dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2004; Lynn et al. , 2005; Planche et al. , 2010)  in a 3D mesoscale context due to the often prohibitive computational costs of memory and CPU (central processing units). This detailed methodology, however, allows better insights into the evolution of cloud-specific processes such as phase changes and collisional processes and describes more closely the interactions with the field of water vapour and temperature and its feedback with cloud dynamics. Only a few studies (e. g. Iguchi et al. , 2008; Fan et al. , 2012)  have been focused on real deep convective systems with a bin microphysics scheme in a 3D dynamical framework, and none of them were applied to an intense precipitating system as usually observed in autumn over the western Mediterranean basin. One major objective of this study is, thus, to test if a bin-resolved microphysics module in a 3D mesoscale model is able to reproduce a real case of intense precipitation using the dataset obtained during IOP7a of HyMeX.
Acknowledgements. This work is a contribution to the  HyMeX programme  and  MUSIC  project, supported by grants  ANR-14CE1-0014  and  MISTRALS/HyMex. Christina Kagkara  was funded by the  MUSIC  project. The model calculations have been carried out at French computer facilities of the  Institut du Développement des ressources en Informatique Scientifique (IDRIS) CNRS at Orsay , the  Centre Informatique National de l'Enseignement Supérieur (CINES) at Montpellier  under the project  A0060105056  and the  Centre Régional de Ressources Informatiques (CRRI) at Clermont-Ferrand. The authors acknowledge the HyMeX database teams ( ES-PRI/IPSL  and  SEDOO/OMP)  for their help in accessing the data ( Joël Van Baelen ,  Sandra Banson  and  Yves Pointin ). The authors extend their gratitude in particular to  Brice Boudevillain  and the OHMCV observatory for providing the KED data, as well as to  Evelyn Freney  for the help regarding the information about initial aerosol population.
Thus, we modeled pupil diameter as the degree of context shift predicted by our model in two discrete events occurring at specific time points after outcome presentation and before the start of the subsequent trial. Resulting discrete signals were then convolved with a gamma probability density function to produce simulated pupil responses for three types of trials: Neutral (no-change), changepoints and oddballs (Figure  7c ). We then used measures of learning and surprise from the Bayesian dynamic learning model in a regressor to see how surprise (changepoint/oddball probability) and learning (trial-wise learning rate) are related to our simulated pupil responses. Our results predict an initial positive correlation with surprise and a later negative correlation with learning. These results provide a qualitative agreement with data from  Nassar 2012  showing that transient pupil dilations reflect changepoint probability in a context with changepoints, but also capture the relationships to early relationship to surprise and late negative relationship to learning observed in O'Reilly 2013.
Pupil Response Simulation: We modeled 480 trials of a predictive inference task for each of the two conditions (oddball, changepoint). We created synthetic pupil traces by defining time points for feedback-locked context shifts, which occurred 400ms after oddball or changepoint, and pre-prediction context shifts at 900ms after oddball events (see eq. 10 & 13). We used measurements of context shift for the respective changepoint and oddball trials (see eq. 11 & 16) at these time points and convolved these measurements with a gamma distribution to create simulated time courses of a pupil response under the assumption that the pupil signal reflects the need for a context shift. We analyzed this signal with a regression model that was applied to all synthetic data in sliding windows of time. Explanatory variables in our model included surprise (changepoint/oddball probability computed from normative model) and learning (trial-by-trial learning rate computed from the normative model).
The quality check for sequencing reads were performed by NanoPlot 1. 3. 1 soft (Q > 7). The reads were assembled using the software SPAdes_3. 12. 0 and Unicycler GPLv3  (Wick et al. , 2017) , 2  and the complete nucleotide sequences of all IncC plasmids were verified by PCR. The assembled sequences were analyzed to identify multi-locus sequence typing (MLST) by the MLST 2. 0  (Larsen et al. , 2012). 3 ingle-nucleotide polymorphism alignments were computed using the CSI phylogeny 1. 4 pipeline  (Kaas et al. , 2014)  according to the default parameters. 4.
