dataesr/scientific-paragraphs-categorization huggingface dataset:
All strains were unable to growth in the presence of mucin as the only carbon source in a defined medium (data not shown). This data agrees with previous results where no evidence of fermentation of porcine gastric mucin by F. prausnitzii was detected  (Lopez-Siles et al. , 2012). Nevertheless, SCFA concentrations and OD 600nm measures taken after 2 days of incubation showed the ability of the different strains to survive but metabolically inactive as it could be deduced by the absence of butyrate in the supernatants of the cultures and the almost minimal OD 600nm recorded (data no shown). A decrease in butyrate production due to non-optimal growth conditions have been already reported for F. prausnitzii A2-165 strain  (Lopez-Siles et al. , 2012). This characteristic pointed out the intrinsic growth requirements of this species which, in addition to be an EOS, needs strain specific nutritional environment and has the ability to switch between substrates derived from the diet or the host  (Lopez-Siles et al.
Lytic Activities Gelatin is a heterogeneous mixture of water-soluble protein that is usually used in microbiological procedures to detect the presence of proteolytic activities. None of the strains were able to degrade gelatin in the conditions recommended by the API gallery supplier (data not shown). However, when the strains were inoculated in the gallery in a defined medium instead of API suspension medium, they were able to degrade partially this compound after 3 days of incubation. This fact suggests that the strains are able to hydrolyze gelatin although, maybe due to the growth limitations present in this culture media, the existence of this compound is not enough to allow the metabolic development of this activity in the strains. The presence of hemolytic activity was tested using blood agar plates.
CONCLUDING REMARKS The development of new probiotic products containing human isolated strains with beneficial properties for the host requires the development of new techniques in order to: (i) isolate strains belonging to the major groups of the intestinal microbiota, (ii) determinate their safe status and (iii) infer in their potential beneficial effects. This study meets these entire three requests. Work with anaerobic and more precisely EOS bacteria are a prerequisite to succeed in the isolation of representative strains that can impact on intestinal homeostasis. For this reason, in this study, we have used a new procedure to isolate EOS strains from feces that has enabled us to build a collection of F. prausnitzii strains. The lack of knowledge about this species prompts us to further analyze their genetic diversity by comparing the new isolates with those already available in the databases.
ACKNOWLEDGMENTS Authors thank  Prof. Juan Evaristo Suárez  for the critical reading of the manuscript and  Stéphanie Courau  and  Pascal Molimard  for fruitful discussions during the project. We gratefully acknowledge  T. Meylheuc  for scanning electron microscopy (MIMA2 platform,  INRA, France)  and  Harry Flint  for the reference Faecalibacterium strains.
FUNDING This paper was a part of FPARIS collaborative project selected and supported by the  Vitagora Competitive Cluster  and funded by the  French FUI (Fond Unique Interministériel ; FUI: n •  F1010012D ), the  FEDER  ( Fonds Européen de Développement Régional ; Bourgogne:  34606 ), the  Burgundy Region , the  Conseil Général 21  and the  Grand Dijon. This work was also supported by  Merck Médication Familiale (Dijon, France)  and  Biovitis (Saint Etienne de Chomeil, France). RM and SM receive a salary from the same grants.
Seventh Framework Programme ( FP7-ICT-2011-7 ) under grant agreements  ERC StG 278242  (ExtendFRET), 288263 (NanoVista),  Spanish Ministry of Economy and Competitiveness ("Severo Ochoa" Programme for Centres of Excellence in  ( SE -0 -0  ) and F S 0 -0 -),  Fundaci CE E (Barcelona)  and  CERCA Programme/Generalitat de Catalunya. P. M. W is supported by the  ICFOstepstone Fellowship , a  COFUND Doctoral Programme of the Marie-Sklodowska-Curie-Action  of the  European Commission. R. R. is supported by the  Erasmus Mundus Doctorate Program Europhotonics  (Grant  159224-1-2009-1-FR-ERA MUNDUS-EMJD ).
Here we investigate how the ring neurons might contribute to behaviour, by simulating the visual input as it would be processed by this small population of visually responsive cells. In particular, we can address why flies are unable to discriminate Funding: ADMD, PG and AP are funded by EPSRC (grant code: EP/P006094/1; https://www. epsrc. ac. uk/). AW is funded by the Fyssen Foundation (http://www. fondationfyssen. fr/en/). ADMD and PG have also received funding from the BBSRC (grant codes: BB/F015925/1 and BB/H013644; http:// www. bbsrc. ac. uk/). AP has also received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 308943 (https://ec. europa. eu/ research/fp7/index_en. cfm). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
To generate the stimuli, an optimisation was performed in MATLAB (fminsearch function) to minimise the ratio of blob difference to difference in activation (G, top) or its inverse (G, bottom). Pairs of stimuli are shown in grey and green whereas in the simulation both are black. F: The corresponding activations of the different R2 RFs to the blobs in G. Two similar patterns give a mean difference in activity  [14]. All of the pattern pairs where flies show significant learning (n = 8) have R2 r. m. s. differences above the overall mean (Fig 3A  and 3B ), whereas 13 out of 18 patterns that flies found more difficult to learn had below-average r. m. s. differences (there were nine pattern pairs for which a significance level was not given that were excluded. ) Across all pattern pairs, we find a significant correlation between the strength of the learning index reported for flies in  [14]  and the r.
25 (Fig 6A ). We take the centroid of the largest excitatory region as the 'centre' of each of the kernels. The excitatory region is then extracted using MATLAB's bwlabeln function (with eightconnectivity) and its centroid, (x, y), with the regionprops function. The mean centroid, ð" x; " yÞ, across flies is then calculated and the kernels are all recentred on this point: In order to calculate the activation for a given RF on presentation of an image the RF must first be resized to have the same number of pixels as the image. This is accomplished by resizing the average RF, " g ði; jÞ, using Matlab's imresize function with bilinear interpolation and then scaled to [-1, 1]. Finally, the filter is thresholded and the excitatory and inhibitory regions are assigned different normalised values: K i;j ¼ " g ði; jÞ Ä S exc ; for " g ði; jÞ > 0; ĝ ði; jÞ ¼ gði þ y À " y; j þ x À " xÞ for 1 i þ y À " y m and 1 j þ x À " x n; À " g ði; jÞ Ä S inh ; for " g ði; jÞ < 0; 0; otherwise: 8 > > > < > > > : where S exc and S inh indicate the sums of excitatory and inhibitory pixels, respectively.
Neural networks The ANNs were implemented using the Netlab toolbox for MATLAB. All networks were two-layer feedforward networks, with 10 hidden units and a linear activation function for the output units. There were 100 training cycles and optimisation was performed with the scaled conjugate gradient method. Random blob stimuli. The stimuli used to train the networks were a series of black 'blobs' on a white background. The blobs were based on ellipses with a fixed ratio between the lengths of the major and minor axes (2 : 1), with the radii modified with complex waves: rðyÞ cos 2 y 2 þ sin 2 y a À 1 þ WðyÞ; y 2 f0; 2pg where a is the length of the major axis and W(θ) is a complex wave defined as: WðyÞ ¼ X n i¼1 W i ðyÞ ¼ X n i¼1 A i sin f i ðy þ 0 i Þ where A i , f i and ϕ i describe the maximum amplitude, frequency and phase shift of the wave, W i (θ), respectively.
This method for generating stimuli allows for a substantial degree of random variation between the stimuli, while not producing shapes that are so irregular as to be unlearnable by a neural network. In these experiments, A i , f i and ϕ i were randomly generated and n = 2. A i was a random value from 0 to 1, f i were random integers from 1 to 30 and ϕ i was a random value from 0 to 2π. The blobs were first generated, according to the above equation, as images of 120 × 270 pixels. For the 'raw view' stimuli, these images were resized, using MATLAB's imresize function, to 3 × 14 pixels, thus giving the same number of inputs as there are R2 and R4d filters (n = 42).
Bars show mean r. m. s. difference between the activation of 28, 56 and 112 RFs to each pattern. For 28 RFs we use the R2 RFs. For 56 and 112 RFs, one and three extra sets of R2 filters, respectively, are added in random, non-overlapping positions on the visual field (see Materials and methods for details). As there is some stochasticity in this process, differences are averaged over 1000 trials with error bars showing standard deviation. F and G: We can also generate shapes that appear similar yet produce a large mean difference in RF activation (G, top) or appear different and produce similar RF activations (G, bottom). The stimuli here are 'blobs' of the form described in Materials and methods. To generate the stimuli, an optimisation was performed in MATLAB (fminsearch function) to minimise the ratio of blob difference to difference in activation (G, top) or its inverse (G, bottom).
