| 1. |
- Eriksson, Jesper, et al.
(författare)
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Temporal patterns of organ dysfunction after severe trauma
- 2021
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Ingår i: Critical Care. - : Springer Nature. - 1364-8535 .- 1466-609X. ; 25:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Understanding temporal patterns of organ dysfunction (OD) may aid early recognition of complications after trauma and assist timing and modality of treatment strategies. Our aim was to analyse and characterise temporal patterns of OD in intensive care unit-admitted trauma patients. Methods We used group-based trajectory modelling to identify temporal trajectories of OD after trauma. Modelling was based on the joint development of all six subdomains comprising the sequential organ failure assessment score measured daily during the first two weeks post trauma. Further, the time for trajectories to stabilise and transition to final group assignments were evaluated. Results Six-hundred and sixty patients were included in the final model. Median age was 40 years, and median ISS was 26 (IQR 17-38). We identified five distinct trajectories of OD. Group 1, mild OD (n = 300), median ISS of 20 (IQR 14-27), had an early resolution of OD and a low mortality. Group 2, moderate OD (n = 135), and group 3, severe OD (n = 87), were fairly similar in admission characteristics and initial OD but differed in subsequent OD trajectories, the latter experiencing an extended course and higher mortality. In group 3, 56% of the patients developed sepsis as compared with 19% in group 2. Group 4, extreme OD (n = 40), received most blood transfusions, had the highest proportion of shock at admission and a median ISS of 41 (IQR 29-50). They experienced significant and sustained OD affecting all organ systems and a 28-day mortality of 30%. Group 5, traumatic brain injury with OD (n = 98), had the highest mortality of 35% and the shortest time to death for non-survivors, median 3.5 (IQR 2.4-4.8) days. Groups 1 and 5 reached their final group assignment early, > 80% of the patients within 48 h. In contrast, groups 2 and 3 had a prolonged time to final group assignment. Conclusions We identified five distinct trajectories of OD after severe trauma during the first two weeks post-trauma. Our findings underline the heterogeneous course after trauma and describe some potentially important clinical insights that are suggested by the groupings and temporal trajectories.
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| 2. |
- Kans, Mirka, 1971-, et al.
(författare)
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Data Driven Maintenance : A Promising Way of Action for Future Industrial Services Management
- 2022
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Ingår i: International Congress and Workshop on Industrial AI 2021. IAI 2021. - Cham : Springer. - 9783030936389 - 9783030936396 ; , s. 212-223, s. 212-223
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Konferensbidrag (refereegranskat)abstract
- Maintenance and services of products as well as processes are pivotal for achieving high availability and avoiding catastrophic and costly failures. At the same time, maintenance is routinely performed more frequently than necessary, replacing possibly functional components, which has negative economic impact on the maintenance. New processes and products need to fulfil increased environmental demands, while customers put increasing demands on customization and coordination. Hence, improved maintenance processes possess very high potentials, economically as well as environmentally. The shifting demands on product development and production processes have led to the emergency of new digital solutions as well as new business models, such as integrated product-service offerings. Still, the general maintenance problem of how to perform the right service at the right time, taking available information and given limitations is valid.The project Future Industrial Services Management (FUSE) project was a step in a long-term effort for catalysing the evolution of maintenance and production in the current digital era. In this paper, several aspects of the general maintenance problem are discussed from a data driven perspective, spanning from technology solutions and organizational requirements to new business opportunities and how to create optimal maintenance plans. One of the main results of the project, in the form of a simulation tool for strategy selection, is also described.
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| 3. |
- Akerlund, Cecilia A., I, et al.
