The role of IFNγ in higher brain function: in health and under chronic stress

Tese de Doutoramento em Ciências da Saúde

Structural and enzymatic characterization of ABgp46, a novel phage endolysin with broad anti-Gram-negative bacterial activity

The present study demonstrates the antibacterial potential of a phage endolysin against Gram-negative pathogens, particularly against multidrug resistant strains of Acinetobacter baumannii. We have cloned, heterologously expressed and characterized a novel endolysin (ABgp46) from Acinetobacter phage vb_AbaP_CEB1 and tested its antibacterial activity against several multidrug-resistant A. baumannii strains. LC-MS revealed that ABgp46 is an N-acetylmuramidase, that is also active over a broad pH range (4.0-10.0) and temperatures up to 50°C. Interestingly, ABgp46 has intrinsic and specific anti-A. baumannii activity, reducing multidrug resistant strains by up to 2 logs within 2 hours. By combining ABgp46 with several organic acids that act as outer membrane permeabilizing agents, it is possible to increase and broaden antibacterial activity to include other Gram-negative bacterial pathogens. In the presence of citric and malic acid, ABgp46 reduces A. baumannii below the detection limit (> 5 log) and more than 4 logs P. aeruginosa and Salmonella Typhimurium strains. Overall, this globular endolysin exhibits a broad and high activity against Gram-negative pathogens, that can be enhanced in presence of citric and malic acid, and be used in human and veterinary medicine.

Hierarchical modelling of timber reference properties using probabilistic methods: Maximum Likelihood Method, Bayesian Methods and Probability Networks

In recent decades, an increased interest has been evidenced in the research on multi-scale hierarchical modelling in the field of mechanics, and also in the field of wood products and timber engineering. One of the main motivations for hierar-chical modelling is to understand how properties, composition and structure at lower scale levels may influence and be used to predict the material properties on a macroscopic and structural engineering scale. This chapter presents the applicability of statistic and probabilistic methods, such as the Maximum Likelihood method and Bayesian methods, in the representation of timber’s mechanical properties and its inference accounting to prior information obtained in different importance scales. These methods allow to analyse distinct timber’s reference properties, such as density, bending stiffness and strength, and hierarchically consider information obtained through different non, semi or destructive tests. The basis and fundaments of the methods are described and also recommendations and limitations are discussed. The methods may be used in several contexts, however require an expert’s knowledge to assess the correct statistic fitting and define the correlation arrangement between properties.