In this task, you are given a abstract of article and corresponding title of an article. Your task is to generate label "yes" if title is right for article, otherwise generate "no".

[Q]: Abstract: Structure-based modeling methods have been used to design a series of disubstituted triazole-linked acridine compounds with selectivity for human telomeric quadruplex DNAs. A focused library of these compounds was prepared using click chemistry and the selectivity concept was validated against two promoter quadruplexes from the c-kit gene with known molecular structures, as well as with duplex DNA using a FRET-based melting method. Lead compounds were found to have reduced effects on the thermal stability of the c-kit quadruplexes and duplex DNA structures. These effects were further explored with a series of competition experiments, which confirmed that binding to duplex DNA is very low even at high duplex:telomeric quadruplex ratios. Selectivity to the c-kit quadruplexes is more complex, with some evidence of their stabilization at increasing excess over human telomeric quadruplex DNA. Selectivity is a result of the dimensions of the triazole-acridine compounds, and in particular the separation of the two alkyl-amino terminal groups. Both lead compounds also have selective inhibitory effects on the proliferation of cancer cell lines compared to a normal cell line, and one has been shown to inhibit the activity of the telomerase enzyme, which is selectively expressed in tumor cells, where it plays a role in maintaining telomere integrity and cellular immortalization.. Title: Rational design of acridine-based ligands with selectivity for human telomeric quadruplexes.
[A]: yes

[Q]: Abstract: Development of the acute and chronic inflammatory responses known as gout and pseudogout are associated with the deposition of monosodium urate (MSU) or calcium pyrophosphate dihydrate (CPPD) crystals, respectively, in joints and periarticular tissues. Although MSU crystals were first identified as the aetiological agent of gout in the eighteenth century and more recently as a 'danger signal' released from dying cells, little is known about the molecular mechanisms underlying MSU- or CPPD-induced inflammation. Here we show that MSU and CPPD engage the caspase-1-activating NALP3 (also called cryopyrin) inflammasome, resulting in the production of active interleukin (IL)-1 and IL-18. Macrophages from mice deficient in various components of the inflammasome such as caspase-1, ASC and NALP3 are defective in crystal-induced IL-1 activation. Moreover, an impaired neutrophil influx is found in an in vivo model of crystal-induced peritonitis in inflammasome-deficient mice or mice deficient in the IL-1 receptor (IL-1R). These findings provide insight into the molecular processes underlying the inflammatory conditions of gout and pseudogout, and further support a pivotal role of the inflammasome in several autoinflammatory diseases.. Title: Gout-associated uric acid crystals activate the NALP3 inflammasome
[A]: yes

[Q]: Abstract: Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon. At the same time, gene selection is very difficult because of the noisy nature of microarray data. As a consequence, gene selection is often performed with machine learning methods. The Random Forest method is particularly well suited for this purpose. In this work, four state-of-the-art Random Forest-based feature selection methods were compared in a gene selection context. The analysis focused on the stability of selection because, although it is necessary for determining the significance of results, it is often ignored in similar studies. The comparison of post-selection accuracy in the validation of Random Forest classifiers revealed that all investigated methods were equivalent in this context. However, the methods substantially differed with respect to the number of selected genes and the stability of selection. Of the analysed methods, the Boruta algorithm predicted the most genes as potentially important. The post-selection classifier error rate, which is a frequently used measure, was found to be a potentially deceptive measure of gene selection quality. When the number of consistently selected genes was considered, the Boruta algorithm was clearly the best. Although it was also the most computationally intensive method, the Boruta algorithm's computational demands could be reduced to levels comparable to those of other algorithms by replacing the Random Forest importance with a comparable measure from Random Ferns (a similar but simplified classifier). Despite their design assumptions, the minimal optimal selection methods, were found to select a high fraction of false positives.. Title: Stem Cells,Cancer and Cancer Stem Cells
[A]:
no