220 research outputs found
Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation
The current burnout rate of radiologists is high due to the large and ever
growing number of Chest X-Rays (CXRs) needing interpretation and reporting.
Promisingly, automatic CXR report generation has the potential to aid
radiologists with this laborious task and improve patient care. Previous CXR
report generation methods are limited by their diagnostic inaccuracy and their
lack of alignment with the workflow of radiologists. To address these issues,
we present a new method that utilises the longitudinal history available from a
patient's previous CXR study when generating a report, which imitates a
radiologist's workflow. We also propose a new reward for reinforcement learning
based on CXR-BERT -- which captures the clinical semantic similarity between
reports -- to further improve CXR report generation. We conduct experiments on
the publicly available MIMIC-CXR dataset with metrics more closely correlated
with radiologists' assessment of reporting. The results indicate capturing a
patient's longitudinal history improves CXR report generation and that CXR-BERT
is a promising alternative to the current state-of-the-art reward. Our approach
generates radiology reports that are quantitatively more aligned with those of
radiologists than previous methods while simultaneously offering a better
pathway to clinical translation. Our Hugging Face checkpoint
(https://huggingface.co/aehrc/cxrmate) and code
(https://github.com/aehrc/cxrmate) are publicly available
Two potential hookworm DAF-16 target genes, SNR-3 and LPP-1: gene structure, expression profile, and implications of a cis-regulatory element in the regulation of gene expression
Background
Hookworms infect nearly 700 million people, causing anemia and developmental stunting in heavy infections. Little is known about the genomic structure or gene regulation in hookworms, although recent publication of draft genome assemblies has allowed the first investigations of these topics to be undertaken. The transcription factor DAF-16 mediates multiple developmental pathways in the free living nematode Caenorhabditis elegans, and is involved in the recovery from the developmentally arrested L3 in hookworms. Identification of downstream targets of DAF-16 will provide a better understanding of the molecular mechanism of hookworm infection. Methods
Genomic Fragment 2.23 containing a DAF-16 binding element (DBE) was used to identify overlapping complementary expressed sequence tags (ESTs). These sequences were used to search a draft assembly of the Ancylostoma caninum genome, and identified two neighboring genes, snr-3 and lpp-1, in a tail-to-tail orientation. Expression patterns of both genes during parasitic development were determined by qRT-PCR. DAF-16 dependent cis-regulatory activity of fragment 2.23 was investigated using an in vitro reporter system. Results
The snr-3 gene spans approximately 5.6 kb in the genome and contains 3 exons and 2 introns, and contains the DBE in its 3′ untranslated region. Downstream from snr-3 in a tail-to-tail arrangement is the gene lpp-1. The lpp-1 gene spans more than 6 kb and contains 10 exons and 9 introns. The A. caninum genome contains 2 apparent splice variants, but there are 7 splice variants in the A. ceylanicum genome. While the gene order is similar, the gene structures of the hookworm genes differ from their C. elegans orthologs. Both genes show peak expression in the late L4 stage. Using a cell culture based expression system, fragment 2.23 was found to have both DAF-16-dependent promoter and enhancer activity that required an intact DBE. Conclusions
Two putative DAF-16 targets were identified by genome wide screening for DAF-16 binding elements. Aca-snr-3 encodes a core small nuclear ribonucleoprotein, and Aca-lpp-1 encodes a lipid phosphate phosphohydrolase. Expression of both genes peaked at the late L4 stage, suggesting a role in L4 development. The 3′-terminal genomic fragment of the snr-3 gene displayed Ac-DAF-16-dependent cis-regulatory activity
Shoulder electromyography-based indicators to assess manifestation of muscle fatigue during laboratory-simulated manual handling task
Muscle fatigue is a risk factor for developing shoulder musculoskeletal disorders. The aim of this study was to identify shoulder electromyographic indicators that are most indicative of muscle fatigue during a laboratory simulated manual handling task. Thirty-two participants were equipped with electromyographic electrodes on 10 shoulder muscles and moved boxes for 45-minutes. The modified rate of perceived exertion (mRPE) was assessed every 5-minutes and multivariate linear regressions were performed between myoelectric manifestation of fatigue (MMF) and the mRPE scores. During a manual handling task representative of industry working conditions, spectral entropy, median frequency, and mobility were the electromyographic indicators that explained the largest percentage of the mRPE. Overall, the deltoids, biceps and upper trapezius were the muscles that most often showed significant changes over time in their electromyographic indicators. The combination of these three indicators may improve the accuracy for the assessment of MMF during manual handling
VLSI technology for smaller, cheaper, faster return link systems
Very Large Scale Integration (VLSI) Application-specific Integrated Circuit (ASIC) technology has enabled substantially smaller, cheaper, and more capable telemetry data systems. However, the rapid growth in available ASIC fabrication densities has far outpaced the application of this technology to telemetry systems. Available densities have grown by well over an order magnitude since NASA's Goddard Space Flight Center (GSFC) first began developing ASIC's for ground telemetry systems in 1985. To take advantage of these higher integration levels, a new generation of ASIC's for return link telemetry processing is under development. These new submicron devices are designed to further reduce the cost and size of NASA return link processing systems while improving performance. This paper describes these highly integrated processing components
