dataesr/scientific-paragraphs-categorization huggingface dataset:
The genes not represented in each of the gene chip platform used were removed from the analysis. This Orthology Filter yielded a filtered expression dataset for each species, where the number of genes and their associated symbols were similar between all species, as illustrated by boxes of the same size (Figure  S2B  in Supplementary Material). In order to be able to rigorously merge the different datasets together, the dynamic ranges of expression values for each gene across all species must be homogenized by setting for each dataset and for each gene across all samples the mean expression to 0 and the variance to 1, a process called data centering and reduction. To prevent this mathematical transformation of the expression data to introduce noise by forcing artifactual expression changes for genes that were unregulated in the initial datasets, it is mandatory to remove all the genes that are not regulated in at least one of the datasets.
This thus requires keeping only the genes that are differentially expressed between at least two cell types for each of the species studied. This was achieved in the second and third steps of the data processing. The second step (Differentially Expressed Gene, DEG, Filter) consisted in identifying independently in each dataset the genes that are differentially expressed between at least two cell types. The identification of DEG was performed by calculating the minimal ratio between each pairwise comparison of cell types and by selecting only the genes for which this minimal ratio was higher than twofold. The third step (DEG intersection) consisted in keeping only the genes that were common to all filtered DEG lists, i. e. , the orthologous genes which expression was modulated across samples in each of the species studied.
The fourth step consisted in data centering and reduction for each dataset, which was performed using the R statistical environment. This step consists in setting, for each dataset, the mean to 0 and the variance to 1, so that all datasets are comparable. In the fifth and final step, the different datasets were merged together simply by aligning their rows based on the common gene symbol extracted from species A. The final cross-normalized expression dataset including the data for all species was then used to perform canonical analyses for classification of samples, namely here hierarchical clustering. generation of conserved cross-species cell Type-specific signatures For each species (human, mouse, sheep, and pig), the transcriptomic fingerprint of each cell type was generated by selecting the genes more highly expressed in the cell type of interest, as compared to all other studied cell types of the same species in the case of "absolute" transcriptomic signatures, or as compared to selected cell populations of the same species in the case of "relative" transcriptomic signatures, using the "Min (test) vs.
real-Time Pcr For relative quantitation of gene expression in subsets, RNA was reverse transcribed using random primers and the Multiscribe reverse transcriptase (Applied Biosystems). Real-time PCR (qPCR) was carried out with 300 nM primers in a final reaction volume of 25 μl of 1 X SYBR Green PCR Master Mix (Applied Biosystems). The primers used to amplify ovine and porcine cDNA were designed with the Primer Express software (v2. 0) using publically available GenBank sequences (Table  S1  in Supplementary Material). PCR cycling conditions were 95°C for 10 min, linked to 40 cycles of 95°C for 15 s and 60°C for 1 min. Real-time qPCR data were collected by the Mastercycler ® e0p realplex-Eppendorf system and 2 -ΔCt calculations for the relative expression of the different genes (arbitrary units) were performed with the Realplex software using GAPDH for normalization.
Control transcripts were chosen based on their high selective expression in specific subsets of mononuclear phagocytes in a conserved manner between mouse and man, i. e. , TCF4 for pDC, FLT3, BATF3 and ZBTB46 for cDC, XCR1 for cDC1, and CD14 for Mo/MP. Data are represented as relative expression levels normalized to maximal expression across cell types, each bar corresponding to a distinct animal. human immune cell subsets  (19)  and chicken cDC  (31). To that aim, we used publicly available transcriptomic data from a selection of human and mouse immune cell types (Data Sheet S1 in Supplementary Material). We established human and mouse transcriptomic fingerprints for B cells, pDC, cDC1, Mo/MP, MoDC, cMo, and ncMo as the list of genes that are expressed at least 1. 5-fold higher in the index cell population than in a large number of other immune cell types (Data Sheet S2 in Supplementary Material).
