dataesr/scientific-paragraphs-categorization huggingface dataset:
Feature selection was performed by (i) constructing a mean-variance trend in the log-counts and retaining genes found to exhibit more variation than expected assuming Poisson-distributed technical noise, as implemented in the makeTechTrend and TrendVar functions from package scran  (54) , and (ii) selecting genes expressed in at least 25 cells (n=22,603). The first 10 PCs of the data were retained for data visualization and clustering analyses. Graph-based clustering was performed by building the shared nearest-neighbor graph with the buildSNNGraph function from scran (54) using a series of k values, and cell clusters were defined with the igraph Walktrap algorithm  (55). Similar clustering results were obtained based on the knn-graphs generated using k=25, 50, 75, and 100, and k=25 was used for all downstream analyses (Supplementary Figure  2A ).
Cell types were predicted using SingleR and the built-in BlueprintEncodeData reference  (56). Based on the clustering and cell-type predictions, we removed cells belonging to clusters associated with lymphoid cell types or low QC metrics from downstream analyses (Supplementary Figures  2B, C ). Accounting for Ambient RNA Contamination in scRNA-Seq Data and Assigning Cell States Droplet-based scRNA-seq methods capture ambient mRNAs present in the cell suspension in addition to cell specific mRNAs. To estimate which cells in our experiment were genuinely expressing mRNAs for CD14, FCGR3A (CD16), and those originating from the virus, we implemented a two-step strategy utilizing the estimateNonExpressionCells function of the SoupX package  (21). This function estimates whether each cell contains significantly more counts of a provided gene-set than would be expected under a Poisson model, given the estimated ambient RNA from its library of origin and the maximum contamination fraction.
Characterizing Monocyte Subsets and Transcriptional Profiles From scRNA-Seq Data Principal components analysis of 6,601 cells at T 0 was used to order monocytes along a differentiation axis separating CD14 + cells from CD16 + cells. We then computed the average percentage of classical and nonclassical monocytes obtained by flow cytometry across the eight donors, weighting each individual by the number of high-quality cells in the scRNAseq data at T 0. Based on these percentages (87. 1% for classical and 7. 6% for nonclassical), we annotated the monocytes on each side of the differentiation axis as classical and nonclassical, respectively, with the remaining 5. 3% of monocytes being annotated as intermediates. Validity of our approach was confirmed by correlating the proportion of monocytes assigned to each subset across the eight donors, with the percentage of classical, intermediate and nonclassical monocytes estimated by flow cytometry.
Differential expression between subsets was assessed for the 4,859 genes expressed at a normalized log 2 count > 0. 1 in any of the 3 subsets. Specifically, Wilcoxon rank tests were implemented in the scran package  (54) , using the findMarkers function and blocking on donor. We considered genes to be differentially expressed (DE) between monocyte subsets when gene expression was significant at an FDR≤1% and log 2 FC>0. 2. The 848 genes that differed between classical (CL) and nonclassical (NC) monocyte subsets were classified according to their behavior in intermediate monocytes  (INT)  At later time points, comparisons between CD16 + and CD16 - monocytes subsets were done based on 5,681 genes expressed with a normalized log 2 count > 0. 1 on average in either subset, in at least one condition and time point.
Characterizing Subset-Specific Responses to IAV Challenge To allow comparison between responses of CD16 + and CD16 - monocytes, 100 cells were subsampled from each subset and cell state, and at each time point. When subsampling, we ensured balanced representation of all donors across each monocyte subset and cell state, by using sampling weights that were inversely proportional to each donor representation in the original dataset. After sampling, a total of 6,669 genes with normalized log 2 counts >0. 1 on average in at least one group (cell-state x subset x time point) was selected for further analyses. For each monocyte subset, differences in expression between cell states (unexposed, bystander, infected) as well as between highand low-IAV transcribing infected cells were performed using the findMarkers function from the scran package (54) and blocked on time point.
Transcription Factor Enrichment Analyses To estimate Transcription Factor (TF) activity and define TFtargets relationships, we ran the R SCENIC pipeline (29) on the expression matrix (pre-normalization) on a random subsample of 4800 cells (100 cells from each donor at each time point and each condition, pre-exclusion of dying and contaminant cells) with default parameters. For each gene, motif-enrichment was considered for either cis-regulatory regions located <10kb from the TSS (distal regulatory elements), or between 500 bp upstream and 100 bp downstream of the promoter (proximal regulatory elements). To do so, motif-enrichment scores for all human genes (hg38 build, refseq_r80), were retrieved from https:// resources. aertslab. org/cistarget and used as input for the Rcistarget package  (29).
Sets of high-confidence targets for the 113 TFs whose activity could be quantified by SCENIC were then extracted and used for enrichment analysis. For each gene module, TF enrichment was assessed using a Fisher's exact test with the 6,669 expressed genes as background (Supplementary Data 3). Resulting p-values were adjusted using a global Benjamini-Hochberg correction for all eight modules and 113 TFs. For each TF, with its targets enriched among one of the eight modules, TF activity inferred by SCENIC was used to test for subset-specific activity using a linear model of the form: TF i ∼ State i + subset i + State i • subset i (2) where TF i is the activity of the TF being tested in cell i, State i is an indicator variable that distinguishes the two cell states being compared (e. g. unexposed and bystander), and subset i is an indicator variable that reflects the CD16 +/-status of cell i.
Gene Ontology Enrichment Analyses All Gene Ontology (GO) enrichment analyses were performed with the GOSeq package using default settings  (59). Background gene sets consisted of all genes that had average log-normalized expression values > 0. 1 in at least one of the groupings being examined, and are described in the text. Only enrichments significant at FDR≤5% are reported. Pseudo-Bulk Estimates From scRNA-Seq Data Pseudo bulk estimates of IAV mRNA levels were computed by measuring, for each donor and time point, the mean percentage of reads of viral origin across all cells from the sample. At each time point, we then used a Pearson's correlation test to compare pseudo-bulk estimates for the 8 donors with IAV mRNA levels obtained in bulk data at 6hpi.
Quantification of Canonical Monocyte Subsets in EVOIMMUNOPOP Samples FlowJo v10. 6. 1 software (60) was used with the gating strategy depicted in Supplementary Figure  8  to quantify monocyte subsets for 174 EVOIMMUNOPOP donors. Population-level differences in proportion of canonical monocyte subsets were assessed using Wilcoxon Rank tests. Correlation of the ratio of CD16 + to CD16 -cells with IAV mRNA levels was assessed using a linear model of the form IAV ∼ ratio + Pop, (3) where 'IAV' are IAV mRNA levels, 'ratio' is the percentage of CD16 + monocytes (nonclassical+intermediates) divided by the percentage of CD16 -monocytes (classical), and 'Pop' is and indicator variable separating AFB from EUB individuals.
Analysis of Bulk RNA-Seq Profiles From the EVOIMMUNOPOP Cohort A combined human-IAV reference was generated by concatenation of the primary human genome assembly (GRCh38) with the 8 segments of the human influenza A virus (IAV) A/USSR/90/1977(H1N1) genome (accession numbers CY010372-CY010379). Comprehensive human gene annotation was obtained from GENCODE (release 27) and merged with the 12 known transcripts of A/USSR/90/1977 (H1N1). RNA-seq reads (FASTQs) for all 970 samples that passed quality control in our previous study  (18)  [accession EGAS00001001895] were mapped to the combined reference with the STAR aligner (v. 2. 5. 0a)  (61)  and assessed for quality with QualiMap 'bamqc' and 'rnaseq'  (62, 63). Expression of viral mRNAs was measured as the percentage of uniquelymapped reads aligning to the IAV genome.
Reassuringly, the mean percentage of RNA-seq reads among samples from the IAV-challenged condition was 5. 86% versus <0. 01% in the other four conditions. Comparison of the percentage of IAV reads between populations was done using a Wilcoxon rank test. StringTie (v. 1. 3. 3) (64) was used to quantify expression levels in transcripts per million mapped reads (TPM) for each annotated transcript. Gene expression data were filtered to remove genes with little evidence of activation (mean zTPM score < -3) (65) in any of the 5 conditions, and their quality was checked by principal component analysis (PCA). As GC content, 5′/3′ bias, date of the experiment and library batch were previously determined to be the strongest confounding factors on transcript expression  (18) , we corrected the data for these factors.
Cell States Deconvolution From Bulk RNA Sequencing To assess the percentage of total transcripts that originate from each cell state across the 199 IAV-challenged samples, we pooled cells from T 6 into 3 groups, based on their assigned cell-state (bystander, infected: high and low IAV-transcribing) and to which we added a 4 th group containing all singlets that were either (i) assigned to cluster numbers 3, 8, 10, and 11 (dying cells) or (ii) discarded based on their high mitochondrial content or low read counts (dead cells). We then estimated pseudo-bulk profiles for each group by summing UMIs across all cells and computing the number of UMIs associated to each gene per million of sequenced UMIs. TPM profiles obtained from bulk data were then normalized to improve comparison with pseudobulk.
Specifically, we first computed a global pseudo-bulk profile of the entire single cell dataset as the average of the pseudo bulk profiles from the 4 cell states (bystander, infected: high and low IAV-transcribing, or dying/dead), weighted by the percentage of UMIs they contribute to the overall pool of cells. To account for the difference in how gene expression is quantified between the two methods (3' end counts for scRNA-seq and full-length gene coverage for bulk RNA-seq), we computed for each gene i a normalization factor s i given by.
The log 2 FC change in gene expression between low IAV-transcribing infected and bystander cells is plotted on the x-axis, while the log 2 FC change in gene expression between low IAV-transcribing infected and high IAV-transcribing infected cells is plotted on the y-axis. Values are plotted based on a meta-analysis across monocyte subsets at T 4. (C) mRNA expression levels of representative IFN-stimulated (MX1) and ribosomal (RPL34) genes across the subsampled dataset. Colors reflect the cell state and subset assignment depicted in Figures 1D, E. IFN, Interferon; FC, fold change.
FUNDING This work was supported by the  Institut Pasteur , the  Collège de France , the  French Government 's  Investissement d'Avenir program ,  Laboratoires d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ) and " Milieu Inteŕieur " ( ANR-10-LABX-69-01 ), the  Fondation de France  ( n°00106080 ), and the  Fondation pour la Recherche Médicale (Equipe FRM   DEQ20180339214 ). MO'N was supported by a  European Molecular Biology Organization long-term fellowship  ( ALTF 229-2017 ).
DATA AVAILABILITY STATEMENT The bulk RNA-seq data used in this study are available at the European Genome Phenome archive under accession number [EGAS00001001895]. The single cell RNA-seq data generated during this study are available at the European Genome Phenome archive under accession number [EGAS00001005000]. Code generated as part of this study is available on Github (https:// github. com/h-e-g/PopDiff_MonocyteIAV).
No feedback on performance was provided. Since the acquisition of our dataset, this widely used paradigm has been criticized in several respects  (Ring et al. , 2015; Desmedt et al. , 2018; Zamariola et al. , 2018). Questionnaires. Once subjects left the MEG room, they filled 4 questionnaires in French: Beck's Depression Inventory (BDI)  (Beck et al. , 1961 ), Peter's et al. Delusions Inventory (PDI)  (Peters et al. , 2004) , the Trait Anxiety Inventory (STAI)  (Spielberger et al. , 1983)  and the Obsessive-Compulsive Inventory (OCI)  (Foa et al. , 2002).
Mean pairwise phase consistency (PPC) was estimated for each independent component  (Vinck et al. , 2010)  with the right clavicle-left abdomen ECG derivation signal in the frequency band 0-25 Hz. Components (up to 3) that exceeded 3 standard deviations from mean PPC were then removed from the continuous data. To correct for blinks, 2-seconds segments of data were used to estimate blink and eyemovement components. Mean PPC was then computed with respect to vertical EOG signal, and components exceeding mean PPC + 3 standard deviations were removed from continuous data. The procedure was iterated until no component was beyond 3 standard deviations or until 3 components in total were removed. Stereotypical blink components were manually selected in two participants as the automated procedure failed to identified them.
Heartbeat-evoked responses Heartbeat-evoked responses were computed on MEG data time-locked to T-wave occurring during the instruction period. T-waves had to be at minimum 400 ms distance from the subsequent Rpeak. In order to avoid contamination by transient visual responses or by preparation to the subsequent visual presentation, we only retained T-waves that occurred at least 300 milliseconds after the onset of the instruction cue and 350 milliseconds before the onset of options presentation. If more than one Twave occurred in this period, HERs for that trial were averaged. HERs were analyzed from T-wave + 10 50 ms to minimize contamination by the residual cardiac artifact  (Dirlich et al. , 1997)  after ICA correction. We verify that differences in HERs between the two types of decision were truly locked to heartbeats, and that a difference of similar magnitude could not arise by locking the data to any time point of the instruction period.
Generalized linear model on response-locked single trials. To analyze how task-related variables are encoded in neural activity during decision, we ran a generalized linear model (GLM) on baseline-corrected (-500 to -200 ms before instruction presentation) single trial MEG data time-locked to button press. We predicted z-scored MEG activity at each time-point and channel using task-relevant experimental variables. For preference-based decisions we modeled MEG activity as:  8,: =  < +  >?@ABC +  DC:?@ABCEF +  GH88@C IJBAA (1) where t and c represent MEG activity at time-point t at channel c,  < is the intercept,  >?@ABCEF are the z-scored ratings of the chosen option,  DC:?@ABCEF is the z-scored rating of the alternative unchosen option and  GH88@C IJBAA is a categorical variable representing motor response (i.
Anatomical MR acquisition and preprocessing An anatomical T1 scan was acquired for each participant on a 3 Tesla Siemens TRIO (n = 2) or Siemens PRISMA (n = 20) or Siemens VERIO (n = 2). Cortical segmentation was obtained by using automated procedure as implemented in the FreeSurfer software package  (Fischl et al. , 2004). The results were visually inspected and used for minimum-norm estimation.
Pupil data analysis Pupil data that contained blinks (automatically detected with EyeLink software and extended before and after by 150 ms), saccades beyond 2 degrees and segments in which pupil size changed abruptly (signal temporal derivative exceeding 0. 3, arbitrary unit) were linearly interpolated. All interpolated portions of the data that exceeded 1 second were removed from further analyses. Continuous pupil data from each experimental block were then band-pass filtered between 0. 01 and 10 Hz (second order Butterworth) and z-scored. 16 subjects were retained for pupil analysis; 5 subjects were excluded due to too low quality of data. Pupil analysis was performed in two ways: 1) averaged pupil diameter in the same time period used for HER computation (i. e. , 300 ms after instruction presentation until 350 ms before options display) and 2) averaged pupil diameter in the time-window spanning 1 second before button press until its execution.
HER effects are specific to preference-based choices Finally, we tested whether the effect of HER was specific to subjective value or whether it is a more general mechanism interacting with all types of decision-relevant evidence. To this aim, we analyzed perceptual discrimination trials using the same approach as for preference-based trials. First, we modeled the single trial response-locked MEG sensor-level data using a GLM (GLM1b) with the parameters accounting for choice in the perceptual task (i. e. contrast of the chosen option -ChosenCtrs -and the contrast of the unchosen option -UnchosenCtrs), as well as response button. The nonparametric clustering procedure revealed the presence of a fronto-central cluster encoding the contrast of the chosen option (ßChosenCtrs, -257 to -25 ms before response, sum(t)= 8121, Monte Carlo p = 0.
This work was supported by funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  670325 , Advanced grant BRAVIUS) and a senior fellowship of the  Canadian Institute For Advanced Research (CIFAR)  program in Brain, Mind and Consciousness to CTB; by a  doctoral fellowship  from the  Ecole des Neurosciences de Paris Ile de France  to DA; and by  ANR-17-EURE-0017. The authors thank  Clémence Alméras ,  Maximilien Chaumon  and  Christophe Gitton  for assistance in data acquisition.
The format of the tiling coordinates and galaxy lists are standard, therefore, it can also be applied to other telescopes, i. e. the GRANDMA (the Global Rapid Advanced Network Devoted to the Multi-messenger Addicts) network  (Antier et al. 2020). The tiling and galaxy lists are sent to the Chinese Multi-Messenger (CMM) server using the VOEvent protocol via brokers. The CMM Service can receive the GRB or Neutrino alert streams from GCN public access by using the pygcn code (Leo Singer 1  ). The GWAC-N provides an interface to automatically receive the GW alerts from the CMM in real time. Several observation planning codes are running to create target/pointing list for all telescopes to perform routine observations. Each planner can insert the target/pointing list into the AOM using a client provided by the GWAC-N.
Two robotic GWAC-F60A/B telescopes and one robotic GWAC-F30 telescope are installed inside of the GWAC dome. The GWAC-F60s are used for the automatic validation of the GWAC OT candidates. They have ∼ 10 degree/second slewing speed and 18'x18' of FoV with the 2Kx2K Andor iKon-L 936 CCDs. The GWAC-F30 with a FoV of 1. 8 • x1. 8 • can complete the gaps of flux coverage and the FoV between the GWAC-A and the GWAC-F60A/B. All the three telescopes are equipped with the Johnson UBVRI filters. With remote controlling and real-time data processing, they can be integrated into the GWAC-N. As a whole, the GWAC-N obtains the capabilities for multiple objectives from survey, queue observations to follow-up observations for many types of targets. The parameters of each type of telescope are summarized in Table  1.
AOM SYSTEM For a highly efficient telescope network, a strong and smart observation management is a key factor. The automatic observation management is the only way to integrate tens of telescopes into a complete network, the GWAC-N. Figure 4. All the sub-systems or modules of the AOM system are shown in the red color. The ToO follow-up sub-system obtains the alerts of the ToO from the CMM database as input targets. The target management sub-system receives all other types of observation requests, and converts them as input targets. For the internal telescopes of the GWAC-N, a client running in the telescope side can monitor the observation and data statues, and transmit them back to the AOM. For the external telescopes, the datalink is customized for different telescopes. No client is installed in the telescope side in current stage.
ToO follow-up sub-system The ToO follow-up sub-system monitors the CMM database for the newly arriving alerts. Currently, three types of events, including the LIGO/Virgo GW, the Swift GRB and the Fermi GRB, are selected by the ToO follow-up sub-system. The sub-system generates a target or a sequence of targets with observation parameters for the alert that meets the alert selection criteria. The observation parameters (such as instrument, observation mode, exposure, etc. ) are set based the observation strategies dedicated to different cases. The alert selection criteria, observation strategies are defined regarding the physical variable behavior of the target and the telescope detection capability to increase the chance of detecting the optical counterpart of the ToO. The details of the selection criteria and observation strategies will be described in another paper  (Han et al.
For the ToO follow-up observations mode, the rankings need to be adopted in both tiling and galaxy targeting strategies. In our system, the probabilities of tiles and galaxies are defined as the second level priority, while the altitude angle is the third level. For the validation mode, the trigger time (receiving time of target in the system) is the second level priority. No third level is needed in this mode. Using these methods, we can generate observation plans with many complex strategies. The scheduler makes a re-sorting process for target list for each telescope based on the priorities, the observability, the status of observation and the telescope, when any update for the target is made in the database. The tables in the Figure  6  demonstrates the sorting sequence for the target list during observations.
Communication center The AOM system is composed of many sub-systems and a database. The communications are very complex and frequent among external interfaces, sub-systems and telescopes. To avoid the conflicts and sequential confusion in the communications, a sequential control mechanism is crucial for the AOM system. In the earlier version, all sub-systems are directly communicating to the database. When a large number of concurrency entries occur in the database, a protecting mechanism will be triggered in the databases from preventing damages of data. These chance faults are rare but fatal to our system. Another key point is for the scheduling. Unlike the multi-instance dispatcher, only one instance of scheduler can be run at time, because to deal with the dynamic information by multiple schedulers easily causes the information confusion.
AOM system workflow Combining with above sub-systems, the overall workflow of the AOM system is described as follows. The targets are manually/automatically inserted into the system by using interfaces provided by the AOM system (all observation request are treated as targets). All the targets are processed and classified by the target management sub-system, then are inserted into the target database. Some targets are sent by the external dispatcher to trigger the follow-up observations with external telescopes. The targets for the GWAC-N will be initially scheduled in order to compute the observation time windows and to make the initial observation plans. The targets having the observation time windows are added in the daily target list and are stored in the database. This daily target list contains all the targets to be observed in a given night.