dataesr/scientific-paragraphs-categorization huggingface dataset:
Acetylated chromatin, particularly super-enhancer regions, is associated to bromodomain and extra-terminal (BET) proteins to facilitate transcriptional activation  [67]. BET inhibitors disrupt the interaction of bromodomains with acetylated histones and lead to loss of enhancer-promoter long-range interactions. The score based on genes associated with super-enhancers built in this study allows one to identify high-risk MM patients that might benefit from BET inhibitors. Several BET inhibitors are currently evaluated in phase I/II in MM patients, including OTX015 (NCT01713582), CPI-0610 (NCT02157636), GSK525762 (NCT01943851), and RO6870810 (NCT03068351)  [68]. We showed that SE-associated risk score was correlated with the response to GSK525762 in HMCLs. Moreover, concerning the 28 genes composing the SE-risk score, the prognostic value of 7 genes (BSG, HK2, HSPA9, IL10, PARP1, PDIA6 and SLC7A5) has already been described in MM  [69] [70] [71] [72] [73] [74] [75] [76] [77] [78] [79]  while the expression of HNRNPC, ILF3, LDHB, MDH1, NCL, PRPS1, RPL8, RPL13A, RPL35, SMARCA4, SPN, STC2 and THY1 was described to be associated to tumorigenesis, drug resistance and/or poor prognosis in other cancers  [80] [81] [82] [83] [84] [85] [86] [87] [88] [89] [90] [91] [92] [93].
The x axis shows the genomic position and the y axis shows signal coverage of ChIP-seq occupancy in units of reads per bin mapped reads. (D) Molecular signature of differentially bound sites localized on gene promoters enriched in lenalidomide-sensitive and -resistant HMCLs was investigated using GSEA database (FDR ≤ 0. 05). No overlap was found for gene promoters enriched in lenalidomide-sensitive HMCLs.
Acknowledgements This work was supported by grants from  INCa (Institut National du Cancer)   PLBIO18-362 PIT-MM  and  PLBIO19   FATidique ,  ANR  ( TIE-Skip ;  2017-CE15-0024-01 ),  ANR-18-CE15-0010-01 PLASMADIFF-3D , SIRIC  Montpellier Cancer  ( INCa_Inserm_DGOS_12553 ),  Labex EpiGenMed  and  Institut Universitaire de France.
The promoter of ANKRD30B and SLAMF6 genes is the most significantly differentially enriched by H3K4me3 modifications in lenalidomide-sensitive group compared to lenalidomide-resistant group and, conversely, the promoter of GPR15 and NKX6-1 genes is the most significantly differentially enriched by H3K4me3 modifications in lenalidomide-resistant group compared to lenalidomide-sensitive group. The x axis shows the genomic position and the y axis shows signal coverage of ChIP-seq occupancy in units of reads per bin mapped reads. (D) Molecular signature of differentially bound sites localized on gene promoters enriched in lenalidomide-sensitive and -resistant HMCLs was investigated using GSEA database (FDR ≤ 0. 05). No overlap was found for gene promoters enriched in lenalidomide-sensitive HMCLs.
The CoCena pipeline was run on each cell line independently. The pipeline identi ed 8, 5, 10, and 4 co-expression modules for CD4 + T cells, CD14 + monocytes, CD19 and NK cells respectively. Analysis of the modules revealed group-speci c enrichment of co-expressed gene modules (Fig. 4A ). Enrichment analyses on each of the modules identi ed associated gene signatures displaying distinct functional characteristics, which distinguish the different sample groups. (Fig. 4B , Fig. 5 ). We then annotated the modules according to their behavior into seven categories: ethnicity speci c independent of disease activity, opposite behaviors according to ethnicity, White speci c, Asian speci c, low disease activity speci c, high disease activity speci c, and high disease activity speci c for White. For example, the IL6 JAK STAT3 signaling pathway was identi ed as having opposite behavior in Asian and Caucasian populations and was enriched in the modules dark grey and gold for CD4 + T cell.
For this reason, we conducted co-expression analyses using CoCena taking demographic and clinical features into account. Coexpression analysis identi ed similarly regulated genes across samples and grouped these genes into modules. Samples were grouped into White-high, Asians-high (high disease activity de ned by a SLEDAI score > = 6) and White-low, Asians-low (low disease activity de ned by a SLEDAI score < 6). Modules were annotated according to their behaviors into seven categories (Fig. 4A ). For example, Interferon gamma response pathway was enriched in modules dark grey and gold for CD4 + T cell (Fig. 4B ). Extended analysis of the modules revealed a prominent enrichment of in ammatory, autoimmune, and interferon pathways (Fig. 4  and Figure  S7 ). Cocena2 further identi ed modules which contained transcription factor signature genes known to be associated with SLE.
