dataesr/scientific-paragraphs-categorization huggingface dataset:
See also Figure  S4  and Tables  S5-6. (F) Enrichment of differentially expressed genes separated by network cluster for the CD86-high vs CD86-low contrast as in (E). Potential Protein-Protein interactions (G), Reactome pathway enrichment (H), and Single Gene Perturbations (I) predicted from differentially expressed genes in the CD86-high vs CD86-low contrast. (J) Network connections for RARA and STAT2 visualized by Gephi software, color coded by contrast, and superimposed onto a grey network backdrop. See also Figure  S6. See also Figure  S7.
Multiple lots of FBS were tested to identify batches leading to minimal baseline induction of CD86 over the course of DC differentiation. Fresh media and cytokines were added to cells (40% by volume) one day after CD14+ cell isolation. On day 4, cells were collected, resuspended in fresh media with cytokines used for infection or stimulation. Immature DCs on day 6 were routinely assessed by flow cytometry surface marker staining to be CD11c+ 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.
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.
Flow cytometry Infected or stimulated DCs were washed with phosphate buffered saline (PBS, Corning) and then exposed to LIVE/DEAD violet (ThermoFisher) in PBS for 15 min at 4 °C in the dark. Cells were either simultaneously stained for surface markers or first washed with PBS and then stained in FACS buffer containing 1% Bovine Serum Albumin (BSA, Roche) and 1 mM EDTA in PBS for 15-30 min in the dark at 4 °C. 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 DCs from multiple donors were infected or stimulated as indicated in the figure legends, 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 RNAsequencing. 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.
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 (parametersbin 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, nonoverlapping, 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 ).
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.
Data was analyzed and quantified with the Image Lab software (Biorad). Quantitative PCR 50,000 to 200,000 cells were lysed in TRIzol reagent (Thermo Fisher) and then RNA was isolated following the manufacturer's instructions with minor modifications. In brief, we performed two sequential chloroform extractions and added Glycoblue (Thermo Fisher) as a carrier prior to precipitation with isopropanol. RNA pellets were washed in 75% ethanol and resuspended in 200 µl of DNase and RNase-free water. 500 µg of RNA was converted into cDNA using Superscript III (ThermoFisher). Quantitative PCR reactions were carried out using TaqMan primer probes (ABI) and TaqMan Fast Universal PCR Master Mix (ThermoFisher) in either a Lightcycler (Roche) or a CFX96 thermocycler (BioRad) in a volume of 10 µl according to the following cycling conditions: 50 °C for 2 min, 95 °C for 2 min, then 55 cycles each of 95 °C for 3 sec, to 60 °C for 30 sec, followed by 95 °C for 5 sec.
Cluster 1 http://amp. pharm. mssm. edu/Enrichr/enrich?dataset=3dhb9 Cluster 2 http://amp. pharm. mssm. edu/Enrichr/enrich?dataset=3dhbc Cluster 3 http://amp. pharm. mssm. edu/Enrichr/enrich?dataset=3dhbo Cluster 4 http://amp. pharm. mssm. edu/Enrichr/enrich?dataset=3dhfq Cluster 5 http://amp. pharm. mssm. edu/Enrichr/enrich?dataset=3dhgc Cluster 6 http://amp. pharm. mssm. edu/Enrichr/enrich?dataset=3dhgi Cluster 7 http://amp. pharm. mssm. edu/Enrichr/enrich?dataset=3dhgn Cluster 8 http://amp. pharm. mssm. edu/Enrichr/enrich?dataset=3dhgp Cluster 9 http://amp. pharm. mssm. edu/Enrichr/enrich?dataset=3dhgq Cluster 10 http://amp. pharm. mssm. edu/Enrichr/enrich?dataset=3dhgr.
Acknowledgments This project was supported the  Howard Hughes Medical Institute (DRL) ; the  Helen and Martin Kimmel Center for Biology and Medicine (DRL) ; by grants from the  National Institutes of Health  ( F32AI093231  to JSJ;  R21AI084633  to DRL;  R01AI025032 ,  R01AI032972 , and  U19AI100627  to AA); State funding from the  Agence Nationale de la Recherche  under " Investissements d'avenir" program  ( ANR-10-IAHU-01 ) (MMM) and  ATIP-Avenir INSERM program  (MMM).
