dataesr/scientific-paragraphs-categorization huggingface dataset:
The global significance observed for the complete dataset with all methods confirms the genus-specificity of viruses: Bathycoccus, Ostreococcus and Micromonas have their own viruses that do not cross the genus boundaries with detectable frequencies. Within each genus, even if viruses tend to be clade-specific, several strains possess a wide intrageneric, and probably interspecific, host range. This is especially true in Micromonas viruses where several strains can infect hosts from the three clades, while this pattern is much less frequent in Ostreococcus viruses. However, this is not strong enough to break the significant cophylogenetic congruence between Micromonas strains and their viruses (P = 0. 01), while between Ostreococcus-Bathycoccus and their viruses the cophylogenetic signal is not significant, as well as between Ostreococcus and their viruses.
The different results obtained with ParaFit and Jane for the Ostreococcus-Bathycoccus dataset may in part be due to the different ways these methods works: ParaFit relies only on distances and the influence of the tree topology on the outcome is far less important than in a method such as Jane. However, this issue exists for the Micromonas as well as for the Bathycoccus-Ostreococcus and Ostreococcus datasets (which contain less taxa, then less data, decreasing the statistical power), and the cophylogenetic congruence in the Bathycoccus-Ostreococcus dataset is only slightly below the significance threshold with Jane. Duplication and sorting probably also play a role here to explain the lack of topological congruence between trees. However, while viruses seems currently unable to switch from a genus to another, the absence of match between host and virus phylogenetic trees at the genus level suggests an early host-switch from Bathycoccus to Micromonas whose colonization by prasinoviruses would then be more recent.
PCR products were cloned using the TOPO TA cloning kit (Invitrogen, Carlsbad, CA, USA) following the protocol provided by the manufacturer. The 18S and ITS2 were concatenated (named here 18S + ITS) after their homogeneity was established using a partition homogeneity test  [75]. We combined the results of the host specificity experimental assessment with molecular data to select a non-redundant virus dataset, and we kept the 51 virus strains differing in term of host specificity or dpo nucleotide sequence and the 22 host strains differing in their susceptibility to viruses or with differences in sequences. The full cross-infection dataset (313 virus strains on the 26 host strains) is available on request to the authors. Sequences alignments were performed with MAFFT v5  [76, 77] , and ambiguously aligned regions were eliminated using GBlocks  [78].
Phylogenetic reconstructions were based on DNA and amino acid (AA) sequences (for dpo), using Bayesian inference (BI) and maximum likelihood (ML). Evolutionary models were selected via Akaike Information Criterion using jModelTest v2  [79]  for DNA sequences and ProtTest  [80]  for AA sequences. Bayesian analysis were carried out done with MrBayes 3. 1. 2 [81] , with 4 chains of 10 6 generations, trees sampled every 100 generations, and burnin value set to 20% of the sampled trees. In BI, coding DNA sequences (dpo) were considered with an evolutionary model designed for coding sequences taking the genetic code into account  [82] [83] [84] , and AA sequences were analyzed with a mixed model  [81]. We checked that standard deviation of the split frequencies fell below 0. 01 to ensure convergence in tree search.
Our strains are deposited for distribution in the Roscoff Culture Collection (http://www. sb-roscoff. fr/Phyto/RCC/). We thank the GENOMER of the Station Biologique de Roscoff, France, for advices on the sequencing procedure. The work was supported by the French National Agency grant PICOVIR ANR-07-BLAN-0210 (coordinator N. G. ). Laure Bellec benefited from a doctoral fellowship from the French Ministry of Education and Research. This research does not involve human subjects, human material, or human data, nor animals or plant subjected to any ethical framework, thus no ethic approval was needed.