Future work will include testing these ndings in other multi-ethnic cohorts and include a multi-omics approach. There are several limitations that should be recognized. Our study is limited by the restriction of transcriptomic data to one time point. At the time of blood collection, patients were not currently in lupus ares. However, since gene expression is dynamic and may re ect cellular responses to an underlying disease process, it is possible that patients transition between transcriptomic clusters as during their disease course. This remains a testable hypothesis in future studies incorporating longitudinal pro ling. Additionally, speci city and sensitivity were low in all cell types and this could be due to the imbalanced dataset. ML models with substantial predictive accuracy can assist clinicians with complicated diagnostic decision making, though extensive study is necessary to construct accurate models for speci c population groups.
Our study is also limited by the fact that all patients within our cohort are currently stable on medication making the identi cation of transcriptomic differences between patients nontrivial as well as the absence of controls. Although there are large numbers of publicly available gene expression pro les of SLE patients, many of these pro les are not annotated with SLEDAI data and are not cell sorted data and do not examine speci c cell types. Furthermore, some data sets which include SLEDAI data show heavy class imbalance, which impedes classi cation. Further work to integrate cross-platform expression data will be crucial to expanding our ability to classify active and inactive SLE patients. In summary, we have identi ed distinct clinical subtypes of SLE using immune cell sorted transcriptomic data that have distinct associations with clinical measures, speci cally with lupus severity index.
Unsupervised clustering approach and analyses Unsupervised K-means clustering was performed on the batch corrected transcriptomic data per cell type using the factoextra package (v 1. 0. 7). The number of clusters, k, was chosen by maximizing cluster stability measured by Jaccard similarity using a bootstrap resampling-based method. The identi ed clusters were then tested for associations with clinical variables such as sex, race, and the ACR criteria. Fisher's exact test was used to evaluate if categorical variables were enriched in a cluster with respect to the others. For continuous variables, we used ANOVA testing.
Differential expression analyses We performed differential expression gene testing with DESeq2 (v. 1. 24. 0 R package) using default settings. Sequence lane, medications, and sex were used as covariates within the DESeq2 model. Statistical signi cance was set a 5% false discovery rate (FDR; Benjamini-Hochberg). The bioconductor package fgsea (v 1. 10. 1), was used for gene set enrichment analysis (GSEA). Differential expression analysis was conducted for several clinical features, speci cally: SLEDAI score  17  , the mean Systemic Lupus International Collaborating Clinics/American College of Rheumatology (SLICC) Damage Index score  18  , lupus severity index, a validated scoring system based on the ACR classi cation criteria  19  , the presence of lupus nephritis, and ethnicity (White vs Asians), Figure  S2-6.
Acknowledgements We are grateful to all of the patients who participated in the study. We are also grateful to  Ms. Bushra Samad , and  Dr. Ellie Seaby  who provided advice on QC methods and discussion of study results. This study was funded through the following grants:  P30 AR070155  (M. S. , G. A. , L. A. C. ),  P60AR053308  (L. A. C. ),  K01LM012381  (M. S. ),  F32 AR070585   NIAMS  (M. G. ),  U01DP005120 CDC (L. A. C. , C. M. L. , J. Y. , M. D. , L. T. , P. K. ), the  Rheumatology Research Foundation   128849A  (C. M. L. ), and the  Lupus Research Alliance  (L. A. C. ).
