Use of Hydrological Models to Predict Risk for Rutting in Logging Operations

Using hydrological models with a high temporal resolution to predict risk for rutting may be a possible method to improve planning of forwarder trails or to schedule logging operations in sites with low bearing capacity to periods when soil moisture content is at a minimum. We have studied whether descriptions of rut variations, collected in 27 logging sites, can be improved by using hydrological data, modeled by Swedish HYdrological Prediction for Environment (S-HYPE). Other explanatory variables, such as field-surveyed data and spatial data, were also used to describe rut variations within and across logging sites. The results indicated that inclusion of S-HYPE data led to only marginal improvement in explaining the observed variations of the ruts in terms of both "rut depths" within the logging sites and "proportion of forwarder trails with ruts" across the logging sites. However, application of S-HYPE data for adapting depth-to-water (DTW) maps to temporal changes of soil moisture content may be a way to develop more dynamic soil moisture maps for forestry applications

A Flexible Spatio-Temporal Model for Air Pollution: Allowing for Spatio-Temporal Covariates

Given the increasing interest in the association between exposure to air pollution and adverse health outcomes, the development of models that provide accurate spatio-temporal predictions of air pollution concentrations at small spatial scales is of great importance when assessing potential health effects of air pollution. The methodology presented here has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the US EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. We present a spatio-temporal framework that models and predicts ambient air pollution by combining data from several different monitoring networks with the output from deterministic air pollution model(s). The model can accommodate arbitrarily missing observations and allows for a complex spatio-temporal correlation structure. We apply the model to predict long-term average concentrations of gaseous oxides of nitrogen (NOx) ─ one of the primary pollutants of interest in the MESA Air study ─ during a ten year period in the Los Angeles area, based on measurements from the EPA Air Quality System and MESA Air monitoring. The measurements are augmented by a spatio-temporal covariate based on the output from a source dispersion model for traffic related air pollution (Caline3QHC) and the model is evaluated using cross-validation. The predictive ability of the model is good with cross-validated R2 of approximately 0.7 at subject sites. The incorporation of a dispersion model output into the overall prediction model was feasible, but the particular implementation of Caline3QHC used here did not improve predictions in a model that also includes road information. However, excluding the road information the inclusion of model output improves predictions and we find some evidence that the source dispersion model can replace road covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, which will be available on CRAN shortly

The dyslexia candidate gene DYX1C1 is a potential marker of poor survival in breast cancer

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Mid-Holocene European climate revisited: New high-resolution regional climate model simulations using pollen-based land-cover

Land-cover changes have a clear impact on local climates via biophysical effects. European land cover has been affected by human activities for at least 6000 years, but possibly longer. It is thus highly probable that humans altered climate before the industrial revolution (AD1750-1850). In this study, climate and vegetation 6000 years (6 ka) ago is investigated using one global climate model, two regional climate models, one dynamical vegetation model, pollen-based reconstruction of past vegetation cover using a model of the pollen-vegetation relationship and a statistical model for spatial interpolation of the reconstructed land cover. This approach enables us to study 6 ka climate with potential natural and reconstructed land cover, and to determine how differences in land cover impact upon simulated climate. The use of two regional climate models enables us to discuss the robustness of the results. This is the first experiment with two regional climate models of simulated palaeo-climate based on regional climate models.Different estimates of 6 ka vegetation are constructed: simulated potential vegetation and reconstructed vegetation. Potential vegetation is the natural climate-induced vegetation as simulated by a dynamical vegetation model driven by climate conditions from a climate model. Bayesian spatial model interpolated point estimates of pollen-based plant abundances combined with estimates of climate-induced potential un-vegetated land cover were used for reconstructed vegetation. The simulated potential vegetation is heavily dominated by forests: evergreen coniferous forests dominate in northern and eastern Europe, while deciduous broadleaved forests dominate central and western Europe. In contrast, the reconstructed vegetation cover has a large component of open land in most of Europe.The simulated 6 ka climate using reconstructed vegetation was 0-5 degrees C warmer than the pre-industrial (PI) climate, depending on season and region. The largest differences are seen in north-eastern Europe in winter with about 4-6 degrees C, and the smallest differences (close to zero) in southwestern Europe in winter. The simulated 6 ka climate had 10-20% more precipitation than PI climate in northern Europe and 10-20% less precipitation in southern Europe in summer. The results are in reasonable agreement with proxy-based climate reconstructions and previous similar climate modelling studies. As expected, the global model and regional models indicate relatively similar climates albeit with regional differences indicating that, models response to land-cover changes differently.The results indicate that the anthropogenic land-cover changes, as given by the reconstructed vegetation, in this study are large enough to have a significant impact on climate. It is likely that anthropogenic impact on European climate via land-use change was already taking place at 6 ka. Our results suggest that anthropogenic land-cover changes at 6 ka lead to around 0.5 degrees C warmer in southern Europe in summer due to biogeophysical forcing. (C) 2022 The Authors. Published by Elsevier Ltd

Mid-Holocene European climate revisited: New high-resolution regional climate model simulations using pollen-based land-cover

