dataesr/scientific-paragraphs-categorization huggingface dataset:
Imaging For all other imaging a Zeiss LSM 780 confocal laser scanning microscope with ZEN 2. 3 software (Carl Zeiss, Jean, Germany) was used. Excitation / emission wavelengths were 405 nm / 475-525 nm for aniline blue, 488 nm / 500-525 nm for GFP, and 561 nm / 560-610nm for RFP.
Expression of jejunal and liver mRNA by real-time PCR RT-qPCR assays were performed as previously described  (Maruo et al. , 2018). Primers obtained from previous studies or designed using PrimerQuest® software were purchased from Sigma (Table  2 ). Data were analysed with the LinRegPCR 2016. 2 program. The expression values of the genes of interest were normalized against three housekeeping genes: hypoxanthine phosphoribosyltransferase 1 (HPRT1), topoisomerase (DNA) II beta (TOP2B) and hydroxymethylbilane synthase (HMBS) and validated with NormFinder software. Gene expression was expressed relatively to the control group.
Statistical analysis Performance and feed conversion data were evaluated in the software R-3. 5. Boxplots were used to visually inspect the data distribution, variability, and outliers. The model assumptions (normality, homoscedasticity, and independence) were inspected via the residual plots. Mixed Effects Models and Generalized Least Squares with subsequent multiple comparisons were used as indicated to test for differences between groups. The significance level was defined as p < 0. 05, and p ≥ 0. 05 to <0. 10 were considered as tendencies. All histology statistical analyses were performed using GraphPad Prism 9. 0. 2 software (GraphPad Software Inc. , La Jolla, USA). Data were expressed as mean ± SEM (standard error of the mean) for normal distribution or median and interquartile range when data were not normally distributed.
They were submitted to statistical analysis, using normality (Shapiro-Wilk) and homogeneity (Bartlett) tests. Significant differences were assessed by one-way ANOVA followed by Tukey's test for parametric data and by Kruskal-Wallis test followed by Dunn's test for non-parametric data. Statistical analysis for all other results were performed by GraphPad Prism 7. 05 (GraphPad Software Inc. , La Jolla, USA). Values were analysed for normality (Shapiro-Wilk) as well as homogeneity of variance (Levene Statistics). Normally distributed homogeneous data were analysed by one-way ANOVA (Dunnett's multiple comparison test). If normal distribution was violated, the Kruskal-Wallis Test was used. Outliers were identified by Grubbs' test and removed, if applicable.
Funding "This research was funded by  Austrian Research Promotion Agency (FFG)  via the research project  Early Stage , grant number  879467  and by the  French ANR  project " EmergingMyco"  ( ANR-18-CE34-0014 ). Amanda L. Hasuda  received a fellowship from the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-CAPES/Cofecub  (Grant  0389/2019 ). This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001. ".
Measurements of pre-and post-hatching maternal care We first measured the effects of rifampicin on four classical forms of earwig maternal egg care: egg grooming, egg defence, delay of maternal return and exploration rate while searching for its eggs  [55, 56]. Egg grooming, which is used by earwig females to deposit chemical substances on the eggs and to clean eggshell from dirt and fungi  [38] , was measured 15 days after egg production. We first isolated each mother for 30 min, then returned them to their Petri dish and gently deposited them at a distance of 5 cm from their clutch, and finally video-recorded their behaviours for the subsequent 15 minutes (SONY© Handycam HDR-CX700 camera). The resulting movies were analysed using the software BORIS v4. 0. 3  [57]  and the total duration of egg grooming was defined as the total amount of time each female spent on cleaning eggs with their mandibles  [38].
We removed each mother from its clutch of eggs, subsequently deposited her at the centre of a square arena (W: 9 cm; L: 9 cm; H: 0. 5 cm) covered by a glass sheet, and then video-tracked its activity during 35 min. The video was done under infra-red light, while the individual video tracking and calculation of exploration rate were conducted using the software ToxTrac v2. 83  [58]. We then measured the effects of rifampicin on two classical forms of post-hatching maternal care: nymphs defence and exploration rate while searching for its nymphs  [44, 55]. These two forms of care were measured 10 and 12 days after egg hatching, respectively, following the above-detailed protocols for egg defence and egg searching. All the measurements of pre-and post-hatching maternal care were conducted in the afternoon and under a red light as earwigs are nocturnal.
