dataesr/scientific-paragraphs-categorization huggingface dataset:
Together, these results showed brain models captured the multifactorial nature of pain experience. Nearly identical conclusions were supported by full brain learner predictions ( † , S4 Table ; S5 and S6 Tables for other models). Standardized path coefficients shown. All parameter estimates computed after removing participant fixed effects. � p < 0. 05, in models using full brain model predictions (shown). † p < 0. 05 in models using full brain learner predictions. Gray arrow, significant factor with full brain model but not learner. Bias corrected bootstrap test for nonzero α, β, τ, and α � β. Underlying data: https://github. com/canlab/petre_scope_of_pain_representation/tree/main/figure6. CV, cross-validation. https://doi. org/10. 1371/journal. pbio. 3001620. g006 While this mediation analysis showed some examples of mediation, it does not generalize to new models which might be learned in new studies.
Models capture pain-specific representations We next investigated the pain specificity of learned models using an independent multimodal dataset of within-participant contrast maps (task versus baseline averaged across trials, or mean high versus low magnitude stimulus evoked blood oxygen level dependent, BAU : PleasedefineBO OLD, https://doi. org/10. 1371/journal. pbio. 3001620. g007 signal) from 18 different studies. These include both thermal, pressure, and visceral (rectal distention) pain (6 studies, 2 per modality, n = 15 participants per study) and studies that shared negative affective dimensions or task demands with pain rating. The latter consisted of tasks focusing on aversive images, social manipulations (romantic rejection and vicarious pain), and aversive sounds, and tasks focused on working memory, response inhibition, and response selection (2 studies × 15 participants each).
Specificity was tested by comparing model scores for multimodal pain studies to those from the cognitive and affective studies with putatively homomorphic evoked responses. To determine whether models were specific to heat pain or specific to more general pain representations, we also tested generalizability by comparing model scores between heat pain versus pressure and visceral pain contrasts. On average models produced greater scores when applied to mechanical, thermal, or visceral pain data than when applied to putatively homomorphic tasks like response inhibition, aversive sounds, or emotional rejection (Fig  8 ; B = 1. 19, t 270 = 18. 5, p < 10e-6, planned contrast, mixed effects model with random participant effects), and although not trained as Distributed models were more pain-specific than local models.
Planned contrasts: black brackets, p < 0. 003 (Sidak threshold for 17 comparisons), gray/black brackets p < 0. 05. Post hoc t test: � p < 0. 003. Dashed gray line: optimal cut point for discrimination (balancing sensitivity and specificity). Legend indicates sensitivity/specificity at the optimal threshold. Underlying data: https://github. com/canlab/petre_scope_of_pain_representation/tree/main/figure8. cRSN, coarse resting-state network; fRSN, fine resting-state network. https://doi. org/10. 1371/journal. pbio. 3001620. g008 classifiers our models nevertheless discriminated pain from putatively homomorphic tasks (sensitivity/specificity for pain versus not pain at optimal cut point, right a24pr (elementary region): 0. 72/0. 72, right salience/ventral attention (fRSN): 0. 77/0. 76, frontoparietal (cRSN): 0.
More importantly, evaluating variability of independent model performances across studies using traditional statistical methods quantitatively informs the performance of new unseen models trained on unseen new participants in new studies in a practical sense. We successfully validated these estimates and illustrated the broader nature of this claim by training new models in new studies on new participants and obtained an identical performance hierarchy favoring multisystem representations. This provides a blueprint for exploiting large heterogeneous datasets to practically quantify information content in the brain, one which can just as readily be applied to other brain phenomena besides pain. Although our findings primarily support a multisystem representation of pain, they also support the notion that specific regions have a privileged role in pain representation.
Data Except for Study V4, data were included in this study if they conformed to several criteria: (1) they included evoked pain stimuli and subjective ratings of the intensity of those stimuli; (2) they were previously published at the time this study was conceived (May 2019); (3) involved healthy participants, without any drug manipulations (excluded one study  [74] ); (4) if the lead authors on those prior publications had prepared a set of single-trial stimulus evoked contrast images (see "General linear model (GLM) analysis" subsection below) in the course of their original analyses for purposes unrelated to this study; and (5) if those contrast images had been made available to the senior author of this study for archival purposes (TW). The latter requirements introduced organic variability across datasets more representative of variability seen across studies in the field.
