A metagenomic comparison of endemic viruses from broiler chickens with runting stunting syndrome and from normal birds

Runting-stunting syndrome (RSS) in broiler chickens is an enteric disease that causes significant economic losses to poultry producers worldwide due to elevated feed conversion ratios, decreased body weight during growth, and excessive culling. Of specific interest are the viral agents associated with RSS which have been difficult to fully characterise to date. Past research into the aetiology of RSS has implicated a wide variety of RNA and DNA viruses however, to date, no individual virus has been identified as the main agent of RSS and the current opinion is that it may be caused by a community of viruses, collectively known as the virome. This paper attempts to characterise the viral pathogens associated with 2 – 3 week old RSS-affected and unaffected broiler chickens using next-generation sequencing and comparative metagenomics. Analysis of the viromes identified a total of 20 DNA & RNA viral families, along with 2 unidentified categories, comprised of 31 distinct viral genera and 7 unclassified genera. The most abundant viral families identified in this study were the Astroviridae, Caliciviridae, Picornaviridae, Parvoviridae, Coronaviridae, Siphoviridae, and Myoviridae. This study has identified historically significant viruses associated with the disease such as chicken astrovirus, avian nephritis virus, chicken parvovirus, and chicken calicivirus along with relatively novel viruses such as chicken megrivirus and sicinivirus 1 and will help expand the knowledge related to enteric disease in broiler chickens, provide insights into the viral constituents of a healthy avian gut, and identify a variety of enteric viruses and viral communities appropriate for further study.

Ordering Quantiles through Confidence Statements

Ranking variables according to their relevance to predict an outcome is an important task in biomedicine. For instance, such ranking can be used for selecting a smaller number of genes to then apply other sophisticated experiments only on genes identified as important. A nonparametric method called Quor is designed to provide a confidence value for the order of arbitrary quantiles of different populations using independent samples. This confidence may provide insights about possible differences among groups and yields a ranking of importance for the variables. Computations are efficient and use exact distributions with no need for asymptotic considerations. Experiments with simulated data and with multiple real -omics data sets are performed and they show advantages and disadvantages of the method. Quor has no assumptions but independence of samples, thus it might be a better option when assumptions of other methods cannot be asserted. The software is publicly available on CRAN.

Self-healing of cracks during ductile regime machining of silicon: Insights from molecular dynamics simulation

During nanoindentation and ductile-regime machining of silicon, a phenomenon known as “self-healing” takes place in that the microcracks, microfractures, and small spallings generated during the machining are filled by the plastically flowing ductile phase of silicon. However, this phenomenon has not been observed in simulation studies. In this work, using a long-range potential function, molecular dynamics simulation was used to provide an improved explanation of this mechanism. A unique phenomenon of brittle cracking was discovered, typically inclined at an angle of 45° to 55° to the cut surface, leading to the formation of periodic arrays of nanogrooves being filled by plastically flowing silicon during cutting. This observation is supported by the direct imaging. The simulated X-ray diffraction analysis proves that in contrast to experiments, Si-I to Si-II (beta tin) transformation during ductile-regime cutting is highly unlikely and solid-state amorphisation of silicon caused solely by the machining stress rather than the cutting temperature is the key to its brittle-ductile transition observed during the MD simulations