Sentence examples for labeling sequence from inspiring English sources

A pseudo-continuous arterial spin labeling sequence was used to acquire BOLD and cerebral blood flow simultaneously in 19 healthy adults (12 F; 20 ± 2 years) during boxcar CO2 and O2 gas inhalation paradigms.

CRFs were introduced initially for solving the problem of labeling sequence data that arises in scientific fields such as bioinformatics and natural language processing.

CBF rates (ml/min/100 g) were measured from a single central slice of CBF map generated from an arterial spin labeling sequence.

Functional images were acquired using a Q2TIPS pulse arterial spin labeling sequence (Luh et al. 1999) with the following parameters: field of view = 192 mm × 192 mm, matrix size = 64 × 64, TR = 2 s, TE = 17 ms, TI1 = 700 ms, TI2 = 1400 ms, T1s = 1050 ms, flip angle = 72°, slice thickness = 7 mm, slice gap = 3 mm.

Illumina provides the WG-DASL® HT-12 v4 system for cDNA labeling, sequence-specific amplification, and hybridization.

By comparison of the labeled sequence length to the domain size, compaction ratios were estimated to 1 129 (p53), 1 235 (p58), and 1 396 (c-myc).

The mean diameters of the gene domains were determined to 103 nm (c-myc), 119 nm (p53), and 123 nm (p58) and did not correlate to the genomic, labeled sequence length.

The second reduction occurs when the label sequence is evaluated in time window of length.

We train a CRF by maximizing the log-likelihood of a label sequence in the training data.

For each input sentence, each of the models automatically generates a label sequence in BIO format, together with the confidence values with which the labels were assigned.

Given an item sequence, a CRF model predicts the most probable label sequence based on functions capturing characteristics of the current token and its context.