dataesr/scientific-paragraphs-categorization huggingface dataset:
2 FPKM in DCL3a RNAi plants +Pi) (Figure  5 -figure  supplement 1H ). Despite a 2 fold difference in transcript abundance, no significant difference in SPX2 transcript abundance was observed between WT and DCL3a RNAi plants under -Pi conditions, suggesting that the Pi responsiveness of SPX2 is maintained in the DCL3a RNAi plants. (Secco et al. , 2015). DOI: 10. 7554/eLife. 09343. 024 The following figure supplements are available for figure  5 : In addition, in order to determine whether DCL3a is required for the stability of methylation at a distinct set of genomic loci upon Pi starvation, assessment of changes in DNA methylation between DCL3a RNAi plants grown in +Pi and -Pi conditions was conducted. Together, 409 PSI DMRs (FDR < 0. 05) were identified in the stressed DCL3a RNAi plants (Figure  5 -figure supplement 2, Figure  5 -source data 3, available at Dryad,  Secco et al.
05) (Figure  6A , Figure  6 -source data 1, available at Dryad,  Secco et al. , 2015). Known PSI marker genes such as SPX3, IPS1, miR399 and PHT1;9 showed >100 fold higher transcript abundance upon Pi starvation. In addition, resupplying Pi for 3 days was sufficient for the transcript abundance of the majority of the PSI genes to return to +Pi like levels. Indeed, of the 4560 PSI genes that displayed differential transcript abundance by 10 days of -Pi, only 10% showed significantly different transcript abundance after 3 days of resupply compared to +Pi. Together, this indicates that the Arabidopsis plants responded to both Pi deprivation and Pi resupply. To examine the extent of DNA methylation changes in response to Pi starvation and recovery in Arabidopsis, high coverage whole genome bisulfite sequencing (91-96% cytosines covered, 92-96% of genome covered) was performed on root samples pooled from 10 plants, in triplicate (Supplementary file 1).
Mapping of RNA-Seq reads, transcript assembly, and abundance estimation using tuxedo suite All RNA-seq analyses were performed as previously reported  (Secco et al. , 2013a). Briefly TopHat2 and Cufflinks2  (Trapnell et al. , 2012 (Trapnell et al. , , 2013) )  packages were used to map sequence reads to the rice IRGSP-1. 0 and Arabidopsis TAIR10 reference genomes and quantitate differential gene transcription (FDR < 0. 05). TEs were classified using the classification of Plant Repeat database (http://plantrepeats. plantbiology. msu. edu/about. html#codes).
2) between 10 days +Pi and 10 days -Pi samples, requiring 3 of 3 -Pi root samples to be differentially methylated compared to +Pi root samples. Persisting differences in DNA methylation levels were defined as being significantly different between 52 days + Pi and 52 days -Pi conditions (t-test, FDR < 0. 05) and significantly different between 52 days Pi resupply and 52 days +Pi (t-test, FDR < 0. 05), but not significant different between 52 days -Pi and 52 days Pi resupply (t-test, FDR >0. 05). Persisting DMRs were identified in CNN context. FDR was calculated using the Benjamini Hochberg method. Full browsing of the entire rice and Arabidopsis datasets can be found at http://www. plantenergy. uwa. edu. au/public/annoj/rice_Pi_starved_methylomes_Secco_et_al. htm and http://www. plantenergy.
Accession numbers Illumina reads of all samples have been submitted to the Sequence Read Archive at the National Center for Biotechnology Information (http://www. ncbi. nlm. nih. gov/sra) under accession number SRP061678 (Stranded RNA-seq for rice after 3, 24 and 52 days of Pi stress), SRP032765 (Rice methylomes under Pi stress), SRP061677 (Stranded RNA-seq and methylomes for the rice DCL3a RNAi experiment), and SRP040029 (Methylomes and stranded RNA-seq for Arabidopsis under Pi stress).
(D) Distribution of non-redundant DMR-associated gene transcription levels identified by RNA-seq upon Pi deprivation. (E) Gene ontology enrichment analysis of non-redundant DMR-associated genes (p-value < 0. 05). Source data for Figure 2 is available at Dryad (Secco et al. , 2015). DOI: 10. 7554/eLife. 09343. 006The following figure supplements are available for figure2:.
