dataesr/scientific-paragraphs-categorization huggingface dataset:
Electrodes with an aberrant signal (excessive noise due to malfunctioning or a bad signal during data collection) were interpolated using a three- This adaptation of the EEfRT was programmed in E-prime (Psychology Software Tools Inc. ; Sharpsburg, PA, USA). The probability and amount of order was randomized across participants. To ensure task comprehension, subjects received oral instructions and were provided with a series of task instruction, followed by a few practice trials prior to starting the experiment.
Data Acquisition EEG signals were recorded using a 256 channel Geodesic Sensor Net (Electrical Geodesics Inc. ; EGI, Eugene, OR, USA). All channels were referenced to the vertex (Cz) and collected with a high impedance amplifier, Net Amp 300 amplifier (Electrical Geodesics) using Net Station 4. 5 software (Electrical Geodesics). Data were recorded continuously with a high-pass filter at 1 Hz and a sampling rate at 1000 Hz. Subjects were instructed to limit body movements, eye blinks, and muscular contractions during task selection and reward feedback.
Data Analysis EEG data analysis was performed using Cartool Software 3. 55  [54]. Raw EEG data were re-referenced offline to a common average reference. Analyses were conducted on the interval around the reward screen for two intervals. The first temporal interval, computed for easy and hard tasks, was related to the SPN. Epochs of 700 ms (500 ms prior to the outcome to 200 ms after) were extracted from the raw data, with a baseline correction of 100 ms applied prior to the selection of an easy or hard task by the participant. The SPN was defined as the mean voltage within 200 ms prior to the reward feedback  [55]. In the second temporal interval, epochs of 700 ms (100 ms prior to reward feedback to 600 ms following reward feedback) were extracted from the raw data and analyzed, with a baseline correction between 100 ms applied before the feedback to the onset of the feedback (100 ms to 0 ms).
Reward anticipation To determine whether the anticipation of the reward was different following the selection of an easy or a hard task, SPN data were analyzed using a repeated measures ANOVA, with repeated measures variables being difficulty (easy/hard) and electrodes (FPz, Fz, FCz, Cz, CPz, Pz). Analysis of the SPN demonstrated a main effect of difficulty on the amplitude of the SPN (F(1,19) = 5. 38; p = 0. 03), with a more negative response for hard choices (Figure  3 ). No interaction was found between the variables difficulty and electrodes (F(5,95) = 1. 50; p = 0. 24). A strong marker of motivational state was indicated by measuring the proportion of hard choices the participants selected. Namely, a strong relationship was found between the total amount of money participants attempted to win and the proportion of hard choices at 10% (t(18) = 7.
To focus on the insula, Cartool software offers the opportunity to select a list of Talairach regions and generates a group of solution points (nodes) that fit within each of the named regions. Activations of the insula were compared following an easy and a hard task within the time window of the SPN. To allow current density measures (indicating activation strength in µ A/mm 3 ) to be extracted from the insula, the inverse solution was estimated for the group of solution points for each time window of interest. A source analysis performed on the same time window as the SPN revealed a main effect of difficulty on the amplitude of the insula (t(19) = -4. 19; p < 0. 001).
04; p = 0. 31). Source reconstruction revealed a main activation in the vicinity of the anterior insula and the lateral prefrontal cortex, as well as an activation of the right temporo-parietal area (Figure  3C ). Because the insula is well known as the main generator of the SPN  [26] [27] [28] [29] , we made the hypothesis that the main activation observed was originating from this area, with an approximation due to the low spatial resolution of LAURA. To focus on the insula, Cartool software offers the opportunity to select a list of Talairach regions and generates a group of solution points (nodes) that fit within each of the named regions. Activations of the insula were compared following an easy and a hard task within the time window of the SPN. To allow current density measures (indicating activation strength in µA/mm 3 ) to be extracted from the insula, the inverse solution was estimated for the group of solution points for each time window of interest.