Statistical analyses Analyses of the α and β-diversity indices. The structure, composition and diversity of the microbial communities were based on the 161 identified bacterial Operational Taxonomic Units (OTUs) (see results) and analysed using PHYLOSEQ R package  [61]  implemented in the FROGSSTAT Phyloseq tools  [62]. Diversity within the gut microbial communities (alpha-diversity) was assessed using two richness indices, which estimate the number of OTUs in the microbiome with correction for subsampling (Chao1; ACE), and three metrics that aim to measure diversity by accounting for evenness or homogeneity (Shannon, Simpson, Inverse Simpson, and Fisher)  [63]. Diversity between the gut microbial communities (beta-diversity) was assessed using 4 measures of community similarity: 1) Jaccard indice, which does not consider phylogeny of OTUs but takes into account their presence/absence; 2) Bray Curtis dissimilarity, which does not consider the phylogeny but considers the number of reads assigned to an OTU (i.
Acknowledgements We thank  Jos Kramer ,  Maximilian Körner , as well as  Nadia Aubin-Horth ,  Gabrielle Davinson , an anonymous reviewer and  Trine Bilde  ( Peer Community in Evolutionary Biology  referees and recommender, respectively) for their insightful comments on this manuscript. This research has been supported by a research grant from the  French Ministry of Research  (to S. V. M. ) and a research grant from the  Agence Nationale de la Recherche  ( ANR-20-CE02-0002  to J. M. ). Version 5 of this preprint has been peer-reviewed and recommended by  Peer Community In Evolutionary Biology  (https://doi. org/10. 24072/pci. evolbiol. 100121).
To measure the voltage-dependence of the inward sodium current steady-state inactivation, a two-pulse voltage-clamp protocol was used. Inactivation properties were studied by applying a conditioning pulse between -110 and +20 mV (10-mV increments) that was long enough (500-ms) to allow the inactivation process to reach its steady-state level. Thereafter, the membrane potential was stepped back to the holding potential (-90mV) for 1 ms to ensure that the activation variable remained constant, and a 15 ms test pulse was applied to -10mV. The inactivation curves were obtained by plotting the amplitude of the peak sodium currents against the conditioning potential (Fig. 7a, b ). The smooth line corresponds to the best fit through the mean data points using a single Boltzmann distribution (see Eq.
2 with NaOH. Neuronal cells were dissociated, identified and maintained at 29 °C for 4 h before experiments were carried out  27. Electrophysiological recordings. Steady-state and voltage-gated ionic currents (voltage-clamp mode) were recorded by using the patch-clamp technique in the whole-cell recording configuration. Membrane potential was recorded under current-clamp mode. Signals were recorded with an Axopatch 200 A amplifier (Axon Instruments, Foster City, CA), filtered at 5 kHz using a 4-pole lowpass Bessel filter. Ionic currents were displayed on a computer with software control pClamp (version 10. 0; Molecular Devices) connected to a digitizer (DIGIDATA 1322; Molecular Devices). Patch pipettes were pulled from borosilicate glass capillary tubes (GC 150T-10; Clark Electromedical Instruments, Harvard Apparatus) using a Sutter P-97 (Sutter Instruments).
2 with CsOH 1M  27. Cholinergic agonists (pulse application 3 s in duration, except when otherwise stated) and pharmacological agents (added to the external solutions) used in the different experiments were applied by a gravity perfusion valve controller system (VC-6 M, Harvard apparatus) controlled by pClamp software (flow rate of perfusion 0. 5 mL/min). The perfusion tube was placed within 10μm from the isolated neuron cell body. When necessary, some compounds were prepared in DMSO and then diluted in the bath solution to obtain the different concentrations tested. The highest concentration used in the electrophysiological recordings of DMSO was 0. 1%. This concentration of solvent was not found to have any effect on the electrophysiological properties of neuron cell body. All compounds were purchased from Sigma Chemicals (L'isle d'Abeau Chesnes, France).
