Returning to the original vocation of the philosophical task and to that defined by the phenomenology of Husserl, we propose to renew the approach towards the experience of falsehood starting from the Greco-Roman to restore a structure to thought and life based on the consideration of a series of «spiritual exercises» and Husserlian phenomenology to go « back to the 'things themselves». This philosophical-phenomenological project seeks to make us understand that in the face of falsehood, a rigorous thought, devoid of prejudices, can be envisaged by suggesting a tension towards a style of «philosophical life», which is consistent with our relationship to others and in our way of life, that is, from an ethical-phenomenological tension capable of placing the body as a psychoanalytic object par excellence according to Sartre’s assertion. Resumen: Retomando la idea de Husserl de ver la filosofía como una vocación de ver las cosas como ""ellas mismas son"", el trabajo de tesis se propone replantear el concepto de ""mentira"" y brindar un acercamiento filosófico a la experiencia de la mentira a partir de la época grecorromana y hasta nuestra época para ofrecer una estructura novedosa al planteamiento del tema y a la vivencia de la mentira basada en la consideración de la puesta en práctica de una serie de « ejercicios espirituales» y de fenomenología husserliana para volver « a las ""cosas mismas"", ofreciendo una alternativa de pensamiento dirigida hacia un estilo de «vida filosófica», que sea coherente con nuestra relación con los demás y con nuestro modo de vida, es decir, desde una tensión ético-fenomenológica capaz de situar al cuerpo como objeto psicoanalítico por excelencia según la afirmación de Sartre.

Abstract: The conditional autoregressive model (CAR model) is the most popular distribution for jointly modeling the \textit{a priori} uncertainty over spatially correlated data. In general, it is used in hierarchical spatial models where it models the uncertainty about random spatial effects. A limitation of the CAR model is its inability to produce high correlations between neighboring areas. We propose a robust model for area data that alleviates this problem. We represent the map by an undirected graph where nodes represent areas and edges connect neighboring nodes on the map. We assign distinct and random weights to the edges. The model is based on a spatially structured $t-$Student multivariate distribution, in which the precision matrix is indirectly constructed assuming a multivariate distribution for the random weights of the edges. Such t- Student distribution spatially correlates the edge weights and induces another $t$-Student model for the spatial effects of the areas that correlates them and is able to accommodate outliers and heavy tail behavior for these effects. More importantly, the proposed model can produce a higher marginal correlation between spatial effects than the CAR model, overcoming one of the main limitations of this model. We adjusted the proposed model to map the incidence of some types of cancer in southern Brazil and compared its performance with several alternative models proposed in the literature. The results show that the proposed model is competitive and provides similar and, in some cases, better results than those obtained by fitting models commonly used to analyze this type of data. In the second proposal, we approach the problem of dimensionality reduction in regression models. One of the most used methods to avoid overfitting and to select relevant variables in regression models with many predictors is the penalized regression technique. Under suchapproaches, variable selection is performed in a non-probabilistic way using some optimization criterion. Bayesian approaches to penalized regression have been proposed assuming an \textit{a priori} distribution for the regression coefficients that plays a role similar to the penalty term in classical statistics: compressing towards zero non-significant coefficients and putting a probability mass significant in coefficients that can be grouped. Generally, such a priori distributions, called shrinkage priors, assume independence between the effects of the covariates, which may not bean appropriate assumption in many cases. In this work, we focus on the dimensionality reduction of categorical variables with many levels. These variables are included in the model through variables dummy inducing sparsity in the design matrix, which can generate overfitting and difficulties in interpreting the results. The effect of the levels of these categorical variables are naturally correlated. To deal with this problem, we propose two a priori shrinkage distributions for the coefficients associated with the levels of categorical variables, correlating them. The proposed distributions are properand, in addition to sparsity, they have the property of grouping similar effects. We illustrate the use of these distributions by applying them to dimensionality reduction in a linear regression. Their performances are analyzed and compared to pre-existing methods through simulated data studies and considering housing price data available on Airbnb. Resumen: El modelo autorregresivo