Going deeper with brain morphometry using neural networks
Brain morphometry from magnetic resonance imaging (MRI) is a consolidated
biomarker for many neurodegenerative diseases. Recent advances in this domain
indicate that deep convolutional neural networks can infer morphometric
measurements within a few seconds. Nevertheless, the accuracy of the devised
model for insightful bio-markers (mean curvature and thickness) remains
unsatisfactory. In this paper, we propose a more accurate and efficient neural
network model for brain morphometry named HerstonNet. More specifically, we
develop a 3D ResNet-based neural network to learn rich features directly from
MRI, design a multi-scale regression scheme by predicting morphometric measures
at feature maps of different resolutions, and leverage a robust optimization
method to avoid poor quality minima and reduce the prediction variance. As a
result, HerstonNet improves the existing approach by 24.30% in terms of
intraclass correlation coefficient (agreement measure) to FreeSurfer
silver-standards while maintaining a competitive run-time
A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning
Objective: Manual contouring and registration for radiotherapy treatment planning and online adaptation for cervical cancer radiation therapy in computed tomography (CT) and magnetic resonance images (MRI) are often necessary. However manual intervention is time consuming and may suffer from inter or intra-rater variability. In recent years a number of computer-guided automatic or semi-automatic segmentation and registration methods have been proposed. Segmentation and registration in CT and MRI for this purpose is a challenging task due to soft tissue deformation, inter-patient shape and appearance variation and anatomical changes over the course of treatment. The objective of this work is to provide a state-of-the-art review of computer-aided methods developed for adaptive treatment planning and radiation therapy planning for cervical cancer radiation therapy. Methods: Segmentation and registration methods published with the goal of cervical cancer treatment planning and adaptation have been identified from the literature (PubMed and Google Scholar). A comprehensive description of each method is provided. Similarities and differences of these methods are highlighted and the strengths and weaknesses of these methods are discussed. A discussion about choice of an appropriate method for a given modality is provided. Results: In the reviewed papers a Dice similarity coefficient of around 0.85 along with mean absolute surface distance of 2-4. mm for the clinically treated volume were reported for transfer of contours from planning day to the treatment day. Conclusions: Most segmentation and non-rigid registration methods have been primarily designed for adaptive re-planning for the transfer of contours from planning day to the treatment day. The use of shape priors significantly improved segmentation and registration accuracy compared to other models
Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis
Deep neural networks are parameterised by weights that encode feature
representations, whose performance is dictated through generalisation by using
large-scale feature-rich datasets. The lack of large-scale labelled 3D medical
imaging datasets restrict constructing such generalised networks. In this work,
a novel 3D segmentation network, Fabric Image Representation Networks
(FIRENet), is proposed to extract and encode generalisable feature
representations from multiple medical image datasets in a large-scale manner.
FIRENet learns image specific feature representations by way of 3D fabric
network architecture that contains exponential number of sub-architectures to
handle various protocols and coverage of anatomical regions and structures. The
fabric network uses Atrous Spatial Pyramid Pooling (ASPP) extended to 3D to
extract local and image-level features at a fine selection of scales. The
fabric is constructed with weighted edges allowing the learnt features to
dynamically adapt to the training data at an architecture level. Conditional
padding modules, which are integrated into the network to reinsert voxels
discarded by feature pooling, allow the network to inherently process
different-size images at their original resolutions. FIRENet was trained for
feature learning via automated semantic segmentation of pelvic structures and
obtained a state-of-the-art median DSC score of 0.867. FIRENet was also
simultaneously trained on MR (Magnatic Resonance) images acquired from 3D
examinations of musculoskeletal elements in the (hip, knee, shoulder) joints
and a public OAI knee dataset to perform automated segmentation of bone across
anatomy. Transfer learning was used to show that the features learnt through
the pelvic segmentation helped achieve improved mean DSC scores of 0.962,
0.963, 0.945 and 0.986 for automated segmentation of bone across datasets.Comment: 12 pages, 10 figure
Preserving photon qubits in an unknown quantum state with Knill dynamical decoupling: Towards an all optical quantum memory
The implementation of polarization-based quantum communication is limited by signal loss and decoherence caused by the birefringence of a single-mode fiber. We investigate the Knill dynamical decoupling scheme, implemented using half-wave plates, to minimize decoherence and show that a fidelity greater than 99% can be achieved in absence of rotation error and fidelity greater than 96% can be achieved in presence of rotation error. Such a scheme can be used to preserve any quantum state with high fidelity and has potential application for constructing all optical quantum delay line, quantum memory, and quantum repeater
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