dataesr/scientific-paragraphs-categorization huggingface dataset:
The bias introduced is corrected at the analysis level, weighting the simulated signal events according to their particle multiplicity in order to reproduce the multiplicity observed in data. The generation of signal events is configured using a Python option file and an EvtGen decay file. They specify the type of particle and the final state. These files are generic and have a syntax which is independent of the collision system. They can thus be used indifferently for the simulation of pp, heavy-ion or fixed-target collisions.
Interface to the EPOS generator The EPOS generator is interfaced to the Gauss software so that it can be used easily in the sequence described in Section 2. The version used in the LHCb software is the version available from the CRMC distribution  [11]. The EPOS implementation is configured to use the "EPOS LHC" model, a minimum impact parameter of 0 and a maximum impact parameter of 20 fm, except for the simulation of PbPb collisions where the range is restricted between 8 fm and 22 fm. The decays of hadrons inside EPOS are switched off, and all particle masses.
However, as experiments were underway, no major effects of Gpr88 inactivation in the ventral striatum were observed, while intriguing interactions were emerging between the dorsolateral and dorsomedial tiers of the striatum. For the sake of clarity, we thus decided to limit the scope of this article to the DLS-DMS interactions in both the effects of the lesion and of Gpr88-KD. The data were accumulated over 17 replication batches. Each batch consisted of 6-10 animals that were randomly distributed across the different experimental conditions, making sure that no 2 rats of the same conditions were housed together. In order to comply with the 3Rs guidelines, the appropriate sample size was calculated based on behavioral data from preliminary experiments, using the G*Power3 software  (28). To reach a statistical power of 0.
The first 24h were considered as an acclimation phase, and were not included in the analysis. After 48h, the position of the bottles was inverted to avoid side preference effects. Bottles were weighed daily in order to calculate the amount of consumed liquids. Sucrose preference was calculated as the percentage of sucrose intake / total intake. However, as the level of sucrose preference exhibited by control rats was lower than what we had observed during pilot experiments (at 0,25% sucrose), the concentration had to be re-evaluated after several batches of animals (0,5%). For this reason, the number of data points for this experiment is relatively lower. General consummatory behavior was also tracked by weighing the food dispenser at the beginning and end of the isolation phase. Social Novelty Discrimination (SND) -Social interaction and selective attention were then evaluated using the social novelty discrimination task (SND) as previously described  (23, 30).
S2A, C ). In situ hybridization (ISH) was performed with antisense digoxygenin-labeled complementary RNA probes designed to recognize Gpr88, 67-kDa glutamate decarboxylase (Gad67), proenkephaline (Penk), prodynorphine (Pdyn) mRNAs as previously described (23) (Fig. S2B, D ). A new probe was also designed following the same procedures to target regulator of G-protein signaling 4 (Rgs4) mRNA (GenBank accession number: NM017214, targeted nucleotides: 325 to 604). Digitization and semi-quantitative analysis -Slides were then digitized using the Axio Scan. Z1 and ZEN software (Zeiss, Oberkochen, Germany). The resulting images were exported for processing in Fiji (NIH, Bethesda, MD, USA)  (34). As fluorescent and colorimetric stainings are not stoichiometrically related to biological content, the signal intensity was not quantified.
Statistical Analyses Data from the experiments were analyzed using the Prism 6. 0 software (GraphPad Software Inc, La Jolla, CA, USA). Different tests were performed depending on the nature of the data and the driving hypotheses that were exposed in the "study design" section. For instance, regarding behavioral data, to assess the effects of the lesion (hypothesis 1), the two groups (SHAM-neg vs 6OHDA-neg) were compared using two-tailed unpaired t-tests, followed by Welch corrections. Then, to evaluate the effects of Gpr88-KD in 6OHDA-lesioned animals (hypothesis 2), the 6OHDA + miR-Gpr88 groups (DLS and DMS) were compared to the 6OHDA + miR-neg animals using one-way ANOVAs followed by Dunnett multiple comparison tests, as the data distribution passed normality tests. However, some of the actimeter, PPI and SND data required the use of 2-way ANOVAs followed by Sidak or Dunnett corrections where appropriate to assess interactions with independent variables (time/prepulse intensity/novelty status).
ACKNOWLEDGMENTS The lentiviral vectors were produced by the in-house iVECTOR facility. In vivo experiments were carried out in the PHENOPARC core facility, and the histological procedures were performed at the HISTOMICS platform, both also situated within the ICM. The authors thank  Annick Prigent  for her time and advice regarding the histological processing of the samples. The PHENOPARC Core is supported by 2 " Investissements d'avenir"  ( ANR-10-IAIHU-06  and  ANR-11-INBS-0011-NeurATRIS ) and the  "Fondation pour la Recherche Médicale ". The research leading to these results has received funding from the program " Investissements d'avenir "  ANR-10-IAIHU-06. B. G. was supported by a scholarship from the  French Ministry of Research , awarded by the  Brain-Cognition-Behavior Doctoral School  of the  Sorbonne University.
