dataesr/scientific-paragraphs-categorization huggingface dataset:
Uncertainty distributions for the removal efficiency The Crystal Ball software (Oracle Corporation, 2016) was used to derive a set of 10,000 E X by assigning uncertainty distribution to the operational and chemical-specific input parameters of the Sim-pleTreat 4. 0 model. Operational parameter distributions were characterised according to  Franco et al. (2013). Characterisation of chemical-specific parameter distributions was based on the available data (SI, S4). When only one value for a parameter was available, the uncertainty characterisation was based on the recommendations of  Jager et al. (1997). When no literature data was available, the estimation accuracy of the methods from EPIweb and of the ones implemented in SimpleTreat  (Sablijc et al. , 1995)  were used. Details on the uncertainties assigned to each physicochemical property are provided in the SI, S4.
The presented results were derived using the program R version 3. 3. 2 (R Core Team, 2016) and additional packages: ggplot2, and Prevalence in product (F X,P ), geometric mean of the inclusion level (Geom. M F mass,X,P ), and geometric standard deviation of the inclusion level (Geom. SD F mass,X,P ) of the chosen chemicals sodium lauryl ether sulfate (SLES), cocamidopropyl betaine (CAPB), cetearyl alcohol (CA), sodium benzoate (SB) and dimethyloldimethyl hydantoin (DMDMH) for the three products shampoo, bodywash, and conditioner prior to 2016.
e. ). However, since p. e. also include industry effluents, this assumption led to an overestimation of the French total population by 8. 5%, and by around 3% for the Dutch population. Similarly, deriving the South Korean population served by a WWTP from the BOD overestimated the total population by around 5%. These assumptions potentially led to an overestimation of the chemical emissions to surface water. Fourth, the estimated total chemical emissions for CPs presented in this study are not representative of total use volumes of these chemicals at the country level. The chemicals we analysed can namely be found in other products. In fact, of the CPs in the Household Products Database (National Institutes of Health and Health and Human Services, 2015) that contain the chemicals studied here, shampoo, conditioner, and bodywash make up approximately 50%  (EWG, 2015).
Acknowledgments We would like to thank  Jacqueline Biesterbos  for sharing the raw data of her study on usage patterns of personal care products in the Netherlands. We would also like to thank  Hyejin Park , Deputy Director at the  Ministry of Environment of South Korea  for her help in collecting data on the locations of sewage treatment plants in South Korea. This project was funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  641459  (RELIEF).
Statistical analysis Data were analysed using SPSS 20 MAC. The distribution of the data was assessed by visual review when plotted as a histogram, with normality further confirmed using the Kolmogorov Smirnov test. All data were normally distributed and presented as mean AE standard deviation. The student t-test was used to compare normally distributed continuous variables between groups. The Chi-squared test, or Fischer test if the sample was less than 5, were used to assess differences in categorical variables between groups. Pearson correlation was used to assess association between HR and BP measurements. We adjusted HR variation to gender with a covariance analysis (ANCOVA). All tests were two-tailed and the cut-off for statistical significance was 0. 05.
Patients The present study focuses on 385 patients of the 438 children treated in the ELAM02 trial (Treating Patients with Childhood Acute Myeloid Leukemia with Interleukin-2; ClinicalTrials. gov NCT00149162). Patient selection was based on the availability of genomic DNA at AML diagnosis. Children aged 0 to 18 years with newly diagnosed AML were enrolled between March 2005 and December 2011. Acute promyelocytic leukemia, therapyrelated AML and Down syndromes were excluded from the ELAM02 trial. The study was approved by the Ethics Committee of Saint-Antoine Paris University Hospital (Assistance Publique-Hôpitaux de Paris) and by the Institutional Review Board of the French Regulatory Agency and was conducted in accordance with the Declaration of Helsinki.
Mutational analysis Genomic DNA from bone marrow aspirates at diagnostic was studied by high-throughput sequencing (HTS) of 36 genes recurrently mutated in myeloid malignancies. Two distinct HTS technologies were used to allow direct cross validation. Firstly, libraries were prepared using the Ampliseq System according to the manufacturer's instructions and run on Ion Proton (Thermofisher, Waltham, MA, USA). Raw data were analyzed with both Torrent Browser (Thermofisher) and SeqNext (JSI Medical System, Los Angeles, CA, USA). Secondly, libraries were also prepared using the Haloplex Target Enrichment System (Agilent Technologies, Santa Clara, CA, USA) and run on MiSeq (Illumina, San Diego, CA, USA). Raw data were processed by SureCall (Agilent Technologies) and SeqNext (JSI Medical System).
