Extraction and Quantification of Atrazine

Undergraduate Basi

Influence of Pyrolysis Temperature and Production Conditions on Switchgrass Biochar for Use as a Soil Amendment

Biochars form recalcitrant carbon and increase water and nutrient retention in soils; however, the magnitude is contingent upon production conditions and thermo-chemical conversion processes. Herein we aim at (i) characterizing switchgrass (Panicum virgatum L.)-biochar morphology, (ii) estimating water-holding capacity under increasing ratios of char: soil; and, (iii) determining nutrient profile variation as a function of pyrolysis conversion methodologies (i.e. continuous, auger pyrolysis system versus batch pyrolysis systems) for terminal use as a soil amendment. Auger system chars produced at 600 °C had the greatest lignin portion by weight among the biochars produced from the continuous system. On the other hand, a batch pyrolysis system (400 °C – 3h) yielded biochar with 73.10% lignin (12 fold increases), indicating higher recalcitrance, whereas lower production temperatures (400 °C) yielded greater hemicellulose (i.e. greater mineralization promoting substrate). Under both pyrolysis methods, increasing biochar soil application rates resulted in linear decreases in bulk density (g cm-3). Increases in auger-char (400 °C) applications increased soil water-holding capacities; however, application rates of \u3e2 Mt ha-1 are required. Pyrolysis batch chars did not influence water-holding abilities (P\u3e0.05). Biochar macro and micronutrients increased, as the pyrolysis temperature increased in the auger system from 400 to 600 °C, and the residence time increased in the batch pyrolysis system from 1 to 3 h. Conversely, nitrogen levels tended to decrease under the two previously mentioned conditions. Consequently, not all chars are inherently equal, in that varying operation systems, residence times, and production conditions greatly affect uses as a soil amendment and overall rate of efficacy

Determination of the Cosmic Distance Scale from Sunyaev-Zel'dovich Effect and Chandra X-ray Measurements of High Redshift Galaxy Clusters

We determine the distance to 38 clusters of galaxies in the redshift range 0.14 < z < 0.89 using X-ray data from Chandra and Sunyaev-Zeldovich Effect data from the Owens Valley Radio Observatory and the Berkeley-Illinois-Maryland Association interferometric arrays. The cluster plasma and dark matter distributions are analyzed using a hydrostatic equilibrium model that accounts for radial variations in density, temperature and abundance, and the statistical and systematic errors of this method are quantified. The analysis is performed via a Markov chain Monte Carlo technique that provides simultaneous estimation of all model parameters. We measure a Hubble constant of 76.9 +3.9-3.4 +10.0-8.0 km/s/Mpc (statistical followed by systematic uncertainty at 68% confidence) for an Omega_M=0.3, Omega_Lambda=0.7 cosmology. We also analyze the data using an isothermal beta model that does not invoke the hydrostatic equilibrium assumption, and find H_0=73.7 +4.6-3.8 +9.5-7.6 km/s/Mpc; to avoid effects from cool cores in clusters, we repeated this analysis excluding the central 100 kpc from the X-ray data, and find H_0=77.6 +4.8-4.3 +10.1-8.2 km/s/Mpc. The consistency between the models illustrates the relative insensitivity of SZE/X-ray determinations of H_0 to the details of the cluster model. Our determination of the Hubble parameter in the distant universe agrees with the recent measurement from the Hubble Space Telescope key project that probes the nearby universe.Comment: ApJ submitted (revised version

The impact thinking framework: a process for advancing research-to-practice initiatives in neuroaesthetics

Neuroaesthetics research explores brain, body and behavioral responses to engagement with the arts and other aesthetic sensory experiences. Evidence indicates that such experiences can help address various psychological, neurological and physiological disorders, and that they can support mental and physical well-being and learning in the general population. The interdisciplinary nature of this work contributes to its impact and promise; however, it also creates challenges as various disciplines approach and define research and practice in varied ways. Recent field-wide reports have noted that a consensus translational framework is needed to support further neuroaesthetics research that can deliver meaningful knowledge and interventions. The Impact Thinking Framework (ITF) was designed to meet this need. Through a description of the framework’s nine iterative steps and a presentation of three case studies, this paper posits that the ITF can support researchers and practitioners in understanding and applying aesthetic experiences and the arts to advance health, well-being, and learning

