dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual PLS-DA Modeling of 'Omics Data Firstly, each 'omics dataset was individually analyzed using PLS-DA. PLS-DA is well adapted for 'omics data (high dimension and collinearities) because linear combinations of spectral features are constructed and few of these are used in the model. Three metabolites (alanine, lactic acid, and phosphocholine) participated in the discrimination of DON-exposed explants from control explants. This low number of metabolites indicated that, despite a clear separation of observations, little information can be extracted from the metabolomic data. This could result from variables containing confounding variability (experimental or instrumental variability for example), which masks biological variability  [8].
Individual PLS-DA Modeling of 'Omics Data Firstly, each 'omics dataset was individually analyzed using PLS-DA. PLS-DA is well adapted for 'omics data (high dimension and collinearities) because linear combinations of spectral features are constructed and few of these are used in the model. Three metabolites (alanine, lactic acid, and phosphocholine) participated in the discrimination of DON-exposed explants from control explants. This low number of metabolites indicated that, despite a clear separation of observations, little information can be extracted from the metabolomic data. This could result from variables containing confounding variability (experimental or instrumental variability for example), which masks biological variability  [8]. To date, very few discriminant metabolites including those detected here have been reported in NMR-based metabolomics investigations of the alterations induced by DON To date, very few discriminant metabolites including those detected here have been reported in NMR-based metabolomics investigations of the alterations induced by DON in pigs  [6, 27, 28].
Fusion of Transcriptomic and Metabolomic Data Methodological issues encountered with the analysis of one single 'omics dataset are worsened by 'omics fusion. Some of these problems are related to the high dimensionality of matrices and biological noise. This latter can hinder the effects of the biological factor and may prevent evidencing the effects of the factor of interest. It is therefore essential to consider these problems when choosing the statistical method to apply for 'omics integration. CCA is designed to select correlated features. However, CCA fails due to ill-, or even un-, conditioned matrices in high dimensions. A regularized method may be applied to solve this issue. Another issue is with selecting relevant and meaningful correlated features from the many thousands of features present in the original datasets, among which many may be noisy.
Treatment of Jejunum Explants Eight 5-week-old piglets were used to generate jejunal explants. The piglets were weaned at 4 weeks and fed ad libitum prior to the experiment. Explants were prepared as already described  [33]. Briefly, 5 cm middle jejunum segments were collected and flushed with William's Medium containing 4. 5 g/L of glucose, 1% of penicillin/streptomycin, 0. 5% of gentamycin, 10% FBS, and 30 mM of amino acids (Ala/Glu). Each segment was opened longitudinally, and multiple pieces of 6 mm diameter were obtained using biopsy punches (Centravet, Lapalisse, France). DON was dissolved in dimethylsulfoxide (DMSO) at 60 mM, before dilution in complete culture media. Control samples were treated with equivalent concentrations of DMSO previously tested to be nontoxic for intestinal explants (data not shown).
[33]. Microarray data and experimental details are available in NCBI's Gene Expression Omnibus  [42]  and are accessible through GEO Series accession number GSE165968. All the data analyses were performed using R (R Core Team, 2018  [43] ) and the Bioconductor package  [44] , as described in GEO accession GSE165968. Raw data (median signal intensity) were filtered, log2-transformed, and normalized using the quantile method  [45]. The transcriptomic matrix includes n = 16 observations and p = 41,436 features.
1 H-NMR-Based Metabolomics Tissue samples (100 mg of tissue) were extracted in methanol/dichloromethane/water (2:2:1. 4, v/v/v) as described by Beckonert et al. [46]. All the 1H NMR spectra were generated using a Bruker Avance III HD spectrometer (Bruker Biospin, Rheinstetten, Germany) operating at 600. 13 MHz for proton resonance frequency using an inverse detection 5 mm 1H-13C-15N-31P cryoprobe. 1 H NMR spectra of aqueous explant extracts were acquired and processed as previously described [47] using 512 transients, a spectral width of 20 ppm, and a relaxation delay of 2 s. NMR spectra were phase-and baseline-corrected and then calibrated (TSP, 0. 0 ppm) using the Topspin software (version 3. 5, Bruker). Then, the NMR data were reduced using the AMIX software (version 3. 9, Bruker) to integrate 0.
