Search

Background Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls. Methods In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty. Results Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD. Limitations The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected. Conclusions The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease.

Electrocardiographic (ECG) signals have been successfully used to perform biometric recognition in a wide range of applications. However, ECG-based biometric systems are usually less accurate than technologies based on other physiological traits. To increase their performance, it is necessary to study novel approaches. Deep learning methods, like Convolutional Neural Networks (CNNs), can automatically extract distinctive features, and have demonstrated their effectiveness for other biometric systems. In this paper, we present Deep-ECG, a CNN-based biometric approach for ECG signals. To the best of our knowledge, this is the first study in the literature that uses a CNN for ECG biometrics. Deep-ECG extracts significant features from one or more leads using a deep CNN and compares biometric templates by computing simple and fast distance functions, obtaining remarkable accuracy for identification, verification and periodic re-authentication. Furthermore, using a simple quantization procedure, Deep-ECG can obtain binary templates that can facilitate the use of ECG-based biometric systems in conjunction with cryptographic applications. We also propose a simple method to enlarge the training dataset of ECG samples, which can increase the performance of deep neural networks. We performed experiments on large sets of samples acquired in uncontrolled conditions, proving the accuracy and robustness ofDeep-ECG in non-ideal scenarios. Furthermore, we evaluated the performance of Deep-ECG for the PTB Diagnostic ECG Database, obtaining identification accuracy better or comparable to the best performing methods in the literature, also for signals with different characteristics with respect to the ones used to train the CNN.

The potential of four regional microalgae species was evaluated in relation to their cell growth and biomass production when cultured in the following alternative media: bio-composts of fruit/horticultural wastes (HB), sugarcane waste and vinasse (VB) chicken excrements (BCE), raw chicken manure (RCM), and municipal domestic sewage (MDS). The cultures were maintained under controlled conditions and their growth responses, productivities, biochemical compositions, and the ester profiles of their biomasses were compared to the results obtained in the synthetic media. The MDS and HB media demonstrated promising results for cultivation, especially of Chlorella sp., Chlamydomonas sp., and Lagerheimia longiseta, which demonstrated productivities superior to those seen when grown on the control media. The highest lipid levels were obtained with the HB medium. The data obtained demonstrated the viability of cultivating microalgae and producing biomass in alternative media prepared from MDS and HB effluents to produce biodiesel.

Background and Objectives In contrast to potassium channel blockers, drugs affecting multiple channels seem to reduce torsadogenic risks. However, their effect on spatial heterogeneity of ventricular repolarization (SHVR) is still matter of investigation. Aim of this work is to assess the effect of four drugs blocking the human ether-a-go-go-related gene (hERG) potassium channel, alone or in combination with other ionic channel blocks, on SHVR, as estimated by the V-index on short triplicate 10 s ECG. Methods The V-index is an estimate of the standard deviation of the repolarization times of the myocytes across the entire myocardium, obtained from multi-lead surface electrocardiograms. Twenty-two healthy subjects received a pure hERG potassium channel blocker (dofetilide) and 3 other drugs with additional varying degrees of sodium and calcium (L‐type) channel block (quinidine, ranolazine, and verapamil), as well as placebo. A one-way repeated-measures Friedman test was performed to compare the V-index over time. Results Computer simulations and Bland-Altman analysis supported the reliability of the estimates of V-index on triplicate 10 s ECG. Ranolazine, verapamil and placebo did not affect the V-index. On the contrary, after quinidine and dofetilide administration, an increase of V-index from predose to its peak value was observed (ΔΔV-index values were 19 ms and 27 ms, respectively, p Conclusions High torsadogenic drugs (dofetilide and quinidine) affected significantly the SHVR, as quantified by the V-index. The metric has therefore a potential in assessing drug arrhythmogenicity.

Due to the currently abundant supply of marine microalgae, which can be found in seawater, as well as microalgae's ability to uptake different chemicals, it appears as a promising raw material with potential for many commercial uses. Despite having a high amount of metal in their biomass, the lipids within marine microalgae can be converted into biodiesel. Analyses of 26 chemical elements (Al, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Sn, Sr, Ti, Tl, V, and Zn) were performed by ICP-OES with the goal of quantifying the inorganic content of marine microalgae's biomass. Regardless of the cultivation media used, microalgae presented differences in their chemical element profile. Strains showed a 12.9% to 36.3% mass of analyzed elements per dry biomass, which represent a relatively high percentage for a feedstock used in biofuels. Among the 36 assayed microalgae, Biddulphia sp., Planktolyngbya limnetica, Amphora sp. (1), Navicula sp. (3) and Synechococcus sp. are most indicated for this purpose as they contain a lower concentration of chemical elements when compared to other samples. However, their profile warns that water quality control is needed for toxic metals such as Ba, Cd, and Pb.

