Viral system algorithm: foundations and comparison between selective and massive infections

This paper presents a guided and deep introduction to Viral Systems (VS), a novel bio-inspired methodology based on a natural biological process taking part when the organism has to give a response to an external infection. VS has proven to be very efficient when dealing with problems of high complexity. The paper discusses on the foundations of viral systems, presents the main pseudocodes that need to be implemented and illustrates the methodology application. A comparison between VS and other metaheuristics, as well between different VS approaches is presented. Finally trends and new research opportunities are presented for this bio-inspired methodology

Protein multiple sequence alignment by hybrid bio-inspired algorithms

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the ‘weighted sum of pairs’ as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BAliBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space

Multi-Target Analysis and Design of Mitochondrial Metabolism.

Analyzing and optimizing biological models is often identified as a research priority in biomedical engineering. An important feature of a model should be the ability to find the best condition in which an organism has to be grown in order to reach specific optimal output values chosen by the researcher. In this work, we take into account a mitochondrial model analyzed with flux-balance analysis. The optimal design and assessment of these models is achieved through single- and/or multi-objective optimization techniques driven by epsilon-dominance and identifiability analysis. Our optimization algorithm searches for the values of the flux rates that optimize multiple cellular functions simultaneously. The optimization of the fluxes of the metabolic network includes not only input fluxes, but also internal fluxes. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the desired Pareto front. We find that the maximum ATP production is linked to a total consumption of NADH, and reaching the maximum amount of NADH leads to an increasing request of NADH from the external environment. Furthermore, the identifiability analysis characterizes the type and the stage of three monogenic diseases. Finally, we propose a new methodology to extend any constraint-based model using protein abundances.PL has received funding from (FP7-Health-F5-2012) under grant agreement no. 305280 (MIMOmics). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PLoS via http://dx.doi.org/10.1371/journal.pone.013382

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Vibration Monitoring: Gearbox identification and faults detection

L'abstract è presente nell'allegato / the abstract is in the attachmen

Super-resolution imaging of cell-surface Sonic hedgehog multimolecular signalling complexes

Sonic hedgehog is a fascinating protein with great responsibility over the formation and upkeep of our bodies. It is widely studied, not least because dysregulation of the Shh signalling pathway leads to repercussions on human health, such as contraction of cancer. Gaining an understanding of its signalling mechanism is central to inventing preventative measures and treatments against this disease. This thesis focuses on the study of the spatial organisation of Shh multimolecular signalling complexes on the surface of producing cells, and those dispatched in the vicinity of those cells, using high-resolution optical imaging beyond the diffraction limit. With un-precedented resolution, the differences in organisation of Shh pre- and post-release from the surface were characterised, and the influence of the lipid modifications of Shh, namely choles-terol and palmitate, investigated. The main findings were that both lipid adducts are necessary for large-scale multimerisation, but not for the formation of small, sub-diffraction limit oligomers. Together with data I collected about the profile of the clusters’ size distributions, I find that electrostatic interactions between the molecules may be the engine driving the multimerisation process. Furthermore, the role of lipid modifications may, at least in part, be to retain Shh on the surface while multimerisation proceeding according to the law of mass action builds upon the small oligomer nucleation sites prepared presumably by the electrostatic interactions in the first place. Other, more indirect lines of evidence again based on the profile of the multimer size distribution insinuated that Shh complexes may not undergo any proteolytic modifications prior to release – contrary to some reports in the literature. The results presented in this thesis are the fruits of a completely fresh and innovative approach to examining Shh, which for the first time delivers concrete dimensional details about the elusive structure of the Shh multimer.Open Acces

Otimização multiobjetivo aplicada ao planejamento sistemático de conservação para espécies de plantas do cerrado brasileiro