X-ray and Sunyaev-Zel'dovich Effect Measurements of the Gas Mass Fraction in Galaxy Clusters

We present gas mass fractions of 38 massive galaxy clusters spanning redshifts from 0.14 to 0.89, derived from Chandra X-ray data and OVRO/BIMA interferometric Sunyaev-Zel'dovich Effect measurements. We use three models for the gas distribution: (1) an isothermal beta-model fit jointly to the X-ray data at radii beyond 100 kpc and to all of the SZE data,(2) a non-isothermal double beta-model fit jointly to all of the X-ray and SZE data, and (3) an isothermal beta-model fit only to the SZE spatial data. We show that the simple isothermal model well characterizes the intracluster medium (ICM) outside of the cluster core in clusters with a wide range of morphological properties. The X-ray and SZE determinations of mean gas mass fractions for the 100 kpc-cut isothermal beta-model are fgas(X-ray)=0.110 +0.003-0.003 +0.006-0.018 and fgas(SZE)=0.116 +0.005-0.005 +0.009-0.026, where uncertainties are statistical followed by systematic at 68% confidence. For the non-isothermal double beta-model, fgas(X-ray)=0.119 +0.003-0.003 +0.007-0.014 and fgas(SZE)=0.121 +0.005-0.005 +0.009-0.016. For the SZE-only model, fgas(SZE)=0.120 +0.009-0.009 +0.009-0.027. Our results indicate that the ratio of the gas mass fraction within r2500 to the cosmic baryon fraction is 0.68 +0.10-0.16 where the range includes statistical and systematic uncertainties. By assuming that cluster gas mass fractions are independent of redshift, we find that the results are in agreement with standard LambdaCDM cosmology and are inconsistent with a flat matter dominated universe.Comment: ApJ, submitted. 47 pages, 5 figures, 8 table

The Effect of Helium Sedimentation on Galaxy Cluster Masses and Scaling Relations

Recent theoretical studies predict that the inner regions of galaxy clusters may have an enhanced helium abundance due to sedimentation over the cluster lifetime. If sedimentation is not suppressed (e.g., by tangled magnetic fields), this may significantly affect the cluster mass estimates. We use Chandra X-ray observations of eight relaxed galaxy clusters to investigate the upper limits to the effect of helium sedimentation on the measurement of cluster masses and the best-fit slopes of the Y_X - M_500 and Y_X - M_2500 scaling relations. We calculated gas mass and total mass in two limiting cases: a uniform, un-enhanced abundance distribution and a radial distribution from numerical simulations of helium sedimentation on a timescale of 11 Gyrs. The assumed helium sedimentation model, on average, produces a negligible increase in the gas mass inferred within large radii (r < r500) (1.3 +/- 1.2 per cent) and a (10.2 +/- 5.5) per cent mean decrease in the total mass inferred within r < r500. Significantly stronger effects in the gas mass (10.5 +/- 0.8 per cent) and total mass (25.1 +/- 1.1 per cent) are seen at small radii owing to a larger variance in helium abundance in the inner region, r < 0.1 r500. We find that the slope of the Y_X -M_500 scaling relation is not significantly affected by helium sedimentation.Comment: 11 pages, accepted for publication in Astronomy and Astrophysic

Comparative diagnostic accuracy studies with an imperfect reference standard - a comparison of correction methods.

BACKGROUND: Staquet et al. and Brenner both developed correction methods to estimate the sensitivity and specificity of a binary-response index test when the reference standard is imperfect and its sensitivity and specificity are known. However, to our knowledge, no study has compared the statistical properties of these methods, despite their long application in diagnostic accuracy studies. AIM: To compare the correction methods developed by Staquet et al. and Brenner. METHODS: Simulations techniques were employed to compare the methods under assumptions that the new test and the reference standard are conditionally independent or dependent given the true disease status of an individual. Three clinical datasets were analysed to understand the impact of using each method to inform clinical decision-making. RESULTS: Under the assumption of conditional independence, the Staquet et al. correction method outperforms the Brenner correction method irrespective of the prevalence of disease and whether the performance of the reference standard is better or worse than the index test. However, when the prevalence of the disease is high (> 0.9) or low (< 0.1), the Staquet et al. correction method can produce illogical results (i.e. results outside [0,1]). Under the assumption of conditional dependence; both methods failed to estimate the sensitivity and specificity of the index test especially when the covariance terms between the index test and the reference standard is not close to zero. CONCLUSION: When the new test and the imperfect reference standard are conditionally independent, and the sensitivity and specificity of the imperfect reference standard are known, the Staquet et al. correction method outperforms the Brenner method. However, where the prevalence of the target condition is very high or low or the two tests are conditionally dependent, other statistical methods such as latent class approaches should be considered

