dataesr/scientific-paragraphs-categorization huggingface dataset:
Quality control We used the simpleaffy Bioconductor package  [10]  to check the quality of each individual CEL file. As can be seen in Fig. 1 , all the CEL files contained a sufficiently large percentage of present calls (N40%) and all the scale factors lie within a 3-fold range, which indicate good quality according to Affymatrix guidelines  [1]. Eighteen CEL files yielded larger beta-actin and GAPDH 3′/5′ ratios than the threshold published in the Affymetrix guidelines (Fig. 1 ), which indicates that RNA degradation might be an issue for these samples. However there is no consensus regarding this quality metrics as to what is acceptable or should be rejected for further analysis, we therefore used all the samples in our study  [3]. Abbreviations: HER2, human epidermal growth factor receptor 2; FISH, fluorescent in situ hybridization; pCR, pathologic complete response.
The first two probes have been tagged as high quality in the CleanEX database  [9]  while the probeset 237469_at exhibited low sequence specificity. Supporting this observation, the first two probesets were highly correlated (Pearson correlation coefficient N 0. 83) while the correlation with the third one is low (0. 56 and 0. 52 respectively). Since the probeset 201291_s_at showed the greatest variance [variance of the 3 probesets:  Fig. 1. Quality controls for the Affymetrix Raw data generated in  [3]. Cel file name for each experiment is provided on the left side, followed by the percentage of present and absent calls (in red) folowing the Affymetrix guidelines. The blue region in the middle of the plot represents the 3-fold region for scale factor as this region is considered as acceptable according to Affymetrix guidelines; any scale factor outside this region is drawn in red as it is considered an indicator of poor quality.
Discussion We described here a unique dataset of patients with ER-breast tumor treated with anthracycline. This dataset is composed of clinical data, including pathological response to anthracycline and genomewide gene expression measured using Affymetrix GeneChip platform. We showed that the gene expression data are of high quality and are concordant with important clinical parameters. This dataset has been recently used in studies published in high impact journals  [7, 8] , which demonstrate the importance and the benefit of data sharing for biomedical research.
Although there is a slight but consistent upward trend in published ''IWM'' articles in the Scopus database, the upward trend in ''weed control'' articles is much stronger (Figure  2 ). Furthermore, during the 17-yr period from 1995 to 2011, ''weed control'' articles outnumbered ''IWM'' articles by a ratio of 14 to 1 (10,359 to 720). One purpose for our simple queries of ''weed control'' and ''weed management'' (Figure  2 ) was to determine if weed scientists were shifting their thinking in terms of how weeds should be managed. If IWM is to become more widely implemented in research programs as well as on the farm, replacing a weed control mentality with a weed management mentality will be necessary. The evolution of strategies from weed control to weed management to IWM in research publications suggests a necessary shift in research emphasis toward more sustainable weed management techniques.
Acknowledgements Research partially funded by the  Centre National d'Etudes Spatiales (R&T) , the  European Research Council  advanced grant " Twelve Labours ", the  Office of Naval Research  (grant  N00014-97-1-0839 ),  STIC-AmSud  ( 11STIC-01 -MMVPSCV ), and the  Uruguayan Agency for Research and Innovation (ANII)  under grant  PR-POS-2008-003.
In this paper we report the assembly of a comprehensive DNA barcode library for a taxonomically very well-known fauna -five of the seven subfamilies of European geometrid moths. We use this data to test the correspondence between the BIN system currently implemented in BOLD and recognised species boundaries. The specific aims of this study are (a) to present a public data release of DNA barcodes for five subfamilies of European geometrids, (b) to critically analyse intraspecific variation and interspecific distances in the barcode region and how they relate to traditionally recognized species, and (c) to test the correspondence between BINs and traditionally recognized species. The latter test is important because DNA barcode records are growing rapidly, and often involve poorly known faunas or include records that have received little or no taxonomic scrutiny.
Specimens collected outside Europe were included for 32 of the 54 missing species, adding an additional 520 specimens. The data set is somewhat geographically biased with Eastern Europe underrepresented. The largest samples derive from Italy (619 sequences; 107 species = 78% coverage of the national fauna), Germany (227 sequences; 55 species = 73%), Spain (215 sequences; 81 species = 48%), United Kingdom (146 sequences; 30 species = 55%), Finland (140 sequences; 43 species = 90%), France (118 sequences; 51 species = 36%) and Greece (88 sequences; 54 species = 50%).
