<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322019000100007</article-id>
<article-id pub-id-type="doi">10.17488/rmib.40.1.7</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Signal-Processing tools for core-collection selection from genetic-resource collection]]></article-title>
<article-title xml:lang="es"><![CDATA[Herramienta de procesamiento de señales para la selección de &#8220;Core-Collection&#8221; desde colección de recursos genéticos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Flores]]></surname>
<given-names><![CDATA[L. I.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Takeya]]></surname>
<given-names><![CDATA[Masaru]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Borrayo]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Guadalajara  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,National Agriculture and Food Research Organization  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Japan</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2019</year>
</pub-date>
<volume>40</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322019000100007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322019000100007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322019000100007&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Selecting a representative core collection (CC) is a proven and effective strategy for overcoming the expenses and difficulties of managing genetic resources in gene banks around the globe. Because of the diverse applications available for these sub-collections, several algorithms have been successfully implemented to construct them based on genotypic, phenotypic, passport or geographic data (either by individual datasets or by consensus). However, to the best of our knowledge, no single comprehensive datasets has been properly explored to date. Thus, researchers evaluate multiple datasets in order to construct representative CCs; this can be quite difficult, but one feasible solution for such an evaluation is to manage all available data as one discrete signal, which allows signal processing tools (SPTs) to be implemented during data analysis. In this research, we present a proof- of-concept study that shows the possibility of mapping to a discrete signal any type of data available from genetic resource collections in order to take advantage of SPTs for the construction of CCs that adequately represent the diversity of two crops. This method is referred to as &#8216;SPT selection.&#8217; All available information for each element of the tested collections was analysed under this perspective and compared when possible, with one of the most used algorithms for CC selection. Genotype-only SPT selection did not prove as effective as standard CC selection did not prove as effective as standard CC selection algorithms; however, the SPT approach can consider genotype alongside other types of information, which results in well-represented Ccs that consider both the genotype and agromorphological diversities present in original collections. Furthermore, SPT-based analysis can evaluate all available data both in a comprehensive manner and under different perspective, and despite its limitations, the analysis renders satisfactory results. Thus, SPT-based algorithms for CC selection can be valuable in the field of genetic resources research, management and exploitation.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La selección de una colección núcleo (core-collection) representativa (CC) es una estrategia comprobada y eficaz para superar los gastos y las dificultades de la gestión de los recursos genéticos en los bancos de germoplasma de todo el mundo. Debido a las diversas aplicaciones disponibles para estas subcolecciones, se han implementado con éxito varios algoritmos para construirlos en base a datos genotípicos, fenotípicos, de pasaporte o geográficos (ya sea por conjuntos de datos individuales o por consenso). Sin embargo, hasta donde tenemos conocimiento, no se han explorado adecuadamente conjuntos de datos integrales hasta la fecha. Por lo tanto, los investigadores evalúan conjuntos de datos múltiples para construir CCs representativos; esto puede ser bastante difícil, pero una solución factible para tal evaluación es administrar todos los datos disponibles como una señal discreta, que permite implementar herramientas de procesamiento de señal (SPT) durante el análisis de datos. En esta investigación, presentamos un estudio de prueba de concepto que muestra la posibilidad de asignar a una señal discreta cualquier tipo de datos disponibles de colecciones de recursos genéticos para aprovechar los SPT para la construcción de CC que representen adecuadamente la diversidad de dos cultivos. Este método se conoce como "selección de SPT." Toda la información disponible para cada elemento de las colecciones analizadas se analizó bajo esta perspectiva y se comparó cuando fue posible, con uno de los algoritmos más utilizados para la selección de CC. La selección de SPT de solo genotipo no resultó tan efectiva como los algoritmos de selección de CC estándar; sin embargo, el enfoque SPT puede considerar el genotipo junto con otros tipos de información, lo que da como resultado CCs bien representados que consideran tanto el genotipo como las diversidades agromorfológicas presentes en las colecciones originales. Además, el análisis basado en SPT puede evaluar todos los datos disponibles, tanto de manera integral y bajo diferentes perspectivas, y a pesar de sus limitaciones, el análisis arroja resultados satisfactorios. Por lo tanto, los algoritmos basados en SPT para la selección de CC pueden ser valiosos en el campo de la investigación, gestión y explotación de recursos genéticos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Core Collection]]></kwd>
<kwd lng="en"><![CDATA[SPT]]></kwd>
<kwd lng="en"><![CDATA[Genotype]]></kwd>
<kwd lng="en"><![CDATA[Genebank]]></kwd>
<kwd lng="es"><![CDATA[Core Collection]]></kwd>
<kwd lng="es"><![CDATA[SPT]]></kwd>
<kwd lng="es"><![CDATA[Banco de germoplasma]]></kwd>
<kwd lng="es"><![CDATA[genotipo]]></kwd>
</kwd-group>
</article-meta>
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