dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. Katherine Hutchinson  received financial support of the  European Union 's  Horizon 2020 Research and Innovation programme   Marie Skłodowska-Curie  grant agreement no. 898058  (Project OPEN). Nicolas Jourdain  received support from the  European Union 's  Horizon 2020 Research and Innovation programme  under grant agreement no. 101003536  ( ESM2025 ). Pierre Mathiot  acknowledges support from the  European Union 's  Horizon 2020 Research and Innovation programme  under grant agreement no. 820575  (TiPACCs). This work was performed using high-performance computing (HPC) resources from  GENCI-IDRIS  (grant  2021-A0100107451 ) and from the  IPSL Mesocentre ESPRI. Financial support. This research has been supported by the  H2020 European Research Council  (grant nos. 898058 ,  101003536 , and  820575 ) and the  Grand Équipement National De Calcul Intensif  (grant no.
The application of decoding models to electrophysiological data has become standard practice in neuroscience. The use of such methods on sensor space data can, however, limit the interpretability of the results, since brain sources cannot be readily estimated from the decoding of sensor space responses. Here, we propose a new method that combines the common spatial patterns (CSP) algorithm with beamformer source reconstruction for the decoding of oscillatory activity. We compare this method to sensor and source space decoding and show that it performs equally well as source space decoding with respect to both decoding accuracy and source localization without the extensive computational cost. We confirm our simulation results on a real MEG data set. In conclusion, our proposed method performs as good as source space decoding, is highly interpretable in the spatial domain, and has low computational cost.
We predict that the source-first pipeline will work well, but will also present a large computational cost, because there are many more brain source points than MEG sensors. Finally, we predict that our newly proposed CSP-first pipeline will be both valid and computationally efficient. To compare these alternatives, we synthesize brain signals and evaluate the three pipelines on their (1) sensitivity (i. e. , how good the decoding performance is), (  2 ) interpretability (how precise their source localization is) and (3) computational load. We also evaluate our method on a public MEG data set. Our analyses show that our CSP-first pipeline successfully and efficiently combines CSP and a linear classifier while ensuring a valid source modeling of its coefficients.
Simulations We simulate oscillatory brain activity data following the experimental design of a real public MEG data set (the so-called sample data set in MNE-Python,  Gramfort et al. (2013) ). Contrary to real MEG data, synthetic data possess a ground truth for source localization, and thus is well-suited for the evaluation of different inverse modeling pipelines. Specifically, we synthesize two distinct data sets which vary in terms of difficulty of decoding and source localization. Specifically, we synthesize (1) a visual and an auditory source in the corresponding distant cortices and (2) two distinct visual sources within the left and right occipital cortices, respectively. The objective of the decoders for each data set is to classify single trials of oscillatory activity into the two classes: (1) visual-audio or (2) visual left and visual right, left-right.
The sensor-space signal-to-noise ratio (SNR) then is manipulated by adding white noise (between -45 and 5 dB in 5 dB steps). The noise level is determined on the first condition and then applied to the second one, such that the conditions are not distinguishable based on a difference in the noise alone, e. g. , during the baseline period. Thus, the sensor data B was generated as follows: B(t) = LS(t) + , (1) where L ∈ R M ×N is the forward model for M sensors and N source points, S(t) ∈ R N ×1 is the simulated source-level activity at time point t, and ∈ R M ×1 is the vector of random white noise added to the sensor-level data. The source-level activity of an active source S active (r, t) at source point r and time point t was simulated as S active (r, t) = h(t)A cos(2πf t -δ) , (2) with h ∈ R 1×T being the vector of a Hanning taper for a time series of length T , A referring to the amplitude, f being the frequency, and δ the phase of the oscillation.
Decoding Decoding is based on an 2 -regularized logistic regression: arg min w = n i y i log p i + (1 -y i ) log(1 -p i ) + C||w|| 2 , (3) where p i = 1 + exp[-x T i w] -1 , x i ∈ R 1×c constitutes a vector of c MEG channels at trial i, w ∈ R c is the decoder's coefficients, y i ∈ [-1 , 1] codes for the category of the trial (e. g. , visual versus auditory), and C is the regularization coefficient. We train the logistic regression with scikit-learn  (Pedregosa et al. , 2011). Except if stated otherwise, all model hyperparameters are kept to their default MNE-Python values (i. e. , intercept liblinear solver, C = 1. 0).
