Contributions of feather microstructure to eider down insulation properties

Insulation is an essential component of nest structure that helps provide incubation requirements for birds. Many species of waterfowl breed in high latitudes where rapid heat loss can necessitate a high energetic input from parents and use down feathers to line their nests. Common eider Somateria mollissima nest down has exceptional insulating properties but the microstructural mechanisms behind the feather properties have not been thoroughly examined. Here, we hypothesized that insulating properties of nest down are correlated to down feather (plumule) microstructure. We tested the thermal efficiency (fill power) and cohesion of plumules from nests of two Icelandic colonies of wild common eiders and compared them to properties of plumules of wild greylag goose Anser anser. We then used electron microscopy to examine the morphological basis of feather insulating properties. We found that greylag goose down has higher fill power (i.e. traps more air) but much lower cohesion (i.e. less prone to stick together) compared to common eider down. These differences were related to interspecific variation in feather microstructure. Down cohesion increased with the number of barbule microstructures (prongs) that create strong points of contact among feathers. Eider down feathers also had longer barbules than greylag goose down feathers, likely increasing their air-trapping capacity. Feather properties of these two species might reflect the demands of their contrasting evolutionary history. In greylag goose, a temperate, terrestrial species, plumule microstructure may optimize heat trapping. In common eiders, a diving duck that nests in arctic and subarctic waters, plumule structure may have evolved to maximize cohesion over thermal insulation, which would both reduce buoyancy during their foraging dives and enable nest down to withstand strong arctic winds

Contributions of feather microstructure to eider down insulation properties

Insulation is an essential component of nest structure that helps provide incubation requirements for birds. Many species of waterfowl breed in high latitudes where rapid heat loss can necessitate a high energetic input from parents and use down feathers to line their nests. Common eider Somateria mollissima nest down has exceptional insulating properties but the microstructural mechanisms behind the feather properties have not been thoroughly examined. Here, we hypothesized that insulating properties of nest down are correlated to down feather (plumule) microstructure. We tested the thermal efficiency (fill power) and cohesion of plumules from nests of two Icelandic colonies of wild common eiders and compared them to properties of plumules of wild greylag goose Anser anser. We then used electron microscopy to examine the morphological basis of feather insulating properties. We found that greylag goose down has higher fill power (i.e. traps more air) but much lower cohesion (i.e. less prone to stick together) compared to common eider down. These differences were related to interspecific variation in feather microstructure. Down cohesion increased with the number of barbule microstructures (prongs) that create strong points of contact among feathers. Eider down feathers also had longer barbules than greylag goose down feathers, likely increasing their air-trapping capacity. Feather properties of these two species might reflect the demands of their contrasting evolutionary history. In greylag goose, a temperate, terrestrial species, plumule microstructure may optimize heat trapping. In common eiders, a diving duck that nests in arctic and subarctic waters, plumule structure may have evolved to maximize cohesion over thermal insulation, which would both reduce buoyancy during their foraging dives and enable nest down to withstand strong arctic winds

NiN-basiskartlegging av utvalgte verneområder i Nordland, 2022

NIBIO har i samarbeid med Sállir Natur AS og Natur og Samfunn AS kartlagt naturtyper og arter i fem verneområder i Nordland i 2022 etter kartleggingsmetodikken Natur i Norge (NiN). Rapporten oppsummerer forhold som kommer dårlig frem i kartobjekter og egenskapsdata som har blitt registrert og rapportert via NiNapp. Rapporten inneholder generelle faglige vurderinger, eventuelle observerte forvaltningsrelevante problemstillinger, praktiske utfordringer i felt, eventuell usikkerhet knyttet til kartleggingsenheter og viser noen utvalgte bilder for verneområdene.NiN-basiskartlegging av utvalgte verneområder i Nordland, 2022publishedVersio

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological systematic review of health technology assessments

Background: Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods: We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results: The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. Conclusions: The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Digital twinning of all forest and non-forest trees at national level via deep learning

Abstract Intelligent forest management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about forests is a prerequisite for such management but is conventionally based on costly plot-scale data, rarely available at resolution of relevance for management strategies. Here, we present a deep learning-based framework that provides location, crown area and height for each individual tree from aerial images at country scale. We quantify and characterize all individual trees in Denmark and show that 26% of the trees grow outside forests, which is typically unrecognized in national inventories. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to Finland, despite markedly dissimilar landscapes and data sources. Our work lays the foundation for a global database, where every tree has its digital twin and is spatially traceable and manageable