cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17044/scilifelab.21602844
title: Real-time single-molecule 3D tracking in E. coli based on cross-entropy minimization

description: <strong>General description</strong>
This item contains all the data, code and analysis objects used in the paper:
"Real-time single-molecule 3D tracking in E. coli based on cross-entropy minimization"
<br>
Elias Amselem*, Bo Broadwater*, Tora Hävermark, Magnus Johansson & Johan Elf.
Dept. Cell and Molecular Biology, Uppsala University, Sweden"
*Equal contribution
<br>
We present a 3D tracking principle that approaches the sub-ms regime. The method is based on the true excitation point spread function and cross-entropy minimization for position localization of moving fluorescent reporters. Our implementation also features a new method for microsecond 3D point spread function positioning and a new estimator for diffusion analysis of tracking data. We successfully applied these methods to track the Trigger Factor protein in living bacterial cells. 
<br>
<br>
<strong>Experimental data description</strong>
<br>
The data provided in this repository is generated by the microscope described in the publication mentioned above. The underlying real-time tracking principle and methods used are outlined and evaluated using the code base and data found in this repository. This includes; the trajectory reconstruction based on the cross-entropy minimization, the  extended covariance estimator (ECVE) for diffusion, simulation for evaluating both trajectory reconstruction and ECVE method, and the Trigger Factor live cell E. coli data with  analysis.
<br>
Each entry includes the raw data with analysis code. In each entry under the folder "TriggerFactor_Code\ProjectMain" 
is the main analysis python file, this file includes instructions on how to run the script. Also, in the folder there are preconfigured main files to generate data used in the manuscript, this is also available in the ScourceData.zip file.
<br>
To understand how to use the code and the structure used please see entry: 
20220722_EXP-22-BL9428_Example_Analysis
and the README.txt file included. Here you find the python requirements (dependencies and versions), and instructions on how to run simulations.  
<br>
<br>
DOI: 10.7483/opendata.cms.n6jd.1u7d
title: /TprimeBToTH_TLep_Hbb_LH_MT2900_MH175_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.21602844.v1
title: Real-time single-molecule 3D tracking in E. coli based on cross-entropy minimization

description: <strong>General description</strong>
This item contains all the data, code and analysis objects used in the paper:
"Real-time single-molecule 3D tracking in E. coli based on cross-entropy minimization"
<br>
Elias Amselem*, Bo Broadwater*, Tora Hävermark, Magnus Johansson & Johan Elf.
Dept. Cell and Molecular Biology, Uppsala University, Sweden"
*Equal contribution
<br>
We present a 3D tracking principle that approaches the sub-ms regime. The method is based on the true excitation point spread function and cross-entropy minimization for position localization of moving fluorescent reporters. Our implementation also features a new method for microsecond 3D point spread function positioning and a new estimator for diffusion analysis of tracking data. We successfully applied these methods to track the Trigger Factor protein in living bacterial cells. 
<br>
<br>
<strong>Experimental data description</strong>
<br>
The data provided in this repository is generated by the microscope described in the publication mentioned above. The underlying real-time tracking principle and methods used are outlined and evaluated using the code base and data found in this repository. This includes; the trajectory reconstruction based on the cross-entropy minimization, the  extended covariance estimator (ECVE) for diffusion, simulation for evaluating both trajectory reconstruction and ECVE method, and the Trigger Factor live cell E. coli data with  analysis.
<br>
Each entry includes the raw data with analysis code. In each entry under the folder "TriggerFactor_Code\ProjectMain" 
is the main analysis python file, this file includes instructions on how to run the script. Also, in the folder there are preconfigured main files to generate data used in the manuscript, this is also available in the ScourceData.zip file.
<br>
To understand how to use the code and the structure used please see entry: 
20220722_EXP-22-BL9428_Example_Analysis
and the README.txt file included. Here you find the python requirements (dependencies and versions), and instructions on how to run simulations.  
<br>
<br>
DOI: 10.17044/scilifelab.21618150
title: Immunoglobulin M libraries for two donors