INTRODUCTION: Technology advancements over the past two decades paired with improvements in costeffectiveness have greatly increased the capacity to conduct large-scale molecular profiling in biomedical research. In translational settings, these advances have enabled the measurement of molecular phenotypes at very high resolutions, reaching the point where it has become possible to routinely generate whole genome, proteome, metabolome, microbiome and transcriptome profiles on an almost routine basis  (1). The current bottleneck is in interpreting the biological significance or potential clinical implications of these molecular signatures. Our group has developed co-expression modules and used them as a framework to analyze and interpret blood transcriptome data  (2) (3) (4). Importantly, we have used the modules as a fixed framework: transcriptional modules are not generated each time a new dataset needs to be analyzed; instead, the same set of pre-existing modules is reused to analyze each new dataset.
Consequently, our team has released only two different module repertoires over a 12-year period, which we and others have used to analyze numerous blood transcriptome datasets [e. g. (5) (6) (7) (8) ]. Other blood transcriptome frameworks have been generated  (9) , as well as those with a more generic transcriptional module repertoire  (10). An inherent benefit of using a collection of gene sets is that it permits the reduction of high-dimensional data. Using a fixed repertoire of modules based on co-expression analyses across a large dataset collection, improves the robustness of analyses of small datasets  (10). As such, efforts can rather focus on the functional interpretation of the collection of gene sets constituting the repertoire. Furthermore, using the same fixed framework to analyze multiple independent datasets means that cross-study comparisons and interpretation are easier and more reliable.
With the construction of this new repertoire we aimed first at increasing the range of immunological states upon which the repertoire is based, which was certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted April 28, 2020. ; https://doi. org/10. 1101/525709 doi: bioRxiv preprint limited to 7 in our earlier attempt. Secondly, considerable efforts were deployed to improve resources to support analysis and interpretation. Thus, the range of disease and physiological states employed for the construction of this new modular repertoire has been expended considerably compared to the first and second generation repertoires. Specifically, 16 input datasets were employed, comprising 985 unique blood transcriptome profiles from: patients with autoimmune, infectious or inflammatory diseases; cancer patients; liver transplant recipients; and pregnant mothers.
Constitution of a collection of datasets covering a wide range of immune states. The development of transcriptional module repertoires relies on identifying gene co-expression events using transcriptome profiling data as a starting point. For this new blood transcriptome module repertoire, we used 16 datasets (GEO ID: GSE100150) that encompassed 985 individual whole blood transcriptome profiles. Each dataset corresponds to a different pathological or physiological state (Table  1 ). These datasets were processed in the same facility and run on Illumina HT12 BeadArrays (details are provided in the method section). Similar to our first two repertoires (Table  2 ), we included data from patients (adult and pediatric) with: systemic lupus erythematosus (SLE), systemic onset juvenile idiopathic arthritis (SoJIA), liver transplants and receiving maintenance immunosuppressive therapy, metastatic melanoma, and infectious diseases [with an expanded range that now includes infections caused by influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV) infections, Mycobacterium tuberculosis, Staphylococcus aureus, and Burkholderia pseudomallei (the agent of Melioidosis) and sepsis caused by other bacteria (Streptococcus pneumoniae, Salmonella spp, Pseudomonas aeruginosa)].
We also added six new conditions to our framework: inflammatory conditions of the skin (juvenile dermatomyositis), lung [chronic obstructive pulmonary disease (COPD)] and circulation (Kawasaki disease); multiple sclerosis (MS); primary immune (B-cell) deficiency; and pregnancy (serving as a physiological variant). Absent from this repertoire (but represented earlier) are patients with type 1 diabetes or with an Escherichia coli infection. In summary, the sixteen datasets that have been assembled capture a wide breadth of immunological responses. This should permit the construction of a transcriptional module repertoire that will prove useful as a generic framework for blood transcriptome data analyses. certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprint (which was not this version posted April 28, 2020. ; https://doi. org/10. 1101/525709 doi: bioRxiv preprint Implementation of a step-wise approach to blood transcriptional module repertoire construction. After collating the 16 input datasets, we next followed a stepwise process to construct a module repertoire and identify co-expression networks (Figure  1 ). We used the module construction algorithm that we have previously implemented (the code is provided in Supplementary File 1). This approach followed four main steps: 1) clustering for each individual dataset; 2) co-clustering, where the number of instances that two genes were included in the same cluster was recorded, with the values ranging from 0 to 16 (i. e. co-clustering observed in none or all of the datasets, respectively); 3) constructing a weighted co-expression network, where the edges between the genes represent at least one co-clustering event in one of the input datasets and the weight is assigned based on the total number of co-clustering events; (supplementary Figure  1 ); and 4) mining the resulting network to identify highly inter-connected sub-networks that form the modules.
