Some of the results appear to be based on a very small number of cells – Figure 2H has fewer than ten events for each group.
9) It seems incredibly surprising that the additional expression of LIN-28 GFP LIN-28 GFPtant backgrounds (increased expression in a very small number of cells; Figure 8B) reflects the dramutantdifferences in the expression of the endogenous LIN-28 at a similar time point/developmental stage describackgroundsexperincreasedtlinexpressionre 8A.
However, using the microarray assay described here, the antibody response to un-glycosylated MUC1 was very weak and only seen in a very small number of patients (see Figure 3A).
Discrimination (AUCs) for LR2 was consistent in both oncology and non-oncology centres with a few exceptions for centres that enrolled a very small number of cases (Supplementary Figure S2).
In regime I where μ A is typically very small (Figure 1(A) and (D)), there is one or more 'mainstream' opinion groups near x ∼ 1 / 2 with a majority of the agents in the system, and a number of smaller, 'extremist' opinion groups at positions closer to the boundaries x = 0, 1.
We chose a marker with a rectangular border to be able to easily detect and localize the marker and chose a 2D barcode as its identity is detectable even when the marker is very small (Figure 4). Figure 4 A marker; ID=4+1024+16384=17412.
The experimental field for this assay was Inabu, which is a high-pressure condition for blast disease, especially panicle blast (Fujii and Hayano-Saito [2007]), in which the difference in parental disease severity was very small (Figure 3C).
This is because: (i) the power factor S 2 GT is not very small (Figure 4) in this case, and (ii) the ratio of κ ph/κ e can be of the order of 1.
Due to the fact that the blue glaze was diffused in the white glaze and its thickness was very small (Figure 2b), it was not practically possible to remove any sample from the blue glaze without contaminating the sample with the white substrate glaze.
On the other hand, the errors generated from the approximate algorithms are very small (Figure 8(b)) - they are kept at the level of 1% for the density-based algorithm and go down to about 0.05% for the algorithm that considers spatio-temporal locality.
