dataesr/scientific-paragraphs-categorization huggingface dataset:
A functional analysis was performed on the selected transcripts by time point, in order to highlight significant functional groups according to the Gene Ontology (GO) process terms using the GeneCodis program with the FDR method at a p value cutoff of 0. 05  [54]. For each time point, a multivariate factorial analysis (MFA) was also performed to obtain an overview of the dataset, which consisted in 433 variables measured for 4 strains (S288c, VL1, cachaça, sake). The data set included a group of individuals described by two types of variables: the normalized expression of the 400 transcripts selected by the sPLA-DA according to the 4 strains, and the 33 volatile compounds produced during the fermentation by the 4 strains. The MFA takes into account the structure of the two groups of data and balances the influence of each group of variables.
Acknowledgements The authors would like to thank all the researchers that kindly provided yeast strains:  Gianni Liti ,  Institute of Genetics UK ,  Rogelio Brandão ,  Laboratório de Fisologia e Bioquı ́mica de Microorganismos Brazil. Authors would like to thank also to  Pierre Delobel ,  Christian Picou  and  Jean-Roch Mouret  that kindly help in the microarrays and fermentations experiments.
Funding Inês Mendes is recipient of a fellowship from the  Portuguese Science Foundation ,  FCT  ( SFRH/BD/74798/2010 ). This work was supported by  FCT  through grant ( PTDC/AGR-ALI/121062/2010 ) and by the strategic programme  UID/BIA/04050/2013  ( POCI-01-0145-FEDER-007569 ) funded by national funds through the FCT I. P. and by the  ERDF  through the  COMPETE2020 -Programa Operacional Competitividade e Internacionalização (POCI).
The LS depolymerisation was monitored by means of size-exclusion chromatography (SEC) and reverse-phase chromatography (RPC). Chromatographic methods and a homemade program were developed to monitor the reaction. The LS used for this work showed a high dispersity of the fragments' molecular weights. The range was very wide, from 10 6 Da to small molecules, the latter of which would be probably produced during the sulfite pulping. The calculated average mass and number molecular weights were respectively M w = 90,357 Da and M n = 16,172 Da. The polydispersity index (PDI) was about 5. 6.
The mean pore size is around 20 nm as given by the constructor. We also placed, before the first column, a guard column TSKGEL G2500PW-GMPW (L × ID = 7. 5 cm × 7. 5 mm, 13 µm particle size) to prevent degradation of the columns. During the SEC analysis, ultrapure water at pH 12 (fixed by the addition of NaOH) was used as eluent. The flowrate was fixed at 0. 6 mL/min and a 280 nm UV detection was used to get the signal. The analysis duration was 60 min. The signal was digitalized at a frequency of 1 Hz. Ten polystyrene sulfonates with average molecular weight going from 970,000 Da down to 896 Da were used as calibrants; in addition, vanillin (152 Da) was added to the set of calibrants to locate monomer elution volume. As we do not have commercial software for the SEC results treatment, we developed a homemade program in the MATLAB language.
First, the program loads and reads the calibrant files, locates the maximum of the signal for each calibrant and searches for the corresponding elution volume. Then, it plots the calibration curve (M w vs. elution volume). A fourth-order polynomial describes it well (R 2 > 0. 99). The equation is determined by a nonlinear least-squares fitting method. Afterwards, the program loads and reads the files corresponding to the different LS depolymerisation experiments. The start and end of the elution curve are determined using a signal derivative method. A linear background subtraction is afterwards performed between these two limits. The calibration equation is then used to calculate the MWD of the lignin. The mean molecular weights in mass M w and in number M n are then calculated as follows: M w = ∑ n i=1 h i M i ∑ n i=1 h i , M n = ∑ n i=1 h i ∑ n i=1 h i M i , where h i and M i are, respectively, the signal value and molecular weight at the index "i".
The eluent flow rate was fixed at 0. 6 mL/min and the column temperature was regulated at 50 • C. UV detection at 280 nm was used to identify and quantify these molecules. Firstly, solutions of the different molecules were prepared and injected to identify their retention times. Checking that the resolution was good for the eight molecules, the method was validated and 4 concentration-level calibration curves were constructed for the concentration determination in LS depolymerisation solutions. As for the SEC analysis, homemade software was developed in the MATLAB R2017a (MathWorks, Natick, MA, USA) language for the processing of the chromatographic data. The first part of the program is dedicated to the calibration curves. For each monomer, the program loads and reads the 280 nm UV signal obtained for the different calibration solution.
