dataesr/scientific-paragraphs-categorization huggingface dataset:
Covariates Data on sex, age, education, smoking habits, alcohol consumption, daily activity, anthropometry, cholesterol, medication use, and diet were obtained from the baseline assessment. As an indicator of socioeconomic status, each participant's average annual income over 5 y (defined as household income after taxation and interest, for the value of the Danish currency in 2015) was used. For hypertension and diabetes mellitus, self-reported data were used due to the underreporting of these diagnoses in the DNPR  (19). Self-reported myocardial infarction at baseline was combined with ICD codes (Supplementary Table  1 ) of ischemic heart disease dated prior to participant enrolment. ICD codes dated prior to participant enrolment were used to identify baseline comorbidities of peripheral artery disease, hemorrhagic stroke, chronic kidney disease, chronic obstructive pulmonary disease, heart failure, and atrial fibrillation (Supplementary Table  1 ).
In line with our prior research, this might indicate that subpopulations prone to atherosclerosis may benefit the most from higher flavonoid intakes, although further investigation is needed to support these preliminary enquiries  (7, 12). The limitations of the present study include possible measurement error relating to the use of self-report subjective measures such as diet, physical activity, etc. While the FFQ used ascertained the frequency of strawberry consumption, it did not capture intakes of other berries, a major food source of anthocyanins. The use of an observational design also restricts our ability to infer causality or to exclude the possibility of confounding by unmeasured or residual factors. Additionally, dietary data and covariate information were obtained at baseline and it is unclear how these might have changed over time.
Sources of support: The  Danish Diet, Cancer  and  Health Study  was funded by the  Danish Cancer Society, Denmark. BHP is supported by an  Australian   Government Research Training Program Stipend Scholarship. NPB is funded by a  National Health and Medical Research Council   Early Career Fellowship (Grant number   APP1159914 ),  Australia. JRL is funded by a  National Heart Foundation   Future Leader Fellowship  (ID:  102817 ). The salary of JMH is supported by a  National Health and Medical Research Council of Australia Senior Research Fellowship, Australia  (Grant number  APP1116937 ). Where authors are identified as personnel of the  International Agency for Research on Cancer/World Health Organization , the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the  International Agency for Research on Cancer/World Health Organization.
Data Availability Cohort 1: ICGC whole genome sequencing and RNA sequencing data for the 67 LMS are available at https://dcc. icgc. org/projects/LMS-FR. Cohort 2: RNA-seq expressions are available on Gene Expression Omnibus under accession GSE71121. RNA-seq raw files (FastQ) are available on sequence read archive under accessions SRP059588 or SRP059588. Mouse K7M2 tumor expression data are available on Gene Expression Omnibus (GEO) under accession GSE157953 and will be released on 09/01/2021 and available before by contacting the corresponding author.
Human Samples Samples used in cohort 1 were collected prospectively by the French Sarcoma Group as part of the ICGC program (International Cancer Genome Consortium). Samples used in cohort 2 were part of the cohort used in Lesluyes et al. [17]. Clinico-pathological data and patient information are summarized in Table  1. All cases were systematically reviewed by expert pathologists of the French Sarcoma Group according to the World Health Organization guidelines  [18]. In cohort 1, selected tumors were primarily untreated (before sampling), not superficial LMS without any additional criteria. In cohort 2, selected tumors were primarily untreated (before sampling) pleomorphic sarcoma. For each patient, frozen and FFPE samples of the primary tumor were collected before any treatment. From those samples, DNA extraction, whole genome sequencing and analysis, RNA extraction, sequencing, and analysis, as well as annotation of variants and breakpoint detection can be found in the supplementary information and in Tables S4-S6.
Cell Lines and Primary Culture The cell lines MG63 (RRID:CVCL_0426; Male) and K7M2 (RRID:CVCL_V455; Female) were given by Dr. Françoise Redini. Those and HEK293T (RRID:CVCL_0063; Female) cells were cultured in DMEM (31-966-021, Life Technologies, Carlsbad, CA, USA). IB106 (UPS; Female) cell is a primary culture established as previously described  [19]  and was cultured in RPMI-1640 (524-000-025, Life Technologies, Carlsbad, CA, USA). Both medium were supplied with 10% fetal bovine serum (S1810-500, Dutscher, Brumath, France), and cells were kept at 37 • C in a humidified chamber containing 5% CO 2. Production and validation of stable ATRX knock-down cell lines, as well as in vitro analyses, can be found in the supplementary information.
