Objectives: Palatal shape contains a lot of information that is of clinical interest. Moreover, palatal shape analysis can be used to guide or evaluate orthodontic treatments. A statistical shape model (SSM) is a tool that, by means of dimensionality reduction, aims at compactly modeling the variance of complex shapes for efficient analysis. In this report, we evaluate several competing approaches to constructing SSMs for the human palate. Setting and Sample Population:This study used a sample comprising digitized 3D maxillary dental casts from 1,324 individuals. Materials and methods: Principal component analysis (PCA) and autoencoders (AE)are popular approaches to construct SSMs. PCA is a dimension reduction technique that provides a compact description of shapes by uncorrelated variables. AEs are situated in the field of deep learning and provide a non-linear framework for dimension reduction. This work introduces the singular autoencoder (SAE), a hybrid approach that combines the most important properties of PCA and AEs. We assess the performance of the SAE using standard evaluation tools for SSMs, including accuracy, generalization, and specificity.
Estimates of individual-level genomic ancestry are routinely used in human genetics, and related fields. The analysis of population structure and genomic ancestry can yield insights in terms of modern and ancient populations, allowing us to address questions regarding admixture, and the numbers and identities of the parental source populations. Unrecognized population structure is also an important confounder to correct for in genome-wide association studies. However, it remains challenging to work with heterogeneous datasets from multiple studies collected by different laboratories with diverse genotyping and imputation protocols. This work presents a new approach and an accompanying open-source toolbox that facilitates a robust integrative analysis for population structure and genomic ancestry estimates for heterogeneous datasets. We show robustness against individual outliers and different protocols for the projection of new samples into a reference ancestry space, and the ability to reveal and adjust for population structure in a simulated case-control admixed population. Given that visually evident and easily recognizable patterns of human facial characteristics co-vary with genomic ancestry, and based on the integration of three different sources of genome data, we generate average 3D faces to illustrate genomic ancestry variations within the 1,000 Genome project and for eight ancient-DNA profiles, respectively. Scientists today have access to large heterogeneous datasets from many studies collected by different laboratories with diverse genotyping and imputation protocols. Therefore, the joint analysis of these datasets requires a robust and consistent inference of ancestry across all datasets involved, where one common strategy is to yield an ancestry space generated by a reference set of individuals 1. Based on open-research initiatives such as the 1,000 Genome project (1KGP) 2 , HapMap project 3 , Human Genome Diversity project (HGDP) 4 , and the POPRES dataset 5 , the potential exists to create reference ancestry latent-spaces at different levels of interest, from worldwide inter-continental to fine-scale intra-continental ancestry. A reference ancestry space allows the researcher to collate multiple datasets facilitating analyses that are more advanced. For example, reference ancestry spaces
23Accurate inference of genomic ancestry is critically important in human genetics, epidemiology, and 24 related fields. Geneticists today have access to multiple heterogeneous population-based datasets 25 from studies collected under different protocols. Therefore, joint analyses of these datasets require 26 robust and consistent inference of ancestry, where a common strategy is to yield an ancestry space 27 generated by a reference dataset. However, such a strategy is sensitive to batch artefacts introduced 28 by different protocols. In this work, we propose a novel robust genome-wide ancestry inference 29 method; referred to as SUGIBS, based on an unnormalized genomic (UG) relationship matrix whose 30 spectral (S) decomposition is generalized by an Identity-by-State (IBS) similarity degree matrix. SUGIBS 31 robustly constructs an ancestry space from a single reference dataset, and provides a robust 32 projection of new samples, from different studies. In experiments and simulations, we show that, 33 SUGIBS is robust against individual outliers and batch artifacts introduced by different genotyping 34 protocols. The performance of SUGIBS is equivalent to the widely used principal component analysis 35 (PCA) on normalized genotype data in revealing the underlying structure of an admixed population 36 and in adjusting for false positive findings in a case-control admixed GWAS. We applied SUGIBS on the 37
Treatment of large acetabular defects and discontinuities remains challenging and relies on the accurate restoration of the native anatomy of the patient. This study introduces and validates a statistical shape model for the reconstruction of acetabular discontinuities with severe bone loss through a two-sided Markov Chain Monte Carlo reconstruction method. The performance of the reconstruction algorithm was evaluated using leave-one-out cross-validation in three defect types with varying severity as well as severe defects with discontinuities. The two-sided reconstruction method was compared to a one-sided methodology. Although, reconstruction errors increased with defect size and this increase was most pronounced for pelvic discontinuities, the two-sided reconstruction method was able to reconstruct the native anatomy with higher accuracy than the one-sided reconstruction method. These findings can improve the preoperative planning and custom implant design in patients with large pelvic defects, both with and without discontinuities.
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