Pronósticos y data mining para la toma de decisiones. Pronóstico sobre la deserción de alumnos de una Facultad

In order to make decisions, it is necessary to understand the reality of the organization and its context. Not only today, but also in the future. Many decisions are based on assumptions about the future. To reduce uncertainty about the future, there are forecasts using quantitative methods. This paper analyzes these methods and how data mining, machine learning and IT solutions, including in called Analytics solutions, can contribute in this way. In addition to explaining the concepts that support it, it is explained how it is possible to apply it to predict the dropout of students for a faculty applying predictive models, through the classification task, using a machine learning tool that works in the cloud. Also invites you to think about the impact that these possibilities can have in the big data era.Para tomar decisiones es necesario entender la realidad de la organización y su contexto. No sólo en la actualidad, sino también en el futuro. Muchas decisiones se basan en supuestos sobre el futuro. Para reducir la incertidumbre sobre el futuro existen los pronósticos usando métodos cuantitativos. Este trabajo analiza estos métodos y como el data mining, machine learning y las soluciones informáticas, cobijadas en las soluciones denominadas “Analytics”, pueden aportar en dicho camino. Para ello más alla de explicar los conceptos que lo sustentan, se expone cómo es posible aplicarlo para pronosticar la deserción de estudiantes para una facultad a partir de aplicar modelos predictivos, mediante la tarea de clasificación, usando una herramienta de machine learning que funciona en la nube. También invita a pensar cual es el impacto, que pueden tener estas posibilidades en la era del big data

Reproducibility of an HPLC-ESI-MS/MS Method for the Measurement of Stable-Isotope Enrichment of in Vivo-Labeled Muscle ATP Synthase Beta Subunit

We sought to evaluate the reproducibility of a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based approach to measure the stable-isotope enrichment of in vivo-labeled muscle ATP synthase β subunit (β-F1-ATPase), a protein most directly involved in ATP production, and whose abundance is reduced under a variety of circumstances. Muscle was obtained from a rat infused with stable-isotope-labeled leucine. The muscle was homogenized, β-F1-ATPase immunoprecipitated, and the protein was resolved using 1D-SDS PAGE. Following trypsin digestion of the isolated protein, the resultant peptide mixtures were subjected to analysis by HPLC-ESI-MS/MS, which resulted in the detection of multiple β-F1-ATPase peptides. There were three β-F1-ATPase unique peptides with a leucine residue in the amino acid sequence, and which were detected with high intensity relative to other peptides and assigned with >95% probability to β-F1-ATPase. These peptides were specifically targeted for fragmentation to access their stable-isotope enrichment based on MS/MS peak areas calculated from extracted ion chromatographs for selected labeled and unlabeled fragment ions. Results showed best linearity (R2 = 0.99) in the detection of MS/MS peak areas for both labeled and unlabeled fragment ions, over a wide range of amounts of injected protein, specifically for the β-F1-ATPase134-143 peptide. Measured stable-isotope enrichment was highly reproducible for the β-F1-ATPase134-143 peptide (CV = 2.9%). Further, using mixtures of synthetic labeled and unlabeled peptides we determined that there is an excellent linear relationship (R2 = 0.99) between measured and predicted enrichment for percent enrichments ranging between 0.009% and 8.185% for the β-F1-ATPase134-143 peptide. The described approach provides a reliable approach to measure the stable-isotope enrichment of in-vivo-labeled muscle β-F1-ATPase based on the determination of the enrichment of the β-F1-ATPase134-143 peptide

Pronósticos y data mining para la toma de decisiones: pronóstico sobre la deserción de alumnos de una facultad

Para tomar decisiones es necesario entender la realidad de la organización y su contexto. No sólo en la actualidad, sino también en el futuro. Muchas decisiones se basan en supuestos sobre el futuro. Para reducir la incertidumbre sobre el futuro existen los pronósticos usando métodos cuantitativos. Este trabajo analiza estos métodos y como el data mining, machine learning y las soluciones informáticas, cobijadas en las soluciones denominadas “Analytics”, pueden aportar en dicho camino. Para ello más alla de explicar los conceptos que lo sustentan, se expone cómo es posible aplicarlo para pronosticar la deserción de estudiantes para una facultad a partir de aplicar modelos predictivos, mediante la tarea de clasificación, usando una herramienta de machine learning que funciona en la nube. También invita a pensar cual es el impacto, que pueden tener estas posibilidades en la era del big data

Las instituciones de educación superior y su rol en la era digital. La transformación digital de la universidad: ¿transformadas o transformadoras?

This article introduces us to the digital age, presenting evidence of the relevance and impact that it has in today’s society. We identify the main disruptive technologies that are formatting it and how is it that the models and processes of the organizations are being transformed generating deep, abrupt and at the same time ephemeral changes. From this assertion we analyze the challenges, risks, opportunities and guidelines that universities must face to address their own digital transformation. This transformation is understood to be inevitable and that it must be addressed without delay but with a critical vision and a clear understanding of the particularities of each institution. It also outlines some ideas about the role that higher education institutions could play to make contributions in building a healthier knowledge society, and what is the strategy that the Inter-American Organization for Higher Education (OUI-IOHE) is putting forth to act as facilitator of this change.Este artículo nos introduce en la era digital, tratando de evidenciar la relevancia e impacto que tiene en la sociedad contemporánea. Se identifican las principales tecnologías disruptivas que la están formateando y cómo, a partir de estas, se están transformando los modelos y procesos de las organizaciones, generando cambios profundos, abruptos y a la vez efímeros. Desde dicho lugar se analizan los desafíos de las universidades en esta era, sus riesgos, oportunidades y lineamientos para encarar la transformación digital de las mismas. Esta transformación se plantea como una necesidad que debe ser abordada sin dilaciones, pero con visión crítica y bajo las particularidades de cada institución. También se intentan esbozar algunas ideas sobre el rol con el que las instituciones de educación superior podrían contribuir para construir una sociedad del conocimiento más saludable, y cuál es la estrategia que está proponiendo la Organización Universitaria Interamericana (OUI) para actuar como facilitadora de este cambio

Sequential muscle biopsies during a 6-h tracer infusion do not affect human mixed muscle protein synthesis and muscle phenylalanine kinetics

Stable isotope tracer experiments of human muscle amino acid and protein kinetics often involve a sequential design, with the same subject studied at baseline and during an intervention. However, prolonged fasting and sequential muscle biopsies from the same area could theoretically affect muscle protein metabolism. The purpose of this study was to determine if sequential muscle biopsies and extended fasting significantly affect parameters of muscle protein and amino acid kinetics in six human subjects. After a 12-h overnight fast, a primed continuous infusion of l-[ring-2H5]phenylalanine was started. After 120 min, we took the first of a series of five hourly muscle biopsies from the same vastus lateralis to measure mixed muscle protein fractional synthetic rate. Furthermore, between 150–180, 210–240, and 330–360 min, we measured leg phenylalanine kinetics using the two-pool and the three-pool arteriovenous balance models. Tracer enrichments were at steady state, and muscle protein FSR and phenylalanine kinetics did not change throughout the experiment (P = not significant). We conclude that a 6-h tracer infusion during extended fasting (up to 18 h) with five sequential muscle biopsies from the same muscle do not affect basal mixed muscle protein synthesis and muscle phenylalanine kinetics in human subjects. Thus, when using a sequential study design over this period of time, it is unnecessary to include a saline only control group to account for these variables