dataesr/scientific-paragraphs-categorization huggingface dataset:
| Effect of the initial assemblage characteristics on the multi-year dynamics of functional diversity Three response variables were studied: realized functional dispersion, species richness, and Simpson's evenness of plant communities over the course of the experiment. The model selection approach was used to relate these variables to the initial assemblage characteristics: initial species richness (low, medium, high), initial functional diversity (low vs. high), and time (years since sowing, continuous variable). To calculate functional dispersion of the assemblages, numerical traits with left-skewed distributions (reproductive height, duration of flowering, flower size, and nectar depth) were log-transformed before computing functional diversity metrics  (Májeková et al. , 2016). To verify that we used uncorrelated predictors, a principal components analysis was performed on several metrics of taxonomic and functional diversity commonly used in the literature (Figure  S3 ), using the ade4 package  (Dray & Dufour, 2007).
TA B L E 3 (Continued) F I G U R E 2 Dynamics of the taxonomic and functional diversity of the eight plant assemblages (means ± standard errors). The data for 2013 are based on the seed assemblages sown, and the data for the next 4 years were obtained from field observations, with six replicates per assemblage. The eight plant assemblages have a low or high functional dispersion (LF or HF), a low, medium, or high species richness (LS, MS, or HS) and are composed of species from two different lists (Table  S2 ). CWM, community weighted mean. In general, we found strong effects of initial species richness on the functional dynamics of the assemblages, particularly in terms of functional dispersion, probably due to the stabilizing effects of the redundancy generated by higher species richness when functional dispersion remained high.
Similarly, while evenness was initially quite high for all eight assemblages, it declined less over time in situations in which functional dispersion was high. This suggests that there is a higher probability of particular species becoming dominant in assemblages with low functional dispersion, with abundances F I G U R E 3 Temporal dynamics of the proportions of species, over all the tested assemblages, as a function of regeneration strategy. The data for 2013 are based on the seed assemblages sown, and the data for the next 4 years were obtained from field observations. The different letters indicate significant differences between groups (p <. 05) and between years, within each category of regeneration strategy. remaining more evenly distributed in functionally-rich assemblages. These temporal dynamics of species richness and evenness may have reinforced the compensation effect of functional redundancy, which may explain an overall stabilizing effect on the functional dispersion of the plant assemblages.
We acknowledge financial support from  National Natural Science Foundation of China Grants 41871011 and 41930641, Laboratoire d'Excellence UnivEarthS  Grant  ANR-10-LABX-0023 ,  Initiative d'Excellence Université de Paris  Grant  ANR-18-IDEX-0001 ,  French National Research Agency  Grants  ANR-12-BS05-001-03/EXO-DUNES  and  ANR-17-CE01-0014/SONO , National Science Center of Poland Grant  2016/23/B/ST10/01700 , and the  French Chinese International Laboratory on Sediment Transport and Landscape Dynamics.
All clusters were filtered for chimeras using VSEARCH  [35]. Operational Taxonomic Units (OTUs) with an abundance inferior to 0. 005% were discarded because they are more likely to be chimeras than rare clusters  [36]  and may lead to a false overestimation of sample richness. Finally, the affiliation of all bacterial OTUs was validated using the EzBioCloud database  [37]  and the sequences from type material in 16S rRNA and ITS databases for BlastN in NCBI  [38]. The remaining chimeras identified were manually filtered, and these cured OTU tables were used to calculate the biostatistics using PhyloSeq R package v1. 28. 0, implemented in FROGS  [39]. Alpha-diversity metrics (Chao1 and inverse Simpson indexes) were computed after rarefaction. The abundance-based dissimilarity (beta-diversity) index, the Bray-Curtis index  [40] , was also calculated after rarefaction.
Sequencing Data and Taxonomic Assignation rDNA libraries were prepared separately for the core and the rind from a total of 32 different ready-to-eat cheeses (Table  S1 ). Two different DNA targets were used for bacteria (V3-V4 and V6-V8 regions from the rDNA 16S) and fungi (ITS1 and ITS2 of the rDNA ITS region) to compare their ability to characterize cheese microbiota using different primer sets (Table  S2 ). A total of 21,616,072 sequence reads were obtained through the Illumina MiSeq sequencing platform. For the bacterial profiles, 4,505,810 and 6,156,446 paired-end reads were sequenced for the V3-V4 and V6-V8 regions, respectively. Overall, 2,588,028 and 3,451,507 non-chimeric reads were assigned, respectively, to 108 and 154 OTUs, having a relative abundance >0. 005% (Table  1 ). The rDNA target V3-V4 allowed the identification of 57 genera, 22 of which were highly abundant (>10% relative abundance in one or more samples).
