dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016) , which also uses spaMM internally, but which is dedicated to performing isoscape analyses and designed to be very simple to use. The latter package also allows for the inference of origin location of environmental materials using mixed models. All these packages are free and open source, similarly to the R environment required to run them. By providing our approach in such a way, we allow for an efficient and objective comparison of different methods. We thus encourage other authors to make their code available and to compare alternative methodology for building isoscapes to ours. We also invite anyone interested to contribute to the development of IsoriX using GitHub (https://github. com/courtiol/IsoriX_project).
The French Institute for Radiation protection and Nuclear Safety (IRSN) is providing reference neutron fluence energy distribution at its standard monoenergetic neutron fields, produced at the AMANDE facility. The neutron energy is assessed by measuring the recoil nuclei energy in a µTPC detector, the LNE-IRSN/MIMAC detector. The knowledge of the ionization quenching factor (IQF) is fundamental to determine the kinetic energy of the recoil nuclei. For some various gases and pressures, discrepancies of about 15% were observed between IQF calculations using the SRIM software and experimental measurements. No data are available for the iC4H10 + 50% CHF3 gas mixture which are used for measurements from a few keV up to 565 keV neutron energies in the µTPC detector. The experimental determination of the IQF is of primary importance to provide reference neutron fluence energy distribution.
To assess this quantity, the Ionization Quenching Factor (IQF) has to be determined. The IQF, which is the ratio of the ionization energy and the kinetic energy (Eq. 1), depends on the gas mixture, the pressure, and the energy of the recoil nuclei, ‫ܨܳܫ‬ ൌ ா ೌǡ ா ೖǡ (1) In the energy range of our interest, and for the chosen gaseous composition, no experimental data are available. The only way to estimate the IQF in our working conditions is to do simulations using SRIM  [6]  software. To perform experimental measurements of the IQF the LPSC has developed a dedicated facility, called COMIMAC  [10].
Electronics and acquisition system The fast electronics as well as the data acquisition software use to perform this experiment has been developed at the LPSC  [11]. In the detector, each particle releases its energy by ionizing the gas; this induces a charge current on the grid of the Micromegas  [12]  which is proportional to the ionization energy deposited. This charge current is then treated by a preamplifier and the output signal is further sent to the FPGA, located on the printed circuit board. The value of the collected charge is recorded and finally sent to the acquisition software. This allows reconstructing for each event a charge collection profile whose amplitude is proportional to the energy deposited by the particle in the detector.
Proton measurements The kinetic energy of proton beams is determined by the extraction voltage applied on the electrodes see Figure  2. This method has been validated by comparison of X-ray sources with electron beams. In this experimental campaign, measurements were performed for eight different proton energy beams, between 1. 5 keV and 25 keV. For each proton beam a minimum of 5000 counts under the proton peak was achieved with a data acquisition time of about 5 minutes each.
Energy calibration By analysing each electron event; it is then possible to plot a histogram about the number of events as a function of the integrated charge value measured on the charge preamplifier profile, which corresponds to the ionization energy released in ADC channels. The spectrum of an electron calibration is shown in Figure  6. The spectrum shows 3 peaks and a continuum background. The peaks correspond respectively to the three energies of electrons produced by X-rays used to calibrate. The continuum observed is induced by the incomplete collection of electrons with energy greater than 10 keV (like X-ray at 22 keV of 109 Cd). The analysis of this spectrum enables to build a calibration curve between ionization energies and ADC channel numbers. In order to determine the parameters associated to this calibration curve, the software MCARE  [13]  is used.
ICRS-13 & RPSD-2016 keV and 0,2 keV and 0,04 keV respectively) are realized with the probability distribution of X-ray for these three energies. As explained before, the software MCARE allows calculating parameters for the energy calibration curve. Moreover, it gives the uncertainty associated to each point on the curve too. This is needed to perform the calculation of uncertainty on IQF measurements, as it will be explained in section 5.
Results Before this experiment, no experimental data on Ionization Quenching Factors were available in literature in our energy range of interest. As seen in the section 1, the only way to estimate the IQF was to use the SRIM simulation software. In Figure  9  are presented the IQF values as a function of proton energy in a 50% C4H10 + 50% CHF3 gas mixture, computed by SRIM simulation in red and those deduced from the data obtained during this experimental campaign. Uncertainties of experimental data are not visible on the Figure  9  because they do not exceed 0,01, which correspond to 1. 16% of uncertainties at 1. 5 keV. The COMIMAC facility allows measuring the Ionization Quenching Factor of a gas mixture in-situ taking into account the pollution in the chamber with low uncertainties. These results will become the reference for the IQF of proton in 50% C4H10 + 50% CHF3 gas mixture at 50 mbar.