Sources of variability include differences in preprocessing choices and statistical designs. Study V4 was introduced subsequently to provide multimodal data (pain and aversive sound) for improved tests of model specificities, but otherwise conforms to the same requirements. Refer to Table  1  for references to publications containing relevant experimental design and data acquisition details and Table  2  for overviews of experimental designs. Data are available as "single trial" statistical contrast maps, hosted at figshare. com (uniformly prepared by BP), and best accessed via the CANlab Single Trials Repository on GitHub (https://github. com/canlab/canlab_single_trials). Each study involved a series of trials of painful thermal stimuli of varying intensities (40. 8 to 50˚C) applied to the left arm, anticipatory cues, noxious stimuli lasting 1 to 20 seconds, and a pain rating period following a variable (jittered) interval  (Fig 1A).
Additionally, many studies included cognitive or affective psychological manipulations like romantic partner hand-holding or manipulations of perceived control over stimulus intensities (Fig 1B  We performed all of our primary analyses in 7 studies (Studies 1 to 7) and used 4 validation studies (Studies V1 to V4) as a post hoc confirmation of the validity of our inferences. Datasets were selected as validation studies semirandomly. We aim for our results to generalize to novel studies and contexts; however, we recognize that we cannot exhaustively include all possible permutations of studies and contexts in our analysis here. To simulate the burden of generalizing to novel pain conditions, we selected validation studies that partially overlapped with the experimental conditions of the training (studies V1, V2, and V3 had thermal intensity variation and V2 had expectation cues) as well as studies that had novel experimental conditions absent from the primary analysis (study V1 had pain reappraisal and V2 and V3 had placebo manipulations).
Additionally, we included studies with multimodal stimuli (V1 and V4) so that we might evaluate pain specificity of decoding models (S1 Text). Importantly, the use of validation studies was separate from the use of CV, which was used extensively for model fitting and evaluation (see MVPA sections below): CV generates an estimate while validation studies put that estimate to the test. Analysts were blind to validation studies until all primary analyses had been completed. In addition to the "single trial" dataset used to test pain intensity decoding, we also assembled a partially (but minimally) overlapping dataset of traditional GLM contrast maps (one contrast per participant representing a mean task effect) from multimodal pain and nonpain studies for indirect evaluation of model pain specificity.
This dataset consisted of 18 previously published studies: 2× acute thermal pain tasks (Study V1 and V2 above), 2× visceral pain tasks  [80, 81] , 2× mechanical pain tasks  [82] , 2× working memory tasks (WM)  [83, 84] , 2× response selection (Inhib)  [85, 86] , 2× response conflict (RespInhib)  [87, 88] , 2× negative emotion induction through visual scenes (AversiveImg)  [89, 90] , 1× social rejection (Rejection)  [91] , 1× vicarious pain (Vicarious Pain)  [45] , and 2× emotionally aversive vignettes from the International Affective Digital Sounds system (Emotion AversiveSnd, including study V4 above)  [88]. Each study contributed 15 contrast maps from 15 participants, and the dataset is designed to balance across studies and task categories, which consist of pain tasks (6×), cognitive demand control tasks (e.
g. , pain rating requires holding pain experience in working memory, inhibition of defensive withdrawal reflexes, etc. ; 6×) and tasks that share aversive emotional qualities with pain (6×). This dataset is a modified version of a previously published dataset  [88]  available at https://neurovault. org/api/collections/3324. Contrasts of stimulus versus baseline from Study V1 and Study V2 were substituted for the original thermal pain studies, since coincidentally the original thermal pain studies from  [88]  are used to train our decoding models here (Study 2 and Study 5 in particular), and we wished to test models on independent data.
fMRI acquisition and preprocessing Briefly, all data were acquired using 3T MRI scanners and were motion corrected, had cerebrospinal fluid and white matter signals linearly removed, and were spatially normalized to a standard MNI152 template, except for Studies 2, 5 and V4. Studies 2 and 5 were acquired at 1. 5T, and none of the 3 had cerebrospinal fluid or white matter signals removed. Please refer to studies referenced in Table  1  for further details on data preprocessing.
