313 research outputs found

    Determining the Time Constant of Arcs at Arbitrary Current Levels

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    For the development and optimization of gas circuit breakers and switchgear, a detailed understanding of the arc related processes is of great importance. Ideally, analytical or numerical models with predicitive capability can be found and used during the design process preceding costly and time-consuming experiments.In the present contribution, we report on a novel measurement and evaluation technique to determine the thermal arc time constant ("thermal inertia") that is commonly used in simple black-box models to describe the arc's dynamical properties. The method is introduced and applied to example arcs under varying blow gas conditions in air

    A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals

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    This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at https://doi.org/10.1088/1361-6579/ab97c1.[EN] Objective: Atrial dominant frequency (DF) maps undergoing atrial fibrillation (AF) presented good spatial correlation with those obtained with the non-invasive body surface potential mapping (BSPM). In this study, a robust BSPM-DF calculation method based on wavelet analysis is proposed. Approach: Continuous wavelet transform along 40 scales in the pseudo-frequency range of 3¿30 Hz is performed in each BSPM signal using a Gaussian mother wavelet. DFs are estimated from the intervals between the peaks, representing the activation times, in the maximum energy scale. The results are compared with the traditionally widely applied Welch periodogram and the robustness was tested on different protocols: increasing levels of white Gaussian noise, artificial DF harmonics presence and reduction in the number of leads. A total of 11 AF simulations and 12 AF patients are considered in the analysis. For each patient, intracardiac electrograms were acquired in 15 locations from both atria. The accuracy of both methods was assessed by calculating the absolute errors of the highest DFBSPM (HDFBSPM) with respect to the atrial HDF, either simulated or intracardially measured, and assumed correct if ¿1 Hz. The spatial distribution of the errors between torso DFs and atrial HDFs were compared with atria driving mechanism locations. Torso HDF regions, defined as portions of the maps with |DF ¿ HDFBSPM| ¿ 0.5 Hz were identified and the percentage of the torso occuping these regions was compared between methods. The robustness of both methods to white Gaussian noise, ventricular influence and harmonics, and to lower spatial resolution BSPM lead layouts was analyzed: computer AF models (567 leads vs 256 leads down to 16 leads) and patient data (67 leads vs 32 and 16 leads). Main results: The proposed method allowed an improvement in non-invasive estimation of the atria HDF. For the models the median relative errors were 7.14% for the wavelet-based algorithm vs 60.00% for the Welch method; in patients, the errors were 10.03% vs 12.66%, respectively. The wavelet method outperformed the Welch approach in correct estimations of atrial HDFs in models (81.82% vs 45.45%, respectively) and patients (66.67% vs 41.67%). A low positive BSPM-DF map correlation was seen between the techniques (0.47 for models and 0.63 for patients), highlighting the overall differences in DF distributions. The wavelet-based algorithm was more robust to white Gaussian noise, residual ventricular activity and harmonics, and presented more consistent results in lead layouts with low spatial resolution. Significance: Estimation of atrial HDFs using BSPM is improved by the proposed wavelet-based algorithm, helping to increase the non-invasive diagnostic ability in AF.This study was supported in part by grants from Sao Paulo Research Foundation (2017/19775-3), Instituto de Salud Carlos III FEDER (Fondo Europeo de Desarrollo Regional PI17/01106) and Generalitat Valenciana Grants (AICO/2018/267).Marques, V.; Rodrigo Bort, M.; Guillem Sánchez, MS.; Salinet, J. (2020). A robust wavelet-based approach for dominant frequency analysis of atrial fibrillation in body surface signals. Physiological Measurement. 41(7):1-14. https://doi.org/10.1088/1361-6579/ab97c1S11441

    Patterns of genetic diversity and linkage disequilibrium in a highly structured Hordeum vulgare association-mapping population for the Mediterranean basin

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    Population structure and genome-wide linkage disequilibrium (LD) were investigated in 192 Hordeum vulgare accessions providing a comprehensive coverage of past and present barley breeding in the Mediterranean basin, using 50 nuclear microsatellite and 1,130 DArT® markers. Both clustering and principal coordinate analyses clearly sub-divided the sample into five distinct groups centred on key ancestors and regions of origin of the germplasm. For given genetic distances, large variation in LD values was observed, ranging from closely linked markers completely at equilibrium to marker pairs at 50 cM separation still showing significant LD. Mean LD values across the whole population sample decayed below r 2 of 0.15 after 3.2 cM. By assaying 1,130 genome-wide DArT® markers, we demonstrated that, after accounting for population substructure, current genome coverage of 1 marker per 1.5 cM except for chromosome 4H with 1 marker per 3.62 cM is sufficient for whole genome association scans. We show, by identifying associations with powdery mildew that map in genomic regions known to have resistance loci, that associations can be detected in strongly stratified samples provided population structure is effectively controlled in the analysis. The population we describe is, therefore, shown to be a valuable resource, which can be used in basic and applied research in barle