Qualitative evaluation on in vivo human brain data Last, we tested the effectiveness of the new formulation on in vivo data from the Human Connectome Project (HCP)  (23). As the ground truth is unknown, we qualitatively assessed the impact of COMMIT2 on known true-positive and false-positive bundles that were manually defined by an expert neuroanatomist. The top row of Fig. 4  shows these known bundles as reconstructed in the original tractogram along with the voxel coverage, i. e. , number of voxels traversed T T R R specificity Sensitivity Fig. 3. Comparison with basic filtering procedures. Receiver operating characteristic curve analysis to assess how COMMIT2 (pink) compares to other filtering procedures present in the literature in terms of discriminating between true-positive and false-positive bundles in the connectome.
of 11 by the streamlines associated with each bundle; the bottom row reports the same bundles after filtering with COMMIT2. Results show that our COMMIT2 filtering procedure does not affect the truepositive bundles, as the voxel coverage is comparable with the original tractogram and the total contributions estimated by COMMIT2 are high. This means that COMMIT2 removes implausible streamlines inside the bundles but recognizes these bundles as fundamental to explain the data and keeps the coverage of the white matter intact. Conversely, the false-positive bundles are markedly thinned, i. e. , the voxel coverage is extremely reduced, and the weights assigned to them by COMMIT2 are close to zero, meaning they are not necessary to explain the signal. In particular, we observe that while the less populated bundles are (almost) completely removed, the more populated ones are extremely thinned in terms of white matter coverage and that their contribution to the resulting connectome is minimal.
Apart from sharing the same convex optimization procedure with the original COMMIT, the method presented here is an important improvement of the previous S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 6 of 11 framework and not only an incremental variant. The possibility to inject priors about brain anatomy and its organization, and not only about microstructural properties, represents a powerful and novel way to tackle the false-positive problem in tractography and brain structural connectivity. When we compared the performance of COMMIT2 with other state-of-the-art filtering techniques (LiFE, SIFT, SIFT2, and COMMIT)  (12) (13) (14) (15) , which are all similar in spirit to our proposed method but are purely data driven, none of them proved effective in reducing IBs. This clearly indicates that adding anatomical priors about the organization of the bundles has indeed a dramatic impact on the specificity of the estimated connectomes.
Besides improving the estimation of brain connectivity, we believe that COMMIT2 is also a promising tool that can be routinely used by neurosurgeons for whom tractography has rapidly become an essential tool for planning surgery. By allowing visualization of the different bundles before the operation, tractography has always been considered as a potential tool, but since its reliability has always been under debate, not all neurosurgeons feel comfortable to trust it. Nevertheless, the continuous progress in MRI scanners has already provided them an easy access to patient's diffusion images through which they can now have access to a more reliable structural connectivity reconstruction. The short computational time required by COMMIT2 can allow its application on any type of whole-brain tractograms that may be built from a large database (like HCP www.
This additional term in the cost function penalizes the contributions at the level of groups and, in practice, promotes (but does not constrain) convergence toward a solution that explains the measured DW-MRI data with the minimum number of bundles; this formulation does not have any prior knowledge about which groups correspond to true-positive or falsepositive bundles. Note that setting  = 0 corresponds to the classical COMMIT. As this formulation represents an extension to the COMMIT framework, we refer to it as COMMIT2 in the manuscript. Without any strong a priori knowledge on the bundles, a classical way to operatively solve this problem is to use the so-called adaptive group lasso  (35) , which penalizes all groups in the same manner regardless of their cardinality. The problem can then be rewritten as argmin x≥0 ‖ Ax -y ‖ 2 2 + ∑ g∈G λ ( g ) ‖ x ( g ) ‖ 2 (5) with  (g) =  √ _ | g | ─ ‖ x NNLS (g) ‖ 2 (6) where |g| is the cardinality of the group g and x NNLS (g) are the weights of the streamlines obtained by solving the NNLS problem in Eq.
Evaluation criteria We assessed the sensitivity and specificity of the resulting connectome using the Tractometer metrics defined in  (21). True positives are described in terms of the valid connection (VC) ratio, which is the proportion of streamlines in the tractogram that connects a correct pair of ROIs, as well as the corresponding number of VBs. Similar metrics can be computed for the false positives, i. e. , invalid connections (ICs) and IBs. To summarize sensitivity and specificity in a single score, we computed the Youden's index J = sensitivity + specificity -1. Sensitivity is defined as the ratio between VB and the number of realpositive bundles (27 in this dataset), and specificity as 1 -IB/N, where N is the number of real negatives (594 in this dataset). N represents the number of ROI pairs that may potentially be connected (incorrectly) by tractography and was computed by reconstructing 10 million streamlines with the probabilistic algorithm, as it is more permissive.
