Models and inference for population dynamics

En el presente trabajo abordamos el problema de la modelización e inferencia de la dinámica de las poblaciones de bacterias. Dado que las mediciones del crecimiento de bacterias en platillos de Petri, pueden fácilmente replicarse bajo las mismas condiciones experimentales, el estudio se centra en los casos donde los datos presentan una estructura jerárquica. El crecimiento de bacterias está muy influido por las condiciones ambientales, por ejemplo niveles de sal, temperatura o acidez y la relación de estos factores con el crecimiento es muy compleja. Por ello, en experimentos bajo distintas condiciones, es fundamental buscar modelos flexibles para relacionar el crecimiento con tales condiciones. En esta tesis, presentamos como objetivo desarrollar modelos predictivos capaces de combinar toda la información disponible, como por ejemplo la repetición de los experimentos, con el fin de lograr predicciones más precisas. Por otra parte, se propone también desarrollar un modelo mas general para el crecimiento aplicable a una gran variedad de microorganismos y bajo un gran número de combinaciones de las condiciones ambientales y ecológicas. Con estos objetivos en mente, proponemos el uso de modelos jerárquicos cuando se observan multiples curvas de crecimiento. De esta manera, la estimación de una única curva es mejorada a través de la información que brindan el resto de las curvas de crecimiento observadas. Adicionalmente, proponemos también el uso de técnicas no paramétricas para modelizar los procesos de crecimiento, sin necesidad de asumir que las poblaciones se comportan según cierta función paramétrica. En particular, utilizamos redes neuronales ya que tienen una gran capacidad de describir el comportamiento de modelos complejos y no lineales. Los procesos de crecimiento pueden presentar ciertas fluctuaciones estocásticas que no se deben a errores de medición. Los modelos que simplemente adicionan un error a una función determinística no son capaces de capturar la variabilidad total de estos procesos. En consecuencia, hemos desarrollado un modelo estocástico que presenta dos características deseables: las trayectorias de crecimiento son no-decrecientes y la función de medias del proceso es proporcional a la función de Gompertz de crecimiento. Finalmente, en este trabajo también se aborda el problema de la estimación de los modelos, para lo cual hemos preferido utilizar inferencia bayesiana ya que, entre otra cosas, brinda un enfoque unificado al tratar con diversos tipos de modelos, como por ejemplo, jerárquicos y redes neuronales. Por otra parte, la inferencia Bayesiana nos permite diferenciar entre distintas fuentes de incertidumbre a través del uso de distribuciones a priori jerárquicas. Asi mismo, permite la incorporación de información previa, ampliamante disponible en ciencias como la microbiologíaIn this dissertation we study the problem of modeling and inference for the dynamics of bacterial populations. Bacterial growth data taken from Petri-dish experiments is easily replicated. Moreover, external factors such as temperature, salinity or acidity of the environment are known to influence bacterial growth and therefore, experiments are often undertaken under a variety of conditions. This implies that often, bacterial growth data present a multilevel structure. The first issue that we wish to to address in this thesis is how to analyze data from multiple experiments in this context. The aim of our study is to develop a predictive model able to combine all available information, such as replicated experiments, in order to get more accurate predictions. Additionally, we wish to develop a more general model for microbial growth for a variety of organism types and under a larger number of combinations of environmental and ecological variables. To accomplish this challenges, we propose the use of hierarchical models when multiple growth curve data are observed. In this way, it is possible to improve the estimation of a single growth curve by incorporating information from the other bacterial growth curves. Additionally, we propose the use of non-parametric techniques to model the growth process, where it is not assumed that the population fits any parameterized model. In particular, we shall introduce models based on neural networks which can be used to fit very complex relationships. A growth process may display some stochastic fluctuations which are not due to measurement errors. Models which simply add an error to a deterministic function cannot necessarily capture the total variability of the growth process. Therefore, it is also important to consider fully stochastic models. Another objective of this thesis is to provide a new, stochastic growth curve model of this type. In general, in the literature on growth curve modeling, most work has been carried out using weighted least squares techniques and other classical approaches. However, the Bayesian approach brings a unified approach to the handling of complex models, such as hierarchical models and neural networks and allows us to differentiate, through the use of hierarchical prior distributions, between various sources of variability, which is an important issue in predictive microbiology. Furthermore, the Bayesian approach permits the incorporation of prior information which is abundant in experimental sciences. One of the main difficulties with the Bayesian approach for practical purposes is that often, complex algorithms have to be devised for the implementation of these techniques, which is a disadvantage to non specialists. Therefore, a further objective of this thesis is to show that Bayesian inference can be implemented for many of the models proposed using a relatively simple algorithm based on a generally available free software package which can be used without the need to fine tune special samplers. In summary, this thesis aims to provide a statistical framework for the analysis of bacterial growth processes. Modeling and prediction play a key role in the field of microbiology as a valuable tool for making recommendations on food safety and human health and hence, improvements in the methods available are of interest. The rest of the thesis is structured as follows. In Chapter 1, we present a brief description of the main population growth models, focusing in the advantages and disadvantages of each one. Then we show that, given a single sample of growth curve data from one of these models, it is straightforward to implement both classical and Bayesian inference for these models. We concentrate on the Bayesian approach which is growing in interest because of its capability to incorporate information from a variety of widely available sources such as laboratory experiments, field measurements and expert judgements and for the possibility to distinguish formally between different sources of uncertainty. In particular, we show that the free software package WinBUGS can be used to implement Bayesian inference for simple bacterial growth models. In Chapter 2, we consider the case when various replications of Petri dish experiments under identical conditions are observed. In such cases, we would expect the individual growth curves to be similar and this suggests the use of hierarchical models to capture the relationship between the diffeerent growth curves. As in Chapter 1, we illustrate that the hierarchical model we use, based on the well known Gompertz curve, can be fitted using WinBUGS. In Chapter 3, we then consider the case of Petri dish experiments under different environmental conditions. The relationship between the growth curve parameters and the environmental factors is complex, and here we consider the use of neural networks to model this relationship. Two basic models are considered. Firstly, we introduce a neural network based secondary model which is based on a Gompertz curve where the parameters of the growth curve are modeled as a function of the environmental factors. Secondly, we consider the direct modeling of the growth curve using neural networks. As previously, inference is carried out using a Bayesian approach implemented via WinBUGS. These first three chapters demonstrate that WinBUGS can be a powerful and fleexible tool able to handle very complex models. We show that in practice, it is relatively straightforward to implement complex models in WinBUGS which allows microbiological researchers to conduct Bayesian inference in a simple way, without the necessity to design complex MCMC algorithms and instead to concentrate on the model building aspects of the problem. In the first three chapters, we concentrate on models in discrete time which have the restriction that, for example they may be difiult to implement if data are observed at irregular time intervals. In contrast, in Chapter 4 we develop a new, continuous time, stochastic growth curve model. We show by means of simulations that our proposed model has the potential to capture the the variability observed in replications of the same experiment under identical conditions. Also, we illustrate that by modifying the parameter values, different shaped growth curves can be generated. Finally, we introduce two approaches to Bayesian inference for our model. Firstly, in a simple case of the model, we introduce a Gibbs sampling algorithm and secondly, for the full model, we consider the use of an approximate Bayesian computing algorithm

