dataesr/scientific-paragraphs-categorization huggingface dataset:
Measuring of vigilance decrement The accuracy of each minute was measured as an F1 score, the harmonic mean of precision and sensitivity, given by F1 = 2 × TP 2 × TP + FP + FN where TP is the true positive rate, FP is the false positive rate and FN is the false-negative rate. F1 score is a particularly suitable accuracy measure for this task because it emphasizes true positives (responses to the rare target) and disregards true negatives (non-responses to non-targets). For reaction time (RT), only trials with correct responses were considered. In each block, trials with outliers (two standard deviations away from mean) were excluded, representing 3. 7% of trials overall. To investigate the effect of group and time on minute-wise performance and the three ratings, all the values were z-scored across participants and mixed-effects generalized linear models (GLM) were conducted using the MATLAB function fitglme with Laplace approximation as the method for estimating model parameters.
Funding This work was supported by the  Wellcome Trust  and  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. S. Z. and M. H. were funded by the  Wellcome Trust  ( 206330/Z/17/Z ). S. M. was funded by an  Medical Research Council (MRC)   Clinician Scientist Fellowship  ( MR/P00878/X ). K. S. was funded by the  Berrow Foundation. A. H. was supported by the  UK Dementia Research Institute Care Research ,  NIHR Technology Centre and Imperial Biomedical Research Centre. P. J. H. was supported by the  NIHR Biomedical Research Centre ,  South London  and  Maudsley NHS Foundation Trust  and  King's College London. W. T. was funded by the  Engineering and Physical Sciences Research Council (EPSRC)  through the  Imperial College Centre for Doctoral Training (CDT)  for Neurotechnology.
Data To quantify the transmission and case reporting of gHAT in the Mandoul focus of Chad, we utilised the WHO HAT Atlas data from 2000 to 2018  [2]. Records within this data set were annually aggregated by mode of screening/detection (i. e. active and passive), and location. In a single record the number of people screened, cases identified and staging (if known) were recorded. In order to fit our model to the focus-level data we aggregated all locations with the Mandoul focus together, but retained information on the number of active (stage 1, stage 2 and stage unknown) cases, passive (stage 1, stage 2 and stage unknown) cases, and the number of people actively screened in each year. To supplement these data, we also used 2019 case and screening data provided by the national control programme of Chad (Programme National de Lutte contre la Trypanosomiase Humaine Africaine; PNLTHA-Chad).
Step 1: Previous model projections corrected for new active screening First we use the previously developed model code and posterior parameterisation from fitting to data from 2000 to 2013  [12]  and use the AS numbers from 2014 to 2019 to update projections for those six years. In Mahamat et al. , screening numbers for 2014 and 2015 were known, but numbers for 2016-2019 were not. Large differences between assumed screening levels and actual screening levels can have a substantial impact on model projections-if there is no screening there would be no active cases, whereas there could be a high number of cases if large proportions of the population were screened. In Mahamat et al. , it was assumed that the screening numbers for 2016-2019 would be 27,265 each year (the same as in 2015), whereas new data from the WHO HAT Atlas and PNLTHA-Chad are somewhat lower-22,0071, 18,144, 18,083 and 12,640 for each of the years respectively.
Availability of data and materials Data cannot be shared publicly because they were aggregated from the World Health Organisation's HAT Atlas which is under the stewardship of the WHO. Data are available from the WHO (contact neglected. diseases@who. int or visit https:// www. who. int/ trypa nosom iasis_ afric an/ count ry/ foci_ AFRO/ en/) for researchers who meet the criteria for access. Model code and outputs produced from this study are available through Open Science Framework https:// osf. io/ rak9d/.
Data from nine participants were excluded. Three were excluded because excessive head movement during acquisition (translations larger than 3 mm or rotations larger than 3°), three were excluded because their electrogastrogram (EGG) spectrum did not show a clear peak that could allow us to identify the frequency of their gastric rhythm, and three more were excluded because ,70% of their EGG cycles was within normogastric range (15-30 s per cycle;  Wolpert et al. , 2020). A total of 63 participants (mean age 23. 95 6 SD 2. 76, 31 females and 32 males, and mean BMI 21 6 SD 1. 8) were included in the analysis described below.
MRI data acquisition MRI was performed at 3 Tesla using a Siemens MAGNETOM Verio scanner (Siemens) with a 32-channel phased-array head coil. The resting-state scan lasted 900 s during which participants were instructed to lay still and fixate on a bull's eye on a gray background. Functional MRI time series of 450 volumes were acquired with an echoplanar imaging (EPI) sequence and the following acquisition parameters: TR = 2000 ms, TE = 24 ms, flip angle = 78°, FOV = 204 mm, and acquisition matrix = 68 Â 68 Â 40 (voxel size = 3 Â 3 Â 3 mm 3 ). Each volume comprised 40 contiguous axial slices covering the entire brain. High-resolution T1-weighted structural MRI scans of the brain were acquired for anatomic reference after the functional sequence using a 3D gradient-echo sequence. The two samples had different anatomic sequences.
