dataesr/scientific-paragraphs-categorization huggingface dataset:
These movements were reinserted at the final stage of rewiring. Disallowed farm-to-market movements were pushed back to the next available market day of the same production type at the same market after the end of the movement restriction period, whereas farm-to-farm movements were delayed to the next legal day. Market movements for particular production types occur only on a limited number of dates during the calendar year, which can result in diverted movements occurring weeks or months after the recorded movement taking place. In order to account for this, movements were shifted to the market with nearest geographical proximity on the next available market day of the originally recorded production type, considering all markets in Scotland. Nearest markets were identified as follows: based on the geo-location data of the recorded market, a list of markets was generated in increasing order of distance from each of Scotland's 891 agricultural parishes.
2. 1) (R Developement Core Team, 2015) and the R package igraph  (Csárdi and Nepusz, 2006). The density of a network indicates how connected the nodes are and is defined as the proportion of links among livestock operations in the network that are currently observed (see  Dubé et al. , 2009; Wasserman and Faust, 1994). A value of one implies that all potential connections are realized. The diameter of the network is the largest shortest path in the network. The bigger the diameter of a network, the less connected a network tends to be. For livestock movement networks, degree assortativity describes the tendency of a holding to be preferably connected to holdings which have degrees similar to their own (degree assortativity close to 1). In networks with disassortative mixing, high-degree holdings tend to be connected to holdings with a low degree and vice versa (degree assortativity close to -1)  (Newman, 2010).
While the number of premises that appear to be violating legally binding standstill requirements could be high, numerous deficiencies and inconsistencies in the available data prevent the full scope of movements during the standstill period from being appropriately categorised. The greater detail of the CTS database resulted in a greater ability to identify standstill exemptions in cattle. Given that many standstill exemptions refer to individual animal characteristics, we were not able to accurately identify these exemptions for the batched sheep data. However, even in cattle -where we were able to identify almost twice as many apparent standstill exemptions than in sheep -there are Table  7  Descriptive summary statistics of FMD simulation output for epidemic size based on number of infections for recorded movements (top row) and rewired movement networks.
Conclusion Our study suggests the usefulness of system-based approaches to identify candidate genes from interacting gene networks in a multi-breed context. We achieved this by exploiting associations between correlated traits. The reluctant inclusion of intergenic SNP leaves the possibility that the AWM approach was not capturing significant SNP such as trans-activators. Nonetheless, the AWM proved to be more efficient for integrating related complex traits and analyzing thousands of SNP and therefore appropriate for the analysis of these complex traits. When applied to our dataset, it predicted gene interactions that are consistent with the known biology of udder morphology and health captured known TF (e. g. , ESR1, FGF2, GLI2, PGR and BTRC), and provided new candidate genes for udder morphology.
In SPR1, Fe and Si deficiency caused an increase in the Pi translocation capacity (Figure  6B ); however, in KH CMU, Fe and Si deficiency did not increase Pi translocation compared with Fe deficiency alone (Figure  6C ). These data indicate a possible differential regulation of the protein involved in modulating Pi root-to-shoot ratio in KH CMU in comparison to SPR1. nutrient deficiency stress, with (+Si) and without (-Si) Si application (-Fe-Pi-Si). Shoots and roots were collected separately. Fe concentration was determined as described in  [22]  and presented as mg/kg DW. DW, dry weight. The black and grey histograms represent SPR1 and KH CMU, respectively. The bars represent the standard errors of the corresponding means of the three replicates. Different lowercase letters indicate significant differences at p < 0.
In SPR1, Fe and Si deficiency caused an increase in the Pi translocation capacity (Figure  6B ); however, in KH CMU, Fe and Si deficiency did not increase Pi translocation compared with Fe deficiency alone (Figure  6C ). These data indicate a possible differential regulation of the protein involved in modulating Pi root-to-shoot ratio in KH CMU in comparison to SPR1. B, C ) rice varieties SPR1 and KH CMU were grown in media containing inorganic phosphate (Pi) in the presence or in the absence of iron (Fe) and silicon (Si). Abbreviations: complete medium (Ct) medium lacking either an individual element, i. e. , iron (Fe) or silicon (-Si), or both (-Fe-Si). Quantitative real-time PCR (qRT-PCR) was performed to analyze OsPHO1;2 mRNA levels; OsActin1 expression level was used as the internal reference.