Statistical analyses Results are expressed as the mean ± SD. Statistical analyses were performed with GraphPad Prism 6 (GraphPad Software) using one-way ANOVA multiple comparisons followed by Tukey correction, or t-test followed by Welch correction. P values were indicated in the legends if considered significant ( Ã <0,05, ÃÃ <0,01, ÃÃÃ <0,001).
Compilation of distributional and climatic data Extant distributional data for the clusioid CPH clade were obtained from online databases (http://www. gbif. org, accessed on January 2017) and from our own collection. A total of 173. 459 data points were collated to exclude ambiguous citations (points in the sea, country centroids, areas out of the native distribution of the species) and repeated occurrences using SpeciesGeoCoder  (Töpel, Zizka, Calió, Scharn, Silvestro et al. , 2017) ; manual verification was also necessary, as this software did not eliminate all ambiguities. The final dataset included 48. 467 occurrences in cells of 5 minutes for Hypericaceae (representing 6 genera and 480 Hypericeae spp. out of 612 described in the family), Podostemaceae (590 occurrences for 38 genera/145 spp.
We tested the impact of calibration on our estimates by considering all models with and without calibration. We initially considered more adaptive models, such as the OU or multiple OU models  (O'Meara, 2012). However, the selective optimum and the root state cannot be identified under the OU model  (Ho & Ané, 2014). We found that this effect concerned all ancestral trait estimates when fitting the OU model on simulated datasets, with highly negatively correlated posterior samples of the selective optimum and ancestral traits. Ho & Ané (2014)  further showed that this problem generalizes to multiple-optima OU models. Finally, extensive tests for the fitting of OU models in RevBayes  (Cornuault, in prep. ) revealed that the inferred number of selective regimes in a multiple-optima OU model, or the uncertainty associated to ancestral trait estimates are highly dependent on the choice of prior for the selection strength parameter of the OU model.
Tip traits were scaled to mean 0 and unit variance before analysis and the root trait received a normal prior with high variance (100) relative to that of tip traits (1): tr ~ normal(0, 10) (3) The trait value at the end of branch i, ti, received the Brownian motion distribution: ti ~ normal(ta(i), i bi) (4) where bi is the length of branch i, and a(i) denotes the index of the ancestor of branch i. The time of calibration received a uniform prior between s and e, the starting and ending times of the calibration branch (i. e. the stem of Hypericum) c ~ uniform( s, e) (5) The calibration branch was cut in two parts, with:  (tc, σk (bk-( τe -τc) ))  (7)  where k is the index of the calibration branch and tc is the trait calibration value (fixed). tc ~ normal(ta(k), k (bk-( c -s))) (6) tk ~ normal This model was implemented in RevBayes and three independent runs of 100,000 iterations were carried out with a thinning of 10, providing a final sample of size 30,000 for temperature variables (scripts are available in Appendix S6).
Diversification analyses To investigate the impact of climate cooling on diversification trajectories in the clusioid CPH clade and answer the question on whether a change in climatic preferences triggered rapid diversification in Hypericum (iv), we inferred rates of lineage diversification using stochastic birth-death models and Bayesian MCMC methods in the R package TESS  (Höhna, May & Moore, 2016). The supertree described above was used to explore diversification dynamics within the CPH clusioid clade, while for Hypericum we used the species-level tree from  Meseguer et al. (2015) , which allowed us to go deeper into diversification patterns within sections in Hypericum. TESS allows comparison among various diversification regimes, including birth-death models where speciation and extinction rates are constant; they vary continuously over time or change episodically in time.
10 Because the number of possible episodic models is infinite, we used reversiblejump MCMC (rjMCMC) approaches in CoMET (TESS) to integrate model uncertainty over all possible episodically-varying birth-death processes including time of rate shifts and mass-extinction events  (May et al. , 2016). We set the expected number of events (speciation and extinction rate shifts, as well as mass-extinction events) to 2 and used the function empiricalHyperPriors=TRUE to determine values of the hyperpriors for the diversification parameters. We run 2 mcmc chains for 1 million iterations each, thinning every 100th and discarding the first 1000 as burnin; the auto-tuning function was used in all analyses. We used the package coda  (Plummer, Best, Cowles & Vines, 2006)  to summarize samples and assess good mixing (ESS) of each mcmc run.
