The summary of the specifications of the cell culture experiments used to estimate the model’s free parameters.

(DOCX)</p

Fits of the model calibrated by C1-5 and C2 to the empirical data of study 2.

Bars indicate the simulations (S-) and the corresponding empirical data (E-) for increasing Mg2+ ion concentrations. The quantities of ALP and OC are reported at day 7 and 21, repectively. The error bars on the empirical data shows the standard deviations. The error bars on the simulation results show the standard deviations obtained during SSIP, i.e. 15% alteration in the estimated parameter values. Stars indicate the statistically significant differences between values given for the empirical data compared to the control, i.e. Mg2+ ion concentration of 0.8 mM (p < 0.05 = *). is the average fitness value of the simulations for the given measurement item.</p

Fits of the model calibrated by C1-5 and C3 to the empirical data of study 3 for the case of IL-10.

Bars indicate the simulations (S) and the corresponding empirical data (E). The quantities of ALP and ARS are reported at day 14 and 21, respectively. The error bars on the empirical data shows the standard deviations. The error bars on the simulation results show the standard deviations obtained during SSIP, i.e. 15% alteration in the estimated parameter values. Stars indicate the statistically significant differences between values given for the empirical data compared to the control, i.e. the applied concentration of 0 ng/ml (p < 0.05 = *). is the average fitness value of the simulations for the given measurement item.</p

The list of the free parameters in the present model, and the inferred values during different calibration schemes.

Those marked by ‘-‘were not inferred during that particular calibrations scenario. (DOCX)</p

Fig 8 -

(A) Dispersity of the inferred values obtained during different runs of C1-5. In total, the calibration process is repeated 200 times in order to reach the stable inferred values, which is achieved by overlapping the mean values of all runs with the mean values of the 1st and 2nd halves of all runs. (B) Dispersity of the parameter values obtained during different calibration scenarios of C1, C2, C3, C4, C5, and C1-5. The values were scaled by dividing by the length of the priors. ED and LD stand for early differentiation and late differentiation, respectively.</p

The fuzzy logic rules in IF/THEN format.

The words in green are the cellular inputs while those in red are cellular outputs. (DOCX)</p

Specification of the software used in this study.

(DOCX)</p

Dispersity of the parameter values obtained during the calibration process for different calibration scenarios of C1, C2, C3, C4, C5 and C1-5.

Individual runs are the results of each calibration process; All samples represent the mean of combined individual runs; First (1st) and second (2nd) halfs of samples indicate the means of the frist half and second half of combined individual runs, respectively. The values were scaled by dividing by the length of the priors. (TIFF)</p

DataSheet1_A comparison of deep learning segmentation models for synchrotron radiation based tomograms of biodegradable bone implants.pdf

Introduction: Synchrotron radiation micro-computed tomography (SRμCT) has been used as a non-invasive technique to examine the microstructure and tissue integration of biodegradable bone implants. To be able to characterize parameters regarding the disintegration and osseointegration of such materials quantitatively, the three-dimensional (3D) image data provided by SRμCT needs to be processed by means of semantic segmentation. However, accurate image segmentation is challenging using traditional automated techniques. This study investigates the effectiveness of deep learning approaches for semantic segmentation of SRμCT volumes of Mg-based implants in sheep bone ex vivo.Methodology: For this purpose different convolutional neural networks (CNNs), including U-Net, HR-Net, U²-Net, from the TomoSeg framework, the Scaled U-Net framework, and 2D/3D U-Net from the nnU-Net framework were trained and validated. The image data used in this work was part of a previous study where biodegradable screws were surgically implanted in sheep tibiae and imaged using SRμCT after different healing periods. The comparative analysis of CNN models considers their performance in semantic segmentation and subsequent calculation of degradation and osseointegration parameters. The models’ performance is evaluated using the intersection over union (IoU) metric, and their generalization ability is tested on unseen datasets.Results and discussion: This work shows that the 2D nnU-Net achieves better generalization performance, with the degradation layer being the most challenging label to segment for all models.</p

Additional file 1: Table S1. of Imaging Invasion: Micro-CT imaging of adamantinomatous craniopharyngioma highlights cell type specific spatial relationships of tissue invasion

Case Details. (DOC 27.5 kb