Analysis of Natural and Artificial Phenomena Using Signal Processing and Fractional Calculus

In this paper we study several natural and man-made complex phenomena in the perspective of dynamical systems. For each class of phenomena, the system outputs are time-series records obtained in identical conditions. The time-series are viewed as manifestations of the system behavior and are processed for analyzing the system dynamics. First, we use the Fourier transform to process the data and we approximate the amplitude spectra by means of power law functions. We interpret the power law parameters as a phenomenological signature of the system dynamics. Second, we adopt the techniques of non-hierarchical clustering and multidimensional scaling to visualize hidden relationships between the complex phenomena. Third, we propose a vector field based analogy to interpret the patterns unveiled by the PL parameters.

Visualizing control systems performance: A fractional perspective

This article presents a novel method for visualizing the control systems behavior. The proposed scheme uses the tools of fractional calculus and computes the signals propagating within the system structure as a time/frequency-space wave. Linear and nonlinear closed-loop control systems are analyzed, for both the time and frequency responses, under the action of a reference step input signal. Several nonlinearities, namely, Coulomb friction and backlash, are also tested. The numerical experiments demonstrate the feasibility of the proposed methodology as a visualization tool and motivate its extension for other systems and classes of nonlinearities.

Measuring data transfer in heterogeneous IoT environments

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

The electron transfer complex between nitrous oxide reductase and its electron donors

J Biol Inorg Chem (2011) 16:1241–1254 DOI 10.1007/s00775-011-0812-9

New spectroscopic and electrochemical insights on a class I superoxide reductase: evidence for an intramolecular electron-transfer pathway

Biochem. J. (2011) 438,485–494 doi:10.1042/BJ20110836

Analysis of the activation mechanism of Pseudomonas stutzeri cytochrome c peroxidase through an electron transfer chain

J Biol Inorg Chem (2011) 16:881–888 DOI 10.1007/s00775-011-0785-8

Electron transfer complex between nitrous oxide reductase and cytochrome c552 from Pseudomonas nautica: kinetic, nuclear magnetic resonance, and docking studies

Biochemistry. 2008 Oct 14;47(41):10852-62. doi: 10.1021/bi801375q

Mediated catalysis of Paracoccus pantotrophus cytochrome c peroxidase by P. pantotrophus pseudoazurin: kinetics of intermolecular electron transfer

J Biol Inorg Chem (2007) 12:691–698 DOI 10.1007/s00775-007-0219-9

Kinetics studies of the superoxide-mediated electron transfer reactions between rubredoxin-type proteins and superoxide reductases

J Biol Inorg Chem (2006) 11: 433–444 DOI 10.1007/s00775-006-0090-0

Overexpression and purification of Treponema pallidum rubredoxin; kinetic evidence for a superoxide-mediated electron transfer with the superoxide reductase neelaredoxin

J Biol Inorg Chem (2004) 9: 839–849 DOI 10.1007/s00775-004-0584-6

Structural and functional studies of PpcA: a key protein in the electron transfer pathways of Geobacter sulfurreducens

Dissertação para obtenção do Grau de Doutor em Bioquímica, ramo de Biotecnologia

The effect of a dialyzable transfer factor (TFd) in the experimental american trypanosomiasis of mice: preliminary report

A dialyzable transfer factor (TFd) was obtained from spleen cells, of mice vaccinated with the avirulent PF strain of Trypanosoma cruzi. This factor reduced significahtly the parasitemia of animals treated before or after the infection with a virulent strain of the same parasite, but does not reduced the mortality rate to a level lower than that of the control mice. It is expected that in a next future, new techniques in the use ofsuch factor will bring better resutts.

The impact of knowledge transfer on innovation in multinational corporations: Exploring the contingent effects

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

A technology market transfer for the Zonadvanced video technology

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics