Numerous publications have stated that metastases are responsible for 90% of cancer deaths, but data underlying this assertion has been lacking. Our objective was to determine what proportions of cancer deaths are caused by metastases. Population‐based data from the Cancer Registry of Norway for the years 2005‐2015 was analyzed. We compared all deaths in the Norwegian population where a cancer diagnosis was registered as cause of death. Deaths caused by cancer, with and without metastases, were analyzed, by sex and tumor group. For solid tumors, 66.7% of cancer deaths were registered with metastases as a contributing cause. Proportions varied substantially between tumor groups. Our data support the idea that the majority of deaths from solid tumors are caused by metastases. Thus, a better understanding of the biology of metastases and identification of druggable targets involved in growth at the metastatic site is a promising strategy to reduce cancer mortality.
Abstract Objectives To estimate the burden of miscarriage in the Norwegian population and to evaluate the associations with maternal age and pregnancy history. Design Prospective register based study. Setting Medical Birth Register of Norway, the Norwegian Patient Register, and the induced abortion register. Participants All Norwegian women that were pregnant between 2009-13. Main outcome measure Risk of miscarriage according to the woman’s age and pregnancy history estimated by logistic regression. Results There were 421 201 pregnancies during the study period. The risk of miscarriage was lowest in women aged 25-29 (10%), and rose rapidly after age 30, reaching 53% in women aged 45 and over. There was a strong recurrence risk of miscarriage, with age adjusted odds ratios of 1.54 (95% confidence interval 1.48 to 1.60) after one miscarriage, 2.21 (2.03 to 2.41) after two, and 3.97 (3.29 to 4.78) after three consecutive miscarriages. The risk of miscarriage was modestly increased if the previous birth ended in a preterm delivery (adjusted odds ratio 1.22, 95% confidence interval 1.12 to 1.29), stillbirth (1.30, 1.11 to 1.53), caesarean section (1.16, 1.12 to 1.21), or if the woman had gestational diabetes in the previous pregnancy (1.19, 1.05 to 1.36). The risk of miscarriage was slightly higher in women who themselves had been small for gestational age (1.08, 1.04 to 1.13). Conclusions The risk of miscarriage varies greatly with maternal age, shows a strong pattern of recurrence, and is also increased after some adverse pregnancy outcomes. Miscarriage and other pregnancy complications might share underlying causes, which could be biological conditions or unmeasured common risk factors.
Victoria Chang, Caro Williams-Pierce, Huaishu Peng
et al.
Social VR introduces new ethical challenges for observational research. The current paper presents a narrative literature review of ethical considerations in observational methods, with a focus on work in HCI. We examine how unobtrusive or selectively disclosed observation is implemented in public face-to-face and social VR settings. Our review extends ethical discussions from traditional public research into the context of social VR, highlighting tensions between observer visibility, data traceability, and participant autonomy. Drawing on insights distilled from prior literature, we propose five constructive guidelines for ethical observational research in public social VR environments. Our work offers key implications for future research, addressing anticipated improvements in platform design, the management of researcher presence, and the development of community-informed consent mechanisms.
Vésteinn Snæbjarnarson, Kevin Du, Niklas Stoehr
et al.
When a vision-language model (VLM) is prompted to identify an entity depicted in an image, it may answer 'I see a conifer,' rather than the specific label 'norway spruce'. This raises two issues for evaluation: First, the unconstrained generated text needs to be mapped to the evaluation label space (i.e., 'conifer'). Second, a useful classification measure should give partial credit to less-specific, but not incorrect, answers ('norway spruce' being a type of 'conifer'). To meet these requirements, we propose a framework for evaluating unconstrained text predictions, such as those generated from a vision-language model, against a taxonomy. Specifically, we propose the use of hierarchical precision and recall measures to assess the level of correctness and specificity of predictions with regard to a taxonomy. Experimentally, we first show that existing text similarity measures do not capture taxonomic similarity well. We then develop and compare different methods to map textual VLM predictions onto a taxonomy. This allows us to compute hierarchical similarity measures between the generated text and the ground truth labels. Finally, we analyze modern VLMs on fine-grained visual classification tasks based on our proposed taxonomic evaluation scheme.