Statistical Analysis Statistical analyses of the data were performed by using the Statistica 9 software (analytical software SX, version 9, Tallahassee, FL, USA). The analysis of variance (ANOVA) was used to detect the differences between the treatments, and the least significant difference (LSD) at p < 0. 05 was used to compare the means. The significance of the correlation coefficients was analyzed by pearson correlations at p < 0. 05.
Bacterial Inocula Pseudomonas fluorescens strain Pf4 (Pf4) was isolated from a forest soil located in Sassello (Savona, Italy) and characterized by  Berta et al. (2014). Pseudomonas sp. 5Vm1K (5Vm) was isolated from the rhizosphere of blueberry plants grown in a larch woodland (Bellino, Cuneo, Italy) and characterized as described by  Bona et al. (2015). P. fluorescens strain 19Fv1t (19Fv) was provided by Mybasol s. r. l (Alessandria, Italy) and characterized by  Bona et al. (2017). Bacterial 16S rDNA sequences were deposited in the NCBI database GenBank with the accession numbers KF234076, KF233995, KF752592 for Pf4, 5Vm, and 19Fv, respectively. Bacterial cells were grown on tryptic soy agar (TSA) at 28 • C for 48 h and suspended in 0. 1M MgSO 4. Bacterial density (600 nm) was adjusted to 10 9 CFU mL -1.
Statistical Analysis, PCA and PCA-DA Analysis Data were analyzed by two-way ANOVA (R software package) using "Fungus" and "Bacterium" as factors. A one-way ANOVA followed by Fisher test (StatView 4. 5, Abacus) with significance cut-off at P < 0. 05 was used to assess differences among the treatments. Data were also treated by multivariate statistical tools as Principal Component Analysis (PCA)  (Massart et al. , 1998; Negri et al. , 2011)  and PCA-Discriminant Analysis (PCA-DA). In PCA-DA  (Hoogerbrugge et al. , 1983)  the space defined by the Principal Components (PCs) is used to identify linear discriminant functions (LDFs) able to separate the samples in the classes present. In two class problems, only one LDF is provided, able to discriminate the samples in the two classes present.
, 1996). Ester enzymatic biosynthesis occurs during ripening and is not affected by homogenization or by other fruit treatments  (Perez et al. , 1992; Ozcan and Barringer, 2011). Such reports are in accordance with our results since the fruits were harvested at maturity and no differences were recorded between fresh and frozen samples (data not shown). Among the esters, only the amount of 3-methyl-1-butyl acetate was affected by inoculation with significant differences between SvPf4-plants and all the other treatments. On the basis of PCA-DA, it was observed that ester concentrations in fruits were mostly affected by FmPf4 and SvPf4 inoculations, suggesting a role for microbial treatments in the determination of the sweet-fruity note in strawberry taste and odor. However, the two clusters of points did not overlap, indicating that the molecules affected in the two treatments were different.
Fruit Mineral Composition Strawberries contain many minerals  (Perez et al. , 1997)  that are involved in plant metabolism, besides having an important role in human diet (mainly K, P, Ca, and Mg). We analyzed the elemental composition of strawberry and we detected a total of 51 elements, all at non-toxic concentrations, among which only Ba, Rb, Sr, Cs (alkali metals), and Co (transitional element) significantly varied between some of the treatments. These variations were due to the fungal inoculation (as shown by two-way ANOVA), suggesting that AMF can influence the uptake of these elements and therefore their availability for the accumulation in fruits  (Taylor and Harrier, 2001; Hart et al. , 2015; Mensah et al. , 2015). To our knowledge, no data were recorded in the literature on the effects of microorganism inoculation on alkali metal accumulation in fleshy fruits.
It was concluded that the methods were suitable to evaluate wine quality using both novice consumers and connoisseurs (expert consumers). Charters and Pettigrew (2007)  used qualitative data obtained from questionnaires and focus groups to understand the complexity and dimensions of the quality perception of Australian consumers based on their level of involvement with the product. From these studies, it became clear that consumers' quality perception is a multi-dimensional concept and is a crucial measurement, since the translation of consumer demands into product specification leads to the development of products accepted by the consumers  (Bredahl et al. , 1998; Verdú Jover et al. , 2004). Measuring quality as perceived by consumers is not always possible, due to logistical matters such as cost implications and the phase of production when the quality measure is needed, for example during product development.
