Disease activity and cognition in rheumatoid arthritis : an open label pilot study

Acknowledgements This work was supported in part by NIHR Newcastle Biomedical Research Centre. Funding for this study was provided by Abbott Laboratories. Abbott Laboratories were not involved in study design; in the collection, analysis and interpretation of data; or in the writing of the report.Peer reviewedPublisher PD

Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images

Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise ($1*1$) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10$\sim$1/100 network parameters and computational cost while achieving comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201

Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis

Presence of a high-dimensional stochastic parameter space with discontinuities poses major computational challenges in analyzing and quantifying the effects of the uncertainties in a physical system. In this paper, we propose a stochastic collocation method with adaptive mesh refinement (SCAMR) to deal with high dimensional stochastic systems with discontinuities. Specifically, the proposed approach uses generalized polynomial chaos (gPC) expansion with Legendre polynomial basis and solves for the gPC coefficients using the least squares method. It also implements an adaptive mesh (element) refinement strategy which checks for abrupt variations in the output based on the second order gPC approximation error to track discontinuities or non-smoothness. In addition, the proposed method involves a criterion for checking possible dimensionality reduction and consequently, the decomposition of the full-dimensional problem to a number of lower-dimensional subproblems. Specifically, this criterion checks all the existing interactions between input dimensions of a specific problem based on the high-dimensional model representation (HDMR) method, and therefore automatically provides the subproblems which only involve interacting dimensions. The efficiency of the approach is demonstrated using both smooth and non-smooth function examples with input dimensions up to 300, and the approach is compared against other existing algorithms

Change in Nutritional Status Modulates the Abundance of Critical Pre-initiation Intermediate Complexes During Translation Initiation \u3cem\u3ein Vivo\u3c/em\u3e

In eukaryotic translation initiation, eIF2∙GTP–Met-tRNAiMet ternary complex (TC) interacts with eIF3–eIF1–eIF5 complex to form the multifactor complex (MFC), while eIF2∙GDP associates with eIF2B for guanine nucleotide exchange. Gcn2p phosphorylates eIF2 to inhibit eIF2B. Here we evaluate the abundance of eIFs and their pre-initiation intermediate complexes in gcn2 deletion mutant grown under different conditions. We show that ribosomes are three times as abundant as eIF1, eIF2 and eIF5, while eIF3 is half as abundant as the latter three and hence, the limiting component in MFC formation. By quantitative immunoprecipitation, we estimate that ∼ 15% of the cellular eIF2 is found in TC during rapid growth in a complex rich medium. Most of the TC is found in MFC, and important, ∼ 40% of the total eIF2 is associated with eIF5 but lacks tRNAiMet. When the gcn2Δ mutant grows less rapidly in a defined complete medium, TC abundance increases threefold without altering the abundance of each individual factor. Interestingly, the TC increase is suppressed by eIF5 overexpression and Gcn2p expression. Thus, eIF2B-catalyzed TC formation appears to be fine-tuned by eIF2 phosphorylation and the novel eIF2/eIF5 complex lacking tRNAiMet

Photoelectrocatalytic degradation of bisphenol A in aqueous solution using a Au-TiO?/ITO film

Author name used in this publication: X. Z. LiAuthor name used in this publication: C. HeAuthor name used in this publication: Y. Xiong2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Dynamical preparation of EPR entanglement in two-well Bose-Einstein condensates

We propose to generate Einstein-Podolsky-Rosen (EPR) entanglement between groups of atoms in a two-well Bose-Einstein condensate using a dynamical process similar to that employed in quantum optics. The local nonlinear S-wave scattering interaction has the effect of creating a spin squeezing at each well, while the tunneling, analogous to a beam splitter in optics, introduces an interference between these fields that results in an inter-well entanglement. We consider two internal modes at each well, so that the entanglement can be detected by measuring a reduction in the variances of the sums of local Schwinger spin observables. As is typical of continuous variable (CV) entanglement, the entanglement is predicted to increase with atom number, and becomes sufficiently strong at higher numbers of atoms that the EPR paradox and steering non-locality can be realized. The entanglement is predicted using an analytical approach and, for larger atom numbers, stochastic simulations based on truncated Wigner function. We find generally that strong tunnelling is favourable, and that entanglement persists and is even enhanced in the presence of realistic nonlinear losses.Comment: 15 pages, 19 figure