The large genetic fragments of the backbone region with accessory modules (including multiple inversions arising from within ARI-A, ARI-B and the ISEcp1bla CMY-2 island) were positioned in inverse orientation relative to the other plasmids in this group (Figure  1A ). Another subtype was comprised of plasmids pKC3-1/2b and pCC1-1/2b, type 1/2b, which contained orf1832, rhs1, and additional type 2 sequence i1 (Figure  1C ). Analysis of the genome of pKC3-1/2b (GenBank accession number MT560001) by Blast analysis revealed that the backbone sequence from 0-bp to 50,800-bp and from 175,375-bp to 193,802-bp showed 99. 97% nucleotide identity to the corresponding backbone region of type 2 IncC plasmid pR55 (GenBank accession no. JQ010984). The remaining backbone, from 50,801-bp to 175,374-bp, showed 99.
99% nucleotide identity to the corresponding backbone region of type 1 IncC plasmid pR148 (GenBank accession no. JX141473). There were different backbone features in the 15 type 1/2 hybrid IncC plasmids, suggesting various homologous recombination between type 1 and type 2 IncC, with IS-mediated rearrangements increasing the diversity of the IncC plamids. Like type 1b IncC plasmid pYDC637 (GenBank accession no. KP056256), the four type 1b IncC plasmids identified in this study (pEC4-1b, pEC11-1b, pEC12-1b, pEC14-1b) (Figure  1D ) lacked the SNPs, which are typically concentrated in a 14. 5-kb part of the type 1a IncC genome  (Ambrose et al. , 2018b). In addition, compared to pR55, the only type 2 IncC plasmid, pEC9-2, contained an extremely simple backbone due to the deletion of a large section of the backbone regions, but still retained all the characteristics of type 2 plasmids in the sequences of i1, i2, orf1847, and rhs2.
Resistance Islands of IncC Plasmids Except for the type 2 plasmid, pEC9-2, and the two type 1/2a plasmids, pEC7-1/2a and pEC13-1/2a, ARI-A islands were identified in the remaining 17 plasmids (Table  2  and Figure  2 ). The structure of ARI-A in the IncC plasmid pRMH760 (GenBank accession no. KF976462) was the first to be described in detail  (Partridge and Hall, 2003; Harmer and Hall, 2017). Similar to pRMH760, there were 10 plasmids in this study have both ends of ARI-A intact, with this sequence flanked by a 5-bp duplication (TTGTA) of the target (Figure  2A , the ARI-A of pEC5-1/2a is not shown). The 10 ARI-A islands were all identified as a complex mosaic structure derived from Tn1696 and composed of a different class 1 integron and multiple resistance units or transposons (Figure  2 ), of which the two Genes coding for proteins are colored according to the following key: blue, transposase genes; yellow, integrase genes; red, antimicrobial resistance genes; green, mercury resistance genes; white, other genes.
In pEC2-1/2a (Figure  3D ), a distinct ARI-B form contains a 90. 0kb segment surrounded by two IS26 elements sharing 99. 99% nucleotide identity with the IncF plasmid pPE15 (GenBank accession no. CP041629), explaining acquisition of newer metal (sil-pco-operon and Tn21 mer ) and antibiotic (aph(3')-Ia, oqxA/B, tet(A), qnrS1, dfrA14) resistance modules with a highly mosaic nature, and suggesting that IS26 promotes diversity of ARI-B in IncC genome.
DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www. ncbi. nlm. nih. gov/genbank/, MT551208; gov/genbank/, MT559985; https://www. ncbi. nlm. nih. gov/gen bank/, MT559986; https://www. ncbi. nlm. nih. gov/genbank/, MT559988; https://www. ncbi. nlm. nih. gov/genbank/, MT559989; https://www. ncbi. nlm. nih. gov/genbank/, MT559990; https:// www. ncbi. nlm. nih. gov/genbank/, MT559991; https://www. ncbi. nlm. nih. gov/genbank/, MT559993; https://www. ncbi. nlm. nih. gov/genbank/, MT559996; https://www. ncbi. nlm. nih. gov/gen bank/, MT559999; https://www. ncbi. nlm. nih. gov/genbank/, MT560000; https://www. ncbi. nlm. nih. gov/genbank/, MT560003; https://www.
