17,985 research outputs found
Experimental study of the decay
The first dedicated search for the rare neutral-kaon decay
has been carried out in the E391a experiment at the
KEK 12-GeV proton synchrotron. The final upper limit of 2.6 at
the 90% confidence level was set on the branching ratio for the decay.Comment: 23 pages, 27 figures, accepted for publication as a regular article
in Physical Review
DNA polymerase B deficiency is linked to aggressive breast cancer: a comprehensive analysis of gene copy number, mRNA and protein expression in multiple cohorts
Short arm of chromosome 8 is a hot spot for chromosomal breaks, losses and amplifications in breast cancer. Although such genetic changes may have phenotypic consequences, the identity of candidate gene(s) remains to be clearly defined. Pol β gene is localized to chromosome 8p12 - p11 and encodes a key DNA base excision repair protein. Pol β may be a tumour suppressor and involved in breast cancer pathogenesis. We conducted the first and the largest study to comprehensively evaluate pol β in breast cancer. We investigated pol β gene copy number changes in two cohorts (n=128 & n=1952), pol β mRNA expression in two cohorts (n=249 & n=1952) and pol β protein expression in two cohorts (n=1406 & n=252). Artificial neural network analysis for pol β interacting genes was performed in 249 tumours. For mechanistic insights, pol β gene copy number changes, mRNA and protein levels were investigated together in 1 28 tumours and validated in 1952 tumours. Low pol β mRNA expression as well as low pol β protein expression was associated high grade, lymph node positivity, pleomorphism, triple negative, basal - like phenotypes and poor survival (ps<0.001). In oestrogen receptor (ER) positive sub - group that received tamoxifen, low pol β protein remains associated with aggressive phenotype and poor survival (ps<0.001). Artificial neural network analysis revealed ER as a top pol β interacting gene. Mechanistically, there was strong positive correlation between pol β gene copy number changes and pol β mRNA expression (p<0.0000001) and between pol β mRNA and pol β protein expression (p<0.0000001). This is the first study to provide evidence that pol β deficiency is linked to aggressive breast cancer and may have prognostic and predictive significance in patients
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
The inability to interpret the model prediction in semantically and visually
meaningful ways is a well-known shortcoming of most existing computer-aided
diagnosis methods. In this paper, we propose MDNet to establish a direct
multimodal mapping between medical images and diagnostic reports that can read
images, generate diagnostic reports, retrieve images by symptom descriptions,
and visualize attention, to provide justifications of the network diagnosis
process. MDNet includes an image model and a language model. The image model is
proposed to enhance multi-scale feature ensembles and utilization efficiency.
The language model, integrated with our improved attention mechanism, aims to
read and explore discriminative image feature descriptions from reports to
learn a direct mapping from sentence words to image pixels. The overall network
is trained end-to-end by using our developed optimization strategy. Based on a
pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we
conduct sufficient experiments to demonstrate that MDNet outperforms
comparative baselines. The proposed image model obtains state-of-the-art
performance on two CIFAR datasets as well.Comment: CVPR2017 Ora
Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion
Computed Tomography (CT) reconstruction is a fundamental component to a wide
variety of applications ranging from security, to healthcare. The classical
techniques require measuring projections, called sinograms, from a full
180 view of the object. This is impractical in a limited angle
scenario, when the viewing angle is less than 180, which can occur due
to different factors including restrictions on scanning time, limited
flexibility of scanner rotation, etc. The sinograms obtained as a result, cause
existing techniques to produce highly artifact-laden reconstructions. In this
paper, we propose to address this problem through implicit sinogram completion,
on a challenging real world dataset containing scans of common checked-in
luggage. We propose a system, consisting of 1D and 2D convolutional neural
networks, that operates on a limited angle sinogram to directly produce the
best estimate of a reconstruction. Next, we use the x-ray transform on this
reconstruction to obtain a "completed" sinogram, as if it came from a full
180 measurement. We feed this to standard analytical and iterative
reconstruction techniques to obtain the final reconstruction. We show with
extensive experimentation that this combined strategy outperforms many
competitive baselines. We also propose a measure of confidence for the
reconstruction that enables a practitioner to gauge the reliability of a
prediction made by our network. We show that this measure is a strong indicator
of quality as measured by the PSNR, while not requiring ground truth at test
time. Finally, using a segmentation experiment, we show that our reconstruction
preserves the 3D structure of objects effectively.Comment: Spotlight presentation at CVPR 201
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