Contrast-enhanced micro-CT imaging in murine carotid arteries : a new protocol for computing wall shear stress

Background: Wall shear stress (WSS) is involved in the pathophysiology of atherosclerosis. The correlation between WSS and atherosclerosis can be investigated over time using a WSS-manipulated atherosclerotic mouse model. To determine WSS in vivo, detailed 3D geometry of the vessel network is required. However, a protocol to reconstruct 3D murine vasculature using this animal model is lacking. In this project, we evaluated the adequacy of eXIA 160, a small animal contrast agent, for assessing murine vascular network on micro-CT. Also, a protocol was established for vessel geometry segmentation and WSS analysis. Methods: A tapering cast was placed around the right common carotid artery (RCCA) of ApoE(-/-) mice (n = 8). Contrast-enhanced micro-CT was performed using eXIA 160. An innovative local threshold-based segmentation procedure was implemented to reconstruct 3D geometry of the RCCA. The reconstructed RCCA was compared to the vessel geometry using a global threshold-based segmentation method. Computational fluid dynamics was applied to compute the velocity field and WSS distribution along the RCCA. Results: eXIA 160-enhanced micro-CT allowed clear visualization and assessment of the RCCA in all eight animals. No adverse biological effects were observed from the use of eXIA 160. Segmentation using local threshold values generated more accurate RCCA geometry than the global threshold-based approach. Mouse-specific velocity data and the RCCA geometry generated 3D WSS maps with high resolution, enabling quantitative analysis of WSS. In all animals, we observed low WSS upstream of the cast. Downstream of the cast, asymmetric WSS patterns were revealed with variation in size and location between animals. Conclusions: eXIA 160 provided good contrast to reconstruct 3D vessel geometry and determine WSS patterns in the RCCA of the atherosclerotic mouse model. We established a novel local threshold-based segmentation protocol for RCCA reconstruction and WSS computation. The observed differences between animals indicate the necessity to use mouse-specific data for WSS analysis. For our future work, our protocol makes it possible to study in vivo WSS longitudinally over a growing plaque

Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy

Mechanical properties of the adventitia are largely determined by the organization of collagen fibers. Measurements on the waviness and orientation of collagen, particularly at the zero-stress state, are necessary to relate the structural organization of collagen to the mechanical response of the adventitia. Using the fluorescence collagen marker CNA38-OG488 and confocal laser scanning microscopy, we imaged collagen fibers in the adventitia of rabbit common carotid arteries ex vivo. The arteries were cut open along their longitudinal axes to get the zero-stress state. We used semi-manual and automatic techniques to measure parameters related to the waviness and orientation of fibers. Our results showed that the straightness parameter (defined as the ratio between the distances of endpoints of a fiber to its length) was distributed with a beta distribution (mean value 0.72, variance 0.028) and did not depend on the mean angle orientation of fibers. Local angular density distributions revealed four axially symmetric families of fibers with mean directions of 0°, 90°, 43° and −43°, with respect to the axial direction of the artery, and corresponding circular standard deviations of 40°, 47°, 37° and 37°. The distribution of local orientations was shifted to the circumferential direction when measured in arteries at the zero-load state (intact), as compared to arteries at the zero-stress state (cut-open). Information on collagen fiber waviness and orientation, such as obtained in this study, could be used to develop structural models of the adventitia, providing better means for analyzing and understanding the mechanical properties of vascular wal

About the computation of the signature of surface singularities z^N+g(x,y)=0

In this article we describe our experiences with a parallel SINGULAR-implementation of the signature of a surface singularity defined by z^N+g(x,y)=0.Comment: 6 page

Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page

Optimized parameter search for large datasets of the regularization parameter and feature selection for ridge regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order with the number of applied regularization parameters, the number of folds in the validation set, the number of input features and the number of data samples in the training set. Our implementation has a computational complexity of the order . This computational cost is smaller than that of regression without regularization for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity and for forward and backward feature selection respectively, with the number of selected features and the number of removed features. This is an order faster than and for the naive implementation, with for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines

Small Hydrophobic Protein of Human Metapneumovirus Does Not Affect Virus Replication and Host Gene Expression In Vitro

Human metapneumovirus (HMPV) encodes a small hydrophobic (SH) protein of unknown function. HMPV from which the SH open reading frame was deleted (HMPVΔSH) was viable and displayed similar replication kinetics, cytopathic effect and plaque size compared with wild type HMPV in several cell-lines. In addition, no differences were observed in infection efficiency or cell-to-cell spreading in human primary bronchial epithelial cells (HPBEC) cultured at an air-liquid interphase. Host gene expression was analyzed in A549 cells infected with HMPV or HMPVΔSH using microarrays and mass spectrometry (MS) based techniques at multiple time points post infection. Only minor differences were observed in mRNA or protein expression levels. A possible function of HMPV SH as apoptosis blocker, as proposed for several members of the family Paramyxoviridae, was rejected based on this analysis. So far, a clear phenotype of HMPV SH deletion mutants in vitro at the virus and host levels is absent

Citizenship:Contrasting Dynamics at the Interface of Integration and Constitutionalism

EUDO Citizenship ObservatoryThis paper explores the different ways in which citizenship has played a role in polity formation in the context of the European Union. It focuses on both the ‘integration’ and the ‘constitution’ dimensions. The paper thus has two substantive sections. The first addresses the role of citizenship of the Union, examining the dynamic relationship between this concept, the role of the Court of Justice, and the free movement dynamic of EU law. The second turns to citizenship in the Union, looking at some recent political developments under which concepts of citizenship, and democratic membership as a key dimension of citizenship, have been given greater prominence. One key finding of the paper is that there is a tension between citizenship of the Union, as part of the EU's ‘old’ incremental constitutionalism based on the constitutionalisation of the existing Treaties, and citizenship in the Union, where the possibilities of a ‘new’ constitutionalism based on renewed constitutional documents have yet to be fully realise

Muscular diacylglycerol metabolism and insulin resistance

Failure of insulin to elicit an increase in glucose uptake and metabolism in target tissues such as skeletal muscle is a major characteristic of non-insulin dependent type 2 diabetes mellitus. A strong correlation between intramyocellular triacylglycerol concentrations and the severity of insulin resistance has been found and led to the assumption that lipid oversupply to skeletal muscle contributes to reduced insulin action. However, the molecular mechanism that links intramyocellular lipid content with the generation of muscle insulin resistance is still unclear. It appears unlikely that the neutral lipid metabolite triacylglycerol directly impairs insulin action. Hence it is believed that intermediates in fatty acid metabolism, such as fatty acyl-CoA, ceramides or diacylglycerol (DAG) link fat deposition in the muscle to compromised insulin signaling. DAG is identified as a potential mediator of lipid-induced insulin resistance, as increased DAG levels are associated with protein kinase C activation and a reduction in both insulin-stimulated IRS-1 tyrosine phosphorylation and PI3 kinase activity. As DAG is an intermediate in the synthesis of triacylglycerol from fatty acids and glycerol, its level can be lowered by either improving the oxidation of cellular fatty acids or by accelerating the incorporation of fatty acids into triacylglycerol. This review discusses the evidence that implicates DAG being central in the development of muscular insulin resistance. Furthermore, we will discuss if and how modulation of skeletal muscle DAG levels could function as a possible therapeutic target for the treatment of type 2 diabetes mellitus