dataesr/scientific-paragraphs-categorization huggingface dataset:
01, fold change41. 5; Supplementary Table  S2 ). To generate a stringent list of auxinresponsive genes for the analysis of cell type-specific expression, we extracted the genes that passed the ANOVA for the treatment factor or interaction and also passed at least one of the four cell type-specific t-tests (Figure  2D ), resulting in a total of 2846 auxin-responsive genes. Measured auxin responses were corroborated at two levels. First, we observed the significant regulation of known auxin-responsive genes in the cell type-specific data set. This includes significant regulation of 22 members of the Aux/IAA family of auxin co-receptors (Calderon-Villalobos et al, 2010), 14 GH3 auxin conjugases  (Hagen et al, 1991) , 18 SAURs  (Hagen and Guilfoyle, 2002)  and 7 LATERAL ORGAN BOUNDARY DOMAIN CONTAINING PROTEIN (LBD) transcription factors  (Shuai et al, 2002)  (Supplementary Figure  S2C-F ; Supplementary Table  S2 ).
RNA extraction and microarray hybridization RNA was extracted from 20 000 sorted cells per replicate using an RNeasy Micro Kit with RNase-free DNase Set according to the manufacturer's instructions  (QIAGEN). RNA was quantified with a Bioanalyzer (Agilent Technologies) and reverse-transcribed, amplified and labeled with WT-Ovation Pico RNA Amplification System and FL-Ovation cDNA Biotin Module V2 (NuGEN). The labeled cDNA was hybridized, washed and stained on an ATH-121501 Arabidopsis full genome microarray using a Hybridization Control Kit, a GeneChip Hybridization, Wash, and Stain Kit, a GeneChip Fluidics Station 450 and a GeneChip Scanner (Affymetrix). Three independent biological replicates were collected for all treatments. The raw data files generated by others and used in this analysis were obtained from the Benfey lab or from http://www.
Data analysis Data normalization, analysis and visualization were performed using freely available code and R-based software (listed in Supplementary information). Raw microarray data were MAS5. 0 normalized with a scaling factor of 250 and log transformed before homoscedastic statistical analysis (Student's t-test and two-way ANOVA; Flexarray). Ambiguous probesets (Supplementary Table  S2 ) were removed from further analysis. False discovery rates (FDR) were calculated based on the P-value distribution (Q-value). For the comparison between statistical tests in separate tissues, equal cutoffs were set at Po0. 01. FDR at this cutoff and q-values for the individual probesets are reported in Supplementary Table  S2. Additionally, a fold-change cutoff of 41. 5 was set for the tissue-specific and intact root t-tests.
To generate the stringent list of 2846 auxin responders, genes had to pass the ANOVA for treatment or interaction (Po0. 01) and at least one of the tissue-specific t-tests (Po0. 01, fold change41. 5). For co-expression analysis, hierarchical clustering was performed on the stringent list of auxin responders with pairwise Pearson's correlation using gene expression lists of replicate sample averages that were row normalized (Multiple Experiment Viewer). Branch length distribution of the HCL tree and the figure of merit (FOM) of iterative K-means clustering runs were used to gauge the expected number of clusters (Multiple Experiment Viewer). A Fuzzy K-means clustering search for dominant expression patterns was executed employing the R script by Orlando and co-workers for the manipulation of large-scale Arabidopsis microarray data sets  (Orlando et al, 2009).
Clusters containing o10 genes were omitted from further analysis. GO-term overrepresentation was analyzed using VirtualPlant with TAIR10 gene annotations. Promoter element enrichment was based on the absence or presence of motifs in the 500-bp upstream of the transcription start sites (hypergeometric distribution test with FDR correction qo0. 01, TAIR10 annotation). Template matching for the isolation of CTSE gene sets was performed using the Pavlidis template matching algorithm (Multiple Experiment Viewer) on previously generated transcriptomic data from 13 non-overlapping tissue marker lines (Supplementary Table  S2 ) with a similarity cutoff of R40. 8.
Microscopy Confocal microscopy was performed with SP5 (Leica) and LSM710 (Zeis) microscopes and software. Cell walls were stained by 10 min incubation in 10 mg/ml propidium iodide (dissolved in water). GUS reporter gene lines were stained in 50 mM phosphate buffer pH 7, 0. 5 mM ferricyanide, 0. 5 mM ferrocyanide, 0. 05% (v/v) Triton X, 1 mM X-Gluc, for 24 h at 371C. The staining reaction was stopped and seedlings were fixed and cleared with ethanol and mounted in water. Staining was visualized with an Axioskop (Zeiss) microscope.
