dataesr/scientific-paragraphs-categorization huggingface dataset:
Normal counterpart B-cell were associated to B cell malignancies according to cell-of-origin classification  (26). For genes with multiple Affymetrix probes, probes were selected according to correlations between GEP and RNA-seq data for MM and MCL cell lines when available (https://www. keatslab. org/data-repository) (n = 19) (Table  S2 ). Given that none of the BAD and HRK probes available gave a correlation with RNA-seq, these genes were excluded from our study. In addition, expression of BBC3 (coding for Puma protein) has not been evaluated because of putative MIR3191/MIR3190 cross-hybridization (Affymetrix HGU133plus2. 0 Annotation,  Revision 35). Factor maps were constructed by FactoMiner and further represented by factoextra package. Data used in the Principal Component for each graph were a subset of the Bcl2-family dataset we firstly constructed.
As observed in the PCA, the BCL2-family profile of the aggressive forms of both FL and MALT segregated apart from their respective indolent forms toward a profile close to the one of DLBCL (Figure  4C ). Of note, we investigated whether BCL2-family expression patterns would differentiate the non-transformed FL/MALT from the transformed one. To do so, an ensemble machine-learning algorithm (random forest) was trained on BCL2-family expression dataset to predict the different B-cell malignancies. Using this trained algorithm on FL and MALT, it classified the transformed forms of the latters as DLBCL, thus efficiently predicting the aggressive transformation in both FL [Odds Ratio  [OR]  for transformation = 31, p = 2x10 -14 ] and MALT (OR = 30, p = 9 × 10 -5 ).
DISCUSSION The BCL2-family is known to be deregulated in cancer, including hematological malignancies  (43). Whereas, most studies focused on the regulation of selective BCL2-family members within a specific pathology, here we provided a global RNA expression analysis of 12 members of the BCL2-family across 10 mature B-cell malignancies and their relative normal counterparts. To do so, we took advantage of the numerous Affymetrix HGU133Plus2. 0 series datasets previously published for mature B cell malignancies and gathered in the GEO database. We controlled the normalization quality by addressing hallmarks expression such as CCND1, SOX11, MKI67, MME, CD200, CD38, or SDC1, confirming malignancies specificities, independently of source series (Figure  S1 ). Using similar data mining strategy, Adams et al.
recently highlighted an overexpression of BCL2 and BCL2L2 in Hodgkin Lymphomas and several NHL (BL, DLBCL, FL, MZL, and MCL)  (44). This overexpression was confirmed in our study with the exception of BL, a discrepancy that might be due to the use of different normal counterparts. Nevertheless, this technology has limitations such as probes aspecificity (HRK, BAD) or cross-hybridization within some probes such as BBC3  (45) , impeding the integration of these critical member of the BCL2 network in the present study (see Material and Methods section). Although this drawback could be resolved using RNA-sequencing technologies, datasets availability was too limited for most of the cellular entities analyzed in the present work. Having these limitations in mind, our analysis provided a global picture of the BCL2-family dysregulation in mature Bcell malignancies, from their transcriptional regulation to their potential use as targeted therapy biomarker.
Various targeted therapies have therefore been tested in combination with selumetinib  [11]  or with the PKC (protein kinase C) inhibitor AEB071  [12]  in UM preclinical models, and particularly in patient-derived xenografts (PDXs). On this basis, this last compound is currently being tested in a clinical trial, currently in combination with a MDM2 inhibitor (NCT02601378). Another recurrent mutation in UM is the bap1 (BRCA1-associated protein 1) gene mutation  [13] , which is associated with a higher risk of metastases  [14, 15]. Sporadic germline bap1 mutations confer a predisposition to several types of cancer, including UM  [16] , confirming its role in tumorigenesis. BAP1 is a deubiquitinating enzyme involved in chromatin structure, cell cycle progression, and differentiation. Although the direct interaction of BAP1 with BRCA1 remains controversial  [17] , several studies report a potential role of BAP1 in DNA damage repair, and particularly in homologous recombination (HR).
Basal Gene and Protein Expression of PARPs in Patient's Tumors and Corresponding PDXs The expression of PARP family genes was evaluated using data generated from previously reported Affymetrix GeneChip-Human Exon 1. 0 ST arrays, including 12 patient tumors and 32 PDXs (15 at passage one, 13 at passage four, and four at passage nine). Two control genes were used to assess positive and negative expression, i. e. , the MAGE1 gene (negative expression) and the MGAT5 gene (positive expression). We observed variable expression levels among all PARP family genes, with high expression of PARP1, PARP4, PARP6, PARP10, PARP12, and PARP14 genes. Moreover, we did not observe variations in gene expression levels between patient's tumors and their corresponding PDXs at three different in vivo passages (Figure  1  and Figure  S1 ).
