dataesr/scientific-paragraphs-categorization huggingface dataset:
Parameters were set at 300°C for ion transfer capillary temperature and 3400 V for needle voltages. Nebulization with nitrogen sheath gas and auxiliary gas were maintained at 40 and 5 arbitrary units, respectively. The spectra were acquired within the m/z mass range of 95-1200 atomic mass units (a. m. u. ), using a resolution of 50 000 at m/z 200 a. m. u. The system was calibrated internally using dibutyl phthalate as lock mass (m/z 279. 1591), giving a mass accuracy below 1 ppm. Peak integration was performed through the XCALIBUR software and, for compounds of interest, integration was checked manually before validation. In most samples, the ion current corresponding to (E )-8-carboxylinalool was complex, suggesting that in addition to its free form, it was also accumulated as multiple glycosylated derivatives, according to their mass and fragmentation patterns.
Acknowledgements We thank  Gis ele Butterlin  and  Anne Alais  ( INRA, Colmar, France ) for help with grape berry sampling and RNA preparation,  Nathalie Jaegli  ( INRA, Colmar, France ) for grape terpene extractions,  Gr egory Lemarquis  ( INRA, Colmar, France ) for providing Gewurztraminer wine samples, and  Benoit Boachon  for background information and technical support. The authors acknowledge the support of the  Agence Nationale de la Recherche  to the  InteGrape  project ( ANR-13-BSV6-0010 ). The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's 7 th  Framework Programme  (FP7/2007-2013) to T. I. under REA Grant Agreement 289217. This paper reflects only the author's views and the  European Union  is not liable for any use that may be made of the information contained herein.
Acknowledgements We would like to thank  Pierre Charneau  and  Nicolas Escriou  for providing the  293T/T7  cells, and  Albin Fontaine , who wrote the R scripts for PD 50 calculations. We thank  Jean Balanant  for technical assistance and  Florence Colbère-Garapin  for assistance with the writing of the manuscript. This work was funded by the  Institut Pasteur  ( PTR 276  and  PTR 484 ).
Plant material, surface sterilisation and culture conditions C. hirta seeds were harvested in French Guyana (GPS coordinates: N04 33ʹ598ʺ; WO 52 12ʹ428ʺ). They were surface-sterilised for 20 min in a diluted commercial bleach solution (2% chlorine) with occasional shaking and finally washed three times (5 min) in sterile-distilled water. Seeds were then placed on solid culture media in square boxes (Duchefa). All the cultures were maintained at 25 C under a 16-h photoperiod (30 mmol m À2 s À1 photosynthetically active radiation). Subculture was performed and/or data were collected after 40 days of culture. To obtain the dry weight, plantlets were lyophilised.
HPLC analysis Separation was performed on a Water liquid chromatography system (a Water 600 pump, a Water 600 controller with a degasser, a Water 2998 Photodiode Array (PDA) detector and Empower 3 Water software for processing) with the temperature set at 35 C, using an ACE 5 AQ C-18 column (250 Â 4. 6 mm and 5 mm internal diameter) and detection set at 215 nm, 280 nm and 340 nm. Separation was achieved using a linear gradient composed of formic acid-acidified (0. 02% v/v) HPLC-grade methanol (A) and water (B) in a mixture from 10:90 to 100:0 (A:B) for 70 min at a flow rate of 0. 6 ml min À1.
Funding was provided by the  Global Environment Fund ,  United Nations Environment Program ,  United Nations Food and Agriculture Organization  ( GEF/UNEP/FAO ) Global Pollination Project, with additional support to the  Food and Agriculture Organization  of the  United Nations  from the  Norwegian Environment Agency  for a project on " Building Capacity in the Science-Policy Interface of Pollination Services ," and from the  International Fund for Agricultural Development  for the development of the sampling protocol (18). Other funding:  Conselho Nacional de Desenvolvimento Científico e Tecnológico Brazil, CONICET Argentina  ( PIP 114-201101-00201 ),  Norwegian Environment Agency  ( 2012/16642 ),  The Research Council of Norway  ( 225019 ), and  Universidad Nacional de Río Negro Argentina  ( PI 40-B-259 ,  PI 40-B-399 ).