confirmation and extension of the conclusions on the similarity Between Mononuclear Phagocyte subsets Through global and simultaneous analysis of the gene-expression Profiling of all cell Types from Mammalian species Using hierarchical clustering In order to confirm the identification of homologous mononuclear phagocytes across species as deduced from GSEA analyses, and to potentially gain more insights into the exact nature of pig and sheep *cDC2 and *Mo/MP, we next processed all the data together for global analysis by hierarchical clustering (Figure  5 ). Only the genes that showed significant variation in their expression across subsets in each species were selected Candidate sheep cell subsets were compared to one another for their relative enrichment in transcriptomic fingerprints (GeneSets) specific of human (a) or mouse (B) mononuclear phagocyte subsets, using GSEA through the Bubble GUM software.
cDC" consisted in the lists of human/mouse genes expressed in cDC to higher levels than in Mo/MP, and reciprocally (Data Sheet S2 in Supplementary Material). All possible pairwise comparisons between sheep cell subsets were performed to assess their respective expression of the transcriptomic fingerprints of human and mouse mononuclear phagocyte subsets, using the Bubble GUM software for calculations and graphical output. Results are represented as bubbles, in a color matching that of the cell subset in which the GeneSet was enriched. Stronger and more significant enrichments are represented by bigger and darker bubbles, as illustrated in the legend box of the figure. Specifically, the surface area of bubbles is proportional to the absolute value of the normalized enrichment score (NES). The color intensity of dots is indicative of the false-discovery rate (FDR) statistical value.
and the resulting datasets were normalized across species. All B cells from the four mammalian species grouped together in a specific branch of the tree, rather than each with other immune cells of the same species. This finding validates hierarchical clustering as an alternative method for identifying homologous mononuclear phagocytes across species. A closer examination of the dendrogram shows that the different cell types grouped in two major branches. The first one encompassed all the known and candidate cells of the monocytic lineages and pig *cDC2, and split further into two subgroups, one including all the identified or candidate MoDC, and the other one including all the identified or candidate Mo/MP and pig *cDC2. The second branch encompassed all the other cell types known or hypothesized not to belong to the monocytic lineage.
However, within the branch of monocytic cells, this analysis grouped Mo/MP by species of origin rather than by cMo vs. ncMo subsets. Similarly, this analysis grouped cDC by species rather than by cDC1 vs. cDC2 subsets. In brief, this analysis is focused on 1926 unique orthologous genes (i) for which a functional and specific ProbeSets was present on the microarrays for each species and (ii) which were found to be differentially expressed in each species between at least two subsets of mononuclear phagocytes. For each species and each of these 1926 genes, the expression data was then transformed to a mean = 0 and a variance = 1, in order to cross-normalize expression values to a similar dynamic range between the different datasets. For each cell type, the initials of the scientific name of the species of origin are indicated as a prefix: Hs, human; Mm, mouse; Ss, pig; and Oa, sheep.
The robustness of the tree was tested by multiscale bootstrap resampling using Pearson's correlation as distance and average linkage as cluster method, with 1000 iterations at 10 different dataset sizes comprised between 50 and 140% of the complete dataset. An AU (approximately unbiased) p-value (percentage) was calculated and placed on the nodes of the cluster dendrogram. Missing percentages correspond to 100%. The expression patterns of genes outside of the cell types of interest may mask similarity between cDC or Mo subsets, as previously reported  (15). Hence, we further evaluated the similarities between subsets of cDC on the one hand, and of Mo/ MP on the other hand, by re-analyzing their gene-expression profiles focusing only on the genes that showed significant variation in their expression across DC subsets (Figure  6 ) or Mo/MP (Figure  7 ) in each species.
