Synechocystis ferredoxin/ferredoxin-NADP+-reductase/NADP+ complex: Structural model obtained by NMR-restrained docking

FEBS Letters 579 (2005) 4585–4590

A further investigation of the cytochrome b5–cytochrome c complex

J Biol Inorg Chem (2003) 8: 777–786

Fractional dynamics in financial indexes

The goal of this study is the analysis of the dynamical properties of financial data series from 32 worldwide stock market indices during the period 2000–2009 at a daily time horizon. Stock market indices are examples of complex interacting systems for which a huge amount of data exists. The methods and algorithms that have been explored for the description of physical phenomena become an effective background in the analysis of economical data. In this perspective are applied the classical concepts of signal analysis, Fourier transform and methods of fractional calculus. The results reveal classification patterns typical of fractional dynamical systems.

Analysis of financial data series using fractional Fourier transform and multidimensional scaling

The goal of this study is the analysis of the dynamical properties of financial data series from worldwide stock market indexes during the period 2000–2009. We analyze, under a regional criterium, ten main indexes at a daily time horizon. The methods and algorithms that have been explored for the description of dynamical phenomena become an effective background in the analysis of economical data. We start by applying the classical concepts of signal analysis, fractional Fourier transform, and methods of fractional calculus. In a second phase we adopt the multidimensional scaling approach. Stock market indexes are examples of complex interacting systems for which a huge amount of data exists. Therefore, these indexes, viewed from a different perspectives, lead to new classification patterns.

Molybdenum-containing proteins from Sulphate Reducing Bacteria: studying proteins with novel cofactors and revealing new features of old enzymes

Dissertação apresentada para a obtenção do Grau de Doutor em Bioquímica, especialidade de Bioquímica-Física pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

A sensor classification strategy for robotic manipulators

In practice the robotic manipulators present some degree of unwanted vibrations. The advent of lightweight arm manipulators, mainly in the aerospace industry, where weight is an important issue, leads to the problem of intense vibrations. On the other hand, robots interacting with the environment often generate impacts that propagate through the mechanical structure and produce also vibrations. In order to analyze these phenomena a robot signal acquisition system was developed. The manipulator motion produces vibrations, either from the structural modes or from endeffector impacts. The instrumentation system acquires signals from several sensors that capture the joint positions, mass accelerations, forces and moments, and electrical currents in the motors. Afterwards, an analysis package, running off-line, reads the data recorded by the acquisition system and extracts the signal characteristics. Due to the multiplicity of sensors, the data obtained can be redundant because the same type of information may be seen by two or more sensors. Because of the price of the sensors, this aspect can be considered in order to reduce the cost of the system. On the other hand, the placement of the sensors is an important issue in order to obtain the suitable signals of the vibration phenomenon. Moreover, the study of these issues can help in the design optimization of the acquisition system. In this line of thought a sensor classification scheme is presented. Several authors have addressed the subject of the sensor classification scheme. White (White, 1987) presents a flexible and comprehensive categorizing scheme that is useful for describing and comparing sensors. The author organizes the sensors according to several aspects: measurands, technological aspects, detection means, conversion phenomena, sensor materials and fields of application. Michahelles and Schiele (Michahelles & Schiele, 2003) systematize the use of sensor technology. They identified several dimensions of sensing that represent the sensing goals for physical interaction. A conceptual framework is introduced that allows categorizing existing sensors and evaluates their utility in various applications. This framework not only guides application designers for choosing meaningful sensor subsets, but also can inspire new systems and leads to the evaluation of existing applications. Today’s technology offers a wide variety of sensors. In order to use all the data from the diversity of sensors a framework of integration is needed. Sensor fusion, fuzzy logic, and neural networks are often mentioned when dealing with problem of combing information from several sensors to get a more general picture of a given situation. The study of data fusion has been receiving considerable attention (Esteban et al., 2005; Luo & Kay, 1990). A survey of the state of the art in sensor fusion for robotics can be found in (Hackett & Shah, 1990). Henderson and Shilcrat (Henderson & Shilcrat, 1984) introduced the concept of logic sensor that defines an abstract specification of the sensors to integrate in a multisensor system. The recent developments of micro electro mechanical sensors (MEMS) with unwired communication capabilities allow a sensor network with interesting capacity. This technology was applied in several applications (Arampatzis & Manesis, 2005), including robotics. Cheekiralla and Engels (Cheekiralla & Engels, 2005) propose a classification of the unwired sensor networks according to its functionalities and properties. This paper presents a development of a sensor classification scheme based on the frequency spectrum of the signals and on a statistical metrics. Bearing these ideas in mind, this paper is organized as follows. Section 2 describes briefly the robotic system enhanced with the instrumentation setup. Section 3 presents the experimental results. Finally, section 4 draws the main conclusions and points out future work.

