dataesr/scientific-paragraphs-categorization huggingface dataset:
The knowledge of past events and long-term series is a base to understand phenomena, analyze climate change and forecast future events. To increase long term series and their accuracy, NUNIEAU software, sort of ³'LJLWL]LQJ 7DEOH´ ZDV GHYHORSHG DQG GLVWULEXWHG IUHHO\ VLQFH E\ &HUHPD ,W LV DQ LQWHUQDWLRQDO UHIHUHQFH LQ WLGH gauge record, but has been improved to widespread its used for river and rainfall record. The SCHAPI (the French national service for hydrometeorology and support to flood forecasting) launched a tender to rescue the old data on rainfall and rivers available. Many improvements have been done to manage different types of rainfall charts. NUNIEAU was developed in French and is also available in English and Spanish version with collaboration of foreign institutes. Main challenge is not only to digitize old data but also assess the quality of digitized data.
Context of hydro-meteorological data rescue and use of NUNIEAU The knowledge of past events and long-term series is a base to understand phenomena, analyze climate change and forecast future events. To increase long-term series and their accuracy, NUNIEAU software was developed and distributed since 181,($8 LV D NLQG RI YLUWXDO ³'LJLWL]HU´ ZLWK QR paper size limit, initially specialized to convert tide gauge records  [1, 2]. One of the main advantages is that you can check each parameter of the digitizing process and modify/correct it if necessary.
Enlargement for other French data type Data rescue is needed not only in Mediterranean Sea but also in the other coasts of French areas. The French national hydrographic institute (SHOM) was interested to use this software instead of being dependant of a digitizer table. Some improvements were made to take account of macro-tidal sea particularity (Figure  2 ). There were some benchmarks in some forecasting service. But few services spent time to do the digitalization and this module was not well tested. The main improvement was the refolding effect (Figure  3 ) which occurs during extreme flows. A test of rainfall module was run with French Meteorological Institute (Météo France). They have also developed their own tool named DIGITALISE and based on digitizer table. Some conclusions of Météo France in one of their ANR EXTRAFLO reports are: x Results are near the same between the two approaches x Differences are visible for very short time step (6 or 15 minutes) x DIGITALISE can lower peaks and NUNIEAU can increase peaks x DIGITALISE is quicker for rain analysis but need to be used by confirmed users with a good knowledge of rainfall measurement x NUNIEAU, more complex in parameters, is more generic (a lot of record can be managed).
Foreign experience Data rescue is anything but simple. It needs time despite the fact that NUNIEAU or other types of tools are available. Moreover, data rescue is often less considered than working on numerical models even if data are the base for a good calibration and understanding. Few people work on these data. Even if a lot of archives are known, few institutions decide to launch huge rescue works. For NUNIEAU software, Survey of India (SOI) wanted to use it. SOI translate a part of the software in English for its internal needs. Some trainees used NUNIEAU rain module in Burkina Faso and Chile in industrial conditions. This means NUNIEAU can be used with few support, by people not involved in the development and for producing long data series in different types of chart format. The two projects aims to produce rainfall Intensity-Duration-Frequency  [10, 11].
NUNIEAU had to be adapted to Chile rain gauge type (Figure  5 ). Chile rainfall chart use an orthogonal axes with no curved Yaxis. It was also an opportunity to compare with other tools  [11] , and the conclusions were: x Software exist but are not available x Software are linked to a type of rainfall chart x Software are not free x Software are program prototypes Some software [12, 13] seem to use more complex image treatment to digitalize rainfall charts. Nevertheless, in some case, they have some problem in recognition. DIGITALISE and NUNIEAU have the same type of problem for rainfall particularly for extreme rainfall. A lot of time, problems come from record bugs on the chart and not on software recognition (come back of the stylus). One of the particularities of NUNIEAU, coming from the sea application is to provide at each time a value of minimum, maximum and barycenter of all the graphs.
This particularity is really important to manage a lot of waves for sea and also river. For rainfall chart, barycenter is used and at each refolding, we manage minimum and maximum to conserve rain. Even if some problems exist in the use of NUNIEAU [14], the work done by Cerema, other French and foreign LQVWLWXWLRQV HQDEOH WR FRQVLGHU 181,($8 DV ³WKH RQO\ publically available software for digitizing tide gauge FKDUW´ >@ :LWK WKH UDLQIDOO FRQFOXVLRQ >@ WKLV sentence could be extended to sea, lagoon, river and rainfall gauge.
