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Furthermore, with pre-selected SNPs, this kernel placed more weight on informative SNPs.
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Basically, it decreases the optimistic bias of the simple resubstitution error estimate (agreement-checking on the training data) by means of suitable bolstering probability density kernels placed at each training data point, producing a nearly-unbiased and low-variance estimator.
The results of our case control study suggest that consumption of contaminated maize kernels placed people in this region of Kenya at risk for life-threatening aflatoxicosis (case-fatality rate of 39%).
Each kernel is placed on the data points and normalized over the data to obtain the kernel estimation.
Assuming an input set of motion images {B 1, B 2, …, B t }, this filter operates in a similar fashion to a two-dimensional kernel density estimator: For every white pixel in every image, a two-dimensional Gaussian kernel is placed (centred) over the pixel and its surrounding neighbours.
A batch of ~10,000 seed (estimated by the average weight of a kernel) were placed in a 1L flask with 400 ml of ultrapure water and soaked overnight (~14 18 hours) at room temperature.
The remaining kernels were placed in paper bags, dried at 105 °C for 1 h, and then dried at 80 °C until constant weight.
Twenty fresh kernels were placed into 5 mL buffer solution (50 mmol L−1 Hepes NaOH, pH 7.5; 8 mmol L−1 MgCl2, 2 mmol L−1 EDTA2, 10 g L−1 PVP-30 and 1 mmol L−1 DTT).
Gaussian kernels were placed around a set of discrete labelled points to estimate the variations in density throughout the region.
Thereafter, the kernels were placed and incubated for 5 d under the same temperature and light regime as described earlier.
Germination paper was dampened with water and the fifteen kernels were placed at the top of the paper.
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