dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors acknowledge the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  InvaCost  project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum  call 2018 on biodiversity scenarios. AN acknowledges funding from  EXPRO  grant no. 19-28807X  ( Czech Science Foundation ) and long-term research development project  RVO 67985939  (The  Czech Academy of Sciences ). CC was supported by  Portuguese National Funds through Fundação para a Ciência e a Tecnologia  ( CEECIND/02037/2017 ;  UIDB/00295/2020  and  UIDP/00295/2020 ).
RNC was funded by a research fellowship from the  Alexander von Humboldt Foundation. TWB acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant no. 747120. MG and CD were funded by the  BiodivERsA-Belmont Forum  Project " Alien Scenarios " ( BMBF/PT DLR 01LC1807C ). NK was partially supported by the  Russian Foundation for Basic Research  (grant no. 19-04-01029-A ) [national literature survey] and the basic project  of Sukachev Institute of Forest SB RAS  (project no. 0287-2021-0011 ) [InvaCost database contribution]. DR thanks  InEE-CNRS who supports the network GdR 3647 'Invasions Biologiques '. Funds for AJT, EA and LBM contracts come from the  AXA Research Fund Chair of Invasion Biology of University Paris Saclay.
The localization of the neutralization epitope of the NTD at the virus/host cell interface is consistent with this high variability as it is submitted to a strong pressure of selection for SARS-CoV-2 variants. Conversely, the ADE epitope, which is on the lateral side of the NTD, is not facing the plasma membrane of the host cell and for this reason is not subjected to such a high selective pressure. The frequency of amino acid sequence variations of the ADE and neutralizing epitopes was analyzed by specific queries of the Los Alamos database over the last four-month period (2021-06-01 to 2021-09-26) (Table  2 ). All the epitopes listed in Figure  3  were analyzed in 929,203 genomes. The ADE epitope of the NTD is highly conserved (>99% for all segments) except for the 64-69 motif at position H69 (variation of 8.
| Data acquisition Horizontal eye movements and eye blinks were monitored using horizontal and vertical bipolar electrooculography (EOG) electrodes, placed on either side in line with the eyes, and above and below the right eye, respectively. Cardiac activity was monitored with bipolar electrocardiography (ECG) electrodes attached at the right clavicle and under the left lower rib. Impedance of electrodes was <10 kΩ measured with SiggiII impedance meter (Easycap GmbH, Inning, Germany). MEG measurements were carried out using a 306-channel whole-scalp F I G U R E 1 Participants performed a sustained (10-30 s) attention task in which rare (10%) targets were detected within continuous tactile stimulation (16 Hz). Trials were followed by subjective attentional ratings on a 8-step visual scale, and a single yes/no target detection response neuromagnetometer system (Elekta Neuromag TRIUX™, Elekta Oy, Helsinki, Finland) at the Centre de Neuroimagerie de Recherche (CENIR MEG-EEG) in Paris, France.
Eye position and pupil diameter were monitored with an EyeLink 1000 (SR Research) and simultaneously recorded with the MEG, ECG, and EOG data as auxiliary data channels and at the same 1-kHz sample rate. This allowed all data to be segmented similarly across modalities. Data were recorded at 1 kHz, low-pass filtered at 330 Hz, and stored for off-line analyses. Anatomical MRI scans (Siemens, MPRAGE, 0. 8-cm isometric voxel size, GRAPPA = 2) for source localization were either recorded at CENIR after MEG measurements, or acquired from participation in a previous study, recorded at the same location.
| Data selection Target trials and false-alarm trials were discarded from further analysis. For each subject, trials were median split between high-and low-attention trials according to the individual distribution of attention-ratings, independent of subsequent artefact rejection and therefore identical between the different measurements. The formula (MATLAB, 2017) used to calculate the membership of each trial to high or low attention was: group = ceil (2 * tiedrank (rating)/n), with rating being the attention rating and n the total number of correct responses. Trials were time locked to the end of the stimulation, that is, 1 s before the onset of the attentional probe. All comparisons between high and low attention were performed on the last 10 s, that is, the minimum trial length, providing identical amounts of data in each trial.
| Artefact removal Continuous MEG data were preprocessed off-line with MaxFilter 2. 2. 10 (Elekta Oy, Helsinki, Finland), using tSSS for artefact removal and head movement compensation with a correlation limit of ≥0. 95 using a single data segment  (Taulu & Kajola, 2005; Taulu & Simola, 2006). MEG data then analysed using the MATLAB-based Fieldtrip toolbox  (Oostenveld et al. , 2011)  and custom functions. Muscle artefacts were detected based on a z value >20 of Hilbert transformed, bandpassed continuous data (110-140 Hz, butterworth, filter order = 9). The whole data (unfiltered, including annotation of the automatically marked artefacts) were then inspected visually in 60-s segments for any SQUID jumps, movement artefacts (large drifts in the data), or remaining muscle artefacts, which were then annotated and used to excludes trials in further analyses.
To remove the influence of EOG and ECG, data were decomposed into independent components  (Makeig et al. , 1996) , using the runica algorithm  (Delorme & Makeig, 2004) , preceded by a principal component analysis (PCA;  Delorme & Makeig, 2004)  to prevent rank-deficiency introduced by MaxFilter. The rank of the data for the PCA was determined by the MATLAB rank function  (MATLAB, 2017). Components reflecting EOG or ECG artefacts were iteratively removed if they correlated more than three σ (standard deviations based on all remaining components channels) with either the EOG or ECG. Finally, a second visual inspection of the data was performed in case any EOG artefacts were not (sufficiently) removed. Time periods of interest that contained any artefact were removed from all subsequent analyses.
| Pupillometry Pupillometry data were first band-pass filtered between 2 and 150 Hz and time-locked to the last stimulation. Saccades and artefacts were detected by means of z thresholding (z > 2), padded by 150 ms, and linearly interpolated. Trials containing more than 50% of unusable data were removed. Differences between high and low attention were normalized to relative change and compared using cluster-based permutation tests  (Maris & Oostenveld, 2007)  to identify temporal clusters of significant difference (p < 0. 05, two-sided, 4000 permutations).
ACKNOWLEDGEMENTS We would like to thank  Margaux Romand-Monnier  and  Clemence Almeras  for their help in subject planning and recording. Part of this work was carried out on the CENIR core facility of ICM. We gratefully acknowledge  Laurent Hugueville  for his help with the experimental setup. This work was supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 670325 , Advanced grant BRAVIUS) and senior fellowship of the  Canadian Institute for Advanced Research (CIFAR) programme in Brain, Mind and Consciousness  to C. T. -B. , as well as from  Agence Nationale de la Recherche  ( ANR-10-LABX-0087 IEC  and  ANR-10-IDEX-0001-02 PSL ).
Introduction Modification of embryonic stem cells and now CRISPR/Cas9 genome editing  [1]  have enabled the generation of approximately a hundred thousand of genetically altered (GA) rodents with a variety of genes, markers or genomic region modifications. The impact of the model creation method, i. e. CRISPR/Cas9 genome editing, pronuclear injection for additive transgenesis (PNI) or homologous recombination in embryonic stem (ES) cells, must first be carefully assessed. For example, unexpected CRISPR/Cas9 genome-editing events are well described and may lead to the generation of a different mutation than that which is expected  [2, 3]. However, many studies do not assess whether the verified mutation(s) then result in the expected mutant mRNA or protein. Recent publications show that improper validation of the consequence of a mutation has a strong impact on data interpretation and research reproducibility.
