dataesr/scientific-paragraphs-categorization huggingface dataset:
Questionnaires Questionnaires used within the new and ongoing HBM campaigns under HBM4EU were harmonized. Questions about socio-demographic characteristics were included in all studies; the majority of studies also captured information on dietary habits and health status of the participant, lifestyle, and residential environment. For some ongoing campaigns questionnaire data needed to be harmonized post factum. Therefore, some information may be available in some studies while not in others. For upcoming HBM studies, base and substance specific harmonized questionnaires are described by  (González-Alzaga et al. , 2022). Specific questionnaires for teenagers have been developed for arsenic and are available in the HBM4EU library for upcoming studies (https://www. hbm4eu. eu/online-library/).
Statistical analysis Statistical analyses were performed in SPSS Statistics 28. Descriptive statistics were calculated. Determinants of variability in As concentration were analysed (ANOVA). Variables considered were among others personal factors such as sex and BMI, questions from the questionnaires related to the living environment as exposure to groundwater, questions related to food consumption, information on socio-economic status (SES; categories for equivalent household income and highest education in household), information on degree of urbanization at the home address (see Table  1 ). The chosen variables were in the first instance based on associations already observed in literature and science based hypotheses about potentially influencing factors. Studies were analysed separately as based on the questionnaires, not all questions were answered in all studies (see Table  1 ).
Exposure determinants In the current study, we used questionnaire information filled out by the teenagers to associate information on food intake with their individual HBM data and this for a large dataset (see Table  A2  in the supplementary material for detailed information on questionnaire answers). Multiple linear regression analysis showed that the sum of As (III), As(V), MMA, and DMA was significantly associated with (recent) seafood and/or rice consumption in all cohorts (p < 0. 05) and not significantly with other food types (see Table  4 ). It is shown in Table  1  which food types were questioned in which study. It also can be seen from Table  1  that there are many gaps in the questionnaire data so more harmonization is needed in advance. Regarding As from drinking water, in the HBM4EU Aligned Studies, the distribution of participants across different categories of use of drinking water was not uniform and often there was a limited amount of participants in some categories (e.
, 2022). In addition, as pointed out by  Hays et al. (2010) , this BE value does not consider carcinogenicity of arsenic. For the Canadian Health Measures Survey (CHMS) St-Amand et al. (  2014 ) set the BE value for the sum of MMA and DMA at 5. 8 μg/L. This was done because of detection problems of arsenite and arsenate  (Aylward et al. , 2013; St-Amand et al. , 2014). In France, INRS stated that the reference value in the general population of As(III)+As(V)+MMA + DMA should be lower than 10 μg/L or 10 μg/g creatinine and As(III)+As(V) should be lower than 2. 2 μg/L  (Fillol et al. , 2010). So a dual guidance value was set. Studies included here (Fig. 1 ) show that P50 value was lower than the BE-value of 6. 4 μg/L except in the dataset of Spain (BEA). P95 values all exceeded the BE value.
The calculated values must be interpreted with caution, particularly as the development of an updated assessment of toxicity of iAs, including the shape of dose-response curves in the low dose region, is ongoing by USEPA (NASEM, 2019). The study addresses exposure, exposure determinants and risk of arsenic in adolescents of the general population. Strengths are the homogenization of protocols under HBM4EU and the QA/QC so that harmonized results for exposure and questionnaire data can be obtained if taken into account a priori. The study covered the different EU regions  (North, South, East, West)  and by selection adolescents it is focusing on a vulnerable population for which the possibility exists that later in life resampling may take place. Some limitations are that not all As species were measured and that also in the questionnaires some information was missing.
Conclusion HBM4EU made a big step in collecting comparable HBM data creating the largest dataset representing As exposure of adolescents living in Europe. Nevertheless, analysis of different As species needs more harmonization in the EU in future campaigns as results of recent analysis are still too heterogeneous. Also future questionnaire data with specific questions on arsenic exposure determinants (e. g. consumption of food supplements, seaweed, selenium status, smoking status) need more harmonization in advance. This will allow a combination of datasets which will increase statistical power. Besides, when addressing certain research questions on the source or determinant of exposure, more attention can go to selection of participants as not each study is a priori designed to answer all the questions for a certain chemical.
