A new finite element formulation of three-dimensional beam theory based on interpolation of curvature

A new finite element formulation of the *kinematically exact finite-strain beam theory* is presented. The finite element formulation employs the generalized virtual work in which the main role is played by the pseudo-curvature vector. The solution of the governing equations is found by using a combined Galerkin-collocation algorith

Insights from Amphioxus into the Evolution of Vertebrate Cartilage

Central to the story of vertebrate evolution is the origin of the vertebrate head, a problem difficult to approach using paleontology and comparative morphology due to a lack of unambiguous intermediate forms. Embryologically, much of the vertebrate head is derived from two ectodermal tissues, the neural crest and cranial placodes. Recent work in protochordates suggests the first chordates possessed migratory neural tube cells with some features of neural crest cells. However, it is unclear how and when these cells acquired the ability to form cellular cartilage, a cell type unique to vertebrates. It has been variously proposed that the neural crest acquired chondrogenic ability by recruiting proto-chondrogenic gene programs deployed in the neural tube, pharynx, and notochord. To test these hypotheses we examined the expression of 11 amphioxus orthologs of genes involved in neural crest chondrogenesis. Consistent with cellular cartilage as a vertebrate novelty, we find that no single amphioxus tissue co-expresses all or most of these genes. However, most are variously co-expressed in mesodermal derivatives. Our results suggest that neural crest-derived cartilage evolved by serial cooption of genes which functioned primitively in mesoderm

A Theoretical Framework for Target Propagation

The success of deep learning, a brain-inspired form of AI, has sparked interest in understanding how the brain could similarly learn across multiple layers of neurons. However, the majority of biologically-plausible learning algorithms have not yet reached the performance of backpropagation (BP), nor are they built on strong theoretical foundations. Here, we analyze target propagation (TP), a popular but not yet fully understood alternative to BP, from the standpoint of mathematical optimization. Our theory shows that TP is closely related to Gauss-Newton optimization and thus substantially differs from BP. Furthermore, our analysis reveals a fundamental limitation of difference target propagation (DTP), a well-known variant of TP, in the realistic scenario of non-invertible neural networks. We provide a first solution to this problem through a novel reconstruction loss that improves feedback weight training, while simultaneously introducing architectural flexibility by allowing for direct feedback connections from the output to each hidden layer. Our theory is corroborated by experimental results that show significant improvements in performance and in the alignment of forward weight updates with loss gradients, compared to DTP.Comment: 13 pages and 4 figures in main manuscript; 41 pages and 8 figures in supplementary materia

Understanding the Neural Bases of Implicit and Statistical Learning

© 2019 Cognitive Science Society, Inc. Both implicit learning and statistical learning focus on the ability of learners to pick up on patterns in the environment. It has been suggested that these two lines of research may be combined into a single construct of “implicit statistical learning.” However, by comparing the neural processes that give rise to implicit versus statistical learning, we may determine the extent to which these two learning paradigms do indeed describe the same core mechanisms. In this review, we describe current knowledge about neural mechanisms underlying both implicit learning and statistical learning, highlighting converging findings between these two literatures. A common thread across all paradigms is that learning is supported by interactions between the declarative and nondeclarative memory systems of the brain. We conclude by discussing several outstanding research questions and future directions for each of these two research fields. Moving forward, we suggest that the two literatures may interface by defining learning according to experimental paradigm, with “implicit learning” reserved as a specific term to denote learning without awareness, which may potentially occur across all paradigms. By continuing to align these two strands of research, we will be in a better position to characterize the neural bases of both implicit and statistical learning, ultimately improving our understanding of core mechanisms that underlie a wide variety of human cognitive abilities

On Embeddability of Buses in Point Sets

Set membership of points in the plane can be visualized by connecting corresponding points via graphical features, like paths, trees, polygons, ellipses. In this paper we study the \emph{bus embeddability problem} (BEP): given a set of colored points we ask whether there exists a planar realization with one horizontal straight-line segment per color, called bus, such that all points with the same color are connected with vertical line segments to their bus. We present an ILP and an FPT algorithm for the general problem. For restricted versions of this problem, such as when the relative order of buses is predefined, or when a bus must be placed above all its points, we provide efficient algorithms. We show that another restricted version of the problem can be solved using 2-stack pushall sorting. On the negative side we prove the NP-completeness of a special case of BEP.Comment: 19 pages, 9 figures, conference version at GD 201

A031 Développement d’un peptido-mimétique de la glycorpotein VI plaquettaire comme outil d’imagerie de la fibrose

