dataesr/scientific-paragraphs-categorization huggingface dataset:
Unexpectedly, this analysis revealed two outlier individuals who showed high expression levels of IL-1b-induced IFN-g (marked by red and blue dots, Figure  2A ). To establish if the observed high levels of IFN-g expression resulted in higher protein secretion, we re-analyzed our previously published protein dataset  (Duffy et al. , 2014)  generated using samples from the same donors and indeed, the two individuals showed the highest levels of IFN-g protein in the culture supernatants (Figure  2B ). The presence of recombinant protein that was used as the stimulus restricted the interpretation of potential positive feedback loops for the given protein (these data points are masked by a gray box, Figure  2B ). In addition to the induction of IFN-g by the two outlier individuals, we also observed higher expression of several IFN-g-induced genes, as compared to the other donors studied (Figures  2C-2E ).
In the case of FLA, Figure  3. TLR-Induced Gene Expression Can Be Represented as a Function of Cytokine-Induced Gene Signatures (A) Correlation circles of unit length were constructed using the 44 gene set and PCA loadings were obtained using the gene expression dataset from the four cytokine stimuli (as defined in Table  1 ). The vectors for TLR-induced gene expression signatures were generated from the median value for the 25 donors, projected onto the correlation circles across the four PC. we suggest this may be occurring due to the engagement of the intracellular sensor NLRC4, in turn activating caspase-1  (Gay et al. , 2014) ; however, the mechanisms underlying FSL activation of the inflammasome also remains uncharacterized. Notably, these analyses also identified the two outlier individuals discussed above, who showed high expression levels of FLAinduced IFN-g (blue and red dots, Figure  3B ).
Next, we performed unsupervised PCA on the TLR-stimulated gene expression data using the entire 572-gene set (Figure  4A ). The first two PCs, capturing 44% of the total variance, segregated all TLR stimuli with the exception of FLA and FSL (shown to have similar gene expression patterns), and to a lesser extent LPS and R848. The clustering achieved with the entire dataset was then compared to a PCA plot built using the 44-gene signature, selected for the four effector cytokines (Table  1 ). Strikingly, the vectors built from the cytokine-gene set fully captured the diversity of responses among the TLR stimuli (Figure  4B ). Moreover, the cytokine-optimized gene set provided improved definition of the clusters, as indicated by a higher silhouette scores (Figure  4C ). This is most evident for the improved discrimination of LPS from R848 (Figure  4B , see PC2; and an increase in the median silhouette score from 0.
Inter-individual Variable Gene Expression Supports Tracing of Cytokine Loops We next extended the concept of correlation among the stimulation conditions to shed light onto possible cytokine loops (A and B) Whole-blood stimulation was performed on 25 healthy donors using TruCulture systems pre-loaded with FSL (maroon), pIC (green), LPS (light blue), FLA (dark blue), GARD (orange), R848 (brown), and ODN (pink). Principle component analysis (PCA) was used to project mRNA expression data from 572 genes (A), PC1 versus PC2 (the percentage of variance captured by each PC is indicated). A parallel PCA was constructed using the mRNA expression data from the filtered set of 44 cytokine-induced genes (from gene lists reported in Table  1 ) (B), PC1 versus PC2 (the percentage of variance captured by each PC is indicated).
(C) Silhouette scores were determined for each sample based on k means clustering (k = 7). Samples are plotted according to TLR stimulus. The red-line indicates a silhouette score of 0. 2 (considered a strong fit). The median silhouette score for 572-gene set was 0. 19; and for the 44-gene set it was 0. 45. (D and E) Whole-blood stimulation was also preformed using HKHP (gray), HKLR (brown), HKSA (blue), HKEC (purple), HKCA (gray-green), BCG (orange), IAV (yellow), and SeV (red). PCA was used to project mRNA expression data from 572 genes (C); and the parallel PCA was constructed using the mRNA expression data from 44 genes. (F) Silhouette scores were determined for each sample based on k means clustering (k = 8). Samples are plotted according to microbial stimulus. Note that IAV and SeV were mixed among two clusters (not depicted); and two samples were misclustered using the 572-gene set versus five samples misclustered using the 44-gene set (not depicted).
