dataesr/scientific-paragraphs-categorization huggingface dataset:
Newspapers: -Most of the newspapers used are regional, but one is published at national level (Le Figaro) and another is printed in Geneva, Switzerland (Le Journal de Genève). Most of the newspapers can be found online or are kept in the departmental or municipal archives. Newspapers describe the causes and consequences of the event and sometimes provide instrumental data and illustrations. They also inform about the public authority response and the past ongoing discussion. Other records: -The national database of historical flooding (Base de Données Historiques sur les Inondations : BDHI) that gathers floods events considered as "remarkable" in the French territory (Ministère de la Transition Ecologique, n. and ONF, 2012 ). Some specific ONF-RTM reports are also included in the HIFAVa. The RTM database was built to assist the management of small tributaries.
3. 2. Characteristics of the HIFAVa database. The database has been built using the free and open-source relational database management system PostgreSQL and is accessible through its package pgAdmin. HIFAVa contains 916 distinct flood impacts caused by 321 flood events. The primary key is the impact ID. Therefore, each impact is recorded as a unique line and described through various variables (Table  1  and Table A2 ). The river that triggered the flood is mentioned when possible (94% of cases). For instance, no river name has been attributed to the impacts related to overland flow in January 1979. The accuracy of the impact location varies from specific addresses (house, bridge, neighborhood) to the municipality scale. When the source is not accurate enough to distinguish distinct locations of several impacts, they are all referenced under a unique impact ID.
In other words, sometimes an event caused numerous impacts registered under distinct ID because it was possible to localize each impact precisely (at the hamlet scale) and sometimes we can only localize the impacts at the municipality scale so they are register under the same ID. The severity of an event can not be estimate by the number of ID registered in the database. The most recent sources are often highly informative, allowing impacts to be more precisely located. Impacts occurring the same day on a given river are expected to be caused by the same flood event. As a result, the date is the key used to connect each impact to a flood event. This "flood event" definition has been extrapolated to impacts occurring the same day in different catchments, assuming that two impacts occurring the same day can be caused by the same hydrometeorological event given the moderate surface area of the Arve catchment.
3. 3. Text mining. The flood impacts of the HIFAVa were categorized through a text quantitative content analysis with the KH Coder software  (Higuchi, 2015). The description of the impacts comes from comments contained in the records. The most frequent words have been gathered in order to determine representative categories of the database content. A category is made of several words assigned to a coding rule. Categories have been inspired by the flash flood impact severity scale of  Diakakis (2020). This analysis has led to the following seven categories with example of the assigned words: -Transport network: e. g. "road", "bridge", "railway", "street". -Urbanized areas and residential buildings: e. g. "house", "town", "basement". -Natural environment: e. g. "forest", "field", "yield", "sediment transport".
-Protection infrastructures and dams: "dikes" and "dam". -Industrial, commercial and recreational facilities: e. g. "mill", "factories", "golf", "camping", "hotel", "school". -Critical installations: "drain", "power transformer". -Victims: "dead", "injured", "missing", "evacuee". https://doi. org/10. 5194/nhess-2021-169 Preprint. Discussion started: 18 June 2021 c Author(s) 2021. CC BY 4. 0 License. A more in-depth analysis will be conducted later on to define severity classes based on the nature of the impacts. This future analysis will be based on the work of  Barriendos et al. , (2003) ,  Llasat et al (2005 Llasat et al ( , 2013) )  and  Boudou et al. (2015). Table  1. Extract of the HIFAVa database showing its structure. Refer to the Online Resource 2 for details about the column contents.
4. 1. Evolving sources over time. During the studied period, the diversity and the quantity of sources in which mentions of impacts have been found fluctuate (Figure  2 ). Among the existing databases used, the BDHI database for instance continuously covers the studied period but was sporadically informative since it only contains two mentions of flood impacts in the Arve catchment, respectively in 1892 and in 1987. By contrast, the SM3A database appears later  (1979)   One of the evolutions of the sources is the increase in the newspaper articles mentioning flood impacts. Following the 1881 press freedom French law, the 1880-1889 decade marks the emergence of articles recording natural hazards, such as flood impacts  (Ferenczi, 1996). However, the 1855 flood in Bonneville was already reported by the swiss newspaper, Le Journal de Genève.
