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Biochar is a recalcitrant carbon-rich solid produced by pyrolysis of organic residues, and its application to soil is considered a promising approach to mitigate climate change, as biochar resists decomposition to readily contributes to soil carbon (C) sequestration. The IPCC provides a basis for future national-scale accounting of the changes in soil C stocks following biochar application to cropland soils. The IPCC Tier 1 approach for biochar is based on fixed emission factors to estimate biochar C sequestration. In contrast, the Tier 2 approach allows countries to use local emission factors and climate data to calculate the contribution of biochar to soil C sequestration. Accurate accounting of biochar C sequestration is essential for ensuring the credibility of C offsetting projects, as well as providing incentives for implementing biochar in C credit schemes, calling for comparative analyses of the different biochar Tier approaches. Here we retrieved biochar samples from local producers and measured their H/Corg to estimate the persistence of biochar in Norwegian croplands post application. Various feedstocks were considered, including forest residues, woody wastes, manure, sludge, and straw. For all biochar samples, the 100-year stable C fraction was calculated at ≥ 0.945, thus exceeding the default Tier 1 value (0.8). Biochar sourced from woody- and forestry residues had a Corg content above the default Tier 1 value (0.77). Based on this and data about national feedstock supplies, we compared the theoretical potential of biochar soil C sequestration to mitigate climate change in Norway, using the IPCC Tier 1 and Tier 2 approaches. Biochar C sequestration in soil was calculated at 0.79 Tg CO2-eq yr−1 and 0.92 to 0.96 Tg CO2-eq yr−1, respectively for the Tier 1 and Tier 2 approaches, thus, underlining that the choice of IPCC Tier approach can have a large impact on the estimated mitigation potential of biochar.

Forest age structure is one of the most important ecological indicators of forest sustainability in terms of biodiversity, forest history, harvesting potentials, carbon storage, and recreational values. The available information on the forest age is most often stand age from forest management plans or national forest inventories. Depending on the definition, stand age is often not a good indicator for the biological age of the dominant trees in a stand. Here, we used 6,998 increment cores from dominant Norway spruce (Picea abies L.) and Scots pine (Pinus sylvestris L.) sampled on National Forest Inventory (NFI) plots throughout Norway to gain a better understanding of the age structure of Norway spruce and Scots pine stands in Norway, and on the relationship between the recorded stand age and the biological age of dominant trees on the NFI plots. In forest with stand ages indicating that the stand was established after the abandonment of selective harvesting in favor of even-aged management dominated by clear-cutting methods (ca.1940 C.E.), we found no systematic difference between the biological age of the sampled trees and the stand age assessed by the NFI. In older stands, there was a large difference between the stand age and the age of the overstory trees with the sampled age trees occasionally being hundreds of years older than the stand age. Our study also reveals that the area of forest with old Norway spruce and Scots pine trees ≥ 160 years old is considerably higher than the corresponding area estimate based on information derived from the stand age only. These results are important as the stand age is often used to characterize status with respect to forest naturalness, biodiversity, guide protection efforts, and describe the appropriate and allowed management activities.

Abstract Background Patients suffering from chronic spontaneous urticaria (CSU) are typically classified as type I or type IIb autoimmune CSU, but further patient stratification is hindered by the lack of biomarkers. Objectives We investigated whether the histamine content of individual basophils differ between patient subtypes in CSU to evaluate its potential as a biomarker. Methods A total of 101 patients diagnosed with CSU were included in the study. The histamine content per circulating basophil was derived from the basophil count in peripheral blood and levels of total cellular blood histamine. These measures, together with results from the serum‐induced basophil histamine release assay (s‐BHRA), were correlated to information on demographics, clinical characteristics, patient reported outcomes and laboratory analyses. Results The histamine content per basophil was significantly different between s‐BHRA positive and ‐negative patients (0.175 vs. 1.40 pg/cell, p