Land-cover changes have a clear impact on local climates via biophysical effects. European land cover has been affected by human activities for at least 6000 years, but possibly longer. It is thus highly probable that humans altered climate before the industrial revolution (AD1750-1850). In this study, climate and vegetation 6000 years (6 ka) ago is investigated using one global climate model, two regional climate models, one dynamical vegetation model, pollen-based reconstruction of past vegetation cover using a model of the pollen-vegetation relationship and a statistical model for spatial interpolation of the reconstructed land cover. This approach enables us to study 6 ka climate with potential natural and reconstructed land cover, and to determine how differences in land cover impact upon simulated climate. The use of two regional climate models enables us to discuss the robustness of the results. This is the first experiment with two regional climate models of simulated palaeo-climate based on regional climate models.Different estimates of 6 ka vegetation are constructed: simulated potential vegetation and reconstructed vegetation. Potential vegetation is the natural climate-induced vegetation as simulated by a dynamical vegetation model driven by climate conditions from a climate model. Bayesian spatial model interpolated point estimates of pollen-based plant abundances combined with estimates of climate-induced potential un-vegetated land cover were used for reconstructed vegetation. The simulated potential vegetation is heavily dominated by forests: evergreen coniferous forests dominate in northern and eastern Europe, while deciduous broadleaved forests dominate central and western Europe. In contrast, the reconstructed vegetation cover has a large component of open land in most of Europe.The simulated 6 ka climate using reconstructed vegetation was 0-5 degrees C warmer than the pre-industrial (PI) climate, depending on season and region. The largest differences are seen in north-eastern Europe in winter with about 4-6 degrees C, and the smallest differences (close to zero) in southwestern Europe in winter. The simulated 6 ka climate had 10-20% more precipitation than PI climate in northern Europe and 10-20% less precipitation in southern Europe in summer. The results are in reasonable agreement with proxy-based climate reconstructions and previous similar climate modelling studies. As expected, the global model and regional models indicate relatively similar climates albeit with regional differences indicating that, models response to land-cover changes differently.The results indicate that the anthropogenic land-cover changes, as given by the reconstructed vegetation, in this study are large enough to have a significant impact on climate. It is likely that anthropogenic impact on European climate via land-use change was already taking place at 6 ka. Our results suggest that anthropogenic land-cover changes at 6 ka lead to around 0.5 degrees C warmer in southern Europe in summer due to biogeophysical forcing. (C) 2022 The Authors. Published by Elsevier Ltd

Healthy Food Intake Index (HFII) - Validity and reproducibility in a gestational-diabetes-risk population

Background: The aim was to develop and validate a food-based diet quality index for measuring adherence to the Nordic Nutrition Recommendations (NNR) in a pregnant population with high risk of gestational diabetes (GDM). Methods: This study is a part of the Finnish Gestational Diabetes Prevention Study (RADIEL), a lifestyle intervention conducted between 2008 and 2014. The 443 pregnant participants (61 % of those invited), were either obese or had a history of GDM. Food frequency questionnaires collected at 1st trimester served for composing the HFII; a sum of 11 food groups (available score range 0-17) with higher scores reflecting higher adherence to the NNR. Results: The average HFII of the participants was 10.2 (SD 2.8, range 2-17). Factor analysis for the HFII component matrix revealed three factors that explained most of the distribution (59 %) of the HFII. As an evidence of the component relevance 9 out of 11 of the HFII components independently contributed to the total score (item-rest correlation coefficients Conclusions: The HFII components reflect the food guidelines of the NNR, intakes of relevant nutrients, and characteristics known to vary with diet quality. It largely ignores energy intake, its components have independent contribution to the HFII, and it exhibits reproducibility. The main shortcomings are absence of red and processed meat component, and the validation in a selected study population. It is suitable for ranking participants according to the adherence to the NNR in pregnant women at high risk of GDM.Peer reviewe

Indolepropionic acid and novel lipid metabolites are associated with a lower risk of type 2 diabetes in the Finnish Diabetes Prevention Study

Wide-scale profiling technologies including metabolomics broaden the possibility of novel discoveries related to the pathogenesis of type 2 diabetes (T2D). By applying non-targeted metabolomics approach, we investigated here whether serum metabolite profile predicts T2D in a well-characterized study population with impaired glucose tolerance by examining two groups of individuals who took part in the Finnish Diabetes Prevention Study (DPS); those who either early developed T2D (n = 96) or did not convert to T2D within the 15-year follow-up (n = 104). Several novel metabolites were associated with lower likelihood of developing T2D, including indole and lipid related metabolites. Higher indolepropionic acid was associated with reduced likelihood of T2D in the DPS. Interestingly, in those who remained free of T2D, indolepropionic acid and various lipid species were associated with better insulin secretion and sensitivity, respectively. Furthermore, these metabolites were negatively correlated with low-grade inflammation. We replicated the association between indolepropionic acid and T2D risk in one Finnish and one Swedish population. We suggest that indolepropionic acid, a gut microbiota-produced metabolite, is a potential biomarker for the development of T2D that may mediate its protective effect by preservation of alpha-cell function. Novel lipid metabolites associated with T2D may exert their effects partly through enhancing insulin sensitivity.Peer reviewe

The Association between HbA1c, Fasting Glucose, 1-Hour Glucose and 2-Hour Glucose during an Oral Glucose Tolerance Test and Cardiovascular Disease in Individuals with Elevated Risk for Diabetes

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Leukocyte Telomere Length in the Finnish Diabetes Prevention Study

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