Monitoring Winter Stress Vulnerability of High-Latitude Understory Vegetation Using Intraspecific Trait Variability and Remote Sensing Approaches

In this study, we focused on three species that have proven to be vulnerable to winter stress: Empetrum nigrum, Vaccinium vitis-idaea and Hylocomium splendens. Our objective was to determine plant traits suitable for monitoring plant stress as well as trait shifts during spring. To this end, we used a combination of active and passive handheld normalized difference vegetation index (NDVI) sensors, RGB indices derived from ordinary cameras, an optical chlorophyll and flavonol sensor (Dualex), and common plant traits that are sensitive to winter stress, i.e. height, specific leaf area (SLA). Our results indicate that NDVI is a good predictor for plant stress, as it correlates well with height (r = 0.70, p < 0.001) and chlorophyll content (r = 0.63, p < 0.001). NDVI is also related to soil depth (r = 0.45, p < 0.001) as well as to plant stress levels based on observations in the field (r = −0.60, p < 0.001). Flavonol content and SLA remained relatively stable during spring. Our results confirm a multi-method approach using NDVI data from the Sentinel-2 satellite and active near-remote sensing devices to determine the contribution of understory vegetation to the total ecosystem greenness. We identified low soil depth to be the major stressor for understory vegetation in the studied plots. The RGB indices were good proxies to detect plant stress (e.g. Channel G%: r = −0.77, p < 0.001) and showed high correlation with NDVI (r = 0.75, p < 0.001). Ordinary cameras and modified cameras with the infrared filter removed were found to perform equally well

Individualister i en fatalistisk bransje : en religionssosiologisk studie av spådomstilbydere

Masteroppgave i religion- Universitetet i Agder 201

Detectability of the degree of freeze damage in meat analytic-tool depends on selection

Novel freezing solutions are constantly being developed to reduce quality loss in meat production chains. However, there is limited focus on identifying the sensitive analytical tools needed to directly validate product changes that result from potential improvements in freezing technology. To benchmark analytical tools relevant to meat research and production, we froze pork samples using traditional (−25 °C, −35 °C) and cryogenic freezing (−196 °C). Three classes of analyses were tested for their capacity to separate different freeze treatments: thaw loss testing, bioelectrical spectroscopy (nuclear magnetic resonance, microwave, bioimpedance) and low-temperature microscopy (cryo-SEM). A general effect of freeze treatment was detected with all bioelectrical methods. Yet, only cryo-SEM resolved quality differences between all freeze treatments, not only between cryogenic and traditional freezing. The detection sensitivity with cryo-SEM may be explained by testing meat directly in the frozen state without prior defrosting. We discuss advantages, shortcomings and cost factors in using analytical tools for quality monitoring in the meat sector

Development of new metrics to assess and quantify climatic drivers of extreme event driven Arctic browning

Rapid climate change in Arctic regions is resulting in more frequent extreme climatic events. These can cause large-scale vegetation damage, and are therefore among key drivers of declines in biomass and productivity (or “browning”) observed across Arctic regions in recent years. Extreme events which cause browning are driven by multiple interacting climatic variables, and are defined by their ecological impact – most commonly plant mortality. Quantifying the climatic causes of these multivariate, ecologically defined events is challenging, and so existing work has typically determined the climatic causes of browning events on a case-by-case basis in a descriptive, unsystematic manner. While this has allowed development of important qualitative understanding of the mechanisms underlying extreme event driven browning, it cannot definitively link browning to specific climatic variables, or predict how changes in these variables will influence browning severity. It is therefore not yet possible to determine how extreme events will influence ecosystem responses to climate change across Arctic regions. To address this, novel, process-based climate metrics that can be used to quantify the conditions and interactions that drive the ecological responses defining common extreme events were developed using publicly available snow depth and air temperature data (two of the main climate variables implicated in browning). These process-based metrics explained up to 63% of variation in plot-level Normalised Difference Vegetation Index (NDVI) at sites within areas affected by extreme events across boreal and sub-Arctic Norway. This demonstrates potential to use simple metrics to assess the contribution of extreme events to changes in Arctic biomass and productivity at regional scales. In addition, scaling up these metrics across the Norwegian Arctic region resulted in significant correlations with remotely-sensed NDVI, and provided much-needed insights into how climatic variables interact to determine the severity of browning across Arctic regions