One mouse in the control group was excluded, due to an important and quick loss of weight, so the final number of units is n = 10 in the ATRX KD group and n = 9 in the ATRX CT one. Tumor sizes were blindly measured twice a week using a caliper, and their volume was calculated using the formula: (L 2 × l)/2. At the end of the experiment, mice were sacrificed by cervical dislocation. Tumors were then weighed and divided into two parts for formalin fixation and nitrogen freezing. Each tumor was stained with HE and analyzed by a pathologist specialized in sarcomas. Growth rates were calculated with the segmental linear regression of GraphPad Prism (GraphPad Software, version 6, San Diego, CA, USA), and statistical analyses were done using an unpaired T-test. For experiments with K7M2 cells, 6-8 weeks-old female NSG (NOD.
At the end of the experiment, mice were sacrificed by cervical dislocation. Tumors were then weighed and divided into two parts for formalin fixation and nitrogen freezing. Each tumor was stained with HE and analyzed by a pathologist specialized in sarcomas. Growth rates were calculated with the segmental linear regression of GraphPad Prism (GraphPad Software, version 6, San Diego, CA, USA), and statistical analyses were done using an unpaired T-test. Survival curves were analyzed with GraphPad Prism using the Kaplan-Meier method.
Mice Tumor RNA Sequencing and Analysis Total RNA was extracted, prepared, and sequenced as described in the supplementary information to obtain more than 20 million paired-end reads with a length of 75 bp each. Bioinformatic analysis was done as previously described  [17]. RNA reads were aligned to the mm10 genome assembly with STAR v2. 6. 0c [20]. Lowquality (score < 20) and duplicated PCR paired-end reads were removed with SAMtools v1. 8  [21]  and PicardTools v2. 18. 2  [22]  (http://broadinstitute. github. io/picard/), respectively. Then, gene expression was quantified with Cufflinks v2. 2. 1  [23] , using RefSeq  [24]  genes (without miRNA and rRNA) from mm10 UCSC Table Browser  [25]  fixed on 2019/01. Differential gene expression was performed by R package DESeq, between ATRX KD and ATRX CT tumors extracted from Balb/c mice.
Quantification and Statistical Analysis Kaplan-Meier analyses were performed for metastasis-free survival and overall survival. To subdivide ATRX expression into two groups, expression was plotted for ATRX WT and altered cases, separately. The intersection between these two density curves was 4. 45 (log2 FPKM) and 2. 77 for cohort 1 and 2, respectively. Differential gene expression (DGE) analyses were performed by R package DESeq. Gene Ontology (GO) analysis was performed on these differentially expressed genes (p < 0. 05 and fold-change >2 or <-2), by R package GOseq. In parallel, significant genes with p < 0. 01 were used to make a heatmap (R package ComplexHeatMap). Every other statistical analysis detail can be found in each figure legend.
Acknowledgments: The authors would like to thank the  Centre Nacional d'Anàlisi Genòmica (CNAG, Barcelona, Spain)  for WG and RNA sequencing services. We acknowledge the personnel of  CREFRE US006  and  Animal Facility A2  for their technical assistance. We thank  Mickaël Michaud  for his technical and theoretical advice and  Michel Charbonneau  and  Nathalie Grandin  for their work on ALT detection in cell lines. We acknowledge  Françoise Redini  for providing us MG63 and K7M2 cell lines.
Characterization of the sedimentary sample The total elemental composition of the sample C5-BjF was determined by ICP-MS after LiBO2 fusion and acid dissolution in an accredited laboratory (CRPG-CNRS, Nancy, France -http://www. crpg. cnrsnancy. fr/SARM/index. html), by CHNS analysis (Flash 2000 Thermo, IC2MP, France) for the organic C and total S, and by ICP-MS for total As and total Se (Agilent 7500ce, IPREM, France). Powdered samples (infra 50 µm fraction) were analyzed by X-ray diffraction (XRD) using a Phillips Panalytical X'Pert Pro apparatus in the same experimental conditions as described by  Bassil et al. (2016b). The phase identifications were made with the X'pert HighScore software, while the semiquantifications were obtained by applying the RIR method  (Chung, 1975; Hillier, 2000)  to major peak areas.
