Browsing by Author "Wilson, Jeremy"

Now showing 1 - 4 of 4
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    Automatic Landmark Placement for Large 3D Facial Image Dataset
    (IEEE, 2019-12) Wang, Jerry; Fang, Shiaofen; Fang, Meie; Wilson, Jeremy; Herrick, Noah; Walsh, Susan; Computer and Information Science, School of Science
    Facial landmark placement is a key step in many biomedical and biometrics applications. This paper presents a computational method that efficiently performs automatic 3D facial landmark placement based on training images containing manually placed anthropological facial landmarks. After 3D face registration by an iterative closest point (ICP) technique, a visual analytics approach is taken to generate local geometric patterns for individual landmark points. These individualized local geometric patterns are derived interactively by a user's initial visual pattern detection. They are used to guide the refinement process for landmark points projected from a template face to achieve accurate landmark placement. Compared to traditional methods, this technique is simple, robust, and does not require a large number of training samples (e.g. in machine learning based methods) or complex 3D image analysis procedures. This technique and the associated software tool are being used in a 3D biometrics project that aims to identify links between human facial phenotypes and their genetic association.
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    Covariate and Co-Structural Influences on Human Facial Morphology: Decoding the Structural Blueprint Behind Facial Shape
    (2025-05) Wilke, Franziska; Walsh, Susan; Roper, Randall; Balakrishnan, Lata; Wilson, Jeremy; Wetherill, Leah; Lapish, Christopher
    The human face is one of the most intricate yet informative structures, serving as a key identifier in forensic investigations, an indicator of medical conditions, and a crucial factor in surgical planning. Over the past few decades, significant effort has been dedicated to understanding the genetic architecture underlying facial morphology. However, this focus often overlooks the substantial influence of covariates, such as biogeographic ancestry, and structural components like the skull. While these factors are acknowledged, their anthropological is frequently reduced to statistical models that bypass anatomical considerations. Furthermore, many of the complex models developed to reconstruct facial shape are not yet practically applicable. This dissertation addresses these gaps by investigating how regional, rather than just global, biogeographic ancestry influences facial morphology and whether genetic models of biogeographic ancestry align with phenotypic expression. Our findings indicate that broad categorizations such as “European” do not fully capture ancestral variation, yet incorporating too many genetic principal components risks overcorrection. To address this, we introduce a novel standardized, phenotype-based approach using consensus faces. Additionally, we present a validated, standardized method for efficiently masking and analyzing the human skull using over 6,000 quasi-landmarks. This methodology is further expanded to include a facial mask, where both the skull and face are intrinsically linked through anatomically corresponding quasi-landmarks. This innovation enables the simultaneous study of facial soft tissue thickness (FSTT), cranial shape, and facial morphology in a computationally efficient manner that has not been previously achieved. The use of correspondence masks permits modeling of the relationship between the skull and face, facilitating craniofacial reconstruction and laying the foundation for an open-source FSTT and facial measurement database. Ultimately, this dissertation explores standardization, global applicability, with the aim of facilitating real-world applications of a scientifically transparent computational approach to facial image projection from skeletal remains. By integrating genetic, anthropological, and statistical approaches, it describes a streamlined methodology that can harness structural knowledge of facial variation to develop practical tools useful in forensic and medical applications. Moreover, it highlights the need for global large-scale collaborative research to further advance this field on both fundamental science and applied levels.