General considerations for mRNA quantification Target organs is selected according to the known pattern of expression of the genes altered and relevant biological systems for the study and, as much as possible, for strong target gene expression (using Bio-GPS as described in Lindner et al  [14] ). The sequence data resource available in Ensembl genome browser (www. ensembl. org) is used to determine the existing splice variants of the gene of interest  [16]. Assays are designed to quantify as many as possible protein coding transcripts and always include the main protein coding transcript proposed by Ensembl. When possible, we take into consideration the exon-intron structure of the gene of interest to reduce the risk of false positives from amplification of any contaminating genomic DNA. For each category of alleles, DNA and the resulting anticipated mRNA maps are built using SnapGene v5 software (www.
Whatever the allele design  [1] , both genomic and functional validation of genetic alterations are highly desirable before phenotyping. For example, RT-qPCR was performed on eight different humanized models generated by insertion of the human cDNA in its mouse counterpart at the ATG position, and it was shown that for one of these eight lines the knock-in allele was not expressed, resulting in the generation of a knock-out model (data not shown). When whole genomic fragments are inserted (pronuclear injection of a human BAC) or are used to replace their mouse counterparts (replacement by homologous recombination), not only exonic regions but also introns and underlying regulatory elements are humanized. Gene expression patterns and their underlying regulatory processes have substantially diverged between mouse and human, with the exception of a subset of core regulatory programs that are largely conserved  [49, 50].
4  & Supplementary Figure  8 ). The data show that only 20% of the lines show correlation between the G0 and G1 generation TCN, whereas for 80% of the lines no correlation is observed (Supplementary Table  1 ). For 25% of the lines this result could be explained by mosaicism in embryos, i. e. the DNA transgene integrates later than the one-cell embryo stage (Supplementary Table  1  and Supplementary Figure  8C, E, I  and O ). Result for an additional 20% of the Fig. 3. mRNA and DNA evaluation of floxed allele inactivation after crossing with Alb-CreER T2 deleter. (A) DNA and remaining mRNA level were analysed 18 weeks after the last tamoxifen (TAM) injection for the Acly cKO/cKO line or 2 weeks after for the 3 other floxed lines. mRNA expression was evaluated using two different designs: an assay that can detect both wild-type or knock-out RNA and an assay that can only detect endogenous WT mRNA.
Acknowledgements The authors would like to thank the staff of  PHENOMIN -Institut Clinique de la Souris  for the generation, breeding and healthcare of the mice and rats. We thank  Louise Tinsley  for her critical reading of the manuscript, and  Claudia Caradec  and  Chadia Nahy  for their contribution to the generation of the data presented. This work was supported by the  National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg (UDS) , and the  Centre Européen de Recherche en Biologie et en Médecine. This study also received support from the  French.
Graph theory thus provides a comprehensive description of topological and dynamical properties of complex brain networks. Most studies seem to corroborate that network topology, to a large Computational Intelligence and Neuroscience extent, determines time-averaged functional connectivity in large-scale brain networks. However, not many tools yet exist to describe dynamic graphs, and only very few consider the relationship between static and dynamic graphical models. Tools for such analysis are made available for researchers, such as GraphVar (https://www. nitrc. org/ projects/graphvar/)  [38, 39] , a GUI-based toolbox for comprehensive graph theoretical analyses of brain connectivity, as well as the construction, validation, and exploration of machine learning models. Dynamic graphical models have also been developed and are available in R (https://github.
com/schw4b/DGM)  [34]. Such DGMs can deal with spatiotemporal activity patterns, allow for loops in networks, and can describe directions of instantaneous couplings between nodes. Furthermore, temporal lags of the hemodynamic response between coupled nodes have been shown to influence quantitative directionality estimates but are known for avoiding false positive estimates  [34]. Very recently, Meunier et al. [40]  presented NeuroPycon (https://github. com/neuropycon), an open-source toolbox for advanced connectivity and graph theoretical analysis of MEG, EEG, and MRI data. Often, one problem is the reproducibility of processing pipelines in neuroimaging studies. To tackle this problem, the NeuroPycon toolbox wraps commonly used neuroimaging software for processing and graph analysis into a common Python environment and provides shareable parameter files.