A high depth of coverage (>1500Â) was obtained for all genes with both HTS technologies, allowing detection of mutations with a variant allele frequency (VAF) until 1%. Frameshift and nonsense variants were always considered as relevant mutations. Single nucleotide variants were retained in the absence of description into public databases of human polymorphisms, and effects on protein function were predicted with 6 established prediction tools: SIFT (Sorting Intolerant From Tolerant), PolyPhen-1, PolyPhen-2, MAPP (Multivariate Analysis of Protein Polymorphism), PhD-SNP (Predictor of human Deleterious Single Nucleotide Polymorphism), and SNAP (Screening for Non-Acceptable Polymorphisms). 8 The presence of the FLT3-internal tandem duplication (ITD) was performed for all patients by fragment analysis as previously described.
Statistical methods Event-free survival (EFS) and overall survival (OS) were estimated by the Kaplan-Meier method and compared by cause-specific hazard Cox models. EFS was measured from the date of diagnosis to the date of the first event (induction failure, relapse, or death) or to the date of last follow-up. Patients who failed to achieve complete remission (CR) were considered as failures at day 60. OS was measured from the date of diagnosis to the date of death from any cause or last follow-up. Data were analyzed and compared without censor at transplant for patients who received allogeneic stem cell transplantation in first CR. Comparisons between patient subgroups were performed by the Mann-Whitney test for continuous variables and by Chi-square or Fisher exact test for categorical variables.
Hazard ratios (HRs) are given with 95% confidence interval (CI). Multivariate analyses assessing the independent effect of the covariates were performed using Cox proportional hazard model. Variables associated with the outcome and a P-value < 0. 10 in univariate analysis or known as validated factors were included in the multivariable models. Then a backward and forward stepwise selection was performed. All P-values were 2-sided and values <0. 05 were considered statistically significant. All statistical tests were performed with the SPSS 22. 0 (IBM Corp. , Armonk, NY) and R3. 2. 3 software packages (R Development Core Team, Vienna, Austria).
Acknowledgments The authors are grateful to  Christophe Roumier  and  Olivier Nibourel  ( Tumor Bank ,  CHU Lille ) for handling, conditioning, and storing patient samples. The work of all clinical research assistants is also acknowledged here. The authors would like to thank all the patients, their families and the staff of all the centers of  Société Française des Cancers de l'Enfant (SFCE)  for their implication in the trial.
Funding/support: This work was supported by the  French National Cancer Institute  ( INCA-DGOS_5797 ) and by a grant from the  French Ministry of Health  ( PHRC-K 2003 no 03142 ). The Trousseau Laboratory received funding from the  Association Laurette Fugain and La Fondation de France  to support molecular study and the  ELAM02 National Tumor Bank. AM-R and ND contributed equally to this work. Author contributions: GL was the principal investigator of the ELAM02 study. AR ensured database management. ML performed statistical analysis. AP, BN, SD, VG, TL, GM, YB, AB, and GL enrolled patients in the study. AM-R, ND, BD, SG,.
Cells grown on coverslips were mounted onto slides with Prolong Gold Antifade moutant (Thermo Fischer Scientitifc) and images were captured using a Leica DMI-4000 microscope (Leica Microsystems) equipped with a Zyla 5. 5 sCMOS camera (Andor). Measurement of the surface area of the cell colonies, the cell-cell contact ecadherin staining and counting of the nuclei were performed with ImageJ 1. 51j8 software (National Institute of Health). Graphic representations and statistical analyses were generated using Prism 5 (Graphpad Software).
Cell migration and invasion Four thousand cells were seeded in 20 μL hanging drops of DMEM supplemented with 7. 5% FCS to obtain spheroids. After 7 days, they were transferred in DMEM-3% FCS supplemented with 1 μg/mL Matrigel (Corning Inc) and cultured for 8 days. Pictures were taken with an AMG Evos microscope 40× objective (Thermo Fisher Scientific Inc) and spheroid surface areas were measured using ImageJ 1. 51j8 software (National Institute of Health).
