Terahertz time-domain imaging for the examination of gilded wooden artifacts.

Terahertz imaging is unlocking unique capabilities for the analysis of cultural heritage artifacts. This paper uses terahertz time-domain imaging for the study of a gilded wooden artifact, providing a means to perform stratigraphic analysis, yielding information about the composition of the artifact, presence of certain materials identifiable through their THz spectral fingerprint, as well as alterations that have been performed over time. Due to the limited information that is available for many historic artifacts, the data that can be obtained through the presented technique can guide proper stewardship of the artifact, informing its long-term preservation.

Automated 2D, 2.5D, and 3D Segmentation of Coral Reef Pointclouds and Orthoprojections.

Enabled by advancing technology, coral reef researchers increasingly prefer use of image-based surveys over approaches depending solely upon in situ observations, interpretations, and recordings of divers. The images collected, and derivative products such as orthographic projections and 3D models, allow researchers to study a comprehensive digital twin of their field sites. Spatio-temporally located twins can be compared and annotated, enabling researchers to virtually return to sites long after they have left them. While these new data expand the variety and specificity of biological investigation that can be pursued, they have introduced the much-discussed Big Data Problem: research labs lack the human and computational resources required to process and analyze imagery at the rate it can be collected. The rapid development of unmanned underwater vehicles suggests researchers will soon have access to an even greater volume of imagery and other sensor measurements than can be collected by diver-piloted platforms, further exacerbating data handling limitations. Thoroughly segmenting (tracing the extent of and taxonomically identifying) organisms enables researchers to extract the information image products contain, but is very time-consuming. Analytic techniques driven by neural networks offer the possibility that the segmentation process can be greatly accelerated through automation. In this study, we examine the efficacy of automated segmentation on three different image-derived data products: 3D models, and 2D and 2.5D orthographic projections thereof; we also contrast their relative accessibility and utility to different avenues of biological inquiry. The variety of network architectures and parameters tested performed similarly, ∼80% IoU for the genus Porites, suggesting that the primary limitations to an automated workflow are 1) the current capabilities of neural network technology, and 2) consistency and quality control in image product collection and human training/testing dataset generation.

An infant burial from Arma Veirana in northwestern Italy provides insights into funerary practices and female personhood in early Mesolithic Europe.

The evolution and development of human mortuary behaviors is of enormous cultural significance. Here we report a richly-decorated young infant burial (AVH-1) from Arma Veirana (Liguria, northwestern Italy) that is directly dated to 10,211-9910 cal BP (95.4% probability), placing it within the early Holocene and therefore attributable to the early Mesolithic, a cultural period from which well-documented burials are exceedingly rare. Virtual dental histology, proteomics, and aDNA indicate that the infant was a 40-50 days old female. Associated artifacts indicate significant material and emotional investment in the child's interment. The detailed biological profile of AVH-1 establishes the child as the earliest European near-neonate documented to be female. The Arma Veirana burial thus provides insight into sex/gender-based social status, funerary treatment, and the attribution of personhood to the youngest individuals among prehistoric hunter-gatherer groups and adds substantially to the scant data on mortuary practices from an important period in prehistory shortly following the end of the last Ice Age.

Cholla cactus frames as lightweight and torsionally tough biological materials.

Biological materials tested in compression, tension, and impact inspire designs for strong and tough materials, but torsion is a relatively neglected loading mode. The wood skeletons of cholla cacti, subject to spartan desert conditions and hurricane force winds, provide a new template for torsionally resilient biological materials. Novel mesostructural characterization methods of laser-scanning and photogrammetry are used alongside traditional optical microscopy, scanning electron microscopy, and micro-computed tomography to identify mechanisms responsible for torsional resistance. These methods, in combination with finite element analysis reveal how cholla meso and macro-porosity and fibril orientation contribute to highly density-efficient mechanical behavior. Selective lignification and macroscopic tubercle pore geometry contribute to density-efficient shear stiffness, while mesoscopic wood fiber straightening, delamination, pore collapse, and fiber pullout provide extrinsic toughening mechanisms. These energy absorbing mechanisms are enabled by the hydrated material level properties. Together, these hierarchical behaviors allow the cholla to far exceed bamboo and trabecular bone in its ability to combine specific torsional stiffness, strength, and toughness. STATEMENT OF SIGNIFICANCE: The Cholla cactus experiences, due to the high velocity desert winds, high torsional loads. Our study has revealed the amazingly ingenious strategy by which the tubular structure containing arrays of voids intermeshed with wood fibers resists these high loads. Deformation is governed by compressive and tensile stresses which are greatest at 45 degrees to the cross section. It proceeds by stretching, sliding, and bending of the wood fibers which are coupled with the pore collapse, resulting in delayed failure and a high torsional toughness.