Specifically, clustering in networks needs to be compared to clustering on the lattice and not random networks. Telesford et al. [43]  proposed a proper metric for such comparison. If brain networks show a small-world network topology, it is mirrored by two principles of information processing: functional segregation on small, quasi-mesoscopic spatiotemporal scales but functional integration on larger, macroscopic spatial and temporal scales. Recently, Sizemore and Bassett  [31]  reviewed existing methods and employed a publicly available MATLAB toolbox (https://github. com/asizemore/Dynamic-Graph-Metrics) to visualize and characterize such dynamic graphs with proper metrics. In  [44] , Sizemore et al. already showed that the algebraic topology is well suited to characterize mesoscale structures of brain connectivity formed by cliques (a set of adjacent vertices) in an otherwise sparsely connected network.
In an early study, Honey et al. [4]  concluded from computational modeling that the inference of structural connectivity from functional connectivity deems impractical. Still, several recent graph theoretical studies elaborate on inferring FC from SC and vice versa. ey provide different metrics for quantifying the SC-FC interrelationship and discuss the decisive role of indirect connections. Abdelnour et al. [48]  added a new twist to inferring FC from SC by considering linear computational GMs rather than the commonly employed nonlinear simulations. ey captured the long-range second-order correlation structure of RSNs, which governs the relationship between its anatomic and functional connectivities. e model applied random walks on a graph (see Appendix A) to structural networks, as deduced by DTI, and predicted the FC structure obtained from the fMRI data of the same subjects.
e study could not only reproduce quantitative measures of SC on a fine-grained cortical dataset, consisting of 998 nodes, but also robustly predict long-range transhemispheric couplings, which are not resolved by DTI. Concerning computational GMs, Huang and Ding  [56]  also considered the question of proper quantification of SC-FC interrelationships. ey showed that conditional Granger causality (cGC) was significantly correlated across subjects with edge weights  [22]  in RSNs, but not with mean fractional anisotropy. e authors concluded that edge weight represents the proper SC measure, while cGC adequately measures FC. Following the question of proper quantification of FC, Meier et al. [57]  compared the structure-function relationship, when using different imaging modalities to assess FC, measured not only with fMRI but also with MEG.
On a more phenomenological level, e Virtual Brain (https://www. thevirtualbrain. org/tvb/zwei) neuroinformatics platform  [69, 70]  provides a brain simulator as an integrated framework which encompasses several neuronal models and their dynamics. It offers multiscale model-based simulations and allows inference of neurophysiological processes underlying functional neuroimaging datasets. Hence, such modeling frameworks generate features, which allow for an understanding of underlying mechanisms beyond computational reproduction. One typical interpretation derived from computational modeling is that the brain at rest resembles a dynamically metastable (A metastable state of a dynamical system is stable against small perturbations but not against large perturbations. It corresponds to a minimum of the free-energy landscape other than the global minimum.
e model has been shown to explain grand average resting-state FCs. However, such average FCs summarize correlated spatial activity distributions but do not reveal their temporal dynamics. In a twostep approach, first, spatiotemporal data arrays have been decomposed, employing tensor decomposition methods, into sets of brain regions, called communities, with similar temporal dynamics. eir related time courses are assessed by an overlapping sliding window technique and could be grouped into four distinct communities resembling wellknown RSNs. Second, data were simulated with this stationary MFM constrained by results from diffusion tensor imaging (DTI) and fiber tracking. e spatiotemporal structure of the network then results solely from fluctuations around a mean FC pattern. More importantly, the four distinct RSNs emerged from this stationary MFM if the network nodes were coupled according to the model-based EC.
e method is rather generative as it only needs weak assumptions about the underlying data, thus is generally applicable to resting-state data and task-based data from arbitrary subject populations. In a related study, Glomb et al. [21]  compared the DTI-based SC with the model-based effective connectivity (EC) using whole-brain computational modeling of the spatiotemporal dynamics of FC evaluated on a sliding window basis. e authors discussed the way node connectivity affects the fitting of simulated to empirical patterns. e resulting tensors are decomposed into a weighted set of communities, whose nodes share similar time courses. Some of these communities resemble known RSNs such as the DMN, whose fluctuations have been linked to cognitive function. Similarity between simulated and empirical spatiotemporal dynamics of ROIs was especially pronounced whenever model nodes were connected by EC rather than SC.
