dataesr/scientific-paragraphs-categorization huggingface dataset:
Although the residuals of preliminary models were Gaussiandistributed, the variances were still heterogeneous. Consequently, we used a variance power function to account for heteroscedasticity in the models, i. e. , Var  (Pinheiro and Bates, 2000, p. 211). Note that the parameter θ i in the variance function is root type dependent. ε ij = σ 2 CSD 2θ i ij Both models were fitted using a generalized least squares (GLS) estimator (function gls in the R package nlme; R Core Team, 2012). The two models were compared to simpler nested models using Akaike's information criterion  (Pinheiro and Bates, 2000, p. 84)  to ensure that they were not overparameterized (Table  2 ). A potential random effect of tree was also tested using a mixed model approach, but the effect was not significant. The empirical correlations calculated from the within-subject residuals (cf.
Fortin et al. , 2008)  were small in all three datasets, demonstrating that there was no need for a random effect of tree in the models. The amount of lost roots was then estimated for all broken root ends using the QR 0 model, because it could be used also for the small CSDs. Because the back transformation of the predictions to the original scale is subject to a bias  (Gregoire et al. , 2008) , we used a naive correction that consisted of adding the prediction error variance to the squared estimate (cf. Fortin et al. , 2008).
RESULTS A good fit was obtained for all models, except with the QR 0 model in the L5 dataset (Figures  3 4 5 6 ; Tables  2 3 4 5 ). Two curves were parabolic (Figure  7 ): that for shallow roots beyond the ZRT and that for higher-order shallow roots. In these cases, the few roots with the largest CSD had a low descendent root volume. Thus, QR 0 may not deliver accurate estimation outside the range of CSD values used for estimation. However, this did not cause inaccuracies in estimates of lost volume, because the CSDs of the largest broken root ends were much smaller than the largest CSDs used to develop the models (1. 5 cm in shallow roots beyond the ZRT and 1 cm in higher-order shallow roots). As a result of the selection procedure, large root sections within the ZRT itself were not included in LR 1 (Figures  3 4 5 ).
Root type strongly influenced the intercepts, first-order, and second-order model coefficients for both models in all three of the datasets (P < 0. 001; Figures  3 4 5 ; Tables  3  and 5 ). γ i varied from 3. 5 to 9 between deep roots and shallow roots. As a consequence, the predicted V d for a 2 cm CSD root in L19 reached 43 cm 3 in deep roots, 102 cm 3 in intermediate depth roots, and 290 cm 3 in shallow, second-order roots beyond the ZRT. Rankings of root categories were fairly consistent across the three datasets. Roots could be roughly divided into four categories according to Data were either restricted to root segments from structural roots or included all root segments. 'Global' denotes that the parameter was fixed for all root types, 'Root type' denotes that it varied by root type, and '/' denotes that it was not included in the model.
DISCUSSION This study has shown that the relationship between root CSD and descendent volume varies largely as a function of root type. In quantifying these relationships, we have demonstrated that it is possible to accurately estimate the amount of missing root material from datasets describing 3D root system architecture. Estimation Frontiers in Plant Science | Functional Plant Ecology    is possible provided that the root dataset contains sufficient information on reasonably intact root axes and segments from which to derive a relationship. Basing the estimate on volume makes for a significant time savings over methods that require biomass to be measured for roots of varying sizes. Root loss estimations are much more accurate when roots are classified by type, and the numerical relationships used to estimate missing volume are stratified according to these classes.
VARIATION IN TAPER The largest difference in γ i was between deep roots and shallow roots beyond the ZRT. For a given diameter in the L19 dataset, LR 1 estimates that deep roots had about six times less (and sinkers had about two times less) root volume descending from them than shallow second-order roots beyond the ZRT. In FBA studies, the taper at branches, when mentioned  (Soethe et al. , 2007) , is close to 1 (i. e. , the sum of CSAs proximal and distal to the branch are equal). This is in accordance with the pipe model  (Shinozaki et al. , 1964). Thus, the slope γ i mainly accounts for tapering between branches of all descendant root segments in an integrative way and also to branching in fine roots. This means that deep roots, the taproot, and to a lesser extent sinkers and oblique roots, have a high overall tapering rate.