Basal Gene and Protein Expression of PARPs in Patient's Tumors and Corresponding PDXs. The expression of PARP family genes was evaluated using data generated from previously reported Affymetrix GeneChip-Human Exon 1. 0 ST arrays, including 12 patient tumors and 32 PDXs (15 at passage one, 13 at passage four, and four at passage nine). Two control genes were used to assess positive and negative expression, i. e. , the MAGE1 gene (negative expression) and the MGAT5 gene (positive expression). We observed variable expression levels among all PARP family genes, with high expression of PARP1, PARP4, PARP6, PARP10, PARP12, and PARP14 genes. Moreover, we did not observe variations in gene expression levels between patient's tumors and their corresponding PDXs at three different in vivo passages (Figures  1  and S1 ).
Western Blot (WB) Analyses Proteins were extracted as described previously  [52]. Lysates were resolved on 4-12% TGX gels (Bio-Rad ® , manufacturer, Marnes La Coquette, France). RNA extraction, array hybridization, and analyses were performed as previously reported  [29]. ), transferred to nitrocellulose membranes (Bio-Rad ® ). Immunoblotting was performed with antibodies against the ubiquitous protein GAPDH and some other specific studied proteins, as shown in the Table  S13. After washes, membranes were incubated with the appropriate secondary horseradish peroxidase-conjugated affinity-purified goat anti-rabbit antibody (Jackson Immuno Research Laboratories, Inc. , Interchim, San Diego, CA, USA). Quantification of protein expression was performed using Multi Gauge software and normalized to GAPDH expression.
Acknowledgments: We wish to thank the Animal Platform  CRP2-UMS 3612 CNRS-US25   Inserm-IRD  ( Faculty of Pharmacy, Paris Descartes University, Paris ), and the animal Platform of the  Institut Curie. The study was partially supported by  "La Fondation de France ", and by  Novartis and Astra-Zeneca laboratories , who are included in the authors. Novartis and  Astra-Zeneca  laboratories provided the molecules (Novartis provided  AEB071 , olaparib and  CGM097. Astra-Zeneca  provided  AZD0156 ,  AZD6738  and olaparib), and  Novartis  financed a part of the experiments.
2011). ALMA data at higher frequencies have recently been published by  (Fogarty et al. 2019)  but no attempt to determine the extent of any absorption against the core was made in that paper. • RXCJ1603. 6+1553 is another relatively poorly studied cluster, likely due to its brightest cluster galaxy being dominated by a flat-spectrum radio core. Like RXCJ1350. 3+0940, the source was selected in the ROSAT Bright Source catalogue (RBS1552) but the bright radio core led to the X-ray source being classified as a BL Lac. HI absorption has been detected close to the galaxy's systemic recession velocity with a peak optical depth of τ max = 0. 125 and FWHM ∼ 400 km s -1  (Geréb et al. 2015 ). • MACSJ0242. 5-2132 contains one of the most radio powerful core sources in the sample presented in  Hogan et al.
DATA PROCESSING AND THE ORIGIN OF THE CN-A AND CN-B ABSORPTION LINES The data presented in this paper were handled using CASA version 5. 1. 1, a software package which is produced and maintained by the National Radio Astronomy Observatory (NRAO)  (McMullin et al. 2007 ). The calibrated data were produced by the ALMA observatory and following their delivery, where needed we made channel maps at maximal spectral resolution. The self-calibration and continuum subtraction of the images were done as part of the pipeline calibration. When converting the frequencies of the observed CO(1-0) spectra to velocities, we use a rest frequency of f CO(1-0) = 115. 271208 GHz. The CN absorption is more complex than that of CO due to its hyperfine structure and the lower spectral resolution with which it was observed.
