dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In the present study, we explored the interplay of differences in timing of migration and the behaviour during the early breeding season. Using bio-logging data we found differences in colony attendance patterns among the three species, determined the foraging areas used during the pre-laying exodus and early incubation, and examined the activity data of tracked birds during the early breeding season. Stable isotopes completed the picture by confirming habitat changes between the interbreeding and early breeding periods and highlighting resource segregation during the latter.
Gut bacteriome GB profiles of the two mouse species were dominated by bacteria of the phyla Firmicutes (40% of all reads), Proteobacteria (25. 3%), Bacteroidetes (20%), Tenericutes (7. 4%) and Deferribacteres (6. 1%, Fig. 1 ). Another six phyla were detected in our dataset (Spirochaetes, TM7, Actinobacteria, Cyanobacteria, Fusobacteria and Acidobacteria) at low abundances (< 1% reads). At both the phylum and class level, GB content was comparable between MM and MS; however, the dominant bacterial phyla and classes tended to vary between gut sections (Fig. 1 , Table  S1 ). In particular, Bacilli (phylum Firmicutes, represented by the genus Lactobacillus), Mollicutes (phylum Tenericures, represented by the genus Mycoplasma) and Gammaproteobacteria (Proteobacteria, represented by the genus Aggregatibacter) were more common in the ileum than the colon or caecum (Fig.
3 ). PERMANOVA analysis indicated that the effect of species and sample location was highly significant for both dissimilarity types, whereas differences between gut sections were only supported by PERMANOVA for relative abundance-based dissimilarity. The effect of sex, despite being marginally significant, explained only < 5% of GB variation (Table  2 ). Using negative binomial GLMMs, we detected 11 OTUs (represented by 11% of all reads in our dataset) whose abundances varied between MM and MS. Remarkably, four Oscillospira, two OTUs from the family Clostidiaceae and one Odoribacter OTU were more abundant in non-commensal MS than commensal MM. The two species also differed in relative abundance of two Helicobacter OTUs, one being more abundant in MM and the other in MS (Fig. 4 ). According to phylogenetic placement analysis, these OTUs represent phylogenetically distant Helicobacter species, where Campylobacter jejuni (GenBank accession: EU127548.
Gut bacteriome genotyping Metagenomic DNA from gut tissue samples was extracted in April 2016 in a laminar flow cabinet using the PowerSoil DNA isolation kit (MO BIO Laboratories Inc. , USA). We failed to collect a caecum sample from one MS individual; consequently, the final dataset included 35 samples of metagenomic DNA (Table  S2 ). Primers flanking the V3-V4 variable region on bacterial 16S rRNA gene (i. e. , S-D-Bact-0341-b-S-17 [CCTACGGGNGGCWGCAG] and S-D-Bact-0785-a-A-21 [GACTACHVGGGTATCTAATCC]) were used during the polymerase chain reaction (PCR) step  [62] , both forward and reverse primers being tagged with 10bp barcodes for sample demultiplexing. For the PCR, we used 8. 2 μl of KAPA HIFI Hot Start Ready Mix (Kapa Biosystems, USA), 0. 56 μM of each primer and 6. 2 μl of DNA template.
PCR conditions were as follows: initial denaturation at 98 °C for 5 min, followed by 30 cycles each of 98 °C (15 s), 55 °C (20 s) and 72 °C (40 s) and a final extension at 72 °C (5 min). The PCR products, together with negative controls (PCR products for blank DNA isolates), were run on 1. 5% agarose gel, the concentration of the PCR product being assessed based on gel band intensity using GenoSoft software (VWR International, Belgium). The samples were subsequently pooled at equimolar concentration and run on 1. 5% agarose gel, with bands of appropriate size excised from the gel and purified using the High Pure PCR product Purification Kit (Roche, Switzerland), according to the manufacturer's instructions. Sequencing adaptors were ligated using TruSeq nano DNA library preparation kits (Illumina, USA) and the resulting amplicon libraries sequenced on a single MiSeq run (Illumina, USA) using v3 chemistry and 2 × 300 bp paired-end reads at Centre de Biologie pour la Gestion des Populations (CBGP, tferrier sur Lez Cedex, France).
