dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential gene expression analysis Differential gene expression analysis was conducted according to the DESeq2 protocol  [41]  implemented in R with the following model design for noble crayfish: sex (male/female) + groups (Control vs Hap A or Hap B challenge) and for marbled crayfish: ~reproduction (yes/no) + groups (Control vs Hap A or Hap B challenge). Independent comparisons were conducted for each sampling point. Raw counts from the Salmon output were used as the input. Transcripts highly similar to the marbled crayfish and noble crayfish mitogenome, respectively, were removed prior to the analysis based on the BLAST hits against the mitogenome (NCBI accession number: KX279347. 1 and NC_020021. 1). Transcripts assigned to bacteria and archaea were also removed based on the DIAMOND search (see 2.
2). results Counts for individual Trinity transcript isoforms were grouped to Trinity genes with the tximport R package  [42]. Lowly expressed genes were filtered out: only genes with the raw counts higher/equal to 10 across at least five samples were retained. The package "EnhancedVolcano"  [43]  was used for the visualisation of the DEGs and "apeglm" for noise removal  [44]. The list of DEGs was exported and their counts, log2fold changes and adjusted p-values (FDR= 0. 1, p-value= 0. 05) together with their respective annotations were merged. Possible overlaps between the DEGs at different time points were inspected using Venn diagrams  [45].
Innate immunity transcripts In this study, we provide experimental evidence of the hepatopancreas involvement in mediation of the crayfish immune response to the A. astaci challenge through the synthesis of immune molecules. Activation of specific pathways and changes in the gene expression landscape are described in detail in sections 3. 4. and 3. 5. Here we provide an overview of genes involved in the immune related pathways and responses (Figure  1 ). Genomic research on non-model organisms is faced by the challenge of annotating large sets of genes from unknown origin. This challenge is particularly evident in Crustaceans  [47, 48] , which are still largely underrepresented in genomic studies. To date, only 48 out of 727 genome assemblies representing Pancrustacea belong to Crustaceans (with the remaining 679 genomes belonging to Hexapoda) (Genomes-NCBI Datasets, accessed: April 2021).
Furthermore, the canonical proPO pathway, considered a core immune response mechanism in the Crustaceans  [49] , is not represented in the KEGG database. Therefore, we conducted the annotation of the innate immunity related genes in the noble crayfish and marbled crayfish transcriptomes using a sequence and domain similarity-based approach. A total of 372 and 353 innate immunity related genes was identified through this approach in noble crayfish and marbled crayfish, respectively (Figure  1 , Table  S2 , Table  S3 , File S2, File S3). The identification of these innate immunity related genes provides a basis for future transcriptomic and genomic studies of the innate immunity in native and invasive freshwater crayfish species. For example, we successfully identified members of the immune signalling Toll pathway.
Hepatopancreas microbiome In the taxonomical classification of the hepatopancreas transcriptome assemblies, 35,879 noble crayfish transcripts and 39,527 marbled crayfish transcripts had a significant BLASTx hit (Figure  2 ). For both species, the majority of the transcripts had a significant hit among the eukaryotic taxa (66% for the noble crayfish and 68% for the marbled crayfish), among which the highest number of hits (51% and 52%, respectively) was assigned to the whiteleg shrimp (Penaeus vannamei). Large proportions of the BLASTx hits were assigned to bacterial taxa (26% and 23%, respectively). Among them, in the noble crayfish, the phylum Proteobacteria was represented with 24% of all hits, with the classes Gammaproteobacteria, contributing 9% of the total, Betaproteobacteria contributing 9% and Alphaproteobacteria contributing to 6% of all hits.
Furthermore, infection with pathogens (such as A. astaci) can cause microbiota dysbiosis  [56]. Despite their obvious importance in metabolic and immunological functioning, microbiome communities of freshwater crayfish are mostly unreported in transcriptomic studies. Creating a knowledge database of the various microbial communities is necessary for understanding the immune response and disease progression in Crustaceans as well as in other Metazoans and might provide an effective tool in disease control  [57].
Among the up-regulated DEGs in the Hap B challenged noble crayfish, we identified a pentraxin domain containing gene (Table  S5 , Pfam: PF00354). The protein product of this gene is 254 aa long (27. 95 kDa), with a signal peptide (1-21 aa) on the N-terminus and only 55. 51% identity with the neuronal pentraxin receptor-like isoform X2 from the whiteleg shrimp (XP_027224174. 1, identified with Blastx). Like the most-well studied pentraxins, (e. g. C-reactive protein (CRP) or Serum-amyloid P component (SAP)), due to its size this pentraxin probably belongs to the group of short pentraxins  [93]. We identified 11 putative pentraxin genes in the noble crayfish and 17 in the marbled crayfish (Figure  1 ). Pentraxins (or pentaxins) represent a multifunctional and evolutionary conserved group of proteins, with a critical role in the humoral innate immune response  [94].
