Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Faculty Publications and Creative Works 2003

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. It serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

A Bioinformatics Study of Protein Conformational Flexibility and Misfolding: a Sequence, Structure and Dynamics Approach

This PhD Thesis titled "A Bioinformatics Study of Protein Conformational Flexibility and Misfolding: a Sequence, Structure and Dynamics Approach" comprises the results and conclusions obtained by us from the study of three different but somehow related research projects, covering aspects of the phenomenon of protein local conformational instability, its relationship with protein function, evolvability and aggregation, and the effect of genetic variations on protein conformational instability related to Conformational Diseases. These projects include the prediction of putative prion proteins in complete proteomes and the study of prion biology from a genomic perspective, the prediction of conformationally unstable protein regions and the existence of a structural framework for linking conformational instability to folding and function, and the establishment of a rationale for assessing the connection among mutations and disease phenotypes in Conformational Diseases.Esta tesis doctoral comprende los resultados y conclusiones obtenidos por nosotros a partir del estudio de tres proyectos de investigación diferentes pero de alguna manera relacionados, cubriendo los aspectos del fenómeno de la inestabilidad conformacional local de la proteína, su relación con la función de la proteína, la capacidad de evolución y agregación, y el efecto de las variaciones genéticas en la inestabilidad conformacional de la proteína relacionados con las enfermedades conformacionales. Estos proyectos incluyen la predicción de presuntas proteínas priónicas en proteomas complejos y el estudio de la biología de priones desde una perspectiva genómica, la predicción de las regiones de proteínas conformacionalmente inestables y la existencia de un marco estructural para la vinculación de la inestabilidad conformacional del plegado y la función, y el establecimiento de una razón fundamental para la evaluación de la relación entre las mutaciones y fenotipos de la enfermedad en enfermedades conformacionales

Soft Computing approaches in ocean wave height prediction for marine energy applications

El objetivo de esta tesis consiste en investigar el uso de técnicas de Soft Computing (SC) aplicadas a la energía producida por las olas o energía undimotriz. Ésta es, entre todas las energías marinas disponibles, la que exhibe el mayor potencial futuro porque, además de ser eficiente desde el punto de vista técnico, no causa problemas ambientales significativos. Su importancia práctica radica en dos hechos: 1) es aproximadamente 1000 veces más densa que la energía eólica, y 2) hay muchas regiones oceánicas con abundantes recursos de olas que están cerca de zonas pobladas que demandan energía eléctrica. La contrapartida negativa se encuentra en que las olas son más difíciles de caracterizar que las mareas debido a su naturaleza estocástica. Las técnicas SC exhiben resultados similares e incluso superiores a los de otros métodos estadísticos en las estimaciones a corto plazo (hasta 24 h), y tienen la ventaja adicional de requerir un esfuerzo computacional mucho menor que los métodos numérico-físicos. Esta es una de las razones por la que hemos decidido explorar el uso de técnicas de SC en la energía producida por el oleaje. La otra se encuentra en el hecho de que su intermitencia puede afectar a la forma en la que se integra la electricidad que genera con la red eléctrica. Estas dos son las razones que nos han impulsado a explorar la viabilidad de nuevos enfoques de SC en dos líneas de investigación novedosas. La primera de ellas es un nuevo enfoque que combina un algoritmo genético (GA: Genetic Algorithm) con una Extreme Learning Machine (ELM) aplicado a un problema de reconstrucción de la altura de ola significativa (en un boya donde los datos se han perdido, por ejemplo, por una tormenta) utilizando datos de otras boyas cercanas. Nuestro algoritmo GA-ELM es capaz de seleccionar un conjunto reducido de parámetros del oleaje que maximizan la reconstrucción de la altura de ola significativa en la boya cuyos datos se han perdido utilizando datos de boyas vecinas. El método y los resultados de esta investigación han sido publicados en: Alexandre, E., Cuadra, L., Nieto-Borge, J. C., Candil-García, G., Del Pino, M., & Salcedo-Sanz, S. (2015). A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction. Ocean Modelling, 92, 115-123. La segunda contribución combina conceptos de SC, Smart Grids (SG) y redes complejas (CNs: Complex Networks). Está motivada por dos aspectos importantes, mutuamente interrelacionados: 1) la forma en la que los conversores WECs (wave energy converters) se interconectan eléctricamente para formar un parque, y 2) cómo conectar éste con la red eléctrica en la costa. Ambos están relacionados con el carácter aleatorio e intermitente de la energía eléctrica producida por las olas. Para poder integrarla mejor sin afectar a la estabilidad de la red se debería recurrir al concepto Smart Wave Farm (SWF). Al igual que una SG, una SWF utiliza sensores y algoritmos para predecir el olaje y controlar la producción y/o almacenamiento de la electricidad producida y cómo se inyecta ésta en la red. En nuestro enfoque, una SWF y su conexión con la red eléctrica se puede ver como una SG que, a su vez, se puede modelar como una red compleja. Con este planteamiento, que se puede generalizar a cualquier red formada por generadores renovables y nodos que consumen y/o almacenan energía, hemos propuesto un algoritmo evolutivo que optimiza la robustez de dicha SG modelada como una red compleja ante fallos aleatorios o condiciones anormales de funcionamiento. El modelo y los resultados han sido publicados en: Cuadra, L., Pino, M. D., Nieto-Borge, J. C., & Salcedo-Sanz, S. (2017). Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms. Energies, 10(8), 1097

