dataesr/scientific-paragraphs-categorization huggingface dataset:
494 0. 0950 31 Hit compound 3. 1 C21H24ClN3O2 2. 667 0. 0693 31 Hit compound 3. 2 C20H29N3O2 2. 518 0. 0933 31 Hit compound 3. 3 C30H35N3O2 2. 556 0. 0894 31 Hit compound 3. 4 C25H32N4O3 2. 656 0. 0717 31 Hit compound 3. 5 C20H23N3O2 2. 708 0. 0587 31 Hit compound 3. 6 C25H33N3O2 2. 540 0. 09913 31 Hit compound 3. 7 C22H27N3O3 2. 691 0. 0633 31 Hit compound 3. 18 C26H37N3O2 2. 471 0. 0471 31 Hit compound 3. 48 C34H43N3O5 2. 635 0. 0763 31 Hit compound 3. 105 C34H40N6O2 2. 658 0. 0712 31 NSC 62914 C31H39O3 2. 480 0. 0957 32 ). *Experimental drug applied for treatment of Ebola patients in Liberia (http://www. ox. ac. uk/ news/2014-11-13-oxford-lead-trial-experimental-drug-ebola-patients) Dataset 3. Experimental drugs selected as candidate for treatment of EVD http://dx. doi. org/10. 5256/f1000research.
To examine whether Fft3 and Sfc4 also interact in vivo, we constructed a double-tagged strain (Fft3-TAP / Sfc4-Myc) and performed a co-immunoprecipitation assay. We observed an enrichment of Sfc4-Myc in the anti-TAP purified material from the double-tagged strain compared to the single-tagged strain (Fig. 6E ), confirming that Fft3 and Sfc4 physically interact in vivo. Taken together, our data indicate that the Fft3 chromatin remodeler directly interacts with the TFIIIC transcription complex at Pol III transcribed loci. [16]. (C) The Fft3 ATPase mutant (fft3-K418R) is expressed at similar levels as the wild type protein (Fft3-myc). Whole-cell lysate was immunoblotted with anti-myc antibodies. A non-tagged wild type strain was included as a control for the specificity of the antibody and actin blot confirms equal loading.
4). Imaging was set up fulfilling the Nyquist criterion in xy and z, with a minimum zoom of 2. For each cell, z-stacks were acquired with 0. 2μm spacing and subjected to a blind 3D deconvolution algorithm using the NIS Elements Advanced Research software version 4. 12. After decapping (removing the top three and bottom three z-stacks) of each nucleus, distances from the center of each FISH spot to the nuclear periphery were measured. Measurements were then assigned to three concentric equal volume zones  [56]. Significance tests were carried out using the Chi-square test.
Deconvolution of each stack was carried out using a 3D non-blind algorithm in NIS Elements (Nikon, High Content Analysis software version 4. 20). After decapping (removing the top three and bottom three zstacks of the nuclear volume) of each nucleus, distances from the center of each mCherry spot to the nuclear periphery were measured in the z-plane with the brightest signal. The radius of each nucleus was measured and used to normalize the distance measurement. Normalized distances were then assigned to one of three concentric equal volume zone  [56]. Significance tests were carried out using a two-sided Chi-square test.
Micrococcal nuclease digestion and sequencing Mononucleosomal DNA fragments were digested and purified as described in  [57]. Fragments were then amplified and labeled with the NEBNext ChIP-Seq Library Prep Master Mix Set for Illumina (NEB #E6240) followed by sequencing on an Illumina Miseq v. 3. Paired-end reads were mapped to the S. pombe genome using Bowtie2  [58]  with standard parameters. DANPOS  [59]  was then used to remove clonal reads, normalize by quantile normalization and calculate nucleosome occupancy. All calculations and visualization were carried out using R. All experiments were done as biological duplicates. MNAse-seq data can be accessed at NCBI GEO under the accession number GSE58013.