PSI DMRs are sorted based on the change in DNA methylation levels at 52 days, in the first generation. Asterisks represent SPX_DMR2, and error bars indicate standard error. (E) Genome browser screenshot of the SPX2 locus, showing the methylation levels in the first generation at 52 days, as well as in the second generation at 10 days. Source data for Figure 4 is available at Dryad (Secco et al. , 2015). DOI: 10. 7554/eLife. 09343. 018 The following figure supplements are available for figure 4: Figure supplement 1. Mitotic transmission of Pi starvation-induced changes in DNA methylation. DOI: 10. 7554/eLife. 09343. 022 Figure supplement 2. Hierarchical clustering of the methylation levels (CNN context) of the 175 PSI DMRs in the progeny of stressed and non-stressed parents. DOI: 10. 7554/eLife.
(E) DNA methylation levels in 5 regions significantly affected in the DCL3a RNAi line. (F) Genome browser representation of the SPX2 locus, showing three DCL3a-dependant methylation regions, also responsive to Pi starvation, indicated by red boxes. Source data for Figure 5 is available at Dryad (Secco et al. , 2015). DOI: 10. 7554/eLife. 09343. 024.
(C and E) DNA methylation levels in the CNN, CG, CHG and CHH contexts in the three Pi treatments (+Pi, -Pi and Pi resupply) in the DMRs associated with Atcopeg1 and MGD3, respectively. Asterisks represents significant differences in the methylation level (t-test < 0. 05). Source data for Figure 6 is available at Dryad (Secco et al. , 2015). DOI: 10. 7554/eLife. 09343. 030.
(D) Hierarchical clustering of the DNA methylation levels in the 175 PSI DMRs in the DCL3a RNAi line vs WT under -Pi (blue) and +Pi (red) conditions. (E) DNA methylation levels in 5 regions significantly affected in the DCL3a RNAi line. (F) Genome browser representation of the SPX2 locus, showing three DCL3a-dependant methylation regions, also responsive to Pi starvation, indicated by red boxes. Source data for Figure 5 is available at Dryad (Secco et al. , 2015). DOI: 10. 7554/eLife. 09343. 024.
Life cycle inventory of system components As shown in Figure  1  and Figure  2 , the life cycle models of the sewer system and the WWTP are constructed from available processes using the SimaPro 7. 3 LCA software package developed by Pré Consultants. The foreground life cycle inventory (LCI) data for the WWTP construction is compiled directly from detailed design documents and vendor-supplied information. Emissions to water (particulate phosphorus and dissolved phosphates, organic nitrogen, ammonium, nitrites, nitrates) and to air (nitrogen gas, nitrous oxide, carbon dioxide and methane) are estimated applying mass balances for phosphorus, nitrogen and carbon compounds in an activated sludge system  (Boutin et al. , 2011). For the sewer system construction, a modular inventory was developed on two levels: (i) the level of basic components (CP) which includes linear sections of concrete pipes, concrete manholes or pumping stations, (ii) the level of combinations (or assemblies) of basic components.
This second level includes sub-assemblies (SE) and sewer subsets (SR), which are network items combining two or more CP elements and require civil engineering works and equipment for their setup. SE elements refer to sewer mains linear sections, while SR elements describe the PVC pipes servicing a determined number of households in residential subdivisions. The detailed construction inventory data for the sewer system is attached in the Supporting Information (Appendix A, Section S2). Estimates of dissolved inorganic nitrogen (DIN) emissions to ground and surface water from leaking sewer infrastructures for the serviced Grabels area were calculated by using the factor of 10. 9 kgDIN•d -1 expressed as total N (geometric mean value from different scenarios investigated in  Divers et al. , 2013  as described in Supporting Information, Appendix A Section S2.
g. , following land sludge application). Table  2  summarises key inventory data obtained for this study with values relative to the functional unit of the urban wastewater system in Grabels. It should be noted that the total nitrates (NO 3 -) emissions to surface water from the WWTP are estimated on the basis of secondary treated effluent quality and land spreading of the stabilised sludge. A conservative scenario was chosen for the land spreading of sludge  (Risch et al. , 2012)  where there is significant leaching of nitrates not retained in the topsoil compared with the nitrates inputs from WWTP secondary treated effluents. With this scenario, almost 75% of nitrates originate from farmland runoff while 25% are from secondary WWTP effluents. The background life cycle inventory data (e. g.
Uncertainty assessment The uncertainty associated to the inventory was estimated by using the Monte-Carlo routine in SimaPro (1500 runs). The uncertainty range for each parameter is provided in Table  3. Uncertainty ranges for unit processes taken from the Ecoinvent database were defined by the "data pedigree" algorithm available in SimaPro as proposed by  Weidema and Wesnaes (1996). This algorithm relates the datum uncertainty to its source characteristics -i. e. reliability of the source, representativeness of the sample, currency of the period, geographical correlation, technological correlation and sample size. The uncertainty ranges associated to the amount of materials used, the transportation distances, the duration of civil works operations, the amount of energy and ancillary chemicals used during WWTP operation were defined according to expert judgement as described in WWTP operation were obtained from literature.
