dataesr/scientific-paragraphs-categorization huggingface dataset:
530 bp long) or even a V1f-UNIr fragment (c. 350 bp long) was used. Although the V1f-UNIr fragment is short, it contained enough phylogenetic information to ascribe sequences to, at least, the species level (see "Results", Additional file 2: Table  S2  and Additional file 3: Data S1). PCR products were purified and sequenced directly with the BigDye technology by Genewiz, Inc. , UK, using the forward primers used in the PCR. Using Geneious 10. 2. 2  [53] , raw sequences were edited, trimmed and checked for being microsporidian sequences via BlastN  [54]  search against the sequences available on GenBank.
Phylogeny reconstruction for microsporidians Four types of microsporidian sequences constituted our dataset: (i) sequences newly produced from our collection of infected G. roeselii individuals; (ii) published SSU sequences representing diversity and divergence of microsporidians already found to infect European freshwater or brackish water amphipods (we did not include sequences outside Europe, e. g. the recently published parasites from Lake Baikal  [34, 35] ); (iii) published SSU sequences for microsporidians infecting other taxa, prioritising freshwater or brackish water invertebrates, when closely related amphipod sequences relative to newly produced sequences were not found; (iv) published SSU sequences representative of the five microsporidian clades (Clades I-V), as determined in the integrative phylogenies presented in literature  [55, 56].
All sequences were aligned using MAFFT7. 388 software  [57] , with the E-IONS-I algorithm using legacy gap penalty option, incorporated in Geneious 10. 2. 2  [53]. Our dataset consisted of sequences of variable lengths depending on both the success in producing new sequences (from 180 to 826 bp) and on various length of the published ones (from 300 to 1448 bp for microsporidians, and 1786 bp for the fungus Basidiobolus ranarum used as an outgroup). All details, including sequence length, are given in Additional file 4: Table  S3. Alignments are given in Additional file 5: Data S2. As some sequences were relatively short, reducing the full dataset to a standard size would, on the one hand, allow defining haplotypes but, on the other hand, would potential induce losing phylogenetic signal. Therefore, we attributed each sequence to haplogroups, defined in such a way that sequences belonged to distinct haplogroups if they differed by one or more variable sites, generating diagnostic features (Additional file 2: Table  S2 ), whatever sequence length.
Four heated chains, each 1,100,000 iterations long, sampled every 200 iterations, were run. The runs reached satisfactory effective sampling sizes (ESS > 200), and the potential scale reduction factor values equalled 1 for all parameters. The 50% majority-rule consensus tree was constructed after the removal of 10% ‛burn-inʼ trees. Four phylogenetic trees were constructed. The first tree contained all haplogroups (i. e. sequences from this study and published sequences) using Basidiobolus ranarum (GenBank: AY635841) as the outgroup  [55]. In this tree, we described novel parasites by conservatively using provisional names, e. g. Microsporidia sp. (hereafter abbreviated Msp) followed by the clade number (from I to V) sensu Vossbrinck et al. [55]  and a superscript roman letter. The three other phylogenies represent detailed analyses for the already identified parasites of the microsporidian species of the genera infecting amphipods: Nosema  [12] , Cucumispora  [9]  and Dictyocoela  [6].
Nosema antherae (Gen-Bank: DQ073396), Vavraia culicis (GenBank: AJ252961), Dictyocoela cavimanum (GenBank: AJ438960) were used as outgroups for the Nosema, Cucumispora and Dictyocoela phylogenies, respectively. Following Grabner et al. [5] , if a newly obtained sequence was > c. 98% similar to a sequence for which a full taxonomic description was available, providing genus and species name, such name was ascribed to the new sequence. Alignments used for building these trees are provided in Additional file 5: Data S2).
Geographical distribution of parasites and potential host specificity In addition to Bayesian trees, we provided a map constructed in Qgis 2. 18. 4 to show the geographical distribution of the three genera Nosema, Cucumispora, Dictyocoela in G. roeselii (including GenBank data). We also added to these maps geographical positions of gammarids' parasite sequences found in the literature (see also Additional file 4: Table  S3 ). We did not analyse geographical distribution of other microsporidian clades, as most of them were present only in single locations.
