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Cells reproduce and divide using an ordered set of processes.
In a finite population setting, this paper considers a partially rank ordered set (PROS) sampling design.
Additional file 1: Appendix F provides sample content of an order set.
Such fields are spatially non-bandlimited, thus require an extremely dense set of samples in order to achieve any useful reconstruction of the field using classical linear reconstruction techniques.
In order to reduce artefacts a set of samples that are similar with respect to developmental features (segment count) was chosen (Table 1).
The proposed framework utilizes different strategies and approximates iteratively the hidden target distribution as a set of samples in order to learn a specialized classifier.
In order to verify whether the differential expression of these miRNAs was not limited to the training set of samples, we have also analyzed a validation set of samples, including 7 complete responders and 5 incomplete responders.
In order to find independent confirmation of polyphyly and to narrow our interpretations, we used a second, independent marker, kaiC, from a more limited set of samples.
A set of samples were prepared with different laser processing parameters and compositions of the precursor in order to study the effect of Al on the coating.
Also an mRNA array was performed on the same set of samples in order to determine if cell-type specific miRNAs downregulated the expression levels of their target genes.
The D-optimal design approach iteratively updates a set of sample configurations in order to increase the determinant |H T ·H|, i.e., minimize the log-determinant of noise variance matrix in (15) to find optimal sample configurations.
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CEO of Professional Science Editing for Scientists @ prosciediting.com