Rat anti Mouse SELE monoclonal antibody, clone 07520

This meta-analysis was undertaken in an attempt to understand the relationships of functional polymorphisms in the SELE and SELP genes to myocardial infarction (MI) risk. The PubMed, CISCOM, CINAHL, Web of Science, Google Scholar, EBSCO, Cochrane Library, and CBM databases were searched for relevant articles published before March 1st, 2013 without any language restrictions. Meta-analysis was conducted using the STATA 12.0 software. Crude odds ratios with 95 % confidence intervals were calculated. The effect of SELE and SELP genetic polymorphisms on the pathogenesis of MI was investigated in this meta-analysis with a total of ten case-control studies, including 2,696 MI patients and 4,724 healthy subjects. Eight single nucleotide polymorphisms were assessed, including polymorphisms 98G/T, 128S/R and 561A/C in the SELE gene, and polymorphisms 715T/P, 599V/L, 290S/N, 562N/D and 2123G/C in the SELP gene. The results of our meta-analysis suggested that SELE genetic polymorphisms might be correlated with an increased risk of MI, especially for 128S/R and 561A/C polymorphisms. A subgroup analysis by ethnicity was conducted to investigate its effects on susceptibility to MI. The results revealed positive significant correlations between SELE genetic polymorphisms and the risk of MI among Asians, but not among Caucasians (all P > 0.05). Nevertheless, no significantly correlations were found between SELP genetic polymorphisms and MI risk (all P > 0.05). In the subgroup analysis by ethnicity, we also did not observe significant associations between SELP genetic polymorphisms and MI risks among both Asians and Caucasians (all P > 0.05). The current meta-analysis suggests that SELE genetic polymorphisms may contribute to the development of MI, especially for the 128S/R and 561A/C polymorphisms among Asians. However, SELP genetic polymorphisms may not be important risk factors in MI.

In techniques such as Dynamic Light Scattering (DLS), Fluorescence Correlation Spectroscopy, and image mining, motion is tracked by the autocorrelation of a signal over logarithmic time scales. For instance the tracking signal in DLS is the scattered light intensity; it remains correlated at time scales where scant changes in the arrangement of the scattering particles occur, but decays exponentially at the time scales of their diffusion. When there are multiple time scales of motion (for instance due to scatterers of different sizes), the correlation curve has more than one exponential fall. Extracting the decay constants or hydrodynamic sizes due to each exponential fall in a multi-species field correlation curve becomes an ill-conditioned mathematical problem. We describe a new algorithm to invert a multi-modal correlation curve by Sequential Extraction of the Late Exponentials (SELE). The idea is that while the inversion of a multi-exponential equation may be ill posed, that of a single exponential is not. So we fit data windows towards to base of the correlation curve to extract the largest contribution species, remove the species contribution from the correlation curve, and repeat the process with the remnant curve. The single exponent can be robustly fitted by least-square minimization with initial guesses generated by an adapted cumutant technique (power-series) that includes stretch coefficients (measure of sample dispersity). The proposed algorithm resolves particle sizes separated by 3X, and is reliable against fluctuations in the correlation curve and to localized regions of suboptimal data. The algorithm can be used to track particle dynamics in solution in multi-species problems such as self-assembly.