Visualizing whole-brain DTI tractography with GPU-based Tuboids and LoD management.

Diffusion Tensor Imaging (DTI) of the human brain, coupled with tractography techniques, enable the extraction of large-collections of three-dimensional tract pathways per subject. These pathways and pathway bundles represent the connectivity between different brain regions and are critical for the understanding of brain related diseases. A flexible and efficient GPU-based rendering technique for DTI tractography data is presented that addresses common performance bottlenecks and image-quality issues, allowing interactive render rates to be achieved on commodity hardware. An occlusion query-based pathway LoD management system for streamlines/streamtubes/tuboids is introduced that optimizes input geometry, vertex processing, and fragment processing loads, and helps reduce overdraw. The tuboid, a fully-shaded streamtube impostor constructed entirely on the GPU from streamline vertices, is also introduced. Unlike full streamtubes and other impostor constructs, tuboids require little to no preprocessing or extra space over the original streamline data. The supported fragment processing levels of detail range from texture-based draft shading to full raycast normal computation, Phong shading, environment mapping, and curvature-correct text labeling. The presented text labeling technique for tuboids provides adaptive, aesthetically pleasing labels that appear attached to the surface of the tubes. Furthermore, an occlusion query aggregating and scheduling scheme for tuboids is described that reduces the query overhead. Results for a tractography dataset are presented, and demonstrate that LoD-managed tuboids offer benefits over traditional streamtubes both in performance and appearance.

Collaborative biomedical data exploration in distributed virtual environments.

Imaging techniques such as MRI, fMRI, CT and PET have provided doctors with a means to acquire high-resolution biomedical images that serve as the foundation for the diagnosis and treatment of diseases. Experts with a multitude of backgrounds, including radiologists, anatomists, psychiatrists and neuroscientists now collaboratively analyze the same images to extract a better understanding of the encoded information. Unfortunately, access to these specialists at the same physical location is not always possible and new tools and techniques are required to facilitate simultaneous and collaborative exploration of volumetric data between spatially separated domain experts. This paper presents CVMED, a collaborative visualization environment for volumetric biomedical data-sets, supporting heterogeneous hardware, rendering and display systems connected via heterogeneous networks. CVMED provides the user with the algorithms and tools for stereoscopic as well as monoscopic data visualization and annotation along with the middleware needed to exchange the resulting visuals between all participants in real-time.

Hybrid-reality: collaborative biomedical data exploration exploiting 2-D and 3-D correspondence.

Doctors and radiologists are trained to infer and interpret three-dimensional information from two-dimensional images. Traditionally, they analyze sets of two-dimensional images obtained from imaging systems based on x-rays, computed tomography, and magnetic resonance. These images correspond to slices or projections to a single plane. With the resolution of the scanners increasing in all directions, so is the complexity of the data that can be used for diagnostic purposes. Using volume rendering techniques, massive stacks of image slices can be combined into a single image and important features stressed, increasing the doctor's ability to extract useful information from the image. A hybrid visualization approach combining 2D slices and 3D visuals is presented, drawing from the best features of both of these approaches. 2D slices emulate conventional medical images while 3D images provide additional information, such as better spatial location of the features in the surrounding structures as well as the 3D shape of features.

CGLX: a scalable, high-performance visualization framework for networked display environments.

The Cross Platform Cluster Graphics Library (CGLX) is a flexible and transparent OpenGL-based graphics framework for distributed, high-performance visualization systems. CGLX allows OpenGL based applications to utilize massively scalable visualization clusters such as multiprojector or high-resolution tiled display environments and to maximize the achievable performance and resolution. The framework features a programming interface for hardware-accelerated rendering of OpenGL applications on visualization clusters, mimicking a GLUT-like (OpenGL-Utility-Toolkit) interface to enable smooth translation of single-node applications to distributed parallel rendering applications. CGLX provides a unified, scalable, distributed OpenGL context to the user by intercepting and manipulating certain OpenGL directives. CGLX's interception mechanism, in combination with the core functionality for users to register callbacks, enables this framework to manage a visualization grid without additional implementation requirements to the user. Although CGLX grants access to its core engine, allowing users to change its default behavior, general development can occur in the context of a standalone desktop. The framework provides an easy-to-use graphical user interface (GUI) and tools to test, setup, and configure a visualization cluster. This paper describes CGLX's architecture, tools, and systems components. We present performance and scalability tests with different types of applications, and we compare the results with a Chromium-based approach.