MRI preprocessing We used the same MRI preprocessing pipeline as described in  Rebollo et al. (2018). Brain imaging data were preprocessed using MATLAB  (MATLAB 2017, MathWorks)  and the Statistical Parametric Mapping toolbox (SPM 8, Wellcome Department of Imaging Neuroscience, University College London, United Kingdom). Images of each participant were corrected for slice timing and motion with six movement parameters (three rotations and three translations). Each participant's structural image was normalized to Montreal Neurologic Institute (MNI) template provided by SPM with affine registration followed by nonlinear transformation  (Friston et al. , 1995; Ashburner and Friston, 1999). The normalization parameters determined for the structural volume were then applied to the corresponding functional images.
EGG preprocessing Data analysis was performed using the FieldTrip toolbox. Data were low-pass filtered below 5 Hz to avoid aliasing and down-sampled from 5000 to 10 Hz. In order to identify the EGG peak frequency (0. 033-0. 066 Hz) of each participant, we first computed the spectral density estimate at each EGG channel over the 900 s of the EGG signal using Welch's method on 200-s time windows with 150-s overlap. Spectral peak identification was based on the following criteria: peaking power larger than 15 mV 2 and sharpness of the peak. Data from the selected EGG channel were then bandpass filtered to isolate the signal related to gastric basal rhythm (linear phase finite impulse response filter, FIR, designed with MATLAB function FIR2, centered at EGG peaking frequency, filter width 60. 015 Hz, filter order of 5).
Heart-rate variability (HRV) preprocessing In order to computer the power and ratio of HRV, we first removed the MRI gradient artifact from the ECG data using the FMRIB plug-in  (Iannetti et al. , 2005; Niazy et al. , 2005, version 1. 21)  for EEGLAB  (Delorme and Makeig, 2004, version 14. 1. 1) , provided by the University of Oxford Center for Functional MRI of the Brain (FMRIB). Data from the ECG channels were then bandpass filtered (1-100 Hz) using a FIR filter, designed with MATLAB function firws. We then retrieved the interbeat interval (IBI) time series by identifying R peaks using a custom semi-automatic algorithm, which combined automatic template matching with manual selection of R peaks for extreme IBIs. Data from eleven participants was discarded at this stage because of noisy ECG recordings.
1): PLV x;y ¼ 1 T X T t¼1 e i w xðtÞÀw yðtÞ ð Þ ; ( 1 ) where T is the number of time samples, and x and y are brain and gastric time series. Statistical procedure for determining regions showing significant gastric-BOLD coupling at the group level We employed a two-step statistical procedure adapted from previous work  (Richter et al. , 2017; Rebollo et al. , 2018)  to determine which voxels are significantly coupled to the stomach at the group level. We first estimated chance-level gastric-BOLD coupling at each voxel and in each participant. To estimate chance-level gastric-BOLD coupling, we computed gastric-BOLD coupling at each voxel between the BOLD data of the participant and the EGG data of the other 62 participants. For each participant and voxel, chance-level PLV was defined as the median of the 62 surrogate PLVs  (Rebollo et al.
Quantification of gastric-bold shared variance To estimate the amount of variance in the BOLD signal that could be accounted for by gastric coupling, we computed, for each resting-state network, the squared coherence coefficient between the EGG and average BOLD time course across all voxels significantly coupled to the gastric rhythm using FieldTrip software. The coherence coefficient measures phase and amplitude consistency across time and is a frequency domain analog of the cross-correlation coefficient in the temporal domain. Therefore, its squared value can be interpreted as the amount of shared variance between two signals at a certain frequency  (Bastos and Schoffelen, 2016). Anatomical characterization of the gastric network across RSNs, cortical gradients, and individual regions First, we calculated the effect sizes across significant gastric network voxels by computing Cohen's d (Eq.
In order to identify the individual regions comprising the gastric network, we used a recent multimodal parcellation of the cerebral cortex  (Glasser et al. , 2016). This parcellation consists of 180 areas per hemisphere and was obtained using a semi-automatized approach in multimodal MRI data that detects sharp changes in cortical thickness, myelin, connectivity, and function. The parcellation was imported to FreeSurfer using the procedure and data available at https://figshare. com/articles/ HCP-MMP1_0_projected_on_fsaverage/3498446/2. For each region of the parcellation, we determined the overlap with the gastric network (percentage of the area in the gastric network), as well as the average effect sizes across significant gastric network vertices. We then compared the spatial layout of the gastric network with the first two gradients of functional connectivity described in  (Margulies et al.
