dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar to Model 1, including α i means that the covariation between OTUs is in terms of species composition only. By drawing the sample effects α i from a normal distribution with a mean that is a linear function of both nonphylogenetic μ(non-phylo) and phylogenetic μ(phylo) host effects (Equation  5 ), we account for the hierarchical structure present in the data. Furthermore, from the loading matrix λ, we computed a covariance matrix as Ω = λλ ⊤ , which we subsequently converted to a correlation matrix for studying the OTU-to-OTU cooccurrence network. For both case studies, we used Markov Chain Monte Carlo (MCMC) to estimate the models via JAGS  (Plummer, 2003)  and the runjags package  (Denwood, 2016)  in R (R Core Team 2016). For each model, we ran one chain with dispersed initial values for 300,000 iterations saving every 10th sample and discarding the first 25% of samples as burn-in.
By doing so, we have also demonstrated how latent factors, both alone or together with measured covariates, can be used for variance partitioning and further visualized as ordinations and co-occurrence networks. At last, depending on the modelled response function, we have illustrated that the variance partitioning of the hierarchy present on the rows can be represented in terms of either relative abundance or species richness. We adapted our proposed model to make use of two published data sets on host-associated microbiota. Although our goal was not to compare the results from these two case studies, such a systematic comparison can be made using a model-based approach like ours. In a broad manner, the data analysed here suggest that markedly different processes are shaping the microbiota harboured by these different host organisms.
Acknowledgements We thank  Dr. Robert W. Thacker  and  Dr. Sarah Hird  for sharing their data sets on the microbiota associated with marine sponges and Neotropical bird species, respectively. We further thank three anonymous reviewers for providing constructive feedback and comments. J. R. B was supported by an  FPI Fellowship  from the  Spanish Government  ( BES-2011-049043 ). J. M. M. was supported by the  French LabEx TULIP  ( ANR-10-LABX-41 ;  ANR-11-IDEX-002-02 ), by the  Region Midi-Pyrenees project  ( CNRS 121090 ) and by the FRAGCLIM.
The sensor heater power consumption was maintained to 53 mW as an optimal working temperature (T = 300 °C). The studied gas concentration was calibrated and delivered by using an autonomous mass flow system. The sensor response was defined as R = Ra/Rg, where Ra is the sensor resistance in air at stationary state and Rg represents the sensor resistance after 1 min of the gas-mixture exposure. It is worth noting that all the mathematical modeling was performed using Star CCM+ software.
m. mellifera  19. These properties make SNPs a powerful tool for testing the breeding stock in A. m. mellifera conservatories and SNP data can be readily incorporated in shared genetic databases, facilitating implementation of a conservation strategy at the European scale. Whilst SNP analysis on whole genome (WG) sequence data may be required in studies concerned with fine-scale relatedness, such deep sequencing is disproportionate when determining introgression levels for the discrimination of A. m. mellifera breeding stocks. Also, while costs have dropped dramatically, WG sequencing is still unaffordable for most stakeholders committed to the long-term sustainability and conservation of honeybees. Costs are accrued as WG analysis requires considerable computing storage and processing power and trained bioinformatics personnel.
Results Assay design, quality control and genotyping accuracy. Of the 144 highly-informative SNPs selected by Muñoz, et al. 20  , the Assay Design software was able to multiplex 127 into four assays (identified by letter M), each containing a variable number of SNPs ranging from 38 in M1 to 24 in M4 (Supplementary Table  S1 ). A total of 573 samples (Fig. 1 ) were genotyped for the four customized assays using the iPLEX MassARRAY system. Of the 573 samples, only seven displayed a SNP call failure rate >30% and these were excluded from further analysis (Supplementary Table  S2 ). Of the 566 remaining samples, 551 displayed a low percentage (<10%) of missing data indicating a high genotyping success rate (96%). The quality control and assessment of the genotyping accuracy of the 127 SNPs (Supplementary Table  S3 ) led to identification of 10 problematic SNPs, of which seven were typed in <80% of the individuals, three were called heterozygous for >10% of the haploid individuals (Supplementary Tables  S1  and S3 ), and three exhibited inconsistent calls among the three genotyping technologies in >5% of the individuals (Fig.