Then, the different peaks are identified using a method based on the signal derivative to identify peak start and end. Afterwards, a linear background subtraction is performed between the two peak limits, and the integral area of the peak is calculated using the trapezoidal rule for the numerical peak integration. Calibration curves of area versus concentration are hence obtained. A linear dependence between these two variables was observed for all the monomers. The calibration equations were determined by linear least-squares fitting. The second part of the program is dedicated to the LS depolymerisation solutions, for which the program performs the same tasks and calculates additionally the corresponding unknown concentrations for each of the identified monomer peaks for which the elution time is known.
We also recorded the average percent leaf area across two leaves that was covered by chlorotic symptoms characteristic of B/CYDVs infection (i. e. , yellowing and reddening) 19 dpi. Tissue chemistry data, i. e. , phosphorus (P), nitrogen (N), and carbon (C) content, were obtained based on above-ground leaf tissue collected 41 dpi. For each sample, leaf tissue was oven dried at 65 • C for 48 h and then ground using a Beadbeater (Biospec). For N and C content, 3 mg of dry and ground tissue from each sample was weighed in a tin capsule. N and C absolute quantification was performed using an elemental (C:H:N) analyzer. For P tissue content analysis, two replicates of 1-1. 5 mg each were prepared in tin capsules. P quantification was performed using a sulfuric acid digestion protocol. A standard curve was constructed from data obtained from samples of apple NIST standard of various weights (from 0 to 6.
For each virus species, transmission rate was expressed as the proportion of infected recipient plants per source plant. Trait data (i. e. , raw values of all traits except for virus induced symptoms) of all experimental plants were analyzed using a principal component analysis (PCA) from the FactoMineR package. All plant trait variables were scaled to unit variance prior to analysis. Correlations between plant trait variables and PCA dimensions were determined based on the contribution of each variable to each dimension and based on the results (i. e. , correlation coefficient and significance test) given by the dimdesc function. The principal components (i. e. , orthogonal dimensions, N = 5) that altogether explained 80% of the variance included in the initial data set were retained for further analysis.
To assess the effect of fertilization and infection on plant traits, we analyzed the effects of host plant N and P supply rate and single-vs. co-infection on coordinates of individual plants on each of the five retained PCA dimension using model averaging  (Grueber et al. , 2011). This approach allowed us to take into account that explanatory variables could be covarying and that there could more than one pertinent model. All the variables were standardized prior to analysis using the standardize function in the arm R package. We used the dredge function in the MuMIn R package to fit all possible models. We estimated parameter values, errors, and AIC-weighted importance using the model. avg function in the MuMIn R package. The "Relative Importance" of each explanatory variable was estimated based on the relativized sum of the Akaike weights summed across all of the models in which the parameter appears that are within four AIC C (i.
ACKNOWLEDGMENTS We thank  Missy Rudeen ,  Kurra Renner ,  Abdulrahman Gamam ,  Amy Kendig ,  Aaron David , and  Eric Lind  as well as undergraduate and graduate students for help in the lab. We also thank  Marty Dekkers  ( University of North Carolina ) and  Benham E. L. Lockhart  ( University of Minnesota ) for sharing expertise and advice on methods for virus detection and transmission. We are grateful to  Stewart Gray  and  Dawn M Smith  ( Cornell University ), and to  Dimitri Mollov  ( Plant Disease Clinic, University of Minnesota ) for sharing and facilitating the reception of BYDV-PAV and CYDV-RPV virus isolates. We are also grateful to  Georgiana May ,  Robert W. Sterner , and  James B. Cotner  for sharing the access to the qPCR, CHN analyser and Varian spectrophotometer piece of equipment.
Results Codling moth putative PRs and Orco. The complete open reading frames (ORFs) for CpomOR1 (JN836674. 1), CpomOR3 (KJ420588), and CpomOrco (JN836672) were sequenced in prior studies  10, 25. For CpomOR6a, a 306 bp sequence was identified in the codling moth antennal transcriptome  10. We performed 5′ RACE-PCR to complete the ORF of 1248 bp (updated to Genbank; accession number JN836671). Then, we compared the ORF sequence with the updated transcript variants found by Walker et al. (2016)   11  , and determined that this sequence corresponds to CpomOR6a. Analysing the translated protein, seven transmembrane domains with an intracellular localization for the N-terminus were predicted for CpomOR6a, CpomOR3, CpomOR1 and CpomOrco (Fig. 1 ), reflecting the typical topology of insect ORs  27.