Cytochemical and immunohistological stainings Paraffin sections were deparaffinized and rehydrated. Aortic root lesions were visualized using hematoxylin-eosin (H&E) stain. Plaque size and necrotic core were quantified using Adobe photoshop CS5 software. Plaque stage was determined according to the method described by de Waard et al. [22]. For further plaque phenotyping, aortic roots were stained for 60 minutes with a 0. 2% Picro Sirius Red solution and incubated for 2 minutes in acidified water (0. 01M HCl). The sections were dehydrated, embedded in pertex (Histolab, Västra Frölunda, Sweden) and collagen content was quantified using Adobe photoshop CS5 software. In addition, sections were incubated with antibodies detecting macrophages (MAC-3; BD Pharmingen, San Jose, CA, USA), smooth muscle cells (SMCs; 1A4; Dako, Glostrup, Denmark), and IgM (polyclonal; Abcam, Cambridge, MA, USA) followed by a horseradish peroxidase (HRP)-conjugated secondary antibody.
Statistical analyses Data are presented as mean±SEM, median (IQR) or n(%). Unpaired t-tests or Mann Whitney U tests were used to assess differences between the 2 groups depending on a normal distribution. Differences in plaque stage were assessed using a Chi square test. All data were analysed using Prism version 5. 0 (GraphPad software, La Jolla, CA, USA) and SPSS version 22. 0 (SPSS Inc. , Chicago, Il, USA). A p<0. 05 was considered statistically significant.
Results Delineating four expression groups. A total of 289 HER2positive (HER2 þ ) BCs with frozen tumour samples identified from the French PHARE/SIGNAL programs  19, 20  were analysed. HER2, ER and PR statuses were defined according to ASCO/CAP guidelines  21  (Methods). All cases were reviewed by breast pathologists and corresponding DNA and RNA samples were subjected to extensive quality controls (Methods). A total of 99 selected tumours were analysed for genome-wide expression profiles, out of which 64 tumours and matched normal DNA were subjected to whole-genome sequencing (WGS) (Supplementary Data 1). An unsupervised hierarchical cluster analysis of the 99 HER2 þ samples delineated four main groups of gene expression, herein referred to as A, B, C and D (Supplementary Fig. 1 ), validated on two external data sets  22, 23  (Methods).
At the end of the process, 99 samples (hereafter called the INCa-HER2 þ data set) met the required quality criteria, out of which 64 were subjected to WGS. Gene expression array processing and quality control. Tumour RNA samples were analysed for expression profiling on Affymetrix U133 Plus 2. 0 GeneChip. Quality control was asserted by using R package affyPLM  45. Raw feature data were normalized using robust multi-array average  46  method in R package affy  47. Probe sets corresponding to control genes were filtered out.
The CIT data set is available on ArrayExpress under accession number E-MTAB-365. First, all expression profiles from CIT and INCa-HER2 þ data sets were normalized together using the robust multi-array average method  46  as above. Then, to maintain the relative proportions of the PAM50 subtypes, each of the 99 INCa-HER2 þ samples was included one by one into the CIT data set and assigned to a PAM50 subtype using the R package genefu 49 v1. 12. 0 with robust scaling of the gene expressions centroids. Unsupervised clustering of transcriptomic array profiles. The clustering of transcriptomic array profiles was performed in two steps. The first step aims at selecting probesets carrying the most differential expression across the data set. This was done by using two criteria: (a) for each probeset, we tested whether its variance across samples was different from median of the variances of all the probesets.
The statistics and criterion used were the same as in the filtering tool of BRB ArrayTools software  50  , where the variance to median ratio is compared with a percentile of the chi-square distribution. Only probesets satisfying a P value of this variance test o10 À 3 were kept. (b) In addition, probesets were ordered by relative s. d. and the top 5% percentile was retained. After this step, we were left with 274 probesets corresponding to 196 known genes. The second step was an agglomerative hierarchical clustering using Pearson correlation as a similarity measure and the Ward's minimum variance linkage method. Validation of RNA groups on TCGA and Metabric HER2 þ data sets. Two publicly available expression profiling data sets were collected for validation purpose. The first one was the Breast Invasive Carcinoma (BRCA) collection of 1,098 tumours from TCGA  22.