(författare)
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Clinical descriptors of disease trajectories in patients with traumatic brain injury in the intensive care unit (CENTER-TBI) : a multicentre observational cohort study
- 2024
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Ingår i: Lancet Neurology. - : Elsevier BV. - 1474-4422 .- 1474-4465. ; 23:1, s. 71-80
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Tidskriftsartikel (refereegranskat)abstract
- Background Patients with traumatic brain injury are a heterogeneous population, and the most severely injured individuals are often treated in an intensive care unit (ICU). The primary injury at impact, and the harmful secondary events that can occur during the first week of the ICU stay, will affect outcome in this vulnerable group of patients. We aimed to identify clinical variables that might distinguish disease trajectories among patients with traumatic brain injury admitted to the ICU. Methods We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) prospective observational cohort study. We included patients aged 18 years or older with traumatic brain injury who were admitted to the ICU at one of the 65 CENTER-TBI participating centres, which range from large academic hospitals to small rural hospitals. For every patient, we obtained pre-injury data and injury features, clinical characteristics on admission, demographics, physiological parameters, laboratory features, brain biomarkers (ubiquitin carboxy-terminal hydrolase L1 [UCH-L1], S100 calcium-binding protein B [S100B], tau, neurofilament light [NFL], glial fibrillary acidic protein [GFAP], and neuron-specific enolase [NSE]), and information about intracranial pressure lowering treatments during the first 7 days of ICU stay. To identify clinical variables that might distinguish disease trajectories, we applied a novel clustering method to these data, which was based on a mixture of probabilistic graph models with a Markov chain extension. The relation of clusters to the extended Glasgow Outcome Scale (GOS-E) was investigated. Findings Between Dec 19, 2014, and Dec 17, 2017, 4509 patients with traumatic brain injury were recruited into the CENTER-TBI core dataset, of whom 1728 were eligible for this analysis. Glucose variation (defined as the difference between daily maximum and minimum glucose concentrations) and brain biomarkers (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were consistently found to be the main clinical descriptors of disease trajectories (ie, the leading variables contributing to the distinguishing clusters) in patients with traumatic brain injury in the ICU. The disease trajectory cluster to which a patient was assigned in a model was analysed as a predictor together with variables from the IMPACT model, and prediction of both mortality and unfavourable outcome (dichotomised GOS-E <= 4) was improved. Interpretation First-day ICU admission data are not the only clinical descriptors of disease trajectories in patients with traumatic brain injury. By analysing temporal variables in our study, variation of glucose was identified as the most important clinical descriptor that might distinguish disease trajectories in the ICU, which should direct further research. Biomarkers of brain injury (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were also top clinical descriptors over time, suggesting they might be important in future clinical practice.
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| 4. |
- Baggesen, Nanna, et al.
(författare)
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Phenological stage of tundra vegetation controls bidirectional exchange of BVOCs in a climate change experiment on a subarctic heath
- 2021
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Ingår i: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 27:12, s. 2928-2944
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Tidskriftsartikel (refereegranskat)abstract
- Traditionally, biogenic volatile organic compound (BVOC) emissions are often considered a unidirectional flux, from the ecosystem to the atmosphere, but recent studies clearly show the potential for bidirectional exchange. Here we aimed to investigate how warming and leaf litter addition affect the bidirectional exchange (flux) of BVOCs in a long‐term field experiment in the Subarctic. We also assessed changes in net BVOC fluxes in relation to the time of day and the influence of different plant phenological stages. The study was conducted in a full factorial experiment with open top chamber warming and annual litter addition treatments in a tundra heath in Abisko, Northern Sweden. After 18 years of treatments, ecosystem‐level net BVOC fluxes were measured in the experimental plots using proton‐transfer‐reaction time‐of‐flight mass spectrometry (PTR–ToF–MS). The warming treatment increased monoterpene and isoprene emissions by ≈50%. Increasing temperature, due to diurnal variations, can both increase BVOC emission and simultaneously, increase ecosystem uptake. For any given treatment, monoterpene, isoprene, and acetone emissions also increased with increasing ambient air temperatures caused by diurnal variability. Acetaldehyde, methanol, and sesquiterpenes decreased likely due to a deposition flux. For litter addition, only a significant indirect effect on isoprene and monoterpene fluxes (decrease by ~50%–75%) was observed. Litter addition may change soil moisture conditions, leading to changes in plant species composition and biomass, which could subsequently result in changes to BVOC emission compositions. Phenological stages significantly affected fluxes of methanol, isoprene and monoterpenes. We suggest that plant phenological stages differ in impacts on BVOC net emissions, but ambient air temperature and photosynthetically active radiation (PAR) also interact and influence BVOC net emissions differently. Our results may also suggest that BVOC fluxes are not only a response to changes in temperature and light intensity, as the circadian clock also affects emission rates.
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| 5. |
- Bergman, Daniel, et al.