01 ppm-wide regions corresponding to the δ 10. 0-0. 5 ppm. The 5. 1-4. 5 ppm region, which includes the water resonance, was excluded in the NMR spectra of aqueous extracts. A total of q = 753 NMR buckets were included in the data matrix. Normalization to the total spectral area was applied to each integration region to account for differences in the sample amount. The metabolomic matrix includes n = 16 observations and p = 753 features. To confirm the chemical struc-tures of the metabolites of interest, 2D 1 H-1 H COSY (correlation spectroscopy) and 2D 1H-13C HSQC (heteronuclear single quantum coherence spectroscopy) NMR spectra were also generated for selected samples. Spectra assignment was based on matching the 1D and 2D data to reference spectra in a homemade reference database, as well as with another database (https://www.
hmdb. ca/, release 3. 0, accessed on 11 May 2021) and reports in the literature. Genome-scale metabolic network (GSMN) analysis was performed on the dataset of discriminant metabolites, as previously described  [48]. To this end, the Sus scrofa GSMN was imported in MetExplore web server from KEGG database (network 4105, downloaded from KEGG database 24 August 2020)  [49, 50]. The network contains 1807 meta-bolic reactions and 1520 metabolites.
Sparse CCA Analyses Canonical correlation analysis  [51]  is a method that studies the correlation relationships between two sets (or blocks) of features measured on the same individuals. Briefly, CCA maximizes the correlation between two linear combinations from the two blocks of features. The coefficients estimated for the definition of the linear combinations are used to explore the relationships between features. In this study, we used Robust Sparse CCA  [13]  for its ability to select relevant correlated features also in presence of outlier observations. Features with non-null loading values in the first pair of linear combinations (transcriptomic and metabolomic datasets) were selected. We adapted scripts from Wilms and Croux  [13]  to perform Robust Sparse CCA.
Features clustered in the same unit were considered to be correlated. A raw matrix or kernel matrix, based on a dissimilarity matrix or Gaussian kernel, were used to learn SOM. Examples of commonly used distance metrics include Euclidean distance and correlation for relative abundance data  [35]. The mixKernel  [35]  (https://CRAN. R-project. org/package=mixKernel, v0. 3, accessed on 11 May 2021) package was used to compute the kernel matrix from the original data. The SOMbrero  [53]  (https://CRAN. R-project. org/package=SOMbrero, v1. 2-4, accessed on 11 May 2021) package was used to perform SOM analyses on raw matrixes or kernel matrixes.
PLS-DA Analyses Partial least squares  [54]  (PLS) regression seeks a mathematical relationship between a quantitative biological factor of interest-drug doses, for example-and several features, such as spectral data. PLS-DA is an extension of PLS regression for categorical factors, DON exposure in this study. PLS-DA analyses were conducted using the ropls package  [55]  (v1. 16. 0). O2-PLS  [54]  is a generalization of the OPLS  [56]  regression method and can be used for combining two 'omics datasets. It models both predictive and systematic variation. The variation present in each of the two matrices is split up into three parts. The first part corresponds to the joint-i. e. , predictive (useful for the prediction of the studied biological factor)-variation. For example, in the transcriptomic data matrix, the joint variation describes expression profiles that are useful for predicting metabolite profiles.
The second part is the orthogonal part (confounding variability-e. g. , physiological, experimental, or technical variations). It corresponds to the unique systematic variation in the transcriptomic data matrix, for example. This part is not useful in predicting the metabolomic data. The final part corresponds to unexplained variance (noise). The OmicsPLS package  [57]  (https://CRAN. R-project. org/package=OmicsPLS, v1. 1. 0, accessed on 11 May 2021) was used to build O2-PLS models.
administration, M. T. -F. ; funding acquisition, M. T. -F. visualization: M. T. -F. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the French National Infrastructure for metabolomics and fluxomics MetaboHUB ANR-11-INBS-010 and by the ExpoMycoPig project ANR-17-Carn012 and by an internal Toxalim grant. Institutional Review Board Statement: All the experimental procedures were conducted under the approval of the French Ministry of Higher Education and Research (decision n • #6303_2016080314392462) and the Ethics committee of Pharmacology-Toxicology, Toulouse-Midi-Pyrénées in animal experimentation Toxcométhique (decision n • TOXCOM/0163/PP, February 2nd 2017). Three authors (I. A. K, I. P. O and P. P. ) have an official agreement from French Veterinary Services authorizing animal experimentation.
