Classifying organisms and artefacts by their outline shapes

We often wish to classify objects by their shapes. Indeed, the study of shapes is an important part of many scientific fields, such as evolutionary biology, structural biology, image processing and archaeology. However, mathematical shape spaces are rather complicated and nonlinear. The most widely used methods of shape analysis, geometric morphometrics, treat the shapes as sets of points. Diffeomorphic methods consider the underlying curve rather than points, but have rarely been applied to real-world problems. Using a machine classifier, we tested the ability of several of these methods to describe and classify the shapes of a variety of organic and man-made objects. We find that one method, based on square-root velocity functions (SRVFs), outperforms all others, including a standard geometric morphometric method (eigenshapes), and that it is also superior to human experts using shape alone. When the SRVF approach is constrained to take account of homologous landmarks it can accurately classify objects of very different shapes. The SRVF method identifies a shortest path between shapes, and we show that this can be used to estimate the shapes of intermediate steps in evolutionary series. Diffeomorphic shape analysis methods, we conclude, now provide practical and effective solutions to many shape description and classification problems in the natural and human sciences.</p

The Digital Bee Brain: Integrating and Managing Neurons in a Common 3D Reference System

The honeybee standard brain (HSB) serves as an interactive tool for relating morphologies of bee brain neurons and provides a reference system for functional and bibliographical properties (http://www.neurobiologie.fu-berlin.de/beebrain/). The ultimate goal is to document not only the morphological network properties of neurons collected from separate brains, but also to establish a graphical user interface for a neuron-related data base. Here, we review the current methods and protocols used to incorporate neuronal reconstructions into the HSB. Our registration protocol consists of two separate steps applied to imaging data from two-channel confocal microscopy scans: (1) The reconstruction of the neuron, facilitated by an automatic extraction of the neuron's skeleton based on threshold segmentation, and (2) the semi-automatic 3D segmentation of the neuropils and their registration with the HSB. The integration of neurons in the HSB is performed by applying the transformation computed in step (2) to the reconstructed neurons of step (1). The most critical issue of this protocol in terms of user interaction time – the segmentation process – is drastically improved by the use of a model-based segmentation process. Furthermore, the underlying statistical shape models (SSM) allow the visualization and analysis of characteristic variations in large sets of bee brain data. The anatomy of neural networks composed of multiple neurons that are registered into the HSB are visualized by depicting the 3D reconstructions together with semantic information with the objective to integrate data from multiple sources (electrophysiology, imaging, immunocytochemistry, molecular biology). Ultimately, this will allow the user to specify cell types and retrieve their morphologies along with physiological characterizations

Machine intelligence for nerve conduit design and production

Nerve guidance conduits (NGCs) have emerged from recent advances within tissue engineering as a promising alternative to autografts for peripheral nerve repair. NGCs are tubular structures with engineered biomaterials, which guide axonal regeneration from the injured proximal nerve to the distal stump. NGC design can synergistically combine multiple properties to enhance proliferation of stem and neuronal cells, improve nerve migration, attenuate inflammation and reduce scar tissue formation. The aim of most laboratories fabricating NGCs is the development of an automated process that incorporates patient-specific features and complex tissue blueprints (e.g. neurovascular conduit) that serve as the basis for more complicated muscular and skin grafts. One of the major limitations for tissue engineering is lack of guidance for generating tissue blueprints and the absence of streamlined manufacturing processes. With the rapid expansion of machine intelligence, high dimensional image analysis, and computational scaffold design, optimized tissue templates for 3D bioprinting (3DBP) are feasible. In this review, we examine the translational challenges to peripheral nerve regeneration and where machine intelligence can innovate bottlenecks in neural tissue engineering

Electric and magnetic fields inside neurons and their impact upon the cytoskeletal microtubules

If we want to better understand how the microtubules can translate and input the information carried by the electrophysiologic impulses that enter the brain cortex, a detailed investigation of the local electromagnetic field structure is needed. In this paper are assessed the electric and the magnetic field strengths in different neuronal compartments. The calculated results are verified via experimental data comparison. It is shown that the magnetic field is too weak to input information to microtubules and no Hall effect, respectively QHE is realistic. Local magnetic flux density is less than 1/300 of the Earth’s magnetic field that’s why any magnetic signal will be suffocated by the surrounding noise. In contrast the electric field carries biologically important information and acts upon voltage-gated transmembrane ion channels that control the neuronal action potential. If mind is linked to subneuronal processing of information in the brain microtubules then microtubule interaction with the local electric field, as input source of information is crucial. The intensity of the electric field is estimated to be 10V/m inside the neuronal cytoplasm however the details of the tubulin-electric field interaction are still unknown. A novel hypothesis stressing on the tubulin C-termini intraneuronal function is presented replacing the current flawed models (Tuszynski 2003, Mershin 2003, Hameroff 2003, Porter 2003) presented at the Quantum Mind II Conference held at Tucson, Arizona, 15-19 March 2003, that are shown in this presentation to be biologically and physically inconsistent

Geometry Processing of Conventionally Produced Mouse Brain Slice Images

Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss. In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space. This is achieved first by constructing a virtual 3D mouse brain model from annotated slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed model generates ARA-based slice images corresponding to the microscopic images of histological brain sections. These image pairs are aligned using a geometric approach through contour images. Histological artifacts in the microscopic images are detected and removed using Constrained Delaunay Triangulation before performing global alignment. Finally, non-linear registration is performed by solving Laplace's equation with Dirichlet boundary conditions. Our methods provide significant improvements over previously reported registration techniques for the tested slices in 3D space, especially on slices with significant histological artifacts. Further, as an application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain. This work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data.Comment: 14 pages, 11 figure