The IQF depends on the amount of pollution and on the pressure of the gas. Indeed, the SRIM simulations on the Ionization Quenching Factor do not show this dependency on the gas pressure. The SRIM simulation, shown in figure  8 , overestimates the IQF of protons in this case. This difference represents the limitations of SRIM calculations on the IQF estimation in this low energy range. Indeed, this software is only optimized and validated for calculations around a few MeV per nucleon when this work concerns energies around a few keV. It is theoretically expected that at higher energies the IQF will be closer than 1. But it appears that the limitations in the energy range of COMIMAC (E < 50 keV) prevent to explore the IQF in the MeV range. However, for fast neutron fields characterization in the MeV range an exploration of the IQF in such a range of energy will be performed in a future work.
Mutagenesis, genetic suppressor screen, and sequencing We performed EMS mutagenesis on a CUC2g-m4 line. Seeds were mutagenized by agitation with 0. 3% EMS for 17 hours, neutralized with 5 ml of 1 M sodium thiosulfate for 5 min, and washed in distilled water. Approximately 6000 mutagenized seeds were sown on soil and M2 seeds were harvested in bulks of 25 plants. Approximately 300 M2 plants per bulk were sown and screened for suppression of the leaf lobbing phenotype by visual inspection. An F2 mapping population was generated by backcrossing folivora to the parental CUC2g-m4 line. Genomic DNA was isolated from ~125 folivora mutants segregating within the F2 and pool sequenced using Illumina technology by the Earlham Institute (formerly The Genome Analysis Centre, Norwich, UK). Sequencing data were analyzed using the SHORE and SHOREmap pipelines for identification of single nucleotide polymorphisms with high frequency within the sequenced folivora population  (Schneeberger et al.
Dissection index calculation The leaf dissection index (DI) shape quantification method is inspired by work reported in  Sicard et al. (2014). DI was calculated as leaf_DI=(leaf_perimeter 2 )/(4π*leaf_area) for each individual leaf. To take into account overall leaf shape, the same calculation was performed for the alpha-hull of each leaf, which is a generalization of the convex hull around the leaf serrations and petiole. Alpha-hull perimeter and area were obtained using the R package alphahull (https://CRAN. R-project. org/package=alphahull). The alpha-hull DI was calculated using the formula ahull_DI=(ahull_perimeter 2 )/ (4π*ahull_area). The final DI value, which was compared between genotypes, was calculated as the ratio between leaf_DI and ahull_DI and represents an integrative value to quantify leaf shape.
Expression data and quantification Laser assisted microdissection and RNA extraction Leaf margins were microdissected with the ZEISS PALM MicroBeam using the Fluar 5x/0. 25 M27 objective. Leaves under 1 mm long were hand-dissected and placed on MMI membrane slides, and microdissected samples were collected in ZEISS AdhesiveCaps. Cutting parameters were adjusted throughout the experiment. Approximately 20 microdissected leaf margins were collected in each sample. RNA was extracted from samples using the Arcturus PicoPure RNA Isolation Kit, following the manufacturer's instructions. RNA quality was controlled using the Agilent RNA 6000 Pico Kit.
Lack of GDP-L-fucose in mur1 loss-of-function mutants leads to leaf serration defects To dissect the role of MUR1 in leaf development, we performed a detailed morphometric analysis of mur1 loss-offunction mutants. mur1-1 and mur1-2 plants have smaller rosettes and leaves that appear smoother than wild-type leaves (Fig. 3A ). The mature leaf shape of 25-day-old L6 leaves from Col-0, mur1-1, and mur1-2 was analyzed using MorphoLeaf software  (Biot et al. , 2016)  (Fig. 3B ). Mean leaf shape analysis showed that both alleles of mur1 mutants produce leaves that are smaller and less serrated than the wild type, suggesting that MUR1 is involved in leaf development. Next, we reconstructed detailed developmental trajectories of leaves using MorphoLeaf  (Biot et al. , 2016). Because the primordia initiation rate is comparable between mur1-1 and Col-0 (Supplementary Fig.
