Norma DIN 476, su uso para desarrollar algunos temas de matemática de un programa de segundo año

Se presenta una propuesta de enseñanza, utilizando un material concreto que nos permite desarrollar algunos temas del programa de Matemática correspondiente al 2do año de la Escuela Industrial Superior de la ciudad de Santa Fe. La selección del material tiene que ver con una búsqueda de relaciones con otras asignaturas del mismo nivel (u otros) porque creemos que la enseñanza y aprendizaje de los contenidos de nuestra área tienen mejor recepción en los alumnos cuando se la contextualiza, cuando se evidencia su necesidad, valor o colaboración en otras áreas de estudio. El abordaje transdisciplinario requiere de mentes creativas, abiertas y capaces de resolver situaciones problemáticas específicas desde muchas perspectivas. Esto indica que el docente debe diseñar estrategias de enseñanza basadas en una concepción cognitiva del aprendizaje, favoreciendo el tratamiento de los contenidos disciplinares desde una perspectiva crítica y reflexiva; en la cual el joven pueda poner en juego sus propias capacidades y posibilidades para participar activamente del proceso y construir el conocimiento.Facultad de Humanidades y Ciencias de la Educació

Ensemble predictive skill depending on ensemble size (case study DREAMiS).

<p>This curve was computed by bootstrapping multiple models from the available 100 models, <i>i.e.</i> we sampled multiple realizations of the individual predictions for the same ensemble size and computed the average value. These curves converge asymptotically and show that the chosen ensemble size parameter is adequate. Equivalent predictions could have been obtained with smaller ensemble sizes.</p

An overview of the characteristics of all case studies approached in this work.

<p>An overview of the characteristics of all case studies approached in this work.</p

MAPK signaling network.

<p>The model by Huang et al. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005379#pcbi.1005379.ref067" target="_blank">67</a>] was used to generate pseudo-experimental data for two sub-problems. The first (MAPKp) partially observed (MAPK-PP, MAPKK-PP and MAPKKK), and the second fully observed MAPKf.</p

Table illustrating the multivariate polynomial interpolation function and model reduction.

<p>Table illustrating the multivariate polynomial interpolation function and model reduction.</p

SELDOM workflow.

<p>The experimental data is used to build an adjacency (a dense DDN) matrix based on the mutual information of all pairs of variables. Through a simple sampling scheme, and limiting the maximum in-degree for each node, a set of more sparse DDNs are generated. Each individual DDN is then used as a scaffold for independent model training and model reduction problems. The resulting models are used to form an ensemble which is able to produce predictions for state trajectories under untested experimental conditions.</p

Heatmap with RMSE scores for different methods and case studies.

<p>The color scheme represents RMSE scores normalized by case-study in order to emphasize differences between methods. The color scale moves from green (low RMSE) to blue (high RMSE). The numeric values of the RMSE scores for each method/case-study are also provided in each corresponding cell. SELDOM B and SELDOM C were clearly the most robust strategies doing very well in all problems.</p

Heatmap with AUPR scores for different methods and case studies.

<p>The color scheme represents AUPR scores normalized by case-study in order to emphasize differences between methods. The color scale moves from red (low AUPR) to yellow (high AUPR). The numeric values of the AUPR scores for each method/case-study are also provided in each corresponding cell. The sparsest version of SELDOM (A) did consistently well in all the case studies. SELDOM B and C did an average job with MAPKf but provided good solutions for all other case-studies. The comparisons are only provided for <i>in silico</i> problems with known solution. Additionally, the solution for the top performing teams in the DREAM challenge is only available for DREAMiS.</p

Logic-Based Models for the Analysis of Cell Signaling Networks

Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks

A systematic atlas of chaperome deregulation topologies across the human cancer landscape

<div><p>Proteome balance is safeguarded by the proteostasis network (PN), an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Since the chaperome is among the central components of the PN, we focused on the chaperome in our study by utilizing a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analyses highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro²) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of chaperome shifts in human cancers and sets the stage for a systematic global analysis of PN alterations across the human diseasome towards novel hypotheses for therapeutic network re-adjustment in proteostasis disorders.</p></div