dataesr/scientific-paragraphs-categorization huggingface dataset:
To assess the significance of the difference between the inferred parameters in the two gene sets, 1000 permutations of genes between the two categories where generated, and parameters inferred independently on the two pseudo-gene sets. We tested two statistics, S(α) = α(effectors) -α(non-effectors) and S(ω a ) = ω a (effectors) -ω a (non-effectors). We computed their respective P-value using the formula (|S ≥ S obs | + 1)/(|S| + 1), where |S| = 1, 000 is the number of permutations performed. We further stratified our dataset according to the local recombination rate, computed in 20 kb windows, using both the previously published genetic maps (r, in cM/Mb)  (Croll et al. 2015)  and population estimates (ρ = 2. Ne. r) from patterns of linkage disequilibrium  (Stukenbrock and Dutheil 2017). We discretized the observed distributions of both r and ρ in 41 and 45 categories, respectively, using the cut2 command from the Hmisc R package in order to have a similar number of genes in each category (comprising between 247 and 258 genes for ρ, and between 67 and 1323 genes for r, the largest value being obtained for genes with r = 0).
For each gene sets, 100 bootstrap replicates were generated by sampling genes randomly in each category. Genes in each replicate were pooled to compute site frequency spectra and the Grapes program run with the GammaExpo distribution of fitness effect  (Galtier 2016). For each recombination category, the mean estimates of α and ω a , as well as the standard error over the 100 replicates, were computed after discarding all replicates with a negative estimate of α or ω a (ranging from 5% to 10% of replicates discarded, depending on the category and analysis). We performed a similar analysis by discretizing the density of TEs in 100 kb windows.
DNA-based characterization Species-specific real-time PCR PCR assays targeting C. psittaci, C. abortus, C. avium and C. gallinacea species were conducted as previously described  [17, 19, 38, 39]. Multi Locus Sequence Typing (MLST) analysis Genotyping by MLST was carried out according to the scheme developed by Pannekoek et al  [18]  targeting seven housekeeping genes: gatA, oppA, hflX, gidA, enoA, hemN and fumC. Target genes were amplified and sequenced using primers and conditions described on the Chlamydiales MLST website (http://pubmlst. org/chlamydiales/). Sequencing of both strands was done by Eurofins (Germany). Sequences for each locus were queried against the online Chlamydiales MLST Databases to determine allelic designations and a subsequent allelic profile was used to determine the sequence type (ST).
DNA was eluted in 50 µL of water and re-purified using the NEBNext Microbiome DNA enrichment kit (NEB, France). Genome sequencing, assembly and draft annotation Libraries were prepared using the Nextera XT kit (Illumina). Whole-genome sequencing was performed using an Illumina MiSeq platform (Illumina) according to the manufacturer's instructions. The MiSeq run was carried out on the DNA preparation, with paired-end reads of 150 bp using MiSeq V2 reagents. The raw reads were trimmed with a quality value of 20 and a window size of 5 using Trimmomatic  [5]  and assembled de novo using SPAdes v3. 11. 1 with the careful parameter and the automatic determination of the k-mer values  [3]. To remove residual mammalian sequences from cell culture constituents, scaffolds from SPAdes were subsequently run on the nucleotide database of NCBI.
Phylogenetic and genome analysis The parameters of the tetranucleotide signature frequency correlation coefficient (tetranucleotide regression) and average nucleotide identity (ANIb) were determined as described previously  [22]  using the program JSpecies v1. 2. 1 with default parameters. Nine phylogenetical markers previously described  [20]  were extracted from 12 Chlamydiaceae reference sequences and one outgroup. Amino acid sequences were aligned with mafft v7  [14]  and were concatenated to build a reference phylogeny using RAxML v8  [30] , with the LG+G+I model and 1000 bootstrap replicates. Pairwise amino acid sequence identities were calculated based on the mafft alignment by Geneious v10 (www. geneious. com). Electron microscopy BGM cells were infected with strain RSHA. Infected cells were harvested at 24, 48, 72 and 96 hours post-infection.
