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Pedro Rodrigues

Bio: Pedro Rodrigues is an academic researcher from University of Porto. The author has contributed to research in topics: Medicine & Data acquisition. The author has an hindex of 24, co-authored 176 publications receiving 7548 citations. Previous affiliations of Pedro Rodrigues include University of Minho & University of Lisbon.


Papers
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Journal ArticleDOI
TL;DR: GeGeant4 as mentioned in this paper is a software toolkit for the simulation of the passage of particles through matter, it is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection.
Abstract: Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Its functionality and modeling capabilities continue to be extended, while its performance is enhanced. An overview of recent developments in diverse areas of the toolkit is presented. These include performance optimization for complex setups; improvements for the propagation in fields; new options for event biasing; and additions and improvements in geometry, physics processes and interactive capabilities

6,063 citations

Proceedings ArticleDOI
16 Oct 2004
TL;DR: The Geant4 simulation toolkit as mentioned in this paper includes a specialised package, implementing a precise treatment of electromagnetic interactions of particles with matter below 1 keV, taking into account detailed features, such as atomic shell effects and charge dependence.
Abstract: The Geant4 simulation toolkit includes a specialised package, implementing a precise treatment of electromagnetic interactions of particles with matter below 1 keV. The Geant4 low energy electromagnetic package provides a variety of models describing the electromagnetic processes of electrons and positrons, photons, charged hadrons and ions, taking into account detailed features, such as atomic shell effects and charge dependence. Those features are relevant to several experimental domains, such as astrophysics, space science and bio-medical research, and have enabled new simulation studies beyond the conventional applications of Geant4 in high energy physics. The design of the package and the physics models implemented are presented.

146 citations

Journal ArticleDOI
TL;DR: The Clear-PEM imaging system for positron emission mammography, under development by the PEM Consortium within the framework of the Crystal Clear Collaboration at CERN, is presented in this paper.
Abstract: The design and evaluation of the imaging system Clear-PEM for positron emission mammography, under development by the PEM Consortium within the framework of the Crystal Clear Collaboration at CERN, is presented. The proposed apparatus is based on fast, segmented, high atomic number radiation sensors with depth-of-interaction measurement capabilities, and state-of-the-art data acquisition techniques. The camera consists of two compact and planar detector heads with dimensions 16.5/spl times/14.5 cm/sup 2/ for breast and axilla imaging. Low-noise integrated electronics provide signal amplification and analog multiplexing based on a new data-driven architecture. The coincidence trigger and data acquisition architecture makes extensive use of pipeline processing structures and multi-event memories for high efficiency up to a data acquisition rate of one million events/s. Experimental validation of the detection techniques, namely the basic properties of the radiation sensors and the ability to measure the depth-of-interaction of the incoming photons, are presented. System performance in terms of detection sensitivity, count-rates and reconstructed image spatial resolution were also evaluated by means of a detailed Monte Carlo simulation and an iterative image reconstruction algorithm.

131 citations

Journal ArticleDOI
TL;DR: A simple yet powerful method for matching the statistical distributions of two datasets, thus paving the way to BCI systems capable of reusing data from previous sessions and avoid the need of a calibration procedure.
Abstract: Objective: This paper presents a Transfer Learning approach for dealing with the statistical variability of electroencephalographic (EEG) signals recorded on different sessions and/or from different subjects. This is a common problem faced by brain–computer interfaces (BCI) and poses a challenge for systems that try to reuse data from previous recordings to avoid a calibration phase for new users or new sessions for the same user. Method: We propose a method based on Procrustes analysis for matching the statistical distributions of two datasets using simple geometrical transformations (translation, scaling, and rotation) over the data points. We use symmetric positive definite matrices (SPD) as statistical features for describing the EEG signals, so the geometrical operations on the data points respect the intrinsic geometry of the SPD manifold. Because of its geometry-aware nature, we call our method the Riemannian Procrustes analysis (RPA). We assess the improvement in transfer learning via RPA by performing classification tasks on simulated data and on eight publicly available BCI datasets covering three experimental paradigms (243 subjects in total). Results: Our results show that the classification accuracy with RPA is superior in comparison to other geometry-aware methods proposed in the literature. We also observe improvements in ensemble classification strategies when the statistics of the datasets are matched via RPA. Conclusion and significance: We present a simple yet powerful method for matching the statistical distributions of two datasets, thus paving the way to BCI systems capable of reusing data from previous sessions and avoid the need of a calibration procedure.