Rare infections (i. e. infections not ascribed to the genera Nosema, Cucumispora and Dictyocoela) Most of the microsporidians representing rare infections in G. roeselii were either phylogenetically close to various microsporidians infecting gammarids (Msp-IVA, Msp-IVB, Msp-IVE, Msp-IVF, Msp-IVG, Msp-VA) or close to microsporidians infecting other Groe Groe crustaceans (Msp-IVC, Msp-IVD) (Fig. 2 ). Among the parasites linked to other gammarid infections, Msp-IVA was found in France and Hungary (populations 37, 39 and 91, Fig. 1 , Additional file 1: Table  S1 ). It was very close to Msp-515 infecting Irish and French populations of G. duebeni (GenBank: FN434086) (97. 7% identity; 311 bp coverage). The Msp-IVB haplogroup was found in four individuals in one French population (# 89, Additional file 1: Table  S1 ).
This sequence showed between 99. 4-99. 7% identity with c. 750 bp coverage to a set of sequences including Msp-JES2002I (Gen-Bank: AJ438964), previously detected in G. pulex from Scotland  [6]  and Msp-I (GenBank: KR871371), from G. roeselii in Germany  [5, 26, 31]. Msp-IVE parasite was found in one individual in population 31 from Romania and was 99. 4% similar (with 327 bp coverage) to Orthosomella sp. (GenBank: KT633994) infecting G. fossarum and the subterranean amphipod Niphargus schellenbergi in Luxembourg  [16]. Msp-IVF and Msp-IVG haplogroups were found in one host individual each, in French populations 84 and 98 (Fig. 1 , Additional file 1: Table  S1 ), respectively. The highest match for these haplogroups in GenBank was Msp-RR2 (Gen-Bank: KR871373) and microsporidians found in the Ruhr region of Germany, infecting G.
pulex, G. fossarum and G. roeselii  [26] , with 99. 7% and 78. 6% identity, respectively (coverage 334 bp). Finally, the sequence of Msp-VA showed 100% identity (246 bp coverage) with Msp-RR1 (GenBank: KR871372) previously found by Grabner et al. [5]  to infect G. pulex and G. roeselii in Germany. In the microsporidian Clade III, Msp-IIIA was found in two individuals from the Hungarian population 46 (Additional file 1: Table  S1 ). This haplogroup is relatively closely related to Pleistophora parasites (Fig. 2 ), notably Pleistophora mulleri (GenBank: AJ438985) infecting G. duebeni celticus (88. 6% identity, coverage 388 bp)  [62] , but was even more closely related to Vavraia culicis infecting mosquitoes (99. 0% identity, coverage 384 bp) (GenBank: AJ252961) (Fig. 2 ). Finally, within the Clade V, Msp-VB was found in one G.
roeselii from Germany, population 77 (Fig. 1 , Additional file 1: Table  S1 ). This parasite had only 75. 7% identity with Msp-RR1 infecting gammarids and 80. 3% identity with Msp-Group H infecting Ephemeroptera (Fig. 2 ). Most of identified parasites have been reported in the literature to infect gammarid hosts; however, they seem to be relatively rare and geographically widespread. Two parasites of G. roeselii were related to other parasites infecting crustaceans. Msp-IVC was found in one G. roeselii individual from the Greek population 12 (Fig. 1 , Additional file 1: Table  S1 ). This sequence is identical (coverage 330 bp) to the sequence of Enterocytospora artemiae found in Artemia franciscana in France, USA and Israel  [63]  (GenBank: JX839889). Similarly, Msp-IVD, infecting three individuals from northern Italy, population 60, was 99.
5% similar, with a coverage of 762 bp, to Parahepatospora carcini infecting, the European shore crab (Carcinus maenas) (GenBank: KX757849)  [11]. The geographical distribution of these rare infections was similar between the geographical regions (Fig. 1 , Additional file 1: Table  S1 ), with 1/26 populations infected in Region 1 (3. 8%; Msp-IVC only), 4/22 (18. 2%) in Region 2a and 6/46 populations infected in Region 2 (13. 0%; all other haplogroups) (Likelihood Ratio χ 2 = 2. 89, P = 0. 23).