2 ). The latter SNPs were also identified as having high rates of missing data or heterozygosity (Supplementary Table  S3 ). Once the 10 SNPs were removed from the datasets, the rates of missing data of the remaining 117 SNPs were low with 113 having <10% and four varying between 10. 4% and 15. 5% (Supplementary Table  S1 ). The genotypes generated for the 117 high-quality SNPs in the MassARRAY platform were highly concordant with those of the Illumina's platforms (99. 9% for the BeadArray and 99. 6% for the HiSeq. 2500). Following the quality control step, 339 of the 573 genotyped samples had no missing data and the highest rate of missing data was 29% but only in two samples (Supplementary Table  S2 ). The final multiplexes contained M1 = 34, M2 = 32, M3 = 28, and M4 = 23 SNPs distributed across the 16 honeybee linkage groups, LGs (Fig.
980 ≤ r ≤ 0. 983) with those inferred from the WG dataset (Table  1 ). The largest plex assay M1 (34 SNPs) and     the smallest M4 (23 SNPs) showed the best and the worst behaviour, respectively, as indicated by most statistics (Table  1 ). The best performance was achieved when the four assays (117 SNPs) were used together (r = 0. 996; mean accuracy = 97. 84%; absolute precision error = 0. 033), although the combination of M1 + M3 (62 SNPs) and M1 + M2 + M3 (94 SNPs) with the highest individual correlations produced equally interesting statistics with mean accuracies >96. 9%, absolute precision error <0. 04, and with over 28 individuals (out of 32) with absolute accuracy error <0. 05. Performance was also assessed by counting purebred A. m. mellifera individuals misclassified as admixed (Q-values > 0.
000), Scotland (0. 006 ± 0. 011) and Netherlands (0. 046 ± 0. 141) with over 80% of the individuals showing a Q-value < 0. 05, although most individuals (91%) of the unprotected populations of Ireland were also very pure (0. 021 ± 0. 022). Populations of Denmark, France and Switzerland exhibited greater Q-values (0. 148 ≤ Q-value ≤ 0. 280) in both protected and unprotected populations with ≤11% of pure individuals. Admixture proportions estimated for A. m. ligustica and A. m. carnica sampled from native and introduced ranges showed that they are very pure (0. 972 ≤ Q-value ≤ 1. 000), excepting for some Swiss colonies (0. 750 ± 0. 296). The commercial breed Buckfast was mostly of C-derived ancestry (0. 806 ± 0. 055). The genotype data were further examined by network analysis. The correlation network graph shown in Fig.
m. mellifera. In addition, we provide the dataset with the genotypes for haploid and diploid individuals of A. m. mellifera, A. m. carnica and A. m. ligustica, which can be used by others in introgression analysis as baseline reference populations with no need for inter-laboratory calibration  18. As opposed to microsatellites, merging of SNP databases is straightforward as there are only two alleles per locus and different platforms will provide the same allele calls. If needed, curation will only involve SNP conversion from different platforms to be on the same DNA strand, which is much simpler than trying to harmonize different microsatellite allele sizes genotyped in different We show that C-lineage introgression can be accurately estimated from haploid, diploid, and combined haploid and diploid datasets (see Supplementary Information for details).
These findings indicate that honeybee conservation managers can choose the software of their preference and, more importantly, can simultaneously analyse workers and drones without biasing estimates of C-lineage introgression in A. m. mellifera colonies. The Assay Design software was able to combine only 127 of the 144 high-graded SNPs 20 into four multiplexes. While the iPLEX protocol allows multiplexing up to 40 SNPs, only assay M1 (38 SNPs) approached the maximum plexing capacity. This is in part due to the relatively small size of the baseline SNP set from which the Assay Design had to work. However, the plex level of each assay can be expanded any time. By using the Replex option of the software, additional high-graded nuclear SNPs or even mitochondrial SNPs can be added to the customized four assays for detecting C-derived genes at both genetic compartments.
Methods Assay design. Muñoz, et al. 20  identified 144 highly informative SNPs for estimating C-lineage introgression in A. m. mellifera. The flanking regions (60 bp of either side) of these SNPs were used to design multiplexed assays with the software Assay Design 4. 0 (Agena BioScience TM ) for genotyping using the Agena BioScience iPLEX chemistry and the MassARRAY ® MALDI-TOF platform (hereafter abbreviated to iPLEX MassARRAY). The software searched for optimal areas within the 120-bp flanking regions to design forward and reverse PCR primers while constructing the different multiplexes. The maximum multiplexing capacity (40 SNPs) allowed by the iPLEX chemistry was attempted whilst preventing hairpin and dimer formation. In addition to the PCR primers, the software designed the iPLEX extension primer placed immediately adjacent to each SNP.