5, a1-a2 ). Subsequent experiments were used to calculate an EC50 of 51. 84 ± 13. 21 μM for codlemone acetate (Fig. 5, a2 ); however, the amplitude at saturating concentrations (18. 91 ± 10. 31, dF) was ~27% of the positive control amplitude (69. 71 ± 27. 29, dF; Fig. 5, a1 ). Interestingly, compared to the positive control, we observed a long lasting codlemone acetate activation of transfected HEK293T cells, which led to a delayed recovery after stimulation (Fig. 5, a1 ). Once we determined the response of CpomOR6a towards codlemone acetate using the HEK293T cell system, in parallel series of experiments we tested activation of CpomOR6a expressed in Drosophila at1 OSNs to a new panel of ligands, including codlemone, pear ester, codlemone acetate and structurally related compounds (Supplementary Dataset S2).
While we did not observe any response to codlemone with either (Supplementary Dataset S2, S3 and Supplementary Fig. S4 ), we did find that CpomOR6a interacts with codlemone acetate and its isomers (Fig. 5 ), pheromone components found in both C. pomonella and related species  22, 38, 39. The lower amplitude of the CpomOrco + OR6a responses to codlemone acetates may suggest that these compounds are partial agonists of the receptor. Another possible explanation is that only approximately 30% of receptors expressed represent heteromeric complexes sensitive to their cognate ligand/s with the majority of the receptors being homomeric (sensitive exclusivily to common agonists, VUAAs). It remains necessary to determine whether a different ratio of OR/Orco expression would yield a higher ratio of heteromeric/homomeric OR complexes and thus different amplitudes in the response.
The identification of a potential PR agonist is one of the first successful associations between an insect PR and a likely pheromone ligand using HEK293T-based heterologous expression  [45] [46] [47]. Among insect ORs, PRs have been especially difficult to functionally characterize  48, 49  , as illustrated even by our own experiments, where CpomOR1 does not produce any response to pheromones, synergists and their combination (Table  1  and Supplementary Dataset S3). These results highlight the importance of developing alternative testing methods. Overall, our findings suggest that the potential codling moth pheromone receptors exhibit biophysical and pharmacological properties surprisingly similar to insect ORs when expressed heterologously: CpomOrco-CpomOR complexes are capable of functioning as ionotropic receptor channels and can be characterized by relatively low conductance and fast gating parameters; CpomOrco-CpomOR complexes are activated by the group of common compounds, VUAAs, and can be efficiently blocked by amiloride derivatives.
The nucleotide sequence was converted to amino acids using the ExPASy translate tool (http://web. expasy. org/translate/), after which transmembrane domains were predicted using TMHMM v2. 0 (http://www. cbs. dtu. dk/services/TMHMM/) and TOPCONS (http://topcons. cbr. su. se/)  50. The Topology of the transmembrane protein was visualized using TOPO 2. 0 (http:// www. sacs. ucsf. edu/cgi-bin/open-topo2. py). The completed sequence of OR6a has been updated in Genbank, JN836671  11.
Cloning of olfactory receptors for heterologous expression in HEK293T cells. In order to produce amplicons suitable for cloning into pDONR221 (Invitrogen Life technologies, Grand Island, NY, USA), we inserted attB regions suitable for BP-clonase-recombination upstream of the CDS primer sequences (attB1 forward region: 5′-GGGGACAAGTTTGTACAAAAAAGCAGGCTTAACA-3′; attB2 reverse region: 5′-GGGGACCACTTTGTACAAGAAAGCTGGGT-3′, Gateway Technology, Invitrogen). CDS primers (Table  2 ) were designed to amplify full-length CpomOR sequences (Genbank database accession numbers, CpomOrco: JN836672; CpomOR6a: JN836671; CpomOR3: KJ420588; CpomOR1: JN836674. 1). For forward primers, a NotI restriction site (5′-GCGGCCGC-3′) followed by the HEK-cell optimized 5′-CACC-3′ Kozak sequence (Dr. Jacob Corcoran, personal communication) and the gene-specific forward sequence, were located downstream of the attB1 sequence.
The stimulating contour washing the cells (~250 μL/min) was switched rapidly to the stimulus contour using a multi-channel rapid solution changer (RSC-160, Bio-Logic, Claix, France) under the software control of Clampex 9 (Molecular Devices, Sunnyvale, CA, USA). Fluorescence imaging was performed using Imaging Workbench 6 software (INDEC BioSystems, Santa Clara, CA, USA). Each cell was assigned a region of interest (ROI) and changes in fluorescence intensity within each ROI were measured and expressed as the fractional change in fluorescence intensity (dF) or in some cases as dF/F0 where F0 is the basal fluorescence level before agonist application. Stored time series image stacks were analysed off-line using Imaging Workbench 6, Clampfit 10. 5 (Molecular Devices), SigmaPlot 11 (Systat Software Inc.