Perturbation of Human DCs in the Face of Diverse Innate Immune and Viral Stimuli With the goal of better understanding how myeloid cells respond to innate immune stimuli, we exposed immature MDDCs to a battery of innate immune agonists and viral challenges, generating datasets composed of RNA sequencing (RNA-seq) and assays for transposaseaccessible chromatin sequencing (ATAC-seq) (Figure  1A ; Table  S1 ). We tracked the kinetics of gene expression and MDDC maturation during HIV-1 infection compared with classic innate agonists by infecting MDDCs with a single-cycle HIV-1 reporter virus (HIV-1-GFP +Vpx), transducing with a non-replicating lentivirus (LKO-GFP +Vpx), or stimulating in parallel with the Toll-like receptor (TLR) 4 agonist lipopolysaccharide (LPS) or the doublestranded RNA mimetic polyinosinic:polycytidylic acid (poly(I:C)) for 2, 8, 24, and 48 h.
Maximum chromatin accessibility for IFNB1, IFNL1, and several ISGs was detected at 24 h post-infection in cells expressing the GFP reporter (Figures  2D, S2C , and S2D). For IFN-related genes; the NF-κB family members RELA, RELB, and REL; and the related TF HIVEP1, chromatin accessibility was higher in HIV-infected, CD86-low MDDCs that were not fully mature compared with CD86-high, mature MDDCs (Figure  2D ), suggesting that chromatin state is linked to MDDC maturation status. To determine whether mapping open chromatin using ATAC-seq in HIV-infected MDDCs offered specific advantages over publicly available datasets, we compared our ATAC-seq peaks to steady-state open chromatin data from CD14+ monocytes (Figures  S2C  and S2D ). Several ISGs displayed high chromatin accessibility at baseline (STAT1, STAT2, IRF1, IRF7, IRF9, OASL, HLA-C, and ISG20), with peaks detected in both MDDC ATAC-seq samples and CD14+ monocyte data available from ENCODE (Figure  S2C ).
, 2019; Siahpirani and Roy, 2017). Key to this study, we extended our previously published computational methods for learning the regulation of gene networks through the Inferelator  (Arrieta-Ortiz et al. , 2015; Bonneau et al. , 2006; Ciofani et al. , 2012; Greenfield et al. , 2010; Madar et al. , 2010)  by integrating results obtained from RNA-seq and ATAC-seq experiments performed at the bulk level in a time course fashion (Figure  3A ). Here, prior information on network architecture is derived from the 88,000 peaks of chromatin accessibility data generated by our ATAC-seq experiments that tracked MDDC responses to HIV infection  (Figures 2A and 3A). Using curated TF binding motif databases, we established possible TF-gene target relationships by searching ATAC-seq peaks for TF motifs that we located within or up to 1 kb upstream of gene bodies (Figure  3B ).
Once TF activity was estimated, the network structure Prior was used to bias model selection of TF-to-gene target regulatory interactions toward edges with Prior information during network inference (Figure  3D ). To improve the overall performance and stability of network inference, several aspects of the method (and key inputs) were tested, such as different sources of Priors (publicly available ENCODE data versus our ATAC-seq data), different TF binding motif databases (HOCOMOCO  [Kulakovskiy et al. , 2013]  versus CisBp 2. 0  [Weirauch et al. , 2014] ), and different model selection methods (Bayesian best subset regression [BBSR] versus elastic net  [EN] ). In addition, because every computational run is subject to stochasticity in the inference procedure, we evaluated whether network performance could be improved by combining hundreds of individual computation runs (selecting model components such as TF activity estimates that were stable across random subsamples of the structure Prior; see STAR Methods).
To evaluate the performance of networks inferred using the parameters described earlier, we used area under precision-recall curves  (Madar et al. , 2010)  to compare the prediction and ranking of our TF-to-gene target regulatory edges against TRRUST v. 2, a gold-standard reference database of human transcriptional regulatory events  (Han et al. , 2018) , and well-known lists of ISGs  (Kane et al. , 2016; Schoggins et al. , 2011; Shaw et al. , 2017). We found that the EN regression model outperformed BBSR when ATAC-seq-based networks were benchmarked against TRRUST v. 2 (Figures  3E  and 3F ). We improved precision-recall on TRRUST and known ISGs by bootstrapping 400 individual EN-ATAC inference runs into a converged network (EN-ATAC x400)  S3C, and S3D). This final EN-ATAC network correctly predicted 90 of 97 core mammalian ISGs  (Shaw et al.
