dataesr/scientific-paragraphs-categorization huggingface dataset:
24 Read summarization was performed with htseq-count v0. 6. 1 using the 12x V1 annotation with default settings. 35 DESeq2 36 was used for the DE analysis of the stress (RNA-seq) datasets, comparing the experiments' treatments against controls. For the grapevine atlas dataset, DE analysis was performed using limma 37 comparing the development progression from young to mature of each organ, except flower-, seedling-, and root-specific tissues. A threshold of false discovery rate (FDR) < 0. 05 and absolute log2FC > 1 represents significant DE genes identified through DESeq2 and limma in each comparison. Estimation of RNA-seq derived transcript abundance was performed with DESeq2 by applying variance-stabilizing transformation (VST) while pre-normalized probe intensities (using Robust Multi-Array Average), were gathered from.
25 All predicted grapevine-Arabidopsis orthologues were downloaded from EnsemblPlants (http://plants. ensembl. org/index. html; 1 March 2016, date last accessed). Expression analysis of Arabidopsis genes was conducted using the AtGenExpress compendium for the 'stress' and 'pathogen' series via the Bio-Analytic Resource for Plant biology database (http://bar. utoronto. ca/; 1 December 2016, date last accessed) expression browser tool.
Construction of GCNs Prior to network construction, genes that are not DE in at least one comparison in each compendium were filtered. A final nonredundant list of DE genes in the stress-responsive and atlas compendia were obtained and used for GCN construction. Principal component analysis was performed on the RNA-seq VST dataset using R (http://www. r-project. org). Pearson's correlation coefficient (PCC) values were used as a measurement of expression similarity between gene pairs. Ranks were assigned to gene pairs based on the PCC values of their genes. The formula for geometric mean of the ranks, MR (AB) ¼ ͱ(Rank(A!B) Â Rank(B!A)), was used to calculate the MR value for each pair-wise genes. 19, 20 Rank(A!B) corresponds to the rank assigned to gene B given the list of co-expression genes from gene A, while vice versa for Rank(B!A).
Numerous CREs are implicated in grapevine stress-responsive and development-dynamic transcription To determine the contribution of CREs in determining stressresponsive and development-dynamic gene transcription, metaanalysis of publicly available next-generation sequencing (RNA-seq) and the grapevine tissue developmental atlas was first performed (Supplementary Fig. S1 ). The survey for stress-related RNA-seq datasets yielded a total of 87 conditions (260 non-averaged) from 10 experiments that considered various abiotic (e. g. drought and shading) and biotic stress (e. g. Botrytis cinerea, Erysiphe necator, and Plasmopara viticola) (Supplementary Table  S3 ). All these conditions also correspond to three different organs: leaf, berry, and flower. Given the incompleteness of sequenced mRNA across tissues and associated developmental stages, the grapevine expression atlas was considered.
We empirically determined that the top 300 most up-and down-regulated genes would provide a reasonable threshold closely satisfying the large fold change requirements (Fig. 3A ; Supplementary Fig. S2 ). These genes typically have a median fold change greater than 4-and 8-fold in the stress and development datasets, respectively. In addition, 10,847 and 8,224 unique genes were within top 300 up-and down-regulated genes in at least one stress and developmental condition, respectively, with an overlap of 5,601 genes that were within the 300 up-and down-regulated genes in both stress-responsive and developmental-dynamic condition. For each 6-mer CRE, the proportion of the genes (containing the respective CRE) that were highly responsive to either stressresponsive and/or development-dynamic was analysed in detail.
CRE-driven modules are biologically relevant in condition-specific GCNs Two GCNs (one stress-and one development-specific) were constructed with gene expression matrices using the MR approach. Coexpression studies in many plants  23  and in grapevine  [28] [29] [30] 61  have shown that direct correlations (e. g. PCC  and rank-based co-expression (e. g. MR) can be used to infer co-expression relationships, albeit rank-based co-expression is generally more robust and is the most popular approach among crop GCNs. 23  In addition, establishment of statistically significant reciprocal ranks, MR in this case, may further improve the robustness of the resulting GCN by retaining strong and significant co-expression. 21  In the two datasets, we empirically chose a MR cut-off of 35 based on 1,000 permutations of each expression matrices, which corresponds to a statistical confidence of P < 0.