FFA-to-TG ratios were calculated. At baseline, FFA-to-TG ratio was not different between the groups. After 1 month of SMFE treatment, FFA-to-TG ratio decreased from 9. 52 AE 4. 85 in untreated patients to 7. 54 AE 2. 36 in treated ones. The count of noninflammatory (comedonal) acne lesions was significantly decreased in the treated group, attesting objectively the decrease of comedogenesis associated with the decrease of FFA (Figure  3beg ). These data indicate that a lower FFA release in the sebum is associated with lower comedogenesis and support the hypothesis.
Western blot analysis (cohort 1) Protein samples were analyzed by western blot using a standard procedure using 10 mg total proteins on 12% polyacrylamide gels (456-1043; Bio-Rad Laboratories, Hercules, CA)  (Fontao et al. , 2020). The following primary antibodies were used: rabbit polyclonal anti-human IgG (NB110-40877, dilution 1:2,000; Novus Biologicals, Littleton, CO) for PLIN2, rabbit polyclonal anti-human IgG (PA5-29654, dilution 1:5,000; Thermo Fisher Scientific, Waltham, MA) for CIDEA, and mouse monoclonal anti-human GAPDH IgG1 (sc-47724, dilution 1:10,000l; Santa Cruz Biotechnology, Dallas, TX) for GAPDH. A goat anti-rabbit IgG (H þ L)-horseradish peroxidase conjugate was used as a secondary antibody (172-1019, dilution 1:200; Bio-Rad Laboratories). The densitometric analysis of protein bands was performed using ImageJ software (National Institutes of Health, Bethesda, MA), as shown in Figure  2e.
05% and 9. 35% of the total days of the trial, respectively  (Fontao et al. , 2020). A weekly follow-up based on digital facilities was established. Cyanoacrylate skin surface biopsies were performed on the forehead at each visit. Clinical acne severity was evaluated by global evaluation of acne  (Dreno et al. , 2011)  and lesions counts. Cohort 2 (skin surface lipids). A total of 38 enrolled and 36 analyzed patients (aged 18-34 years, mean age of 24. 2 years, four males, 32 females) with mild to moderate acne were enrolled in a clinical trial aimed at assessing the modulation of sebum lipids and acne score following topical treatment with a low concentration of SMFE on the face (1. 75% in an oil-in-water emulsion). According to the French legislation, this trial using a topical cosmetic formulation (ClinicalTrials.
ACKNOWLEDGMENTS We thank  Patrizia Arboit  from the  Proteomics Platform as well as Mylene Docquier  and  Christelle Barraclough  from the  Genomics Platform of the University of Geneva  ( Geneva, Switzerland ) for their helpful discussions and their technical assistance. We thank  Kerstin Grosdemange  for her technical assistance. This study was funded by Fonds  UF1024 ,  University of Geneva.
SSR genotyping After confirming the location of the Or Deb2 gene on Chr4 through BSA, a set of SRR markers from this Chr4, mapped by  Tang et al. (2002)  and  Yu et al. (2003)  and identified by ORS and CRT prefixes, was screened for polymorphisms between the parental lines DEB2 and IAS-31. PCR analyses were performed as described by  Pérez-Vich et al. (2004). SSR amplification products were separated on 3% (w/v) Metaphor® (BMA, Rockland, ME, USA) in 1 × TBE buffer with SaveView Nucleic Acid Stain (NBS Biologicals Ltd. , Huntingdon, UK) incorporated in the gels and visualized under UV light. A 100 bp DNA ladder (Solis BioDyne, Tartu, Estonia) was used as a standard molecular weight marker to get an approximate size of DNA fragments. Bands were scored manually using Quantity One® 1-D Analysis Software (Bio-Rad Laboratories Inc, Hercules, CA, USA).
Resistance gene candidate (RGC) genotyping Since some RGC homologs to intracellular Nucleotide-binding Leucine-Rich Repeat (NLR or NB-LRR) proteins, which consist in a central NB domain and a C-terminal LRR, have been reported to map to the upper half of Chr4  (Radwan et al. 2008) ; they were used to develop more SNP markers. These NB-LRR RGC sequences were as follows: RGC41 (GenBank accession EF559382), RGC126 (EF559405), RGC127 (EF559406), RGC128 (EF559407), RGC129 (EF559408), RGC130 (EF559409), RGC133 (EF559412), RGC225 (EF559430), and RGC246 (EF559450). Primers were designed from these sequences with Lasergene SeqBuilder Pro software within the DNASTAR, Inc. package (Table  S1 ) and used to amplify the parental lines IAS-31 and DEB2. PCR reactions were performed using 30 µl of reaction mixture containing 1 × PCR buffer, 1.