Evaluation We evaluate the methods on their decoding performance as well as on their interpretability (i. e. , spatial precision). To this end, we compare the obtained source patterns across methods regarding (1) localization precision and (2) source spread. Finally, we apply our three decoding pipelines to a real MEG data set. Additionally, to investigate the role of the regularization parameter C in the logistic regression, we perform a grid-search for C. The value for C that optimizes the decoding performance is searched among 10 values between 0. 0001 and 1000, evenly spaced on a logarithmic scale. This is done for all three decoding pipelines and 200 simulations, but only one of the two contrasts (the visual-audio contrast). Decoding performances All decoding approaches are embedded in a 5-fold stratified cross-validation which includes the whole decoding pipeline.
Real data To validate our simulation results with real data, we test our analyses on the SPM faces data set as distributed with MNE-Python. This MEG face perception data set is openly available as the "Multi-modal Face Dataset" from the SPM software (https://www. fil. ion. ucl. ac. uk/spm/data/mmfaces/). During the recording, faces and scrambled faces were presented to the participant in random order (cf. Henson et al. , 2003). We aimed to decode the high-frequency activity induced by faces versus scrambled faces, and based the choice for the processing parameters on the face-processing literature  (Dobel et al. , 2011; Itier and Taylor, 2004; Liu et al. , 2018; Zion-Golumbic and Bentin, 2007). Specifically, the MEG data is filtered between 60-95 Hz to isolate oscillatory activity in the gamma band that is not dominated by the event-related fields and not influenced by line noise.
0 performs comparably to the optimal regularization over 200 runs for all three approaches. As expected, Figure  4B  shows that the optimal parameter exhibits a high variance for lower input SNRs, while a regularization with the lowest parameter of C = 0. 0001 seems to be optimal for higher input SNRs. Computational time Analyses were done on a high-performance computing cluster, and simulation realizations were distributed across different computing nodes. Unsurprisingly, the power-first pipeline proved to have the lowest computational time with an average time of 0. 07 s across the 200 simulation runs (Fig. 5 ). This result is expected because this pipeline computes both the decoding models and the patterns based on sensor-space data which is fairly low-dimensional. By contrast, the source-first pipeline appears to be the slowest approach with an average time of 156.
HTseq-counts files were analyzed with the R software, using also EdgeR package version 3. 24. 3 (McCarthy et al. , 2012). Genes which did not have at least one read after a count per million normalization in at least one half of the samples were discarded. Raw counts were normalized using TMM method, and count distribution was modeled with a negative binomial generalized linear model where the treatment, the time, and the double interaction between treatment and time were considered and where the dispersion is estimated by the EdgeR method  (Robinson et al. , 2009). A likelihood ratio test was performed to evaluate an infection effect at a given time point. Raw p-values were adjusted with the Benjamini-Hochberg procedure to control the false discovery rate (FDR). The raw files were imported to R software for data analysis (http://www.
r-project. org/). After that, principal component analysis was done on normalized data. Differentially expressed genes (DEGs) were then selected based on FC>1 or FC<-1 (log2). Clustering took place by HCE3. 0 software with DEGs average linkage (UPGMA) Euclidean distance. After that, gene ontology was done through ShinyGo visualization  (Ge et al. , 2020)  and with Mapman analysis.
univ-perp. fr). Illumina sequence reads in the FastQ format were processed using QIIME2 (version 2021. 4)  (Bolyen et al. , 2019). The DADA2 workflow  (Callahan et al. , 2016)  was used to merge paired-end reads, perform filtering, dereplication, chimera removal, and identify representative sequences of amplicon sequence variants (ASVs). The taxonomic affiliations for the rRNA gene sequences 16S and ITS2 were, respectively, assigned using a pre-trained naive Bayes classifier on the basis of the SILVA 138 database  (Quast et al. , 2013)  and the UNITE v8 database  (Nilsson et al. , 2019). The sequencing data were processed under R v3. 4 (www. rproject. org) using the R-Studio (http://www. rstudio. com/) Phyloseq package  (McMurdie and Holmes, 2012). Beta diversity for all conditions was calculated using the Bray-Curtis dissimilarity and compared using analysis of similarities (ADONIS) on normalized data (999 permutations) through Vegan R packages  (Oksanen et al.