Workflow of the statistical analysis of free sorting and quality scoring data to determine drivers of quality FIGURE 1 Schematic presentation of the statistical analysis workflow. Statistical analysis of the data was performed as a three-step process. One-way ANOVA on the quality scores was performed as the first step to determine whether samples differed significantly. During the second step the sorting configuration was determined using DISTATIS, followed by AHC to identify groups of samples. Lastly sensory attributes that drove the quality scoring of the wines were determined correlating the correspondence analysis (CA) deviates with the quality scores. defined as (1) the flavour, including retronasal aroma and the perception of basic tastes (sweet, sour, bitter) and (  2 ) mouthfeel (concerning astringency, body) and (3) the length of the aftertaste.
Pearson's correlation coefficients between the attributes and the DISTATIS product coordinates were calculated using the contingency table and the data of the first two dimensions of the DISTATIS analysis. Two Pearson's correlation coefficients were thus calculated for each descriptor; one between each attribute frequencies and the coordinates of the first dimension, and the second between the attribute frequencies and the coordinates of the second dimension. These correlation coefficients were projected as the x-and y-coordinates of the descriptors onto the DISTATIS compromise map to obtain a plot representing the similarity information from the sorting exercise as well as the descriptors assigned to the groups  (Faye et al. , 2004; Cartier et al. , 2006 , Abdi et al. , 2007). Pearson's correlations coefficients and projections onto the DISTATIS space were executed using Microsoft Excel  (Microsoft Corporation, wwwmicrosoft.
Identifying drivers of quality During step three of the data analysis the drivers of quality were identified (Fig. 1 ). For this purpose, correspondence analysis (CA) was performed on the contingency table that contained the descriptors used to describe the groups that were created during the free sorting step. CA was conducted to obtain a descriptor-based sensory space which represents the relationship between the sensory characteristics of the samples. This space was used to correlate to the sensory characteristics to the perceived quality. Standardized deviates (also called Pearson residuals) were calculated for each descriptor from the formula provided below. These deviates indicate the magnitude of deviation from independence between wines and descriptors. Negative deviates indicate less occurrence of a descriptor with a wine as would be expected under independence, and positive deviates more occurrence of a descriptor with a wine as expected under independence.
Panel consensus and differences between individual judges' sorting data The consensus among the individual judges was investigated by inspection of the DISTATIS plot that showed the judge configuration (Fig. 3A ). It is evident that the panel consensus was good. This is in line with the findings from the study by  Torri et al. (2013)  who inferred that the good consensus observed between experts' description of wines could be ascribed to their use of a common language that stemmed from experience gained from evaluating good quality wines. In the present study, judge 11 could be considered as an outlier. This was confirmed by performing a STATIS Principal Component Analysis (PCA) shown in Fig. 3B. It is clear that this judge's calculated weight (Fig. 3B ) was lower than that of the other judges.
Data collection and management Data on the HPAI-H5N8 outbreaks that occurred in poultry in France during the 2016-17 epidemic (November 2016-March 2017) were obtained from the Direction Générale de l'Alimentation (DGAl) of the French Ministry of Agriculture, Paris, France. An outbreak was defined as the detection of at least one laboratory-confirmed HPAI-H5N8 infected animal (by virus isolation or PCR) in a domestic poultry holding. Data included the list of laboratory-confirmed outbreaks, the species involved, the geographical locations (Cartesian coordinates) and the date of suspicion by passive or active surveillance (as further described). Outbreaks for which the precise location was missing were given the coordinates of the centroid of the commune (smallest administrative unit in France, with a median area of 10 km 2 ) where they occurred.
The coordinates of the communes' centroids were obtained from GEOFLA  [8]. Data on duck holding census in France was obtained from the DGAl. The data included the list of duck holdings, the geographical locations (Cartesian coordinates) and the date of culling during the 2016-17 epidemic. All geographical data were projected to RGF93/Lambert-93 (EPSG: 2154) and processed using R software version 3. 3. 2 [9].