Acknowledgements HBM4EU is co-financed under  Horizon 2020  (grant agreement  No733032 ). The authors thank all investigators of the contributing studies for their participation and contribution to the joint  HBM4EU  survey and the national programme owners for their financial support. Also thanks to the participating teenagers and their families, the field workers that collected the samples. The  FLEHS IV study  was conducted within the framework of the  Flemish Center of Expertise on Environment and Health  ( FLEHS 2016 ( FLEHS -2020 ) ) and funded by the  Flemish Government, Department of Environment & Spatial Development. We thank the teenagers and their families that participated in the study, the field workers from the  Provincial Institute of Hygiene  and  VITO  for the sample and data collection.
825 -1. 55 -13. 55 10. 45 Arsenobetaine 0. 002 0. 04 0. 02 0. 07 SG 0. 541 3. 63 -8. 08 15. 34 BMI 0. 013 -0. 03 -0. 05 -0. 01 Recent rice consumption <0. 001 No 97 -0. 31 -0. 48 -0. 14 <0. 001 Yes 51 Reference = 0 NUTS2 regions <0. 001 Model <0. 001 BEA Intercept 0. 110 7. 92 -1. 51 17. 36 Arsenobetaine <0. 001 0. 15 0. 11 0. 18 SG 0. 217 -5. 81 -15. 06 3. 43 Matrix (sampling method) 0. 865 Morning urine 197 -0. 01 -0. 14 0. 12 0. 865 Spot urine 74 Reference = 0 NUTS1 regions 0. 015 Model <0. 001 NUTS: Nomenclature Of Territorial Units for Statistics. CI: Confidence Interval; LL: lower limit; UL: upper limit. SG and matrix (first morning urine; spot urine) forced into the model. All datasets were normalized for specific gravity (SG).
For det andre er de biomekaniske egenskapene til et glioblastom heterogene og skiller seg fra friskt vev. For det tredje er stive områder innad i svulsten forbundet med høyere aggressivitet og kortere overlevelse. The research presented in this thesis was conducted at the Department for Physics and Computational Radiology, Oslo University Hospital between 2018 and 2022. Funding was provided by the Research Council of Norway (grant 261984), South-Eastern Norway Regional Health Authority (grant 2017073), and the European Research council's Horizon 2020 Framework Programme (project 758657). This PhD would not have been possible without the contributions of many colleagues: First and foremost, thank you, Kyrre Eeg Emblem. I could not have asked for a better main supervisor. Your scientific knowledge and experience is a true inspiration.
Glioblastoma The Global Cancer Observatory database reported that roughly 300,000 patients (1. 6 % of all new cancer cases) were diagnosed with a brain tumor in 2018, and 241,000 patients (2. 5 % of all cancer deaths) died from this disease  [52]. In Norway, there are about 400 new cases annually of cancer in the central nervous system  [53]. Despite their relatively low frequency of occurrence compared with other cancers, brain tumors cause a disproportionate amount of morbidity and mortality, partly because of the critical location of the tumor mass  [54]. The 5-year survival rates for malignant brain tumors are the third lowest among all types of cancer  [55]. The most commonly occurring malignant brain tumor is GBM, which accounts for 14 % of all primary central nervous system tumors  [56].
In Papers II and III, a variation of the curl method was used: The data was filtered in Fourier space using a 11 th order Blackman-Harris filter  [95]  before the curl operator was applied in the image space. The final choice of reconstruction method to be used in patients was based on the recommendation of the developer of the reconstruction methods, deeming the curl method more robust  [31]. The MRE analysis resulted in maps of the shear storage modulus G , the shear loss modulus G , the magnitude of the shear modulus |G * | and the shear phase angle φ. After reconstruction of MRE maps, further image analysis as described below was performed using Matlab (version R2021a, MathWorks, Natick, MA, USA). In Paper I, MRE data quality was assessed by the shear-compression wave ratio, meaning the ratio between the amplitude of the curl and the amplitude of the divergence of the displacement data.