Network Clustering, Differential Gene Expression Analysis, and TF Enrichment Tests Define Key Subregulatory Groups We used this MDDC transcriptional response network to group the expression changes observed into sets of coregulated genes with high-confidence regulatory subnetworks. We first applied a cutoff to visualize the top 75,000 regulatory edges that displayed the highestconfidence beta scores (Figure  4A ) and then partitioned the network using Louvain modularity clustering into 10 major neighborhoods of TF and target gene communities, with each cluster encompassing at least 1% of the total number of genes in the network (MDDC network topology can be freely explored using Gephi software by downloading the Gephiformatted supplemental file, available in the STAR Methods section). We were able to assign putative biological functions to 7 of 10 clusters based on pathway enrichment scores (see STAR Methods).
When differentially expressed genes were assessed in CD86high versus CD86-low conditions in a similar fashion, pathway analysis predicted strong enrichment of protein-protein interactions with the RARA partner molecule RXRA, pathways associated with nuclear receptor transcription, MyD88 signaling, and gene expression changes resembling published datasets from RARA perturbation (highest among other inflammatory activators) (Figures  6G-6I ). These analyses implicated RARA/RXRA nuclear hormone receptor signaling to be involved in regulating MDDC maturation.
Many of these connections were detected using the Search Tool for the Retrieval of Interacting Genes (STRING) database  (Szklarczyk et al. , 2015) , highlighting the position of RARA close to TFs involved in controlling cell fate, lipid and sterol metabolism, IFN signaling, and inflammation (Figure  7B ). Knockdown of cGAS or STAT2 led to a pronounced loss of innate immune activation in MDDC during infection with HIV-1-GFP (Figures  7C  and 7D ). In contrast, knockdown of RARA upregulated CD86 at baseline and potentiated MDDC maturation during infection, suggesting RARA acts as a negative regulator of inflammation. Consistent with this notion, expression levels of many predicted targets of RARA increased in response to LPS and poly(I:C) treatment and to a lesser degree in response to HIV-1-GFP (Figure  S7A ).
DISCUSSION Multiple TFs are known to drive the innate response downstream of pathogen sensing in myeloid DCs  (Ivashkiv and Donlin, 2014) , but it remains unclear how production of IFN, differential expression of thousands of host genes, and DC maturation are coordinately regulated across the genome in response to HIV detection. To address this issue, we employed a systems biology approach to understand how MDDCs respond to stimulation, generating steady-state and time series RNA-seq under a battery of viral and innate immune perturbations combined with ATAC-seq measurements of chromatin accessibility (Figure  1A ). In this study, we inferred a transcriptomic network linking 542 TFs to 21,862 gene targets, revealed unexpected roles for TFs in the innate response through validation experiments (PRDM1 and RARA), and provided tools for further network exploration that accompany this manuscript (see STAR Methods).
Computational strategies that combine ATAC-seq and gene expression data have recently been employed to identify key regulators of tissue-resident memory CD8+ T cells  (Milner et al. , 2017) , define TF specificity across cell types  (Cusanovich et al. , 2018) , and chart the landscape of human cancers  (Corces et al. , 2018). Here, we improved the stability of network inference by performing ensembles of analyses, with each run subsampling information from a structured network Prior that was generated from our ATAC-seq data. By modeling changes in gene expression to be a function of combined TF activities, we reduced the complexity of multiple factors known to influence gene expression into a single parameter (such as changes in TF expression, post-translational modifications, and epigenetic marks on target genes).
In doing so, our network revealed strong enrichment of TFs known to regulate the innate response, notably including IRF3, a key factor that is activated by phosphorylation and, because it is constitutively expressed, is rarely identified in pathway analyses that emphasize changes in TF expression  (Amit et al. , 2009). Our network Prior only considered chromatin accessibility from 1 kb upstream through target gene bodies, which may have biased the network toward proximal factors, and it did not cleanly distinguish between TFs with overlapping motif preferences. Future network inference models will likely incorporate distal enhancer elements and improved TF motif databases. To date, this study reports one of the largest gene regulatory networks that has been charted in mammalian cells. Because our full reported network (500,000 edges) is potentially promiscuous, we have provided beta values (Table  S5 ) that can be used to truncate edges at any specified cutoff to yield smaller networks composed of high-confidence connections.
