Integration of geochemical and biological modifications in mining areas: waters, sediments and diatoms

The development of mining activities over thousands of years in the region of Aljustrel is nowadays visible as a vast area of ore tailings, slag and host rocks of sulphides mineralization. The generation of acidic waters by the alteration of pyritic minerals - Acid Mine Drainage (AMD) - causes a significant impact on the river system both in the south of the village (Rib ª. Água Forte) and in the north of it (Rib ª. Água Azeda and Barranco do Farrobo), which is reflected in extremely low pH values (< 3) and high concentrations of As, Cd, Cu, Fe, Mn, Pb, Zn and sulphates. This study aimed to assess the environmental impacts extent, integrating geochemical (surface waters and stream sediments) and biological (diatoms) parameters. Three groups of sites were defined, based on sediments and water analysis, which integration with diatom data showed the same association of groups: Group 1- impacted, with acidic pH (1.9-5.1), high metal contents (0.4-1975 mg L-1) and Fe-Mg-sulphate waters, being metals more bioavailable in waters in cationic form (Me2+); mineralogically the sediments were characterized by phyllosilicates and sulphates/oxy-hydroxysulphate phases, easily solubilized, retaining a high amount of metals when precipitated; dominant taxon was Pinnularia aljustrelica (a new species); Group 2- slightly impacted, weak acid to neutral pH (5.0-6.8), metal contents not so high (0.2-25 mg L-1) and Fe-Mg-sulphate to Mg-chloride waters; dominant taxa were Brachysira neglectissima and Achnanthidium minutissimum; Group 3- unimpacted, alkaline pH (7.0-8.4), low metal contents (0-7 mg L-1) with Mg-chloride waters. In this group, metals were associated to the primary phases (e.g. sulphides), not so easily available; the existence of high chloride contents explained the presence of typical taxa of brackish/marine (e.g. Entomoneis paludosa) waters. Taxonomical aspects of the diatoms were studied (discovery of a new species: Pinnularia aljustrelica Luis, Almeida et Ector sp. nov.), as well as morphometric (size decrease of diatoms valves, as well as the appearance of deformed valves of Eunotia exigua in Group 1 and A. minutissimum in Group 2) and physiological (effective to assess the effects of metals/acidity in the photosynthetic efficiency through PAM Fluorometry) aspects. A study was carried out in an artificial river system (microcosm) that aimed to mimic Aljustrel’s extreme conditions in controlled laboratory conditions. The chronic effects of Fe, SO42- and acidity in field biofilms, inoculated in the artificial rivers, were evaluated as well as their contribution to the communities’ tolerance to metal toxicity, through acute tests with two metals (Cu and Zn). In general, the effects caused by low pH values and high concentrations of Fe and SO42- were reflected at the community level by the decrease in diversity, the predominance of acidophilic species, the decrease in photosynthetic efficiency and the increase of enzymatic (e.g. catalase, superoxide dismutase) and non-enzymatic activities (e.g. total glutathione and total phytochelatins). However, it was possible to verify that acidity performed a protective effect in the communities, upon Cu and Zn addition. A comparative study between Aljustrel mining area and New Brunswick mining area was carried out, both with similar mining and geological conditions, reflected in similar diatom communities in both mines, but in very different geographic and climatic areas.

Mining biomedical information from scientific literature

The rapid evolution and proliferation of a world-wide computerized network, the Internet, resulted in an overwhelming and constantly growing amount of publicly available data and information, a fact that was also verified in biomedicine. However, the lack of structure of textual data inhibits its direct processing by computational solutions. Information extraction is the task of text mining that intends to automatically collect information from unstructured text data sources. The goal of the work described in this thesis was to build innovative solutions for biomedical information extraction from scientific literature, through the development of simple software artifacts for developers and biocurators, delivering more accurate, usable and faster results. We started by tackling named entity recognition - a crucial initial task - with the development of Gimli, a machine-learning-based solution that follows an incremental approach to optimize extracted linguistic characteristics for each concept type. Afterwards, Totum was built to harmonize concept names provided by heterogeneous systems, delivering a robust solution with improved performance results. Such approach takes advantage of heterogenous corpora to deliver cross-corpus harmonization that is not constrained to specific characteristics. Since previous solutions do not provide links to knowledge bases, Neji was built to streamline the development of complex and custom solutions for biomedical concept name recognition and normalization. This was achieved through a modular and flexible framework focused on speed and performance, integrating a large amount of processing modules optimized for the biomedical domain. To offer on-demand heterogenous biomedical concept identification, we developed BeCAS, a web application, service and widget. We also tackled relation mining by developing TrigNER, a machine-learning-based solution for biomedical event trigger recognition, which applies an automatic algorithm to obtain the best linguistic features and model parameters for each event type. Finally, in order to assist biocurators, Egas was developed to support rapid, interactive and real-time collaborative curation of biomedical documents, through manual and automatic in-line annotation of concepts and relations. Overall, the research work presented in this thesis contributed to a more accurate update of current biomedical knowledge bases, towards improved hypothesis generation and knowledge discovery.