dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison to the Northern pike genome We compared the obtained European grayling chromosomes to the chromosomes of Northern pike (Esox lucius), a species that represents the closest sister group to Salmonids prior to the salmonid-specific whole genome duplication with an available genome assembly. The genome assembly was downloaded from the NCBI Genome database (RefSeq assembly GCA_000721915. 1). Conserved synteny between European grayling and Northern pike was determined by aligning European grayling and Northern pike chromosome sequences using the nucmer tool in MUMmer 3. 0 aligner  (Kurtz et al. 2004 ) and keeping hits with identity $80. 0 and matchcount $100. Homeologous European grayling chromosome pairs were named according to the Northern pike chromosome naming convention  (Rondeau et al.
Data availability The PacBio reads, chromosome-level sequences and unmapped scaffolds over than 2000 base pairs have been deposited at NCBI SRA and Genomes under BioProject ID PRJNA464295. Scripts have been deposited to GitHub under the link https://github. com/tiinasa/graylinggenome. Supplemental material available at Figshare: https://doi. org/10. 25387/ g3. 7728512.
We confirmed the absence of chromosomal fusions and the somewhat high abundance of pericentric inversions in European grayling and highlighted the differences compared to the large number of chromosomal fusions that have taken place in the Atlantic salmon. We also highlighted novel and already described instances of transposable elements with a role in driving these different genome evolution   (2013) , doi: 10. 5061/dryad. r42qf) of those salmonids that have diploid chromosome number (2n) and the number of chromosome arms (NF) available in  Phillips and Ráb (2001). processes. We further identified similar homeologous regions under residual tetrasomy in European grayling as in the genomes of many other salmonid species and discussed the potential underlying evolutionary causes behind the distinctive karyotype evolution of Thymallinae among salmonids as well as the role of genomic rearrangements in speciation.
ACKNOWLEDGMENTS This work was supported by the  Academy of Finland  (project numbers 287342 and 302873). We thank the  Finnish Centre for Scientific Computing  for providing computational resources. SV and LAV are supported by  Center for Computational Inference in Evolutionary Life Science (CELS)  and the  Department of Biosciences, University of Oslo. YG and RG contributions were supported by the  Agence Nationale de la Recherche  ( ANR Blanc   SVSE 7 2011 , project  SDS ).
Complementarity between the two methods Due to the difference in the nature of the data obtained by each method (frequencies for the dishcomposition task, and ordinal data for the questionnaire), each dataset was converted into numerical format using the appropriate multivariate analysis. For the dish-composition task results, a Correspondence Analysis (CA) was carried out on the frequency of choice for each product in each scenario. The first three dimensions accounted for 96% of the total variance (respectively, 60, 28 and 8% for axes 1 to 3). In contrast, a Multiple Correspondence Analysis (MCA) was carried out on the questionnaire results. The first three dimensions accounted for 73% of the total variance (respectively 47, 16 and 10% for axes 1 to 3). A Pearson Principal Component Analysis (PCA) was then performed, using the principal coordinates of the CA and the MCA as active variables.
Acknowledgment The authors thank  Francine Griffon  for her precious logistic and technical help in the preparation of the study and for data collection,  Joanna Loglisci  for her help in data collection,  Jacques Maratray  for development of the online questionnaire, and  Cedric Serrano  for taking the photographs and then standardizing the food images. We thank  Claire Chabanet  and  Dominique Valentin  for their advice.
Statistics All statistical analyses were performed using XLSTAT 2012  (Addinsoft 2012). Frequencies were compared using a Wilks G 2 likelihood ratio test completed with a computation of significance by cell (Fisher's test). Comparison of fecundity, copulation latency and duration was carried out with a Kruskall-Wallis test with Conover-Iman multiple pairwise comparisons (P = 0. 05, with a Bonferroni correction). CH and/or behavioral data were analyzed using Principal Components Anaysis (PCA; type Pearson's correlation matrix) with standardized values. For qPCR analysis, significant differences in transcript levels ratio between control and sample strain were detected with the Relative Expression Software Tool (REST, REST-MCS beta software version 2; Pfaffl 2001) where the iteration number was fixed at 2000.
