Determination of crystal orientation from micrographs using a MATLAB program

Crys.m is a MATLAB routine that combines a micrograph of a crystal with a scaleable, rotatable three-dimensional cage structure to determine the orientation of the crystal axes. The example presented here uses the morphology of tetragonal lysozyme. Rotation of the cage until it aligns with the crystal in the image yields the orientation of the c axis of the crystal relative to the image normal. This analysis can be used for quantitative determination of crystal orientation effects induced by electric, magnetic and/or gravitational fields

Hydrothermal activity and magma genesis along a propagating back-arc basin: Valu Fa Ridge (southern Lau Basin)

Valu Fa Ridge is an intraoceanic back-arc spreading center located at the southern prolongation of the Lau basin. Bathymetric observations as well as detailed sampling have been carried out along the spreading axis in order to trace hydrothermal and volcanic activity and to study magma generation processes. The survey shows that widespread lava flows from recent volcanic eruptions covered most of the Vai Lili hydrothermal vent field; only diffuse low-temperature discharge and the formation of thin layers of siliceous precipitates have been observed. Evidence of present-day hydrothermal activity at the Hine Hina site is indicated by a thermal anomaly in the overlying water column. Our studies did not reveal any signs of hydrothermal activity either above the seismically imaged magma chamber at 22°25′S or across the southern rift fault zone (22°51′S). Lavas recovered along the Valu Fa Ridge range from basaltic andesites to rhyolites with SiO2 contents higher than reported from any other intraoceanic back-arc basin. On the basis of the highly variable degrees of crystal fractionation along axis, the development of small disconnected magma bodies is suggested. In addition, the geochemical character of the volcanic rocks shows that the transition zone from oceanic spreading to propagating rifting is located south of the Hine Hina vent field in the vicinity of 22°35′S

Measurement of the B_s^0→K^+K^- lifetime relative to the B_d^0→K^+π^- lifetime

The study of B decays to charmless charged hadrons offers an opportunity to improve our understanding CP violation and to search for New Physics beyond the Standard Model. We present an analysis to make a measurement of the B_s^0→K^+K^- lifetime relative to B_d^0 lifetime which removes systematic bias introduced to the lifetime by distance of flight based selections

Tunable coaxial resonators based on silicon optical fibers

Thermal tuning of a coaxial fiber resonator with a silica cladding surrounding an inner silicon core is investigated. By pumping the silicon with below bandgap light, it is possible to redshift the WGM resonances

Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that 1) jointly optimising a third conditioning neural network that pre-processes the input image, can effectively extract patient-specific features for conditioning; and 2) combining multiple generative models trained separately with heuristically pre-disjointed training data sets can adequately mitigate the problem of mode collapse. Trained with diagnostic T2-weighted MR images from 143 real patients and 73,216 3D dense displacement fields from finite element simulations of intraoperative prostate motion due to transrectal ultrasound probe pressure, the proposed models produced physically-plausible patient-specific motion of prostate glands. The ability to capture biomechanically simulated motion was evaluated using two errors representing generalisability and specificity of the model. The median values, calculated from a 10-fold cross-validation, were 2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced approach demonstrates the feasibility of applying state-of-the-art machine learning algorithms to generate organ motion models from patient images, and shows significant promise for future research.Comment: Accepted to MICCAI 201

Treatment-seeking rates in malaria endemic countries

BACKGROUND: The proportion of individuals who seek treatment for fever is an important quantity in understanding access to and use of health systems, as well as for interpreting data on disease incidence from routine surveillance systems. For many malaria endemic countries (MECs), treatment-seeking information is available from national household surveys. The aim of this paper was to assemble sub-national estimates of treatment-seeking behaviours and to predict national treatment-seeking measures for all MECs lacking household survey data. METHODS: Data on treatment seeking for fever were obtained from Demographic and Health Surveys, Malaria Indicator Surveys and Multiple Cluster Indicator Surveys for every MEC and year that data were available. National-level social, economic and health-related variables were gathered from the World Bank as putative covariates of treatment-seeking rates. A generalized additive mixed model (GAMM) was used to estimate treatment-seeking behaviours for countries where survey data were unavailable. Two separate models were developed to predict the proportion of fever cases that would seek treatment at (1) a public health facility or (2) from any kind of treatment provider. RESULTS: Treatment-seeking data were available for 74 MECs and modelled for the remaining 24. GAMMs found that the percentage of pregnant women receiving prenatal care, vaccination rates, education level, government health expenditure, and GDP growth were important predictors for both categories of treatment-seeking outcomes. Treatment-seeking rates, which varied both within and among regions, revealed that public facilities were not always the primary facility type used. CONCLUSIONS: Estimates of treatment-seeking rates show how health services are utilized and help correct reported malaria case numbers to obtain more accurate measures of disease burden. The assembled and modelled data demonstrated that while treatment-seeking rates have overall increased over time, access remains low in some malaria endemic regions and utilization of government services is in some areas limited

Signalling paediatric side effects using an ensemble of simple study designs

Background: Children are frequently prescribed medication `o-label', meaning there has not been sucient testing of the medication to determine its safety or eectiveness. The main reason this safety knowledge is lacking is due to ethical restrictions that prevent children from being included in the majority of clinical trials. Methods: Multiple measures of association are calculated for each drug and medical event pair and these are used as features that are fed into a classifier to determine the likelihood of the drug and medical event pair corresponding to an adverse drug reaction. The classier is trained using known adverse drug reactions or known non-adverse drug reaction relationships. Results: The novel ensemble framework obtained a false positive rate of 0:149, a sensitivity of 0:547 and a specificity of 0:851 when implemented on a reference set of drug and medical event pairs. The novel framework consistently outperformed each individual simple study design. Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions eectively

A novel semi-supervised algorithm for rare prescription side effect discovery

Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of undesirable side effects. Side effects that occur in more than one in a thousand patients are likely to be signalled efficiently by current drug surveillance methods, however, these same methods may take decades before generating signals for rarer side effects, risking medical morbidity or mortality in patients prescribed the drug while the rare side effect is undiscovered. In this paper we propose a novel computational meta-analysis framework for signalling rare side effects that integrates existing methods, knowledge from the web, metric learning and semi-supervised clustering. The novel framework was able to signal many known rare and serious side effects for the selection of drugs investigated, such as tendon rupture when prescribed Ciprofloxacin or Levofloxacin, renal failure with Naproxen and depression associated with Rimonabant. Furthermore, for the majority of the drug investigated it generated signals for rare side effects at a more stringent signalling threshold than existing methods and shows the potential to become a fundamental part of post marketing surveillance to detect rare side effects

Technology Foresight: A Bibliometric Analysis to Identify Leading and Emerging Methods

Foresight studies provide essential information used by the government, industry and academia for technology planning and knowledge expansion. They are complicated, resource-intensive, and quite expensive. The approach, methods, and techniques must be carefully identified and selected. Despite the global importance of foresight activities, there are no frameworks to help one develop and plan a proper foresight study. This paper begins Keywords: technology foresight; strategic foresight; adaptive foresight; Social Network Analysis (SNA); bibliometric tools; data mining; text mining. to close this gap by analyzing and comparing different schools of thought and updating the literature with the most current tools and methods. Data mining techniques are used to identify articles through an extensive literature review. Social Network Analysis (SNA) techniques are used to identify and analyze leading journals, articles, and researchers. A framework is developed here to provide a guide to help in the selection of methods and tools for different approaches