<?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>S0188-95322023000400105</article-id>
<article-id pub-id-type="doi">10.17488/rmib.44.4.7</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Performance Evaluation of Biomedical Time Series Transformation Methods for Classification Tasks]]></article-title>
<article-title xml:lang="es"><![CDATA[Evaluación del Rendimiento de Métodos de Transformación de Series Temporales Biomédicas para Tareas de Clasificación]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ku-Maldonado]]></surname>
<given-names><![CDATA[Carlos Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Molino-Minero-Re]]></surname>
<given-names><![CDATA[Erik]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Autónoma de México  ]]></institution>
<addr-line><![CDATA[Yucatán ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Autónoma de México Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas ]]></institution>
<addr-line><![CDATA[Yucatán ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<volume>44</volume>
<numero>spe1</numero>
<fpage>105</fpage>
<lpage>116</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322023000400105&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322023000400105&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322023000400105&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The extraction of time series features is essential across various fields, yet it remains a challenging endeavor. Therefore, it's crucial to identify appropriate methods capable of extracting pertinent information that can significantly enhance classification performance. Among these methods are those that translate time series into different domains. This study investigates three distinct time series transformation approaches for addressing time series classification challenges within biomedical data. The first method involves a response vector transformation, while the other two employ image transformation techniques: RandOm Convolutional KErnel Transform (ROCKET), Gramian Angular Fields, and Markov Transition Fields. These transformation methods were applied to five biomedical datasets, exploring various format configurations to ascertain the optimal representation technique and configuration for input, which in turn improves classification performance. Evaluations were conducted on the effectiveness of these methods in conjunction with two classification algorithms. The outcomes underscore the significance of these time series transformation techniques as facilitators for enhanced classification algorithms documented in current literature.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La extracción de características de series temporales es esencial en diversos campos, pero sigue siendo un desafío. Por lo tanto, es crucial identificar métodos apropiados capaces de extraer información pertinente que pueda mejorar significativamente el rendimiento de clasificación. Entre estos métodos se encuentran aquellos que traducen las series temporales a diferentes dominios. Este estudio investiga tres enfoques distintos de transformación de series temporales para abordar los desafíos de clasificación de series temporales en datos biomédicos. El primer método implica una transformación de vector de respuesta, mientras que los otros dos emplean técnicas de transformación de imagen: RandOm Convolutional KErnel Transform (ROCKET), Gramian Angular Fields y Markov Transition Fields. Estos métodos de transformación se aplicaron a cinco conjuntos de datos biomédicos, explorando diversas configuraciones de formato para determinar la técnica y configuración de representación óptima para la entrada, lo que a su vez mejora el rendimiento de clasificación. Se realizaron evaluaciones sobre la efectividad de estos métodos en conjunción con dos algoritmos de clasificación. Los resultados subrayan la importancia de estas técnicas de transformación de series temporales como facilitadoras para mejorar los algoritmos de clasificación documentados en la literatura actual.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[biomedical data]]></kwd>
<kwd lng="en"><![CDATA[classification]]></kwd>
<kwd lng="en"><![CDATA[convolutional neural networks]]></kwd>
<kwd lng="en"><![CDATA[time series]]></kwd>
<kwd lng="en"><![CDATA[transformations]]></kwd>
<kwd lng="es"><![CDATA[clasificación]]></kwd>
<kwd lng="es"><![CDATA[datos biomédicos]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales convolucionales]]></kwd>
<kwd lng="es"><![CDATA[series temporales]]></kwd>
<kwd lng="es"><![CDATA[transformaciones]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>[1]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Middlehurst]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Schäfer]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Bagnall]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Bake off redux: a review and experimental evaluation of recent time series classification algorithms]]></article-title>
<source><![CDATA[arXiv]]></source>
<year>2023</year>
<numero>2304</numero>
<issue>2304</issue>
</nlm-citation>
</ref>
<ref id="B2">
<label>[2]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ismail Fawaz]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Forestier]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Weber]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Idoumghar]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Muller]]></surname>
<given-names><![CDATA[P.-A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep learning for time series classification: a review]]></article-title>
<source><![CDATA[Data Min. Knowl. Discov.]]></source>
<year>2019</year>
<volume>33</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>917-63</page-range></nlm-citation>
</ref>
<ref id="B3">
<label>[3]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Xiong]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Fault Diagnosis Method Based on Encoding Time Series and Convolutional Neural Network]]></article-title>
<source><![CDATA[IEEE Access]]></source>
<year>2020</year>
<volume>8</volume>
<page-range>165232-46</page-range></nlm-citation>
</ref>
<ref id="B4">
<label>[4]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Garcia]]></surname>
<given-names><![CDATA[G. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Michau]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Ducoffe]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Sen Gupta]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Fink]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms]]></article-title>
<source><![CDATA[Proc. Inst. Mech. Eng. O J. Risk Reliab.]]></source>
<year>2022</year>
<volume>236</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>617-27</page-range></nlm-citation>
</ref>
<ref id="B5">
<label>[5]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lines]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Taylor]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Bagnall]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Hive-cote: The hierarchical vote collective of transformation-based ensembles for time series classification]]></source>
<year>2016</year>
<conf-name><![CDATA[ 16international conference on data mining (ICDM)]]></conf-name>
<conf-date>2016</conf-date>
<conf-loc>Barcelona, Spain </conf-loc>
<page-range>1041-6</page-range></nlm-citation>
</ref>
<ref id="B6">
<label>[6]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Yan]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Oates]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<source><![CDATA[Time series classification from scratch with deep neural networks: A strong baseline]]></source>
