The Experts below are selected from a list of 267 Experts worldwide ranked by ideXlab platform
Ola Dale - One of the best experts on this subject based on the ideXlab platform.
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Choosing the Unit of Measurement counts: the use of oral morphine equivalents in studies of opioid consumption is a useful addition to defined daily doses.
Palliative Medicine, 2011Co-Authors: Kristian Svendsen, P. C. Borchgrevink, O. M. S. Fredheim, K Hamunen, A. Mellbye, Ola DaleAbstract:Aim: Defined daily dose (DDD) is the most common Measurement Unit used in drug consumption studies. The DDD for opioids may not reflect their relative clinical potencies. The aim of this study was to explore whether opioid consumption data may be interpreted differently when adding oral morphine equivalent (OMEQ) dose as a Measurement Unit compared with using DDD.Methods: The equianalgesic ratio of each opioid relative to morphine was tabulated. Data on opioid consumption expressed in DDD were converted to OMEQs using the equianalgesic ratios. The opioid consumption was compared in three different study settings: clinical data from an opioid switching study, trends within one country and a comparison between countries.Results: Using DDD, the opioid consumption in Norway between 2004–2008 increased of 6.7%, while the increase was 23.6% using OMEQ. While DDD/1000 inhabitants/day showed that Sweden had the highest consumption of opioids among the Nordic countries, OMEQ/1000 inhabitants/day showed that Denmar...
Pieter G N Kramers - One of the best experts on this subject based on the ideXlab platform.
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an aggregate public health indicator to represent the impact of multiple environmental exposures
Epidemiology, 1999Co-Authors: Augustinus E M De Hollander, Johan M Melse, Erik Lebret, Pieter G N KramersAbstract:We present a framework to aggregate divergent health impacts associated with different types of environmental exposures, such as air pollution, residential noise, and large technologic risks. From the policy maker's point of view, there are at least three good reasons for this type of aggregation: comparative risk evaluation (for example, setting priorities), evaluation of the efficiency of environmental policies in terms of health gain, and characterizing health risk associated with geographical accumulation of multiple environmental exposures. The proposed impact measure integrates three important dimensions of public health: life expectancy, quality of life, and number of people affected. Time is the Unit of Measurement. "Healthy life years" are either lost by premature death or by loss of quality of life, measured as discounted life years within a population. Severity weights (0 for perfect health, 1 for death) are assigned to discount the time spent
Mark W Dunlop - One of the best experts on this subject based on the ideXlab platform.
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water addition evaporation and water holding capacity of poultry litter
Science of The Total Environment, 2015Co-Authors: Mark W Dunlop, P J Blackall, Richard M StuetzAbstract:Litter moisture content has been related to ammonia, dust and odour emissions as well as bird health and welfare. Improved understanding of the water holding properties of poultry litter as well as water additions to litter and evaporation from litter will contribute to improved litter moisture management during the meat chicken grow-out. The purpose of this paper is to demonstrate how management and environmental conditions over the course of a grow-out affect the volume of water A) applied to litter, B) able to be stored in litter, and C) evaporated from litter on a daily basis. The same Unit of Measurement has been used to enable direct comparison-litres of water per square metre of poultry shed floor area, L/m(2), assuming a litter depth of 5cm. An equation was developed to estimate the amount of water added to litter from bird excretion and drinking spillage, which are sources of regular water application to the litter. Using this equation showed that water applied to litter from these sources changes over the course of a grow-out, and can be as much as 3.2L/m(2)/day. Over a 56day grow-out, the total quantity of water added to the litter was estimated to be 104L/m(2). Litter porosity, water holding capacity and water evaporation rates from litter were measured experimentally. Litter porosity decreased and water holding capacity increased over the course of a grow-out due to manure addition. Water evaporation rates at 25°C and 50% relative humidity ranged from 0.5 to 10L/m(2)/day. Evaporation rates increased with litter moisture content and air speed. Maintaining dry litter at the peak of a grow-out is likely to be challenging because evaporation rates from dry litter may be insufficient to remove the quantity of water added to the litter on a daily basis.
Henderson, Luke A - One of the best experts on this subject based on the ideXlab platform.
