dataesr/scientific-paragraphs-categorization huggingface dataset:
9. (D) Comparison of calcium responses using Indo-1 or BD PBX upon thapsigargin or anti-TCR crosslinking. 3A9 T cells were loaded with Indo-1 or BD PBX as indicated in Materials and Methods. Cells were stimulated 1 min after the start point (red arrow) with thapsigargin, or a complex of antibody against CD3-biotin/streptavidin. Data were normalized to the baseline before stimulation to allow comparison between recordings. (E) Calcium response upon thapsigargin stimulation of purified mouse CD4+ T lymphocytes. Mouse CD4+ T lymphocytes were purified from spleen and lymph nodes and overnight serum starved. Cells were loaded with BD PBX and analyzed by flow cytometry under thapsigargin stimulation (red arrow in the right panel) with (red) or without (black) cell surface labeling with an anti CD4 coupled to APC antibody (left panel).
Flow cytometry Cells were analyzed on a LSR I flow cytometer (Becton Dickinson) with Cell Quest software or LSR II (for Fura Red/BD PBX and Indo-1 AM acquisitions) using the FACSDiva software. PBX calcium indicator was observed over time on the FL1 channel with an excitation by an Argon laser 488 nm and a 530/ 30 nm emission filter at 37uC, maintained using a water bath. Data analysis was performed with FlowJo software and the median intensity of fluorescence was plotted vs. time after exclusion of dead cells and cell debris.
Acknowledgments We thank  Lionel Costa  and  Fabien Conchonaud  for initiating this project, and are indebted to the imaging (ImagImm) and cytometry CIML core facilities for their technical assistance and to  Philippe Pierre  for providing the C4H3 hybridoma. We are grateful to  Marc Baje  ´noff, Sebastien Mailfert, and  Tomasz Trombik  for their constructive comments. We thank  E. Witty  ( AngloScribe ) for editing the English.
ASa's PhD thesis was supported by fellowships from the  French ''Ministere de la Recherche' ' and from the  ARC (Association pour la Recherche contre le Cancer ). MCP was funded by  FRM (Fondation pour la recherche medicale, France ). CB was funded by the  Agence Nationale de la Recherche  ( ANR Blanc-Nanodigicode ). This work was also funded by a specific grant from the  Agence Nationale de la Recherche  ( ANR-10-BLAN-1509  and  ANR-10-INBS-04-01 France Bio Imaging ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Chromatographic conditions consisted of a cationic exchange column (Eicom CAX, 2. 0 Â 200 mm, 5 mm particle size) maintained at 40 C. The mobile phase (final pH 6. 2) consisted of sodium phosphate C 2 H 7 NO 2 (100 mM), Na 2 SO 4 (50 mM), EDTA (0. 125 mM), KCl (8 mM) and 25% methanol, and was delivered at a constant flow rate of 0. 30 mL/min. Detection was performed with a WATERS 2465 electrochemical detector equipped with a glassy carbon working electrode set at an oxidation potential of þ0. 430 V. Detector signal output is acquired and analyzed with chromatography application software (Empower Chromatography Manager, Waters). Quantification of chromatographic peaks was based on the comparison with calibration curves derived from authentic standards of NA, 5-HT and DA prepared in acidified Ringer's solution and carried through the respective analytical procedure.
Statistical analysis Binding studies were analyzed using built-in equations from the non-linear regression program Prism v4. 03 (GraphPad Software, San Diego, CA, USA). Inhibition constants (Ki) were calculated from estimates of concentrations required to inhibit binding by 50% (IC 50 ) using the built-in "Chen-Prusoff approximation": In vitro reuptake inhibition data (human) were analyzed by non-linear regression to determine an IC 50 ; concentration-response data (rat) were analyzed according to a 4-parameter sigmoid curve fit logistic model to determine an IC 50. (Prism v4. 03 software). K i ¼ IC 50 =ð1 À ð½LIGAND=K In vitro seminal vesicle assay data were analyzed by a one-way ANOVA with the concentration as the between-subjects factor, followed by a HolmeSidak post-hoc test for comparison with the appropriate control group.