We also compared the performance of our novel formulation to other state-of-the-art filtering techniques: LiFE (13), SIFT  (14) , SIFT2  (15) , and COMMIT  (12). For each method, we downloaded the software package developed by the original authors-i. e. , https://github. com/brain-life/encode for LiFE, https://mrtrix. org for SIFT/SIFT2, and https://github. com/daducci/COMMIT for COMMIT-and run the code with default parameters. Last, we investigated the accuracy in the estimation of the actual weights of the edges in the ground truth connectome. However, as each technique assumes a different definition of connection strength between two brain regions, to validate these weights, we first normalized each connectome to its maximum value and then computed the root of the sum of squared differences between them and the normalized ground truth connectome.
Synthetic phantom description and processing We quantitatively evaluated our novel approach using a realistic digital phantom with known ground truth developed for the reconstruction challenge organized in 2013 at the International Symposium on Biomedical Imaging (ISBI) using Phantomas (https://github. com/ecaruyer/phantomas). This simulated dataset is shown in Fig. 1A  and consists of 27 ground truth fiber bundles that were specifically designed to mimic real fiber configurations typically encountered in the brain (Fig. 1B ). These include complex arrangements of bending, crossing, and branching fibers at various angles and with different curvatures; in addition, three spherical regions corresponding to fast diffusive compartments such as in brain ventricles were added. The corresponding DW-MR signal was generated using the composite hindered and restricted model of diffusion  (24)  along 64 directions with b = 3000 s/mm 2 and adding Rician noise with a signal-to-noise ratio of 30.
The intra-axonal signal frac-tion of this phantom was computed from the geometry of the ground truth streamlines. Connectomes were constructed from the reconstructions obtained with both deterministic and probabilistic tractography using the 53 gray matter ROIs as network nodes. We used the MRtrix software  (36)  as it is a popular processing suite to analyze DW-MR data. First, we computed the fiber orientation distributions (FODs) in each voxel using constrained spherical deconvolution  (37) , with l max = 8. Then, we reconstructed 1 million streamlines with both deterministic (SD_ STREAM) and probabilistic (iFOD2) algorithms using default parameters and performed the tracking using the white matter mask as the seed region. Last, we assigned each end point of a streamline to a node if that point fell within 2 mm from one of the 53 gray matter ROIs (default setting).
In vivo data processing We also tested COMMIT2 on in vivo data using data from the HCP repository (www. humanconnectome. org). We downloaded the preprocessed diffusion data corresponding to subject 100307 and the structural T1-weighted image with the corresponding standard Desikan-Killiany  (38)  parcellation in 85 gray matter ROIs performed with FreeSurfer (http://surfer. nmr. mgh. harvard. edu/). Detailed processing methods applied to all HCP open-access data are described in  (23). We performed whole-brain anatomically constrained tractography (A) Receiver Operating Characteristic curve analysis to assess how COMMIT2 compares to the state-of-the-art filtering procedures when their estimated connectomes are further thresholded. Connections were gradually removed from the connectomes based on the streamline count for raw tractograms and SIFT, and on the streamline weight sum for LiFE, SIFT2, COMMIT and COMMIT2.
Acknowledgments Funding: This work was supported by the  Rita Levi Montalcini Programme  for young researchers of the  Italian Ministry of Education, University and Research  and by the  Swiss National Science Foundation  under grant  205320_175974. Author contributions: S. S. , J. -P. T. , and A. D. conceptualized the problem. S. S. , M. O. -P. , M. B. , and A. D. developed, implemented, and tested the technical framework. L. P. manually segmented the in vivo VB and IB toward which we compared our method. M. O. -P. and M. D. conceptualized and developed the validation framework. S. S. , M. O. -P. , M. B. , L. P. , M. D. , J. -P. T. , and A. D. wrote the manuscript.
Confocal imaging Protein localization was analysed at the indicated time points with the confocal laser scanning microscope LSM880 from Zeiss (Oberkochen, Germany), using a 40x or 63x water-immersion objective and the ZENblack software. EYFP and Citrine were excited using a 514 nm laser collecting emission between 516-556 nm; RFP and mCherry were excited using a 561 nm laser with an emission spectrum of 597-634 nm, Chlorophyll A was excited with a 561 nm laser and the emission spectrum was 661-682 nm. Focal plane images were processed with the ZENblue software (Zeiss) for adjustment of brightness and contrast. Maximum Z-projection images were processed with ImageJ.