Infections ascribed to the genus Nosema A total of 96 G. roeselii individuals were infected by microsporidia for which partial SSU rDNA sequences were ascribed to the genus Nosema (Additional file 1: Table  S1 ). Three haplogroups of Nosema were identified in our study (Fig. 3 ), all belonging to Nosema granulosis (Fig. 3 ). Infections with N. granulosis represented 30. 4% of all the microsporidian infections in G. roeselii. Nosema granulosis 1 was the most frequent haplogroup, infecting 76 individuals from 19 populations across 8 countries (Fig. 3 ). The haplogroups N. granulosis 1 and 2 showed, respectively, 100% and 99. 8% identity with N. granulosis already found in G. roeselii from France (GenBank: AY584251) (Additional file 4: Table  S3 )  [19]. The haplogroup N. granulosis 3 was 100% identical with N.
granulosis infecting the subterranean amphipod Niphargus schellenbergi but also G. fossarum individuals from Luxembourg and G. pulex from Poland (Additional file 4: Table  S3 ) (GenBank: KP633991 and KM657357, respectively)  [16]. This haplogroup had c. 98% identity with N. Fig. 2  Bayesian phylogenetic reconstruction based on partial small ribosomal subunit rDNA alignment. Labels in bold are parasites of G. roeselii found in the present study. These labels show the name of the parasite, the country where it was found (two letter ISO code, see Additional file 1: Table  S1 ), the number of infected populations (=pop. ), and the total number of infected individuals (=ind. ). Labels with accession numbers are parasite sequences taken from GenBank. These labels show the accession number, the parasite name given in the associated publication, the order of the host (except for amphipod hosts where the family is provided; if G.
It is worth noting that the repartition of infections with N. granulosis 1 was uneven among the host geographical regions. Only   S3  for further details). Dot colors match clade colors on the phylogenetic tree. The Bayesian phylogenetic reconstruction is based on a small ribosomal subunit rDNA alignment of Nosema granulosis. Nosema antherae was used as the outgroup and divergent lineages were ascribed a color code. Sequences from the present study are in bold and labels include haplogroup names, the total number of G. roeselii infected individuals (=ind. ), plus other hosts found infected by the same haplogroup. Sequences from GenBank are all other Nosema granulosis haplogroups (Additional file 4: Table  S3 ). Labels include the accession number, the species name given in the associated publication and the host species abbreviated names.
roeselum 1 to 5 were 99. 7% similar to the closest D. roeselum sequence (GenBank: MG773219,  [24] ). Dictyocoela roeselum 1, 3, 5 were found in western and northern Europe (Regions 2a and 2b), while haplogroups 2 and 4 were found in Region 2b only (Fig. 5 , Additional file 1: Table  S1 ). Dictyocoela roeselum infections in eight G. roeselii individuals were associated with short sequences (see "Methods"): we were unable to assert if they belong to D. roeselum 4 or 5 (7 individuals), or D. roeselum 1 or 5 (one individual) (Fig. 5 , Additional file 1: Table  S1  and Additional file 4: Table  S3 ). A single haplogroup (called D. muelleri 1) infecting G. roeselii showed 100% identity with the sequence used originally to identify D. muelleri (Terry et al. [6] ; parasite found in G. duebeni from northern Europe).
Sequences from the present study are in bold and labels include haplogroup names, the total number of G. roeselii infected individuals (=ind. ), plus other hosts found infected by the same haplogroup. Sequences from GenBank represent all other Cucumispora haplogroups, following Bojko et al. [20]  (Additional file 4: Table  S3 ). Labels include, in this order, the accession number, the microsporidia species name given in the associated publication and the host species abbreviated name(s). For abbreviations of host species names see Additional file 4: Table  S3. *Parasites for which the sequence did not allow to distinguish their assignment between the haplogroups indicated by the vertical bar. Abbreviation: PP: Bayesian posterior probability phylogeny than the other parasites. For example, N.
The Bayesian phylogenetic reconstruction is based on a small ribosomal subunit rDNA alignment of Dictyocoela spp. Dictyocoela cavimanum was used as the outgroup and divergent lineages were ascribed a color code. Sequences from the present study are in bold and labels include haplogroup names, the total number of G. roeselii infected individuals (=ind. ), plus other hosts found infected by the same haplogroup. Sequences from GenBank are only a representative panel of Dictyocoela diversity, divergence and host range (see Additional file 4: Table  S3  and Additional file 3: Figure  S1  for complete data). Labels include, in this order, the accession number, the microsporidia species name given in the associated publication and the host species abbreviated name(s). For abbreviations of host species names see Additional file 4: Table  S3.