ACKNOWLEDGEMENTS Research funding was provided by the  Spanish government  through a  PhD grant  ( AP-2007-01698 ) to ASM, and projects  CGL2009-13322-C01 ,  CGL2012-40129-C01 , and  CGL2015-67849-P  ( MINECO/FEDER)  to I. S. ASM was also supported by a  Marie-Curie   FP7-COFUND (AgreenSkills fellowship-26719 ). JC is supported by  Marie-Curie actions People   H2020 ,  MSCA-IF-EF-ST-708207. The authors are grateful to  Martin Godefroid ,  Graham Slater  and to  Sebastian Höhna  for help with the analyses.
Bioinformatics-guided structure elucidation The strong correlations between certain sequence motifs present in PKS domains and the stereochemistry of the resulting polyketide chains has been exploited in several cases to predict   [116]. When a conserved Y is present (indicated with the triangle), a (2S)-methyl stereochemistry is observed, while the presence of a conserved V at the same position correlates with (2R)-methyl stereochemistry. The grey bar indicates residues involved in binding the NADPH cofactor 12. Residue numbering is based on that of E. coli QOR (PDB ID 1QOR). Reprinted and adapted with permission from  [116]. Copyright (2010) American Chemical Society. and/or corroborate absolute stereochemical assignments made newly-discovered natural products (for example, elansolid  [117] , the disciformycins  [118] , hygrobafilomycin  [119] , phormidiolide  [120] , and haprolid  [121] ), either by manual inspection of domain sequences or by more sophisticated methods including hidden Markov model (HMM)-based sequence classification (e.
The genome of CIRM 1, previously sequenced and annotated, is complete, whereas the other strains are in a draft state and deposited in several scaffolds in the EMBL database. In order to evaluate strain diversity, a phylogenetic tree was reconstructed, based on the concatenation of orthologous proteins in the core genome, i. e. proteins present in a single copy in all strains (1011 out of 4971 proteins) (Fig. 2 ). Twelve strains were preselected in pairs from this phylogenetic tree for additional proteomic studies. The strains were chosen in pairs so that: (i) members of a pair would be close by in the tree (genetically similar) but exhibit contrasted phenotypic properties, and (ii) the pairs would span the complete diversity of the phylogenetic tree, so as to mitigate the effect of the shared evolutionary history of strains (see Fig.
All methods combined, the number of unique proteins identified for each strain ranged from 21 (CIRM 121) to 59 (CIRM 134). For the 12 strains, a total of 509 proteins were identified, representing 174 different proteins (many proteins were identified in several. All bacterial strains were implemented at a bacterial density corresponding to a multiplicity of infection (MOI) of 10, and the supernatants collected were analysed using ELISA. Immunocompetent PBMC cells were stimulated by bacteria for 24 h and the data are expressed in pg/ml as means + /-SEM of the results concerning four separate french healthy blood donors. The Pf strains are ordered by their decreasing induction of IL-10, which is highly variable. strains). Within these proteins, 23 could not be linked to a cluster because they corresponded to parts of bacterial genomes with poor quality sequences (inherent to draft genomes).
The fourth was LspA, a protein with a predicted molecular weight of 115 kDa and a glucosaminidase domain, which was detected in CIRM 456, 516 and 139. Finally, among the 174 proteins identified, one, represented by PFCIRM129_08670, was common to all 12 strains. The sequence of this protein contains a peptide signal and its predicted function is a secreted cell-wall peptidase. 77 proteins were identified in only one of the 12 strains, and could represent the specific proteome of each strain. All the other proteins identified were strain-dependent and could potentially be responsible for the strain-dependency of the anti-inflammatory properties of Pf. These proteomic data are summarised in the supplemental data online, Table  S4. Identification by association analysis of candidate genes potentially involved in the anti-inflammatory properties of P.