After loading, cells were washed two times in saline. The glass coverslips were then mounted in a recording chamber (Warner Instruments, Hamden, CT, USA) connected to a gravity perfusion system allowing drug application. Imaging experiments were performed with an inverted Nikon Eclipse Ti microscope (Nikon, Tokyo, Japan) equipped with epifluorescence. Excitation light was provided by a 75-W integral xenon lamp. Excitation wavelengths (340 nm and 380 nm) were applied using a Lambda DG4 wavelength switcher (Sutter instrument, Novato, CA, USA). Images were collected with an Orca-R2 CCD camera (Hamamatsu photonics, Shizuoka, Japan) and recorded on the computer with Imaging Workbench software (version 6, Indec BioSystems Santa Clara, CA, USA). Experiments were carried out at room temperature. Intracellular calcium level was expressed as the ratio of emitted fluorescence (340/380 nm).
4). After 2 h of color development, the reaction was monitored at 405 nm, with a Bio-Tek EL808 Ultra Microplate reader (Biotek Instruments). The AChE activity from AcerKis neuronal cells was expressed as relative activity compared to Kis one. Data were analyzed with GraphPad Prism v8 (GraphPad software, Inc) and displayed as mean ± S. E. M. Transcriptional analysis by RT-qPCR. Neuronal cells were dissociated from 10 emerging females mosquito heads. Neuronal suspension was aspirated to remove cell debris and centrifuged at 13,000 g for 10 min. Pellets were stored at -80 °C until use. Total RNA were extracted from frozen pellets with Nucleospin ® RNA Plus kit (Macherey Nagel) and eluted in 60 µL of RNase-and DNase-free water. The total mRNA was precipitated with glycogen, sodium acetate and ethanol at -80 °C overnight.
Larval mortality was corrected by the Abbott formula to take into account control mortality (<10%), and data were analyzed by the log-probit method of Finney with the R software using the R script BioRssay  [77] [78] [79] [80]  The programmed script uses the iterative method of maximum likelihood to fit a linear regression between the logarithm of concentration and the probit of mortality. The goodness-of-fit was estimated by a weighted chi-squared test. It also estimates the lethal concentrations and the slope of the regression lines with their confidence intervals. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Acknowledgements S. P. was supported by a doctoral fellowship of the  Direction Générale de l'Armement, l'Agence de l'Innovation de Défense (Ministère des Armées)  and the  Région Pays de la Loire (France). We thank the Vectopole platform (IRD, Montpellier),  B. Scheid  for providing technical support and  C. Ginibre  for the rearing and maintenance of the mosquito populations). The Vectopole is a platform of the ' Vectopole Sud' Network  and is part of the  LabEx CeMEB  ( ANR-10-LABX-04-01 ).
Animals, Phenotypes, and 50K Genotypes Details on phenotypes and 50K-genotype quality checks are described in  Teissier et al. (2018). The milk performance data set was provided by the French national milk records system and the udder traits records by the breeding company Capgenes (Mignaloux-Beauvoir, France). Phenotypes for milk production were considered over the whole lactation period: 250-d milk yield (MY, in kg), 250-d protein and fat yields (PY and FY, respectively, both in kg), and 250-d somatic cell score (LSCS). The type traits were fore udder (FU), teat orientation (TO), udder floor position (UFP), udder profile (UP), and rear udder attachment (RUA). Type traits were only measured once per animal, mainly during their first lactation and sometimes in their second. Phenotypes, pedigree data, and genotypes were obtained from the official genetic evaluation of January 2016  (Larroque et al.
, 2011). Phenotype data were retained only for French Saanen goats born between 1980 and 2017. The final data set is described for each trait in Table  1. The pedigree consisted of 2,177,617 individuals and was complemented by defining unknown parent groups. Sixteen groups were defined according to the year of birth of the descendants  : before 1975, between 1975 and 1980, between 1980 and 1983 , and then every 2 years. Males and females were pooled together in unknown parent groups because there were few animals with unknown dams. Genotypes were acquired with the Illumina Goat-SNP50 BeadChip. A total of 1,207 genotypes (394 males, 813 females) were retained for French Saanen goats after the quality check step. The quality check step was previously described in  Talouarn et al. (2020). It implies removing all individuals with a call rate below 95% or showing pedigree inconsistency.
