Toe pressure and toe brachial index are predictive of cardiovascular mortality regardless of the most diseased arterial segment in symptomatic lower-extremity artery disease-A retrospective cohort study.

OBJECTIVE: Although lower extremity arterial disease (LEAD) is most often multisegmental, the predominant disease location and risk factors differ between patients. Ankle-brachial index (ABI), toe-brachial index (TBI), and toe pressure (TP) are predictive of outcome in LEAD patients. Previously, we reported a classification method defining the most diseased arterial segment (MDAS); crural (CR), femoropopliteal (FP), or aortoiliac (AOI). Current study aimed to analyze the associations between MDAS, peripheral pressure measurements and cardiovascular mortality. MATERIALS AND METHODS: We reviewed retrospectively 729 consecutive LEAD patients (Rutherford 2-6) who underwent digital subtraction angiography between January, 2009 to August, 2011 and had standardized peripheral pressure measurements. RESULTS: In Cox Regression analyses, cardiovascular mortality was associated with MDAS and non-invasive pressure indices as follows; MDAS AOI, TP <30 mmHg (HR 3.00, 95% CI 1.13-7.99); MDAS FP, TP <30 mmHg (HR 2.31, 95% CI 1.36-3.94), TBI <0.25 (HR 3.20, 95% CI 1.34-7.63), ABI <0.25 (HR 5.45, 95% CI 1.56-19.0) and ≥1.30 (HR 6.71, 95% CI 1.89-23.8), and MDAS CR, TP <30 mmHg (HR 4.26, 95% CI 2.19-8.27), TBI <0.25 (HR 7.71, 95% CI 1.86-32.9), and ABI <0.25 (HR 2.59, 95% CI 1.15-5.85). CONCLUSIONS: Symptomatic LEAD appears to be multisegmental with severe infrapopliteal involvement. Because of this, TP and TBI are strongly predictive of cardiovascular mortality and they should be routinely measured despite the predominant disease location or clinical presentation.

Effects of heavy metal pollution on red wood ant (Formica s. str.) populations.

We studied the species composition, mound population densities, relative abundance and colony sizes of red wood ants along a well known air pollution gradient of a copper smelter in Southwest Finland. The dominant species, Formica aquilonia, was further studied for heavy metal (Al, Cu, Cd, Ni, Zn, As, Pb, Hg) levels and morphological characters (body mass, head width, labial gland disease) of workers. We found five species belonging to Formica s. str., and two of them showed changes in their relative abundance, which could not be explained by natural habitat differences. Nest mound volumes were 34% smaller in the polluted area, suggesting that smaller colonies can be maintained there. The heavy metal levels in F. aquilonia workers were higher in the polluted area for all metals, except Hg. The largest relative differences between the study areas (polluted/unpolluted) were found for As (4.1), Ni (2.4), Cu (2.1) and Pb (1.8). Morphological characters of workers were not related to the heavy metal levels. Our data showed that red wood ants can tolerate relatively high amounts of heavy metals and maintain reproducing colonies even in a heavily polluted area, but on the basis of smaller colony sizes, pollution stress may also cause trade-offs in reproduction.

Adaptive algorithm for blind separation from noisy time-varying mixtures.

This article addresses the problem of blind source separation from time-varying noisy mixtures using a state variable model and recursive estimation. An estimate of each source signal is produced real time at the arrival of new observed mixture vector. The goal is to perform the separation and attenuate noise simultaneously, as well as to adapt to changes that occur in the mixing system. The observed data are projected along the eigenvectors in signal subspace. The subspace is tracked real time. Source signals are modeled using low-order AR (autoregressive) models, and noise is attenuated by trading off between the model and the information provided by measurements. The type of zero-memory nonlinearity needed in separation is determined on-line. Predictor-corrector filter structures are proposed, and their performance is investigated in simulation using biomedical and communications signals at different noise levels and a time-varying mixing system. In quantitative comparison to other widely used methods, significant improvement in output signal-to-noise ratio is achieved.

Nonlinear filtering of multivariate images under robust error criterion.

A class of nonlinear filters for multivariate data is introduced. A robust error criterion is minimized. Approximate algorithms for computing the filter output are developed. A polynomial signal model is used in applications where the signal amplitude has to be retained with high fidelity. Simulated data and RGB color image data are used in experiments.

A robust nonlinear filter for image restoration.

A class of nonlinear regression filters based on robust estimation theory is introduced. The goal of the filtering is to recover a high-quality image from degraded observations. Models for desired image structures and contaminating processes are employed, but deviations from strict assumptions are allowed since the assumptions on signal and noise are typically only approximately true. The robustness of filters is usually addressed only in a distributional sense, i.e., the actual error distribution deviates from the nominal one. In this paper, the robustness is considered in a broad sense since the outliers may also be due to inappropriate signal model, or there may be more than one statistical population present in the processing window, causing biased estimates. Two filtering algorithms minimizing a least trimmed squares criterion are provided. The design of the filters is simple since no scale parameters or context-dependent threshold values are required. Experimental results using both real and simulated data are presented. The filters effectively attenuate both impulsive and nonimpulsive noise while recovering the signal structure and preserving interesting details.