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We developed custom software (Rabbit) for assembling sequences with large overlaps (>2 kb).
The identification of binary sequences with large merit factor (small mean-squared aperiodic autocorrelation) is an old problem of complex analysis and combinatorial optimization, with practical importance in digital communications engineering and condensed matter physics.
In particular, the proposed method based on particle swarm optimization is not only much faster, but also remarkably more accurate (about 2 dB higher in terms of the Peak Signal-to-Noise-Ratio) than the competing methods on video sequences with large motion.
Therefore, it should predict well the quality of the loss-affected sequences with large occurrence probability.
For sequences with large smooth texture areas like "PeopleOnStreet", the algorithm saves more than 20%% coding time.
For sequences with large motion, such as Soccer, the proposed method improves the objective quality (up to 1.0 dB), compared with TDWZ.
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We found that use of candidate genomic sequences with larger numbers of uninterrupted tandem repeats and use of overlapping BAC sequences with alleles which differed by two or more repeats led to higher rates of STRP inclusion into the Screening Sets.
This is also a significant improvement compared to the artifact reduction accomplished by a T1-FSE sequence with large bandwidth (−62%).
However, the estimation is normally coarse due to the lack of the other part of information, especially for video sequence with large motion.
In particular, as a test sequence with large global motion area, sequence "RaceHorses" has a huge amount of movement information, so the small size CU in this sequence also accounts for nearly 0.90 at RA condition.
First, we postulated that a cleavage sequence with large KM and moderate kcat might both decrease background and increase fold induction.
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