Other categories also strongly impacted include photochemical oxidant formation, particulate matter formation, and natural land transformation with the disposal processes of concrete and bitumen. An increase of the lifespan from the reference 30 years to 100 years shows a reduction in the range of 45 to 60% in the same midpoint categories. Hence, the relative contribution of sewer systems to the overall environmental impacts would be larger for shorter lifespans and smaller for longer lifespans as this is more driven by construction rather than operation aspects. Finally, the uncertainty associated to sewer construction is quite low as shown on Figure  8. For the interpretation on this result, it should be noted that this uncertainty is estimated (i) from expert judgement for foreground system (quantity of materials and hours of civil work as described in Table  3 ) and (ii) from data quality indicators for all background processes used from Ecoinvent database.
Chemical analyses Total organic C concentration was measured with an organic C analyser (Shimadzu, Kyoto, Japan). Total N content was measured with the same apparatus equipped with a total nitrogen module (TNM-1) detector. The glucose concentration was measured by means of a glucose assay (Sigma-Aldrich, Seelze, Germany). Fourier transform infrared (FTIR) spectra were recorded on a Bruker IFS66 v/s spectrometer (Bruker Scientific Instruments, Billerica, MA, USA). Data were collected in diffuse reflectance mode. Each spectrum was the result of 1000 consecutive scans at a resolution of 4 cm À1. A Perkin-Elmer TurboMass/ Autosystem XL with a Frontier Lab Double Shot pyrolyser was used for py-GC/MS (Perkin-Elmer, Waltham, MA, USA). Pyrolysis data were acquired and processed with QCALIBUR 1. 4 SR1 software (Thermo Finnigan, San Jose, CA, USA), and peaks were identified by comparison with published and stored data (National Institute of Standards and Technology (NIST) library).
, 2000) , libraries were sequenced (RNA-Seq) using a HiSeq2000 instrument (Illumina Inc. , San Diego, CA, USA) in single-read mode and with a read length of 50 bp (IGA Technology Services, Udine, Italy; http://www. igatechnology. com). The reads were mapped onto the corresponding genomes by means of TOPHAT (v. 2. 0. 8b)  (Kim et al. , 2013). The transcript abundances were normalized using the R package DESeq  (Anders & Huber, 2010)  for each fungus by dividing the expression values by size factors to adjust for different sequence depth between the two conditions. The size factor for each sample was calculated as follows: the read count of each gene was divided by the geometric mean of the expression across the SOM and MMN samples. Significantly differentially expressed genes (SOM vs MMN) were identified with DESeq (ttest, false discovery rate, q-value < 0.
Annotations Gene models and annotations for each of the fungal species were retrieved from the Joint Genome Institute (JGI) MycoCosm database (Table  S1 ). The SIGNALP algorithm (v. 4. 0) was used for additional searches for prediction of secretion signals  (Petersen et al. , 2011). Conserved protein domain families (Pfam) were identified by searching through the Pfam family protein database (pfam_scan. pl tool with default settings)  (Finn et al. , 2014). Orphans were defined as sequences not having matches in the Pfam database or homologues in other organisms as revealed by BLASTP searches  (Altschul et al. , 1990)  against the UniProt database (cut-off 1E< 10 À5 ). Putative peptidases were annotated by searching the proteome of each fungus against the MEROPS database  (Rawlings et al. , 2012).
Annotations on gene models encoding enzymes active on carbohydrates (CAZymes) were retrieved from the Carbohydrate-Active Enzymes database (CAZy; http://www. cazy. org/), including the extended set of auxiliary activities (AAs) to cover redox enzymes that act in conjunction with CAZymes  (Cantarel et al. , 2009; Levasseur et al. , 2013). Annotations for various types of PODs in all nine genomes were retrieved from the PeroxiBase database  (Fawal et al. , 2013). Gene models representing tyrosinases were retrieved directly from the JGI database by the use of InterPro term IPR002227 as a search term. All gene models of CAZymes, auxiliary activities, PODs and tyrosinases detected were inspected manually, and the selected and filtered gene model in the JGI database was modified if necessary. Genes encoding multidomain naturalproduct-biosynthesis enzymes (polyketide synthases, nonribosomal peptide synthetases and related enzymes) were annotated in a two-step process: first, on the basis of homology by means of BLAST and a conserved domain search  (Marchler-Bauer et al.