ObjectifLa glycoprotéine VI est le récepteur d’activation des plaquettes par les collagènes de type I et de type III. Nous avons émis l’hypothèse que nous pourrions développer une sonde spécifique du collagène basée sur la spécificité de GPVI et que cette sonde permettrait de visualiser la fibrose in vivo par une méthode non invasive.MéthodesUn anticorps bloquant la liaison de GPVI au collagène a été utilisé pour cribler une banque peptidique permettant d’identifier un motif peptidique cyclique. La capacité du peptide à mimer la GPVI a été analysée par des études de liaison et de compétition en phase solide. La liaison au collagène tissulaire a été analysée par histochimie. L’imagerie in vivo a été réalisée par injection du peptide-marqué au Tc-99m dans un modèle de fibrose cicatricielle sur infarctus du myocarde chez le rat, scintigraphie et autoradiographieRésultatsLe peptide, nommé collagelin, se lie de manière spécifique à l’anticorps anti GPVI 9O12.2 et aux collagènes I et III in vitro et la liaison est inhibée par GPVI indiquant que le peptide mime GPVI. Cependant le collagelin n’inhibe pas l’agrégation des plaquettes induite par le collagène. Les études d’histochimie montrent que le collagelin se lie au collagène tissulaire sur coupe d’aorte et de queue de rat indiquant que le collagelin se comporte comme un traceur du collagène. Dans le modèle d’infarctus cicatriciel, une accumulation du collagelin radiomarqué est observée dans la zone cardiaque par scintigraphie planaire et tomographie chez les animaux avec MI mais pas chez les animaux contrôles ni avec un peptide contrôle. L’accumulation du traceur dans les zones de fibrose a été mise en évidence ex vivo par superposition des images d’autoradiographies et d’histologie sur coupes congelées.ConclusionNous avons produit un peptide qui mime en partie le site de liaison de GPVI au collagène. Ce peptide se comporte comme un traceur spécifique du collagène in vitro et in vivo. Nous proposons que ce traceur pourrait être utile pour le diagnostic et le suivi évolutif de la fibrose dans un grand nombre de pathologies

A New Mechanistic Scenario for the Origin and Evolution of Vertebrate Cartilage

The appearance of cellular cartilage was a defining event in vertebrate evolution because it made possible the physical expansion of the vertebrate “new head”. Despite its central role in vertebrate evolution, the origin of cellular cartilage has been difficult to understand. This is largely due to a lack of informative evolutionary intermediates linking vertebrate cellular cartilage to the acellular cartilage of invertebrate chordates. The basal jawless vertebrate, lamprey, has long been considered key to understanding the evolution of vertebrate cartilage. However, histological analyses of the lamprey head skeleton suggest it is composed of modern cellular cartilage and a putatively unrelated connective tissue called mucocartilage, with no obvious transitional tissue. Here we take a molecular approach to better understand the evolutionary relationships between lamprey cellular cartilage, gnathostome cellular cartilage, and lamprey mucocartilage. We find that despite overt histological similarity, lamprey and gnathostome cellular cartilage utilize divergent gene regulatory networks (GRNs). While the gnathostome cellular cartilage GRN broadly incorporates Runx, Barx, and Alx transcription factors, lamprey cellular cartilage does not express Runx or Barx, and only deploys Alx genes in certain regions. Furthermore, we find that lamprey mucocartilage, despite its distinctive mesenchymal morphology, deploys every component of the gnathostome cartilage GRN, albeit in different domains. Based on these findings, and previous work, we propose a stepwise model for the evolution of vertebrate cellular cartilage in which the appearance of a generic neural crest-derived skeletal tissue was followed by a phase of skeletal tissue diversification in early agnathans. In the gnathostome lineage, a single type of rigid cellular cartilage became dominant, replacing other skeletal tissues and evolving via gene cooption to become the definitive cellular cartilage of modern jawed vertebrates

Computing Schematic Layouts for Spatial Hypergraphs on Concentric Circles and Grids

Set systems can be visualized in various ways. An important distinction between techniques is whether the elements have a spatial location that is to be used for the visualization; for example, the elements are cities on a map. Strictly adhering to such location may severely limit the visualization and force overlay, intersections and other forms of clutter. On the other hand, completely ignoring the spatial dimension omits information and may hide spatial patterns in the data. We study layouts for set systems (or hypergraphs) in which spatial locations are displaced onto concentric circles or a grid, to obtain schematic set visualizations. We investigate the tractability of the underlying algorithmic problems adopting different optimization criteria (e.g. crossings or bends) for the layout structure, also known as the support of the hypergraph. Furthermore, we describe a simulated-annealing approach to heuristically optimize a combination of such criteria. Using this method in computational experiments, we explore the trade-offs and dependencies between criteria for computing high-quality schematic set visualizations