DNA analysis PCR amplification and DNA sequencing was performed at the Canadian Centre for DNA Barcoding following standard high-throughput protocols  [21, 22]  that can be accessed at http://www. dnabarcoding. ca/pa/ge/research/protocols. PCR amplification with a single pair of primers usually recovered a 658 bp region near the 5' terminus of the mitochondrial cytochrome c oxidase I (COI) gene that includes the standard 648 bp barcode region for the animal kingdom  [1]. All barcoded voucher specimens are listed in Appendix S1 and Appendix S2. A DNA extract from each specimen is cryopreserved at the Canadian Centre for DNA Barcoding and in the DNA-Bank facility of the Bavarian State Collection of Zoology (see http:// www. zsm. mwn. de/dnabank/). All sequence records together with images, voucher deposition details, GenBank accession numbers, GPS coordinates, sequence and trace files are available on BOLD as a single citable dataset (http://dx.
Data analysis The analyses of genetic distances were restricted to sequences > 500 bp unless stated otherwise (in single cases, shorter sequences are discussed in Appendix S1). Sequence divergences for the barcode region were quantified using the Kimura 2 Parameter model, employing the analytical tools in BOLD (BOLD alignment, pairwise deletion). Genetic distances between species are reported as minimum pairwise distances, while intraspecific variation is reported as mean and maximum pairwise distances. Each specimen with a sequence longer than 500bp (listed in Appendix S1, similarities visualized in a neighbor joining tree, Appendix S5, records analysed in March 2013) automatically gained a BIN assignment on BOLD. It should be noted that BIN assignments are dynamically updated as new records are added to BOLD.
Traditional and BIN species delineations In total 2,150 European individuals of the five geometrid subfamilies, including many older specimens (maximum 90y, mean 10. 3y), were submitted for DNA barcoding. Sixteen (0. 8%) generated sequences that proved to be contaminants, while 393 (18%) generated no sequence information. Sequences were obtained for 1,741 European geometrid specimens representing 187 species. Most sequences (1,610 individuals belonging to 183 species and 224 BINs; cf. Figures  1 2 ) were longer than 500 bp, meeting the length requirement for DNA barcode status  [14]. Additional data for 32 European species was based on specimens collected outside Europe (see Appendix S1). Most were from Turkey or the Middle East (20 species), but some derived from North Africa (9) and Russia (3).
Acknowledgements Many colleagues at the  Biodiversity Institute of Ontario  contributed to the success of this study. We are particularly grateful to  Evgeny Zakharov. We are grateful to  Vlad Dinca  for commenting on the manuscript. We thank  Stoyan Beshkov  (Sofia),  Ernst Brockmann  ( Lich-Oberbessingen ),  Feza Can (Hatay) ,  Omar Cao (Nuoro) ,  Bernard Dardenne (Rouen) ,  Thibaud Decaens (Rouen) ,  Camiel Doorenweerd  (Leiden),.
Acknowledgements The authors wish to thank the  Pays de la Loire region  for the Envoleo grant and the  European Erasmus commission  for the Erasmus grant through the engineering school of  Centrale Nantes , which enable the student to carry out the project. The authors also wish to thank the  European Commission  through  CEHIDRO (Hydraulic Research Centre  from the  Civil Engineering Department of Instituto Superior Técnico (IST)-University of Lisbon ) and research project  EU  HYLOW from the  EU 7th Framework Program for Research and Technological Development , (Grant no. 212423 ), which contributed to the development of this research.
Statistical Analysis The genotype data were first used to estimate genotypes for other common variants across the region in the study subjects by imputation, with IMPUTE v. 2. 2 and the March 2012 release of the 1000 Genomes Project as reference panel. Genotypes at 2,291 SNPs could be imputed with imputation r 2 > 0. 3. Per-allele ORs for each SNP were estimated by logistic regression, including study and principal components (seven in Europeans, two in Asians, and two in African Americans) as covariates, to allow for potential population stratification as previously described. 6 o determine the minimal number of SNPs independently associated with breast cancer for each ethnicity, forward stepwise logistic regression analysis was applied (with the R function step) to all SNPs with a MAF > 0.