Expression of Sl-ARF genes in different tomato tissues To gain clues on the physiological function of tomato ARFs, the spatio-temporal expression of individual members of the gene family was examined at the transcriptional level using qRT-PCR. Transcript accumulation could be assessed for 15 ARF genes in different tissues including root, stem, leaves, flower and fruit at various developmental stages. For the remaining 7 tomato ARF genes, transcript detection was unsuccessful in any of the samples tested suggesting their extremely low expression in these tissues. The data indicate that the expression of ARF genes is ubiquitous in all tissues with most genes being expressed in reproductive tissues suggesting their putative role in flower and fruit development (Figure  3 ). Heatmap representation (Figure  4 ) allowed the clustering of tomato ARFs into two main groups based on their expression pattern: group I (Sl-ARF1, Sl-ARF2A, Sl-ARF2B, Sl-ARF4, Sl-ARF7A, Sl-ARF6B and Sl-ARF18) are genes preferentially expressed in roots and group II Sl-ARFs in the areal part of the plant.
Auxin and ethylene regulation of Sl-ARF genes Screening for cis-acting elements corresponding to Auxin Response Elements (AuxRE) within the promoter regions using the Place database (http://www. dna. affrc. go. jp/PLACE/ signalscan. html) identified conserved (TGTCTC) and degenerate (TGTCCC) motifs in most tomato ARF promoters. In addition to these AuxRE, Sl-ARF promoters contain conserved Ethylene-Response motifs, the so-called ERELEE4 motif found in the promoter of tomato E4 gene (AWTTCAAA) (Table  S2  in File S1). The presence of these cis-regulatory elements suggests a potential regulation of ARF genes by both auxin and ethylene. To test the responsiveness of tomato ARF genes to both hormones, transcript accumulation was assessed by qRT-PCR in seedlings treated with auxin or ethylene. All Sl-ARFs were found to be auxin-responsive after 2 hour treatment (Figure  5A ), with Sl-ARF4, Sl-ARF5 and Sl-ARF2A showing the highest up-regulation whereas Sl-ARF1, Sl-ARF7 Sl-ARF10 displayed the most significant down-regulation.
Expression of Sl-ARF genes during tomato fruit set The expression of a high number of Sl-ARFs in reproductive tissues (Figure  3  and 4 ) along with the previously reported role of auxin in controlling the fruit set process, prompted us to investigate the expression of Sl-ARF genes during the flower-tofruit transition. To determine the expression dynamics throughout the fruit set process, transcript accumulation of tomato ARFs was monitored by RNA-seq approach at flower buds, anthesis and posanthesis stages (young fruit at 4 DPA). For each stage, RNA libraries were generated from three independent biological replicates and subjected to Illumina mRNA-Seq technology sequencing (Data desposited at NCBI SRA database under accession number SRP029978). Reads were then mapped on the tomato genome sequence and read counts were determined as described in Maza et al.
To further validate the occurrence of the alternative splicing forms and assess the relative levels of the various splice variants, a semi quantitative PCR approach was conducted. To this purpose, two pairs of primers were designed, one aiming to specifically amplify the retained intron fragment while the second pair was designed in the margins of the two exons framing the retained intron. A PCR product with the expect size was detected for all genes confirming the presence of the splice variant in each RNA extraction (Figure  7B ). Interestingly, the data indicate that the abundance of the Sl-ARF8B_int11 transcript variant decreases dramatically in young fruits whereas the global expression of the corresponding Sl-ARF8B gene increases significantly. This finding suggests that the down-regulation of the Sl-ARF8B_int11 transcript variant may potentially play a role in the regulation of the flower to fruit transition.
In silico Identification of the tomato ARFs All the ARF gene sequences (ITAG2. 3_gene_models. gff3) are download from the Sol Genomics Network (http://solgenomics. net/), and analyzed in Notepad++ software. The NLS location was searched using cNLS Mapper (http://nls-mapper. iab. keio. ac. jp/ cgi-bin/NLS_Mapper_form. cgi). All the obtained sequences were sorted for the unique sequences and these were further used for B3, AUX_RESP, and Aux/IAA domain search using InterProScan Sequence Search (http://www. ebi. ac. uk/Tools/pfa/iprscan/ ). The UTR of Sl-ARFs were found by two steps, first, the whole tomato genome and Sl-ARF gene structures (ITAG2. 3_gene_models. gff3) were loaded into the Java, and then, the complete cDNA sequences from RNA-Seq data including three stages (flower bud, anthesis and post-anthesis) were blast with Sl-ARF gene structures to identify the final 59 or 39 UTRs in Sl-ARFs.
