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A new fuzzy learning method of the rule modifier is developed, where the modification value of each rule is based on the fuzzy firing weight.
The modification value of each rule is based on the fuzzy firing weight, so that the learning of the rule bases is reasonable.
A modification value corresponds to the expected change in Chi square value (model fit) if certain parameter restrictions (e.g., uncorrelated measurement errors) are relaxed.
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Motifs with a modification quality value >50 are considered.
The ζ-potential of FITC-modified MSN was slightly positive (+3.89 mV), and after APTES modification, this value further increased to +22.7 mV, as expected.
Looking at this formula, it is intuitive that the notion of interaction is equivalent to one of the possible definitions of the notion of effect modification: the value of x1 modifies the effect of x2 and vice versa.
More specifically, the term hemicellulose synthesis was highly significantly enriched (p-value < 3.6e-17) as was the term cell wall modification (p-value < 6.4e-12; Additional file 9A).
However, we found a negative trend between number of older siblings and ALL diagnosed at age 0 4 years (OR per sibling 0.85, 95% CI 0.68 1.06; p = 0.141) and a positive trend for ALL diagnosed at age 5 9 (OR 1.34, 95% CI 1.05 1.72; p = 0.019), with some evidence for an effect modification (p-value for interaction = 0.040).
(B) Histogram of modification quality values for all bases.
We found no significant seasonal effect modification (p-value = 0.71).
(A) Scatter plot of modification quality values and per-strand coverage of 101,019 bases detected as methylated in the C. coli BFR-CA-9557 BFR-CA-9557 BFR-CA-9557
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com