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The emphasis on digitizing and computational modelling of physical phenomena began in the telecommunication sector with the appearance of the large-scale integration microprocessors and micro-controllers, them was possible to produce architectures with enough processing and memory capacity to supply the needs of the applications, specially the health applications. One of those is the Valsalva Maneuver (VM), used as a test that verifies the functional integrity of the autonomic nervous system and stands out in scientific research as an instrument to evaluate functions and control of the cardiovascular system. The aim of this device is to improve the execution of VM giving more control of the manoeuvre and reduce the repetitions, in tests that use tilt-table, or bed rest together with the VM, the position of the patient turns hard to visualize the pressure that are doing, this created device gives a one hand solution that integrates all that are need to perform the manoeuvre, and an IEEE 802.11 connection that allows the motorization of the process through a computer or a smartphone, and save the data of intrathoracic pressure at the end for analysis.

Practices of using active methodologies as facilitating tools in the teaching-learning process in higher education are increasing, especially in bioengineering courses. The university is a good model for facilitating the implementation of innovative processes due to its role and proximity to science, teaching and research. To embrace this challenge, the university is reaching a broader interaction with the community, from the area of scientific education that includes the awakening of children to science and research. Thus, this article reports on the pilot experience of the Laboratory of Cardiovascular Autonomic Function of the Cardiovascular Center of the University of Lisbon, in the approach of different academic and scientific knowledge together with society as a means to stimulate the pleasure of science and research in children.

Myocardial infarction (MI) is a high prevalence cardiovascular disease with a tendency to increase. Although MI therapy has increased its effectiveness, the loss of myocardial function in consequence of MI is still responsible for major subsequent complications as rhythm changes and heart failure. In human hearts, MI is characterized by cardiomyocyte death and fibrin deposition, which is later replaced by scar tissue. In opposition, zebrafish heart undergoes minimal scaring and cardiac tissue can undergo full functionally regeneration in 60 to 120 days. Therefore, this animal model has been used in cardiovascular research as a model for studying mechanisms of cardiac regeneration. The hearts of zebrafish is anatomically different from the human heart, though the electrocardiogram (ECG) pattern and the critical conduction pathways are comparable to the human's. The objective of our full project is the implementation of an ECG recording system with 3 orthogonal electrodes in 2 planes, in order to create a 4D recording of the zebrafish cardiac electrical activity. Here we present our preliminary work related to a non-invasive ECG monitoring with two electrodes.

Usually, transport reactions are added to genome-scale metabolic models (GSMMs) based on experimental data and literature. This approach does not allow associating specific genes with transport reactions, which impairs the ability of the model to predict effects of gene deletions. Novel methods for systematic genome-wide transporter functional annotation and their integration into GSMMs are therefore necessary. In this work, an automatic system to detect and classify all potential membrane transport proteins for a given genome and integrate the related reactions into GSMMs is proposed, based on the identification and classification of genes that encode transmembrane proteins. The Transport Reactions Annotation and Generation (TRIAGE) tool identifies the metabolites transported by each transmembrane protein and its transporter family. The localization of the carriers is also predicted and, consequently, their action is confined to a given membrane. The integration of the data provided by TRIAGE with highly curated models allowed the identification of new transport reactions. TRIAGE is included in the new release of merlin, a software tool previously developed by the authors, which expedites the GSMM reconstruction processes., This work was partially supported by a PhD grant (SFRH /BD/47307/2008) and by the ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness), and National Funds through the FCT within the projects FCOMP-01-0124-FEDER-009707 (HeliSysBio—molecular Systems Biology in Helicobacter pylori) and PTDC/EIAEIA/115176/2009. The authors would also like to thank the FCT Strategic Project PEst-OE/EQB/LA0023/2013 and the Projects “BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes”, REF. NORTE-07-0124-FEDER-000028 and “PEM – Metabolic Engineering Platform”, project number 23060 , both cofunded by the Programa Operacional Regional do Norte (ON.2 – O Novo Norte), QREN, FEDER., info:eu-repo/semantics/publishedVersion

Lately, heart rate variability (HRV) and blood pressure adaptations have been studied in relation to Space Exploration. Most data obtained so far, indicate a decrease in heart rate (HR), mainly explained by a decrease in the low frequency component (LF) of HRV and an increase in the high frequency band (HF), indicating a predominant vagal modulation during space flights. In order to validate our laboratory and in-house analysis software, as potential tools for space life sciences, 14 normal subjects participated in a 2 hours protocol of simulated weightlessness (HDT-6o) in addition to a Mental Stress Maneuver. All cardiac components were collected, and after wavelets analysis, preliminary results show a significant decrease in HR (p=0,0128). The LF/HF ratio shows a clear tendency to decrease, with a comprehensible increase in HF. During Mental Stress, HR was increased, correlating with higher LF, alongside a general decrease in HF. Overall, our preliminary results validate our laboratory, for studies in the field of human space physiology.