Introduction Behavioural weight loss programmes are generally accepted as being beneficial in reducing cardiometabolic risk and improving patient-reported outcomes. However, prospective data from large real-world cohorts are scarce concerning the mid-term and long-term impact of such interventions. The objective of this large prospective cohort study (n>10 000 participants) is to demonstrate the effectiveness of the standardised Nutritional and Psycho-Behavioural Rehabilitation programme (RNPC Programme) in reducing the percentage of subjects requiring insulin and/or other diabetes drug therapy, antihypertensive drugs, lipid-lowering therapies and continuous positive airway pressure therapy for obstructive sleep apnoea after the end of the intervention. The rate of remission of hypertension, type 2 diabetes and sleep apnoea will also be prospectively assessed.Methods This is a prospective multicentre observational study carried out in 92 RNPC centres in France. Participants will follow the standardised RNPC Programme. The prospective dataset will include clinical, anthropometric and biochemical data, comorbidities, medications, body composition, patient-reported outcome questionnaire responses, sleep study data with objective measurements of sleep apnoea severity and surrogate markers of cardiovascular risk (ie, blood pressure and arterial stiffness). About 10 000 overweight or obese participants will be included over 2 years with a follow-up duration of up to 5 years.Ethics and dissemination Ethical approval for this study has been granted by the Ethics Committee (Comité de protection des personnes Sud-Est I) of Saint-Etienne University Hospital, France (SI number: 23.00174.000237). Results will be submitted for publication in peer-review journals, presented at conferences and inform the design of a future randomised controlled trial in the specific population identified as good responders to the RNPC Programme.Trial registration number NCT05857319.

Abstract To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Abstract Background Nordic countries excel in cancer care, but studies on uptake, costs, or managed entry agreements of cancer medicines have not been conducted recently. The aim of this study was to examine the uptake and availability of orally administered new cancer medicines in Nordic countries. Orally administered cancer medicines enable and are used in the community as part of outpatient care. Firstly, we studied the distribution, costs and adoption of managed entry agreements of these medicines, and secondly, uptake of and managed entry agreements for cancer medicines used in outpatient care that were granted marketing authorization in Europe in 2010–2021. Methods An E-mail survey of competent authorities, meaning pharmaceutical service organizers, payers or other government or non-government actors developing pharmaceutical service operations, in Denmark, Finland, Iceland, Norway, and Sweden in April-June 2022. The data were analysed using frequencies and percentages for descriptive analysis. Results The distribution of cancer medicines has similarities in Finland, Iceland, Norway, and Sweden, where cancer medicines can be distributed both via hospitals or hospital pharmacies for inpatient use, and via community pharmacies for outpatient use. In Denmark, cancer medicines are predominantly distributed via publicly funded hospitals. In all countries that provided data on the costs, the costs of cancer medicines had notably gone up from 2010 to 2021. The number of reimbursable medicines out of new cancer medicines varied from 36 products in Denmark and Iceland to 51 products in Sweden, out of 67 studied products. Managed entry agreements, often with confidential discounts, were in use in all Nordic countries. The number of agreements and the cancer types for which agreements were most often made varied from three agreements made in Iceland to 35 agreements made in Finland, out of 67 studied products. Average days from authorization to reimbursement of new cancer medicines varied from an average of 416 to 895 days. Conclusions Nordic countries share similar characteristics but also differ in terms of the details in distribution, adopted managed entry agreements, market entry, and availability of new orally administered cancer medicines used in the outpatient care. The costs of cancer medicines have increased in all Nordic countries during the last decade. Due to differences in health care and because orally administered cancer medicines can be dispensed at community and hospital pharmacies in all studied countries other than Denmark, the number of reimbursable medicines and managed entry agreements vary between countries. However, Nordic countries show good agreement for 2010 to 2021 in entry and reimbursement decisions of novel cancer medicines.

Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy.