This article investigates crime patterns across European countries in 2022 using Compositional Data Analysis (CoDA) to address limitations of traditional statistical approaches in dealing with the relative nature of crime data. Recognizing crime types as components of a whole, we employ CoDA to explore relationships between different crime categories while respecting their inherent interdependencies. The study utilizes k-means clustering to group countries based on their crime profiles, identifying three distinct clusters largely aligning with geographical locations. This clustering is visualized through t-SNE and geographic mapping, revealing regional similarities. Further analysis using Robust Principal Component Analysis on identified crime clusters reveals insightful relationships between specific crime types, such as homicide, smuggling, and financial crimes, and how their prevalence varies across countries. The findings reveals distinct crime patterns across Europe, highlighting regional commonalities while also highlighting divergences like Norway and Latvia that deviate from their expected geographical classifications. Moreover, the study identifies specific crime groups; for example, it pairs countries high in corruption and smuggling, such as Austria, with those countries that exhibit a higher relevance to homicide and smuggling, such as Luxembourg. It also points to the presence of financial crimes like fraud in countries such as Romania and Estonia.
Lukas Herrmann, Ángel F. García-Fernández, Edmund F. Brekke
et al.
Despite their theoretical advantages, track-before-detect (TBD) methods remain largely absent from real-world multi-target tracking applications due to their computational complexity and limited scalability. This paper presents a scalable hybrid tracking framework that combines a TBD multi-target tracking algorithm with a detection-based multi-target tracking algorithm for coastal radar surveillance. In particular, the approach uses an integrated existence Poisson histogram-probabilistic multi-hypothesis tracking (IE-PHPMHT)-based TBD module with a conventional Poisson multi-Bernoulli Mixture (PMBM) point tracker. The system processes raw radar data through land clutter suppression, cell-wise detection, and clustering-based feature extraction. High-threshold detections are used to track strong targets via the point tracker, while low-threshold detections are employed for adaptive birth in the TBD module, enabling early initiation and sustained tracking of weak or ambiguous targets. Validated using real X-band radar data from the Trondheim Fjord, Norway, the approach demonstrates robust multi-target tracking performance in a full-scale application with a large observation area under resource constraints, highlighting its suitability for operational deployment in complex maritime environments needed for coastal surveillance and to support autonomy.
Snow avalanches present significant risks to human life and infrastructure, particularly in mountainous regions, making effective monitoring crucial. Traditional monitoring methods, such as field observations, are limited by accessibility, weather conditions, and cost. Satellite-borne Synthetic Aperture Radar (SAR) data has become an important tool for large-scale avalanche detection, as it can capture data in all weather conditions and across remote areas. However, traditional processing methods struggle with the complexity and variability of avalanches. This chapter reviews the application of deep learning for detecting and segmenting snow avalanches from SAR data. Early efforts focused on the binary classification of SAR images, while recent advances have enabled pixel-level segmentation, providing greater accuracy and spatial resolution. A case study using Sentinel-1 SAR data demonstrates the effectiveness of deep learning models for avalanche segmentation, achieving superior results over traditional methods. We also present an extension of this work, testing recent state-of-the-art segmentation architectures on an expanded dataset of over 4,500 annotated SAR images. The best-performing model among those tested was applied for large-scale avalanche detection across the whole of Norway, revealing important spatial and temporal patterns over several winter seasons.