Bioinformatics analysis Skewer  [64]  was used for both sample demultiplexing and detection and trimming of gene-specific primers. In the next step, reads of low quality (expected error rate per paired-end read > 1) were eliminated. Dada2  [65]  was used for denoising of quality-filtered reads and quantification of 16S rRNA and ITS2 haplotypes (hereafter operational taxonomic units; OTUs) in each sample. Next, UCHIME  [66]  was employed for detection of chimeric OTUs. The gold. fna (available at: https:// drive5. com/uchime/gold. fa) and UNITE databases  [67]  were used as references for chimeras filtering from the GB and GM datasets, respectively. After elimination of chimeric haplotypes, taxonomy for non-chimeric OTUs was assigned at 80% posterior confidence by RDP Classifier  [68]. The GreenGenes database version gg.
13. 8  [69]  was used for bacterial OTU annotation, and the UNITE database  [67]  for fungal OTU annotation. Bacterial OTU sequences were aligned using PyNast  [70]  and their phylogenetic tree constructed using FastTree  [71]. We did not conduct phylogenetic reconstruction for ITS2 OTUs as ITS2 evolution is driven to a large extent by insertions and deletions, which complicates identification of homologous positions for phylogenetically disparate taxa. OTU tables (i. e. OTU read counts in individual samples), OTU sequences, their taxonomic annotations and phylogeny along with sample metadata were merged into separate GB and GM databases using the package phyloseq  [72]  in R (R Core Team 2015).
Statistical analysis The GM database comprised 589,406 high-quality sequences grouped in 153 non-chimeric OTUs. The number of ITS2 reads per sample ranged between 1826 and 32,002 (median = 17,896). Only basic descriptive tools were used in GM analysis as the sample structure was highly homogenous (detailed below). The GB database comprised 1,088,282 high quality sequences grouped in 1634 non-chimeric OTUs. As the number of bacterial reads varied between samples (range = 1415 -58,106, median = 29,188), we rarefied the OTU table to achieve even sequencing depth per sample and used the downsampled database for further statistical analysis unless otherwise stated. The Shannon index was used as an alpha diversity measure, with per-sample Shannon diversities included as a response variable in generalised linear mixed effect models (GLMM), while sex, sampling location, gut section and species identity were included as alpha diversity predictors.
To account for within-individual covariance, individual identity was included as a constraint for permutations (i. e. strata). We reported marginal probability values, i. e. significance of the variable in question controlled for the effect of all other variables included in the PERMANOVA model. To identify OTUs exhibiting variation in abundance between gut sections and between the two species, we employed mixed models (from R package BhGLM  [74] ) assuming negative binomial error distribution. These analyses were only conducted for a subset of dominant OTUs (represented by > 0. 1% reads and present in > 5 samples, n = 119). The marginal effect of the predictor in question (i. e. effect of species identity statistically controlled for systematic variation between gut sections and vice versa) was tested using likelihood-ratio tests, with qvalue  [75]  used as a multiple testing correction method.
As in the previous analyses, individual identity was included as a random effect. Tukey post-hoc tests were employed after likelihood ratio testing to identify specific gut section pairs where the OTU in question exhibited significant variation in abundance. In specific cases, phylogenetic placement was used to provide a more detailed insight into OTU taxonomy. A set of reference 16S rRNA sequences exhibiting > 97% similarity with the OTUs in question was extracted from the Silva database v. 132  [76]. Sequences were aligned using mafft  [77]  and a phylogenetic tree constructed using RAxML  [78]. Bootstrap analysis based on 1000 replications was used to estimate support for the tree nodes.
Acknowledgements Access to computing and storage facilities owned by parties and projects contributing to the  National Grid Infrastructure MetaCentrum , provided under the programme "Projects of  Large Infrastructure for Research, Development, and Innovations"  ( LM2010005 ), is greatly appreciated. We acknowledge the  Core Facility Genomics ,  Central European Institute of Technology, Masaryk University , supported by the  National Center for Medical Genomics research infrastructure  ( LM2015091  funded by  Ministerstvo Školství ,  Mládeže a Tělovýchovy, Czech Republic ), and especially  Filip Pardy  and  Boris Tichý  for their support while obtaining the scientific data presented in this paper.
Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet, most of the literature is concerned with classification of outcomes defined at the event-level. Here, we focus on predicting continuous outcomes from M/EEG signal defined at the subjectlevel, and analyze about 600 MEG recordings from Cam-CAN dataset and about 1000 EEG recordings from TUH dataset. Considering different generative mechanisms for M/EEG signals and the biomedical outcome, we propose statistically-consistent predictive models that avoid source-reconstruction based on the covariance as representation. Our mathematical analysis and ground-truth simulations demonstrated that consistent function approximation can be obtained with supervised spatial filtering or by embedding with Riemannian geometry.
, 2008; D€ ahne et al. , 2014a , 2013) , whereas  Dmochowski et al. (2012)  have proposed variants of Canonical Correlation Analysis (CCA)  (Hotelling, 1992; D€ ahne et al. , 2014b)  without orthogonality constraint to focus on shared directions of variation between related datasets or by proposing shared envelope correlations as optimization target  (D€ ahne et al. , 2014b). This yields a two-step procedure: 1) spatial filters model the correlation induced by the leadfields and provide unmixed time-series 2) some non-linear transforms such as logarithms are applied to these time-series as the validity of linear equations is now secured. A more recent single-step approach consists in learning directly from spatially correlated power-spectra with linear models and Riemannian geometry  (Barachant et al.
, 2017 ). Yet, systematic comparisons against additional baselines and competing regression models on larger datasets and other outcomes are missing. Importantly, the majority of approaches have focused on event-level prediction problems instead of subject-level prediction and have never been systematically compared in terms of their statistical properties and empirical behavior. Here, we will explicitly focus on subject-level as contrasted to event-level prediction, both, theoretically and at the level of data analysis. Note that the present article does not focus on event-level prediction with generalization across subjects  (Halme and Parkkonen, 2018; Westner et al. , 2018; Olivetti et al. , 2014) , which is a distinct and more complex problem inheriting its structure from, both, event-level and subject-level regression.
Event-level regression: Cortico-muscular coherence We first focused on event-level regression of continuous electromyogram (EMG) from MEG beta activity. Data acquisition. We analyzed one anonymous subject from the data presented in  (Schoffelen et al. , 2011)  and provided by the FieldTrip website to study cortico-muscular coherence  (Oostenveld et al. , 2011). The MEG recording was acquired with 151 axial gradiometers and the Omega 2000 CTF whole-head system. EMG of forceful contraction of the wrist muscles (bilateral musculus extensor carpi radialis longus) was concomitantly recorded with two silver chloride electrodes. MEG and EMG data was acquired at 1200Hz sampling-rate and online-filtered at 300Hz. For additional details please consider the original study  (Schoffelen et al. , 2011).
Data processing and feature engineering. The analysis closely followed the continuous outcome decoding example from the MNE-Python website  (Gramfort et al. , 2014). We considered 200 s of joint MEG-EMG data. First, we filtered the EMG above 20 Hz using a time-domain firwin filter design, a Hamming window with 0. 02 passband ripple, 53 dB stop band attenuation and transition bandwidth of 5Hz (-6 dB at 17. 5 Hz) with a filter-length of 661 ms. Then we filtered the MEG between 15 and 30 Hz using an identical filter design, however with 3. 75 Hz transition bandwidth for the high-pass filter (-6 dB at 13. 1 Hz) and 7. 5 Hz for the low-pass filter (-6 dB at 33. 75 Hz). The filter-length was about 880 ms. Note that the transition bandwidth and filter-length was adaptively chosen by the default procedure implemented in the filter function of MNE-Python.
Subject-level regression with MEG: Age prediction In a second MEG data example, we considered a subject-level regression problem in which we focused on age prediction using the Cam-CAN dataset  (Taylor et al. , 2017; Shafto et al. , 2014). Data acquisition. Cam-CAN dataset contains 643 subjects with resting state MEG data, between 18 and 89 years of age. MEG was acquired using a 306 VectorView system (Elekta Neuromag, Helsinki). This system is equipped with 102 magnetometers and 204 orthogonal planar gradiometers inside a light magnetically shielded room. During acquisition, an online filter was applied between around 0. 03 Hz and 1000Hz. To support offline artifact correction, vertical and horizontal electrooculogram (VEOG, HEOG) as well as electrocardiogram (ECG) signal was concomitantly recorded.