The first-order root tapers strongly below this point to form the taproot. Prediction of V d in the first-order root would therefore be unreliable using a single set of parameters for both the root stump and the taproot. In our database the root stump was never broken, so there was no practical reason to estimate its descendent volume. All trees in this study were from the same species, the same genetic provenance, and grown in the same stand. As no tree effect was detected, these trees probably exhibit low intra-population genetic variability, low plasticity to the micro-environment, and a minimal effect of tree size for the studied relationship. A small but significant increase in γ i was observed with age for certain root types. However, we could not assess whether there is a global age effect, because, in L5, the number of segments was only sufficient to provide reliable estimates in the first-order root and in shallow roots.
A further improvement of our method would be to split the estimates of lost root volume into root types or root diameter categories (Le  Goff and Ottorini, 2001). This technique could also be used to estimate structural root length and root number. However, fine roots make up a large proportion of root length and number. It could also be used to estimate the number of fine roots branching from the structural roots, possibly on a sub-sample of intact root branches carefully excavated and digitized, including counts of fine roots. A recurrent procedure could be used to compute parameters of QR 0 from all the segments of the root systems whether they carry large broken root ends or not. This would entail attributing a lost root volume (from QR 0 ) to all broken root ends of the database, and recomputing a corrected descendant root volume for each segment.
ROOT LOSS IN LARGER TREES The low fraction of missing roots in our database can be partly attributed to the fact that we worked in shallow, sandy soils, excavated during the wet season, and dealt with root systems that were mainly composed of long, shallow roots and sinkers. The amount of lost roots is expected to be larger without preliminary removal of understory plants and soil preparation. Root loss would also be large in stony, hard, or deeper soils, or for heart root systems (i. e. , those with a large proportion of oblique roots originating from the root stump;  Danjon et al. , 2013). Even in deep soils with deep-rooting species, only few structural roots can be found below a 4 m depth  (Christina et al. , 2011). While low, the root biomass losses reported here may be slight overestimates.
There was high variability in the database used herein, despite the fact that it described a single, monospecific stand with a single provenance. Exceptions may be found: for example, roots specialized for starch storage, roots with a specialized mechanical function (the taproot in Q. petraea;  Reubens et al. , 2009) , or adventitious roots originating in stems, as in the banyan, mangrove, and certain orchids. The datasets of  Kalliokoski et al. (2010)  and  Nielsen and Hansen (2006)  suggest that there can also be a large inter-species and inter-soil-type variability for γ i. Insofar as it describes a rate of tapering, the magnitude of γ i may vary with root type consistently across species. For example, the magnitude may correspond to the strategy used for anchorage, water uptake, or nutrient absorption.
ACKNOWLEDGMENTS We thank  INRA ,  UR1263 Ephyse  ( Pierre Trichet and Michel Sartore ), and  INRA ,  UE0570 Forêt Pierroton , for installing and maintaining the experimental stand. We thank  Florence Courdier ,  Bernard Issenhut ,  Didier Bert ,  Annabel Porté ,  Frédéric Lagane ,  Jean-Paul Chambon ,  Frédéric Bernier ,  Rim Khlifa ,  Nicolas Cheval ,  Pierre Trichet , and  Marie-Charlotte Descorps  for the uprooting and measurements. Fundings were provided by  Conseil Régional d'Aquitaine and the French state Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt ,  Fortius  project. L5 measurements were funded by the  European Community  (contract no. AIR3-CT93-1269 ). Additional support was provided by a grant from  USDA-NIFA  ( 2009-35900-06016 ).
The 15K custom microarray (Agilent Technologies) contains 8,691 60-mer oligonucleotides specific for the 2134 predicted genes and long intergenic region of the NEM316 strain  [90]. Arrays were scanned in an Axon 4000B dual laser scanner and treated as described  [90]. Raw data have been submitted to the ArrayExpress database under the Accession number E-MEXP-3703 (http://www. ebi. ac. uk/arrayexpress/). Hierarchical clustering were done with Cluster 3. 0 and visualized with Java TreeView.