granulosis is ancient in G. roeselii, and that host-parasite co-diversification occurred after an initial ancestral infection. This hypothesis will be interesting to test in the future, by addressing the following issues. First, even if SSU rDNA is a useful marker for microsporidian phylogeny, it might not appear to be variable enough in this case for a detailed exploration of N. granulosis diversification history. Thus, additional markers such as RPB1  [68]  could be useful to the dataset to step forward in addressing specificity. Secondly, the precise host diversification process in the secondary diversification area (Region 2a) remains to be explored in details  [47]. It will notably be useful to understand if variation within the host MOTU C fits variation within N. granulosis. Thirdly, all N.
duebeni were published, but the haplogroup Msp-1049 (GenBank: FN434092) falls within this clade. Some major differences can nevertheless be noted between the infection patterns in G. roeselii and G. duebeni. First, some infections that we qualified as "rare" in G. roeselii are more common in G. duebeni. This is the case of Pleistophora-Vavraia-like parasites, being present in seven populations of G. duebeni but only in one of G. roeselii. Similarly, but in larger proportions, Msp-505-515 were found in only three populations of G. roeselii (Msp-IVA in our study), but in 10 populations of G. duebeni  [18]. Since parasites of this group have also been identified in other species of Gammarus  [5, 31] , it is tempting to suggest, following  [5] , that they are ubiquitous in Gammarus and may show a horizontal transmission pattern as well as low host specificity.
Statistical methods The following statistical methods were used: descriptive analysis (e. g. frequencies, mean, median), bivariate analysis (crosstabs), multivariate analysis (logistic regression). The results were presented as odds ratio (including forest plot), confidence interval and p-value (Pearson's Chi squared). Multivariable regression model was used to reduce the potential impact of confounding factors. Statistical analysis was restricted to subjects with complete data on the variables involved in the analysis. No data imputation method was involved. All analyses were performed using IBM SPSS version 23 and 24. A significance level of 0. 05 was assumed.
Fluorescence correlation spectroscopy FCS measurements were performed on a Leica TCS SP8 SMD. Cells were transfected with Actin-GFP (addgene 21948) using FuGENE® HD (Promega, Mannheim, Germany) 24 h before the FCS measurement. The effective volume (V eff ) and structure parameter (κ) were measured before each experiment using 1 nM ATTO488 dye solution (ATTO-TEC GmbH, Siegen, Germany). For every nucleus three different points were measured (45 s per point). After the zero-time point measurement, Doxo [250 nM] was added. FCS curves were analyzed with the Picoquant SymPhoTime V 5. 2. 4. 0 software and fitted with a single diffusing species and a triplet state.
Acknowledgements This study was financially supported by the  German Research Council (DFG) :  FOR1406 ,  SFB1032  (project  B08 ), and by  NIM-Nanosystems Initiative Munich. We thank  Jeremy M. Stark  ( Beckman Research Institute of the City of Hope, USA ) for kindly providing the U2OS reporter cells, and  Jana Peliskova  and  Kerstin Loske  for expert technical assistance.
Sequence processing Fastq files were processed with DADA2 v1. 6. 0  [36] , using the parameters described in the workflow for "Big Data: Paired-end"  [37]. The only modification made relative to this protocol was a change in the truncLen argument according to the quality of the sequencing run. Each run was analysed separately. Taxonomic affiliations for amplicon sequence variants (ASV) generated with DADA2 were assigned with a naive Bayesian classifier on the RDP trainset 14 for bacteria  [38]  and the UNITE 7. 1 database for fungi  [39]. Only ASV detected in both technical replicates were conserved to ensure robustness  [40]  and were then added together. ASV classified as "Cyanobacteria/Chloroplast", or not classified at the phylum level, were discarded from the datasets. This resulted in suppression of 1.
A complete model combining all the factors could not have been used because the experimental design did not include an oilseed rape monoculture field plot. Of note oilseed rape monoculture is considered as an agronomic nonsense. Thus, a first model including the plant effect but not the cropping system effect (plant * cropping season * sampling period * sampling plot) was applied using the data set from plots in rotation only (WOW and OWO). The effect of the cropping system (monoculture; rotation) was estimated separately using a second model (cropping system * cropping season * sampling period) applied on the data set from wheat residues only; the sampling plot factor was not included in this second model as it would have been confounding with the cropping system factor. Shannon index was calculated for both bacterial and fungal communities with the ggpubr package in R  [45]  and Faith's phylogenetic diversity was calculated for bacterial communities with the picante package  [46].
