dataesr/scientific-paragraphs-categorization huggingface dataset:
hysterophorus can persist either through recolonisation or the maintenance of seed banks  [57]. The more granular spatial resolution of the CORINE database is reflected in a larger set of habitat classes than the FAO dataset. Both of these factors make the CORINE database inherently less likely to create confusing interpretation problems with spatial intersections, as happened with the FAO dataset. However, the limitation usually lies in the spatial resolution of the location records for invasive alien species, rather than the habitat/ land use data. This is especially marked for species location data collected prior to the widespread availability of GPS units. Hence, it is unclear whether chasing a finer-scale, globallyconformal, land use/habitat type classification would result in a more accurate assessment of the non-climatic habitat risk factors.
Whilst the fine spatial resolution of the CORINE database may be highly valued for risk assessment in the EPPO region, the lack of conformal global coverage is clearly a drawback for estimating non-climatic habitat risk factors for invasive alien species that have little or no history in the risk assessment area. The large size of the CORINE database also created practical challenges for spatial analyses in geographical information systems, sometimes requiring the dataset to be split in two for spatial intersections. One option for pest risk analysts is to sacrifice some precision for potentially greater accuracy, employing the FAO method and dataset as we have demonstrated here. Another option is to use a hybrid two-phase method combining the insights gained through the FAO dataset analysis with expert opinion to select classes from the CORINE database.
Model limitations The CLIMEX model was fitted using the best available data and understanding available at the time of the analysis. However, we should be mindful that climate and distribution data are imperfect. The spatial resolution of the distribution data varied, and the estimated precision was not always reported. The mismatch between the resolution of the land use dataset and the species distribution data had the potential to pick up spurious habitat associations; hence we were careful to scrutinise low frequency associations. We should also be mindful that the CLI-MEX Compare Locations model is a simplification of the complex ecological processes that define a species niche. The land use classification in the FAO dataset and the identification of the irrigated areas will doubtless contain minor spatial and classification errors.
The mis-fitting points at the dry end of P. hysterophorus' range indicate a limit to the spatial precision in the global irrigated area database. However, despite these sources of potential errors, the analysis appears suitable for its intended purpose-to provide an indication of areas at risk of invasion should P. hysterophorus be introduced. Each of the mis-fitting points was in close spatial association with areas that were indicated as being suitable, and for which there were location records. This underscores the notion that the resulting maps should be used in aggregate to inform regional risk patterns, rather than being scrutinised at the level of an individual cell. In the extreme xeric and cold limits habitat suitability will be more subject to unusual micro-habitat variations that cannot be accounted for with global datasets and modelling.
Advancing pest risk modelling In this paper we applied two advances in pest risk modelling: spatially-explicit irrigation scenarios, and the inferential derivation of non-climatic habitat classes. Both methods are relatively easy to apply using a GIS with the freely available irrigation and land use datasets. The explicit irrigation scenario method allows the niche model to describe the species niche using biologically realistic parameters. In the absence of this method, the model would be unable to identify correctly the habitats at risk in xeric environments, either under-predicting (biologically realistic parameters), or over-predicting (using biologically unrealistic parameters that allow persistence in xeric environments). The inferential method of identifying suitable land use classes can clearly provide a degree of rigour to the downscaling process.
[41]l summary of composite invasion risk to Europe from Parthenium hysterophorus by habitat class according to the CORINE environmental database, considering climate with irrigation scenarios applied according to the GMIAV5 database[41]. Habitat classes are listed in descending order of area at risk under the current climate scenario. Land use is assumed to remain static under the future climate scenario. The cells where the Ecoclimatic Index is positive, indicating potential for persistent populations to establish. ‡ Compared with the baseline area at risk under historical climate.
SDS-PAGE. Proteins were separated on 10% polyacrylamide gels. Samples diluted (Vol/Vol, 4/1) in the Laemmli's sample buffer (2% SDS, 5% ßmercaptoethanol, 10% glycerol and 62 mM Tris-HCl, pH6. 8) were loaded with 60 μg proteins per lane. SDS-PAGE analyses were performed following the method described by  Laemmli (1970)  using Mini-Protean II electrophoresis unit (125 V for 2h). After the run, gels were stained overnight in Coomassie Brilliant Blue G-250 (PageBlue Protein Staining Solution, FERMENTAS). Image Analysis. Zymographic gels were scanned with an Image Scanner III using Image Master Platinum software (GE Healthcare, Uppsala, Sweden). Each liver sample was processed individually, and the value for main bands was calculated by using Image Master Platinum application. Each gel also included a pool of all samples run and was used as an internal standard.
