<?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>2448-5705</journal-id>
<journal-title><![CDATA[Inter disciplina]]></journal-title>
<abbrev-journal-title><![CDATA[Inter disciplina]]></abbrev-journal-title>
<issn>2448-5705</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Centro de Investigaciones Interdisciplinarias en Ciencias y Humanidades]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2448-57052020000100055</article-id>
<article-id pub-id-type="doi">10.22201/ceiich.24485705e.2020.20.71195</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[On the stability and adaptability of human physiology: Gaussians meet heavy-tailed distributions]]></article-title>
<article-title xml:lang="es"><![CDATA[Sobre la estabilidad y adaptabilidad de la fisiología humana: la gaussiana se encuentra con distribuciones de cola pesada]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fossion]]></surname>
<given-names><![CDATA[Ruben]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sáenz-Burrola]]></surname>
<given-names><![CDATA[Ariel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zapata-Fonseca]]></surname>
<given-names><![CDATA[Leonardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Autónoma de México Instituto de Ciencias Nucleares Centro de Ciencias de la Complejidad]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Autónoma de México Facultad de Medicina ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional Autónoma de México Facultad de Medicina ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2020</year>
</pub-date>
<volume>8</volume>
<numero>20</numero>
<fpage>55</fpage>
<lpage>81</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2448-57052020000100055&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2448-57052020000100055&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2448-57052020000100055&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Recent technological advances allow to monitor in baka non-invasive and continuous way a wide variety of physiological variables. A surprise of the last few decades is that most - if not all - of these variables are always fluctuating, even when the monitored subject is in resting conditions, and the general interpretation is that the statistics of these fluctuations reflect the dynamics of the underlying regulatory mechanisms. The objective of the present contribution is to offer an explanation why a large variability may be a signature of good health for some variables, e.g., heart rate variability, whereas it is interpreted as a risk factor for other variables, like blood pressure variability. Control theory suggests that variables may be classified into 2 categories, depending on the roles they play in the regulatory mechanism, and we argue that the statistics of the corresponding time series may reflect these different functions. We illustrate with experimental time series that regulated variables, such as blood pressure and core temperature, which are to be maintained within a restricted range around a predefined setpoint, correspond to time series that obey a normal (Gaussian) distribution with small variability around a representative average value. On the other hand, effector variables, such as heart rate and skin temperature, oppose or adapt to a multitude of perturbations from the environment, and we show that the corresponding time series exhibit a large variability and obey heavy- tailed distributions that may span various scales. With ageing and/or chronic degenerative disease, effector variables lose variability and adaptive capacity and consequently regulated variables lose stability and become more variable. Although the above results on the variability of time series have been obtained for the specific case of human physiology, they may be applicable as well to other complex dynamical systems where regulatory control mechanisms are active.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Los avances tecnológicos recientes permiten monitorizar de forma continua y no invasiva una amplia variedad de variables fisiológicas. Una sorpresa de las últimas décadas es que la mayoría si no todas de estas variables siempre fluctúan, incluso cuando el sujeto monitoreado se encuentra en condiciones de reposo, y la interpretación general es que las estadísticas de estas fluctuaciones reflejan la dinámica de los mecanismos subyacentes. El objetivo de la presente contribución es ofrecer una explicación de por qué una gran variabilidad puede ser una firma de buena salud para algunas variables, por ejemplo, la variabilidad de la frecuencia cardíaca, mientras que se interpreta como un factor de riesgo para otras variables, por caso, la variabilidad de la presión arterial. La teoría de control sugiere que las variables se pueden clasificar en 2 categorías, dependiendo de los roles que desempeñan en el mecanismo regulatorio, y argumentamos que las estadísticas de las series de tiempo correspondientes pueden reflejar estas funciones diferentes. Ilustramos con series temporales experimentales que las variables reguladas, como la presión arterial y la temperatura central, que deben mantenerse dentro de un rango restringido alrededor de un punto de ajuste predefinido, corresponden a series temporales que obedecen a una distribución normal (gaussiana) con una pequeña variabilidad alrededor de un representante valor promedio. Por otro lado, las variables efectoras, como la frecuencia cardíaca y la temperatura de la piel, se oponen o se adaptan a una multitud de perturbaciones del entorno, y mostramos que las series de tiempo correspondientes muestran una gran variabilidad y obedecen a las distribuciones de cola pesada que pueden abarcar varias escamas. Con el envejecimiento y/o la enfermedad degenerativa crónica, las variables efectoras pierden variabilidad y capacidad de adaptación y, por consiguiente, las variables reguladas pierden estabilidad y se vuelven más variables. Aunque los resultados anteriores sobre la variabilidad de las series de tiempo se han obtenido para el caso específico de la fisiología humana, pueden ser aplicables también a otros sistemas dinámicos complejos donde los mecanismos de control regulatorio están activos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[control theory]]></kwd>
<kwd lng="en"><![CDATA[homeostasis]]></kwd>
<kwd lng="en"><![CDATA[continuous monitoring]]></kwd>
<kwd lng="en"><![CDATA[time series]]></kwd>
<kwd lng="en"><![CDATA[fractals]]></kwd>
<kwd lng="en"><![CDATA[complexity]]></kwd>
<kwd lng="en"><![CDATA[resilience]]></kwd>
<kwd lng="en"><![CDATA[robustness]]></kwd>
<kwd lng="en"><![CDATA[stability]]></kwd>
<kwd lng="en"><![CDATA[adaptability]]></kwd>
<kwd lng="en"><![CDATA[early-warning signals]]></kwd>
<kwd lng="en"><![CDATA[loss of complexity hypothesis]]></kwd>
<kwd lng="es"><![CDATA[teoría de control]]></kwd>
<kwd lng="es"><![CDATA[homeostasis]]></kwd>
<kwd lng="es"><![CDATA[monitoreo continuo]]></kwd>
<kwd lng="es"><![CDATA[series temporales]]></kwd>
<kwd lng="es"><![CDATA[fractales]]></kwd>
<kwd lng="es"><![CDATA[complejidad]]></kwd>
<kwd lng="es"><![CDATA[resiliencia]]></kwd>
<kwd lng="es"><![CDATA[robustez]]></kwd>
<kwd lng="es"><![CDATA[estabilidad]]></kwd>
<kwd lng="es"><![CDATA[adaptabilidad]]></kwd>
<kwd lng="es"><![CDATA[señales de alerta temprana]]></kwd>
<kwd lng="es"><![CDATA[hipótesis de pérdida de complejidad]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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