dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS Marina Díaz-Beyá  is supported by  ISCIII  ( Rio Hortega   CM13/00205 ). This research was in part supported by  Fundación Española de Hematologia y Hemoterapia  (beca de investigación MDB). This research is also supported by grants from  Fondo de Investigaciones Sanitarias/Instituto de Salud Carlos   III PI13/00999  (PI: JE),  RETICS RD12/0036/0010  (JE; MDB) and SDCSD from  School of Medicine, University of Barcelona ,  AECC-Catalunya 2013  (AN) (sponsored by  Mat Holding ), and grants  AGAUR 2014SGR-1281 ,  ISCIII RD12/0036/0071  and  PI014/00450  (JS). Anna Cordeiro  is an APIF fellow of the  University of Barcelona.
Most SPR data came from three independent experiments performed on different chips in which the initial amount of protein fi xed could vary substantially, leading to response unit (RU) variations. These data were thus presented as percentage of a reference value set at 100% rather than RU. The percentages of dissociation were calculated as follows: (R120 -R150)/R120 × 100. For single molecule binding (OA and DHA), values for the dissociation and association rate constants, ka and kd, and the response at equilibrium R eq were determined using the Biacore software. R eq was then plotted as a function of OA and DHA concentration noted [A] and fi tted to a hyperbola using SigmaPlot software (v11): R eq = R max [A]/(K D + [A]), where the equilibrium constant K D is the dissociation constant (inverse of the association or affi nity constant), [A] the analyte concentration, and R max the maximal response.
Statistical analysis Analysis by group. Results are expressed as means ± SD. Differences between more than two groups of unpaired data were tested by the nonparametric Kruskal-Wallis test. The nonparametric Mann-Whitney U test was used as a post hoc test when the Kruskal-Wallis test showed signifi cant differences between groups. Differences between two groups of unpaired data were tested using the nonparametric Mann-Whitney U test. Correlations between two groups of paired data were tested using the nonparametric Spearman test. Values of P < 0. 05 were considered signifi cant. All statistical analyses were performed using Statview software, version 5. 0 (SAS Institute, Cary, NC). ANOVA. Associations between the lipid types (either cholesterol, phospholipid, or free fatty acid), hydrodynamic radius, zeta potential, and the binding and dissociation phases of lCD36 and lSR-BI were further assessed by univariate and multivariate Simple micelles.
4A  ). In contrast, the increase in cholesterol concentration did not modify the R120 and therefore the same association for lSR-BI was obtained compared with controls, but ANOVA using SAS software version 9. 3 (SAS Institute). The multivariate model included the three lipid types (cholesterol, phospholipid, and free fatty acid) as explanatory variables; partial effect size and signifi cance level were estimated using semi-partial Eta-square coeffi cient and type III sum of squares.
Effect of micellar free fatty acid type on micelle interactions with lCD36 and lSR-BI As shown in Fig. 6  , the absence of fatty acid drastically decreased micelle binding to both lCD36 and lSR-BI Fig. 3. Comparison between mixed micelle and cholesterol-taurocholate micelle interactions with lCD36 and lSR-BI. Mixed micelles (white bars) were composed of 0. 5 mM OA, 0. 16 mM lysophosphatidylcholine, 0. 3 mM monoolein, 0. 04 mM phosphatidylcholine, 0. 1 mM cholesterol, and 5 mM taurocholate. Cholesterol-taurocholate micelles (simple micelles, black bars) were composed of 0. 1 mM cholesterol and 5 mM taurocholate. Data are means ± SD of three assays with three independent micelle batches. Micelle binding (R120) and dissociation (R150) were measured as presented in the Material and Methods. The intensity-weighted mean radius and the zeta potential of the mixed micelles at various dilutions were determined by photon correlation spectroscopy at 25°C.
All mixed micelles were composed of 0. 16 mM lysophosphatidylcholine, 0. 3 mM monoolein, 0. 04 mM phosphatidylcholine, 0. 1 mM cholesterol, and 5 mM sodium taurocholate. Only the free fatty acid composition differed between the different conditions: no free fatty acid or 0. 5 mM of either PA, OA, LA, ALA, ARA, EPA, or DHA. Micelle binding (R120) and dissociation (R150) were measured according to the Material and Methods. Data are means ± SD of three assays on three different NTA chips with three independent micelle batches. An asterisk indicates a signifi cant difference ( P < 0. 05) compared with the control (mixed micelle with OA standardized at 100%, hatched bar). range of cholesterol concentrations, whereas micelle binding to CD36 was optimal for a cholesterol concentration of around 0.
Micelle binding (R120) and dissociation (R150) were measured as presented in the Material and Methods. Data of the complete model are expressed in R-square, i. e. , 62% of the variation of zeta was explained by the combined effect of cholesterol concentration, phospholipid type, and fatty acid composition in the micelles. Eta-square coeffi cient corresponds to the semi partial effect of either cholesterol, phospholipids, or fatty acids in the model, i. e. , adjusted for variations of the two other explanatory factors. a P < 0. 001. b P < 0. 01. protein II (LIMPII)  ( 38 ). The authors showed that the helical bundle face and the apex region forming the head of both proteins were important for ligand binding. We suggest that optimally sized structures would bind more effi ciently to this specifi c area than other structures.
NUMERICAL MODEL The numerical approach is developed using COMSOL Multiphysics software, which is based on finite elements method (FEM) with ALE (Arbitrary Lagrangian-Euleurian) formulation. Many parameters affect the numerical solution since they affect not only the convergence of the problem studied, but also the quality of results. These factors include the choice of mesh quality and the number of mesh used in our study and is given by the Figure  2. The maximum time step T max 0. 01s , the relative tolerance is  r  10 -3 , and the absolute tolerance is held fixed at  a  10 -5. The average number of iteration in each time step was 5-10 and the results demonstrate that convergence criterion is reached when the error is less than 10 -6.