Four Head-Position Indicator (HPI) coils were used to track head motion. For subsequent source-localization the head shape was digitized. The recording lasted about 8 min. For additional details on MEG acquisition, please consider the reference publications on the Cam-CAN dataset  (Taylor et al. , 2017; Shafto et al. , 2014). MNE model for regression with source localization. To compare the datadriven statistical models against a biophysics-informed method, for this dataset, we included a regression pipeline based on anatomically constrained minimum norm estimates (MNE) informed by the individual anatomy. Following common practice using the MNE software we used Q ¼ 8196 candidate dipoles positioned on the cortical surface, and set the regularization parameter to 1=9  (Gramfort et al. , 2014).
Concretely, we used the MNE inverse operator as any other spatial filter by multiplying the covariance with it from both sides. We then retained the diagonal elements which provides estimates of the source power. To obtain spatial smoothing and reduce dimensionality, we averaged the MNE solution using a cortical parcellation encompassing 448 regions of interest from  Khan et al. (2018). For preprocessing of structural MRI data we used the FreeSurfer software  (Fischl, 2012 , http://surfer. nmr. mgh. harvard. edu/). Data processing and feature engineering. This large dataset required more extensive data processing. We composed the preprocessing pipeline following current good practice recommendations  (Gross et al. , 2013; Jas et al. , 2018; Pernet et al. , 2018). The full procedure comprised the following steps: suppression of environmental artifacts, suppression of physiological artifacts (EOG/ECG) and rejection of remaining contaminated data segments.
Since no continuous head monitoring data were available at the time of our study, we performed no movement compensation. The origin of internal and external multipolar moment space was estimated based on the head-digitization. To mitigate ocular and cardiac artifacts, we applied the signal space projection method (SSP)  (Uusitalo and Ilmoniemi, 1997). This method learns principal components on data-segments contaminated by artifacts and then projects the signal into to the subspace orthogonal to the artifact. To reliably estimate the signal space dominated by the cardiac and ocular artifacts, we excluded data segments dominated by high-amplitude signals using the 'global' option from autoreject  (Jas et al. , 2017). To preserve the signal as much as possible, we only considered the first SSP vector based on the first principal component.
Subject-level regression with EEG: Age prediction In a third data example, we considered a subject-level age prediction problem, as in Cam-CAN, but focused on EEG from the Temple University (TUH) EEG Corpus  (Harati et al. , 2014) , one of the largest publicly available database of clinical EEG recordings. This ongoing project currently includes over 30,000 EEGs spanning the years from 2002 to present. This analysis is theoretically important as it supports assessing translation to clinical EEG data. It is also useful as it allows us to evaluate our regression models in a complementary setting where A is sample-dependent, as in Cam-CAN, but covariance matrices are full rank. This analysis is therefore appropriate to primarily investigate the particular model violation of sample-dependent mixing matrices.
Unfortunately, the absence of associated MRI data prevented us from conducting source localization. Data acquisition. We used the TUH "Abnormal EEG Corpus", a subset of TUH EEG Corpus that have been annotated as normal or abnormal by medical experts. From this dataset we focussed on the 1385 healthy patients, from both training and evaluation sets, whose EEG has been annotated as normal. Their age ranges between 10 and 95 years (mean 44. 3y, std 16. 5y, 775 females, 610 males). EEG was acquired using several generations of  Nicolet EEG system (Natus Medical Inc. ) , equipped between 24 and 36 channels. All sessions have been recorded with an average reference electrode configuration, sampled at 250Hz minimum. The minimal recording length for each session was about 15 min. For additional details on EEG acquisition, please consider the reference publications on the TUH dataset  (Harati et al.
