3D representation and analysis of enthesis morphology

This comparison of methods for assessing the development of muscle insertion sites, or entheses, suggests that three‐dimensional (3D) quantification of enthesis morphology can produce a picture of habitual muscle use patterns in a past population that is similar to one produced by ordinal scores for describing enthesis morphology. Upper limb skeletal elements (humeri, radii, and ulnae) from a sample of 24 middle‐aged adult males from the Pottery Mound site in New Mexico were analyzed for both fibrous and fibrocartilaginous enthesis development with three different methods: ordinal scores, two‐dimensional (2D) area measurements, and 3D surface areas. The methods were compared using tests for asymmetry and correlations among variables in each quantitative data set. 2D representations of enthesis area did not agree as closely as ordinal scores and 3D surface areas did regarding which entheses were significantly asymmetrical. There was significant correlation between 3D and 2D data, but correlation coefficients were not consistently high. Intraobserver error was also assessed for the 3D method. Cronbach's alpha values fell between 0.68 and 0.73, and error rates for all entheses fell between 10% and 15%. Marginally acceptable intraobserver error and the analytic versatility of 3D images encourage further investigation of using 3D scanning technology for quantifying enthesis development. Am J Phys Anthropol 152:417–424, 2013. © 2013 Wiley Periodicals, Inc.     

Journey to the centre of the cell: Virtual reality immersion into scientific data

Visualization of scientific data is crucial not only for scientific discovery but also to communicate science and medicine to both experts and a general audience. Until recently, we have been limited to visualizing the three‐dimensional (3D) world of biology in 2 dimensions. Renderings of 3D cells are still traditionally displayed using two‐dimensional (2D) media, such as on a computer screen or paper. However, the advent of consumer grade virtual reality (VR) headsets such as Oculus Rift and HTC Vive means it is now possible to visualize and interact with scientific data in a 3D virtual world. In addition, new microscopic methods provide an unprecedented opportunity to obtain new 3D data sets. In this perspective article, we highlight how we have used cutting edge imaging techniques to build a 3D virtual model of a cell from serial block‐face scanning electron microscope (SBEM) imaging data. This model allows scientists, students and members of the public to explore and interact with a “real” cell. Early testing of this immersive environment indicates a significant improvement in students’ understanding of cellular processes and points to a new future of learning and public engagement. In addition, we speculate that VR can become a new tool for researchers studying cellular architecture and processes by populating VR models with molecular data.      

Photogrammetry: a useful tool for three‐dimensional morphometric analysis of small mammals

Recently, the improvement of methods for shape analysis has revolutionized the field of morphometrics. While three‐dimensional (3D) imaging technology is increasingly available, many studies of 3D structures still use two‐dimensional (2D) data, even when this may result in the loss of important information. This is particularly conspicuous in the study of small mammals, as devices precise enough for 3D digitization of small objects are the most expensive. Thus, the development of low‐cost methods aimed to recover 3D shape from small mammals would be of wide interest. Photogrammetry allows for obtaining 3D data with a lower cost than other 3D techniques, but it has not been previously applied to the study of small mammals. Accordingly, here we test the suitability of photogrammetric techniques to obtain 3D landmarks on mouse skulls as a model for small mammals. Shape and size of 3D models obtained with photogrammetric techniques were consistent among replicates, even when different sets of photographs were used. The linear measurements obtained from 3D models produced here were highly correlated with measurements obtained with callipers on actual crania, and differences among both sets of measures were smaller than those among individuals in most of the tested measures. These results show for the first time that photogrammetry is a precise technique for 3D shape analysis of small mammals. Photogrammetry also proved to be accurate for obtaining linear measurements between 3D landmarks; however, further studies are needed to demonstrate that this technique is also accurate to recreate 3D shapes.     

Technical Note: Guidelines for the digital computation of 2D and 3D enamel thickness in hominoid teeth

The study of enamel thickness has received considerable attention in regard to the taxonomic, phylogenetic and dietary assessment of human and non‐human primates. Recent developments based on two‐dimensional (2D) and three‐dimensional (3D) digital techniques have facilitated accurate analyses, preserving the original object from invasive procedures. Various digital protocols have been proposed. These include several procedures based on manual handling of the virtual models and technical shortcomings, which prevent other scholars from confidently reproducing the entire digital protocol. There is a compelling need for standard, reproducible, and well‐tailored protocols for the digital analysis of 2D and 3D dental enamel thickness. In this contribution we provide essential guidelines for the digital computation of 2D and 3D enamel thickness in hominoid molars, premolars, canines and incisors. We modify previous techniques suggested for 2D analysis and we develop a new approach for 3D analysis that can also be applied to premolars and anterior teeth. For each tooth class, the cervical line should be considered as the fundamental morphological feature both to isolate the crown from the root (for 3D analysis) and to define the direction of the cross‐sections (for 2D analysis). Am J Phys Anthropol 153:305–313, 2014. © 2013 Wiley Periodicals, Inc.     

