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

Fixed-Gap Tunnel Junction for Reading DNA Nucleotides

Previous measurements of the electronic conductance of DNA nucleotides or amino acids have used tunnel junctions in which the gap is mechanically adjusted, such as scanning tunneling microscopes or mechanically controllable break junctions. Fixed-junction devices have, at best, detected the passage of whole DNA molecules without yielding chemical information. Here, we report on a layered tunnel junction in which the tunnel gap is defined by a dielectric layer, deposited by atomic layer deposition. Reactive ion etching is used to drill a hole through the layers so that the tunnel junction can be exposed to molecules in solution. When the metal electrodes are functionalized with recognition molecules that capture DNA nucleotides <i>via</i> hydrogen bonds, the identities of the individual nucleotides are revealed by characteristic features of the fluctuating tunnel current associated with single-molecule binding events

Hydrodynamics of Diamond-Shaped Gradient Nanopillar Arrays for Effective DNA Translocation into Nanochannels

Effective DNA translocation into nanochannels is critical for advancing genome mapping and future single-molecule DNA sequencing technologies. We present the design and hydrodynamic study of a diamond-shaped gradient pillar array connected to nanochannels for enhancing the success of DNA translocation events. Single-molecule fluorescence imaging is utilized to interrogate the hydrodynamic interactions of the DNA with this unique structure, evaluate key DNA translocation parameters, including speed, extension, and translocation time, and provide a detailed mapping of the translocation events in nanopillar arrays coupled with 10 and 50 μm long channels. Our analysis reveals the important roles of diamond-shaped nanopillars in guiding DNA into as small as 30 nm channels with minimized clogging, stretching DNA to nearly 100% of their dyed contour length, inducing location-specific straddling of DNA at nanopillar interfaces, and modulating DNA speeds by pillar geometries. Importantly, all critical features down to 30 nm wide nanochannels are defined using standard photolithography and fabrication processes, a feat aligned with the requirement of high-volume, low-cost production

Hydrodynamics of Diamond-Shaped Gradient Nanopillar Arrays for Effective DNA Translocation into Nanochannels

Effective DNA translocation into nanochannels is critical for advancing genome mapping and future single-molecule DNA sequencing technologies. We present the design and hydrodynamic study of a diamond-shaped gradient pillar array connected to nanochannels for enhancing the success of DNA translocation events. Single-molecule fluorescence imaging is utilized to interrogate the hydrodynamic interactions of the DNA with this unique structure, evaluate key DNA translocation parameters, including speed, extension, and translocation time, and provide a detailed mapping of the translocation events in nanopillar arrays coupled with 10 and 50 μm long channels. Our analysis reveals the important roles of diamond-shaped nanopillars in guiding DNA into as small as 30 nm channels with minimized clogging, stretching DNA to nearly 100% of their dyed contour length, inducing location-specific straddling of DNA at nanopillar interfaces, and modulating DNA speeds by pillar geometries. Importantly, all critical features down to 30 nm wide nanochannels are defined using standard photolithography and fabrication processes, a feat aligned with the requirement of high-volume, low-cost production

Supplementary Table 1. Results of proteomics data including phosphoproteomics and cytokine level measurements for primary normal human bronchial epithelial cells (NHBE) and normal rat bronchial epithelial cells (NRBE) cells exposed to 52 stimuli.

<p>Supplementary Table 1. Results of proteomics data including phosphoproteomics and cytokine level measurements for primary normal human bronchial epithelial cells (NHBE) and normal rat bronchial epithelial cells (NRBE) cells exposed to 52 stimuli.</p> <p>The file contains the median of bead fluorescence intensities measured for each protein in every sample (cell lysate and corresponding supernatant for phosphoproteins and cytokines, respectively). For each stimulus, sample replicates have been extracted from 3 independent wells. The results are reported for (a) 19 phosphoproteins, with in addition the measurements for 2 control beads (Control A: Phycoerythrin-coated beads used as positive control bead; Control B: BSA-coated beads devoid of antibody used as negative control bead), and for the actin; (b) 22 cytokines.</p

Stability of model performance.

<p>Model stability was evaluated for SC1 (A), SC2 (B, left) and SC3 (B, right) by scoring final predictions on 1000 different random subsets of the test set samples (each subset was 60 patients, ~80% of the week 13 test set). The resulting distribution of scores was plotted against each teams overall challenge rank. Note, the center horizontal line of each box indicates the median score. Challenge ranks are ordered from highest to lowest, where a rank of 1 indicates the highest rank.</p

The role of patient outcome and proteomics data in determining prediction accuracy.

<p>A) The probability density of prediction accuracy evaluated separately for CR and Resistant patients. (B) Comparison of individual model accuracy for CR and Resistant patients (right) compared to the distribution over the population (left). The midline of the box plot indicated median accuracy while the lower and upper box edge indicated 25^th and 75^th percentile. (C) The distribution of scores obtained using scrambled RPPA data for the two top performing teams in SC1 (Rank #1 and Rank #2). For each metric, the score obtained using the original RPPA data (not scrambled) is indicated by a diamond. (D) Heat map showing the percent difference in score (average of BAC and AUROC) between predictions obtained using the original RPPA data (not scrambled) and predictions made using data where each protein was scrambled separately over 100 assessments. The y-axis indicates the result for each scrambled protein assessment, 1–100, while the x-axis indicates each protein.</p

Model performance.

<p>The performance of each model was tracked during each week of the challenge. Each sub-challenge was scored using two different metrics. BAC and AUROC were used for SC1, while CI and PC were chosen for SC2 and SC3. The score of the highest performing model was determined each week, either using each metric independently, or by averaging both metrics, and is shown for SC1 (A), SC2 (B), and SC3 (C). Note, if the highest score for any week did not exceed the previous weeks score, the previous score was maintained. The probability density of the final scores (normalized to a maximum of 1) was also determined and for each metric in SC1 (B), SC2 (D), and SC3 (F). The probability density of the null hypothesis, determined by scoring random predictions, is also indicated.</p