Acknowledgements FONDECYT  1160334 ,  FONDECYT 1181829 ,  FONDECYT 1171369 ,  ANID-ECOS  Project  C18B04 ,  Pontificia Universidad Católica de Chile ,  Museo Regional de Iquique, Facultad de Ciencias Sociales Universidad de Chile, Universidad Arturo Prat  ( Estación Canchones ),  Instituto de Investigaciones Arqueológicas, Museo Gustavo Le Paige. We are grateful to  Marcela Vidal  for the graphic design of Figure 1 and to  Dr. Evandro Ferrada  for critically reading the manuscript. We also thank  Sandrine Chay  ( BPMP, SAME platform, Montpellier, France ) for technical support with plant Fe determination.
Quantitative PCR reactions consisted of 3 µL gDNA template, 3. 6 µL nuclease free water, 0. 45 µL of each primer (10µM), and 7. 5 µL of iQ SYBR Green Supermix (Bio-Rad). PCR amplification were performed on a CFX qPCR system (Bio-Rad) with cycles as follows: 95°C 3 min followed by 40 cycles of 95°C 15 secs and 30 secs for hybridization. A dilution scale made from a pool of all gDNA samples was used for assessing PCR efficiency. Data were analyzed using the ∆∆Ct method  (Pfaffl, 2001)  taking into account PCR efficiency. Two control genes (the P450 AAEL007808 and the chloride channel protein AAEL005950) shown to be present as single copies in multiple Ae. aegypti strains and populations  (Faucon et al. , 2015)  were used for normalization. For each gene, CNV were expressed as mean relative gDNA quantity as compared to the fully susceptible line Bora-Bora.
Statistical analysis All statistical analyses were performed with R v3. 6. 2 (R Core Team, 2013), using the package lme4 for all mixed models  (Bates et al. , 2015). Mortality data were statistically compared across conditions by using a Generalized Linear Model (GLM) with mixed effects (binomial family) in which the replicates were included as a random factor. For comparing mean CNV obtained from pools of mosquitoes, normalized gDNA levels obtained for each gene were Log2 transformed and compared across conditions using a GLM with mixed-effects in which the replicates were included as a random factor. For comparing CNV obtained from individual mosquitoes, normalized gDNA levels were Log2 transformed and compared between genes using a one-way ANOVA. A Pearson's product moment correlation coefficient test was used to compare normalized gDNA quantities obtained from qPCR and ddPCR.
Acknowledgements The views expressed in this publication are those of the authors and do not necessarily reflect the official policy or position of the  Department of the Navy, Department of Defense , nor the  U. S. Government. This work was conducted in the framework of the  U. S. Naval Medical Research Unit TWO  projects  BIO-LAO-2  (work unit number  D1425 ) and ARBOVEC-PLUS (work unit number  D1428 ), in support and funded by the  Department of Defense Global Emerging Infections Surveillance Program and Military Infectious Disease Research Program. I (IWS and JCH) am a military Service member. This work was prepared as part of my official duties. Title 17, U. S. C. , §105 provides that copyright protection under this title is not available for any work of the  U. S. Government. Title 17, U.
Data accessibility & Supplementary material The sequence data from this study have been deposited to the European Nucleotide Archive (ENA; http://www. ebi. ac. uk/ena) under the accession numbers PRJEB37991 (RNA-seq data) and PRJEB37993 (whole genome pool-seq data). Data, scripts and supplementary material have been deposited to Zenodo (https://zenodo. org/record/4243761#. X6JBUWgzZPZ).
Acknowledgements This work was supported by the  French National Programme EC2CO-Biohefect/Ecodyn/Dril/MicrobiEn/BAC-RIP. We acknowledge the  Laboratoire des RadioIsotopes of the University of Antananarivo, Madagascar , for providing access to the experimental station in Lazaina and the greenhouse. We thank  Modeste Rakotondramanana ,  Marie-Paule Razafimanantsoa ,  Andrianantenaina Hilaire Damase Razafimahafaly , and  Eric N. Randrianasolo  for laboratory assistance. We also thank the two anonymous reviewers who allowed us to improve the quality of the manuscript.