m. carnica and seven A. m. ligustica, previously genotyped using the GoldenGate ® Assay in the BeadArray platform of Illumina 13 , were added to the dataset to have a better representation of C-lineage. Genomic DNA was extracted from the head, antennae, thorax (entire or ~half), legs, or abdomen of adults or immatures (larvae or pupae) of a single individual, multiple individuals (extracted, then pooled), or a pool of individuals (mixed tissue, then extracted) per colony in 561 samples (Supplementary Table  S2 ). The extraction methods included phenol-chloroform, CTAB, commercial kits (Qiagen EZ1 DNA tissue kit, Omega bio-tek EZNA kit), and magnetic beads using the KingFisher ™ Flex Purification System. These represent the wide array of tissues and extraction methods commonly used in honeybee research  27.
SNP genotyping and quality control. A total of 573 samples (561 plus 12 DNA pools, Supplementary Table  S2 ) were genotyped for the 127 SNP loci multiplexed in the four assays using the iPLEX chemistry and the MassARRAY ® MALDI-TOF genotyping platform 39. The genotypes generated for the 573 samples (Supplementary Table  S11 ) were subjected to quality control filters to discard SNP loci and samples with poor or inconsistent amplification. SNPs and samples with missing data >20% (Supplementary Table  S1 ) and >30% (Supplementary Table  S2 ), respectively, were excluded from the dataset (Supplementary Table  S3 ). frequent allele, and (iv) the least frequent allele. The sensitivity of the genotyping system in detecting C-lineage ancestry in the different pools was also assessed via introgression analysis.
m. mellifera; Supplementary Table  S2 ). Additionally, a subset of four colonies (two A. m. mellifera, one A. m. carnica, and one Buckfast) from Scotland and England was represented by both a pool of 16 workers and one individual worker. For a better C-lineage representation, nine samples of A. m. carnica and 7 of A. m. ligustica (each representing a single individual and colony), previously genotyped using the GoldenGate ® Assay  13  , were added to the dataset. Q-values were inferred from the genotypes of single and pooled samples using ADMIXTURE. The genotype data were further examined by network analysis using the software Graphia Professional (Kajeka Ltd, Edinburgh, UK). For each sample, SNPs were scored 0 when same as reference (A. m. carnica), 1 for heterozygous and 2 for homozygous different to reference, i.
Acknowledgements We are deeply indebted to  João Costa  ( Instituto Gulbenkian Ciência, Oeiras, Portugal ), for designing the multiplexes and SNP genotyping. José Rufino  provided computational resources at the  Polytechnic Institute of Bragança, Portugal. DH was supported by a PhD scholarship ( SFRH/BD/84195/2012 ) from the  Portuguese Science Foundation (FCT). KAB receives a PhD fellowship from the  Irish Research Council. MP was supported by the  Swiss Federal Office for Agriculture FOAG  and the  Fondation Sur-la-Croix , Basel. IM was supported by  Saavedra Fajardo  fellowship from the  Fundación Séneca  ( 20036/SF/16 ). MAP is a member of and receives support from the  COST Action FA1307  (SUPER-B). Funding for genotyping of Irish honeybees was gratefully received from the  Eva Crane Trust , the  Native Irish Honeybee Society  and the  Department of Agriculture, Food  and the  Marine  ( 16/GR/09 ).
MB and TCF are funded by an  Institute Strategic Grant  from the  Biotechnology and Biological Sciences Research Council (BBSRC)  ( BB/JO1446X/1 ). Financial support for this research was provided to MAP and LG by 2013-2014  BiodivERsA/FACCE-JPI  joint call for research proposals, with the national funders FCT (Portugal), " Agence Nationale de la Recherche" (France) , and  "Ministério de Economia y Competividade" (Spain).
, 1980). The A6 was carefully desheathed to facilitate penetration of bathapplied drugs. The recording electrodes were connected to the input of high-impedance amplifier, whose outputs were passed to a numeric oscilloscope (PCSGU250, Velleman instruments, France) and a computer connected with Minidigidata 1B (Axon instruments, USA). Variation of postsynaptic polarization was monitored on a chart recorder and the EPSPs were evoked by electrical stimulations of the ipsilateral cercal nerve XI using a dual pulse stimulator (Campden 915, USA). The mixture was applied during 3 min under the same conditions as previously published  (Buckingham et al. , 1997; Thany, 2009) , with a micropump fast perfusion (Harvard Apparatus) that produced a constant solution exchange (500 μL/min). For pretreatment, all compounds were bath-applied for at least 10 min before a single application of the mixture.