(2020). MS-DIAL data were then cleaned by using MS-CleanR based on Fraisier-Vannier et al. (  2020 ). An in-house database constructed on MS-FINDER version 3. 52 model was used for peak annotation  (Tsugawa et al. , 2016; Chervin et al. , 2022). C, H, O, N, P, and S atoms were utilized principally in Formula finder. Databases based on Medicago (genus) and Fabaceae (family) and dictionary of natural product were constituted and used (DNP-CRC press, DNP on DVD v. 28. 2). Through a successive ranking based on a genus database, then family database, and finally generic database, annotation prioritization was performed using the final MS-CleanR output. The generic databases were KNApSAcK, PlantCyc, and PubChem available within MS-FINDER. Normalized LC-MS dataset created by using MS-CleanR was imported in R for multivariate data analysis.
The mixOmics package  (Rohart et al. , 2017)  was used to perform principal component analysis (PCA) as a first exploratory step before discriminant modeling. All data were centered and scaled to unit variance. To consider the effect of dpi, data were sloped between 3 to 7 dpi and 3 to 14 dpi. Partial Least-squares Determinant Analysis (PLS-DA) model was applied on this transformed dataset to extract features correlated to each sample class. The loading weights of each variable were extracted from the two first components to rank the top 50 loadings of the discriminant model. Finally, these 50 features (m/z × RT pairs) were used to construct a clustered image map by using euclidian distance and ward agglomeration for both rows (features) and columns (samples).
Statistics and data analysis The general statistical analysis was done using SAS 9. 4 software, and graphs were made by using Graphpad prism 8. 0. 2 software (San Diego, CA, USA). Transcriptomic and metagenomic data were analyzed using R software as well as EdgeR package versions 3. 24. 3 and R v3. 4 (http://www. r-project. org/) using the R-Studio (http://www. rstudio. com/), respectively. Statistical analyses for metabolomics were done using SIMCA (version 14. 1, Umerics, Umea, Sweden). All data were scaled by unit variance scaling before the multivariate analysis. The partial least squares-discriminant analysis (PLS-DA) and orthogonal projection to latent structure using discriminant analysis was used to separate data according to M. truncatula growing conditions. The general statistics was performed using SAS 9.
Pythium oligandrum strongly activates M. truncatula defense responses and isoflavonoid metabolism In order to better understand the molecular basis of plant growth promotion and protection caused by P. oligandrum, we probed A17 M. truncatula responses to P. oligandrum oospores with transcriptome and metabolome analyses within an in vitro culture system at 3, 5, 7, and 14 dpi. These time points were chosen to observe the early responses of root colonization by P. oligandrum. For transcriptomic studies, after RNA sequencing and mapping on model genes (Supplementary Material S1), we performed a principal component analysis (Figure  3A ). The difference between mock and P. oligandrum datasets could be observed as soon as day 3 post-inoculation but was more noticeable at 5 and 7 days post-inoculation, and then it tends to decrease at 14 days.
Based on retention time and mass spectra, major peaks were annotated and assigned through a comparison with standards and data from the literature sources. Mass spectrometry was carried out in positive and negative ionization modes. The total number of ions retained following MS-CleanR sorting was 474 (Supplementary Material S2). A global metabolomic analysis based on PCA validated our dataset by regrouping the samples according to their group of origin (Figure  5A ). Then, a supervised PLS-DA resulted in the clear separation of control and P. oligandrum-treated plants at 7 and 14 dpi (Figure  5A ). We then plotted the 46 most significant variables which supported the PLS-DA separation of the groups (Figure  5B ) (Supplementary Material S2). The annotation of these variables revealed that salicylic acid 2-beta-D-glucoside (variable neg_226) was the most strongly accumulated in M.