condicional (modelo CAR) es la distribución más popular para modelar conjuntamente la incertidumbre \ textit {apriori} sobre datos correlacionados espacialmente. En general, se utiliza en modelos espaciales jerárquicos donde modela la incertidumbre sobre los efectos espaciales aleatorios. Una limitación del modelo CAR es su incapacidad para producir altas correlaciones entre áreas vecinas. Proponemos un modelo robusto para datos de área que alivia este problema. Representamos el mapa mediante un gráfico no dirigido donde los nodos representan áreas y los bordes conectan los nodos vecinos en el mapa. Asignamos pesos distintos y aleatorios a los bordes. El modelo se basa en una distribución multivariante $ t- $ Student estructurada espacialmente, en la que la matriz de precisión se construye indirectamente asumiendo una distribución multivariante para los pesos aleatorios de los bordes. Tal distribución t-Student correlaciona espacialmente los pesos de los bordes e induce otro modelo $ t $ -Student para los efectos espaciales de las áreas que los correlaciona y es capaz de acomodar valores atípicos y un comportamiento de cola pesada para estos efectos. Más importante aún, el modelo propuesto puede producir una mayor correlación marginal entre los efectos espaciales que el modelo CAR, superando una de las principales limitaciones de este modelo. Ajustamos el modelo propuesto para mapear la incidencia de algunos tipos de cáncer en el sur de Brasil y comparamos su desempeño con varios modelos alternativos propuestos en la literatura. Los resultados muestran que el modelo propuesto es competitivo y proporciona resultados similares y, en algunos casos, mejores que los obtenidos mediante el ajuste de modelos comúnmente utilizados para analizar este tipo de datos. En la segunda propuesta abordamos el problema de la reducción de dimensionalidad en modelos de regresión. Uno de los métodos más utilizados para evitar el sobreajuste y seleccionar variables relevantes en modelos de regresión con muchos predictores es la técnica de regresión penalizada. Bajo tales enfoques, la selección de variables se realiza de forma no probabilística utilizando algún criterio de optimización. Se han propuesto enfoques bayesianos para la regresión penalizada asumiendo una distribución \ textit {a priori} para los coeficientes de regresión que juega un papel similar al término de penalización en la estadística clásica: comprimir hacia cero coeficientes no significativos y poner una masa de probabilidad significativa en coeficientes que se pueden agrupar. Generalmente, tales distribuciones a priori, llamadas a priori de contracción, asumen independencia entre los efectos de las covariables, lo que puede no ser una suposición apropiada en muchos casos. En este trabajo, nos enfocamos en la reducción de dimensionalidad de variables categóricas con muchos niveles. Estas variables se incluyen en el modelo a través de variables ficticias que inducen escasez en la matriz de diseño, lo que puede generar sobreajustes y dificultades en la interpretación de los resultados. El efecto de los niveles de estas variables categóricas está naturalmente correlacionado. Para abordar este problema, proponemos dos distribuciones de contracción a priori para los coeficientes asociados a los niveles de variables categóricas, correlacionándolos. Las distribuciones propuestas son adecuadas y, además de escasa, tienen la propiedad de agrupar efectos similares. Ilustramos el uso de estas distribuciones aplicándolas a la reducción de dimensionalidad en una regresión lineal. Sus resultados se analizan y comparan con métodos preexistentes a través de estudios de datos simulados y teniendo en cuenta los datos de precios de la vivienda disponibles en Airbnb.

Abstract: Chain elongation fermentation in open cultures systems can tolerate complex organic waste and convert it into useful biochemicals (commodities) such as caproic acid. In previous chain elongation research, fatty alcohols such as hexanol have been also documented. Hexanol has a potential application as biofuel, is less corrosive, watersoluble, and toxic than caproic acid. Moreover, the hexanol market size seems more attractive than the one of caproic acid. This research aims to produce hexanol via a chain elongation process in an upflow anaerobic sludge blanket (UASB) system. Granular sludge formation was also considered due to its advantages such as high biomass concentration and settleability. The highest selectivity (based on total carbon products in the effluent) and productivity of caproate was 82.49 % on day 35 and 10.25 g L-1 d -1 on day 33, whereas for hexanol was 1.66 % and 0.19 g L-1 d -1 , both on day 77. CO2 supply had a positive effect on chain elongation activity and hexanol formation. Selectivity and productivity of hexanol was not negatively affected despite methane formation. Throughout the whole study, suspended biomass was the dominant form. A thick biofilm layer on the inner wall of the reactor was present from day 19 to 76 and black aggregates became more visible from day 76 onwards, although not significantly. The process was severely inhibited by substrate (ethanol) toxicity during the last phase (36.77 ± 3.23 g L-1 ).