Specific primers RSHA-F (5'-ATTTCCAACACGCACTGCAT-3') and RSHA-R (5'-TGGGACTAGGTGTTTCTCCCT-3') as well as a specific probe RSHA-P (5'-FAM-GGACAACATGCCTAGATGAAGA -TAMRA 3') were designed using the primer3 software and synthesised by Eurofins MWG Operon. Amplification was performed using the 2X TaqMan Universal PCR Master Mix (Applied Biosystems, Courtaboeuf, France). The final volume of the reaction mixture was 20 μL including 10 μL of master mix, 2 μL of DNA sample, 0. 6 μM of each primer, 0. 1 μM of the probe and sterile PCR water. Amplification was carried out at a Viia7 thermocycler (Applied Biosystems) using the following cycling parameters: heating at 95 °C for 10 min, 50 cycles of 95 °C for 15 s and 56 °C for 1 min. For all PCR reactions, DNA of the isolate RSHA, arbitrarily defined as the reference strain for C.
Results Molecular characterization of the RSHA isolate DNA from the RSHA isolate tested positive using the C. psittaci incA-based real-time PCR assay, but negative using C. psittaci ompA-based real-time PCR. The same result was obtained with primers targeting ompA of C. abortus and with species-specific C. avium and C. gallinacea real-time PCR systems. MLST genes were successfully amplified using primers designed for C. psittaci  [18]. Nucleotide sequences were obtained for all 7 MLST genes of the RSHA isolate, and their analysis revealed a new sequence type (sequence type number 196), which was not represented in the Chlamydiales database (http://pubmlst. org/chlamydiales/). Comparative phylogenetic analysis of the concatenated MLST sequences from RHSA and the corresponding MLST sequences of the RTH strain  [12]  places the RSHA isolate on the same clade as the RTH isolate, i.
caviae, C. gallinacea, C. felis, C. pecorum and C. psittaci strains were tested using this protocol. The results confirmed the specificity of the assay (data not shown). Measurements on dilution series of cell culture aliquots with defined numbers of inclusion-forming units (IFUs) indicated detection limits in the range of 10 IFUs. As the target oxaA sequence of strain RTH is identical to that of strain RSHA, it is safe to assume that it would be detected by this assay as well.
abortus strains (unpublished data). Preliminary genotyping of this strain based on 7 MLST target genes identified a new sequence type located at an intermediate position between C. psittaci and C. abortus (Figure  1 ). Intermediate strains from birds have been previously reported  [1, 9, 35]. Recently, some of these strains were assigned to the species of C. abortus  [32]  based on 16S rRNA, ompA, and MLST analyses. Analysis of WGS data of the RSHA isolate revealed that the criteria for assignment to a new chlamydial species within the family Chlamydiaceae were fulfilled. Indeed, according to the recent classification scheme proposed by Pillonel et al. [20] , sequence identity values of ≥ 80% for 16S rRNA and 23S rRNA, and of ≥ 92. 5% for 16S rRNA, ≥ 91% for 23S rRNA confirm the assignment of strain RHSA to the order Chlamydiales and family Chlamydiaceae, respectively.
psittaci-like strain RTH was isolated from a wild red-tailed hawk (Buteo jamaicensis) showing signs of respiratory distress and diarrhea  [16]. The genome of this strain was recently sequenced and further analysis suggested that RTH represents a new species in the family Chlamydiaceae  [12]. Although the 23S rRNA sequence could not be extracted from the RTH genome sequence owing to incomplete assembly and low coverage, comparison of the RTH and RSHA 16S rRNA and MLST sequences revealed complete sequence identity, with the exception of a single nucleotide polymorphism in the 16S rRNA gene sequence, thus strongly suggesting a close evolutionary relationship between these two strains. Unfortunately, none of the nine taxonomically conserved protein sequences could be fully extracted from the published RTH genome sequence data (SRA061571).
Acknowledgements We thank  Andreas Naber  and  Alexander Münchinger  (both  KIT ) for discussions. We acknowledge support by the  Excellence Cluster  " 3D   Matter Made  to  Order"  ( 3DMM2O ) funded through the  German Excellence Strategy via Deutsche Forschungsgemeinschaft (DFG)  and by the  Carl Zeiss Foundation. We acknowledge additional support by the  Helmholtz program  " Science and Technology of Nanosystems " (STN) and through the associated " Virtual Materials Design" (VIRTMAT)  project as well as by the  Karlsruhe Nanostructure Service Laboratory (NSL). T. F. has been supported by the  Karlsruhe School of Optics & Photonics (KSOP). T. F. and J. K. have been supported by the  Heidelberg Karlsruhe Strategic Partnership (HEiKA)  graduate school Functional Materials. J. K. acknowledges support by the  Hector Fellow Academy.