; I. R. and C. Tsupported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Program Grant Agreement 670325  (to C. T. -B. ) as well as by the  FrontCog  grant  ANR-17-EURE-0017. I. R. was supported by a grant from  DIM Cerveau et Pensée and Fondation Bettencourt-Schueller. We thank  Benoît Béranger  and  Juliette Klamm  for their help during data acquisition.
The notebooks for metabolomics data preparation and microbially-related molecules establishment are available on this repository (https://github. com/lfnothias/emp_metabolomics). Amplicon and shotgun metagenomic sequence data are submitted to the European Nucleotide Archive under Project: PRJEB42019 (https://www. ebi. ac. uk/ena/browser/view/PRJEB42019). Raw and demultiplexed amplicon and shotgun sequence data, the feature-table for full-length rRNA operon analysis, feature-tables for LC-MS/MS classical molecular networking and feature-based molecular networking, and the feature-table for GC-MS molecular networking data are available for download and analysis through Qiita at https://www. qiita.
Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale.
In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus).
A resource for multi-omics in microbial ecological research Here we generated data for 880 environmental samples that span 19 major environments contributed by 34 principal investigators as part of the Earth Microbiome Project 500 (EMP500). The EMP500 is a previously unreported sample set for multi-omics protocol development and data exploration (Fig. 1  and Supplementary Table  1 ). To normalize sample collection for this and future studies, we updated and followed the existing Earth Microbiome Project (EMP) sample submission guide (https://earthmicrobiome. org/protocols-and-standards/emp500sample-submission-guide/)  50  , which we highlight here to encourage its use. In parallel, we followed standardized protocols for sample collection, sample tracking, sample metadata curation, sample shipping and data release, which are also detailed on the EMP website (https:// earthmicrobiome.
Resource https://doi. org/10. 1038/s41564-022-01266-x (GC-MS)) (Supplementary Table  2 ). To foster exploration of this previously unreported dataset, we have made the raw sequence and metabolomics data publicly available through Qiita (https://qiita. ucsd. edu; study ID: 13114)  51  and GNPS (https://gnps. ucsd. edu; MassIVE IDs: MSV000083475, MSV000083743)  52  , respectively. We also provide complete protocols for laboratory and computational workflows for both metagenomics and metabolomics data for use by the broader community (available on GitHub at https://github. com/biocore/emp/blob/ master/methods/methods_release2. md). We hope that the dataset and workflows presented here serve as useful tools for others, in addition to providing a framework for launching additional future studies.
Metabolite intensities reveal habitat-specific distributions In total, we generated untargeted metabolomics data (that is, LC-MS/ MS) for 618 of 880 samples (Supplementary Table  2 ), resulting in 52,496 unique molecular structures, or metabolites, across all samples. We then refined that dataset to include only putative, microbially related metabolites (that is, defined as being produced, modified by, or otherwise associated with a microbe), resulting in 6,588 metabolites across all samples (12. 55% of all metabolites). Focusing on this subset, we found that although the presence/absence of major classes of microbially related metabolites is relatively conserved across habitats, their relative intensities (that is, analogous to relative abundances for microbes) reveal specific chemistry that is lacking or enriched in particular environments (Fig.
Turnover and nestedness are related to the environment Next, we examined whether metabolite diversity among environments (that is, beta-diversity) was driven by either turnover (that is, the replacement of features) or nestedness (gain/loss of features leading to differences in richness)  1, 53. We first looked at turnover. We already noted similarity in the clustering of samples by environment between microbially related metabolite and microbial taxon datasets (Fig. 3c, d ). We also observed a strong correlation between sample-sample distances based on metabolites vs microbial taxa (Table  1 ). Interestingly, we observed a stronger effect of salinity when comparing samples on the basis of microbial taxa vs metabolites (PERMANOVA on salinity: pseudo-F = 40. 94 for microbes vs 8. 25 for metabolites, P = 0.
001 for both tests) (Fig. 3c, d ). Furthermore, when focusing on the separation of samples within a single environment such as soil, we observed much more variability between metabolite and microbial taxon datasets (Mantel r = 0. 32 for soil vs 0. 43 for all environments, P = 0. 001 for both tests). This highlights the unique composition among soil samples from distinct locations (Extended Data Fig. 3 ), and also the insight that was gained from analysis at different scales (that is, only soils vs all habitats). To assess whether metabolite profiles were more similar to those for microbial taxa vs microbial functions, we annotated our metagenomic reads to profile enzymes. We found the separation of samples based on microbial functions to be unique and largely driven by animal gut samples as compared to separation based on either metabolites or microbial taxa (Extended Data Fig.