General linear model (GLM) analysis This study used beta-series analysis  [92]  to estimate brain response to each evoked noxious stimulus (a "single trial"). The GLM design matrix included separate regressors for each stimulus event and nuisance parameters (e. g. , head motion). This study excluded trials with variance inflation factors >2. 5. We thus obtained a separate parameter estimate brain map for stimulus evoked activity for each stimulation (trial), which represents heat versus baseline. For computational tractability, we averaged these maps within pain intensity quartile after data standardization (see "Data standardization" below for discussion).
Data standardization We demeaned data across studies (subtracted the study mean map from all constituent contrast maps) and standardized all single trial images (mean voxel value is 0, and standard deviation of voxel values within trial image is 1). Thus, individual images differed from one another in the spatial configuration of their pain response but were matched on net response. Although global intensity information may be task related, it is also affected in idiosyncratic ways by preprocessing decisions, image acquisition parameters, and spurious physiological noise. We also z-scored pain ratings within study to partially control for quantitative differences in the rating scales and calibrations used  (S1 Fig). This preserved between-participant differences withinstudy. Standardized imaging data and pain ratings were averaged within the pain rating quartiles for each participant.
Study-wise MVPA and learner generalization performance We defined the learner as the combination of algorithm (PCR) and the modeling space (regional, cRSN, fRSN, pain pathways, Neurosynth, or full brain). For learner performance estimates, we did not aggregate data across studies, instead defining 7 nonoverlapping datasets, one for each study (using the complete participant sample available in each case, in contradistinction with the multistudy analysis where participants were balanced across studies), and trained separate patterns for each. This study-wise MVPA used a total of 171 participants and 9,773 trials (16 to 30 participants, 256-2592 trials per study MVPA, Table  2 ). As for the multistudy analysis, we averaged data within-pain rating quartile within participant and evaluated the performance of MVPA patterns using 2 × 5 repeated k-fold nested CV.
Since fMRI data were quartiled for computational tractability, and z-scored for between study normalization, these data were also quartiled and (in the aggregated data) z-scored prior to regression analyses. The sum of wedges equals model variance explained. Residual model error is not shown, but completes the circles. We do not consider between-pAU : Pleasenotethatasperstyle; thetermsubject=sshouldnotbeusedforhumanpatients:Hence; al articipant differences further in this study, but we display them here to better characterize nonexperimental sources of pain variability. See S1 Table for standardized regression coefficients. Underlying data: https://github. com/ canlab/petre_scope_of_pain_representation/tree/main/figure1. Illustrated arm adapted from Da Vinci's Vitruvian man [101]. fAU : Abbreviationlistshavebeencomp MRI, functional magnetic resonance imaging.
Model weights are shown on representative brain slices, with yellow/orange indicating positive weights (pro-pain) and blue negative weights (antipain), thresholded with 5,008 bootstrap samples, voxel-wise p < 0. 05, uncorrected. While the relatively small number of significant weights suggests low model stability, the pattern spatial similarity matrix (correlations in patterns of model weights) shows that these weights were highly consistent across modeling spaces. Across all 6 model spaces, we commonly observed frontal midline deactivations, and cingulate, insular and cerebellar activations, supporting a nested modeling space interpretation. Underlying data: https://github. com/canlab/petre_scope_of_pain_representation/tree/main/figure4. cRSN, coarse resting-state network; CV, cross-validation; fRSN, fine resting-state network.
3 in favor of cRSN = fRSN). Post hoc comparison showed a full brain model outperformed the best single region (FullBrain -Regions = 0. 34(0. 10), F 1,7. 9 = 12. 39, p = 0. 008). Models were trained after pooling data across 7 datasets, yielding 90 participants per training fold. (B) We retrained cross-validated models separately for each study to infer whether findings would generalize to new PCR models trained on new datasets. All obtained models showed significant predictive performance (p < 0. 01, random participant and study intercept, random study effect), but comparison of performance across models from each study showed that distributed representations continued to outperform modular representations (planned comparison, mean(PainPathway, Neurosynth, FullBrain) -mean(regions, fRSN, cRSN) = 0.