Shared CSF Biomarker Profile in Idiopathic Normal Pressure Hydrocephalus and Subcortical Small Vessel Disease

Introduction: In this study, we examine similarities and differences between 52 patients with idiopathic normal pressure hydrocephalus (iNPH) and 17 patients with subcortical small vessel disease (SSVD), in comparison to 28 healthy controls (HCs) by a panel of cerebrospinal fluid (CSF) biomarkers. Methods: We analyzed soluble amyloid precursor protein alpha (sAPPα) and beta (sAPPβ), Aβ isoforms −38, −40, and −42, neurofilament light protein (NFL), glial fibrillary acidic protein (GFAP), myelin basic protein (MBP), matrix metalloproteinases (MMP −1, −2, −3, −9, and −10), and tissue inhibitors of metalloproteinase 1 (TIMP1). Radiological signs of white matter damage were scored using the age-related white matter changes (ARWMC) scale. Results: All amyloid fragments were reduced in iNPH and SSVD (p < 0.05), although more in iNPH than in SSVD in comparison to HC. iNPH and SSVD showed comparable elevations of NFL, MBP, and GFAP (p < 0.05). MMPs were similar in all three groups except for MMP-10, which was increased in iNPH and SSVD. Patients with iNPH had larger ventricles and fewer WMCs than patients with SSVD. Conclusion: The results indicate that patients with iNPH and SSVD share common features of subcortical neuronal degeneration, demyelination, and astroglial response. The reduction in all APP-derived proteins characterizing iNPH patients is also present, indicating that SSVD encompasses similar pathophysiological phenomena as iNPH

Arctic browning: Impacts of extreme climatic events on heathland ecosystem CO2 fluxes.

Extreme climatic events are among the drivers of recent declines in plant biomass and productivity observed across Arctic ecosystems, known as "Arctic browning." These events can cause landscape-scale vegetation damage and so are likely to have major impacts on ecosystem CO2 balance. However, there is little understanding of the impacts on CO2 fluxes, especially across the growing season. Furthermore, while widespread shoot mortality is commonly observed with browning events, recent observations show that shoot stress responses are also common, and manifest as high levels of persistent anthocyanin pigmentation. Whether or how this response impacts ecosystem CO2 fluxes is not known. To address these research needs, a growing season assessment of browning impacts following frost drought and extreme winter warming (both extreme climatic events) on the key ecosystem CO2 fluxes Net Ecosystem Exchange (NEE), Gross Primary Productivity (GPP), ecosystem respiration (Reco ) and soil respiration (Rsoil ) was carried out in widespread sub-Arctic dwarf shrub heathland, incorporating both mortality and stress responses. Browning (mortality and stress responses combined) caused considerable site-level reductions in GPP and NEE (of up to 44%), with greatest impacts occurring at early and late season. Furthermore, impacts on CO2 fluxes associated with stress often equalled or exceeded those resulting from vegetation mortality. This demonstrates that extreme events can have major impacts on ecosystem CO2 balance, considerably reducing the carbon sink capacity of the ecosystem, even where vegetation is not killed. Structural Equation Modelling and additional measurements, including decomposition rates and leaf respiration, provided further insight into mechanisms underlying impacts of mortality and stress on CO2 fluxes. The scale of reductions in ecosystem CO2 uptake highlights the need for a process-based understanding of Arctic browning in order to predict how vegetation and CO2 balance will respond to continuing climate change

Calibration of dosemeters used in mammography with different X ray qualities: Euromet Project No. 526

The effect of different X ray radiation qualities on the calibration of mammographic dosemeters was investigated within the framework of a EUROMET (European Collaboration in Measurement Standards) project. The calibration coefficients for two ionization chambers and two semiconductor detectors were established in 13 dosimetry calibration laboratories for radiation qualities used in mammography. They were compared with coefficients for other radiation qualities, including those defined in ISO 4037-1, with first half value layers in the mammographic range. The results indicate that the choice of the radiation quality is not crucial for instruments with a small energy dependence of the response. However, the radiation quality has to be chosen carefully if instruments with a marked dependence of their response to the radiation energy are calibrate

Long-term results of open and endovascular revascularization of superficial femoral artery occlusive disease