description: D19 and D46 human Immunoglobulin M (IgM) libraries, sequenced by Illumina Miseq and publised in the paper titled Adaptive immune receptor genotyping using the <em>corecount </em>program.
The D19 and D46 datasets consists of bulk sequencing data from IgM libraries obtained by IgM specific cDNA synthesis and sequencing in forward and reverse directions using the Illumina MiSeq platform (2 x 300 bp kit). 
Further details will be available in Narang et al., 2023 (doi: 10.3389/fimmu.2023.1125884)
DOI: 10.17044/scilifelab.21618150.v1
title: Immunoglobulin M libraries for two donors

description: D19 and D46 human Immunoglobulin M (IgM) libraries, sequenced by Illumina Miseq and publised in the paper titled Adaptive immune receptor genotyping using the <em>corecount </em>program.
The D19 and D46 datasets consists of bulk sequencing data from IgM libraries obtained by IgM specific cDNA synthesis and sequencing in forward and reverse directions using the Illumina MiSeq platform (2 x 300 bp kit). 
Further details will be available in Narang et al., 2023 (doi: 10.3389/fimmu.2023.1125884)
DOI: 10.7483/opendata.cms.15ih.ysow
title: /TprimeBToTH_TLep_Hbb_LH_MT2900_MH175_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.21694799
title: RNA sequencing data from the CLL cell line HG3 (HG-3) by Oxford Nanopore Technologies based direct RNA long-read sequencing.

description: This is the sequencing data from direct RNA long-read sequencing by Oxford Nanopore Technologies of the chronic lymphocytic leukemia cell line HG3 (HG-3).Total RNA was extracted using the RNAeasy kit (Qiagen, Hilden, Germany). Sequencing libraries were generated from ~5 ?g of high-quality RNA (RIN &gt;9.7) as assessed by Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA). The direct RNA Sequencing kit (SQK- RNA002; Oxford Nanopore Technologies, Oxford, UK) was used according to the manufacturerÕs recommendations. Sequencing libraries were sequenced on a PromethION (Oxford Nanopore Technologies, Oxford, UK) using PromethION R9.4.1 flow cells (Oxford Nanopore Technologies, Oxford, UK). Basecalling was performed using Guppy v6.1.2 and passed FASTQ files were analyzed using nf-core/nanoseq v1.1.0 pipeline1. The FASTQ files were aligned to the reference genome hg19 using Minimap2 v2.1717. The resulting bam and index file are deposited here.The data has been used in the publication by Hagerstrand and Oder et al. The non-canonical BAF chromatin remodeling complex is a novel target of spliceosome dysregulation in SF3B1-mutated chronic lymphocytic leukemia, Leukemia 2024 (https://doi.org/10.1038/s41375-024-02379-4).Researchers are welcome to use the data contained in the dataset for any scientific projects according to license CC BY-NC-ND 4.0.
DOI: 10.7483/opendata.cms.o1a8.3zgo
title: /TprimeBToTH_TLep_Hbb_LH_MT2900_MH200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.98d4.s0zj
title: /TprimeBToTH_TLep_Hbb_LH_MT2900_MH200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.21792356
title: Comparing the blood response to hyperbaric oxygen with high intensity exercise - a cross-over study in healthy volunteers to evaluate biomarkers of oxidative stress (EPM 2019-01864)