Acknowledgements We would like to thank  Hidehiro Fukaki  ( Kobe University ) for the pLBD33::GUS reporter. This work was supported by grants from the  NSF  ( DBI-0519984 ) and  NIH  ( R01-GM078270 ) to KDB, the  NIH  ( R01-GM086632 ) to DCB, the  NIH  ( GM43644 ),  Howard Hughes Medical Institute and Gordon and Betty Moore Foundation  to ME, the  EMBO (LTF)  to IE, the  Vaadia-BARD  ( FI-431-10 ) to ES, and the  Research Foundation of Flanders  to SV. Author contributions: BORB helped conceptualize, design, perform and write this work, SV helped conceptualize and write this work, GK.
The number of differentially expressed genes is comparable with those observed for xrn4  (38, 39). However, there is no significant overlap between urt1 and xrn4 data sets because only one gene (At5g49280) is misregulated both in urt1 and xrn4. To test a potential impact of URT1 on mRNA degradation rates, half-lives of five short-lived transcripts (i. e. half-lives ranging between 20 and 40 min) were determined following inhibition of transcription by cordycepin or actinomycin D. The degradation rates of the five mRNAs were not significantly affected regardless of their respective up-regulated, down-regulated or unchanged expression level in urt1 (Supplementary Figure  S4B  and C ). Therefore, the variation of steady-state levels of these mRNAs in urt1 is rather due to indirect effects or multifactorial causes that remain to be identified.
ACKNOWLEDGEMENTS The authors are grateful to  Benjamin Stupfler  and  Barbara Class  for technical help. FUNDING Centre National de la Recherche Scientifique (CNRS, France) ;  Agence Nationale de la Recherche  ( ANR, France)  [ ANR-2010-BLAN-1707  to D. G. and J. M. D. ], [ ANR-2010-LABX-36  to D. G. ] in the frame of the programme d'investissements d'avenir]. Funding for open access charge:  Centre National de la Recherche Scientifique (CNRS), France.
[3]  based on the religious structuring of Y-chromosomal variation within Lebanon, but here we see it is genome-wide. In order to assess the proportion of putative ancestral components in the Lebanese, an unsupervised clustering method (ADMIXTURE)  [10]  was applied to the Lebanese dataset (Figure  S1A ). At K = 2, which showed the lowest cross-validation error (Figure  S1B ), Christians present one major component (,82% on average per individual), which is also found in Druze and in lower frequencies in Muslims; in contrast, the second component is almost exclusive to Muslims with a lower representation in Druze. At K = 3 and K = 4, new components most abundant in Lebanese Muslims are shown, probably reflecting recent admixture after the split from the other Lebanese groups.
Subjects, genotyping, and comparative datasets Samples were collected from 1,341 Lebanese subjects with informed consent approved by the IRB of the Lebanese American University. Genotyping was performed on Illumina 610K or 660K bead arrays. PLINK  [29]  was used for data management and quality control. Genotyping success rate was set to 99%, sex-linked and mitochondrial SNPs removed, keeping 505,859 SNPs. After LD pruning (excluding r2. 0. 4) 244,919 SNPs remained. 75 Lebanese samples (Figure  S3 , Table  S1 ) were selected through a stratified random sampling taking into consideration the distribution of the religious groups in Lebanon and merged with 994 samples from literature representing 48 populations (Table  S2 ). The selected Lebanese data set is available at: bhusers. upf. edu/ ,mhaber/PLOS/ Text S1 Description of the ROLLOFF analysis.
Morphological Analysis The accessions grown in greenhouse conditions were evaluated for morphological characters such as plant habit, plant height, presence or not of trichomes on the stem, leaf color, leaflet shape, flower type, flower dimensions, pod shape, pod surface, number of seeds per pod, seed size and shape. Macroscopy images were obtained using either an optical Macroscope (Nikon AZ100, Champigny-sur-Marne, France) or a Digital Presenter UF-130ST (Samsung, Korea). Seed length was determinated using the ImageJ software (Wayne Rasband, National Institutes of Health, USA; http://imagej. nih. gov/ij).
