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In our approach, motion intention estimation solves these problems, and previous rejection of data higher than a threshold provides additional robustness.
Excluding data from 2004, which represented only six months of data, higher case loads and positivity rates were observed in 2005, 2008, 2009 and 2012 in Alberta.
NGS offers methodological advantages such as increased throughput, decreased RNA input, consistency and quality of data, higher detection depth, analysis of all RNA populations, and discovery of novel molecules.
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They are composed of large volumes of data, high speed data production and information with a high data variability.
They involve the collection of more than 100 terabytes of data; high-speed, real-time streaming of data; or projects that may start with smaller data sets but the data is growing by 60 percent or more a year.
In order to preserve the intrinsic structure of data, higher-order tensor analysis method was applied to feature extraction.
The specific characteristics of this kind of data (high-transmission rate with limited bandwidth) make standard encryption algorithms inadequate.
These inconsistencies were apparently owing to poor quality of methodology, weakness of data, high levels of heterogeneity and publication bias.
Interestingly, and importantly, Problems 2 and 3 are both more acute with large amounts of data (high S, L, K).
Taking into account this amount of data, high-throughput tools using the SSU rRNA biomarker such as phylogenetic oligonucleotide arrays (POAs) have been developed.
Following cleaning of data, high quality reads were obtained for control and Cd-treated leaf and root samples (Additional file 2: Table S2).
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CEO of Professional Science Editing for Scientists @ prosciediting.com