We anticipate further refinements to our network rendered possible through the use of the tools we have included as companion applications to this manuscript (i. e. , network visualization and exploration via Gephi and our Jupyter widget; see STAR Methods) and through subsequent experimental validation. The final gene regulatory network reported here (75,000 edges) exhibited a high level of precision-recall against TFs targeting known ISGs and correctly predicted prominent roles for IRF, STAT, and NF-κB family members in the IFN response. We recovered 90 of 97 core mammalian ISGs as targets of IRFs; the remaining 7 core ISGs were predicted to be downstream of STAT and AP-1 family TFs (Table  S5 ). Of the 22 IFN species measured by RNA-seq, we found IFNL1 to be upregulated more than any type I IFN, including IFNB1, during stimulation with LPS, poly(I:C), or HIV-1.
293FT female cells (Life Technologies Cat# R70007, RRID:CVCL_6911) were cultured in Dulbecco's modified Eagle's medium (DMEM, Thermo Fisher) that was supplemented with 10% FBS, P/S, 10mM HEPES, and supplemented with 0. 1 mM MEM non-essential amino acids (Thermo Fisher), 6 mM glutamine, and 1 mM sodium pyruvate (Thermo Fisher). HL116 male cells were cultured in DMEM supplemented with 10% FBS, P/S, 10mM HEPES, 0. 1 mM MEM non-essential amino acids, 6 mM glutamine, 1 mM sodium pyruvate, and HAT supplement (Thermo Fisher; hypoxanthine (5 mM), aminopterin (20 μM) and thymidine (0. 8 mM)) diluted 1:50 as recommended. All cell lines were thawed from early passages, kept in culture no longer than 4 weeks, and were regularly tested for mycoplasma contamination (every 6 months). All cells were maintained at 37°C and 5% CO2.
METHOD DETAILS Plasmids-HIV-1-GFP has been used previously to study immune responses in human DCs  (Manel et al. , 2010)  and is env-vpu-vpr-vif-nef-, with the GFP open reading frame in place of nef. We generated virus like particles packaging Vpx from the plasmid pSIV3+ (based on SIVmac251, GenBank acc. no. M19499), which has been described elsewhere  (Mangeot et al. , 2000). Target sequences for shRNA vectors are listed in Table  S7. All lentiviral constructs were transformed into Stbl3 bacteria (ThermoFisher) for propagation of plasmid DNA. All plasmids were prepped and purified using Nucleobond Xtra Maxi Kit (Takara) or similar maxi prep reagents. Virus and virus-like particle production-To produce recombinant lentiviral vectors, reporter viruses, and Vpx-containing virus-like particles, we transfected plasmids into 293FT cells as previously described  (Johnson et al.
Cells were then washed with PBS and fixed with 0. 5% paraformaldehyde (Electron Microscopy Sciences) diluted in PBS. Cells were either sorted on a FACSAria II or data was acquired on an LSR II flow cytometer (BD Biosciences). Data were then analyzed using FlowJo software (FlowJo LLC). RNA-seq-MDDCs from multiple donors were infected or stimulated as indicated above, stained with anti-human CD86 and a live-dead viability dye and then sorted into serum on a FACSAria II (BD Biosciences). Cells were lysed in TRIzol reagent (Thermo Fisher), RNA was isolated according the manufacturer's instructions, and then samples were submitted to HudsonAlpha Institute for Biotechnology (https://hudsonalpha. org/) for library preparation and RNA-sequencing. 50bp-length single-end sequences were aligned to the human genome (hg38) using STAR version 2.
4. 2a. Samtools 0. 1. 19 was used to filter alignments to a MAPQ score threshold of 30. Counts per gene were called using featureCounts version 1. 4. 6 and gencode v24 genome annotation. Samtools 0. 1. 19 was used to filter alignments to a MAPQ score threshold of 30. We confirmed the presence of HIV-1 or HIV-2 sequences in infected samples yet these sequences were not taken into account during differential expression analysis. Differential expression analysis was performed separately for each RNA-seq experiment using DESeq2 version 1. 10. 1 and R version 3. 2. 3, using time and treatment together as the design parameter. Genes were considered differentially expressed in a given comparison if the FDR-adjusted p value was below 0. 1. ATAC-seq-MDDCs from three unique donors were infected in the presence of Vpx to match infection conditions as performed for RNA-seq at 2, 8, 24, and 48 h after infection.