These solutions are essential for multicenter population-based studies, as well as for assays with ambitions for clinical deployment. Using the reference data presented herein, we tested the hypothesis that responses to TLR ligands or whole microbes can be captured by the transcriptional signature of key effector cytokines. We tested a total of 23 stimulation systems, all built into whole-blood syringes for point of care sampling. Using linear SVM learning, it was possible to identify a 44-gene set, selected based on their ability to differentially cluster cytokine-induced genes. Strikingly, these same genes, when applied to the stratification of responses to TLR ligands or microbes, resulted in improved discrimination among the stimuli as indicated by a marked improvement in silhouette scores.
If extended to the study of disease states, it may be possible to classify, for example, subsets of rheumatoid arthritis patients that are responsive to IL-1b versus TNF-a blockade  (Gibbons and Hyrich, 2009; McInnes and Schett, 2007). This reference dataset and the applied analytical approach offers a useful resource to the community, nevertheless, several specific limitations should be highlighted. First, some of the employed TLR stimuli may engage secondary pathways in addition to their commonly ascribed receptors. Notably, the observation that FLA is highly correlated with the IL-1b-induced gene signature suggests that it may also trigger NLRC4 within the wholeblood stimulation systems. This may occur within neutrophils, which express high levels of the NLRC4 inflammasome and release IL-1b  (Chen et al.
, 2014). If correct, it would also help to explain why, despite the high prevalence of dominant-negative forms of TLR5 in Europeans  (Barreiro et al. , 2009; Hawn et al. , 2003) , all 25 donors showed an induced response after FLA stimulation  (Barreiro et al. , 2009). Alternatively, TLR sensor pathways on platelets and neutrophils may be unique in their ability to engage caspase-1  (Hayashi et al. , 2003). We also observed that IAV and SeV were highly correlated with pIC, suggesting that the latter is engaging RIG-I like receptors (RLRs) in addition to TLR3. We also acknowledge that, in the natural setting, human immune responses typically occur in mucosal tissues and, as such, stromal cells and tissue resident immune populations such as macrophages and ILCs may need to be considered to fully apply our dataset to physiologic and pathologic responses.
Lastly, our analyses consider a single analytical time point only, thus capturing a snapshot of the complexity inherent in dynamic immune responses. Finally, it is our aim with this resource paper to highlight the growing need to make data more accessible and easier to explore. In line with recent efforts  (Gorenshteyn et al. , 2015; Speake et al. , 2015) , we have thus developed an online R-Shiny application software that will allow readers to fully query the dataset based on their specific questions. This application software was built as a direct companion to the presented analyses with publically available R-scripts and downloading options for gene expression data. In sum, the data resource presented here and the available online tools provide a foundation for association studies, kinetic analyses, and in vivo mechanistic experimentation.
Donors Samples were obtained as part of the Milieur Interieur Healthy Donor Cohort (https://www. clinicaltrials. gov/; NCT01699893). The study protocol was designed and conducted in accordance with the ethical principles of the Declaration of Helsinki and Good Clinical Practices as outlined in the ICH Guideline for Good Clinical Practices. The data were collected under pseudo-anonymized conditions: the identity of the subject is coded in a way that does not allow third-party persons to detect the identity of the person. All subjects (12 male, 13 female) aged 30-39 years old, gave informed consent and were considered as healthy based on medical history, clinical examination, laboratory results, and electrocardiography (ECG). More specific details on criteria to define healthy can be found in previously published work  (Thomas et al.
High-Throughput Standardized RNA Extraction Samples were randomized and extracted in groups of 95. Cell pellets in Trizol LS were thawed on ice 60 min prior to processing. To complete thawing and RNA release, tubes were vortexed twice for 5 min at 2,000 rpm. Before processing, a centrifugation (3,000 3 g for 5 min at 4 C) of the thawed samples was performed to pellet the cellular debris generated during the Trizol lysis. The barcoded tubes were loaded in the rack module of the Freedom EVO platform (TECAN) and scanned for sample traceability. For extraction, a modified protocol of the NucleoSpin 96 RNA tissue kit (Macherey-Nagel) was developed and adapted to the Freedom EVO integrated vacuum system. The detailed script for the operation of the TECAN system is provided online (http://www. milieuinterieur.