Next, we adopted a recently proposed 'bottom-up' approach by Ma and co-workers  13  to identify gene expression modules potentially driven by CREs of co-expressed genes. Using this approach, 245 and 289 CRE-driven modules (Supplementary Tables  S7  and S8 ) in stress-and development-specific GCNs, respectively, were enriched (FDR < 0. 05) with at least one MapMan BIN category (Supplementary Table  S2 ). Although modular structures selfmanifested in most CRE-driven modules,  13  we applied community clustering to ensure robust final submodules. 38 In total, 880 and 1,095 CRE-driven submodules were enriched (FDR < 0. 05) in the stress and development-specific GCNs, respectively, with at least one MapMan BIN category (Supplementary Table  S2 ). Interestingly, these enriched submodules corresponded to more 'global' CREdriven modules, 293 and 314 for stress and development, respectively, demonstrating that CRE-driven submodules have higher predictive performance compared with interpreting 'global' CRE-driven modules as a whole.
CACGTG enriched co-expression network is involved in ABA metabolism and light-related functions The G-box (CACGTG) was found to be highly enriched for stressresponsive genes having scored a CF, Z-score, and FE of 2. 7, 3. 7, and 4. 5, respectively. To investigate whether the enrichment observed has a biological relevance, a gene network was constructed for the stress dataset. A total of 842 genes composed a network comprising a total of 20 community clusters. Some of these clusters were significantly enriched with different MapMan BIN categories (Table  1 ). Focus is given to the core modules with bZIP and bHLH TFs since previous research has shown that the G-box CRE has been associated with the binding of these TFs. 44, 46 The presence of these TFs within the modules of the CACGTG network would suggest that the TFs contribute to the regulation of genes and processes pertaining to the selected module.
Module I (Fig. 4A  and B ) had a MapMan ontology enrichment pertaining to ABA metabolism. In this module six bZIPs and one bHLH were identified, of which one bZIP encodes the grapevine ABSCISIC ACID RESPONSE ELEMENT-BINDING FACTOR2 (VviABF2, VIT_18s0001g10450) involved in regulating berry ripening in an ABA-dependent manner. 53 However, direct implication of VviABF2 in regulating stress response networks remains to be established. In Arabidopsis, previous research has shown that this homologous bZIP is involved in ABA mediated stress responses including drought. 62 In the VviABF2 subnetwork (k ¼ 2) of Module I (Fig. 4A ), members of the ABA signaling pathway were immediately connected with VviABF2 including a homolog (VviABI1, VIT_11s0016g03180) of an Arabidopsis protein phosphatase 2C, ABA insensitive 1, an essential component of the ABA signaling network,  63  and two homeodomain-leucine zipper TFs (VviHB7, VIT_15s0048g02870 and VviHB12, VIT_02s0025g02590), involved in the signal transduction pathways mediating growth in response to drought.
64 In addition, VviABF2 might be involved indirectly (k ¼ 2) in the regulation of a 9-cis-epoxycarotenoid dioxygenase 2 (VviNCED3, VIT_19s0093g00550), encoding the putative rate-limiting step of ABA biosynthesis, and a protein phosphatase 2C gene (VviPP2C9, VIT_06s0004g05460) involved in ABA signalling. 63 To determine the involvement of this subnetwork in grapevine stress responses, meta-analysis of stress-related RNA-seq datasets described above was performed. Water deficit resulted in higher gene expression and greater up-regulation of module genes especially in leaves compared with berries (Fig. 4C ). This observation is in agreement with the role of ABA metabolism and signaling in response to drought,  63  while highlighting differences in ABA metabolism and signaling pathways that arise between organs.
GCC-box network highlights pathogen response and signalling in stress dataset Although individual CRE modules may reveal important biological insights, many CRE modules often overlap  13  as underlying genes are shared between similar as well as distinct CRE modules. In the case of similar CREs, the canonical GCC-box sequence (GCCGCC), a well-known binding site for DREB and ERF subfamilies of AP2/ ERFs TFs, mediates ethylene and jasmonate signaling in response to pathogens. 43, 55 Recent studies now show that GCC-related elements (GCCGTC and GCCGGC) are also CREs targeted by DREB and ERF TFs. 5  In this study, GCC-box, and GCC-related CREs (GCCGTC and GCCGGC) were found to be enriched. GCNs were built for these CREs. Each of three CREs' GCNs contained at least one module with an AP2/ERF TF: Modules I and VI for GCCGCC, Module II for GCCGTC, and Modules I, III, and IV for GCCGGC (Supplementary Table  S7 ).
