289 research outputs found

    Approaches Toward Combining Positron Emission Tomography with Magnetic Resonance Imaging

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    Positron emission tomography (PET) and magnetic resonance imaging (MRI) provide complementary information, and there has been a great deal of research effort to combine these two modalities. A major engineering hurdle is that photomultiplier tubes (PMT), used in conventional PET detectors, are sensitive to magnetic field. This thesis explores the design considerations of different ways of combining small animal PMT-based PET systems with MRI through experimentation, modelling and Monte Carlo simulation. A proof-of-principle hybrid PET and field-cycled MRI system was built and the first multimodality images are shown. A Siemens Inveon PET was exposed to magnetic fields of different strengths and the performance is characterized as a function of field magnitude. The results of this experiment established external magnetic field limits and design studies are shown for wide range of approaches to combining the PET system with various configurations of field-cycled MRI and superconducting MRI systems. A sophisticated Monte Carlo PET simulation workflow based on the GATE toolkit was developed to model the Siemens Inveon PET. Simulated PET data were converted to the raw Siemens list-mode format and were processed and reconstructed using the same processing chain as the data measured on the actual scanner. A general GATE add-on was developed to rapidly generate attenuation correction sinograms using the precise detector geometry and attenuation coefficients built into the emission simulation. Emission simulations and the attenuation correction add-on were validated against measured data. Simulations were performed to study the impact of radiofrequency coil components on PET image quality and to test the suitability of various MR-compatible materials for a dual-modality animal bed

    Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)

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    Reactive gases and aerosols are produced by terrestrial ecosystems, processed within plant canopies, and can then be emitted into the above-canopy atmosphere. Estimates of the above-canopy fluxes are needed for quantitative earth system studies and assessments of past, present and future air quality and climate. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) is described and used to quantify net terrestrial biosphere emission of isoprene into the atmosphere. MEGAN is designed for both global and regional emission modeling and has global coverage with ~1 km<sup>2</sup> spatial resolution. Field and laboratory investigations of the processes controlling isoprene emission are described and data available for model development and evaluation are summarized. The factors controlling isoprene emissions include biological, physical and chemical driving variables. MEGAN driving variables are derived from models and satellite and ground observations. Tropical broadleaf trees contribute almost half of the estimated global annual isoprene emission due to their relatively high emission factors and because they are often exposed to conditions that are conducive for isoprene emission. The remaining flux is primarily from shrubs which have a widespread distribution. The annual global isoprene emission estimated with MEGAN ranges from about 500 to 750 Tg isoprene (440 to 660 Tg carbon) depending on the driving variables which include temperature, solar radiation, Leaf Area Index, and plant functional type. The global annual isoprene emission estimated using the standard driving variables is ~600 Tg isoprene. Differences in driving variables result in emission estimates that differ by more than a factor of three for specific times and locations. It is difficult to evaluate isoprene emission estimates using the concentration distributions simulated using chemistry and transport models, due to the substantial uncertainties in other model components, but at least some global models produce reasonable results when using isoprene emission distributions similar to MEGAN estimates. In addition, comparison with isoprene emissions estimated from satellite formaldehyde observations indicates reasonable agreement. The sensitivity of isoprene emissions to earth system changes (e.g., climate and land-use) demonstrates the potential for large future changes in emissions. Using temperature distributions simulated by global climate models for year 2100, MEGAN estimates that isoprene emissions increase by more than a factor of two. This is considerably greater than previous estimates and additional observations are needed to evaluate and improve the methods used to predict future isoprene emissions

    Towards Emotion Recognition: A Persistent Entropy Application

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    Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (calm, happy, sad, angry, fearful, disgust and surprised)

