dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was produced within the framework of the  European  project  "Diversification through Rotation, Intercropping ,  Multiple Cropping , Promoted with  Actors and value-Chains towards Sustainability" (DiverIMPACTS) , funded by the  European Commission  under Grant Agreement number  727482. This work was also partly funded by the STIMUL (Scenarios towards integrating multiscale land use tools) flagship project as part of the " Investments d'Avenir" Programme (LabEx BASC ;  ANR-11-LABX-0034 ), and by the  CLAND institute of convergence  ( 16-CONV-0003 ). We thank  A. Bône  for assistance with the literature search and  A. Gardarin  for assistance in the categorization of outcomes. We are grateful to the authors who provided additional information.
Plant material and experiments The panel consisted of 60 maize (Zea mays. L. ) hybrids released in Europe from 1950 and 2015, which were the most successful in their generation according to the Arvalis database (https://www. arvalisinstitutduvegetal. fr) and experts in the field. These hybrids belonged to midearly/mid-late maturity groups, with leaf number from 14 to 18, and originated from the programmes of selection of different breeding companies based in Europe.
2017). Virtual 3D representations were computed using a space carving algorithm  (Kutulakos & Seitz 2000)  and represented by a set of 3D voxels of 0. 512 cm 3 each. This voxel set was then transformed into a triangular mesh using the marching cube algorithm  (Lorensen & Cline 1987 ) and a 90% decimation  (Schroeder et al. 1992 ) implemented in a VTK library  (Schroeder et al. 1995). This transformation into triangular meshes was achieved in order to obtain a 3D surface reconstruction that conserved the plant leaf area estimated by regressions with pixel number. The open-source software pipeline Phenomenal (https://github. com/openalea/phenomenal; Fournier et al. 2015)  processed the images, stored intermediate results and distributed the computation on the grid via the cyber-infrastructure InfraPhenoGrid  (Pradal et al.
SI 2 B); ii) grouping leaves into azimuthal directions by applying a cluster analysis based on matrix distance of voxel azimuths, and considering the azimuth of the detected radial peaks as cluster medoids (medoids with difference in azimuth below 15° were merged to form a unique cluster) (Fig. SI 2C ). Each group was characterized by the number of voxels forming it, an azimuth (median azimuth of the grouped voxel) and a radius (median radius of the grouped voxels); iii) clustering the groups into two main azimuthal directions using a hierarchical cluster analysis (hclust procedure of the R software; R Core Team, 2017) applied on group azimuths weighted by the number of voxels per group (Fig. SI 2D ). This clustering analysis allowed estimation of σaz, which was computed as the mean difference between group azimuths with the associated main direction.
Imaging in the field experiment In the field experiment, vertical upward-looking hemispherical images (2640 × 1760) were taken using a Sigma SD-14 camera equipped with a fisheye lens of 8 mm focal. For each micro-plot and date, 10 images were taken early in the morning (before 8 am). At each sampling date (June 15 th , June 21 st , June 29 th , and July 19 th ), the camera was oriented North with the lens kept horizontally towards the sky and images were distributed over two diagonal segments placed between the two central rows of each micro-plot as detailed in  Baret et al. (2018). Images are available at http://www. phis. inra. fr/ under an Open Source license (CC-BY-NC-SA). Images were analysed using the Ilastik (Ilastik software, version 1. 21. 7) (Sommer et al. 2011 ) built-in pixel classification machine learning procedure based on a training set of contrasting images involving micro-plots of different hybrids at different dates.
Simulation of light interception and radiative variables in virtual canopies Radiation interception efficiency (RIE) was estimated considering the 3D representation of each plant in a virtual toric field to represent a homogeneous canopy under three contrasting plant densities (5. 5, 9 and 12. 5 plants m -2 ) with 0. 8 m row spacing. The toricity allows one to remove border effects as if the canopy was infinite. Light interception was estimated using the Caribu light propagation model  (Chelle & Andrieu 1998) , combined with a sky radiance distribution model mixing SOC  (Moon & Spencer 1942)  and clear sky  (Perez et al. 2002)  distributins according the mixing ratio proposed by  Mardaljevic (2000). These models were calibrated with daily meteorological data to reproduce appropriate sun path over a day (segmented into 46 directions).
