Review of presentations at the 6th European Lupus Meeting 3-5 March 2005.

The 6th European Lupus Meeting was held at the Royal College of Physicians of London and was attended by 450 delegates. The conference brought together leading speakers from Europe and North America who reviewed current knowledge and exciting new developments in both clinical and basic science aspects of systemic lupus erythematosus. This review summarizes the major points covered in each session

Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds

A novel multi-scale operator for unorganized 3D point clouds is introduced. The Difference of Normals (DoN) provides a computationally efficient, multi-scale approach to processing large unorganized 3D point clouds. The application of DoN in the multi-scale filtering of two different real-world outdoor urban LIDAR scene datasets is quantitatively and qualitatively demonstrated. In both datasets the DoN operator is shown to segment large 3D point clouds into scale-salient clusters, such as cars, people, and lamp posts towards applications in semi-automatic annotation, and as a pre-processing step in automatic object recognition. The application of the operator to segmentation is evaluated on a large public dataset of outdoor LIDAR scenes with ground truth annotations.Comment: To be published in proceedings of 3DIMPVT 201

Annus Mirabilis - a guide to the 6th European Lupus meeting 3-5 March 2005

This article has no abstract

Arginine mutation alters binding of a human monoclonal antibody to antigens linked to systemic lupus erythematosus and the antiphospholipid syndrome

Objective: Previous studies have shown the importance of somatic mutations and arginine residues in the complementarity-determining regions (CDRs) of pathogenic anti-double-stranded DNA (anti-dsDNA) antibodies in human and murine lupus, and in studies of murine antibodies, a role of mutations at position 53 in VH CDR2 has been demonstrated. We previously demonstrated in vitro expression and mutagenesis of the human IgG1 monoclonal antibody B3. The present study was undertaken to investigate, using this expression system, the importance of the arginine residue at position 53 (R53) in B3 VH. Methods: R53 was altered, by site-directed mutagenesis, to serine, asparagine, or lysine, to create 3 expressed variants of VH. In addition, the germline sequence of the VH3-23 gene (from which B3 VH is derived) was expressed either with or without arginine at position 53. These 5 new heavy chains, as well as wild-type B3 VH, were expressed with 4 different light chains, and the resulting antibodies were assessed for their ability to bind to nucleosomes, -actinin, cardiolipin, ovalbumin, 2-glycoprotein I (2GPI), and the N-terminal domain of 2GPI (domain I), using direct binding assays. Results: The presence of R53 was essential but not sufficient for binding to dsDNA and nucleosomes. Conversely, the presence of R53 reduced binding to -actinin, ovalbumin, 2GPI, and domain I of 2GPI. The combination B3 (R53S) VH/B3 VL bound human, but not bovine, 2GPI. Conclusion: The fact that the R53S substitution significantly alters binding of B3 to different clinically relevant antigens, but that the alteration is in opposite directions depending on the antigen, implies that this arginine residue plays a critical role in the affinity maturation of antibody B3

Droplet dynamics in confinement

This study is to understand confinement effect on the dynamical behaviour of a droplet immersed in an immiscible liquid subjected to a simple shear flow. The lattice Boltzmann method, which uses a forcing term and a recoloring algorithm to realize the interfacial tension effect and phase separation respectively, is adopted to systematically study droplet deformation and breakup in confined conditions. The effects of capillary number, viscosity ratio of the droplet to the carrier liquid, and confinement ratio are studied. The simulation results are compared against the theoretical predictions, experimental and numerical data available in literature. We find that increasing confinement ratio will enhance deformation, and the maximum deformation occurs at the viscosity ratio of unity. The droplet is found to orient more towards the flow direction with increasing viscosity ratio or confinement ratio. Also, it is noticed that the wall effect becomes more significant for the confinement ratios larger than 0.4. Finally, the critical capillary number, above which the droplet breakup occurs, is found to be mildly affected by the confinement for the viscosity ratio of unity. Upon increasing the confinement ratio, the critical capillary number increases for the viscosity ratios less than unity, but decreases for the viscosity ratios more than unity

Deep roots: Improving CNN efficiency with hierarchical filter groups

We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root. This allows a significant reduction in computational cost and number of parameters compared to state-of-the-art deep CNNs, without compromising accuracy, by exploiting the sparsity of inter-layer filter dependencies. We validate our approach by using it to train more efficient variants of state-of-the-art CNN architectures, evaluated on the CIFAR10 and ILSVRC datasets. Our results show similar or higher accuracy than the baseline architectures with much less computation, as measured by CPU and GPU timings. For example, for ResNet 50, our model has 40% fewer parameters, 45% fewer floating point operations, and is 31% (12%) faster on a CPU (GPU). For the deeper ResNet 200 our model has 25% fewer floating point operations and 44% fewer parameters, while maintaining state-of-the-art accuracy. For GoogLeNet, our model has 7% fewer parameters and is 21% (16%) faster on a CPU (GPU).Microsoft Research PhD Scholarshi

Investigating the molecular targets of antiphospholipid antibodies.