Microdialysis data were analyzed using a one-way ANOVA with the treatment as the between-subjects factor, followed if appropriate by a HolmeSidak post-hoc test for comparison with the appropriate vehicle-treated group (for NE and 5-HT measures collated across t 0e240 post-injection). In addition, for the time-course analysis, NE 5-HT and DA measures were analyzed using a two-way ANOVA with the treatment as the between-subjects and time as the within-subject factors, followed if appropriate by a one-way ANOVA for post-hoc comparisons with the vehicleinjected group. For behavioral studies, data were analyzed with a one-way ANOVA with the dose as the between-subjects factor, followed by a HolmeSidak post-hoc test for comparison with the appropriate vehicle-treated group. ANOVAs were run with the Sigmastat 3.
Acknowledgments We wish to express our gratitude to  Christelle Benas ,  Joël Besnard ,  Nathalie Consul-Denjean ,  Nathalie Danty ,  Alexandra Galinier ,  Jérôme Floutard ,  Jean Laval ,  Nathalie Leduc ,  Nathalie Malfètes ,  Anne-Marie Ormière ,  Gaëlle Pulou ,  Veronique Ravailhe , for their expert technical assistance.
After 3 washes in PBS 1X, slices were incubated for 2 h at room temperature with secondary fluorophore-coupled antibodies against goat, mouse or rabbit. After 3 washes of 10 min in PBS 1X slices were mounted with Mowiol. For vascular density, sections were observed with a Leitz DMRB microscope (Leica, Wetzlar, Germany) equipped for fluorescent microscopy. Images were digitized by a 1392 × 1040 resolution cooled CCD camera (Cool Snap, Princeton Instrument, Trenton, NJ, USA) on a computer with Cool Snap software and transferred to Adobe Photoshop Elements (version 4) for image processing. For ZO-1, VEGF and VEGFR-2 immunostaining staining sections were observed using a confocal microscope (Zeiss 510 Meta, Göttingen, Germany) equipped with an x63 objective (oil, numeric opening 1. 4). We used an argon laser (excitation 488, emission 505-530 nm) for alexa 488, a helium laser (excitation 543, emission 585-615 nm) for Texas red and a krypton-argon laser (excitation 647 nm, emission 660-700 nm) for alexa 647.
Cytokine array The profile of cytokines released into the culture medium was analyzed by proteome profiler using rat cytokine array (R&D Systems, Minneapolis, MN, USA, # ARY008) according to the manufacturer's protocol. Results were analyzed by densitometry using ImageJ. For heatmap analysis, a difference between optical density of control slices and treatment conditions was calculated. Then the heatmap was generated using the MeV software (Boston, MA, USA).
Background: Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The robust identification of ChIP-enriched regions is frequently complicated by noisy measurements. This identification can be improved by accounting for dependencies between adjacent probes on chromosomes and by modeling of biological replicates. Results: MultiChIPmixHMM is a user-friendly R package to analyse ChIP-chip data modeling spatial dependencies between directly adjacent probes on a chromosome and enabling a simultaneous analysis of replicates. It is based on a linear regression mixture model, designed to perform a joint modeling of immunoprecipitated and input measurements.
Arabidopsis dataset analysis To illustrate the benefit of using MultiChIPmixHMM compared to standard ChIPmix, we use a normalized ChIP-chip data set of the model plant Arabidopsis thaliana by  [6]  to compare the identification of genomic regions marked by histone H3 tri-methylated at lysine 27 (H3K27me3). We applied both methods to analyze the two biological replicates and identified probes enriched in H3K27me3 using a stringent cutoff of 1α = 0. 99. Since ChIPmix does not handle multiple replicates, both replicates were analyzed separately and only probes declared as enriched in both replicates were finally considered as enriched (considering probes declared enriched for at least one of the replicates leads to similar results). Considering the decodings of individual probes, ChIPmix and MultiChIPmixHMM provide the same status prediction (non-enriched or enriched) for more than 90% of the probes.