Identification of orthologues and phylogenetic analyses Orthologues were identified with the PROTEINORTHO program  (Lechner et al. , 2011). Principal component analysis (PCA) of orthologues was performed with the QLUCORE EXPLORER software (v. 2. 2; Qlucore AB, Lund, Sweden) with default settings. For rescaling of the gene expression data before the PCA, clustering analyses, and phylogenetic analyses, the expression values of the orthologues were normalized by means of the DESeq package  (Anders & Huber, 2010)  and were then log 2 (counts + 1) transformed. The construction of a species tree for the nine species included in this study was based on a phylogenetic analysis of 3148 oneto-one (1 : 1) orthologues. These orthologues were aligned with MAFFT (v. 7. 147)  (Katoh & Standley, 2013)  with default settings, and the resulting alignments were trimmed with GBLOCKS (v.
The APE package in R was used to construct the NJ expression tree and to perform the bootstrap analysis with 1000 replicates  (Paradis et al. , 2004). Proteins encoding laccases (CAZy subfamily AA1_1) in all nine fungal genomes were retrieved from the CAZy database (http://www. cazy. org/). The 72 proteins were aligned with MAFFT (v. 7. 147)  (Katoh & Standley, 2013 ) and subsequently trimmed with GBLOCKS  (Talavera & Castresana, 2007)  using options to reduce the stringency (-b1 = 2 -b2 = 2 -b3 = 8 -b4 = 5 -b5 = h -b6 = y). PROTTEST (v. 2. 4)  (Abascal et al. , 2005)  was used to identify the most suitable evolutionary model. On the basis of the PROTTEST results, the Whelan and Goldman (WAG) protein substitution model with a gamma shape parameter and a proportion of invariant sites (P-Invar) was chosen for the phylogenetic reconstruction using RAXML with 1000 bootstrap replicates  (Stamatakis, 2006).
ITOL was used to visualize the tree and map the differential gene expression to the tree  (Letunic & Bork, 2011). A phylogenetic analysis of the aspartate protease family was performed using the alignment of the Pfam domain PF00026 (eukaryotic aspartyl protease). In total, 323 proteins containing this domain were identified in the nine analysed basidiomycete genomes. The domain sequences were extracted and retrieved using hmmalign in the HMMER3 package (v. 3. 1b1) (Eddy, 2011) , and the sequences were subsequently aligned with  MAFFT (v. 7. 147). The alignment was further trimmed with GBLOCKS with the settings described for the.
Global transcriptional responses The transcriptional profiles of all fungi after growth on the SOM extract and on a mineral nutrient MMN medium were compared using RNA sequencing (Table  S1 ). For comparison of the transcriptomes among the nine species, the genes were classified into orthologous groups (Table  S4 ). In total, we identified 10 939 groups, of which 3148 were identified as putative 1 : 1 orthologues that were the best reciprocal BLAST hits shared by all nine species. The 1 : 1 orthologues constituted 14-25% of the total number of genes in each of the fungal genomes. The remaining genes were found in orthologous groups shared by two to eight species, in groups of so-called co-orthologues (i. e. genes that are duplicated within a lineage), or were species specific. Depending on the species, 16-29% of the transcripts were significantly upregulated in the SOM extracts (Table  S1 ), and between 23 and 32% of those were 1 : 1 orthologues (Table  S4 ).
Secretome Between 6 and 9% of all SOM-up-regulated genes (q < 0. 01) were predicted to encode secreted proteins (Table  S1 ). The   S6. up-regulated secretome contained a large fraction of genes that were either species specific or shared with a limited set of other species (Tables  S4 ). Still, the functional annotation terms of the proteins in the up-regulated secretome were similar. The SOMinduced secretomes contained a large fraction (38-60%) of genes encoding extracellular enzymes such as oxidases, hydrolases and peptidases (Fig. 4 ). Apart from these enzymes, the up-regulated secretomes also contained cell wall proteins including members of the hydrophobin family (Pfam domain PF01185). Hydrophobins are small (c. 100 amino acid (aa) residues) cysteine-rich proteins that can self-assemble and form aggregations of proteins on surfaces of filamentous ascomycetes and basidiomycetes (W€ osten, 2001).