The RIR values used for the identification are available in JCPDS Pdf2 mineralogical database. Based on experimental peaks correction using corundum ( -Al2O3) as an external standard, this method is simple and efficient when clay minerals do not have specific chemical compositions and show great variabilities of peak intensities due to structural disorder  (Xiang et al. , 2018) , which is the case for the kaolinite-rich studied samples. Diffractograms presented in Figure  1  were normalized to the peak intensity of kaolinite at 7. 19 Å. Thin sections (26x46mm; 30 µm thickness) were prepared from undisturbed samples after impregnation at room temperature and under atmospheric pressure with a two-component epoxy resin (Araldite 2020) diluted with 20% acetone. The thin sections were observed using an optical polarized light microscope (Nikon Eclipse E600 POL) and then using a scanning electron microscope JEOL 5600-LV [Low Vacuum] (IC2MP, UP, Poitiers) equipped with a secondary electron detector Everhart -Thornley and a backscattered electron detector Centaurus (KE Development ic, Cambridge) with a Bruker energy dispersive X-ray spectrometer for scanning electron microanalyses (SEMA).
European Centre for Medium-Range Weather Forecasts Ensemble The ECMWF ensemble consists of one control member and 50 exchangeable members of 2-m temperature from 3-up to 360-h lead times twice a day. Only the initialization time 00:00 UTC is presented and used in this study. The grid of the ensemble is 0. 25°regular and the members are bilinearly interpolated to 2056 locations in Western Europe where observations are available, see Figure  1. Data span 3 years from 1 August 2017 to 31 July 2020. The results presented in Section 3 are based on a leave-one-year-out cross-validation strategy.
Results All results presented in the following come from 3-fold cross-validation based on the 3 years of data at hand. For each station, lead time, and season, three regression models are estimated using two seasons while the remaining season is used for validation. In the following, the raw ensemble is denoted by RAW. For the postprocessed methods, quantiles of order 1  52 , 2 52 , • • • , 51  52 are computed and used in the verification process.
Data We selected two ferredoxin structures (FdI and FdII) from the Protein Data Bank (PDB)  [20, 21]. Biological assemblies of these proteins are homodimeric (as assigned by their authors) with cyclic complex symmetry. Both molecules belong to the same CATH homologous superfamily 3. 30. 70. 20  [22, 23] , the alpha-beta plaits. We also accessed two other PDB structures for comparative analysis in Section 3. 8: a globular, monomeric type III antifreeze protein and a symmetric, homodimeric complex of Rad50 domains from Mre11-helix hairpins involved in chromosomal maintenance. Basic information of all four molecules summarized in Table  1  while their 3D presentation is available in Figure  1. The structure of Ferredoxin I (FdI, PDB code: 1FXR  [24] , Figure  1a ) is a homodimer with 64 amino acids and one [4F4S] cluster in each chain.
These ligands are located in pockets located at opposite sides of the symmetric complex. Each ligand interacts with 11 residues from the chain, detected in accordance with PDBsum atom distance criterion for non-bonded contacts  [28, 29]  (two residues are assumed to be in contact if there is at least one pair of non-hydrogen atoms-one from each residue-with Euclidean distance not The structure of Ferredoxin I (FdI, PDB code: 1FXR  [24] , Figure  1a ) is a homodimer with 64 amino acids and one [4F4S] cluster in each chain. These ligands are located in pockets located at opposite sides of the symmetric complex. Each ligand interacts with 11 residues from the chain, detected in accordance with PDBsum atom distance criterion for non-bonded contacts  [28, 29]  (two residues are assumed to be in contact if there is at least one pair of non-hydrogen atoms-one from each residue-with Euclidean distance not greater than 3.
9 Å). Among these residues are four cysteines (C11, C14, C17 and C54), experimentally determined to coordinate the iron-sulfur cluster. The structure of Ferredoxin II (FdII, PDB code: 1FXD  [25] , Figure  1b ) is a 58 amino acid monomer, although it is isolated as a tetramer (according to Kissinger et al. , "there are four molecules in the unit cell, but it is apparent from an examination of the crystal packing interactions that their arrangement is unlikely to be relevant to tetramer formation"  [25] ). PDB file data of 1FXD provides symmetry operators in REMARK 350, application of which results in a perfectly symmetric homodimeric complex. A single [3F4S] cluster is present in each chain, involved in non-bonded contacts with 10 residues. Ligand coordination is handled by C8, C14 and C50.