Determinación de la actividad antagonista de microorganismos ambientales frente a Escherichia coli ATCC 25922

Las enfermedades transmitidas por alimentos (ETA) son causantes de problemas en salud pública, de la población peruana y otros países. Los patotipos de Escherichia coli pertenecen a los grupos de patógenos causantes de estas infecciones. En el tratamiento contra estos agentes infecciosos se utilizan antibióticos; sin embargo, debido al incremento de la resistencia bacteriana, estos se han vuelto ineficaces. En la búsqueda de nuevas alternativas de tratamiento, el presente trabajo tiene como objetivo determinar la actividad antagonista de microorganismos ambientales contra Escherichia coli ATCC 25922. Para este estudio, la actividad antimicrobiana de 33 cepas de Bacillus y 32 cepas de levaduras fueron evaluadas mediante el método de difusión en pozos y el método de inóculo en superficie (spot technique), respectivamente. Las levaduras no mostraron actividad antagonista mientras que las cepas de Bacillus BS3, BS4, BS17 y BS21 presentaron dicha actividad. La cepa BS4 mostró un mayor tamaño en el diámetro de los halos de inhibición con valores de 27.00 ± 2.65 mm y 15.67 ± 1.15 cuando se utilizó el cultivo bacteriano (CB) y el sobrenadante libre de células (SLC), respectivamente. Además, la actividad antagonista se mantuvo constante a las 24, 48 y 72 horas de incubación. De acuerdo a los resultados obtenidos, se concluye que el uso del cultivo bacteriano evidenció mayor actividad antagonista en comparación al sobrenadante libre de células. Los resultados sugieren que los metabolitos secretados por estas cepas de Bacillus tendrían potencial antimicrobiano contra Escherichia coli ATCC 25922.Perú. Universidad Nacional Mayor de San Marcos. Vicerrectorado de Investigación y Posgrado. Programa de Promoción de Trabajo de Investigación para obtener el grado académico de Bachiller. Código: B20100240