Biopsy extraction, Paper III Surgery was performed by a single experienced neurosurgeon, except for one patient. Biopsies were taken from regions within the preplanned volume amenable for surgery, and covered both T1-weighted-contrast enhancing regions, and regions with hyperintense FLAIR signal that were nonenhancing on T1-weighted images. From each tumor, 2-7 biopsies were taken. Biopsies were taken early in the resection, before any major change in navigational precision. The surgeon evaluated tumor consistency as 1) softer than normal brain tissue, 2) similar in consistency as normal brain tissue, or 3) firmer than normal brain tissue  [107]. Biopsies were snap frozen immediately after extraction. The surgeon was shown the MRE data before the surgery, but the biopsy locations were chosen according to the surgeon's evaluation of tissue with differing stiffness.
External patient data sets, Paper III In Paper III, we analyzed survival data and transcriptomic profiles of patients in publicly available data sets. Data were retrieved from two data sets: The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC)  [110, 111]. TCGA contains genomic, epigenomic, transcriptomic and proteomic data and is publicly available for the research community. The CPTAC database has as its main focus proteomic profiling using mass spectroscopy. Through the National Cancer Institute Genomic Data Commons Data Portal, we could access RNA transcription profiles from 265 patients, 168 from TCGA and 97 from CPTAC  [112].
In Paper III, comparisons of MRE measurements in tumor and NAWM were performed using the Wilcoxon signed rank test. Spearman rank-order correlation is a nonparametric test of the strength and direction of association between two variables. In Paper I, Spearman rank-order correlation was used to assess the correlation between stiffness and MRE data quality. In Paper II, the correlation between the different functional measurements was assessed by this test. In Paper III, MRE measurements and tumor volumes were compared using the Spearman's rank order test. In Paper I, test-retest reliability was estimated using both absolute and relative indices. Relative reliability was calculated in Stata (release 16, StataCorp, College Station, TX) using intraclass correlation coefficients (ICC) with 95 % confidence intervals, based on an absolute-agreement, two-way mixed effects model  [113].
In Paper III, logistic regression using Stata was used to compare the surgeon's stiffness assessment with the MRE stiffness measurement. Differential expression analysis of RNA sequencing data and dimensionality reduction was performed using the DESeq2 package of R (version 4. 1. 1, R Core Team 2021). To examine whether stiffness was a strong source of variance within RNA sequencing data in Paper III, we performed dimensionality reduction by both principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). The purpose of dimensionality reduction methods is to separate data points according to the variables that vary the most between different groups (in our case, biopsies). Using a linear transformation, the data is converted to a lower dimensional space with as small an error as possible.
Differential gene expression analysis between the 'stiff' and the 'soft' biopsies per patient found that 196 genes were differentially expressed (adjusted p<0. 05). Of these, 122 were upregulated in 'stiff' biopsies while 74 were upregulated in 'soft' biopsies. Normalized expression levels of differentially expressed genes in every biopsy showed that 'stiff' or 'soft' biopsies tend to cluster together, and biopsies within individual patients also showed similar expression profiles. Patient-wise leave-one-out cross-validation identified a set of 43 genes that were found to be differentially expressed in every iteration. To evaluate the structural and functional importance of the differentially expressed genes, we performed a gene set enrichment analysis of genes associated with increased biopsy stiffness using the Gene Ontology and Reactome databases  [120] [121] [122].
