Although the rate of PDCoV infection detected in mainland China in this study was relatively low, the results may not accurately reflect the prevalence of PDCoV in mainland China because the tested samples were collected from only 4 provinces.
Among them, jackknife test is deemed the least arbitrary [ 13] because the test sample and training samples are always open.
Among these three methods, jackknife test is deemed to be the least arbitrary and can always provide a unique result for a given dataset and a given prediction model because both the training samples and the test samples are fixed [ 16].
Thus, our MDAhet classifier is not strictly speaking a single-sample predictor because the gene expression value of a test sample needs to be renormalised (a simple centering and scaling) across all the test samples in the same cohort, before classification is performed.
Because the normal testing samples were located nearby the normal population G _N, while the inner-race fault, outer-race fault, and ball fault testing samples were located far away.
Promising marker combinations are identified in the training sample, but more reliable FPR and TPR measurements are made in the test sample because it involves different data.
In some cases, the time difference between the arrival of a rupture front at two neighboring strain gauges could not be determined (e.g., the red and purple gauges in Figure 3D) because of the small size of the test sample and the limited sampling rate.
Obviously, contrasting with the MDs of the normal testing samples, the MDs of the slipper loose and valve plate wear testing samples were quite large, because the normal testing samples were located nearby the normal population G _N, while the fault testing samples were located far away, i.e., the slipper loose and valve plate wear samples were in an abnormal condition.
Another limitation was that the percentage of laboratory confirmation was low (< 5%%), because the purpose of testing samples from HFMD cases is to determine the predominant virus circulating in Wuhan, rather than to identify further patients with the disease.
More and more attention has been paid to the invariant texture analysis, because the training and testing samples generally have not identical or similar orientations, or are not acquired from the same viewpoint in many practical applications, which often has negative influences on texture analysis.