Choice of the Best Targets for Metabarcoding A comparison of different rDNA regions for monitoring the bacterial microbiota in cheese provided similar profiles (Figure  1 ). At the order taxonomic level, the major taxa identified were Lactobacillales (70%), Micrococcales (8-10%), Oceanospirillales, Pseudomonadales, Enterobacterales, Corynebacteriales, Bacillales and Rhizobiales (Dataset S1). For bacteria, V3-V4 generated fewer chimeric sequences and fewer low-abundant OTUs than V6-V8 (33. 7% compared to 37. 0%) (Table  1 ). In addition, V6-V8 provided ten ambiguous profiles out of the 94 samples sequenced (c06y15, c28y15, r01y15, r03y15, r06y15, r18y15, r21y15, r22y15, r23y15, r15y15; see Section 2 for the sample nomenclature) that could be associated with background or unspecific sequencing.
In contrast, only two ambiguous profiles were present using V3-V4 (Figure  1 ; r01y15, r15y15), therefore suggesting that V3-V4 was a better bacterial target for metabarcoding studies in cheese ecosystems. The two samples suspected to correspond to background or unspecific amplification, namely r01y15 and r15y15, were removed for further analysis. Fungal metabarcoding performed using ITS1 and ITS2 regions (Figure  2 ) generated a low proportion of chimeric sequences and rare OTUs, corresponding to 5. 0% and 10. 2% of the sequences, respectively (Table  1 ). The fungal profiles were different between ITS1 and ITS2. For ITS1, Saccharomycetales (52%), Hypocreales (21%), Eurotiales (17%) and Microascales (9%) were detected as the most abundant orders, while in ITS2 profiles, most sequence reads were affiliated to Saccharomycetales (73%), Eurotiales (12%), Hypocreales (10%) and Microascales (4%) (Dataset S1).
Microbial Landscape of Quebec's Terroir Cheeses Cheese core ecosystems were largely dominated by starter and ripening cultures  [11]. Overall, these cheese cores ecosystems were composed of OTUs affiliated with 47 bacterial genera where the most abundant were Lactococcus (54%), Streptococcus (37%) and the Lactobacillaceae family (7%), the latter being present mostly in washed cheese cores (Dataset S1 and Figure  3a ). The fungal OTUs were mostly affiliated with the genera Geotrichum, Debaryomyces, Penicillium, Fusarium, Kluyveromyces, Cyberlindnera and Scopulariopsis (Dataset S1 and Figure  3b ). Fungal core ecosystems showed specific profiles related to rind type. Geotrichum (46%) and Debaryomyces (20%) were the most abundant yeasts in washed cheese cores, while the surface of the bloomy-rind cheeses was dominated by Geotrichum (52%) and Penicillium (28%) (Dataset S1 and Figure  3b ).
The maximum estimated OTU number reached 30 for fungi and 42 for bacteria, while the maximum inverse of the Simpson α-diversity index reached 3. 183 and 2. 516 for both kingdoms, respectively (Tables  2  and S1 ). Cheese rind ecosystems were dominated by lactic acid bacteria (LAB), ripening strains and members of the secondary microbiota found in cheeses. On the surface of the cheese sampled, the average fungal richness (18 OTUs) and inverse Simpson index (1. 919) were lower than the bacterial richness (34 OTUs) and inverse Simpson index (3. 601), no matter the type of rind (Table  2 ). Overall, the cheese rind's ecosystems were composed of OTUs affiliated to 54 bacterial genera where the most abundant were Lactococcus, Brevibacterium, Glutamicibacter, Corynebacterium, Halomonas, Brachybacterium, Staphylococcus, Psychrobacter, Serratia, Streptococcus, Pseudoalteromonas and several other less abundant genera (Figure  3a , Dataset S1).
The fungal OTUs were mostly affiliated with the genera Geotrichum, Penicillium, Debaryomyces, Fusarium and Scopulariopsis (Figure  3b , Dataset S1). These rind ecosystems showed, at the genus taxonomic level, specific populations depending on the type of rind. The highest bacterial richness and α-diversity were observed in washed rinds (maximum richness of 46 OTUs and inverse Simpson index of 7. 268), followed by bloomy and natural rinds (Table  2 ). The richness and inverse Simpson index of fungal cheese rinds were similar in all rinds and to those observed in cheese cores (Table  2 ). Washed rinds were dominated by halotolerant psychrophilic and coryneform bacteria belonging to the genera Halomonas (20%), Corynebacterium (13%), Glutamicibacter (13%), Psychrobacter (12%), Staphylococcus (10%) and Brevibacterium (10%) (Figure  3a , Dataset S1).