Heritability Heritabilities were estimated using the following mixed-effect model for each variable: (eq2) Where Yijsd is the phenotypic value of the considered variable for plant i for the water scenario s and the day d, µsd is the overall mean, Gjsd is the effect of genotype j and Eijsd the residual effect. Models were fitted for each water scenario and every day after sowing. Broad sense heritabilities on plant leaf area (LA) and the seven architectural traits (hwhorl, θ, rhPAD, bPAD, radius, σaz and Δrow) were calculated as: (eq3) The variance components were estimated using the 'lmer' procedure of the 'lme4' R package (R Core Team, 2017).
The improved distribution of absorbed light applied to low and high densities We performed simulations with three plant densities because it has been stated that part of the genetic progress originates from the plant ability to grow at high densities. Studied densities were 9 plants m -2 , common in Europe, plus a high density, 12. 5 plants m -2 , increasingly used for silage, and a low density of 5. 5 plants m -2 that was used at the beginning of XX th century in southern regions. These simulations were performed with the 3D representations of plants in the greenhouse regardless of plant density, thereby neglecting the adaptation of plant architecture in the considered range of plant densities. Indeed, calculations performed on datasets collected on previous studies suggested a low interaction between these adaptations and generations of selection  (Sangoi et al.
Acknowledgements This work was supported by the " Infrastructure Biologie Santé" Phenome  funded by the  National Research Agency  ( ANR-11-INBS-0012 ) and the  Agence Nationale de la Recherche  project  ANR-10-BTBR-01  (Amaizing). FT was also supported by the  EU  project  H2020 731013 (EPPN 2020  ). We are grateful to all members at the  DIAPHEN and PhenoArch platforms  for providing technical support, conducting the experiments and collecting data.
Finally, machine learning algorithms have gained increasing attention to handle high dimensional datasets where the number of potential explanatory variables vastly exceeds the number of observations, and to select an optimal subset of variables for classification or prediction of particular phenotypes. The importance scores generated by random forest and support vector machines, two methods considered as state-of-the art of machine learning algorithms, have been recently applied to whole-genome molecular markers for prediction of RFI in beef cattle  [14, 15]. Support vector machine, random forest, elastic net, and nearest shrunken centroid algorithms have been also successfully tested for their ability to classify extreme pigs on high/low RFI from RNAseq data in liver and duodenum  [16]. When applied on microarray datasets from human subjects to predict health outcomes  [17] , the best prediction accuracy was obtained for the gradient tree boosting machine (GTB) among seven machine learning approaches including random forest and support vector machine.
Likely, this adds to the difficulty to find common candidates to explain and predict FE. Combining different molecular datasets to provide a larger number of animals and wider ranges of experimental conditions may be a relevant strategy to obtain a robust description of predictors involved in the phenotypes of interest  [19, 20]. This study aimed to identify important muscle genes for prediction of FE in growing pigs. Experiments run on two pig lines divergently selected for RFI that have been already analyzed separately  [4, 8]  and included differences in selection generations, feed allowance and diet composition, sex and live weight of the pigs were combined. The GTB algorithms were then used as a resampling machine learning approach to re-examine muscle microarray datasets and to predict different FE traits.
Descriptive statistics on merged molecular datasets Microarrays data from longissimus muscle of 71 barrows and female pigs of two lines divergently selected for RFI and reared under different experimental conditions were reanalysed from available molecular repositories. A total of 20,405 expressed annotated molecular probes were successfully matched over repositories and included in a new merged dataset. In this new dataset, pigs were ascribed to low or high RFI groups according to their genetic lines. Traits related to FE were available from references publications  [21, 22]  or newly calculated from data obtained on littermates in the selection farms  [3]. Descriptive statistics are shown in Table  1. The RFI breeding values (RFI-BV) ranged from -108. 7 to 91. 6 g/ d, and the mean value was significantly lower, as expected from selection, for pigs of the low RFI line than for pigs of the high RFI line.
