dataesr/scientific-paragraphs-categorization huggingface dataset:
Uncertainties are the interannual variability over the averaged period. Abbreviations are included for north (N), south (S), east (E), west (W), eastern boundary current (EBC), western boundary current (WBC), sea-to-air flux of total carbon (F CO tot 2 ), anthropogenic carbon (F CO ant 2 ). Surface areas indicated as "from LA14" actually differ slightly from those published in LA13 as they have been modified for subsequent computations (Goulven G. Laruelle, personal communication, January 2015). 2. Interactive illustrations can be found at http://lsce-datavisgroup. github. io/CoastalCO2Flux/. the Arctic polar regions, the simulated uptake is too low, with 52 Tg C yr -1 from the model vs. 86 Tg C yr -1 from LA14.
Acknowledgements. We are grateful to the associate editor  J. Middelburg  for managing the peer-reviewing process and to two referees,  N. Gruber  and  P. Regnier , for their helpful comments. We thank  G. G. Laruelle  for providing the raw data version of the Laruelle et al. (2014) database, and we are grateful to  C. Nangini  and  P. Brockmann  for their help with the GIS analysis, data treatment, and data visualization. We also thank  J. Simeon  for the first implementation of the ORCA05-PISCES model. The research leading to these results was supported through the  EU   FP7  project  CARBOCHANGE  (grant  264879 ), the  EU   H2020  project  CRESCENDO  (grant  641816 ), and the  EU   H2020  project  C-CASCADES (Marie Sklodowska-Curie  grant  643052 ). Simulations were made using HPC resources from  GENCI-IDRIS  (grant  x2015010040 ).
Standardized differences were used to compare the balance in baseline characteristics between groups, with a standardized difference <10% generally considered to indicate successful balance  [19]. The overall balance in all baseline characteristics was measured using the Cstatistic  [20]. Models for weighted samples used a robust variance estimator  [21, 22]. Kaplan-Meier curves and weighted Kaplan-Meier curves were used to estimate the probability of OS and PFS, and cumulative incidence and weighted cumulative incidence curves were used to estimate the probability of NRM, relapse, and acute and chronic GVHD. Missing data were handled through multiple imputations by chained equations methods  [23, 24]. A total of 20 independent imputed datasets were generated and analyzed separately. Variables used for multiple imputation were not limited.
Database searching and criteria Tandem mass spectrometry raw data were assigned to peptide sequences with the MASCOT search engine (version 2. 3. 02) from Matrix Science, the following parameters: full-trypsin specificity, a mass tolerance of 5 ppm on the parent ion and 0. 5 Da on the MS/MS, carboxyamidomethylated Cys (þ 57. 0215) as a fixed modification and oxidized methionine (þ15. 9949) as a variable modification, and a maximum of two missed cleavages. All peptide matches with a score below a p-value of 0. 05 were filtered by the IRMa 1. 28. 0 parser  [10]. A protein was considered validated when at least two different peptides were identified when considering all the samples. In terms of protein identification, the false-positive rate estimated using the appropriate decoy database resulted below 0.
2e, f ). Taken together, these results demonstrate that miR-200c/141 and PTPN6 transcriptions are tightly associated in a variety of cell lines at basal state and upon oxidative stress. Pri-miR-200/PTPN6 transcripts are coupled in ovarian cancer. As miR-200c/141 have been repeatedly defined as key regulators of human ovarian tumorigenesis  5, 7, 9, 10, 15, [32] [33] [34] [35] [36]  , we next wondered whether the correlation of expression between miR-200c/141 and PTPN6 was also observed in HGSOC. We first observed that the expression of the two miRNAs (Fig. 3a ) and their corresponding primary transcript (Fig. 3b ) were correlated with PTPN6 mRNA levels in a cohort of 107 HGSOC (Fig. 3a, b ). Correlation between    miR-200c/141 and PTPN6 expression was further validated in the publicly available The Cancer Genome Atlas (TCGA) database  51  composed by 574 HGSOC patients (Fig.
4c , right). Moreover, we also detected a significant correlation between the expression levels of this intermediate transcript and PTPN6 primary transcript in HGSOC (Fig. 4c , left). Importantly, a read-through from the PTPN6 gene was also detected in RNA-seq data generated from a set of cell lines in the ENCODE project and visualized using the UCSC Genome Browser (Supplementary Fig. 4 ). The levels of expression of the intermediate transcript, while being much lower than mature (exonic) PTPN6 mRNA as expected, reached the same expression rate as the pri-PTPN6 primary (intronic) transcript (Supplementary Fig. 4 ). We next demonstrated that this intermediate transcript resulted from the bypass of the usual polyadenylation site of the PTPN6 gene (Fig. 4d, e ). Indeed, we detected both spliced and unspliced transcripts extending from PTPN6-coding sequence to the downstream region of its usual polyadenylation site, in both mouse and human cells at basal states and following H 2 O 2 exposure (Set 2 primers, Fig.