1c ). Therefore, this strategy is better suited for sex chromosomes that have either a low or intermediate level of divergence. However, it will still provide X copies of sex-linked genes in old systems, as long as appropriate SNPs are present in the dataset (fig. 1c ). Crosses can be obtained in any organism that can be grown in controlled conditions and are a common resource because they are needed for building genetic maps. Hundreds of sex-linked genes were identified this way in species with unavailable fully sequenced genomes such as Silene latifolia  (Bergero and Charlesworth 2011; Chibalina and Filatov 2011)  or in species without any genomic resource such as Rumex hastatulus  (Hough et al. 2014 ) and Rumex acetosa  (Michalovova et al. 2015). Although this RNA-seq cross-based strategy is very promising for studying sex chromosomes in non-model organisms, the existing approaches have a number of limitations due to the fact that inference of sex-linkage was done with empirical filters and without a statistical framework.
Once RNA-seq reads have been mapped to a reference transcriptome, individuals need to be genotyped in order to study allele segregation in the cross (fig. 1 ). Genotyping was either done by filtering the number of reads at each locus with fixed thresholds  (Bergero and Charlesworth 2011; Chibalina and Filatov 2011)  or using genotypers designed for DNA-seq data  (Hough et al. 2014; Michalovova et al. 2015). This is problematic in the case of sex-linked genes where the Y allele is frequently less expressed than the X (reviewed in  Bachtrog 2013)  and can be confounded with a sequencing error in RNA-seq data. Read number thresholds determined empirically for a given dataset can be sub-optimal for another dataset with different sequencing depth. In many cases, progeny individuals were pooled separately for each sex for sequencing in order to lower costs  (Bergero and Charlesworth 2011; Chibalina and Filatov 2011; Michalovova et al.
2015). However, this makes it impossible to differentiate between all individuals of the pool or only a few being heterozygous, a criterion that is crucial to disentangle sex-linked genes from autosomal ones (fig. 1 ). Finally, sex-linked genes were filtered for having more than a given number of sex-linked SNPs  (Chibalina and Filatov 2011; Hough et al. 2014) , and for not having any autosomal SNPs  (Bergero and Charlesworth 2011; Michalovova et al. 2015 ). These arbitrary filters clearly limit the application of this strategy to only specific datasets and probably prevent the detection of many true sex-linked genes. Also, a method allowing the study of UV systems is currently lacking. Here, we propose a probabilistic method called SEX-DETector that solves the caveats of previous RNA-seq crossbased approaches and works on any sex chromosome type (XY, ZW, and UV).
Bayesian Information Criterion Test for the Presence of Sex Chromosomes The Maximum Likelihood framework of the method allows the use of a model selection strategy to assess the presence of sex-linked genes in the dataset. A model M with the three possible segregation types can be compared with a model with only autosomal segregation type using the Bayesian information criterion (BIC), defined such that BICðMÞ ¼ À2log Lðy M Þ þ y M log n Where BICðMÞ is the BIC value of model M; Lðy M Þ is the likelihood of the model, y M is the number of free parameters of the model and n is the sample size. The model with the lower BIC value is chosen. It is also possible to test for a X/Y versus a Z/W system by comparing both BIC values. In case a model with sex chromosomes fits best the data but no sexlinked genes are inferred, then it means there are no sex chromosomes in the dataset.
Samples were treated additionally with QiagenDNase. RNA quality was assessed with an Aligent Bioanalyzer (RIN larger than 9) and quantity with an Invitrogen Qubit. An intron-spanning PCR product was checked on an agarose gel to exclude the possibility of genomic DNA contamination. Then, the two extractions of the same individual were pooled. Individuals were tagged and then pooled for sequencing. Samples were sequenced by GATC, Konstanz, Germany on an Illumina HiSeq2000 following an Illumina paired-end protocol (fragment lengths 150-250bp, 100 bp sequenced from each end). A normalized 454 library was generated for S. latifolia using bud extracts from four different developmental stages. The RNA-seq data used in this study is available under the European Nucleotide Archive accession number PRJEB14171  (Zemp et al.
Assembly Adaptors, low quality and identical reads were removed. The transcriptome was then assembled using Trinity  (Haas et al. 2013)  on the combined 10 individuals described previously as well as the six individuals from  (Muyle et al. 2012 ) and the normalized 454 sequencing that was transformed to illumina using 454-to-illumina-transformed-reads (because Trinity cannot take 454 reads as input). Then, isoforms were collapsed using /trinity-plugins/rsem-1. 2. 0/rsem-preparereference. PolyA tails and ribosomal RNAs were removed using ribopicker. ORFs were predicted with Trinity's transcripts_to_best_scoring_ORFs. pl (this step is facultative and SEX-DETector can work on coding or non-coding sequences). In order to increase the probability of assembling X and Y sequences in the same contig, ORFs were further assembled using CAP3 (cap3 -p 70, Version 15 October 2007, Huang and Madan 1999) inside of Trinity components (in Trinity, components can group contigs that are alternative transcripts of the same gene or paralogs).
