dataesr/scientific-paragraphs-categorization huggingface dataset:
To define a theta threshold to delineate putative NREM1 periods, we used data acquired during the sequence game to give us an estimate of the baseline theta amplitude during waking. We defined the 80th percentile of theta amplitudes from the sequence game as the theta threshold; i. e. , bins during rest periods exhibiting theta amplitude higher than this threshold were scored as "putative NREM1. " The number of bins used as "putative NREM1" and "putative waking" were equalized so that statistical tests could be compared between states without either set having a statistical power advantage (Table  S2 ). To balance the number of bins, the same number of bins scored as "putative NREM1" were used for "putative waking" in each Rest period, but using the bins exhibiting the lowest theta amplitude.
All statistical analyses were performed using MATLAB (https://www. mathworks. com/products/ matlab. html). Statistical values, dispersion and precision measures (e. g. , Mean, SEM) can be found in the figures, figure legends, and/or in the main text. Jarosiewicz et al. , 2015). The participant was then invited to relax with his eyes closed, and nap if desired, during a pre-task rest period (Rest1). Following Rest1, the participant performed the 4-target sequence game, consisting of 66 presentations of the repeated sequence (in this example, red-teal-yellow-blue), interspersed with 22 control sequences. Following the game, the participant was invited to relax with his eyes closed again (Rest2). (B) Participant T9 at his home during a research session. (C) Performance in the game, divided into control and repeated sequences.
ACKNOWLEDGMENTS The authors would like to thank participants  T9  and  T10  and their families and caregivers;  Sobhan Naderi Parizi ,  Damien Lesenfants , and  Tommy Hosman  for their contributions to this research; and  Beth Travers ,  Yuliya Mironovas , and  Dave Rosler  for administrative support. This work was supported by  U. S. Office of Naval Research  grant  N00014-13-1-0672 ;  NINDS  ( UH2NS095548 ;  5U01NS098968 );  NIDCD  ( R01DC009899 );  Department of Veterans Affairs  ( B6453-R; A2295-R ); and the  Fyssen Foundation. This work was supported in part by the  Center for Neurorestoration and Neurotechnology  ( N2864-C; N9228-C ) from the  United States Department of Veterans Affairs, Rehabilitation Research and Development Service, Providence, RI. The contents do not represent the views of the  U.
Then, half of the volume was withdrawn for analysis ("P1" samples). The bottles were then sealed, in order to trigger natural carbonation. This phase (P2) was performed at 26 • C for 10 days. At the end, the samples were analyzed ("P2" samples). Open and closed IC do not strictly reflect aerobic or anaerobic systems, but rather the production process of kombucha making in industrial conditions. Dissolved oxygen concentrations were not measured in samples. A description of the research samples and abbreviations is detailed as Supplementary Data (Table  S2 ). The populations of yeasts and/or bacteria during fermentation were determined by plating successive decimal dilutions of samples on WL agar for yeasts and MRS agar for bacteria, with technical triplicates for each biological triplicate.
Sucrose Utilization Strategies as a Basis of Microbial Interactions in Kombucha The experimental results obtained in this study confirm the metabolic interplay between yeasts and acetic acid bacteria that has been theorized for kombucha made from sugared black tea  [17, 49, 51, 52]. They highlight, for the first time, the key role of yeasts and their metabolisms in terms of invertase activity and the fermentation capacity, which can differ, depending on yeast strains (Table  1 ). Bibliographic resources and the NCBI database have reported the existence of genes encoding invertase in S. cerevisiae (internal and extracellular enzyme) [53-56] and B. bruxellensis (internal and extracellular enzyme)  [57] [58] [59] [60] , but few data is available for H. valbyensis. However, genomic studies on the Hanseniaspora genus reported the loss of the SUC gene of the branch to which H.
Supplementary Materials: The following are available online at http://www. mdpi. com/2304-8158/9/7/963/s1, Figure  S1 : Sum-up diagram of the experiment, Table  S2 : Description of research samples with abbreviations, Figure  S3 : Hypothetical "snowball effect" explaining sucrose hydrolysis by acetic acid bacteria. Funding: This work was supported by the Regional Council of Bourgogne-Franche Comté, the "Fond Européen de DEveloppement Régional (FEDER) and funded by Biomère (Paris, France).