ACKNOWLEDGEMENTS The authors gratefully acknowledge the anonymous referee for their comments, which helped us to improve the paper. We thank  Tom Oosterloo  for generously providing the H I detection of NGC 6868. T. R. is supported by the  Science and Technology Facilities Council (STFC)  through grant  ST/R504725/1. A. C. E. acknowledges support from  STFC  grant  ST/P00541/1. M. G. is supported by the  Lyman Spitzer Jr. Fellowship (Princeton University ) and by  NASA Chandra  grants  GO7-18121X  and  GO8-19104X. S. B. and C. O. are grateful for support from the  Natural Sciences and Engineering Research Council of Canada. G. R. T. acknowledges support from the  National Aeronautics and Space Administration (NASA)  through  Chandra Award Number   GO7-8128X8 , issued by the Chandra X-ray Center, which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of  NASA  under contract  NAS8-03060.
(2017)   [24]  validated the PXRF for in situ measurement of Hg in soils. Hu et al. (2017)   [25]  employed PXRF and Vis-NIR sensors to assess soil heavy metals pollution in Hangzhou, China. However, further application of PXRF has been deterred by drawbacks such as its high detection limit and low accuracy for certain heavy metals and because it is easily disturbed by environment conditions or other soil properties, such as water and pre-treatment steps. Co-kriging (COK) is the extension of ordinary kriging (OK) by incorporating auxiliary variables. The auxiliary variables should be more easily obtained than target variables  [26, 27]. The use of PXRF data as an auxiliary variable in COK mapping heavy metals distribution instead of direct use of PXRF data to employ interpolation is more promising than OK interpolation based on laboratory measurements or PXRF estimation values, because it can include more information to facilitate spatial estimation of soil heavy metals.
PXRF The calibration dataset (N = 97) was scanned using a Niton XL2 GOLDD XRF Analyzer (Thermo Fisher Scientific Inc. , Waltham, MA, USA) to determine the Cr, Pb, Cd, As and Ni concentrations. The instrument was calibrated before measurement and then corrected every 30 samples. The instrument was equipped with an Ag anode operating at a maximum of 45 kV and 80 µA. Before analysis, ground soils were put in a 31 mm X-ray sample cup and then covered with an X-ray film. Three parallel samples were scanned for each soil sample and the averaged measurement values were used to represent the content of heavy metals of soil samples. The time needed for each measurement was 90 s.
Data collection Table  1  provides a snapshot of the study sample, sampling criteria and methods adopted to collect data with each of these groups. We adopted criterion sampling  [28]  based on age to enrol children in our study. All children under the age of 5 years who were present at the study site were assessed for eligibility, recruited, and enrolled in the study. In all, 131 children (male: 46%, female 53%); (mean age: 31. 7 months) participated in the study. In 35% cases (N = 46), we used a local calendar to arrive at an approximate age of the child as the parents were unable to report exact age. While this calendar was useful in recalling events of the immediate past, it may have  Semi-structured interviews led to some age heaping around local festivals in the past years. Since the WHO Anthro software uses exact birthdate of the child, we have also reported results for a truncated sample that excludes this 35% of the sample.
In order to capture the perceptions of fathers, two Focus Group Discussions [Group 1 (N = 10), Group 2 (N = 8)] were conducted with men who had at least one child under the age of five. The FGDs focused on migration patterns, income utilization, and access to food and healthcare. Through snowball sampling  [31] , we also reached out to other stakeholders (N = 13)construction union leaders, staff of not for profit organizations, maternal health activists, doctors and academic researchers working on health or migration and government employees. Semi-structured interviews were undertaken with this group of participants, which focused on the larger context. By applying various methods of data collection (observation, interviews and focus group discussions) and by reaching out to various types of respondent groups as shown in Table  1  (mothers, fathers and other stakeholders) we were able to triangulate our data, which helped us to achieve a more complete understanding of the issues under study  [32].
Data analysis The anthropometric measurements were used to calculate z-scores of height-for-age (HAZ), weight-for-height (WHZ), and weight-for-age (WAZ), with the help of the World Health Organization's software 'Anthro' to categorize nutritional status as stunted, wasted or underweight respectively among children who were two standard deviations below the reference population. The interviews and FGDs were conducted in Hindi and Gujarati, languages spoken by the construction workers, and translated and transcribed into English. The first author and her research assistant are familiar with both the languages respectively, and thus were able to assess the accuracy of the translated transcripts. 4 Observational notes were also coded and analysed. Using MAXQDA, we employed a combination of a priori coding and emergent coding.