Resumen: Esta tesis consta de dos capítulos. El primero es una reflexión teórica sobre la relación entre los estudios feministas y el campo de la investigación en salud mental de los refugiados. En él, sostengo que los enfoques feministas pueden contribuir a la investigación sobre la salud mental de los refugiados cuestionando algunos de sus límites y supuestos epistemológicos, haciendo visibles a las comunidades marginadas y amplificando sus voces, y reconociendo las implicaciones sociales y políticas de este tipo de investigación. Este esfuerzo pretende alinear las perspectivas de la academia y el activismo de una manera que resulte relevante para los sujetos implicados. El segundo artículo es un trabajo de investigación empírica que se basa en un estudio cualitativo realizado con nueve mujeres solicitantes de asilo de América Latina en el que se adoptó un enfoque feminista. Contribuye a la investigación actual sobre las mujeres refugiadas al ocuparse de la narración de la experiencia vivida por las mujeres colombianas y venezolanas llegadas a Quebec como de asilo en términos de su corporeidad. A partir de entrevistas en profundidad con estas mujeres, sostengo que la salud mental y el bienestar no son incorpóreos, sino que están anclados en roles de género, prácticas, rituales y normas sociales que tienen una dimensión corporal y son culturalmente específicos. Considerar el lugar del cuerpo en la experiencia de la migración implica examinar sus dimensiones sensoriales, táctiles, somáticas y quinestésicas. De esta manera, esta investigación ofrece nuevas posibilidades para un análisis crítico de las intersecciones entre género, bienestar emocional y asilo. Abstract: This thesis consists of two chapters. The first paper is a theoretical reflection about the relationship between feminist scholarship and the field of refugee mental health research. Here, I argue that feminist approaches can contribute to refugee mental health research by questioning some of its epistemological boundaries and assumptions, by rendering marginalized communities visible and amplifying their voices, and by recognizing the social and political implications of this type of research, in an attempt to align academia and activism in ways that are relevant for the implicated subjects. The second paper is an empirical research paper drawing from a qualitative study conducted with nine women asylum seekers from Latin America in which a feminist approach undertaken. It contributes to the current research on refugee women by examining the narration of the lived experience of Colombian and Venezuelan women arrived in Quebec as asylum claimants in terms of their corporeality. Drawing from indepth interviews with these women, I argue that mental health and wellbeing are not disembodied, but rather anchored in gender roles, practices, rituals, and norms that have a corporeal dimension and are culturally specific. Considering the place of the body in the experience of migration implies examining its sensorial, tactile, somatic and kinesthetic dimensions. This brings about new possibilities for a critical analysis of the intersections between gender, wellbeing and asylum.

Abstract: Because there is nothing intelligent to say about a massacre. Everybody is supposed to be dead, to never say anything or want anything ever again. Everything is supposed to be very quiet after a massacre, and it always is, except for the birds.” All wars are atrocious, and the task of incorporating such inglorious events into a nation’s collective identity becomes understandably hard. But it needs to be done, otherwise, the memory of the war might be trivialized and forgotten and, hence, are more prone to be repeated. War is bound to replicate itself without confrontation and awareness. Mosul city was established on the western bank of the Tigris River in the 6th Century, just across the water from the ruins of the Assyrian city of Nineveh. The city flourished during the ancient era establishing itself as an epicentre of the Islamic world, therefore it is rich with ancient ruins and cultural heritage. The war which erupted in Iraq didn’t aim to destroy only its defence, but also its identity. Entire urban blocks of Mosul’s city were damaged so severely, that when it comes to reconstruction it cannot simply restore the former fabrics, either of space or architecture. The war has changed everything, and most critically the structure of society itself. The spatial reconstruction will mostly require more than just physical rehabilitation and restoration of the cities devastated infrastructure and housing. In order to provide sensations for the need of diversity and unity, more urgent concern should be given to restoring public facilities and daily utilities. Architectural and landscape design can be judiciously used in healing the social structure. How can common spaces be rearranged to create conditions for a peaceful and creative coexistence among citizens which survived such horrors? How can the location and the design of such utilities be done in a way as to meet the needs for intimacy and peace? This thesis study intends to recommend an urban and architectural regeneration project, to be taken upon during the redevelopment of the old city of Mosul. Furthermore, a set of strategies that may ensure the preservation of the urban identity of the area, while redeveloping and coping with the environmental, socioeconomic and political changes have been identified for reclaiming the urban identity and promoting the sense of belonging.

