Liquidity, business cycles and monetary policy: A simulation

A Masters Thesis, presented as part of the requirements for the award of a Research Masters Degree in Economics from NOVA – School of Business and Economics

A nanopore-based stochastic detection method: Single molecule characterisation of nanoparticles using ()- hemolysin

Dissertation presented to obtain the Ph.D degree in Chemistry.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Angiotensin II receptor blockade: is there truly a benefit of adding an ACE inhibitor?

We assessed the blockade of the renin-angiotensin system (RAS) achieved with 2 angiotensin (Ang) antagonists given either alone at different doses or with an ACE inhibitor. First, 20 normotensive subjects were randomly assigned to 100 mg OD losartan (LOS) or 80 mg OD telmisartan (TEL) for 1 week; during another week, the same doses of LOS and TEL were combined with 20 mg OD lisinopril. Then, 10 subjects were randomly assigned to 200 mg OD LOS and 160 mg OD TEL for 1 week and 100 mg BID LOS and 80 mg BID TEL during the second week. Blockade of the RAS was evaluated with the inhibition of the pressor effect of exogenous Ang I, an ex vivo receptor assay, and the changes in plasma Ang II. Trough blood pressure response to Ang I was blocked by 35+/-16% (mean+/-SD) with 100 mg OD LOS and by 36+/-13% with 80 mg OD TEL. When combined with lisinopril, blockade was 76+/-7% with LOS and 79+/-9% with TEL. With 200 mg OD LOS, trough blockade was 54+/-14%, but with 100 mg BID it increased to 77+/-8% (P<0.01). Telmisartan (160 mg OD and 80 mg BID) produced a comparable effect. Thus, at their maximal recommended doses, neither LOS nor TEL blocks the RAS for 24 hours; hence, the addition of an ACE inhibitor provides an additional blockade. A 24-hour blockade can be achieved with an angiotensin antagonist alone, provided higher doses or a BID regimen is used.