Genetic linkage analysis, Or Deb2 mapping, and candidate gene analysis Genetic linkage analysis was run with MAPMAKER v. 3. 0 (Whitehead Institute, Cambridge, MA;  Lander et al. 1987)  using segregation data for SSRs, SNPs, and RGC-SNPs, as well as for Or Deb2 gene. The genotypes for Or Deb2 gene were inferred from their corresponding phenotypes based on the F 2 and F 2:3 evaluations for broomrape resistance to races G TK and F GV , as described above. Two-point analysis was used to group the marker loci. A LOD threshold of 6 and a maximum recombination fraction of 0. 3 were used as linkage criteria. Three-point and multi-point analyses were used to determine the order and interval distances between the markers. Recombination fractions were converted to centiMorgans (cM) using the Kosambi mapping function.
Linkage group maps were drawn using the MapChart software  (Voorrips 2002). Linkage analyses were performed in consecutive SSR, RGC-SNP, and SNP marker genotyping rounds, each one reducing the Or Deb2 mapping interval, as explained in the results section. A final linkage analysis was carried out using genotyping data from the complete mapping population of the closest markers to the Or Deb2 gene, in order to refine their position. For the candidate gene analysis, Or Deb2 closest markers were mapped on the HanXRQr2. 0-SUNRISE reference sunflower genome sequence (https:// www. helia gene. org/ HanXR Qr2. 0-SUNRI SE). Positions in the previous sunflower genome assembly (https:// www. helia gene. org/ HanXRQ-SUNRI SE) were also determined in order to compare with previous reports in which only this assembly was used.
2004) , and established using the following parameters: a cluster must contain at least two genes, the distance between two neighboring genes should be < 200 kb, and no more than eight genes should be present between the neighboring genes  (Jupe et al. 2012). Finally, for specific protein coding genes, detailed in the results section, protein domains were analyzed by the InterProScan program (http:// www. ebi. ac. uk/ Tools/ pfa/ iprsc an/), and homology between amino acid sequences was calculated using Lasergene MegAlign Pro software within the DNASTAR, Inc. package. Multiple sequence alignments were generated by using the Clustal omega program, and a phylogenetic tree was constructed by maximum likelihood (RAxML) algorithm.
S1 ). TAIR BlastP analyses showed that each half of the longer proteins had homology to the kinase region of At5g38900 or At5g38900 (Fig. S1 ). However, a unique serine/threonine active site (InterPro IPR008271) (Ser/Thr AS) and ATP binding site (ATP-BS) (InterPro IPR017441) were present in the four proteins (Fig. S1 ). In the longer (about 600 aa) CrRLK1L-1 kinases, these unique Ser/Thr AS and ATP-BS were located in the left-kinase domain which showed higher homology with the kinase region of the Arabidopsis genes than the right domain (Fig. S1 ). No transmembrane or extracellular domains were observed in any of the four RLK-Pelle-CrRLK1L-1 kinases (Fig. S1 ). PANTHER family classification system grouped the four sunflower CrRLK1L-1 kinase genes in the OS07G0166700 protein family (PTHR27003) [Os07g0166700 (LOC434248) encodes a putative brassinosteroid LRR receptor kinase] (Fig.
38 Mbp region (12 of them were located in a window from 6. 5 to 7. 4 Mbp, 4 were the CrRLK1L-1 kinase genes in the Or Deb2 -1. 38 Mbp region, located from 8. 2 to 8. 7 Mbp, and one of them was located at 11. 5 Mbp). BlastP searches against the non-redundant (nr) GenBank protein database revealed the best matching putative homologues to the four sunflower RLK-Pelle-CrRLK1L-1 kinases in other plant species to be two Lactuca sativa receptor-like protein kinase FERONIA proteins (LOC111885056 and LOC111896681) (e-values lower than 1 × 10 -100 ), which had also two kinase domains (Prosite PS50011), one serine/threonine active site (InterPro IPR008271) and one ATP binding site (InterPro IPR017441), and lacked any transmembrane or extracellular domains. The four protein coding genes in the other group were classified as putative non-specific serine/threonine protein kinases [receptor-like proteins (RLPs) according to NCBI Annot.
