Increasing the quality of seismic interpretation

Acknowledgments E. Macrae was funded by an NERC Open CASE Ph.D. award (NE/F013728/1) with Midland Valley Exploration Ltd. as the industry partner. We thank 763 geoscientists for their participation, and in particular, the REs who gave their time freely to the project. M. Scott (University of Glasgow, UK) is thanked for assisting with the statistical analysis. Four reviewers are thanked for their constructive comments that improved the manuscript.Peer reviewedPublisher PD

Automated high accuracy, rapid beam hardening correction in X-Ray Computed Tomography of multi-mineral, heterogeneous core samples

X-ray Computed Tomography scanning is an innovative procedure that allows representing the internal structure of samples. Among its several purposes, X-ray CT is widely used for investigation of petrophysical properties of porous media. To provide accurate results, it is necessary to have high quality scan images, free of artefacts. One of the most problematic artefacts is beam hardening, which, in cylindrical shapes, increases the attenuation values with increasing distance from the centre. Until now, no automatic solution has been proposed for cylindrically-shaped cores that is both computationally feasible and applicable to all geological media. A new technique is here introduced for correcting beam hardening, using a linearization procedure of the beam hardening curve applied after the reconstruction process. We have developed an automated open source plug-in, running on ImageJ software, which does not require any a priori knowledge of the material, distance from the source or the scan conditions (current, energy), nor any segmentation of phases or calibration scan on phantom data. It is suitable for expert and non-expert use, alike. We have tested the technique on μCT scan images of a plastic rod, a sample of loose sand, several heterogeneous sandstone core samples (with near-cylindrical shapes), and finally, on an internal scan of a Berea sandstone core. The Berea core was also scanned using a medical X-ray CT scanner with a fan-beam geometry, as opposed to a cone beam geometry, showing that our algorithm is equally effective in both cases. Our correction technique successfully removes the beam hardening artefact in all cases, as well as removing the cupping effect common to internal scans. For a Berea Sandstone, with a porosity of 20%, porosity calculated using the corrected scan is 20.54%, which compares to a value of 14.24% using the software provided by the manufacturer

2010 ACVIM Small Animal Consensus Statement on Leptospirosis: Diagnosis, Epidemiology, Treatment, and Prevention

This report offers a consensus opinion on the diagnosis, epidemiology, treatment, and prevention of leptospirosis in dogs, an important zoonosis. Clinical signs of leptospirosis in dogs relate to development of renal disease, hepatic disease, uveitis, and pulmonary hemorrhage. Disease may follow periods of high rainfall, and can occur in dogs roaming in proximity to water sources, farm animals, or wildlife, or dogs residing in suburban environments. Diagnosis is based on acute and convalescent phase antibody titers by the microscopic agglutination test (MAT), with or without use of polymerase chain reaction assays. There is considerable interlaboratory variation in MAT results, and the MAT does not accurately predict the infecting serogroup. The recommended treatment for optimal clearance of the organism from renal tubules is doxycycline, 5 mg/kg PO q12h, for 14 days. Annual vaccination can prevent leptospirosis caused by serovars included in the vaccine and is recommended for dogs at risk of infection

Cystic fibrosis mice carrying the missense mutation G551D replicate human genotype phenotype correlations

We have generated a mouse carrying the human G551D mutation in the cystic fibrosis transmembrane conductance regulator gene (CFTR) by a one-step gene targeting procedure. These mutant mice show cystic fibrosis pathology but have a reduced risk of fatal intestinal blockage compared with 'null' mutants, in keeping with the reduced incidence of meconium ileus in G551D patients. The G551D mutant mice show greatly reduced CFTR-related chloride transport, displaying activity intermediate between that of cftr(mlUNC) replacement ('null') and cftr(mlHGU) insertional (residual activity) mutants and equivalent to approximately 4% of wild-type CFTR activity. The long-term survival of these animals should provide an excellent model with which to study cystic fibrosis, and they illustrate the value of mouse models carrying relevant mutations for examining genotype-phenotype correlations

HIV-infected sex workers with beneficial HLA-variants are potential hubs for selection of HIV-1 recombinants that may affect disease progression