Yeast-2-hybrid assay The yeast-2-hybrid screen and data analysis were performed by Hybrigenics, Paris, France (www. hybrigenics. com). The full-length open-reading frame of the fft3 gene (SPAC25A8. 01c) was cloned into the pB27 vector as a C-terminal fusion to LexA. The construct was use as bait to screen a S. pombe cDNA library. 62. 4 million interactions were tested. Co-immunoprecipitation 50 ml of log phase growing cells were pelleted and resuspended in 500μl lysis buffer (20 mM Tris pH7. 5, 150mM NaCl, 3% glycerol, 0. 05% Igepal, 0. 5mM EDTA, 0. 5 mM DTT and protease inhibitors) and lysed in a FastPrep machine (5x30sec at 6. 5) with 1 volume of glass beads. The extract was centrifuged for 20 min, 14,000 rpm at 4°C, and the supernatant was incubated with 60μl 50% IgG-bead slurry for 40 min at 4°C.
Northern blots 2. 5ug of total RNA was electrophoresed in 10% polyacrylamide, 8M urea, 0. 5x TBE gel and blotted onto Hybond Nx membrane (GE Healthcare) in 0. 3x TBE using a semi dry blotter, followed by UV crosslinking. Oligonucleotide probes were 32 P labelled using T4 PNK (USB Affymetrix) and hybridized in 5x Denhardt's solution, 6xSSC, 10mM EDTA, 0. 5% SDS, 0. 1mg/ml salmon sperm DNA (Invitrogen) at respective temperatures for 16 hours. The blots were washed 3 times in 2xSSC 0. 1% SDS and exposed to Phosphorimager screens (Fuji). Screens were scanned using Molecular Imager FX (BioRad). Band intensities were quantified using the Quantity One software (BioRad). Probe sequences are listed in S2 Table.
Data analysis All analysis was performed in R (http://www. r-project. org) using the Bioconductor (http:// www. bioconductor. org) packages "affy", "affxparser" and "preprocessCore" with standard parameters. CEL-files were imported and normalized as described in  [7]. For visualization, all probes were used, while only probes with one match in the genome were used for calculations and significance tests. To determine probe distributions for the subtelomeres, probes mapping to the following regions were used: 1-98950 bp and 5496300-5579133 bp on chromosome 1, 1-96400 bp and 4437300-4539804 bp on chromosome 2. For probe distributions across tRNA genes, probes were used which overlapped with sequence features marked "tRNA" in the GFF-annotation file downloaded from the Pombase website (ftp://ftp.
sanger. ac. uk/pub/yeast/pombe/GFF/ pombe_09052011. gff). For averaging of microarray scores across different genomic features, the "feature" column was used for all items with the following exceptions: features matching "LTRTF2", "Tf2" (together with the feature category "protein_conding_gene"), "origin_of_replication" and "external_name = intron" were annotated as LTRs, Tf2s, replication origins and introns, respectively. Microarray data published elsewhere was used as processed data when available or otherwise processed as described above. Boxplot were created in R using the "boxplot" function with standard parameters. Significance tests between data subsets (subtelomeric and tRNA probes vs. all other probes) were performed using a circular permutation as described in  [7]. Microarray data can be accessed at NCBI GEO under the accession number GSE58013.
Methods We employed the polyfunctionality index on two datasets selected for their unique ability to evaluate the polyfunctional imprint on T cell efficacy. 1) HIV-specific CD8 + T cells and 2) Leishmania major-specific CD4 + T cells were analysed for their capacity to secrete multiple effector molecules, kill target cells and control infection. Briefly, employing the Polyfunctionality Index algorithm we determined the parameter estimates resulting in optimal correlation between T cell polyfunctionality and T cell efficacy.
It is custom to analyse the effect of T cells at different levels, 1) pathogen clearance and clinical recovery, 2) target killing, cellular help and recruitment of innate immune cells and 3) effector molecules expressed by T cells. Whereas T cell efficacy is typically evaluated extrinsically (level 1 and 2), their functionality is more often analysed intrinsically (level 3). Indeed, T cell functionality assays have the advantage of being applicable to large cohorts as well as many cell types and subsets in a standardized manner, with readouts that can be highly multiparametric. Here, we focus on how to associate or even predict extrinsic T cell efficacy from intrinsic T cell functionality. Using highly multiparametric datasets of T cell polyfunctionality we also propose a widely applicable analytical strategy, which objectively identifies the importance of individual and combinatorial effector functions.