Altogether, our comparative analyses of the gene-expression profiles of mononuclear phagocyte subsets across mammals FigUre  7  | completing homology assignment of pig cDc2 Dc candidate to non-classical Mo subset by unsupervised cross-species hierarchical clustering focused on Mo subsets. An unsupervised cross-species hierarchical clustering analysis was performed as described in Figure  5 , but focused only on cells from the monocyte branch of the tree obtained in Figure  5. The corresponding filtered dataset included 191 unique orthologous genes found regulated between cMo and ncMo from human (Hs), mouse (Mm), and pig (Ss). Sheep data could not be included in this analysis due to lack of data on subsets of sheep monocytes. indicated that the complex specialization of these cells into distinct subsets is conserved across mammals for both DC and Mo.
The two methodologies that we used to assess subset homologies across species, i. e. , hierarchical clustering and GSEA, display complementary functionalities. Hierarchical clustering on filtered, centered, reduced, and aggregated datasets has the advantage of integrating all samples together into a single analysis and of providing a global overview of the homologies between cell subsets of various species  (15, 17, 18, 74, 75). However, the integration of distinct datasets requires a cross-normalization procedure which consists in a rather profound mathematical transformation of the data. The normalization procedure artificially increases the variance for genes with only small differences in their initial signal intensities between the different cell types studied. Conversely, it comparatively decreases the variance for genes with high differences in their initial signal intensities between the different cell types studied.
To limit the biases that this normalization introduces, it is thus necessary to select only the orthologous genes that vary strongly in their expression across the cell types examined within each species. Another corollary is that this analysis can only be applied to genes that have known orthologs in all species. If one ortholog is missing in only one species, the gene must be removed from the analysis. Hence, this method should be used with caution, only under conditions where dataset normalization does not yield too strong biases in gene-expression profiles. It is also not appropriate when the structures of the different datasets are too different (i. e. , the number and potential identities of cell types vary too much across datasets), because the dynamic ranges of gene expression between datasets are not expected to be the same and should therefore not be forced to similarity.
Third, GSEA can perform cross-platform comparison without any cross-normalization thus without any supplementary artificial manipulation of the expression data. Finally, it can be performed on multiple datasets, even if their structures are different. However, GSEA presents the limitation of performing pairwise comparisons whose results can be integrated and visualized with our Bubble GUM software, but it nevertheless does not provide a global trans-species overview of subset homology. Overall, in order to increase confidence in the interpretation of the results, it is important to combine both approaches and verify that they both lead to consistent conclusions. Our subset assignment methodology demonstrates similarity or proximity between subsets across species but not strict identity. Besides possible intrinsic transcriptomic differences between species, one of the reasons that explain this limitation is the process of subset identification itself, which makes use of different surface markers.
Sometimes, low signal-to-noise ratio for certain ProbeSets can also be responsible for the loss of putative interesting signature genes, such as ZNF521 (Zfp521 in mouse) found to be highly specific of pDC in mouse and human while the pig and sheep orthologous ProbeSet remains at the background level whatever the cell type considered. Recent technological advances now allow performing high throughput RNA sequencing at single cell levels with high sensitivity and processivity, which could solve most of the above issues; indeed, all expressed genes should be detected without any bias and analysis at the single cell level should alleviate any issue of cross-contamination between cell types. Therefore, the generation of gene-expression data for many individual cells of the same type should increase statistical power to define genes co-expressed at the single cell level and defining cell type-specific transcriptomic modules  (22).
Finally, the interpretation of the RNA-seq data on single cells is still largely based on the transcriptomic/molecular identity of cell types that are deduced from microarray analysis of purified cell pools. Hence, our work constitutes a major advancement in the field and is a necessary step before an eventual, later, refinement of the definition of cell subsets and their associated molecular signatures using single cell RNA-seq. The canonical gene-expression signatures that we generated can be used to distinguish and identify cell subsets in other vertebrate species. The cDC1 signature and the cDC2 vs. cDC1 signatures could be evaluated in chicken cDC sorted as single cells to determine whether this population includes only cDC1, as suggested by the trans-vertebrate hierarchical clustering, or a mixture of cDC1 and cDC2.
