dataesr/scientific-paragraphs-categorization huggingface dataset:
5% Triton X-100 for 45 min, followed by a 45 min washing step with a 50 mM Tris HCl solution containing 5 mM CaCl 2 and 1 µM ZnCl 2 plus 2. 5% Triton X-100, pH 7. 5. Gels were then incubated for 24 h at 37 °C with a solution of 50 mM TrisHCl containing 10 mM CaCl 2 , 200 mM NaCl, pH7. 5. Finally, gels were stained with 0. 5% Coomassie Brilliant Blue R-250 and destained with decolorizing solution (25% v/v isopropanol plus 10% v/v acetic acid). Enzyme activity, seen as clear bands against a blue background was analysed by densitometry with ImageJ image processing system. The zymographic activity was expressed as a percentage in relation to a standard internal sample that saturates at a density of 50%. Data corresponding to different gels were normalized using internal control samples. Statistical analysis.
Data were graphed and analysed using GraphPad Prism version 5 software (GraphPad Software). All the data were tested for normality using the Kolmogorov Smirnoff test before performing parametric statistical tests. The data obtained using cell lines were normally distributed. Differences in BrdU incorporation, clonogenic ratio and MTT data were analysed by analysis of variance (ANOVA) followed by Tukey's post-test. Differences in the expression levels of PRLR and PRL mRNA were assessed by Student's t test, whereas the proportion of positive vs. negative patients was analysed by χ 2 test. Nonlinear correlation analysis was used to analyse differences in the scratch assay. Correlation between PRL and PRLR expression was evaluated by Pearson analysis. Kaplan-Meier survival values were calculated for the low and high expression of PRL and PRLR.
Acknowledgements We thank  Dr. Mirtha Guitelman  ( Hospital General de Agudos Carlos G. Durand, Buenos Aires, Argentina ) and  Dr. Adriana Oneto  ( Centro de Diagnóstico TCba, Buenos Aires, Argentina ) for their advice and support in this project. We thank  Dr. Julia Halperin  ( Universidad Maimonides, Buenos Aires, Argentina ) for kindly donating the plasmids encoding for the long and short isoforms of mouse PRLR. This work was funded by  Consejo Nacional de Investigaciones Científicas y Tecnológicas  (doctoral fellowship awarded to  AS Asad  and  S Orrillo) ,  Agencia Nacional de Promoción Científica y Tecnológica  ( PICT 2015-3309  to  M Candolfi ;  PICT 2014-0334  to  A Seilicovich , doctoral fellowships to  AJ Nicola Candia  and  CF Zuccato) ,  Instituto Nacional del Cáncer (Argentina, Asistencia Financiera IV  to  M Candolfi ),  Fundación Bunge y Born  ( "Jorge Oster" fellowship  to  AJ Nicola Candia ) and the  University of Buenos Aires.
We present results of the MUSE-ALMA Halos, an ongoing study of the Circum-Galactic Medium (CGM) of low redshift galaxies (z ≤ 1. 4), currently comprising 14 strong H i absorbers in five quasar fields. We detect 43 galaxies associated with absorbers down to star formation rate (SFR) limits of 0. 01-0. 1 M yr -1 , found within impact parameters (b) of 250 kpc from the quasar sightline. Excluding the targeted absorbers, we report a high detection rate of 89 per cent and find that most absorption systems are associated with pairs or groups of galaxies (three to eleven members). We note that galaxies with the smallest impact parameters are not necessarily the closest to the absorbing gas in velocity space. Using a multi-wavelength dataset (UVES/HIRES, HST, MUSE), we combine metal and H i column densities, allowing for derivation of the lower limits of neutral gas metallicity as well as emission line diagnostics (SFR, metallicities) of the ionised gas in the galaxies.
The astrometry solution and the correction for geometry, together with flux calibrations, were all combined within the scipost command. Finally, individual exposures were combined including field rotation. The detailed description of the reduction procedure can be found in  Péroux et al. (2019). We refrained from using the pipeline sky subtraction method, due to unsatisfactory results. Instead, removal of sky emission lines was performed on the final datacube with a Principal Component Analysis (PCA) algorithm  (Husemann et al. 2016). The code creates PCA component of selected sky regions used further to remove sky residuals. In addition to MUSE observations we used readily available, reduced archival HST imaging for the galaxy identification: WFPC2 (Wide Field Planetary Camera 2) in filter F702W of quasars Q0152-2001 (PI: Steidel, ID:6557), Q1211+1030, Q1232-0224 (PI: Bergeron, ID:5351) and Q2131-1207 (PI: Maccheto, ID:5143) and WFC3 (Wide Field Camera) IR-F140W of Q1130-1449 (PI: Bielby, ID: 14594), with exposure times from 10 to 50 minutes.