Imaging polyphenolic therapeutic compounds in a eukaryotic model microbe

Flavonoids are polyphenolic metabolites that have a range of physiological and developmental functions in plants. They are the focus of much work as potential therapeutics, although investigation of specific mode of action remains a notably under-researched area. Monitoring transport and location of flavonoids in cells is difficult because, despite a role in UV-absorption in plants, they emit only low levels of fluorescence. Visualising them in plants is possible using the Naturstoff reagent (NA), reported historically to be a polyphenol-fluorescence-enhancing stain. We explored therefore whether this agent was effective during preclinical assessment of polyphenolic therapeutics in a microbial-model. The eukaryote Dictyostelium discoideum has been shown to be a useful model when identifying novel drug targets for treating various diseases. For example, in the case of polycystic kidney disease, naringenin decreased Dictyostelium cell division whereas a polycystin-2-null Dictyostelium line was resistant to the flavonoid, and, subsequently, naringenin treatment proved to reduce cyst-formation in mammalian-kidney model cell lines1. To monitor transport and site of action of the drugs investigated in such studies, we developed a method using NA-staining in this model organism. A range of polyphenolics were assayed in cells, cell-extracts and the cell-washes, and NA-enhanced imaging was evaluated in parallel with LCMS-quantification. NA-enhanced fluorescence of compounds at therapeutically relevant concentrations proved an effective and qualitative measure of transport and localisation in Dictyostelium, and could be used in concert with localisation dyes. Fluorescence-enhancement is limited to a subset of flavonoids, however, and not more widely applicable in our studies to date

The cyanobacterial rhomboid protease is a regulator of the CCM

Cyanobacteria are aquatic photosynthetic bacteria and useful models for study of the chloroplast and photosynthesis. We are studying a ‘rhomboid’ membrane-located proteases in Synechocystis sp. PCC 6803, which appears to function as a previously undiscovered regulator of the carbon concentrating mechanism (CCM) of this phototroph. Rhomboids are almost ubiquitous across evolution, and are known to activate diverse cellular processes via proteolysis of their specific, membrane-sequestered substrates. Although this well-conserved family has solved crystal structures of bacterial enzymes such as Escherichia coli GlpG, ironically, most work has been carried out on eukaryotic representatives. Following our study of the Arabidopsis thaliana chloroplast RBL10 protease, we identified cyanobacterial orthologues with the aim of discovering if roles might be conserved between these and organellar rhomboids. Molecular biology and reverse-genetics studies were made on slr1461, a mutant in the single rhomboid protease of Synechocystis. When photosynthetic parameters were investigated, it could be seen that inactivation of slr1461 did not affect nonphotochemical quenching, unlike the chloroplast rbl10 mutant, but Slr1461 was required for reduction of photosynthetic activity in mixotrophic conditions. This reduction allows cyanobacteria to avoid expending energy on the uptake of CO2 when an organic carbon source can be utilised: as might be expected, therefore, Slr1461 transcription was linked with downregulation of genes encoding proteins facilitating high-affinity CO2 import under high CO2 and mixotrophic conditions. Quantitative RT-PCR of CCM network genes suggested that Slr1461 is located upstream of known regulators, including another membrane protease, the Slr0228 FtsH, and a central, controlling transcription factor NdhR