FigUre5| confirming and completing homology assignment of sheep and pig candidate Mo/MP, pDc, and cDc by unsupervised hierarchical clustering with human and mouse cell types. The datasets of each species were filtered and cross-normalized in order to allow mixing them all together for global analysis of the relationships between sheep, pig, mouse, and human mononuclear phagocyte subsets by using unsupervised hierarchical clustering. In brief, this analysis is focused on 1926 unique orthologous genes (i) for which a functional and specific ProbeSets was present on the microarrays for each species and (ii) which were found to be differentially expressed in each species between at least two subsets of mononuclear phagocytes. For each species and each of these 1926 genes, the expression data was then transformed to a mean = 0 and a variance = 1, in order to cross-normalize expression values to a similar dynamic range between the different datasets.
For each cell type, the initials of the scientific name of the species of origin are indicated as a prefix: Hs, human; Mm, mouse; Ss, pig; and Oa, sheep. The robustness of the tree was tested by multiscale bootstrap resampling using Pearson's correlation as distance and average linkage as cluster method, with 1000 iterations at 10 different dataset sizes comprised between 50 and 140% of the complete dataset. An AU (approximately unbiased) p-value (percentage) was calculated and placed on the nodes of the cluster dendrogram. Missing percentages correspond to 100%.
FigUre6| confirming homology assignment of sheep cDc1 and cDc2 candidates by unsupervised cross-species hierarchical clustering focused on cDc subsets. An unsupervised cross-species hierarchical clustering analysis was performed as described in Figure5, but focused only on cDC subsets. The corresponding filtered dataset included 868 unique orthologous genes found regulated between cDC1 and cDC2 from human (Hs), mouse (Mm), and sheep (Oa). Pig cDC could not be included in this analysis due to the lack of data on proper pig cDC2.
FigUre8| Unsupervised cross-species hierarchical clustering including a chicken dataset demonstrates a conserved organization of vertebrate mononuclear phagocytes in the two main lineages of Mo/MP vs. cDc. An unsupervised cross-species hierarchical clustering analysis was performed as described in Figure5, but including geneexpression data from chicken (Gg prefix for Gallus gallus) and focused only on the cell types commonly sorted in all five vertebrate species, i. e. , B cells, Mo/MP, and cDC. The corresponding filtered dataset included 388 unique orthologous genes found regulated across cell subsets in each species.
Vu Manh et al. Trans-species mononuclear phagocyte subset homology Frontiers in Immunology | www. frontiersin. org Commission 7th Framework Programme (Initial Training Network FishforPharma, PITNGA-2011-289209). TPVM was supported through the PhyloGenDC ANR grant and the European Research Council (FP7/2007-2013 Grant Agreement no. 281225 to MD for the Systems Dendritic project). The Supplementary Material for this article can be found online at http://journal. frontiersin. org/article/10. 3389/fimmu. 2015. 00299 references.
acknowledgments The authors thank Chantal Kang and Michel Bonneau from the Centre d'Imagerie Interventionnelle for providing pig blood and the Unité Commune d'Expérimentation Animale du Centre de Jouyen-Josas for providing sheep blood and spleen. We thank Christelle Thibault-Carpentier from the Plate-forme Biopuces (Strasbourg, France) for performing one of the microarray experiments (http:// www-microarrays. u-strasbg. fr). This work was supported by institutional funding from Agence Nationale de la Recherche (ANR) PhyloGenDC, ANR-09-BLAN-0073-02. We thank the European Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Vu Manh et al. Trans-species mononuclear phagocyte subset homology Frontiers in Immunology | www. frontiersin. org Commission 7th Framework Programme (Initial Training Network FishforPharma, PITNGA-2011-289209). TPVM was supported through the PhyloGenDC ANR grant and the European Research Council (FP7/2007-2013 Grant Agreement no. 281225 to MD for the Systems Dendritic project). The Supplementary Material for this article can be found online at http://journal. frontiersin. org/article/10. 3389/fimmu. 2015. 00299 references supplementary Material.
