Assessing long-term medical remanufacturing emissions with Life Cycle Analysis

The unsustainable take-make-dispose linear economy prevalent in healthcare contributes 4.4% to global Greenhouse Gas emissions. A popular but not yet widely-embraced solution is to remanufacture common single-use medical devices like electrophysiology catheters, significantly extending their lifetimes by enabling a circular life cycle. To support the adoption of catheter remanufacturing, we carried out a holistic comparative evaluation of virgin manufactured and remanufactured carbon emissions with Life Cycle Analysis (LCA). We followed ISO modelling standards and NHS reporting guidelines to ensure industry relevance. We conclude that remanufacturing may lead to a reduction of up to 61% per turn (burden-free) and 58% per life (burdened). Our extensive sensitivity analysis and industry-informed buy-back scheme revealed up to 49% long-term emission reductions per remanufactured catheter life. Our comprehensive results encourage a collaborative approach to remanufacturing to optimise emission savings across a catheter's life cycle.Comment: 23 pages, 9 figures, 7 table

Enabling Group-Based Learning in Teacher Education: A Case Study of Student Experience

“Teacher education ill prepares pre-service teachers for the classroom.” Research conducted in a teacher education program at Edith Cowan University (ECU) responded to this criticism. This longitudinal case study selected group work (i.e., group-based learning) to investigate the quality of its teacher education program. Phase one explored teacher educators\u27 perceptions of group-based learning. Phase two explored preservice teachers\u27 perceptions and experience of group-based learning. This phase used student ‘voice’ (i.e., through focus groups, confirmed field notes, summary sheets) to convey their ideas and experiences when studying in a group and/or implementing group-based learning in the classroom. This paper discusses phase two findings which show the importance of consistency and coherence in understanding group-based learning principles and practices, and the broad ‘conditions’ and ‘actions’ that enable meaningful learning. The research has enabled ECU teacher educators to enhance the quality of the teacher education program

Enabling Group-Based Learning in Teacher Education: A Case Study of Student Experience

“Teacher education ill prepares pre-service teachers for the classroom.” Research conducted in a teacher education program at Edith Cowan University (ECU) responded to this criticism. This longitudinal case study selected group work (i.e., group-based learning) to investigate the quality of its teacher education program. Phase one explored teacher educators\u27 perceptions of group-based learning. Phase two explored pre-service teachers\u27 perceptions and experience of group-based learning. This phase used student ‘voice’ (i.e., through focus groups, confirmed field notes, summary sheets) to convey their ideas and experiences when studying in a group and/or implementing group-based learning in the classroom. This paper discusses phase two findings which show the importance of consistency and coherence in understanding group-based learning principles and practices, and the broad ‘conditions’ and ‘actions’ that enable meaningful learning. The research has enabled ECU teacher educators to enhance the quality of the teacher education program

The impact of participant mental health on attendance and engagement in a trial of behavioural weight management programmes: secondary analysis of the WRAP randomised controlled trial.

BACKGROUND: Low attendance and engagement in behavioural weight management trials are common. Mental health may play an important role, however previous research exploring this association is limited with inconsistent findings. We aimed to investigate whether mental health was associated with attendance and engagement in a trial of behavioural weight management programmes. METHODS: This is a secondary data analysis of the Weight loss referrals for adults in primary care (WRAP) trial, which randomised 1267 adults with overweight or obesity to brief intervention, WW (formerly Weight Watchers) for 12-weeks, or WW for 52-weeks. We used regression analyses to assess the association of baseline mental health (depression and anxiety (by Hospital Anxiety and Depression Scale), quality of life (by EQ5D), satisfaction with life (by Satisfaction with Life Questionnaire)) with programme attendance and engagement in WW groups, and trial attendance in all randomised groups. RESULTS: Every one unit of baseline depression score was associated with a 1% relative reduction in rate of WW session attendance in the first 12 weeks (Incidence rate ratio [IRR] 0.99; 95% CI 0.98, 0.999). Higher baseline anxiety was associated with 4% lower odds to report high engagement with WW digital tools (Odds ratio [OR] 0.96; 95% CI 0.94, 0.99). Every one unit of global quality of life was associated with 69% lower odds of reporting high engagement with the WW mobile app (OR 0.31; 95% CI 0.15, 0.64). Greater symptoms of depression and anxiety and lower satisfaction with life at baseline were consistently associated with lower odds of attending study visits at 3-, 12-, 24-, and 60-months. CONCLUSIONS: Participants were less likely to attend programme sessions, engage with resources, and attend study assessments when reporting poorer baseline mental health. Differences in attendance and engagement were small, however changes may still have a meaningful effect on programme effectiveness and trial completion. Future research should investigate strategies to maximise attendance and engagement in those reporting poorer mental health. TRIAL REGISTRATION: The original trial ( ISRCTN82857232 ) and five year follow up ( ISRCTN64986150 ) were prospectively registered with Current Controlled Trials on 15/10/2012 and 01/02/2018.The WRAP trial was funded by the National Prevention Research Initiative through research grant MR/J000493. The intervention was provided by WW (formerly Weight Watchers) at no cost via an MRC Industrial Collaboration Award. Five year follow up of the WRAP trial was funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (RP-PG-0216-20010). RAJ, ALA, SJG, and SJS are supported by the Medical Research Council (MRC) (Grant MC_UU_00006/6). The University of Cambridge has received salary support in respect of SJG from the National Health Service in the East of England through the Clinical Academic Reserve. All funding bodies had no role in the design of the study and collection, analysis and interpretation of the data, and in the writing of the manuscript