1, the median as 26. 5 and the range as 12-169. Functional profiling and enrichment results were generated using multiple tools (GSAn, Literature Lab, IPA, DAVID, KEGG, Biocarta, OMIM, and GOTERM). We also determined the extent of overlap with the previous modular repertoire and a set of modules constituted at Emory University and our previous generation of modules  (9). For module-level analyses, we determined the proportion of the constitutive transcripts that differ in abundance levels between study groups (e. g. cases vs. controls; pre-treatment vs. post-treatment). By this approach, two values, corresponding to the percent of transcripts that are (i) increased and (ii) decreased, are derived. The cutoff points can be chosen based on user preferences. For example, cutoffs can be based on statistics, fold changes and/or differences with or without multiple testing correction for group comparisons.
From here, the extent of differential expression at the module level can be displayed as a "fingerprint", assigning each module to a fixed position on a grid plot and color-coding it according to the level of increased or decreased abundance for the constituting transcripts (Figure  2 ). For this we then performed a second tier of clustering to group the 382 modules into 38 "aggregates", with each row on the grid displaying the modules corresponding to one such aggregate. Segregation into distinct aggregates was based on similarities in abundance levels observed across the collection of 16 datasets. By this approach, we derived two levels of granularity (i. e. modulelevel vs. module aggregate-level) with the number of variables for interpretation constrained to a more manageable number. The overall result is that changes in expression levels for each row on the fingerprint grid will tend to be coordinated, which was not the case of prior iterations of such certified by peer review) is the author/funder.
Illustrative use case of fingerprint grid plot representations We next illustrate the analysis and visualization approach described above with a use case. It will primarily focus on SLE, a disease which blood transcriptome signature has been wellcharacterized. Fingerprints of other reference disease cohorts employed for module construction will be included to provide additional context. As mentioned, data interpretation is facilitated by tiered dimension reduction: the first vertical reading of the fingerprint grid permits visualization of changes across the aggregates, while the horizontal reading permits visualization of changes within an aggregate and across modules. As pointed out earlier, in the fingerprint grid, the number and intensity of the spots represents qualitative as well as quantitative differences.
More specifically, for the module aggregate A1, the most marked decreases were observed for modules associated with protein synthesis (dark purple color at positions 1, 5, 11 and 19 on row A1). It is possible to go a step further and "aggregate" the changes observed by row, thereby reducing the dimensions for a given dataset even further. In this case, we reduced the dataset from 382 modules to 27 "aggregates" (Figure  3A ). Users can decide taking this extra step depends on the desired level of resolution: module-level or aggregate-level. For example, mapping changes at the aggregate level, the simplest framework possible, is optimal when looking at signatures in a broader context. But at the same time our earlier work showed that distinct interferon modules are biologically and clinically meaningful, also it can also be indicated to work instead at the modulelevel  (11).
In depth functional annotation of fixed transcriptional module repertoires Based on earlier iterations, the expectation is for this repertoire to be of use over a period spanning at least 5-6 years. We thus put great effort into functionally annotating the repertoire. We followed two main annotation approaches: (1) concurrently running ontologies, pathways or literature-term profiling analyses; and (2) determining for each module the patterns of gene expression for select reference transcriptome datasets. We compiled the resulting information for all 382 modules and have since made it available via an interactive web application Here, it is possible to zoom in and out, determine spatial relationships and interactively browse the very large compendium of analysis reports and heatmaps generated as part of our annotation efforts.
(Figure  4 ; Links for each aggregate are listed in Table  3. ; A demonstration video is available: https://youtu. be/8ajESET2mqI) Below, we describe briefly how we conducted our annotations (see also Experimental Procedures). Step 1 -Functional profiling: We conducted gene ontology profiling for each of the 382 modules using DAVID  (12) , GOTERM, and GSAn  (13). GSAn interactive reports were uploaded to a custom web portal (https://ayllonbe. github. io/modulesV3/index. html). We also performed certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted April 28, 2020. ; https://doi. org/10. 1101/525709 doi: bioRxiv preprint 13 pathway enrichment analyses using KEGG, Biocarta and Ingenuity Pathway, as well as literature term enrichment with Literature Lab.
