Initially, all radiocarbon data are used (hereafter Model A) and the model displays high-precision dates (Table 3; Fig. 5; Additional file 3: Figure S3).
Using such a platform, we verified tumor-targeted delivery of MSCs by in vivo multimodal imaging in an orthotopic U87MG glioblastoma model, displaying higher tumor uptake than particles without MSCs.
Both models display high confidence factors (~70%) and global QMEAN scoring functions of about - 4.5.
The CoMFA and CoMSIA models displayed high external predictability (rpred2 0.919 and 0.908) and good statistical robustness.
The developed CoMFA and CoMSIA models display high external predictability (r2pred of 0.954 and 0.935, respectively) and good statistical robustness.
Unique to our maximum difference approach is the ability to account for all (possibly exponential number of) Boolean models displaying high fit to the available data.
Females of all three models display higher Aβ load burden and plaque quantity as compare to the male counterparts.
SNP calling procedures were assessed with reference to Support Vector Regression with linear kernel (SVR-lin) and Ridge Regression BLUP, since these models displayed higher predictive accuracies than other genomic selection models in following analyses.
The new model displayed a higher oil saturation as compared to the old model, resulting in a higher stock tank oil in place (STOIP).
First, the prediction model displayed a higher power to explain the variance of PME compliance in the low SDRT group (56%) than in the high SDRT group (39%).
The linear model displayed a higher slope between 2014 and 2020 than the logarithmic model (Table 3).