Definitions of Metabolic Health and Risk of Future Type 2 Diabetes in BMI Categories: A Systematic Review and Network Meta-analysis.

OBJECTIVE: Various definitions of metabolic health have been proposed to explain differences in the risk of type 2 diabetes within BMI categories. The goal of this study was to assess their predictive relevance. RESEARCH DESIGN AND METHODS: We performed systematic searches of MEDLINE records for prospective cohort studies of type 2 diabetes risk in categories of BMI and metabolic health. In a two-stage meta-analysis, relative risks (RRs) specific to each BMI category were derived by network meta-analysis and the resulting RRs of each study were pooled using random-effects models. Hierarchical summary receiver operating characteristic curves were used to assess predictive performance. RESULTS: In a meta-analysis of 140,845 participants and 5,963 incident cases of type 2 diabetes from 14 cohort studies, classification as metabolically unhealthy was associated with higher RR of diabetes in all BMI categories (lean RR compared with healthy individuals 4.0 [95% CI 3.0-5.1], overweight 3.4 [2.8-4.3], and obese 2.5 [2.1-3.0]). Metabolically healthy obese individuals had a high absolute risk of type 2 diabetes (10-year cumulative incidence 3.1% [95% CI 2.6-3.5]). Current binary definitions of metabolic health had high specificity (pooled estimate 0.88 [95% CI 0.84-0.91]) but low sensitivity (0.40 [0.31-0.49]) in lean individuals and satisfactory sensitivity (0.81 [0.76-0.86]) but low specificity (0.42 [0.35-0.49]) in obese individuals. However, positive (0.4) likelihood ratios were consistent with insignificant to small improvements in prediction. CONCLUSIONS: Although individuals classified as metabolically unhealthy have a higher RR of type 2 diabetes compared with individuals classified as healthy in all BMI categories, current binary definitions of metabolic health have limited relevance to the prediction of future type 2 diabetes.The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement n° 115372, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. This work was supported by the Netherlands Organization for Scientific Research (NWO), and the Medical Research Council UK (grant no. MC_U106179471). A.A. is supported by a Rubicon grant from the NWO (Project no. 825.13.004).This is an author-created, uncopyedited electronic version of an article accepted for publication in Diabetes Care. The American Diabetes Care Association (ADA), publisher of Diabetes Care, is not responsible for any errors or omissions in this version of the manuscript or any version derived from it by third parties. The definitive publisher-authenticated version will be available in a future issue of Diabetes Care in print and online at http://care.diabetesjournals.org

The SAMI Galaxy Survey: Shocks and Outflows in a normal star-forming galaxy

We demonstrate the feasibility and potential of using large integral field spectroscopic surveys to investigate the prevalence of galactic-scale outflows in the local Universe. Using integral field data from SAMI and the Wide Field Spectrograph, we study the nature of an isolated disk galaxy, SDSS J090005.05+000446.7 (z = 0.05386). In the integral field datasets, the galaxy presents skewed line profiles changing with position in the galaxy. The skewed line profiles are caused by different kinematic components overlapping in the line-of-sight direction. We perform spectral decomposition to separate the line profiles in each spatial pixel as combinations of (1) a narrow kinematic component consistent with HII regions, (2) a broad kinematic component consistent with shock excitation, and (3) an intermediate component consistent with shock excitation and photoionisation mixing. The three kinematic components have distinctly different velocity fields, velocity dispersions, line ratios, and electron densities. We model the line ratios, velocity dispersions, and electron densities with our MAPPINGS IV shock and photoionisation models, and we reach remarkable agreement between the data and the models. The models demonstrate that the different emission line properties are caused by major galactic outflows that introduce shock excitation in addition to photoionisation by star-forming activities. Interstellar shocks embedded in the outflows shock-excite and compress the gas, causing the elevated line ratios, velocity dispersions, and electron densities observed in the broad kinematic component. We argue from energy considerations that, with the lack of a powerful active galactic nucleus, the outflows are likely to be driven by starburst activities. Our results set a benchmark of the type of analysis that can be achieved by the SAMI Galaxy Survey on large numbers of galaxies.Comment: 17 pages, 15 figures. Accepted to MNRAS. References update