0 -4. 5), metallicity (log(Z/Z = -4. 0 -1. 0 and n H density (log(n H ) = -4. 5 -0. 0). N(H i) and redshift are constrained from the observations and serve as an input. To determine the most probable model, we use as constraints the measured metal line column densities (Mg ii, Mg i and Fe ii in almost all cases). We run the MCMC chain over the grid, using the PyIGM package routines 2. For the resulting Probability Density Functions (PDF) of each of the parameters, we adopt the median value of the computed [Fe/H] metallicity (see Table  2 ). For a detailed description of the method, its extension and assumptions in the UV background we refer the reader to  Fumagalli et al. (2016)  and  Wotta et al. (2019b).
After narrow-band identification of the sources at the redshift of the absorber, we extracted all continuum sources from the MUSE white light image using the MUSELET source finder, part of the MPDAF package provided by the MUSE GTO team  (Bacon et al. 2016). MUSELET creates from a cube: (i) a white light image; (ii) R, G, B images corresponding to 1/3 of the wavelength range each; (iii) a set of narrow-band images based on the average of 5 wavelength planes (total width of 6. 25 Å). It then runs SExtractor  (Bertin & Arnouts 1996)  on these products, resulting in a catalogue of continuum sources. We classified all of the reported sources as either emission line galaxies, absorption line galaxies, stars, unknown continuum sources (without emission or absorption lines) or artifacts. We inspected all extracted spectra and used [O ii] and [O iii] emission lines or Ca K+H absorption lines to determine the redshift of each source.
(2019). Discoveries of multiple galaxies associated with low-z absorbers challenge the classical view of the CGM connected to the single halo of an isolated galaxy. Cuts in both impact parameter and velocity difference between absorber and galaxy differ between published MUSE absorber studies, making the comparison of results not straightforward. MusE GAs FLOw and WInd (MEGAFLOW), a MUSE survey of Mg ii absorbers,  (Schroetter et al. 2016 ) allows for a 1000 km s -1 velocity difference between the galaxy and the absorber, and in case of multiple detections, the lowest-impact-parameter object is selected. Quasar Sightline And Galaxy Evolution (QSAGE), an OVI absorption oriented MUSE program  (Bielby et al. 2019) , supported by a larger impact parameter coverage with HST grisms, uses a tight limit of 400 km s -1 (expected outflow velocity).
CONCLUSIONS We report the first results of MUSE-ALMA Halos, a unique multi-wavelength data set aimed at understanding the complexity of the Circum-Galactic Medium, the interactions with the galaxies and the environmental effect on the high H i column density absorbers. The survey consists of five quasar fields for which we identified 14 absorbers at redshift z < 1. 4. We searched for the galaxies associated with the absorber within the MUSE field of view (covering 200-250 kpc in impact parameter, depending of the system's redshift) and within ∆v = 1000 km s -1 from the absorber redshift. Each absorber has an H i column density measured either from the literature or from archival FOS spectra. We used MUSE IFU observations to identify galaxies associated with the gas seen in absorption, finding altogether 43 such systems yielding a 89 per cent detection rate among the nontargeted absorbers.
We find large dispersion in velocities between galaxies and absorbing gas, sometimes pointing to a more distant galaxy as the main candidate for the host. We compare the parameters of the systems in our sample with canonical relations of the absorbers and their host galaxies, N(H i)-b and W r (2796)-b. We find that the W r (2796)-b relation follows the general trend while defining an envelope of param-eters below which no further objects are detected. Thanks to our multi-wavelength dataset we can compare the lower limits of metallicities of the absorbing gas with the metallicity of the galaxies as measured in the emission. We find that the overall metallicity is always higher in the associated galaxies than in the absorbing gas probed by the QSO. However, we are not sensitive to the low metallicity systems below 12+(O/H) = 7.
ACKNOWLEDGEMENTS We thank the referee for the useful comments. CP thanks the  Alexander von Humboldt Foundation  for the granting of a Bessel Research Award held at MPA. AK acknowledges  STFC  grant  ST/P000541/1  and  Durham University. RA thanks  CNRS  and  CNES  ( Centre National d'Etudes Spatiales ) for support for her PhD. Authors thank  C. Howk  and  N. Lehner  for their comments and suggestions. AH thanks  A. Bittner ,  M. Kozubal  and  D. Wylezalek  for comments helping to improve this manuscript.