Dose Physical parameters were obtained from Scifinder (Scifinder, 2015; Chemical Abstracts Service: Columbus, OH, 2015; accessed Jan-Nov 2015) and Chemspider (http://www. chemspider. com/). Compounds were diluted in either DMSO or ethanol (Sigma Aldrich) depending on their solubility properties. Each compound was tested at 100 μM (10 s stimulus duration). VUAA1 100 μM was used as a positive control in all cases (stimulus duration 5. 0 s). Responses to codlemone acetate (for CpomOR6a) and pear and methyl esters (for CpomOR3) were normalized to the 250 μM VUAA3 evoked calcium signal response amplitude. Following modification, the Hill equation, ( Electrophysiology and data analysis. GFP positive HEK293T cells were visualized using either an Axiovert 100 inverted microscope (Carl Zeiss, Inc. , München, Germany) equipped with a mercury vapour compressed-arc lamp (HBO100) coupled to widefield fluorescence filter set (1114-459, Carl Zeiss, Inc.
) or Olympus IX-71 inverted microscope described above (Olympus Corp. ). The OR channel activity was investigated using whole-cell and outside-out patch-clamp recordings. The currents were measured with a 200B patch-clamp amplifier (Molecular Devices) and a digital interface (Digidata 1320 A, Molecular Devices), low pass filtered at 5. 0 kHz, sampled at 1-2 kHz. Analysis of the data was carried out using pCLAMP 9. 2/10. 5 software (Molecular Devices) and SigmaPlot 10 (Systat Software Inc). OR channel related currents were investigated at a holding potential of -50/+50 mV unless otherwise specified. The polarity of the currents/voltages was presented relative to intracellular membrane surface. Appropriate corrections for liquid junction potentials were made when necessary. Patch pipettes were fabricated from borosilicate capillary glass (BF150-86-10, Sutter Instrument, CA, USA) using a Flaming-Brown micropipette puller (P-87, Sutter Instrument).
Signals coming from the olfactory sensory neurons were amplified 10 times with a probe (INR-02, Syntech, Hilversum, the Netherlands), digitally converted through an IDAC-4-USB (Syntech) interface, and visualized and analysed with the software Autospike v. 3. 4 (Syntech). A constant flow of 0. 65 m/s of humidified air (charcoal-filtered) was delivered through a glass tube to the antenna. The panel of odorants was given to the insect by inserting pipettes containing a piece of filter paper with the correspondent stimulus in a lateral hole of the glass tube and puffing a flow of 2. 5 mL of air during 0. 5 s through the pipette. For CpomOR1 and CpomOR6a the panel of odorants was prepared by applying 10 μL of a solution of 1. 0 μg/μL of the compounds in Supplementary Dataset S2 and Supplementary Dataset S3, for a total amount of 10 μg per stimulus.
Acknowledgements We thank  Dr. Barry W. Ache ,  Dr. Mark Martindale ,  Dr. Kirill Ukhanov ,  Dr. Barbara Battelle  and  Dr. Daniel Dixon  ( all from Whitney Laboratory for Marine Bioscience, University of Florida ) for stimulating discussion, excellent technical assistance and general support. We thank  Prof. Angela Bassoli  and  Dr. Gigliola Borgonovo  for.
-10 soils (PS1-PS10), from fields pastured by bovines for at least 60 years, were sampled in the Lake Aydat catchment (Massif Central, France) in order to describe the fecal molecular distribution in grazing areas and their transfer to soils. -A total of 46 samples were retrieved from two lacustrine sediment cores to reconstruct past and recent land uses at a catchment scale. Two catchments with distinct anthropogenic contexts were selected: (i) Lake Montcineyre catchment (Massif Central, France) located twenty kilometers away from Aydat (Fig. 1 ). Archaeological and historical data attest toagropastoral activities since the Middle Ages  (Charbonnier, 1999; Bayard, 2010 and references therein). However, despite the presence of archaeological settlements in the catchment since the Neolithic, agropastoral activities and their impact on the local ecosystems remain poorly understood at catchment scale  (Chassiot, 2015; Chassiot et al.
