(A) Auxiliary marking points to ensure complete and accurate seating of the prosthesis. by Long Li (6555)

Cinacalcet administered early in the inactive phase markedly decrease parathyroid Ki-67 index. by Søren Egstrand (10906087)

Accuracy chart of each model validation set. by Hongwei Bai (1447999)

Data sets for model creation and external validation. by Junichi Kushioka (12236447)

Accuracy chart of each model validation set. by Hongwei Bai (1447999)

Loss rate graph of each model validation set. by Hongwei Bai (1447999)

Loss rate graph of each model validation set. by Hongwei Bai (1447999)

Group-level narrow- and broad-band spectral changes after hemispherotomy reveal a marked EEG slowing of the isolated cortex, robust across patients. by Michele Angelo Colombo (22446342)

“…This decrease was larger in the disconnected than in the contralateral cortex. …”

Map of Pakistan with marked Punjab province. by Sumaira Sumaira (21453523)

Setting of experimental environment and model parameters. by Guozhu Sui (21672798)

Biases in larger populations. by Sander W. Keemink (21253563)

“…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …”

The parameters of gas exchange markedly decreased in maize <i>zmstomagen1/2</i> mutant compared with in wild type (WT). by Juan-Juan Xu (18590868)

PLB is a target gene of miRNA 221. by Hongyu Li (1332669)

Gene set enrichment analysis (GSEA) of the DEGs. by Wei He (131453)

Subjects:

Experimental validation. by Wan-Jing Zhen (20427813)

Subjects:

Validation results for the different scenario parameter sets and the resulting KGE_mean value. by Fabian Mitze (21084251)

The attraction force generated by the attractor set on the robot. by Long Di (9977453)

The update step size of the attractor set in different regions. by Long Di (9977453)

Expression of lipid biosynthetic genes is decreased with polyamine depletion. by Yazmin E. Cruz-Pulido (20285238)

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”