Domain analysis of kinesin heads In the initial part of our studies we have identified dynamic domains in the structure of Kinesin Heavy Chain (taken from PDB Databank( id:3kin)  13  ) and analyzed deformations (low-frequency domain motions) which have been obtained with a simplified mechanical model proposed by Hinsen  14. The method is essentially based on observation that the low frequency modes describing motion of domains in proteins are influenced by anharmonic effects and in realistic environments become strongly overdamped, thus independent of the applied force field details. The structure has been analyzed with DomainFinder application  15. Firstly, an approximate normal mode analysis has been performed. Then, 16 modes of lowest frequencies have been stored for further domain and deformation analysis.
MD simulations' setup A structure of a motor domain, belonging to a kinesin-like protein KIF3B (a Kinesin-2 family's member)  19  , has been obtained from the PDB Databank( id:3b6u)  20. A sequence of 19 amino acids, 17 of which are considered to be a functional part known as a neck linker, has then been extracted and optimized. In order to do so, Steepest Descent and Conjugate Gradients algorithms have been employed. The neck linker chain was subsequently placed in a box of water molecules (a Simple Point Charge model), and the whole system was optimized again. Next, a simulation of Molecular Dynamics (MD) was scheduled, with a goal of achieving a state of at least near equilibrium, producing a 6ns long trajectory (with a time step duration ∆t = 2 × 10 -3 ps). A distribution of end-to-end distances was then created and used to determine the mean end-to-end distance of the equilibrated linker chain.
Additionally, the above steps of modelling have been repeated for three "intermediate" structures between the original neck linker and the Ala-only polymer. Namely, by selecting some of amino acid residues (either Asparagine, or Proline, or Lysine) and substituting them with alanine residues, "mutant" versions of the neck-linker have been created. The equilibrium simulations of those three altered linker structures lasted 5ns, 6ns and 3ns for no-Asparagine, no-Lysine and no-Proline chains, respectively. Finally, the whole process involving all 5 different sequences has been repeated for different stretching force values (130, 400, 700 and 1000 kJ * mol -1 * nm -1 ). The 4. 5. 5 version 21 of the GROMACS package  22, 23  and the OPLS-aa force field  24, 25  have been employed to perform all MD simulations.
The 10 most intense ions were fragmented by higher-energy collisional dissociation (nce = 30) with a resolution of 17,500, with AGC target set to 10 6 ions (maximum filling time 250 ms and minimum AGC target of 3 × 10 3 ), and dynamic exclusion set to 20 s. MS and MS/MS data were acquired using the Xcalibur software (Thermo Scientific).
Database Searches and Results Processing Data were processed automatically using Mascot Distiller software (version 2. 6, Matrix Science). Peptides and proteins were identified using Mascot (version 2. 6, Matrix Science) through concomitant searches against TAIR (version 10. 0), classical contaminants database (homemade), and their corresponding reversed databases. Trypsin/P was chosen as the enzyme and three missed cleavages were allowed. Precursor and fragment mass error tolerance were set, respectively, to 10 ppm and 25 mmu. Variable peptide modifications allowed during the search were: carbamidomethylation (C), acetyl (Protein N-ter), oxidation (M), and diGlycine (K). The Proline software (http:// proline. profiproteomics. fr) was used to filter the results: conservation of rank 1 peptide-spectrum match (PSM) with a minimal length of 7 and a minimal score of 25.
PSM score filtering was used to reach a False Discovery Rate (FDR) of PSM identification below 1% by employing the target-decoy approach. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol et al. , 2019) partner repository with the dataset identifier PXD019412 and 10. 6019/PXD019412".
| Implementation The code was implemented using PyNEST, the Python bindings of the NEST simulator  (Eppler, Helias, Muller, Diesmann, & Gewaltig, 2009) , based on version 2. 10 of NEST. Individual neurons were simulated using the iaf_ alpha_psc_mutisynapse model so as to allow the use of two types of excitatory synapses (to implement the different dynamics of NMDA and AMPA receptors). The model code is available at https://github. com/benoi t-girar d/sBCBG.