Selection Criteria for Gene Expression Analysis NanoString nCounter, a hybridization-based multiplex assay, was selected after comparison with multiple gene expression technologies (microarray, qPCR-based methods) (Table  S1 ). All assays were performed at the genomic platform (Institut Curie), with the exception of the cross platform control comparison performed at Institut Pasteur, Paris. The Human Immunology v2 gene code set was selected as it covers 25 immunology-related gene networks as illustrated by the use of KEGG charts (Figure  S2 ). The code set contains a total of 594 probes (15 correspond to housekeeping genes), of which 572 probes were included in downstream analysis after removing probes mapping to multiple genes and probes aligning to polymorphic regions with greater than two SNPs (Table  S2 ).
To this end, the probes were mapped against the human genomic sequence (GRCh37/hg19) with GSNAP  (Wu and Nacu, 2010) , a splice-aware aligner. A total of 573 out of 594 probes were mapped with 100% identity to the genome. Twelve probes mapped with one to two mismatches in the middle of the sequence, eight probes were misaligned in the first/last 1-9 bp, and one probe did not map at all (PECAM1 located on HG183_PATCH). The misaligned probes were realigned manually using BLASTN against Ab-initio cDNAs database. Of the 594 probes, 15 mapped to more than one genomic location (see Table  S2 ). We removed from further analysis KIR_Activating_Subgroup_1 probe, which mapped to three different genomic locations, as well as three other KIR probes that mapped to multiple locations: KIR_Activating_Subgroup_2, KIR_Inhibiting_Subgroup_1, and KIR_Inhibiting_Subgroup_2.
Gene Expression Analysis Total mRNA were diluted with RNase-free water at 20 ng/ml in the 12-strip provided by NanoString. We analyzed 100 ng (5 ml) of total RNA from each sample using the Human Immunology kit v2 according to manufacturer's instructions. Each sample was analyzed in a separate multiplexed reaction including in each, eight negative probes and six serial concentrations of positive control probes. Negative control analysis was performed to determine the background for each sample. Of note, we observed variable expression of two negative control probes (NEG B, NEG F), which cross-reacted with bacterial nucleic acid present in two of the TruCulture systems (HKSA and BCG, respectively, Figures  S1D  and S1E ), and thus these probes were not used for data normalization. Data was imported into nSolver analysis software (version 2.
These genes were selected using geNorm method  (Vandesompele et al. , 2002) , an established approach for identification of stable housekeeping genes, from the 15 candidate genes provided by NanoString. Statistical Analysis, Data Visualization, and Software Principal component analysis (PCA) or singular value decomposition (SVD) was used to decompose the data matrix in a way that is amenable for dimension reduction  (Alter et al. , 2000). The decomposition was used to orthogonally project both the rows and the columns of the data matrix into lower dimensional space in an optimal way-optimal signifying the retention of as much of the original variance in the dataset as possible. For a comprehensive overview of PCA and the exploratory analysis using dual PCA and the accompanying PCA biplots, we refer to  Fontes (2012).
The respective 2D PCA projection plots were made with the R package ''FactoMineR'' (version 1. 28) to compute PCA scores and projected coordinates. Silhouette analysis was used to study the separation distance among the TLR and microbial stimuli. K means clustering was performed using the Open CV library  (Bradski and Kaehler, 2008) ; with settings equal to 100 iterations and 500 attempts and the silhouette scores were computed  (Bradski and Kaehler, 2008; Steinhaus, 1956). Cluster number was selected based on the number of stimuli represented in the PCA (k = 7 for TLR, k = 8 for microbes). Note, silhouette coefficients near +1 indicate that the sample is far away from the neighboring clusters; a value of 0 indicates that the sample is on or very close to the decision boundary between two neighboring clusters, and negative values indicate that those samples might have been assigned to the wrong cluster.
Bootstrapped hierarchical clustering analysis was performed using the ''pvclust'' R package (version 1. 3-2) using a Spearman-based dissimilarity metric. One thousand trees were sampled to evaluate the robustness of each cluster. Correlation matrices were plotted using the R graphics package ggplot2 (version 1. 0. 0). Plots were exported from the Qlucore Omics Explorer 3. 1 or created using the ggplot2 package (version 1. 0. 0) on the R platform (version 3. 1. 1). Stimulus signatures, consisting of gene lists specific for each of the four cytokines, were created by training a support vector machine (SVM) for each individual stimulus versus null. This approach was used to define stimulus signatures set by a discrete number of variables. In order to discover reasonably complex gene interaction networks among the four stimuli, SVMs were optimized from 12-57 gene subsets ($2%-10% of the total gene number).
