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Many studies have been made on nematodes, especially Caenorhabditis Elegans, which are used as a model organism. In many studies, the size of the nematode is important. This article describes a method of measuring the length, volume and surface area of nematodes from photographs. The method uses the imaging software ImageJ, which is in the public domain. Two macros are described. [https://www.selleckchem.com/products/pim447-lgh447.html Pim inhibitor] The first converts the images into binary form, and the second uses several built-in functions to measure the length of the worm and its diameter along its length. If it is assumed that the worm has a circular cross-section, then the volume and surface area of the nematode can be calculated. This is a cheap and easy technique. © The Author(s) 2020. Published by Oxford University Press.Bioinformatics methods are increasingly needed and used to analyze and interpret extensive datasets many of which are produced by diverse high-throughput technologies. Unfortunately, it is quite common that published articles do not contain sufficient information to allow the reader to fully comprehend and repeat computational and other studies. Guidelines were developed for reporting studies and results from sequence alignment. Brief and concise checklist of required data items was compiled making it easy to provide necessary details. Implementation of the guidelines requires similar meticulous attitude toward details as other parts of publications. If the journal does not allow reporting full details in the main article, it can be provided in supplementary material. It is important to make the alignments available. Systematic and detailed description of bioinformatics analyses adds to the value of papers and makes it easier for the scientific community to evaluate, understand, verify, and extend the published articles and their results. © The Author(s) 2020. Published by Oxford University Press.In matrix-assisted laser-desorption and ionization mass spectrometry, spectral differences are frequently observed using different growth media on agar plates and/or different growth times in culture, which add undesirable analytical variance. In this article, we explore an approach to the above problem based upon the rationale that, while protein expression in fungal mycelium may well vary under different growth conditions, this might not apply to the same extent in fungal spores. To this end, we have exploited the fact that while mycelium is generally anchored to the fungal-growth substrate, some fungi produce physically-isolated spores which, as such, are amenable to manipulation using dielectrophoresis (the translational motion of charged or uncharged matter caused by polarization effects in a non-uniform electrical field). Such fields can be conveniently generated through the charging of an insulator using the triboelectric effect (the transfer of charge between two objects through friction when they are rubbed together). In this study, polystyrene microbiological inoculating loops were used in combination with nylon-fabric rubbing to harvest fungal spores from five species from within the genus Penicillium, which were grown on agar plates containing two different media over an extended time course. In terms of average Bruker spectral-comparison scores, our method generated higher scores in 80% of cases tested and, in terms of average coefficients of variation, our method generated lower spectral variability in 93% of cases tested. Harvesting of spores using a rapid, inexpensive and simple dielectrophoretic method, therefore, facilitates improved fungal identification for the Penicillium species tested. © The Author(s) 2019. Published by Oxford University Press.Long non-coding RNAs (lncRNAs) makeup a considerable part of the non-coding human genome and had been well-established as crucial players in an array of biological processes. In spite of their abundance and versatile roles, their functional characteristics remain largely undiscovered mainly due to the lack of suitable genetic manipulation tools. The emerging CRISPR/Cas9 technology has been widely adapted in several studies that aim to screen and identify novel lncRNAs as well as interrogate the functional properties of specific lncRNAs. However, the complexity of lncRNAs genes and the regulatory mechanisms that govern their transcription, as well as their unique functionality pose several limitations the utilization of classic CRISPR methods in lncRNAs functional studies. Here, we overview the unique characteristics of lncRNAs transcription and function and the suitability of the CRISPR toolbox for applications in functional characterization of lncRNAs. We discuss some of the novel variations to the classic CRISPR/Cas9 system that have been tailored and applied previously to study several aspects of lncRNAs functionality. Finally, we share perspectives on the potential applications of various CRISPR systems, including RNA-targeting, in the direct editing and manipulation of lncRNAs. © The Author(s) 2019. Published by Oxford University Press.Sample entropy is a powerful tool for analyzing the complexity and irregularity of physiology signals which may be associated with human health. Nevertheless, the sophistication of its calculation hinders its universal application. As of today, the R language provides multiple open-source packages for calculating sample entropy. All of which, however, are designed for different scenarios. Therefore, when searching for a proper package, the investigators would be confused on the parameter setting and selection of algorithms. To ease their selection, we have explored the functions of five existing R packages for calculating sample entropy and have compared their computing capability in several dimensions. We used four published datasets on respiratory and heart rate to study their input parameters, types of entropy, and program running time. In summary, NonlinearTseries and CGManalyzer can provide the analysis of sample entropy with different embedding dimensions and similarity thresholds. CGManalyzer is a good choice for calculating multiscale sample entropy of physiological signal because it not only shows sample entropy of all scales simultaneously but also provides various visualization plots. MSMVSampEn is the only package that can calculate multivariate multiscale entropies. In terms of computing time, NonlinearTseries, CGManalyzer, and MSMVSampEn run significantly faster than the other two packages. Moreover, we identify the issues in MVMSampEn package. This article provides guidelines for researchers to find a suitable R package for their analysis and applications using sample entropy. © The Author(s) 2019. Published by Oxford University Press.