Pythium oligandrum influences the composition of the rhizosphere microbiota We then investigated the effect of root colonization by P. oligandrum on the rhizosphere of M. truncatula (F83005. 5) plants sown in potting soil. A total of 14 samples comprising seven controls and seven P. oligandrum-inoculated plants were harvested at day 56 post-sowing, and DNA from their rhizosphere was extracted and subjected to PCR. The 16S rRNA dataset retained 194,708 reads, and the Internal Transcribed Spacer dataset retained 422,171 reads, which were assigned respectively to 1,413 bacterial amplicon single variants and 374 fungal ASVs (Supplementary Materials S3, S4). The data were normalized to account for unequal sequencing depth, resulting in a minimum sampling depth of 3,834 for 16S and 10,556 sequences per sample for ITS.
05) among the fungal community upon P. oligandrum treatment. In order to determine more precisely whether particular bacterial or fungal genera are affected by P. oligandrum in their rhizospheric abundance, we performed a DEseq2 analysis on the individual ASVs of the dataset. A total of 17 bacterial ASVs and 20 fungal ASVs were differentially occurring upon P. oligandrum inoculation. We confirmed that P. oligandrum largely reduced rhizosphere colonization by the Streptomyces-related ASV219 belonging to Actinobacteria and found out that it impacts either positively or negatively 10 ASVs belonging to Planctomycota and four ASVs affected to Alphaproteobacteria (Supplementary Material S5). In addition, the oomycete inoculation increased the abundance of nine fungal ASVs related to Basidiomycota, among which six were belonging to the Clitopilus genus.
The proven advantages of P. oligandrum still make it a great candidate for further studies in biocontrol industry and sustainable agriculture. , Reǵion Occitanie, CPER 2007 -2013 Technoviv, CPER 2015 -2020 Technoviv2). The Laboratoire de Recherche en Sciences Veǵetales belongs to TULIP Laboratoire d'Excellence (ANR-10-LABX-41; ANR-18-EURE-0019) and was also supported by the Agence Nationale de la Recherche (LabCom BioPlantProtec ANR-14-LAB7-0001) and the Reǵion Occitanie (projet GRAINE-BioPlantProducts).
Acknowledgments We warmly thank  Joshua Bougon-Ronin  and  Valeŕie Arnal  from the  R&D  staff of  DE SANGOSSE company (Agen, France ) for support within the BioPlantProducts projects and in the frame of industrial secondment during the ITN-PROTECTA. We are grateful to  Eve Toulza ,  Jean-Francois Allienne ,  Margot Doberva , and  Michèle Laudiéfrom  the  Bio-Environment platform  ( Universitéde Perpignan Via Domitia, France ) for their technical support in library preparation and sequencing.
Funding This research was funded by the European Union's Horizon 2020 Research and Innovation Program [grant agreement numbers 766048 (MSCA-ITN PROTECTA) and 774340 (Organic-PLUS)]. The metabolomics studies were carried out at MetaToul-AgromiX platform with financial support from the French National Infrastructure for Metabolomics and Fluxomics (grant MetaboHUB-ANR-11-INBS-0010). The bioenvironment platform is funded by the platform (UPVD.
We present version 2. 3 of SModelS, a public tool for the fast reinterpretation of LHC searches for new physics on the basis of simplified-model results. The main new features are a database update with the latest available experimental results for full Run 2 luminosity, comprising in particular a large variety of electroweak-ino searches, and the ability to combine likelihoods from different analyses. This enables statistically more rigorous constraints and opens the way for global likelihood analyses for LHC searches. The physics impact is demonstrated for the electroweak-ino sector of the minimal supersymmetric standard model.
Introduction The lack of evidence for new physics in the LHC data puts stringent constraints on masses and couplings of new particles, predicted by theories beyond the standard model (BSM). However, searches for new physics at the LHC are made on a channel-by-channel basis in specific final states, and the results are typically presented in the context of simplified models. To obtain a comprehensive view of how the plethora of LHC results constrain new physics and which regions of parameter space remain viable -or perhaps even untested-it is therefore important to reinterpret the results of these searches in the context of realistic theoretical scenarios. This is particularly relevant for BSM theories which feature a large number of new particles with potentially very complex decay patterns, such as supersymmetric theories, including the minimal supersymmetric standard model (MSSM).