The dcuA gene encodes a transport protein that is involved in C4-dicarboxylate transport, probably in the context of aspartate import. Finally, the pep gene encodes a peptidase. Four clusters were represented by genes encoding proteins of unknown function (Pouf): pouf 10785, pouf 10930, pouf 04790 and pouf 08235. ). The localisations were predicted by SurfG + software, as described by Barinov et al. 20  and the categories were as follows: (C) cytoplasmic protein, PSE: Protein surface exposed, M: membrane protein, SEC: secreted protein. Inactivation of the selected candidate genes. In order to evaluate the role of the candidate genes in the anti-inflammatory properties of Pf, we pursued several strategies. When the regression coefficient was positive, inactivation of the candidate genes was attempted in Pf CIRM 129, the most anti-inflammatory strain.
In order to decipher the molecular components associated with anti-inflammatory strains of the Pf species, we implemented a strategy that combined three levels of analysis: genomics, transcriptomics and surface proteomics. Such a strategy has never previously been described in the literature, many studies having focused on the role of specific surface components in these anti-inflammatory effects. As an example, several lactobacillus species are able to produce S-layers, a crystalline array constituted of self-assembling S-layer proteins subunits  26. These The localisation corresponds to the category predicted in silico with SurfG + software, as described by Barinov et al. 20. C cytoplasmic protein, PSE: Protein surface exposed, M: membrane protein, SEC: secreted protein. b Gene [P/T/G]i corresponds to the I-th candidate gene according to proteomic, transcriptomic and genomic data analysis.
Statistical analysis. Results are expressed as the mean ± standard error of the mean (SEM). Statistical analyses were performed using GraphPad Prism 6 software (GraphPad Software Inc. , La Jolla, CA, USA). Non-parametric Mann-Whitney tests were used to calculate significance levels for measurements. Values of P < 0. 05 were considered statistically significant. Clustering of orthologous proteins based on whole genomes sequences. The whole genome sequencing, assembly and annotation of the 21 strains of Pf were conducted as previously described  6. To establish the correspondence between the gene repertoires of the 21 Pf strains, we used single-linkage clustering whereby two predicted coding sequences were considered as belonging to the same gene cluster when blastp revealed a local alignment with at least 80% identity and accounting for at least 88% of the protein length (and E-value < 0.
RNA concentrations were quantified using a Nanodrop. RNA quality (RIN) was evaluated using an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA). RNA labeling and hybridization were performed by the 'Biopuces GeT plateforme' (INSA Toulouse, France). All of the RNA samples had a RIN value greater than 8. 3, indicating a good RNA integrity. The ratios 260/230 and 260/280 were greater than 1, 8. A pangenomic 4 × 72 K Nimblegen microarray (Roche Diagnostics, France) was designed. For this purpose, we used the Oligowiz software  42  and a reference pangenome composed of a representative sequence for each gene cluster to build a repertoire of possible isothermal probes of length between 45 nt and 65 nt. A relatively high target melting temperature 90 °C (+ /-5 °C) was selected to account for the high G + C-content of Pf (67%).
Finally, the log2-transformed measures made on biological replicates were averaged for the subsequent correlation analysis with the immunological phenotype data. All microarray data are under submission with Gene Expression Omnibus (GEO; https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?token= yzenwcgivjslroj&acc= GSE87574). Proteomic analysis of bacterial surface. Surface proteins of several strains were detected using three complementary methods: trypsin-shaving, guanidine hydrochloride extraction and CyDye Cyanine surface labeling. All these methods were previously fully described in ref. 19. Association between anti-inflammatory phenotype of P. freudenreichii strains and the genomic, transcriptomic and proteomic data. The strategy used to associate genomic, transcriptomic and surface proteomic informations with anti-inflammatory phenotypes was to perform a regression study of phenotypes against omics features.
The p-value and fraction of variance explained (R 2 ) by the omics effect were computed by comparing the full and nested models using either an ANOVA (p-value) or predicted IL-10 levels in both models (R 2 ) (R functions: lmer from package lme4  43  for the mixed models, anova and predict for the p-value and R-squared). The regression coefficient, which gives the direction (sign) and size of the omics effect, was recovered from the full model. Each cluster of orthologues for which omics information was available was tested individually and we corrected p-values using the fdr procedures to account for multiplicity of testing. Selection of candidate genes to be inactivated. Following the genomic, transcriptomic and proteomic correlation analysis, the clusters of orthologous were ordered according their R 2 (fraction of IL-10 variance explained) for each omics type.