Multiparametric cell analysis generates very large combinatorial datasets. For instance, 3 bimodial parameters divide cells into 2 3 = 8 socalled boolean combination subsets. In the case of T cell polyfunctionality, the complexity of such boolean datasets has been reduced to enable associations with clinical and biological parameters. Two main approaches have been applied in attempt to reduce complexity of T cell polyfunctionality, 1) polyfunctionality profiles characterized by the frequency of cells positive for 0, 1, 2, until n simultaneous parameters presented as pie diagrams (n+1 dimensions) and 2) quantitative polyfunctionality characterized by the frequency of cells that produce a defined number of functions (Fig  1A ). Polyfunctionality profiles have been instrumental for our understanding of cellular functionality, but remain difficult to interpret with appropriate statistical inference.
Selection of T cell polyfunctionality data Contrary to the analytical approaches so-far utilized for T cell polyfunctionality analysis, the polyfunctionality index provides several components enabling an objective and quantitative assessment of the degree to which T cell efficacy is associated with T cell polyfunctionality. Moreover, we can robustly grade the impact of individual functional parameters. In two of our previous studies we showed association between T cell polyfunctionality and T cell mediated target killing  [9]  as well as vaccine induced control of Leishmania major infection  [10]. We therefore selected these two datasets to more thoroughly understand the contribution of individual functional parameters with respect to T cell efficacy.
Polyfunctional CD8 + T cell responses towards HIV-1 One experimental dataset was composed of T cell polyfunctionality and target killing capacity of HIV-specific CD8 + T cell clones analysed as previously described. [9]  Briefly, T cell clones from 3 HLA B Ã 2705 HIV-1 seropositive patients were stimulated for 6 hours with serial dilutions (10 -6 -10 -12 M) of cognate peptide (p24 Gag KK10; residues 263-272) and analysed on a BD LSRII apparatus (BD Biosciences) for intracellular expression of IFN-γ, TNF-α, IL-2 and MIP-1β as well as surface displayed marker of recent degranulation, CD107a.
Polyfunctional CD4 + T cell responses against Leishmania major A second experimental dataset was composed of polyfunctionality profiles of Leishmania major-specific CD4 + T cells from the spleen of mice vaccinated against Leishmania major. These data were associated with vaccine induced protective capacity measured as the lesion size post infectious challenge as previously described. [10]  Briefly, mice were vaccinated with a recombinant leishmanial polyprotein (MML) either expressed by a replication-deficient adenovirus or as protein with CpG as adjuvant. Vaccination with adenovirus was delivered once either intramuscular (leg) or subcutaneous (foot). Protein vaccination was administrated three times in intervals of two weeks. A group of mice were vaccinated with sub-lethal doses of live Leishmania major.
Flow cytometry data analysis Data analysis was accomplished with FlowJo (TreeStar Inc) software. Polyfunctionality analysis was performed using Pestle and Spice softwares (Mario Roederer, ImmunoTechnology Section, VRC/NIAID/NIH)  [14]  as well as FunkyCells Boolean Dataminer software (www. FunkyCells. com). Primary polyfunctionality data for the two datasets analysed in the present manuscript can be found in S1 and S2 Tables. Human blood samples were obtained following acquisition of the study participants' written informed consent and the study protocol was reviewed and approved by local ethics committee of Pitié-Salpêtrière Hospital, Paris. This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of The National Institutes of Health.
Modelling T cell efficacy using the special polyfunctionality index For the experiments assessing the polyfunctionality of HIV-specific CD8 + T cells,  [9]  we employed non-linear least-squares regression with the dependent variable being percent killing and the independent variable being the resulting series expansion of the special polyfunctionality index  (7) , where F i was determined using FunkyCells software (www. FunkyCells. com) and i and n were constants. Both the slope (β 1 ) in relation to the polyfunctionality index and constant (β 0 ) parameter estimates were obtained for this model. The parameter q was also directly calculated from this model. A total of four separate experiments were conducted to assess the polyfunctionality of vaccine induced CD4 + T cell responses towards Leishmania major.
FunkyCells. com) in conjunction with the Generalized Reduced Gradient non-linear iterative computation included in the Frontline Systems Solver add-in for Microsoft Excel. Graphical representation of r 2 as a function of q can be accomplished using FunkyCells Track Function software (www. FunkyCells. com). For both these methods, robustness of q was evaluated via bootstrapping by randomly selecting with replacement 90% of the original data (overall for experiments involving HIV-specific CD8 + T cell polyfunctionality and within experiments involving Leishmania major-specific CD4 + T cell polyfunctionality) and re-running the q computation above. 95% two-sided confidence intervals (CIs) were then constructed using the non-studentized pivotal method.