The touristic specificity of valley and its international stature can explain the media coverage. We can assume that, floods are more likely to be reported in newspapers as when they happen in a location known by the reader. In addition, the strong emergence of the RTM since 1940 (Figure  2 ) can explain the rise in impacts caused by small tributaries illustrates by the Figure  5. Following the floods of the Rhône, the Loire and the Garonne in 1854, the 1858 law against urban flooding places flood control at the heart of the national legislation for the first time. In the following, the RTM department was formed for the reforestation of mountains slope in order to prevent the reproduction of major floods. The department became quickly efficient and since 1860 collected numerous reports. Built for the study and management of small tributaries, the RTM database became the main source of information since 1930 for the HIFAVa database.
However, in order to overcome the bias induced by the recording of impacts according to their location, we have aggregated the impacts at the municipality level. That is to say, all the impacts reported for a given municipality that were caused by the same event (thus the same day) are recorded under the same line in the database. This results in 562 "aggregated" impacts instead of 917 impacts initially. From these data, we have redrawn Figure  3  (Figure  A2 ) comparing the moving average of impacts and associated events. We can see that the trends are significantly the same. There is an increase of impacts (here starting soon as 1890s) and a late increase of events (1990s). Thus, the way the impacts are stored in the database (by location or by municipality) does not affect the observed temporal changes in impacts.
Nature of the flood impacts. The quantitative analysis of text content reveals the distribution of the impact categories by river and illustrates the diversity of the catchment in terms of land use and economic development (Figure  7 ). This analysis of text content is particularly relevant because it allows to overcome the database scarcity of quantitative information on the severity of the flood. Indeed, it is difficult to estimate the severity of the flood event as the flow rate and the water height are only exceptionally mentioned. However, a link can be made between the impact nature and the severity of the event  (Barriendos et al. , 2019). The number of occurrences of the words determining the category is here named as the number of mentions. For the overall Arve catchment, damage to the transport network is the most frequent (406 mentions), followed by damage to urbanized areas (286) and natural environment (253).
Conclusions. We collected historical information from 1850 onwards to document flood impacts in the Arve valley, an Alpine catchment characterized by a high hydrological and socioeconomic diversity. The analysis of the HIFAVa database led to acknowledge the rise in the number of impacts starting in 1920 and well-marked from 1960. This rise does not seem to be related to increased flood hazard since it does not follow changes in flood occurrence, except partially for the latest period. Instead, more frequent impact could be explained by increased exposure related to the demographic growth (tourism and economic attractiveness of Geneva) and/or by the evolution of the sources, in particular the emergence of the RTM. There are two main types of flood events causing impacts in the catchment, e.
d. ; Boudou, 2015) has been used. -The database created by the department of Restoration of Mountainous Areas (Restauration des Terrains en Montagne : RTM) from the public institution managing the French public forests (Office National des Forêts : ONF). The ONF-RTM database gathers transcriptions of observations of the RTM officials as well as information collected from diverse primary sources. These data (labelled RTM in the HIFAVa https://doi. org/10. 5194/nhess-2021-169 Preprint. Discussion started: 18 June 2021 c Author(s) 2021. CC BY 4. 0 License. database) are freely available through the open-access and online ONF-RTM database (RTM.
Participants A total of 108 participants took part in the study in exchange for 10 €. Participants in the MR group received an additional bonus of on average 5. 6 €. Participants with empty cells in any of the phases by trial types conditions were excluded from the analysis (14 participants in total). Thus, the data of 94 participants were included in the analysis (48 in the MR group, 46 in the PA group, 76 females, M age = 22. 34, s. d. = 3. 36, range = 18-35). The current sample size was based on related behavioural studies in the motivational and emotional domain (see  [24, 45] ), and is consistent with studies using the AX-CPT paradigm cited above (e. g. [20] ). Participants were recruited from the online recruiting of Ghent University. All participants were right-handed, with a normal colour perception, normal or corrected-to-normal vision and no reported history of diagnosed mental disorders.
