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Fries, N. & Rydén, P. (2024). Data-driven process adjustment policies for quality improvement. Expert systems with applications, 237, Article ID 121524.
Åpne denne publikasjonen i ny fane eller vindu >>Data-driven process adjustment policies for quality improvement
2024 (engelsk)Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 237, artikkel-id 121524Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Common objectives in machine learning research are to predict the output quality of manufacturing processes, to perform root cause analysis in case of reduced quality, and to propose intervention strategies. The cost of reduced quality must be weighed against the cost of the interventions, which depend on required downtime, personnel costs, and material costs. Furthermore, there is a risk of false negatives, i.e., failure to identify the true root causes, or false positives, i.e., adjustments that further reduce the quality. A policy for process adjustments describes when and where to perform interventions, and we say that a policy is worthwhile if it reduces the expected operational cost. In this paper, we describe a data-driven alarm and root cause analysis framework, that given a predictive and explanatory model trained on high-dimensional process and quality data, can be used to search for a worthwhile adjustment policy. The framework was evaluated on large-scale simulated process and quality data. We find that worthwhile adjustment policies can be derived also for problems with a large number of explanatory variables. Interestingly, the performance of the adjustment policies is almost exclusively driven by the quality of the model fits. Based on these results, we discuss key areas of future research, and how worthwhile adjustment policies can be implemented in real world applications.

sted, utgiver, år, opplag, sider
Elsevier, 2024
Emneord
Process adjustment policy, Quality improvement, Cost reduction, Prediction, Local explanations, Simulation
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-208103 (URN)10.1016/j.eswa.2023.121524 (DOI)2-s2.0-85171612846 (Scopus ID)
Forskningsfinansiär
Vinnova, 2015-03706Umeå University
Merknad

Originally included in thesis in manuscript form.

Volume 237, Part B.

Tilgjengelig fra: 2023-05-08 Laget: 2023-05-08 Sist oppdatert: 2023-10-02bibliografisk kontrollert
Bayisa, F., Ådahl, M., Rydén, P. & Cronie, O. (2023). Regularised semi-parametric composite likelihood intensity modelling of a Swedish spatial ambulance call point pattern. Journal of Agricultural Biological and Environmental Statistics, 28(4), 664-683
Åpne denne publikasjonen i ny fane eller vindu >>Regularised semi-parametric composite likelihood intensity modelling of a Swedish spatial ambulance call point pattern
2023 (engelsk)Inngår i: Journal of Agricultural Biological and Environmental Statistics, ISSN 1085-7117, E-ISSN 1537-2693, Vol. 28, nr 4, s. 664-683Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Motivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events in the Swedish municipality Skellefteå during 2014–2018 in order to identify important spatial covariates and discern hotspot regions. Our large-scale multivariate data point pattern of call events consists of spatial locations and marks containing the associated priority levels and sex labels. The covariates used are related to road network coverage, population density, and socio-economic status. For each marginal point pattern, we model the associated intensity function by means of a log-linear function of the covariates and their interaction terms, in combination with lasso-like elastic-net regularized composite/Poisson process likelihood estimation. This enables variable selection and collinearity adjustment as well as reduction of variance inflation from overfitting and bias from underfitting. To incorporate mobility adjustment, reflecting people’s movement patterns, we also include a nonparametric (kernel) intensity estimate as an additional covariate. The kernel intensity estimation performed here exploits a new heuristic bandwidth selection algorithm. We discover that hotspot regions occur along dense parts of the road network. A mean absolute error evaluation of the fitted model indicates that it is suitable for designing prehospital resource dispatching strategies. Supplementary materials accompanying this paper appear online.

