Since non-market activity does not have a clear criterion to distinguish work from non-work, nor necessary from non-necessary social labor, an arbitrary element seems to creep in that makes standards of fairness difficult to apply to gendered household bargains between men and women dividing up waged and non-waged work.
This makes standard protein structure assessment inexplicably difficult, however a few members of class A GPCR had their structure solved.
It has been argued that patient volume, limited funding, the shortage of health care workers, a generally overburdened public health care system and other factors make these standards difficult to meet in resource-limited settings like South Africa [ 5, 15- 20].
Often, the seabed habitat at such sites is variable, e.g. a mix of rocky ledges, boulders and soft sediment patches, particularly at high energy tidal stream and wave sites, making standard monitoring methodology difficult to implement.
The proposed Lgr5 ligands, Rspondin [ 29– 31] and Norrin [ 32], do not activate classical G protein or ß-arrestin dependent signaling pathways, making standard screening assays difficult.
A strength of simulations is that intervention effects can be estimated in the context of differing scenarios (e.g., whether or not patients misreport their adherence barriers, making standard tailoring more difficult) as well as in the context of random variability in key parameters, such as patients' daily adherence behavior.
The lack of biomarkers of exposure for many contaminants makes setting scientific standards difficult.
However, all the studies were homogenous in terms of methodology, all of them being retrospective, single-center with small-sample size cohorts including many case series and case reports, making standard scale or checklists difficult to apply.
In addition, many tools make use of uneven coverage as a tool to detect copy number variants or duplicated regions making standard methods to discover SNPs difficult to use as the uneven coverage and allele specific gene expression will cause deviations from these expectations.
Similarly, the lack of standards makes it difficult to compare data among different studies.
