The Role of the Thoracic Spine during Breathing in Osteogenesis Imperfecta: A Combined Traditional Morphometry and 3D Geometric Morphometrics Research

OsteogenesisImperfecta (OI) is a rare disease with respiratory problems, which are usually attributed to the secondary effects of scoliosis and rib fractures and to severe restrictive pulmonary disease. Conventional morphometry has already been studied in OI patients but three-dimensional geometric morphometrics (3D GMM) has never been used to assess how the thoracic spine shape changes during maximal breathing. A total of 6 adult subjects with OI type III and 16 healthy controls underwent a spirometric study and two computed tomography scans in maximal inspiration and expiration. Shape data by means of 3D GMM and Cobb angle values of scoliosis and kyphosis were obtained and their relationship with spirometric values was analysed using regressions and mean shape comparisons. No differences in kyphosis (p = 0.285) and scoliosis Cobb values (p = 0.407) were found between inspiration and expiration in OI patients. The 3D GMM analysis revealed significant shape differences between OI and control subjects (p < 0.001) that were related to the inspiration (p = 0.030) and not to the expiration (p = 0.079). Nevertheless, no significant relation was found between thoracic spine shape, scoliosis, kyphosis and breathing outcomes in both OI patients and controls. There were thoracic spine shape differences during maximal breathing between OI patients and controls that were mainly related to the inspiration.

Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data

A helideck is an essential structure in an offshore platform, and it is crucial to maintain its structural integrity and detect the occurrence of damage early. Because helidecks usually consist of complex lattice truss members, precise measurements are required for structural health monitoring based on accurate modal parameters. However, available sensors and data acquisition are limited. Therefore, we propose a two-step damage detection process using an artificial neural network. Based on the mode shape database collected from 137,400 damage scenarios by finite element analysis, the neural network in the first step was trained to estimate the mode shapes of the entire helideck model using the selected mode shape data obtained from the limited measuring points. Then, the neural network in the second step is consecutively trained to detect the location and amount of structural damage to individual parts. As a result, it is shown that the proposed procedure provides the damage detection capability with only a quarter of the entire mode shape data, while the estimation accuracy is sufficiently high compared to the single network directly trained using all mode shape data. It was also found that, compared to the network directly trained from the same data, the proposed technique tends to detect minor damages more accurately.

Data-Efficient Machine Learning on 3D Engineering Data

Decisions in engineering design are closely tied to the 3D shape of the product. Limited availability of 3D shape data and expensive annotation present key challenges for using Artificial Intelligence in product design and development. In this work we explore transfer learning strategies to improve the data-efficiency of geometric reasoning models based on deep neural networks as used for tasks such as shape retrieval and design synthesis. We address the utilization of problem- related and un-annotated 3D data to compensate for small data volumes. Our experiments show promising results for knowledge transfer on mechanical component benchmarks.

On the morphospace of eurypterine sea scorpions

ABSTRACT Eurypterids (sea scorpions) are a group of extinct, marine euchelicerates that have an extensive Palaeozoic record. Despite lacking a biomineralised exoskeleton, eurypterids are abundantly preserved within select deposits. These collections make statistical analyses comparing the morphology of different genera possible. However, eurypterid shape has not yet been documented with modern geometric morphometric tools. Here, we summarise the previous statistical assessments of eurypterid morphology and expand this research by presenting landmark and semi-landmark analyses of 115 eurypterid specimens within the suborder Eurypterina. We illustrate that lateral compound eye morphology and position drives specimen placement in morphospace and separates proposed apex predators from more generalist forms. Additionally, evidence for size clusters in Eurypterus that may reflect ontogeny is uncovered. We highlight the use of geometric morphometric analyses in supporting the naming of new taxa and demonstrate that these shape data represent a novel means of understanding inter-generic ontogenetic trajectories and uncovering developmental changes within the diverse euarthropod group.

Mixing Worlds

OverviewSpatial and shape data represented by 3D digital models have become a central component of our archaeological datasets. Immersive visual and audio interaction with these models offers an intuitive way to use these data. The mixing of the virtual with the real world suits archaeological work particularly well, and the technologies of augmented reality (AR) and mixed reality (MR) enable this type of interaction. Much past work on these technologies has involved public engagement, but they also hold the potential for valuable deployment directly in archaeological practice and research, especially the seamless integration offered by MR. This review examines the range of experiments archaeologists are currently undertaking with AR and MR, and it looks to the future applications of these technologies.