Abstract: Background: Seasonal Influenza is considered to be a cyclic and ordered sequence of values, influenced by external factors that can be predicted and used to detect disease outbreaks and monitoring. In machine learning, the key challenges that limit these analyses are in model explainability and limitations associated with ecological bias. Aim: Determine the best environmental variable selection method to predict Seasonal Influenza in Norway, using an environmental medicine approach combined with machine learning techniques. Methods: This is a quasi-experimental study that compares three approaches (non-variable selection, isolate component, and multipollutant mixture), represented in five methods (univariable, bivariable, multivariable AME, multivariable PCA, multivariable LDA). Per method, the best co-variable combination will be performed, following the internal rules of each method. The best covariable combination is the result of three components: variable selection, validation data set and lag. The first one involves 13 environmental variables (temperature, relative humidity, specific humidity, air pressure, wind speed, precipitation, CO, NO, NO2, O3, PM10, PM2,5 and SO2); second one compares a test dataset compiled from the data from 2019, last year (2018) and a synthetic environmental (avg. 2013-2018) data set in the validation process; and the third one compares a combination of lag from 0 to 12. All the predictions are made using ARIMA algorithm. The evaluation is given in terms of MAE, MSE, RMSE, OR. The training set is from 2 Jun 2013 (week 22/2013) to 28 May 2018 (week 21/2018), and test set is from (week 22/2018) to (week 21/2019) with a window of predictions of 52 weeks. Results: The increment of dimensionality in the environmental variable selection introduce different noise levels and optimize the prediction. Considerations that impact the explainability, usability, ecological bias and performance will be described. Conclusion: The increment of dimensionality in the variable selection has a better impact on performance than using complex algorithms.

Abstract: Campylobacter infections are the main bacterial cause of gastroenteritis in the UK, causing an estimated 500 thousand cases per year. Health authorities investigate outbreaks to identify the source, control the spread and understand the cause. Outbreak detection mechanisms are potentially improved by the increasing availability of whole-genome sequence alongside other epidemiological data. However, techniques mixing genomics and other epidemiological factors are still underdeveloped. This project aims to develop and apply outbreak detection methods using surveillance data collected from two regions in the UK. The approaches proposed in this thesis are based on an existing spatialtemporal Bayesian hierarchical model, where cases are labelled as potential outbreaks if they comprise an elevated number of cases compared to the expected sporadic count. The model is adjusted to include genetic data using Gaussian random fields, exploiting the capacity of whole-genome sequencing to discriminate closely related isolates. Moreover, a Markov Chain Monte Carlo algorithm is implemented to obtain the posterior distribution of the model parameters. In particular, a sampling strategy is proposed to improve the convergence of the chain for the parameters describing the Gaussian random field. The project dataset is analysed using a spatial-temporal, a spatial-genetic and a temporalgenetic version of the model, where each version explores different types of outbreaks. The proposed approach demonstrates how to organise genetic sequences into a highdimensional structure and incorporate them into a Bayesian framework. Also, the MCMC sampling algorithm improves the mixing of the chain to estimate the posterior distribution of the model parameters. Finally, all model versions provide the probability that each reported infection is part of a potential outbreak. Comparing the potential outbreaks found by each model provides insights to estimate the real outbreaks. It also identifies cases that are potentially part of a diffuse real outbreak hard to detect by existing approaches. Despite the capability of the model, it requires predefined outbreak sizes and therefore is not flexible at capturing many shapes. Autocorrelated models are a potential improvement to be explored. Resumen: Las infecciones por Campylobacter son la principal causa bacteriana de gastroenteritis en el Reino Unido, provocando un estimado de 500 mil casos por año. Las autoridades sanitarias investigan los brotes para identificar la fuente, controlar la propagación y comprender la causa. Los mecanismos de detección de brotes se mejoran potencialmente con la creciente disponibilidad de datos del genoma completo de las bacterias junto con otros datos epidemiológicos. Sin embargo, las técnicas que combinan genética y otros factores epidemiológicos aún están poco desarrolladas. Este proyecto tiene como objetivo desarrollar y aplicar métodos de detección de brotes utilizando datos de vigilancia recopilados en dos regiones del Reino Unido. Los enfoques propuestos en esta tesis se basan en un modelo jerárquico bayesiano espacio-temporal existente, donde los casos se etiquetan como brotes potenciales si presentan un número elevado de casos