In our case, the optimization was performed with an evolutionary algorithm; 1,000 different runs were carried out with population sizes of 400 and duration of 1,500 generations, thus representing a total of 600 million model simulations. It permitted to find reasonable values for about 50 parameters, while the translation to LIF required the adjustment of five additional parameters (the constant inputs to the neurons in each modelled population) and the choice to have each connection between two neurons relying on three synapses. In order to avoid putting too much a priori hypotheses about the function of the circuit, we suggest to rely as much as possible on anatomical data and on function-independent measurements and to test a posteriori the operation of the model, possibly using tests driven by hypotheses on the function at that step of the process.
ACKNOWLEDGEMENTS BG thanks the  Japan Society for the Promotion of Science  for the long-term Invitational Fellowship for Research in Japan that supported his stay at  OIST , allowing the collaboration with the  Neural Computation Unit of Prof. K. Doya , that seeded the research presented in the present paper. This work was supported by Post-K Application Development for Exploratory Challenges hp160266, hp170251, hp180223 and hp190157 from the  Ministry of Education, Culture, Sports, Science, and Technology (MEXT) of Japan and Okinawa Institute of Science and Technology Graduate University  research support to KD.
Introduction Variants in the non-coding part of the genome can result in severe diseases and phenotypes with a high expressivity  [1]  and the number of variants identifiable by GWAS can explain only a modest fraction of the phenotypic variation  [2]. However, the pathomechanism underlying these variants are often difficult to dissect. Factor IX (FIX) constitutes a serine protease in the enzyme cascade of the intrinsic coagulation pathway. The F9 gene is located on the X chromosome consisting of 8 exons  [3]. Diseasecausing variants in the F9 gene cause complete or partial deficiency of the corresponding coagulation factor, ultimately leading to hemophilia B (OMIM:306900) of varying severity with an incidence of 1 in 25. 000 births  [4]. The FIX mutation database (http://www. factorix. org; [5]  lists 1083 different variants.
Of these, 122 are non-coding and four occur in the 3' untranslated region (3'UTR). These non-coding variants can have severe consequences and in addition, they are often part of regulatory sequences, which otherwise would escape our notice  [1]. In 1993 a 3'UTR variant (Chr. X, NC_000023. 11:g. 139563228A>G) was described in four families with moderate to severe hemophilia B  [6]. Affected individuals showed FIX activity between <1 and 4. 5% of normal. At present, the FIX mutation database lists 21 patients from different geographic regions with this variant (http://www. factorix. org; [7, 8] ; S1 Table ). The variant may generate a 5' splice site (5'SS) by an A to G transition at position +1157 downstream of the stop codon (c. 2545A>G; NM_000133) in the 3' UTR  [6]. However, the underlying pathophysiological mechanism remained unsolved.
Bioinformatic analysis and statistics MicroRNA binding sites were analyzed using the online prediction tool miRDB  [44]. RBPmap  [27]  is available here: http://rbpmap. technion. ac. il/. Splice site prediction was performed using NNSPLICE 0. 9 version  [45]  and MaxEnt score  [15]. Statistical analysis (Bonferroni's Multiple Comparison test) was performed using the GraphPad Prism software.
The efficiency of these enhanced oil recovery (EOR) techniques comes with larger and faster changes in the stress field at depth, increasing their potential for triggering local anomalous seismicity  (Murray, 2013; Rubinstein and Mahani, 2015). The fact that extensive extraction and injection of fluids in the subsoil can trigger moderate-sized earthquakes is thus now commonly accepted, in particular where gas is extracted  (Foulger et al. , 2018, and references therein). The HiQuake database of potentially induced earthquakes built by  Foulger et al. (2018)  hosts 38 earthquakes associated with EOR techniques in general and with waterflooding in particular, as observed for instance in the Wilmington and Inglewood oil fields, USA, in the 20th century  (Nicholson and Wesson, 1992). Unraveling the contributions of (i) injection for EOR, (ii) oil extraction itself, or (iii) wastewater disposal in inducing seismicity and deformation is challenging since these operations are often occurring simultaneously.