Within each tower, the energy deposits in ECAL and HCAL cells are summed to define the calorimeter tower energies, subsequently used to provide the energies and directions of hadronic jets. Muons are measured in gas-ionization detectors embedded in the steel flux-return yoke outside the solenoid; drift tubes and resistive-plate chambers (RPC) provide coverage in the barrel, while cathode strip chambers (CSC) and RPC provide coverage in the endcaps. Extensive forward calorimetry complements the coverage provided by the barrel and endcap detectors. The first level (L1) of the CMS trigger system, composed of custom hardware processors, uses information from the calorimeters and muon detectors to select the most interesting events in a fixed time interval of less than 4 µs. The high-level trigger (HLT) processor farm further decreases the event rate from around 100 kHz to around 400 Hz, before data storage.
Simulated signal Monte Carlo (MC) events for this analysis are generated in three stages. In the first stage we use PYTHIA 8. 153  [21]  to generate pp → g g and pp → t t events. The colored sparticles are hadronized with the default parameters in the RHADRONS package included in PYTHIA 8. 153. These parameters influence technical aspects of the hadronization process, e. g. the fraction of produced R-hadrons that contain a gluino and a valence gluon, which is set to 10%. Because of the nature of the stopped-particle technique, these parameters do not have a significant effect on the phenomenology. The passage of the R-hadrons through the detector is simulated with GEANT4 9. 4. p03 [22]. A phase-space driven "cloud model" of R-hadron interactions with the material of the CMS detector  [23, 24] , referred to as the "generic" model in Ref.
We classify these events as to whether the halo originates from the clockwise or counterclockwise beam, and bin them by their geometric location in the endcap muon system. After integrating these distributions, we measure a halo veto inefficiency of 1 × 10 -5. This inefficiency is multiplied by the number of halo events vetoed from the search sample yielding an average halo background estimate of 8. 0 ± 0. 4 events, where the uncertainty in the estimate is owing to the limited size of the data samples used. To determine the rate at which cosmic ray muons escape detection by the cosmic muon veto, we generate a sample of 300 million simulated cosmic events using CMSCGEN  [28] , which is based on the air shower program CORSIKA  [29] , and has been validated in  [30]. After requiring a substantial energy deposit in the calorimeter and the absence of any hits in the muon endcap system that would cause the event to be classified as a halo muon, we estimate the inefficiency of the cosmic muon veto by dividing the number of events that escape this veto by the total number of events.
The inefficiency obtained in this manner is roughly 0. 5%. After multiplication by the number of cosmic ray muon events vetoed from the search sample, this corresponds to a predicted cosmic background of 5. 2 ± 2. 5 events, where the uncertainty in the estimate is owing to the limited size of the data samples used. Finally, the background owing to instrumental noise is estimated by considering data recorded in 2010. Figure  2  shows the measured noise rates in both periods. In these plots all selection criteria except those that are designed to reject noise are applied. Additionally, only events at least 5 BX from a bunch are considered. This is done to reduce halo contamination in the distributions, which is abundant directly after a bunch crossing. There is a greater variation in the 2012 noise rate because of increased halo background, which can mimic HCAL noise if no CSC hits are present.
To perform the analysis at the higher jet energy thresholds, the threshold is applied to the simulated signal to calculate the minimum energy of E g or E t and ε reco for each E thresh. We repeat the analysis of the 2010 data to estimate the instrumental noise rate at the increased threshold, and then the cosmic and beam halo rates are determined as for the analysis with the 70 GeV jet energy threshold. The resultant contributions of each background source to each The left-hand axes present expected and observed 95% CL upper limits on top squark and gluino pair production cross sections using the cloud model of R-hadron interactions, as a function of particle lifetime. The NLO+NLL cross sections shown were obtained with NLL-FAST  [37]. The right-hand axis shows the quantity σ × B × ε stopping × ε reco , which is more model independent.