The antiphospholipid syndrome (APS) is characterised by the presence of pathogenic antiphospholipid antibodies (aPL) that bind phospholipid via the N-terminal domain I (DI) of the protein cofactor beta2-glycoprotein I (beta2GPI). This thesis aims to investigate and identify the nature of this interaction at the molecular level. In order to study DI, a system for expressing and purifying sufficient quantities of this protein was developed using Escherichia coli (E.coli) as the expression host. This was achieved primarily by synthesising a gene with all codons optimised for E.coli, tight regulation of expression and recovery of DI from the periplasm to facilitate folding. Recombinant purified DI bound clinically relevant human monoclonal and polyclonal purified IgG aPL in both solid an fluid phase assays. Hypotheses were generated that identified candidate epitopes for aPL binding as potentially involving residues D8, D9, E23, E26, E27, R39, G40, R43 and the DI-II interlinker region. In total, 15 mutants of DI targeting these areas were created and computer modelling employed to predict the likely structural effects of these mutations upon DI. Expressed DI mutants were then tested in both solid and fluid phase assays for binding to polyclonal IgG derived from patients with APS and compared to wild-type DI. Some mutations, such as those targeting R39, caused loss of binding to aPL in the fluid phase whilst others caused enhanced binding over wild-type DI. In conclusion, this thesis demonstrates that DI of 62GPI may be expressed using E.coli and binds clinically relevant IgG aPL. Detailed mutational studies support the concept that aPL bind discontinuous epitopes on DI involving regions D8 and D9, R39 to R43 and the DI-II interlinker which are in close proximity to each other in the tertiary structure. The ability to produce a mutant of DI with enhanced binding over wild-type holds therapeutic potential

Scanning Electron Microscopy - Electron Beam Induced Current and Deep Level Transient Spectroscopy Studies of GaAs(In) Layers grown by Molecular Beam Epitaxy

Electrically active defects in indium-doped (0.6%) GaAs layers grown by Molecular Beam Epitaxy (MBE) on Si-doped (≈1x1018 cm-3) GaAs substrates have been studied by the combination of two techniques: Scanning Electron Microscope - Electron Beam Induced Current (SEM-EBIC) technique, and Deep Level Transient Spectroscopy (DLTS). The epilayers studied were three microns thick. No electrically active defects were revealed by the EBIC micrographs in the top one micron of the epilayers, whereas a large number of non-propagating misfit dislocations were observed at the epilayer/substrate interface. DLTS measurements made in the dislocation free top region of the epilayer showed the presence of three well known traps, which had previously been observed to also exist near the interface. It is concluded that these traps are not related to misfit dislocations

IgG anti-apolipoprotein A-1 antibodies in patients with systemic lupus erythematosus are associated with disease activity and corticosteroid therapy: an observational study.

IgG anti-apolipoprotein A-1 (IgG anti-apoA-1) antibodies are present in patients with systemic lupus erythematosus (SLE) and may link inflammatory disease activity and the increased risk of developing atherosclerosis and cardiovascular disease (CVD) in these patients. We carried out a rigorous analysis of the associations between IgG anti-apoA-1 levels and disease activity, drug therapy, serology, damage, mortality and CVD events in a large British SLE cohort

Refining Architectures of Deep Convolutional Neural Networks

© 2016 IEEE. Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks [11, 27]. However, a question of paramount importance is somewhat unanswered in deep learning research - is the selected CNN optimal for the dataset in terms of accuracy and model size? In this paper, we intend to answer this question and introduce a novel strategy that alters the architecture of a given CNN for a specified dataset, to potentially enhance the original accuracy while possibly reducing the model size. We use two operations for architecture refinement, viz. stretching and symmetrical splitting. Stretching increases the number of hidden units (nodes) in a given CNN layer, while a symmetrical split of say K between two layers separates the input and output channels into K equal groups, and connects only the corresponding input-output channel groups. Our procedure starts with a pre-trained CNN for a given dataset, and optimally decides the stretch and split factors across the network to refine the architecture. We empirically demonstrate the necessity of the two operations. We evaluate our approach on two natural scenes attributes datasets, SUN Attributes [16] and CAMIT-NSAD [20], with architectures of GoogleNet and VGG-11, that are quite contrasting in their construction. We justify our choice of datasets, and show that they are interestingly distinct from each other, and together pose a challenge to our architectural refinement algorithm. Our results substantiate the usefulness of the proposed method