Their fungal ecosystem was mostly composed of Geotrichum (35%), Fusarium (28%), Debaryomyces (22%) and Scopulariopsis (14%) (Figure  3b , Dataset S1). Bloomy rinds showed a different microbiota composed mostly of the fungi Geotrichum (58%), Penicillium (34%) and Debaryomyces (7%), and the bacteria Lactococcus (44%), Serratia (13%), Streptococcus (6%), Glutamicibacter (6%) and many less abundant genera (Figure  3a , Dataset S1).
The most dissimilar fungal core ecosystem (Bray-Curtis index of 0. 908) was observed in a washed-rind cheese core (c13), where proportions of Cyberlindnera, Kluyveromyces and Geotrichum changed from 64%, 22% and 11% to 84%, 14% and 2% respectively, from 2015 to 2018 (Figure  3b , Dataset S1). For c16 (Bray-Curtis 0. 772), the major yeast genera found in 2015 were Fusarium (49%), Kluyveromyces (19%), Geotrichum (18%), Penicillium (8%) and Debaryomyces (3%). In 2018, these proportions switched to Geotrichum (87%), Penicillium (12%) and Kluyveromyces (1%) (Figure  3b , Dataset S1). High dissimilarities in the bacterial ecosystems were observed on the surface of two washed-rind cheeses (r12 and r13) and one bloomy cheese (r06). For the bloomy cheese rind (r06), the Bray-Curtis index was 0. 769, explained by the high abundance of the starter cultures Lactococcus (51%) and Streptococcus (38%) in 2015 and by halotolerant psychrophilic bacteria (Psychrobacter, Glutamicibacter and Staphylococcus; totaling 79%) in 2018 (Figure  3a , Dataset S1).
For the first time, the bacterial and fungal microbiota of Quebec's terroir cheeses were studied using metabarcoding. Cheese rinds and cores were analyzed separately because of their different microbial composition  [54] [55] [56]. Due to their contact with the external environment, cheese rind ecosystems generally have a higher α-diversity than cores  [57, 58]. Here, when comparing ecosystems, the bacterial α-diversity (calculated using Chao1 and inverse Simpson indexes) was generally higher in rinds than in cores, whereas these indexes were similar for fungal populations  [11]. Moreover, cheeses cores and rinds showed a higher α-diversity for washed-ripened cheeses than natural-and bloomy-ripened cheeses (Tables  2  and S1 ). The predominant bacteria found in cheese cores belonged to the genera Lactococcus, Streptococcus and the Lactobacillaceae family (Figure  3a , Dataset S1).
Fungal communities of cheese cores and rinds showed similar composition for a given variety and were mainly composed of Geotrichum, Penicillium, Debaryomyces, Fusarium, Kluyveromyces, Scopulariopsis and Cyberlindnera (Dataset S1). These dairy yeasts and molds frequently occur in cheeses because they are inoculated as ripening cultures or originate from the indigenous microbiota of the milk or of the dairy plant environment  [73, [77] [78] [79]. They were found in various proportions, without obvious predominance in either rind type, except for Penicillium, which was more abundant in bloomy-rind cheeses and was well known to contribute to the development of their characteristic appearance and flavor (Figure  3b )  [78]. In the washed-rind cheese ecosystems, Fusarium (28%) was the second most abundant fungi identified in this study (Dataset S1).
Although most commonly found in bloomy-rind cheeses  [65] , Fusarium can be added to the smearing solutions to reduce rind stickiness  [80]. Scopulariopsis (14%, Dataset S1), usually associated with wrapped washed-and natural-rind cheeses  [81] , can contribute to cheese flavor through ammonia production  [82]. Although generally considered spoilage molds  [83] [84] [85] , Fusarium and Scopulariopsis could have been used as ripening agents for their technological properties  [78]. The metabarcoding method used in this study allowed the detection of the major contributors to cheese ecosystems and the influence of technological practices, such as the choice of optimal ripening and adjunct cultures, on each cheese variety studied (bloomy-, natural-and washed-rind). These data also provide information on the natural microbiota that could develop during cheese ripening.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/microorganisms10071381/s1, Dataset S1: Relative abundance of bacteria and fungi in all cheese core and rind samples. Compared distribution of the bacteria and fungi using all metabarcoding targets (V3-V4, V6-V8, ITS1, ITS2); Table  S1 : Cheese samples description and biostatistics describing cheese microflora. The richness (estimated OTUs) has been calculated with the Chao1 index, the α-diversity is evaluated with the inverse of Simpson index and the β-diversity is evaluated for the same sample, at different year using Bray-Curtis dissimilarity index, Table  S2 : Primers sequences for the preparation of the samples. Name of primers or adapters, target, DNA region and sequences.