, 2014). Data processing, feature engineering and model evaluation. We applied minimal preprocessing to the raw EEG data. We first selected the subset of 21 electrodes common to all subjects (A1, A2, C3, C4, CZ, F3, F4, F7, F8, FP1, FP2, FZ, O1, O2, P3, P4, PZ, T3, T4, T5, T6). We then discarded the first 60 s of every recording to avoid artifacts occurring during the setup of the experiment. For each patient we then extracted the first 8 min of signal from the first session, to be comparable with Cam-CAN. EEG recordings were downsampled to 250Hz. Finally, we excluded data segments dominated by high-amplitude signals using the 'global' option from autoreject  (Jas et al. , 2017)  that computes adaptive rejection thresholds. Note that the absence of linear projection to preprocess raw data (as SSS or SSP in Cam-CAN) ensures the data are full rank.
Statistical modeling We used ridge regression  (Hoerl and Kennard, 1970)  to predict from the vectorized covariance matrices and tuned its regularization parameter by generalized cross-validation  (Golub et al. , 1979)  on a logarithmic grid of 100 values in ½10 À5 ; 10 3 on each training fold of a 10-fold cross-validation loop. For each model described in previous sections ('diag', 'upper', SPoC, Riemann), we standardized the features enforcing zero mean and unit variance. This preprocessing step is standard for penalized linear models. To compare models against chance, we estimated the chance-level empirically through a dummy-regressor predicting the mean of the training data. Uncertainty estimation was obtained from the cross-validation distribution. Note that formal hypotheses testing for model comparison was not available for any of the datasets analyzed as this would have required several datasets, such that each average cross-validation score would have made one observation.
Software All analyses were performed in Python 3. 7 using the Scikit-Learn software  (Pedregosa et al. , 2011) , the MNE software for processing M/EEG data  (Gramfort et al. , 2014)  and the PyRiemann package  (Congedo et al. , 2013)  for manipulating Riemannian objects. We used the R-programming language and its ecosystem for visualizing the results (R Core Team, 2019;  Allaire et al. , 2019; Wickham, 2016; Clarke and Sherrill-Mix, 2017). Code used for data analysis can be found on http s://github. com/DavidSabbagh/meeg_power_regression.
Event-level regression with MEG: Predicting continuous muscle contraction We then probed the regression models on real MEG data where the true data-generating mechanism is not a priori known and multiple model violations may occur simultaneously. In a first step, we considered a problem where the unit of observation was individual behavior of one single subject with some unknown amount of noise affecting the measurement of the outcome. In this scenario, the mixing matrix is fixed to the extent that the subject avoided head movements, which was enforced  by the experimental design. Here, we analyzed one MEG dataset with concomitant EMG recordings that we chose as outcome (See section Event-level regression: Cortico-muscular coherence in Methods for details). At the time of the analysis, individual anatomical data was not available, hence we constrained the analysis to the sensor-space.
Subject-level regression with MEG: Predicting age We then turned our attention to a regression problem that imposes the important model violation of varying source geometry due to individual anatomy while providing a clean outcome with virtually no measurement noise: predicting age from MEG. We analyzed resting-state MEG from about 600 subjects of the Cam-CAN dataset  (Shafto et al. , 2014; Taylor et al. , 2017)  focusing on the power spectral topography and between-sensor covariance of nine frequency bands as features (for details see Table  2 ). In this problem, each sample consists of MEG signals recorded from different persons, hence different brains. On theoretical grounds, one may therefore expect individual cortical folding, size and proportional composition of the head and its tissues to induce important distortions to the signal that may pose severe problems to purely data-driven approaches.
Subject-level regression with clinical EEG: Predicting age The results on subject-level regression based on MEG suggest the importance of model violations due to individual head geometry. Importantly, with traditional cryogenic MEG, the sensor array is not fixed relative to the head, rendering head-positioning and head-movements factors contributing to model violations due to individual signal geometry. How would the present results generalize to clinical EEG? In that setting, sensors are fixed relative to the head but, in general, fewer sensors are used. Here, we analyzed resting-state EEG (21 sensors) from about 1000 subjects of the TUH dataset  (Harati et al. , 2014). As with previous analysis of the Cam-CAN data, each data point had its own mixing matrix. Likewise, the EEG recordings from TUH were sufficiently long to support accurate covariance estimation, hence, rendering model order search less important for shrinkage.