Stimuli and procedure The experiment was programmed and presented using Matlab 2018b and the Psychophysics Toolbox  [46]. The experiment was run on a desktop computer, with a monitor at a viewing distance of royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 8: 202002 approximately 80 cm (display resolution: 1920 × 1080 pixels). Responses were collected via a QWERTY keyboard with arrow keys '←' and '→' serving as the left and right response keys used in the experiment. Participants were free to position the keyboard at a comfortable position between themselves and the monitor. Similar to previous work in the emotion domain (e. g. [25] ), faces with happy expression from the NimStim face stimulus set  [47]  were used as PA stimuli. Each face stimulus with happy expression was matched with a face stimulus with the same identity but neutral expression also taken from the NimStim, for a total of eight face stimuli (two females and two males, with both a positive and a neutral expression).
In addition, eight object pictures chosen from the bank of standardized stimuli (BOSS) dataset  [48]  were used to signal the gain or absence of MR (figure  1  for an example of one set of object stimuli and one set of affective stimuli, and see the Appendix for the exact stimuli used in this study). Each trial of the AX-CPT consisted of a sequence of two letters presented centred on the screen (e. g. A followed by X) on a black background, with the first letter serving as a cue and the second letter serving as probe. The letters presented during the experiment consisted in either A, B, D, E, F or G, used as cues, and X, M, P, S, U or Z, used as probes (letter colour: white; letter font: default Matlab font; font size: 60). A target sequence consisted of the cueprobe sequence 'A followed by X' (i.
To assess the effect of contingency compared with B, contingent and non-contingent presentation of either object pictures signalling the gain of MR (MR group) or face pictures with a happy expression (PA group) were manipulated in two separated phases, i. e. CO phase and NCO phase. We elaborate on this below. 202002 RGB = [ In the CO phase of the MR group, two object pictures taken from the BOSS dataset were randomly selected to signal the gain or absence of MR (figure  1 ). In the CO phase of the PA group, two face stimuli with the same face identity were randomly selected, i. e. one face with happy expression and one face with a neutral expression (figure  1 ). None of the face stimuli were associated with MR. The CO phase entailed 80 trials of the AX-CPT paradigm (56 AX, 8 AY, 8 BX and 8 BY trials, with trials order randomized).
Multiplexed peptide fractions were analyzed by LC/MS-MS using an Orbitrap Fusion Lumos Mass Spectrometer. MS/MS data were processed using Spectrum Mill v7 prerelease (Agilent Technologies), and protein quantitative values were further analyzed using R. Details on proteomic sample processing, LC/MS-MS conditions, and data analysis are provided in the Supplementary Data. The original mass spectra and the protein sequence database used for searches have been deposited in the public proteomics repository MassIVE (http://massive. ucsd. edu) under the identifier MSV000086902 and are accessible at http://massive. ucsd. edu.
Protein Degradation Reporter Analysis Cells stably expressing the respective construct in the protein degradation reporter vector Artichoke or Cilantro 2 were seeded at a density of 10,000 to 20,000 cells per well in 96-well culture plates with 200 μL of media per well. Different concentrations of thalidomide analogues were printed in three to four biological replicates using the Tecan D300e Digital Dispenser. After 20 hours of drug treatment, flow cytometry using the CytoFLEX instrument (Beckman Coulter) was performed to quantify EGFP and mCherry fluorescence. In FlowJo software, a parameter was derived that calculated the EGFP/ mCherry fluorescence ratio on a single-cell basis. The geometric mean of the ratio for a given drug-treated sample was normalized to the average of DMSO-treated controls.
In Silico Structural Modeling Our findings from the reporter assay suggested ZMYM2 recruitment to CRBN occurs through the MYM-type zinc finger residues 423-466. In the absence of a ZMYM2 structure, a homology model for the ZMYM2 degron residues 421-453 was built in Maestro software  (Schrödinger; ref. 48)  using the structure of the N-terminal MYM-type zinc finger of human ZMYM5 [Protein Data Bank (PBD) 2DAS] as a template. ZMYM2 residues 454-466 could not be modeled due to a lack of structural coverage in PDB 2DAS. A model of avadomide bound to DDB1-CRBN (PDB 4TZ4) was generated in Maestro. The ZMYM2 ternary complex model was assembled in PyMOL  (Schrödinger; ref. 49) , superimposing the CRBN-avadomide model with CRBN from PDB 5HXB and then superimposing ZMYM2 homology model residues 424-431 onto GSPT1 residues 570-577 from PDB 5HXB.