Gene Expression Data Sets We gathered clinicopathological and gene expression data of clinical pancreatic carcinoma samples from ten publicly available data sets  [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] , which comprise at least one probe set representing XPO1 (Supplementary Table  S1 ). Data were collected from the National Center for Biotechnology Information (NCBI)/Genbank GEO, ArrayExpress, and TCGA databases. The samples were profiled using whole-genome DNA microarrays (Affymetrix, Agilent) and RNASeq (Illumina). The pooled data set contained 1052 samples, including 573 primary PAC samples, 73 metastatic samples, and 95 normal pancreatic samples. Our institutional board approved the study. A total of 573 PAC samples that were informative for overall survival were included in the present analysis.
Gene Expression Data Analysis Data analysis required pre-analytic processing. First, we separately normalized each DNA microarray-based data set by using quantile normalization for the available processed Agilent data, and Robust Multichip Average (RMA) with the non-parametric quantile algorithm for the raw Affymetrix data sets. Normalization was done in R using Bioconductor and the associated packages. Subsequently, we mapped hybridization probes across the different technological platforms present. We used SOURCE (http://smd. stanford. edu/cgi-bin/source/sourceSearch) and EntrezGene (Homo sapiens gene information db, release from 04/27/2017), ftp://ftp. ncbi. nlm. nih. gov/gene/) to retrieve and update the Agilent annotations, and NetAffx Annotation files (www. affymetrix. com; release from 01/12/2008) for the Affymetrix annotations.
The probes were then mapped according to their EntrezGeneID and, when multiple probes represented the same GeneID, we retained the one with the highest variance in a particular dataset. For the RNA-seq data, we used the available normalized RNASeq data that we log 2 -transformed. Subsequently, we corrected the ten studies for batch effects using z-score normalization. Briefly, for each separate XPO1 expression value in each study, subtracting the mean of the gene in that dataset divided by its standard deviation, mean, and standard deviation only being measured on primary cancer samples transformed the value. XPO1 expression in tumors was measured as discrete value after comparison with mean expression in the 573 primary tumors: high expression was defined by value > mean and low expression by value ≤ mean.
We separately applied different multigene classifiers to each sample in each data set: the subtype classifiers that were reported by Bailey  [61] , Collisson  [58] , and Moffitt  [60] , and the 25-gene prognostic signature that we recently developed  [64]. Finally, to explore the biological pathways linked to XPO1 expression in pancreatic cancer more-in-depth, we applied a supervised analysis by using the largest data set (TCGA: 150 samples) as a learning set, and the other data sets as independent validation sets (423 samples). In the learning set, we compared the whole-genome expression profiles between tumors with (N = 76) versus without (N = 74) high XPO1 expression using a moderated t-test with an empirical Bayes statistic  [65]  being included in the limma R packages. False discovery rate (FDR)  [66]  was applied to correct the multiple-testing hypothesis and the following thresholds: p < 1.
0 × 10 -5 and q < 1. 0 × 10 -5 defined significant genes. Ontology analysis of the resulting gene list was based on the GO biological processes of the Database for Annotation, Visualization and Integrated Discovery (DAVID; david. abcc. ncifcrf. gov/). We verified the robustness of the resulting gene list in the validation set (295 tumors with and 283 without high XPO1 expression) by computing a metagene-based prediction score defined by the difference between the "metagene XPO1-high" (mean expression of all genes upregulated in the "XPO1-high" class) and the "metagene XPO1-low" (mean expression of all genes upregulated in the "XPO1-low" class) for each tumor. This score was then compared between the "XPO1-high" and "XPO1-low" samples.
1), tumor size (T4, T2, T3 vs. T1), regional lymph node status (positive vs. negative), and XPO1 expression ("high" vs. "low"). Variables with a p-value < 0. 05 were tested in multivariate analysis. All of the statistical tests were two-sided at the 5% level of significance. Statistical analysis was done using the survival package (version 2. 30) in the R software (version 2. 15. 2; http://www. cran. r-project. org/). We followed the reporting REcommendations for tumor MARKer prognostic studies (REMARK criteria)  [67].