Low Temperature Opacities

Previous computations of low temperature Rosseland and Planck mean opacities from Alexander & Ferguson (1994) are updated and expanded. The new computations include a more complete equation of state with more grain species and updated optical constants. Grains are now explicitly included in thermal equilibrium in the equation of state calculation, which allows for a much wider range of grain compositions to be accurately included than was previously the case. The inclusion of high temperature condensates such as Al$_2$O$_3$ and CaTiO$_3$ significantly affects the total opacity over a narrow range of temperatures before the appearance of the first silicate grains. The new opacity tables are tabulated for temperatures ranging from 30000 K to 500 K with gas densities from 10$^{-4}$ g cm$^{-3}$ to 10$^{-19}$ g cm$^{-3}$. Comparisons with previous Rosseland mean opacity calculations are discussed. At high temperatures, the agreement with OPAL and Opacity Project is quite good. Comparisons at lower temperatures are more divergent as a result of differences in molecular and grain physics included in different calculations. The computation of Planck mean opacities performed with the opacity sampling method are shown to require a very large number of opacity sampling wavelength points; previously published results obtained with fewer wavelength points are shown to be significantly in error. Methods for requesting or obtaining the new tables are provided.Comment: 39 pages with 12 figures. To be published in ApJ, April 200

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef

The SAMI Galaxy Survey: a new method to estimate molecular gas surface densities from star formation rates

Stars form in cold molecular clouds. However, molecular gas is difficult to observe because the most abundant molecule (H_2) lacks a permanent dipole moment. Rotational transitions of CO are often used as a tracer of H_2, but CO is much less abundant and the conversion from CO intensity to H2 mass is often highly uncertain. Here we present a new method for estimating the column density of cold molecular gas (Σ_(gas)) using optical spectroscopy. We utilize the spatially resolved Hα maps of flux and velocity dispersion from the Sydney-AAO Multi-object Integral field spectrograph (SAMI) Galaxy Survey. We derive maps of Σ_(gas) by inverting the multi-freefall star formation relation, which connects the star formation rate surface density (Σ_(SFR)) with Σ_(gas) and the turbulent Mach number (M). Based on the measured range of Σ_(SFR) = 0.005-1.5M⊙ yr^(−1) kpc^(−2) and M=18–130, we predict Σ_(gas) = 7–200 M⊙ pc^(−2) in the star-forming regions of our sample of 260 SAMI galaxies. These values are close to previously measured Σ_(gas) obtained directly with unresolved CO observations of similar galaxies at low redshift. We classify each galaxy in our sample as ‘star-forming’ (219) or ‘composite/AGN/shock’ (41), and find that in ‘composite/AGN/shock’ galaxies the average Σ_(SFR), M and Σ_(gas) are enhanced by factors of 2.0, 1.6 and 1.3, respectively, compared to star-forming galaxies. We compare our predictions of Σ_(gas) with those obtained by inverting the Kennicutt–Schmidt relation and find that our new method is a factor of 2 more accurate in predicting Σ_(gas), with an average deviation of 32 per cent from the actual Σ_(gas)

Performance deficits of NK1 receptor knockout mice in the 5 choice serial reaction time task: effects of d Amphetamine, stress and time of day.

Background The neurochemical status and hyperactivity of mice lacking functional substance P-preferring NK1 receptors (NK1R-/-) resemble abnormalities in Attention Deficit Hyperactivity Disorder (ADHD). Here we tested whether NK1R-/- mice express other core features of ADHD (impulsivity and inattentiveness) and, if so, whether they are diminished by d-amphetamine, as in ADHD. Prompted by evidence that circadian rhythms are disrupted in ADHD, we also compared the performance of mice that were trained and tested in the morning or afternoon. Methods and Results The 5-Choice Serial Reaction-Time Task (5-CSRTT) was used to evaluate the cognitive performance of NK1R-/- mice and their wildtypes. After training, animals were tested using a long (LITI) and a variable (VITI) inter-trial interval: these tests were carried out with, and without, d-amphetamine pretreatment (0.3 or 1 mg/kg i.p.). NK1R-/- mice expressed greater omissions (inattentiveness), perseveration and premature responses (impulsivity) in the 5-CSRTT. In NK1R-/- mice, perseveration in the LITI was increased by injection-stress but reduced by d-amphetamine. Omissions by NK1R-/- mice in the VITI were unaffected by d-amphetamine, but premature responses were exacerbated by this psychostimulant. Omissions in the VITI were higher, overall, in the morning than the afternoon but, in the LITI, premature responses of NK1R-/- mice were higher in the afternoon than the morning. Conclusion In addition to locomotor hyperactivity, NK1R-/- mice express inattentiveness, perseveration and impulsivity in the 5-CSRTT, thereby matching core criteria for a model of ADHD. Because d-amphetamine reduced perseveration in NK1R-/- mice, this action does not require functional NK1R. However, the lack of any improvement of omissions and premature responses in NK1R-/- mice given d-amphetamine suggests that beneficial effects of this psychostimulant in other rodent models, and ADHD patients, need functional NK1R. Finally, our results reveal experimental variables (stimulus parameters, stress and time of day) that could influence translational studies