Using co-linearity to assess the redundancy of HIV-specific CD8 + T cell effector molecules We first wanted to examine the probability distribution of polyfunctionality for T cells secreting a specific effector molecule. Using FunkyCells software (www. FunkyCells. com), mean frequencies of each boolean combination were calculated. These frequencies can be viewed as the conditional probability of cells secreting i number of effector molecules given that they secrete effector molecule j. In the regression analysis, we viewed the redundancy of certain effector molecules as a colinearity problem. In order to assess this, we used linear regression to model the frequency of cells expressing one specific effector molecule against the frequency of all other effector molecules with intercept. This resulted in the correlation coefficient, b R 2 j , which was then used to calculate the centered variance inflation factor (VIF) for chemokine j: VIF j ¼ 1 1 À b R 2 j High VIF (>5) represents strong co-linearity with at least one other effector molecule and suggests that the specific cytokine can be removed from the model.
Control of Leishmania major infection is associated with T cell polyfunctionality Whereas the above dataset is based on robust and elaborate in vitro analysis of human T cell functionality, we wanted to investigate whether quantitative assessment of T cell polyfunctionality represents a marker of in vivo T cell efficacy. We previously reported that polyfunctionality of vaccine induced Leishmania major-specific CD4 + T cells were strongly correlated with the protective capacity against an infectious challenge with live Leishmania major. [10]  Indeed, the size of the skin lesion induced by the Leishmania major infection was inversely correlated with the frequency of antigen-specific CD4 + T cells producing IFN-γ, TNF-α and IL-2, simultaneously. Employing the same methodology as outlined for the in vitro generated dataset on T cell killing above, we used the polyfunctionality index to further understand the relationship between T cell polyfunctionality and protection against Leishmania major infection.
However, the variability of this dataset, did not allow the polyfunctionality index parameters to converge. We performed a power analysis using fixed parameters from previous models (S2 Fig) , demonstrating that 75 groups of mice would be needed to achieve 80% power in order to determine a significant difference for a true φ i = 1. The inherent data variability is most likely due to the in vivo nature of the mouse experiments. A larger study is required to reduce some of this variability, thus allowing full evaluation on whether certain effector molecules contribute more to the predictive capacity of polyfunctionality with regards to vaccine protection.
Information redundancy between effector molecules From our previous studies it is clear that effector molecules expressed as a result of cellular stimulation, such as antigen-specific stimulation, appear in quantities ranked according to effector molecules but increasing with the intensity of stimulation in a synchronous manner. [9]  Cellular pathways activated by antigen-specific stimulation and leading to expression of effector molecules have been shown to include a significant amount of redundancy. [16]  We therefore wanted to evaluate if the effector molecules measured in our experimental dataset would be redundant in a polyfunctional setting. We first analysed the probability of expressing 1, 2, until n simultaneous effector molecules for cells expressing a given effector molecule. This analysis was performed for all effector molecules within our datasets.
For the dataset charting polyfunctionality of HIV-specific CD8 + T cell clones; CD107a, IFN-γ and TNF-α have very similar probability distributions, secreting equally zero, one or two other effector molecules. On the contrary, MIP-1β was mostly secreted alone or with another effector function, while IL-2 was almost always secreted by itself (Fig  4A ). This analysis suggests that there might be redundancy between CD107a, IFN-γ and TNF-α. Consequently, we analysed the variance inflation factors (VIF) for each effector  molecule based on the modelling of target killing (Table  3  and Fig 4B ). This analysis (model 1) showed that CD107a presents substantial co-linearity with at least one other effector molecule (VIF CD107a = 24. 62). We consequently removed CD107a from the model and reanalysed the VIF associated with the remaining effector molecules (model 2).
Indeed, for HIV-specific CD8 + T cells we associated two very closely related measures, T cell killing and polyfunctionality of T cells presented to the same target in the same type of in vitro assay. Conversely, measuring the efficacy of Leishmania major-specific CD4 + T cells as the capacity of a mouse to control Leishmania major infection is more biologically distant to the T cell polyfunctionality assay. Noteworthy, our results observed for datasets derived from these two biologically and clinically distinct phenomena are very homogeneous. To know if our observations are widely applicable, the outlined approach should be applied in more studies associating T cell polyfunctionality with T cell efficacy. Single-cell polyfunctionality is measurable by multiple techniques for many cell types and many different panels of effector molecules.