Together, these results showed brain models captured the multifactorial nature of pain experience. Nearly identical conclusions were supported by full brain learner predictions ( † , S4 Table;S5 and S6 Tables for other models). Standardized path coefficients shown. All parameter estimates computed after removing participant fixed effects. � p < 0. 05, in models using full brain model predictions (shown). † p < 0. 05 in models using full brain learner predictions. Gray arrow, significant factor with full brain model but not learner. Bias corrected bootstrap test for nonzero α, β, τ, and α � β. Underlying data: https://github. com/canlab/petre_scope_of_pain_representation/tree/main/figure6. CV, cross-validation.
3, p < 1e-6). Additionally, the full brain models specifically outperformed the best elementary region models in Study 2 (mixed effects, random participant intercept, F 1,44 = 10. 1, p = 0. 0027) and overall (mixed effects, random participant intercept, and fixed study effects,F 1,201 = 6. 49, p = 0. 012; right). NS p > 0. 05 (not significant), � p < 0. 05, �� p < 0. 01, ��� p < 0. 001, ; BF > 10, "strong" evidence of null. Gray bars previously shown in Fig 5. Error bars: SEM, random participant intercepts, random study (gray bars) or fixed study (otherwise). Violin plot width indicates likelihoods estimated by inferential models fit to the original dataset (7 studies; Fig 5), vertically constrained to 95% confidence intervals. Underlying data and validated models: https://github. com/ canlab/petre_scope_of_pain_representation/tree/main/figure7.
Bars are grouped into pain tasks (blue), cognitive demand control tasks (red), and nonpainful aversive tasks (yellow). Planned contrasts: black brackets, p < 0. 003 (Sidak threshold for 17 comparisons), gray/black brackets p < 0. 05. Post hoc t test: � p < 0. 003. Dashed gray line: optimal cut point for discrimination (balancing sensitivity and specificity). Legend indicates sensitivity/specificity at the optimal threshold. Underlying data: https://github. com/canlab/petre_scope_of_pain_representation/tree/main/figure8. cRSN, coarse resting-state network; fRSN, fine resting-state network.
Data storage was supported by the  University of Colorado Boulder "PetaLibrary". Research was funded by  NIMH   R01 MH076136  (TDW),  NIDA   R01 DA046064  (TDW) and  NIDA   R01 DA035484  (TDW). Research was additionally supported by funding from  NCCIH ZIA AT000030 (LYA) ,  NIDA   K01 DA045735  (EARL), and the  Serra Hunter fellow lecturer program  (ML). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Here we report two antimicrobial peptides (AMPs), HG2 and HG4 identified from a rumen microbiome metagenomic dataset, with activity against multidrug-resistant (MDR) bacteria, especially methicillin-resistant Staphylococcus aureus (MRSA) strains, a major hospital and community-acquired pathogen. We employed the classifier model design to analyse, visualise, and interpret AMP activities. This approach allowed in silico discrimination of promising lead AMP candidates for experimental evaluation. The lead AMPs, HG2 and HG4, are fast-acting and show anti-biofilm and anti-inflammatory activities in vitro and demonstrated little toxicity to human primary cell lines. The peptides were effective in vivo within a Galleria mellonella model of MRSA USA300 infection. In terms of mechanism of action, HG2 and HG4 appear to interact with the cytoplasmic membrane of target cells and may inhibit other cellular processes, whilst preferentially binding to bacterial lipids over human cell lipids.
An enhanced understanding of microbiomes facilitates the identification and utilisation of some of the beneficial traits that are conveyed by these microbiomes  16. Several metagenomic datasets from the rumen have been generated in the last few years, illustrating some of the beneficial traits of the rumen microbiome including the presence of large numbers of novel glycosyl hydrolases  17  , esterases  18  , lipolytic enzymes  18  and more recently, antimicrobial compounds  14, 16  , possessing therapeutic potential for the treatment of MDR bacteria. Here, we combined the application of metagenomics, using one of the largest rumen metagenomic dataset  17  available, with advanced computational analytic tools and chemical models to identify and characterise AMP candidates for the treatment of MDR infections.