The electrification of trucks progresses slowly, with extended charging times as a major concern for transportation companies. In the comparison of electric versus diesel trucks, an aspect often neglected is that regulations on driver working hours affect both types of trucks. In particular, mandatory break times offer opportunities for electric trucks to be charged while drivers rest and, therefore, without necessarily implying additional time over the traditional route duration. To this aim, this paper develops a mathematical programming model that allows to synchronize break times of the drivers with charging times of the trucks. We implement this model using data on real-world truck specifications and charging station infrastructure from Northwest Germany. Our results indicate that under average conditions, the current features of batteries and charging stations are sufficient for electric trucks to perform routes at very similar times as combustion engine trucks. We also study how variations in features such as usable battery size or charging rates due to aging or ambient conditions affect route duration. Our results show that in these cases synchronization of charging and break times is crucial to keep the competitiveness of electric trucks with respect to diesel trucks.
I 2023 kom boken Religionshermeneutikk. Forståing i ei polarisert tid av Øystein Brekke. Denne ambisiøse essayboka på vel 350 sider ble umiddelbart en viktig bok å lese og diskutere i fagmiljøene som forholder seg til religion og livssyn i utdanningsfeltet. Året etter ble boken utgangspunkt for en helt ny sjanger på Norsk religionspedagogisk forskerforums (NoReFo) årlige konferanse: Forfatter møter kritikere, en paneldebatt med tre inviterte kritikere i møte med forfatteren, før tilhørerne i salen fikk anledning til å komme med sine spørsmål og kommentarer. Debatten sparket i gang hele konferansen, og skapte stor entusiasme. Den ble også tatt opp, og nå er opptaket transkribert og publisert her, i en egen temaseksjon av dette Prismet-nummeret. Slik kan både de som ikke kunne være til stede på NoReFo-konferansen og de som ikke husker alt som ble sagt, lese både kritikernes innspill, kommentarene fra salen og Øystein Brekkes svar.
Torkel Bjørnskau, Ingeborg Storesund Hesjevoll, Rikke Ingebrigtsen
et al.
This study explores the characteristics of bicycle, e-bike, and e-scooter accidents that are not included in official Norwegian accident statistics, focusing on findings from the ReCyCLIST project in Agder County. Traditional accident reporting systems overlook most incidents involving vulnerable road users (VRUs), particularly single accidents, which represent the majority of such cases. ReCyCLIST introduced a digital self-reporting tool deployed in hospitals and clinics, collecting 671 accident cases between June 2022 and April 2024. The study analyses 487 incidents that occurred in traffic environments, revealing that 73% were single accidents, predominantly caused by infrastructure issues or loss of balance, rather than collisions. The data also highlight demographic differences in accident patterns by age, gender, and vehicle type. Notably, women were more frequently involved in e-scooter accidents, and men were overrepresented in racing bike collisions. Multivariate analysis shows that vehicle type, especially racing bikes, is a strong predictor of collisions. The findings emphasize the critical role of underreported single accidents and provide actionable insights for urban planning and policy development aimed at improving micromobility safety.
Transportation engineering, Transportation and communications
Aya K. H. Mahdy, Valentina Schöpfel, Gert Huppertz-Hauss
et al.
IntroductionCrohn’s disease (CD) is a clinical subset of inflammatory bowel disease that is characterized by patchy transmural inflammation across the gastrointestinal tract. Although the exact etiology remains unknown, recent findings suggest that it is a complex multifactorial disease with contributions from the host genetics and environmental factors such as the microbiome. We have previously shown that the T cell repertoire of individuals with CD harbors a group of highly expanded T cells which hints toward an antigen-mediated pathology.MethodsWe simultaneously profiled the αβ and γδ T cell repertoire in addition to the B cell repertoire of both the blood and the colonic mucosa of 27 treatment-naïve individuals with CD and 27 age-matched symptomatic controls.ResultsRegardless of disease status, we observed multiple physiological differences between the immune repertoire of blood and colonic mucosa. Additionally, by comparing the repertoire of individuals with CD relative to controls, we observed different alterations that were only detected in the blood or colonic mucosa. These include a depletion of mucosal-associated invariant T (MAIT) cells and an expansion of TRAV29/DV5-TRAJ5+ clonotypes in the blood repertoire of individuals with CD. Also, a significant depletion of multiple IGHV3-33-IGHJ4+ and IGHV3-33-IGHJ6+ clonotypes in the blood and gut IGH repertoire of individuals with CD.DiscussionOur findings highlight the importance of studying the immune repertoire in a tissue-specific manner and the need to profile the T and B cell immune repertoire of gut tissues as not all disease-induced alterations will be detected in the blood.
