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The first episode of psychosis is a critical period in the emergence of cardiometabolic risk.
We set out to explore the influence of individual and lifestyle factors on cardiometabolic outcomes in early psychosis.
This was a prospective cohort study of 293 UK adults presenting with first-episode psychosis investigating the influence of sociodemographics, lifestyle (physical activity, sedentary behaviour, nutrition, smoking, alcohol, substance use) and medication on cardiometabolic outcomes over the following 12 months.
Rates of obesity and glucose dysregulation rose from 17.8% and 12%, respectively, at baseline to 23.7% and 23.7% at 1 year. Little change was seen over time in the 76.8% tobacco smoking rate or the quarter who were sedentary for over 10 h daily. We found no association between lifestyle at baseline or type of antipsychotic medication prescribed with either baseline or 1-year cardiometabolic outcomes. Median haemoglobin A1c (HbA1c) rose by 3.3 mmol/mol in participants from Black and minority ethnic (BME) groups, with little change observed in their White counterparts. At 12 months, one-third of those with BME heritage exceeded the threshold for prediabetes (HbA1c >39 mmol/mol).
Unhealthy lifestyle choices are prevalent in early psychosis and cardiometabolic risk worsens over the next year, creating an important window for prevention. We found no evidence, however, that preventative strategies should be preferentially directed based on lifestyle habits. Further work is needed to determine whether clinical strategies should allow for differential patterns of emergence of cardiometabolic risk in people of different ethnicities.
Habitat prioritization and corridor restoration are important steps for reconnecting fragmented habitats and species populations, and spatial modelling approaches are useful in identifying suitable habitat for elusive tropical rainforest mammals. The Endangered Bornean banteng Bos javanicus lowi, a wild bovid endemic to Borneo, occurs in habitat that is highly fragmented as a result of extensive agricultural expansion. Based on the species’ historical distribution in Sabah (Malaysia), we conducted camera-trap surveys in 14 forest reserves during 2011–2016. To assess suitable habitat for the banteng we used a presence-only maximum entropy (MaxEnt) approach with 11 spatial predictors, including climate, infrastructure, land cover and land use, and topography variables. We performed a least-cost path analysis using Linkage Mapper, to understand the resistance to movement through the landscape. The surveys comprised a total of 44,251 nights of camera trapping. We recorded banteng presence in 11 forest reserves. Key spatial predictors deemed to be important in predicting suitable habitat included soil associations (52.6%), distance to intact and logged forests (11.8%), precipitation in the driest quarter (10.8%), distance to agro-forest and regenerating forest (5.7%), and distance to oil palm plantations (5.1%). Circa 11% of Sabah had suitable habitat (7,719 km2), of which 12.2% was in protected forests, 60.4% was in production forests and 27.4% was in other areas. The least-cost path model predicted 21 linkages and a relatively high movement resistance between core habitats. Our models provide information about key habitat and movement resistance for bantengs through the landscape, which is crucial for constructive conservation strategies and land-use planning.
Agriculture as a social-ecological system embraces many disciplines. This book breaks through the silos of individual disciplines to bring ecologists and economists together to consider agriculture through the lens of resilience. It explores the economic, environmental and social uncertainties that influence the behaviour of agricultural producers and their subsequent farming approach, highlighting the importance of adaptability, innovation and capital reserves in enabling agriculture to persist under climate change and market volatility. The resilience concept and its relation to complexity theory is explained and the characteristics that foster resilience in agricultural systems, including the role of biodiversity and ecosystem services, are explored. The book discusses modelling tools, metrics and approaches for assessing agricultural resilience, highlighting areas where interdisciplinary thinking can enhance the development of resilience. It is suitable for those researching sustainable agriculture or those engaged in agricultural policy decisions and analysis, as well as students of ecology, agriculture and socioeconomics.
Inherent in the use of radioisotope sources with secondary fluorescers is the background produced by scattering of the source photons from the exciter system. A Monte Carlo program has been developed that is capable of simulating the backscattered photon spectrum as a function of the system geometry, including shielding and collimation variations. This computer program generates the scattered photon spectrum incident on both the sample and detector. The program is applied to a commercially available exciter system to study the effect of specific geometric design changes on the scattered spectrum.
The Monte Carlo simulation method that has been previously developed and demonstrated for EDXRF analysis with annular radioisotope excitation sources is extended to systems using secondary fluorescer X-ray machines for excitation. Comparisons of the Monte Carlo predictions with experimental results indicate that the modification is valid.