Acknowledgments We are grateful for technical support and experimental help from  Christel Kulibaba-Mattern  and  Elke Sauberzweig. We thank  Karin Schumacher  for the VMA12 construct,  Klaus Harter  for the BRI1 construct and  Roger Innes  for PBS1-HA and AvrPphB-myc clones. We would also like to thank  Thomas Stanislas , the  El Kasmi lab, Nishimura lab  and the  Dangl Lab NLR group  for critical comments and discussions on the project. We thank the  University of Tübingen , the  Deutsche Forschungsgemeinschaft  (grant no. DFG-CRC1101  -project  D09  to F. E. K. ) and the  Reinhard Frank Stiftung  (Project 'helperless plant' to F. E. K. ) for the financial support to F. E. K. , the  National Science Foundation  (grant  IOS-1758400  to M. T. N. and J. L. D. ) and  National Institutes of Health  (grants  GM107444  to  J.
L. D)  for the financial support M. T. N. and J. L. D. M. T. N. was also supported by startup funds from  Colorado State University , and J. L. D. is a  Howard Hughes Medical Institute (HHMI) Investigator. Y. J. and M. C. C. were supported by  ERC  no. 3363360-APPL under FP/2007-2013 and  ANR  (caLIPSO;  ANR-18-CE13-0025-02 ) to Y. J. ,  ANR JC/JC Junior Investigator Grant  ( INTERPLAY ;  ANR-16-CE13-0021 ) and a  SEED Fund ENS   LYON-2016  to M. C. C.
After reducing the 33 variables to eight, using the described three-step procedures, these eight variables were used to investigate the importance of (i) the opponent (La. neglectus, La. niger or no opponent), (ii) the status (resident vs. colonizer) and their interaction. In order to do that, we performed eight mixed models using the package glmmTMB  [37]. The identity of the 20 combinations of source colonies and status was introduced in the model as a random effect. M1, M3, M7 and M8 were fitted with linear models, M2 and M4 were fitted with linear models performed following a square root transformation of the dependent variables, M5 and M6 were fitted with generalized linear models using Poisson family. The significance of each explanatory term was tested using Chi-squared tests. For every significant variable, post hoc, pairwise contrasts among treatments were performed by calculating the least square means (package lsmeans;  [38] ), and t-tests were adjusted to the number of tests using Tukey corrections.
Acknowledgments: We thank  Rumsaïs Blatrix  ( CNRS ,  UMR 5175-CEFE ) and  Bernard Kaufmann  ( CNRS, UMR 5023-LEHNA ) for their invaluable help in the field, and  Bianca Dupont ,  Zoé Simon  and  Eliabelle Mauduit  for their major help to monitor the competitive interactions in the lab. We also thank  Frederic Mery  for the equipment loan,  Muriel Deparis  and  Palmyre Pasteau  for their help in colonies maintenance.
2% had peak heights above. Since peak height may reflect affinity for a particular element, DNAbinding affinity does not distinguish between well-characterized differentiation-associated MITF targets and non-differentiation MITF targets. To eliminate the possibility that the expression of HA-tagged MITF and the endogenous protein could produce different results, we also re-analyzed a ChIP-seq dataset obtained from melanocytes using an anti-MITF antibody  (Webster et al. , 2014). The results were similar with differences likely accounted for by the reduced number of peaks in the Webster et al. dataset compared to ours, reflecting the high ChIP efficiency when using the anti-HA antibody. Thus, the consensus MITF recognition motif is similar (Figure  S1E ), mean peak height tends to reduce when the binding site varies from the consensus (Figure  S1F ) and binding to genes such as ATF4 and LAMP1 is located in a similar position (Figure  S1G ).
1. 0 (Zhang et al. , 2008)  and associated with a gene if they fell within 20 kb of the gene body of a RefSeq gene using CisGenome  (Ji et al. , 2008). De novo consensus motifs were predicted with MEME  (Machanick and Bailey, 2011)  by interrogating the 60 bp around the summits of peaks. The coordinates of all CATGTG and CACGTG motifs were determined genome-wide and overlapped with peak coordinates using BedTools  (Quinlan and Hall, 2010). Melanoma patient H3K27ac ChIP-Seq from GEO series GSE6066  (Verfaillie et al. , 2015)  and bigwig files analyzed as above. Melanocyte MITF ChIP-Seq from GSE50681  (Webster et al. , 2014)  was processed as above. All additional steps were performed in R  (Sato et al. , 1997). To exclude bioinformatics pipeline-specific artifacts, inducible MITF ChIP-Seq samples were processed in two independent ways with similar results obtained with both methods.