TVL₁ Planarity Regularization for 3D Shape Approximation

The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within. This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy

Feature Lines for Illustrating Medical Surface Models: Mathematical Background and Survey

This paper provides a tutorial and survey for a specific kind of illustrative visualization technique: feature lines. We examine different feature line methods. For this, we provide the differential geometry behind these concepts and adapt this mathematical field to the discrete differential geometry. All discrete differential geometry terms are explained for triangulated surface meshes. These utilities serve as basis for the feature line methods. We provide the reader with all knowledge to re-implement every feature line method. Furthermore, we summarize the methods and suggest a guideline for which kind of surface which feature line algorithm is best suited. Our work is motivated by, but not restricted to, medical and biological surface models.Comment: 33 page

Ductal-lobar organisation of human breast tissue, its relevance in disease and a research objective: vector mapping of parenchyma in complete breasts (the Astley Cooper project)

A human breast has many lobes, which are highly variable in size and shape, each with one central duct, its peripheral branches and their associated glandular tissues. Realising the potential of new endoductal approaches to breast diagnosis and improving our understanding of breast cancer precursors will require greatly improved knowledge of this ductal-lobar anatomy and the distribution of cancer precursors within it. This architecture is very challenging to study in its entirety: whole-breast lobe mapping has only been achieved for two human breasts. Clearly, much more efficient techniques are required. Streamlined data capture and visualisation of breast parenchymal anatomy from thin and thick sections in a vector format would allow integrated mapping of whole-breast structure with conventional histology and molecular data. The 'Astley Cooper digital breast mapping project' is proposed as a name for this achievable research objective. Success would offer new insights into the development of breast cancer precursor lesions, allow testing of the important 'sick lobe' hypothesis, improve correlation with imaging studies and provide 'ground truth' for mathematical modelling of breast growth

Codon reassignment in the Escherichia coli genetic code

Most organisms, from Escherichia coli to humans, use the ‘universal’ genetic code, which have been unchanged or ‘frozen’ for billions of years. It has been argued that codon reassignment causes mistranslation of genetic information, and must be lethal. In this study, we successfully reassigned the UAG triplet from a stop to a sense codon in the E. coli genome, by eliminating the UAG-recognizing release factor, an essential cellular component, from the bacterium. Only a few genetic modifications of E. coli were needed to circumvent the lethality of codon reassignment; erasing all UAG triplets from the genome was unnecessary. Thus, UAG was assigned unambiguously to a natural or non-natural amino acid, according to the specificity of the UAG-decoding tRNA. The result reveals the unexpected flexibility of the genetic code

Aldosterone and the mineralocorticoid receptor in renal injury: A potential therapeutic target in feline chronic kidney disease

There is a growing body of experimental and clinical evidence supporting mineralocorticoid receptor (MR) activation as a powerful mediator of renal damage in laboratory animals and humans. Multiple pathophysiological mechanisms are proposed, with the strongest evidence supporting aldosterone‐induced vasculopathy, exacerbation of oxidative stress and inflammation, and increased growth factor signalling promoting fibroblast proliferation and deranged extracellular matrix homeostasis. Further involvement of the MR is supported by extensive animal model experiments where MR antagonists (such as spironolactone and eplerenone) abrogate renal injury, including ischaemia‐induced damage. Additionally, clinical trials have shown MR antagonists to be beneficial in human chronic kidney disease (CKD) in terms of reducing proteinuria and cardiovascular events, though current studies have not evaluated primary end points which allow conclusions to made about whether MR antagonists reduce mortality or slow CKD progression. Although differences between human and feline CKD exist, feline CKD shares many characteristics with human disease including tubulointerstitial fibrosis. This review evaluates the evidence for the role of the MR in renal injury and summarizes the literature concerning aldosterone in feline CKD. MR antagonists may represent a promising therapeutic strategy in feline CKD

CT Image Segmentation Using FEM with Optimized Boundary Condition

The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery