Characterizing chemical exposure : focus on children's environment

Children are constantly exposed to chemicals in food, water, dust, air and consumer products. Compared to adults, children often have a higher exposure to chemicals due to physical and behavioural factors. Because of their unique exposure patterns, exposure assessments in adults are not directly transferrable to children. Therefore, there is a need for exposure assessments performed for children, with focus on the environments were children spend a large part of their time. The overall objectives of this thesis were to find an approach to overview existing exposure information and to generate new knowledge about chemical exposures in children. In addition, the thesis aims to identify and evaluate the importance of different exposure sources, such as foods, personal care products and indoor environments, for children’s chemical exposure. In study I, we developed an automatic classifier with the ability to retrieve and categorize published exposure information, based on data presented in scientific abstracts. In this study, a taxonomy for exposure information was created and nearly 3700 abstracts relevant for chemical exposure were manually annotated. Based on this annotated corpus, Natural Language Processing (NLP) techniques were used to extract semantic and syntactic features relevant for scientific texts on chemical exposure. Using these features, a supervised machine learning algorithm was trained to automatically classify abstracts according to the structure of the exposure taxonomy. The performance of the developed classifier was generally good and its applicability was demonstrated in several case studies. In conclusion, this automatic classifier has potential to constitute the foundation for a text mining tool to extract relevant exposure information from large amounts of text. In study II, we used a harmonized protocol to study the exposure to phthalates, BPA, parabens and triclosan in 98 Swedish mothers and their children (6-11 years old). Urine samples were collected and the mothers answered a questionnaire about their residential environment, sociodemographic factors, and the mother and child’s dietary habits and use of personal care products. Different foods were the main exposure determinants for most phthalates and BPA, whereas use of personal care products and cosmetics were the major determinants for the exposure of parabens and diethyl phthalate (DEP). Children had higher internal levels of most phthalates and BPA, than their mothers, reflecting their higher exposure to chemicals originating from foods and the indoor environment. The mothers had higher levels of parabens and DEP compared to the children. However, the levels were significantly correlated between the mothers and their children, indicating common exposure sources in the home environment. In study III and IV, we measured phthalates, non-phthalate plasticizers, bisphenols, brominated flame retardants (BFRs) and organophosphate esters (OPEs) in dust from 100 preschools. In addition, phthalate metabolites, bisphenols and one OPE were measured in urine from 113 children attending these preschools and BFRs and OPEs were measured in hand wipes from 100 children. The estimated intakes of individual chemicals via preschool dust were below available health based reference values. However, for some of these chemicals, reference values are either lacking or are uncertain, due to insufficient toxicity data. The levels of currently strictly regulated chemicals in dust were higher in older preschools, whereas the levels of chemicals now substituting these old ones were higher in newer preschools. Furthermore, the presence of certain products in the preschools was shown to have impact on the levels of chemicals in dust. For five out of eleven BFRs and OPEs significant correlations were found between preschool dust and children’s hand wipes. In addition, the levels of an OPE in urine and dust were significantly correlated. These results indicate that preschool dust may be an important source to children’s exposure of these compounds. Levels of phthalates and BPA in preschool dust were not significantly correlated to respective metabolites in urine and the relative contribution from dust to the total exposure of these compounds was low or moderate, indicating that other sources are more important

Identifying Patterns of Cancer Disease Mechanisms by Mining Alternative Representations of Genomic Alterations

Cancer is a complex disease driven by somatic genomic alterations (SGAs) that perturb signaling pathways and consequently cellular function. Identifying combinatorial patterns of pathway perturbations would provide insights into common disease mechanisms shared among tumors, which is important for guiding treatment and predicting outcome. However, identifying perturbed pathways is challenging, because different tumors can have the same perturbed pathways that are perturbed by different SGAs. We started off by designing a novel semantic representation that captures the functional similarity of distinct SGAs perturbing a common pathway in different tumors. This representation was used alongside the nested hierarchical Dirichlet process topic model in order to identify combinatorial patterns in altered signaling pathways. We found that the topic model was able to capture the functional relationships between topics. It was also able to identify cancer subtypes composed of tumors from different tissues of origin that exhibit different survival rates. These results led us to investigate the performance of the methodology on pan-cancer data, as well as in conjunction with cancer driver data. The results revealed that the framework was still able to identify clinically relevant features in pan-cancer. However, the addition of driver data decreased the noise in the data and improved the separation of tumors in the clustering results. This provided support for including the use of driver data in our methodology. In order to have gene representations independent of literature, we developed a biological representation that could identify functionally related genes. Its performance when used alongside topic modeling was tested. We found that the topic association patterns separated tumors by their tissue of origin. But, analyzing some of the cancer types on an individual basis still led to significant differences in survival. Our studies show the potential for using alternative representations in conjunction with topic modeling to investigate complex genomic diseases. With further research and refinement of this methodology, it has the potential to capture the relationship between pathways involved in cancer. This would contribute to a better understanding of cancer disease mechanisms and treatment

Systematic Analysis of the Factors Contributing to the Variation and Change of the Microbiome

abstract: Understanding changes and trends in biomedical knowledge is crucial for individuals, groups, and institutions as biomedicine improves people’s lives, supports national economies, and facilitates innovation. However, as knowledge changes what evidence illustrates knowledge changes? In the case of microbiome, a multi-dimensional concept from biomedicine, there are significant increases in publications, citations, funding, collaborations, and other explanatory variables or contextual factors. What is observed in the microbiome, or any historical evolution of a scientific field or scientific knowledge, is that these changes are related to changes in knowledge, but what is not understood is how to measure and track changes in knowledge. This investigation highlights how contextual factors from the language and social context of the microbiome are related to changes in the usage, meaning, and scientific knowledge on the microbiome. Two interconnected studies integrating qualitative and quantitative evidence examine the variation and change of the microbiome evidence are presented. First, the concepts microbiome, metagenome, and metabolome are compared to determine the boundaries of the microbiome concept in relation to other concepts where the conceptual boundaries have been cited as overlapping. A collection of publications for each concept or corpus is presented, with a focus on how to create, collect, curate, and analyze large data collections. This study concludes with suggestions on how to analyze biomedical concepts using a hybrid approach that combines results from the larger language context and individual words. Second, the results of a systematic review that describes the variation and change of microbiome research, funding, and knowledge are examined. A corpus of approximately 28,000 articles on the microbiome are characterized, and a spectrum of microbiome interpretations are suggested based on differences related to context. The collective results suggest the microbiome is a separate concept from the metagenome and metabolome, and the variation and change to the microbiome concept was influenced by contextual factors. These results provide insight into how concepts with extensive resources behave within biomedicine and suggest the microbiome is possibly representative of conceptual change or a preview of new dynamics within science that are expected in the future.Dissertation/ThesisDoctoral Dissertation Biology 201