Antibodies for Ser5P RNA polymerase II (ab5131) were also obtained from Abcam, UK. In these experiments primer pairs from the genomic region of 8q24 (see Table  S1 ) were used as negative control. Each ChIP has yielded similar results in at least two independent experiments. For the rs2981578 TaqMan assay, titrations were carried out to show that Ct values were directly proportional to input for each of the alleles. The error bars denote the standard deviation in three technical replicates. The data are also presented in the form of allelic discrimination (AD) plots. The AD plots are obtained from a TaqMan assay in which two different fluorophores are each linked to a probe detecting the two different alleles. Amplification of each allele was followed with an Applied Biosystems Real Time PCR machine (7900HT) and the data analyzed with the SDS software.
Chromatin Conformation Capture Chromatin conformation capture (3C) libraries were generated with EcoRI as described previously. 18 3C interactions were quantitated by real-time PCR (qPCR) with primers designed within EcoRI restriction fragments (Table  S3 ). qPCR was performed on a RotorGene 6000 with MyTaq HS DNA polymerase (Bioline) with the addition of 5 mM of Syto9, annealing temperature of 66 C, and extension of 30 s. 3C analyses were performed in two independent experiments with each experiment quantified in triplicate. BAC clone RP11-62L18 covering the 10q26 region was used to create an artificial library of ligation products in order to normalize for PCR efficiency. Data were normalized to the signal from the BAC clone library and, between cell lines, by reference to a region at within GAPDH (MIM 138400).
Analysis of Protein-DNA Interactions By using data from ENCODE, we examined DNase hypersensitivity and known transcription factor binding at each of the potential causative SNPs in risk signals 1, 2, and 3 (Table  1 ) and selected for further analysis five SNPs (rs35054928, rs2981579, rs2912779, rs2981578, and rs45631563) that were found to be present in a DNase hypersensitive site (DHS) in MCF-7 cells (UCSC Genome Browser), because confirmed GWAS hits have been shown to be enriched in open chromatin. 21 Electrophoretic mobility shift assays (EMSAs) were carried out for these variants. For rs35054928 we detected binding by two protein complexes. Competition experiments suggested that the strong (lower) band is likely to be E2F1 (Figure  2A ), which has also been reported to bind at this site in vivo (UCSC Genome Browser), whereas the weaker band could be competed with SP1 oligonucleotides (Figure  2A ).
ChIP was also carried out with SP1 antibodies but no enrichment or allele-specific binding was detectable at this site (data not shown). Having detected allele-specific binding by FOXA1 and E2F1, we examined the transcriptional activity of the locus by assaying binding of RNA polymerase II at the risk SNPs via ChIP. We used antibodies against the serine-5-phosphorylated (Ser5P) form of this enzyme, which catalyzes transcriptional initiation and elongation and has been shown to be a good read-out for transcriptional activity. 23 Figure  5  shows that Ser5P-Pol II can be precipitated at the rs2981578 site. In the heterozygous cell line MDA-MB-134, there was a 3. 8-fold greater enrichment of the C over the T allele (Figure  5A ), which is visualized in the allelic discrimination plot (Figure  5B ).
Microarrays We hand selected 13-to 15-h svb R9 or pri 1 embryos using GFP balancers. We subjected 200 embryos to trizol (Invitrogen, Life Technologies, Carlsbad, CA, USA) extraction and RNA quality was monitored using Agilent Chip. Five independent samples of each genotype were used for microarrays (Affymetrix, Santa Clara, CA, USA; IGBMC, Strasbourg, France). Data extraction and normalization were performed using Affymetrix software and statistical analyses with R. A more than two-fold difference in expression levels between mutant genotypes was the most efficient criterion to retrieve Svb downstream genes (with a false discovery rate of 0. 01 for pri). The top 150 genes down-regulated in both pri and svb mutants defined the set of Svb-regulated genes. One-hundred genes showing irrelevant variation of their expression (P-value > 0.
ChIP-seq A svb rescue construct (RSQ8)  [34]  was used for ChIP-seq experiments. It expresses a Svb-GFP protein under the control of two svb enhancers (medial and proximal) driving specific expression in epidermal trichome cells. Stocks were expanded to fill three population cages. Adults were allowed to lay eggs for 2 hours on apple juice plates covered with yeast. Embryos deposited on the plates were aged for 12 h at 25°C. Chromatin was collected from approximately 100 mg of whole embryos for each replicate chromatin collection. ChIP was done with an anti-GFP antibody as described  [8]. Data presented are from two independent replicates. Peaks were called for single replicates using MACS P < 0. 00001 for downstream computational analyses. MACS was used to call loose criteria peaks for two replicates of RSQ8 12-to 14-h embryos.
