Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE

Introducing discrete frequency infrared technology for high-throughput biofluid screening

Accurate early diagnosis is critical to patient survival, management and quality of life. Biofluids are key to early diagnosis due to their ease of collection and intimate involvement in human function. Large-scale mid-IR imaging of dried fluid deposits offers a high-throughput molecular analysis paradigm for the biomedical laboratory. The exciting advent of tuneable quantum cascade lasers allows for the collection of discrete frequency infrared data enabling clinically relevant timescales. By scanning targeted frequencies spectral quality, reproducibility and diagnostic potential can be maintained while significantly reducing acquisition time and processing requirements, sampling 16 serum spots with 0.6, 5.1 and 15% relative standard deviation (RSD) for 199, 14 and 9 discrete frequencies respectively. We use this reproducible methodology to show proof of concept rapid diagnostics; 40 unique dried liquid biopsies from brain, breast, lung and skin cancer patients were classified in 2.4 cumulative seconds against 10 non-cancer controls with accuracies of up to 90%

Rationale and design for SHAREHD: a quality improvement collaborative to scale up Shared Haemodialysis Care for patients on centre based haemodialysis.

BACKGROUND: The study objective is to assess the effectiveness and economic impact of a structured programme to support patient involvement in centre-based haemodialysis and to understand what works for whom in what circumstances and why. It implements a program of Shared Haemodialysis Care (SHC) that aims to improve experience and outcomes for those who are treated with centre-based haemodialysis, and give more patients the confidence to dialyse independently both at centres and at home. METHODS/DESIGN: The 24 month mixed methods cohort evaluation of 600 prevalent centre based HD patients is nested within a 30 month quality improvement program that aims to scale up SHC at 12 dialysis centres across England. SHC describes an intervention where patients who receive centre-based haemodialysis are given the opportunity to learn, engage with and undertake tasks associated with their treatment. Following a 6-month set up period, a phased implementation programme is initiated across 12 dialysis units using a randomised stepped wedge design with 6 centres participating in each of 2 steps, each lasting 6 months. The intervention utilises quality improvement methodologies involving rapid tests of change to determine the most appropriate mechanisms for implementation in the context of a learning collaborative. Running parallel with the stepped wedge intervention is a mixed methods cohort evaluation that employs patient questionnaires and interviews, and will link with routinely collected data at the end of the study period. The primary outcome measure is the number of patients performing at least 5 dialysis-related tasks collected using 3 monthly questionnaires. Secondary outcomes measures include: the number of people choosing to perform home haemodialysis or dialyse independently in-centre by the end of the study period; end-user recommendation; home dialysis establishment delay; staff impact and confidence; hospitalisation; infection and health economics. DISCUSSION: The results from this study will provide evidence of impact of SHC, barriers to patient and centre level adoption and inform development of future interventions to support its implementation. TRIAL REGISTRATION: ISRCTN Number: 93999549 , (retrospectively registered 1st May 2017); NIHR Research Portfolio: 31566

Superfund, Hedonics, and the Scales of Environmental Justice

Environmental justice (EJ) is prominent in environmental policy, yet EJ research is plagued by debates over methodological procedures. A well-established economic approach, the hedonic price method, can offer guidance on one contentious aspect of EJ research: the choice of the spatial unit of analysis. Environmental managers charged with preventing or remedying inequities grapple with these framing problems. This article reviews the theoretical and empirical literature on unit choice in EJ, as well as research employing hedonic pricing to assess the spatial extent of hazardous waste site impacts. The insights from hedonics are demonstrated in a series of EJ analyses for a national inventory of Superfund sites. First, as evidence of injustice exhibits substantial sensitivity to the choice of spatial unit, hedonics suggests some units conform better to Superfund impacts than others. Second, hedonic estimates for a particular site can inform the design of appropriate tests of environmental inequity for that site. Implications for policymakers and practitioners of EJ analyses are discussed

Differential risk of ST-Segment Elevation Myocardial Infarction in male and female smokers

Background Smoking is a well-documented risk for acute ST-segment elevation myocardial infarction (STEMI). The differential effect between sexes has yet to be quantified. Objectives The purpose of this study was to differentiate the effect of smoking on increased risk of STEMI between sexes. Methods For this retrospective ecological cohort study, all patients at a U.K. tertiary cardiothoracic center who presented between 2009 and 2014 with acute STEMI were combined with population data to generate incidence rates of STEMI. Age-standardized incidence rate ratios (IRRs) using the Poisson distribution were calculated comparing STEMI rates between smokers and nonsmokers stratified by sex and 3 age groups (18 to 49, 50 to 64, and >65 years). Results A total of 3,343 patients presented over 5,639,328 person-years. Peak STEMI rate for current smokers was in the 70 to 79 years age range for women (235 per 100,000 patient-years) and 50 to 59 years (425 per 100,000 patient-years) in men. Smoking was associated with a significantly greater increase in STEMI rate for women than men (IRR: 6.62; 95% confidence interval [CI]: 5.98 to 7.31, vs. 4.40; 95% CI: 4.15 to 4.67). The greatest increased risk was in women age 18 to 49 (IRR: 13.22; 95% CI: 10.33 to 16.66, vs. 8.60; 95% CI: 7.70 to 9.59 in men). The greatest risk difference was in the age 50 to 64 years group, with IRR of 9.66 (95% CI: 8.30 to 11.18) in women and 4.47 (95% CI: 4.10 to 4.86) in men. Conclusions This study quantifies the differential effect of smoking between sexes, with women having a significantly increased risk of STEMI than men. This information encourages continued efforts to prevent smoking uptake and promote cessation