Different cell wall remodelling genes are enriched in subnetworks of the CCWACC development-specific network Differences in biological function enrichments were also observed for many CRE submodules constructed from the developmental and stress-specific GCNs. For example, CCWACC commonly referred to as an AC-element, is one of the three main R2R3-MYB binding sites. 10 It showed bona fide CRE properties (CF 1. 90, Z-score 3. 61, and FE 1. 86 in stress-responsive, and CF 1. 87, Z-score 3. 10, and FE 1. 87 in development-dynamic genes), and was highly enriched for biological functions in both sets of genes. However, in the developmental dataset greater enrichment for biological pathways, especially cell wall metabolism, was observed compared with stress. Therefore, focus will be given to the development-specific CCWACC network.
6D ). The difference in cell wall metabolism pathway enrichment is not limited to the CCWACC network, but to other developmental submodules that are potentially regulated by ARF (TGTCGG and TGTCGA),  5, 11  NAC (TTGCGT and TTCCTT),  85  and AP2/ERF (ACCGACA). 6, 43 8. AGATTCT developmental network module is specifically enriched in photosynthesis-related genes Although CREs and CRE-driven subnetworks, such as those previously discussed, show that strong positional bias towards the TSS and FE scores provide reasonable proxies for identifying bona fide CREs and biologically relevant modules, some CREs and CRE-driven subnetworks lack these properties. For example, the 7-mer CRE AGATTCT (CF 2. 69, Z-score 0. 13, and FE 6. 91 in the developmental dataset), whose network contains a G2-like TF. DNA-binding specificities of lesser known plant TF families such as G2-like, a subclass of GARP proteins, have been recently characterized.
5 The G2-like TF, GLK, is implicated in photosynthesis and chloroplast development in many plants  86  and is a key factor regulating the latitudinal gradient of ripening  87  and various quality traits in fruits. 88 Arabidopsis GLK1 recognizes the AGATTCT CRE sequence. 5 The AGATTCT network in the developmental dataset contained 113 genes and was highly enriched for many photosynthesisrelated genes, especially in modules I and II (Table  2 ) suggesting that GLK-regulated gene networks are highly conserved across plants including grapevine. Present in module I (Fig. 6C ) are VviGLK1, the homolog of Arabidopsis GLK1 (VviGLK1, VIT_12s0028g03100), and its likely target genes which encode for photosystem I polypeptide subunits (VviPSAN, VIT_04s0044g01410; VviPSAF, VIT_00s0125g00280; VviPSAD-2, VIT_05s0020g03180) and light harvesting complexes (VviCAB1-1, VIT_10s0003g02890; VviCAB1-2, VIT_10s0003g02900; VviLHB6, VIT_12s0055g01110).
One gene involved in cellular redox homeostasis (VviCDSP32, VIT_18s0001g00820) was also implicated in this module. The expression survey across the developmental dataset revealed that module I genes are highly expressed in immature organs of the berry pericarp, flesh, skin, and seed, as well as in rachis, buds, and leaves, and gradually decrease their expression as development of each organ progresses (Fig. 6D ). Therefore, the consistent down-regulation of module genes may reflect an active role of photosynthetic genes and VviGLK1 in the immature-to-mature phase transition of grapevine organs, similar to the observations in other plants. 86 Additionally in grape berries, these module genes may be critical for the establishment of differing ripening gradients during the onset of ripening, as seen with the role of GLK1 in tomatoes.
Conclusion In this study, we have elucidated the properties of a large selection of known plant CREs in grapevine promoters at the genome-scale. We observed that many CREs fulfill the widely-adopted criterions defining authentic plant CREs. We corroborated these findings through extensive biological pathway enrichment analysis suggesting the characterized CRE structure has strong biological relevance. Together, these assessments have elucidated many potential targets of a large repertoire of grapevine TFs for the first time. Large-scale transcriptome analysis using the developmental atlas dataset and a selection of stress-related RNA-seq datasets demonstrated that the distribution of CREs in gene promoters explains close to half of the DE across the various abiotic and biotic stresses, and developmental comparisons evaluated.
Acknowledgements G. B. is indebted to I. M. and his team at the LOMC of the  CNRS in Le Havre , and to  Christophe Dumouchel  at  CORIA in Rouen , for their hospitality during three sabbatical stays in September 2014, September 2015, and December 2016 and acknowledges the inspiring atmospheres at the two laboratories. MC. R. was supported by the  LABEX EM C 3  under the project  TUVECO.