DNA Isolation, Amplification, Typing and Sequencing Total genomic DNA was isolated from young leaves of plants using the CTAB extraction method  [29]. Primers for RAPD markers were designed by Operon Technologies (Alameda, Calif, USA) (Table  S6  A in File S1). SSR motifs present in A. indica and in A. evenia IRLF6945 ESTs (F. Cartieaux, unpublished data) were The A. evenia EST library (F. Cartieaux, unpublished data) was used to identify single nuclear genes : CYP1, eiF1a, Sucrose Synthase, SUI1 genes, a gene coding for a putative 2OG-Fe(II) oxygenase, and a legume-specific gene homolog to Glyma07g16420 and Glyma18g37410 identified in Glycine max. Corresponding primers were designed using the Primer3 program (Table  S7  in File S1) and PCR reactions were performed using the Go Taq kit (Promega).
The sequence comprising the ITS1-5. 8S rDNA gene-ITS2 region was amplified with primers listed Table  S7  in File S1. For PCR products with mixed sequences, they were cloned into pGEM-T Easy (Promega) following the manufacturer's instructions. Individual transformed E. coli colonies were used as template for subsequent PCR amplification and sequencing. The obtained sequences were deposited in GenBank. The references for all the sequences used in the phylogenetic analyses are listed Table  S8  in File S1.
Data Analysis RAPD bands and SSR alleles obtained for the A. evenia accessions were scored visually from gel images and converted into binary data by assigning 1 to presence or 0 to absence of a character or bands. They were then used to develop binary data matrix of A. evenia accessions. The DendroUPGMA software (http://genomes. urv. cat/UPGMA/) was used to produce for the different data sets a similarity matrix based the Dice similarity coefficient and a bootstrap of 100 replicates. The resulting data were further processed for cluster analysis using the un-weighted pair group average method with arithmetic mean algorithm (UPGMA) and the Neighbour program of the software package Phylip 3. 5. The resulting clusters were presented as dendograms (tree phenogram). For the phylogenetic analyses, the sequence of the 6 single nuclear genes was concatenated in order to obtain a well-resolved phylogeny.
Concatenated single-gene sequences and ITS sequences were aligned using default settings in ClustalX  [30]  and were manually corrected in Genedoc v2. 6  [31]. Phylogenetic reconstructions were performed using the MEGA v4 program  [32]  using the Neighbor Joining method and the Kimura-2 parameter model. Bootstrap (1,000 replicates) analyses were performed on data to place confidence estimates on groups contained in the most parsimonious trees generated. The data are presented as rooted trees using A. denticulata and A. ciliata as outgroups.
Acknowledgments We are grateful to  L. G. Santos Mele  ´ndes (CIAT, Columbia) and to  S. Norton  ( AusPGRIS, Autralia ) for provision of the A. evenia accessions used in this study. We thank  Mickae ¨l Bourge  ( Imagif Platform, CNRS, Gif-sur-Yvette ) for its help in cytometry.
This work was supported by grants from the  French National Research Agency  ( ANR-SESAM-2010-BLAN-170801 ) and conjointly from the  Agropolis Fondation  and the  French National Research Agency  ( program ''Investissements d'avenir' '  ANR-10-LABX-0001-01 ,  Project ''Aeschynomene mapping '' id:  1202-009 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These routers are often off-the-shelf cheap low-end devices, providing just networking services. For the provision of services, the router is connected over a wired LAN with a home computer (e. g. a PC or laptop), where the services are hosted, and via a wireless link with the community network. In this demonstration paper we present the community home gateway, a system attached to a wireless community network router. The design and implementation of the community home gateway system aims at avoiding the obstacles of a closed home gateway, and allows the gateway's hardware to be part of a cloud infrastructure that is managed by P2P services. To this end, the system offers cloud infrastructure services based on resource virtualization in these low-end devices, achieved with Linux containers (LXC) ported to the OpenWRT operating system.
IV. DEMONSTRATION The demonstration at the P2P 2013 conference is a live demo of the community home gateway in two parts: 1) A live demo of the access to community home gateways deployed in the Community-Lab testbed. For this purpose we will connect to the Web interface of the Community-Lab portal and connect to testbed nodes integrated in the Guifi. net community network. 2) A live demo of operational community home gateways (4-5 nodes) deployed at the conference demo location and interconnected through wireless routers. Virtual resources are created with Linux containers in the community home gateways. In order to demonstrate platform and application services, a set of decentralized applications (e. g. file-sharing, video-streaming, storage) will be running in the virtualized resources of the community home gateways.