The physiological signals are a challenge in terms of identification and processing due to its lack of stationarity and linearity. The autonomic output to the cardiovascular system is classically quantified by analysis of heart rate variability. The Fast Fourier Transform (FFT) and Discrete Wavelet Transform applied to the cardiac signals showed limitations in terms of time and frequency resolutions. With this work we propose the application of a Modified Hilbert-Huang Transform for tachogram and systogram evaluation with the introduction of a new method to smooth fluctuations endpoint. This work presents a comparative study using FFT and Hilbert-Huang methodologies. It is known that patients and animal models with diseases involving sympathoexcitation, eg.: Metabolic Syndrome (MetS), show increases in low frequencies band. Therefore, these mathematical tools are evaluated by analyzing experimental data from two animal species: MetS-rabbits (n=6) and MetS-rats (n=6) and their respective lean age and sex-matched controls (n=6 for each group). Modified Hilbert-Huang Transform analysis showed a sympathoexcitatory condition in MetS comparing to controls both in a rabbit and in a rat model of MetS. These significant differences on sympathetic tone were also confirmed by FFT but, the parasympathetic activity, was similar between normal and MetS animals. Results and its physiological interpretation clearly show the suitability of the modified Hilbert-Huang Transform as an alternative methodology for the dynamic assessment of the autonomic nervous system in MetS.

Recent clinical research directs up to provide personalized health care, adapted to each patient and to his clinical situation. With this work, we showed that a wearable device with heart rate (HR), respiratory and temperature sensors could be used, under certain technical and environmental requirements, for a reliable autonomic evaluation outside the confinement of an autonomic laboratory.

In silico cell simulation approaches based in the use of genome-scale metabolic models (GSMMs) and constraint-based methods such as Flux Balance Analysis are gaining importance, but methods to integrate these approaches with omics data are still greatly needed. In this work, the focus relies on fluxomics data that provide valuable information on the intracellular fluxes, although in many cases in an indirect, incomplete and noisy way. The proposed framework enables the integration of fluxomics data, in the form of 13C labeling distribution for metabolite fragments, with GSMMs enriched with carbon atom transition maps. The algorithms implemented allow to infer labeling distributions for fragments/metabolites not measured and to build expressions for the relevant flux ratios that can be then used to enrich constraint-based methods for flux determination. This approach does not require any assumptions on the metabolic network and reaction reversibility, allowing to compute ratios originating from coupled joint points of the network. Also, when enough data do not exist, the system tries to infer ratio bounds from the measurements.

The identification of genetic modifications leading to mutant strains able to overproduce compounds of industrial interest is a challenging task in Metabolic Engineering (ME). Several methods have been proposed but, to some extent, none of them is suitable for all the specificities of each particular strain optimization problem. This work proposes an integrated framework that allows its users to configure and fine tune all the various steps involved in a strain optimization strategy, including the loading of models in distinct formats, the definition of a suitable phenotype simulation method and the choice and configuration of the strain optimization engine. Moreover, it is designed to suit the needs of users skilled at programming, as well as less advanced users. The framework includes a GUI implemented as the strain optimization plug-in for the OptFlux workbench (version 3), a reference platform for ME (http://www.optflux.org). All the code is distributed under the GPLv3 licence and it is fully available (http://sourceforge.net/projects/optflux/)., This work is partially funded by ERDF- European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within projects ref. COMPETE FCOMP- 01-0124-FEDER-015079 and PTDC/EBB-EBI/104235/2008. This work is also funded by National Funds through the FCT within project PEst-OE/EEI/UI0752/2011. The work of PM was supported by the FCT through the Ph.D. grant SFRH/BD/61465/2009.

Heart rate variability is a classical method to quantify the autonomic outflow to the cardiovascular system. The current methods in frequency and time-frequency domains have shown some limitations in resolution. In this paper, we propose a modified Hilbert-Huang algorithm to the assessment of autonomic activity in heart rate signals. These approaches are evaluated while analyzing both simulated signals and experimental data from tachograms associated with cardiac autonomic blockade of patients with paroxysmal atrial fibrillation.

The purpose of this work is to discuss the ability of wavelet analysis and cross-spectral algorithm to follow up the short-term cardiovascular function changes during cardiac arrhythmia. It was analyzed the (a) coherence between systolic blood pressure (SBP) and cardiac oscillations in low frequency (LF) and high frequency (HF) band; (b) fluctuations of phase; (c) heart rate variability (HRV) and blood pressure variability (BPV) as a LF power and HF power in frequency and time frequency domain. The analysis was performed on blood pressure and electrocardiogram signals from one rabbit anaesthetized, paralyzed and mechanically ventilated with self-terminating atrial and ventricular fibrillation after 7 minutes of cardiac arrhythmia. Arrhythmia was preceded by a) decreased coherence; b) increase in fluctuations of phase between signals; c) increased spectral energy associated with respiratory frequency in blood pressure signal; d) increased sympathetic outflow to the heart and e) decreased HRV. Cardiac arrhythmia was characterized mainly by increased LF power in blood pressure and cardiac signal. During self-termination of dysrhythmia a larger increased in total BPV and HRV was recorded. These results suggest that important information about both neuronal cardio-circulatory control and risk for spontaneous arrhythmia might be provided by combined analysis of frequency, phase, time-frequency analysis of blood pressure and cardiac oscillation.