SYBR Power master mix (Applied Biosystems, #4367659) was used for qPCR with primers at 300 nM final concentration each. GAPDH, U6 snRNA and CYCLOPHYLIN B were used as loading controls. TaqMan qRT-PCR assay was used for detection of mature miRNAs. Reagents, primers and probes were obtained from Applied Biosystems. RT reactions and real-time qPCR were performed according to manufacturer protocols from 50 ng of RNA per sample. Primers and probes are specific for each miRNA and are designed by the manufacturer. U6 snRNA and miR-16 were used as loading controls. qPCR reactions were performed in a Chromo4 apparatus (Bio-Rad). Relative expression was calculated using the comparative cycle threshold method (2 DDCt ) and the Opticom Monitor 3 software. qPCR primers were designed using PrimerQuest (Integrated DNA Technologies) and were purchased from Sigma.
During the first ten cycles of the PCR reactions, the annealing temperature increased 0. 5 °C every cycle, from 60°to 65 °C and was maintained at 65 °C for the following cycles. The reactions were stopped after either 25, 30, 35 or 40 cycles. The PCR products were loaded on 0. 8% agarose gel then transferred onto Hybond N þ membrane (GE Healthcare, # RPN203B). Membranes were hybridized with g-ATP 32 P-labelled primers (5 0 -TGACCCTGTATATAGCCCAGCCA-3 0 and 5 0 -AGCAAACAAAGCCTGGGAGAGAGA-3 0 ) in ULTRAhyb Ultrasensitive Hybridization Buffer (Ambion, # AM8669). Signals were detected with Typhoon and quantified with Multi Gauge software. All primer sequences used for PCR and qPCR are given in Supplementary Table  2. Amplified bands were purified, cloned in TA-vector (Invitrogen#45. 0030) and submitted to sequencing in order to verify that they corresponded to the expected intermediate transcript sequence.
Only samples enriched in at least 65-70% epithelial cells have been considered in our study. miRNA and mRNA levels were assessed by qPCR using TaqMan and SYBR assays as described in ref. 7. Microarray data are accessible in the Gene Expression Omibus under the accession number GSE26193. All the data from the TCGA cohort was obtained from the TCGA Data Portal (https://tcga-data. nci. nih. gov/tcga/). Patient characteristics and clinical features of the two cohorts analysed in our study are listed in Supplementary Table  1 , in order to facilitate their comparison. TCGA data are provided from tumour samples with at least 60% of enrichment in epithelial tumour cells, as mentioned in the Supplementary Information in ref. 51. Copy number analyses in ovarian cancer samples. For the Curie cohort, SNP 100K data were analysed using Partek Software.
Raw data were imported into Partek using default parameters and the Partek baseline for copy number calculation, followed by a GC-adjustment. The segmentation parameters used were 15 markers, a signal-to-noise ratio of 0. 3 and a P-value of 0. 001. The threshold used for defining amplified/deleted patients in the PTPN6 and miR-200c/141 region was ±0. 3. For the TCGA cohort, copy number data were downloaded from: https://tcga-data. nci. nih. gov/tcga/ (Level 3 data, MSKCC 1x1M-G447A). Data were imported and analysed using R version 2. 12. 2. Segments defined in TCGA data were used to classify unchanged, amplified or deleted patients in the region of interest. The classification was made according to segment mean values in a window of 150 kb in the chromosome 12p13 including PTPN6 and miR-200c/141.
NCI-60 and Sanger Cell Line Project data sets. For the NCI-60 panel, Agilent microarray data were obtained from CellMiner Database (http://discover. nci. nih. gov/cellminer/loadDownload. do) and miRNA qPCR data were obtained from the Israel lab http://dtp. nci. nih. gov/mtargets/download. html. For the Sanger Cell Line Project panel, microarray data were obtained from the Broad Institute website (http://www. broadinstitute. org/cgi-bin/cancer/ publications/pub_paper. cgi?mode=view&paper_id=189). Affymetrix Plus 2. 0 probeset 206687_s_at was used for PTPN6 levels. All the non-haematopoietic cell lines were included in the analyses. Methylated DNA Immunoprecipitation. MeDIP experiments were performed using MagMEDIP kit from Diagenode (#C02010020), according to the manufacturer's instructions.