ACKNOWLEDGMENTS We want to thank two anonymous referees for their extensive work to improve the ms. This work was partly funded supported by the  Centre National de la Recherche Scientifique (INSB) , the  Burgundy Regional Council  ( PARI 2012 ), the  Université de Bourgogne , the  ANR (Gustaile)  and the  CONICYT  ( MEC 80140013 ).
Determining the experimental temperature range Short preliminary experiments were conducted to determine experimental temperatures. Bugdorms of 30 adult Ae. aegypti and Ae. albopictus were placed inside a climate chamber (Sanyo MLR-351H, Osaka, Japan) with a Hero4 GoPro camera for 20 min to test each temperature across the range of 2-14˚C. As immobilisation did not occur above 12˚C, a narrower range of 2-10˚C was selected for further investigation. To monitor the temperature inside the climate chambers, Data loggers (Onset Hobo data loggers, Bourne, MA, USA) were placed inside.
Data Description The 1000 Plant transcriptomes initiative (1KP) sequenced and analysed transcribed RNA from 1,342 samples representing 1,173 green plant and chloroplast bearing species, including examples of all major taxa within the Viridiplantae: streptophyte and chlorophyte green algae, bryophytes, ferns, angiosperms, and gymnosperms. Importantly, our selection criteria were not biased towards the model organisms and crop species where other plant sequencing efforts have historically focused. While many of the samples were selected for the phylogenomic analyses, others were motivated by different subprojects. Major articles describing the project have been published elsewhere  [1, 2]. The most recent papers  [1, 3]  are focused on large-scale phylogenomic analyses made possible by the breadth of this dataset.
While all of the 1,342 samples were used in one analysis or another, published over the course of the last ten years, not all of the samples were judged of adequate quality for every analysis. Since each publication uses different criteria for sample data quality filtering, each necessarily uses a different subset of the sample data; for example the final/capstone paper  [1]  used only 1,124 species. This Data Note describes the complete dataset and provides additional details on the sample and sequence processing, as well as our quality assessments of these data. It supplements and replaces our earlier work  [4]  outlining plans for the 1KP efforts.
De novo assembly Once the data were transferred from BGI, the FastQ files were given a uniform name based on a quasi-random 4-letter identification code. A list of all the samples and their ID code is included in the associated data. These identifiers also distinguish otherwise identical repeated samples and provide a stable reference when a sample's species identification is changed. Quality filtered reads were assembled using the SOAPdenovo-Trans transcript assembler (version 2012-04-05)  [10]. No additional pre-processing of the data was performed. This largely used the program defaults, with the slight modification of increasing the k-mer length to 25 bp and reducing the number of processor threads to 1. This reduced thread count allowed us to more efficiently use our computer resources.
Both the internal FillGap module and the external GapCloser post-processor (supplied with SOAPdenovo-Trans) were run. An example of the commands used for one of the assemblies (dataset AEPI, Lineum leonii): These commands refer to a configuration file named config, which specified the expected insert size, maximum read length, and read-sequence filenames. The contents of this file were as follows: max rd len = 120 [LIB] avg ins = 200 rank = 1 q1 = AEPI-read 1. fq q2 = AEPI-read 2.
BLAST searches Thanks to the support of China National GeneBank (CNGB), a BLAST search service  [15]  allows public searches against the assemblies and protein translations. CNGB developed the service using NCBI BLAST+ (version 2. 6. 0) [16]. It integrates all public datasets from CNGB applications, BGI projects, and external data sources and provides a comprehensive and convenient sequence searching. A specialized interface for BLAST searching the 1KP dataset allows limiting the search to specific families, orders, or 25 higher-level clades. For assemblies, there are 21,398,790 nucleotide sequences, 6,188,419,272 bases in total. And for the Transpipe protein translations, there are 103 million protein sequences comprising more than 47 billion amino acids in total.
Unfortunately, this is not always the case. Some analyses are further protected by looking at the whole of the available transcriptome, whereby the many genes from the target species will overpower a few contaminants. Single gene family analyses do not have this advantage and must rely on other methods to reject non-plant genes. Another possibility is significant contamination during sample processing when plant RNA is transferred between adjacent samples, or when whole samples are accidentally mislabelled. Given the potential contamination problems, we tried to identify them in the sequence data by comparing the assembled sequences by BLASTn to a reference set of nuclear 18S ribosomal RNA (rRNA) sequences from the SILVA small subunit (SSU) rRNA database  [18, 19]. The BLASTn alignment to an assembly with the lowest expectation value is taken to indicate that the assembly has a taxonomic origin similar to that of the reference sequence.