Transcript levels were measured using a StepOnePlus Real-Time PCR System (ThermoFisher Scientific). The obtained cDNA (2 µL) was amplified in the presence of 10-µL KAPA SYBR FAST qPCR Kit (Nippon Genetics Co. Ltd. , Tokyo, Japan), 0. 4-µL specific primers (0. 2 µM final concentration), and 7. 2-µL sterile water. Transcript levels were quantified using a relative standard curve with ACTIN3 as the internal standard. A dilution series of total cDNAs were used as templates to generate the standard curves. The primer sequences used in the experiments are shown in Supplementary Data 3. Immunoblot analysis of GLN isoproteins. Immunoblot analysis of GLN proteins was performed based on a previously described method  40. Shoots were harvested, frozen with liquid N 2 , and stored at -80 °C until use.
The signal intensities for each band corresponding to GLN1s and GLN2 isoproteins were quantified using Image J software, version 10. 2. After detection, the membrane was stained with the GelCode Blue Stain Reagent (ThermoFisher Scientific). Microarray analysis. Total RNA was extracted as described above. RNA quality was assessed using an Agilent 2100 bioanalyzer (Agilent Technologies). RNA amplification, labeling, hybridization, and scanning with the 3′ IVT Express Kit (Affymetrix) and the GeneChip Arabidopsis Genome ATH1 Array (Affymetrix) were conducted according to the manufacturer's instructions. The data set from the microarray chips was normalized by the Microarray Suite 5. 0 (MAS5) method (Affymetrix). When the signal detection of a transcript was labeled "Absent" or "Marginal", the sequence was omitted from the subsequent quantitative analysis.
Cations were determined in positive ion mode (applied voltage 25 kV). For MS stabilization, 5 mM ammonium acetate (for anions) or 0. 1% formic acid (v/v) (for cations) in 50% (v/v) methanol was used as sheath solution, applied to the capillary at 10 μL min -1 using an isocratic HPLC pump (Agilent 1200 series) equipped with a 1:100 splitter. The capillary voltage (± 3500 V) and the drying nitrogen gas (at 320 °C) flow (8 L min -1 ) were held constant for approximately 30 min during each electrophoresis run. Quantitative accuracy was determined using known concentrations of standard reference compounds using Agilent MassHunter Software, version B. 07. 00. Determination of ammonium. Ammonium was extracted and its concentration was determined with slight modifications to the method reported by Hachiya et al.
Micrographs were recorded using a JEM-1400 (JEOL) transmission electron microscope. Statistical and cluster analyses. All statistical analyses were conducted using R software, version 3. 6. 2. Two-sided tests were used for Welch's t-test. Adjustments were made for Tukey-Kramer's multiple comparison test. Hierarchical clustering was performed using Gene Cluster software, version 3. 0, with "Correlation (uncentered)" as the similarity metric and "Average linkage" as the clustering method. The results were visualized using Java TreeView software, version 1. 1. 6r4. Other details of analyses are provided in the "Results" section and in the table and figure legends.
Acknowledgements Seeds of the A thaliana NR-null mutant and gln1;2 gln1;3 were kindly provided by  Dr. Nigel M. Crawford  ( University of California, San Diego ) and by  Dr. Soichi Kojima  ( Tohoku University, Sendai ), respectively. This work was supported by the  Building of Consortia for the Development of Human Resources in Science and Technology , by the  Japan Society for the Promotion of Science KAKENHI  Grant No. JP17K15237 , by the Program for Promoting the  Enhancement of Research Universities, Nagoya University  by the  Inamori Foundation , by the  Agropolis Foundation  No. 1502-405 , by a  Grant-in-Aid for Young Scientists  from  Shimane University , and by the joint research fund from  Yanmar Holdings Co. , Ltd. FOX lines were developed by the  Plant Genome Project  of  RIKEN PSC , and were provided by the  RIKEN BRC  through the  National BioResource  Project of the  MEXT/AMED, Japan.
A single N. tenuis was placed at the base of the Y-tube stem, and its first choice was recorded over a 10 min observation period. The insect was considered to have made a choice when it climbed and penetrated 3 cm inside a given arm. Eighty insects were tested per choice test and 10 insects were tested per day. Insects which did not choose either arm within the observation period (i. e. nonresponsive insects) accounted for 1-10% of all the insects tested and were not included in the data analysis. One plant was used for 10 insects, and after five insects the Y-tube was replaced by another one, and the positions of the volatile sources were switched between the olfactometer arms to account for positional bias. Between the choice assays, the Y-tubes were cleaned with dichloromethane, and before they were used again on another day they were cleaned with Teepol odourless detergent and hot water, rinsed with acetone and distilled water, then ovendried at 150 °C overnight.