sted, utgiver, år, opplag, sider
Springer, 2023
Emneord
Bandwidth selection, Cyclic coordinate descent algorithm, Emergency alarm, Inhomogeneous Poisson process, Lasso-like elastic-net, Multivariate point process
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-206937 (URN)10.1007/s13253-023-00534-5 (DOI)000981152000001 ()2-s2.0-85152546695 (Scopus ID)
Forskningsfinansiär
Vinnova, 2018-00422Region VästerbottenNorrbotten County CouncilRegion VästernorrlandRegion Jämtland Härjedalen
Tilgjengelig fra: 2023-04-28 Laget: 2023-04-28 Sist oppdatert: 2024-01-05bibliografisk kontrollert
Kurtz, S. L., Rydén, P. & Elkins, K. L. (2023). Transcriptional signatures measured in whole blood correlate with protection against tuberculosis in inbred and outbred mice. PLOS ONE, 18(8), Article ID e0289358.
Åpne denne publikasjonen i ny fane eller vindu >>Transcriptional signatures measured in whole blood correlate with protection against tuberculosis in inbred and outbred mice
2023 (engelsk)Inngår i: PLOS ONE, E-ISSN 1932-6203, Vol. 18, nr 8, artikkel-id e0289358Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Although BCG has been used for almost 100 years to immunize against Mycobacterium tuberculosis, TB remains a global public health threat. Numerous clinical trials are underway studying novel vaccine candidates and strategies to improve or replace BCG, but vaccine development still lacks a well-defined set of immune correlates to predict vaccine-induced protection against tuberculosis. This study aimed to address this gap by examining transcriptional responses to BCG vaccination in C57BL/6 inbred mice, coupled with protection studies using Diversity Outbred mice. We evaluated relative gene expression in blood obtained from vaccinated mice, because blood is easily accessible, and data can be translated to human studies. We first determined that the average peak time after vaccination is 14 days for gene expression of a small subset of immune-related genes in inbred mice. We then performed global transcriptomic analyses using whole blood samples obtained two weeks after mice were vaccinated with BCG. Using comparative bioinformatic analyses and qRT-PCR validation, we developed a working correlate panel of 18 genes that were highly correlated with administration of BCG but not heat-killed BCG. We then tested this gene panel using BCG-vaccinated Diversity Outbred mice and revealed associations between the expression of a subset of genes and disease outcomes after aerosol challenge with M. tuberculosis. These data therefore demonstrate that blood-based transcriptional immune correlates measured within a few weeks after vaccination can be derived to predict protection against M. tuberculosis, even in outbred populations.

sted, utgiver, år, opplag, sider
Public Library of Science (PLoS), 2023
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-212816 (URN)10.1371/journal.pone.0289358 (DOI)37535648 (PubMedID)2-s2.0-85166566465 (Scopus ID)
Tilgjengelig fra: 2023-08-16 Laget: 2023-08-16 Sist oppdatert: 2023-08-16bibliografisk kontrollert
Fries, N. & Rydén, P. (2022). A comparison of local explanation methods for high-dimensional industrial data: a simulation study. Expert systems with applications, 207, Article ID 117918.
Åpne denne publikasjonen i ny fane eller vindu >>A comparison of local explanation methods for high-dimensional industrial data: a simulation study
2022 (engelsk)Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 207, artikkel-id 117918Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Prediction methods can be augmented by local explanation methods (LEMs) to perform root cause analysis for individual observations. But while most recent research on LEMs focus on low-dimensional problems, real-world datasets commonly have hundreds or thousands of variables. Here, we investigate how LEMs perform for high-dimensional industrial applications. Seven prediction methods (penalized logistic regression, LASSO, gradient boosting, random forest and support vector machines) and three LEMs (TreeExplainer, Kernel SHAP, and the conditional normal sampling importance (CNSI)) were combined into twelve explanation approaches. These approaches were used to compute explanations for simulated data, and real-world industrial data with simulated responses. The approaches were ranked by how well they predicted the contributions according to the true models. For the simulation experiment, the generalized linear methods provided best explanations, while gradient boosting with either TreeExplainer or CNSI, or random forest with CNSI were robust for all relationships. For the real-world experiment, TreeExplainer performed similarly, while the explanations from CNSI were significantly worse. The generalized linear models were fastest, followed by TreeExplainer, while CNSI and Kernel SHAP required several orders of magnitude more computation time. In conclusion, local explanations can be computed for high-dimensional data, but the choice of statistical tools is crucial.