Living bacteria rheology: Population growth, aggregation patterns, and collective behaviour under diferente shear flows

The activity of growing living bacteria was investigated using real-time and in situ rheology-in stationary and oscillatory shear. Two different strains of the human pathogen Staphylococcus aureus-strain COL and its isogenic cell wall autolysis mutant, RUSAL9-were considered in this work. For low bacteria density, strain COL forms small clusters, while the mutant, presenting deficient cell separation, forms irregular larger aggregates. In the early stages of growth, when subjected to a stationary shear, the viscosity of the cultures of both strains increases with the population of cells. As the bacteria reach the exponential phase of growth, the viscosity of the cultures of the two strains follows different and rich behaviors, with no counterpart in the optical density or in the population's colony-forming units measurements. While the viscosity of strain COL culture keeps increasing during the exponential phase and returns close to its initial value for the late phase of growth, where the population stabilizes, the viscosity of the mutant strain culture decreases steeply, still in the exponential phase, remains constant for some time, and increases again, reaching a constant plateau at a maximum value for the late phase of growth. These complex viscoelastic behaviors, which were observed to be shear-stress-dependent, are a consequence of two coupled effects: the cell density continuous increase and its changing interacting properties. The viscous and elastic moduli of strain COL culture, obtained with oscillatory shear, exhibit power-law behaviors whose exponents are dependent on the bacteria growth stage. The viscous and elastic moduli of the mutant culture have complex behaviors, emerging from the different relaxation times that are associated with the large molecules of the medium and the self-organized structures of bacteria. Nevertheless, these behaviors reflect the bacteria growth stage.

Avoiding healthy cells extinction in a cancer model

We consider a dynamical model of cancer growth including three interacting cell populations of tumor cells, healthy host cells and immune effector cells. For certain parameter choice, the dynamical system displays chaotic motion and by decreasing the response of the immune system to the tumor cells, a boundary crisis leading to transient chaotic dynamics is observed. This means that the system behaves chaotically for a finite amount of time until the unavoidable extinction of the healthy and immune cell populations occurs. Our main goal here is to apply a control method to avoid extinction. For that purpose, we apply the partial control method, which aims to control transient chaotic dynamics in the presence of external disturbances. As a result, we have succeeded to avoid the uncontrolled growth of tumor cells and the extinction of healthy tissue. The possibility of using this method compared to the frequently used therapies is discussed. (C) 2014 Elsevier Ltd. All rights reserved.

Bio-inspired anatomy for autonomous DPWS-compliant automation components

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

Particle swarm design of digital circuits

Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge. Particle swarm optimization (PSO) is a form of SI, and a population-based search algorithm that is initialized with a population of random solutions, called particles. These particles are flying through hyperspace and have two essential reasoning capabilities: their memory of their own best position and knowledge of the swarm's best position. In a PSO scheme each particle flies through the search space with a velocity that is adjusted dynamically according with its historical behavior. Therefore, the particles have a tendency to fly towards the best search area along the search process. This work proposes a PSO based algorithm for logic circuit synthesis. The results show the statistical characteristics of this algorithm with respect to number of generations required to achieve the solutions. It is also presented a comparison with other two Evolutionary Algorithms, namely Genetic and Memetic Algorithms.