The FCR was 2. 75 kg/kg on average (min = 2. 25; max = 3. 28). It was 27 MJ/kg BW on the net energy basis (FCRe: min = 22; max = 32). As expected, pigs of the low RFI line had lower feed conversion ratios (FCR, FCRe) than pigs of the high RFI line (P < 0. 001). Because pigs were reared under different experimental conditions, a large range of values for feed intake (FI) was observed (min =1725 g/day; max = 3026 g/day). A large range of FCR values was covered in the merged dataset, with some interpenetration between pigs of the low and high RFI lines (Fig. 1 ). Principal Component Analysis (PCA) was then used to handle the 20,405 expressed probes in longissimus muscle on a common frame, to detect outliers and visualize links between variables. The first principal component (PC1) summarized 46.
8% of the observed variance, whereas the second principal component (PC2) summarized 13. 7% of the variability. Pigs of the low RFI group were opposed to pigs of the high RFI group on PC1 (Fig. 2 ), whereas PC2 did not allow any distinction between the two RFI groups. The FCR was significantly (P < 0. 001) correlated with PC1 (r = 0. 53) and PC2 (r = -0. 41). The FCRe was also significantly (P < 0. 001) correlated with PC1 (r = 0. 49) and PC2 (r = -0. 44). The partition of pigs due to their genetic lines (low or high RFI) and the datasets of origin is shown in Suppl. File 1.
80; RMSEP = 0. 09), and no more difference was observed in model prediction quality between low RFI (R 2 = 0. 71; RMSEP = 0. 10) and high RFI (R 2 = 0. 75; RMSEP = 0. 09) lines. A detailed representation of the regression slopes when calculated within the RFI groups and datasets of origin is available in Additional file 5.
Moreover, the longissimus muscle was found as the primary affected site, among four examined tissues including liver, by a divergent selection for RFI in growing pigs  [4]. Therefore, we focused on microarray datasets obtained from longissimus muscle in different studies. Good accuracy was found (R 2 ~0. 65-0. 70) for gradient tree boosting (GTB) models in prediction of RFI-BV, the measure of the net FE which was used as the selection criterion, and FCR, another measure of FE that is more easily obtained in most circumstances in pig farms. The error of prediction for FCR was less than 8% when calculated on all pigs. While data were included in the model without any (supervised) indication of genetic lines of origin, the deviation between observed and predicted values was higher for pigs from the high RFI line than for pigs from the low RFI line.
Bad prediction concerned seven pigs with very high FCR, with no apparent bias arising from diets and feeding regimen. This suggests that the model cannot handle very high FCR values because they might be under a different metabolic control not captured in the dataset. In support, the metabolic phenotypes described in the referenced publications  [21, 22]  indicated higher glycaemia (1. 7 vs 1. 4 g/L) and leptinemia (1. 9 vs 2. 87 ng/L) but lower blood phospholipids concentrations (0. 83 vs 1. 0 g/ L) in the seven pigs for which prediction of FCR largely deviated from the observations. These blood parameters may refer to something associated to energy homeostasis. Therefore, GTB models were built to predict FCR when expressed on a net energy basis. The prediction accuracy (R 2 ) was slightly lower for FCRe than for FCR, and this may sign how the environment influences the two traits  [24].
Predictive models were built from microarrays transcriptomics dataset to identify the most important annotated expressing probes in the longissimus muscle able to predict breeding values of RFI, and feed-conversion-ratio (FCR) and net energy-based feed conversion ratio (FCRe) values. The lists of these probes identified as VIP (very important variables in prediction) were then uploaded by their corresponding gene name in the VENNY tool. Venn diagram was edited to enlighten commonalities between the lists of unique genes identified as VIP for the three traits protein family which plays a role in immune regulation and basic cellular processes involving protein folding and trafficking. In juvenile pigs, FKBP5 was proposed as a candidate gene for a better understanding of the stress response, notably due to its connexion with the glucocorticoid receptor  [27].