DATA AND SOFTWARE AVAILABILITY The RNA-seq data have been deposited in the Gene Expression Omnibus (GEO) database under ID code GSE125817. The ATAC-seq data have been deposited in the GEO database under ID code GSE125918. Both can be accessed from the GEO series code GSE125919. Software and Algorithms R N/A https://www. rproject. org/ Integrative Genomics Viewer (IGV) N/A http://software. bro adinstitute. org/soft ware/igv/ FlowJo Version 8. 7 FlowJo LLC https://www. flowjo. com/ FlowJo Version 9. 9. 4 FlowJo LLC https://www. flowjo. com/ LEGENDplex Software -Version 8. 0 LEGENDplex http://www. vigenet ech. com/LEGEND plex7. htm Image Lab software -Version 5. 2. 1 BioRad http://www. biorad. com/frfr/product/imagelabsoftware?ID=KRE 6P5E8Z GraphPad Prism 6. 0 GraphPad https://www. graph pad.
Both methods use a pseudo-counts to avoid zeros and can not take into account potential covariates. Current network inference methods such as SPIEC-EASI and SparCC do not fully consider the structure of metagenomic data involving sparsity, overdispersion, compositionality or covariate inclusion. We therefore propose a novel inference framework involving copula Gaussian graphical models  [44]. This model provides a general and integrative framework for network inference. We called our method MAGMA for Microbial Association Graphical Model Analysis. MAGMA allows to take into account all aspects of the data, while relying on the well-known properties of a latent Gaussian graphical model. We implemented our method in R and provide a package called rMAGMA available on a Git repository at https://gitlab.
We use graphical lasso (glasso) inference  [42]  from the R huge package to estimate a sparse precision matrix. In the sparse estimation of the precision matrix Θ, the problem is to maximize the penalized log likelihood l pen Z, Θ = log |Θ| -trace SΘ -ρ Θ 1. (10) S denotes the empirical covariance of the Z transformed data matrix, Θ 1 is the L 1 norm and ρ ∈ R + 0 is a sequence of non-negative penalty parameters. Penalized inference of graphical models results in a collection of OTU networks associated with the estimated precision matrix Θ ρ for different values of ρ. In order to infer the most parsimonious network given the available data, one need to weigh the fit of the data relative to the complexity of the data  [52, 53]. To select the penalty parameter ρ, we consider three approaches: rotation information criterion (ric)  [54] , stability approach for regulation selection (stars)  [55]  and extended Bayesian information criterion (ebic)  [56].
All these approaches are encoded in the R package huge used by MAGMA. In summary, MAGMA inference comprises of the following steps: 1. Adjust the marginal OTU abundances to ZINB distributions according to equations (4) and (5). 2. Approximate the latent data Z according to equation  (7). 3. Estimate a sparse precision matrix Θ ρ according to equation  (10). 4. Select the penalty ρ * that best balances fit and complexity via ric/stars/ebic. 5. Identify the OTU network from non-zero elements of Θ ρ *.
Generation of realistic data sets To measure the performance of network inference tools, we should simulate datasets of known structure and tried to recover the associations that we simulated. SPIEC-EASI  [40]  proposes a simulation procedure, however it is unable to reproduce variations in sequencing depth, which is considered an essential feature  [57]. Our procedure first generates an association network G with d vertices and e edges (Figure  1 ). The topology of the generated network can be selected to be either band, block, cluster, hub,.
Effect of the sample size Data were simulated with different number of samples n. We then inferred the association network and measured the quality of edge recovery as shown in Figure  2A. As the number of samples increases, the AUC increases and tends to one. Asymptotically, the method correctly recovers all the simulated links. The approximation of the copula Gaussian graphical model made by MAGMA allowed to recover the network with hundreds of samples. With 200 simulated OTUs, 200 to 300 of samples are sufficient recover almost the entire network correctly. (C) the condition number of the simulated correlation matrix k varying from 1 to 100; (D) the structure of simulated graph (band, block, cluster, hub, random and scale free). If they did not vary, the parameters were fixed at: n = 100, k = 10, random graph structure (Erdos-Renyi), marginal count data simulated according to a ZINB.
Effect of the distribution of read counts To measure the flexibility of our method to model misspecification, we varied the distribution of the simulated data. The results are shown in Figure  2B. Zero-inflated Poisson, negative binomial and zeroinflated negative binomial all performed roughly similar, suggesting that MAGMA is quite robust to model misspecification, as it assumes underlying ZINB data. It is striking that dependence networks with underlying Poisson distributed read count data were able to be reconstructed significantly better (average AUC > 0. 95), suggesting that the zero-inflation and, particularly, over-dispersion makes network reconstruction more difficult (average AUC ≈ 0. 85).
The addition of an irrelevant covariate effect to the method does not have a negative impact on the AUC. As the strength of the confounding increases, MAGMA that accounts for this confounding has an increasing advantage in recovering the network structure over applying MAGMA that ignores the covariate. We conclude that careful modelling of the read count distribution F ij is particularly relevant: the inference quality of the association network relative to the agnostic MAGMA increases when the covariate effect is gets stronger. for the agnostic MAGMA method vs. the MAGMA method with covariate effect. We considered 20 simulation iterations for networks of size 200 with an average degree of 2, number of samples n = 100, conditioning number k = 10, graph structure was random, read count data were simulated according to a ZINB.