Data analysis All of the HPAI-H5N8 outbreaks that were detected by passive surveillance (i. e. with origin of suspicion based on the appearance of clinical signs) in poultry in France during the 2016-17 epidemic (November 2016-March 2017) were included in the analyses to study how the epidemic progressed over time and space. Outbreaks identified through enhanced active surveillance of flocks were not considered. Enhanced active surveillance involved testing samples collected on flocks before pre-emptive culling, before transport between premises, when showing an epidemiological link with an outbreak and when located in the restriction zones. The reason for not including outbreaks detected by active surveillance was that this would have likely increased the detection probability of infected holdings in the vicinity of reported outbreaks and bias estimations.
The measures were prompted by the increasing number of outbreaks reported in the Landes department at the beginning of February 2017  [10]. Global spatio-temporal clustering of HPAI-H5N8 outbreaks detected by passive surveillance was investigated in the first and second study periods using the space-time K-function (Supplement 1)  [11] [12] [13]. The space-time K-function analysis was conducted using a maximum space-time window of 30 km and 30 days. The overall significance of space-time clustering was assessed by generating 9,999 Monte Carlo random permutations. The excess risk attributable to spacetime interaction within distance s and time t (D 0 (s,t)) was calculated and visually inspected. The analyses were performed in R software version 3. 3. 2 [9]  using the 'splancs' package  [14].
The mask was created with pixels in which the smoothed density of outbreak reports was higher than one and applied to the TPRS analysis to avoid an edge effect on the estimated values. The analyses were performed in R software version 3. 3. 2 [9]  using the 'fields' package  [21]. To analyse the relationship between the spread rate and the duck holding density, we used the census duck holding database to calculate, for each outbreak, the number of susceptible duck holdings present within 10 km from the infected holding during the week of the outbreak (Supplement 2). A linear regression was used to assess the statistical association between the disease spread rate at the outbreak location and the density of susceptible duck holdings around outbreaks. The majority of outbreaks affected duck flocks (81.
Acknowledgements The authors gratefully acknowledge the  Direction Générale de l'Alimentation (DGAl, France) of the French Ministry of Agriculture  for providing the data, particularly  Marie-Cécile Moisson  for providing additional detailed information about the data, and  Séverine Rautureau ,  Marie-Pierre Donguy ,  Evelyne Bouvier ,  Sophie Lebouquin-Leneveu  and  Axelle Scoizec  for providing useful comments on this research study. They are also very grateful to  Jean Artois  ( SpELL ULB, Belgium ) for advice in relation to spatial maps and geocoding, and to  Mattias Delpont  ( ENVT, France ) for providing detailed information on the French poultry industry. They are grateful to  Grace Delobel  for checking the English language of this paper. This work was carried out within the framework of the  Chaire de Biosécurité at the École Nationale Vétérinaire de Toulouse (ENVT) , which is funded by the  French Ministry of Agriculture  and was supported by the  UMR IHAP 1225 ENVT-INRA.
Using DFB diode lasers or ICLs/QCLs, OF-CEAS absorption spectra are acquired in a small spectral region (~ 1. 5 GHz), by scanning the laser frequency at a relatively fast rate (~ 10 Hz). However, the response time is not limited by this rate but rather by the gas exchange inside the measurement volume. Therefore, the cell is designed with minimal dead space and a small sample volume of less than 20 cm 3. The gas sample can be injected in a continuous flow with a stabilized, relatively low cell pressure, and its exchange time depends on the flow and pressure. It can be made below 1 s, for example, by setting a flow of 8 mL/s with a cavity pressure of 140 mbar  [46]. If required, it can be further improved using a lower pressure and/or a higher pumping rate. Finally, the design of the spectrometer is robust and compact: the optical assembly and all the electronics for real-time control and data acquisition fit inside a 19″ chassis.
Acknowledgements B. H. acknowledges support from the  H2020-MSCA-ITN-2015 ,  IMPACT  project (nr. 674911 ). G. A. D. R. acknowledges support from the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the Department of Health. F. M. S. acknowledges support from the  Swedish Research Council  ( 2013-6031 ) and the  Kempe Foundations  ( SMK-1446 ). The authors thank the anonymous reviewers for their detailed and constructive comments.