Acknowledgements We are grateful to  Nathalie Dostatni ,  Alain Jacquier ,  Stephan Vagner  and  Moshe Yaniv  for fruitful discussions and comments on the present work. We acknowledge  Elphege Nora  for advices on 3C experiments. We thank surgeons and pathologists, as well as the  Biological Resource Center of Institut Curie , for providing human HGSOC. The results presented here are in part based upon data generated by the  TCGA Research Network : http://cancergenome. nih. gov/. We thank  Melissa Cardon  and  Yann Kieffer  for their help in analysing TCGA data sets. L. B. was supported by fundings from the  Ecole Polytechnique  and the  Institut Curie. The experimental work was also supported by grants from the  Institut National de la Santeet de la Recherche Medicale (Inserm)  and the  Institut Curie.
Interestingly, in contrast with the preserved mRNA levels (data not shown), the immunostaining of the endocytic receptor megalin/cubilin complexes revealed evidence of increased cubilin protein expression (Figure  5B ) with no significant changes in megalin protein abundance (data not shown). Given that megalin/cubilin receptors are responsible for the uptake of glomerular filtrate proteins by the proximal tubules, we measured proteinuria in both WT and CEP mice. The results showed reduced amount of total urinary proteins and urinary albumin in CEP compared to WT mice (Figure  5C  and D ), suggesting induced function of the megalin/cubilin endocytic receptor in these mice. The toxicity of Hb on renal function was evaluated by the measurement of both serum urea levels and creatinine clearance calculations, and by the histological examination of kidney sections.
Acknowledgments The authors are very grateful to  Catherine Vernimmen ,  Olivier Thibaudeau ,  Margarita Hurtado-Nedelec  and  Valérie Andrieu  for their help with the animal work and FACS analysis, and to  Joel Poupon  ( Laboratoire de Toxicologie Biologique, Hôpital Lariboisière, Paris ) for urinary iron determinations by atomic absorption spectrometry.
Funding INSERM and the  Université Paris Diderot, France  supported this work. Part of this work is funded by the  labex GR-Ex , reference  ANR-11-LABX-0051 , by the program " Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02 , and by the program " University of Sorbonne Paris Cité, Excellence Initiative , IDEX", reference Hemir. SM was supported by the  Université Paris Diderot  and by the  Société Française d'Hématologie. BM was supported by a grant from the  Fondation pour la Recherche Médicale Française. Iron and heme metabolisms in hemolytic mouse model haematologica | 2017;  102(2 ).
2 %). The following antibodies were used: goat anti-β-actin HRP 1/5000 (Santa Cruz, Dallas, Texas, USA), mouse anti-claudin-5 2 μg/mL (Life Technologies), rabbit anti-ZO-1 2 μg/mL (Life Technologies), rabbit anti-occludin 2 μg/mL (Life Technologies), rabbit anti-HOXA9 1. 5 μg/mL (Novus biological, Littleton, CO, USA), rabbit anti-HOXB7 0. 5 μg/mL (Proteintech, Rosemont, IL, USA), rabbit anti-TNFR1 0. 5 μg/mL (Proteintech), and rabbit anti-TNFR2 0. 5 μg/mL (Proteintech). After washing, membranes were incubated with appropriate secondary horseradish peroxidase (HRP)-conjugated IgG antibodies 1/2000 (Jackson Immunoresearch). Finally, proteins were detected using a chemiluminescence kit (Roche Diagnostics, Mannheim, Germany) revealed with the G:Box chemi xx6 system (Syngene, Cambridge, UK). Films were digitized using GeneSys software (Syngene) and optical densities of the bands were assessed using the NIH ImageJ software.
Funding The  UMR7259  laboratory acknowledges funding from the  CNRS  and from  Aix-Marseille Université (AMU). Financial support to  VECT-HORUS  is acknowledged from the  Fonds Unique Interministériel  ( FUI/MEDUL  project), to MK, the  UMR7259 laboratory and VECT-HORUS  from the  Agence Nationale de la Recherche  ( ANR ) for the TIMPAD and PREVENTAD collaborative projects, for the  DHUNE  project supported by  A*MIDEX , and to MK from LECMA.
