When the uniseqs were queried against the nr protein database, about 76.2% of them had no significant homology with proteins of known functions, which may be related to the difficulty of functional characterization of genes in rust fungi.
However, some difficulties of micelle characterization by ITC still remain; most micelles have aggregation numbers which are too high to be measured by ITC and even when n is sufficiently low, dilution enthalpies can obscure determination.
These relationships are inconsistent with our molecular data, which strongly support close affinities of P. persidis with the genus Acanthobrama, also pointing on the difficulties of morphological characterization of miniaturized cyprinids [ 23, 63].
Apart from this, histological data show that up to 45%% of nodal metastases in RC are ≤4 mm, which increases the difficulty of the accurate characterization of LNs.
Compared with genetic study of T2D, the progress of genetic study of DN has been slowed down mainly due to the difficulty of collection and characterization of subjects [ 31].
These pitfalls are most often due to difficulty in characterization of nodes that have intermediate levels of uptake of the contrast rather than complete uptake (i.e. a black node) or non-uptake (i.e. a white node).
Although this version of reductionism is no longer widely accepted, primarily because of the difficulty of giving a satisfactory characterization of the distinction between theoretical and observational statements in science, the question of the reducibility of one science to another remains controversial.
This could be due to the difficulty of performing in situ characterization on NPs embedded within the active layer.
Furthermore, stepwise solid phase peptide synthesis (SPPS) of branching peptides such as MAPs commonly encounters the difficulty of purification to homogeneity and characterization of the final products.
Other approaches using high throughput metabolite analysis focus on a subset of useful information while avoiding the difficulties of comprehensive metabolite characterization; metabolic fingerprinting uses signals from hundreds to thousands of metabolites for rapid sample classification via statistical analysis [ 52].
