Future permafrost conditions along environmental gradients in Zackenberg, Greenland

The future development of ground temperatures in permafrost areas is determined by a number of factors varying on different spatial and temporal scales. For sound projections of impacts of permafrost thaw, scaling procedures are of paramount importance. We present numerical simulations of present and future ground temperatures at 10 m resolution for a 4 km long transect across the lower Zackenberg valley in northeast Greenland. The results are based on stepwise downscaling of future projections derived from general circulation model using observational data, snow redistribution modeling, remote sensing data and a ground thermal model. A comparison to in situ measurements of thaw depths at two CALM sites and near-surface ground temperatures at 17 sites suggests agreement within 0.10 m for the maximum thaw depth and 1 °C for annual average ground temperature. Until 2100, modeled ground temperatures at 10 m depth warm by about 5 °C and the active layer thickness increases by about 30%, in conjunction with a warming of average near-surface summer soil temperatures by 2 °C. While ground temperatures at 10 m depth remain below 0 °C until 2100 in all model grid cells, positive annual average temperatures are modeled at 1 m depth for a few years and grid cells at the end of this century. The ensemble of all 10 m model grid cells highlights the significant spatial variability of the ground thermal regime which is not accessible in traditional coarse-scale modeling approaches

Future permafrost conditions along environmental gradients in Zackenberg, Greenland

The future development of ground temperatures in permafrost areas is determined by a number of factors varying on different spatial and temporal scales. For sound projections of impacts of permafrost thaw, scaling procedures are of paramount importance. We present numerical simulations of present and future ground temperatures at 10 m resolution for a 4 km long transect across the lower Zackenberg valley in northeast Greenland. The results are based on stepwise downscaling of future projections derived from general circulation model using observational data, snow redistribution modeling, remote sensing data and a ground thermal model. A comparison to in situ measurements of thaw depths at two CALM sites and near-surface ground temperatures at 17 sites suggests agreement within 0.10 m for the maximum thaw depth and 1 °C for annual average ground temperature. Until 2100, modeled ground temperatures at 10 m depth warm by about 5 °C and the active layer thickness increases by about 30%, in conjunction with a warming of average near-surface summer soil temperatures by 2 °C. While ground temperatures at 10 m depth remain below 0 °C until 2100 in all model grid cells, positive annual average temperatures are modeled at 1 m depth for a few years and grid cells at the end of this century. The ensemble of all 10 m model grid cells highlights the significant spatial variability of the ground thermal regime which is not accessible in traditional coarse-scale modeling approaches

Attendance in a national screening program for diabetic retinopathy:a population-based study of 205,970 patients

AIMS: A nationwide diabetic retinopathy (DR) screening program has been established in Denmark since 2013. We aimed to perform an evaluation of adherence to DR screenings and to examine whether non-adherence was correlated to DR progression. METHODS: The population consisted of a register-based cohort, who participated in the screening program from 2013 to 2018. We analyzed age, gender, marital status, DR level (International Clinical DR severity scale, none, mild-, moderate-, severe non-proliferative DR (NPDR) and proliferative DR (PDR)), comorbidities and socioeconomic factors. The attendance pattern of patients was grouped as either timely (no delays > 33%), delayed (delays > 33%) or one-time attendance (unexplained). RESULTS: We included 205,970 patients with 591,136 screenings. Rates of timely, delayed and one-time attendance were 53.0%, 35.5% and 11.5%, respectively. DR level at baseline was associated with delays (mild-, moderate-, severe NPDR and PDR) and one-time attendance (moderate-, severe NPDR and PDR) with relative risk ratios (RRR) of 1.68, 2.27, 3.14, 2.44 and 1.18, 2.07, 1.26, respectively (P < 0.05). Delays at previous screenings were associated with progression to severe NPDR or PDR (hazard ratio (HR) 2.27, 6.25 and 12.84 for 1, 2 and 3+ delays, respectively). Any given delay doubled the risk of progression (HR 2.28). CONCLUSIONS: In a national cohort of 205,970 patients, almost half of the patients attended DR screening later than scheduled or dropped out after first screening episode. This was, in particular, true for patients with any levels of DR at baseline. DR progression in patients with delayed attendance, increased with the number of missed appointments

Diabetic retinopathy as a potential marker of Parkinson's disease:a register-based cohort study

Neurodegeneration is an early event in the pathogenesis of diabetic retinopathy, and an association between diabetic retinopathy and Parkinson’s disease has been proposed. In this nationwide register-based cohort study, we investigated the prevalence and incidence of Parkinson’s disease among patients screened for diabetic retinopathy in a Danish population-based cohort. Cases (n = 173 568) above 50 years of age with diabetes included in the Danish Registry of Diabetic Retinopathy between 2013 and 2018 were matched 1:5 by gender and birth year with a control population without diabetes (n = 843 781). At index date, the prevalence of Parkinson’s disease was compared between cases and controls. To assess the longitudinal relationship between diabetic retinopathy and Parkinson’s disease, a multivariable Cox proportional hazard model was estimated. The prevalence of Parkinson’s disease was 0.28% and 0.44% among cases and controls, respectively. While diabetic retinopathy was not associated with present (adjusted odds ratio 0.93, 95% confidence interval 0.72–1.21) or incident Parkinson’s disease (adjusted hazard ratio 0.77, 95% confidence interval 0.56–1.05), cases with diabetes were in general less likely to have or to develop Parkinson’s disease compared to controls without diabetes (adjusted odds ratio 0.79, 95% confidence interval 0.71–0.87 and adjusted hazard ratio 0.88, 95% confidence interval 0.78–1.00). In a national cohort of more than 1 million persons, patients with diabetes were 21% and 12% were less likely to have prevalent and develop incident Parkinson’s disease, respectively, compared to an age- and gender-matched control population without diabetes. We found no indication for diabetic retinopathy as an independent risk factor for incident Parkinson’s disease