Learning with tangible interfaces

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

Interoperability on e-learning 2.0: the PEACE case study

The confluence of education with the evolution of technology boosted the paradigm shift of the face-to-face learning to distance learning. In this scenario e-Learning plays an essential role as a facilitator of the teaching/learning process. However new demands associated with the new Web paradigm require that existent e-Learning environments characterized mostly by monolithic systems begin interacting with new specialized services. In this decentralized scenario the definition of a strategy of interoperability is the cornerstone to ensure the standardization communication among systems. This paper presents a definition of an interoperability strategy for an e-Learning environment at our School (ESEIG) called PEACE – Project for ESEIG Academic Content Environment. This new interoperability model relies on the application of several coordination and integration standards on several services, controlled by teachers and students, and included in the PEACE environment such as social networks, repositories, libraries, e-portfolios, intelligent tutors, recommendation systems and virtual classrooms.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Thermodynamic and kinetic modelling of the redox properties of tetrahaem cytochromes C3

Dissertação apresentada para obtenção do grau de Doutor em Bioquímica,especialidade Bioquímica-Física, pela Universidade Nova de Lisboa, Faculdade de Cincias e Tecnologia

P53 and Cancer-Associated Sialylated Glycans Are Surrogate Markers of Cancerization of the Bladder Associated with Schistosoma haematobium Infection

BACKGROUND: Bladder cancer is a significant health problem in rural areas of Africa and the Middle East where Schistosoma haematobium is prevalent, supporting an association between malignant transformation and infection by this blood fluke. Nevertheless, the molecular mechanisms linking these events are poorly understood. Bladder cancers in infected populations are generally diagnosed at a late stage since there is a lack of non-invasive diagnostic tools, hence enforcing the need for early carcinogenesis markers. METHODOLOGY/PRINCIPAL FINDINGS: Forty-three formalin-fixed paraffin-embedded bladder biopsies of S. haematobium-infected patients, consisting of bladder tumours, tumour adjacent mucosa and pre-malignant/malignant urothelial lesions, were screened for bladder cancer biomarkers. These included the oncoprotein p53, the tumour proliferation rate (Ki-67>17%), cell-surface cancer-associated glycan sialyl-Tn (sTn) and sialyl-Lewisa/x (sLea/sLex), involved in immune escape and metastasis. Bladder tumours of non-S. haematobium etiology and normal urothelium were used as controls. S. haematobium-associated benign/pre-malignant lesions present alterations in p53 and sLex that were also found in bladder tumors. Similar results were observed in non-S. haematobium associated tumours, irrespectively of their histological nature, denoting some common molecular pathways. In addition, most benign/pre-malignant lesions also expressed sLea. However, proliferative phenotypes were more prevalent in lesions adjacent to bladder tumors while sLea was characteristic of sole benign/pre-malignant lesions, suggesting it may be a biomarker of early carcionogenesis associated with the parasite. A correlation was observed between the frequency of the biomarkers in the tumor and adjacent mucosa, with the exception of Ki-67. Most S. haematobium eggs embedded in the urothelium were also positive for sLea and sLex. Reinforcing the pathologic nature of the studied biomarkers, none was observed in the healthy urothelium. CONCLUSION/SIGNIFICANCE: This preliminary study suggests that p53 and sialylated glycans are surrogate biomarkers of bladder cancerization associated with S. haematobium, highlighting a missing link between infection and cancer development. Eggs of S. haematobium express sLea and sLex antigens in mimicry of human leukocytes glycosylation, which may play a role in the colonization and disease dissemination. These observations may help the early identification of infected patients at a higher risk of developing bladder cancer and guide the future development of non-invasive diagnostic tests.