2016). InterPro analysis of the four sunflower RLPs revealed that they had a large LRR region (InterPro IPR032675, IPR001611) spanning the majority of the protein sequence which was predicted to be extracellular, and lacked an intracellular kinase domain (Fig. S1 ). Chr04g0142271 (Fig. S1 ) and Chr04g0142391, which were 93% identical, also showed small transmembrane and cytoplasmic domains at the C-terminus protein region, being the domain constitution very close to that of their homologous Arabidopsis RLP genes. PANTHER grouped these two genes and Chr04g0142241 in the "receptor-like protein 14" (PTHR48062) family, which was represented in sunflower by 17 genes. Again, the majority of these sunflower genes (14 out of 17) were located at Chr4, very close to the Or Deb2 -1. 38 Mbp region [1 of them was located at 7.
85 Mbp, 3 were the RLP genes in the Or Deb2 -1. 38 Mbp region (located from 7. 97 to 8. 66 Mbp), and 10 of them were in a window from 9. 35 to 10. 78 Mbp]. BlastP searches against the non-redundant (nr) GenBank protein database revealed the best matching putative homologues to the three RLPs Chr04g0142241, Chr04g0142271 and Chr04g0142391 in other plant species to be the Lactuca sativa RLP 12 (LOC11190055) (e-value 0. 0), which also had a large non-cytoplasmic LRR region and small transmembrane and cytoplasmic domains at the C-terminus protein region.
Material and methods Morphological characters and anatomy are not relevant for species delimitation in the Bythinella genus  This study  (Bichain et al. , 2007a, b). We thus relied on the genetic distances between specimens to delimit species. Following a barcoding approach, we used the pairwise distribution of the genetic distances calculated with the Barcode gene COI to identify a gap in the distribution, referred to as the "barcoding gap"  (Puillandre et al. , 2012) , between intra and interspecific distances. A total of 1074 COI sequences of Bythinella species downloaded from GenBank (Dataset 1) were used to estimate the barcoding gap (supplementary material 1). MEGA 10. 05  (Tamura et al. , 2011)  was used to calculate the p-distances (following  Srivathsana and Meier, 2012)  between each pair of specimens.
Sequences were automatically aligned using CLUSTALW multiple alignments implemented in BIOEDIT 7. 2. 5  (Hall, 1999). The accuracy of automatic alignments was confirmed by eye. Only a few gaps, unambiguously aligned, were inferred for the 28S, ITS and 16S genes: they were conserved for the analyses. We obtained 34 sequences for the COI gene (Tab. 1), resulting after alignment in a 557 bp fragment. Seven additional sequences downloaded from GenBank were included the dataset (two sequences for both B. viridis and B. lancelevei, three sequences for B. carinulata). A total of 25 sequences were obtained for the 16S gene, and four additional sequences were included in the dataset (two for B. viridis, one For phylogenetic analyses of the COI gene fragment, B. badensis  Boeters, 1981 and B. cebennensis (Dupuy, 1849)  were chosen as outgroups, following the results of  Benke et al.
Results COI gene phylogeny of the genus Bythinella (based on Dataset 1) confirmed the monophyly of all studied populations (results not shown). Within this clade, corresponding to clade Vof  Benke et al. (2009) , B. viridis and B. lancelevei topotypes could not be distinguished. Some populations of the putative B. carinulata formed distinct supported clade, but these were not monophyletic (Fig. 2 ). These results confirm those of  Benke et al. (2009). The 16S gene was little variable (overall p-distance = 0. 01). The topotypes of B. viridis and B. lancelevei shared the same haplotype for this gene. Regarding the nuclear genes, all the specimen studied shared the same haplotype for the 28S fragment. The ITS gene fragment was a bit more variable (overall p-distance = 0. 07). Topotype specimens of B.
Acknowledgments. This study was performed with financial support of the DREAL Picardie, and the  Parc naturel régional de Lorraine. Sequencing of the specimens was performed by  Lise-Marie Pigneur  ( Université de Lièges ),  Stephane Dreano  ( Université de Rennes I)  and  Sebastian Berg  ( Animal Ecology and Systematics, Justus Liebig University Giessen ).
A significant difference among groups was observed in kidney weight. Comparison of means revealed that kidney weights were lower in ducks exposed to FBDONZEN than in ducks fed the control diet, whereas kidney weight did not differ in any of the other groups. Although a numerical decrease in the liver weight was also observed in ducks exposed to FBDONZEN, the difference among groups was not significant (ANOVA, p = 0. 0891). By contrast, no differences were found among groups in the relative organ weights of kidney and liver (data not shown). The effects of FUS on the weights and the length of some sections of the gastro digestive tract were also measured. Although significant differences in duodenum weight, in the jejunum and in ileum length were observed among groups (ANOVA), none of the groups exposed to FUS differed from the group of control ducks not exposed to FUS.