Availability and requirements MultiChIPmixHMM is publicly available as an R package from CRAN  [14]. Two functions are implemented and refer to the models describe before. To distinguish between the model and the function, the first letter of the name of the function is a lower case: (i) multiChIPmixHMM for modeling spatial dependencies and multiple replicates and (ii) multiChIPmix to model multiple replicates ignoring spatial dependencies between probes. Both functions take as input a vector of filenames (one biological replicate per file), and display as output a file containing the enriched conditional probability and status of each probe.
STIMULI AND PROCEDURE The experiment was programmed using the Matlab software and the Psychtoolbox library  (Brainard, 1997) , and was run on a 64 bits dell laptop (2. 9 GHz processor). The stimuli were presented on a 15. 6 screen with a resolution of 1920 × 1080 pixels and a refresh rate of 60 Hz. The task was divided in four blocks. The participants were asked to perform a brightness-reproduction task during two blocks, and a size-reproduction task during two other blocks. The order of the four blocks followed a latin square design, leading to four possible orders. Each order was run by six of the 23 participants reported here, except one order run by 5 participants. In both the brightness and the size reproduction tasks, the design was as follows (all stimuli presented on a black background): after presentation of a fixation cross at the center of the screen for a duration of 500 ms, a first square was presented for 500 ms, also centered on the screen, and flanked with four symbols outside of each corner.
These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here.
Contrary to PCA and ICA, uBLU does not impose orthogonality or independence on the factors, as well as the GB-GMF algorithm. These relaxed assumptions might better represent what is known about the preponderance of overlap and dependency in biological pathways. Finally, uBLU naturally accommodates Bayesian estimation of the number of factors, like BFRM. Note that BFRM has been specifically developed for gene expression data  [1]. In this paper we provide comparative studies that establish quantitative performance advantages of the proposed constrained model and its corresponding uBLU algorithm with respect to PCA, NMF, BFRM and GB-GMF for timevarying gene expression analysis, using synthetic data with known ground truth. We also illustrate the application of uBLU to the analysis of a real gene expression dataset from a recent viral challenge study  [18]  in which several subjects were administered viral inoculum and gene expression time course data were collected over a period of several days.
The representation (1) is rank deficient since A has rank N -1. This presents algorithmic challenges for solving the unmixing problem. Several algorithms have been proposed in the context of hyperspectral imaging to solve similar problems  [6, 19]. Most of these algorithms perform unmixing in a two step procedure where M is estimated first using an endmember extraction algorithm (EEA) followed by a constrained linear least squares step to estimate A. A common (but restrictive) assumption in these algorithms is that some samples in the dataset are "pure" in the sense that the linear combination of (2) contains a unique factor, say m r , with factor score a r,i. Recently, this assumption has been relaxed by applying a hierarchical Bayesian approach, called Bayesian linear unmixing (BLU). The resulting algorithm requires the number R of factors to be specified (see  [13]  for details).
Simulation scenario Several synthetic datasets D 1 ,. , D 4 were generated. The experiments presented here correspond to the expression values of G = 512 genes (for datasets D 1 , D 3 and D 4 ) or G = 12000 genes (for dataset D 2 ) with N = 128 samples. Each sample is composed of exactly R = 3 factors mixed using the linear mixing model in  (1). The factors of the first dataset D 1 have been generated so that only a few genes affect each factor. For the second dataset D 2 , realistic factors have been extracted from real genetic datasets. The third dataset D 3 has been generated enforcing the factors to be orthogonal but not necessarily positive whereas in the forth dataset, D 4 , factors are orthogonal and positive. These simulation conditions are summarized in Table  1. In each case, the R = 3 factors were mixed in random proportions (factor scores), with positivity and sum-toone constraints.
The proposed uBLU algorithm, the PCA, NMF and GB-GMF methods were implemented in Matlab 7. 8. 0 (R2009a). The BFRM software (version 2. 0) was downloaded from  [22]  and implemented with default values for the parameters. All methods were implemented on an Intel(R) Core(TM)2 Duo processor. Simulation results are reported in Tables 2, 3, 4 and 5. Note that the positivity and sum-to-one constraints that are enforced on the data for the proposed uBLU algorithm avoid the scale ambiguity inherent to any factor decomposition problem. Conversely, for the other factor decomposition methods (PCA, NMF, BFRM and GB-GMF), if {M, A} is an admissible solution, {MB, B T A} is also admissible for any scaling and permutation matrix B. Hence a re-scaling is required to identify appropriate permutations before computing MSEs and GMSEs.