The effect of each factor on the Shannon index was assessed with two complementary ANOVA. A Kruskal-Wallis test also performed to assess significant differences in microbial diversity with time for each cropping season and sampling plot. Wilcoxon pairwise tests were also performed to compare the effects of sampling periods. Divergences were considered significant if p < 0. 05. The effect of each factor on the Bray-Curtis dissimilarity index was assessed with two complementary PERMANOVA using the adonis2 function of the vegan R package (version 2. 4-4  [47]  with "margin" option, used to determine the effect of each term in the model, including all other variables, to avoid sequential effects. They were visualized by multidimensional scaling (MDS) on the Bray-Curtis dissimilarity index with the phyloseq package in R (version 1.
22. 3 [48]  and completed for bacterial communities by incorporating phylogenetic distances using the UniFrac distance matrix. After the aggregation of ASV for each sampling condition "sampling period/cropping year * crop within a rotation", the betapart R package  [49]  was used to determine whether temporal changes in community composition were due to turnover (i. e. replacement of ASV between two sampling periods) or nestedness (gain or loss of ASV between two sampling periods). The effect of the plant on the microbial communities associated with residues during degradation was also assessed with PERMANOVA on each sampling period, for each cropping season. The genus composition of fungal and bacterial communities was analysed with a cladogram based on genus names. Only genera observed in three biological samples harvested on the same plot were incorporated into the cladogram.
Results The bacterial and fungal communities associated with wheat (W) and oilseed rape (O) crop residues were characterised on three plots: the wheat monoculture (WWW), and two oilseed rape-wheat rotation plots (WOW and OWO) (Fig. 1 ). We assessed the composition of these microbial communities four times per year, during two consecutive cropping seasons (in October, December, February, and May). An additional time point (in July) was also included for identification of the micro-organisms present on the plant before contact with the soil. The analysis of raw sequence datasets for the 150 samples of wheat and oilseed rape residues collected over the two cropping seasons resulted in the grouping of 14,287,970 bacterial and 9,898,487 fungal reads into 2,726 bacterial and 1,189 fungal amplicon sequence variants (ASV).
Although our study reveals the presence of genera or species reported in the literature as biocontrol agents, it has not yet shown any interaction between them and the pathogens. This experimental study (sampling effort, residue treatments, etc. ) was not designed to characterize such interactions. A strategy involving the inference of microbial interaction networks from metabarcoding datasets might help to identify the species beneficial against pathogens, through competition, antagonism or parasitism. This however requires a more analytical, comparative experimental approach, that goes beyond the only description of shifts in natural communities composition: for example, using different "treatments" in a broad sense (e. g. artificial inoculation with a species or a group of species, change of biotic or abiotic environmental conditions, etc.
Results of the ANOVA performed to assess the effects of plant (wheat; oilseed rape), cropping season(2015-2016; 2016-2017), sampling period (July, October; December; February; May) and sampling plot on the Shannon index of the fungal and bacterial communities present in oilseed rape and wheat residues from the plots in rotation (OWO; WOW). The effect of the cropping system (monoculture; rotation) was estimated separately with a second ANOVA performed on the wheat residue samples dataset only (wheat in monoculture, i. e. in WWW; wheat in rotation, i. e. in WOW and in OWO).
Structure of the bacterial communities present in oilseed rape and wheat residues visualized by incorporating phylogenetic distances using the UniFrac distance matrix. MDS were performed on the overall dataset and faceted according to the sampling period. Each point represents one sample corresponding to a cropping season (shape: 2015-2016; 2016-2017; 2017-2018) and crop within a rotation (colour: oilseed rape in rotation, i. e. in WOW and OWO; wheat monoculture, i. e. in WWW; wheat in rotation, i. e. in WOW and OWO).
Acknowledgements This study was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr)and was supported by  France Génomique National Infrastructure , funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). We thank  Martial Briand  ( INRA, UMR IRHS ) and  Dr. Gautier Richard  ( INRA, UMR IGEPP ) for assistance with bioinformatic analyses, and  Dr. Thierry Rouxel  ( INRA, UMR BIOGER ) for improving and clarifying this manuscript. We thank  Julie Sappa  for her help correcting our English. We finally thank the reviewers for their insightful comments on the paper, as these comments led us to improve the correctness of several analyses.