sted, utgiver, år, opplag, sider
Elsevier, 2022
Emneord
Interpretable model, Local Explanations, Shapley values, Simulation, Statistical process control
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-197996 (URN)10.1016/j.eswa.2022.117918 (DOI)000827577500009 ()2-s2.0-85133195266 (Scopus ID)
Forskningsfinansiär
Vinnova, 2015-03706
Tilgjengelig fra: 2022-07-11 Laget: 2022-07-11 Sist oppdatert: 2023-09-05bibliografisk kontrollert
Shenoi, V. N., Brengdahl, M. I., Grace, J. L., Eriksson, B., Rydén, P. & Friberg, U. (2022). A genome-wide test for paternal indirect genetic effects on lifespan in Drosophila melanogaster. Proceedings of the Royal Society of London. Biological Sciences, 289(1974), Article ID 20212707.
Åpne denne publikasjonen i ny fane eller vindu >>A genome-wide test for paternal indirect genetic effects on lifespan in Drosophila melanogaster
Vise andre…
2022 (engelsk)Inngår i: Proceedings of the Royal Society of London. Biological Sciences, ISSN 0962-8452, E-ISSN 1471-2954, Vol. 289, nr 1974, artikkel-id 20212707Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Exposing sires to various environmental manipulations has demonstrated that paternal effects can be non-trivial also in species where male investment in offspring is almost exclusively limited to sperm. Whether paternal effects also have a genetic component (i.e. paternal indirect genetic effects (PIGEs)) in such species is however largely unknown, primarily because of methodological difficulties separating indirect from direct effects of genes. PIGEs may nevertheless be important since they have the capacity to contribute to evolutionary change. Here we use Drosophila genetics to construct a breeding design that allows testing nearly complete haploid genomes (more than 99%) for PIGEs. Using this technique, we estimate the variance in male lifespan due to PIGEs among four populations and compare this to the total paternal genetic variance (the sum of paternal indirect and direct genetic effects). Our results indicate that a substantial part of the total paternal genetic variance results from PIGEs. A screen of 38 haploid genomes, randomly sampled from a single population, suggests that PIGEs also influence variation in lifespan within populations. Collectively, our results demonstrate that PIGEs may constitute an underappreciated source of phenotypic variation.

sted, utgiver, år, opplag, sider
The Royal Society, 2022
Emneord
Drosophila, lifespan, paternal indirect genetic effects
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-203053 (URN)10.1098/rspb.2021.2707 (DOI)000796005500013 ()35538781 (PubMedID)2-s2.0-85130003769 (Scopus ID)
Forskningsfinansiär
Sven och Lilly Lawskis fond för naturvetenskaplig forskningCarl Tryggers foundation Helge Ax:son Johnsons stiftelse Lars Hierta Memorial FoundationStiftelsen Längmanska kulturfonden
Tilgjengelig fra: 2023-01-17 Laget: 2023-01-17 Sist oppdatert: 2023-03-24bibliografisk kontrollert
Dracheva, E., Norinder, U., Rydén, P., Engelhardt, J., Weiss, J. M. & Andersson, P. L. (2022). In Silico Identification of Potential Thyroid Hormone System Disruptors among Chemicals in Human Serum and Chemicals with a High Exposure Index. Environmental Science and Technology, 56(12), 8363-8372
Åpne denne publikasjonen i ny fane eller vindu >>In Silico Identification of Potential Thyroid Hormone System Disruptors among Chemicals in Human Serum and Chemicals with a High Exposure Index
Vise andre…
2022 (engelsk)Inngår i: Environmental Science and Technology, ISSN 0013-936X, E-ISSN 1520-5851, Vol. 56, nr 12, s. 8363-8372Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Data on toxic effects are at large missing the prevailing understanding of the risks of industrial chemicals. Thyroid hormone (TH) system disruption includes interferences of the life cycle of the thyroid hormones and may occur in various organs. In the current study, high-throughput screening data available for 14 putative molecular initiating events of adverse outcome pathways, related to disruption of the TH system, were used to develop 19 in silico models for identification of potential thyroid hormone system-disrupting chemicals. The conformal prediction framework with the underlying Random Forest was used as a wrapper for the models allowing for setting the desired confidence level and controlling the error rate of predictions. The trained models were then applied to two different databases: (i) an in-house database comprising xenobiotics identified in human blood and ii) currently used chemicals registered in the Swedish Product Register, which have been predicted to have a high exposure index to consumers. The application of these models showed that among currently used chemicals, fewer were overall predicted as active compared to chemicals identified in human blood. Chemicals of specific concern for TH disruption were identified from both databases based on their predicted activity.