The fact that FdI is bound to the cluster by four cysteines, and FdII by three, has already been described  [9]. The structure of type III antifreeze protein (PDB code: 9MSI  [26] , Figure  1c ) is a 66 amino acid monomer with no partner molecules present along its structure. The structure of Rad50 domain (PDB code: 1L8D  [27] , Figure  1d ) is a component of the Mre11-Rad50-Nbs1 complex, which plays role in several DNA-related processes. Unlike the other three proteins presented here, it assumes the form of a long coiled coil, limited in the PDB file to the neighborhood of a hook-shaped region in the middle of its sequence. Pfam  [30]  domain segment for this structure is 423-474 (coiled-coil zn hook). Hopfner et al. inform that Rad50 forms dimeric complexes through coordination of a zinc ion by four conserved cysteines (two per chain) located in this hook  [27].
Tools and Websites Images of the protein structures were rendered with PyMOL  [45, 46]. Charts were plotted using Matplotlib library  [47]. FOD results were calculated using state-of-the-art open-source Python libraries for scientific computation  [48, 49]. Sequence and structure similarity measurements were carried out on Matras server  [50, 51]. Solvent-accessible surfaces were resolved using MSMS program  [52, 53]. Online calculation of fuzzy oil drop hydrophobicity profiles and related data is available at http://fod. cm-uj. krakow. pl web server. MIR and SMIR data was downloaded from the SPROUTS web server at http://sprouts. rpbs. univ-paris-diderot. fr.
Sequence Analysis To measure the sequence and structure similarities between FdI and FdII, their PDB structures were submitted to Matras server, which is known to be among the best tools for 3D structure alignment  [54, 55]. Based on data received from Matras, sequence similarity between FdI and FdII is measured at 41. 4% after the introduction of six insertions in the shorter sequence (FdII): one between N5 and D6, one between D37 and S38 and two padding residues at each chain terminus. This alignment, as well as the secondary structure composition and non-bonded contacts is presented in Figure  2. Secondary structure similarity between FdI and FdII is determined to be 91. 4%, with 100% of beta sheet match. Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 30 hydrophobic status. That is where protein-protein interacting residues are located.
Given the above statement, one may ask whether type III AFPs also have similar T vs. O map features. To answer it, we analyzed a representative of this family accessible by PDB code 9MSI. It is naturally a monomer, mostly unstructured, with its size and shape (Figure  1c ) slightly resembling FdII (cealign RMSD: 6. 61 Å/40 aa, 9. 11 Å/48 aa for FdI). RD for the whole molecule is 0. 29. All its segments also exhibit very strong accordance (Table  S1 ). A striking similarity can be seen between its T and O profiles (Figure  10a ), especially on the map (Figure  10c ) where not a single residue falls outside the accordance stripe (marked by the dashed lines). There are no members of D 2 , D 3 , B 2 and B 3 classes and just three insignificant representatives of class B 1 , located deeply within the class Z square: S24, P33 and D36.
The accordance classes (C and S) capture most of the residues at third level with only 2 and 8 of them respectively being at level 1. This shows that even with similar RD value, hydrophobicity map characteristic of type III AFP is clearly different from the one exhibited by ferredoxins. It correctly signals that a lack of protein-protein and protein-ligand activity should be expected from it. The three proteins investigated so far: FdI, FdII and type III AFP are globular, relatively small and highly accordant with 3D Gauss function (RD ≈ 0. 3). To showcase how the presented approach handles a vastly different structure, we also included 1L8D in this analysis. It is the PDB code for Rad50 domain from Mre11-Rad50-Nbs1 complex. Its hairpin shape and exposition of all residues to the solvent nearly guarantees that no stable hydrophobic core will be found in it (Figure  1d ).
Similar, but weaker phenomenon can be observed in whole 1L8D dataset. This suggests that hydrophobicity may play a role in initial formation of contact between Rad50 units even though the dimer is not hydrophobically stable. It partially fits FOD expectation where accordant complex is made from discordant monomers. It is worth mentioning here that most structures we worked with so far subscribe to "monomers accordant, dimer accordant" and "monomers discordant, dimer discordant" themes. Classification of FOD residues proposed in this paper has high flexibility, allowing choosing of class level suitable for given structure. Higher level (i. e. , 3) means narrower search but focused on the most outstanding data, while lower level (i. e. , 1) can be either ignored or included in further analysis, depending on available number of members of significant classes.