In this paper, a detailed study on the possibility and significance of performing a parametric estimation of sample entropy (SampEn) is proposed. SampEn is a non-linear metric, meant to quantify regularity of a time series. It is widely employed on biomedical signals, especially on heart rate variability. Results relevant to approximate entropy, a related index, are also reported. An analytical expression for SampEn of an autoregressive (AR) model is derived first. Then we study the feasibility of a parametric estimation of SampEn through AR models, both on synthetic and real series. RR series of different lengths are fitted to an AR model and then expected values of SampEn (SampEn μ ) are estimated. Values of SampEn, computed from real beat-to-beat interval time series (obtained from 72 normal subjects and 29 congestive heart failure patients), with m = 1 and r = 0.2, are within the standard range of SampEn μ more than 83% (for series length N = 75) and 28% (for N = 1500) of the cases. Surrogate data have been employed to verify if departures from Gaussianity are to account for the mismatch. The work supports the finding that when numerical and parametric estimates of SampEn agree, SampEn is mainly influenced by linear properties of the series. A disagreement, on the contrary, might point those cases where SampEn is truly offering new information, not readily available with traditional temporal and spectral parameters.

Using a simple stochastic model of ventricular repolarization and the equivalent surface source (ESS) model, an electrophysiological formulation relating surface ECG to variations at the myocytes' level, we recently pointed out a few theoretical results regarding T-wave alternans (TWA). In this paper, stimulated by the comments of John E. Madias on our paper (J Electrocardiol, 2012), we further explored the consequences implied by the theoretical model. First, we verified the reproducibility of TWA measures, in clinically stable patients repeatedly tested. The sensitivity to displacement was evaluated simulating lead mislocations of up to 20mm. The numerical simulations were performed on data obtained solving the inverse electrocardiographically problem from three subjects (ECGSIM). The results showed that TWA sensitivity varies across leads, being maximal in V1 and decreases towards V6. Globally, the maximal percent error found was 6.1%. Thus, TWA measures do not seem to add more stringent requirements on lead placement's precision, than the usual diagnostic practice. Finally, we further discussed the implications of normalizing TWA measures. While clinical studies are necessary to sort out the issue, the theoretical model suggests that normalization might be appropriate only is certain cases.

T-wave alternans (TWA) is an alteration of the ECG T-wave which repeats every other beat. An alternating pattern has been also observed at myocytes level, involving both action potential duration and morphology (mainly in phases 2 and 3). While this might happen in a specific region (i.e., myocardial ischemia), it can also involve the entire myocardium. It is still unclear how alternations at the myocytes level are reflected on surface ECG modification of T-waves, especially when in vivo human hearts are considered. We have recently proposed a simple stochastic model of ventricular repolarization (IEEE Trans. Biomed. Eng., 2011), which takes into account both repolarization heterogeneity across the myocardium as well as random beat-to-beat variations in cells' activity. In this work, we generalized that model incorporating a term which describes myocytes alternans related to T-wave variability. Starting from the model and using the electrophysiological formulation developed by van Oosterom, we derived an analytical formula relating surface ECG to variations at the myocytes' level. Several theoretical results were then obtained. First, temporal small random variations in repolarization heterogeneity affect the precision of TWA estimates in a significant way. Second, TWA theoretically differs across leads, but multilead configuration can be used to reduce the effect of noise. Finally, the dependency between TWA and T-wave amplitude was analyzed.

The dominant T wave (DTW) reflects the derivative of the repolarization phase of the transmembrane potential of myocytes. T-wave alternans (TWA) is defined as an alteration of this repolarization that repeats every other beat. We investigate if the DTW can offer new insight on TWA. We first proved that the DTW estimate obtained through singular value decomposition is optimal, because it minimizes the norm of the residuals. Then we suggested an optimal estimate of the vector of lead factors, in the case in which the DTW is given. Finally, we derived a mathematical relationship between observable TWA on electrocardiogram and DTW morphology. The relationship depends on the slope of the repolarization phase of the myocytes' transmembrane potentials and on the dispersion of the repolarization times. Based on this finding, a new index meant to quantify TWA was defined and termed amplitude of dominant T-wave alternans (ADTWA). A preliminary validation of the index was performed using the synthetic records contained in the Computers in Cardiology 2008 data set. They were obtained from 5 electrocardiogram models to which TWA was added at different extents. We found a linear relationship between the TWA amplitude and the ADTWA metric (R(2) = 0.9898 ± 0.100 across all models). Moreover, the root mean square error between actual and estimated TWA amplitudes was 10.9 μV (ADTWA) vs 12.9 μV obtained with the classical spectral method.

Siderastrea stellata is a common scleractinian coral that inhabits shallow reefs off the coast of Brazil. This species is considered to be very resistant to temperature and salinity variations and water turbidity, demonstrating great ecological plasticity and adaptability to environmental changes. Samples of S. stellata were taken from the Cabo Branco coastal reefs near Joao Pessoa, Brazil, every month for two years and analyzed using PCR and Restriction Fragment Length Polymorphisms of SSU rDNA techniques. The data indicated that during the study period S. stellata hosted only one SSU rDNA genotype of Symbiodinium with the RFLP pattern of clade C. The presence of clade C zooxanthellae in S. stellata in northeastern Brazilian reefs shows the wide geographical distribution of this clade, and it may aid bleaching recovery in S. stellata. Furthermore, the association of S. stellata with a zooxanthellae clade considered to be one of most resistant to bleaching may help to explain the high ecological plasticity of this scleractinian species, its capacity to reverse bleaching, and its high resistance and resilience to environmental disturbances.