Data The transcriptomic data are deposited in the BioProject PRJNA435671 (https://www. ncbi. nlm. nih. gov/bioproject/?term¼PRJNA435671). Table  1  summarizes the total number of reads and the corresponding RNA-Seq assembly statistics. Supplementary document 1 consists in the hemp contig sequences obtained from de novo transcriptome assembly using the datasets originated with the 3 experiments. Supplementary document 2 refers to the annotation. Supplementary document 3 reports the quality control metrics of the raw reads (the majority of the sequences have a PHRED score >35, indicative of high quality) obtained from the newly performed RNA-Seq on hypocotyls treated or not with jasmonic acid.
Raw sequences reads were uploaded in CLC Genomics Workbench 9. 0. 1. Sequences were filtered and trimmed as follows: sequence length >35 bp, sequence quality score <0. 01, no ambiguity in the sequence, trimming using Illumina adaptors, hard trim of 5 bp at the 5 0 end and 2 bp at the 3' end. The de novo assembly, with sequences from the 3 experiments, was performed with an auto wording size of 20, an auto bubble size of 50 and a minimum contig length of 300 bp. The reads were mapped back to the assembly with a mismatch, insertion and deletion cost of 3, a length fraction and similarity >0. 95. The assembly was then annotated using Blast2GO PRO version 3. 0 against the Arabidopsis thaliana nonredundant database.
Detection of co-expressed genes with WGCNA within hypothalamus and blood DEG lists To detect gene subsets in our DEG lists, we used the R package WGCNA to identify and cluster co-expressed gene modules (see Methods). As shown in Fig. 3 , WGCNA separated for hypothalamus (Fig. 3a ) and blood (Fig. 3c ) different co-expression groups (noted by a color) for both "LE > CT" (in red) and "LE < CT" (in blue) DEG lists. Interestingly, 2 modules of the same DEG list were not positively correlated in the blood (Fig. 3d , pink color in the correlation matrix) with the blue and purple modules for the red "LE > CT" DEG list and the red and turquoise modules of the blue "LE < CT" DEG list, while all modules were positively correlated in the hypothalamus (Fig. 3b ). The plots of module eigengenes of these two pairs can be found in Fig.
Focus on genomic regions concentrating differentially expressed genes We searched for groups of three or more DEG in close physical proximity (i. e. , side by side) along the genome that had a significant pairwise expression correlation (|r| > 0. 7 & p FDR < 10 -4 ), hypothesizing that such genes might be co-regulated by a local common mechanism. We found two such proximal co-expressed gene groups in the hypothalamus (Fig. 4a  and b ), composed of RPS6KA2, MPC1 and SFT2D1 for the first one (Fig. 4a ) and C1QA, C1QB and C1QC for the second (Fig. 4b ), genes that belong to the black WGCNA module, which was enriched in immunity-related genes.
The mechanism of the regulation of the FADS2 and NAPE-PLD transcription that can be direct or indirect, remains to be elucidated. Interestingly, the arachidonic acid is also a precursor of the prostaglandins  [32] , which has been shown to be involved in feed intake regulation along with complement system molecules  [33]. Among the eight modules detected by WGCNA using the lists of hypothalamic DEG, the black module was composed of over-expressed genes related to immunity. Three of them, C1QA, C1QB and C1QC were detected as colocalized and co-expressed genes. The co-localization and strong co-expression of C1QA, C1QB and C1QC strongly suggest a mechanism of common regulation. These three genes code for the A, B and C polypeptide chains composing the C1q molecule, a subcomponent of the C1 complex involved in the complement activation  [34].
RNA-seq data acquisition Paired-end sequencing was conducted on all samples using an Illumina HiSeq3000 (Illumina, California, USA) system, with 2 × 150 bp. Libraries with an on average 465bp insert were prepared following Illumina's instructions by purifying poly-A RNAs (TruSeq RNA Sample Prep Kit). Illumina adapters containing indexing tags were added for subsequent identification of samples. Samples were PCR-amplified, and quantitative PCR was then performed for library quantification (QPCR NGS Library Quantification kit). Eight samples were filled on one lane within a flow cell with 2 samples for each of the four line × diet groups to minimize the inter-lane bias. After sequencing, the indexed adapter sequences were trimmed using CASAVA v. 1. 8. 2 software (Illumina). We obtained an average of 90 million reads per sample (84 million for the adipose tissue, 98 million for the blood, 86 million for the hypothalamus and 90 million for the liver), for a grand total of 11 billion reads.