We selected the subset of 114 samples defined as HER2 þ in the original paper, out of which 75 had associated Agilent G4502A microarray expression data. The second validation set came from the Metabric breast cancer collection  23. We selected a subset of 122 HER2-amplified tumours with IHC status equal to 2 þ or 3 þ and copy number of ERBB2 locus gained according to SNP6 array (no IHC-FISH status was available in this data set). Raw Illumina HT-12-v3 expression profiling data were obtained after data access authorization and normalized using R package beadarray  51. To allow the comparison between expression data from different array technologies, we reduced the RNA group signature defined with probesets to genes by using the best genes according to JetSet  52. This resulted in 196 genes out of which 162 and 180 were defined in TCGA and Metabric data respectively.
We used two different and complementary methods for validation on both sets. The first method is a single sample predictor (SSP) method. First the centroid of each RNA group (labelled A, B, C, D) was computed on the centred-reduced INCa data set. Then, for each single external sample (that is, from TCGA or Metabric), we computed the Spearman rho correlation coefficient with each of these centroids and the sample was assigned to the RNA group with the largest correlation coefficient or to no group (O) if the correlation coefficient was o0. 1. Independently, the whole external data set was clustered into four clusters (labelled 1, 2, 3, 4) using the same agglomerative hierarchical clustering method as before. The stability of the RNA groups in the external data set was then evaluated by examination of the (A, B, C, D, O) Â (1, 2, 3, 4) contingency table and practically measured by the fraction of the most abundant RNA label in each cluster (that is, 100% if all clusters are composed of a single RNA group and, about 25% if RNA groups are spread randomly across clusters).
The second method (joined) consisted in merging the independently centred-reduced INCa and external data sets. This joined set was then clustered into four clusters (labelled 1, 2, 3, 4) using the same agglomerative hierarchical clustering method as before and the stability of the RNA groups was then evaluated by examination of the (A, B, C, D) Â (1, 2, 3, 4) contingency table of the data reduced to the INCa-HER2 subset (that is, for which the actual RNA group labels are known). A RNA group label can further be assigned to each of the four clusters (by considering the majority label in the INCa-HER2 subset) and, eventually, to each external sample. Finally, for both methods, clinical and biological metadata (namely ER status, PAM50 subtypes and P53 mutation status) per RNA group were also compared.
Results are shown in Supplementary Figs 11 À 15. Supplementary Fig. 11  displays the RNA groups obtained on the original INCa-HER2 data set using all of the 196 probesets as the reference. Supplementary Figs 12 and 13 (resp. 14 and 15) displays the results obtained on the TCGA (resp. Metabric) data set for both SSP and joined methods. The two methods gave similar results, with 76% (TCGA) and 81% (Metabric) of samples classified in the same RNA group by both methods. The overall stability of RNA groups is good (80% (SSP), 75% (joined) for TCGA and 66% (SSP), 73% (joined) for Metabric). Groups A and D appeared more stable than B and C. Finally, in terms of metadata, the same general features were observed for the three data sets: (a) over-representation of ER þ (resp. ER À ) in groups A and B (resp.
C and D); over-representation of luminal B (resp. HER2-enriched and basal) in groups A and B (resp. C and D). As for TP53 mutations, the situation is less clear-cut: the under-representation of TP53 mutations in group A is observed both for INCa and TCGA data sets but is not significant for the Metabric data set. However it should be pointed out that (a) this data set exhibits a lot of TP53 undetermined cases (51%) and (b) for determined cases the fraction of TP53 mutated cases is unusually low (28% versus 44% and 47%, respectively in INCa and TCGA data sets). Finally, all cases in the present study were included in the PHARE/SIGNAL cohorts (NCT00381901-RECF1098, www. e-cancer. fr) with a median follow-up at 58 months (interquartile range 46. 5-62. 5). In this smaller subset and on this period, recurrence events were very limited (4 out of all 99 cases) and the relationship between RNA groups and outcome could not be analysed.
Gene expression signature projection. Gene signatures for mammary stem cell, progenitor and mature luminal  26  were projected by using the single sample extension of GSEA  53  where gene expression values for each single sample were rank-normalized, and an enrichment score was produced using the empirical cumulative distribution functions of the genes in the signature and in the remaining genes. SNP array processing. Illumina Omni1 Quad and OmniExpress SNP arrays quality control and normalization was performed using GenomeStudio Genotyping Module. A supplementary normalization step was applied using the tQN algorithm  54. Allelic CNVs were analysed using GAP  55  and ASCAT 2. 0 (ref. 56). Tumour purity was estimated using both approaches, with a good correlation, although GAP estimates were found to be always above ASCAT estimates (Supplementary Fig.