05) by pooling hormone concentration data across genotypes, where no genotypic effect was observed (n = 3). All in vitro precocious germination experiments were repeated at least three times with a minimum of 30 seeds per genotype and treatment. The experimental design was completely randomized, and the statistical analysis performed using χ 2 test for homogeneity of the binomial distribution. A proportion test analysis was performed when significant differences between treatments were observed. Statistical tests were considered significant when P ≤ 0. 05. All statistical analyses were performed using Rstudio software.
ACKNOWLEDGMENTS We thank  Mr. R. Creasy ,  Mr. B. Piasini , and  Mr. L. Hodgson  for glasshouse expertise. We also thank  Dr. David Nichols  and  Assoc. Prof. Noel Davies  ( Central Science Laboratory, University of Tasmania ) for mass spectrometry,  Toby Ling  for sample preparation,  Prof. Lewis Mander  for providing labeled GAs, and  Prof. Jerry Cohen  for labeled 4-Cl-IAA.
Results To study the global impact of allopolyploidy on sRNA populations, we analyzed a total of $15,000,000 Illumina sequence reads of 18-26-nt in size (bioinformatic treatment, normalization of the read counts, and further analyses of the sequencing data are detailed in Materials and Methods). These reads were generated from sRNA-enriched libraries constructed from three resynthesized B. napus allotetraploids at two different generations of selfing and their diploid progenitors B. oleracea and B. rapa (fig. 1 ). To allow integration of the current data with available functional data generated from the same plant material  (Albertin et al. 2006 (Albertin et al. , 2007)) , we specifically collected and used stem tissues.
The Global Expression of miRNAs Was Not Significantly Affected by Allopolyploidy We first considered the subset of sRNAs corresponding to miRNAs. We identified conserved miRNAs by comparing the entire set of unique reads for the 21-and 24-nt sRNA classes to the plant miRBase database (release 19). No conserved miRNAs were identified among the 24-nt reads in accordance with the rarity of 24-nt miRNAs in the plant miRBase. Among the 21-nt class, more than one third of the sRNA reads represented previously known miRNAs (fig. 3A ). In total, we retrieved 133 unique known miRNAs corresponding to 28   S3 , Supplementary Material online) according to the classification published in miRBase (Kozomara and Griffiths-Jones 2014); these included 24 different miRNA families that had been previously identified in Brassica.
The Expression Changes of sRNAs Derived from TEs in Response to Allopolyploidy Were TE Dependent For each of the two 21-and 24-nt classes, we identified sRNAs derived from TEs (thereafter denominated siRNAs for small interfering RNAs, Axtell 2013) by subjecting the sequence reads to BLAST analyses using Repbase in addition to a locally developed Brassica TE database (see Materials and Methods), first with no distinction between TE families. For the two sRNA classes of 21-and 24-nt long taken as a whole, TEderived sRNAs corresponded to 13. 91% (representing 669,168 raw read counts), 7. 96% (242,119), and 12. 04% (277,461) of the reads from the diploid parents and the S1 and S5 generations, respectively. Among the 21-nt class, relatively few reads were similar to TE sequences corresponding on average to 7.
6B  and supplementary fig. S3 , Supplementary Material online). We undertook the identification of the molecular origins of these S1-specific 21-nt-siRNA populations. No clear information was retrieved from exploitation of the Brassica genome sequences publically available. Nevertheless, exploitation of unannotated B. napus scaffolds available to us led to sequence alignments between 15-and 20-nt in length, with no mismatch, for all the 68 siRNAs analyzed, with a total of 246 different B. napus unannotated scaffolds retrieved (data not shown); the siRNAs under survey may thus originate from genomic sites that were not integrated in the published assembled Brassica genomes. We also exploited B. napus mRNAseq data; the data were generated from high-throughput sequencing of RNAs extracted from meiocytes of two different oilseed rape varieties that were both analyzed at two different ploidy levels, haploid versus diploid (NCBI BioProject PRJNA362706,  Lloyd et al.
napus EST, previously identified when analyzing the population of transcripts in young flowers and flower buds (Evalue of 3e-90 with 96% identity covering 201 bp of the cDNA sequence #CX187929). Four of the retrieved mRNAseq reads matching four different siRNAs of interest (the viruslike derived "siRNA21. 26" and "siRNA21. 39," in addition to "siRNA21. 21" and "siRNA21. 56") were quantified by qRT-PCR; they displayed the same dynamics of expression as the corresponding siRNAs: the results revealed significant transcript amounts in the F1 hybrids, the S0 and S1 generations, but no expression in the diploid progenitors or in the S3 and S5 generations (fig. 6B  and supplementary fig. S3 , Supplementary Material online). PCR amplifications on Brassica DNA extracts, using primers designed in each of the two viral-like transcripts of interest, gave multiband profiles (fig.