89,90 Sr and 137 Cs accumulated in the fetus were assumed as the sources of internal in utero exposure. The fetal biokinetic and dosimetric models for Sr adapted for the Techa River population were used for dose calculation  [16] [17] [18] [19]. ICRP-88 models  [20]  were applied to calculate the in utero doses from 137 Cs. Dietary intakes of 89,90 Sr and 137 Cs for adult Techa riverside residents  [21, 22]  were adjusted for the increase in food consumption by pregnant women  [23]. External in utero doses were calculated using TRDS-2009D, considering exposure of the maternal uterus as a surrogate of the fetal exposure. Postnatal doses were also calculated based on TRDS-2009D, using the individual data on residence in specific Techa riverside villages. The doses due to intakes of short-lived radionuclides were considered.
Data Collection The methods of follow-up of the members of both cohorts were developed and consolidated by experience many years ago  [11, 25]. The vital status of the cohort members was regularly assessed on the basis of the data derived from the Chelyabinsk and Kurgan oblasts' address bureaus, the Mayak Unified Computer Registry, and the results of interviews with relatives; the address bureaus were the most accurate source. For incidence analyses, data from the cancer registry were used, and the mortality analyses were based on data from cause-of-death registries. The basic sources of information on cancer cases included medical records (notifications of the first diagnosed cancer cases, patient medical records, operation log books, results of analyses performed by the cytology and pathology laboratories, etc.
In utero dose, mGy All solid cancers (ICD-   [28]. Although the data from animal studies on radiation-induced embryonic or fetal death appears rather inconsistent, increased lethality was observed in studies of mice after exposures in the dose range 0. 05-0. 5 Gy on day 7 after conception  [29, 30]. To the contrary, positive dependence of solid cancer incidence RR on postnatal dose was observed in UPEC. In the highest dose category (> 80 mGy) RR was statistically significantly increased. Pooling the datasets of the two cohorts (TRCIU and MWOC) resulted in substantially higher statistical power for both cancer mortality and cancer morbidity. The strengths of the study are the large sample size, long-term follow-up, and individualized dose estimates. However, the study also has some limitations.
Acknowledgments The authors would like to thank the staff of the  Urals Research Center for Radiation Medicine (Chelyabinsk, Russian Federation ) and the  Southern Urals Biophysics Institute (Ozyorsk, Russian Federation) of the Federal Medical Biological Agency  for tremendous long-term work on following up the members of the study cohorts;  John Harrison ,  Richard Haylock ,  Svetlana Carr , and other representatives of  Public Health England  for coordinating the  SOLO  project, and for their support and encouragement throughout the study; and also the Scientific Advisory Board members  Christian Streffer ,  Lynn Anspaugh , and  Dale Preston  for valuable and constructive suggestions and discussions of the results of the research.
Complementary Data and Statistical Analysis In case of specific increase of performance immediately after the adaptation training, in functional or oculomotor parameters, we performed long-term analyses thanks to paired-t tests on the same parameter at 1 month. These long-term analyses compared the immediate post-phase following the adaptation training with the pre-phase at 1 month (visit V3 or V4). Note that in case of specific and simultaneous (functional and oculomotor) increase of performance following the adaptation training, additional analyses were performed on oculomotor parameters such as number of saccades and were also checked at 1 month. Statistical analyses were performed with the Statistica 10 software (Statsoft, Tulsa, OK). Post-hoc Least Significant Difference (LSD) Fischer was performed following a significant interaction in ANOVAs.
Most of the effort has been dedicated to "chat room" data as it is the kind of data which is the most accessible on the WEB. (Achille, 2005)  constituted a corpus in French. (Forsyth and Martell, 2007)  and  (Shaikh et al. , 2010)  describe similar corpora in English. (Cadilhac et al. , 2013)  have studied the relational structure of such conversations through a deep discursive analysis of chat sessions in an online video game. This kind of data falls under the informal register whereas we are interested in this paper in understanding the mechanisms of a more formal kind of CMC: dialog chat in contact centers. This study is realized in the context of the DATCHA project, a collaborative project funded by the French National Research Agency, which aims at performing unsupervised knowledge extraction from very large databases of WEB chat conversations between operators and clients in customer contact centers.
ACKNOWLEDGMENTS This work was funded by  GlaxoSmithKline  and by the  Ligue Nationale de Lutte Contre le Cancer. No funding or sponsorship was received for the publication of this article. We thank  Anne Visbecq  ( KP1916 ) for writing and editorial support in the preparation of this manuscript, and this support was funded by  UNICANCER. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this manuscript, take responsibility for the integrity of the work as a whole, and have given final approval for the version to be published.