sted, utgiver, år, opplag, sider
American Chemical Society (ACS), 2022
Emneord
conformal prediction, endocrine disruption, environmental health, QSAR
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-196482 (URN)10.1021/acs.est.1c07762 (DOI)000815124300001 ()35561338 (PubMedID)2-s2.0-85131097293 (Scopus ID)
Forskningsfinansiär
Swedish Research Council Formas, 2018-02264Swedish Environmental Protection Agency, 215-20-010Mistra - The Swedish Foundation for Strategic Environmental Research, 2018/11
Tilgjengelig fra: 2022-06-17 Laget: 2022-06-17 Sist oppdatert: 2024-03-22bibliografisk kontrollert
Källberg, D., Vidman, L. & Rydén, P. (2021). Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes. Frontiers in Genetics, 12, Article ID 632620.
Åpne denne publikasjonen i ny fane eller vindu >>Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes
2021 (engelsk)Inngår i: Frontiers in Genetics, E-ISSN 1664-8021, Vol. 12, artikkel-id 632620Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Cancer subtype identification is important to facilitate cancer diagnosis and select effective treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data can be used to detect novel subtypes, but only a subset of the features (e.g., genes) contains information related to the cancer subtype. Therefore, it is reasonable to assume that the clustering should be based on a set of carefully selected features rather than all features. Several feature selection methods have been proposed, but how and when to use these methods are still poorly understood. Thirteen feature selection methods were evaluated on four human cancer data sets, all with known subtypes (gold standards), which were only used for evaluation. The methods were characterized by considering mean expression and standard deviation (SD) of the selected genes, the overlap with other methods and their clustering performance, obtained comparing the clustering result with the gold standard using the adjusted Rand index (ARI). The results were compared to a supervised approach as a positive control and two negative controls in which either a random selection of genes or all genes were included. For all data sets, the best feature selection approach outperformed the negative control and for two data sets the gain was substantial with ARI increasing from (−0.01, 0.39) to (0.66, 0.72), respectively. No feature selection method completely outperformed the others but using the dip-rest statistic to select 1000 genes was overall a good choice. The commonly used approach, where genes with the highest SDs are selected, did not perform well in our study.

sted, utgiver, år, opplag, sider
Frontiers Media S.A., 2021
Emneord
cancer subtypes, feature selection, gene selection, high-dimensional, RNA-seq
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-181727 (URN)10.3389/fgene.2021.632620 (DOI)000626903100001 ()2-s2.0-85102373666 (Scopus ID)
Tilgjengelig fra: 2021-03-24 Laget: 2021-03-24 Sist oppdatert: 2023-09-05bibliografisk kontrollert
Evengård, B., Destouni, G., Kalantari, Z., Albihn, A., Björkman, C., Bylund, H., . . . Orlov, D. (2021). Healthy ecosystems for human and animal health: Science diplomacy for responsible development in the Arctic. Polar Record, 57, Article ID e39.
Åpne denne publikasjonen i ny fane eller vindu >>Healthy ecosystems for human and animal health: Science diplomacy for responsible development in the Arctic
Vise andre…
2021 (engelsk)Inngår i: Polar Record, ISSN 0032-2474, E-ISSN 1475-3057, Vol. 57, artikkel-id e39Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Climate warming is occurring most rapidly in the Arctic, which is both a sentinel and a driver of further global change. Ecosystems and human societies are already affected by warming. Permafrost thaws and species are on the move, bringing pathogens and vectors to virgin areas. During a five-year project, the CLINF - a Nordic Center of Excellence, funded by the Nordic Council of Ministers, has worked with the One Health concept, integrating environmental data with human and animal disease data in predictive models and creating maps of dynamic processes affecting the spread of infectious diseases. It is shown that tularemia outbreaks can be predicted even at a regional level with a manageable level of uncertainty. To decrease uncertainty, rapid development of new and harmonised technologies and databases is needed from currently highly heterogeneous data sources. A major source of uncertainty for the future of contaminants and infectious diseases in the Arctic, however, is associated with which paths the majority of the globe chooses to follow in the future. Diplomacy is one of the most powerful tools Arctic nations have to influence these choices of other nations, supported by Arctic science and One Health approaches that recognise the interconnection between people, animals, plants and their shared environment at the local, regional, national and global levels as essential for achieving a sustainable development for both the Arctic and the globe.