The accompanying data include 2 accessory files containing details of this SILVA-based SSU validation for each sample  [20]. The first lists whether the sample is overall judged to be validated as containing the expected taxon and whether it had alignments to any other plant sequences (described as "worrisome contamination"). The second file, more detailed, lists each scaffold identified as being 18S-like sequence, and which reference sequence it matched against. It must be emphasized, however, that these files (and indeed this entire section) describe how we removed contaminations from the final analyses. Every publication did their best to removed potential contamination or otherwise inadequate datasets. For example, the final/capstone publication  [1]  used a subset of only 1,124 species.
Pairwise cross-contamination of assemblies Cross-contamination between datasets was also identified by a genome-scale sequence search pipeline, adapted from previous studies  [21] [22] [23]. Briefly, each pair of assemblies (nucleotide) was compared and a threshold identity level established, above which sequences are likely to be contamination between the pair. While best for identifying technical contamination between libraries (e. g. , due to mixing of RNA samples), this technique could also detect other biological contamination events (e. g. , contamination of pairs of libraries with common commensal organisms). An additional search step, using the entire 1KP sequence library, identified the probable origin of each sequence. The pairwise comparison used LAST v. 963 (RRID:SCR 006119)  [24]  with the -cR01 option, and the respective matches were grouped and ordered by similarity.
This code is available from GitHub  [25]. The output of such an analysis is pairs of apparent orthologs whose sequence similarities are higher than the cutoff in one or both libraries, i. e. , potential contamination. To discriminate donors and recipients in each contaminant pair, each of these potential contaminants was searched against all the non-contaminant assemblies by BLASTn, using the optionmax target seqs 3  [26]. Queries with ≥1 of the 3 best alignments against a sequence from the same family, or from a taxon separated by <2 branches within the 1KP tree  [2] , were excluded from the list of potential contaminants, whereas sequences that yielded best hits exclusively against more distantly related taxa were verified as potential contaminants. Clean and contaminant FASTA sequence files for each library are available in the accompanying data.
Completeness of gene set Two different approaches were used to estimate transcriptome completeness. First, BUSCO v1  [29]  was applied with default settings, using the eukaryote and embryophyte conserved gene datasets (eukaryota odb9, embryophyta odb9) as the query databases. Second, conditional reciprocal best BLAST (CRBB) hits were calculated using CRB-BLAST  [30]  with default parameters. The predicted coding sequences were used as queries against the set of 248 core eukaryotic genes (CEGs) distributed with the.
Some of the weaker sensitivity to the BUSCOembryophyte set is attributable to our sampling species outside of this phylum, which may not have the homologous genes; however, the difference is present when only the embryophyte samples are considered (not shown). Percentage CEG abundance was calculated as number of CEGs with a CRBB hit divided by 248, the number of CEGs used. The percentage BUSCO abundance was calculated as 100% minus the missing percentage. Samples with low abundance by these measures should be treated with caution because the observed transcriptome incompleteness may indicate problems in library preparation or other types of poor sample quality. For these reasons the taxonomic analyses in  [1]  excluded samples with <57. 5% BUSCO abundance. Table  3  shows the percentages of complete genes found for each of the 3 references at several percentile levels of the whole dataset.
Re-use potential Because many of the samples are from poorly studied taxa, these are the first-large scale sequence data to be made available for many species. We expect these sequences to be of broad interest to the plant sciences community, whether researchers merely use our sequences, supplement them with their own sequences, or develop PCR primer and probe sets to collect entirely new sequence data. 1. Tables with list of samples/assemblies and corresponding ENA/NCBI references and GigaDB links. 2. The major part of the provided data have a directory for each assembly. This is named based on the 4-letter code and a species name. Within the directory are a FASTA file containing the SOAPdenovo-Trans assembly, translations of the scaffolds to amino acids, the subset of the nucleotide sequence corresponding to the translation, and tab-separated (text) files with tables of Transrate outputs assessing the assemblies and lists of the reference sequence that each translation is based on.