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DTI-based upper limit of voxel free water fraction.
eScholarship University of California, 2018Co-Authors: Macey, Paul M, Thomas M Albert, Henderson, Luke AAbstract:Background:Free water (FW) in neuroimaging is non-flowing extracellular water in the cranium and brain tissue, and includes both cerebral spinal fluid (CSF) and fluid in intercellular space or edema. For a region such as a voxel (spatial Unit of Measurement in neuroimaging), the FW fraction is defined as the volume fraction of FW within that volume. Quantifying the FW fraction allows estimating contamination by fluid of neuroimaging or magnetic resonance spectroscopy Measurements within a voxel. New method:An upper limit to the fraction of FW within a voxel, based on any diffusion tensor imaging (DTI) sequence including a standard single shell at one b-value, can be derived from the standard diffusion tensor by scaling the third eigenvalue of the diffusion tensor. Assuming a two-compartment model, the diffusivity of a voxel is a combination of tissue and FW diffusivity. FW fraction is FW volume divided by voxel volume. Assuming FW diffuses equally in all directions, the diffusivity component is representable by a single, non-tensor diffusivity value. Since the diffusivity of water is known for a given temperature, and brain temperature is relatively constant, the FW diffusivity value can be assumed constant. The third eigenvector of the voxel diffusion tensor is the direction of least diffusivity and since the FW component of diffusivity is equal in all directions, we show that FW diffusivity cannot be lower than the third eigenvalue. Assuming FW contributes proportionally to voxel diffusivity, we show that the third eigenvalue divided by water diffusivity (as a constant based on known water diffusivity at 36.7 °C) forms an upper limit on the FW-fraction (fUL ). Results:We calculated fUL for 384 subjects from the IXI dataset. Values mostly ranged from 0.1 to 0.6, and were closely related to radial diffusivity.Comparison with Existing Methods:fUL is easily calculated from any DTI data, but is not a true estimate of FW-fraction. Conclusions:The fUL measure offers a starting point in calculating the true FW-fraction of a voxel, or an easy-to-calculate voxel characteristic
Luke A. Henderson - One of the best experts on this subject based on the ideXlab platform.
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DTI-based upper limit of voxel free water fraction
Elsevier, 2018Co-Authors: Paul M. Macey, Albert M. Thomas, Luke A. HendersonAbstract:Background: Free water (FW) in neuroimaging is non-flowing extracellular water in the cranium and brain tissue, and includes both cerebral spinal fluid (CSF) and fluid in intercellular space or edema. For a region such as a voxel (spatial Unit of Measurement in neuroimaging), the FW fraction is defined as the volume fraction of FW within that volume. Quantifying the FW fraction allows estimating contamination by fluid of neuroimaging or magnetic resonance spectroscopy Measurements within a voxel. New method: An upper limit to the fraction of FW within a voxel, based on any diffusion tensor imaging (DTI) sequence including a standard single shell at one b-value, can be derived from the standard diffusion tensor by scaling the third eigenvalue of the diffusion tensor. Assuming a two-compartment model, the diffusivity of a voxel is a combination of tissue and FW diffusivity. FW fraction is FW volume divided by voxel volume. Assuming FW diffuses equally in all directions, the diffusivity component is representable by a single, non-tensor diffusivity value. Since the diffusivity of water is known for a given temperature, and brain temperature is relatively constant, the FW diffusivity value can be assumed constant. The third eigenvector of the voxel diffusion tensor is the direction of least diffusivity and since the FW component of diffusivity is equal in all directions, we show that FW diffusivity cannot be lower than the third eigenvalue. Assuming FW contributes proportionally to voxel diffusivity, we show that the third eigenvalue divided by water diffusivity (as a constant based on known water diffusivity at 36.7 °C) forms an upper limit on the FW-fraction (fUL). Results: We calculated fUL for 384 subjects from the IXI dataset. Values mostly ranged from 0.1 to 0.6, and were closely related to radial diffusivity.Comparison with Existing Methods: fUL is easily calculated from any DTI data, but is not a true estimate of FW-fraction. Conclusions: The fUL measure offers a starting point in calculating the true FW-fraction of a voxel, or an easy-to-calculate voxel characteristic. Keywords: Medical imaging, Neuroscienc