[9, 13, 18]  The approach outlined in the present work is applicable to all types of single-cell polyfunctionality measures. Our study defines a robust methodology to identify the most important parameters (polyfunctionality and/or effector molecules) contributing to a given polyfunctionality analysis. We therefore believe that past and future studies including single-cell polyfunctionality measures could readily benefit from our analytical approach to increase biological insight. Two important aspects of our work are to 1) identify potential redundancy between effector molecules and 2) define if non-redundant effector molecules contribute differentially or equally to the immune correlate defined by the polyfunctionality index. To answer the first question we performed a variance inflation factor analysis of our datasets.
For the five effector molecules analysed in our in vitro CD8 + T cell assay, we successfully identified three non-redundant effector molecules (IFN-γ, MIP-1β and IL-2). To define their contribution to the polyfunctionality immune correlate, we employed the modified general polyfunctionality index. In concordance with the variance inflation factor analysis this model demonstrated similar model fit compared to the model with five effector functions. None of the three effector molecules measured in the Leishmania major vaccination dataset were redundant. Of note, redundancy is here uniquely linked to our predictive model and by no means linked with the biological roles of the monitored effector molecules. The second question was answered using regression model analysis. The regression models established for the two experimental settings presented here demonstrate that polyfunctionality (q) is an important immune correlate of T cell efficacy.
Of note, our mathematical model permits the identification of the better immune correlate of T cell efficacy between polyfunctionality and response size. Response size is here defined as functional T cells irrespective of the number of simultaneously produced effector molecules. Our in vitro and in vivo data unanimously demonstrate that polyfunctionality is a superior immune correlate as compared with response size. From the HIV-specific target killing dataset we fully delineated all parameter estimates including estimates of the impact of individual effector molecules (φ-values). Of note, no effector molecule impacted the polyfunctionality index immune correlate significantly different compared to other effector molecules. We recognize that the in vitro nature of our dataset could hampen the effect of certain effector molecules, such as chemo-attractants, whose effect would be better represented in in vivo studies.
The study was funded by  INSERM , the  University Pierre et Marie Curie   EMERGENCE" program  (www. upmc. fr)," Fondation pour l'Aide a la Recherche sur la Sclerose En Plaques " (www. ARSEP. org) and  ARTHRITIS Fondation COURTIN  (http://www. fondation-arthritis. org). A. B. was awarded a post-doctoral fellowship with the " Agence nationale de recherche sur le Sida et les hépatites " (www. accordance with the Polyfunctionality Index.
Building the C-Model The WHO MCS collected data on various socio-demographic and clinical-obstetric characteristics, severity markers, and complications that were analysed as candidate predictors for CS. First, we assessed the reference population and the WHO MCS country data using the Robson classification. Secondly, we carried out univariate analyses of the reference population to explore the relationship of several candidate predictors and the occurrence of CS. Third, a logistic regression random effects model was used to determine the relationship between candidate predictors and CS. As part of the regression analysis, the reference population was divided in two subpopulations: 'A1' (used for model building), and 'A2' (used for testing the internal validity of the prediction model). 23 To maximise representativeness and at the same time ensure the ability to test the internal validity of the model, 90% of the reference population was randomly allocated to population 'A1' and the remainder (10%) to population 'A2'.
Testing the C-Model The internal validity of C-Model was assessed in subpopulations A1 and A2. The discriminatory power was assessed through ROC curves. Classification tables, accuracy tests, and model performance tests were also carried out. An international call for validation studies of the C-Model was issued in February 2014. Members of the WHO MCS research network, the International Network of Obstetric Surveys Systems (INOSS), and independent researchers received an invitation to test the C-Model in their databases. To ensure standardisation, an external validation protocol with detailed instructions including variable recoding, computing probabilities of CS using the C-Model coefficients, generating ROC curves, and other information were provided. The researchers that responded to the call returned their results via e-mail and the information was compiled in a master spreadsheet (Supporting Information Appendix S2).