Thomas Nils Nipen, Håvard Homleid Haugen, Magnus Sikora Ingstad
et al.
A data-driven model (DDM) suitable for regional weather forecasting applications is presented. The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regional area of interest and maintains a lower resolution elsewhere on the globe. The model is based on graph neural networks, which naturally affords arbitrary multi-resolution grid configurations. The model is applied to short-range weather prediction for the Nordics, producing forecasts at 2.5 km spatial and 6 h temporal resolution. The model is pre-trained on 43 years of global ERA5 data at 31 km resolution and is further refined using 3.3 years of 2.5 km resolution operational analyses from the MetCoOp Ensemble Prediction System (MEPS). The performance of the model is evaluated using surface observations from measurement stations across Norway and is compared to short-range weather forecasts from MEPS. The DDM outperforms both the control run and the ensemble mean of MEPS for 2 m temperature. The model also produces competitive precipitation and wind speed forecasts, but is shown to underestimate extreme events.
Taridzo Chomutare, Therese Olsen Svenning, Miguel Ángel Tejedor Hernández
et al.
\textbf{Trial design} Crossover randomized controlled trial. \textbf{Methods} An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a user study in Norway and Sweden. Participants were randomly assigned to two groups, and crossed over between coding complex (longer) texts versus simple (shorter) texts, while using our tool versus not using our tool. \textbf{Results} Based on Mann-Whitney U test, the median coding time difference for complex clinical text sequences was 123 seconds (\emph{P}\textless.001, 95\% CI: 81 to 164), representing a 46\% reduction in median coding time when our tool is used. There was no significant time difference for simpler text sequences. For coding accuracy, the improvement we noted for both complex and simple texts was not significant. \textbf{Conclusions} This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for complex clinical coding tasks. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood.
Riboswitches reside in the untranslated region of RNA and regulate genes involved in the biosynthesis of essential metabolites through binding of small molecules. Since their discovery at the beginning of this century, riboswitches have been regarded as potential antibacterial targets. Using fragment screening, high-throughput screening and rational ligand design guided by X-ray crystallography, lead compounds against various riboswitches have been identified. Here, we review the current status and suitability of the thiamine pyrophosphate (TPP), flavin mononucleotide (FMN), glmS, guanine, and other riboswitches as antibacterial targets and discuss them in a biological context. Further, we highlight challenges in riboswitch drug discovery and emphasis the need to develop riboswitch specific high-throughput screening methods.
Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance. These challenges include data availability, biased outcomes, data quality, lack of transparency, and underperformance on unseen datasets from different distributions. The scarcity of large-scale, precisely labeled, and diverse datasets are the major challenge for clinical integration. This scarcity is also due to the legal restrictions and extensive manual efforts required for accurate annotations from clinicians. To address these challenges, we present \textit{GastroVision}, a multi-center open-access gastrointestinal (GI) endoscopy dataset that includes different anatomical landmarks, pathological abnormalities, polyp removal cases and normal findings (a total of 27 classes) from the GI tract. The dataset comprises 8,000 images acquired from Bærum Hospital in Norway and Karolinska University Hospital in Sweden and was annotated and verified by experienced GI endoscopists. Furthermore, we validate the significance of our dataset with extensive benchmarking based on the popular deep learning based baseline models. We believe our dataset can facilitate the development of AI-based algorithms for GI disease detection and classification. Our dataset is available at \url{https://osf.io/84e7f/}.