Funding: This work was funded by  Novalait Inc. , the  Fonds de recherche du Québec-Nature et technologies (FRQ-NT) , the  Quebec Consortium for Industrial Bioprocess Research and Innovation (CRIBIQ)  (Grant ID  2016-LG-195927 ) and the  Natural Sciences and Engineering Research Council of Canada   Discovery Grant  ( NSERC-RGPIN-2017-06388 ). Eric Dugat-Bony was supported by the  AgreenSkills + fellowship program  which received funding from the  EU's Seventh Framework Programme  (Grant  ID FP7-609398 , AgreenSkills + contract).
Norovirus ORF2 Cloning The E12905 isolate is a GII. 17 HuNoV similar to the epidemic Kawasaki 308 variant  (Chan et al. , 2015). The GII. 17 strain (E12905, variant Kawasaki 308, GenBank number KU587626) was first amplified using 5′-TCCGCCCTGCAGATGAAGA TGGCGTCGAATG-3′ sense primer (PstI site is underlined) and RT 5′-TGGGTCGCGGCCGCTTACTGAGCCCTCCTT CG-3′ antisense primer (NotI site is underlined). Following amplification and digestion, the PCR product was resolved by electrophoresis, gel-purified and cloned into pVL1392 plasmid vector before transfection. The ORF2 of the GII. 4 2012 variant was also cloned into pVL1392 plasmid vector. The primers used for the amplification of the 2012 variants were described previously  (de Rougemont et al. , 2011). Details about the cloning strategies are available upon request.
Gene Synthesis Gene synthesis and cloning were provided by Life Technologies (Saint-Aubin, France) and Genecust (Ellange, Luxembourg). The ORF2 coding sequence of GII. 17-JC129 strain (GenBank number KY406981, variant CS-E1) and GII. 17-CUHK-NS-360 strain (GenBank number KP902565, variant Kawasaki 323) were synthesized and cloned into pUC with PstI (CUHK-NS-360 strain) and BamHI (JC129 strains) restriction sites directly located upstream of the first ORF2 start codon. All constructs were engineered with NotI restriction site at the 3′ end, which was directly located downstream from the ORF2 stop codon. Following digestion and gel purification, the fragments were subcloned into plasmid vector pVL1392 (CUHK-NS-360 strains) and pVL1393 (JC129 strains).
The plates were then washed three times with PBS buffer and incubated with 500 ng/well of purified VLPs diluted with PBS and 4% skimmed milk (PBS-4% blotto) for 1 h at 37°C. Each experiment was performed in triplicate. Purified 2012 variant GII. 4 VLPs (GenBank number KM406485) were used as a positive control. Cloning and VLP production methods of the 2012 variant is similar to previous work (de  Rougemont et al. , 2011). For saliva and affinity assays, attached GII. 17 VLPs were then detected with rabbit polyclonal serum (gracious gift from bioMérieux, Marcy l'Etoile, France) raised against E12905 GII. 17 isolates (Kawasaki 308 variant). The polyclonal serum was diluted 10,000 fold in PBS buffer-4% blotto and incubated for 1 h at 37°C prior to incubation with a 2,000-fold dilution of peroxidase-conjugated anti-rabbit antibody (Sigma, France) in PBS-4% blotto.
Genetic Analysis Four hundred seventy-five complete ORF2 amino acid sequences were retrieved from GenBank, of which 93 are unique sequences, and were used for genetic analysis. Alignment and sequence analysis were performed using the MEGA suite v. 10  (Kumar et al. , 2018). The sequences were first aligned using the MUSCLE algorithm, then manually checked for deletions. Homology between strains was estimated, based upon the number of amino acid changes, using MEGA without taking into account the deletions. Phylogenetic trees were generated from 1,000 replicates using the neighbor-joining and the maximum parsimony methods.