CalS2 Callose synthase TA45471_4113 Forward GCGTCGGTTAGAGTTCATCA 1200 Reverse GTCTCGCTACAGGCATCTCC  qRT-PCR results were analyzed using SmartChip qPCR Software (v. 2. 8. 6. 1) for melting and amplification curves. Wells with multiple melting peak as well as those with bad amplification curves were discarded. Then, transcript accumulation of defense and effector genes was calculated by using the relative quantification method described by  Pfaffl (2001). To calculate qRT-PCR efficiencies from calibration curves (E= 10 [-1/slope] ) and thus perform the inter-chip normalization, each qRT-PCR run x primer set included a dilution range. Genes with amplification effectiveness beyond the range 1. 7 -2. 2 were discarded (i. e. defense genes THT, CalS2, WRKY1 and effector genes EPI1, PiNPP1. 1, PexRD2).
Acknowledgements The authors thank  Romain Mabon  and  Alexander Kröner  who developped the primers and qRT-PCR protocol for effectors analysis, and  Viviane Lecomte  for microbiology assays support. We are most grateful to  Biogenouest Genomics  and the  Human & Environmental Genomics core facility of Rennes (Biosit/OSUR)  for its technical support. The authors are grateful to the  INRA Genetic Resources Center BrACySol of Ploudaniel  for providing the plant material used in the tests. C. Thomas is supported by a CIFRE PhD grant from  Bretagne Plants Innovation , acting on behalf of  ACVNPT (Association des Créateurs de Variétés Nouvelles de Pomme de Terre ). The authors also acknowledge  N. Seal , English consultant, for the manuscript correction.
FUNDING This project was supported by  Agropolis Fondation  under the reference ID  1403-057  through the  «Investissements d'avenir» programme (Labex Agro :  ANR-10-LABX-0001-01 ) under the frame of I- SITE MUSE  ( ANR-16-IDEX-0006 ) and the  NERC/DFID   Future Climate For Africa programme  under the  AMMA-2050  project, grant number  NE/M019934/1.
Confocal microscopy Flocculation was analyzed by confocal microscopy. Briefly, selected flocculent and non-flocculent strains were grown at 30 and 40 °C as described above. ScFerm was selected as the non-flocculent yeast. Cells were resuspended in citrate buffer (50 mM, pH=4. 0; containing 8 mM CaCl 2 ; Sigma-Aldrich, Merck) and then 0. 4 % (m/V) Solophenyl Flavine 7GFE 500 (AK Scientific, Inc. , Union City, CA, USA) was added. Incubation was conducted at room temperature in darkness. The samples were washed with citrate buffer. Images were visualized and acquired with a confocal microscopy TCS SP5 X (Leica Microsystems, New York, NY, USA) at magnifications 20´ and 40´ with and without 4. 0 digital zoom (confocal microscopy software LAS X v. 2. 0. 2. 14392; Leica Microsystems) at 490 and 510-550 nm for excitation and emission, respectively.
cerevisiae S288c (NM_001179342. 1) sequence. Then, using this FLO5 sequence, a new primer set was designed (FLO5-F2 5'-GCATCAGGAAGTACGGAAGTCA-3' and FLO5-R2 5'-TGCTGCATTCGAATATGTGGA-3'), which corresponds to a 110-bp fragment. MEGA v. 4. 1  (29)  program was used to align and design these primer sets. PCR products were amplified in reaction mix that contained 2 µL MgCl 2 (50 mM; Bioline, Taunton, MA, USA), 2 µL dNTP mix (100 mM; Bioline), 2. 5 µL PCR buffer (Bioline), 1. 5 µL each primer (50 ng), 0. 2 U Taq polymerase (Bioline), and 3 µL DNA template (100 ng). Initial denaturalization time was 5 min at 94 °C, followed by 35 cycles of 1 min at 94 °C, 1 min at 61 °C and 1 min at 72 °C, with a final extension cycle of 10 min at 72 °C. PCR products were verified and purified from an agarose gel (1 %) using Wizard SV gel and PCR clean-up kit (Promega, Madison, WI, USA).