| Numerical analyses Prior all statistical analyses, a ln(y + 1) transformation of microbial biomass was applied  (Jongman, ter Braak, & van Tongeren, 1995). The effects of water level treatments and sampling depth on each individual microbial group, phenoloxidase activity, and PPMR in both sites were tested with ANOVAs followed by post hoc analyses (Tukey's multiple comparisons of means test). Assumptions for normality and homoscedasticity of the data were previously tested. The effects of water level treatments (Sphagnum moisture, arcsine transformed), depth, phenoloxidase, pH, and phenols on microbial assemblages (log-transformed) in each site were assessed using redundancy analyses (RDA). The proportion of variance explained by explanatory variables was calculated using variance partitioning.
The interactions between Sphagnum moisture and depth (Sphagnum segments) were also tested. The significance of the model and of each explanatory variable included in the model was tested using 999 permutations. The response of individual microbial groups to shifts in moisture conditions in both peatlands (merged data sets) was assessed using linear regressions. Assumptions for normality and homoscedasticity of the data were previously tested. We used the Indicator Value (IndVal) approach to quantify to what extent microbial groups are sensitive to changes in Sphagnum water content (i. e. , wet or dry conditions; the two datasets were merged)  (Dufrêne & Legendre, 1997). IndVal scores were calculated using the TITAN method  (Baker & King, 2010)  with permutation tests to assess the uncertainty in these scores.
Then, the permuted IndVal scores were standardized (z scores) by subtracting the mean of randomized permutations from the observed IndVal, and dividing by its permuted SD. High z scores indicated high biomass of the microbial group when Sphagnum moisture is high, and low values indicate the opposite. We used multiple (linear) regression to test linkages between phenoloxidase (dependent variable) and PPMR, pH, polyphenols (independent variables), which showed no significant effects of neither pH nor polyphenols. We made a new corrected model with phenoloxidase (dependent variable) and PPMR (independent variable). All statistical and multivariate analyses were performed in RStudio (RStudio Team 2012) using R version 3. 1. 2 (R Core Team 2012) using vegan package  (Oksanen et al. , 2013). S1 ).
| PPMR, phenoloxidase activity, and watersoluble phenolic content In Forbonnet, the PPMR decreased between MW and MD by 37% (p <. 05) in upper segments but remained stable in lower segments (p =. 60; Figure  5a ). In Linje, similar tendencies were found with a decrease of PPMR in upper segments between W and ED treatments (-35%, p <. 01) (Figure  5b ). In lower segments, PPMR remained stable (Figure  5b ), as in Forbonnet peatland. We further found significant relationship between PPMR and Sphagnum moisture content (r =. 48, p <. 001, Figure  5c ) for the pooled data sets of Forbonnet and Linje peatlands. The phenoloxidase activity in Forbonnet peatland was higher in the drier conditions than in the wetter ones, but this increase was stronger in upper segments (+60%, p <. 01) than in lower segments (+30%, p =.
This research was supported by grants from Switzerland through the Swiss Contribution to the enlarged European Union (Project CLIMPEAT: PSPB-013/2010), the National Science Centre, Poland (grants: No. NN306060940, 2015/19/N/NZ8/00172), and the French National Agency for Research (ANR Program "Vulnerability: Environment-Climate" (ANR-07-VUL-010), project PEATWARM: Effect of moderate warming on the functioning of Sphagnum peatlands and their function as carbon sink). The authors would like to thank Łukasz Lamentowicz and Richard J. Payne for their help during the fieldwork and Matthieu Bueche for technical assistance during the collection of flow cytometry data. Narodowe Centrum Nauki, Grant/Award Number: 2015/17/B/ST10/01656.
Funding information Narodowe Centrum Nauki , Grant/Award Number:  2015/19/N/NZ8/00172  and  NN306060940 ;  French National Agency for Research , Grant/Award Number:  ANR-07-VUL-010 ; Switzerland through the  Swiss Contribution  to the enlarged  European Union , Grant/Award Number:  PSPB-013/2010 ;  Narodowe Centrum Nauki , Grant/Award Number:  2015/17/B/ ST10/01656.