Electrical cell impedance measurements Electrode-containing arrays 8W10E+ (Ibidi, Cat. No. 72040) were equilibrated with 400 μL of DMEM-7. 5% FCS for 1 h on the ECIS Z Theta apparatus (Ibidi). 200 μL of medium was then removed and replaced by 200 μL of cell suspension containing 250. 000 cells +/-10 μM MK-2206 (Selleck Chemicals, Cat. No. S1078), 10 μM Rapamycin (Selleck Chemicals, Cat. No. S1039) or 10 μM Erlotinib (Selleck Chemicals, Cat. No. S7786). Electrical properties of the cells were then measured for 15 h. Raw data were exported from the ECIS software and used to generate graphical representations with the Prism software (Additional files 1, 2, 3, 4, 5, 6, 7, 8 and 9). Phenotypic differences were determined via analyses over a 4 h period of the most robust increase in electrode resistance (measured at 4000 Hz) during the cell attachment phase (Additional file 9).
The relevance of these findings in mouse models have been recently reported for human breast tumors  [29, 30]. Gene expression datasets obtained from breast cancer cell lines and clinical samples revealed a strong association between high akt1 expression, low expression of mesenchymal markers and better patient survival. Collectively, these results strongly suggest that AKT1 activity promotes early stages of tumorigenesis but restricts the tumor cell metastatic potential. However, these results have never been extended to non-breast cancer models. Our study suggests that AKT1 specific activity is also involved in the maintenance of the epithelial phenotype of HNSCC cells. An important implication is that AKT1 may also be predictive of the invasive capacities and aggressiveness of HNSCCs. Enhanced AKT/mTOR activity is common in oral carcinomas  [38]  and alterations of the PI3K/Akt/mTOR pathway are found in a large majority of HNSCCs  [39].
5 h (ai). The cell spreading/attachment phase was determined via calculation of the increased electrical resistance during the period of 4-8 h following cell plating (aii). bi, bii Electrode resistance measured at 4000 Hz for CAL33 cells treated with the pan-AKT inhibitor MK-2206 (MK), the mTORC1 inhibitor Rapamycin (Rapa) or the EGF receptor inhibitor Erlotinib (Erlo) was observed for up to 12. 5 h (bi). Changes in electrical resistance between 4 and 8 h after cell spreading were then quantified (bii). ci, cii Electrode resistance measured at 4000 Hz for De-troit562 or CAL27 cells +/-MK-2206 (MK) was observed for up to 12. 5 h (ci). As above, changes in electrical resistance between 4 and 8 h after cell spreading were then quantified (cii). Each dataset was generated from at least three independent experiments.
One-way ANOVA with Bonferroni's post-test: *** p < 0. 001, *p < 0. 05, n. s. : non-significant as compared to control MK2206 on recurrent and metastatic HNSCC resulted in a partial response and was not moved to phase III so far (ClinicalTrials. gov identifier NCT01349933). A significant consideration is that treatments targeting the PI3K or all AKT isoforms may promote the invasive capacities of cancer cells in some cases. These counterintuitive results may explain why PI3K and AKT inhibitors are not included yet in clinical practices  [49]. The possibility that some targeted therapies may increase the invasive and thus metastatic potential despite a reduction of the tumor burden should therefore be scrutinized. We described a screening technique to determine potential pro-metastatic effects of a drug by electrical properties measurements.
Alterations in the PI3K/AKT/mTOR pathway occur in 38% of all cancers as demonstrated on nearly 20,000 tumors of diverse origins  [52]. More specifically in HNSCCs, pooled results from several databases showed that the PIK3CA gene, encoding the p110alpha catalytic subunit of the PI3K, is amplified in approximately 70% of HNSCC cell lines and 20% of HNSCCs clinical cases analyzed  [53]. These results pinpoint the importance of the PI3K/AKT pathway dysregulation in cancers. The CAL33 and Detroit562 cell lines that adopted a mesenchymal phenotype upon AKT or mTORC1 inhibition bear a H1047R activating mutation in PIK3CA. Conversely, the CAL27 cell line does not bear this mutation and does not display comparable modifications in the same experimental conditions. Maintenance of the epithelial phenotype of CAL33 and Detroit562 cells could rely on the constitutive activity of the PI3K/AKT1 axis that is absent in CAL27 cells.
1b ). Lighten et al. note a correlation between the number of ST9 alleles and the number of microsatellite alleles within populations and suggest that ST9 alleles are subject to drift. Thus, ST9 may represent a specialized MHC locus that evolves differently from other MHC supertypes. In support of this, alleles comprising ST9 map best to a different scaffold in the guppy genome (LG18: 22 Mb) than alleles of the other supertypes: (unplaced scaffold 55 kb or unplaced scaffold 1. 5 kb). Importantly, our analyses showed that the central observation of Lighten et al. -of weaker population-genetic structuring of supertypes compared to structuring of MHC alleles-appears to be entirely driven by ST9, as indicated by jack-knife removal of each supertype (Supplementary Figs  1, 2 ). The effect of ST9 does not appear to be solely due to its high frequency in the dataset (Supplementary Table  1 ), which further suggests that this supertype may be experiencing different evolutionary pressures from the other supertypes.