BackgroundFirst-line treatment for patients with superficial femoral arterial (SFA) occlusive disease has yet to be determined. This study compared long-term outcomes between primary SFA stent placement and primary femoral-popliteal bypass. Periprocedural patient factors were examined to determine their effect on these results.MethodsAll femoral-popliteal bypasses and SFA interventions performed in consecutive patients with symptoms Rutherford 3 to 6 between 2001 and 2008 were reviewed. Time-dependent outcomes were analyzed using the Kaplan-Meier method and log-rank test. Cox proportional hazards were performed to determine predictors of graft patency. Multivariate analysis was completed to identify patient covariates most often associated with the primary therapy.ResultsA total of 152 limbs in 141 patients (66% male; mean age, 66 ± 22 years) underwent femoral-popliteal bypass, and 233 limbs in 204 patients (49% male; mean age, 70 ± 11 years) underwent SFA interventions. Four-year primary, primary-assisted, and secondary patency rates were 69%, 78%, and 83%, respectively, for bypass patients and 66%, 91%, and 95%, respectively, for SFA interventions. Six-year limb salvage was 80% for bypass vs 92% for stenting (P = .04). Critical limb ischemia (CLI) and renal insufficiency were predictors of bypass failure. Claudication was a predictor of success for SFA stenting. Three-year limb salvage rates for CLI patients undergoing surgery and SFA stenting were 83%. Amputation-free survival at 3 years for CLI patients was 55% for bypass and 59% for SFA interventions. Multivariate predictors (odds ratios and 95% confidence intervals) of covariates most frequently associated with first-line SFA stenting were TransAtlantic Inter-Society Consensus II A and B lesions (5.9 [3.4-9.1], P < .001), age >70 years (2.1 [1.4-3.1], P < .001), and claudication (1.7 [1.1-2.7], P = .01). Regarding bypass as first-line therapy, claudicant patients were more likely to have nondiabetic status (5.6 [3.3-9.4], P < .001), creatinine <1.8 mg/dL (4.6 [1.5-14.9], P = .01), age <70 years (2.7 [CI, 1.6-8.3], P < .001), and presence of an above-knee popliteal artery target vessel (1.9 [CI, 1.1-3.4] P = .02).ConclusionIndication, patient-specific covariates, and anatomic lesion classification have significant association when determining surgeon selection of SFA stenting or femoral-popliteal bypass as first-line therapy. Patients with SFA disease can have comparable long-term results when treatment options are well matched to patient-specific and anatomic characteristics

An artificial intelligence approach to remotely assess pale lichen biomass

Although generally given little attention in vegetation studies, ground-dwelling (terricolous) lichens are major contributors to overall carbon and nitrogen cycling, albedo, biodiversity and biomass in many high-latitude ecosystems. Changes in biomass of mat-forming pale lichens have the potential to affect vegetation, fauna, climate and human activities including reindeer husbandry. Lichens have a complex spectral signature and terricolous lichens have limited growth height, often growing in mixtures with taller vegetation. This has, so far, prevented the development of remote sensing techniques to accurately assess lichen biomass, which would be a powerful tool in ecosystem and ecological research and rangeland management. We present a Landsat based remote sensing model developed using deep neural networks, trained with 8914 field records of lichen volume collected for > 20 years. In contrast to earlier proposed machine learning and regression methods for lichens, our model exploited the ability of neural networks to handle mixed spatial resolution input. We trained candidate models using input of 1 x 1 (30 x 30 m) and 3 x 3 Landsat pixels based on 7 reflective bands and 3 indices, combined with a 10 m spatial resolution digital elevation model. We normalised elevation data locally for each plot to remove the region-specific variation, while maintaining informative local variation in topography. The final model predicted lichen volume in an evaluation set (n = 159) reaching an R2 of 0.57. NDVI and elevation were the most important predictors, followed by the green band. Even with moderate tree cover density, the model was efficient, offering a considerable improvement compared to earlier methods based on specific reflectance. The model was in principle trained on data from Scandinavia, but when applied to sites in North America and Russia, the predictions of the model corresponded well with our visual interpretations of lichen abundance. We also accurately quantified a recent historic (35 years) change in lichen abundance in northern Norway. This new method enables further spatial and temporal studies of variation and changes in lichen biomass related to multiple research questions as well as rangeland management and economic and cultural ecosystem services. Combined with information on changes in drivers such as climate, land use and management, and air pollution, our model can be used to provide accurate estimates of ecosystem changes and to improve vegetation-climate models by including pale lichens.Peer reviewe