For all four cytokine stimuli, the optimal classifier determined by the SVM cross validation scheme corresponded to the smallest gene set size. The identified gene lists had perfect accuracy upon ten repeated complete SVM test runs. This shows that a small number of selected variables can predict the specific stimulus used. The small overlap between the established stimulus signatures indicates that the selection strikes a reasonable balance between capturing the complexity in the data and at the same time identifying those important individual genes. The open source C++ software library OpenCV was used to build and evaluate the SVMs  (Burges, 1998; Chang and Lin, 2011). For comparison, a kNN classifier was also tested, using the implementation in the OpenCV library with default parameter settings, which gave exactly the same stimulus signatures.
ACKNOWLEDGMENTS This work benefited from support of the  French government's Invest in the Future program  managed by the  Agence Nationale de la Recherche  ( ANR , reference  10-LABX-69-01 ). We thank  Stephanie Thomas  for management of the  Milieu Interieur Consortium , and  Haiyin Chen ,  Sebastian Amigorena , and  Shannon Turley  for critical review of the manuscript.
We found that the latter set-up offered a higher speed (a few microseconds for one pixel compared to a few ms with the first configuration) for both the data writing and reading  [24]. For ODS applications, efficient SHG is required as well as a significant SHG contrast between the "0" and the "1" bits, which correspond to the two different molecular states. We previously determined  [23]  the absolute values of the second order NLO susceptibilities, before and after the dimerization process, for three coumarin-based polymers (P8, P9, P11). These values were obtained by the Maker fringes technique using picosecond laser pulses. However the low repetition rate of a picosecond laser system cannot be adapted for data storage purposes. We therefore used a Ti:Sapphire laser source (Tsunami, Spectra-Physics, Santa Clara, CA, USA) with 120 fs pulse duration and a repetition rate of 80 MHz for our developed ODS setup (see Experimental Section).
25). The lateral resolution is about 1 μm for the illuminated area. The transmitted NLO signal (SHG) is filtered with IR blocking filters (NT48-071, Edmund Optics Ltd. , York, UK, and BG39, Schott, Mainz, Germany) and detected by a fast photomultiplier (H744P-40, Hamamatsu) coupled with a photon counter (C9744, Hamamatsu, Hirakuchi, Japan). The samples (thin films deposited on glass microscope slides) are placed on X, Y, Z, motorized stages (Newport, Irvine, CA, USA) and driven by a high performance 8-axes motion controller (XPS, Newport) and computer controlled in order to keep always the focal point in the plane of the sample  [24]. We have developed a Labview™ program (National Instrument, Austin, TX, USA) to control the micro-positioning of the sample, the light polarization, the electric signals for the scanner and the photon counter in order to store the signal for each position of the scanning laser.
25). The lateral resolution is about 1 µm for the illuminated area. The transmitted NLO signal (SHG) is filtered with IR blocking filters (NT48-071, Edmund Optics Ltd. , York, UK, and BG39, Schott, Mainz, Germany) and detected by a fast photomultiplier (H744P-40, Hamamatsu) coupled with a photon counter (C9744, Hamamatsu, Hirakuchi, Japan). The samples (thin films deposited on glass microscope slides) are placed on X, Y, Z, motorized stages (Newport, Irvine, CA, USA) and driven by a high performance 8-axes motion controller (XPS, Newport) and computer controlled in order to keep always the focal point in the plane of the sample  [24]. We have developed a Labview™ program (National Instrument, Austin, TX, USA) to control the micro-positioning of the sample, the light polarization, the electric signals for the scanner and the photon counter in order to store the signal for each position of the scanning laser beam on the sample and to produce an SHG image, pixel after pixel, at the maximum frequency of 1 MHz.
