Inference of peak density of indirect branches to detect ROP attacks

Author(s):  
Mateus Tymburibá ◽  
Rubens E. A. Moreira ◽  
Fernando Magno Quintão Pereira
Keyword(s):  
2003 ◽  
Vol 49 (11) ◽  
pp. 1865-1872 ◽  
Author(s):  
Ian G Davies ◽  
John M Graham ◽  
Bruce A Griffin

Abstract Background: A predominance of small, dense LDL (sdLDL) confers in excess of a threefold increase in coronary heart disease (CHD) risk. The conventional method for the detection of sdLDL, salt density gradient ultracentrifugation (DGUC) has been superseded by more rapid techniques. This report presents novel methodology for the separation of sdLDL by a combination of iodixanol density gradient centrifugation and digital photography. Methods: LDL subclasses were separated in 3 h from prestained plasma on a self-forming density gradient of iodixanol. LDL subclass profiles were generated by digital photography and gel-scan software. Plasma samples from 106 normo- and dyslipidemic individuals were used to optimize the gradient for the resolution of LDL heterogeneity. A subgroup of 47 LDL profiles were then compared with LDL subclasses separated by salt DGUC. Results: The peak density of the predominant LDL band correlated significantly with the relative abundance (as a percentage) of sdLDL as resolved by salt DGUC (P <0.001). As shown previously, LDL isolated at a lighter density in iodixanol compared with salt gradients. A predominance of sdLDL corresponded to a peak density on iodixanol of 1.028 kg/L. This density and the area under the LDL profile lying above this density were sensitive and specific markers for the prediction of a predominance of sdLDL (P <0.001) and showed predictable associations with plasma triglycerides (r = 0.59; P <0.001) and HDL (r = −0.4; P <0.001). Conclusions: This simple method for the detection of sdLDL can differentiate a predominance of sdLDL, is highly reproducible, and can be used preparatively to isolate sdLDL.


2014 ◽  
Vol 32 (5) ◽  
pp. 571-580 ◽  
Author(s):  
C.-C. Lee

Abstract. This study aims to assess the predictability of IRI-2012 on the equatorial F1 layer during solar minimum. The observed characteristics of F1 layer by the Jicamarca digisonde are compared with the model outputs. The results show that the time range for F1-layer appearance of observation is longer than that of IRI-2012, by at least 1 h in the early morning and later afternoon. In IRI-2012, there are three options for the occurrence probability of F1 layer: IRI-95, Scotto-97 no L, and Scotto-97 with L options. The first option predicts the probability well, but the last two underestimate the probability. The peak density of F1 layer (NmF1) of observation is very close to that of IRI-2012. For the F1 peak height (hmF1), the modeled values are smaller than the observed ones. The observed seasonal variation of hmF1 is not found in the modeled results. Nevertheless, the observed diurnal variation of hmF1 is similar to the modeled results with the B0 choices of Bil-2000 and ABT-2009. Regarding the shape parameter, the values of D1 (the shape parameter of F1 layer in observation) are much greater than the values of C1 (the shape parameter of F1 layer in IRI-2012). The D1 values are 3–6 times the C1 values. The diurnal variation of D1 is similar to that of C1, but the seasonal variation of D1 is not.


Author(s):  
Jeremy Stromer ◽  
Leila Ladani

Peak density is an ultrasound measurement, which has been found to vary according to microstructure, and is defined as the number of local extrema within the resulting power spectrum of an ultrasound measurement. However, the physical factors which influence peak density are not fully understood. This work studies the microstructural characteristics which affect peak density through experimental, computationa,l and analytical means for high-frequency ultrasound of 22–41 MHz. Experiments are conducted using gelatin-based phantoms with glass microsphere scatterers with diameters of 5, 9, 34, and 69 μm and number densities of 1, 25, 50, 75, and 100 mm−3. The experiments show the peak density to vary according to the configuration. For example, for phantoms with a number density of 50 mm−3, the peak density has values of 3, 5, 9, and 12 for each sphere diameter. Finite element simulations are developed and analytical methods are discussed to investigate the underlying physics. Simulated results showed similar trends in the response to microstructure as the experiment. When comparing scattering cross section, peak density was found to vary similarly, implying a correlation between the total scattering and the peak density. Peak density and total scattering increased predominately with increased particle size but increased with scatterer number as well. Simulations comparing glass and polystyrene scatterers showed dependence on the material properties. Twenty-four of the 56 test cases showed peak density to be statistically different between the materials. These values behaved analogously to the scattering cross section.