Primary Human Hematopoietic Cell Experiments Cryopreserved primary CD34 + hematopoietic stem and progenitor cells from healthy donors were obtained from Lonza. Cryopreserved bone marrow samples collected at the time of diagnosis from patients with ZMYM2-FGFR1-positive hematologic malignancy were obtained from Gustave Roussy or Paris Saint-Louis Hospital biobanks. The studies were approved by institutional review boards and performed in accordance with the Declaration of Helsinki. All patients gave their informed written consent. Cytogenetic and molecular analyses performed in bone marrow patient samples are described in the Supplementary Data (Supplementary Methods; Supplementary Table  S5 ). Patient CD34 + cells were purified from bone marrow total or mononuclear cells using the AutoMACS and MACS CD34 microbeads (Miltenyi Biotec).
Acknowledgments The authors thank  Anna Molineaux  and  J. Dora Levin  for administrative support and assistance. The authors are grateful to  Ebert lab and Carr lab  (to D. R. Mani  and  Karsten Krug ) members for advice and helpful discussions. This work was supported by the  NIH  ( R01HL082945 ,  P01CA108631 , and  P50CA206963 ), the  Howard Hughes Medical Institute , and  Celgene  (to B. L. Ebert). S. A. Carr was supported by grants from the  NCI Clinical Proteomic Tumor Analysis  Consortium grants  NIH/NCI   U24-CA210986  and  NIH/NCI   U01 CA214125. R. S. Sellar was supported by an  Intermediate Fellowship  grant from The  Kay Kendall Leukaemia Fund. The authors thank  Gustave Roussy's and Saint-Louis Hospital's CRB Biobanks  for managing patient biological samples.
then stained with antibodies against surface proteins as previously indicated. Samples were acquired using BD FACS Canto II, Invitrogen Attune and BD LSR Fortessa flow cytometers, and data were analyzed with FlowJo software. For all analyses in CD4 + T cells, Foxp3 + regulatory T cells (Tregs) were excluded. According to the gating strategy used for PBMCs (within viable cells), CD8 + T cells were defined as CD3 + CD8 + , while CD4 + T cells were defined as CD3 + CD8 -cells. For tumors (within viable cells), cell subsets were defined as CD8 + :CD3 + CD8 + and CD4 + :CD3 + CD4 +. Because PMA/Ionomycin stimulation downregulates CD4 expression, for assays which include this stimulation protocol we used the same strategy mentioned for PBMCs for all samples (see Supplementary Table  2  for detailed information).
Microarray Analysis DP and DN CD4 + T cells from the blood of three luminal (ER + PR + HER2 -) BC patients and three age-matched HDs were sorted based on KLRG-1 and CD57 expression using the following strategy: live CD4 + CCR7 -CD25 int/-(CD4+, effector/ memory and conventional T cells) and lysed with TCL buffer (Qiagen, Les Ulis, France). RNA was isolated with RNA purification kit (Norgen, Ontario, Canada), and its integrity was evaluated with Agilent RNA 6000 pico kit. A 100 ng of RNA was used to synthesize cDNA using reverse-transcription reactions according to the standard Affymetrix protocol. Analyses were performed using Affymetrix Human Gene 2. 1 ST arrays. Microarray data have been deposited in the GEO database under accession code: GSE142080. Gene expression data were normalized using RMA algorithm from oligo package (version 1.
42. 00) and annotated with manufacturer CDF (version 8. 7. 0) in R (version 3. 4. 0). Principal component analysis was performed using R on 29,298 probesets. Probesets with a log2 expression value inferior to 4 in all samples were discarded. After elimination of background, the expression matrix of 10,756 genes was used for downstream analysis. Differentially expressed genes (DEGs) between groups were obtained using the linear model process from the R limma package (version 3. 34. 8) (27) , considering in the model the tumor presence (BC patients and HDs) and the cell status (DP and DN). Only the DEGs with p-values ≤0. 05 (Benjamini-Hochberg correction at 0. 25) have been considered. Heat map of selected genes was produced with Heatmapper  (28). Generated DEG lists were analyzed using the HTML5 gene list enrichment analysis tool EnrichR  (29, 30) ; selected pathways and ontologies from specified databases were listed.