One possible explanation for the different take on this might be that around a quarter of their respondents were recruited via MDPI, an OA publisher platform, and therefore were likely to be more familiar with OA and have more experience in publishing in OA journals. It could also be down to the fact that respondents were heavily skewed towards engineering and technology and more informed about what open access was and how it could benefit them. This brings us to the difficulties of doing studies in this area and how careful we should be about coming to hard conclusions. The closed-question approach to predatory journals saw them embedded in a question about the disad- ) and we sought to test the hypothesis derived from the interview data that there were thought to be too many predatory journals going by the number of emails received.
Not surprisingly, we discovered significant cohort effects within the CGS and FGMS data sets in which different generations of CF individuals have different CFRD prevalence rates. To account for these differences, we defined cohort based on the decade in which an individual was born and adjusted for cohort effects when constructing the prediction model. For instance, individuals born in the 1970s or the 1980s were grouped into separate cohorts. Moreover, we excluded French and Canadian participants born before 1970 for all subsequent analyses. In CF, the standard of care is to employ annual OGTT testing to conclude the presence of CFRD, but there is poor adherence to this time-consuming test that requires an overnight fast. 15 In the CGS, CFRD status was determined using a combination of chart review and the Canadian CF patient registry.
The age-dependent model defined in the CGS shows excellent agreement when validated in the FGMS, demonstrating that our approach has selected stable predictors generalizable to other populations. The risk classifier also shows slightly better performance at predicting CFRD risk later in life (e. g. , AUC = 0. 71, age = 28 in FGMS) in both study cohorts. Of note, our model outperforms univariate PRS regardless of the chosen p value cutoff (Appendix F). To further investigate model performance between CGS and FGMS, we plotted univariate log HR and the 95% CI for each selected predictor (Fig. 1c ). Increase in CFRD risk for females and those with at least one copy of the type 2 diabetes risk allele (rs7903146[T]) show good agreement in both studies. Those with at least one copy of the PRSS1 (rs1964986(C)) and those with at least one copy of the SLC26A9 risk variant (rs4077468[A]) also show similar increases in CFRD risk in both independent data sets.
However, several predictors including MI, the variants rs12318809 (SLC5A8), rs7822917 (NRG1), and rs959173 (CAV1) have much weaker effects in the FGMS. The effect size of the CFTR score is comparable in the FGMS and CGS, albeit with a wider CI for the FGMS since relatively fewer individuals carry mild CFTR pathogenic variants in the FGMS. Wider CIs can also be observed for other predictors due to a smaller sample size in FGMS. Consequently, the ability of our model to stratify CFRD risk based on the CFTR score may be underutilized in the FGMS and leads to underestimated performance at younger ages. Winner's curse, in which the associations of selected predictors in the training data set are more likely to be overestimated, might also be a contributing factor. 28 ince AUC(t) only measures a model's ability to rank individuals based on their estimated risk, we further evaluated a more clinically relevant metric by comparing CFRD prevalence rates between individuals with the highest and lowest 10% risk.
ACKNOWLEDGEMENTS The authors thank the patients and families who participated in the CGS and the FGMS in the contributing CF centers across Canada and France. We also express our gratitude to the clinical research assistants, collaborators, and principal investigators involved in both the CGS and FGMS. The study is indebted to the group of FGMS investigators that make external validation of the tool possible. Funding was provided by  Cystic Fibrosis Foundation   STRUG17PO ;  Canadian Institutes of Health Research  ( MOP 258916 ,  MOP 117978 ,  MOP 388348 ,  MOP167282 ),  Cystic Fibrosis Canada  ( 2626 ), and the  CFIT Program  funded by the  SickKids Foundation and CF Canada; Natural Sciences and Engineering Research Council of Canada  ( RGPIN-2015-03742 ,  250053-2013 ); This work was funded by the  Government of Canada through Genome Canada  ( OGI-148 ) andsupported by a grant from the  Government of Ontario ; and  Institut National de la Santé et de la Recherche Medicale, Assistance Publique Hopitaux de Paris, Université Pierre et Marie Curie Paris ,  Agence Nationale de la Recherche  ( R09186DS ),  DGS ,  Association Vaincre La Mucoviscidose, Chancellerie des Universite´s (Legs Poix ),  Association Agir Informer Contre la Mucoviscidose ,  GIS-Institut des Maladies Rares.