Civilizing the tropics: The highlanders' failed attempt to transform the Colombian Amazon, 1850--1930

The subject of this dissertation is the nature of the environmental transformations, both symbolic and physical, that took place in Colombia between 1850 and 1930. This period begins with the attempt by the Colombian elite to leave behind colonial ties, overcome economic disorganization, and link Colombia to the international market. These efforts were part of a general project to “civilize” this tropical country. The period closes with the transition toward an industrialization and urbanization process led by the Colombian state during the 1930s. ^ Frequently, environmental studies as an academic field are dominated by biological concerns. However, most environmental thinking accepts their interdisciplinary nature. Under this framework not only spatial but also symbolic concerns are key elements in understanding environmental transformations. ^ This study finds that despite several attempts to transform the Colombian landscape physically, most of the substantive changes were localized and circumscribed to the Andean region. Other changes were mainly symbolic. This dissertation thus uses the Amazon as one of several regions that did not experience significant changes in the forest canopy. While highlanders originally dreamed of the Amazon as an untapped El Dorado, their failed attempts to exploit the region caused them to imagine it as a nightmarish “green hell”. ^ This dissertation concentrates on three pairs of concepts: tropicality/civilization, landscape/territory, and symbolic/material changes. It presents both a general vision of Colombia and case studies of three regions: Cundinamarca, and Cauca Valley are used to compare with the Amazon region that is developed at length. Whereas mainstream Colombian histories have either fixated on the Andean highlands or, in a relegated second place, on the Caribbean region, this dissertation attempts to significantly contribute to the historiography of Colombia by focusing on the largely neglected Amazonian region. ^ To understand imageries about Colombia's landscape, the dissertation relies on travel writings, chorographic descriptions and maps. It also makes uses legal documents and other published primary sources, including literary pieces and memoirs. ^

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^