Semi-automatic segmentation of subcutaneous tumours from micro-computed tomography images

Cataloged from PDF version of article.This paper outlines the first attempt to segment the boundary of preclinical subcutaneous tumours, which are frequently used in cancer research, from micro-computed tomography (microCT) image data. MicroCT images provide low tissue contrast, and the tumour-to-muscle interface is hard to determine, however faint features exist which enable the boundary to be located. These are used as the basis of our semi-automatic segmentation algorithm. Local phase feature detection is used to highlight the faint boundary features, and a level set-based active contour is used to generate smooth contours that fit the sparse boundary features. The algorithm is validated against manually drawn contours and micro-positron emission tomography (microPET) images. When compared against manual expert segmentations, it was consistently able to segment at least 70% of the tumour region (n = 39) in both easy and difficult cases, and over a broad range of tumour volumes. When compared against tumour microPET data, it was able to capture over 80% of the functional microPET volume. Based on these results, we demonstrate the feasibility of subcutaneous tumour segmentation from microCT image data without the assistance of exogenous contrast agents. Our approach is a proof-of-concept that can be used as the foundation for further research, and to facilitate this, the code is open-source and available from www.setuvo.com. © 2013 Institute of Physics and Engineering in Medicine

Power requirements for electron cyclotron current drive and ion cyclotron resonance heating for sawtooth control in ITER

13MW of electron cyclotron current drive (ECCD) power deposited inside the q = 1 surface is likely to reduce the sawtooth period in ITER baseline scenario below the level empirically predicted to trigger neo-classical tearing modes (NTMs). However, since the ECCD control scheme is solely predicated upon changing the local magnetic shear, it is prudent to plan to use a complementary scheme which directly decreases the potential energy of the kink mode in order to reduce the sawtooth period. In the event that the natural sawtooth period is longer than expected, due to enhanced alpha particle stabilisation for instance, this ancillary sawtooth control can be provided from > 10MW of ion cyclotron resonance heating (ICRH) power with a resonance just inside the q = 1 surface. Both ECCD and ICRH control schemes would benefit greatly from active feedback of the deposition with respect to the rational surface. If the q = 1 surface can be maintained closer to the magnetic axis, the efficacy of ECCD and ICRH schemes significantly increases, the negative effect on the fusion gain is reduced, and off-axis negative-ion neutral beam injection (NNBI) can also be considered for sawtooth control. Consequently, schemes to reduce the q = 1 radius are highly desirable, such as early heating to delay the current penetration and, of course, active sawtooth destabilisation to mediate small frequent sawteeth and retain a small q = 1 radius.Comment: 29 pages, 16 figure

Efficient Bayesian inference for natural time series using ARFIMA processes

Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long memory (LM). LM implies that these quantities experience non-trivial temporal memory, which potentially not only enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LM. In this paper we present a modern and systematic approach to the inference of LM. We use the flexible autoregressive fractional integrated moving average (ARFIMA) model, which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LM, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g., short-memory effects) can be integrated over in order to focus on long-memory parameters and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data and the central England temperature (CET) time series, with favorable comparison to the standard estimators. For CET we also extend our method to seasonal long memory

Actinobacillus actinomycetemcomitans lipopolysaccharide induces interleukin-6 expression through multiple mitogen-activated protein kinase pathways in periodontal ligament fibroblasts

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73410/1/j.1399-302X.2006.00314.x.pd

A Fully Bayesian Approach for Combining Multilevel Failure Information in Fault Tree Quantification and Corresponding Optimal Resource Allocation

This paper presents a fully Bayesian approach that simultaneously combines basic event and statistically independent higher event-level failure data in fault tree quantification. Such higher-level data could correspond to train, sub-system or system failure events. The full Bayesian approach also allows the highest-level data that are usually available for existing facilities to be automatically propagated to lower levels. A simple example illustrates the proposed approach. The optimal allocation of resources for collecting additional data from a choice of different level events is also presented. The optimization is achieved using a genetic algorithm