Mixing datasets encompassing different rearing conditions would have also maximized the chances for genericity of the candidates. However, the next steps will be to test the ability of the identified muscle genes to Abbreviations used: FCR Feed conversion ratio, FCRe Net energy feed conversion ratio, RFI Residual feed intake, VIP Very important variable in prediction. Muscle transcriptomes from pigs (n = 71) of two lines divergently selected for RFI and reared under different conditions were considered. The qPCR technology was used to assess expression levels of target genes that were identified by a gradient tree boosting procedure as very important for prediction (VIP) of RFI, FCR or FCRe individual values. ANOVA was then used to evaluate the differences in expression levels of those genes between the two RFI lines bold face highlights significant differences (P < 0.
Conclusions This study demonstrates the feasibility of finding few molecular predictors of complex traits such as feed efficiency from microarray datasets. Good accuracy of prediction models was obtained for RFI, FCR, and FCRe to a lesser extent, by using the expression levels of 6 to 267 expressed genes in longissimus muscle of pigs under different diets, feeding regimen and years. Other studies are required to validate these candidate genes in different studies and confirm the generality of the obtained predictions as planed via the combination of experimental designs. Our study can be viewed as a proof of concepts that small subset of expressed genes can be identified as a proxy of complex traits such as feed efficiency. Further studies must be conducted to apply the same procedures to peripheral blood as a relevant and easy sampling source of biological information.
Microarray data sets In the merged dataset, pigs were classified into low or high RFI groups, respectively, according to their genetic lines (n = 40 for the high RFI line and n = 31 for the low RFI line). Selection principles have been described by Gilbert and colleagues  [3]. Breeding values for RFI (RFI-BV) were newly estimated for these pigs by running a genetic evaluation of the trait combining data obtained on the full pedigree, based on their relative performance recorded in the selection farm (INRA GenESi, Rouillé, France). The RFI-BV were-64. 2 + 18. 5 g/d on average in low RFI pigs and + 54. 2 + 16. 3 g/d in high RFI pigs. Feed conversion ratio (FCR) was calculated from individually measured daily feed intake (FI) and average daily gain (ADG) during the test periods as explained in the referenced publications  [21, 22].
Net energy-based feed conversion ratio (FCRe) was obtained by considering net energy composition of the different diets together with daily FI and ADG of each pig. Descriptive statistics and gradient tree boosting procedure Processing of molecular data In the original publications, raw spot intensities obtained after hybridization reactions have been submitted to quality filtration (intensity, uniformity, saturation, and outlier detection) and intensities of filtered spots have been log2 transformed. Microarrays datasets from the different experiments were merged in a single new dataset by using the probes ID as references. This merged dataset included 20,405 annotated muscle probes expressed in the 71 pigs. In the merged dataset, the molecular data were normalized by mean centering, i. e.
Therefore, at each stage of gradient boosting, it was assumed that there was some imperfect model so that the gradient boosting algorithm was improving it by constructing a new model that added an estimator to provide a better model. Moreover, the accuracy of the algorithm was typically improved by introducing randomization through training the base learner on different randomly selected datasets at each iteration. As opposed to neural networks, this methodology is not sensitive to data errors and needs no time-consuming data preparation, pre-processing or imputation of missing values. In the present study, GTB prediction models were generated with the Salford Predictive Modeler 8. 0 (SPM 8. 0®) software. In the current study, about 1114 to 1500 trees were created for each FE trait, with each tree typically containing about six terminal nodes as recommended for boosting  [35].
Each tree was built on a randomly selected bootstrap sample, by using 70% of the original dataset for learning (n = 50 pigs) and a randomly selected subset of variables. Consequently, each bootstrap sample called "out-of-bag" data (OOB) excluded 30% of the data that were used for testing in the validation step (n = 21 pigs). The random partition of the muscle samples between learning and validation sets in relation to the datasets of origin was carefully checked. Significant variables were selected using the Gini index to evaluate discriminative ability defined as: Gi ¼ 1- X j pj jjt ð Þ Where pj ( | ) is the estimated class probability for feature or node in a decision tree and is an output data. Only the variables that improved Gini index and minimized the OOB error rate were retained as VIP.