Evidence synthesis and linkage for modelling the cost-effectiveness of diagnostic tests : preliminary good practice recommendations

Objectives: To develop preliminary good practice recommendations for synthesising and linking evidence of treatment effectiveness when modelling the cost-effectiveness of diagnostic tests. Methods: We conducted a targeted review of guidance from key Health Technology Assessment (HTA) bodies to summarise current recommendations on synthesis and linkage of treatment effectiveness evidence within economic evaluations of diagnostic tests. We then focused on a specific case study, the cost-effectiveness of troponin for the diagnosis of myocardial infarction, and reviewed the approach taken to synthesise and link treatment effectiveness evidence in different modelling studies. Results: The Australian and UK HTA bodies provided advice for synthesising and linking treatment effectiveness in diagnostic models, acknowledging that linking test results to treatment options and their outcomes is common. Across all reviewed models for the case study, uniform test-directed treatment decision making was assumed, i.e., all those who tested positive were treated. Treatment outcome data from a variety of sources, including expert opinion, were utilised for linked clinical outcomes. Preliminary good practice recommendations for data identification, integration and description are proposed. Conclusion: Modelling the cost-effectiveness of diagnostic tests poses unique challenges in linking evidence on test accuracy to treatment effectiveness data to understand how a test impacts patient outcomes and costs. Upfront consideration of how a test and its results will likely be incorporated into patient diagnostic pathways is key to exploring the optimal design of such models. We propose some preliminary good practice recommendations to improve the quality of cost-effectiveness evaluations of diagnostics tests going forward

Bayesian joint analysis of cluster weak lensing and Sunyaev-Zel'dovich effect data

As the quality of the available galaxy cluster data improves, the models fitted to these data might be expected to become increasingly complex. Here we present the Bayesian approach to the problem of cluster data modelling: starting from simple, physically motivated parameterised functions to describe the cluster's gas density, gravitational potential and temperature, we explore the high-dimensional parameter spaces with a Markov-Chain Monte-Carlo sampler, and compute the Bayesian evidence in order to make probabilistic statements about the models tested. In this way sufficiently good data will enable the models to be distinguished, enhancing our astrophysical understanding; in any case the models may be marginalised over in the correct way when estimating global, perhaps cosmological, parameters. In this work we apply this methodology to two sets of simulated interferometric Sunyaev-Zel'dovich effect and gravitational weak lensing data, corresponding to current and next-generation telescopes. We calculate the expected precision on the measurement of the cluster gas fraction from such experiments, and investigate the effect of the primordial CMB fluctuations on their accuracy. We find that data from instruments such as AMI, when combined with wide-field ground-based weak lensing data, should allow both cluster model selection and estimation of gas fractions to a precision of better than 30 percent for a given cluster.Comment: 13 pages, 7 figures, submitted to MNRAS; accepted 14/8/03 after minor revisio

Unmet clinical needs for COVID-19 tests in UK health and social care settings

There is an urgent requirement to identify which clinical settings are in most need of COVID-19 tests and the priority role(s) for tests in these settings to accelerate the development of tests fit for purpose in health and social care across the UK. This study sought to identify and prioritize unmet clinical needs for COVID-19 tests across different settings within the UK health and social care sector via an online survey of health and social care professionals and policymakers. Four hundred and forty-seven responses were received between 22nd May and 15th June 2020. Hospitals and care homes were recognized as the settings with the greatest unmet clinical need for COVID-19 diagnostics, despite reporting more access to laboratory molecular testing than other settings. Hospital staff identified a need for diagnostic tests for symptomatic workers and patients. In contrast, care home staff expressed an urgency for screening at the front door to protect high-risk residents and limit transmission. The length of time to test result was considered a widespread problem with current testing across all settings. Rapid tests for staff were regarded as an area of need across general practice and dental settings alongside tests to limit antibiotics use