    Galaxy And Mass Assembly: Galaxy Zoo spiral arms and star formation rates

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    Understanding the effect spiral structure has on star formation properties of galaxies is important to complete our picture of spiral structure evolution. Previous studies have investigated connections between spiral arm properties and star formation, but the effect that the number of spiral arms has on this process is unclear. Here, we use the Galaxy And Mass Assembly (GAMA) survey paired with the citizen science visual classifications from the Galaxy Zoo project to explore galaxies’ spiral arm number and how it connects to the star formation process. We use the votes from the GAMA-Kilo Degree Survey Galaxy Zoo classification to investigate the link between spiral arm number and stellar mass, star formation rate, and specific star formation rate (sSFR). We find that galaxies with fewer spiral arms have lower stellar masses and higher sSFRs, while those with more spiral arms tend towards higher stellar masses and lower sSFRs, and conclude that galaxies are less efficient at forming stars if they have more spiral arms. We note how previous studies’ findings may indicate a cause for this connection in spiral arm strength or opacity

    Estimations of isoprenoid emission capacity from enclosure studies: measurements, data processing, quality and standardized measurement protocols

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    The capacity for volatile isoprenoid production under standardized environmental conditions at a certain time (ES, the emission factor) is a key characteristic in constructing isoprenoid emission inventories. However, there is large variation in published ES estimates for any given species partly driven by dynamic modifications in ES due to acclimation and stress responses. Here we review additional sources of variation in ES estimates that are due to measurement and analytical techniques and calculation and averaging procedures, and demonstrate that estimations of ES critically depend on applied experimental protocols and on data processing and reporting. A great variety of experimental setups has been used in the past, contributing to study-to-study variations in ES estimates. We suggest that past experimental data should be distributed into broad quality classes depending on whether the data can or cannot be considered quantitative based on rigorous experimental standards. Apart from analytical issues, the accuracy of ES values is strongly driven by extrapolation and integration errors introduced during data processing. Additional sources of error, especially in meta-database construction, can further arise from inconsistent use of units and expression bases of ES. We propose a standardized experimental protocol for BVOC estimations and highlight basic meta-information that we strongly recommend to report with any ES measurement. We conclude that standardization of experimental and calculation protocols and critical examination of past reports is essential for development of accurate emission factor databases.JRC.H.7-Climate Risk Managemen

    On predicting the outcomes of chemotherapy treatments in Breast cancer

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    Chemotherapy is the main treatment commonly used for treating cancer patients. However, chemotherapy usually causes side effects some of which can be severe. The effects depend on a variety of factors including the type of drugs used, dosage, length of treatment and patient characteristics. In this paper, we use a data extraction from an oncology department in Scotland with information on treatment cycles, recorded toxicity level, and various observations concerning breast cancer patients for three years. The objective of our paper is to compare several different techniques applied to the same data set to predict the toxicity outcome of the treatment. We use a Markov model, Hidden Markov model, Random Forest and Recurrent Neural Network in our comparison. Through analysis and evaluation of the performance of these techniques, we can determine which method is more suitable in different situations to assist the medical oncologist in real-time clinical practice. We discuss the context of our work more generally and further work.Postprin

    Galaxy And Mass Assembly:Galaxy Zoo spiral arms and star formation rates

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    Understanding the effect spiral structure has on star formation properties of galaxies is important to complete our picture of spiral structure evolution. Previous studies have investigated connections between spiral arm properties and star formation, but the effect that the number of spiral arms has on this process is unclear. Here, we use the Galaxy And Mass Assembly (GAMA) survey paired with the citizen science visual classifications from the Galaxy Zoo project to explore galaxies’ spiral arm number and how it connects to the star formation process. We use the votes from the GAMA-Kilo Degree Survey Galaxy Zoo classification to investigate the link between spiral arm number and stellar mass, star formation rate, and specific star formation rate (sSFR). We find that galaxies with fewer spiral arms have lower stellar masses and higher sSFRs, while those with more spiral arms tend towards higher stellar masses and lower sSFRs, and conclude that galaxies are less efficient at forming stars if they have more spiral arms. We note how previous studies’ findings may indicate a cause for this connection in spiral arm strength or opacity
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