A subordinated stochastic process model

We introduce a new stochastic model for non-decreasing processes which can be used to include stochastic variability into any deterministic growth function via subordination. This model is useful in many applications such as growth curves (children’s height, fish length, diameter of trees, etc.) and degradation processes (crack size, wheel degradation, laser light, etc.). One advantage of our approach is the ability to easily deal with data that are irregularly spaced in time or different curves that are observed at different moments of time. With the use of simulations and applications, we examine two approaches to Bayesian inference for our model: the first based on a Gibbs sampler and the second based on approximate Bayesian computation (ABC)

Bayesian hierarchical modelling of bacteria growth

Bacterial growth models are commonly used in food safety. Such models permit the prediction of microbial safety and the shelf life of perishable foods. In this paper, we study the problem of modelling bacterial growth when we observe multiple experimental results under identical environmental conditions. We develop a hierarchical version of the Gompertz equation to take into account the possibility of replicated experiments and we show how it can be fitted using a fully Bayesian approach. This approach is illustrated using experimental data from Listeria monocytogenes growth and the results are compared with alternative models. Model selection is undertaken throughout using an appropriate version of the deviance information criterion and the posterior predictive loss criterion. Models are fitted using WinBUGS via R2WinBUGS

Current Debates on the Migrant Vote in the City of Buenos Aires

El objetivo del presente artículo de investigación1 es abordar los debates actuales en torno a la extensión de los derechos políticos de las personas migrantes en la Ciudad Autónoma de Buenos Aires. Desde 2017, como parte del largo proceso de autonomización de la ciudad iniciado con la reforma de la Constitución Nacional en 1994, se está debatiendo un nuevo Código Electoral en la Comisión de Asuntos Constitucionales de la Legislatura Porteña. Sin duda, se trata de una oportunidad única para promover cambios vitales en las leyes que regulan y limitan fuertemente el voto migrante en este distrito y avanzar así en el reconocimiento efectivo de los derechos políticos del 13.5% de la población que reside en la ciudad y ha sido sistemáticamente silenciado y sub-representado.The objective of this article is to examine the current debates regarding the extension of political rights of migrant people in the Autonomous City of Buenos Aires. As part of a long process for the city to become autonomous that began with the 1994 national constitutional reform, a new Electoral Code is under debate since 2017 within the City Legislature’s Commission on Constitutional Matters. Without doubt, it is a unique opportunity to promote vital changes in the laws that firmly regulate and limit the migrant vote within the district, and in that way advance the effective recognition of the political rights of 13.5% of the population that resides in the city and has been systematically silenced and under-represented.Fil: Penchaszadeh, Ana Paula. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Sociales. Instituto de Investigaciones "Gino Germani"; ArgentinaFil: Rivadeneyra Palacios, Lourdes. Red de Migrantes y Refugiadxs de la Argentina; Argentina. Central de los Trabajadores de la Argentina; Argentin

Bayesian modeling of bacterial growth for multiple populations

Bacterial growth models are commonly used for the prediction of microbial safety and the shelf life of perishable foods. Growth is affected by several environmental factors such as temperature, acidity level and salt concentration. In this study, we develop two models to describe bacterial growth for multiple populations under both equal and different environmental conditions. Firstly, a semi-parametric model based on the Gompertz equation is proposed. Assuming that the parameters of the Gompertz equation may vary in relation to the running conditions under which the experiment is performed, we use feed forward neural networks to model the influence of these environmental factors on the growth parameters. Secondly, we propose a more general model which does not assume any underlying parametric form for the growth function. Thus, we consider a neural network as a primary growth model which includes the influencing environmental factors as inputs to the network. One of the main disadvantages of neural networks models is that they are often very difficult to tune which complicates fitting procedures. Here, we show that a simple, Bayesian approach to fitting these models can be implemented via the software package WinBugs. Our approach is illustrated using real experimental Listeria Monocytogenes growth data

