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

Additional file 2 of A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin

Table S2. Classification Test Binders. Set of binders in the test set including computational analysis. The peptides in Column A represent the test data set, sorted by Column B. The highest measured values (MaxIVIG) are given in Column B, in Column C (mBuffer) the mean of secondary antibody control, and in Column D (mIVIG) the mean of all IVIG measures. In Column E, the sum of all computational methods are summed up whose predictions were correct as outlined in the remaining columns, where the EL-Manzalawy, LuĹĄtrek, PWM, Pythia, Barbarini et al. [2] (Pavia), and PWM2 methods are given. (XLS 1218 kb

Additional file 3 of A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin

Table S3. Classification Test Non-Binders. Set of non-binders in the test set including computational analysis. The peptides in Column A represent the test data set, sorted by Column B. The highest measured values (Max IVIG) are given in Column B, in Column C (mBuffer) the mean of secondary antibody control, and in Column D (mIVIG) the mean of all IVIG measures. In Column E, the sum of all computational methods are summed up whose predictions were correct as outlined in the remaining columns, as in Additional file 2: Table S2. (XLS 1218 kb

Epitope Predictions Indicate the Presence of Two Distinct Types of Epitope-Antibody-Reactivities Determined by Epitope Profiling of Intravenous Immunoglobulins

<div><p>Epitope-antibody-reactivities (EAR) of intravenous immunoglobulins (IVIGs) determined for 75,534 peptides by microarray analysis demonstrate that roughly 9% of peptides derived from 870 different human protein sequences react with antibodies present in IVIG. Computational prediction of linear B cell epitopes was conducted using machine learning with an ensemble of classifiers in combination with position weight matrix (PWM) analysis. Machine learning slightly outperformed PWM with area under the curve (AUC) of 0.884 vs. 0.849. Two different types of epitope-antibody recognition-modes (Type I EAR and Type II EAR) were found. Peptides of Type I EAR are high in tyrosine, tryptophan and phenylalanine, and low in asparagine, glutamine and glutamic acid residues, whereas for peptides of Type II EAR it is the other way around. Representative crystal structures present in the Protein Data Bank (PDB) of Type I EAR are PDB 1TZI and PDB 2DD8, while PDB 2FD6 and 2J4W are typical for Type II EAR. Type I EAR peptides share predicted propensities for being presented by MHC class I and class II complexes. The latter interaction possibly favors T cell-dependent antibody responses including IgG class switching. Peptides of Type II EAR are predicted not to be preferentially presented by MHC complexes, thus implying the involvement of T cell-independent IgG class switch mechanisms. The high extent of IgG immunoglobulin reactivity with human peptides implies that circulating IgG molecules are prone to bind to human protein/peptide structures under non-pathological, non-inflammatory conditions. A webserver for predicting EAR of peptide sequences is available at <a href="http://www.sysmed-immun.eu/EAR" target="_blank">www.sysmed-immun.eu/EAR</a>.</p></div

Performance comparison of the tested classifiers for IVIG binding prediction on the test set peptides.

<p>ROC analysis for our machine learning approach with an ensemble classifier (ML-advanced), the machine learning method of El-Manzalawy et al. (2008) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078605#pone.0078605-ElManzalawy1" target="_blank">[22]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078605#pone.0078605-ElManzalawy2" target="_blank">[23]</a> and a PWM approach using an PWM derived from the training set. Both machine learning approaches were trained on the original training set (“original”: three times more non-binding than binding peptides) and on the balanced training set (“balanced”: equal number of binding and non-binding peptides) and finally applied on the test set. AUC values are indicated as well. Note that the curves based on the original and balanced training set of our ML-advanced method show almost complete overlap.</p

Analysis of amino acid enrichment in IVIG “binding” versus “non-binding” peptides for all, the 1^st degree classifiable and 1^st degree unclassifiable peptides of the training set.

<p>Amino acid propensities for the indicated groups were determined by dividing the frequency of amino acids in “binding” peptides (recognized by IVIG) by the frequency of amino acids in non-binding peptides. Results are sorted by decreasing epitope propensity assigned to the whole training set group. A propensity score >1 means that an amino acid is more likely to occur in the “binding” peptides, a score <1 more frequent in the “non-binding” ones, respectively. The analysis is position-independent.</p

Data set preparation and computational workflow for the prediction of epitope-antibody-reactivities (EAR) determined for IVIG antibodies.

<p>Rectangles represent groups of peptides (numbers in each group are indicated), boxes with rounded corners indicate the applied classification approaches. ¹All peptides printed on the microarrays ²Removal of false positive (binding) peptides (e.g. those reactive with secondary antibodies) ³Separation of peptide set according to signal intensities of EAR into non-binders, binders and unassigned peptides ⁴Classification approach ML-advanced = machine learning with an ensemble classifier ⁵Number of peptides predicted to be non-binding/binding, separated into those predicted correctly (underlined) and incorrectly ⁶Classification approach PWM = position weight matrix ⁷Classification approach ML-simple = simplified machine learning using human-understandable attributes ⁸Capital letters A–H indicate subsets of peptides assigned in supplementary information table S1 and explained there in the legend.</p

Ratio heat map based on amino acid propensities of IVIG “binding” versus “non-binding” peptides for the 2^nd degree unclassifiable peptides.

<p>The rows represent the individual amino acids, the columns the positions within the 15mer peptide. The heat map color reflects the ratio between the PWM values (frequency of the occurrence of a given amino acid at a given peptide position) for the “binding” and the “non-binding” peptides in the set. Pink color indicates high propensity (overrepresentation in “binding” peptides), while blue color indicates low propensity (underrepresentation in “binding” peptides). Standard hierarchical clustering using Euclidean distance was performed on rows and columns.</p

Distribution of peptides initially scored as 1^st degree classifiable and unclassifiable by ML-simple using PWM measures.

<p>Peptides of the training set were assigned to the groups 1^st degree classifiable and unclassifiable by our “simplified machine learning using human-understandable attributes” (ML-simple) approach. They were further divided into peptides reacting with IVIG (“binding”; panel A) or not reactive with IVIG (“non-binding”; panel B). In a next step each peptide was assigned values using two ratio PWMs. The x-axis values derive from a PWM that was based on all peptides present in the training set. They are calculated by multiplying the ratios of the relative frequencies of each amino acid at each position in a peptide sequence for the group “binding” (panel A) and “non-binding” (panel B), respectively. The y-axis values were calculated in the same way, however, only the 1^st degree unclassifiable peptides present in the training set were used as input of the PWM. Each peptide is represented by one dot. Peptides in red in panel A correspond to the type I EAR while those in black depict the type II EAR.</p