Each site had three N levels to test the [CO 2 ] × N interactions. We also compiled results from a series of Soil-Plant-Atmosphere-Research (SPAR) chamber experiments1  6, 24. The SPAR chamber experiments conducted in Gainesville, Florida, USA, included six levels of [CO 2 ], so are suitable to test model responses to various levels of [CO 2 ]. Another SPAR experiment with three levels of N was conducted to test the [CO 2 ] × N interactions. With these datasets and collectively 16 rice models, uncertainty analyses using a multi-model ensemble and multi-experimental facilities are possible. Our objectives are: (i) to determine the variation of yield prediction among existing commonly used rice models of different types, (ii) to identify the processes/mechanisms/factors that cause this uncertainty, (iii) to analyze to what extent the models differ in simulating [CO 2 ] × N interactions, and (iv) to propose possible directions for model improvement.
Discussion Crop response to increasing [CO 2 ] has been identified as a major source of uncertainty in predicting crop production 6. In this study, we confirmed substantial variation in yield prediction in response to E-[CO 2 ] among rice crop models, which was much greater than the experimental variation. Here we used data from FACE and SPAR chamber experiments to identify the sources of uncertainty. Experiments with small pots could limit the response to E-[CO 2 ]  25  , but the SPAR chamber experiments used large containers containing soil of 50 cm depth, so the effect of the limited rooting zone was assumed to be minimized. Despite the large differences between models, the mean computed over all model simulations was close to the observed yield and yield response to E-[CO 2 ], as previously reported  [10] [11] [12] 26.
Model simulations for the FACE experiments. Each modeling group received calibration data that covers all three N levels with both ambient and elevated [CO 2 ] treatments (Shizukuishi in 1999 and Wuxi in 2002). These data sets were used to test and improve model performance under E-[CO 2 ]. Each group could determine how to use the available data to perform calibration or modify the functions, and the final model equations and parameters were recorded and reported to the AgMIP-Rice team leader. Then the same final model, except for cultivar and soil parameters that were site dependent, was run for all N × [CO 2 ] combinations of field experiments conducted in 1998 and 2000 at Shizukuishi, and in 2001 and 2003 at Wuxi. Model simulations for the SPAR experiments. Each modeling group received all required crop management, soil and weather data, phenology dates (anthesis and maturity), seasonal biomass accumulation and yield for the "ambient" [CO 2 ] (330 µmol mol -1 ) treatment for all experiments.
Experimental procedures and data collection A group of 70 rummy-nose tetras (Hemigrammus rhodostomus) were used in our experiments. This tropical freshwater species swims in a highly synchronised and polarised manner. Inside an experimental tank, a ring-shaped corridor 10 cm wide with a circular outer wall of radius 35 cm was filled with 7 cm of water of controlled quality (Electronic Supplementary Material, Figure  S1A ). For each trial, n fish were randomly sampled from their breeding tank (n ∈ {1, 2, 4, 5, 8, 10, 20}). Each fish only participated in a single experiment per day. For each group size, we performed between 9 and 14 replications (see Electronic Supplementary Material, Table  S1 ). Trajectories of the fish were recorded by a Sony Handy-Cam HD camera filming from above the set-up at 50Hz in HDTV resolution (1920×1080p).
Acknowledgments We thank  Daniel Calovi  for his help to track experiments and his insightful comments. This study was supported by supported by the project  ANR-11-IDEX-0002-02  - Transversalité -MUSE -"Multidisciplinary study of emergence phenomena " and by grants from the  CNRS  and  Université Paul Sabatier  (project  Dynabanc ). V. L. was supported by doctoral fellowships from the scientific council of the  Université Paul Sabatier. L. J. was funded by a grant from the  China Scholarship Council  ( CSC NO. 201506040167 ).
The outputs from these models were daily ragweed pollen levels for 50 km × 50 km grid cells across Europe. Two estimates of current levels were produced (based on CHIMERE or WRF/ RegCM), as were 12 estimates of future levels (based on CHIMERE or WRF/RegCM, plus the 2 RCP and 3 plant invasion scenarios). For the present analysis, these 14 estimates of current and future ragweed pollen were combined with health and population data to quantify their public health significance using ragweed sensitization rate (RSR) as a health consequence. Sensitization to ragweed is related to long-term pollen exposure; thus, these daily pollen outputs were aggregated to provide estimates, on a 50 km × 50 km grid, of average total season ragweed pollen. To produce quantitative estimates of the potential impact of future ragweed pollen levels on RSRs, we first needed to generate a dose-response curve that would be representative of populations in locations with varying ragweed pollen levels in addition to characteristics that might confound the association between pollen levels and RSRs.