(författare)
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Pre-existing canine anti-IgG antibodies: implications for immunotherapy, immunogenicity testing and immunoassay analysis
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- One of the most enigmatic features of humoral immunity is the prevalent presence of circulating autoantibodies against IgG. These autoantibodies consist of several subsets, including rheumatoid factors, anti-Fab/anti-F(ab′)2-autoantibodies, and anti-idiotypic antibodies. Anti-IgG autoantibodies can impair the safety and efficacy of therapeutic antibodies and interfere with immunogenicity tests in clinical trials. They can also cross-react with allospecific IgG, presenting as heterophilic antibodies that interfere with diagnostic immunoassays. Owing to these factors, recent years have seen a resurgent interest in anti-IgG autoantibodies, but their underlying clinical significance, as well as biological roles and origins, remain opaque. Increased knowledge about canine anti-IgG autoantibodies could facilitate the development of canine immunotherapies and help in understanding and counteracting immunoassay interference. This study investigated the clinical significance and interconnection of heterophilic antibodies, anti-Fab, and anti-F(ab′)2-autoantibodies in dogs. We performed a 2-year prospective follow-up of dogs with heterophilic antibodies and analyzed serum for anti-Fab and anti-F(ab′)2-autoantibodies. Canine heterophilic antibodies can persist for at least 2 years in serum. A widespread occurrence of anti-Fab and anti-F(ab′)2-autoantibodies was found, with reactivity to cryptic epitopes in the IgG hinge region and sporadic cross-reactivity with mouse IgG. Canine anti-Fab and anti-F(ab′)2-autoantibodies are thus potential sources of clinical immunogenicity and immunoassay interference.
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| 6. |
- Christiansen, Morten K, et al.
(författare)
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Incidence, Predictors, and Success of Ventricular Tachycardia Catheter Ablation in Arrhythmogenic Right Ventricular Cardiomyopathy (from the Nordic ARVC Registry).
- 2020
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Ingår i: The American journal of cardiology. - : Elsevier BV. - 1879-1913 .- 0002-9149. ; 125:5, s. 803-811
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Tidskriftsartikel (refereegranskat)abstract
- Catheter ablation may reduce ventricular tachycardia (VT) burden in arrhythmogenic right ventricular cardiomyopathy (ARVC) patients. However, little is known about factors predicting need for ablation. Therefore, we sought to investigate predictors and use of VT ablation and to evaluate the postprocedural outcome in ARVC patients. We studied 435 patients from the Nordic ARVC registry including 220 probands with definite ARVC according to the 2010 task force criteria and 215 mutation-carrying relatives identified through cascade screening. Patients were followed until first-time VT ablation, death, heart transplantation, or January 1st 2018. Additionally, patients undergoing VT ablation were further followed from the time of ablation for recurrent ventricular arrhythmias. The cumulative use of VT ablation was 4% (95% confidence interval [CI] 3% to 6%) and 11% (95% CI 8% to 15%) after 1 and 10 years. All procedures were performed in probands in whom cumulative use was 8% (95% CI 5% to 12%) and 20% (95% CI 15% to 26%). In adjusted analyses among probands, only young age predicted ablation. In patients undergoing ablation, risk of recurrent arrhythmias was 59% (95% CI 44% to 71%) and 74% (95% CI 59% to 84%) 1 and 5 years after the procedure. Despite high recurrence rates, the burden of ventricular arrhythmias was reduced after ablation (p=0.0042). Young age, use of several antiarrhythmic drugs and inducibility to VT after ablation were associated with an unfavorable outcome. In conclusion, twenty percent of ARVC probands developed a clinical indication for VT ablation within 10 years whereas mutation-carrying relatives were without such need. Although the burden of ventricular arrhythmias decreased after ablation, risk of recurrence was substantial.
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| 7. |
- Cuba Gyllensten, Amaru
(författare)
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Quantifying Meaning
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Distributionella semantikmodeller är en klass av maskininlärningsmodeller med syfte att konstruera representationer som fångar semantik, i.e. mening, av objekt som bär mening på ett datadrivet sätt. Denna avhandling är särskilt inriktad på konstruktion av semantisk representation av ord, en strävan som har en lång historia inom datalingvistik och som sett dramatiska utvecklingar under de senaste åren.Det primära forskningsmålet med denna avhandling är att utforska gränserna och tillämpningarna av distributionella semantikmodeller av ord, i.e. word embeddings. I synnerhet utforskar den relationen mellan modell- och embeddingsemantik, det vill säga hur modelldesign påverkar vad ord-embeddings innehåller, hur man resonerar om ord-embeddings, och hur egenskaperna hos modellen kan utnyttjas för att extrahera ny information från embeddings. Konkret introducerar vi topologiskt medvetna grannskapsfrågor som berikar den information som erhålls från grannskap extraherade från distributionella sematikmodeller, villkorade likhetsfrågor (och modeller som möjliggör dem), konceptutvinning från distributionella semantikmodeller, tillämpningar av embbeddningmodeller inom statsvetenskap, samt en grundlig utvärdering av en bred mängd av distributionella semantikmodeller.
