Macroscopic Anisotropic Bone Material Properties in Children with Severe \u3cem\u3eOsteogenesis imperfecta\u3c/em\u3e

Children with severe osteogenesis imperfecta(OI) typically experience numerous fractures and progressive skeletal deformities over their lifetime. Recent studies proposed finite element models to assess fracture risk and guide clinicians in determining appropriate intervention in children with OI, but lack of appropriate material property inputs remains a challenge. This study aimed to characterize macroscopic anisotropic cortical bone material properties and investigate relationships with bone density measures in children with severe OI. Specimens were obtained from tibial or femoral shafts of nine children with severe OI and five controls. The specimens were cut into beams, characterized in bending, and imaged by synchrotron radiation X-ray micro-computed tomography. Longitudinal modulus of elasticity, yield strength, and bending strength were 32–65% lower in the OI group (p \u3c 0.001). Yield strain did not differ between groups (p ≥ 0.197). In both groups, modulus and strength were lower in the transverse direction (p ≤ 0.009), but anisotropy was less pronounced in the OI group. Intracortical vascular porosity was almost six times higher in the OI group (p \u3c 0.001), but no differences were observed in osteocyte lacunar porosity between the groups (p = 0.086). Volumetric bone mineral density was lower in the OI group (p \u3c 0.001), but volumetric tissue mineral density was not (p = 0.770). Longitudinal OI bone modulus and strength were correlated with volumetric bone mineral density (p ≤ 0.024) but not volumetric tissue mineral density (p ≥ 0.099). Results indicate that cortical bone in children with severe OI yields at the same strain as normal bone, and that their decreased bone material strength is associated with reduced volumetric bone mineral density. These results will enable the advancement of fracture risk assessment capability in children with severe OI

Evaluating Descriptive Metrics of the Human Cone Mosaic

Purpose: To evaluate how metrics used to describe the cone mosaic change in response to simulated photoreceptor undersampling (i.e., cell loss or misidentification). Methods: Using an adaptive optics ophthalmoscope, we acquired images of the cone mosaic from the center of fixation to 10° along the temporal, superior, inferior, and nasal meridians in 20 healthy subjects. Regions of interest (n = 1780) were extracted at regular intervals along each meridian. Cone mosaic geometry was assessed using a variety of metrics − density, density recovery profile distance (DRPD), nearest neighbor distance (NND), intercell distance (ICD), farthest neighbor distance (FND), percentage of six-sided Voronoi cells, nearest neighbor regularity (NNR), number of neighbors regularity (NoNR), and Voronoi cell area regularity (VCAR). The “performance” of each metric was evaluated by determining the level of simulated loss necessary to obtain 80% statistical power. Results: Of the metrics assessed, NND and DRPD were the least sensitive to undersampling, classifying mosaics that lost 50% of their coordinates as indistinguishable from normal. The NoNR was the most sensitive, detecting a significant deviation from normal with only a 10% cell loss. Conclusions: The robustness of cone spacing metrics makes them unsuitable for reliably detecting small deviations from normal or for tracking small changes in the mosaic over time. In contrast, regularity metrics are more sensitive to diffuse loss and, therefore, better suited for detecting such changes, provided the fraction of misidentified cells is minimal. Combining metrics with a variety of sensitivities may provide a more complete picture of the integrity of the photoreceptor mosaic

Use of uncertain external information in statistical estimation

A product’s life cycle hinges on its sales. Product sales are determined by a combination of market demand, industrial production, logistics, supply chains, labor hours, and countless other factors. Business-specific questions about sales are often formalized into questions relating to specific quantities in sales data. Statistical estimation of these quantities of interest is crucial but restricted availability of empirical data reduces the accuracy of such estimation. For example, under certain regularity conditions the variance of maximum likelihood estimators cannot be asymptotically lower than the Cramer-Rao lower bound. The presence of additional information from external sources therefore allows the improvement of statistical estimation. Two types of additional information are considered in this work: unbiased and possibly biased. In order to incorporate these two types of additional information in statistical estimation, this manuscript minimizes mean squared error and variance. Publicly available Walmart sales data from 45 stores across 2010-2012 is used to illustrate how these statistical methods can be applied to use additional information for estimating weekly sales. The holiday effect (sales spikes during holiday weeks) adjusted for overtime trends is estimated with the use of relevant external information

