A detecção remota e os SIG na produção animal - análise da contribuição e situação actual

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Freshwater arsenic detoxification through selenium-enriched food supplements. A proteomic approach

Dissertação apresentada para a obtenção do grau de doutor em Bioquímica pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Genetic characterization of Portuguese Fasciola hepatica isolates

Dissertation presented to obtain the Master Degree in Molecular, Genetics and Biomedicine

Gastrointestinal symbionts of chimpanzees in Cantanhez National Park, Guinea-Bissau with respect to habitat fragmentation

One of the major factors threatening chimpanzees (Pan troglodytes verus) in Guinea-Bissau is habitat fragmentation. Such fragmentation may cause changes in symbiont dynamics resulting in increased susceptibility to infection, changes in host specificity and virulence. We monitored gastrointestinal symbiotic fauna of three chimpanzee subpopulations living within Cantanhez National Park (CNP) in Guinea Bissau in the areas with different levels of anthropogenic fragmentation. Using standard coproscopical methods (merthiolate-iodine formalin concentration and Sheather's flotation) we examined 102 fecal samples and identified at least 13 different symbiotic genera (Troglodytella abrassarti, Troglocorys cava, Blastocystis spp., Entamoeba spp., Iodamoeba butschlii, Giardia intestinalis, Chilomastix mesnili, Bertiella sp., Probstmayria gombensis, unidentified strongylids, Strongyloides stercoralis, Strongyloides fuelleborni, and Trichuris sp.). The symbiotic fauna of the CNP chimpanzees is comparable to that reported for other wild chimpanzee populations, although CNP chimpanzees have a higher prevalence of Trichuris sp. Symbiont richness was higher in chimpanzee subpopulations living in fragmented forests compared to the community inhabiting continuous forest area. We reported significantly higher prevalence of G. intestinalis in chimpanzees from fragmented areas, which could be attributed to increased contact with humans and livestock.

Quantification and characterization of the phenolic compounds of kenaf leaves

Dissertation for the Master Degree in Technology and Food Security

O contrato de Project Finance

Currently, Angola portrays a notorious economic growth and due to recent innovative legislations, it has become the major investment attracting pole, especially in Sub-Saharan Africa, having, thus, an extraordinary potentiality for a rapid and sustainable development, likely to place her in outstanding positions in the world economic ranking. Yet, such economic growth entails demanding levels of intensive investment in infrastructure, what has been reported of the Angolan Government to be unable to respond to, save if recurring to very high index of external debt, poisoning, in this way, the future budgeting of the country. Due to these infrastructure investment shortages, the cost of production remains highly onerous and the cost of life extremely unaffordable. On this account, the current study disserts about the contract of Project Finance; an alternative finance resource given as a viable solution for the private financing of infrastructure, aiming to demonstrate that such contractual figure, likewise the experience of several emerging economies and others, is a contract bid framework to take into account in today’s world. It refers to a financing technique – through which the Government may satisfy a common need (for example, the construction of a public domain or public servicing), without having to pay neither offer any collateral – based on a complex legal-financial engineering, arranged throughout a coalition of typical and atypical agreements, whereby it is mandatory to look back at the basic concepts of corporate law. More than just a simple financial study, the dissertation at stake analyses the nature and legal framework of Project Finance, which is a legally atypical and innominate contract, concluding that there is a relevant need for regulating and devoting a special legal regime in the Angolan jurisdiction for this promising legal form in the contemporary corporate finance world.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.