, 2015). The CS-E1 variant could be divided into two groups, 1978 and 2002, as proposed previously  (Qian et al. , 2019). However, the percentage of homology was too high to truly differentiate two variants within CS-E1. Additionally, we constructed a minimum spanning tree including all the complete GII. 17 capsid sequences available on GenBank by the end of year 2018 (N = 475). The topology of the minimum spanning tree clearly showed three distinct variants within the GII. 17 genotype: Kawasaki 308, Kawasaki 323, and CS-E1 variants (data not shown). Kawasaki 323 variant was circulating before it was replaced by the Kawasaki 308 variant  (Matsushima et al. , 2015). The CS-E1 variant is the oldest to be described, with only few sequences registered in GenBank  (Zheng et al. , 2006). The amino acid homology within each variant ranges between 96.
A B FIGURE 1 | Genetic analysis of GII. 17 human norovirus (HuNoV). (A) Neighbor-joining dendrogram of the 93 unique amino acid sequences corresponding to GII. 17 ORF2. Variants are indicated on the right side of the dendrograms. For each variant, one isolate was selected for VLP production. The GenBank registration number of the isolate is indicated on the tree. (B) Summary of the alignment of the 93 amino acid sequences of GII. 17. The sequences were divided into the three GII. 17 variants, CS-E1, Kawasaki 323, and Kawasaki 308. S, P1, and P2 domains are indicated above the graph. Variations within each variant and between variant are indicated by black and red bars, respectively. Deleted amino acid residues are indicated by asterisks. The absence or poor binding on synthetic carbohydrates for Kawasaki 308 and Kawasaki 323 variants was surprising when a marked attachment was observed using phenotyped saliva (Figure  3 ).
Interestingly, CS-E1 VLP only produced faint binding with 50-fold diluted saliva. Overall, these experiments suggested that the binding capacity toward HBGA has increased with the recent evolution of GII. 17 HuNoVs, with the highest binding activity observed for the Kawasaki 308 strain. Additionally, the data suggested that the binding capacity for the GII. 17 Kawasaki 308 variant was lesser than that observed for the GII. 4 2012 variant, which circulated at the same period. The next step was to ascertain recognition of HBGAs on native glycans by the GII. 17 using Kawasaki 308 VLPs.
Histological Analysis of VLP Binding to Duodenal and Colonic Tissues Virus-like particles have previously been used to demonstrate that HBGAs are the natural ligands of HuNoVs using saliva binding assays and histological assays  (Marionneau et al. , 2002; Green et al. , 2020). The role of the HBGA was later confirmed in enterocytes where HBGAs are highly expressed and HuNoVs replicate  (Karandikar et al. , 2016). Here, we intestinal tissue to study GII. 17-HBGA interactions. For the GII. 17 variants including the CS-E1 variant, we observed VLP attachment to the duodenal mucosa (Figure  7 , panels A-C). However, binding intensity was lower for CS-E1 VLPs. In addition, binding was abolished when the duodenal tissues were pretreated with NaIO 4 , demonstrating the importance of glycan structures (data not shown).
We then hypothesized that Le b might be involved into the recognition of GII. 17 HuNoVs. Incubation of the duodenal section with a Le b -specific antibody abrogated attachment for Kawasaki 308 and 323 variants. At this point, histological analysis confirmed that recent GII. 17 variants (i. e. , Kawasaki 308 and 323 variants) were more efficient binders since they showed an extended binding profile encompassing A, B, H, and Lewis antigens (i. e. , Le b ). Additionally, for CS-E1 binding assays, our data suggested that the use of histological sections was physiologically more relevant than saliva binding assays. In the next set of experiments, we characterized GII. 17 binding using healthy duodenal sections from a blood group A donor. VLP binding was observed for the Kawasaki 308 and 323 variants but not for the CS-E1 variant.
17 VLPs. It is clear that SPR and ELISA using synthetic carbohydrates provide interesting information about HuNoV binding capacity. Nevertheless, they may not entirely reflect the carbohydrate presentation on human tissues. To verify this hypothesis, binding assays were then performed using histological sections. Here, binding of the three GII. 17 variants all involved HBGA. Therefore, the data suggests that, similar to assays in synthetic carbohydrates, saliva assays might not entirely reflect the binding status and poor binders, such as CS-E1, might not possess the capacity to attach to HBGA from saliva. Our data are coherent with previous studies showing that saliva binding status might not entirely reflect binding capability at the intestinal level considering that HBGA conformation and concentration might vary between intestinal and saliva samples  (Ruvoen-Clouet et al.
FUNDING This study was partially funded by  Santé Publique France , the  National reference Center for Viral Gastroenteritis  and the public hospital of Dijon. The authors acknowledge the support of biophysical and nanocharacterization facilities of the  Clinical-Innovation Proteomic Platform (CLIPP, Besançon, France). At the time of the study,  Siwar Ayouni  was sponsored by a fellowship from  Campus France.