Discussion The primary objective of this PhD project was to use MRE in patients with GBM to explore the biomechanical properties of tumors and to probe how biomechanics relate to tissue function and genetics. In Paper I, we scanned healthy subjects twice to assess repeatability of MRE in the healthy human brain. We also evaluated the effect of different reconstruction methods and varying MRE data quality on stiffness estimates. In Paper II, we used MRE, DSC, and DTI in healthy subjects and patients with GBM and mapped the differences between tumor and healthy tissue. In Paper III, we further examined tumor heterogeneity by performing RNA sequencing of biopsies from different locations in the tumor. These are our main findings: • MRE in brain is robust in terms of repeatability. Absolute stiffness values vary with the reconstruction method.
Besides the one that we use, there are several alternatives: phase-to-noise ratio, displacement-to-noise ratio, and octahedral shear strain signal-to-noise ratio  [30, 136]. Further standardization of how to report data quality and recommended cutoff values would be valuable to make MRE studies more comparable. Sharing of raw data could also enable research reproducibility and method comparison. To this end, we have shared our MRI data from both healthy subjects and patients with GBM on the open respository Zenodo: DOI 10. 5281/zenodo. 4926005 [137].
MRE of the healthy brain In Papers I and II, we used data from healthy subjects to examine the healthy brain and its subregions. We found white matter to be stiffer than gray matter. This is consistent with the findings in most publications on MRE in the healthy brain  [40, 124, [138] [139] [140] [141]. This can be explained by the fact that white matter consists of bundles of myelinated axons and therefore is more structured than gray matter  [40]. We found cortical gray matter to be stiffer than deep gray matter. This is different from what was measured in a study of 134 healthy subjects, where deep gray matter was reported to be stiffer than white and cortical gray matter  [40]. An earlier study reported that stiffness was lower in deep gray matter than in white matter  [140] , while a third study found that stiffness of white and gray matter was very similar in both adults and pediatric subjects  [96].
This has been shown to increase vascular perfusion and improve drug delivery. We are currently performing an ongoing clinical trial at Oslo University Hospital on the use of losartan in patients with GBM and brain metastases (ClinicalTrials. gov Identifier: NCT03951142). The hypothesis of the study is that losartan will improve the effect of traditional cancer treatment by alleviating solid stress of the tumor microenvironment to improve tissue perfusion. MR imaging, including MRE, will be used to assess the effect of the drug  [213].
Limitations in our work The applicability of the results from our studies is limited by the relatively small sample of patients and healthy subjects. In Paper III, the sample size of biopsies per tumor is also small. With a handful of biopsies taken of a large and heterogeneous tumor, it is difficult to ensure that the sampling is representative of the whole tumor. While the surgeon had access to the MRE data prior to surgery, the MRE images were not consulted during surgery, and the chosen locations of the biopsies were based on varying tissue stiffness as evaluated by the neurosurgeon. The classification of biopsies used in the differential gene expression analysis was based on MRE measurements, which did not correlate with the surgeon's evaluation. Had the biopsy locations been chosen based on MRE, we could have sampled tumor regions that differed even more from each other in terms of |G * |.
Therefore, an absolute division based on |G * | norm would disregard the information of which patient the biopsies were from and make interpretation less meaningful and useful, as differences between patients could be greater than differences between stiff or soft biopsies. Biopsies were classified using |G * | norm values rather than the surgeon's division of soft or stiff biopsies because we wanted to examine the biological significance of MRE measurements. We also performed the differential gene expression analysis with biopsies classified according to the surgeon's evaluation, but no significant differences in gene expression was found using this definition of stiff and soft biopsies. To understand the importance of differentially expressed genes in 'stiff' versus' soft' biopsies, we evaluated independent survival data from public data sets.
6 Within one experimental design, both reliability and repeatability should be high in order to track changes over time or to separate between normal and abnormal tissue stiffness. Researchers have reported brain MRE repeatability as measured by coefficients of variation (CV), with values below 10% for subregions in the healthy brain, and below 2% for the brain as a total. 7-10   In order to trust stiffness estimates, the underlying data need to be of sufficient quality, both in terms of wave propagation and data acquisition. 11 As the tissue displacement caused by the shear waves are the underlying signal prior to processing, MRE data quality is here quantified by the ratio between the shear waves and the compressional waves. The aim of this study was to assess repeatability and test-retest reliability of MRE in the human brain, and to evaluate the effect of different reconstruction methods and varying MRE data quality on stiffness estimates.