Statistical estimation with possibly incorrect model assumptions

We combine a consistent (base) estimator of a population parameter with one or several other possibly inconsistent estimators. Some or all assumptions used for calculating the latter estimators may be incorrect. The suggested in the manuscriptapproach is not restricted to parametric families and can be easily used for improvingefficiency of estimators built under nonparametric or semiparametricmodels. The combined estimator minimizes the mean squared error (MSE) in a family of linear combinations of considered estimators when all variances and covariancesused in its structure are known. In real life problems these variances and covariances are estimated generating an empirical version of the combined estimator.The combined estimator as well as its empirical version are consistent. The asymptotic properties of these estimators are presented. The combined estimator is applicable when analysts can use several different procedures for estimating the same population parameter. Different assumptions are associated with the use of each of non-base estimators. Our estimator is consistent in the presence of wrongassumptions for non-base estimating procedures. In addition to theoretical resultsof this manuscript, simulation studies describe properties of the estimator combiningthe Kaplan-Meier estimator with the censored data exponential estimator of a survival curve. Another set of simulation examples combine semi-parametricCox regression with exponential regression on right censored data

Use of uncertain additional information in newsvendor models

The newsvendor problem is a popular inventory management problem in supply chain management and logistics. Solutions to the newsvendor problem determine optimal inventory levels. This model is typically fully determined by a purchase and sale prices and a distribution of random market demand. From a statistical point of view, this problem is often considered as a quantile estimation of a critical fractile which maximizes anticipated profit. The distribution of demand is a random variable and is often estimated on historic data. In an ideal situation, when the probability distribution of demand is known, one can determine the quantile of a critical fractile minimizing a particular loss function. When a parametric family is known, maximum likelihood estimation is asymptotically efficient under certain regularity assumptions and the maximum likelihood estimators (MLEs) are used for estimating quantiles. Then, the Cramer-Rao lower bound determines the lowest possible asymptotic variance for the MLEs. Can one find a quantile estimator with a smaller variance then the Cramer-Rao lower bound? If a relevant additional information is available then the answer is yes. This manuscript considers minimum variance and mean squared error estimation which incorporate additional information for estimating optimal inventory levels

Motion Analysis Strategy Appropriate for 3D Kinematic Assessment of Children and Adults with Osteogenesis Imperfecta

Human motion analysis provides a quantitative means of assessing whole body and segmental motion of subjects with musculoskeletal pathologies. This chapter describes a low cost motion analysis appropriate for complete three-dimensional (3D) assessment of upper and lower extremity kinematics. The system has been designed to support lower cost outreach efforts that require accuracy and resolution on the order of classical fixed lot systems such as Vicon. The focus of this work addresses the assessment needs typically seen in adults and children with osteogenesis imperfect (OI) experiencing ambulatory and upper extremity challenges

Creep Evaluation of (Orthotic) Cast Materials During Simulated Clubfoot Correction

The Ponseti method is a widely accepted and highly successful conservative treatment of pediatric clubfoot that relies on weekly manipulations and cast applications. However, the material behavior of the cast in the Ponseti technique has not been investigated. The current study sought to characterize the ability of two standard casting materials to maintain the Ponseti corrected foot position by evaluating creep response. A dynamic cast testing device (DCTD) was built to simulate a typical pediatric clubfoot. Semi-rigid fiberglass and rigid fiberglass casting materials were applied to the device, and the rotational creep was measured at various constant torques. The movement was measured using a 3D motion capture system. A 2-way ANOVA was performed on the creep displacement data at a significance level of 0.05. Among cast materials, the rotational creep displacement was found to be significantly different (p-values ≪ 0.001). The most creep displacement occurs in the semi-rigid fiberglass (approximately 1.0 degrees), then the rigid fiberglass (approximately 0.4 degrees). There was no effect of torque magnitude on the creep displacement. All materials maintained the corrected position with minimal change in position over time