10, p-value < 0. 001; 95% confidence interval (CI): 3. 66, 8. 04] with an adjusted R 2 = 0. 384. The coefficient for study type (atopic vs. general population) was 9. 35 (SE 3. 70, p-value 0. 014; 95% CI: 1. 94, 16. 76). A plot of the association between mean pollen counts and RSR, adjusted for study population type, is presented in Figure  1. We then used the model coefficient and the estimated ln-mean pollen count in each 50 km × 50 km grid cell to estimate an RSR for each grid cell. This operation was performed on our 2 current and 12 future estimates of ragweed pollen across Europe. We combined the gridded RSR data with NUTS3 boundaries (subdivisions of the 28 EU countries into regions of 150,000-800,000 residents) and population data, to estimate the number of sensitized individuals at the NUTS3 level.
These data were aggregated to the NUTS2 level (regions with 0. 8-3 million residents) because many NUTS3 areas are smaller in size than the 50 km × 50 km grid cells. NUTS data were sourced from the Statistical Office of the European Union (Eurostat). For 12 non-EU European countries, we used boundaries and population data from the Global Administrative Areas database  (DIVA-GIS 2015). Projections of population change were obtained from the World Bank databank (World Bank 2015) and applied equally to all NUTS2 areas within a country to estimate population counts for 2041-2060. Burbach et al. (2009)  indicated that only a proportion of patients sensitized to ragweed experience symptoms and presented estimates of clinically relevant sensitization rates for ragweed in different European countries.
Results The pollen RSR dose-response function was applied to the 14 ragweed pollen maps on a 50 km × 50 km grid, and the corresponding population sensitized to ragweed at the NUTS2 level was mapped and tabulated at European and country levels. Initially focusing upon the reference plant invasion scenario, 6 RSR maps and countrylevel data on the sensitized population were produced (2 current + 4 future). These differed according to the regional climate/ pollen model (CHIMERE or WRF/RegCM), time period (baseline or future), and RCP scenario used. These maps are presented in Figure  S2  and Table  S3. Estimates of the sensitized population based on airborne pollen levels generated using the WRF/ RegCM model suite were 27-39% higher than corresponding estimates based on the CHIMERE model suite for both the baseline period and the future period.
Here, the two model suites are considered to be equally plausible because they both show a similar performance in simulating pollen amounts compared with limited available observations  (Hamaoui-Laguel et al. 2015). Therefore, the estimated population affected was averaged across the two model suites for all further analysis. However, in the numerical results, the data for both model suites are additionally reported in square brackets as an indicator of uncertainty; the following format is used: [CHIMERE, WRF/ RegCM]. All subsequent results are presented for RCP4. 5 (i. e. , a moderate degree of climate change) for the sake of simplicity, bearing in mind that the numbers of sensitized individuals in the future are very similar between RCP4. 5 and RCP8. 5. Our best estimates of the current and future population sensitized to ragweed from the pollen RSR dose-response relationship shown in Figure  1   Sensitization is projected to increase in countries with an existing ragweed problem, such as Romania and Italy, partly as a result of increased pollen production by established plant populations; however, the greatest proportional increases are likely to be in areas where ragweed sensitization is currently relatively uncommon, such as Germany, France, and Poland.
INTRODUCTION Salmonella enterica subspecies enterica serovar Dublin (S. Dublin) is one of the most frequently encountered Salmonella in cattle in the European Union. Data from the ANSES Salmonella Network (jointly with the National Reference Laboratory) show that this serovar contends with the Typhimurium serovar for the top spot in the ranking of the serovars most frequently isolated in cattle in France. Between 2002 and 2010, S. Dublin was outright the most common one (European Food Safety Authority  [EFSA], 2015). Infected cattle may develop several clinical signs mainly characterized by i/ diarrhea, pneumonia and death in calves and adult cattle, and ii/ abortion and decreased milk yield in cows  (Nielsen et al. , 2013). Beyond the significant economic losses caused in the bovine sector, S.