Structure-function relationships in a glycosyltransferase, a phosphatase and an oxidoreductase

Dissertation presented to obtain the Ph.D degree in Biochemistry

The recognition of work

The following article argues that recognition structures in work relations differ significantly in the sphere of paid work in contrast to unpaid work in private spheres. According to the systematic approach on recognition of Axel Honneth three different levels of recognition are identified: the interpersonal recognition, organisational recognition and societal recognition. Based on this framework it can be stated that recognition structures in the sphere of paid work and in private spheres differ very much. Whereas recognition in private spheres depends very much on personal relations, thus on the interpersonal level, recognition in employment relationships can be moreover built on organisational structures. Comparing recognition structures in both fields it becomes apparent, that recognition in field of employment can be characterised as much more concrete, comparable and measurable. Therefore, it can be concluded that the structural differences of recognition contribute to the high societal and individual importance of employment in contrast to unpaid work in private spheres.

Analysis and recognition of similar environmental sounds

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Informática

Molecular modelling of ABC transporters: from ATP hydrolysis to substrate transport

Dissertação para a obtenção de grau de doutor em Bioquímica pelo Instituto de Tecnologia Química e Biológica da Universidade Nova de Lisboa

Automatic musical instrument recognition for multimedia indexing

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Functional and therapeutical implications of ligand recognition by the scavenger-like lymphocyte receptors CD5 and CD6

Dissertação para obtenção do Grau de Mestre em Genética Molecular e Biomedicina

Ditopic molecular architectures for the recognition of anionic species

Dissertation presented to obtain the Ph.D degree in Chemistry

Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Human activity recognition for an intelligent knee orthosis

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Intention recognition, commitment and their roles in the evolution of cooperation

Dissertação para obtenção do Grau de Doutor em Informática

Bacterial peptidoglycan biosynthesis and recognition by the innate immune system

Dissertation presented to obtain the Ph.D degree in Biology

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.

Coupling organic substrate removal to methane production in a fully biological microbial electrolysis cell

Microbial electrolysis cells (MECs) are an innovative and emerging technique based on the use of solid-state electrodes to stimulate microbial metabolism for wastewater treatment and simultaneous production of value-added compounds (such as methane). This research studied the performance of a two-chamber MEC in terms of organic matter oxidation (at the anode) and methane production (at the cathode). MEC‟s anode had been previously inoculated with an activated sludge, whereas the cathode chamber inoculum was an anaerobic sludge (containing methanogenic microorganisms). During the experimentation, the bioanode was continuously fed with synthetic solutions in anaerobic basal medium, at an organic load rate (OLR) of around 1 g L-1 d-1, referred to the chemical oxygen demand (COD). At the beginning (Run I), the feeding solution contained acetate and subsequently (Run II) it was replaced with a more complex solution containing soluble organic compounds other than acetate. For both conditions, the anode potential was controlled at -0.1 V vs. standard hydrogen electrode, by means of a potentiostat. During Run I, over 80% of the influent acetate was anaerobically oxidized at the anode, and the resulting electric current was recovered as methane at the cathode (with a cathode capture efficiency, CCE, accounting around 115 %). The average energy efficiency of the system (i.e., the energy captured into methane relative to the electrical energy input) under these conditions was over 170%. However, reactor‟s performance decreased over time during this run. Throughout Run II, a substrate oxidation over 60% (on COD basis) was observed. The electric current produced (57% of coulombic efficiency) was also recovered as methane, with a CCE of 90%. For this run the MEC‟s average energy efficiency accounted for almost 170 %. During all the experimentation, a very low biomass growth was observed at the anode whereas ammonium was transferred through the cationic membrane and concentrated at the cathode. Tracer experiments and scanning electron microscopy analyses were also carried out to gain a deeper insight into the reactor performance and also to investigate the possible reasons for partial loss of performance. In conclusion, this research suggests the great potential of MEC to successfully treat low-strength wastewaters, with high energy efficiency and very low sludge production.