40, 41 Using the function 'MDSplot' of the RF package, a multidimensional scaling (MDS) plot  42  was performed to visualise the similarity among healthy plant and T. absoluta-infested plant categories. A sparse partial least square discriminant analysis (sPLS-DA) biplot was performed in the mixOmics package  43  to illustrate how the discriminating VOCs were correlated with healthy and T. absoluta-infested plants. The function 'perf' and the 'leave-one-group-out' cross-validation method in the mixOmics package, as well as the sPLS-DA parameters (R2X, R2Y and Q2) served to validate the sPLS-DA model. 44 A clustering heatmap, using the function 'cim' in the mixOmics package,  44  was performed to illustrate variations in the emission of the most discriminating VOCs across replicates of healthy and T.
ACKNOWLEDGEMENTS We acknowledge the financial support provided by the  French National Research Agency (ANR)  through  CIRAD  ( Award  no. ANR-16-CE32-0010-01 ), the  University of Pretoria  and the  National Research Foundation  through the  NRF  grants for C. W. W. P (Grant no. CPRR160502163617 ) and A. A. Y (IFRR Grant no. 109380  and Y-rated Researchers Grant no. RDYR180504326262 ). The authours also gratefully acknowledge financial support provided by the following icipe core donors: the  UK Foreign, Commonwealth & Development Office , the  Swedish International Development Cooperation Agency , the  Swiss Agency for Development and Cooperation , the  Federal Democratic Republic of Ethiopia  and the  Kenyan Government. P. M. A. was supported by the  University of Pretoria  and the  German Academic Exchange Service   In-Region Postgraduate Scholarship (Personal Grant  no.
Data analysis The single tree stem volume of Norway spruce and Scots pine was estimated using the functions developed by  Brandel (1990)  while Lodgepole pine volume was estimated with a function developed by  Eriksson (1973). DBH, HT, and HL were used to calculate Norway spruce stem volume, while only DBH and HT were used for Scots pine and Lodgepole pine calculations. Stem volume for all trees was estimated by assigning volume to caliper trees in diameter classes using an algorithm described in  Nilsson et al. (2010). In addition to volume production,  Periodic Annual Increment (PAI) was calculated for each species using the total volume yield difference between the two measurements. Site index (top height (m) at total age 100 years) was estimated with generally applied site curves from measured heights at the last measurement.
Individual tree diameters were used to calculate shape and scale parameters for each planting design for all the replicates and species. Stand heterogeneity was also quantified computing Gini coefficients of stem diameters within the different stands and rectangular designs. The ineq package in R version 3. 2. 5 (R Core Team, 2015) was used to calculate the Gini coefficients. The effects of different planting designs on external wood-quality properties that may affect the value of lumber and stability of the tree and stand was described by stem ovality, branch thickness (BT) at breast height, height to the first living crown (HL), and height-diameter ratio (HD). Stem ovality was calculated using the two-directional DBH of individual trees. Relative ovality was calculated by subtracting the twodirectional diameter of the tree and divided it by the average diameter of the tree.
External wood quality External wood quality properties measured in this study did not depend on different planting designs for Norway spruce, Scots pine, and Lodgepole pine. The spatial design has an insignificant effect on the branch thickness (BT) and there was no difference in branch thickness depending on their orientation to the row (Table  3 ). Stem ovality also did not differ significantly at any planting design for all the species in the last measurement (Table  3 ) or in the first measurement (data not shown). The height of the living crown (Fig. 3 ) and height-diameter ratio (Fig. 4 ) were also not significantly different among different planting designs for any of the species used in this experiment (Table  2 ).
Sampling description Soil samples were collected along the frontal (M1-3) and lateral (M4) moraines of the Chamser Kangri glacier chronosequence (5711, 5710, 5700 and 5598 m a. s. l. , M1-M4 respectively). The age of the chosen soils was estimated to be <1, 25, 50 and > 100 years, for M1 to M4, respectively, according to the slope and relative ice cover loss of 0. 4% km -2 year -1  (Dvorský et al. , 2015). Sites M1 and M2 had no vegetation cover, M2 and M3 had weakly and well-developed soil biological crusts, respectively, M3 was sparsely covered by Thylacospermum caespitosum (Caryophyllaceae   S2 ). The average soil temperature (5 cm below surface) during the week (23/2017) of sampling was 0. 18°C, which corresponded to previous years values (based on long-term data collected from installed microclimatic stations TOMST®, Figure  S1A ).