Transformation assays were performed in three independent replicates. After 16 h, GFP expression was analyzed and quantified by flow cytometry (FACS Calibur II instrument, BD Biosciences, San Jose, CA) on the flow cytometry platform, IRF31, Inserm, Toulouse and and cell sorting platform, INSERM UPS UMR 1048, Toulouse RIO imaging platform. Data were analyzed using Cell Quest software. For each sample, 100 to 1000 protoplasts were gated on forward light scatter and the GFP fluorescence per population of cells corresponds to the average fluorescence intensity of the population of cells after subtraction of autofluorescence determined with non transformed BY-2 protoplasts. The data are normalised using an experiment, in presence of 50 mM 2. 4 D, with protoplasts transformed with the reporter vector in combination with the vector used as the effector plasmid but lacking Sl-ARF coding region.
RNA isolation and Quantitative RT-PCR Total RNA from fruit was extracted according to the method of Hamilton et al. [57]. Total RNA from leaves and seedlings was extracted using a Plant RNeasy Mini kit (Qiagen) according to the manufacturer's instruction. Total RNA was treated by DNase I to remove any genomic DNA contamination. First strand cDNA was reverse transcribed from 2 mg of total RNA using Omniscript kit (Qiagen) according to the manufacturer's instruction. The qRT-PCR analysis was performed as previously described  [3]. The sequences of primers are listed in Table  S3  in File S1. Relative fold differences were calculated based on the comparative Ct method using the Sl-Actin as an internal standard. To determine relative fold differences for each sample in each experiment, the Ct value of genes was normalized to the Ct value for Sl-Actin-51 (accession number Q96483/Solyc11g005330) and was calculated relative to a calibrator using the formula 2 2DDCt.
RNA-Sequencing and RT-PCR Total RNA was extracted from bud, flower and post-flower (4DPA) for three biological repeats using a TRIZOL Reagent (invitrogen) according to the manufacturer's instruction. Total RNA was treated by DNase I to remove any genomic DNA contamination and checked by RNA gel and Agilent RNA 6000 Nano Assay, which the RIN value above 7 was determined to be qualified. After that, the best RNA were sent out for deep RNA sequencing using Illumina Hiseq2000 and the reads generated were mapped to the tomato genome sequence SL2. 40. The data are desposited at NCBI SRA database under the accession number SRP029978 The gene expression was calculated for each annotated tomato gene (iTAG2. 30). For continuous validation, first strand cDNA was synthesized as previously described and PCR was performed using primers designed from the intron and exon of 7 Sl-ARF genes.
Acknowledgments This work benefited from the networking activities within the European funded  COST ACTION FA1106 ''Qualityfruit' '. The authors thank  C. Pecher  and  A. Zakaroff-Girard  for their technical assistance and expertise in flow cytometry (Cytometry and cell sorting platform, INSERM UPS UMR 1048, Toulouse RIO imaging platform). The authors also thank  O. Boucher  and  N. Marsaud  for their technical assistance and expertise in RNA-seq data production (plateforme genotoul, GeT).
30 genome annotation c Length of the corresponding Coding Sequence (CDS) in base pairs. d Conserved Domains found in PFAM database: B3 means DNA binding domain, ARF means Auxin response Factor conserved domain, AUX/IAA means AUX/IAA dimerization domain, AD means transcriptional activation domain, RD means transcriptional repression domain. e Corresponding names in Wu et al. ; accession numbers are in the parentheses. f Gene Model modification type: UTR means that the UTR sequence have been identified and annotated, CDS means that the the coding sequence have been corrected. g New locations in.
In a previous study  [2] , a literature review was performed in order for the most suitable simplified LCA tools to be identified within the food sector. These tools were then evaluated by some experts and rated OPEN ACCESS on the basis of several pre-set criteria. These criteria included ISO compliance, user-friendly interface, broad focus, limited data requirement or adaptability to existing databases, relevance to the life cycle step of agriculture, and ease of integration with EPD, POEMS and/or other communication tools. The simplified LCA tools were then ranked using decision-making methodologies, belonging to the family of Multi-Attribute Utility Theory. For this paper, some of the best rated tools were tested and the results were examined in parallel to those of a full LCA. The case study was performed in the framework of a small family-managed wine making firm in the region of Abruzzo, Italy, called Palazzo Centofanti.