Acknowledgements We thank  Stéphane Verger  for his help with SHORE and SHOREmap pipelines;  Jasmine Burguet  for advice with R analysis; and  Herman Höfte  and  Aline Voxeur  for discussions. This work was supported by the  Agence National de la Recherche  grants  LEAFNET  ( ANR-12-PDOC-0003 ) and  MORPHOLEAF  ( ANR-10-BLAN-1614 ). MC was partly supported by the  Fundacion Alfonso Martin Escudero. The IJPB benefits from the support of the  Labex Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ).
The mlasso method has been implemented in the glmnet R package and assumes that there is some underlying subset of the coefficients in X which are related to all components of the response Y. The multivariate lasso (mlasso) problem is stated as: Bmlasso = argmin B∈R p×q ⎧ ⎨ ⎩ Y -XB 2 F + λ q j=1 B jT 2 ⎫ ⎬ ⎭ , where • F denotes the Frobenius norm, and B jT denotes the jth row of B. The MRCE method proposed by  Rothman et al. (2010)  has been implemented in the MRCE R package  (Rothman, 2017). The multivariate regression with covariance estimation (MRCE) method producing a sparse estimator of B which depends on the inverse of the covariance matrix Ω = Σ -1 is stated as: ( B, Ω) = argmin B∈R p×q ,Ω∈R q×q ⎧ ⎨ ⎩ L(B, Ω) + λ 1 j =j |w j j | + λ 2 q j=1 q k=1 |β jk | ⎫ ⎬ ⎭ , ( 22 ) where L(B, Ω) is the negative log-likelihood function and Ω = [w j j ].
Note that BGL-SS and BSGS-SS are designed for univariate response but are also used in the multivariate response simulation (viewed as q univariate regressions) and the results are pooled to obtain prediction performance. The different Bayesian methods used have been implemented in our R package MBSGS  (Liquet and Sutton, 2017 ) available on CRAN. The posterior mean and posterior median are both used as our Bayes estimators and we compare their variable selection and prediction performance. For our Bayesian methods, we generate data from the full posterior distribution with a Gibbs Sampler, running 20000 iterations in which the first 10000 are burn-ins. For MBGL-SS and BGL-SS, we set a = b = 1 for the hyperparameters relating to π 0. For MBSGS-SS and BSGS-SS, hyperparameters relating to the Beta distributions of π 0 and π 1 are chosen to be a 1 = a 2 = c 1 = c 2 = 1.
, 2010)  to perform the lasso method for univariate and multivariate responses. The SGL R package  (Simon et al. , 2013)  is used to perform the group and sparse group lasso methods for the univariate setting. The MRCE R package  (Rothman, 2017)  is used to perform the multivariate regression with covariance estimation. The tuning parameters for the frequentist methods are calibrated using 5-fold cross-validation.
True models The data have been generated from the following multivariate model: Y = XB + E where E ∼ MN n×q (0, Σ, I n ), B = [B 1 , B 2 , B 3 ] and q = 3. For all models, we assumed strong levels of correlation between the first and second outcomes, and weaker levels for the other pairwise correlations. Specifically, we defined Σ = ⎛ ⎝ 1 0. 95 0. 5 0. 95 1 0. 3 0. 5 0. 3 1 ⎞ ⎠. ( 23 ) We considered the following nine simulations setting: • Model 1. We simulated data sets with n = 100 observations and p = 20 covariates divided into four groups with five covariates each. We randomly sampled 60 observations to train the model and used the remaining 40 for comparing performance prediction. Let B T = ⎛ ⎝ 0. 3 -1 0 0. 5 0. 01 0 5 0. 8 5 0 5 0. 2 -1. 1 0 0. 6 0. 02 0 5 0. 7 5 0 5 0. 1 -1. 2 0 0.
Application to real data In this section, we present the results of the application of our approaches to investigate genetic regulation. To discover the genetic causes of variation in the expression (i. e. transcription) of genes, gene expression data are treated as a quantitative phenotype while genotype data (SNPs) are used as predictors, a type of analysis known as expression Quantitative Trait Loci (eQTL). Here, we use a dataset which comes from a larger study  (Heinig et al. (2010) ) from which we selected the Hopx genes, as in  Petretto et al. (2010). This dataset has been also analyzed by  Liquet et al. (2016a)  who used a Bayesian model to identify a parsimonious set of predictors that explains the joint variability of gene expression in four tissues (adrenal gland, fat, heart, and kidney).