The root mean square error (RMSE) was then calculated as the square root of the difference between the realized and the predicted observation within the OOB data after permuting each predictor variable in the training dataset divided by the number of trees. The adjusted coefficient of determination (R 2 ) was also computed. For FCR, predicted (X) and observed (Y) values were further compared (X -Y), using the GLM procedure. The model was considered unbiased when the intercept was not different from 0 and the slope was not significantly different from 1. The quality of the relationships was evaluated on the basis of RMSE of prediction (RMSEP), obtained by a leave-one-out cross-validation from the value of the predicted residual sum of squares (PRESS) statistics  [36]. Model evaluation was performed for all pigs, and RFI line by RFI line.
Functional pathways represented across the VIP The gene ontology terms for biological processes (GOBP) were automatically searched within each list of VIP which were uploaded by their official gene symbol. The Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics resource database (v6. 7; http://david. abcc. ncifcrf. gov) was used, with Homo sapiens as background for mapping and enrichment analysis. The results were downloaded using the "Functional annotation clustering" option of the DAVID tool, and medium clustering stringency was selected to generate the functional groups across the genes based on a priori knowledge  [37]. For each term, the enrichment (E) score (measured by the geometric mean of the EASE score of all enriched annotations terms for each cluster) and the modified Fisher exact P-value were obtained.
Quantitative real-time PCR (qPCR) Expression levels of 24 target genes identified as common VIP for at least two FE traits and (or) participating to pathways judged as biologically-relevant for one FE trait, were further determined in the same longissimus muscle samples (n = 71) where microarrays data have been generated. The SmartChip Real-Time PCR system (Wafergen) available at the Human and Environmental Genomics (GEH) technological core facilities (Rennes, France), was used. Total RNA was extracted as described previously  [4, 8]. First-strand cDNA synthesis was performed with 1 μg of total RNA used for microarray analysis, by using High Capacity RNA to cDNA Kit (Applied Biosystems, Foster City, USA). Primers (Additional file 7) were designed from porcine sequences available in Ensembl or NCBI databases using Primer Express® v3.
0 software (Applied Biosystems). Detailed information on the primer sequences (forward and reverse) is provided in Additional file 4. Amplification reactions were carried out using LightCycler 480 SYBR Green 1 Master (Roche Diagnostics, Meylan, France) with a final cDNA concentration of 1 ng/μL and a primer concentration of 500 nM dispensed using the WaferGene SmartChip Multisample Nanodispenser. Amplification conditions were as follows: 5 min at 95 °C followed by 50 cycles of 30 s at 95 °C, 30 s at 60 °C and 30 s at 72 °C, followed by 15 s at 95 °C and 1 min at 60 °C. Specificity of the amplification products was checked by dissociation curve analysis. As stated by the GeNorm algorithm (https://genorm. cmgg. be/), RPL4 and TBP1 were identified as the most stable housekeeping genes among other tested reference genes, and were used to calculate the normalization factor (NF).
Linear regression models Differences in expression values of the 24 genes between low and high RFI groups were first evaluated using variance analysis (GLM procedure) for the effect of RFI line on the SAS software (SAS, Cary NC). Then, regression models on mRNA levels of these 24 genes were applied to determine the prediction accuracy for RFI, FCR and FCRe, respectively, considering RSQUARE selection (i. e. , the independent variables that best predict the dependent variable by linear regression) or stepwise option (i. e. , the variables are added one by one to the model). As recommended, P < 0. 15 was used as threshold to retain significant variables in the stepwise regression model. Additional regression analyses were also performed using final BW as an explicative factor of FCR in supplement to expression levels of the target genes.
Number of probes and encoded genes identified as VIP for feed efficiency traitsMachine learning procedure (gradient tree net boosting) was applied on microarrays dataset (20,405 expressed annotated probes) generated from the longissimus muscle of 71 growing pigs to identify models able to predict residual feed intake (RFI), feed conversion ratio (FCR) and net energy-based feed conversion ratio (FCRe). A randomly selected bootstrap pig sample (n = 50) was used for learning, whereas the remaining pigs (n = 21) was used for validation test. The first rounds led to model stabilization with 384 molecular probes as very important variables (VIP) for RFI-BV prediction, 421 probes for FCR prediction and 318 probes for FCRe prediction, respectively, out of the 20,405 expressed annotated probes.