Moreover, when PCA, NMF, BFRM and GB-GMF methods are run for R = 4, we only considered the 3 factors yielding the 3 smallest SADs values. These results show that the uBLU method is more flexible since it provides better unmixing performance across all of the considered synthetic datasets D 1 ,. , D 4 as compared to other existing factorization methods (PCA, NMF, BFRM and GB-GMF). Moreover, uBLU has the following advantages: i) it is fully unsupervised and does not require the number of factors to be specified as a prior knowledge, ii) due to the constraints, the factors and factor scores are estimated without scale ambiguity. The disadvantage is the execution time: uBLU requires more computation due to the Gibbs sampling.
Details on data collection We briefly describe the dataset. For more details the reader is referred to  [14, 18]. H3N2 dataset consists of the gene expression levels of N = 267 Affymetrix chips collected on 17 healthy human volunteers experimentally infected with influenza A/Wisconsin/67/2005 (H3N2). A clinical symptom score was assigned to each sample by clinicians who participated in the study. Nine of the 17 subjects (those labeled Z01, Z05, Z06, Z07, Z08, Z10, Z12, Z13, and Z15 in Figure  1c ) became clinically ill during the study. These labels are only used as ground truth to quantify performance and are not available to the uBLU algorithm. The challenge consists of inoculating intranasally a dose of 10 6 TCID 50 Influenza A manufactured and processed under current good manufacturing practices (cGMP) by Baxter BioScience.
Peripheral blood microarray analysis was performed at multiple time instants corresponding to baseline (24 hours prior to inoculation with virus), then at 8 hour intervals for the initial 120 hours and then 24 hours for two further days. Each sample consisted of over G = 12000 gene expression values after standard microarray data normalization with RMA using the custom brain array cdf  [14]. No other preprocessing was applied prior to running the five unsupervised methods (uBLU, PCA, NMF, BFRM, and GB-GMF).
The different factors are depicted in Figure  1b  where the G genes have been reordered so that the dominant genes are grouped together in each factor. Factors are then ordered with respect to their maximum loading. Specifically, the k-th sharp peak in the figure occurs at the gene index that has maximal loading in factor k. Genes to the right of this dominant gene up to the (k + 1)-st peak also dominate in this k-th factor, but at a lower degree. uBLU identifies a strong factor (the first factor, in red) by virtue of its significantly larger proportion of highly dominant genes. Many of the genes in this strong factor are recognizable as immune response genes that regulate pattern recognition, interferon, and inflammation pathways in respiratory viral response. A very similar factor was found in a different analysis  [14, 18]  of this dataset and here we call it the "inflammatory component".
The dominant gene contributors to this inflammatory component correspond to well-known transcription factors controlling immune response, inflammatory response and antigen presentation -see Table  6. The reader is referred to  [14, 18]  for more details on clinical determination of symptom scores and biological significance of the inflammatory component genes. For comparison we applied a supervised version of the proposed uBLU algorithm to the H3N2 dataset. This was NCI-curated pathway associations of group of genes contributing to uBLU inflammatory component, whose factor scores are shown in Figure  1  (Source: NCI pathway interaction database http://pid. nci. nih. gov). Genes in uBLU factor are significantly better represented in the NCI-curated pathways than the genes in NMF (compare p-values here to those in Table  8 ).
This is confirmed by the evaluation of the Fisher criteria (  22 ) between these two regions (see Table  7 ). Indeed, denote by μ pos , σ 2 pos the empirical mean and variance of the scores associated with the N pos samples in the acute symptomatic state (bright colored samples in the lower right rectangle of Figure  1c ). Denote Figure  3  Factor loadings ranked by decreasing dominance for H3N2 challenge data. uBLU shows a particularly strong component (Figure  1b ), the group 1, that corresponds to the well-known inflammatory pathway. NMF and PCA algorithms also reveal an inflammatory component, but it includes fewer relevant genes than uBLU. See Figure  4  for the corresponding factor scores. Measure of the Fisher linear discriminant measure (  [23] , p. 119) between post-onset symptomatic samples and the other samples on heatmaps (Figure  4 ), reconstruction error (RE) between the observed data and the MAP estimators, computational times (for an implementation in MATLAB 7.