Acknowledgments: This work was supported by the  Région des Pays de la Loire  (Programs MoltechAnjou 2009 and Movamol 2011) and by  OSEO  and " Fonds Pays de la Loire Territoires d'Innovation" (Program Stovomol 2013 ). Acknowledgments: This work was supported by the  Région des Pays de la Loire  (Programs MoltechAnjou 2009 and Movamol 2011) and by  OSEO  and " Fonds Pays de la Loire Territoires d'Innovation" (Program Stovomol 2013 ).
In the present study, experiments were carried out in a single-cylinder downsized SI engine with different rates of oxygen (15% to 27% by volume in the total mixture of intake gases except fuel) and equivalence ratios (from 0. 45 to 1). Therefore, the oxygen volume fraction is due to oxygen enrichment or nitrogen dilution. The study of the impact of controlling the oxygen concentration on the combustion characteristics and emissions was performed at 1 400 rpm, at several loads (Indicated Mean Effective Pressure (IMEP) from 400 to 1 000 kPa). For each operation point, the spark advance and the intake pressure were adjusted simultaneously in order to maintain the load and obtain a minimum value of the indicated Specific Fuel Consumption (SFC). The effect of the oxygen concentration on the engine combustion characteristics was simulated by using the commercial software AMESim, with the combustion model developed by IFP Energies nouvelles, and an adapted algorithm was used to avoid residual gas calibration.
Then, the air is recomposed with oxygen enrichment or nitrogen dilution for engine applications  [13]. The first purpose of this paper is to investigate experimentally the impact of controlling the oxygen concentration on engine performance and pollutant emissions, and to give a global overview of the optimized engine operating conditions with the lowest Specific Fuel Consumption (SFC) and exhaust gas emissions. In order to provide more details about the specificity of combustion processes with oxygen control, such as turbulent flame velocity evolution during the engine cycle or flame wrinkling, the AMESim commercial software with the combustion model developed by Richard et al. [14]  was used. The AMESim software provides the following advantages: first, it enables the various physical domains involved in vehicle and engine system simulation to be addressed with a high level of detail  [15].
EXPERIMENTAL SETUP 1. Engine Characteristics Experiments were carried out in a single-cylinder SI engine (PSA EP6) characterized by a four-valve pent-roof chamber with a displacement volume (V cyl ) of 399. 5 cm 3 and a compression ratio of 10. 5. The bore, stroke and connecting rod lengths were 77 mm, 85. 8 mm and 138. 5 mm, respectively. The engine was driven by an electric motor at a fixed engine speed, and equipped with an optical encoder mounted on the main shaft, giving a 0. 1 Crank Angle Degree (CAD) as resolution. A conventional spark plug with an electrode space of 1 mm was used. A timer card ensured synchronization of the various trigger signals and data acquisition systems. The engine sucks in the air through a thermal mass flowmeter and all other flows were evaluated from this reference flow.
Cylinder pressure was recorded with a water-cooled AVL quartz pressure transducer connected to a charge amplifier at 0. 1 CAD resolutions. The linearity (<±0. 6%) of the transducer was verified starting from a maximum pressure of 10 MPa (> maximum pressure in the engine). In this work, the absolute cylinder pressure was deduced by equalizing the in-cylinder pressure at 20 CAD after inlet valve opening timing to the intake mean absolute pressure. Thus, 100 consecutive cycles of cylinder pressure data were acquired by a PC equipped with a National Instruments acquisition board. The exhaust emissions of gases (NO x , HC, CH 4 , CO, CO 2 and O 2 ) were measured by a classical emission analyzer (from Environment S. A. ) with an accuracy <2% of the measured value and 1% of full scale.
Calibration Process The calibrations were accomplished by coupling AMESim with a Matlab program, to input experimental results in the requisite format. Figure  4  illustrates the simulation process for all the engine operating points. First, experimental data (such as intake pressure, intake temperature, exhaust pressure, exhaust gas temperature, equivalence ratio, advanced ignition crank angle and in-cylinder pressure) are read by a Matlab program. In this program, Matlab-AMESim coupling was used to optimize the in-cylinder data. The second part of this algorithm focuses on two-zone AMESim model simulation with oxygen control. In this part, the AMESim development tool, AMESet, was applied to induce laminar burning velocity as a sub-routine inside the main combustion model. In addition, the main two-zone combustion model was modified to adapt it to the oxygen control.