Influence of chronic azithromycin treatment on the composition of the oropharyngeal microbial community in patients with severe asthma

Background: This study of the oropharyngeal microbiome complements the previously published AZIthromycin in Severe ASThma (AZISAST) clinical trial, where the use of azithromycin was assessed in subjects with exacerbationprone severe asthma. Here, we determined the composition of the oropharyngeal microbial community by means of deep sequencing of the amplified 16S rRNA gene in oropharyngeal swabs from patients with exacerbationprone severe asthma, at baseline and during and after 6 months treatment with azithromycin or placebo. Results: A total of 1429 OTUs were observed, of which only 59 were represented by more than 0.02% of the reads. Firmicutes, Bacteroidetes, Fusobacteria, Proteobacteria and Actinobacteria were the most abundant phyla and Streptococcus and Prevotella were the most abundant genera in all the samples. Thirteen species only accounted for two thirds of the reads and two species only, i.e. Prevotella melaninogenica and Streptococcus mitis/pneumoniae, accounted for one fourth of the reads. We found that the overall composition of the oropharyngeal microbiome in patients with severe asthma is comparable to that of the healthy population, confirming the results of previous studies. Long term treatment (6 months) with azithromycin increased the species Streptococcus salivarius approximately 5-fold and decreased the species Leptotrichia wadei approximately 5-fold. This was confirmed by Boruta feature selection, which also indicated a significant decrease of L. buccalis/L. hofstadtii and of Fusobacterium nucleatum. Four of the 8 treated patients regained their initial microbial composition within one month after cessation of treatment. Conclusions: Despite large diversity of the oropharyngeal microbiome, only a few species predominate. We confirm the absence of significant differences between the oropharyngeal microbiomes of people with and without severe asthma. Possibly, long term azithromycin treatment may have long term effects on the composition of the oropharygeal microbiome in half of the patients

Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research

A Survey on Feature Selection Algorithms

One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations. DOI: 10.17762/ijritcc2321-8169.16043

HETEROSCEDASTIC DISCRIMINANT ANALYSIS COMBINED WITH FEATURE SELECTION FOR CREDIT SCORING

Credit granting is a fundamental question and one of the most complex tasks that every credit institution is faced with. Typically, credit scoring databases are often large and characterized by redundant and irrelevant features. An effective classification model will objectively help managers instead of intuitive experience. This study proposes an approach for building a credit scoring model based on the combination of heteroscedastic extension (Loog, Duin, 2002) of classical Fisher Linear Discriminant Analysis (Fisher, 1936, Krzyśko, 1990) and a feature selection algorithm that retains sufficient information for classification purpose. We have tested five feature subset selection algorithms: two filters and three wrappers. To evaluate the accuracy of the proposed credit scoring model and to compare it with the existing approaches we have used the German credit data set from the study (Chen, Li, 2010). The results of our study suggest that the proposed hybrid approach is an effective and promising method for building credit scoring models