Cytotoxic T lymphocyte (CTL) responses against the HIV Gag protein are associated with lowering viremia; however, immune control is undermined by viral escape mutations. The rapid viral mutation rate is a key factor, but recombination may also contribute. We hypothesized that CTL responses drive the outgrowth of unique intra-patient HIV-recombinants (URFs) and examined gag sequences from a Kenyan sex worker cohort. We determined whether patients with HLA variants associated with effective CTL responses (beneficial HLA variants) were more likely to carry URFs and, if so, examined whether they progressed more rapidly than patients with beneficial HLA-variants who did not carry URFs. Women with beneficial HLA-variants (12/52) were more likely to carry URFs than those without beneficial HLA variants (3/61) (p < 0.0055; odds ratio = 5.7). Beneficial HLA variants were primarily found in slow/standard progressors in the URF group, whereas they predominated in long-term non-progressors/survivors in the remaining cohort (p = 0.0377). The URFs may sometimes spread and become circulating recombinant forms (CRFs) of HIV and local CRF fragments were over-represented in the URF sequences (p < 0.0001). Collectively, our results suggest that CTL-responses associated with beneficial HLA variants likely drive the outgrowth of URFs that might reduce the positive effect of these CTL responses on disease progression

Simulating spatial and temporal evolution of multiple wing cracks around faults in crystalline basement rocks

Fault zones are structurally highly spatially heterogeneous and hence extremely complex. Observations of fluid flow through fault zones over several scales show that this structural complexity is reflected in the hydrogeological properties of faults. Information on faults at depth is scarce, hence, it is highly valuable to understand the controls on spatial and temporal fault zone development. In this paper we increase our understanding of fault damage zone development in crystalline rocks by dynamically simulating the growth of single and multiple splay fractures produced from failure on a pre-existing fault. We present a new simulation model, MOPEDZ (Modeling Of Permeability Evolution in the Damage Zone surrounding faults), that simulates fault evolution through solution of Navier's equation with a combined Mohr-Coulomb and tensile failure criteria. Simulations suggest that location, frequency, mode of failure and orientation of splay fractures are significantly affected both by the orientation of the fault with respect to the maximum principal compressive stress and the conditions of differential stress. Model predictions compare well with published field outcrop data, confirming that this model produces realistic damage zone geometries

A Microsoft-Excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.BACKGROUND: The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves. METHODS: We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL's interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation. RESULTS: We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software. CONCLUSIONS: Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows for more efficient and transparent critical appraisal of network meta-analyses, enhanced standardization of reporting, and integration with health economic evaluations which are frequently Excel-based.CC is a recipient of a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (funding reference number—CGV 121171) and is a trainee on the Canadian Institutes of Health Research Drug Safety and Effectiveness Network team grant (funding reference number—116573). BH is funded by a New Investigator award from the Canadian Institutes of Health Research and the Drug Safety and Effectiveness Network. This research was partly supported by funding from CADTH as part of a project to develop Excel-based tools to support the conduct of health technology assessments. This research was also supported by Cornerstone Research Group

A Data Science and Machine Learning Approach to Measure and Monitor Physical Activity in Children

Physical Activity is a fundamental component for the maintenance of a healthy lifestyle. Recommendations for physical activity levels are issued by most governments as part of public health measures. Therefore, it is vital for regulatory purposes, that there are reliable measurements of physical activity. However, the techniques and protocols used in existing physical activity research, including laboratory-based measurement, have received increasingly critical scrutiny in recent times. Consequently, physical activity researchers have begun to explore the use of wearable sensing technology to capture large amounts of data and the use of machine learning techniques, specifically artificial neural networks, to produce classifications for specific physical activity events. This paper explores this idea further and presents a supervised machine learning approach that utilises data obtained from accelerometer sensors worn by children in free-living environments. The paper posits a rigorous data science approach that presents a set of activities and features suitable for measuring physical activity in children in free-living environments. A Multilayer Perceptron neural network is used to classify physical activities by activity type, using ecologically valid data from body worn accelerometer sensors. A rigorous reproducible data science methodology is presented for subsequent use in physical activity research. Our results show that it was possible to obtain an overall accuracy of 92% using the initial data set, and 99.8% using interpolated cases