Mycotoxins in Liver The concentration of mycotoxins in the liver was analyzed by UHPLC-MSMS after sample clean-up on immunoaffinity column and isotopic dilution, as previously described  [29, 30]. The UHPLC MSMS system was a 1260 model, the detector was a 6410 triple quad, both from Agilent (Agilent, Santa Clara, CA, USA). Mycotoxins were separated on a Poroshell 120 column (3. 0 × 50 mm, 2. 7 µ). Detection was done in positive electro-spray ionization mode in the following conditions: temperature at 300 • C, gas delivered at 10 L/min, nebulization at 25 psi and capillary voltage at 4000 V. Two transitions were used as qualifiers for FB1, FB2, FB3, ZEN, α-ZOL, β-ZOL, ZAN, α-ZAL and β-ZAL, whereas one qualifier was used for the internal standards. MassHunter quantitative analysis software (Agilent) was used to analyze the chromatograms.
Supplementary Materials: The following are available online at https://www. mdpi. com/2072-665 1/13/2/152/s1, Table  S1 : Amount of corn and amount of toxins administered to ducks. Funding: This study was supported by CASDAR grants (project 2012-2015 MYCOVOL). The authors would like to thank the RMT Quasaprove for supporting the project.
Acknowledgements. This work is part of a project validated by the " Pôle de Compétitivité Valorial" (programme 214 ) and was funded by the  Conseil Régional de Bretagne ,  Conseil Départemental des Côtes d'Armor ,  Conseil Régional des Pays de la Loire , and  Saint Brieuc Agglomération. We thank  Zoopole Développement ,  Ploufragan (France)  for their help in preparation of the project, and we acknowledge partners involved in the global project:  CI-DEF-CIPC, CECAB, Deltavit (groupe CCPA ),  Techna France Nutrition, Terrena, Triskalia, Sanders ,  Coopérative Le Gouëssant ,  MG2Mix , and  Selvet Conseil. We also thank  Dr. Paul Brown  for proofreading the English manuscript. The authors declare that they have no competing or financial interests.
Results Data description. Our dataset includes the results of 80 independent experiments reported in 22 articles published from 1982-2015 that compare the efficacy of calendar-based and DSSbased strategies in reducing fungal disease incidence with fungicide treatments. Each experiment included data collected for at least one calendar-based strategy, one DSS-based strategy, and an untreated control plot. The dataset includes a total of 328 disease incidence data items collected for 80 untreated controls, 99 calendar-based strategies, and 149 DSS-based strategies (Supplementary Table  S1 ). The dataset included a total of 16 core DSSs, with additional modifications and different action thresholds. The DSSs comprised both empirical (correlative) and mechanistic (processbased) modelling approaches.
S1a ). The locations of the experiments covered 6 Köpen-Geiger climate classes  17, 18  : Aw (equatorial with dry winter), Cfa (warm temperate, fully humid, hot summer), Csa (warm temperate, dry and hot summer), Csb (warm temperate, dry and warm summer), Dfa (snow, fully humid, hot summer) and Dfb (snow, fully humid, warm summer) (Supplementary Data 1). The experiments targeted different crops, pathogens, and fungicides. A total of n = 44 experiments were conducted on non-woody crops, including wheat, asparagus, lettuce, strawberry, and tomato. The remaining n = 36 experiments were conducted on woody crops, including apple, pear, grape, and mandarin (Supplementary Table  S3  and Supplementary Fig. S1b ). The dataset covered both fungal and fungal-like pathogens from the classes of the Dothideomycetes (Alternaria alternata, A.
These fungicides covered all FRAC  19  categories with regard to resistance risk: low risk (FRAC codes M01, M03, M04, M05, and P07), low to medium risk (FRAC code 12), medium risk (FRAC codes 3 and 9), medium to high risk (FRAC codes 2 and 7) and high risk (FRAC codes 1, 4 and 11) (Supplementary Data 1). The spread of the disease incidence data was very large, ranging from 1. 5% to 100% in the untreated controls (Fig. 1a ), revealing the presence of experiments with very low, moderate, and very high disease pressure in the dataset. Disease incidences were lower in calendar-based and DSS-based strategies than in their untreated counterparts in the majority of the experiments (Fig. 1a, b ). This observation remained valid when considering different subgroups of experiments corresponding to different locations, crops, pathogens, or types of fungicide (Supplementary Figs.