Those peaks were then used for an IDR (Irreproducible Discovery Rate) analysis (IDR = 0. 02). DNA sequencing libraries were generated with Nextera DNA Sequencing Library kits (VWR, Radnor, PA, USA) Details are given in Additional files 3 and 4. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [66] and are accessible through GEO series accession number GSE48791.
Motif detection and genome analysis Detection of motifs enriched in Svb-dependent and Svbindependent epidermal genes was performed using cis-TargetX  [40]. For de novo motif discovery, genomic sequences of enhancer and negative regions were processed through a C++ program and statistical operations performed within the R software, as described  [47]. To compute the cross-correlation between conserved motif instances and Svb ChIP-Seq data, we defined a 10 kb window centered around each ChIP peak, collected distances of each motif to the peak center and plotted these values using a 500 bp bin. In the cases of Svb-regulated and control genes, each ChIP peak was associated with the nearest transcription start site. Further details are available in Additional files 3 and 4.
Acknowledgements We are grateful to the  Bloomington Drosophila Stock Center and Drosophila Genomic Resource Center  for providing us with flies and molecular clones. We are indebted to  B Ronsin  ( Toulouse RIO Imaging ),  P Valenti  and  O Bohner  for excellent technical assistance. We also thank  C Hermann ,  N Negre ,  E Preger-Ben Noon ,  A Vincent  and members of the  SP/FP lab  for critical reading of the manuscript. This work was supported by grants from  ANR Blanc 'Netoshape' ,  Association pour la Recherche contre le Cancer  (n°3832, n°1111,  SFI20101201669  and fellowships to IF and DM) and  University Paul Sabatier  (cisdecode). SA was supported by grants from  FWO  ( G. 0704. 11N  and  G. 0640. 13 ) and  HFSP  ( RGY0070/2011 ).
AV-45 PET scans All participants underwent a PET scan using AV-45 amyloid marker. All scans were performed on the same hybrid PET/CT scanner (Biograph 6 TruePoint Hirez; Siemens Medical Solutions) using 3-D detection mode producing images with voxels of 1×1×1. 5 mm and a spatial resolution of approximately 5 mm full-width at half-maximum at the centre of the field of view). Both CT and PET scans were acquired. Cerebral emission scans were started 50 min after injection of 3. 7 MBq/kg weight of AV-45 and lasted for 20 min. PET data were corrected for partial volume effects using the point spread function (PSF) modelling implemented by Siemens (HD-PET©). Briefly the manufacturer previously measured the response of a point source from many points in its FOV and incorporated measured PSFs in the reconstruction algorithms.
Image processing Route 1: Template-based AV-45 quantification AV-45-PET scans were linearly registered with 12 degrees of freedom (DOF) and trilinear interpolation onto a template from Avid (http://www. avidrp. com/), which is in the Montreal Neurological Institute (MNI) space, using FLIRT (FSL library tools)  [22]. Regional cortical AV-45 mean SUV was measured in each subject using a Matlab (The MathWorks®) script developed in-house (Fig. 1a ). Mean global SUVs, SUVs in the five lobes, and SUVs in specific regions of interest (ROIs: orbitofrontal, anterior cingulate, posterior cingulate and precuneus) were calculated. These regions were selected as they have shown different AV-45 binding in studies comparing AD patients to CN subjects  [8, 9, 12, 16, 23]. These regions were defined using the anatomical automatic labelling (AAL) atlas  [24]  provided by MRIcron software, that was linearly registered (12 DOF, trilinear interpolation) onto the AV-45 template and binarized.
To minimize the bias due to nonspecific white matter binding, the AAL atlas was masked for grey matter. To do so, we used the grey matter probability map from SPM8 (Wellcome Trust Centre for Neuroimaging, London, UK), that was binarized using a threshold of 0. 3meaning that only voxels with a probability greater than 30 % of being grey matter were selected. SUVs were then normalized (SUVr) to the whole cerebellar mean uptake (vermis excluded) using Matlab and pooled by group for statistical comparison (Fig. 1a ).