RNA-seq data analysis All the analyses were performed with R version 3. 4. 2. The trimmed mean of M-values (TMM) scaling factor method was used for library size normalization  [52]  using the R/ Bioconductor package edgeR  [53]  version 3. 12. 1. In each tissue, the expressed genes were selected if their FPKM expressions were over 0. 1 in at least 80% of the samples of a group line × diet (FPKM expression being obtained after TMM normalization using "rpkm" function from edgeR package). Differential expression analysis was performed using the R/Bioconductor package edgeR  [53]  based on a generalized negative binomial model for model fitting. We used the "edgeR-Robust" method to account for potential outliers when estimating per gene dispersion parameters  [54]. P-values were corrected for multiple testing using the Benjamini-Hochberg approach  [55]  to control the false discovery rate (FDR), and genes were identified as significantly differentially expressed if p FDR < 0.
Co-expression module detection with WGCNA We used the R package WGCNA  [57]  to detect coexpression modules based on gene expression data and a weighted correlation network. Briefly, WGNCA screens for clusters (called modules) of highly correlated genes in the expression dataset. Indeed, while within a list of over-or under-expression in one condition versus another one, one can expect all the genes to be positively correlated to one another, such list can be split into modules of genes with a higher expression correlation among them than with the rest of the list. These genes are more likely to share a common regulation and a common biological function and therefore may highlight more specifically one pathway. In addition, it may happen that a gene subset is not correlated with the other subsets of the same DEG list because of factors other than the one used for the differential expression analysis.
Acknowledgements The authors thank the staff of the  INRA experimental poultry unit  ( UE1295 PEAT, Nouzilly, France ) for producing and rearing animals, and the technicians of the research units for helping to measure birds. The authors also thank  F. Boissel  and  M. Bellier  for helping with RNA extraction,  M. Lessire  for valuable advices in defining the LE diet.
In this study, we investigated the temporal responses of stream invertebrate communities to flow intermittence across river basins presenting contrasting spatial drying patterns. Using data collected across 11 river basins over two years, we quantified taxonomic and functional beta diversity in space and time to test the following hypotheses. H1: in downstream-drying basins (DDB), we hypothesised that fast recolonisation of intermittent sites upon rewetting through active and passive drift from upstream perennial sites (Fig. 1a ) would lead to a low spatial taxonomic beta diversity at the basin scale (Fig. 1b ). In contrast, for upstreamdrying basins (UDB), being more isolated, intermittent sites should be more slowly recolonized (Fig. 1a ), leading to a high spatial taxonomic beta diversity at the basin scale due to low dispersal rates (Fig.
Trait composition analyses To describe the trait composition of stream invertebrate communities, 39 categories of 8 biological traits were used  (Tachet et al. 2010 , see selected traits in Supplementary material Appendix 3A). We only considered biological traits that may be related to drying (i. e. lifespan, resistance forms,  Datry et al. 2014) , and no ecological traits were used in our analyses  (Aspin et al. 2018). Information from Schmidt-Kloiber and Hering (2015) was used to complete information for taxa missing from  Tachet et al. (2010). These traits characterise life-cycle features, resilience and resistance features, physiology, morphology, reproduction and feeding behavior. Within the database, each taxon was coded according to its affinity to each category of a trait using a fuzzycoding approach  (Chevenet et al.
1994). The affinity of each genus to each category was coded from 0, for no affinity, to 3 for the strongest affinity, except for feeding and locomotion categories coded from 0 to 5. From the 231 taxa analyzed,  trait information for seven taxa (mostly crustaceans) was unavailable and these taxa were omitted. Trait modalities and values are provided in Supplementary material Appendix 3B. Aerial dispersers were separated from strict aquatic dispersers based on traits in  Tachet et al. (2010)  to form two subsets of the original dataset, and this classification was confirmed by Bertrand Launay, an expert on invertebrate biology.
Data analysis For each reach, the duration of drying events (TotDur) in days and the number of drying events (TotNum) were quantified from the water state logger data and 12 visual observations. To analyse the spatial variation of community composition, spatial beta diversity (pairwise differences of composition between sampling sites for each sampling date) was calculated for both taxonomic and functional diversity. For taxonomic beta diversity, the presence-absence Jaccard index was calculated for each basin and partitioned into its two additive components 'replacement' and 'richness difference'  (Podani and Schmera 2011, Legendre 2014 ). To calculate functional beta diversity matrix, we first computed taxon-by-taxon Gower distances from the trait matrix and, second, we generated a dendrogram using hierarchical clustering analysis on these distances with the unweighted pair group method with arithmetic mean  (Cardoso et al.
