Population-Wide Emergence of Antiviral Resistance during Pandemic Influenza

Background: The emergence of neuraminidase inhibitor resistance has raised concerns about the prudent use of antiviral drugs in response to the next influenza pandemic. While resistant strains may initially emerge with compromised viral fitness, mutations that largely compensate for this impaired fitness can arise. Understanding the extent to which these mutations affect the spread of disease in the population can have important implications for developing pandemic plans. Methodology/Principal Findings: By employing a deterministic mathematical model, we investigate possible scenarios for the emergence of population-wide resistance in the presence of antiviral drugs. The results show that if the treatment level (the fraction of clinical infections which receives treatment) is maintained constant during the course of the outbreak, there is an optimal level that minimizes the final size of the pandemic. However, aggressive treatment above the optimal level can substantially promote the spread of highly transmissible resistant mutants and increase the total number of infections. We demonstrate that resistant outbreaks can occur more readily when the spread of disease is further delayed by applying other curtailing measures, even if treatment levels are kept modest. However, by changing treatment levels over the course of the pandemic, it is possible to reduce the final size of the pandemic below the minimum achieved at the optimal constant level. This reduction can occur with low treatment levels during the early stages of the pandemic, followed by a sharp increase in drug-use before the virus becomes widely spread. Conclusions/Significance: Our findings suggest that an adaptive antiviral strategy with conservative initial treatment levels, followed by a timely increase in the scale of drug-use, can minimize the final size of a pandemic while preventing large outbreaks of resistant infections

Minería de datos para el descubrimiento de patrones en enfermedades respiratorias en Bogotá, Colombia

Trabajo de InvestigaciónEl presente proyecto se basa en la aplicación de minería de datos mediante el algoritmo de clustering K- means que permita la generación de un modelo descriptivo con el análisis de los datos y con el objetivo de identificar posibles comportamientos en enfermedades respiratorias en la ciudad de Bogotá. El conjunto de clústeres generados por la herramienta RapidMiner es la recopilación de datos de un periodo de cinco años de 2012 a 2016, en donde se contemplan el número de casos asociados a 184 diagnósticos de enfermedades respiratorias y la edad de los pacientes corresponde de 0 a 5 años.Trabajo de Investigación1. GENERALIDADES 2. OBJETIVOS 3. JUSTIFICACIÓN 4. DELIMITACIÓN 5. MARCO REFERENCIAL 6. METODOLOGÍA 7. FUENTES DE EXTRACCIÓN Y SUS VARIABLES 8. DISEÑO 9. SELECCIÓN DE ALGORITMOS DE CLUSTERING 10. RECONOCER PATRONES A PARTIR DE LA INFORMACIÓN RECOPILADA 11. CONCLUSIONES 12. TRABAJOS FUTUROS 13. REFERENCIAS BIBLIOGRÁFICAS 14. ANEXOSPregradoIngeniero de Sistema

Computational proteomics with Jupyter and Python

Proteomics based on mass spectrometry produces complex data in large quantities. The need for flexible computational pipelines, in the context of big data, in proteomics and other areas of science, has prompted the development of computational platforms and libraries that facilitate data analysis and data processing. In this respect, Python appears to be one of the winners among programming languages in terms of popularity and development. This chapter shows how to perform basic tasks using Python and dedicated libraries in a Jupyter framework: from basic search result summarizations to the creation of MS1 chromatograms

Metabolomics Data Processing Using OpenMS

This chapter describes the open-source tool suite OpenMS. OpenMS contains more than 180 tools which can be combined to build complex and flexible data-processing workflows. The broad range of functionality and the interoperability of these tools enable complex, complete, and reproducible data analysis workflows in computational proteomics and metabolomics. We introduce the key concepts of OpenMS and illustrate its capabilities with a complete workflow for the analysis of untargeted metabolomics data, including metabolite quantification and identification

OpenMS: a flexible open-source software platform for mass spectrometry data analysis

High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease