In this paper we analyze the performance that can be achieved by means of Proportional-Integral-Derivative-Acceleration (PIDA) controllers and we compare it with that achievable by means of PID controllers in order to evaluate the cost/benefit ratio when an action proportional to the second derivative of the control error is added to the control law. In particular, different process transfer functions are considered and the tuning of the controllers is determined with genetic algorithms by minimizing the integrated absolute error. Both the set-point following and load disturbance rejection tasks are evaluated. In order to provide a fair comparison, a constraint on the maximum sensitivity is also posed so that the achieved robustness is taken into account.

BACKGROUND: During the last decade, with the aim to solve the challenge of post-genomic and transcriptomic data mining, a plethora of tools have been developed to create, edit and analyze metabolic pathways. In particular, when a complex phenomenon is considered, the creation of a network of multiple interconnected pathways of interest could be useful to investigate the underlying biology and ultimately identify functional candidate genes affecting the trait under investigation. RESULTS: PANEV (PAthway NEtwork Visualizer) is an R package set for gene/pathway-based network visualization. Based on information available on KEGG, it visualizes genes within a network of multiple levels (from 1 to n) of interconnected upstream and downstream pathways. The network graph visualization helps to interpret functional profiles of a cluster of genes. CONCLUSIONS: The suite has no species constraints and it is ready to analyze genomic or transcriptomic outcomes. Users need to supply the list of candidate genes, specify the target pathway(s) and the number of interconnected downstream and upstream pathways (levels) required for the investigation. The package is available at https://github.com/vpalombo/PANEV.

UNLABELLED: The GHap R package was designed to call haplotypes from phased marker data. Given user-defined haplotype blocks (HapBlock), the package identifies the different haplotype alleles (HapAllele) present in the data and scores sample haplotype allele genotypes (HapGenotype) based on HapAllele dose (i.e. 0, 1 or 2 copies). The output is not only useful for analyses that can handle multi-allelic markers, but is also conveniently formatted for existing pipelines intended for bi-allelic markers. AVAILABILITY AND IMPLEMENTATION: https://cran.r-project.org/package=GHap CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Genome Wide Association Studies between molecular markers and phenotypes are now routinely run in model and non-model species. However, tools to estimate the probability of association of functional units (e.g. genes) containing multiple markers are not developed for species other than humans. Here we introduce MUGBAS (MUlti species Gene-Based Association Suite), software that estimates the P-value of a gene using information on annotation, single marker GWA results and genotype. The software is species and annotation independent, fast, highly parallelized and ready for high-density marker studies. AVAILABILITY AND IMPLEMENTATION: https://bitbucket.org/capemaster/mugbas