4) or moderately heritable (0. 1 < h 2 ≤ 0. 4), respectively  [13]. All protocols for IT phenotyping are fully detailed in Flori et al. [13]. Briefly, for the IL2, IL10 and IFNγ dosage, 1:5 diluted blood was stimulated with a mixture of PMA and Iono for 48 hours and the levels of cytokines released in the supernatants were measured using in-laboratory developed ELISA tests. For TNFα, 1:5 diluted blood was stimulated with a mixture of PMA, Iono and LPS for 24 hours and the cytokine levels were measured using a commercial ELISA kit (DuoSet ELISA development kits, R&D Systems, USA). Phagocytosis was measured from total blood using the Phagotest kit (ORPEGEN Pharma, Heidelberg, Germany). The CD4 -/CD8 + and TCRγδ + cell counts were quantified by Fluorescence-Activated Cell Sorting (FACS), using FACScan and CELLQuest software (Becton Dickinson, Oxford, UK).
Statistical analysis of microarray expression data All microarray analyses, including pre-processing, normalization and statistical analysis were carried out using 'Bioconductor' packages in R programming language (version 2. 15). The homogeneity of the background was systematically checked on each microarray by the boxplot and image plot procedures of the linear models for microarray data ('Limma' library; version 3. 14. 4). Furthermore, PCA was performed with 'FactoMineR' library (version 1. 23) to detect if any particular array contributed largely to variability in the gene expression data, that is, whether it retained most of the information (Additional file 14: Figure  S4  and Additional file 15: Figure  S5 ). Finally, after examining the resulting diagnostic plots, we analysed a total of 141 microarrays (Table  2 ).
The list of DE genes for each IT was uploaded into IPA (IPA; ver. 5. 5, Ingenuity Systems, Redwood City, CA) to identify relevant categories of molecular functions, cellular components and biological processes. Using this approach, we identified statistically overrepresented functional GO annotations, and determined their up-or down-expression, and group-specific transcriptional networks. All listed or reconstructed cellular pathways were derived from the expert annotated database that is provided by the Ingenuity Knowledge Base. The IPA annotations follow the GO annotation principle, but are based on a proprietary knowledge database of over 10 6 protein-protein interactions. The IPA output included biological functions and signalling pathways with statistical assessment of the significance of their representation based on Fisher's Exact test.
Validation of the transcriptome results by RT-qPCR on a subset of genes To technically validate the data generated in the microarray study, quantitative RT-qPCR was carried out on selected candidate genes (Additional file 3). The genes were selected based on the following strategies: (1) genes with significant DE levels between the phenotypes of interest that spanned a dynamic range of at least log2 (ratio) > 0. 485; (2) genes with a coefficient of determination greater than 0. 8 with respect to the first principal component in the PCA; and (3) genes with biological interest (e. g. SLA-1 and IL10RA; Additional file 3). PCR primers specific to these genes were designed using ABI Primer Express software version 2. 0 (Applied Biosystems, USA) and designed with the melting temperatures of 58°C to 60°C and resulting products between 100 and 150 bp.
Determination of potential blood biomarkers for immunocompetence The top 50 DE genes between the H and L groups for IL2, IL10 production, PHAG activity and CD4 -/CD8 + cell counts were selected to identify potential blood biomarkers (n = 200) in a validation set. To evaluate the predictive value of these selected gene signatures from the experimental sets, first, we mapped the gene signatures in a validation set of 74 animals. The validation set was sampled from the same original population to avoid biological and technical sources of dataset variation. These 74 animals corresponded to (1) animals that showed no differential expression between the H and L groups for the levels of IFNγ, TNFα and γδ T lymphocyte counts and IgG-Mh (n = 72), and (2) two animals that contributed to increase the coefficient of variation for CD4 -/CD8 + cell counts, and that had been removed from the experimental set for this IT.