ncbi. nlm. nih. gov/genbank/, MT560002; https:// www. ncbi. nlm. nih. gov/genbank/, MT560001; https://www. ncbi. nlm. nih. gov/genbank/, MT559998; https://www. ncbi. nlm. nih. gov/genbank/, MT559987; https://www. ncbi. nlm. nih. gov/gen bank/, MT559994; https://www. ncbi. nlm. nih. gov/genbank/, MT559995; https://www. ncbi. nlm. nih. gov/genbank/, MT559997; https://www. ncbi. nlm. nih. gov/genbank/, MT559992; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRF000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRG000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRH000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRI000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRJ000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRK000000000; https:// www. ncbi. nlm.
nih. gov/genbank/, JACXRL000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRM000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRN000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRO000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRP000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRQ000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRR000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRS000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRT000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRU000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRV000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRW000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXRE000000000; https:// www. ncbi. nlm. nih. gov/genbank/, JACXSK000000000.
Bioinformatic analysis. Raw fastq files were demultiplexed and quality filtered (< 1 expected error per read) and gene-specific primers were trimmed using skewer  26. Next, we denoised the fastq files using dada2  27  to obtain 16S rRNA haplotypes (hereafter OTUs). Chimeric OTUs were eliminated by uchime  28  and taxonomy for non-chimeric OTUs was assigned using RDP classifier (> 80% confidence  29  ) and the Silva database version 132  30. BLCA  31  was used in specific cases for species-level assignment. The OTU sequences were then aligned by PyNAST  32  and their phylogeny reconstructed using FastTree  33. PCR triplicates were prepared for each sample, all of them being sequenced twice on two Illumina MiSeq runs (i. e. , six technical replicates per sample in total). We found high consistency in community profile composition among identical technical replicates between the two MiSeq runs (Mantel test: r = 0.
991, p < 0. 001), as well as between technical replicates within the first and second runs (Mantel test: r > 0. 99, p < 0. 001). To avoid microbial diversity inflation due to PCR and sequencing artifacts, we eliminated all OTUs that were present just once within the set of PCR replicates for a given sample, e. g. 34. We also excluded OTUs that were not assigned to any bacterial phylum, or those that were of chloroplast or mitochondrial origin. Furthermore, using Decontam  35  , we eliminated 82 OTUs corresponding to putative contaminants. The resulting abundance matrix, sample metadata, taxonomic assignations, haplotype sequences and their phylogeny were merged in a single phyloseq database  36  for further analysis. The final dataset included 6,618,602 high-quality reads that passed all quality-filtering steps.
Statistical analysis. Shannon indices and number of OTUs were calculated after dataset normalization by rarefaction and used as alpha diversity measures. Variation in alpha diversity between species, between oral vs. vaginal samples, and during the course of captivity exposure was assessed using Analysis of variance (ANOVA) or Linear mixed effect models (LMM; lmer function in the R package lme4). If necessary, individual identity was included as a random effect in the LMM to account for repeated sampling of the same mouse. Box-Cox transformation of the alpha diversity indices was applied in order to achieve a normal distribution of ANOVA/LMM residuals. Variation in microbiota composition was examined using Principal Coordinate Analysis (PCoA) and PERMANOVA (adonis2 function in the R package vegan) running on relative abundance-based (Bray-Curtis) and prevalence-based (binary Jaccard) dissimilarities.
In parallel to the PERMANOVA analyses, we searched for specific OTUs where changes in abundance played a role in the compositional variation, using Joint Species Distribution Models (JSDM) from the package boral  37. For the JDSM, we assumed a negative binomial distribution of OTU counts and only considered OTUs present in > 5 samples. Individual identity was modeled via random effects and log-scaled sequencing coverage per sample as a model offset. Explanatory variables in the JSDM were the same as those included in the corresponding PERMANOVA tests. Two latent variables were estimated during the fitting process to account for residual correlation of OTU abundances. JSDM coefficients were estimated via 50,000 iterations of a Markov Chain Monte Carlo simulation using default priors. The thinning interval was set to 20 iterations and the first 50 iterations were discharged as a burn in.