Micro-computed tomography-based phenotypic approaches in embryology: procedural artifacts on assessments of embryonic craniofacial growth and development

Background  

Growing demand for three dimensional (3D) digital images of embryos for purposes of phenotypic assessment drives implementation of new histological and imaging techniques. Among these micro-computed tomography (μCT) has recently been utilized as an effective and practical method for generating images at resolutions permitting 3D quantitative analysis of gross morphological attributes of developing tissues and organs in embryonic mice. However, histological processing in preparation for μCT scanning induces changes in organ size and shape. Establishing normative expectations for experimentally induced changes in size and shape will be an important feature of 3D μCT-based phenotypic assessments, especially if quantifying differences in the values of those parameters between comparison sets of developing embryos is a primary aim. Toward that end, we assessed the nature and degree of morphological artifacts attending μCT scanning following use of common fixatives, using a two dimensional (2D) landmark geometric morphometric approach to track the accumulation of distortions affecting the embryonic head from the native, uterine state through to fixation and subsequent scanning.     

Bioactivity landscape modeling: Chemoinformatic characterization of structure-activity relationships of compounds tested across multiple targets

Characterizing structure-activity relationships (SAR) of sets of compounds screened across different targets is crucial in several drug discovery endeavors. To this end, chemoinformatic approaches are emerging to characterize SARs using the concept of multi-target activity landscapes. Herein, we present the Structure multiple Activity Similarity (SmAS) maps and the Structure multiple Activity Landscape Index (SmALI) as general approaches to navigate through and quantify the most informative regions of multi-target activity landscapes. These methods are extensions of SAS maps and SALI metric used for single targets. To illustrate the use of these methods, SmAS maps and SmALI values were employed for characterizing the SAR of three benchmark sets of compounds screened with different target families. As a follow up of our work, we employed four 2D and 3D structure representations to obtain consensus models for each data set. For the three data sets, we identified pairs of compounds with high structure similarity but very different bioactivity profile across the corresponding targets of each family that is, multi-target activity cliffs. Also, we identified pairs of compounds with low structure similarity but similar bioactivity profile across the different targets that is, multi-target scaffold hops. The consensus SmAS maps and mean SmALI metric are complementary chemoinformatic tools to systematically describe multi-target activity landscapes.     

Evaluating statistical methods using plasmode data sets in the age of massive public databases: an illustration using false discovery rates

Plasmode is a term coined several years ago to describe data sets that are derived from real data but for which some truth is known. Omic techniques, most especially microarray and genomewide association studies, have catalyzed a new zeitgeist of data sharing that is making data and data sets publicly available on an unprecedented scale. Coupling such data resources with a science of plasmode use would allow statistical methodologists to vet proposed techniques empirically (as opposed to only theoretically) and with data that are by definition realistic and representative. We illustrate the technique of empirical statistics by consideration of a common task when analyzing high dimensional data: the simultaneous testing of hundreds or thousands of hypotheses to determine which, if any, show statistical significance warranting follow-on research. The now-common practice of multiple testing in high dimensional experiment (HDE) settings has generated new methods for detecting statistically significant results. Although such methods have heretofore been subject to comparative performance analysis using simulated data, simulating data that realistically reflect data from an actual HDE remains a challenge. We describe a simulation procedure using actual data from an HDE where some truth regarding parameters of interest is known. We use the procedure to compare estimates for the proportion of true null hypotheses, the false discovery rate (FDR), and a local version of FDR obtained from 15 different statistical methods.     

Nouveaux regards sur la Grotte de La Peña (San Román de Candamo,Asturies, Espagne)

This article presents the first results of the interdisciplinary study of the Palaeolithic art of La Peña Cave (San Román de Candamo) recently declared a UNESCO World Heritage Site. The systematic exploration of the cave and the application of new technologies for documenting Palaeolithic graphs have allowed us to locate new heretofore unknown sets and to extend the number of known decorated topographic units. The making of a new topographical survey allows us to locate the previously known catalogue (figurative representations and signs) precisely as well as the new findings of paintings and engravings. In parallel to this, the use of different geotechnologies has allowed us to obtain digital models of the original structure of the La Peña Cave of San Román de Candamo (Spain), and also to obtain orthophotos that faithfully reflect the cave's dimensional and textural characteristics for subsequent technical study by a team of prehistorians.     

Improved and Robust Detection of Cell Nuclei from Four Dimensional Fluorescence Images