Microbial data illustration: Human Microbiome Project In this section, we present the analysis of the 16S variable region V3-5 data from the Human Microbiome Project (HMP)  [4, 5]. The study collected microbiomes of healthy individual at various body sites. The data was retrieved on the qiita data platform  [58, study ID 1928]. This study brings together a total of 6,000 samples from 18 different the human body sites. A total of 10,000 microbial species occupy the human ecosystem. We first studied a stool microbiota sample, comparing MAGMA with SparCC and SPIEC-EASI. Second, we analyzed the stool and saliva microbiota in a single study in order to show the usefulness of the covariate implementation in the MAGMA method.
Figure3: The network recovery ability (AUC) as a function of the confounding covariate strength γ for the agnostic MAGMA method vs. the MAGMA method with covariate effect. We considered 20 simulation iterations for networks of size 200 with an average degree of 2, number of samples n = 100, conditioning number k = 10, graph structure was random, read count data were simulated according to a ZINB.
Figure4: Comparison of inference methods considering a covariate effect. Boxplots of the AUC criterion for different network inference methods. We considered 20 simulation iterations for networks of size 200 with an average degree of 2, number of samples n = 300, condition number k = 5, graph structure was random and read count data were simulated according to a ZINB. The quantitative covariate parameter γ was drawn from a N (0, 2), when covariate was effective. CoNet designates network obtained from Spearman correlation p-values by 100 iterations of permutation and bootstrap. For SparCC, the correlation threshold parameter was equal to 0. 3 and 100 iterations were done in the outer loop and 20 in the inner loop. raw Glasso, npn SKEPTIC, SPIEC-EASI, and MAGMA were network obtained by graphical lasso inference from raw data, nonparanormal SKEPTIC transformation, clr transformation and MAGMA transformation respectively.
9 AUCFigure 4 : 94 Figure4: Comparison of inference methods considering a covariate effect. Boxplots of the AUC criterion for different network inference methods. We considered 20 simulation iterations for networks of size 200 with an average degree of 2, number of samples n = 300, condition number k = 5, graph structure was random and read count data were simulated according to a ZINB. The quantitative covariate parameter γ was drawn from a N (0, 2), when covariate was effective. CoNet designates network obtained from Spearman correlation p-values by 100 iterations of permutation and bootstrap. For SparCC, the correlation threshold parameter was equal to 0. 3 and 100 iterations were done in the outer loop and 20 in the inner loop. raw Glasso, npn SKEPTIC, SPIEC-EASI, and MAGMA were network obtained by graphical lasso inference from raw data, nonparanormal SKEPTIC transformation, clr transformation and MAGMA transformation respectively.
Observations and Data Reduction The z = 3. 632 1 strongly lensed SMG, G09v1. 97 was observed in the 2 mm atmospheric window (Band 4) with the Atacama Large Millimeter/submillimeter Array (ALMA), in the project C. Yang et al. :  ALMA imaging of a z = 3. 63 strongly lensed starburst galaxy   -3). ν sky gives the sky frequencies of the line centers and the continuum. ( a) The 2 mm continuum image was made by combining a line-free spectral window SPW-3 and all the line free channels from SPW-0, SPW-    1. The root mean square (RMS) of the data reaches 0. 21 mJy beam -1 in a 50 km s -1 channel width. We have also included in this study ALMA Band 7 continuum archive data of G09v1. 97 centered at 343. 494 GHz (ADS/JAO. ALMA#2013. 1. 00358. S, PI: S. Eales, for further discussion of this dataset, see  Amvrosiadis et al.
2018) , which allow us to have a better constrain of the lens model and perform a detailed analysis of the spatial distribution and properties of dust emission. The observed frequency corresponds to a wavelength of 0. 873 mm, or ∼ 188 µm in the rest-frame. The observations were performed on 01-Sep-2015 in good weather conditions, with 3. 1 minutes on source time and a maximum baseline of 1. 6 km, yielding a synthesis beam of 0. 19×0. 12 (PA = 60 • ). The bandpass, flux and phase calibrators were J0739+0137, J0510+1800, and J0839+0104, respectively. The absolute flux calibration uncertainty in Band 7 is 10%. Table  1  summarizes the details of the observations. Both datasets were calibrated using the ALMA calibration pipelines, with only minor flagging required. The calibrated data were then imaged and CLEANed using tclean within CASA 3  version 5.
