A new electrophoresis technique to separate microsatellite alleles*

Analysis of large numbers of SSR (simple sequence repeats: microsatellites) reactions can be tedious, time-consuming and expensive. The objective of this study was to report a new electrophoresis method to analyze and visualize SSR data quickly and accurately and compare it to the ability of four other electrophoresis methods. Individual PCR reactions consisting of DNA from several Cornus florida L. (flowering dogwood) cultivars and two SSR primer pairs were assembled for analysis using the following three methods: agarose gel, polyacrylamide gel and QIAxcel System. Two separate PCRreactions consisting of the same components plus a fluorescent-labeled primer were set up for analyses using the CEQTM 8000 Genetic Analysis System and ABI 3130xl DNA Sequencer. These fiveelectrophoretic methods were assessed for advantages and disadvantages. Polyacrylamide gels had highest resolution of alleles, whereas agarose gels had the lowest. However, with both separationmedia, it was difficult to score the size of alleles. Capillary electrophoresis with the CEQTM 8000 Genetic Analysis System and ABI 3130xl DNA Sequencer easily separated products and determined allelic size, but was more expensive than electrophoresis using either agarose or polyacrlamide gels. The QIAxcel System had lower  esolution than CEQTM 8000 Genetic Analysis System and ABI 3130xl DNA Sequencer. However, QIAxcel System was rapid and cost effective compared to the two widely used capillary sequencers, and also provided a computer generated gel image. For researchers in small to intermediate-sized laboratories, the QIAxcel System using a twelve channel, sieving-gel cartridge is an affordable device for SSR assays used for mapping and population diversity analysis

A toolbox of machine learning software to support microbiome analysis

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.</p

Multi-Response Optimisation of Turning Process Parameters with GRA and TOPSIS Methods

The research deals with the optimisation of CNC turning process parameters to determine the optimal parametric combination that provides the minimal surface roughness (Ra) and maximal material removal rate. The experiment was conducted by the CNC turning process of S355J2 carbon steel. Data from the Taguchi design of experiments were the subject of analysis with Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In the present study, three process parameters, such as cutting speed, feed rate and depth of cut, were chosen for the experimentation. It was found that 250 m/min cutting speed, 0.10 mm/rev feed rate and 1.8 mm depth of cut presented the optimal parametric combination by both used multi-objective optimisation methods. Analysis of variance (ANOVA) at a 95 % confidence level was used to determine the most significant parameters. Finally, the accuracy of GRA and TOPSIS results were validated by confirmation experiments.</jats:p