p21-activated kinases and gastrointestinal cancer

Abstractp21-activated kinases (PAKs) were initially identified as effector proteins downstream from GTPases of the Rho family. To date, six members of the PAK family have been discovered in mammalian cells. PAKs play important roles in growth factor signalling, cytoskeletal remodelling, gene transcription, cell proliferation and oncogenic transformation. A large body of research has demonstrated that PAKs are up-regulated in several human cancers, and that their overexpression is linked to tumour progression and resistance to therapy. Structural and biochemical studies have revealed the mechanisms involved in PAK signalling, and opened the way to the development of PAK-targeted therapies for cancer treatment. Here we summarise recent findings from biological and clinical research on the role of PAKs in gastrointestinal cancer, and discuss the current status of PAK-targeted anticancer therapies

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

Preparation of TiO?/ITO and TiO?/Ti photoelectrodes by magnetron sputtering for photocatalytic application

Author name used in this publication: X. Z. LiAuthor name used in this publication: N. GrahamAuthor name used in this publication: Y. WangAuthor name used in this publication: C. He2005-2006 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe