dataesr/scientific-paragraphs-categorization huggingface dataset:
Optical transient target (supernova, flare) Thanks to the large sky coverage of the GWAC-A, the fast follow-up capability of the GWAC-F60A/B and GWAC-F30 and the dedicated online data processing pipeline of each telescope, the GWAC-N is not only capable to independently detect optical transients in the sky but also to identify the types of the candidates in realtime. Since 2018,  The routine survey mode of the current GWAC-A covers ∼20000 square degree of observable sky (galactic plane not included) on a clear night. The center of the sky coverage of the survey shifts ∼ 1 degree along the longitude each day, while most of survey area is consistent in successive observation nights, which means a supernova survey with 1 day cadence can be made using the GWAC-A data. We adopt the ASAS-SN supernova detection relation  (Fig 6.
Variable and Periodic object The most of sky coverages of the GWAC-A's survey are consistent in successive observation nights, which means one given sky area can be monitored for days or dozens of days. With a high cadence observation mode (15 seconds per image), the GWAC-A can monitor the variables or the periodic objects in the sky area and obtain their variation. The online data processing pipeline of the GWAC-A can measure the photometric features for all sources in the images. Using neural network mechanism, researchers analyze the massive data of the GWAC-A to detect and to classify variable and periodic sources  (Qiu et al. 2018, Turpin et al. 2020B).
Moving object With its large field of view and high cadence, the GWAC-A can monitor hundreds asteroids on an observation night. The GWAC-A also has the capability to detect the decameter asteroids and meteors  (Shugarov 2019, Xu et al. 2020B). They are valuable for the researchers in these fields. Our team works on the algorithm and database to recognize and morphology analyze them from the GWAC data. The Figure  10  shows some moving objects automatically detected in the GWAC-A images by an algorithm for selecting moving objects. The accuracy over 85% can be reached for the meteor candidate selections by using the algorithm (Xu et al. 2020B).
The AOM will fully support the operations of the GWAC-N and greatly enlarge the scientific return of the GWAC-N. Its technology and mechanism, or as a whole, the AOM can be adapted to other world-wide, general purposed, telescope networks. The code of the whole AOM system is available at the Github 2 for public download. The AOM is not only used to manage the operations of telescopes but also to manage the data. A server named the Data Center (DC) is installed inside of the AOM. In the past, the massive data taken from the GWAC-N telescopes bring huge workload to the scientists and operators to collect and find images for certain observations. Currently, the data of the ToO follow-up observations taken by the GWAC-F60, the GWAC-F30 are automatically collected and uploaded to the DC by the AOM.
ACKNOWLEDGEMENT The GWAC team at the NAOC is grateful for financial assistance from the National K&D Program of China (grant No. 2020YFE0202100) and the National Natural Science Foundation of China  (Grant No. 11533003, 11973055, U1831207, 11863007). This work is supported by the Strategic Pioneer Program on Space Science, Chinese Academy of Sciences, grant Nos. XDA15052600 & XDA15016500 and by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDB23040000. Damien Turpin acknowledges the financial support of the CNES postdoctoral program. We thank the staffs of the Xinglong and the Jilin observatories at which the TNT and the CGFT telescopes are operated. We would like to thank Sarah Antier, David Corre, Jean-Grégoire Ducoin for their very helpful discussions during the developments of the AOM system.
e. minks, ferrets, cats, dogs, cat-tle, sheep, goats, horses and all other receptive species) antibodies directed against the major nucleocapsid protein of SARS-CoV-2. Plates were sensitized with a purified recombinant N antigen. Optical density (OD) was measured at 450 nm using Multiskan GO software (Thermo Scientific, Waltham, MA, USA). The test was validated when the optical density of positive control (OD PC ) was ≥0. 35 and a mean ratio of positive (OD PC ) and negative (OD NC ) control was higher than three. The optical density of each sample (OD N ) was used to calculate the sample to positive (S/P) ratio (expressed as a %) where S/P = 100 × (OD N -OD NC )/ (OD PC -OD NC ). When the S/P score was lower than 50% by ELISA, samples were considered negative. They were considered as positive when it was higher than 60% and doubtful when 50 < P/S score <60%.
SARS-CoV-2 genotyping and phylogeny The genomic study was performed for all obtained sequences and hallmarks of mutations were retrieved to identify the variant. Phylogenetic reconstruction was performed using the IQ-TREE software with the GTR Model and 1000 ultrafast bootstrap repetitions after alignment of genomes using MAFFT v. 7  (Colson et al. , 2021). The tree was visualized with the iTOL (Interactive Tree Of Life) software as previously described  (Katoh & Standley, 2013).
ACKNOWLEDGEMENT The authors would like to thank  Innovative Diagnostics  which provide them the ELISA ID Screen SARS-CoV-2 kit. We are grateful to  Marielle Bedotto ,  Ludivine Brechard ,  Delphine Rivière  and  Camille Fligny  for their technical help. A part of this study was supported by the  Institut Hospitalo-Universitaire (IHU) Méditerranée Infection , the  National Research Agency  under the " Investissements d'avenir" programme (reference   ANR-10-IAHU-03 ), the  Region Provence-Alpes-Côte d'Azur and European funding ERDF PRIMI. The development and validation of the mutiplex SARS CoV2 test by Scanelis was supported by a grant from the  Defense Innovation Agency (French Ministry of the Armed Forces).
Results For each subject, whole-brain tractograms derived from tractography and preprocessed rs-fMRI time series were combined to generate a 4-dimensional tw-dFC dataset with the same spatial and temporal resolution of the original fMRI time series. In this framework, each white matter voxel's time series reflects the dynamic changes in functional connectivity occurring at the endpoints of the white matter pathways traversing that voxel. Figure  1. Overview of the workflow. After diffusion-MRI and fMRI preprocessing steps, wholebrain tractography and resting-state fMRI are merged to generate subject-specific track-weighted dynamic functional connectivity (tw-dFC) time series. Independent component analysis (ICA) is then applied at the group level to classify the tw-dFC signal into spatially independent component (IC) maps and their associated time courses, which are the basis for following analyses.
In particular, reproducibility of IC spatial maps was evaluated at three different levels on the primary dataset: intrasubject reproducibility (test-retest), inter-subject reproducibility (split-half) and inter-cohort reproducibility (comparison with the validation dataset). IC spatial maps were also annotated by calculating percentage overlap with regions of interest from known GM (Rolls et al. , 2020) and WM atlases (Hua et al. , 2008; Yeh et al. , 2018). IC time courses, instead, were employed to compute between-components correlation (functional network connectivity, FNC), perform connectivitybased IC classification and predict cognitive tasks. Finally, the time series of IC underwent a further clustering analysis aimed at identifying stable or quasi-stable patterns of component activity weights which tend to reoccur over time and across subjects, an analogy to "brain states" described in the dynamic functional connectivity literature.
For each of these three group ICA runs, and both for the principal dataset (HCP) and the validation dataset (LEMON), peak activation for each independent component (IC) was localized in the white matter; all the components' mean power spectra show higher low-frequency spectral power and for all the reconstructed components there was no or minimal overlap with known vascular, ventricular, and meningeal sources of artifacts (Supplementary File 1). To ensure stability of estimation, each ICA algorithm was repeated 20 times in ICASSO  27. On the principal datasets, the cluster stability/quality index (Iq) over 20 ICASSO runs was very high (> 0. 9) for all the components. Similar results were obtained on the validation dataset, except for the n=100 run, where nearly all components obtained moderate-to-high Iq values (>0.
8) (Supplementary Figure  1 ). For the lower-dimensionality ICA10, the resulting components mostly consisted of white matter pathways linking nodes of well-known gray matter functional networks derived from rs-fMRI literature: default mode network (IC3), lateral (IC5) and medial (IC1) sensorimotor network, right (IC6) and left (IC7) frontoparietal network, lateral (IC9) and medial (IC9) visual network and auditory network (IC4) (Figure  2 ). Independent components (ICs) identified by the ICA10 on the main dataset reveal white matter structures corresponding to large-scale brain connectivity networks. Group spatial maps for each component are thresholded at z > 1, binarized and volume-rendered on a glass-brain underlay for visualization purposes. Each render shows left, right, superior, and anterior 3D views, along with the putative large-scale network name attributed to each component.
White matter components show high intra-subject, inter-subject and inter-cohort reproducibility The reproducibility of the results was evaluated at three different levels on the primary dataset: intrasubject reproducibility (test-retest), inter-subject reproducibility (split-half) and inter-cohort reproducibility (comparison with the validation dataset). All the components derived from ICA10, ICA20 and ICA100 were successfully replicated in the testretest reproducibility analysis, showing very high or moderate-to-high intra-subject similarity. Pearson's correlation was employed to quantify similarity between group spatial maps. Specifically, the ICA10 showed the highest test-retest reproducibility (all components with Pearson r = 1, meaning absolute identity between the paired components from the two datasets), while the ICA20 and ICA100 showed a decreasing trend (ICA20: median Pearson r = 0.
95, IQR = 0. 98 -0. 85) and the ICA100 (median Pearson r = 0. 92, IQR = 0. 95 -0. 87). For the white matter parcellation, a different trend was observed; while the ICA10 obtained the highest DSC values between corresponding components (median DSC= 0. 88, IQR = 0. 90 -0. 85), the ICA100 run showed higher spatial overlap (median DSC = 0. 81, IQR = 0. 86 -0. 75) than the ICA20 (median DSC = 0. 71, IQR = 0. 85 -0. 59) (Figure  4B ). Finally, many of the components resulting from the primary dataset (HCP) were totally or partially replicated in the validation dataset (LEMON). To ease with interpretation of the results, we subdivided correlation values into three ranks: high correlation (r > 0. 66), moderate correlation (0. 33 < r > 0. 66) low or absent correlation (r < 0. 33). For the ICA10, 4 components showed high correlation (40%), 5 components (50%) showed moderate correlation and 1 component (10%) showed low or absent correlation.
For the ICA20, 12 components (60%) showed high correlation, 4 components (20%) showed moderate correlation and 4 components showed low or absent correlation. Finally, for the ICA100, 74 components (74%) showed high correlation, 21 components (21%) showed moderate correlation and 5 components (5%) showed low or absent correlation (Figure  4C ). Due to the heterogeneity of the results between the two datasets, DSC between paired components from the two datasets was not calculated. However, corresponding components were visually inspected to check for similar coverage of cortical, subcortical, and white matter regions. Visual inspection confirmed that highly correlated components covered roughly the same cortical, subcortical, and white matter regions (i. e. , they have substantially the same anatomical meaning).
Resting-state and task-based white matter networks are correlated together Given that resting-state patterns of brain activity are often related to regional coactivation during behavioral tasks 2 , we sought to investigate the involvement of resting-state white matter dynamic connectivity components in task-modulated activity by correlating them with distinct, task-dependent white matter networks obtained from track-weighted, task-based fMRI data ("Functionnectome"). In brief, the Functionnectome algorithm maps the function signal from fMRI to tractography-derived priors of white matter anatomy. It has been applied to task-based fMRI data from the HCP dataset to obtain group task-based activation maps for the HCP motor, working memory and semantic language tasks  28. As expected, we found high correlation between resting-state and task-based white matter networks.
Discussion In the present work we employed tw-dFC to incorporate structural and dynamic functional connectivity information from a large dataset of human subjects into a unified analysis framework  23. In such a framework, the components identified by the ICA process can be interpreted as spatially independent functional units of white matter, composed by fiber bundles sharing coordinated fluctuations of functional connectivity at their endpoints. On a coarse ICA dimensionality scale, our findings complement and expand current knowledge on resting-state brain networks, as tw-dFC-derived components could be associated with well-known grey matter resting-state networks. Indeed, these ICs mostly consisted of white matter pathways linking nodes of gray matter functional networks as described in rs-fMRI literature during the last decades  1, 2, 7.
In addition, spatial tw-dFC components are highly stable both across subjects of the same sample and when compared to those obtained from a validation dataset (Figure  4 ). This last result is of utmost importance, as reproducibility is one of the key issues of modern-days neuroimaging research  42. Noteworthy, the validation dataset showed several demographical (larger age range, different gender proportion) and technical differences both in DWI (single shell, low b-value, no filtering) and rs-fMRI (lower temporal resolution, different denoising pipeline). Although a proper formal evaluation of the effects of these variables on the results is warranted in future studies, this finding suggests that tw-dFC-based components may be robust to experimental conditions, and, by consequence, able to capture actual features of brain activity and connectivity regardless of technical differences in data processing.
Although our results were obtained from nuisancecorrected fMRI data, preprocessed independently using two state-of-art denoising algorithms  59  , disentangling neuronal from non-neuronal signal in dynamic connectivity may be challenging even with the existing tools. On the other hand, constraining the dynamic functional connectivity analysis to voxels which are structurally connected by streamlines, as in tw-dFC, could help mitigate such drawback  23. Nevertheless, it is generally well-known that tractography-derived priors may not entirely reflect the ground-truth white matter anatomy, being affected by relatively high false positive rates  47. In the present work we employed a probabilistic tractography algorithm paired with trackfiltering in the main dataset (multi-shell data), and the same algorithm without track-filtering in the validation dataset (single shell data).
Albeit finding generally good external reproducibility, it remains difficult to rule out how much of the differences observed between the two datasets were due to tractography. It is very likely that optimizing the tw-dFC map generation by integrating a tractography pipeline with high robustness to noise could help raising the external reproducibility of the resulting tw-dFC components, and the impact of different signal modeling and tracking algorithms (such as deterministic or global tractography) is yet to be evaluated. Finally, since the quality of tw-dFC maps depends on the correct alignment of tractography and BOLD-fMRI data, non-linear registration of tractograms, as well as proper distortion correction of both DWI and BOLD data, are critical steps that need accurate control to ensure the good quality of the results.
Primary dataset (HCP) Structural, diffusion and resting-state functional MRI data of 210 healthy subjects (males=92, females=118, age range 22-36 years) were retrieved from the HCP repository (https://humanconnectome. org). Data have been acquired by the Washington University, University of Minnesota and Oxford University (WU-Minn) HCP consortium. The Washington University in St. Louis Institutional Review Board (IRB) approved subject recruitment procedures, informed consent and sharing of de-identified data. MRI data were acquired on a custom-made Siemens 3T "Connectome Skyra" (Siemens, Erlangen, Germany), provided with a Siemens SC72 gradient coil and maximum gradient amplitude (Gmax) of 100 mT/m (initially 70 mT/m and 84 mT/m in the pilot phase), to improve acquisitions of diffusionweighted imaging (DWI).
High resolution T1-weighted MPRAGE images were collected using the following parameters: voxel size = 0. 7 mm , TR = 2400 ms, TE = 2. 14 ms. DWI data were acquired using a single-shot 2D spin-echo multiband Echo Planar Imaging (EPI) sequence and equally distributed over 3 shells (b-values 1000, 2000, 3000 mm/s 2 ), 90 directions per shell, spatial isotropic resolution of 1. 25 mm. For rs-fMRI, a gradient-echo EPI resolution was acquired with the following parameters: voxel size = 2mm isotropic, TR = 720 ms, TE = 33. 1 ms, 1200 frames, ~15 min/run. Scans were acquired along two different sessions on different days, with each session consisting of a left-to-right (LR) and a right-to-left (RL) phase encoding acquisition; in the present work, we employ left-to-right and rightto-left acquisitions from a single session only (first session)  24, [60] [61] [62].
Validation dataset (LEMON) We obtained structural, diffusion and rs-fMRI data of 213 healthy subjects (males=138, females=75, age range 20-70 years) from the Leipzig Study for Mind-Body-Emotion Interactions (LEMON) dataset (http://fcon_1000. projects. nitrc. org/indi/retro/MPI_LEMON. html). The study was carried out in accordance with the Declaration of Helsinki and the study protocol was approved by the ethics committee at the medical faculty of the University of Leipzig. MRI was performed on a 3T scanner (MAGNETOM Verio, Siemens Healthcare GmbH, Erlangen, Germany) equipped with a 32-channel head coil. High resolution structural MRI scans were acquired using a MP2RAGE sequence and with the following parameters: voxel size =1 m, TR = 5000 ms, TE = 2. 92 ms. Single-shell DWI data were acquired using a multi-band accelerated sequence with spatial isotropic resolution= 1.
DWI preprocessing For the HCP dataset, DWI scans were retrieved in a minimally preprocessed form which includes eddy currents, EPI susceptibility-induced distortion and motion correction, as well as linear registration of structural and DWI images  60. In contrast, the LEMON DWI scans were obtained in raw format and underwent preprocessing through the dedicated pipeline included in the MRtrix3 software (https://www. mrtrix. org/). It features denoising using Marchenko-Pastur principal component analysis (MP-PCA), removal of Gibbs ringing artifacts, eddy currents, distortion (by exploiting the available reverse-phase encoding scans) and motion correction using EDDY and TOPUP FSL's tools, as well as bias field correction using the N4 algorithm  63, 64.
Resting state fMRI preprocessing Both HCP and LEMON rs-fMRI data were obtained in preprocessed and denoised form. HCP data minimal preprocessing included the following steps: field inhomogeneity-related artifact correction, motion correction, registration to standard space (MNI152, 2mm resolution), high pass temporal filtering (> 2000 s full width at half maximum) for removal of slow drifts  60  , artifact components identification using ICA-FIX  65  and regression of artifacts and motion-related parameters  62. Minimally preprocessed data were additionally band-pass filtered (0. 01-0. 09 Hz) and the global WM and CSF signal was regressed out to further improve ICA-based denoising  66. The LEMON dataset processing pipeline included removal of the first 5 volumes to allow for signal equilibration, motion and distortion correction, artifact detection (rapidart) and denoising using component-based noise correction (aCompCor), mean-centering and variance normalization of the time series as well as spatial normalization to MNI 152, 2mm resolution template  25.
Tractography and track-weighted dynamic functional connectivity (tw-dFC) Whole-brain tractograms were obtained for each subject using the following pipeline: first, diffusion signal modeling was performed on the preprocessed DWI data within the constrained-spherical deconvolution (CSD) framework, which estimates white matter Fiber Orientation Distribution (FOD) function from the diffusion-weighted deconvolution signal using a single fiber response function (RF) as reference  68. Specifically, multi-shell HCP DWI data underwent multi-shell multi-tissue (MSMT) CSD signal modeling, an optimized version of the CSD approach which allows for separate response function calculation in WM, GM and CSF, reducing the presence of spurious FOD in voxels containing GM and/or CSF  69. To achieve a similar result on the single-shell LEMON data, the diffusion signal was modeled by using single-shell 3-tissue CSD, a variant of the MSMT model optimized for RF estimation in single-shell datasets.
SS3T-CSD signal modeling was performed using MRtrix3Tissue 70 , a fork of MRtrix3 software. After signal modeling, whole-brain tractography was performed using the IFOD2 algorithm with default parameters, and by applying the anatomically constrained tractography (ACT) frameworks, which makes use of the 5TT map previously obtained from segmentation to improve the biological plausibility of the resulting streamlines  71, 72. For the high b-value, multi-shell HCP data, the spherical-deconvolution informed filtering of tractograms (SIFT)  73  algorithm was applied to further improve the fit between the reconstructed streamlines and the underlying DWI data, starting from a generated tractogram of 10 million streamlines and filtering it to a final whole-brain tractogram of 1 million streamlines.
Since the SIFT algorithm is best suited for high b-value datasets, it was not applied on the single-shell, low b-value LEMON dataset, where instead a whole brain tractogram of 5 million streamlines was generated (note that the number of streamlines is uninfluential to the tw-dFC contrast generation). Whole-brain tractograms for each subject of both datasets were transformed to the MNI 152 standard space by applying the non-linear transformations obtained from structural images. Since each subject's rs-fMRI volumes were already registered to the MNI 152 template, all the following analyses took place in standard space. For each subject, whole-brain tractograms derived from tractography and preprocessed rs-fMRI time series were combined to generate a 4-dimensional tw-dFC dataset with the same spatial and temporal resolution of the original fMRI time series.