Satellite data We use remotely sensed CO 2 observations from three different instruments, namely GOSAT, the Thermal Emission Sounder (TES), and the Atmospheric Infrared Sounder (AIRS). As most HIPPO profiles took place over the oceans, SCIAMACHY was not included in the analysis because it lacks a dedicated Glint Mode measurement. While GOSAT CO 2 is representative of the column-averaged dry air mole fraction (XCO 2 ), both TES and AIRS are most sensitive to the atmosphere around 500 and 300 hPa, respectively.
Comparisons of column-averaged mixing ratios Here, we look at XCO 2 , derived using absorption spectroscopy of reflected sunlight recorded by near-infrared spectrometers such as SCIAMACHY, GOSAT, or Orbiting Carbon Observatory-2 (OCO-2). In this paper, we only used data from GOSAT as it is the only instrument having sampled in Glint mode during the HIPPO investigation. SCIAMACHY data have not been used as it has no dedicated glint mode and the SCIAMACHY products (e. g. ,  Reuter et al. , 2011)  are limited to retrievals over land. For the comparison of column-averaged mixing ratios, we need to extend the HIPPO profiles to the top of atmosphere. For this, we use the respective atmospheric model for comparison. In addition, we computed the average HIPPO XCO 2 for each campaign using all the data and subsequently removed it from individual measurements, both from the HIPPO, model and satellite data.
HIPPO XCO 2 is computed as the average of MACC and CT2013B extended HIPPO profiles with the difference between the two used as uncertainty range for HIPPO. In Fig. 7 , the scatter plot of HIPPO vs. GOSAT is depicted. It is obvious that the data density is far lower than that of the models because (a) HIPPO 1 is not overlapping in time and (b) only a subset of HIPPO profiles is matched with enough co-located GOSAT soundings. This gives rise to a reduced dynamic range in XCO 2 , from about a -1. 5 to 3 ppm difference to the campaign average. However, both slope and r 2 are also in excellent agreement with HIPPO and only very few points are exceeding a 1 ppm difference. Those that are. Left: scatter plot of normalized (with campaign average) XCO 2 computed from individual HIPPO profiles (x axis) against corresponding GOSAT data.
As before, the error bar for GOSAT is derived as the standard error in the mean and the model error bar by using the variability of HIPPO XCO 2 using the two different models to extrapolate to the (and the average of the two is defined as HIPPO XCO 2. The center box spans a range from -0. 5 to 0. 5 ppm, a strict requirement for systematic biases (GHG-CCI, 2014). The green and red shaded areas indicated regions where either the GOSAT data meet the 0. 5 ppm requirement but the models do not (green) or vice versa (red). Given the small amount of samples, it is premature to draw strong conclusions but it appears that somewhat more points lie in the green area. It also has to be pointed out that pure measurement unsystematic noise also contributes to the scatter in GOSAT. For MACC, there is even a noticeable correlation between MACC-HIPPO and GOSAT-HIPPO with an r 2 of 0.
5 ppm (green) as well as where the models deviate less than GOSAT (red). The white cells on the outer edges indicate areas where both deviate more than 0. 5 ppm overall. Figure  9  depicts the HIPPO 2S campaign in more detail, showing the exact flight patterns and the differences with respect to MACC (MACC-HIPPO) at each measurement point (upper panel). For the sake of simplicity, we only show MACC here. The measured CO concentrations are shown in the lower panel. There is enhanced carbon monoxide (CO) at higher altitudes, indicating long-range transport of biomass burning at the time of overflight, which can explain the apparent model-HIPPO mismatch. The features span several degrees of latitude, excluding coarse model resolution as a reason for missing the plume. Thus, we hypothesize that the mismatch is caused by either underestimated CO emissions from the Global Fire Emissions Database (GFED,  Randerson et al.
Data availability CarbonTracker CT2013B data are available at http:// carbontracker. noaa. gov. MACC data are available at https: //atmosphere. copernicus. eu. TES data are available at http: //tes. jpl. nasa. gov/data/. HIPPO data are available at http: //hippo. ornl. gov. AIRS data are available at http://disc. sci. gsfc. nasa. gov/uui/datasets?keywords=AIRS. GOSAT data processed by NASA/JPL as well as a general CO 2 repository are available at http://co2. jpl. nasa. gov. Acknowledgements. Funded by NASA Roses ESDR-ERR 10/10-ESDRERR10-0031, "Estimation of biases and errors of CO 2 satellite observations from AIRS, GOSAT, SCIAMACHY, TES, and OCO-2". We thank the entire HIPPO team for making these measurements possible and the NIES and JAXA GOSAT teams for designing and operating the GOSAT mission and generously sharing L1 data with the ACOS project.