Accurate estimation of site productivity is essential for forest projections and scenario modelling. We present and evaluate models to predict site index (SI) and whether a site is productive (potential total stem volume production [greater than or equal to] 1 [m.sup.3]*[ha.sup.-1][year.sup.-1]) in a wall-to-wall high-resolution (16 m * 16 m) SI map for Norway. We investigate whether remotely sensed data improve predictions. We also study the advantages and disadvantages of using boosted regression trees (BRT), a machine-learning algorithm, to create high-accuracy SI maps. We use climatic and topographical data, soil parent material, a land resource map, and depth to water, together with Sentinel-2 satellite images and airborne laser scanning metrics, as predictor variables. We use the SI observed at more than 10 000 National Forest Inventory (NFI) sample plots throughout Norway to fit BRT models and validate the models using 5822 independent temporary plots from the NFI. We benchmark our results against SI estimates from forest monitoring inventories. We find that the SI from BRT has root mean squared error (RMSE) ranging from 2.3 m (hardwoods) to 3.6 m (spruce) when tested against independent validation data from the NFI temporary plots. These RMSEs are similar or marginally better than an evaluation of SI estimates from operational forest management plans where SI normally stems from manual photo interpretation. Key words: machine learning, BRT, ALS, Norway spruce, Scots pine, Introduction Site productivity is a key variable in most forest management decisions such as species selection, stand density, or number and timing of thinnings. Forest site productivity is the production [...]

Cut-to-length harvesters collect useful information for modeling relationships between forest attributes and airborne laser scanning (ALS) data. However, harvesters operate in mature forests, which may introduce selection biases that can result in systematic errors in harvester data-based forest attribute maps. We fitted regression models (harvester models) for volume (V), height (HL), stem frequency (N), above-ground biomass, basal area, and quadratic mean diameter (QMD) using harvester and ALS data. Performances of the harvester models were evaluated using national forest inventory plots in an 8.7 Mha study area. We estimated biases of large-area synthetic estimators and compared efficiencies of model-assisted (MA) estimators with field data-based direct estimators. The harvester models performed better in productive than unproductive forests, but systematic errors occurred in both. The use of MA estimators resulted in efficiency gains that were largest for HL (relative efficiency, RE = 6.0) and smallest for QMD (RE = 1.5). The bias of the synthetic estimator was largest for N (39%) and smallest for V (1%). The latter was due to an overestimation of deciduous and an underestimation of spruce forests that by chance balanced. We conclude that a probability sample of reference observations may be required to ensure the unbiasedness of estimators utilizing harvester data. Key words: cut-to-length harvester data, model-assisted estimation, national forest inventory, airborne LiDAR, large- area estimation, 1. Introduction The increased availability of wall-to-wall airborne laser scanning (ALS) data during the past 20 years has revolutionized the mapping of forest resources (Maltamo et al. 2021). Many countries [...]

ABSTRACT: Bovine mastitis is one of the most important diseases in modern dairy farming, as it leads to reduced welfare and milk production and increased need for antibiotic use. Clinical mastitis in Denmark is most often treated with a combination of local and systemic treatment with penicillin. The objective of this randomized clinical trial was to assess whether worse results could be expected with local intramammary treatment with penicillin compared with a combination of local and systemic treatment with penicillin in terms of the bacteriological cure of mild and moderate clinical mastitis cases caused by gram-positive bacteria. We carried out a noninferiority trial with a noninferiority margin set to a relative reduction in bacteriological cure of 15% between these 2 treatment groups to assess the effect of reducing the total antibiotic use by a factor of 16 for each treated case. Clinical mastitis cases from 12 Danish dairy farms were considered for enrollment. On-farm selection of gram-positive cases was carried out by the farm personnel within the first 24 h after a clinical mastitis case was detected. A single farm used bacterial culture results from the on-farm veterinarian, whereas the other 11 farms were provided with an on-farm test to distinguish gram-positive bacteria from gram-negative or samples without bacterial growth. Cases with suspected gram-positive bacteria were allocated to a treatment group: either local or combination. Bacteriological cure was assessed based on the bacterial species identified in the milk sample from the clinical mastitis case and 2 follow-up samples collected approximately 2 and 3 wk after ended treatment. Identification of bacteria was carried out using MALDI-TOF on bacterial culture growth. Noninferiority was assessed using unadjusted cure rates and adjusted cure rates from a multivariable mixed logistic regression model. Of the 1,972 clinical mastitis cases registered, 345 (18%) met all criteria for inclusion (full data). The data set was further reduced to 265 cases for the multivariable analysis to include only complete registrations. Streptococcus uberis was the most commonly isolated pathogen. Noninferiority was demonstrated for both unadjusted and adjusted cure rates. The unadjusted cure rates were 76.8% and 83.1% for the local and combined treatments, respectively (full data). The pathogen and somatic cell count before the clinical case had an effect on the efficacy of treatment; thus efficient treatment protocols should be herd- and case-specific. The effect of pathogen and somatic cell count on treatment efficacy was similar irrespective of the treatment protocol. We conclude that bacteriological cure of local penicillin treatment for mild and moderate clinical mastitis cases was noninferior to the combination of local and systemic treatment using a 15% noninferiority margin. This suggests that a potential 16-fold reduction in antimicrobial use per mastitis treatment can be achieved with no adverse effect on cure rate.