<year>2017</year>
<conf-name><![CDATA[ International Joint Conference on Neural Networks (IJCNN)]]></conf-name>
<conf-date>2017</conf-date>
<conf-loc>Anchorage, AK, USA </conf-loc>
<page-range>1578-85</page-range></nlm-citation>
</ref>
<ref id="B7">
<label>[7]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ismail Fawaz]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Lucas]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Forestier]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Pelletier]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Inceptiontime: Finding alexnet for time series classification]]></article-title>
<source><![CDATA[Data Min. Knowl. Discov.]]></source>
<year>2020</year>
<volume>34</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>1936-62</page-range></nlm-citation>
</ref>
<ref id="B8">
<label>[8]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dau]]></surname>
<given-names><![CDATA[H. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Bagnall]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Kamgar]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Yeh]]></surname>
<given-names><![CDATA[C.-C. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Gharghabi]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Ratanamahatana]]></surname>
<given-names><![CDATA[C. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Keogh]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The UCR time series archive]]></article-title>
<source><![CDATA[IEEE/CAA J. Autom. Sin.]]></source>
<year>2019</year>
<volume>6</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>1293-305</page-range></nlm-citation>
</ref>
<ref id="B9">
<label>[9]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dempster]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Petitjean]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Webb]]></surname>
<given-names><![CDATA[G. I.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels]]></article-title>
<source><![CDATA[Data Min. Knowl. Discov.]]></source>
<year>2020</year>
<volume>34</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>1454-95</page-range></nlm-citation>
</ref>
<ref id="B10">
<label>[10]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Oates]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Imaging time-series to improve classification and imputation]]></article-title>
<source><![CDATA[arXiv]]></source>
<year>2015</year>
<numero>1506</numero>
<issue>1506</issue>
</nlm-citation>
</ref>
<ref id="B11">
<label>[11]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Faouzi]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Janati]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[pyts: A Python Package for Time Series Classification]]></article-title>
<source><![CDATA[J. Mach. Learn. Res.]]></source>
<year>2020</year>
<volume>21</volume>
<numero>46</numero>
<issue>46</issue>
<page-range>1-6</page-range></nlm-citation>
</ref>
<ref id="B12">
<label>[12]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chollet]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Keras]]></article-title>
<source><![CDATA[GitHub]]></source>
<year>2015</year>
</nlm-citation>
</ref>
<ref id="B13">
<label>[13]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pedregosa]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Varoquaux]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Gramfort]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Michel]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Scikitlearn: Machine learning in Python]]></article-title>
<source><![CDATA[J. Mach. Learn. Res.]]></source>
<year></year>
<volume>12</volume>
<numero>85</numero>
<issue>85</issue>
<page-range>2825-30</page-range></nlm-citation>
</ref>
<ref id="B14">
<label>[14]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tanu]]></surname>
</name>
<name>
<surname><![CDATA[Kakkar]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<source><![CDATA[Accounting for order-frame length tradeoff of Savitzky-Golay smoothing filters]]></source>
<year>2018</year>
<conf-name><![CDATA[ 5International Conference on Signal Processing and Integrated Networks (SPIN)]]></conf-name>
<conf-date>2018</conf-date>
<conf-loc>Noida, India </conf-loc>
<page-range>805-10</page-range></nlm-citation>
</ref>
<ref id="B15">
<label>[15]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Kang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Forecasting with time series imaging]]></article-title>
<source><![CDATA[Expert. Syst. Appl.]]></source>
<year>2020</year>
<volume>160</volume>
</nlm-citation>
</ref>
<ref id="B16">
<label>[16]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gonzalez-Zapata]]></surname>
<given-names><![CDATA[A. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Fraga]]></surname>
<given-names><![CDATA[L. G. de la]]></given-names>
</name>
<name>
<surname><![CDATA[Ovilla-Martinez]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Tlelo-Cuautle]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Cruz-Vega]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Enhanced FPGA implementation of Echo State Networks for chaotic time series prediction]]></article-title>
<source><![CDATA[Integration]]></source>
<year>2023</year>
<volume>92</volume>
<page-range>48-57</page-range></nlm-citation>
</ref>
<ref id="B17">
<label>[17]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ben Said]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Erradi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning]]></article-title>
<source><![CDATA[arXiv]]></source>
<year>2019</year>
<numero>1911</numero>
<issue>1911</issue>
</nlm-citation>
</ref>
<ref id="B18">
<label>[18]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ni]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Zeng]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An efficient astronomical seeing forecasting method by random convolutional Kernel transformation]]></article-title>
<source><![CDATA[Eng. Appl. Artif. Intell.]]></source>
<year>2024</year>
<volume>127</volume>
</nlm-citation>
</ref>
<ref id="B19">
<label>[19]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Marco-Blanco]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Cuevas]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Time Series Clustering With Random Convolutional Kernels]]></article-title>
<source><![CDATA[arXiv]]></source>
<year>2023</year>
<numero>2305</numero>
<issue>2305</issue>
</nlm-citation>
</ref>
<ref id="B20">
<label>[20]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dhariyal]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Nguyen]]></surname>
<given-names><![CDATA[T. Le]]></given-names>
</name>
<name>
<surname><![CDATA[Ifrim]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Back to Basics: A Sanity Check on Modern Time Series Classification Algorithms]]></article-title>
<source><![CDATA[arXiv]]></source>
<year>2023</year>
<numero>2308</numero>
<issue>2308</issue>
</nlm-citation>
</ref>
<ref id="B21">
<label>[21]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Middlehurst]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Large]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Flynn]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Lines]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Bostrom]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Bagnall]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[HIVE-COTE 2.0: a new meta ensemble for time series classification]]></article-title>
<source><![CDATA[Mach. Learn.]]></source>
<year>2021</year>
<volume>110</volume>
<numero>11-12</numero>
<issue>11-12</issue>
<page-range>3211-43</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