[105]. e authors integrated individual SC and FC data with neuronal population dynamics to infer neurophysiological properties on multiple scales. In their study, EEG was used to record electrical potentials at the scalp surface of individuals. ese source activities were integrated into an individualspecific model, which simulated brain activity distributions and predicted person-specific fMRI time series and spatial topologies of RSN activities. In addition, neurophysiological mechanisms underlying several experimental observations from various functional imaging modalities could be successfully predicted. Whole-brain computational modeling can also answer questions concerning the reproducibility and consistency of resting-state fMRI. Donnelly-Kehoe et al. [106]  demonstrated that the estimation of parameters, which describe the dynamical regime of the Hopf model (It denotes a model of a dynamic system which exhibits a critical point in the phase space where its stability switches and a periodic solution arises.
Machine Learning Approaches Machine learning approaches were first applied to fMRI datasets to deduce signal components in a purely datadriven fashion. Such explorative techniques allow to detect spatially segregated regions in the brain, associated with individual functions  [116, 117]. ese blindly identified source components can then help to define nodes in graphical models of brain networks and set the spatial layout for brain connectivity. Also, such blind source separation techniques were applied to EEG recordings in order to identify relevant source signals and to separate them from artifacts in the data  [118] [119] [120]. Like in fMRI, such intrinsic components can be used to define nodes in functional networks and provide a data-driven perspective on brain connectivity in EEG studies  [121].
Especially in the absence of stimulus-driven tasks, like in resting-state fMRI  [116]  or in resting-state EEG  [121] , such exploratory techniques have proven to be a promising alternative to atlasbased definitions of brain networks. Most notably, principal component analysis (PCA) and independent component analysis (ICA) (While PCA extracts components with maximal variance from the data, ICA applies a stronger condition and maximizes for statistical independent components  [133] )  [134]  are frequently employed to analyze biomedical and neuroimaging datasets, especially EEG and fMRI data  [117, 118]. While most studies on brain connectivity still rely on atlas-based definitions of graph nodes in brain networks, simulations have shown that data-driven derivations of such nodes with ICA can be beneficial for graphical analysis  [36].
ICA is intrinsically a multivariate approach, and hence, each independent component (IC) groups brain activity into similar response patterns thereby providing a natural measure of functional connectivity (FC). ICA comes in two flavors extracting either spatially (sICA) or temporally (tICA) independent component maps. e basic principle of spatial and temporal ICA is illustrated in Figure  5 , but these studies predominantly rely on sICA due to the abundance of requisite data samples. Spatial ICA has been applied first to fMRI datasets by McKeown et al. [117] , while tICA followed shortly afterwards  [135]. Also, few studies dealing with spatiotemporal ICA have been performed  [136] [137] [138] [139] [140] [141]. ese various modes of ICA all share the limitation that the user has to identify the underlying sources.
To resolve this issue, constrained ICA  [142, 143]  or ICA with reference  [144, 145]  has been proposed to extract one or more ICs, which are as similar as possible to given reference signals. us, a priori information of the desired sources is used to form constraints in either the spatial or temporal domain. is approach has also been extended to the spatiotemporal domain. e constrained stICA algorithm searches for maximally independent sources that correspond to constraints in both spatial and temporal domains  [137]. is also exhibits improved performance for the analysis of fMRI datasets. Early work studied cortical functional connectivity (FC) in a seed-based approach, where the time course of any chosen seed voxel was correlated with the time courses of all other voxels to reveal two-point correlations of cortical activity in response to external stimuli and task requirements.