The analysis of heart rate variability is nowadays a common method for noninvasive evaluation of the autonomic nervous system integrity. In this work we developed an integrated and modular system — FisioSinal — capable of clinical and laboratorial evaluation of the behavior of autonomic nervous system using cardiovascular signals in humans and animal models. The computational tools that were included seek to cover the currently most validated methodologies: stochastic analysis, descriptive statistics, auto-regression, fast Fourier transform, discrete Wavelets transform and baroreflex sensitivity index. Due to its limitations, we developed and validated a new analytical tool based on Hilbert-Huang transform. FisioSinal was validated and tested by analyzing a synthesized signal. The clinical applicability was demonstrated through an analysis of the cardiovascular signals of a normal individual and a patient with paroxysmal atrial fibrillation, undergoing autonomic a provocation maneuver.

The present paper describes a novel approach to evaluate the mass sensitivity of a Love wave (LW) sensor. A ZnO/XY LiNbO3 delay line based device with a sensing film was modeled and simulated applying the 3D- Finite Element Method (FEM). The radio frequency signal delays due to the thickness changes induced to the sensing film were registered and measured at the output Interdigital Transducer (IDT). The time delays were associated with phase shifts to determine the phase sensitivity of the sensor. A phase sensitivity of 600 [cm2/g] was obtained, which is in good agreement with literature.

Over the last few years, a growing number of techniques has been successfully proposed to tackle diverse challenges in the Biomedical Text Mining (BioTM) arena. However, the set of available software tools to researchers has not grown in a similar way. This work makes a contribution to close this gap, proposing a framework to ease the development of user-friendly and interoperable applications in this field, based on a set of available modular components. These modules can be connected in diverse ways to create applications that fit distinct user roles. Also, developers of new algorithms have a framework that allows them to easily integrate their implementations with state-of-the-art BioTM software for related tasks., This work was supported in part by the research projects recSysBio (ref. POCI/BIO/60139/2004) and MOBioPro (ref. POSC/EW59899/2004) of the University of Minho, financed by the Portuguese Fundaao para a Ciencia e Tecnologia. The work of SC is supported by a PhD grant from the same institution (ref. SFRH/BD/22863/2005).

The analysis of cellular behavior and functionality is the most challenging aim of systems biology. The extensive analysis of the interactions between different classes of intra-cellular molecules reacting to genetic/environment changes can elucidate the mechanisms of regulation involved on different cellular processes. We propose a novel framework that enables the integrated analysis of metabolic and regulatory networks. The framework takes advantage on publicly available data repositories, sustaining the inference of knowledge from the integrated network. Since it is based on logic programming, it provides users with a powerful language to query information using both first and second order predicates. Also, it supports network topology analysis, motif finding and robustness evaluation. In this work, as an illustrative case study, our framework is used to build a model of the bacterium Escherichia coli K12.

In Metabolic Engineering, the identification of genetic manipulations that lead to mutant strains able to produce a given compound of interest is a promising, while still complex process. Evolutionary Algorithms (EAs) have been a successful approach for tackling the underlying in silico optimization problems. The most common task is to solve a bi-level optimization problem, where the strain that maximizes the production of some compound is sought, while trying to keep the organism viable (maximizing biomass). In this work, this task is viewed as a multiobjective optimization problem and an approach based on multiobjective EAs is proposed. The algorithms are validated with a real world case study that uses E. coli to produce succinic acid. The results obtained are quite promising when compared to the available single objective algorithms., This work was supported by the Portuguese FCT project POSC/EIA/59899/2004

In the last decade, new techniques have been introduced for analysis of nonstationary cardiovascular signal. Widespread used are the short time Fourier transform (STFT) and the autoregressive (AR) model. The present paper presents a wavelet method for noninvasive evaluation of autonomic status after pharmacological modulation by analyzing rats' electrocardiographic (ECG) signal transient phenomena. Some methods of preprocessing R-R signal are presented that make time-frequency analysis of the heart rate variability signal more reliable. The results of an implemented algorithm for multiresolution wavelet analysis of rats ECG signal after ethanol modulation of autonomic balance are presented. The proposed approach shows excellent numerical and computational performance. Clearly visualization of the respiratory behavior is accomplished with wavelet decomposition. The studies reported here draw attention to the feasibility of noninvasive access to the circuitry that governs cardiac autonomic function.