ToxNav germline genetic testing and PROMinet digital mobile application toxicity monitoring: Results of a prospective single-center clinical utility study-PRECISE study.

INTRODUCTION: In this study (PRECISE), we assess the clinical utility of a germline DNA sequencing-based test (ToxNav) for mutations in DPYD and ENOSF1 genes to alter clinician-prescribed fluoropyrimidine doses and the use of a digital application (PROMinet) to record patient-reported chemotherapy toxicity. MATERIALS AND METHODS: Adult patients with a histological diagnosis of colorectal cancer (CRC) who consented to fluoropyrimidine-based chemotherapy were recruited prospectively and given a digital application to monitor and record associated toxicities. Patient samples were analyzed for 18 germline coding variants in DPYD and 1 ENOSF1 variant. RESULTS: Genetic testing was performed for 60 patients and identified one patient at increased risk of fluoropyrimidine-based toxicities. Uptake of genetic testing was high and results were available on average 17 days from initial clinical encounter. Patient-reported chemotherapy toxicity identified differences in 5-fluorouracil vs capecitabine regime profiles and identified profiles associated with subsequent need for chemotherapy dose reduction and hospital admission. DISCUSSION: The PRECISE clinical trial demonstrated that a germline DNA sequencing-based test can provide clinically relevant information to alter clinicians' fluoropyrimidine prescription. The study also obtained high volume, high granularity patient-reported toxicity data that might allow the improvement and personalization of chemotherapy management

Geographically weighted elastic net logistic regression

This paper develops a localized approach to elastic net logistic regression, extending previous research describing a localized elastic net as an extension to a localized ridge regression or a localized lasso. All such models have the objective to capture data relationships that vary across space. Geographically weighted elastic net logistic regression is first evaluated through a simulation experiment and shown to provide a robust approach for local model selection and alleviating local collinearity, before application to two case studies: county-level voting patterns in the 2016 USA presidential election, examining the spatial structure of socio-economic factors associated with voting for Trump, and a species presence–absence data set linked to explanatory environmental and climatic factors at gridded locations covering mainland USA. The approach is compared with other logistic regressions. It improves prediction for the election case study only which exhibits much greater spatial heterogeneity in the binary response than the species case study. Model comparisons show that standard geographically weighted logistic regression over-estimated relationship non-stationarity because it fails to adequately deal with collinearity and model selection. Results are discussed in the context of predictor variable collinearity and selection and the heterogeneities that were observed. Ongoing work is investigating locally derived elastic net parameters

Local niche differences predict genotype associations in sister taxa of desert tortoise

Aims: To investigate spatial congruence between ecological niches and genotype in two allopatric species of desert tortoise that are species of conservation concern. Location: Mojave and Sonoran Desert ecoregions; California, Nevada, Arizona, Utah, USA. Methods: We compare ecological niches of Gopherus agassizii and Gopherus morafkai using species distribution modelling (SDM) and then calibrate a pooled-taxa distribution model to explore local differences in species-environment relationships based on the spatial residuals of the pooled-taxa model. We use multiscale geographically weighted regression (MGWR) applied to those residuals to estimate local species-environment relationships that can vary across the landscape. We identify multivariate clusters in these local species-environment relationships and compare them against models of (a) a geographically based taxonomic designation for two sister species and (b) an environmental ecoregion designation, with respect to their ability to predict a genotype association index for these two species. Results: We find non-identical niches for these species, with differences that span physiographic and vegetation niche dimensions. We find evidence for two distinct clusters of local species-environment relationships that when mapped, predict an index of genotype association for the two sister taxa better than did either the geographically based taxonomic designation or an environmental ecoregion designation. Main conclusions: Exploring local species-environment relationships by coupling SDM and MGWR can benefit studies of biogeography and conservation. We find that niche separation in habitat selection conforms to genotypic differences between sister taxa of tortoise in a recent secondary contact zone. This result may inform decision making by agencies with regulatory or land management authority for the two sister taxa addressed here.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]