description:   
The dataset comprise experimental data (baseline and physiological parameters), qPCR data and RNA seq data from 10 healthy subjects from the HIITO<sub>2</sub> pilot study.
<br>
10 healthy volunteers performed a 28 min High Intensity Interval training (HIIT) session and inhaled Hyperbaric ocygen (HBO<sub>2</sub>) for 28min in an experimental crossover design. We evaluated superoxide by EPR and saturation (SpvO<sub>2</sub>) in peripheral venous blood. ROS, miR-210/miR-34a in plasma and gene expression in PBMCs.
<br>
PBMCs were isolated from blood using Ficoll-Hypaque density gradient centrifugation (BD CPT-tubes). PBMC were isolated. Total RNA including miR was extracted form PMCs with miRNeasy Mini Kit (Qiagen). MicroRNA from plasma was extracted with miRNeasy Serum/Plasma Kit (Qiagen). RNA concentration and purity was checked with Nanodrop 2000 (Kodak) and QC was performed with Agilent TapeStation. RNA sequencing was performed by BEA at Karolinska Institutet Huddinge with Illumina Hiseq 2000.
 
For all subjects there is detailed information on subject characteristics, blood gas analyses, qPCR data from plasma and PBMC (miR-34a and miR-210), RNAseq data from PBMC. 
<br>
Experimental data was collected on paper CRF and transferred to an excel document stored in a pseudonymised database. RNA seq and other datasets are identified through the same pseudonymisation and saved on separate excel files. 
<br>
Any researcher interested in individual data can contact the PI to discuss their idea and data requirements, whereafter data transfer agreements can be drafted. New ethical permission may be required.
DOI: 10.7483/opendata.cms.nz6u.dryo
title: /TprimeBToTH_TLep_Hbb_LH_MT2900_MH250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.21792356.v1
title: Comparing the blood response to hyperbaric oxygen with high intensity exercise - a cross-over study in healthy volunteers to evaluate biomarkers of oxidative stress (EPM 2019-01864)

description:   
The dataset comprise experimental data (baseline and physiological parameters), qPCR data and RNA seq data from 10 healthy subjects from the HIITO<sub>2</sub> pilot study.
<br>
10 healthy volunteers performed a 28 min High Intensity Interval training (HIIT) session and inhaled Hyperbaric ocygen (HBO<sub>2</sub>) for 28min in an experimental crossover design. We evaluated superoxide by EPR and saturation (SpvO<sub>2</sub>) in peripheral venous blood. ROS, miR-210/miR-34a in plasma and gene expression in PBMCs.
<br>
PBMCs were isolated from blood using Ficoll-Hypaque density gradient centrifugation (BD CPT-tubes). PBMC were isolated. Total RNA including miR was extracted form PMCs with miRNeasy Mini Kit (Qiagen). MicroRNA from plasma was extracted with miRNeasy Serum/Plasma Kit (Qiagen). RNA concentration and purity was checked with Nanodrop 2000 (Kodak) and QC was performed with Agilent TapeStation. RNA sequencing was performed by BEA at Karolinska Institutet Huddinge with Illumina Hiseq 2000.
 
For all subjects there is detailed information on subject characteristics, blood gas analyses, qPCR data from plasma and PBMC (miR-34a and miR-210), RNAseq data from PBMC. 
<br>
Experimental data was collected on paper CRF and transferred to an excel document stored in a pseudonymised database. RNA seq and other datasets are identified through the same pseudonymisation and saved on separate excel files. 
<br>
Any researcher interested in individual data can contact the PI to discuss their idea and data requirements, whereafter data transfer agreements can be drafted. New ethical permission may be required.
DOI: 10.7483/opendata.cms.5pfy.oflt
title: /TprimeBToTH_TLep_Hbb_LH_MT2900_MH250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.21904578
title: Mutational Signature Contribution of Matched Primary and Recurrent Breast Tumors

description: Mutational signature contribution table reports COSMIC mutational signatures (v2; https://cancer.sanger.ac.uk/signatures/signatures_v2/) for matched primary and locally recurrent tumors (n = 27 + 27). "SUBSTITUTION.TABLE" contains the number of nucleotide substitutions (type by sample). "MUT.SIG.contribution" reorts the fractional contribution of all 30 COSMIC signatures in each samples. "MUT.SIG.contrib.DELTA" reports pair-wise contribution difference (DELTA = contribution.recurrence - contribution.primary) for paired samples.
<br>
This dataset was used for Figure 2 in the following manuscript:
"Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. <em>accepted for publication</em>
DOI: 10.17044/scilifelab.21904578.v1
title: Mutational Signature Contribution of Matched Primary and Recurrent Breast Tumors