1. 1, with a Briggs robust weighting factor of -0. 5 for the Band 4 dust continuum to generate CLEAN-component models. All the line free channels were combined by using the MS-MFS algorithm  (Rau & Cornwell 2011)  with multiple Taylor terms nterms=2 during the CLEAN process. We then performed several iterations of phase-only self-calibration until the S/N stopped improving. The typical phase variations are within ± 50 deg and change smoothly with time. Accordingly, the corresponding gaincal solutions were applied to the entire dataset. After subtracting the dust continuum for the line emission data cube, the datasets were then CLEANed with a Briggs robust weighting factor of -0. 5 for the dust continuum (using again nterms=2, combining all line-free channels), -0. 2 for the CO emission, and 0.
Finally, there is extended emission which connects the southern and northern components along the eastern side of the ring. We also show the 1. 94 mm dust continuum superimposed on the Keck-II/NIRC2 Ks-band image in Fig. 1. The figure shows the two foreground deflecting galaxies, the southern galaxy at z = 0. 626 (G1) and the northern one at z = 1. 002 (G2), with the redshifts obtained from the William Herschel Telescope (WHT) ACAM spectroscopy through the detection of the Mg absorption and the [O II]λλ3726,3729 doublet lines, respectively. A Hubble Space Telescope (HST) image taken in the F110W band, which was obtained as part of the ID 12488 Snapshot program (PI: Negrello), also confirms such a compound lens configuration. 4  The unusual line-of-sight configuration, with two deflecting galaxies, complicates the model of the gravitational potential (Section 4).
Integrated Spectra The continuum-subtracted spectrum integrated over the entire source is shown in Fig. 4. The upper panel shows the combined spectra of the 2 mm windows SPW-0 and SPW-1 of the H 2 O and H 2 O + lines, while the lower shows the CO(6-5) line covered by SPW-2. In addition to the strong CO(6-5) and H 2 O emission lines, there is a series of H 2 O + emission lines including the dominant H 2 O + feature, i. e. , H 2 O + (2 11 -2 02 ) (5/2-5/2) and H 2 O + (2 02 -1 11 ) (5/2-3/2) (based on the analysis of the expected relative strengths of the H 2 O + submm lines in Arp 220 by  González-Alfonso et al. 2013). We also detect an emission line at 745 GHz, which will be discussed at the end of this section. After extracting the spectra integrated over the entire spatial region of the source, the emission lines were fitted with multiple Gaussian profiles using the Levenberg-Marquardt leastsquare minimization code MPFIT  (Markwardt 2009).
Lens Modeling In order to derive the intrinsic properties of G09v1. 97, a lens model needs to be built using both the high-resolution ALMA imaging data and the optical/near-IR images to constrain the gravitational potential of the deflectors. We stress that the main focus of this paper is to study the properties of the background lensed source, and the detailed structure of the deflecting mass distribution will be presented in a subsequent study. Similar to the parametric lens model in  Bussmann et al. (2013) , the two foreground deflectors (G1 and G2, see Fig. 1 ) are assumed to be singular isothermal ellipsoid (SIE) mass distributions centered on the two foreground galaxies. The parameter encoding the strength of the deflector, namely the depth of the lensing potential, is represented by the velocity dispersion σ v (note here σ v is not the velocity dispersion of the ISM gas).
Nevertheless, we are able to account for synthesized beam and noise correlation in the CLEANed images for our ALMA data using the methods described in  Gavazzi et al. (2011). Although fitting visibilities in the uv-plane would overcome the caveats related to sidelobes and correlated noise of interferometric data (e. g. ,  Bussmann et al. 2012 Bussmann et al. , 2013;; Hezaveh et al. 2013; Nightingale & Dye 2015; Spilker et al. 2016; Leung et al. 2017; Enia et al. 2018) , this is not implemented yet in sl_fit. Dye et al. (2018)  showed that image-and uv-plane model fitting can yield highly consistent results with ALMA data with sufficiently high uv-plane coverage and thus small dirty beam side-lobes. Since our ALMA data has such sufficiently high uv-plane coverage and S/N, we safely assume that any information loss in the source plane as a result of our image-plane analysis is negligible.
With the improved depth and spatial resolution of our ALMA data, we can perform modeling with a more complex intrinsic light distribution. Therefore, we assume that the dust emission consists of two exponential profiles with several free parameters, e. g. , positions (x, y), sizes (effective radius R eff ), ellipticities (q, which equals the minorto-major axis ratio) and orientations (PA). Those parameters can differ between each source component, although, by construction, the geometry (size, orientation, position) remains the same at 1. 94 and 0. 87 mm. Only the flux can differ across the two bands. In other words, each of the two background components has a constant magnification across its entire far-IR spectral energy distribution (SED). Two such sources sharing the same center would mimic a unique source with an SED gradient, should the data demand it, but these model assumptions also allow us to model two spatially distinct internally homogeneous sources.