Literature review A critical literature review was performed by analyzing published articles using the scholarly databases Web of Science, EBSCO and ScienceDirect. These databases identified the most relevant academic papers published on mathematical modelling of food products/processes (i. e. modelling food processes, quality/food safety of food products and environmental models). There were no geographical restrictions applied, while searching was limited to studies that were published in the last decade. The majority of publications related to modelling in the food sector were focused on: (i) food technology process-based modelling; (ii) modelling food products (quality) and risks from a food safety/environmental point of view; (iii) a combination of the two. Complexity of analyzing this topic is related to the level of understanding a certain phenomenon.
Statistical processing Likert scale data were considered as ordinal values and non-parametric statistical tests have been used since data were not normally distributed. A cluster analysis was conducted in order to classify the observed statements. A two-step cluster analysis using country type, company size and food sector as categorical variables was performed. The Mann-Whitney U test was used to uncover statistically significant differences among the clusters. A principal component analysis (PCA) was run on 16 statements that measured desired needs for modelling in various application areas as well as on 10 statements measuring awareness of environmental impacts to gain a better understanding of the overall correlations in the two data sets. The suitability of PCA was assessed prior to analysis.
Acknowledgement The authors would like to acknowledge the networking support by the  COST Action CA15118 (Mathematical and Computer Science Methods for Food Science and Industry -FoodMC)  as well the help in conducting the survey provided by  Prof. Dr Serafim Bakalis  ( Nottingham University ),  Dr. Efstathios Kaliviotis  ( Cyprus University of Technology ),  Mirza Uzunović  ( University of Sarajevo ) and  Erifili P. Nika  ( Agricultural University of Athens ).
| Data analyses To determine whether the host-plants of each bee species have significantly different sterol composition, data were first square root transformed and standardized using the Wisconsin double standardization ("wisconsin" function, R-package vegan,  Oksanen et al. , 2018)  prior to the multivariate analysis (i. e. , bee species as categorical variable with four levels, all host-plants were considered equally for a given bee species). The Wisconsin double standardization is a method which first standardizes the data by sterol maximum standardization and afterward by sample total standardization (i. e. , normalization to percent abundance). We then performed a perMANOVA using the Bray-Curtis dissimilarities as a measure of ecological distance and 999 permutations ("adonis" function, R-package vegan).
An advantage of this method is that the procedure is less dependent on data distribution than constrained methods. When perMANOVA returned significant p-value (p <. 05), multiple pairwise comparisons were conducted on the data to detect precisely the differences and p-values were adjusted using Bonferroni's correction to avoid increases of type error I due to multiple testing. Indicator Species Analyses (Indval;  Dufrêne & Legendre, 1997)  were finally performed using the "indval" function from the labdsv package (Roberts, 2012) TA B L E 1 Four selected bee species, sampled localities, degree of floral specialization, and the main hostplant from the sampled population. Classification  APGIII (2009)  to identify the pollen sterols that were indicator of host-plants exploited by a given bee species.
This analysis calculates an indicator value based on relative abundance (specificity) and relative frequency (fidelity) for each sterol to identify the compound(s) with the highest indicator value for each sample. A p-value was calculated for each sterol-bee combination to assess whether pollen sterols are significantly found in association with a given bee species. All pvalues were adjusted using Holm's correction, to avoid increases of type error I due to multiple testing. Both similarities and dissimilarities were visually assessed on a nonmetric multidimensional scaling (nMDS) ordination using the "metaMDS" function from the package vegan. This function transforms the data using the Wisconsin double standardization, applies Bray-Curtis dissimilarities, runs NMDS multiple times with random starts to avoid local optima, and rotates the axes of the final configuration so that the variance of points is maximized on the first dimension.
We determined the appropriate number of axes to use by obtaining stress values for ten replicates NMDS runs for each number of dimensions between one and four. We set the maximum number of random starts for each run at 500. For the final number of dimensions, we selected the lowest number of axes that had a stress value ≤0. 2 (conventional cutoff;  McCune & Grace, 2002). Similar statistical procedure was used to compare the host-pollen (i. e. , plant species as categorical variable with 11 levels), brood cell provisions, tissues of larvae, and nonemerged females among the four bee species (i. e. , bee species as categorical variable with four levels). To detect sterol(s) that might be involved in growth and developmental processes of the selected bee species, we compared the tissues from larvae and nonemerged females for each bee species using a perMANOVA on transformed data (i.