Illumina HiSeq2000/ HiSeq2500 genome analysers and Illumina paired-end sequencing protocols were used for all samples, read lengths ranging from 100 to 126 base pairs. Paired-end reads were aligned to the human genome (GRCh37) using the BWA aligner  57. Alignments were refined using GATK  58  and Picard (http://broadinstitute. github. io/ picard/) software suites. Duplicates were removed from the sample BAM files for further analysis. Raw and mapped sequences from all produced HiSeq lanes were checked using in-house pipelines that collect a set of important metrics reflecting the overall quality of the sequencing data. Lanes showing poor quality were manually discarded. SNV variant calling. Somatic SNVs were called using MuTect 59 v1. 1. 5 part of the GATK suite. To improve performance, data from dbSNP Build 132 and COSMIC v65 (http://www.
Annotated somatic variants were further processed to classify those in potential promoter or enhancer regions. Somatic SNVs in potential promoter regions were extracted if they localize around H3K4me3 histone marks. Somatic variants in potential enhancer regions were extracted if they localize around H3K4me1 or H3K27ac histone marks. In addition, mutations were further annotated using FANTOM5 predictions  61  , which uses cap analysis of gene expression to detect potentially active transcription from promotor or enhancer regions. In both cases (promotors and enhancers regions) we computed, per patient, the fraction of somatic SNVs located in these regions (that is, number of SNVs in regions divided by the total number of SNVs in patient). Copy-number analysis. The analysis of CNVs from whole-genome sequence data was performed in three steps.
Somatic SVs were further filtered according to several criteria: at least 2 discordant read pairs per cluster; at least 2 soft-clipped reads per cluster; at least 15 aligned clipped bases and at least 1 breakpoint should be located in a mappable region  63. Structural variants were then classified as intra-chromosomal event (deletion, duplication, inversion) or inter-chromosomal (breakpoints on different chromosomes) according to discordant read pairs type. BFB amplification analysis. We used two independent sources of information for testing the BFB amplification mechanism for the ERBB2 amplicon: the CNV patterns and discordant read pairs and clipped reads orientation at breakpoints. The main hallmark of BFB is that the CNV pattern follows some specific sequences  43, 64. A technical difficulty is that this requires the determination of tumoral absolute integer copy number (aCN), whereas NGS data primarily provides relative read counts (rRC, see section 'Copy-number analysis'); aCN linearly relates to rRC as a function of the contamination by normal DNA and the mean ploidy of the tumour.
21The trial was sponsored by the French National Cancer Institute (INCa), approved by the Central Ethical Committee (Comitede Protection des Personnes, CHU Besanc¸on) on 15 May 2006. It was done in compliance with the principles of Good Clinical Practice and the Declaration of Helsinki and registered at ClinicalTrials. gov,numberNCT00381901. Patients were eligible if they were over 18 years of age with histologically confirmed invasive early breast cancer with HER2 overexpression and had provided signed informed consent. For each patient, tumour tissue as well as matched blood samples were collected. Tumours were snap-frozen in liquid nitrogen upon surgical removal after pathologist's review and were stored in the corresponding hospital's biological resources center. The clinical HER2 status was assessed in accordance with the ASCO/CAP guidelines21.
HER2 protein expression was scored by performing immunohistochemistry (IHC) on a tumour section from FFPE blocks for all patients. Cases displaying a 3 þ score were considered positive and cases only displaying a 2 þ score were tagged as equivocal. In equivocal cases, HER2 gene amplification status was further determined using either fluorescence in situ hybridization (FISH) or chromogenic in situ hybridization (CISH) and only those showing an HER2 gene amplification were then considered positive. ER and PR statuses were also established using IHC. Breast pathologists reviewed all cases for ER, PR and HER2 status. Corresponding pathological, clinical and follow-up data were obtained from the INCa PHARE/ SIGNAL clinical database.
Acknowledgements This work has been funded, through the  HER2  þ project of the  ICGC Breast Cancer Working group , by  Institut National du Cancer (INCa ; grants nos 226-2009,  02-2011 ,  41-2012 ,  144-2008 ,  06-2012 ). We acknowledge additional support from  LYric  (grant  INCa -4662 ) and from the  ABS4NGS  project (grant  ANR-11-BINF-0001-03 ).