Coverage, Continuity and Visual Cortical Architecture

The primary visual cortex of many mammals contains a continuous representation of visual space, with a roughly repetitive aperiodic map of orientation preferences superimposed. It was recently found that orientation preference maps (OPMs) obey statistical laws which are apparently invariant among species widely separated in eutherian evolution. Here, we examine whether one of the most prominent models for the optimization of cortical maps, the elastic net (EN) model, can reproduce this common design. The EN model generates representations which optimally trade of stimulus space coverage and map continuity. While this model has been used in numerous studies, no analytical results about the precise layout of the predicted OPMs have been obtained so far. We present a mathematical approach to analytically calculate the cortical representations predicted by the EN model for the joint mapping of stimulus position and orientation. We find that in all previously studied regimes, predicted OPM layouts are perfectly periodic. An unbiased search through the EN parameter space identifies a novel regime of aperiodic OPMs with pinwheel densities lower than found in experiments. In an extreme limit, aperiodic OPMs quantitatively resembling experimental observations emerge. Stabilization of these layouts results from strong nonlocal interactions rather than from a coverage-continuity-compromise. Our results demonstrate that optimization models for stimulus representations dominated by nonlocal suppressive interactions are in principle capable of correctly predicting the common OPM design. They question that visual cortical feature representations can be explained by a coverage-continuity-compromise.Comment: 100 pages, including an Appendix, 21 + 7 figure

Pubertal mouse mammary gland development - transcriptome analysis and the investigation of Fbln2 expression and function

Mouse mammary gland morphogenesis at puberty is a complex developmental process, regulated by systemic hormones, local growth factors and dependent on the epithelial/epithelial and epithelial/stromal interactions. TEBs which invade the fat pad are important in laying down the epithelial framework of the gland at this time point. The objective of this thesis was to use a combination of ‘pathway-‘ and ‘candidate gene analysis’ of the transcriptome of isolated TEBs and ducts and associated stroma, combined with detailed analyses of selected proteins, to further define the key proteins and processes involved at puberty. Using GeneChip® Mouse Exon 1.0 ST Arrays we identified the epithelial-, epithelial-stromal- and stromal transcriptomes of TEBs and ducts and defined the major functional pathways/biological processes in each compartment. By ranking the transcripts according to their expression levels and known functions in other systems, we identified genes of potential importance for pubertal mammary morphogenesis. We focused our study on Upk3a and Fbln2 and their protein products. Upk3a could only be detected at mRNA level and thus further analysis was based on Fbln2. We demonstrated that Fbln2 V1 and Fbln2 protein are predominantly expressed in the epithelium and stroma of TEBs. Using hormone primed mice we demonstrated that Fbln2 expression and localisation in the mouse mammary gland is positively regulated by E2 and P. Furthermore, by a combination of further in silico analysis, in vitro functional assays, IHC and IF we identified Vcan, Lama1, Fbn1, ColVIαIII, ColIVαI, ColXVIIIαI, Eln, Per, Acan, Nid, Itgb3 and Itga5 as potential binding partners of Fbln2 in mammary gland. Finally, we reported lack of an obvious mammary phenotype in Fbln2 KO-/- mice at puberty but demonstrated that this may be attributed to the over-compensation by Fbln1. This thesis demonstrates the benefit of DNA microarray analysis in studying pubertal development of mouse mammary gland. It identifies Fbln2 as a potential pubertal mammary regulator which by interacting with various ECM proteins at different sites of mammary milieu may contribute to an array of structural and migratory functions during mammary morphogenesis. These data substantially add to the understanding of the development of mammary gland at puberty and reveal many potential avenues for further investigations

Computer Vision Based Early Intraocular Pressure Assessment From Frontal Eye Images

Intraocular Pressure (IOP) in general, refers to the pressure in the eyes. Gradual increase of IOP and high IOP are conditions or symptoms that may lead to certain diseases such as glaucoma, and therefore, must be closely monitored. While the pressure in the eye increases, different parts of the eye may become affected until the eye parts are damaged. An effective way to prevent rise in eye pressure is by early detection. Exiting IOP monitoring tools include eye tests at clinical facilities and computer-aided techniques from fundus and optic nerves images. In this work, a new computer vision-based smart healthcare framework is presented to evaluate the intraocular pressure risk from frontal eye images early-on. The framework determines the status of IOP by analyzing frontal eye images using image processing and machine learning techniques. A database of images from the Princess Basma Hospital was used in this work. The database contains 400 eye images; 200 images with normal IOP and 200 high eye pressure case images. This study proposes novel features for IOP determination from two experiments. The first experiment extracts the sclera using circular hough transform, after which four features are extracted from the whole sclera. These features are mean redness level, red area percentage, contour area and contour height. The pupil/iris diameter ratio feature is also extracted from the frontal eye image after a series of pre-processing techniques. The second experiment extracts the sclera and iris segment using a fully conventional neural network technique, after which six features are extracted from only part of the segmented sclera and iris. The features include mean redness level, red area percentage, contour area, contour distance and contour angle along with the pupil/iris diameter ratio. Once the features are extracted, classification techniques are applied in order to train and test the images and features to obtain the status of the patients in terms of eye pressure. For the first experiment, neural network and support vector machine algorithms were adopted in order to detect the status of intraocular pressure. The second experiment adopted support vector machine and decision tree algorithms to detect the status of intraocular pressure. For both experiments, the framework detects the status of IOP (normal or high IOP) with high accuracies. This computer vison-based approach produces evidence of the relationship between the extracted frontal eye image features and IOP, which has not been previously investigated through automated image processing and machine learning techniques from frontal eye images