2. 3), filtered based on mapping score  (MAPQ > 30, Samtools (0. 1. 19 )), and duplicates removed (Picard version 1. 120). Only pairs that aligned uniquely and concordantly to non-mitochondrial human chromosomes were retained. For establishing the network Prior, a merged ATAC-seq Sam file was generated by combining all ATAC-seq conditions. Peak calling was performed with Peakdeck version 1. 1 (parameters -bin 160, -STEP 25, -back 5000, -npBack 10000) using the start and end locations of the pairs to define fragment lengths, with a p value of 1e-4 as the cutoff, outputting 87,681 unique, non-overlapping, high-confidence peaks. Putative binding events were discovered by finding motif occurrences using HOCOMOCO version 10 (  Kulakovskiy et al. , 2013)  as the motif database, with position weight matrices for 601 of 640 human TFs discovered using FIMO (Memesuite version 4.
10. 1) with a p value cutoff of 1e-4. When scored against our gold standards (see below) HOCOMOCO performed better than the curated CisBP 2. 0 motif database  (Weirauch et al. , 2014)  composed of JASPAR  (Mathelier et al. , 2016) , TRANSFAC  (Matys et al. , 2006) , and other motif collections, possibly due to low redundancy in motif position weight matrices. Bedtools (version 2. 25) was used to generate a Markov model of order 1 from all peak intervals to use as a background peak file (parameter "-bgfile"). For each TF, the target set was further refined by filtering out binding sites below the top quartile, ranked by the FIMO p value. TF binding sites were linked to a target gene if the peak fell within a 1kb window of the gene body, creating a Prior matrix with 601 columns and 24909 rows (Table  S4 ).
These clusters were then labeled with identifiers from Enrichr pathway analysis. For complete pathway analysis results for the top 10 clusters, please see: Network Visualization-Network visualization software Gephi Version 0. 9. 1  (Bastian et al. , 2009)  was used to visualize the network using the Force Atlas2 layout algorithm  (Jacomy et al. , 2014). For visualizing smaller network components with louvain clustering, we used a jp_gene_viz visualization tool developed at the Simons Foundation. This tool is publicly accessible online using Binder as a resource to complement this publication (https:// mybinder. org/v2/gh/flatironinstitute/dc_network/master). Instructions for Visualizing the Gephi-formatted MDDC HIV-Response Network -Download the Gephi open graph visualization platform from the following web address: https://gephi.
org/. Download the EN-ATAC x400 final network from the following link: https://www. dropbox. com/s/4pzwdnn340ary49/EN_ATAC_400_network_final. gephi?dl=0. Once the Gephi software has been installed, start the program and open the downloaded EN_ATAC network file. The graph window will show the top 75,000 edges in the ensemble network, with clusters color-coded as indicated in the manuscript. Zoom in or out by scrolling up or down while the cursor is positioned in the graph window. The graph can be re-centered by clicking and holding the control button and then moving the cursor. To view a specific gene and its connections predicted in the top 75k edges, in the "Filters" window, click on the folder, "Topology," and then double-click on "Ego Network. " In the bottom box that appears, labeled "Node ID," enter your gene of interest, then click "OK" and click "Filter.
decrypt_widget()" is reached. Enter "nick" into the key box and then click "decrypt. " When the decrypt script has finished running, navigate back to the Home tab to select the binder file "Visualize network. ipynb" or a network browswer with appended heatmaps, "Network and Heatmap. ipynb. " These binder files will open additional browser tabs. Click the "Run" button to run each step of the widget down to the cell that contains "L. gene_click(). " The subnetwork browser will load after several seconds, which can then be explored by entering a gene or multiple genes into the "match" box and selecting a variety of layouts and regulatory connections. Gene Set Enrichment Analysis (GSEA)-GSEA was performed by comparing each condition in the time series RNA-seq data to the corresponding mock time point.
36883 gene features for each condition were ranked by the signal to noise metric of GSEA and the analysis was performed using the standard weighted enrichment statistic against 3815 human gene sets contained in the Molecular Signatures Database that included all (H) Hallmark gene sets, (C2) curated gene sets, and (C3) motif gene sets. In a first pass analysis, the normalized enrichment score (NES) was calculated using 500 gene set permutations and once relevant gene sets were identified a more stringent statistical analysis was performed using 1000 phenotype permutations. Full GSEA results can be accessed through the following link: https://www. dropbox. com/sh/2u4psikt2tyz4r0/ AAB4Re3YKtOA5lqPEEAwfR7Aa?dl=0 Immunoblotting-1 million cells were lysed in 100 μL of RIPA buffer (50mM Tris HCl, 150mM NaCl, 0.