In vivo virulence assay Two-days old neonatal Sprague-Dawley rat pups (Janvier, Le Genest Saint Isle, France) were used for mortality curve experiments. Randomized groups of 10 neonatal rat pups were infected by intraperitoneal (i. p. ) injection with 5610 6 bacteria in 100 ml PBS. Survivals were monitored for five days after injection and two independent experiments were carried out. The eight predicted transmembrane domains with a probability greater than 0. 5 are illustrated above with red boxes. The four transmembrane domains containing the conserved Abi-domain signature are highlighted with a yellow box. (B) Identification of Abi-domain proteins in the genome of S. agalactiae, S. pneumoniae, and S. pyogenes. Proteins containing the Abi-domain (Pfam domain PF02517, Wellcome Trust Sanger Institute, UK) were retrieved from a representative genome of each species.
Acknowledgments We would like to thank  Carmen Buchrieser ,  Cecile Wandersman  and  Alexandra Gruss  for their critical reading of the manuscript;  Daniel Ladant  and  Gouzel Karimova  for plasmids and advice on double hybrid system;  Cosmin Saveanu  and  Thomas Rolland  for their expertise and assistance with bio-informatics; and  Marcia Dinis  for assistance during in vivo experiments.
This work was supported by grants from the  National Institute of Health  ( NIH R01 AI052455-06A1  to DTG and PTC), the  Agence Nationale de la Recherche  ( ANR-10-BLAN-1321  FattyBact to CP and  PTC , and Grant  ANR-08-StrepRespire  to CP), and by financial supports from the  Pasteur Institute  (to PTC),  CNRS  (to CP, PG, and PTC),  INSERM and UniversiteParis Descartes  (to CP). SB was the recipient of a postdoctoral fellowship from the  Region Ile-de-France (DIM MalInf). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Microarray experiment and analysis Sixteen microarrays (2 stimuli * 2 times * quadruplicate) were hybridized using the ARK-Genomics 17 K slide (ArrayExpress accession number A-MEXP-1592) in a two-color dye swap experimental design (Additional file 2). RNA labeling was carried out with the fluorescent cyanine dyes Cy3 or Cy5 and hybridizations performed in a GeneTac automated hybridization station (Genomic Solutions) as described previously  [29]. The data were extracted using BlueFuse  [30]. Raw data were imported in R for filtering and normalization. Data were first filtered according to spot quality (uniformity and saturation) provided by BlueFuse. Spots with the worst quality (flag = E and quality = 0) were excluded, resulting in a removal rate of 33% to 45% per slide. Lowess normalization commonly used to correct the dye bias effect was not applied since MA-plots did not show any significant deviation from linearity.
The log 2 -intensity of each dye was analyzed separately, since Bossers et al. [31]  showed that this method enhances the reproducibility and the sensitivity of the detection of differentially expressed genes. Data were then reduced and centered within slide (mean intensity equal to zero and variance equal to one on average for the spots of one slide) to allow comparison across slides. Differences of expression between conditions were analyzed probe by probe by analysis of variance (ANOVA) using the SAS MIXED procedure. Probes with less than 16 observations were excluded from the analysis, resulting in an average of 28. 5 observations per probe (± 9). Finally, 9559 probes and 272 770 observations (per dye) were retained. The ANOVA model included the fixed effect of color and the effect of the combination between stimulation (LPS, SaS and reference) and time (3 h, 6 h).
The p-values of the tests were corrected with a 1% false discovery rate (FDR) with Benjamini-Hochberg correction  [32]. Fold-changes (FC) were computed as the exponential (base 2) of the ANOVA-estimated-log 2 -intensity-ratio of samples from two conditions. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO series accession number GSE47599. To reveal functional connections between the regulated transcripts, a network and pathway analysis of the differentially expressed genes was performed by using Genomatix GePS (Genomatix Software GmbH, Munich, Germany). Lists of differentially expressed genes were input into the system with human ortholog gene names, along with their fold changes. Signal transduction pathways such as canonical and proprietary (Genomatix), molecular functions (Gene Ontology) and biological processes (GO) were listed provided the associated p value was > 10 -5 (the threshold was lowered to 10 -4 whenever none were > 10 -5 ), and the number of genes in the list falling in the pathway > 5.