2014). Then, functional beta diversity was calculated and partitioned for each basin based on the site-by-taxon matrix and the dendrogram using the R function 'beta' of package 'BAT'  (Cardoso et al. 2014). Pairwise beta diversity measures between sites were averaged for each river basin for each sampling date. For a given sampling date, when less than four sites were sampled (the others being dry), spatial beta diversity was not computed. There were 3 UDB with on average 9 (± 3) sampling dates (25 beta diversity measures in total) and 8 DDB with on average 10 (± 2) sampling dates (80 beta diversity measures in total). To analyse the temporal variation of community composition, temporal beta diversity (pairwise differences of composition between sampling dates for each sampling site) was calculated for each site and partitioned into its two additive components 'replacement' and 'richness difference' using the same functions as described above for spatial taxonomic and functional diversity.
Pairwise beta diversity measures between sampling dates were averaged on each site, there were 18 sites in UDB and 48 sites in DDB. As several approaches are used in the literature to assess functional redundancy  (Lozanovska et al. 2018) , we carried out two different types of analyses. First, it was assessed with the 'SYNCSA' package as the difference between species diversity and Rao's quadratic entropy based on their functional dissimilarity  (de Bello et al. 2007, Debastiani and Pillar 2012) , for each basin separately and for the entire dataset. With this definition, maximal value of functional redundancy is equal to species diversity which can vary across samples, thus we divided this number by species diversity, so functional redundancy scales from 0 (no redundancy) to 1 (full redundancy).
Values were compared between spatial drying patterns with Kruskal-Wallis tests. Then, functional redundancy was also assessed by fitting a hyperbolic curve of functional richness against taxonomic richness from the entire dataset with the form y = ex/(d + x) where e is the asymptotic limit of the curve and d is the half-saturation constant. This allowed us to see if functional trait saturation was reached for a high species richness  (Schriever et al. 2015) , as the concept of functional redundancy relies on the saturation of functionally similar species in a community  (Rosenfeld 2002). To test the hypothesis H1, mixed-effect models were used on spatial taxonomic and functional beta diversity and on their respective replacement and richness difference components using function 'lmer' of the lme4 package.
Each full model included the sampling date as random effect and the spatial drying pattern as fixed effect (UDB or DDB). There were six full models: for spatial taxonomic beta diversity, taxonomic replacement, taxa richness difference, for spatial functional beta diversity, functional replacement and functional richness difference. To test the significance of the effect of the spatial drying pattern, each of these six full models was compared with a likelihood ratio test to a null model with a random intercept and sampling date as random effect. This was computed for the entire dataset and separately for aerial and strict aquatic dispersers, as loss of aquatic connectivity may differently affect communities if they are able to disperse overland or not; hence, there were 18 tests in total for this section.
In each model, river was a random effect. The significance of each model was tested by a comparison with the reduced version of the model with a likelihood ratio test (the fourth model with the third, the third model with the second, etc. ). Each model was first fitted for the entire dataset and, then, separately for subsets of the aerial dispersers and strict aquatic dispersers. In total, there were 36 tests for H2 (3 datasets × 6 community variables × 2 quantitative drying events variables × 1 likelihood test) and 72 tests for H3 (3 datasets × 6 community variables × 2 quantitative drying events variables × 2 likelihood tests with and without interaction).
H1: effect of the spatial drying pattern on spatial beta diversity Spatial taxonomic beta diversity was significantly higher for UDB compared to DDB when considering all taxa and aerial dispersers but not strict aquatic dispersers (Table  3 , Fig. 3 ). There was no difference for the replacement component when considering all taxa and aquatic dispersers. For aerial dispersers, replacement was higher in UDB than DDB (Table  3 , Fig. 3 ). For the three datasets, richness difference was higher in UDB than DDB (Table  3 , Fig. 3 ). On average, across all basins, replacement was higher than richness difference when considering all invertebrates and aerial dispersers (respectively 0. 152 versus 0. 102, F = 1880, p < 0. 001*** and 0. 144 versus 0. 103, F = 1880, p < 0. 001***). For aquatic dispersers, however, replacement was lower than richness difference (0.