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Two types of corrections for multiple testing (simple and robust) were applied [ 7].
One concern on PacBio sequencing is its relatively high error rate, but this can be effectively improved through implementing two types of corrections, i.e., by constructing consensus sequence reads from raw PacBio subreads and by alignment with the reads generated from appropriate SGS platforms [ 13– 15].
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These two types of correction are commonly performed using SPM and FSL.
In this section, we discuss three types of corrections that can be easily visually annotated using SegAnnDB.
A group of eight presbyopic subjects wore in succession four types of correction.
This paper makes a clear distinction between two types of areal correction coefficients: one for correction of historical data at given time moments and one for rainfall at given return period levels.
Two types of error correction neuron were proposed to be required, false-positive and false-negative error correctors.
Two types of simulated extreme correction were performed: 1) complete coronal correction only (C method) and 2) complete coronal correction with complete derotation of vertebral bodies (C + D method).
Two types of multiple test corrections were performed.
We applied two types of widely used correction strategies, i.e., the Bonferroni correction which controls false positives but potentially increases false negatives and the false discovery rate (FDR) correction (at q = 0.05) which generates less false negatives at the cost of increased false positives than the Bonferroni correction.
For this purpose, four types of tropospheric corrections have been used: (i) microwave radiometer (MWR) observations, (ii) tropospheric corrections computed from meteorological models, (iii) GPS observations and (iv) synoptic station data.
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