A minimal version of a Protein Tyrosine Phosphatase

Phosphatase and tensin homologue (PTEN) protein belongs to the family of protein tyrosine phos-phatase. Mutations on the phosphatase and tensin homologue (PTEN) protein are highly observed in diverse types of human tumors, being mostly identified on the phosphatase domain of the protein. Although PTEN is a modular protein composed by a phosphatase domain and a C2 domain for mem-brane anchoring, this work aimed at developing a minimal version of PTEN´s phosphatase domain. The minimal version (Small Domain) comprises a 28 residue peptide, with the PTEN 8-mer catalytic peptide accommodated between a α-helix and β-turn as observed in PTEN native structure. Firstly, a de novo prediction of the Small Domain´s secondary structure was carried out by molecular modeling tools. The stability of the predicted structures were then evaluated by Molecular Dynamics. Automated molecular docking of PTEN natural substrate PIP3, its analogue (Inositol) and a PTEN inhibitor (L-tar-tare) were performed with the modeled structure, and PTEN used as a positive control. The gene en-coding for Small Domain was designed and cloned into an expression vector at N-terminal of Green Fluorescence Protein (GFP) encoding gene. The fusion protein was then expressed in Escherichia coli cells. Different expression conditions have been explored for the production of the fusion protein to minimize the formation of inclusion bodies.

Epidemic store for massive scale systems

The MAP-i doctoral program of the Universities of Minho, Aveiro and Porto

Grafting of adipic anhydride to carbon nanotubes through a Diels-Alder cycloaddition/oxidation cascade reaction

Accepted Manuscript

HEP-Frame: A software engineered framework to aid the development and efficient multicore execution of scientific code

This work was supported by FCT (Fundação para a Ciência e Tecnologia) within Project Scope (UID/CEC/00319/2013), by LIP (Laboratório de Instrumentação e Física Experimental de Partículas) and by Project Search-ON2 (NORTE-07-0162- FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework, through the European Regional Development Fund.

Monitoring and analysis of queries in distributed databases

Dissertação de Mestrado em Engenharia Informática

Ensino, aprendizagem e avaliação no Ensino Superior: Perspetivas e práticas

Tese de Doutoramento em Ciências da Educação - Especialidade de Desenvolvimento Curricular

Avaliação de um processo de aconselhamento na construção de carreira: estudo de caso

Dissertação de mestrado integrado em Psicologia

Construção de um manual de codificação e interpretação de memórias autobiográficas

Dissertação de mestrado integrado em Psicologia

The role of deviant-letter position in cognate word processing

Dissertação de mestrado integrado em Psicologia

Resolução da ambivalência em terapia narrativa

Dissertação de mestrado integrado em Psicologia

Momentos de inovação e mudança na terapia cognitivo comportamental para a bulimia nervosa

Dissertação de mestrado em Psicologia Aplicada

Restabelecimento da colaboração terapêutica num caso de sucesso seguido em Terapia Narrativa

Dissertação de mestrado integrado em Psicologia

Parallel improved Schnorr-Euchner enumeration SE++ for the CVP and SVP

The Closest Vector Problem (CVP) and the Shortest Vector Problem (SVP) are prime problems in lattice-based cryptanalysis, since they underpin the security of many lattice-based cryptosystems. Despite the importance of these problems, there are only a few CVP-solvers publicly available, and their scalability was never studied. This paper presents a scalable implementation of an enumeration-based CVP-solver for multi-cores, which can be easily adapted to solve the SVP. In particular, it achieves super-linear speedups in some instances on up to 8 cores and almost linear speedups on 16 cores when solving the CVP on a 50-dimensional lattice. Our results show that enumeration-based CVP-solvers can be parallelized as effectively as enumeration-based solvers for the SVP, based on a comparison with a state of the art SVP-solver. In addition, we show that we can optimize the SVP variant of our solver in such a way that it becomes 35%-60% faster than the fastest enumeration-based SVP-solver to date.

Um modelo para a gestão do conhecimento organizacional no contexto dos serviços partilhados com recurso à utilização do e-Learning

Tese de Doutoramento em Tecnologias e Sistemas de Informação

Self-narrative reconstruction after dilemma-focused therapy for depression: a comparison of good and poor outcome cases

Objective: The aim of this study is to improve the understanding of self-changes after an intervention for depression focused on implicative dilemmas, a type of cognitive conflict related to identity. As recent research has highlighted the relevance of identity-related dilemmas in clients with depression, we sought to assess the way in which clients resolve such inner conflicts after a tailored dilemma-focused intervention and how this is reflected in the clients’ self-narratives. Method: We used three instruments to observe differences between good (n = 5) and poor (n = 5) outcome cases: (i) the Repertory Grid Technique to track the resolution of dilemmas, (ii) the Change Interview to compile clients’ accounts of changes at posttreatment, and (iii) the Innovative Moments Coding System to examine the emergence of clients’ novelties at the Change Interview. Results: Groups did not differ in terms of the number and relevance of client-identified significantly helpful events. However, between-group differences were found for the resolution of dilemmas and for the proportion of high-level innovative moment (IM) types. Furthermore, a greater self-narrative reconstruction was associated with higher levels of symptom improvement. Conclusions: Good outcome cases seem to be associated with the resolution of conflicts and high-level IMs.