Simulation results also suggest that the multivariate Bayesian group lasso with spike and slab prior is strongly influenced by a combination of different group size structures and high correlation between predictors. The multivariate Bayesian sparse group selection with spike and slab prior does not suffer in this situation. This approach seems to be the most powerful method for variable selection and prediction performance in the presence of group structure data except in the extreme case of independent predictors and small sample size. All numerical results from this article can be reproduced using our R package MBSGS  (Liquet and Sutton, 2017)  available on CRAN. We have illustrated our methods on a challenging dataset involving gene expression data (q = 4, n = 29) and SNP explanatory variables (p = 770) with the group structure (G = 20) defined by chromosomes.
Model 4. The simulation setting is the same as for the previous model except for the true B. Let 2, some relevant covariates are non-zero for two responses and zero for the other one. • Model 5. We simulated data with n = 100 and p = 40 covariates divided into 4 groups with 10 covariates each. We randomly sampled 60 observations to train the model and used the remaining 40 for comparing performance prediction. Let simulated in the same way as in model 2. • Model 6. The simulation setting is the same as for the previous model except for the true B. LetB T = ⎛ ⎝ 0 10(2 5 , 0 5 ) 0 10 , (2 5 , 0 5 ) 0 10 (1 5 , 0 5 ) 0 10 , (1 5 , 0 5 ) 0 10 (0. 5 5 , 0 5 ) 0 10 , (0. 5 5 , 0 5 ) Model 7. We simulated data with n = 240 and p = 500 covariates divided into 50 groups with 10 covariates each. We randomly sampled 200 observations to train the model and used the remaining 40 for comparing performance prediction.
Acknowledgments The authors wish to thank  Xiaofan Xu  and  Malay Ghosh  for sharing the code implemented in Xu and Ghosh (2015). The authors will also to thank  Dr. Marc Chadeau-Hyam  for his valuable inputs and comments on the paper and the model. The authors would also like to acknowledge the  ARC Centre of Excellence for Mathematical and Statistical Frontiers  for supporting this research. Finally, we thank the  High Performance Computing and Research Support (HPC)  group from  Queensland University Technology.
•• Model 4. The simulation setting is the same as for the previous model except for the true B. Let 2, some relevant covariates are non-zero for two responses and zero for the other one. • Model 5. We simulated data with n = 100 and p = 40 covariates divided into 4 groups with 10 covariates each. We randomly sampled 60 observations to train the model and used the remaining 40 for comparing performance prediction. Let simulated in the same way as in model 2. • Model 6. The simulation setting is the same as for the previous model except for the true B. LetB T = ⎛ ⎝ 0 10(2 5 , 0 5 ) 0 10 , (2 5 , 0 5 ) 0 10 (1 5 , 0 5 ) 0 10 , (1 5 , 0 5 ) 0 10 (0. 5 5 , 0 5 ) 0 10 , (0. 5 5 , 0 5 ) Model 7. We simulated data with n = 240 and p = 500 covariates divided into 50 groups with 10 covariates each. We randomly sampled 200 observations to train the model and used the remaining 40 for comparing performance prediction.
, 2013; Ruf et al. , 2011). In addition, loxP site insertions in the mouse genome generated by transposition have been captured in the TRACER resource with precise location and orientation (http://tracerdatabase. embl. de), reducing the time needed to generate new DS models to 3 years  (Chen et al. , 2013). However, a major revolution is now underway with the development of an even faster method -CRISPR-mediated rearrangement (CRISMERE)which is based on CRISPR/Cas9 genome-editing technology  (Birling et al. , 2017). Two pairs of small guide RNA (sgRNA), each pair selected either upstream or downstream of the region of interest, are injected with the Cas9 nuclease into one-cell mouse embryos that are reimplanted. Newborns are analysed for chromosomal modifications and are bred to select carrier individuals with the new rearrangement in the next generation.
, 2008; Ahmed et al. , 2015; Rueda et al. , 2010; Lockrow et al. , 2011; Hanson et al. , 2013). A memantine clinical study is currently underway to assess the effects of this treatment on adolescents with DS (https://www. clinicaltrials. gov/). Prenatal or early postnatal treatment with fluoxetine, which targets the serotoninergic system, rescued neurogenesis, long-term memory and synaptic plasticity in Ts65Dn mice  (Guidi et al. , 2014; Stagni et al. , 2015). Adult treatment enhanced neurogenesis without improving learning/memory deficits at a high dose of fluoxetine  (Heinen et al. , 2012) ; however, an improvement in short-term memory was observed using a low dose (Begenisic   et al. , 2014). As seen with fluoxetine treatment, early administration of lithium, another antidepressant drug acting also upon the serotonin pathway, fully rescued short-and longterm memory, and LTP as well as neurogenesis in Ts65Dn mice  (Contestabile et al.