InSAR data and processing We use data from the European Space Agency (ESA) Sentinel-1 Synthetic Aperture Radar (SAR) constella-tion, consisting of two satellites launched respectively in April 2014 (S1A) and April 2016 (S1B). Sentinel-1 operates in a burst mode (the so-called TOPS mode; De Zan and Guarnieri, 2006), which allows for generating ∼ 250 km wide across-track images, which are acquired in three contiguous subswaths. Each Sentinel-1 satellite operates on a sun-synchronous orbit with a cycle time of 12 d. The two satellites are phased at 180 • , enabling a revisit time of 6 d for interferometry. According to the acquisition plan designed by ESA, systematic acquisitions are carried out over Europe, including Albania, providing an optimal temporal coverage for our area of interest. We process independently SAR data acquired on two overlapping tracks in descending (T153) and ascending (T175) geometries, consisting respectively of 120 and 109 images over the 2014-2018 period (Figs.
S2, S3 ). Sentinel-1 data processing is carried out using the NSBAS software  (Doin et al. , 2011) , which includes routines from the ROI_PAC software  (Rosen et al. , 2004). The originality of NSBAS is to enable the application of a series of corrections prior to phase unwrapping in order to minimize unwrapping errors, which remain the primary limitation of the InSAR techniques in areas of low coherence. The specific Sentinel-1 processing technique used in NSBAS is described in  Grandin (2015)  and  Grandin et al. (2016). After selecting the common bursts in our image set, a master image is chosen on the basis of its central position in terms of spatial baseline and time of acquisition within the image stack (Figs. S2, S3 ). We use the 30 m SRTM (Shuttle Radar Topography Mission) digital elevation model  (Farr and Kobrick, 2000)  to compute the simulation in the master geometry.
S8 ). No significant surface deformation can be identified, suggesting that the Patos-Marinza context is unique. A possible hypothesis for this strong deformation occurring in this intensely operated oil field could be that gas may still be retrieved coeval with extraction of heavy oil, leading to pressure drops larger than expected in the reservoir. An important gas leakage has indeed been observed near the most subsiding area in 2015  (Report CAO, 2018). Surface deformation has been monitored over a wide range of oil and gas fields worldwide, and several physical models have been proposed and validated with real data (e. g. , van Thienen-Visser and Fokker, 2017). In the simplest elastic case, analytical solutions developed for buried points or finite spherical sources  (Mogi, 1958) , tensile dislocations  (Okada, 1985) , and circular sill-like horizontal cracks  (Fialko et al.
MM coordinated the ALBA project and the redaction of the present paper. All authors participated in the interpretation and discussion of the results. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. InSAR data were processed using the NSBAS chain, mainly developed at ISTerre and IPGP as part of a French community effort to provide tools for the Data and Services for the Solid Earth (ForM@Ter). The figures of this manuscript were done using the free and open-source GMT library (http://gmt. soest. hawaii. edu/, last access: 16 March 2020) and QGIS software (http: //qgis. osgeo. org, last access: 16 March 2020). The authors would like to thank one anonymous reviewer and the editor Simon McKlusky for their comments that helped improve the paper, as well as Tara Shreve for proofreading this paper.
Thus, the main aim of this study was to characterize the functional segregation and integration of the brain network associated with the experience of pain in healthy subjects. We tested the hypothesis that the modular organization of the (sub)cortical nodes during pain can shape the temporal structure of nociceptive-related activation previously defined with iEEG  (Bastuji et al. 2016). Taking as standpoint the brain areas identified by human iEEG as being involved in the transition from nociception to conscious pain  (Bastuji et al. 2016) , we used graph theory with modular analysis on functional magnetic resonance imaging (fMRI) recorded during noxious heat stimuli. Pain-related modular partitions were based on the weighted functional correlation matrices during pain, from two independent sets of fMRI data including 60 healthy participants, one being used for main analysis (n = 36) and the second for replication/validation (n = 24).
Preprocessing Identical preprocessing was applied to the 2 datasets, using Statistical Parametric Mapping software (SPM12, Welcome Trust Centre for Neuroimaging) running on MATLAB R2014b (Math-Works, Naticks). The first two functional scans from each BOLD session were deleted. Preprocessing included motion correction, co-registration of the structural and functional images and normalization with the segmentation procedure into the Montreal Neurological Institute (MNI) space. No spatial smoothing was applied before regional parcellation to avoid increasing artificially the correlation of signals in atlas-based graph functional activity  (Alakörkkö et al. 2017; Gargouri et al. 2018).