130 citations

Journal ArticleDOI
TL;DR: The results support the utilization of yeast as a useful model to screen in vivo for natural antioxidants with putative health beneficial effects.
Abstract: Quercetin, the major flavonol found in several fruits and vegetables, is a natural antioxidant with potential anticancer and antiaging activities. In this paper, the effect of quercetin in Sacharomyces cerevisiae cells submitted to oxidative stress was studied. Hydrogen peroxide resistance increased in cells pretreated with quercetin. Cellular protection was correlated with a decrease in oxidative stress markers, namely, levels of reactive oxygen species, glutathione oxidation, protein carbonylation, and lipid peroxidation. The acquisition of H2O2 resistance was not associated with the induction of antioxidant defenses or with iron chelation. Oxidative stress is a limiting factor for longevity. In agreement, quercetin also increased 60% chronological life span. These results support the utilization of yeast as a useful model to screen in vivo for natural antioxidants with putative health beneficial effects.

124 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: GeGeant4 as mentioned in this paper is a software toolkit for the simulation of the passage of particles through matter, it is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection.
Abstract: Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Its functionality and modeling capabilities continue to be extended, while its performance is enhanced. An overview of recent developments in diverse areas of the toolkit is presented. These include performance optimization for complex setups; improvements for the propagation in fields; new options for event biasing; and additions and improvements in geometry, physics processes and interactive capabilities

6,063 citations

Journal ArticleDOI
W. B. Atwood1, A. A. Abdo2, A. A. Abdo3, Markus Ackermann4  +289 moreInstitutions (37)
TL;DR: The Large Area Telescope (Fermi/LAT) as mentioned in this paper is the primary instrument on the Fermi Gamma-ray Space Telescope, which is an imaging, wide field-of-view, high-energy gamma-ray telescope, covering the energy range from below 20 MeV to more than 300 GeV.
Abstract: (Abridged) The Large Area Telescope (Fermi/LAT, hereafter LAT), the primary instrument on the Fermi Gamma-ray Space Telescope (Fermi) mission, is an imaging, wide field-of-view, high-energy gamma-ray telescope, covering the energy range from below 20 MeV to more than 300 GeV. This paper describes the LAT, its pre-flight expected performance, and summarizes the key science objectives that will be addressed. On-orbit performance will be presented in detail in a subsequent paper. The LAT is a pair-conversion telescope with a precision tracker and calorimeter, each consisting of a 4x4 array of 16 modules, a segmented anticoincidence detector that covers the tracker array, and a programmable trigger and data acquisition system. Each tracker module has a vertical stack of 18 x,y tracking planes, including two layers (x and y) of single-sided silicon strip detectors and high-Z converter material (tungsten) per tray. Every calorimeter module has 96 CsI(Tl) crystals, arranged in an 8 layer hodoscopic configuration with a total depth of 8.6 radiation lengths. The aspect ratio of the tracker (height/width) is 0.4 allowing a large field-of-view (2.4 sr). Data obtained with the LAT are intended to (i) permit rapid notification of high-energy gamma-ray bursts (GRBs) and transients and facilitate monitoring of variable sources, (ii) yield an extensive catalog of several thousand high-energy sources obtained from an all-sky survey, (iii) measure spectra from 20 MeV to more than 50 GeV for several hundred sources, (iv) localize point sources to 0.3 - 2 arc minutes, (v) map and obtain spectra of extended sources such as SNRs, molecular clouds, and nearby galaxies, (vi) measure the diffuse isotropic gamma-ray background up to TeV energies, and (vii) explore the discovery space for dark matter.

3,666 citations

Journal ArticleDOI
TL;DR: It is suggested that long term consumption of diets rich in plant polyphenols offer protection against development of cancers, cardiovascular diseases, diabetes, osteoporosis and neurodegenerative diseases.
Abstract: Polyphenols are secondary metabolites of plants and are generally involved in defense against ultraviolet radiation or aggression by pathogens. In the last decade, there has been much interest in the potential health benefits of dietary plant polyphenols as antioxidant. Epidemiological studies and associated meta-analyses strongly suggest that long term consumption of diets rich in plant polyphenols offer protection against development of cancers, cardiovascular diseases, diabetes, osteoporosis and neurodegenerative diseases. Here we present knowledge about the biological effects of plant polyphenols in the context of relevance to human health.

3,370 citations