The lower genetic structuring of supertypes is therefore not a general feature of the dataset. Another argument which Lighten et al. used to support balancing selection acting on supertypes, a deficit of "supertype homozygotes", is unconvincing because the authors could not assign alleles to loci (genotypes consisted of up to nine alleles, implying a minimum of five co-amplifying loci in some individuals), meaning that true zygosity is not known. Furthermore, the unique properties of ST9 also mean that the observed homozygosity deficit needs to be treated cautiously (6. 7% of individuals are ST homozygotes in the full dataset, but 29. 1% if ST9 and all its alleles are removed). The theoretical argument of Lighten et al. is based on simulations suggesting that supertypes may persist nearly indefinitely without much change in their antigen-binding properties, shown by stable positions of supertypes in paratope space.
Methods Phylogenetic analysis. The relationships between 539 class II exon 2 alleles reported in the Supplementary Data 1 of Lighten et al. 1  were reconstructed from full 209 bp nucleotide sequences using the neighbor Joining method in MEGA 7  11. The matrix of evolutionary distances was computed using the Jukes-Cantor method. The robustness of the relationships was assessed with 500 bootstrap replicates. Population genetics. We downloaded Lighten et al. 's population-genetic data and analysis scripts from Ben J. Ward's GitHub repository for the paper (https://github. com/BenJWard/Supertypes_RedQueen_TSE) on 17/04/2017, and followed their annotated analysis to produce the data used for Supplementary Fig. 2  and the topleft panel of Supplementary Fig. 1. This analysis first calculates the observed MHC D EST  12  between all population pairs in the dataset, and uses each population's mean pairwise D EST as the red, 'observed' dots in the plots.
We then repeated these analyses removing each supertype in turn, i. e. removing all instances of all alleles in the focal supertype, and thereby any individuals that were homozygous for the focal supertype. One population (Cumana, n = 6) was homozygous for ST13 and was removed from that supertype's analysis. To compare the effects of removing each supertype, we calculated the mean and SD of the difference between the red and blue dots for each jack-knifed dataset (second column of Supplementary Table  1 ). We also calculated the Spearman correlation coefficient between each jack-knifed dataset's red dots and the red dots of the full dataset (third column of Supplementary Table  1 ). To assess the degree to which each supertype's jack-knifed dataset might be influenced by supertype membership size (number of alleles) or supertype frequency, we performed three additional sets of randomised deletions for each supertype.
In the first, we repeatedly removed all occurrences of a random subset of unique nucleotide sequences from the dataset, corresponding in size to the number of sequences in the focal supertype (columns 7-8 of Supplementary Table  1 ). In the second, we made randomized deletions that matched the frequency of the focal supertype but without systematically removing any particular nucleotide sequences (columns 9-10 of Supplementary Table  1 ). The third was a spatially structured version of the second, matching the number of random deletions within each population to the frequency of the focal supertype within each population (columns 11-12 of Supplementary Table  1 ).
Acknowledgements We thank  Mateusz Konczal ,  Magda Herdegen-Radwan  and  Joshka Kaufman  for their comments on earlier versions of this manuscript. This research was partly supported by the  Jagiellonian University  (grants,  DS/WB/INoS/756/2018 ,  DS/WB/INoS/757/2018 ) and  Adam Mickiewicz University in Poznan  ( DS/S/P-B/48 ).
Formal Analysis With Pint and NuSMV In the above section, the conclusion regarding the loss of apoptotic stable state relies on stochastic simulations, which, in general, may not offer a complete coverage of the possible trajectories. Therefore, one may want to formally verify whether the loss of reachable stable apoptosis state is total or not. First, we show how to use Pint  (Paulevé, 2017)  to predict combinations of mutations which are guaranteed to prevent the activation of apoptosis. Next, we use the software NuSMV  (Cimatti et al. , 2002)  to evaluate formally the Notch++/p53--   double mutant. Finally, we use MaBoSS to assess the efficiency of new combinations of mutations predicted by Pint.