Segmentation-free direct methods are quite efficient for automated nuclei extraction from high dimensional images. A few such methods do exist but most of them do not ensure algorithmic robustness to parameter and noise variations. In this research, we propose a method based on multiscale adaptive filtering for efficient and robust detection of nuclei centroids from four dimensional (4D) fluorescence images. A temporal feedback mechanism is employed between the enhancement and the initial detection steps of a typical direct method. We estimate the minimum and maximum nuclei diameters from the previous frame and feed back them as filter lengths for multiscale enhancement of the current frame. A radial intensity-gradient function is optimized at positions of initial centroids to estimate all nuclei diameters. This procedure continues for processing subsequent images in the sequence. Above mechanism thus ensures proper enhancement by automated estimation of major parameters. This brings robustness and safeguards the system against additive noises and effects from wrong parameters. Later, the method and its single-scale variant are simplified for further reduction of parameters. The proposed method is then extended for nuclei volume segmentation. The same optimization technique is applied to final centroid positions of the enhanced image and the estimated diameters are projected onto the binary candidate regions to segment nuclei volumes.Our method is finally integrated with a simple sequential tracking approach to establish nuclear trajectories in the 4D space. Experimental evaluations with five image-sequences (each having 271 3D sequential images) corresponding to five different mouse embryos show promising performances of our methods in terms of nuclear detection, segmentation, and tracking. A detail analysis with a sub-sequence of 101 3D images from an embryo reveals that the proposed method can improve the nuclei detection accuracy by 9 over the previous methods, which used inappropriate large valued parameters. Results also confirm that the proposed method and its variants achieve high detection accuracies ( 98 mean F-measure) irrespective of the large variations of filter parameters and noise levels.     

Spectral embedding finds meaningful (relevant) structure in image and microarray data

Background  

Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented.     

GEANT4 Monte Carlo simulations for virtual clinical trials in breast X-ray imaging: Proof of concept

Virtual clinical trials (VCT) are in-silico reproductions of medical examinations, which adopt digital models of patients and simulated devices. They are intended to produce clinically equivalent outcome data avoiding long execution times, ethical issues related to radiation induced risks and huge costs related to real clinical trials with a patient population. In this work, we present a platform for VCT in 2D and 3D X-ray breast imaging. The VCT platform uses Monte Carlo simulations based on the Geant4 toolkit and patient breast models derived from a cohort of high resolution dedicated breast CT (BCT) volume data sets. Projection images of the breast and three-dimensional glandular dose maps are generated for a given breast model, by simulating both 2D full-field digital mammography (DM) and 3D BCT examinations. Uncompressed voxelized breast models were derived from segmented patient images. Compressed versions of the digital breast phantoms for DM were generated using a previously published digital compression algorithm. The Monte Carlo simulation framework has the capability of generating and tracking ~10⁵ photons/s using a server equipped with 16-cores and 3.0 GHz clock speed. The VCT platform will provide a framework for scanner design optimization, comparison between different scanner designs and between different modalities or protocols on computational breast models, without the need for scanning actual patients as in conventional clinical trials.     

Towards a Tralfamadorian view of the embryo: multidimensional imaging of development

Biological problems such as embryonic development require tools to follow cell and tissue movements as well as the distribution of active genes. A variety of emerging imaging techniques offer the capability of fully rendering the three-dimensional structure of the embryo, and some offer the possibility of following changes directly over time. The data sets that result offer both new insights and new challenges. A framework of digital atlases will soon offer the integration of different imaging modalities and permit users to interact with multidimensional data sets.     

Linkage disequilibrium clustering‐based approach for association mapping with tightly linked genomewide data

Genomewide association studies (GWAS) aim to identify genetic markers strongly associated with quantitative traits by utilizing linkage disequilibrium (LD) between candidate genes and markers. However, because of LD between nearby genetic markers, the standard GWAS approaches typically detect a number of correlated SNPs covering long genomic regions, making corrections for multiple testing overly conservative. Additionally, the high dimensionality of modern GWAS data poses considerable challenges for GWAS procedures such as permutation tests, which are computationally intensive. We propose a cluster‐based GWAS approach that first divides the genome into many large nonoverlapping windows and uses linkage disequilibrium network analysis in combination with principal component (PC) analysis as dimensional reduction tools to summarize the SNP data to independent PCs within clusters of loci connected by high LD. We then introduce single‐ and multilocus models that can efficiently conduct the association tests on such high‐dimensional data. The methods can be adapted to different model structures and used to analyse samples collected from the wild or from biparental F₂ populations, which are commonly used in ecological genetics mapping studies. We demonstrate the performance of our approaches with two publicly available data sets from a plant (Arabidopsis thaliana) and a fish (Pungitius pungitius), as well as with simulated data.     

Arbor 3D: an interactive environment for examining phylogenetic and taxonomic trees in multiple dimensions

This paper examines a new technique for the visualization of and the interaction with trees, objects frequently used to convey hierarchical relationships in biological data. Motivated by the quality of 2D tree interaction, we adapt the planar tree-of-life metaphor to a virtual, semi-immersive 3D environment. A 3D environment extends the utility of this metaphor by allowing the user to view an entire data set in a single screen. Interrogation of the tree is implemented using 3D input devices. This real-time interrogation of the tree itself provides a quick means by which to qualitatively analyse the hierarchical data. In this paper, we describe the techniques underlying the implementation of such an environment. We conclude by considering the utility of tree metaphors as a basis for the representation of highly dimensional data sets. AVAILABILITY: Arbor3D (source code, a binary executable for SGI IRIX 6.4, Perl parsers, and sample Newick data files) are available via the Internet (http://xian.tamu.edu/Arbor3D/). Arbor3D can be displayed in "CAVE simulator" mode on an SGI workstation screen, or as an interactive virtual environment on a projection workbench. CONTACT: druths@rice.edu; echen@cs.rice.edu; leland@xian.tamu.edu