Mapping, Genotyping, and Segregation Inference Illumina reads from the 10 individuals of the cross were mapped onto the assembly using BWA (version 0. 6. 2, bwa aln -n 5 and bwa sample;  Li and Durbin 2009). The libraries were then merged using SAMTOOLS (Version 0. 1. 18; Li et al. 2009). The obtained alignments were locally realigned using IndelRealigner (GATK)  (McKenna et al. 2010; DePristo et al. 2011 ) and were analyzed using Reads2snp (Version 2. 0, -fis 0 -model M2 -output_genotype best -multi_alleles acc -min_ coverage 3 -par false)  (Tsagkogeorga et al. 2012 ) in order to genotype individuals at each loci while allowing for biases in allele expression and not cleaning for paralogous SNPs as X/Y SNPs tend to be filtered out by paraclean (the program that removes paralogous positions)  (Gayral et al.
Simulations Simulated genotypes were used in order to test the effect of various parameters on the sensitivity and specificity of SEX-DETector. Sequences were first simulated for two parents (or a single parent in the case of a UV system) using the program ms to generate a coalescent tree  (Hudson 2002 ; see supplementary figure S1, Supplementary Material online and then the program seq-gen to generate sequences using the ms tree and molecular evolution parameters (version 1. 3. 2x, seq-gen -mHKY -l contig_length -f 0. 26 0. 21 0. 23 0. 3 -t 2 -s theta)  (Rambaut and Grassly 1997). Different types of sequences were generated: either autosomal (ms 4 1 -T) or X/Y (ms 4 1 -T -I 2 3 1 -n 2 0. 25 -n 1 0. 75 -ej XY_divergence_time 2 1 -eN XY_divergence_time 1) or X-hemizygous (same parameters as X/Y but no Y sequence drawn) or U/V (ms 2 1 -T -I 2 1 1 -n 2 0.
latifolia data  (Ka ¨fer et al. 2013). The rate of genotyping error (e) was set to 0. 01 and the rate of Y genotyping error (p 2het ) was set to 0. 13 as inferred by SEX-DETector on the observed S. latifolia data. Five types of datasets were simulated, with ten repetitions for each set of parameters and 10,000 contigs simulated for each dataset:. Effect of X-Y divergence: Five different X-Y divergence times in units of 4N e generations were tested, either S. latifolia X-Y divergence time (4. 5 My) or 10 times or 100 times older or younger. The proportion of X-hemizygous contigs among sex-linked contigs was set accordingly to X-Y divergence time: 0. 002, 0. 02, 0. 2, 0. 6, and 1 for 45,000 years, 450,000 years, 4. 5 My, 45 My, and 450 My divergence time, respectively. As well as the proportion of Y genotyping error (because Y expression is known to decrease with X-Y divergence): 0, 0.
Sex chromosome size was set to 10% and X-Y/Z-W divergence to 4. 5 My. Effect of the number of individuals in U/V systems: Eight different numbers of offspring individuals of each sex were tested for the U/V system: 1, 2, 3, 4, 5, 6, 7, or 8 individuals of each sex. Sex chromosome size was set to 10% and U-V divergence to 4. 5 My. For each simulated dataset, segregation types were inferred using SEX-DETector and were compared with the true segregation types in order to compute sensitivity and specificity values.
Testing Our Pipeline's Performance Using a Silene latifolia Dataset The SEX-DETector pipeline (fig. 2 ) was run on a cross dataset sequenced by RNA-seq in the plant Silene latifolia. This dioecious species was interesting for benchmarking our method/ pipeline as its sex chromosomes are well known: they are relatively recent (~5 My old)  (Rautenberg et al. 2010 ) but old enough to be clearly heteromorphic (the X is 400 Mb and the Y is 550 Mb)  (Matsunaga et al. 1994 ). X-Y synonymous divergence ranges from 5% to 25%  (Bergero et al. 2007) , S. latifolia thus represents a system of intermediate age. Also, a tester set of 209 genes for which segregation type has been established is available in this species (supplementary table  S2 , Supplementary Material online). The dataset that was used here consists of a cross (two parents and four offspring of each sex).
We used our tester set to measure the performance of our pipeline, i. e. , estimate its sensitivity (the capacity to detect true sex-linked genes) and specificity (the capacity not to assign autosomal genes as sex-linked, see "Materials and Methods" section). About 83% of the known sex-linked genes expressed in the RNA-seq data used here (i. e. flower bud) were detected, indicating a high sensitivity. We obtained a specificity of 99% for this dataset as one gene, OxRZn, was supposedly wrongly assigned as a sex-linked gene by SEX-DETector. However, this gene was earlier assessed as autosomal based on the absence of male specific alleles  (Marais et al. 2011 ) and SEX-DETector assigned it to a sex-linked category because out of the four SNPs detected in this gene, all where inferred to be X-hemizygous, and all were without genotyping error.