Aydat catchment soils In soil samples retrieved from lake Aydat catchment (Massif Central, France), previous studies based on fecal biomarkers evidenced recent husbandry practices  (Lavrieux et al. , 2012; Zocatelli et al. , 2012). Here, this was achieved by re-exploiting the dataset of fecal biomarker distribution and comparing it to the reference samples. The distribution of sterols and bile acids was quite similar over soil samples (Table  2 , Fig. 4 ), with TC varying between 0. 56 and 26. 9mg/g soil, and with 8-37% of coprostanols (ethCp and Cp; Table  2 ). Samples also contained significant amounts of LC and DOC (0. 08-0. 36 and 0. 84-4. 25 µg/g). No HDOC, which is specific of pig feces, was detected. Associated to the distribution of sterols, the identification of DOC and the absence of C and CDOC argue for a fecal input from cows to the soil, consistent with current husbandry in the catchment.
, 2007; Madsen, 2014). In the sediments of Lake Igaliku, a maximum of Sr2 at 26 is recorded and evidences herbivorous livestock grazing around Lake Igaliku; low Cp contents and the absence of CDOC, C and UDOC indicate no significant human fecal contribution while a similar fecal biomarker imprint was identified during the last century. High contents of ethCp and the presence of DOC alone suggest that Norse livestock was mainly composed of sheep, as confirmed by archaeozoological data  (McGovern, 1983; Edvardsson et al. , 2007; Madsen, 2014). Considering the climatic and environmental conditions of south Greenland (positive temperatures only seven months a year and difficulties to cultivate fodders;  Austrheim et al. , 2008; Cappelen, 2014) , as sheep are more resistant to cold conditions and needed less fodder than cows, it would be logical that the Norse Greenlandic livestocks were mainly constituted of sheep, as nowadays.
Acknowledgments The work received financial support from the  OTARIE Project (Région Centre, European Council)  and the  ERODE  Project ( EC2CO ,  CNRS/INSU ). We thank theInstitut  National de la Recherche Agronomique (INRA)  for the reference samples, Pascal Joyeux (INRAP), and  Emmanuel Chapron  ( GEODE UMR 5602 CNRS,University of Toulouse ). We also thank the  French Polar Institute (IPEV)  and the  ANR CEP&S "Green Greenland "  project  ( ANR-10-CELP-0008 ) and particularly  Vincent Bichet ,  Emilie Gauthier ,  Hervé Richard  ( Chronoenvironnement UMR 6249,University Franche-Comté )and anonymous donors for sampling authorizations and/or providing some of the collection and reference samples. We would also like to thank the two anonymous reviewers for suggestions and comments which have helped to improve the quality of this manuscript.
Fungal identification A polyphasic approach was employed to identify the isolated strains. First, fungi were identified according to their macroscopic, microscopic and physiological features (S1 Fig) on the basis of specific taxonomical keys, following the indications provided from Dictionary of the Fungi  [18]  and from the Mycobank databases (http://www. mycobank. org/). Subsequently, molecular analyses were performed by sequencing specific genomic DNA regions.
it was necessary to amplify the Actin gene using primers ACT512F/ACT783R  [24]. For those strains identified as Penicillium spp. the βtubulin gene was amplified using the primer pair Bt2a/Bt2b  [25]. PCR products were purified and sequenced at Macrogen Europe (Amsterdam, The Netherlands). Consensus sequences were obtained by using Sequencer 5. 0 (Gene Code Corporation, http://www. genecodes. com). Taxonomic assignments were inferred by querying with the Blastn algorithm (default setting), hosted at NCBI (National Center for Biotechnology Information-http://www. ncbi. nlm. nih. gov) the newly generated sequences against the nucleotide database of NCBI (GenBank). Pairwise alignments were also performed at http://www. cbs. knaw. nl against the CBS-Knaw Fungal Biodiversity Centre (Centraalbureau voor Schimmelcultures) database.
Similarity values equal or higher than 98% (e-value > e -100 ) were considered credible and the results were confirmed morphologically. Sequences related to fungi isolated in this study were deposited at the NCBI database (GenBank accession no. KP671714-KP671750; KR014346-KR014380; KT313376 -KT313393; KT587307-KT587334; KU315005-KU315009; KX988016-KX988018; KY081460-KY081463; KY081637). When low sequence similarity (< 98%) did not allow genus and/or species determination, or when the strain remains sterile in pure culture, the taxonomic position was inferred through phylogenetic analysis. A full phylogenetic analysis was performed on LSU sequences, since comparable ITS and SSU sequences of fungi studied in this paper are rare in public databases and/or poorly informative. Four sequences datasets were properly composed following Suetrong et al.