Reverse transcription and PCR analysis (RT-qPCR) From the samples used in the microarray analysis, total RNA was reverse transcribed to cDNA: 1 μg of RNA was incubated with 1 μg of random primers (Promega, Madison, WI, USA) for 10 min at 65°C and then for 5 min on ice in a final volume of 10 μL. Reverse transcription was carried out by adding avian myeloblastosis virus (AMV) reverse transcriptase buffer (Promega), 4 mM deoxynucleoside triphosphate (dNTP) (Promega), 15 U of AMV reverse transcriptase (Promega), and 40 U of RNasin (Promega) to the mixture. The mixture was incubated for 1. 5 h at 42°C and 5 min at 95°C. Diluted cDNA samples were stored at 4°C until use. The same cDNA samples were used for microarray and qPCR determinations. All primers (Table  2 ) used in this study were designed by using Clone Manager 9 (Scientific & Educational Software, Cary, NC, USA) using publicly available bovine sequences and were purchased from Eurogentec (Liège, Belgium).
Statistical analyses Statistical analyses were performed with the StatXact software (Cytel software Corp. , Cambridge, MA, USA) or the Prism software (version 5. 0; GraphPad). A probability level of < 0. 05 was considered significant. Comparisons of paired samples were done with the Friedman test followed by Bonferroni post-test comparison and the comparison of unpaired samples with the Kruskal-Wallis test.
Transcriptome profiling of the response of bMEC to LPS preparation and SaS The reaction of MEC to LPS and SaS was investigated through gene expression profiling at a rather early time post-exposure in order to gain insight into the response of the cells likely to contribute to the triggering of the inflammatory response. Data were validated for 9559 probes on the basis of the Reml with a Benjamini-Hochberg correction of 1%. Microarray analysis of bMEC RNA obtained 3 h and 6 h postexposure to LPS or SaS revealed differential expression of hundreds of genes by comparison with unstimulated cells (Additional file 4). More genes were differentially expressed at 6 h than at 3 h post-exposure, and following exposure to LPS than to SaS (Table  3 ). Also, the proportion of downregulated genes was higher with SaS than with LPS at 6 h post-exposure (Table  3 ).
Overall, this suggests that the activation pathways and the early chemokine and cytokine production preceded the defense and stress responses, which was not unexpected. A functional analysis of the genes up-regulated after stimulation was performed with Genomatix GePS to display canonical pathways and to create networks. In response to LPS, most of the highest ranking signal transduction pathways, molecular functions and biological processes were connected to inflammatory and immune responses (Table  6 ). In contrast, the signal transduction pathways associated with the response of MEC to SaS at 6 h post-exposure were connected with cell cycle and DNA repair (Table  7 ). Nevertheless, at 3 h post-exposure to SaS, the AP-1 transcription factor network was identified, along with the pro-inflammatory IL-17A pathway (Table  7 ).
Acknowledgements Authors acknowledge the help of SIGENAE staff members (http://www. sigenae. org/)for their contribution to microarray annotation. This work was funded in part by the  European Network of Excellence Eadgene  ( EU  Contract No. FOOD-CT-2004-506416 ); EG was also supported by the  Biotechnology and Biological Sciences Research Council  ( Institute Strategic Programme Grant ),  ANR Genanimal and APIS-GENE.
Methods MicroRNA microarray, data processing, and statistical methods. Quantification of microRNA expression by microarray was performed as in  48. Briefly, 5 mg of RNA from each cell type was labeled with biotin and hybridization was carried out on a miRNA-chip (ArrayExpress accession number A-MEXP-258  49  ), which contained 238 probes for mature miRNA and 143 probes for precursor miRNAs. Hybridization signals were detected by biotin binding of a Streptavidin-Alexa647 conjugate (one-color signal) using a GenePix 4000B scanner (Axon Instruments). Images were quantified using the GenePix Pro 6. 0 (Axon Instruments). Raw data were analyzed in BRB-ArrayTools developed by Dr. Richard Simon and Amy Peng Lam (Version: 3. 6. 1,May 2008; National Cancer Institute) http:// linus. nci. nih. gov/BRB-ArrayTools.