Integrating snow science and wildlife ecology in Arctic-boreal North America

Snow covers Arctic and boreal regions (ABRs) for approximately 9 months of the year, thus snowscapes dominate the form and function of tundra and boreal ecosystems. In recent decades, Arctic warming has changed the snowcover\u27s spatial extent and distribution, as well as its seasonal timing and duration, while also altering the physical characteristics of the snowpack. Understanding the little studied effects of changing snowscapes on its wildlife communities is critical. The goal of this paper is to demonstrate the urgent need for, and suggest an approach for developing, an improved suite of temporally evolving, spatially distributed snow products to help understand how dynamics in snowscape properties impact wildlife, with a specific focus on Alaska and northwestern Canada. Via consideration of existing knowledge of wildlife-snow interactions, currently available snow products for focus region, and results of three case studies, we conclude that improving snow science in the ABR will be best achieved by focusing efforts on developing data-model fusion approaches to produce fit-for-purpose snow products that include, but are not limited to, wildlife ecology. The relative wealth of coordinated in situ measurements, airborne and satellite remote sensing data, and modeling tools being collected and developed as part of NASA\u27s Arctic Boreal Vulnerability Experiment and SnowEx campaigns, for example, provide a data rich environment for developing and testing new remote sensing algorithms and retrievals of snowscape properties

Glucose tolerance is associated with differential expression of microRNAs in skeletal muscle: results from studies of twins with and without type 2 diabetes.

AIMS/HYPOTHESIS: We aimed to identify microRNAs (miRNAs) associated with type 2 diabetes and risk of developing the disease in skeletal muscle biopsies from phenotypically well-characterised twins. METHODS: We measured muscle miRNA levels in monozygotic (MZ) twins discordant for type 2 diabetes using arrays. Further investigations of selected miRNAs included target prediction, pathway analysis, silencing in cells and association analyses in a separate cohort of 164 non-diabetic MZ and dizygotic twins. The effects of elevated glucose and insulin levels on miRNA expression were examined, and the effect of low birthweight (LBW) was studied in rats. RESULTS: We identified 20 miRNAs that were downregulated in MZ twins with diabetes compared with their non-diabetic co-twins. Differences for members of the miR-15 family (miR-15b and miR-16) were the most statistically significant, and these miRNAs were predicted to influence insulin signalling. Indeed, miR-15b and miR-16 levels were associated with levels of key insulin signalling proteins, miR-15b was associated with the insulin receptor in non-diabetic twins and knockdown of miR-15b/miR-16 in myocytes changed the levels of insulin signalling proteins. LBW in twins and undernutrition during pregnancy in rats were, in contrast to overt type 2 diabetes, associated with increased expression of miR-15b and/or miR-16. Elevated glucose and insulin suppressed miR-16 expression in vitro. CONCLUSIONS: Type 2 diabetes is associated with non-genetic downregulation of several miRNAs in skeletal muscle including miR-15b and miR-16, potentially targeting insulin signalling. The paradoxical findings in twins with overt diabetes and twins at increased risk of the disease underscore the complexity of the regulation of muscle insulin signalling in glucose homeostasis.JB-J was supported by a grant from the Danish PhD School for Molecular Metabolism. The study was supported by grants from the Danish Medical Research Council, the Danish Strategic Research Council. The Novo Nordisk Foundation, the Danish Ministry of Science, Technology and Innovation. DSF-T was supported by the Biotechnology and Biological Sciences Research Council project grant BB/F-15364/1. SEO is a British Heart Foundation Senior Fellow (FS/09/029/27902).This is the final version of the article. It was first published by Springer at http://link.springer.com/article/10.1007%2Fs00125-014-3434-

Snow and vegetation seasonality influence seasonal trends of leaf nitrogen and biomass in Arctic tundra

Abstract Climate change, including both increasing temperatures and changing snow regimes, is progressing rapidly in the Arctic, leading to changes in plant phenology and in the seasonal patterns of plant properties, such as tissue nitrogen (N) content and community aboveground biomass. However, significant knowledge gaps remain over how these seasonal patterns vary among Arctic plant functional groups (i.e., shrubs, grasses, and forbs) and across large geographical areas. We used three years of in situ field vegetation sampling from an 80,000-km² area in Arctic Alaska, remotely sensed vegetation data (daily normalized difference vegetation index [NDVI]), and modeled output of snow-free date to determine and model the seasonal trends and primary controls on leaf percent nitrogen and biomass (in grams per square meter) among Arctic vegetation functional groups. We determined relative vegetation phenology stage at a 500-m spatial scale resolution, defined as the number of days between the date of the seasonal maximum NDVI and the vegetation field sampling date, and relative snow phenology stage (90-m spatial scale) was determined as the number of days between the date of snow-free ground and the sampling date. Models including relative phenology stage were particularly important for explaining seasonal variability of %N in shrubs, graminoids, and forbs. Similarly, vegetation and snow phenology stages were also important for modeling seasonal biomass of shrubs and graminoids; however, for all functional groups, the models explained only a small amount of seasonal variability in biomass. Relative phenology stage was a stronger predictor of %N and biomass than geographic position, indicating that localized controls on phenology, acting at spatial scales of 500 m and smaller, are critical to understanding %N and biomass