjugated PMO and limiting steps in their internalization pathway. Affinity for heparin (taken as a model heparan sulfate), hydrophobicity, cellular uptake, intracellular distribution and splicing correction have been monitored. Spacing between the charges, hydrophobicity of the linker between the Arg-groups and Arg-stereochemistry influence splicing correction efficiency. A significant correlation between splicing correction efficiency, affinity for heparin and ability to destabilize model synthetic vesicles has been observed but no correlation with cellular uptake has been found. Efforts will have to focus on endosomal escape since it appears to remain the limiting factor for the delivery of these splice-redirecting ON analogs.

A: Delivery of steric block morpholino oligomers by (R-X-R) 4 peptides: structure-activity studies
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Q: Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online.

A: Bayesian mixture analysis for metagenomic community profiling
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Q: Epidemiological and laboratory studies have shown that green tea and green tea catechins exert beneficial effects on a variety of diseases, including cancer, metabolic syndrome, infectious diseases, and neurodegenerative diseases. In most cases, (−)-epigallocatechin gallate (EGCG) has been shown to play a central role in these effects by green tea. Catechins from other plant sources have also shown health benefits. Many studies have revealed that the binding of EGCG and other catechins to proteins is involved in its action mechanism. Computational docking analysis (CMDA) and X-ray crystallographic analysis (XCA) have provided detailed information on catechin-protein interactions. Several of these studies have revealed that the galloyl moiety anchors it to the cleft of proteins through interactions with its hydroxyl groups, explaining the higher activity of galloylated catechins such as EGCG and epicatechin gallate than non-galloylated catechins. In this paper, we review the results of CMDA and XCA of EGCG and other plant catechins to understand catechin-protein interactions with the expectation of developing new drugs with health-promoting properties.

A:
molecules Computational Molecular Docking and X-ray Crystallographic Studies of Catechins in New Drug Design Strategies
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