Data leading to negative shear modulus values are removed and a weighted averaging of the shear modulus based on residual error is also performed. In this case, only first-order spatial derivatives are needed, since no curl operator is applied. The derivatives of u were computed by least squares polynomial fitting. 20  Final stiffness maps were spike-filtered for both reconstructions using a sliding 3 × 3 pixel window. If the central pixel of the sliding window carried the largest or the smallest entry of all values within the sliding window, and this entry was 3 standard deviations away from the average value of the surrounding eight values, this central value was replaced by the average value of the neighboring pixels. Diffusion analysis was performed in nordicICE (NordicNeuroLab, Bergen, Norway) using the following preprocessing steps: automatic detection of noise threshold, noise level cutoff, and motion correction.
Functional annotation of stiffness-associated gene expression To evaluate the structural and functional importance of differentially expressed genes, we performed a gene set enrichment analysis (GSEA) of genes associated with increased biopsy stiffness using the Gene Ontology (GO) and Reactome databases  (19, 20). The GO-terms with the highest association to 'stiff' biopsies represent components of the extracellular matrix, cellular adhesion and innate immunity (Fig. 3A ). Similarly, among the most significantly enriched Reactome pathways associated with 'stiff' biopsies were extracellular matrix organization, integrin cell surface interactions, and neutrophil degranulation (Supplementary Fig. 2 ). In contrast, GO-terms and Reactome pathways with highest association with 'soft' biopsies largely represented normal neuronal functions such as regulation of membrane potential and neurotransmitter receptor complex although associations to DNA methylation and rRNA regulation were also found.
CR1, PLAGL1, COL4A1, and COL5A2 have all been shown to correlate with shorter survival  (40) (41) (42) (43). When our data was compared to previously published transcriptomic profiles of GBM samples, we found that median survival was significantly shorter in patients with tumors that exhibited the gene expression signal associated with 'stiff' biopsies. This indicates that the genetic and molecular processes we detect in 'stiff' tumor biopsies play a role in the malignant progression of tumors. It should be noted that while our data consists of multiple biopsies per patient with MRE stiffness evaluations, only one tissue biopsy per patient was available in the external data sets. We assume that this tissue biopsy is representative of the entire tumor. We do not claim that the tumors in these datasets are stiff or soft, rather, we examined the prognostic relevance of stiff or soft expression signals for GBM patients.
Invasive species cost data collection and filtering We compared biological invasion costs to natural hazards at the global scale, the regional scale and at a representative national scale with high data availability (i. e. , the USA). To quantify economic losses generated by the impacts of biological invasions, we used data from the InvaCost database version 4. 1 -the most up-to-date, comprehensive, standardised and robust data compilation and description of economic cost estimates associated with invasive species worldwide (https://doi. org/10. 6084/m9. figshare. 12668570  Diagne et al. , 2020)  -downloaded using the data() function from the invacost R package version 1. 1-4 (R Core Team 2020)  (Leroy et al. , 2020). We filtered the database to only include costs that were actually incurred (classified as observed in the "Implementation" field) and labelled as highly reliable ("Method reliability" field).
The filtered dataset is available in Supplementary Material Data S1. Highly reliable observed damage costs for the USA from invasive species were obtained by filtering InvaCost 4. 1 data as described above and retaining entries attributed to "United States of America" in the "Official country" field. We adapted the TDWG World Geographic Scheme for Recording Plant Distributions Standard (TDWG, 2001) to reclassify InvaCost field "Geographic region" and obtain the breakdown of highly reliable observed damage costs by continental regions. The list of countries and matching regions is available in Supplementary Material Data S2. As costs may be recorded as a single or multi-year estimate, to obtain annualised costs we expanded the database using the expandYearlyCosts() function from the invacost R package version 1.