Il diluvio azteco, l’evangelizzazione e la persistenza dei modelli narrativi nelle società indigene messicane

L’articolo propone una riflessione su un tema mitologico rinvenuto dai primi evangelizzatori dei popoli amerindiani nella tradizione religiosa nahua preispanica, nel quale i primi uomini sopravvissuti a un diluvio per certi versi simile a quello biblico offendono gli dèi e vengono da questi trasformati in cani, da cui poi discenderebbero alcune delle popolazioni della Mesoamerica. Un motivo narrativo che, mescolatosi con la versione vetero-testamentaria dell’arca, persiste nella narrativa orale di molti gruppi indigeni del Messico contemporaneo. Attraverso il raffronto e una sommaria analisi delle prime versioni del racconto azteco del diluvio e di quelle raccolte recentemente in una comunità indigena di Oaxaca, l’articolo propone alcune considerazioni circa le ragioni della persistenza di certi elementi narrativi e i criteri che hanno ispirato la loro rielaborazione e adattamento nell’attuale versione del racconto del diluvio huave.The article proposes a reflection on a mythological theme found by the early evangelizers of the American people in the pre-Hispanic Nahua tradition, in which the first survivors of a flood in some respects similar to that of the Bible, offend the gods and are transformed into dogs, which in turn give origin to some of the peoples of Mesoamerica. A narrative motif that, blended with the arch-testamentary version of the ark, persists in the oral narrative of many indigenous groups in contemporary Mexico. By comparing and analyzing the early versions of the Aztec tale of the flood and those recently collected in an indigenous community in Oaxaca, the article offers some considerations about the reasons for the persistence of some narrative elements and the criteria that have inspired their re-elaboration and adaptation into the current version of the Huave flood story

Media 9: Computational toolbox for optical tweezers in geometrical optics

Originally published in JOSA B on 01 May 2015 (josab-32-5-B11

Media 7: Computational toolbox for optical tweezers in geometrical optics

Originally published in JOSA B on 01 May 2015 (josab-32-5-B11

Media 2: Computational toolbox for optical tweezers in geometrical optics

Originally published in JOSA B on 01 May 2015 (josab-32-5-B11

Characteristics of the structural MRI sample.

<p>Characteristics of the structural MRI sample.</p

Differences between groups in the nodal functional degree.

<p>Significant decreases in the nodal degree of regions from Module III or fronto-parietal network in Parkinson’s disease patients with mild cognitive impairment (PD-MCI) compared to controls (CTR) after FDR corrections.</p

Structural brain networks in controls, MCI patients, and AD patients.

<p>From left to right: weighted correlation matrices of 82 regions, binary correlation matrices after fixing density at 15%, and corresponding brain graphs from A) controls (CTR), B) patients with amnestic mild cognitive impairment (MCI), and C) Alzheimer’s disease (AD) patients.</p

Types of graphs.

<p>Graphs can be classified based on their edge weights (<i>weighted</i> or <i>binary</i>) and directionality (<i>directed</i> or <i>undirected</i>). It is possible to transform a directed graph into an undirected one by symmetrization (i.e. by removing the information about the edge directions), and a weighted graph into a binary one by thresholding (i.e. by assigning a value of 1 to the edges above a given threshold and 0 to those below threshold).</p

BRAPH workflow.

<p>Workflow for a graph theory analysis in BRAPH and relative graphical user interfaces (GUIs). A) The brain regions are defined in the <i>GUI Brain Atlas</i>. B) The data of the subjects are imported in the <i>GUI Cohort</i> and the user can define groups and edit their age, gender and other relevant data. C) The connectivity matrix is calculated in the <i>GUI Graph Analysis</i> after selecting the parameters defining the type of correlation, how to deal with negative correlation coefficients, and which type of graph to analyze: D) binary undirected graphs at a fixed density (<i>GUI Graph Analysis BUD</i>); E) binary undirected graphs at a fixed threshold (<i>GUI Graph Analysis BUT</i>); F) weighted undirected graphs (<i>GUI Graph Analysis WU</i>).</p

Differences between groups in nodal structural measures.

<p>Nodes showing significant differences between groups in the nodal degree and nodal local efficiency after FDR corrections. Orange indicates increases in nodal measures, while blue indicates decreases.</p