Progeny genotypes were obtained by random segregation of alleles from the parents and a genotyping error layer was added (see "Materials and Methods" section). About 10,000 contigs were simulated for each dataset. SEX-DETector was run on every dataset (see supplementary table  S5 , Supplementary Material online for details on the inferences). In order to know how many offspring of each sex should be sequenced to achieve the best sensitivity and specificity tradeoff using SEX-DETector, we varied the number of progeny individuals in the simulations. For an X/Y or Z/W system, optimal results were obtained when sequencing five progeny individuals of each sex (fig. 6A ); sequencing more progeny individuals did not improve the results further. This suggests that sequencing 12 individuals (two parents and five progeny individuals of each sex) may be sufficient to achieve optimal performances with SEX-DETector on an X/Y or Z/W system.
SEX-DETector Identifies Unknown Sex Chromosomes Using Model Selection It is common that the sex determination system is unknown in species with separated sexes, i. e. , it is unknown whether they have sex chromosomes and if they do, what the system is (Z/W or X/Y). The likelihood-based framework of SEX-DETector allows us to test for these assumptions by comparing the model fit to the data using the Bayesian Information Criterion (BIC, see "Materials and Methods" section). In species for which sex determination is unknown, it is possible to compare models with and without sex chromosomes, and, if sex chromosomes are detected, it is possible to compare models with X/Y or Z/W system. This model selection strategy was tested on empirical and simulated data. In the S. latifolia dataset, the best model inferred by SEX-DETector was a model with sex chromosomes as expected, with 1357 sex-linked contigs (which represents 9% of the contigs with a posterior probability higher than 0.
8). In the S. vulgaris data set (a species without sex chromosomes), no sex-linked contigs were inferred, the best model fit to the data was thus a model without sex chromosomes as expected (see "Materials and Methods" section). In order to know from which proportion of sex-linked genes sex chromosomes can be detected, we compared models on simulated data with varying numbers of sexlinked contigs out of 10,000 simulated contigs (table 2 and supplementary table S6, Supplementary Material online). When no sex-linked contigs were simulated, as expected the best model was the one without sex chromosomes. This was also the case when a single sex-linked contig was simulated. In this case, SEX-DETector could not detect it due to lack of information in the dataset. When ten or more sex-linked contigs were simulated, the best model was the one with sex chromosomes as expected.
This was done for the 332 inferred X-hemizygous genes in the S. latifolia dataset, and only five of them had a significant match with a male-specific contig. This suggests that very few true X/Y gene pairs were wrongly inferred as X-hemizygous due to a too divergent Y. In S. latifolia, X-Y synonymous divergence ranges from 5% to 25%  (Bergero et al. 2007). This is comparable to regions in human sex chromosomes that stopped recombining last: the mean X-Y synonymous divergence for strata 3, 4, and 5 in humans is respectively 30%, 10%, and 5%  (Skaletsky et al. 2003). SEX-DETector will therefore perform best in species with sex chromosomes of young or intermediate age, but will also work on recent strata of old systems. A complete list of cases where inferences could be difficult (e. g. , presence of pseudoautosomal genes, X chromosome inactivation and imprinting) along with possible solutions is provided in the online SEX-DETector user manual (p.
Model comparison using SEX-DETector on empirical datasets in Silene latifolia (with sex chromosomes) and S. vulgaris (without sex chromosomes) and simulated X/Y datasets with varying number of sex-linked contigs out of 10,000 simulated contigs. The best model is chosen as the one having the lowest BIC value (see "Materials and Methods" section and supplementary table S6, Supplementary Material online for details).
Acknowledgments We thank  Alex Widmer  for access to the RNA-seq data sets and comments on the manuscript,  Nicolas Galtier  and  Sylvain Glemin  ( ISEM-Montpellier ) for useful discussions about Reads2snp,  Vincent Miele  ( LBBE ) for SEX-DETector profiling and advice on code performance,  Khalid Belkhir  ( ISEM-Montpellier ) for providing and adapting Galaxy wrappers for analyses used upstream of SEX-DETector (BWA,  Reads2snp ) and  Philippe Veber  ( LBBE ) for help with Galaxy. We thank the editor and three anonymous referees for their helpful comments. This work was supported by  Agence Nationale de la Recherche  grants to GABM (grants numbers:  ANR-11-BSV7-013 ,  ANR-11-BSV7-024 ;  ANR-14-CE19-0021 ) and  SNF  project to  Alex Widmer  ( SNF 31003A_141260 ).