Life Cycle Inventory The tool requires the data to be entered online per FU, using its incorporated database, according to the following stages: pre-manufacture, manufacture, product packaging, and product distribution. In this case study, some of the materials/processes were missing from the internal database (e. g. , cork, as a packaging material for the bottle stoppers); therefore, they were replaced by similar ones or omitted. Furthermore, the water-and airborne emissions were calculated using the EcoInvent manuals  [1]. Finally, the transport was calculated as a percentage of the FU for each distribution in the three Italian regions, as follows: (the percentage of the total mass transported) × (FU). For example, for the case of the Lazio region, 20% of the total amount of the product is distributed there.
Life Cycle Impact Assessment and Interpretation As soon as all data is inserted, the user can decide to complete the inventory and view the results of eVerdEE that come in a table format. As it can be deduced by Table  1 , pre-manufacturing (which corresponds to the agricultural stage) is the most responsible stage for mineral resources consumption and for eutrophication  [1]. Packaging and distribution seem to be highly impacting for climate change and for a set of other environmental impact categories  [1]. Finally, the normalization scores showed that the highest score was received by non-renewable energy consumption followed by eutrophication and climate change  [1].
BilanProduit BilanProduit  [4] , designed by ADEME France, is a tool, available only in French, the simplification of which is at the level of LCI  [5]. This takes into account the principal stages of a product's life cycle: the raw materials, manufacturing procedures, means of transport and sources of energy, and is designed for when performing an LCA intended for eco-design  [5]. The tool is downloadable in the form of a Microsoft Excel file after registration and offers an incorporated database (using mainly entries from EcoInvent 2. 0 (Ecoinvent Centre, Zürich and Lausanne, Switzerland). Detailed tips for the use of the tool are provided online but also within the file itself. The environmental impact categories tackled by BilanProduit are: energy and resource consumption, greenhouse effect, acidification, eutrophication, photochemical pollution, aquatic ecotoxicity and human toxicity.
Life Cycle Inventory Within the sheets that follow the initial ones, the user can insert the processes regarding the system under study. The sheets include the phases of production, transports, use, and end of life. For every entry selected in the production sheet, the user needs to specify the phase in the life cycle that it is connected to. For this case study, the connection was performed by correlating the entries to the agricultural and vinification phases. It is to be noted that this tool appeared to be lacking in specific fertilizers' entries within its database. Therefore the fertilizers used in the agricultural phase were omitted, whilst the database provided sufficient entries for the pesticides used during the same phase. As far as transport is concerned, the ones related to the raw materials were included in the production phase whilst under the "transport" entry only the transport of the final product was considered.
Life Cycle Inventory Within the page of LCI the users get to see an overview of the data that was inserted initially (e. g. , mass, volume, etc. ) and then a list of five different categories/phases, where the inventory data is inserted directly online: ingredients, packaging, processing, shipping and consumer. Similar to BilanProduit and eVerdEE, the Carbonostics tool comes with an incorporated database. As soon as an entry is selected, the user can view a set of information about it, including the data source and e-link. Then, it is possible to include for the specific process the mass, extra mass for waste, cost and water use, depending on the kind of results to be obtained, and some extra information such as carbon sequestration. For this study, the ready-made entry "Grapes, wine, average, Europe"-including the production of grapes for winemaking-was selected from the category "ingredients".
This was because not all the necessary processes to describe the agricultural phase were available in the database. As a consequence, the whole grape-growing phase was approximated by the aforementioned entry. As regards the "packaging" category, all types of packaging are included, from primary to tertiary, along with their transport to the firm. In the "processing", the energy consumption during the agricultural phase was not inserted, as this was included in the process of grapes production, as described above. Finally, the distance travelled was calculated as the weighted average of the distance of the transported good to the three regions and was inserted in the category of "shipping", as there was no entry for "quantity" that needed to be shipped.