Protease activity was considered as the combination of computerized measurements of both intensity and volume of main bands for each sample. Then, relative protease activity was estimated as protease activity/pool activity within each gel. According to the procedure described by  Marino et al. (2013)  the identification of proteins molecular weight was done by comparison with a known molecular weight standard (precision plus protein standard-broad range, Bio-Rad, Marnes la Coquette, France). Bands were analyzed using Image Studio Lite 3. 0 free online software (www. licor. com/islite) to determine the signal intensity (optical intensity) of the defined band. The relative quantity of each band was determined as signal intensity of the defined band/signal intensity of the pool. Band Identification by Mass Spectrometry.
Bands of interest were manually excised in our laboratory and analyzed by the spectrometry platform PEFM (Plate-Forme d'Exportation du Métabolism, INRA de Theix). Proteins were extracted, and Trypsin digested was analyzed by nano-LC-MS/MS coupled to an LTQ-Orbitrap mass spectrometer according to the method described by  Theron et al. (2011b). MS/MS spectra were processed by Mascot software against the Gallus gallus (SwissProt-TrEmbl) and Caïrina moschata-Anas platyrhynchos (NCBI) databases. Protein identification was validated when at least 3 peptides originating from one protein showed significant identification Mascot score (P-value of < 0. 005). Only proteins with a corresponding molecular weight and with at least 10 matching peptides were retained in this study.
Statistical Analysis For statistical analysis, the General Linear Model procedure of SAS (2011) software was used initially with a 2-way Anova to compare the effects of chilling rates and times post-mortem. When the interaction between chilling rates and times was not significant (P-value of >0. 05), independent 1-way Anovas were further performed. When appropriate, means were compared according to Student-Newman and Keuls' test. Correlation values were performed according to Pearson (Proc Corr, SAS 2011).
The cartridges were used at most one time on each fly and five times in total. Odorant stimulations were performed by inserting the tip of the pipette into a hole in the glass tube and generating a 500 ms air pulse (0. 6 L. min -1 ), which reached the permanent air flux while going through the stimulation cartridge. Action potentials were recorded from at1 sensilla using electrolytically sharpened tungsten electrodes (TW5-6, Science Products, Hofheim, Germany). The reference electrode was inserted into the eye and the recording electrode was inserted at the base of the sensillum using a motorcontrolled PatchStar micromanipulator (Scientifica, Uckfield, United Kingdom). The electrical signal was amplified using an EX-1 amplifier (Dagan Corporation, Minneapolis, MN, USA), high-pass (1 Hz) and low-pass (3 kHz) filtered and digitized (10 kHz) through a Digidata 1440A acquisition board (Molecular Devices, Sunnyvale, CA, USA) then recorded and analyzed using the pCLAMP ™ 10 software (Molecular Devices).
Phylogenetic Analysis An amino acid sequence dataset was created, including every full-length candidate PR sequence identified in Lepidoptera (see Supplementary Table  S2 ). Bombyx mori OR6 and Ctenopseustis obliquana OR22 were also included to serve as an external group. The 149 amino acid sequences were aligned using the online version of MAFFT v. 7  (Katoh and Standley, 2013) , with the G-INS-i algorithm  (Katoh et al. , 2005)  and default parameters. Phylogenetic reconstruction was performed using the maximum likelihood method. The JTT+I+G+F substitution model  (Jones et al. , 1992) , was determined as the best-fit model of protein evolution by ProtTest 2. 4  (Abascal et al. , 2005). Tree reconstruction was performed using PhyML 3. 0  (Guindon et al. , 2010) , with both SPR (Subtree Pruning and Regrafting) and NNI (Nearest Neighbor Interchange) methods for topology improvement.