Expression Analysis From Public Datasets CD4, KLRG1 and B3GAT1 (as CD57) z-score expressions were extracted from databases available at cBioPortal for Cancer Genomics  (32, 33). This portal stores expression data and clinical attributes. The z-score for CD4, KLRG1, and CD57 mRNA expression is determined for each sample by comparing mRNA expression to the distribution in a reference population harboring typical expression for the gene. The query "CD4" or "KLRG1" or "B3GAT1" was performed in all datasets available but only two TCGA datasets for breast invasive carcinoma allow statistical analysis by reaching sufficient patient number for gene level criterions, such as >+1 (high expression) or <-1 (low expression) for each mRNA expression z-scores relative to all samples. The resulting number of each patient population in accordance with the level of mRNA expression is indicated in the corresponding figures.
Statistical and Bioinformatics Analysis GraphPad Prism software was used for statistical analysis. For comparisons of HD and BC patients, unpaired two-tailed Student's t-tests or one-way ANOVA with Sidak's post-test was used. For T cell subset comparisons (within HD or BC group) paired two-tailed Student's t-tests or repeated measures one-way ANOVA with Tukey's post-test was used. Graphs always represent mean ± SEM. p-values <0. 05 were considered statistically significant.
Within CD4 + T cells from BC patients, SA-bgal activity was similar in the DP and SP cells but significantly higher in these subsets compared to the DN cells (Figures  2C, D ). No significant differences were observed in SA-bgal activity among the T cell subsets from HDs and BC patients (data not shown). Regarding the expression of gH2AX, a DNA damage response-related phosphorylated histone, we determined that DP CD4 + cells from BC patients exhibited the higher frequency of cells expressing this protein in comparison to the DN T subset from BC patients (Figure  2E ) as well as the DP subset from HDs (Figure  2F ). We next evaluated the proliferative response of CD4 + T cells from BC patients. Because KLRG-1 expression was completely downregulated after stimulation of PBMCs with anti-CD3/CD28 beads, proliferation was first analyzed in CD57 -versus CD57 + cells.
High Expression of CD4 + KLRG-1 + CD57 + Correlated With Increased Overall Survival for Breast Cancer Patients To explore the possible contribution of the KLRG1 + CD57 + CD4 + T cell subset in the clinical prognosis of breast cancer, we collected publicly available TCGA datasets of patients with breast carcinoma to link gene expression data with overall survival (OS). For this study, we analyzed the mRNA expression for CD4, KLRG-1, and B3GAT1 (CD57) to define a senescent CD4+T signature. We used the increased or decreased expression (represented as high or low, respectively) of these three genes as the value of the mRNA expression for each of these cell markers compared to the mean of the corresponding expression for each patient included in the analyzed cohort. The lower expression of the signature genes was found positively correlated with a lower survival rate when we analyzed the OS of patients from two invasive breast carcinoma cohorts from the TCGA consortium (TCGA-PanCancer, and TCGA-Cell 2015) (Figures  6A, B ).
In fact, single cell transcriptome studies of the CD4 + T EMRA lymphocytes identified cells with features of terminal CD4 + -CTL effector phenotype (KLRG-1 high , CD28 low , CD27 low ). Moreover, Peguillet et al. (50)  reported that CD25 -CD127 -CD4 + T cells are expanded in blood from patients with uveal melanoma and breast cancer patients undergoing neo-adjuvant chemotherapy compared to healthy donors. These CD4 + T cells are highly differentiated effector cells and display cytotoxic features. Interestingly, during neo-adjuvant chemotherapy in patients with breast cancer the increase of CD25 -CD127 -CD4 + T cells correlated with tumor regression. More recently, Cachot et al. (51)  confirmed the presence of cytolytic tumor-specific CD4 + T cells by mining single cell RNAseq datasets from melanoma patients.
ACKNOWLEDGMENTS We thank  Dr. P. Abadie  and  Dr. M. P. Crespo  for Flow Cytometry technical support at  CIBICI-CONICET ,  Facultad de Ciencias Químicas, UNC. The  Surgery Department-Hospital Rawson-Argentina  for collection of human samples. We acknowledge the help of  C Guerin ,  S Grondin , and  A. Viguierrom  the flow cytometry core at  Institut Curie ;  A. Salomon  from the  Department of Pathology of Institut Curie ;  D. Loirat  from the  Medical Oncology Department  and  M. Milder  from the  Clinical Immunology Laboratory, Institut Curie , for collection of human samples. We thank the  ICGex NGS platform of the Institut Curie  ( S. Lameiras ,  S. Baulande ,  M. Bohec ) for technical help with transcriptomic experiments.