0 Hz, were processed using a Data Sciences analog converter and routed to an analog-to-digital (AD) converter (Eagle PC30, Data Sciences International, St. Paul, MN, USA), which digitized EEG and activity signals. Subsequently data were transferred to a computer and graphically displayed. An on-line fast Fourier transformation  (FFT)  program was used to analyze EEG data and generate power density values from 0. 0 to 20. 0 Hz at a resolution of 0. 5 Hz. FFT data were further averaged between 0 to 20 Hz at 10-s intervals. Sleep data and FFT results were saved to hard disk every 10 s for additional off-line analysis. Number of animal movements related to telemetry receiver generated transistortransistor logic pulses were viewed as measures of activity. Data were gathered on the 1st and 3rd days after α S1 -CH treatment and percentage power densities were calculated.
Determination of sleep behaviors using EEG signals Times elapsed in wakefulness, NREM sleep, or REM sleep was determined using digitized data using animal sleep analysis software Sleep-Sign 2. 1 (Kissei Comtec, Matsumoto, Japan). Briefly, this software identifies wakefulness as highfrequency, low-amplitude EEG, NREM sleep as spikes interspersed with slow waves, and REM sleep as δ-waves (0. 75 to 4. 0 Hz) with θ-wave activity (5. 0 to 9. 0 Hz) of peak frequency 7. 5 Hz.
, 2017). We investigated whether the protein expressions of the GABA A receptor subunits α 1 , β 1 , and γ 3 were modulated in the hypothalami of rats treated with α S1 -CH. Treatment using α S1 -CH at 150 mg/kg or 300 mg/kg increased the protein expression of β 1 , but the protein expression level of α 1 and glutamic acid decarboxylase (GAD 65/67 ; catalyzes the formation of GABA in neuronal tissues) were unaltered (Fig. 5 ). Although the level of γ 3 tended to be elevated by α S1 -CH treatment, this was not statistically significant. Wakefulness, total sleep, REM sleep, and NREM sleep were determined after administering α s1 -CH (150, 300 mg/kg) orally to rats once daily for 3 days. Sleeping was determined using telemetric cortical EEG records and analyzed using Sleep Sign2. 1 software. To compare each group versus naïve control, we used the Newman-Keuls test.
RNA extraction and RT-qPCR Total RNA was extracted using Isol-RNA lysis reagent (5Prime) and treated with RNase-free DNAse (Euromedex, Souffelweyersheim, France) to avoid genomic DNA contamination. Total RNA was reversetranscribed using the iScript cDNA Synthesis Kit (Bio-Rad, Hercules, USA). The qPCR reactions were performed on a MyIQ instrument (Bio-Rad, Hercules, USA) using the iQ SYBR Green Supermix (Bio-Rad, Hercules, USA). Each reaction was performed in triplicate, and all results were normalized to tubulin and rp-49 mRNA levels; the relative amount of mRNAs were calculated using the ΔΔCt method. All error bars represent SEMs. REST software was used to compare the qPCR sets of data. Asterisks indicate statistical significance (*p < 0. 05, **p < 0. 01, ***p < 0. 001)  (Pfaffl et al. , 2002).
Protein crystallization, data collection and structure resolution GSTD2 was used at 7 mg ml -1 prior to crystallization trials. Crystallization assays were performed using the hanging-drop vapor diffusion method setup in grease-sealed 24 well Linbro plates (Hampton research, USA). The Linbro wells were filled with 500 μl of crystallization screening solution. Crystals between 200 and 300 μm appeared after 1 week in 0. 1 M succinic acid-phosphate-glycine (SPG) buffer with 25% PEG 1500 at pH 4. 0 from the PACT premier commercial screen (Molecular Dimensions, United Kingdom). The crystal was mounted on a LithoLoop (Molecular Dimensions, United Kingdom), cryoprotected and flash-frozen in liquid nitrogen. X-ray diffraction intensities were collected on the ID-29 beamline  (de Sanctis et al. , 2012)  at the European Synchrotron Radiation Facility (ESRF, Grenoble, France).
Diffraction data were collected from 1800 images; individual frames consisted of 0. 1°steps. X-ray diffraction data were integrated, scaled and merged using the XDS software program suite  (Kabsch, 2010). Molecular replacement was performed with a truncated form of D. melanogaster GSTD1 as a model (PDB ID: 3EIN) with deletions in the Nterminus and in some loops  (1-56, 79-87, 142-154). Two molecules per asymmetric unit were correctly placed by  Phaser (McCoy et al. , 2007). When possible, reconstruction of missing areas was performed using Arp/warp classic  (Langer et al. , 2008) , and deleted amino acids (in the N-and C-terminus regions) were manually placed using coot software  (Emsley and Cowtan, 2004 ) (Table  S1 ).