Two decisional conflicts in Heisenberg

Se describe, por una parte, el doble conflicto de decisión que se presenta cuando el individuo desea y simultáneamente no desea seguir un curso de acción, en el físico Werner Heisenberg. Conflicto respecto a las decisiones de exiliarse o permanecer en la Alemania nazi y respecto a en qué grado colaborar científicamente con el régimen nazi en la construcción de una bomba atómica. Por otra parte, se propone a discusión el hecho de que en el verano de 1945 mientras Oppenheimer era celebrado por su éxito en coordinar el proyecto Manhattan que posibilitó la destrucción atómica de Hiroshima y Nagasaki, Heisenberg permanecía detenido por su colaboración científica con el régimen nazi aún cuando ésta fuera parsimoniosa y limitada. Planteándose con ello el problema moral, pero también práctico de la relación entre la ciencia y el poder, cuando los científicos obligados a servir a este son juzgados posteriormente según las victorias o derrotas de dicho poder que personalmente no les son imputables.A decision conflict is present when a person wants and, simultaneously, not wants to follow an action course. This paper describes firstly the double decision conflict on the physicist Werner Heisenberg: Conflict regarding decisions of going into exile or to remain in the Nazi Germany, and with regard to the degree of scientific collaboration with the Nazi regime to create an atomic bomb. Secondly, discusses the fact that on the summer of 1945 while Oppenheimer was celebrated by his success in coordinating the Manhattan project, which made possible the atomic destruction of Hiroshima and Nagasaki, Heisenberg was in detention for his scientific collaboration with the Nazi regime even when the collaboration was parsimonious and limited. Finally, the Author discusses the ethical and practical problem of the relation between science and power: when scientists forced to serve power are judged lately according to victories and defeats of such power that personally are not imputed to them

Additional file 2: Figure S1. of BCM: toolkit for Bayesian analysis of Computational Models using samplers

Description: Overview of the sampling results of each inference of the comparison with existing software packages. (PDF 1 mb

Additional file 1: of BCM: toolkit for Bayesian analysis of Computational Models using samplers

Supplementary Information. Description: Supplementary information describing the methodological details and all settings that were used for each inference. (DOCX 49 kb

Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates - Fig 1

<p><b>A</b> The generative model for the conditional dependencies graph and precision matrix. <b>B</b> The generative model for structural connectivity and the precision matrix, based on both BOLD time series <b>X</b> and probabilistic streamline counts <b>N</b>. Latent variables, observed variables and hyperparameters are indicated in white, yellow and grey, respectively.</p

Subcortical connectivity for one subject using the data fusion model.

<p>From left to right: the empirical streamline log-counts, the mean posterior connection probability matrix and the mean posterior partial correlation matrix. Note the reduction in connectivity, in particular between the hemispheres, compared to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004534#pcbi.1004534.g007" target="_blank">Fig 7</a>. The connections for the left hemisphere (LH) and the right hemisphere (RH) are separated by the dashed lines.</p

Effect of different sample sizes in recovery of ground truth connectivity, for the BGGM approach as well as for the graphical LASSO with <i>λ</i> ∈ {5, 100, 1 000, 10 000}.

<p>Error bars indicate one standard deviation over the 50 runs. For the BGGM approach, the error bars indicate one standard deviation over the expectations of the runs.</p

Subcortical connectivity for one subject.

<p>From left to right: the empirical correlation matrix, the mean posterior connection probability matrix and the mean posterior partial correlation matrix. The connections for the left hemisphere (LH) and the right hemisphere (RH) are separated by the dashed lines.</p

Comparison of competitive gene-set analysis results at different SNP cut-offs.

<p>Comparison of gene set -log10 p-values from the CD data competitive gene-set analysis at different SNP p-value cut-offs for ALIGATOR (top row), INRICH (middle row) and MAGENTA (bottom row). The highest cut-off on the horizontal axis is compared to each of the lower cut-offs. P-values for gene sets not evaluated at the lower cut-off are shown in grey. The shown correlations are for the -log10 p-values for gene-sets evaluated at both cut-offs. Horizontal and vertical grey dotted lines demarcate the p = 0.05 nominal significance threshold.</p

Comparison of gene analysis results for different test-statistics.

<p>Gene -log10 p-values from the CD data gene analysis in MAGMA for three different gene test-statistics, comparing analyses using (A) the mean <i>χ</i>² statistic with the top <i>χ</i>² statistic, (B) the mean <i>χ</i>² statistic and the PC regression model and (C) the top <i>χ</i>² statistic and the PC regression model. P-values below 10^–8 are truncated to 10^–8 (grey points) to preserve the visibility of the other points.</p

Competitive gene-set p-values for MAGMA and INRICH significant gene-sets.

<p>Significant p-values are highlighted in bold. MAGMA p-values compared against a Bonferroni-corrected threshold of 0.05/1320 = 0.000038. For INRICH, corrected p-values (not shown) are compared against a threshold of 0.05; corrected p-value for all three significant gene-sets is 0.049.</p><p>^a p-values were not computed because fewer than two genes in the set overlapped with an associated interval; p-values are therefore effectively equal to 1</p><p>Competitive gene-set p-values for MAGMA and INRICH significant gene-sets.</p