4 ). However, correlations in sample-sample distances between microbial functional data and other datasets were strong (Table  1 ). In the absence of complete turnover in metabolites and microbial taxa across environments, apparent in the overlap of clusters representing different habitats in our ordinations (Fig. 3c, d ), we quantified nestedness. Nestedness describes the degree to which features in one environment are nested subsets of another environment, and can provide insight into community assembly dynamics  1, 53. We found that samples were significantly nested on the basis of both metabolites (Extended Data Fig. 5 ) and microbial taxa (Extended Data Fig. 6 ), and that certain environments were consistently nested within others, although this pattern varied between datasets. For example, on the basis of microbial taxa, we observed host-associated samples to be nested within free-living ones (Extended Data Fig.
Metabolite-microbe co-occurrences are habitat-specific In addition to exploring relationships between metabolite and microbial diversity, we sought to explicitly quantify metabolite-microbe co-occurrence patterns. Beyond relating metabolites to the microbes that potentially interact with them, certain metabolite-microbe pairs may have stronger associations with the environment than any one feature set alone and may serve as emergent indicators. In particular, we examined associations between metabolites and the environment (for example, Fig. 3a, c ) while also considering each metabolite's co-occurrence with all microbes in the dataset (Extended Data Fig. 1 ). In that regard, we first generated metabolite-microbe co-occurrences learned from both LC-MS/MS-and shotgun metagenomic profiles across all samples, for a cross-section of 6,501 microbially related metabolites and 4,120 microbial taxa (Extended Data Figs.
Correlations with amplicon sequence data and GC-MS data To begin to explore the additional data generated for EMP500 samples, including GC-MS and amplicon sequence data (that is, bacterial and archaeal 16S and full-length rRNA operon, eukaryotic 18S, fungal ITS), we compared sample-sample distances (that is, beta-diversity) between each pair of datasets. Beyond providing insight into how certain community data are related, strong correlations between datasets may indicate similarity in the structuring of features among samples or habitats. Importantly, we found further support for a strong relationship between microbially related metabolites and microbial taxa (LC-MS/MS vs 16S; r = 0. 27, P = 0. 001) (Table  1 ). The relationships between the metabolomics data (that is, LC-MS/MS or GC-MS) and sequence data from eukaryotes (that is, 18S or ITS) were weaker (for example, LC-MS/MS vs ITS; r = 0.
Discussion Here we discuss some of the caveats and limitations of our study, and further highlight how our approach advances understanding of microbial community dynamics and functional diversity. Due to their extensive nature, we provide additional important points of discussion as Supplementary Information. We begin by recognizing that certain environments included in EMPO are represented here by only a handful of samples (Fig. 1 ) and/or a single sample set (Supplementary Table  1 ), and note that we had to exclude them from some of our analyses due to low representation (for example, machine learning and co-occurrence analyses). We recommend that future efforts focus on additional sampling of these environments to further generalize our findings to those habitats. Similarly, we hope to expand sampling geographically to broaden our scope of inference, as many important environments and locations could not be included here (or, indeed, in the EMP's 27,000-sample dataset  1  ).
Here we performed multi-omics integration of a dataset encompassing a diversity of environmental sample types representing several habitats, generated using standardized methods allowing for robust meta-analysis with data from other studies using the same approach. Our approach illustrates that recent advances in computational annotation tools offer a powerful toolbox to interpret untargeted metabolomics data  41. We anticipate that parallel advances in metagenomic sequencing, genome assembly and genome mining will improve the discovery and classification of functional products from among microbes and provide additional insight into these findings. By following standardized methods available on GitHub and making this dataset publicly available in Qiita and GNPS, this study will serve as an important resource for continued collaborative investigations.
Dataset description Sample collection. Our research complies with all relevant ethical regulations following policies at the University of California, San Diego (UCSD). Animal samples that were sequenced were not collected at UCSD and are not for vertebrate animals research at UCSD following the UCSD Institutional Animal Care and Use Committee (IACUC). Samples were contributed by 34 principal investigators of the Earth Microbiome Project 500 (EMP500) Consortium and are samples from studies at their respective institutions (Supplementary Table  1 ). Relevant permits and ethics information for each parent study are described in the 'Permits for sample collection' section below. Samples were contributed as distinct sets referred to here as studies, where each study represented a single environment (for example, terrestrial plant detritus).