Route 2: Cortical MRI-based AV-45 quantification Grey matter was segmented from the 3-D T1 anatomical image of each participant using FAST and FIRST (FSL library tools), and the resulting grey matter mask was binarized using a threshold of 0. 3as used for route 1  [25]. Then, for each subject, the CT scan obtained during PET data acquisition was linearly registered (12 DOF) onto the relative T1 anatomical image. The transformation matrix obtained was applied to the AV-45 image of the subject so that the AV-45 image was in the T1 space. The grey matter mask from T1 segmentation (see above) was applied to the transformed AV-45 image. The AAL atlas  [24]  was also registered from MNI space to each individual T1 space. To do this, the linear transformation matrix from the registration of each single subject's T1 onto MNI space was first calculated.
The inverse of this matrix was then applied for each subject to the AAL atlas to obtain ROIs in the subject's space. Finally, the registered atlases were binarized. The cerebral regions studied were the same as those studied in route 1. Values of regional cortical AV-45 mean SUV were obtained for each subject using the same Matlab script as in route 1 (Fig. 1b ). SUVs were normalized (SUVr) to the whole cerebellar mean uptake (vermis excluded, identical definition to that used in route 1) and pooled by group for statistical comparison.
Results Twenty-two patients with prodromal AD and 17 CN subjects were included in the study (see supplementary data 1 for individual profiles on research criteria). The two groups were matched for age and level of education (see Table  1  for clinical and neuropsychological data). Visual assessment of AV-45 PET scans revealed amyloidpositive profiles in 18 patients out of 22, and in 2 CN subjects out of 17. Fig. 1  AV-45 quantification procedures: a cortical PET template-based (route 1) and b cortical MRI-based (route 2). a In route 1, AV-45 acquisitions were linearly registered onto a template from Avid (http:// www. avidrp. com/), and regional uptake was quantified for each subject in the MNI space using the AAL atlas  [24]  that was masked with a grey matter probability map. Uptake values were collected using Matlab.
b In route 2, for each subject, the CT scan acquired together with the AV-45 image was registered onto the MRI anatomical space defined by the T1 image of the subject concerned (1). The transformation matrix obtained was then applied to the AV-45 image of the subject so that the AV-45 image was in the T1 space  (2). A binarized grey matter mask obtained from MRI segmentation was applied to the transformed AV-45 image (3). The AAL atlas was also registered onto each individual T1 space using the inverse of the transformation matrix from the T1 registration onto MNI space (4). Regional cortical uptake was collected for each subject using Matlab  (5). Arrows represent transformation (Subj subject).
Classification and features 16S rDNA gene sequence analysis A representative genomic 16S rRNA gene sequence of A. finegoldii AHN2437 T was compared using NCBI BLAST  [13, 14]  under default settings (e. g. , considering only the high-scoring segment pairs (HSPs) from the best 250 hits) with the most recent release of the Greengenes database  [15]  and the relative frequencies of taxa and keywords (reduced to their stem  [16] ) were determined, weighted by BLAST scores. The most frequently occurring genera were Alistipes (84. 4%) and Bacteroides (15. 6%) (19 hits in total). Regarding the three hits to sequences from members of the species, the average identity within HSPs was 98. 7%, whereas the average coverage by HSPs was 98. 0%. Regarding the nine hits to sequences from other members of the genus, the average identity within HSPs was 96.
5%, whereas the average coverage by HSPs was 100. 1%. Among all other species, the one yielding the highest score was Alistipes shahii (AB554233), which corresponded to an identity of 97. 2% and an HSP coverage of 100. 0%. (Note that the Greengenes database uses the INSDC (= EMBL/NCBI/DDBJ) annotation, which is not an authoritative source for nomenclature or classification. ) The highestscoring environmental sequence was AY643083 (Greengenes short name 'Isolation finegoldii blood two patients colon cancer Alistipes finegoldii; clone 3'), which showed an identity of 100. 0% and an HSP coverage of 99. 4%. The most frequently occurring keywords within the labels of all environmental samples which yielded hits were 'human' (11. 5%), 'fecal' (8. 1%), 'intestin' (5. 5%), 'biopsi' (4. 2%) and 'mucos' (4.
Genome project history This organism was selected for sequencing on the basis of its phylogenetic position  [34] , and is part of the Genomic Encyclopedia of Bacteria and Archaea project  [35]. The genome project is deposited in the Genomes OnLine Database  [23]  and the complete genome sequence is deposited in GenBank. Sequencing, finishing and annotation were performed by the DOE Joint Genome Institute (JGI) using state of the art sequencing technology  [46]. A summary of the project information is shown in Table  2.