Abstract Aim To examine whether patients' body mass index is associated with missed hip fracture care consistent with national guideline‐recommended care. Design A nationwide, population‐based cohort study using prospectively collected data from the Danish Multidisciplinary Hip Fracture Registry. Methods The study population consisted of 39,835 patients ≥65 years admitted with a hip fracture and discharged between 1st of January 2012 and 29th of November 2017. National guideline‐recommended care consists of preoperative optimization, early surgery, mobilization within 24 h, basic mobility assessment, nutrition screening, post‐discharge rehabilitation program, and osteoporotic and fall prophylaxis. We used binomial regression to estimate the relative risk for the fulfilment of the individual measures with 95% confidence interval. Multiple imputation method was applied to handle missing values of body mass index. Results The overall fulfilment of the individual measures ranged from 43% for pre‐operative optimization to 95% for receiving a post‐discharge rehabilitation program. The obese patients had a lower fulfilment of surgery within 36 h compared to patients with normal weight. No differences in fulfilment of the other measures were found. However, patients with missing data on body mass index had the highest risk of missed care. In conclusion, patients with missing BMI values had the highest risk of missed care. The obese patients had a slightly higher risk of long waiting times for surgery than normal‐weighted patients. No Patient or Public Contribution This study was done based on population‐based data from medical registries and data was analysed by the authors only.

Forest management planning often relies on Airborne Laser Scanning (ALS)-based Forest Management Inventories (FMIs) for sustainable and efficient decision-making. Employing the area-based (ABA) approach, these inventories estimate forest characteristics for grid cell areas (pixels), which are then usually summarized at the stand level. Using the ALS-based high-resolution Norwegian Forest Resource Maps (16 ​m ​× ​16 ​m pixel resolution) alongside with stand-level growth and yield models, this study explores the impact of three levels of pixel aggregation (stand-level, stand-level with species strata, and pixel-level) on projected stand development. The results indicate significant differences in the projected outputs based on the aggregation level. Notably, the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation, ranging from −301 to +253 ​m3⋅ha−1 for single stands. The differences were, on average, higher for broadleaves than for spruce and pine dominated stands, and for mixed stands and stands with higher variability than for pure and homogenous stands. In conclusion, this research underscores the critical role of input data resolution in forest planning and management, emphasizing the need for improved data collection practices to ensure sustainable forest management.

Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories. We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees. Unlike object reconstruction methods, our approach is based on simple metrics computed on vertical slices that summarize information on point distances, angles, and geometric attributes of the space between and around the points. Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans. We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios. Our approach provides a simple, clear, and scalable solution that can be adapted to different situations both for research and more operational mapping.

Abstract The European Union (EU) set clear climate change mitigation targets to reach climate neutrality, accounting for forests and their woody biomass resources. We investigated the consequences of increased harvest demands resulting from EU climate targets. We analysed the impacts on national policy objectives for forest ecosystem services and biodiversity through empirical forest simulation and multi-objective optimization methods. We show that key European timber-producing countries – Finland, Sweden, Germany (Bavaria) – cannot fulfil the increased harvest demands linked to the ambitious 1.5°C target. Potentials for harvest increase only exists in the studied region Norway. However, focusing on EU climate targets conflicts with several national policies and causes adverse effects on multiple ecosystem services and biodiversity. We argue that the role of forests and their timber resources in achieving climate targets and societal decarbonization should not be overstated. Our study provides insight for other European countries challenged by conflicting policies and supports policymakers.