Support for the estimated parameters was assessed based on 95% posterior credible intervals. We also applied phylogenetic placement analysis for selected OTUs by extracting all reference 16S rRNA sequences corresponding to the same genus as the OTUs in question from the Silva database and clustering them at 99% similarity using vsearch  38. Representative sequences for clusters exhibiting > 97% sequence similarity with any OTU were used for phylogenetic reconstruction using RAxML 39 , assuming the GTRI substitution model after mafft alignment  40. Bootstrap analysis (1,000 replicates) was conducted to assess the robustness of phylogenetic clades. All the figures were generated in R software (https ://cran. r-proje ct. org/).
Acknowledgements This research was supported by  MICOBION  project funded from  EU H2020  (No. 810224 ) and the  National Program for Sustainability II  ( LQ1604 ) from the  Ministry of Education, Youth and Sports of the Czech Republic. TM was funded by the  Grant Agency of Charles' University (GAUK ; No. 1191419 ), PH was funded by the  European Social Fund  and the state budget of the  Czech Republic  ( CZ. 1. 07/2. 3. 00/30. 0061 ),and JK was funded by a grant from the  Czech Grant Agency (GAČR ; No. 18-17796Y ). The funders had no role in the study design, data collection, analysis, decision to publish, or preparation of the manuscript. We are grateful to  Martina Černá  and  Leona Havrdová  for taking care of the experimental animals.
com/htwan gtw/Wang2 020_taskS CCA/tree/maste r/ repor ts/manus cript for raw canonical vector loadings of the functional connectivity data). The right-hand panel shows word clouds that describe the results of a quantitative meta-analysis of this functional data performed using Neurosynth (see "Methods"). explained significant variance in the data collected in the experience sampling phase of the experiment. We conducted a multiple multivariate regression in which the average responses to the experience sampling questions were the dependent variables. We included scores for each individual for each mode of brain-behaviour co-variation as explanatory variables. Age, gender and mean frame displacement were included as nuisance covariates. This analysis revealed that the variation in mode 2 was associated with the experience sampling questions at the multivariate level (F(164, 13) = 2.
Methods participants. Two hundred and seven healthy participants were recruited from the University of York (132 females, 75 males; age range = 18-31 years, M = 20. 21, SD = 2. 36). This analysis included two data sets with some shared measurements and the same MRI protocol. Participants were right-handed native English speakers with normal or corrected-to-normal vision and no history of psychiatric or neurological illness. Participants underwent MRI scanning, completed a 1-h online questionnaire. Of the first cohort, participants attended three (165 participants; 99 females, 66 males; age range = 18-31 years, M = 20. 43, SD = 2. 63) 2-h behavioural testing sessions to complete a battery of cognitive tasks. The second cohort (42 participants; 33 females, 9 males; age range = 18-23 years, M = 19.
79, SD = 1. 37) underwent two 2-h behavioural testing sessions to complete a battery of cognitive tasks. The behavioural sessions took place within a week of the scan. Twenty-nine participants were excluded from the multivariate pattern analysis because they failed to complete all of the behavioural testing sessions or failed the imaging data quality check. In total, 178 participants (113 females, 65 males; age range = 18-25 years, M = 19. 81, SD = 1. 66) were included in the multivariate pattern analysis and the comparison with cognitive performance. Participants were rewarded with either a payment of £10 per hour or a commensurate amount of course credit. All participants provided written consent prior to the fMRI session and the first behavioural testing session. Approval for the study was obtained from the ethics committee of the University of York Department of Psychology and the University of York Neuroimaging Centre.
All data acquisition methods used in this study were in accordance with international, national, and institutional guidelines. Resting-state data preprocessing. All preprocessing and denoising steps for the functional MRI data were carried out using the SPM software package (Version 12. 0) and Conn functional connectivity toolbox (Version 17. f), based on the MATLAB platform (Version 17. a). The first three functional volumes were removed to achieve steady-state magnetisation. The remaining data were first corrected for motion using six degrees of freedom (x, y, z translations and rotations), and adjusted for differences in slice-time. Subsequently, the highresolution structural images were co-registered to the mean functional image via rigid-body transformation, segmented into grey matter, white matter and cerebrospinal fluid probability maps, and all functional volumes were spatially normalized to Montreal Neurological Institute (MNI) space using the segmented images and a priori templates.