description: Mutational signature contribution table reports COSMIC mutational signatures (v2; https://cancer.sanger.ac.uk/signatures/signatures_v2/) for matched primary and locally recurrent tumors (n = 27 + 27). "SUBSTITUTION.TABLE" contains the number of nucleotide substitutions (type by sample). "MUT.SIG.contribution" reorts the fractional contribution of all 30 COSMIC signatures in each samples. "MUT.SIG.contrib.DELTA" reports pair-wise contribution difference (DELTA = contribution.recurrence - contribution.primary) for paired samples.
<br>
This dataset was used for Figure 2 in the following manuscript:
"Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. <em>accepted for publication</em>
DOI: 10.17044/scilifelab.21904581
title: RNAseq-derived Read Counts for Primary and Locally Recurring Breast Cancers

description: Read counts from RNAseq analysis for all 75 breast cancer samples included in the study "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E.
<br>
"RNA.RAW.COUNTS" for all genes are reported here. "RNA.SCALED.COUNTS" report on filtered (for missing data) reads with matching Uniprot/swissprot identifier. Reads were then scaled and centered across samples and genes.
<br>
This dataset was used for Figure 3 in the following manuscript:
"Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. <em>accepted for publication</em>
DOI: 10.7483/opendata.cms.4zyr.h92g
title: /TprimeBToTH_TLep_Hbb_LH_MT2900_MH25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.21904581.v1
title: RNAseq-derived Read Counts for Primary and Locally Recurring Breast Cancers

description: Read counts from RNAseq analysis for all 75 breast cancer samples included in the study "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E.
<br>
"RNA.RAW.COUNTS" for all genes are reported here. "RNA.SCALED.COUNTS" report on filtered (for missing data) reads with matching Uniprot/swissprot identifier. Reads were then scaled and centered across samples and genes.
<br>
This dataset was used for Figure 3 in the following manuscript:
"Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. <em>accepted for publication</em>
DOI: 10.17044/scilifelab.21904584
title: Mass Spectrometry-derived Protein Intensities

description: Mass spectrometry analysis (data-independent acquisition) derived intensities are reported here for all breast tumor samples (n = 75). RAW data files for these samples are accessible via  ProteomeXchange  with the dataset identifiers PXD032266 (S samples) and PXD037428 (V samples). Protein intensities were Log2 transformed and scaled (samples and proteins).
<br>
This dataset was used for Figure 5 in the following manuscript:
 "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. <em>accepted for publication</em>
DOI: 10.17044/scilifelab.21904584.v1
title: Mass Spectrometry-derived Protein Intensities

description: Mass spectrometry analysis (data-independent acquisition) derived intensities are reported here for all breast tumor samples (n = 75). RAW data files for these samples are accessible via  ProteomeXchange  with the dataset identifiers PXD032266 (S samples) and PXD037428 (V samples). Protein intensities were Log2 transformed and scaled (samples and proteins).
<br>
This dataset was used for Figure 5 in the following manuscript:
 "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. <em>accepted for publication</em>
DOI: 10.5281/zenodo.14653016
title: DisinfoAppear

description: A dataset for a novel link classification task related to the spread of disinformation content online. The dataset focuses on links appearing within fact-checking and debunking articles written by journalists to counter the spread of popular disinformation. Each item in the dataset is a classification of a link appearing within a fact-checking article in one of three categories based on the textual context of the link within the article.The classification categories are Misinformation Appearance, Supporting Evidence and Other.
DOI: 10.5281/zenodo.14653017
title: DisinfoAppear

description: A dataset for a novel link classification task related to the spread of disinformation content online. The dataset focuses on links appearing within fact-checking and debunking articles written by journalists to counter the spread of popular disinformation. Each item in the dataset is a classification of a link appearing within a fact-checking article in one of three categories based on the textual context of the link within the article.The classification categories are Misinformation Appearance, Supporting Evidence and Other.
DOI: 10.7483/opendata.cms.nx9h.xliu
title: /TprimeBToTH_TLep_Hbb_LH_MT2900_MH25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.tbknm3
title: Occurrence Download

description: A dataset listing the 49 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Madagascar",
 "TaxonKey is Sphagnum L."
 ]
}