In this paper we study the type IV Knorr Held space time models. Such models typically apply intrinsic Markov random fields and constraints are imposed for identifiability. INLA is an efficient inference tool for such models where constraints are dealt with through a conditioning by kriging approach. When the number of spatial and/or temporal time points become large, it becomes computationally expensive to fit such models, partly due to the number of constraints involved. We propose a new approach, HyMiK, dividing constraints into two separate sets where one part is treated through a mixed effect approach while the other one is approached by the standard conditioning by kriging method, resulting in a more efficient procedure for dealing with constraints. The new approach is easy to apply based on existing implementations of INLA. We run the model on simulated data, on a real data set containing dengue fever cases in Brazil and another real data set of confirmed positive test cases of Covid-19 in the counties of Norway. For all cases we get very similar results when comparing the new approach with the tradition one while at the same time obtaining a significant increase in computational speed, varying on a factor from 2 to 4, depending on the sizes of the data sets.
This paper presents a modeling framework for tracking the spawning migration of the capelin, which is a fish species in the Barents Sea. The framework combines an individual-based model (IBM) with artificial neural networks (ANNs). The ANNs determine the direction of the fish's movement based on local environmental information, while a genetic algorithm and fitness function assess the suitability of the proposed directions. The framework's efficacy is demonstrated by comparing the spatial distributions of modeled and empirical potential spawners. The proposed model successfully replicates the southeastward movement of capelin during their spawning migration, accurately capturing the distribution of spawning fish over historical spawning sites along the eastern coast of northern Norway. Furthermore, the paper compares three migration models: passive swimmers, taxis movement based on temperature gradients, and restricted-area search, along with our proposed approach. The results reveal that our approach outperforms the other models in mimicking the migration pattern. Most spawning stocks managed to reach the spawning sites, unlike the other models where water currents played a significant role in pushing the fish away from the coast. The temperature gradient detection model and restricted-area search model are found to be inadequate for accurately simulating capelin spawning migration in the Barents Sea due to complex oceanographic conditions.
Ingrid Ask Torvik, Robert Moshiro, Hege Ersdal
et al.
Early bag-mask ventilation (BMV) administered to non-breathing neonates at birth in the presence of birth asphyxia (interruption of placental blood flow) has reduced neonatal mortality by up to 50% in low- and middle-income countries. The neurodevelopmental outcome of neonates receiving BMV remains unknown. Using the Malawi Developmental Assessment Tool (MDAT), infants who received BMV at birth were assessed at 6 months, evaluating gross motor, fine motor, language and social skills. A healthy cohort with no birth complications was assessed with the same tool for comparison. Mean age-adjusted MDAT z-scores were not significantly different between the groups. The number of children having developmental delay defined as a z-score ≤ −2 was significantly higher in the resuscitated cohort for the fine motor and language domain and overall MDAT z-score. The prevalence of clinical seizures post discharge was significantly higher in the resuscitated group and was associated with neurodevelopmental delay. Infants with developmental delay or seizures were more likely to have a 5 min Apgar < 7 and a longer duration of BMV. Most children receiving BMV at birth are developing normally at 6 months. Still, there are some children with impaired development among resuscitated children, representing a subgroup of children who may have suffered more severe asphyxia.
This article presents experiences of using selected aspects of Nature of Science as guidelines for inclusive science education in biology at the upper-secondary level in the Swedish school system. First, the purpose and background of the project is described, along with a discussion on inclusive science education and the specific interpretation of Nature of Science used. Then, some concrete examples of tasks and teaching strategies used are provided. Finally, an evaluation of the project is presented, based on students’ experiences and findings from a questionnaire, and the experiences are discussed in relation to the purpose of the project. As no comparisons have been made, it is not possible to claim that this teaching is more inclusive, or better in any other regard, as compared to any other teaching. Hopefully, however, the text can provide inspiration and a basis for further development work with a similar focus.