OBI: A computational tool for the analysis and systematization of the positive selection in proteins

There are multiple tools for positive selection analysis, including vaccine design and detection of variants of circulating drug-resistant pathogens in population selection. However, applying these tools to analyze a large number of protein families or as part of a comprehensive phylogenomics pipeline could be challenging. Since many standard bioinformatics tools are only available as executables, integrating them into complex Bioinformatics pipelines may not be possible. We have developed OBI, an open-source tool aimed to facilitate positive selection analysis on a large scale. It can be used as a stand-alone command-line app that can be easily installed and used as a Conda package. Some advantages of using OBI are: • It speeds up the analysis by automating the entire process • It allows multiple starting points and customization for the analysis • It allows the retrieval and linkage of structural and evolutive data for a protein through We hope to provide with OBI a solution for reliably speeding up large-scale protein evolutionary and structural analysis.Fil: Calvento, Julián H.. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Bulgarelli, Franco Leonardo. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; ArgentinaFil: Velez Rueda, Ana Julia. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Optimización de problemas multi-objetivo de empaquetado de palets mediante algoritmos evolutivos

En este trabajo se ha implementado un algoritmo evolutivo multi-objetivo paralelo, para resolver el problema de empaquetamiento en dos dimensiones con restricciones, para una aplicación de transporte de palets en camiones. El transporte de palets en camiones tiene una gran importancia en Andalucía y especialmente en el campo almeriense donde a diario salen hacia Europa cientos de camiones cargados de productos del campo. El problema de empaquetamiento en dos dimensiones (2DPP) consiste en insertar un conjunto de objetos caracterizados por tener un alto y ancho específico, en el menor número de camiones posibles donde el alto y ancho es igual para todos. A partir de esta definición existen multitud de variantes al problema. En la variante multi-objetivo del problema, además de minimizar el número de camiones, se intenta minimizar el balanceo de carga de los mismos intentando colocar la carga de las piezas de la mejor forma posible para que el centro de gravedad del camión quede lo más cercano posible al centro deseado. El algoritmo se ha aplicado a una variante del problema donde se trata de insertar un conjunto de palets con su alto, ancho y peso específico, en el menor número de camiones posible y con el mejor balanceo de carga, evitando una serie de restricciones añadidas al problema. Cada palet corresponde a un cliente, con lo cual todos los palets de un mismo cliente deben de ir en el mismo camión, cada camión no puede ir cargado con más de 25000 kilos y el centro de gravedad debe de ir lo más próximo al eje del camión. Para la optimización de este problema hemos implementado un algoritmo evolutivo TPMOEA, este tipo de algoritmos están inspirados en la teoría de la evolución de Darwin y en el desarrollo de la informática evolutiva. Los algoritmos evolutivos son técnicas de optimización y búsqueda de soluciones basadas en la selección natural y genética que permiten resolver problemas no lineales en los que interviene un alto número de variables en problemas complejos. El algoritmo ha sido implementado con un conjunto de operadores evolutivos diseñados para obtener soluciones de gran calidad para un conjunto de instancias establecidas

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Texture-boundary detection in real-time

Boundary detection is an essential first-step for many computer vision applications. In practice, boundary detection is difficult because most images contain texture. Normally, texture-boundary detectors are complex, and so cannot run in real-time. On the other hand, the few texture boundary detectors that do run in real-time leave much to be desired in terms of quality. This thesis proposes two real-time texture-boundary detectors – the Variance Ridge Detector and the Texton Ridge Detector – both of which can detect high-quality texture-boundaries in real-time. The Variance Ridge Detector is able to run at 47 frames per second on 320 by 240 images, while scoring an F-measure of 0.62 (out of a theoretical maximum of 0.79) on the Berkeley segmentation dataset. The Texton Ridge Detector runs at 10 frames per second but produces slightly better results, with an F-measure score of 0.63. These objective measurements show that the two proposed texture-boundary detectors outperform all other texture-boundary detectors on either quality or speed. As boundary detection is so widely-used, this development could induce improvements to many real-time computer vision applications

Simultaneous modelling and clustering of visual field data

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn the health-informatics and bio-medical domains, clinicians produce an enormous amount of data which can be complex and high in dimensionality. This scenario includes visual field data, which are used for managing the second leading cause of blindness in the world: glaucoma. Visual field data are the most common type of data collected to diagnose glaucoma in patients, and usually the data consist of 54 or 76 variables (which are referred to as visual field locations). Due to the large number of variables, the six nerve fiber bundles (6NFB), which is a collection of visual field locations in groups, are the standard clusters used in visual field data to represent the physiological traits of the retina. However, with regard to classification accuracy of the data, this research proposes a technique to find other significant spatial clusters of visual field with higher classification accuracy than the 6NFB. This thesis presents a novel clustering technique, namely, Simultaneous Modelling and Clustering (SMC). SMC performs clustering on data based on classification accuracy using heuristic search techniques. The method searches a collection of significant clusters of visual field locations that indicate visual field loss progression. The aim of this research is two-fold. Firstly, SMC algorithms are developed and tested on data to investigate the effectiveness and efficiency of the method using optimisation and classification methods. Secondly, a significant clustering arrangement of visual field, which highly interrelated visual field locations to represent progression of visual field loss with high classification accuracy, is searched to complement the 6NFB in diagnosis of glaucoma. A new clustering arrangement of visual field locations can be used by medical practitioners together with the 6NFB to complement each other in diagnosis of glaucoma in patients. This research conducts extensive experiment work on both visual field and simulated data to evaluate the proposed method. The results obtained suggest the proposed method appears to be an effective and efficient method in clustering visual field data and 3 improving classification accuracy. The key contributions of this work are the novel model-based clustering of visual field data, effective and efficient algorithms for SMC, practical knowledge of visual field data in the diagnosis of glaucoma and the presentation a generic framework for modelling and clustering which is highly applicable to many other dataset/model combinations