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| 8. |
- Eskilsson, Claes, et al.
(författare)
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A hybrid linear potential flow - machine learning model for enhanced prediction of WEC performance
- 2023
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Ingår i: Proceedings of the 15th European Wave and Tidal Energy Conference.
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Konferensbidrag (refereegranskat)abstract
- Linear potential flow (LPF) models remain the tools-of-the trade in marine and ocean engineering despite their well-known assumptions of small amplitude waves and motions. As of now, nonlinear simulation tools are still too computationally demanding to be used in the entire design loop, especially when it comes to the evaluation of numerous irregular sea states. In this paper we aim to enhance the performance of the LPF models by introducing a hybrid LPF-ML (machine learning) approach, based on identification of nonlinear force corrections. The corrections are defined as the difference in hydrodynamic force (vis- cous and pressure-based) between high-fidelity CFD and LPF models. Using prescribed chirp motions with different amplitudes, we train a long short-term memory (LSTM) network to predict the corrections. The LSTM network is then linked to the MoodyMarine LPF model to provide the nonlinear correction force at every time-step, based on the dynamic state of the body and the corresponding forces from the LPF model. The method is illustrated for the case of a heaving sphere in decay, regular and irregular waves – including passive control. The hybrid LPF-model is shown to give significant improvements compared to the baseline LPF model, even though the training is quite generic.
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| 9. |
- Eskilsson, Claes, et al.
(författare)
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Estimation of nonlinear forces acting on floating bodies using machine learning
- 2023
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Ingår i: Advances in the Analysis and Design of Marine Structures. - London : CRC Press. - 9781003399759 ; , s. 63-72
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Numerical models used in the design of floating bodies routinely rely on linear hydrodynamics. Extensions for hydrodynamic nonlinearities can be approximated using e.g. Morison type drag and nonlinear Froude-Krylov forces. This paper aims to improve the approximation of nonlinear forces acting on floating bodies by using machine learning (ML). Many ML models are general function approximators and therefore suitable for representing such nonlinear correction terms. A hierarchical modelling approach is used to build mappings between higher-fidelity simulations and the linear method. The ML corrections are built up for FNPF, Euler and RANS simulations. Results for decay tests of a sphere in model scale using recurrent neural networks (RNN) are presented. The RNN algorithm is shown to satisfactory predict the correction terms if the most nonlinear case is used as training data. No difference in the performance of the RNN model is seen for the different hydrodynamic models.
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| 10. |
- Eskilsson, Claes, et al.
(författare)
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Hierarchical Approaches to Train Recurrent Neural Networks for Wave-Body Interaction Problems
- 2023
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Ingår i: The Proceedings of the 33rd International Ocean and Polar Engineering Conference.
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Konferensbidrag (refereegranskat)abstract
- We present a hybrid linear potential flow - machine learning (LPF-ML) model for simulating weakly nonlinear wave-body interaction problems. In this paper we focus on using hierarchical modelling for generating training data to be used with recurrent neural networks (RNNs) in order to derive nonlinear correction forces. Three different approaches are investigated: (i) a baseline method where data from a Reynolds averaged Navier Stokes (RANS) model is directly linked to data from a LPF model to generate nonlinear corrections; (ii) an approach in which we start from high-fidelity RANS simulations and build the nonlinear corrections by stepping down in the fidelity hierarchy; and (iii) a method starting from low-fidelity, successively moving up the fidelity staircase. The three approaches are evaluated for the simple test case of a heaving sphere. The results show that the baseline model performs best, as expected for this simple test case. Stepping up in the fidelity hierarchy very easily introduce errors that propagate through the hierarchical modelling via the correction forces. The baseline method was found to accurately predict the motion of the heaving sphere. The hierarchical approaches struggled with the task, with the approach that steps down in fidelity performing somewhat better of the two.
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