 The dataset's 49 records were derived from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071170-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17044/scilifelab.21904587
title: GSEA/GSVA-based Analysis of Primary Breast Tumors with/without Recurrence

description: GSEA comparsion between primary breast tumors with a matched recurrence (S sample, n = 27) and tumors with no recurrence (V samples, n = 21). GSEA output is provided for RNA and protein mass spectrometry data (database: HALLMARKS). GSVA scores for key enriched pathways (E2F targets and allograft rejection) are provided too along with immunohistochemical analysis of poliferation marker Ki-67 and leukocyte counts.
<br>
This dataset was used for Figure 6 in the following manuscript:
 "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. <em>accepted for publication</em>
DOI: 10.17044/scilifelab.21904587.v1
title: GSEA/GSVA-based Analysis of Primary Breast Tumors with/without Recurrence

description: GSEA comparsion between primary breast tumors with a matched recurrence (S sample, n = 27) and tumors with no recurrence (V samples, n = 21). GSEA output is provided for RNA and protein mass spectrometry data (database: HALLMARKS). GSVA scores for key enriched pathways (E2F targets and allograft rejection) are provided too along with immunohistochemical analysis of poliferation marker Ki-67 and leukocyte counts.
<br>
This dataset was used for Figure 6 in the following manuscript:
 "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. <em>accepted for publication</em>
DOI: 10.17044/scilifelab.21904590
title: Clinical Data of Matched Primary and Locally Recurrent Breast Cancer Samples

description: Clinical metadata of all samples included in the study  "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E..
<br>
File reports clinical data of 27 primary breast cancers and their associated ipsilateral breast tumor recurrences (samples marked with S). Additionally, a cohort of 21 primary breast tumors with no recurrence is reported (samples marked with V). Data includes age at diagnosis of primary tumor, time to recurrence (S samples) or follow-up (V samples), Estrogen receptor status (positive/negative), progesterone receptor status (positive/negative), ERBB2 status (normal/amplified), proliferation marker Ki-67 (low/high), tumor grade (1/2/3), and adjuvant therapies (yes/no).
<br>
This dataset was used for Figure 1-6 in the following manuscript:
 "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. <em>accepted for publication</em>
DOI: 10.17044/scilifelab.21904590.v2
title: Clinical Data of Matched Primary and Locally Recurrent Breast Cancer Samples

description: Clinical metadata of all samples included in the study  "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E..
<br>
File reports clinical data of 27 primary breast cancers and their associated ipsilateral breast tumor recurrences (samples marked with S). Additionally, a cohort of 21 primary breast tumors with no recurrence is reported (samples marked with V). Data includes age at diagnosis of primary tumor, time to recurrence (S samples) or follow-up (V samples), Estrogen receptor status (positive/negative), progesterone receptor status (positive/negative), ERBB2 status (normal/amplified), proliferation marker Ki-67 (low/high), tumor grade (1/2/3), and adjuvant therapies (yes/no).
<br>
This dataset was used for Figure 1-6 in the following manuscript:
 "Proteogenomics decodes the evolution of human ipsilateral breast cancer". De Marchi T, Pyl PT, Sjöström M, Reinsbach SE, DiLorenzo S, Nystedt B, Tran L, Pekar G, Wärnberg F, Fredriksson I, Malmström P, Fernö M, Malmström L, Malmström J, Nimèus E. <em>accepted for publication</em>