XPO1 Expression and Associated Biological Processes To explore the biological alterations that are associated with the XPO1 expression status, we applied supervised analysis to the TCGA data set (N = 150). We identified 268 genes that were differentially expressed between the tumors with (N = 76) versus without (N = 74) XPO1 upregulation, including 191 genes that were upregulated and 77 genes that were downregulated in the "XPO1-high" samples (Supplementary Table  S3 ). Ontology analysis (Supplementary Table  S4 ) revealed the strong involvement of genes that were overexpressed in the "XPO1-high" tumors in cell cycle, nuclear division, DNA repair, signal transduction, chromosome segregation, DNA replication, and RNA processing. Ontologies that were associated with the genes underexpressed in the "XPO1-high" tumors were fewer and mainly related to metabolism and development.
Discussion The need for new therapeutic and/or prognostic targets is crucial in PAC. We have analyzed XPO1 mRNA expression in 573 clinical PAC samples because of the promising therapeutic value of XPO1 inhibitors in oncology and the paucity of data in the literature: high expression was associated with shorter OS in multivariate analysis. To our knowledge, this is by far the largest study analyzing XPO1 expression in PAC. Our analysis was based on mRNA expression rather than protein expression as measured using immunohistochemistry (IHC) for several reasons: i) avoiding the limitations of IHC with different non-standardized protocols for XPO1; ii) working on an available large series of clinical samples; and, iii) searching for associations with expression of other genes on a whole-genome scale.
tipulae gene annotations (CEW1_nOt2) using snpEff  (Cingolani et al. 2012). Scripts used for this processing pipeline can be found at: https://github. com/ fabfabBesnard/Andalusian_Mapping. The candidate scaffolds were also analyzed using Pindel  (Ye et al. 2009)  to identify large deletions or insertions, which were confirmed later by visual inspection with the Tablet software  (Milne et al. 2013). The genetic loci with names such as cov-3 and cov-4 were defined by genetic complementation tests  (Dichtel et al. 2001; Louvet-Vallée et al. 2003; Dichtel-Danjoy and Félix 2004a,b). When the locus was defined by more than one mutant allele, as described in  Besnard et al. (2017)  for cov-3, we subjected two alleles to the genetic mapping-bysequencing approach and determined the intersection between the resulting lists of candidate genes with lesions in the genetic interval, thus pointing to a single common gene.
Data availability Supplemental Tables are available through the Figshare portal (https://doi. org/10. 25386/genetics. 7624559): Table  S1. List of strains used in this study. Table  S2. Sequences of DNA primers used in this study. Sequencing primers to verify by Sanger sequencing the mutations identified by the mapping-by-sequencing approach, and to identify the molecular lesions in additional alleles. Table  S3. Sequences of smFISH probes used in this study. The fluorophore coupled to each probe is noted at the end of the set name. Table  S4. smFISH quantifications, distance measurements, and vulval cell fates used in this study. Code for mutant identification is available at https:// github. com/fabfabBesnard/Andalusian_Mapping. Sequencing reads used for mapping-by-sequencing can be find at the Sequence Read Archive portal under the bioproject number PRJNA514975.
Acknowledgments We thank  Aurélien Richaud  for performing a smFISH experiment;  Joao Picao Osorio  for comments on the manuscript;  Michalis Barkoulas  for his advice on Oscheius smFISH;  Mark Blaxter 's laboratory for their continuous support of nematode genomics as well as for maintaining the Caenorhabditis website (http://www. caenorhabditis. org/); Benjamin Podbilewicz  for advice on the immunofluorescence experiments;  Sana Dieudonné ,  Aurélien Richaud , and  Clément Dubois  for advice and help in the use of the mapping-by-sequencing technique; the Associate Editor  Meera Sundaram  and the anonymous reviewers for helpful input; and WormBase. Some strains were provided by the  Caenorhabditis Genetics Center , which is funded by the  National Institutes of Health Office of Research Infrastructure Programs  ( P40 OD-010440 ).
Data collection This is a retrospective, multicenter registry-based study performed by the Acute Leukemia Working Party (ALWP) of the European society for Blood and Marrow Transplantation (EBMT) and by Eurocord. EBMT registry is a voluntary working group of more than 500 transplant centers, participants of which are required once a year to report all consecutive stem cell transplantations and follow-up. Audits are routinely performed to determine the accuracy of the data. Eurocord collects data on CBT performed in >50 countries worldwide and >500 transplant centers, mainly EBMT centers. Inclusion criteria were adult (≥18 years) patients, de novo or secondary AML, primary refractory (defined as absence of CR (<5% marrow blasts) achievement after induction chemotherapy 1 ) or in first or second relapse at transplantation, transplantation between 2004 and 2015, and either a 10/10 HLA-matched unrelated donor (UD 10/10), a 9/10 HLA-matched unrelated donor (UD 9/10), or a single or double CBT.