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    Exploring the Effects of Ancestry on Inference and Identity Using Bioinformatics
    (2023-08) Herrick, Noah; Walsh, Susan; Picard, Christine; Wilson, Jeremy; Balakrishnan, Lata; Roper, Randall
    Ancestry is a complex and layered concept, but it must be operationalized for its objective use in genetic studies. Critical decisions in research analyses, clinical practice, and forensic investigations are based on genetic ancestry inference. For example, in genetic association studies for clinical and applied research, investigators may need to isolate one population of interest from a worldwide dataset to avoid false positive results, or in human identification, ancestry inferences can help reveal the identity of unknown DNA evidence by narrowing down a suspect list. Many studies seek to improve ancestry inference for these reasons. The research presented here offers valuable resources for exploring and improving genetic ancestry inference and intelligence toward identity. First, analyses with ‘big data’ in genomics is a resource-intensive task that requires optimization. Therefore, this research introduces a suite of automated Snakemake workflows, Iliad, that was developed to give the research community an easy-to-learn, hands-off computational tool for genomic data processing of multiple data formats. Iliad can be installed and run on a Google Cloud Platform remote server instance in less than 20 minutes when using the provided installation code in the ReadTheDocs documentation. The workflows support raw data processing from various genetic data types including microarray, sequence, and compressed alignment data, as well as performing micro-workflows on variant call format (VCF) files to merge data or lift over variant positions. When compared to a similar workflow, Iliad completed processing one sample’s raw paired-end sequence reads to a human-legible VCF file in 7.6 hours which was three-times faster than the other workflow. This suite of workflows is paramount towards building reference population panels from human whole-genome sequence (WGS) data which is useful in many research studies including imputation, ancestry estimation, and ancestry informative marker (AIM) discovery. Second, there are persistent challenges in ancestry inference for individuals of the Middle East, especially with the use of AIMs. This research demonstrates a population genomics study pertaining to the Middle East, novel population data from Lebanon (n=190), and an unsupervised genetic clustering approach with WGS data from the 1000 Genomes Project and Human Genome Diversity Project. These efforts for AIM discovery identified two single nucleotide polymorphisms (SNPs) based on their high allelic frequency differences between the Middle East and populations in Eurasia, namely Europe and South/Central Asia. These candidate AIMs were evaluated with the most current and comprehensive AIM panel to date, the VISAGE Enhanced Tool (ET), using an external validation set of Middle Eastern WGS data (n=137). Instead of relying on pre-defined biogeographic ancestry labels to confirm the accuracy of validation sample ancestry inference, this research produced a deep, unsupervised ADMIXTURE analysis on 3,469 worldwide WGS samples with nearly 2 million independent SNPs (r2 < 0.1) which provided a genetic “ground truth”. This resulted in 136/137 validation samples as Middle East and provided valuable insights toward reference samples with varying co-ancestries that ultimately affects the classification of admixed individuals. Novel deep learning methods, specifically variational autoencoders, were introduced for visualizing one hundred percent of the genetic variance found using these AIMS in an alternative method to PCA and presents distinct population clusters in a robust ancestry space that remains static for the projection of unknown samples to aid in ancestry inference and human identification. Third, this research delves into a craniofacial study that makes improvements toward key intelligence information about physical identity by exploring the relationship between dentition and facial morphology with an advanced phenotyping approach paired with robust dental parameters used in clinical practice. Cone-beam computed tomography (CBCT) imagery was used to analyze the hard and soft tissue of the face at the same time. Low-to-moderate partial correlations were observed in several comparisons of dentition and soft tissue segments. These results included partial correlations of: i) inter-molar width and soft tissue segments nearest the nasal aperture, the lower maxillary sinuses, and a portion of the upper cheek, and ii) of lower incisor inclination and soft tissue segments overlapping the mentolabial fold. These results indicate that helpful intelligence information, potentially leading towards identity in forensic investigations, may be present where hard tissue structures are manifested in an observable way as a soft tissue phenotype. This research was a valuable preliminary study that paves the way towards the addition of facial hard tissue structures in combination with external soft tissue phenotypes to advance fundamental facial genetic research. Thus, CBCT scans greatly add to the current facial imagery landscape available for craniofacial research and provide hard and soft tissue data, each with measurable morphological variation among individuals. When paired with genetic association studies and functional biological experiments, this will ultimately lead to a greater understanding of the intricate coordination that takes place in facial morphogenesis, and in turn, guide clinical orthodontists to better treatment modalities with an emphasis on personalized medicine. Lastly, it aids intelligence methodologies when applied within the field of forensic anthropology.
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    A GIS Approach to Understanding Mississippian Settlement Patterns in the Central Illinois River Valley
    (2020-07) Swoveland, Kayla Jan; Wilson, Jeffery; Wilson, Jeremy; Lulla, Vijay
    Geographic Information Science (GIS) technologies have helped to further the research of archaeologists almost since the inception of the field. Archaeologists have long made observations rooted in what would become GIS, but it wasn’t until the early 21st century that science was able to back up these observations. From the seemingly simple task of organizing and storing spatial data to more robust statistical and spatial calculations, GIS has quickly become a valuable tool used by archeologists to better understand past populations. This research applied GIS to help understand the regional distribution of settlement locations from the Mississippian Period (AD 1050-1450) in the central Illinois River Valley (CIRV) of west-central Illinois. Settlement distribution was examined in two contexts, first in the context of larger, more “metropolitan” site placement in relation to smaller, more transitory sites. Secondly, site distribution was examined to see what, if any, pattern existed between site placement and a set of ecological factors. The results found that while smaller sites were prevalent around many of the larger sites, a few metropolitan sites did have a larger number of smaller sites surrounding them, supporting the idea of certain Mississippian sites serving as hubs. Additionally, it was demonstrated that several different types of GIS based analyses were particularly effective in helping to identify these patterns, thus solidifying and improving the role of GIS in the field of archaeology.