To do so we used the software fastcodeml  (Valle et al. , 2014) , a recent program designed to reduce the calculation time of branch-site models developed in the codeml suite of programs (formerly defined in codeml using the parameters model = 2 and NSsites = 2). We split the full tree up into 6 independent clades having large support values (>0. 95) and we performed the selection tests on each one of them. We made full use of fastcodeml parallelized algorithms by specifying computations with 8 threads on a computer cluster (parameter -nt 8). For each of the clades we reported the branches that have at least one site under positive selection and noted the associated probability that a diversifying selection occurred at this site along the branch under consideration.
Amino Acid Sites under Positive Selection Next, we searched for regions in Spodoptera spp. OR6 and OR13 that could be responsible for the functional divergence of these receptors. We carried out a large-scale analysis of selective pressures acting on Lepidoptera PR genes using fastcodeml  (Valle et al. , 2014) , a software implementation of branch-site probabilistic models of evolution that were introduced in  (Yang and Nielsen, 2002). We found evidence for positive selection in numerous branches of the PR phylogeny, notably in the one leading to the pair of orthologs SlitOR6/SlituOR6, which both detect (Z,E)-9,12-14:OAc (Figure  5A ), and in the one leading to Spodoptera OR13 orthologs, detecting (Z,E)-9,12-14:OAc and (Z)9-14:OAc (Figure  5C ). We then mapped the candidate sites under positive selection in those two branches, as inferred by the Bayes empirical Bayes approach, on the predicted topology of Spodoptera OR6 (Figure  5B ) and OR13 (Figure  5D ).
Acknowledgments The authors thank  Dr Barry Dickson  for providing the Or67d GAL4 mutant knock-in fly line and  Marie-Christine François  for insect rearing and technical help. This work has been funded by  INRA ,  Université Pierre et Marie Curie (UPMC)  and an  INRA   doctoral fellowship  to  AdF. XS  received a grant from the  China Scholarship Council (CSC).
Results: We used physical map of wheat chromosome arm 3DS to validate the method with two different DNA sequence datasets. The first comprised 567 genes ordered along the chromosome arm based on syntenic relationship of wheat with the sequenced genomes of Brachypodium, rice and sorghum. The second dataset consisted of 7,136 SNP-containing sequences, which were mapped genetically in Aegilops tauschii, the donor of the wheat D genome. Mapping of sequence reads from individual BAC pools to the first and the second datasets enabled unambiguous anchoring 447 and 311 3DS-specific sequences, respectively, or 758 in total.
BAC-pool sequencing The original target for the sequencing was at least 10x coverage for each of BAC pools. Finally, about 180 million reads were generated in three Illumina HiSeq2000 lines. Mean coverage of BAC pools reached nearly 35x. However, the coverage ranged from 5. 9 for pool p09 to 166. 5 for pool rP (for complete statistics see Additional file 1). So large differences in coverage of BAC pools were not expected. To investigate the effect of sequencing depth, we selected and mapped reads representing different depth of pool rP from 1x to 50x to Genome-Zipper sequence dataset and counted the sequences identified in the pool. This parameter reached a plateau at 30x coverage (Figure  2 ).
Read alignment optimization Reads of BAC pool rP were aligned using MOSAIK to GenomeZipper sequence dataset with the aim to optimize alignment parameters. The BAC pool was considered positive for a particular sequence if "covered region" (length of the sequence region covered by reads from the pool) was at least 80% of the sequence length. Hash size (k-mer length for alignment) was optimized first without any limitation of alignment candidate threshold. Time needed for read mapping, decreased dramatically for hash size between ten and fifteen (from ~10 hours to 160 seconds). Further decrease of computation time is shown in Figure  3a. The sensitivity of alignment was not influenced by hash size, and the number of sequences found in the pool rP remained 43 for any hash size used (Figure  3b ).
3 sequences per 100 BAC clones were detected in plate pools, 16. 0 sequences per 100 clones in row pools and 16. 9 sequences per 100 clones in column pools. This phenomenon could reflect larger growth differences among 384 clones in plate pools as compared to 240 and 160 clones in row and column pools, respectively. Underrepresentation of a particular clone (containing the target sequence) in the pool could then lead to false negative result for the pool after read alignment. Twenty nine to ninety five positive sequences per BAC pool (out of 7,136) were identified in Ae. tauschii sequence dataset (for details see Additional file 1). Mean values were 75. 1, 65. 7 and 49. 0 positive sequences per plate, row and column pool, respectively. As for the GenomeZipper dataset, plate pool p09 had the smallest number of positive sequences, and plate pools showed the lowest number of detected sequences.