1980 ◽  
Vol 58 (4) ◽  
pp. 623-625 ◽  
Author(s):  
Terry D. Beacham

A 2-year livetrapping study on Townsend's vole (Microtus townsendii) on Reifel Island in the Fraser River delta in British Columbia, Canada, showed that there was an early stop to summer breeding in the peak phase summer compared with the increasing phase summer. Selective dispersal and death of early-maturing voles may account for this result. A delay occurred in the onset of breeding in the decline phase. Voles in peak density populations had the highest median weights at sexual maturity, and males matured at heavier weights than did females.


Ecography ◽  
1998 ◽  
Vol 21 (2) ◽  
pp. 135-139 ◽  
Author(s):  
Juha Laakkonen ◽  
Antti Oksanen ◽  
Timo Soveri ◽  
Heikki Henttonen

2010 ◽  
Vol 31 (5) ◽  
pp. 509-530 ◽  
Author(s):  
M. T. A. H. Muella ◽  
E. R. de Paula ◽  
P. R. Fagundes ◽  
J. A. Bittencourt ◽  
Y. Sahai

1997 ◽  
Vol 243 (4) ◽  
pp. 831-835 ◽  
Author(s):  
H. Steen ◽  
J. C. Holst ◽  
T. Solhøy ◽  
M. Bjerga ◽  
E. Klaussen ◽  
...  

1978 ◽  
Vol 20 (4) ◽  
pp. 221-227 ◽  
Author(s):  
D. J. Whitehouse

The effects of quantization, sampling and the numerical model on the digital assessment of peak density, peak height distribution and peak curvatures are investigated. It is shown that there are practical limits which should be adhered to in order to avoid erroneous results.


2015 ◽  
Vol 85 (4) ◽  
pp. 217-232 ◽  
Author(s):  
Eduardo Garza-Gisholt ◽  
Ryan M. Kempster ◽  
Nathan S. Hart ◽  
Shaun P. Collin

The eyes of five ray species (Taeniura lymma, Neotrygon kuhlii, Pastinachus atrus, Himantura uarnak and Urogymnus asperrimus) from the same taxonomic family (Dasyatidae) and the same geographic region (Ningaloo Reef, Western Australia) were studied to identify differences in retinal specializations that may reflect niche specialization. The topographic distributions of photoreceptors (rods and all cones) and ganglion cells were assessed and used to identify localized peaks in cell densities that indicate specializations for acute vision. These data were also used to calculate summation ratios of photoreceptors to ganglion cells in each species and estimate the anatomical spatial resolving power of the eye. Subtle differences in the distribution of retinal neurons appear to be related to the ecology of these closely related species of stingrays. The main specialization in the retinal cell density distribution is the dorsal streak that allows these animals to scan the substrate for potential prey. The blue-spotted fantail ray, T. lymma, showed the highest peak density of rods (86,700 rods mm-2) suggesting a specialization for scotopic vision. The highest peak density of cones (9,970 cones mm-2) was found in H. uarnak, and the highest peak density of ganglion cells (4,500 cells mm-2) was found in P. atrus. The proportion of rods to cones in the dorsal streak was higher in the two smaller species (12.5-14:1 in T. lymma and N. kuhlii) than the larger stingrays (6-8:1 in P. atrus, H. uarnak and U. asperrimus). Visual specializations in different sympatric species are subtle but may reflect specializations to specific ecological niches.


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