sted, utgiver, år, opplag, sider
Cambridges Institutes Press, 2021
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-189220 (URN)10.1017/S0032247421000589 (DOI)000721257900001 ()2-s2.0-85118198981 (Scopus ID)
Tilgjengelig fra: 2021-11-12 Laget: 2021-11-12 Sist oppdatert: 2023-09-05bibliografisk kontrollert
Law, S. R., Kellgren, T., Björk, R., Rydén, P. & Keech, O. (2020). Centralization Within Sub-Experiments Enhances the Biological Relevance of Gene Co-expression Networks: A Plant Mitochondrial Case Study. Frontiers in Plant Science, 11, Article ID 524.
Åpne denne publikasjonen i ny fane eller vindu >>Centralization Within Sub-Experiments Enhances the Biological Relevance of Gene Co-expression Networks: A Plant Mitochondrial Case Study
Vise andre…
2020 (engelsk)Inngår i: Frontiers in Plant Science, E-ISSN 1664-462X, Vol. 11, artikkel-id 524Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Gene co-expression networks (GCNs) can be prepared using a variety of mathematical approaches based on data sampled across diverse developmental processes, tissue types, pathologies, mutant backgrounds, and stress conditions. These networks are used to identify genes with similar expression dynamics but are prone to introducing false-positive and false-negative relationships, especially in the instance of large and heterogenous datasets. With the aim of optimizing the relevance of edges in GCNs and enhancing global biological insight, we propose a novel approach that involves a data-centering step performed simultaneously per gene and per sub-experiment, called centralization within sub-experiments (CSE). Using a gene set encoding the plant mitochondrial proteome as a case study, our results show that all CSE-based GCNs assessed had significantly more edges within the majority of the considered functional sub-networks, such as the mitochondrial electron transport chain and its complexes, than GCNs not using CSE; thus demonstrating that CSE-based GCNs are efficient at predicting canonical functions and associated pathways, here referred to as the core gene network. Furthermore, we show that correlation analyses using CSE-processed data can be used to fine-tune prediction of the function of uncharacterized genes; while its use in combination with analyses based on non-CSE data can augment conventional stress analyses with the innate connections underpinning the dynamic system being examined. Therefore, CSE is an effective alternative method to conventional batch correction approaches, particularly when dealing with large and heterogenous datasets. The method is easy to implement into a pre-existing GCN analysis pipeline and can provide enhanced biological relevance to conventional GCNs by allowing users to delineate a core gene network. Author Summary Gene co-expression networks (GCNs) are the product of a variety of mathematical approaches that identify causal relationships in gene expression dynamics but are prone to the misdiagnoses of false-positives and false-negatives, especially in the instance of large and heterogenous datasets. In light of the burgeoning output of next-generation sequencing projects performed on a variety of species, and developmental or clinical conditions; the statistical power and complexity of these networks will undoubtedly increase, while their biological relevance will be fiercely challenged. Here, we propose a novel approach to generate a "core" GCN with enhanced biological relevance. Our method involves a data-centering step that effectively removes all primary treatment/tissue effects, which is simple to employ and can be easily implemented into pre-existing GCN analysis pipelines. The gain in biological relevance resulting from the adoption of this approach was assessed using a plant mitochondrial case study.

sted, utgiver, år, opplag, sider
Frontiers Media S.A., 2020
Emneord
correlation, gene co-expression network, metabolism, method, plant mitochondria
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-173437 (URN)10.3389/fpls.2020.00524 (DOI)000542980000001 ()32582224 (PubMedID)2-s2.0-85086578832 (Scopus ID)
Forskningsfinansiär
Swedish Research Council, 621-2014-4688Swedish Research Council, 340-2013-5185The Kempe FoundationsCarl Tryggers foundation
Tilgjengelig fra: 2020-07-10 Laget: 2020-07-10 Sist oppdatert: 2024-01-17bibliografisk kontrollert
Andersson-Evelönn, E., Vidman, L., Källberg, D., Landfors, M., Liu, X., Ljungberg, B., . . . Degerman, S. (2020). Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma. Journal of Translational Medicine, 18(1), Article ID 435.
Åpne denne publikasjonen i ny fane eller vindu >>Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma
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2020 (engelsk)Inngår i: Journal of Translational Medicine, ISSN 1479-5876, E-ISSN 1479-5876, Vol. 18, nr 1, artikkel-id 435Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background: Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables.

Methods: A novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression.

Results: The “triple classifier” which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress (pCIP5yr) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis.

Conclusions: The triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC.

Emneord
Clear cell renal cell carcinoma, Classification, DNA methylation, Prognosis, Directed cluster analysis
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-176921 (URN)10.1186/s12967-020-02608-1 (DOI)000594136300002 ()33187526 (PubMedID)2-s2.0-85095955809 (Scopus ID)
Forskningsfinansiär
The Kempe FoundationsSwedish Research CouncilRegion Västerbotten
Tilgjengelig fra: 2020-11-19 Laget: 2020-11-19 Sist oppdatert: 2023-03-24bibliografisk kontrollert
Organisasjoner