L 1 regularisation conducted (a) feature selection (i. e. , select only relevant components) and (b) model estimation (i. e. , determine what combination of components best disentangles the neurocognitive relationship) in an identical process. This way we handle adverse behaviours of classical linear models in high-dimensional data. A reliable and robust open-source implementation of the SCCA method was retrieved as R package from CRAN (PMA, penalized multivariate analysis, version 1. 0. 928). The amount of L 1 penalty for the functional connectomes and cognitive task performance were chosen by cross-validation. The procedure is described in the section below. Before SCCA, we regressed age, sex, head motion indicated by mean frame-wise displacement  53  out of both the connectivity and cognitive task data to ensure that these potential confounders did not drive results.
The implementation of the confound removal method was retrieved from Python library Nilearn (https ://nilea rn. githu b. io/, version 0. 3. 1). As features that do not vary across subjects cannot be predictive of individual differences, we limited our analysis of connectivity data to the top 5% most variable connections, as measured by median absolute deviation, which is more robust against outliers than standard deviation  57. Thus the input data to the subsequent analysis consist of 247 ROI-ROI connectivity and 13 cognitive task measures. We first conducted SCCA to uncover the hidden structure that combines the task measures and the functional connectivity data. The SCCA model selection is detailed in "Model selection" and Fig. 6. The theoretical validity of latent variables was later examined by predicting the ongoing thought report.
We also explore the associated cortical thickness change associated with the functional neurocognitive modes. For the cortical thickness analysis, please refer to "Cortical thickness analysis". For the details of multiple multivariate regression, please see "Group level regression analysis". Model selection. The model selection process was conducted in two parts: L 1 penalty coefficient selection and component selection. For the L 1 penalty coefficient selection, we performed a grid search combined with crossvalidation (CV) to avoid over-fitting  58. Of each penalty pair on the search grid, fivefold cross-validation was performed to search for the best out-of-sample the rank-1 canonical correlation. We then decomposed the full dataset with the selected L 1 penalty coefficients (see the top panel of Fig.
6 ). The K-Fold CV was conducted by the implementation in Python library scikit-learn (https ://sciki t-learn. org/stabl e/, version 0. 18. 2). We performed SCCA with the optimal hyper-parameters on the full data set and saved all decompositions. A thousand permutations were applied to determine the mode(s) that occur above chance (see the bottom panel of Fig. 6 ). We constructed an empirical null distribution for the set of canonical components by holding the functional connectivity data in place and permuting the row order of self-reports data. The permutation scheme specifically disturbed the link between individual differences in the dataset. The emerging distribution of no-association effects in our analysis setup provided the basis for testing the robustness of the components in the hypothetical population.
The neuro-cognitive scores were calculated from averaging the canonical variates in the significant modes. A Type III multiple multivariate regression with Pillai's trace test was applied to the data. Each of the latent components describing the neurocognitive mechanism from the SCCA was the independent variables, and the 13 measures from MDES were the dependent variables. We thus asked how well variability in the MDES items can be explained our robust components of brain-behaviour association across individuals. We hoped to describe the neuro-cognitive components by the linear combination of the self-report questions collected via MDES. The analysis was conducted in Python library Statsmodels (https ://www. stats model s. org/stabl e/index. html).
Cortical thickness analysis. We explore the relationship between the SCCA modes (i. e. their canonical variates) and cortical thickness to understand the structural effect of the neurocognitive components. The surfstat toolbox for Matlab  60  was used for structural covariance network analysis, as in previous studies  61, 62. There is a well-established negative correlation between age and cortical thickness  60  and gender also influences cortical thickness  61. Consequently, these variables were included as covariates of no interest. The model fitted at a surface point i is shown as bellow: We determined significant clusters in this model using random field theory for nonisotropic images  62  which controlled the Family-Wise Error rate at p < 0. 05. We also explored the effect of the neurocognitive score.