Classification of mite taxa into the abovementioned groups was based on a database available at Alterra, established based on taxonomic information in combination with the broad range of ecological literature about feeding habits (e. g. ,  Bhattacharyya, 1962; Hartenstein, 1962; Karg, 1962; Ito, 1971; Luxton, 1972). For the Lusignan LTO, we tested for differences in both biomass and taxon richness per feeding guild using linear mixed models in SPSS version 22 (IBM corp. ), with land use treatment as categorical fixed variable and block as random variable. With respect to taxon richness, we only analyzed differences for the six main feeding guilds, since a further differentiation resulted in too low numbers of taxa per group to produce valid statistics. In case of significant overall differences, LCD post-hoc tests were used to test for pairwise differences between treatments.
Because we were mainly interested in the development of the mite community following a transition from arable land to grassland, and to reduce the number of pairwise comparisons, we focused on the differences between the fields that were converted to grassland but had different historic land use up to 11 years before our sampling event (AAG and GAG; see Section Site Description for exact code descriptions), and the difference between each of those and the permanent arable fields (AAG-AAA and GAG-AAA). For the Veluwe LTO experiment, effects of time since the transition to grassland on biomass and taxon richness were also tested using linear mixed models in SPSS, using time since transition as a continuous fixed variable and site as random variable. For this experiment, biomass data per feeding guild were log transformed prior to all analyses to comply with assumptions for normality.
We tested separate models for the total community and each of the feeding guilds (again restricting to the six main guilds with respect to taxon richness). In some cases where no significant linear relation was found, mite biomass or taxon richness seemed to converge to a certain maximum value. Therefore, in such cases we also tested an inverse regression model (y = b 0 + b 1 / x) and an S-curve regression model (ln(y) = b 0 + b 1 / x) via the curve estimation option in SPSS. Differences in taxonomic composition among the nine fields of the Veluwe experiment, as well as among the fields with different land use treatments at Lusignan, were analyzed with the help of principle component analysis (PCA) in  PCOrd (v. 6. 0; McCune and Mefford, 1999) , based on presence/absence scores per taxon per site or field.
FUNDING This research was funded by the  European Commission  through the  FP7 project EcoFINDERS (Ecological Function  and Biodiversity Indicators in European Soils; www. ecofinders. eu;grant number  FP7-264465 ) and by the strategic research program KBIV "Sustainable spatial development of ecosystems, landscapes, seas and regions" which is funded by the  Dutch Ministry of Economic Affairs  and carried out by  Wageningen University and Research Centre.
other is often reduced or lost  (Volkov et al. , 1999; Kovarik et al. , 2005; Doyle et al. , 2008; Weiss-Schneeweiss et al. , 2012; Mahelka et al. , 2013; and reviewed by Volkov et al. , 2007; Buggs et al. , 2012; Weiss-Schneeweiss et al. , 2013). Homogenization may be mediated by different genetic events including locus loss, reduction of copies or replacement with newly amplified ones  (Nieto Feliner and Rossell o, 2012). Theoretical models suggest that non-homologous recombination and gene conversion are the mechanisms driving rDNA homogenization processes  (Zimmer et al. , 1980; Dover, 1982). The genus Brassica is known to harbour large variability in the number of rDNA loci, ranging between two and five per haploid set  (Maluszynska and Heslop-Harrison, 1993; Hasterok et al. , 2006;  PlantrDNAdatabase -http://www.
Plant material Oilseed rape cultivars were chosen according to the genetic diversity of the species with mainly ssp. oleifera cultivars but also two accessions of ssp. rapifera ('Rutabagas 22' and 'Rutabaga 95') and one of ssp. pabularia ('Asparagus kale'). Fourteen spring B. napus cultivars ('Asparagus kale', 'Brutor', 'Loras', 'Nachan', 'Norin 1', 'Norin 6', 'Norin 9', 'Norin 10', 'Oro',  'Spok', 'Stellar', 'Taichung', 'Yudal' and 'Westar')  and seven winter cultivars  ('Darmor', 'Maxol', 'Mohican', 'Petranova', 'Rutabaga 22', 'Rutabaga 95' and 'Tapidor')  were used for genetic and expression analysis of rDNA (Supplementary Data Table  S1 ). As controls of presumed diploid progenitors, we used Z1, a doubled haploid line of B. rapa provided by AAFC, Canada, and HDEM, a doubled haploid line of B.