The genetics of symbiotic nitrogen fixation: comparative genomics of 14 Rhizobia Strains by resolution of protein clusters.

The symbiotic relationship between legumes and nitrogen fixing bacteria is critical for agriculture, as it may have profound impacts on lowering costs for farmers, on land sustainability, on soil quality, and on mitigation of greenhouse gas emissions. However, despite the importance of the symbioses to the global nitrogen cycling balance, very few rhizobial genomes have been sequenced so far, although there are some ongoing efforts in sequencing elite strains. In this study, the genomes of fourteen selected strains of the order Rhizobiales, all previously fully sequenced and annotated, were compared to assess differences between the strains and to investigate the feasibility of defining a core ?symbiome??the essential genes required by all rhizobia for nodulation and nitrogen fixation. Comparison of these whole genomes has revealed valuable information, such as several events of lateral gene transfer, particularly in the symbiotic plasmids and genomic islands that have contributed to a better understanding of the evolution of contrasting symbioses. Unique genes were also identified, as well as omissions of symbiotic genes that were expected to be found. Protein comparisons have also allowed the identification of a variety of similarities and differences in several groups of genes, including those involved in nodulation, nitrogen fixation, production of exopolysaccharides, Type I to Type VI secretion systems, among others, and identifying some key genes that could be related to host specificity and/or a better saprophytic ability. However, while several significant differences in the type and number of proteins were observed, the evidence presented suggests no simple core symbiome exists. A more abstract systems biology concept of nitrogen fixing symbiosis may be required. The results have also highlighted that comparative genomics represents a valuable tool for capturing specificities and generalities of each genome.bitstream/item/74069/1/ID-34062.pd

An exploratory social network analysis of academic research networks

For several decades, academics around the world have been collaborating with the view to support the development of their research domain. Having said that, the majority of scientific and technological policies try to encourage the creation of strong inter-related research groups in order to improve the efficiency of research outcomes and subsequently research funding allocation. In this paper, we attempt to highlight and thus, to demonstrate how these collaborative networks are developing in practice. To achieve this, we have developed an automated tool for extracting data about joint article publications and analyzing them from the perspective of social network analysis. In this case study, we have limited data from works published in 2010 by England academic and research institutions. The outcomes of this work can help policy makers in realising the current status of research collaborative networks in England

A new web-based genomics resource for bioinformatics analysis of Rhipicephalus (Boophilus) microplus: CattleTickBase

No abstract availabl

Confidence intervals of success rates in evolutionary computation

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 12th annual conference on Genetic and evolutionary computation , http://dx.doi.org/10.1145/1830483.1830657Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examinated in detail. We address some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical distribution with a large corpus of tools available that can be used in the context of EC research. One of those tools, confidence intervals (CIs), is studie

Adapting Searchy to extract data using evolved wrappers

This is the author’s version of a work that was accepted for publication inExpert Systems with Applications: An International Journal. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications: An International Journal, 39, 3 (2012) DOI: 10.1016/j.eswa.2011.08.168Organizations need diverse information systems to deal with the increasing requirements in information storage and processing, yielding the creation of information islands and therefore an intrinsic difficulty to obtain a global view. Being able to provide such an unified view of the -likely heterogeneous-information available in an organization is a goal that provides added-value to the information systems and has been subject of intense research. In this paper we present an extension of a solution named Searchy, an agent-based mediator system specialized in data extraction and Integration. Through the use of a set of wrappers, it integrates information from arbitrary sources and semantically translates them according to a mediated scheme. Searchy is actually a domain-independent wrapper container that ease wrapper development, providing, for example, semantic mapping. The extension of Searchy proposed in this paper introduces an evolutionary wrapper that is able to evolve wrappers using regular expressions. To achieve this, a Genetic Algorithm (GA) is used to learn a regex able to extract a set of positive samples while rejects a set of negative samples.The authors gratefully acknowledge Mart´ın Knoblauch for his useful suggestions and valuable comments. This work has been partially supported by the Spanish Ministry of Science and Innovation under the projects ABANT (TIN 2010-19872), COMPUBIODIVE (TIN2007-65989) and by Castilla-La Mancha project PEII09-0266-6640

Microarray evidence for off target effects in tick RNA interference experiments, and the lack of strong correlation between DSRNA and antibody phenotypes in tick In vitro treatments for vaccine candidate screening

No abstract availabl

Constraint Satisfaction in Current Control of a Five-Phase Drive with Locally Tuned Predictive Controllers

The problem of control of stator currents in multi-phase induction machines has recently been tackled by direct digital model predictive control. Although these predictive controllers can directly incorporate constraints, most reported applications for stator current control of drives do no use this possibility, being the usual practice tuning the controller to achieve the particular compromise solution. The proposal of this paper is to change the form of the tuning problem of predictive controllers so that constraints are explicitly taken into account. This is done by considering multiple controllers that are locally optimal. To illustrate the method, a five-phase drive is considered and the problem of minimizing x- y losses while simultaneously maintaining the switching frequency and current tracking error below some limits is tackled. The experiments showed that the constraint feasibility problem has, in general, no solution for standard predictive control, whereas the proposed scheme provides good tracking performance without violating constraints in switching frequency and at the same time reducing parasitic currents of x- y subspaces.Ministerio de Ciencia, Innovación y Universidades de España RTI2018-101897-B-I0

An empirical study on the accuracy of computational effort in Genetic Programming

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. F. Barrero, M. D. R-Moreno, B. Castaño, and D. Camacho, "An empirical study on the accuracy of computational effort in Genetic Programming", in IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 1164 - 1171Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error.This work was partially supported by the MICYT project ABANT (TIN2010-19872) and Castilla-La Mancha project PEII09- 0266-664

A decision support system for logistics operations

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-13161-5_14Proceedings of 5th International Workshop Soft Computing Models in Industrial and Environmental ApplicationsThis paper describes an Artificial Intelligence based application for a logistic company that solves the problem of grouping by zones the packages that have to be delivered and propose the routes that the drivers should follow. The tool combines from the one hand, Case-Based Reasoning techniques to separate and learn the most frequent areas or zones that the experienced logistic operators do. These techniques allow the company to separate the daily incidents that generate noise in the routes, from the decision made based on the knowledge of the route. From the other hand, we have used Evolutionary Computation to plan optimal routes from the learning areas and evaluate those routes. The application allows the users to decide under what parameters (i.e. distance, time, etc) the route should be optimized.We want to thank Antonio Montoya for his contribution in the tool developed. This work has been supported by the Espi & Le Barbier company and the public projects funded by the Spanish Ministry of Science and Innovation under the projects COMPUBIODIVE (TIN2007-65989), V-LeaF (TIN2008-02729-E/TIN) and by Castilla-La Mancha project PEII09- 0266-6640

Effects of the lack of selective pressure on the expected run-time distribution in genetic programming

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. F. Barrero, M. D. R-Moreno, B. Castano, and D. Camacho, "Effects of the lack of selective pressure on the expected run-time distribution in genetic programming", in IEEE Congress on Evolutionary Computation, CEC 2013, pp. 1748 - 1755Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time distribution. In particular, we explore the influence of the selective pressure on the run-time distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time distribution well described by the Weibull probability distribution.This work has been partly supported by Spanish Ministry of Science and Education under project ABANT (TIN2010- 19872)