All animals included in the validation set were analysed with the same SLA-RI/NRSP8-13 K microarrays and normalised as described above. Two different statistical methods were applied to quantify the association between gene expression and the different ITs and to detect which of these genes could be considered as potential gene biomarkers: (1) the sPLS and (2) the rCCA. sPLS maximised the covariance between two datasets by searching for linear combinations of the variables. Furthermore, it imposed sparsity within the context of partial least squares and thereby carried out dimension reduction and variable selection simultaneously  [21, 22]. To evaluate the statistical significance of covariation between the expression of genes and the distinct ITs, we performed the M-fold or leave-one-out cross-validation, estimating the mean squared error of prediction (MSEP), the R 2 and the Q 2 for each IT in the dataset.
An X variable contributed significantly to the prediction if Q 2 ≥ (1-0. 95 2 )  [61]. rCCA identified and quantified the correlation between two datasets and regularized the empirical covariance matrices of X and Y by adding a multiple to the identity matrix  [23]. The 'mixOmics' library (version 4. 1. 4) in R was used to carry out sPLS and rCCA analyses  [62, 63]. We used the 'cim' function to plot the sPLS results and the 'plotVar' and 'network' functions to generate the images from rCCA. On the one hand, the 'cim' function plotted the association matrices for X and Y variables. Increasing values were translated into colours from blue (negative association) to red (positive association). On the other hand, the variable plot ('plotVar') made it possible to identify the structure of the correlation between the two sets of variables X and Y.
Acknowledgements This work was supported by grants from the  French National Agency  ( IMMOPIG  Project,  ANR-06-GANI-08 ) and the  Animal Genetics division of INRA. The authors acknowledge the expertise of the  Biological Resources Centre  dedicated to livestock genomics (CRB GADIE, Jouy-en-Josas, INRA), and are grateful to  Hélène Hayes  ( INRA ) who helped with the editing of the manuscript. Lastly, the authors thank  Guylaine Gely-Meissonnier , who prepared the reference RNA.
This is one of the reasons why size selective number concentration measurements are of particular use for aerosol-climate trend analysis. Unfortunately, long datasets of such measurements are scarce (four stations in this analysis), and concentrated in Europe. Although recently significant improvements in the site density for such measurements have been done globally in the con-text of the GAW network or the ACTRIS EU-Infrastructure project, they will only benefit future studies. Particles across the whole particle size spectrum are not equally relevant to climate. Numerous and highly variable nano-particles with diameters less than approximately 50 nm do not act as CCN before they grow to larger sizes, (e. g. Mc-Figgans et al. , 2006)  although they can make up the bulk of the aerosol number population.
As of today, nearly 30 stations are regularly reporting number concentration information, many including the number size distribution information, to the World Data Center for Aerosols (WDCA) but very few records span over more than 5 yr. As discussed in Collaud  Coen et al. (2013) , analyses of long-term trends for aerosol physical properties require at least 10 yr long records, which explains why very few studies have been published on aerosol particle number concentration trends. Our aim is to provide reliable information on aerosol number concentration decadal trends. We study the similarities to aerosol optical properties trends (using results from the companion paper) and discuss potential causes for the long-term trends. We also evaluate the trends of aerosol number size distributions in locations where such information is available, and study the applicability of aerosol particle number concentration trends to CCN-sized aerosol properties in these environments.
Instrumentation The aerosol particle number concentrations were measured with a variety of condensation particle counters (condensation nuclei counters), which differed significantly in type and performance from site-to-site (Table  1 ). The sites generally operated similar or often the very same instrument through the analysis period, and if the instrumentation change was considered major, the data were either split into two parts (stations SPO and SMO) or a part of the time series was not included in the analysis (e. g. station MHD measurements in 1990's). The main criterion for having a dataset of one station was at least 10 yr of relatively continuous measurements. Longer gaps are mentioned in Table  2 , and can affect the trend results. The number size distribution measurements were obtained by custom-made mobility particle size spectrometers (differential mobility particle sizer, DMPS) systems, which stayed relatively unchanged during the whole measurement period.
In Hyytiälä, the DMPS inversion routine has changed during the period, but the measurements were considered to be comparable by the data providers. All of the stations included in the size distribution analyses had either constant monitoring of the instrument by measurement personnel (Hyytiälä) or effective site calibration routines. Table  1. Measurement sites and instrumentation used in the trend analysis. The lower detection limit in particle diameter (LDL) is indicated as a nominal value from the instrument manufacturer  (Liu and Kim (1977)  for GE CNC NO7006), and the particle range used for the size distribution measurements. The start times of each measurement are given in ISO 8601 format (YYYY-MM-DD). Last column lists the dataset acronym.