A Monte Carlo model that predicts the entire photon, spectrum for energy-dispersive X-ray fluorescence (EDXRF) analyzers excited by radio-isotope sources from multielement homogeneous samples is developed and demonstrated. The components of the photon spectrum include: (1) the and Kα and Kβ characteristic primary, secondary and tertiary X rays from both the unscattered and scattered source photons, (2) the characteristic X rays excited by other characteristic X rays that have been scattered, and (3) the scattered source photons from single, double, and multiple scatters in the sample.
The computer code NCSMCXF based on this model has been developed. It is capable of handling up to 20 elements per sample and provides a detailed account of the intensities of the X rays and backscattered source photons per unit source decay as well as a summary of the relative intensities from all elements present in the sample. Cubic splines are used within the code for photoelectric and total scattering cross sections and two-variable cubic splines for angular coherent and incoherent scattering distributions for efficiency in both computation time and storage. The code also provides the pulse-height spectrum of the sample by using the appropriate Si(Li) detector response function. The Monte Carlo predictions for benchmark experimental results on two alloy samples of known composition indicate that the model is very accurate. This approach is capable of replacing most of the experimental work presently required in EDXRF quantitative analysis.
A previous Monte Carlo model that uses the simple assumption of spherical homogeneous particles to approximate sample heterogeneities has been modified to improve the computer execution time requirements for the heterogeneous sample case. A new technique for photon tracking in this medium is used and reduces the computation time requirement by half.
The error introduced by sample scattering in EDXRF analysis is evaluated by Monte Carlo simulation. This is accomplished by deriving a Monte Carlo model capable of simulating single Compton and Rayleigh scatters from the exciting photon source and from fluorescent X rays in homogeneous samples. The model also includes primary, secondary, and tertiary fluorescence events. (1) Results are given for Ni-Fe-Cr ternary samples for various exciting energies with and without scattering and indicate that errors as large as 2% can be attributed to this effect.
A review of the application of the Monte Carlo, fundamental parameters method to XRF fluorescence analysis for the reduction of matrix effects is made. The analytical solutions arising from theoretical equations are given along with the restrictive assumptions that are necessary to this approach. The extensions of the fundamental parameters method by the Monte Carlo simulation to practical situations that require much less restrictive assumptions are outlined. The average angle approach to the use of the analytical solutions is investigated by comparison with the Monte Carlo method. Future extensions of the fundamental parameters method by the Monte Carlo approach are discussed.
A new analysis principle for energy-dispersive X–ray fluorescence has been identified and investigated as to feasibility. It consists of: (1) generating the complete spectral response for a sample of known (assumed) composition by Monte Carlo simulation,(2) keeping track of the individual elemental responses within the Monte Carlo simulation for use as library spectra, (3) use of the library least–squares (linear) analysis method to obtain the elemental amounts for any unknown sample spectrum, and (4) iterating these steps if the unknown amounts are too fax from the assumed composition originally used.
This principle has been investigated for a radioisotope source excited EDXRF system consisting of a 109Cd source and a Si(Li) detector for a Cu-Ni alloy sample (CDA Alloy 715) and a stainless steel sample (304 Stainless Steel) and found to give excellent results. This analysis principle makes unique use of the Monte Carlo “forward” simulation method to provide the elemental library spectra for use in the library least-squares method of analysis.
Vulnerability to depression can be measured in different ways. We here examine how genetic risk factors are inter-related for lifetime major depression (MD), self-report current depressive symptoms and the personality trait Neuroticism.
We obtained data from three population-based adult twin samples (Virginia n = 4672, Australia #1 n = 3598 and Australia #2 n = 1878) to which we fitted a common factor model where risk for ‘broadly defined depression’ was indexed by (i) lifetime MD assessed at personal interview, (ii) depressive symptoms, and (iii) neuroticism. We examined the proportion of genetic risk for MD deriving from the common factor v. specific to MD in each sample and then analyzed them jointly. Structural equation modeling was conducted in Mx.
The best fit models in all samples included additive genetic and unique environmental effects. The proportion of genetic effects unique to lifetime MD and not shared with the broad depression common factor in the three samples were estimated as 77, 61, and 65%, respectively. A cross-sample mega-analysis model fit well and estimated that 65% of the genetic risk for MD was unique.
A large proportion of genetic risk factors for lifetime MD was not, in the samples studied, captured by a common factor for broadly defined depression utilizing MD and self-report measures of current depressive symptoms and Neuroticism. The genetic substrate for MD may reflect neurobiological processes underlying the episodic nature of its cognitive, motor and neurovegetative manifestations, which are not well indexed by current depressive symptom and neuroticism.