Sequence-Derived Information Quality Check of Sequence Data and Imputation. Sequence data are described in  Talouarn et al. (2020). The sequence data were retrieved from the Var-Goats project (http: / / www. goatgenome. org/ vargoats. html). The 37 French Saanen individuals came from VarGoats child projects PRJEB37276, PRJEB37276, and NextGen PRJEB5900. The final data set comprised 33 French Saanen goats (31 males and 2 females); 4 individuals were removed as their mean coverage was below 5. All of these were also genotyped with the Illumina GoatSNP50 BeadChip. A wide QTL region was previously identified in Saanen goats on CHI19 between 24. 72 and 28. 38 Mb  (Martin et al. , 2018; Mucha et al. , 2018; Talouarn et al. , 2020). This region is associated with production traits (milk, fat, and protein yields), stature, udder type and health, as well as semen volume.
Sequence quality check and soft filtering processes were described in detail by  Talouarn et al. (2020)  and resulted in keeping  23,337,436 variants, including 539,476 variants on chromosome 19 and 22,269 variants between 24. 72 and 28. 38  Mb. Before imputing available 50K genotypes, imputation was necessary to fill in the gaps of the sequenced panel. A combination of AlphaImpute (v. 1. 9; Hickey et al. , 2012)  and  FImpute (v. 3. 0; Sargolzaei et al. , 2014 ) was used because it gave higher concordance rates between 50K genotypes and sequence than using only one software and minimized computation time. The mean concordance rate between 50K-genotypes and sequence data, calculated on the 33 sequenced Saanen goats, was 98. 43% (±1. 35) after filtering and imputation. No missing genotypes remained for sequenced Saanen after these steps.
The 178 positions were extracted from imputed sequence data to quantify the effect of the increased representation of chromosome 19 (+178 variants) on the accuracy of genomic evaluations. To determine whether gains in precision were only due to the increased SNP density, another 178 variants were also randomly selected on chromosome 19 using PLINK software  (Purcell et al. , 2007). Among them, only 11 were located in the QTL region of chromosome 19.
'. , i m i i p p where m is the number of variants, p i is the estimated allele frequency at the locus i, and M is a centered matrix of variant genotypes. For udder traits, which were measured only once in the life of goats, the genomic evaluation model did not include a permanent environmental effect, and was as follows: y = Xβ + Zu + e, [1b] where y, u, and e are the same as previously stated; β includes 3 combined fixed effects: herd × parity × year, age at scoring × year, and days in milk at scoring × year. Variance components were estimated by using the restricted maximum likelihood method in the remlf90 software  (Misztal et al. , 2002)  and ssGBLUP analyses were performed with the blup90iod2 software  (Misztal et al. , 2002). Weighted Single-Step GBLUP. The same model was used to perform WssGBLUP.
The WssGBLUP was applied to each trait studied using blupf90 family software  (Misztal et al. , 2002). The SNP effects and SNP weights were estimated using postGSf90 software  (Misztal et al. , 2002). Our objective was to take advantage of the sequence information for chromosome 19 to increase the accuracy of genomic evaluations for the traits related to the QTL region. The WssGBLUP is based on iterative ssGBLUP in which weights for SNP variances are used to form the genomic relationship matrix G. This method is used to give more weight to causal mutations, variants that are in high LD with a causal mutation or variants within a QTL region with a relatively large effect. The genomic relationship matrix G was built differently. The solutions for genomic breeding values from ssGBLUP can be decomposed into SNP effects as follows  (Wang et al.
Tested Scenarios Several scenarios were tested, based on either ssGB-LUP or WssGBLUP methods, and using 4 different sets of new markers in addition to the Illumina GoatSNP50 BeadChip (Table  2 ). We wanted to see how we could expect the next version of the Illumina GoatSNP50 BeadChip to perform if used for the genomic evaluations (geno_50kv2QTL). As mentioned previously, the 178 additional variants selected in the QTL region of chromosome 19 and imputed in our data set were extracted for that purpose. To assess the relevance of the results, another scenario consisted of including 178 randomly selected variants located over the whole length of chromosome 19 (geno_50kv2_random). In scenarios 3 and 4, all imputed variants from the sequence on chromosome 19 (n = 539,476) (geno_50kseqCHI19) or only those in the QTL region  (22, 269; geno_50kseqQTL)  were chosen, respectively.