Network Construction: Pain Stimulation-based Correlation Matrices Graph analysis was performed under the graphpype functions of Neuropycon package  (Meunier et al. 2020 ; https://neuropyco n. github. io/graphpype/. ) using the open-source Python package "nipype"  (Gorgolewski et al. 2011). Raw time-series averaged over the voxels within each ROI were extracted for each subject. Head movement parameters, as well as average signals in the white matter and CSF were regressed out from ROI time series, and residuals were high-pass filtered (>0. 01 Hz) to remove the scanner drift component of the signal and then normalized into Zscore for each session. Functional connectivity (FC) was derived from time series, to describe patterns of statistical dependence among neural elements  (Smith 2012). To compute stimulation-based correlation matrices, we used the methods described by  Meunier et al.
Network Topological Features The computation of graph metrics was mostly based on "Radatools" software, a set of freely distributed applications to analyze complex networks (http://deim. urv. cat/&#x007E; sergio. gomez/radatools. php). The following common global network properties were computed at various link densities individually  (Rubinov and Sporns 2010 ): 1) clustering coefficient (C), a measure of the degree to which nodes in a graph tend to cluster together; 2) shortest path length (L), defined as the average minimal number of links needed to reach any node from each node; 3) modularity (Q), defined as a set of nodes that have many intramodular connections but sparser intermodular connections, indicating a decomposability of the system into smaller subsystems  (Bullmore and Bassett 2011) ; and 4) assortativity (A), or the correlation coefficient between the degree of a node and the average degree of its neighbors.
Weighted Signed Modularity and Hub Region Detection Modular decomposition of a graph aims at determining subgraphs (i. e. , modules or communities) whose nodes interact significantly more strongly together than they do with nodes belonging to other modules  (Fortunato 2010 ). Several algorithms have been proposed to detect the modular partition  (Newman 2006) , and here we used the one described by  Gómez et al. (2009) , implemented in Radatools software, which enables the analysis Downloaded from https://academic. oup. com/cercorcomms/article/1/1/tgaa088/6006204 by guest on 14 April 2022 of modular structure in weighted signed graphs. Radatools is one of the few tools to offer modular partition on weighted signed networks; it was previously applied in neuroimaging studies  (Meunier et al.
Validation Analysis The same procedure and analysis pipeline described above was also applied separately to the second group (n = 24 subjects), recorded during a separate session and considered here as a validation cohort. The experimental pain stimuli were longer (i. e. , 25 s) than in group 1, but was calibrated to induce the same pain intensity rated 6/10 by the participant (Fig. 2 ). Data were not recorded in the exact same technical conditions (i. e. , type of scanner, headcoils, number and duration of functional sessions), but these external differences participated to the generalizability of the findings. Pain stimulation-based correlation matrices were created and weighted signed modular analysis was applied an identical procedure as described above for group 1, in an attempt to replicate results obtained with the first set of data.
DATA For the experiments, we use the Corpus of Historical American English (COHA)  1. It contains more than 400 million words of text from the 1810s-2000s. As a historical corpus, it is smaller than the widely used Google books corpus 2  but it has the advantage of being balanced by genre (fiction, magazines, newspapers, and non-fiction texts, gathered from various Web sources) for each decade. We focus our experiments on the most recent data in this corpus, from the 1960s to the 1990s (1960s has around 2. 8 million and 1990s 3. 3 million words), to match the evaluation corpus. The fine-tuning of the model is also done only on this subset. We rely on a small human-annotated dataset  [13]  to conduct the evaluation. The dataset consists of 100 words from various frequency ranges, labelled by five annotators according to the level of semantic change between the 1960s and the 1990s.
They use a 4-points scale from "0: no change" to "3: significant change", and the inter-rater agreement was 0. 51 (p <0. 01, average of pair-wise Pearson correlations). The most significantly changed words from the dataset are, for example, user and domain; words for which the meaning remain intact, are for example justice and chemistry. This dataset is a valuable resource and has been used to evaluate methods for measuring semantic change in previous research  [8, 11]. Following previous work, we use the average of the human annotations as semantic change score. For evaluation, we compute Pearson and Spearman rank correlations between a model output and this score. The notion of the best model is based on Spearman correlations.