CCaLC CCaLC  [8]  is a simplified tool the simplification of which, as in the case of Carbonostics, is mainly related to the phase of Life Cycle Impact Assessment (LCIA), as it is advertised to focus mainly on one environmental impact category, namely climate change. However, the user can actually obtain results for some other impact categories, as well: water footprint, acidification, eutrophication, ozone depletion, photochemical smog and human toxicity  [9]. The tool is a Microsoft Excel file, available online and can be downloaded after registration. At the moment, an executable version of the software is under development that will render it more flexible and faster to use  [9]. The file contains an incorporated database, but also provides the possibility to use a part of the EcoInvent database reaching overall more than 6000 items.
Life Cycle Inventory As mentioned above, the tool comes with a generic schematic representation of a product's life cycle, which includes raw materials, production, storage, use, waste management and transport (that connects all of these entries to each other). By clicking on the various entries, the user can insert the data directly in the file and has two options for every entry: to use either the CCaLC database or the EcoInvent one. For this study, most of the data was selected from the incorporated database (in order to test the robustness of the tool). Only two entries were inserted from the EcoInvent database, namely the compost (used in the agricultural phase) and the paper (used for the label-in packaging-in the vinification phase). For the "raw materials" entry, the user needs to include the raw materials used for this product.
As the various phases were created differently and for meeting different needs of the tools, some processes ended up in different phases for each one of the tools under study. For example for eVerdEE, two different analyses were performed and then one of them (agriculture) was inserted in the second one as a component. In the BilanProduit analysis the production phase had to include both the agriculture and the wine making phases. Carbonostics had a ready-made entry for the agricultural phase and CCaLC provided the possibility to model all phases separately. Furthermore, the fact that in the various incorporated databases there were different processes missing has led to a heterogeneous comparison of the results even for cases where the modeling was similar for two tools. For instance, eVerdEE and CCaLC were similarly modeled, but different processes were missing for each of the related databases, rendering their parallelism quite heterogeneous.
This type of background generally appears in dilepton t t events and the estimation is done using a fully data-driven method. The charge flip rate is measured inside the Z boson invariant mass peak using the ratio of SS to OS electron pairs. The probability that one electron has its charge mis-measured varies from 10 -4 to 0. 02 in the range 0 ≤ |η| ≤ 2. 5 and 20 < p T < 200 GeV. Another reducible background, so called fake lepton background, is caused by leptons from decays of hadrons, especially b hadrons. Other candidates can result from mis-measurement of hadronic jets. The fake lepton estimation is performed in data using a matrix method after loosening the lepton identification and isolation criteria. This method requires the measurement of lepton identification efficiency and lepton fake rate as input.
For comparison, results for this model from other ATLAS searches using the same data are shown in Figure  3  right. The exclusion lines show the interplay between SR1b, which performs best at a large mass difference between the gluino and the LSP, and SR3b, which performs best at a small mass difference (compressed region) between the gluino and the LSP. At 95% CL gluinos are excluded up to masses of 900 -1020 GeV for LSP masses below 550 GeV. Figure  6  presents the 95% CL exclusion limits for this model in the (m 0 , m 1/2 ) parameter space. The measurement allows one to exclude a large region of the mSUGRA/CMSSM parameter space. ∼ tb + → g ~ = 118 GeV 1 ± χ ∼ ) = 60 GeV, 1 0 χ ∼ ), m( g ) < m( t , m( 1 ± χ ∼ b → t , t ~ t → g ~2 same-charge leptons + jets =8 TeV s ,.
Data analysis Epidemiologic data (age, sex, height) and spine deformity data (etiology, deformity reduction, Cobb angle) were analyzed. Hemodynamic parameters (pulse, arterial blood pressure) were collected thanks to the peri-operative anesthesiologic diagram. Surgical analyzed data included primary or secondary surgical procedure, number of sublaminar bands used and location of sublaminar band implantation. Intra-operative neurophysiologic monitoring data were found on peri-operative reports and checked by the referent neurologist. A monitoring alert was defined as a reduction of at least 50 % of the amplitude and/or an increase of at least 50 % of their latency, with or without post-operative neurologic deficiency. An alert was linked to SB if situated at the thoracic level, after lesional level determination by the spinal electrode, and without a link to other implants and/or spine deformity correction.