The tests showed a significant dependence of the determined mechanical parameters on the cross-sectional area. The larger the cross-sectional area, the lower the obtained value of stress. By contrast, the value of the stiffness coefficient is independent of the cross-sectional area. The differences in mechanical parameters are related mainly to water absorption into structures of the AF during hydration. This is confirmed by microscopic analysis of geometric dimensions of the AF during hydration conducted in real time. The greatest changes occurred in the radial direction, where the thickness increased by 2.05 mm, while in the axial direction the main change concerned an increase in height by 0.69 mm. There were negligible changes in the circumferential direction.

This study evaluated the changes of height and bulging occurring in individual layers of the annulus fibrosus of the intervertebral disc for 3 load scenarios (axial compression, flexion, and extension). The numerical model of a single motion segment of the thoracic spine was analysed for 2 different configurations, ie, for the model of a physiological segment and a segment with the posterior column removed. In the physiological segment, all annulus fibrosus layers decrease in height regardless of the applied load, bulging outside the intervertebral disc. Removal of the posterior column increases mobility and disrupts the load transfer system, with the lamellae bulging into the intervertebral disc.

In previous work, a modified version of the Bayesian information criterion (mBIC) was proposed to locate multiple interacting quantitative trait loci (QTL). Simulation studies and real data analysis demonstrate good properties of the mBIC in situations where the error distribution is approximately normal. However, as with other standard techniques of QTL mapping, the performance of the mBIC strongly deteriorates when the trait distribution is heavy tailed or when the data contain a significant proportion of outliers. In the present article, we propose a suitable robust version of the mBIC that is based on ranks. We investigate the properties of the resulting method on the basis of theoretical calculations, computer simulations, and a real data analysis. Our simulation results show that for the sample sizes typically used in QTL mapping, the methods based on ranks are almost as efficient as standard techniques when the data are normal and are much better when the data come from some heavy-tailed distribution or include a proportion of outliers. (2006). The most difficult part in constructing an appropriate regression model is the decision on the number of its components (i.e., the QTL number). This decision is particularly important in cases where the trait is influenced by some linked QTL. In such situations, the estimated QTL locations may depend substantially on the number of QTL in the model. When the size of the model is underestimated, for example, two linked QTL may be easily represented as one putative QTL in the middle between two real QTL locations. The opposite situation occurs when the size of the model is overestimated, leading to the incorrect identification of spurious QTL.Since the size of the chosen model depends on the level of significance used for including or deleting its components, the choice of the corresponding threshold value may substantially influence the results of the analysis. The corresponding problem exists also in the framework of Bayesian statistics, where the final estimates of the QTL locations depend on the prior distribution of the QTL number. In the setting of classical statistics, a systematic approach for the comparison of different models is provided by model selection criteria. Different model selection criteria serve different purposes. If the purpose of the study is the choice of markers for marker-assisted selection, then one should consider the criteria aiming at minimizing the prediction error, like, e.g., the Akaike information criterion (Akaike 1974). Note that the prediction does not suffer much from including several markers closely linked to a QTL. However, in the case when the purpose of the study is to identify real QTL, then consistent criteria, like, e.g., the Bayesian information criterion (BIC) (Schwarz 1978), seem to be a better choice.The classical model selection criteria were developed on the basis of asymptotic arguments and assuming that the sample size is large in comparison to the size of the analyzed models. This assumption is no longer...

BackgroundBiomechanical factors influence stress in the aortic wall. The aim of this study was to assess how the diameter and shape of the vessel, blood pressure and longitudinal systolic aortic stretching (SAS) caused by the contraction of the myocardium influence stress in the aortic wall.MethodsThree computational models of the non-dilated aorta and aneurysms of the ascending aorta and aortic root were created. Then, finite elements analyses were carried out. The models were subjected to blood pressure (120 mmHg and 160 mmHg) and longitudinal systolic aortic stretching (0 mm, 5 mm, 10 mm and 15 mm). The influence of wall elasticity was examined too.ResultsBlood pressure had a smaller impact on the stress than the SAS. An increase in blood pressure from 120 mmHg to 160 mmHg increased the peak wall stress (PWS) on average by 0.1 MPa in all models. A 5 mm SAS caused a 0.1–0.2 MPa increase in PWS in all the models. The increase in PWS caused by a 10 mm and 15 mm SAS was 0.2 MPa and 0.4 MPa in the non-dilated aorta, 0.2–0.3 MPa and 0.3–0.5 MPa in the aneurysm of the ascending aorta, and 0.1–0.2 MPa and 0.2–0.3 MPa in the aortic root aneurysm model, respectively. The loss of elasticity of the aneurysmal wall resulted in an increase of PWS by 0.1–0.2 MPa.ConclusionsAortic geometry, wall stiffness, blood pressure and SAS have an impact on PWS. However, SAS had the biggest impact on wall stress. The results of this study may be useful in future patient-specific computational models used to assess the risk of aortic complications.

Introduction: The aim of our cross-sectional study is to determine whether there is a link between sex, skeletal class and mandibular asymmetry in orthodontic patients, with tooth length and asymmetry in tooth length on contralateral sides of the mandible. Methods: As the source for relevant data to answer this question, 3D cone-beam tomography (CBCT) images of a total of 95 future orthodontic patients were retrospectively selected from private practice records and were analyzed. The CBCT images were part of routine orthodontic diagnosis. Patients were divided into three groups (Class I, Class III with asymmetry and Class III without asymmetry) based on skeletal variables assessed on orthodontic cephalometric images and frontal photos of the face. Three null hypotheses were developed, and a series of statistical tests was performed in order to support or reject them. Results: We have established that there exists a sexual dimorphism in some of the teeth’s lengths in our sample. Furthermore, we failed to find a link between mandibular asymmetry and asymmetry in tooth length. We have also found a link between skeletal class and tooth length differences in some of the analyzed measurements. Conclusions: Computational models used to design orthodontic appliances and to plan orthodontic treatment should be more individualized to consider a patient’s sex and skeletal class.

Wall stress can indicate the areas susceptible to the formation of an intimal tear and subsequent aortic dissection. Stress analysis may be a valuable tool to predict the risk of aortic dissection in patients with aortic dilatation.

No abstract

No abstract