Positive pool detection Alignment of reads from individual BAC pools to GenomeZipper sequence dataset resulted in a variable number of positive pools per individual sequence (Figure  5a ). 407 (71. 8%) GenomeZipper sequences were found in at least one pool and the remaining 160 sequences were not scored in any of the fifty pools. To explain this, we screened the pools with primers specific for ten of the sequences using PCR. Out of ten markers, eight identified at least one positive pool after PCR screening the pools (data not shown), which were prepared in the same way as for sequencing. This indicates high level of false negative results. As mentioned above, sequencing depth could influence the identification of pools containing target sequences. Thus, the pools with lower sequence depth could be more frequently false scored as negative.
Further, individual clones in pools could be under-represented in the sequence reads, and hence not covering particular sequence by reads enough to reach the threshold. Finally, duplicated regions among sequences with 100% identity could not be covered by any read as only reads mapping to unique positions were used for the analysis. Similarly to GenomeZipper dataset, alignment of reads from BAC pools to Ae. tauschii sequence dataset resulted in a variable number of positive pools per sequence (Figure  5a ). Excessive number of pools was positive for several sequences, and for three sequences even all fifty pools were positive (all three represent transposable elements). This fact led to the modification of BAC address deconvolution script and all markers with more than five positive pools in any of the dimensions (plate, row, column) were considered repetitive and were not assigned to any of the BAC clones detected by the script.
Out of the 7,136 Ae. tauschii sequences, 506 (7. 1%) were detected in at least one BAC pool. While GenomeZipper was constructed specifically for 3DS chromosome arm, Ae. tauschii sequences originate from all seven chromosomes. This led to lower fraction of sequences detected in pools as compared to the GenomeZipper dataset. On the other hand, it allowed us to screen pools for sequences originating from other genome regions and thus estimate the rate of false positive results. For non-target chromosomes (i. e. 1D, 2D, 4D, 5D, 6D and 7D) the frequency of sequences scored in at least one pool ranged from 1. 2% for chromosome 7D to 2. 8% for chromosome 6D (Figure  5b ). These results indicate low level of false positive results, which we estimated below 3%. 35. 2% sequences genetically mapped to Ae.
BAC address deconvolution Each BAC clone is present in a single plate, row and column pool (e. g. BAC clone at position F17 in MTP plate no. 3 is present in pools p03, rF and c17; see Methods for description of BAC pool preparation). Thus, it should be possible to deconvolute positive pools to individual clones using the information about the presence of individual clones in pooled samples. In other words, each combination of single plate, row and column pools could be unambiguously deconvoluted to a single BAC clone. Thus, it was possible to link sequences with one positive pool in each dimension to a particular BAC clone (anchor_type_1). This type of sequences represented 19. 4% and 19. 2% of all sequences scored in at least one pool using GenomeZipper and Ae. tauschii datasets, respectively (Figure  6 ).
These two approaches resulted in anchoring 95 (23. 3%) and 8 (2%) sequences from Geno-meZipper dataset. Using the same strategy for Ae. tauschii dataset, 125 (24. 7%) and 12 (2. 4%) sequences were anchored to physical map contigs. Altogether, 182 (44. 7%) sequences from GenomeZipper and 234 Ae. tauschii datasets were linked with physical map contigs. Within the Ae. tauschii dataset, 191 sequences were assumed to originate from chromosome arm 3DS representing 42. 0% of sequences mapped genetically to chromosome arm 3DS. After the deconvolution, some sequences remained not anchored as no positive BAC clone was identified for them even if they were positive for at least one pool. Majority of them were represented by sequences with a missing positive pool in at least one of the dimensions (plate, row or column; unequivocal deconvolution is not possible in such case) and sequences for which no overlap/match of any two candidate clones was identified in physical map (for details see Figure  6 ).
