Spatial variance in the 12-lead ECG and mechanical dyssynchrony

INTRODUCTION

Electrical transmission disorders have a deleterious effect on cardiac depolarization, resulting in a disorganized ventricular contraction that reduces global mechanical efficiency; this mechanical dyssynchrony can be corrected by cardiac resynchronization therapy. However, despite adjustments in the electrical criteria selection of QRS for the recognition of mechanical dyssynchrony, a significant proportion of patients do not currently respond to this therapy.

PURPOSE

To find if a new predictor of dyssynchrony, the electrocardiogram spatial variance, is a better marker of mechanical dyssynchrony than QRS duration.

METHODS

Forty-seven electrocardiograms and 47 strain (2D speckle tracking) echocardiograms were prospectively collected simultaneously in consecutive, non-selected patients; the left ventricular mechanical dispersion was measured in all the cases. The electrocardiographic analysis of variance was made with a digital superposition of the electrocardiographic leads and generates different indexes of variance of both QRS complex and repolarization phase.

RESULTS

ROC analysis probed that the best area under the curve (AUC) value correlated with left ventricular mechanical dispersion and was obtained combining several spatial variance markers (considering depolarization and repolarization spatial variance together; AUC = 0.97); the same analysis using the QRS duration versus mechanical dispersion showed a significantly lower AUC value (AUC = 0.64).

CONCLUSION

Spatial variance combining depolarization and repolarization markers is a superior predictor of left ventricular mechanical dispersion than QRS duration.

Eliminating redundant spatial variation to better understand the variance of interest of soil potentially toxic elements at different sampling scales in different soil types south of Nanjing, China

Sampling scale and prediction of spatial distribution are essential in surveys of soil metal pollution. Sufficient sampling density encompassing the principal spatial sources of variance and prediction of polluted areas with the help of soil maps makes pollution evaluation more reliable and subsequent soil remediation assessment more efficient. Two soil sampling schemes, using 232 points at 2-km intervals in 2002 for sampling at county scale and 109 points at 200-1000-m intervals in 2012 at town scale, were used to study the potentially toxic metals Cu, Cd, Cr, Hg, Ni, Pb, Zn, and the metalloid As in an urban-rural hinge area. We focused on finding characteristics of the explanatory power of soil type toward different sampling scales from 200 to 2000 m, a routine sampling scale in practice for remediation of soil potentially toxic elements (PTEs). We also attempted to eliminate the redundant spatial variation to better understand the variance of soil PTEs. Spatial variation of PTEs at different scales was compared and estimated using soil map units based on geostatistical methods. The explanatory power of the soil map units selected at different scales was significantly different at P < 0.01 and the smaller scales better explained the spatial variance. Anthropic activities profoundly affected the contents of PTEs in soils and the amounts of anthropogenic pollutants released often exceed the contribution from natural sources. Variances of interest of Cr and Cu were underestimated by 72.4 and 32.8%, respectively, due to soil type as a factor but were overestimated for other elements by percentages following the sequence Zn (45.4%) > Hg (28.6%) > Pb (28.8%) > Ni (26.73%) > As (13.7%) > Cd (10.5%). Eliminating variances of zero interest would be helpful in increasing the effectiveness of remediation of metal-contaminated soils.

Spatial variance of POPs and heavy metals in transformer oil-contaminated soil around Tamil Nadu

The persistent organic pollutants in the environment are one of the global issues to their unregulated disposal and informal recycling. This study investigates the contamination of soil with polychlorinated biphenyls (PCBs), polyaromatic hydrocarbons (PAHs), phenolic compounds and heavy metals via spillage of transformer oil (TO). Fresh TO (FTO), used TO (UTO) and soil samples were analysed using GC-MS to confirm the presence of 8 PCB congeners, 16 PAHs and 24 types of phenolic compounds and using inductively coupled plasma optical emission spectrometry to confirm the presence of 7 heavy metals. The chromatographic analysis revealed the levels of mono-, di-, tri-, tetra-, penta-, hexa-, hepta- and octachlorobiphenyls in FTO to be 5.63, 25.24, 0.195, 0.185, 2.169, 1.023 and 5.28 mg/L and the level of mono-, di-, tri-, tetra-, penta- and hexachlorobiphenyls in UTO to be 0.27, 1.21, 1.31, 0.80, 1.77 and 3.90 mg/L. Analysis of soil from 10 different TO-contaminated sites showed the presence of PCBs, PAHs, phenolic compounds and heavy metals in the concentration range of 0.53-42.87 mg/kg, 3.19-246.6 μg/kg, 0.01-4086.45 μg/kg and 1.0-401.3 mg/kg, respectively. The variation in the concentration of these compounds and heavy metals among different sampling sites was determined using principal component analysis (PCA), metric multi-dimensional scaling (MDS) and Bray-Curtis cluster analysis (Bu-CA). The toxicity equivalence factor and the mechanism involved in the disruption of endocrine system are discussed. Thus, this study exemplifies the need for complete ban of PCB-containing TOs in developing countries and urges the need for technology for the disposal of TO.

Characterizing scale-specific environmental factors affecting soil organic carbon along two landscape transects

Soil organic carbon (SOC) is one of the most important soil properties affecting many other soil and environmental properties and processes. In order to understand and manage SOC effectively, it is important to identify the scale-specific main factors affecting SOC distributions, which in this study occurred in a watershed on the Loess Plateau. Two transects were selected that passed along the upper slopes on each side of the main gully of the Liudaogou watershed. Transect 1 (3411-m length) had 27 sampling sites at 131-m intervals; transect 2 (3597 m length) had 30 sampling sites at 124-m intervals. The two transects were chosen in order to compare landscape patterns of differing complexity that were in close proximity, which reduced the effects of factors that would be caused by different locations. The landscape of transect 1 was more complex due to the greater diversity in cultivation. Multivariate empirical mode decomposition (MEMD) decomposed the total variation in SOC and five selected environmental factors into four intrinsic mode functions (IMFs) and a residual according to the scale of occurrence. Scale-specific correlation analysis was used to identify significant relationships between SOC and the environmental factors. The dominant scales were those that were the largest contributors to the total SOC variance; for transect 1, this was the IMF 1 (scale of 403 m), whereas for transect 2, it was the medium scale of the IMF 2 (scale of 688 m). For both transects, vegetation properties (vegetation cover and aboveground biomass) were the main factors affecting SOC distributions at their respective dominant scales. At each scale, the main effective factors could be identified although at the larger scales, their contributions to the overall variance were almost negligible. The distributions of SOC and the factors affecting it were found to be scale dependent. The results of this study highlighted the suitability of the MEMD method in revealing the main scale-specific factors that affect SOC distributions, which is necessary in understanding and managing this important soil property.