Validation of control of allergic rhinitis and asthma test for children (CARATKids)--a prospective multicenter study

BACKGROUND: Control of Allergic Rhinitis and Asthma Test for Children (CARATKids) is the first questionnaire that assesses simultaneously allergic rhinitis and asthma control in children. It was recently developed, but redundancy of questions and its psychometric properties were not assessed. This study aimed to (i) establish the final version of the CARATKids questionnaire and (ii) evaluate its reliability, responsiveness, cross-sectional validity, and longitudinal validity. METHODS: A prospective observational study was conducted in 11 Portuguese centers. During two visits separated by 6 wk, CARATKids, visual analog scale scales and childhood asthma control test were completed, and participant's asthma and rhinitis were evaluated by his/her physician without knowing the questionnaires' results. Data-driven item reduction was conducted, and internal consistency, responsiveness analysis, and associations with external measures of disease status were assessed. RESULTS: Of the 113 children included, 101 completed both visits. After item reduction, the final version of the questionnaire has 13 items, eight to be answered by the child and five by the caregiver. Its Cronbach's alpha was 0.80, the Guyatt's responsiveness index was -1.51, and a significant (p < 0.001) within-patient change of CARATKids score in clinical unstable patients was observed. Regarding cross-sectional validity, correlation coefficients of CARATKids with the external measures of control were between 0.45 and -0.69 and met the a priori predictions. In the longitudinal validity assessment, the correlation coefficients between the score changes of CARATKids and those of external measures of control ranged from 0.34 to 0.46

Employing machine learning for reliable miRNA target identification in plants

<p>Abstract</p> <p>Background</p> <p>miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions.</p> <p>Result</p> <p>In the present work, a Support Vector Regression (SVR) approach has been implemented for plant miRNA target identification, utilizing position specific dinucleotide density variation information around the target sites, to yield highly reliable result. It has been named as p-TAREF (plant-Target Refiner). Performance comparison for p-TAREF was done with other prediction tools for plants with utmost rigor and where p-TAREF was found better performing in several aspects. Further, p-TAREF was run over the experimentally validated miRNA targets from species like <it>Arabidopsis</it>, <it>Medicago</it>, Rice and Tomato, and detected them accurately, suggesting gross usability of p-TAREF for plant species. Using p-TAREF, target identification was done for the complete Rice transcriptome, supported by expression and degradome based data. miR156 was found as an important component of the Rice regulatory system, where control of genes associated with growth and transcription looked predominant. The entire methodology has been implemented in a multi-threaded parallel architecture in Java, to enable fast processing for web-server version as well as standalone version. This also makes it to run even on a simple desktop computer in concurrent mode. It also provides a facility to gather experimental support for predictions made, through on the spot expression data analysis, in its web-server version.</p> <p>Conclusion</p> <p>A machine learning multivariate feature tool has been implemented in parallel and locally installable form, for plant miRNA target identification. The performance was assessed and compared through comprehensive testing and benchmarking, suggesting a reliable performance and gross usability for transcriptome wide plant miRNA target identification.</p

The Populus holobiont: dissecting the effects of plant niches and genotype on the microbiome

Background: Microorganisms serve important functions within numerous eukaryotic host organisms. An understanding of the variation in the plant niche-level microbiome, from rhizosphere soils to plant canopies, is imperative to gain a better understanding of how both the structural and functional processes of microbiomes impact the health of the overall plant holobiome. Using Populus trees as a model ecosystem, we characterized the archaeal/bacterial and fungal microbiome across 30 different tissue-level niches within replicated Populus deltoides and hybrid Populus trichocarpa × deltoides individuals using 16S and ITS2 rRNA gene analyses. Results: Our analyses indicate that archaeal/bacterial and fungal microbiomes varied primarily across broader plant habitat classes (leaves, stems, roots, soils) regardless of plant genotype, except for fungal communities within leaf niches, which were greatly impacted by the host genotype. Differences between tree genotypes are evident in the elevated presence of two potential fungal pathogens, Marssonina brunnea and Septoria sp., on hybrid P. trichocarpa × deltoides trees which may in turn be contributing to divergence in overall microbiome composition. Archaeal/bacterial diversity increased from leaves, to stem, to root, and to soil habitats, whereas fungal diversity was the greatest in stems and soils. Conclusions: This study provides a holistic understanding of microbiome structure within a bioenergy relevant plant host, one of the most complete niche-level analyses of any plant. As such, it constitutes a detailed atlas or map for further hypothesis testing on the significance of individual microbial taxa within specific niches and habitats of Populus and a baseline for comparisons to other plant species