8. 0 (R2009a) on a 3 GHz Intel(R) Core(TM)2 Duo processor), and corresponding number of iterations. by μ pos , σ 2 pos the same parameters for the remaining samples. The Fisher linear discriminant measure (  [23] , p. 119) is defined as μ pos -μ pos 2 N pos σ 2 pos + (N -N pos )σ 2 pos. ( 22 ) To compare the biological relevance of the inflammatory genes found by uBLU to those found by the other methods we performed gene enrichment analysis (GEA). Here we only report GEA comparisons between uBLU and NMF. Tables  6  and 8  show the pathway enrichment associated with the top 200 genes found by uBLU and NMF, respectively, using NCI pathway interaction database (http://pid. nci. nih. gov). The uBLU genes are significantly better associated with the NCI-curated pathways than the NMF genes. In particular, the two most enriched pathways, IFN-gamma and PDGFR beta signaling, associated with the uBLU genes have much higher statistical significance (lower p-value) than any of the pathways associated with NMF.
Conclusions This paper proposes a new Bayesian unmixing algorithm for discovering signatures in high dimensional biological data, and specifically for gene expression microarrays. An interesting property of the proposed algorithm is that it provides positive factor loadings to ensure positivity as well as sum-to-one constraints for the factor scores. NCI-curated pathway associations of group of genes contributing to NMF inflammatory component, whose factor scores are shown in Figure  4  (Source: NCI pathway interaction database http://pid. nci. nih. gov). Genes in uBLU factor are significantly better represented in the NCI-curated pathways than the genes in NMF (compare p-values here to those in Table  6 ).
Finally, as any Bayesian algorithm, the Monte Carlo-based procedure investigated in this study provides point estimates as well as confidence intervals for the unknown parameters, contrary to many existing factor decomposition methods such as PCA or NMF. Simulation results performed on synthetic and real data demonstrated significant improvements. Indeed, when applied to real time-evolving gene expression datasets, the uBLU algorithm revealed an inflammatory factor with higher contrast between subjects who would become symptomatic from those who would remain asymptomatic (as determined by comparing to ground truth clinical labels). In this study, the time samples were modeled as independent. Future works include extensions of the proposed model to account for time dependency between samples.
Generation of samples according to ) 25 For more details on how we generate realizations from this truncated distribution, see  [13]. The five black colored pixels of the heatmaps indicate samples that were not assayed. Note that when only the sum-to-one constraint is applied, non-negativity is not guaranteed. However, for this dataset the sum-to-one factor scores turn out to take on non-negative values for the inflammatory factor (but not for the other factors).
Acknowledgements  P. de Anna  and  T. Le Borgne  acknowledge the financial support of the  European Commission  through  FP7  projects  ITN  project,  IMVUL  project (Grant Agreement  212298 ) and  Marie Curie ERG grant ReactiveFlows  (Grant Agreement Number  230947 ). A. Tartakovsky was supported by the  ASCR Office of the US Department of Energy. D. Bolster  was supported by  NSF  grant  EAR-1113704. M. Dentz  acknowledges the support of the  FP7 EU  project  PANACEA  (Grant Agreement No. 282900 ).
Such features, obtained from empirical evidence, will be used in the following to define the metapopulation model, by creating a realistic synthetic network of mobility connecting a number of urban areas. Since we are interested in exploring how non-homogeneous travel habits, coupled with non-homogeneous mixing patterns, may drive the conditions for the spatial invasion of an epidemic, we collected age travel statistics across different countries. These are typically obtained from travel surveys at airports, and collect a variety of information about passengers and their travel behavior including demographic data. Figure  1B  shows Demographic data for the age distribution of the population by country was obtained from Eurostat  [20]  for European countries, and from the U. N. database  [21]  for Mexico.