Bayesian modelling of bacterial growth for multiple populations

Bacterial growth models are commonly used for the prediction of microbial safety and the shelf life of perishable foods. Growth is affected by several environmental factors such as temperature, acidity level and salt concentration. In this study, we develop two models to describe bacterial growth for multiple populations under both equal and different environmental conditions. Firstly, a semi-parametric model based on the Gompertz equation is proposed. Assuming that the parameters of the Gompertz equation may vary in relation to the running conditions under which the experiment is performed, we use feed forward neural networks to model the influence of these environmental factors on the growth parameters. Secondly, we propose a more general model which does not assume any underlying parametric form for the growth function. Thus, we consider a neural network as a primary growth model which includes the influencing environmental factors as inputs to the network. One of the main disadvantages of neural networks models is that they are often very difficult to tune which complicates fitting procedures. Here, we show that a simple, Bayesian approach to fitting these models can be implemented via the software package WinBugs. Our approach is illustrated using real experimental Listeria Monocytogenes growth data

Depression, anxiety and delinquency: Results from the Pittsburgh Youth Study

Purpose: The main aim of this research is to investigate to what extent within-individual changes in anxiety and depression are related to within-individual changes in theft and violence. Methods: The youngest sample of the Pittsburgh Youth Study (PYS), a prospective longitudinal survey of 503 boys followed up from age 7 onwards, was analyzed. Depression and anxiety were measured for boys from ages 11 to 22 as were moderate and serious forms of self-reported theft and violence. A hierarchical linear random effects model was used to investigate anxiety and depression as potential causes or outcomes of these forms of delinquency. Results: The results showed that the between-individual correlations were consistently higher than the corresponding within-individual correlations, and provided little evidence to discern the directionality of the potential relationships between depression, anxiety and delinquency. Using a random effects approach, there was limited evidence that prior depression or anxiety was related to later offending, but there was evidence that offending (particularly theft and serious violence) was associated with later increases in anxiety, and to a lesser extent depression. Conclusions: This study indicates that depression and anxiety were outcomes of offending. If replicated, this would suggest that evidence-based interventions which reduced offending would have a desirable influence in reducing depression and anxiety. However, interventions for depression should still form part of responsive interventions. More research which explores within-individual changes in longitudinal studies with repeated measures is needed

Actas del Seminario Internacional Transformaciones Territoriales y la Actividad Agropecuaria : Tendencias globales y emergentes locales (La Plata, 2016)

Esta publicación recapitula los trabajos presentados en el Seminario internacional Transformaciones territoriales y la actividad agropecuaria: Tendencias globales y emergentes locales,en mayo de 2016. Del encuentro participaron equipos de investigación de la UNLP, pertenecientes a la Facultad de Humanidades y Ciencias de la Educación y a la Facultad de Ciencias Agrarias y Forestales, y equipos de la Universidad Federal de Viços, Brasil. Las contribuciones abordan discusiones teórico-metodológicas y estudios de caso de actividades agropecuarias y transformaciones territoriales en espacios rurales.Facultad de Humanidades y Ciencias de la Educació

Actas del Seminario Internacional Transformaciones Territoriales y la Actividad Agropecuaria : Tendencias globales y emergentes locales (La Plata, 2016)

Esta publicación recapitula los trabajos presentados en el Seminario internacional Transformaciones territoriales y la actividad agropecuaria: Tendencias globales y emergentes locales,en mayo de 2016. Del encuentro participaron equipos de investigación de la UNLP, pertenecientes a la Facultad de Humanidades y Ciencias de la Educación y a la Facultad de Ciencias Agrarias y Forestales, y equipos de la Universidad Federal de Viços, Brasil. Las contribuciones abordan discusiones teórico-metodológicas y estudios de caso de actividades agropecuarias y transformaciones territoriales en espacios rurales.Facultad de Humanidades y Ciencias de la Educació