mayor al recuento esporádico esperado. El modelo se ajusta para incluir datos genéticos utilizando campos aleatorios Gaussianos, aprovechando la capacidad del genoma completo para discriminar casos estrechamente relacionados. Además, se implementa usando algoritmos de Markov Chain Monte Carlo para obtener la distribución posterior de los parámetros del modelo. En particular, se propone una estrategia de muestreo para mejorar la convergencia de la cadena de Markov para los parámetros del campo Gaussiano. El conjunto de datos del proyecto se analiza utilizando una versión espacial temporal, una espacial-genética y una temporal-genética del modelo, donde cada versión explora diferentes tipos de brotes. El enfoque propuesto demuestra cómo organizar secuencias genéticas en una estructura de múltiples dimensiones e incorporarlas en un marco bayesiano. Además, el algoritmo de muestreo MCMC mejora la mezcla de la cadena para estimar la distribución posterior de los parámetros del modelo. Por último, todas las versiones del modelo estiman la probabilidad de que cada infección sea parte de un posible brote. La comparación entre los posibles brotes encontrados por cada modelo proporciona información para estimar cuáles son los brotes reales. También identifica casos que son potencialmente parte de un brote real difícil de detectar por los enfoques existentes. A pesar de las múltiples ventajas del modelo, este requiere tamaños de brotes predefinidos y, por lo tanto, no es flexible para capturar brotes de formas irregulares. Los modelos autocorrelacionados proveen una mejora potencial para el modelo que debe explorarse.

Abstract: Ethical innovation in the skincare industry has been mainly concerned with biodegradable packaging concerns and so-called green formulations. This interpretation of what ethics means is a response to the current dominant discourses of sustainability, in which nature is seen as a perfect and idyllic set of goods and resources that has to be managed in the same way a bank account is administered. This journal attempts to invite skincare consumers to create other ways of relating to nature and sustainability, by re-thinking the meanings of these concepts.To help consumers rethink their position within nature in the skincare industry, the sense of touch and its capacity to confer understanding and raise consciousness is introduced as an ethical strategy for doing so.

Abstract: Biological and physiological changes in response to stress are crucial to achieving adaption to environmental constraints. Typical stress responses activate the sympathetic nervous system and the hypothalamic-pituitary-adrenal cortical axis, which aim to maintain homeostasis in response to demands and influence various biological responses at neuroendocrine, cellular, and immune levels. In particular, there is compelling evidence showing the devastating effects of high-stress levels on immune responses and brain functionality. In that sense, data suggest that a sustained stress-physiological response predicts decreases in immunity and leads to allostatic load, the wear, and tear of biological systems. Similarly, in cancer research, chronic stress levels have been reported to impair the bodysurveillance systems and be related to cancer incidence, progression, and mortality. Despite the evidence demonstrating the associations between stress and cancer, there are limited data on whether lifetime stress exposure relates to clinical indicators of cancer aggressiveness. Considering the evidence showing the adverse effects of prolonged stress exposure on cancer disease, this thesis aimed to explore the relationship between lifetime stress exposure and the clinical characteristics of tumor-aggressiveness in a cohort of patients affected with triple-negative breast cancer (TNBC). Likewise, as research has shown that coping responses moderate the stress-physiological response, we aimed to investigate the role of coping responses in moderating the association between tumor aggressiveness and lifetime stress exposure. According to the scientific literature, high-stress levels would increase physiological arousal and allostatic load favoring a debilitated immune system. Thus, we hypothesized finding positive associations between lifetime stress exposure and tumor-aggressiveness. In the same line, we suspected that adaptative coping styles would be negatively related to tumoraggressiveness. The participants of this study were 29 women diagnosed with TNBC who were treated in the University Hospital for Gynecology Pius-Hospital (Oldenburg, Germany). Correlation and moderation regression analyses with the use of composite scores were conducted. Overall, we could not demonstrate significant associations between lifetime stress exposure and coping responses on the clinical indicators of tumor aggressiveness in this cohort of patients. Possible explanations for these results are discussed, and suggestions for future research are provided. This study was the first to investigate the effect of lifetime stress exposure and coping response on the aggressive tumor characteristics of TNBC. We provided a deep theoretical framework to understand the interplay between biological and psychological aspects of cancer disease, and we hope that our recommendations may serve as a base for upcoming studies.