We review viscoplastic flow over inclined surfaces, focusing on constant-flux extrusions from small vents and the slumping of a fixed volume of material. Lubrication theory is used for shallow and slow flows to reduce the governing equations to a non-linear diffusion-type equation for the local fluid depth; this model is used as the basis for exploration of the problem. Theory is compared to experiments. A number of complications and additional physical effects are discussed that enrich real situations.

Recently, the research efforts in the context of electrocardiographical recording during atrial fibrillation (AF) has been directed to broaden the understandings on the electrophysiological and structural remodelling occurring during the arrhythmia and on characterizing the different types of AF. Following this line, both surface ECG and endocardial electrograms have been thoroughly studied and a series of linear and non-linear parameters were computed either directly on the electrograms or on the derived activation series. In this paper, we reviewed some signal processing methods used to characterize surface ECG and endocardial electrograms during AF, focusing on spectral and non-linear analysis. In particular, parametric and non-parametric methods for spectral analysis of the residual ECG, i.e. atrial waves obtained from surface ECG after removing ventricular activity, and endocardial recordings are described. The different purposes of spectral analysis (exploring autonomic functions, analysis of spontaneous AF behaviour and predicting therapeutic effects) are illustrated with some examples. In addition, we described some more recent non-linear methods applied to AF, assessing the organization of atrial signals as well as ventricular response in AF. In particular, methods derived from embedding time series and based on entropy computation are illustrated and exemplified.

Riassunto Le cure domiciliari sono un’assistenza sanitaria e sociale erogata dalle ASL, in collaborazione con il medico e/o pediatra di famiglia e di altri operatori secondo le necessita rilevate a domicilio del paziente in modo continuo e integrato. Essa permette ai cittadini che ne hanno bisogno di essere assistiti a casa con programmi personalizzati, evitando il ricovero in ospedale per un tempo oltre il necessario. Quando necessaria, deve essere richiesta al Distretto sanitario di residenza da parte del Medico di medicina generale o dal Pediatra di famiglia, ma puo anche essere richiesta dalla persona interessata, dai suoi familiari o dagli operatori dei Servizi sociali del Comune.

This article explores the Kuramoto model describing the synchronization of a population of coupled oscillators. Two versions of this model are considered: a discrete version suitable for a population with a finite number of oscillators, and a continuum model found in the limit of an infinite population. When the strength of the coupling between the oscillators exceeds a threshold, the oscillators partially synchronize. We explore the transition in the continuum model, which takes the form of a bifurcation of a discrete mode from a continuous spectrum. We use numerical methods and perturbation theory to study the patterns of synchronization that form beyond transition, and compare with the synchronization predicted by the discrete model. There are similarities with instabilities in ideal plasmas and inviscid fluids, but these are superficial.

Drug-induced alterations of repolarization heterogeneity are checked to prevent the risk of lethal arrhythmias. Before drugs approval, FDA mandates a “thorough QT study.” In the last 14 years, the test avoided the withdrawal from the market of related drugs. However, it is questioned if the low specificity of the test might have hindered molecules from being commercialized. Research of alternative indices, linked with the physiology, is therefore supported by regulatory agencies. A new measure of ventricular repolarization heterogeneity (V-index) was recently proposed; it is based on a stochastic model that links the electrical potential of the myocytes with the surface ECG and it provides an estimate of the heterogeneity of the repolarization times. After being tested on synthetic signals, the metric is now undergoing a validation procedure. In this work, we performed a retrospective study to assess the metric as a function of the sotalol concentration. Since sotalol leads to an increased dispersion of the repolarization times, due to the block of the beta-adrenergic receptors, it provides a good test bench for the V-index. Briefly, 12-lead digital ECG recordings of 38 healthy subjects were collected in three consecutive days. At the first day no drugs were administrated. A 160-mg dose of sotalol was injected at the second day and 320-mg dose at the third one. The serum concentration of the drug was assessed at predefined intervals during the second and the third days. The QT interval and the V-index were computed at the same instants to support a proper comparison. The goals were as follows: evaluation of i) sensitivity of the metric to the effects of the drug and ii) time evolution of the metric as a function of serum concentration. All measures were collected during rest conditions and only on stable beats. A beat was defined stable whether its RR value was within of an interval of 20 ms around the local mean. The metric showed a different mean value in presence of sotalol (baseline: 26.17 ± 9.23 ms vs single-drug dose: 36.00 ± 14.51 ms; p b 0.001 paired t-test). The cross-correlation among QT and V-index showed a high level of similarity (N0.9, p b 0.001). The results confirmed a study performed on the same data set using only the QT interval (Extramiana et al. 2010, Ann Noninvasive Electrocardiol). Further studies are still necessary, but the research showed that quantification of repolarization heterogeneity through the V-index is effective. Moreover, being directly linked to a physiological model, the Vindex could offer additional insights in risk stratification.