6% agarose gel, respectively. Genome and transcriptome sequencing were performed at GenomeScan. The DNA was fragmented using a Focused-ultrasonicator (Covaris). The NEBNext Ultra DNA library prep kit and NEBNext Ultra directional RNA library prep kit for Illumina (catalog numbers E7370S/L and E7420S/L, respectively) were used according to the manufacturer's instructions. The quality and yield after sample preparation were measured with Lab-on-a-Chip analysis or a fragment analyzer. Illumina cBot and HiSeq 2500 standard Illumina primers and the HiSeq control software HCS version 2. 2. 58 were used according to the manufacturer's protocols for clustering and DNA sequencing, with concentrations of 8. 0 pM DNA and 16. 0 pM cDNA. The Illumina data analysis pipeline RTA version 1. 18. 64 and bcl2fastq version 1.
8. 4 were used for image analysis, base calling, and quality checking. fastqFilter version 2. 05, a GenomeScan in-house pipeline, was used for adapter removal and quality checking; bases with a Phred score above Q22 and reads longer than 36 bp passed the filtering. For the assembly, ABySS version 1. 3. 7  (7) , with a k-mer length of 64, was used. Scaffolds shorter than 500 bp were removed. A total of 1,367 contigs were used for the assembly of the 34. 66-Mb genome. The GC content was 48. 91%, as assessed by QUAST  (8). The genome of Phlebia brevispora was used as a gene-finding trainer for the HMM-based algorithm Glimmer (version 3. 02)  (9). Mapped mRNA-Seq reads were used by the CodingQuarry software tool  (10)  for an evidence-based method of gene finding. Using the two methods combined, gene models for 13,785 genes were obtained.
Gene models encoding lignin degradation-related proteins are present in multiple copies (four manganese peroxidases, four lignin peroxidases, and four laccases). Accession number(s). This whole-genome shotgun project has been deposited in DDBJ/ENA/GenBank under the accession number MLYV00000000. The version described in this paper is version MLYV02000000 and is also available through MycoCosm  (11).
Trait variation and heterosis in a wide range of A. thaliana genotypes We generated 450 hybrids by manual crosses between 415 inbred accessions (Fig. 2A ). We then measured four traits, vegetative dry mass, plant lifespan, growth rate and total fruit number, in the 450 hybrids, the 415 inbred parents, plus a further 35 inbred accessions. The parental combinations for the hybrids had been chosen to represent a wide range of genetic, geographic and phenotypic distances (Fig. 2B, C ). Traits were strongly variable between genotypes: vegetative dry mass M varied between 1. 3 and 2,218 mg, plant lifespan between 24 and 183 days, growth rate between 0. 04 and 40. 4 mg d -1 , and fruit number between 5 and 400 (Table  S1 ). Across the entire dataset of hybrids and inbreds, a high proportion of the phenotypic variance could be explained by genotypic differences (broad-sense heritability): from 80% for growth rate to 94% for vegetative dry mass (Table  S2 ).
Availability of data and material The datasets supporting the conclusions of this article are included within the article and its additional files. Codes are available on Github (https://github. com/fvasseur/AnalysisHeterosis), and phenotypic data are available on the Dryad repository (https://datadryad. org/review?doi=doi:10. 5061/dryad. 4978rp4) [96]. Correspondence and requests for materials should be addressed to weigel@weigelworld. org or franc. vasseur@gmail. com.
Genome-wide RNA sequencing using endoreduplicated nuclei from fruit pericarp: experimental setup and data preprocessing To analyze the influence of endoreduplication on gene expression, RNA-Seq was performed using RNA extracted from purified nuclei sorted based on their DNA content by FACS. Nuclei were prepared from pericarp cells of 30 day-post-anthesis (dpa) fruits because at this developmental stage the immature green tomato fruits have completed their growth and display a wide range of ploidy levels (from 2C to 512C) (Figure  1 )  (Joubès et al. , 1999; Cheniclet et al. , 2005). Four populations of endoreduplicated nuclei, namely of 4C, 8C, 16C, and 32C DNA content, were sorted on a per-fixed number (ca. 100,000 nuclei) and total nuclear RNA was extracted for the construction of cDNA libraries.