Data from this microarray is available at GSE50697. Growth, migration and invasion assays. Cell growth rates were determined by counting cells after the indicated number of days in culture. For migration cells were serum-starved overnight and scratch wounds were created using a sterile pipette tip on the cell monolayer. The distance between the two edges at multiple points was quantified using Photoshop CS4 (Adobe) at the indicated timepoints. For invasion, the number of cells invaded through a PET track-etched membrane towards serumcontaining media was quantified by staining with methylene blue. Sphere and non-adherent culture. were grown as in  29, 30. Spheres larger than 50 micrometers were counted. For measuring cell survival under nonadherent conditions, plates were coated with 1% agarose and cells were plated in their own media.
Mice were assessed periodically for tumor growth via the intraperitoneal injection of D-Luciferin (Caliper LifeSciences) at 150 mg/kg in PBS and bioluminescent imaging using the IVIS imaging system 200 series (Xenogen Corporation). All mice were monitored for tumor growth and lung metastasis. Once mammary gland tumors reached 1. 5 cm in diameter, mice were euthanized and their organs harvested and fixed using Bouin's fixative. Associations with differentiated cell gene expression and triple negative breast cancer. To evaluate the methylation of the miR-203 promoter in breast cancer, we acquired the methylation data (Illumina Human Methylation 450 array) for 672 breast tumor samples in TCGA by downloading the beta values (a measure of the percent methylation) processed by the Broad Firehose pipeline.
We distinguished the tumors into ones that are triple-negative and others using the clinical annotations provided by the TCGA. Then, using annotations from Illumina, we identified the probes targeting the region upstream of the miR-203 transcription start site. We compared the methylation levels of the triple negative and non-triple negative tumors using a two-sided t-Test with unequal variance. To score the association of miR-203 with stem cells, we generated a gene expression signature that consists of the genes with at least a 2-fold change in gene expression and are correlated with the expression of miR-203. Using a SAM analysis  51  we found 66 genes that are positively correlated with miR-203, and 65 negatively correlated genes. Then, we collected data from the Gene Expression Omnibus 25 Affymetrix gene expression data sets that contained both differentiated cells and either embryonic stem (ES) or induced pluripotent stem (iPS) cells.
Acknowledgements We acknowledge the entire  Mani Lab  for invaluable discussion and advice. Also, we appreciate the assistance of  Dr. Rudy Guerra  of  Rice University  for additional statistical analysis. This work was supported by  DOD   Postdoctoral Fellowship   BC101115  (JHT). JTC is supported by  NIH   R00LM009837  and grant  R1006  from the  Cancer Prevention and Research Institute of Texas. Flow cytometry, and animal imaging were in part funded by the  Cancer   Center Support Grant  from the  National Cancer Institute  ( CA16672 ). This work was supported in part by an  MD   Anderson Research Trust Fellow Award , funded by the  George and Barbara Bush Endowment for Innovative Cancer Research (SAM). GM was also funded by the following non-for-profit organizations:  Fondation Nadine Midy, Foundation Nelia et Amadeo Barletta (F.
"SPARC Report No. 1 -Trends in the vertical distribution of ozone" (SPARC, 1998) provided a comprehensive summary of available data and methods for trend analysis at that time. Its main objectives were to understand the limitations of the data, to assess the consistency between various data records and create a reference database for the analysis of long-term ozone changes as a function of altitude. One of the main reasons Figures for this painstaking approach was to avoid misinterpreting instrument drifts as actual ozone changes. Trend analysis is made more complex by the addition of new satellite records (e. g. Aura, Envisat, Odin, and SCISAT) where each covers a shorter time period than the traditionally used long-term records (e. g. ozonesondes, Umkehr, lidars, SAGE, HALOE). Adding to the problem, some of the long-term platforms have ceased taking measurements (e.
, 2011). Clearly, an understanding of atmospheric transport, circulation and wave breaking patterns is important for our understanding of ozone recovery. The consideration of uncertainties and artifacts is crucial in ozone trend analyses, particularly when the ozone recovery signal is small as compared to natural geophysical variability. As methods for trend and attribution analysis become more advanced, the uncertainties and instrumental artifacts buried in currently available datasets start to play a bigger role. Therefore, detailed information about measurement uncertainties, data jumps due to changes in instrumentation and drifts during long-term operation of a given instrument are essential. There have been several attempts to produce historical global ozone profile data sets; these include satellite-ozonesonde combinations starting in 1979 when the first global satellite measurements became available (e.