Ages and Abundances of Red Sequence Galaxies as a Function of LINER Emission Line Strength

Although the spectrum of a prototypical early-type galaxy is assumed to lack emission lines, a substantial fraction (likely as high as 30%) of nearby red sequence galaxy spectra contain emission lines with line ratios characteristic of low ionization nuclear emission-line regions (LINERs). We use spectra of ~6000 galaxies from the Sloan Digital Sky Survey (SDSS) in a narrow redshift slice (0.06 < z < 0.08) to compare the stellar populations of red sequence galaxies with and without LINER-like emission. The spectra are binned by internal velocity dispersion and by emission properties to produce high S/N stacked spectra. The recent stellar population models of R. Schiavon (2007) make it possible to measure ages, [Fe/H], and individual elemental abundance ratios [Mg/Fe], [C/Fe], [N/Fe], and [Ca/Fe] for each of the stacked spectra. We find that red sequence galaxies with strong LINER-like emission are systematically 2-3.5 Gyr (10-40%) younger than their emission-free counterparts at the same velocity dispersion. This suggests a connection between the mechanism powering the emission (whether AGN, post-AGB stars, shocks, or cooling flows) and more recent star formation in the galaxy. We find that mean stellar age and [Fe/H] increase with velocity dispersion for all galaxies. Elemental abundance [Mg/Fe] increases modestly with velocity dispersion in agreement with previous results, and [C/Fe] and [N/Fe] increase more strongly with velocity dispersion than does [Mg/Fe]. [Ca/Fe] appears to be roughly solar for all galaxies. At fixed velocity dispersion, galaxies with fainter r-band luminosities have lower [Fe/H] and older ages but similar abundance ratios compared to brighter galaxies.Comment: 25 pages, 17 figures, Accepted for publication in ApJ as of 16 July 2007; acceptance status updated, paper unchange

Convolutional LSTM Networks for Subcellular Localization of Proteins

Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model on the other hand are designed to handle sequences. In this study we demonstrate that LSTM networks predict the subcellular location of proteins given only the protein sequence with high accuracy (0.902) outperforming current state of the art algorithms. We further improve the performance by introducing convolutional filters and experiment with an attention mechanism which lets the LSTM focus on specific parts of the protein. Lastly we introduce new visualizations of both the convolutional filters and the attention mechanisms and show how they can be used to extract biological relevant knowledge from the LSTM networks

Clopidogrel Enhances Periodontal Repair in Rats Through Decreased Inflammation

Aim We hypothesized that platelet inactivation induced by drugs might interfere with periodontal repair in experimental periodontitis by suppressing the release of biological mediators from platelets at the site of injury. Material and Methods 60 rats were randomly assigned to 6 groups (n=10) and ligatures were placed around lower first molars of three groups. The other three groups were used as negative controls. Ligatures were removed after 10 days of periodontitis induction and all groups were submitted to treatment with aspirin (Asp) (30 mg/kg), clopidogrel (Clop) (75 mg/kg) or NaCl 0.9% intragastrically once daily for 3 days. Periodontal tissue was assessed by the measurement of CXCL12, CXCL4, CCL5 and PDGF by ELISA; histomorphometric analysis of PMN infiltration, attachment loss, bone loss and osteoclast numbers and quantification of blood vessels by imunnohistochemistry. Results During periodontal repair and treatment with NaCl 0.9%, CCL5 was decreased and CXCL12 increased when compared to negative control groups. Asp and Clop did not affect CCL5 expression, decreased CXCL12 but only Clop decreased CXCL4 and PDGF content compared to saline-treated animals. Clop increased blood vessel number, reduced PMN count, and decreased attachment and bone loss, also decreased osteoclast number in animals submitted or not to periodontal repair. Conclusion Systemic administration of Clop during 3 days improved the repair process associated with experimental periodontal disease, suggesting that it may have therapeutic value under situations where tissues undergo a transition from inflammation to repair