This metagenomic dataset contains more than 268 Gb, or 1. 5 billion read pairs, of metagenomic DNA from microorganisms that colonised plant fibre during incubation in the rumen of fistulated cows. De novo assembly of reads resulted in more than 2. 5 million predicted open reading frames at an average of 542 bp and 55% predicted full-length genes. We employed the classifier model design, a feature extraction method using molecular descriptors for amino acids for the analysis, visualisation, and interpretation of AMP activities, and the in silico discrimination of active and inactive peptides in order to define a small number of promising new lead AMP candidates for chemical synthesis and experimental evaluation. We also show the innocuity ex vivo, and the anti-MRSA efficacy of two of these AMPs both in vitro and in vivo.
RESULTS In silico identification of AMPs using computational analysis After applying the first selection criteria (i. e. protein sequences with a maximum length of 200 amino acids (AAs) and not more than 5% unknown AAs (marked by X, *)  19  , the computational analysis identified 917,636 sequences (36%) of the 2,547,270 predicted protein sequences in the Hess et al. dataset (termed Library 'Cow'). Of these 917,636 sequences, only 829 sequences fulfilled the criteria of AA distances (AAD) <0. 2 or small AA pair distances (AAPD) <1. 45, ensuring high possibility of these sequences being AMPs  20, 21. For example, only 65 sequences met these AAD and AAPD criteria in the first 68,274 sequences analysed, with isolated points outside a relatively dense distribution area as illustrated in Fig. 1a.
Descriptor computations generated positively charged loading-hydrophobicity plots, indicating that the selected sequences from the Library 'Cow' are represented in only a small portion of all AMP regions from the Library 'AMP' (see Fig. 1b ). Results from each computational step used in the identification of potentially novel AMPs from the Hess et al. rumen metagenomic dataset  17  is summarised in Supplementary Table  1. Six promising AMP sequences (Hess-Gene 1-6 (HG1-HG6) (Supplementary Table  2 ) were identified from the 829 sequences as holding the greatest potential for being active AMPs. The corresponding nucleotide sequence and additional information for the identified genes can be found in Supplementary Table  3. Two of these six promising candidates, HG2 (MKKLLLILFCLALA-LAGCKKAP) and HG4 (VLGLALIVGGALLIKKKQAKS), the shortest length AMPs containing 22 and 21 AA residues respectively, were selected for subsequent characterisation.
Sequence homology analysis using NCBI's BLASTP  22  against the non-redundant (nr) protein sequences (see Table  1 ) suggests that HG2 has similarity (71%) to an ABC transporter substrate-binding protein from Megasphaera elsdenii and many uncharacterised proteins, while HG4 has similarity to a hypothetical protein from Megasphaera elsdenii (77%). It is important to note that the AMPs, HG2 and HG4 match only a small portion of their homologous sequences (see Table  1 ), which are automatically curated unreviewed sequences, and this may indicate the novelty of the identified AMPs from the rumen metagenomic dataset. In addition, eggNOG mapper  23  annotations revealed that the HG2 and HG4 bearing sequences map to different membrane-associated proteins including transport and receptor proteins, amongst other functions (see Supplementary Table  4 ).
This puts HG2 into the small group of AMPs, representing <1% of AMPs deposited in the APD3 database for which a hydrophobicity ratio of ≥72% has been reported  19. and Hilpert Library 46 ), green coloured signs are AMP hits identified from library 'Cow' , and finally selected peptides HG2 (magenta) and HG4 (red). Three-dimensional modelling of peptide structures Three-dimensional structural modelling of HG2 and HG4 suggests that these peptides have a high proportion of helical content (Fig. 2 ). In the case of HG2 (Fig. 2a ), the cysteine bond stabilises the capping of the helix and the C-terminus region. Noteworthy is the clear amphipathic nature of the helix with the hydrophobic residues, particularly, Leu, aligned in a typical Leu-zipper motif. The N-and C-termini include a high proportion of the positively charged residues (Lys), which contribute to the segregation of charges along the peptide.