Acknowledgements This work was supported by grants from the  Swedish Research Council (VR)  (to A. T. and P. P. ), the  Knut and Alice Wallenberg Foundation  (to A. T. ), the strategic research programme  Biodiversity and Ecosystem Services in a Changing Climate (BECC)  (to A. T. ) and the  French National Research Agency through the Laboratory of Excellence ARBRE  ( ANR-11-LABX-0002-01 ) (to F. M. ). Genome sequencing was conducted by the  US Department of Energy Joint Genome Institute , supported by the  Office of Science of the US Department of Energy. We also thank  Annegret Kohler  and  Nicolas Cichocki  for handling the fungal cultures, and principal investigators from the  Mycorrhizal Genomics Initiative Consortium  for providing the strain cultures and access to the genome sequences before publication.
In a related vein, a population genomic analysis of D. simulans compared with sister species D. yakuba and D. melanogaster found that nucleotide diversity and divergence fluctuate on large scales across the genome; these fluctuations are probably related to natural selection, and not the mutation rate  (Begun et al. 2007). If these fluctuations were caused by recombination being mutagenic, then nucleotide divergence between species should be positively correlated with recombination, which is not the case in the D. simulans data set. Instead, genomic regions with more recombination may allow polymorphic loci to escape the effects of selection at other sites  (Hahn 2008). A recent study mapped recombination rates at fine scales over a significant portion of the D. pseudoobscura and D. miranda 4.
Synonymous Substitutions in the LTEE We identified all synonymous substitutions in the genome sequences of single clones isolated from each of the 12 independently evolved populations after 40,000 generations of the LTEE  (Lenski and Travisano 1994; Wiser et al. 2013). Six of these clones derived from lineages that had evolved mutations in mutS, mutL, or mutT  (Sniegowski et al. 1997; Barrick et al. 2009; Wielgoss et al. 2013). The genomic reads for all 12 populations have been deposited at the NCBI Sequence Read Archive where the accession numbers are SRP001369  (Barrick et al. 2009 ), SRP004752  (Blount et al. 2012) , SRP045228  (Raeside et al. 2014) , SRP060289  (Wielgoss et al. 2011) , and SRP060314 (this study). Across the entire genome, we identified a total of 1,518 synonymous substitutions, which are summarized by population in figure  3.
Acknowledgments The authors thank  Iñigo Martincorena ,  Vaughn Cooper ,  Luis Zaman ,  Justin Meyer ,  Mike Wiser , and  Caroline Turner  for discussions and comments. This work was supported, in part, by a  National Defense Science and Engineering Graduate Fellowship  to R. M. ; a grant from the  National Science Foundation  ( DEB-1019989 ) to R. E. L. ; and the  BEACON Center for the Study of Evolution in Action  ( National Science Foundation Cooperative Agreement   DBI-0939454 ).
Système national d'information inter-régime de l'assurance maladie Échantillon généraliste des bénéficiaires a b s t r a c t French health insurance databases are organized since 2003 into a huge digital data warehouse, the Système national d'information inter-régime de l'assurance maladie (SNIIR-AM). It covers the entire French population (65 million inhabitants). In order to facilitate studies on more frequent conditions, a random sample of 1/97th of national health system beneficiaries has been built since 2005, called the échantillon généraliste des bénéficiaires (EGB). The aim of this article is to describe the main characteristics of the SNIIR-AM and the EGB, to detail their accessibility according to French law, and to present their strengths and limits. It is illustrated with the most recent studies conducted in these databases.
These databases include demographic, out-hospital reimbursement (including drug dispensing), medical (costly longterm diseases, occupational diseases, sick-leaves. ), and in-hospital data. All these data are prospectively recorded, individualized, made anonymous and linkable. Consequently, the SNIIR-AM is a very useful data source for epidemiological, pharmacoepidemiological and health economics studies, particularly for rare diseases. The EGB is appropriate for long-term research on more frequent diseases.
Mots clés : Assurance maladie Base de données médico-administrative France Système national d'information inter-régime de l'assurance maladie Échantillon généraliste des bénéficiaires r é s u m é Les bases de données de l'assurance maladie sont collectées depuis 2003 dans un vaste entrepôt numérique, le Système national d'information inter-régime de l'assurance maladie (SNIIR-AM). La résultante en est une des plus grandes bases médico-administratives au monde, couvrant 65 millions de personnes. Afin de faciliter l'étude de cohortes de patients atteints de maladies plus fréquentes, un échantillon au 1/97 e des assurés à l'assurance maladie a été constitué depuis 2005 : l'échantillon généraliste des bénéficiaires (EGB). L'objectif de cette mise au point est de présenter les grandes lignes de l'architecture du SNIIR-AM et de l'EGB, leurs modalités d'accès, leurs intérêts et leurs limites.