Data availability. Raw data have been uploaded to the European Genome-phenome Archive (EGA; http://www. ebi. ac. uk/ega) under the overarching study accession number EGAS00001001431. This study includes all data from wholegenome sequencing, genotyping arrays and gene expression arrays used in this work. Access to whole-genome sequences and genotyping arrays is subjected to the ICGC data access authorization (DAC: EGAC00001000010 and Policy: EGAP00001000037). The CIT data set is available on ArrayExpress (http:// www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-365. METABRIC expression data sets are available at EGA under study accession number EGAS00000000083. TCGA expression data set is available on the TCGA data portal (https://tcga-data. nci. nih. gov/docs/publications/brca_2012).
5, 100 mM KCl, and 5-2000 μ M 45 CaCl 2. The 45 Ca 2+ -uptake was quenched by cold buffer containing 10 mM EGTA and filtrated on GF/C filters, as outlined before  13, 17, 22. The K m and V max values were measured in at least three independent experiments (data are presented as mean ± SEM) with GraFit 7. 1 (Erithacus Software, Ltd. )  10, 13, 17. The k cat was calculated as k cat = V max /[E] t , where [E] t was determined by the GFP assay (see above).
The solvents used were A-0. 4% formic acid, 2% acetonitrile in water, and solvent B-0. 4% formic acid in 95% acetonitrile. The outlet of the analytical column was directly connected to an electrospray ionization (ESI) source of an Apex-ULTRA Qe Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer (Bruker Daltonics) equipped with a 9. 4 T superconducting magnet. ESI-FT-ICR MS was calibrated externally using arginine clusters, resulting in a mass accuracy below 2 ppm. For LC-MS/MS the instrument was operated in data-dependent mode by selecting the six most intense ions in each MS scan for an MS/MS analysis using collisionally induced dissociation in a quadrupole. The data were searched by MASCOT (MatrixScience) against single-protein databases containing the NCX sequence and against a database containing pepsin (to exclude possible autolysis products).
The proteins were deuterated for 15 sec, or for 2 or 20 min at 4 °C. Back-exchange quench and digestion were performed using 50 μ L of porcine pepsin (Sigma) solution at 0. 23 mg/mL in 100 mM glycine-HCl, pH 2. 5 at 4 °C for 2 min. The peptides were desalted for 6 min using a HPLC pump (Agilent Technologies) with 0. 03% TFA in water, at a flow rate of 100 μ L/min. The peptides were separated using another HPLC pump (Agilent Technologies) at 50 μ L/min for 6 min with 15-50% gradient B (Buffer A: TFA 0. 03% in water; Buffer B: acetonitrile 95%, TFA 0. 03% in water), followed by 9 min at 50% B and 1 min at 100% B. The peptide masses were measured using an electrospray-TOF mass spectrometer (Agilent 6210) in the 300-1300 m/z range. Each deuteration experiment was conducted in triplicate. The Mass Hunter (Agilent Technologies) software was used for data acquisition.
ERRα is more highly expressed in CRPC patients and their associated bone metastases than normal prostate and non-metastasizing PCa To determine whether ERRα is involved in PCa bone lesions, we first assessed ERRα mRNA expression (ESRRA) levels during disease progression by performing a meta-analysis of data from the gene expression omnibus (GEO; GSE69129, GSE21034 and GSE32269) (Figure  1A -1C, Supplementary Table  S1 )  [24, 25, 26]. We found that ERRα expression was significantly higher in CRPC compared to normal prostate (P = 0. 0172)(Figure  1A ) and (P = < 0. 05, n = 22 (normal) vs n = 41 (CRPC)) (Figure  1B ). Higher ERRα expression was also observed in primary tumors from CRPC patients who had developed bone metastases compared to androgen-sensitive PCa patients (P < 0. 005, (PCa) vs (CRPC bone Mets))(Figure  1B ) and (P = 0.
0178, (PCa) vs (CRPC who all developed bone metastases)) (Figure  1C ). In the dataset GSE21034, we also found that ERRα mRNA was significantly higher in primary cancerous prostate lesions from CRPC who developed bone metastatic lesions (n = 5) compared to patients with had developed other types of metastases (brain, lung, bladder, colon or lymph nodes) (n = 41) (P < 0. 05; Figure  1B ) suggesting that ERRα is associated with advanced prostate cancer and bone metastases. Immunohistochemistry also revealed that ERRα protein expression in human PCa cells was maintained in the associated bone metastases (Figure  1D ), suggesting that ERRα is an overall poor prognostic factor for bone metastases from CRPC.