    Mapping adaptation of barley to droughted environments

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    Identifying barley genomic regions influencing the response of yield and its components to water deficits will aid in our understanding of the genetics of drought tolerance and the development of more drought tolerant cultivars. We assembled a population of 192 genotypes that represented landraces, old, and contemporary cultivars sampling key regions around the Mediterranean basin and the rest of Europe. The population was genotyped with a stratified set of 50 genomic and EST derived molecular markers, 49 of which were Simple Sequence Repeats (SSRs), which revealed an underlying population sub-structure that corresponded closely to the geographic regions in which the genotypes were grown. A more dense whole genome scan was generated by using Diversity Array Technology (DArT®) to generate 1130 biallelic markers for the population. The population was grown at two contrasting sites in each of seven Mediterranean countries for harvest 2004 and 2005 and grain yield data collected. Mean yield levels ranged from 0.3 to 6.2 t/ha, with highly significant genetic variation in low-yielding environments. Associations of yield with barley genomic regions were then detected by combining the DArT marker data with the yield data in mixed model analyses for the individual trials, followed by multiple regression of yield on markers to identify a multi-locus subset of significant markers/QTLs. QTLs exhibiting a pre-defined consistency across environments were detected in bins 4, 6, 6 and 7 on barley chromosomes 3H, 4H, 5H and 7H respectivel

    Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry

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    Background: The rotational activation created by spiral waves may be a mechanism for atrial fibrillation (AF), yet it is unclear how activation patterns obtained from endocardial baskets are influenced by the 3D geometric curvature of the atrium or 'unfolding' into 2D maps. We develop algorithms that can visualize spiral waves and their tip locations on curved atrial geometries. We use these algorithms to quantify differences in AF maps and spiral tip locations between 3D basket reconstructions, projection onto 3D anatomical shells and unfolded 2D surfaces. Methods: We tested our algorithms in N = 20 patients in whom AF was recorded from 64-pole baskets (Abbott, CA). Phase maps were generated by non-proprietary software to identify the tips of spiral waves, indicated by phase singularities. The number and density of spiral tips were compared in patient-specific 3D shells constructed from the basket, as well as 3D maps from clinical electroanatomic mapping systems and 2D maps. Results: Patients (59.4±12.7 yrs, 60% M) showed 1.7±0.8 phase singularities/patient, in whom ablation terminated AF in 11/20 patients (55%). There was no difference in the location of phase singularities, between 3D curved surfaces and 2D unfolded surfaces, with a median correlation coefficient between phase singularity density maps of 0.985 (0.978-0.990). No significant impact was noted by phase singularities location in more curved regions or relative to the basket location (p>0.1). Conclusions: AF maps and phase singularities mapped by endocardial baskets are qualitatively and quantitatively similar whether calculated by 3D phase maps on patient-specific curved atrial geometries or in 2D. Phase maps on patient-specific geometries may be easier to interpret relative to critical structures for ablation planning

    Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

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    Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. Objective: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. Methods: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. Results: DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p 15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). Conclusions: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF

    Comparative performance of the stable isotope signatures of carbon, nitrogen and oxygen in assessing early vigour and grain yield in durum wheat

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    The present paper studied the performance of the stable isotope signatures of carbon (δ13C), nitrogen (δ15N) and oxygen (δ18O) in plants when used to assess early vigour and grain yield (GY) in durum wheat growing under mild and moderate Mediterranean stress conditions. A collection of 114 recombinant inbred lines was grown under rainfed (RF) and supplementary irrigation (IR) conditions. Broad sense heritabilities (H2) for GY and harvest index (HI) were higher under RF conditions than under IR. Broad sense heritabilities for δ13C were always above 0·60, regardless of the plant part studied, with similar values for IR and RF trials. Some of the largest genetic correlations with GY were those shown by the δ13C content of the flag leaf blade and mature grains. Under both water treatments, mature grains showed the highest negative correlations between δ13C and GY across genotypes. Flag leaf δ13C was negatively correlated with GY only under RF conditions. The δ13C in seedlings was negatively correlated, under IR conditions only, with GY but also with early vigour. The sources of variation in early vigour were studied by stepwise analysis using the stable isotope signatures measured in seedlings. The δ13C was able to explain almost 0·20 of this variation under RF, but up to 0·30 under IR. In addition, nitrogen concentration in seedlings accounted for another 0·05 of variation, increasing the amount explained to 0·35. The sources of variation in GY were also studied through stable isotope signatures and biomass of different plant parts: δ13C was always the first parameter to appear in the models for both water conditions, explaining c. 0·20 of the variation. The second parameter (δ15N or N concentration of grain, or biomass at maturity) depended on the water conditions and the plant tissue being analysed. Oxygen isotope composition (δ18O) was only able to explain a small amount of the variation in GY. In this regard, despite the known and previously described value of δ13C as a tool in breeding, δ15N is confirmed as an additional tool in the present study. Oxygen isotope composition does not seem to offer any potential, at least under the conditions of the present study
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