Weighted Single-Step GBLUP Our objective using WssGBLUP was to compare the integration of sequence data for the QTL region of chromosome 19, i. e. , geno_50kseqQTL (22,269 sequence variants in the QTL region) or geno_50kv2QTL (178 SNP in the QTL region) with a simple evaluation using 50K genotypes (geno_50k). This was performed using a standard WssGBLUP model. We also attempted to use windows of consecutive markers (WssGBLUP windows ). The highest weight in the window was assigned to all the SNP in the same window. Because the average spacing in the QTL region was smaller than when solely using 50K genotypes for all 4 approaches, we implemented windows of 2. 4 Mb which is the average distance covering 40 SNP with the Illumina GoatSNP50 BeadChip. This window size has proven to be efficient in previous studies using 50K genotypes  (Teissier et al.
Accuracy of Genomic Predictions The reference population used to assess the accuracy of genomic evaluation comprised only genotyped males, even if the genotypes of females were also used in ss-GBLUP and WssGBLUP evaluations. This reference population was split into 2 subsets: a training set and a validation set. The training population consisted of 248 sires born between 1998 and 2007 and genotyped with the Illumina GoatSNP50 BeadChip. All the information for these animals (genotype, pedigree with their ancestry and progeny, and phenotypes of their progeny) was retained in the data sets to estimate the GEBV. The validation population consisted of 146 bucks born between 2008 and 2012. All of their progeny with phenotypes were removed from the data set. The GEBV estimated in these conditions and the daughter yield deviation (DYD) computed from the official genetic evaluation of January 2016, were compared for the 146 animals in the validation set.
The DYD were the average performance values for the daughters corrected for fixed and random environmental effects and half of the merit of their dams. The DYD were weighted by effective daughter contributions as described by  Van-Raden and Wiggans (1991). The accuracy of genomic predictions was assessed by calculating the Pearson correlation between the GEBV estimated with each model and DYD. To compare the Pearson correlations obtained with the different scenarios and methods, we used the Hotelling-Williams test as implemented in the multilevel R package  (Williams, 1959).
Variant Selection As a result of the efforts of the VarGoats Consortium, a large amount of sequence data are now available to the research community. However, sequence data comprise vast numbers of variants  (23, 337, 436  for the whole genome after the filtering process). Careful variant selection is therefore crucial to avoid burdening genomic evaluations with too much information. In this study, we assessed various options for selecting variants. We are aware that the imputation quality is lower than in other species which might lead to a deg-  radation of predictions as suggested by  Perez-Enciso et al. (2015). However, the genomic predictions were improved in our study. Further investigations will be needed once the update of the current chip will be validated to confirm the improvement we observed with more reliable genotypes.
It therefore seems likely that sequencing more individuals could further improve evaluations by lifting imputation errors and refining the QTL region. Nevertheless, it is important to bear in mind that imputation has to be performed to get sequence information for every genotyped individual. This is a time-consuming process and imputed sequences represent a large amount of data that require significant storage capacities. In our study, we also show that the future version of the Illumina GoatSNP50 BeadChip is promising if the additional variants selected in the QTL region (178 SNP variants) are added for routine genomic evaluations. Indeed, the mean gain of accuracy was 4. 9% with ssGBLUP and ranged from -0. 1% (TO and LSCS; not significant) to 10. 1% (FY). This updated chip has an increased density within the QTL region of chromosome 19 with 308 SNP (130 SNP from version 1 + 178 new selected variants) located between 23 and 30 Mb with an expected average spacing of 22,675 bp (ranging from 4 to 166,413 bp).
This might explain why the selected variants tend to introduce noise in the evaluations for this particular trait, and adding weights to the variants within the region might have amplified the phenomenon. Nevertheless, we reasonably question whether the ratio between the gain in accuracy and the time spent preparing the data (quality control, imputation, data formatting for software) is significant enough to implement the procedure on a routine basis. In the WssGBLUP windows models, regardless of the method used to build the windows, the QTL information tended to be smoothed (Figure  4 ). All weighted scenarios using sequence information from the QTL region (geno_50kseqQTL) led to significant decreases in the accuracy of genomic evaluations for both type traits (except for UFP and RUA) and LSCS compared with simple ssGBLUP on 50K genotypes.