ANTICIPATED RESULTS With this protocol, we showed how the Python interface and Jupyter integration of GINsim, bioLQM, MaBoSS, and Pint ease      the delineation of sophisticated re-executable computational analyses of qualitative models of biological networks, combining and chaining different software with a unified interface. Leaning on the CoLoMoTo Docker image and on the companion Jupyter notebook, we have demonstrated the benefits of this framework by revisiting the analysis of a recent Boolean model of the signaling network controlling cancer cell metastasis. We could reproduce results previously obtained with GINsim and MaBoSS, which demonstrate that the Notch++/p53--double mutant can suppress the apoptotic outcome. Furthermore, a formal analysis of trajectories with Pint enabled us to deduce novel "anti-apoptotic" combinations of mutations, including a triple mutant that forbids even transient activation of apoptosis, which were subsequently quantified using MaBoSS.
As POLE3 and POLE4 possess histone folds that resemble those of H2A or H2B  (Gnesutta et al. , 2013) , a structural model of POLE3 was superposed onto H2B and POLE4 onto H2A, using Phyre2  (Kelley et al. , 2015)  and a defined nucleosome structure as a template (PDB: 1s32). The resulting structural model almost completely superimposes with the recently published crystal structure of yeast Dpb3-Dpb4  (He et al. , 2017)  (Figure  S1D ). The modeled POLE3-POLE4 dimer revealed conserved residues Phe44 in POLE3 and Phe74 in POLE4, located in the central a helix (a2) of their respective histone folds, as having high potential to interact via pi stacking of their side chains  (Figures 1B and S1C). As shown in Figure  1C , mutation of Phe44 in POLE3 and Phe74 in POLE4 to alanine or aspartic acid, respectively, was sufficient to abolish interaction between POLE3 and POLE4, validating our structural model.
Peptides were separated on a reverse phase column (Acquity UPLC BEH C18 column 1. 7 mm, 100 mm x 1 mm (Waters, UK) and detected on a SYNAPT G2-Si HDMS mass spectrometer (Waters, UK) over a m/z of 300 to 2000, with the standard electrospray ionization (ESI) source with lock mass calibration using [Glu1]-fibrino peptide B (50 fmol/mL). The mass spectrometer was operated at a source temperature of 80 C and a spray voltage of 2. 6 kV. Spectra were collected in positive ion mode. Peptide identification was performed by MS e  (Silva et al. , 2005)  using an identical gradient of increasing acetonitrile in 0. 1% v/v formic acid over 11 min. The resulting MS e data were analyzed using Protein Lynx Global Server software (Waters, UK) with an MS tolerance of 5 ppm. Mass analysis of the peptide centroids was performed using DynamX software (Waters, UK).
Coverslips were washed in PBS and mounted in DAPI containing mounting media (Invitrogen, see key resource table ). Images were finally acquired using Zeiss Axio Imager M1 microscope and ORCA-ER camera (Hamamatsu). Image analysis was performed using Volocity 6. 3 (Improvision) and CellProfiler softwares. H3. 1-SNAP assay in vivo HeLa cells stably expressing H3. 1-SNAP-3xHA were previously characterized  (Ray-Gallet et al. , 2011). Details of SNAP tag assays are described in Clement et al. , 2016. For quench-chase-pulse experiments, cells were incubated in complete medium containing 10 mM of SNAP-Cell Block (New England Biolabs) to quench SNAP tag activity, washed twice with PBS, and incubated for 30 min in complete medium to allow SNAP-Cell Block to diffuse out. Cells were then chased for 2 hr in complete medium.
ACKNOWLEDGMENTS We would like to thank  Derek Davies  for assisting with FACS analysis,  Nicola O'Reilly  for biotinylated H3 and H4 tail synthesis,  Anja Groth  for valuable discussion and providing U2OS GFP-RPA/RFP-PCNA as well as  U2OS MCM2  WT and HBD mutant cell lines,  Tom Owen-Hughes  for providing the H3(C110E) mutant protein, and  Julian Stingele  for helping with microscope quantification of PCNA experiments. R. B. is funded by the  Francis Crick Institute. J. M. S. and S. L. M. are funded by the  UK Medical Research Council. Work in the G. A. lab was supported by  La Ligue Nationale contre le Cancer (Equipe labellisee Ligue) , the  Agence Nationale de la Recherche  ( ANR-11-LABX-0044_DEEP ,  ANR-12-BSV5-0022-02  '' CHAPINHIB,' '  ANR-16-CE12-0024  '' CHIFT,''  and  ANR-16-CE11-0028  ''REPLICAF''), and the  European Research Council (ERC)  ( ERC-2015-ADG-694694  ''ChromADICT'').