About 76 glitches in 25 pulsars have been reported to date. Most glitches are ‘giant’, with fractional increases of frequency Δν0/ν0∼ 10−6. 25 glitches were analysed and presented by Shemar and Lyne, who detected them mainly at Jodrell Bank during the monitoring of a sample of 279 pulsars in a regular timing programme up to MJD 49500. This paper is a continuation of their work up to MJD 50500. We present the detection and analysis of a further 14 glitches in 9 pulsars, 6 of which have glitched for the first time since monitoring had started. Eleven of these glitches are small (Δν0/ν0∼ 10−9) and below the completeness threshold of Shemar & Lyne. We report a giant glitch in PSR B1930+22, the second largest reported hitherto, with a Δν0/ν0= 4.5 × 10−6. We also report four recent glitches in PSR B1737−30 which continues to exhibit frequent glitches. Few of these pulsars show any recovery after the glitch.

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Searching of new actions aimed at the reduction of the microbial contamination of food and food contact surfaces are extremely important. RCI method has been already described as an effective technique of microbial and abiotic pollution removal from air. However, our studies provide new, additional data related to evaluation the RCI efficacy against microbes on different surfaces, both in planktonic and biofilm form.

BackgroundSince experimental elucidation of gene function is often laborious, various in silico methods have been developed to predict gene function of uncharacterized genes. Since functionally related genes are often expressed in the same tissues, conditions and developmental stages (co-expressed), functional annotation of characterized genes can be transferred to co-expressed genes lacking annotation. With genome-wide expression data available, the construction of co-expression networks, where genes are nodes and edges connect significantly co-expressed genes, provides unprecedented opportunities to predict gene function. However, the construction of such networks requires large volumes of high-quality data, multiple processing steps and a considerable amount of computation power. While efficient tools exist to process RNA-Seq data, pipelines which combine them to construct co-expression networks efficiently are currently lacking.ResultsLSTrAP (Large-Scale Transcriptome Analysis Pipeline), presented here, combines all essential tools to construct co-expression networks based on RNA-Seq data into a single, efficient workflow. By supporting parallel computing on computer cluster infrastructure, processing hundreds of samples becomes feasible as shown here for Arabidopsis thaliana and Sorghum bicolor, which comprised 876 and 215 samples respectively. The former was used here to show how the quality control, included in LSTrAP, can detect spurious or low-quality samples. The latter was used to show how co-expression networks are able to group known photosynthesis genes and imply a role in this process of several, currently uncharacterized, genes.ConclusionsLSTrAP combines the most popular and performant methods to construct co-expression networks from RNA-Seq data into a single workflow. This allows large amounts of expression data, required to construct co-expression networks, to be processed efficiently and consistently across hundreds of samples. LSTrAP is implemented in Python 3.4 (or higher) and available under MIT license from https://github.molgen.mpg.de/proost/LSTrAP Electronic supplementary materialThe online version of this article (10.1186/s12859-017-1861-z) contains supplementary material, which is available to authorized users.

The biocidal properties of silver nanoparticles (AgNPs) prepared with the use of biologically active compounds seem to be especially significant for biological and medical application. Therefore, the aim of this research was to determine and compare the antibacterial and fungicidal properties of fifteen types of AgNPs. The main hypothesis was that the biological activity of AgNPs characterized by comparable size distributions, shapes, and ion release profiles is dependent on the properties of stabilizing agent molecules adsorbed on their surfaces. Escherichia coli and Staphylococcus aureus were selected as models of two types of bacterial cells. Candida albicans was selected for the research as a representative type of eukaryotic microorganism. The conducted studies reveal that larger AgNPs can be more biocidal than smaller ones. It was found that positively charged arginine-stabilized AgNPs (ARGSBAgNPs) were the most biocidal among all studied nanoparticles. The strongest fungicidal properties were detected for negatively charged EGCGAgNPs obtained using (−)-epigallocatechin gallate (EGCG). It was concluded that, by applying a specific stabilizing agent, one can tune the selectivity of AgNP toxicity towards desired pathogens. It was established that E. coli was more sensitive to AgNP exposure than S. aureus regardless of AgNP size and surface properties.