We synthesized and compiled the analyses (Supplementary File 2) and used them to identify convergences and attribute functional titles to the different modules. Functional titles could not be attributed in all cases, due to a lack of convergence or poor enrichment in one or several of the analyses. Step 2 -Expression patterns in reference transcriptome datasets: We used transcriptome datasets as a reference to improve characterization and biological interpretation of the module framework. Two different datasets were used. The first was contributed by  Novershtern et al. and comprised  the transcriptome profiles of 38 human hematopoietic cell populations  (14). The second was contributed by Speake et al. and comprised the RNAseq profiles of six circulating leukocyte populations from patients with various immune-associated diseases  (15).
We generated heatmaps for each module to display the abundance patterns of the constitutive transcripts for each dataset. Overall, this resource serves two purposes. First, it provides access to information that is necessary for interpreting transcriptome fingerprints generated via module-level analyses. Second, it helps us to improve the attribution of functional titles and roles to the different modules and aggregates. Indeed, although the transcriptional module repertoire is fixed over time, we anticipate that the functional annotations will continue to evolve over its lifespan.
From the six modules, four seemed to be "core" interferon modules (M15. 86, M10. 1, M8. 3, M15. 127), while the remaining two (M13. 17, M15. 64) were associated to the interferon literature to a lesser degree. These latter two modules were more strongly associated with the Herpes simplex virus than the other four modules while the four core modules were preferentially associated with hepatitis. The heatmaps available via the Prezi interface, which depict the gene expression profiles for the A28 modules provide additional perspectives. For example, the dataset from Speake et al. comprised samples of patients with MS collected immediately before and 24 h after the administration of their first dose of interferon-b (Supplementary Figure  3  [GEO ID GSE60424); certified by peer review) is the author/funder.
All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted April 28, 2020. ; https://doi. org/10. 1101/525709 doi: bioRxiv preprint  (15) ]. Despite the small number of subjects in this category, we observed a clear pattern of response to interferon in vivo across all six modules. This observation confirms the functional associations obtained from enrichment analyses. Next, we examined the degree of changes for the six interferon modules across the 16 input datasets (Figure  8B ). The first cluster showing the highest induction levels comprised SLE, influenza infection, HIV infection and active M. tuberculosis infection (noted as C1 on the figure). Interferon has antiviral properties; therefore, it is no surprise to see viral infections included in this set.
We previously showed using our second generation repertoire that the interferon signature characterizing SLE comprises distinct "sub-signatures" at the module level  (11). In this earlier work we observed a sequential increase for a set of three interferon modules (including M1. 2, M3. 4 & M5. 12). M1. 2 showed a higher degree of sensitivity, followed by M3. 4 and then M5. 12. Based on this finding, we could stratify SLE patients based on whether one, two or all three of those interferon modules were activated. By combining functional profiling with a reference dataset, we concluded that the modules responded differently to each interferon type: interferon-a induced an increase in the abundance of genes belonging to M1. 2; interferon-b induced an increase in the abundance of genes belonging to M1.
The copyright holder for this preprint (which was not this version posted April 28, 2020. ; https://doi. org/10. 1101/525709 doi: bioRxiv preprint modules to re-classify the adult SLE dataset profiled in our earlier study [GEO ID GSE49454  (11) ]. The resulting clustering and stratification mirrored our earlier findings made using the three interferon modules (Supplementary Figure  4 ). Overall these observations confirm that interferon "sub-signatures" may be employed for patient stratification. This may be relevant in the tailoring of biologics targeting interferon which are under development for the treatment of SLE. However, whether utilizing six modules from the new repertoire would necessarily be an improvement over the three from the second-generation repertoire that were employed previously remains to be determined.
DISCUSSION: We have developed a new resource for the analysis and interpretation of blood transcriptome data. At its core is a fixed, transcriptional module framework. This framework has been constructed via co-expression analyses carried out across a wide range of diseases and physiological states. It constitutes the third iteration released by our group since 2008. Another blood transcriptome repertoire was developed and made available by our collaborators from Emory University  [Li et al. : (9) ]. While methodological differences exist for the construction of such frameworks the principle remains the same: i. e. utilization of reference datasets for constitution of reusable sets of transcriptional modules. Collections of gene sets are commonly used for the interpretation of transcriptome profiling data [Gene Set Enrichment Analyses (GSEA): (25)  (26) ].
e. the analysis and interpretation of blood transcriptome data). Zhou and Altman recently described the development of another fixed transcriptional module repertoire via co-expression analysis and discuss the advantages of such approaches  (27). In brief, they assembled a collection of 2753 public datasets that were deposited in the NCBI GEO that encompassed 97,049 unique transcriptome profiles. They used independent component analysis for resolving co-expression relationships which led to the identification of a set of 139 transcriptional modules. They went on to demonstrate the biological relevance of these sets and advantages their use presents for improving robustness of analyses, especially for smaller datasets. Altogether this article provides additional arguments in favor of the reuse of fixed transcriptional module repertoires, an approach that to date has not seen widespread use so far.