Analysis with decreasing stringency of positive pool detection Based on the results of the initial analysis (lower number of anchored sequences, tendency to false negative results), we performed the analysis with successively lower value for covered region (region covered by reads from particular pool). We decreased the covered region from 80% to 30% of sequence length in steps of 10% (see Additional file 2 for complete datasheet). With the GenomeZipper dataset, the threshold 30% of covered region resulted in at least one positive pool for 541 (95. 4%) sequences. The increased number of sequences found in pools confirmed false negative results obtained after the initial stringent analysis, which was indicated by PCR screening (see above). 441 sequences from GenomeZipper dataset could be anchored to BAC clones based on less stringent criteria (30% of length of particular sequence covered by pool reads).
Genome Zipper Ae. tauschii all Ae. tauschii 3DS Anchoring results for sequences with at least one positive pool. For both sequence datasets (GenomeZipper, Ae. tauschii), about half of sequences were anchored to BAC clones/physical map contigs. A particular BAC pool was considered positive if its reads covered at least 80% of particular sequence after read alignment to complete sequence set. A majority of the remaining sequences were not anchored either due to missing positive pool in one of the dimensions (no_positive_BAC) or no candidate clone overlap during the deconvolution (if more than one clone is positive; no_clone_ovlp). the pipeline. In case of six sequences, identification of additional positive pools under less stringent conditions resulted in identification of different clones/contigs after BAC address deconvolution.
Utility of BAC pools sequencing for physical map anchoring We were able to anchor 311 sequences from Ae. tauschii dataset genetically mapped to chromosome arm 3DS using combination of high and low stringency anchoring conditions (see Additional file 5 for complete sequence list and their BAC/contig addresses). Under the same conditions, 447 sequences represented in 3DS Genome-Zipper dataset were assigned to physical map contigs. This represent 78. 8% of 567 non-redundant gene fragments (454 reads) organized in wheat 3DS GenomeZipper (see Additional file 5 for complete sequence list and their BAC/contig addresses). Recently, Poursarebani et al. [38]  used similar approach for physical map anchoring. Authors used short sequence tags produced from whole genome profiling (WGP)  [39]  to anchor sequences to physical map of wheat chromosome 6A.
Methods Preparation of DNA pools BAC library specific for wheat chromosome arm 3DS (TaaCsp3DShA;  [37] ) was used to validate the BAC pool sequencing strategy for physical map anchoring. The library comprises 36,864 clones with mean insert size 110 kbp. A physical map was build using HIFC technique  [8]  in Fingerprinted Contigs software (FPC V9. 3;  [11] ) following "physical map assembly guideline" established by TriticeaeGenome project and accepted by the International Wheat Genome Sequencing Consortium (IWGSC). Briefly, each clone was fragmented using a cocktail of five restriction enzymes. Fragment ends produced by four of them were labelled by one of four different fluorescent dyes. DNA fragments were subsequently analysed using capillary electrophoresis, and physical map was built based on the number of shared fragments (with the same size and label) among clones at stringency cutoff 1e-75 to produce robust contigs.
Sequences used for the mapping We mapped two different sequence datasets to BAC clones from MTP of 3DS arm. The first was produced using GenomeZipper pipeline  [27]. Briefly, the chromosome arm was flow-sorted, its DNA amplified according to Šimková et al. [46]  and sequenced using Genome Sequencer FLX. One full run resulted in 945,769 reads [NCBI-SRA: SRR1611613] which were used for GenomeZipper calculation. The GenomeZipper pipeline reconstructs gene order based on comparative analysis and synteny conservation with already sequenced grass species. We used Brachypodium  [28] , rice  [29]  and sorghum  [30]  genome sequence in this study. The GenomeZipper of 3DS comprises 578 non-redundant reads, which correspond to 498 unique positions on the chromosome arm (see Additional file 6 for complete GenomeZipper data sheet).
567 stringent non-redundant reads with length 504 ± 53 bp were used in this work. The remaining eleven reads had multiple positions in the linear gene order and hence were excluded from the analysis. The second dataset was derived from genetic map of Aegilops tauschii. Sequences with length 477 ± 170 bp underlying 7,136 SNPs  [47]  which mapped to any of the seven Ae. tauschii chromosomes were selected and used in this work (see Additional file 7 for list of sequences used).