Abstract: Most socioeconomic studies on illicit economies have been approached through the lens of war economics, hence, a wide variety of them are rooted in the greed and grievance literature. On contrast, recent studies (Eventon & Gutierrez, 2020, p. 10) (Gutierrez, 2020) (International Journal of Drug Policy, 2021) have found that illicit economies, particularly on illicit crops, arise as a source of livelihoods for impoverished farmers that provides opportunities to enhance agricultural practices and local development such as infrastructure, land access, access to credit, and supplies for cropping coca, also for other licit products and services. In addition, its origin is not only the result of poverty or "lack of development", but of a modernisation process in which coca has emerged as an "autonomous form of development." (Gootenberg, 2021). The aim of this study which dialogues with these new analytical frames, is measuring the influence that subnational inequality measures have in explaining the variation of coca area –among municipalities identified as coca leaf producers— and contrasting them with municipal poverty measures. I will argue that coca crops expansion adapts to different land regimes, but particularly in uneven distribution of land, where coca interacts with other conditions of economic precarity (Rigg, et al., 2016). Based on a longitudinal analysis of 217 Colombian municipalities, I contrast the effect of Gini of land owning and the Dependency on National Transfers on coca area from 2014 and 2018. The results feeds into and contributes to an Agrarian Change perspective to explain the influence of structures and mechanisms that shape rural change (Cramer & Richards, 2011); and a subnational analysis to make visible “within-country heterogeneity” of the association coca and land inequality. Resumen: La mayoría de los estudios socioeconómicos sobre las economías ilícitas se han abordado a través de los lentes de la economía de la guerra, por lo que una gran variedad de ellos están arraigados en la literatura de la codicia y el agravio. Por el contrario, estudios recientes (Eventon & Gutierrez, 2020, p. 10) (Gutierrez, 2020) (International Journal of Drug Policy, 2021) han encontrado que las economías ilícitas, en particular sobre los cultivos ilícitos, surgen como una fuente de sustento para los agricultores empobrecidos que proporciona oportunidades para mejorar sus prácticas agrícolas y el desarrollo local, tales como la infraestructura, el acceso a la tierra, el acceso al crédito y los suministros para el cultivo de coca, también para otros productos y servicios lícitos. Además, su origen no es sólo el resultado de la pobreza o la "falta de desarrollo", sino de un proceso de modernización en el que la coca ha surgido como una "forma autónoma de desarrollo". (Gootenberg, 2021). El objetivo de este estudio, que dialoga con estos nuevos marcos analíticos, es medir la influencia que tienen las medidas de desigualdad subnacional en la explicación de la variación de la superficie de coca -entre los municipios identificados como productores de hoja de coca- y contrastarlas con las medidas de pobreza municipal. Argumentaré que la expansión de los cultivos de coca se adapta a diferentes regímenes de tierra, pero particularmente en la distribución desigual de la tierra, donde la coca interactúa con otras condiciones de precariedad económica (Rigg, et al., 2016 ). A partir de un análisis longitudinal de 217 municipios colombianos, contrasto el efecto del Gini de la propiedad de la tierra y la Dependencia de las Transferencias Nacionales sobre la superficie de coca de 2014 y 2018. Los resultados alimentan y contribuyen a una perspectiva de Cambio Agrario para explicar la influencia de las estructuras y mecanismos que moldean el cambio rural (Cramer & Richards, 2011); y un análisis subnacional para hacer visible la "heterogeneidad dentro del país" de la asociación coca y desigualdad de la tierra.