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.Nesta tese, propôs-se a aplicação de conceitos de Otimização Multiobjetivo (MOO) e de Computação Bioinspirada a problemas de Planejamento Sistemático de Conservação (SCP). Foram estudados três problemas específicos. No primeiro, buscou-se o menor conjunto de populações locais a serem conservadas para representar a diversidade genética de uma espécie vegetal do Cerrado. O método proposto foi capaz de identificar uma maior diversidade de soluções com a quantidade mínima de populações ao mesmo tempo em que refinou os resultados, indicando as combinações com maior diversidade intraespecífica e maior possibilidade de persistência ao longo do tempo. No segundo problema, buscou-se: (i) selecionar um conjunto de amostras geneticamente complementares a uma coleção de germoplasma de plantas já existente; (ii) definir uma core collection para uma coleção de germoplasma. Com a utilização de MOO foi possível identificar os indivíduos exatos que deveriam ser selecionados para complementar o germoplasma. Ademais, definiu-se um protocolo para tratar um grande volume de amostras a fim de estabelecer uma core collection. A abordagem proposta pode ser usada para construir core collections com máxima riqueza alélica, bem como ser estendido a casos de conservação in situ. Por fim, no terceiro problema, SCP foi associado à estimativa da ocorrência de espécies projetada para o futuro com base em simulações climáticas objetivando definir prioridades de conservação. O método proposto identificou locais com: (i) alta prioridade para conservação; (ii) risco significativo de investimento; e, (iii) que poderiam tornar-se atrativos no futuro. Foi proposto, também, um algoritmo multiobjetivo baseado em Sistemas Imunológicos Artificiais, o Multi-Objective Artificial Immune System (MAIS). MOO permitiu trabalhar com instâncias de problemas com mais de duas dimensões, possibilitando maior confiabilidade na indicação do portfolio de soluções, aumentando, assim, o poder de decisão do método computacional e a qualidade da informação fornecida aos tomadores de decisão. O presente trabalho é pioneiro no país ao resolver problemas de SCP usando técnicas avançadas de otimização, colaborando para a implantação da área de Ecoinformática no Brasil.This thesis proposes a more sophisticated, yet general, solution to the systematic conservation planning problem (SCP) based on multi-objective optimization (MOO) and bio-inspired computing. We worked with three problems using data from plants of the Brazilian Cerrado biome. In the first problem, we looked for the smallest set of local populations of a plant species aiming its conservation. The method was able to find a larger portfolio of solutions and to refine the results as well, indicating solutions with more intra-specific diversity and higher probability of persistence throughout time. In the second problem, we aimed: (i) to select a set of individuals genetically complementary to an existing plant germplasm collection; and, (ii) to define a core collection for a germplasm collection. We were able to identify within a population of several individuals, the exact accessions/samples that should be chosen in order to preserve the species diversity. Moreover, we defined a method (a protocol) to deal with large amounts of accessions in the context of MOO. The proposed approach can be used to help constructing collections with maximal allelic richness and can also be extended to the in situ conservation. Finally, in the third problem, we applied MOO to SCP associated to climate forecasting, in a dynamic spatial prioritization analysis for biodiversity conservation. Our method was able to identify sites: (i) of high priority for conservation; (ii) with significant risk of investment; and, (iii) that may become attractive in the future. We also proposed a constrained multi-objective artificial immune system algorithm (MAIS). The MOO approach to SCP increases reliability by including additional objectives, which while increasing the complexity, significantly augments the amount and quality of information used to provide users with an improved decision support system. This thesis is pioneer in solving the SCP problem using advanced optimization techniques contributing to the insertion and consolidation of the new area of ecoinformatics in Brazil

Theoretical and Empirical Evaluation of Diversity-preserving Mechanisms in Evolutionary Algorithms: On the Rigorous Runtime Analysis of Diversity-preserving Mechanisms in Evolutionary Algorithms

Evolutionary algorithms (EAs) simulate the natural evolution of species by iteratively applying evolutionary operators such as mutation, recombination, and selection to a set of solutions for a given problem. One of the major advantages of these algorithms is that they can be easily implemented when the optimisation problem is not well understood, and the design of problem-specific algorithms cannot be performed due to lack of time, knowledge, or expertise to design problem-specific algorithms. Also, EAs can be used as a first step to get insights when the problem is just a black box to the developer/programmer. In these cases, by evaluating candidate solutions it is possible to gain knowledge on the problem at hand. EAs are well suited to dealing with multimodal problems due to their use of a population. A diverse population can explore several hills in the fitness landscape simultaneously and offer several good solutions to the user, a feature desirable for decision making, multi-objective optimisation and dynamic optimisation. However, a major difficulty when applying EAs is that the population may converge to a sub-optimal individual before the fitness landscape is explored properly. Many diversity-preserving mechanisms have been developed to reduce the risk of such premature convergence and given such a variety of mechanisms to choose from, it is often not clear which mechanism is the best choice for a particular problem. We study the (expected/average) time for such algorithms to find satisfactory solutions for multimodal and multi-objective problems and to extract guidelines for the informed design of efficient and effective EAs. The resulting runtime bounds are used to predict and to judge the performance of algorithms for arbitrary problem sizes, further used to clarify important design issues from a theoretical perspective. We combine theoretical research with empirical applications to test the theoretical recommendations for their practicality, and to engage in rapid knowledge transfer from theory to practice. With this approach, we provide a better understanding of the working principles of EAs with diversity-preserving mechanisms. We provide theoretical foundations and we explain when and why certain diversity mechanisms are effective, and when they are not. It thus contributes to the informed design of better EAs