Its working principle is to decompose the signatures of full BSM scenarios into simplified-model components, which are then confronted against the experimental constraints from a large database of results. So far  [7] , constraints on a particular BSM point were considered purely on an analysis-by-analysis basis, i. e. for each experimental search separately. The new version presented in this paper now provides the possibility to combine likelihoods from different analyses, under the assumption that they are approximately uncorrelated. As we will show, this enables statistically more rigorous constraints and opens the way for global likelihood analyses with SMODELS. Besides the support of analysis combinations, the entire statistical computation has been refactored in v2. 3. Moreover, support was added for simplified likelihoods in the formalism of  [18]  (SLv2, Gaussian with a skew).
The full list of improvements, including small bug fixes, can be found in the release notes. The other significant news in version 2. 3 is the database update with the latest available experimental results for full Run 2 luminosity, comprising in particular the full suit of (re)usable electroweak-ino (EW-ino) searches. Concretely, results from 9 ATLAS and 12 CMS publications were added to the database. With this, the SMODELS v2. 3. 0 database covers a total of 111 ATLAS and CMS searches (78 from Run 2 and 33 from Run 1); 17 of the ATLAS and 13 of the CMS searches are for full Run 2 luminosity. The physics impact of both, the database update and the new feature of analysis combination, is demonstrated by means of a case study of the EW-ino sector of the MSSM. The paper is organised as follows.
We begin in section 2 by reviewing the likelihood calculation in SMODELS. The combination of SRs within an analysis is explained in section 2. 1, and the combination of likelihoods of orthogonal analyses in section 2. 2. The new analyses in the database are presented in section 3, and the EW-ino case study in section 4. Section 5 contains a summary and conclusions. Four appendices complete the paper: Appendix A provides a complete list of the experimental results in the v2. 3. 0 database, Appendix B explains the new functionality of database add-ons, and Appendix C gives examples of combining more than two analyses. Finally, Appendix D contains auxiliary plots supplementing the results presented in section 4. It is assumed that the reader is familiar with the concepts and usage of SMODELS.
HistFactory statistical models ATLAS searches are often based on HISTFACTORY  [24]  for their statistical modelling. Following  [10] , the collaboration has started to provide JSON serialisations of the full HISTFACTORY workspaces for results with full Run 2 luminosity (139/fb) on HEPDATA. Thus the full set of nuisance parameters, changes under systematic variations, and observed data counts are provided at the same fidelity as used in the experiment. SMODELS supports the usage of these JSON-serialised statistical models since v1. 2. 4  [12]  via an interface to the PYHF package  [25] , a pure-python implementation of the HISTFACTORY family of statistical models. This means that with combineSRs=True, whenever a HISTFAC-TORY statistical model is available in the database, the evaluation of the likelihood is relegated to PYHF (where, internally, the calculation is again based on the asymptotic formulas of  [21] ).
It has to be noted here that the evaluation of full HISTFACTORY models, which can have hundreds of nuisance parameters, can be very CPU intensive, in particular when combining analyses. For this reason, the official SMODELS database contains mostly simplified HISTFAC-TORY models  [26] , which were derived from the full ones by means of the SIMPLIFY  [27]  tool. The currently only exception is the ATLAS-SUSY-2019-08 analysis  [28] , for which the SIM-PLIFY'ed statistical model does not reproduce the results from the full one well enough, and therefore the full one is kept as the default. In any case, when CPU performance is not an issue, the SIMPLIFY'ed statistical models in the database can be replaced by the full ones through a "full_llhds" database add-on as explained in Appendix B. Some minor technical fixes have been implemented with respect to  [12] , without changing the code's core.
The most noticeable changes include the suppression of the datasetOrder field from the globalInfo. txt file in the database entry, and the possibility to not remove the control regions from the HISTFACTORY statistical model by adding includeCRs = True in globalInfo. txt. The latter is used for two analyses so far: ATLAS-SUSY-2018-32  [29]  and ATLAS-SUSY-2019-09  [30]. For all other analyses that make use of a HISTFACTORY statistical model, includeCRs = False by default.