Funding This work has been supported by the  National Centre for Scientific Research  [ Centre National de la Recherche Scientifique (CNRS) ], the  French National Institute of Health and Medical Research [Institut National de la Santéet de la Recherche Medicale (INSERM)] , the  University of Strasbourg [Universitéde Strasbourg (UDS)], the 'Centre Europeén de Recherche en Biologie et en Medecine' , the  European Commission  ( AnEUploidy  project to V. L. J. T. , E. M. C. F. , Y. H. and J. M. D. ;  Fifth Framework Programme   LSHG-CT-2006-037627 ), the  French State funds through the 'Agence Nationale de la Recherche ' under the frame programme  Investissements d'Avenir  ( ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT ,  ANR-10-INBS-07 PHENOMIN ), from the  EU   FP7 large-scale integrated project GENCODYS (Sixth Framework Programme  grant  241995 ) to Y.
H. and under  ANR-09-MNPS-008  to J. M. D. V. L. J. T. and E. M. C. F. were funded by  Wellcome Trust  grants  080174 ,  098327  and  098328. V. L. J. T. was also supported by the  Francis Crick Institute  ( FC001194 ), which receives its core funding from the  MRC, Cancer Research UK  and the  Wellcome Trust. E. Y. was supported in part by grants from the  National Institutes of Health  ( R01NS66072 ,  R01HL91519 ,  R21GM114645  and  P30CA16056 ), The Children's  Guild Foundation and Roswell Park Alliance Foundation, Roswell Park Cancer Institute. The  Jeŕôme Lejeune Foundation (Fondation Jeŕôme Lejeune)  supports the work of J. M. D. , E. Y. , Y. H. and V. B. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The PRZM and MACRO models are then used to execute a total of four simulation runs for the PPP. Soil parametrisation and weather input data (i. e. the meteorological station) for PRZM and MACRO are defined by the respective FST and the climate cluster. Four 30-year time series of substance edge-of-field losses via surface run-off and erosion (in dissolved phase and in sediment, respectively) and drainage are provided as data outputs. Additionally the pesticide deposition from spray drift (drift values to FOOTPRINT approach, ref. Table  S3 ) and from atmospheric transport (according to German model EVA) are included in the exposure assessment. For these inputs the STEP-3 model (equivalent to the TOXSWA model) calculates 30-year time series of PECsw and PECsed (temporal resolution 1 h) for the two water body types 'ditch' and 'stream'.
19 The structure of exposure assessment based on GERDA was mainly kept consistent with the tiered FOCUSsw approach for the approval of active substances in the EU. Step 1 and Step 2 of the FOCUSsw approach therefore are adopted without any modification as Step 1 and Step 2 in the GERDA-STEPS tool (Step 2 calculates only the Cluster North-EU). Step 3 computes exposure endpoints using Germany-specific soil-climate scenarios, determined as described above. Programme structure, user interface, parameter input and data output is nearly identical with the FOCUS SWASH software apart from replacing the FOCUSsw run-off and drainage scenarios by national-specific FST-CC scenario (for some further modifications see Table  S3 ). The assignment of a generic PPP to the PPP to be assessed and the selection of FST-CC combinations from its CDF occur internally within the GERDA-STEPS software program.
The dimensioning of the FOCUSsw water body types 'ditch' and 'stream' with respect to their width/depth (w/d) ratio of 3. 33 probably does not reflect the real situation for most of the streams and ditches in the EU. For example, from a country-wide channel geometry analysis for the Netherlands, a trapezoidal ditch with a surface width of 2. 62 m and a depth of 0. 23 m, i. e. a w/d ratio of 11. 4, was stipulated as a reference water body for a national specific spray drift scenario. 9 A comparable national database on water body geometry does not exist for Germany; a review on a number of local studies revealed a median w/d ratio of approximately 10 for smaller, freely flowing water bodies in Germany (cf. Kubiak et al. 32  and Table  S7 ). Furthermore, a full probabilistic approach of spray drift calculation has to take into account the frequency distributions of wind speed, wind direction, and distance between treated fields and water stretches as main factors.
We then propose a deep convolutional neural network architecture, name HLA-CNN, for the task of HLA class I-peptide binding prediction. Experimental results show combining the new distributed representation with our HLA-CNN architecture acheives state-of-the-art results in the majority of the latest two Immune Epitope Database (IEDB) weekly automated benchmark datasets. We further apply our model to predict binding on the human genome and identify 15 genes with potential for self binding.