For UD, HLA-A, HLA-B, HLA-C, HLA-DR, and HLA-DQ were typed at the allelic level. For CBT, HLA-compatibility requirements followed the current practice of antigen level typing for HLA-A and HLA-B and allele level typing of HLA-DRB1. CB units were 4-6/6 HLA-A, HLA-B, and HLA-DRB1 matched to the recipient and to the other unit in case of double CBT in most patients  11, 12. HLA disparities between each unit and the recipient and between the two units were not necessarily at the same loci. Grading of acute and chronic GVHD was performed using established criteria  13. For the purpose of this study, all necessary data were collected according to EBMT guidelines.
Statistical analyses Data from all patients meeting the inclusion/exclusion criteria were included in the analyses. Start time was date of transplant for all endpoints. Neutrophil engraftment was defined as first of 3 consecutive days with a neutrophil count of at least 0. 5 × 10 9 /L. Cumulative incidence functions were used for relapse incidence and non relapse mortality (NRM) in a competing risk setting, since death and relapse were competing together. For estimating the cumulative incidence of engraftment and chronic GVHD, death was considered as a competing event. Overall (OS) and leukemia-free (LFS) survivals were estimated using the Kaplan-Meier estimates. GVHD and relapse-free survival (GRFS) was defined as being alive with neither grade III-IV acute GVHD, extensive chronic GVHD nor disease relapse  14.
The estimation of propensity score was performed using generalized boosted models  15. We weighted the 3 groups receiving either CBT, 10/10, or 9/10 UD by estimating the Average Treatment Effect (ATE). We checked the balance between the groups looking to ATE Weighted means. Then, we used pairwise ATEs to fit weighted KM and Cox models. All tests were two sided. The type I error rate was fixed at 0. 05 for determination of factors associated with time to event outcomes. Analyses were performed using the R statistical software version 3. 4. 0. Propensity score analysis was performed using the mnps function of the Twang package (http://cran. rproject. org/web/packages/twang/ vignettes/twang. pdf), and weighted analyses using the survey package.
These results are in contrast with those observed in AML patients in CR with or without MRD, where CBT did at least as good as UD allo-HCT  [8] [9] [10]. There are some limitations in our study. They include the heterogeneity in patients characteristics between the different groups and the lack of relevant data in the database such as blast counts at transplantation, comorbidity score, or donor T cell reconstitution after allo-HCT. We tried to address the heterogeneity question by comparing transplantation outcomes between the three different groups by using both multivariate Cox models and propensity weighted Cox models. In summary, these data suggest that in AML patients with active disease at transplantation, allo-HCT with UD results in better transplantation outcomes than CBT.
MeTHods source/study populations The presently studied population consisted of five of seven cohorts from the European collaboration on acute decompensated heart failure database with exact data available on left ventricular ejection fraction. 11 Details on the search strategy, source gathering and explicit information on data collection for these prospective ADHF cohorts have been reported previously. 11 In addition to these five cohorts, data from the Can NT-proBNP guided therapy during hospital admission for acute decompensated heart failure reduce mortality and readmissions? (PRIMA II) trial was used for the analyses. 18 20 The PRIMA II was a randomised controlled trial investigating the effect of NT-proBNP-guided (targeting a >30% NT-proBNP at discharge) versus conventional therapy in patients with ADHF, demonstrating a neutral effect.
As a measure of attainability for a number of preset NT-proBNP levels at discharge and NT-proBNP reduction percentages, the percentage of patients attaining these levels was determined for patients with HFpEF and HFrEF. Finally, the presence of comorbidities of established prognostic relevance was compared between patients with HFpEF and patients with HFrEF in a low (<3000 pg/mL) and high (>3000 pg/mL) category of discharge NT-proBNP levels. The Fisher's exact test was used to make a comparison. Normally distributed were compared using the Student's t-test. Other continuous data were compared using the Mann-Whitney U test. To accommodate for the different cohorts, separate baseline hazard functions were used to adjust for between-study differences. For the multivariable model, we performed multiple imputation pooling algorithms (n=10) to correct for missing values.