6C ) suggesting a dispersed location of these putative virus-derived sequences in the Brassica genome; this recalls the main features of endogenous viral elements, EVEs, that were demonstrated to be present in all eukaryotic genomes  (Feschotte and Gilbert 2012) , and mainly disseminated across the genome as truncated copies  (Bertsch et al. 2009 ). Altogether, our data have demonstrated the high mobilization of specific 21-nt-siRNA populations in the very first generations of the neoallopolyploid formation, including the early step of the interspecific hybridization represented by the F1. Their mobilization was concomitant with the expression of noncoding transcripts that also appeared specific to the early generations of the resynthesized B. napus allotetraploids. Production of 21-and 24-nt siRNAs Similar in Sequence Was Highly Reorganized during the Early Stages of the Neoallotetraploid Formation Expression levels were assayed by Northern blot hybridization for four of the sixty-eight 21-nt siRNAs that displayed significantly higher levels of accumulation in the S1 generation; they were considered as anonymous as their genomic origins could not be determined precisely.
Bioinformatic Analyses Quality control and preprocessing-A total of 23,102,628 sequence reads were obtained after Illumina sequencing of the eleven sRNA libraries that were specifically constructed for the present study (fig. 1 ). The quality control tool for highthroughput sequence data FastQC (Version 0. 10. 1; http:// www. bioinformatics. babraham. ac. uk/projects/fastqc/) was used for validation of the sequencing data to be further surveyed. Sequences of adaptors, as well as empty reads, low complexity reads and reads smaller than 18-nt and bigger than 26-nt were removed by using Filter Tool from the UEA sRNA toolkit  (Moxon et al. 2008). In a subsequent step, Filter Tool was used to remove reads similar to transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs). To increase the sensitivity of Filter Tool, we built Brassica-specific tRNA and rRNA databases from de novo predictions using locally the tRNAscan-SE 1.
21 software  (Lowe and Eddy 1997; Schattner et al. 2005 ) and the RNAmmer 1. 2 software  (Lagesen et al. 2007 ) on the genome sequence of B. rapa (B. rapa Genome Sequencing Project Consortium,  Wang et al. 2011). We predicted a total of 1,138 tRNA sequences and 79 rRNA sequences; these sequences were then added to the whole set of t/ rRNAs registered in the Rfam 10. 1 RNA family database  (Griffiths-Jones et al. 2003). Sequence reads matching t/ rRNAs (no mismatches allowed, 100% coverage) were eliminated from our present analysis. Finally, a total of 14,917,073 sequence reads were further analyzed. Quantification of sRNA populations and normalization-Each sRNA library was analyzed individually; read counts were normalized to allow direct comparisons between sRNA data from the different libraries (see below, statistical analysis).
Size distribution of sRNAs was first computed for comparisons between generations. Then, full annotation of the sRNA classes of interest was performed to tentatively identify their origins. Identification of known and putative miRNAs-Each of the eleven short-read libraries was searched for known miRNAs using miRProf (from the UEA sRNA toolkit,  Moxon et al. 2008 ) that identifies sequence reads matching already registered miRNAs in miRBase (version 19, database: plant_mature, with the options: group-organisms, group-family, and collapse-match-groups were enabled; Griffiths-Jones 2004, http://www. mirbase. org/). A locally developed Perl script was used to estimate expression levels for each known miRNA retrieved from our sequence data; results were summarized in supplementary table  S3 , Supplementary Material online.
Identification of sRNAs derived from TEs-Sequences of sRNAs that were not identified as miRNAs were compared with the Repbase database and a home-made Brassica TE database (Sarilar V, Martinez Palacios P, Joets J, Alix K, unpublished data) using Blast (word size ¼ 21 or 24), and only perfect matches were further considered. Annotation of individual TE-derived siRNAs was made according to the TE class/order/superfamily they originated from, following the TE classification from  Wicker et al. (2007). Search for the genomic origins of sRNAs of interest-Search for the genomic origins of the sRNA populations specifically expressed in the S1 generation was first performed by alignments of the reads against Brassica genome resources (genome of B. rapa,  Wang et al. 2011; genome of B. napus, Chalhoub et al.