Previous cognitive behavioral studies based on Acquired Equivalence Associative learning Task (AEALT) showed a strong relation between hippocampus and basal ganglia in associative learning. However, experimental behavioral studies of patients with Generalized Tonic Clonic (GTC) epilepsy remained sparse. The aim of the present study is to integrate a classical behavioral cognitive analysis with a computational model approach to investigate cognitive associative learning impairments in patients with GTC epilepsy. We measured the accuracy of associative learning response performance in five GTC epileptic patients and five control subjects by using AEALT, all subjects were matched in age and gender. We ran the task using E-Prime, a neuropsychological software program, and SPSS for data statistical analysis.
Computational Model Structure and Description For computational and simulation study, we introduced an abstract connectionist model (for similar study; see  Gluck and Rumelhart, 1990; Dayan and Abbott, 2001; Chapter 9 Classical Conditioning and Reinforcement Learning, and Moustafa et al. , 2009) , which is based on the structural analysis of AEALT  (Moustafa et al. , 2000; Myers et al. , 2003; Herzallah et al. , 2010; Moustafa and Gluck, 2011). We used it to explore the behavioral and cognitive significance of associative learning in GTC epileptic patients. We represented the connection between hippocampus and basal ganglia as two modules connected to each other by one-to-one connection (see Figure  2 ). 1. Hippocampus Module: It is a two layered network module consists of 10 patches and 20 nodes, each is considered to be a separate representation code of the input  (Grossberg, 1988; Hemmen and Schulten, 1991; Hertz et al.
Accordingly, we computed Q-values for direct actor for this chosen action. Also, we represented the learning rate value as basal ganglia input and the connection between both modules by one-to-one connection. By the end, we computed the mean and variance of the accuracy, correct performance of the subject. Importantly, we fitted the model with the average of experimental block trials that GTC epileptic and control subjects performed during each phase of AEALT (see Figure  3 ), before running the simulation for each subject. If β is small, then, the softmax probability approaches one or zero more slowly, and the action choices of the subject are more variable and random. We adjusted βvalue over simulation trials, according to the value of the hippocampus input, which represented the hippocampus strength.
Computational Modeling Our abstract model-based analysis of AEALT  (Moustafa et al. , 2000; Myers et al. , 2003; Herzallah et al. , 2010; Moustafa and Gluck, 2011) , showed a strong connection between hippocampus and basal ganglia in GTC epileptic patients. This connection was represented in our connectionist model by two modules (hippocampus and basal ganglia), which connected to each other by one-to-one connection. We introduced different values of hippocampus input representing differential states hippocampus strength. Then, we associated each of these values with different values of the basal ganglia input representing the learning rate values. In our simulation, we considered two different conditions based on the strength of hippocampus (hippocampus input) while fixing the values of basal ganglia input (the learning rate values) to have range of values in each condition between 0, 0.
Molecular dynamics simulations Molecular dynamics simulations of the apo form of YsxC (PDB: 1SUL, chain B,  [10]  and of the C-terminal mutants del(K190-R195) and del(K182-R195) were performed using the coarse-grain protein model PaLaCe  [14]  implemented in the Molecular Modeling Toolkit (MMTK)  [15]. By adopting this model residue-specific interactions involved only one to two pseudoatoms per side-chain. At the same time an atomistic description of the backbone allowed secondary structure hydrogen bonding to be taken into account and the dynamics of folded proteins to be simulated without conformational restraints. The C-terminal residues from the crystallographic structure of the full-length chain were manually removed to obtain the starting structure of the two mutants. Each protein was initially relaxed (5000 steps of steepest descent followed by 500 steps of conjugate gradient energy minimization), then heated up to 300 K in 500 ps.
Representative structures were visualized in PyMOL (PyMOL Molecular Graphics System, Version 1. 1 Schrödinger, LLC). Global compactness was measured in terms of radius of gyration, normalized by a theoretical radius of gyration for globular proteins  [19]. The GDP-binding residues were defined as those having at least one non-hydrogen atom at less than 5 Å from GDP in the crystallographic structure of YsxC bound to GDP (PDB: 1SVI,  [10]. The degree of burial of the binding residues was calculated in the apo forms in terms of circular variance (CV) using the coarse-grained protein model  [20]. CV ranges from 0 (completely buried) to 1 (completely exposed) and, as shown earlier, is closely linked to the degree of hydration  [21]. The burial profile of the GDP-binding site of the apo forms was defined as the CV value averaged over all the pseudoatoms of the binding residues, as a function of the time.
Acknowledgments This work was supported by the  'Agence Nationale de la Recherche ', grant  N°ANR-08-Blan-0143  to J. -M. J. and by the grant  N°ANR-11-BINF-0003  to N. C. C. W. -P. thanks  Bastien Hermant  for his technical help throughout this project. We thank  Richard Lavery  ( BMSSI, Lyon ) for his substantial contribution to analysis and interpretation of molecular dynamics simulation experiments.
2b  and 3  only the divergence at 270 MeV was considered. Yet, a careful analysis of the angularly-resolved electron spectra ) Figure  3  | Influence on the electron beam divergence of the peak electron density in the second gas jet (the laser-plasma lens). The divergence was measured for an electron energy of 270 MeV. The distance between the two stages is L = 2. 3 mm. Data points were averaged over at least 11 shots; the vertical error bar correspond to the standard error of the mean, and the horizontal bar to the precision on the backing pressure. The analysis of the interferograms leads to an additional systematic error on the density of ±5%. in Fig. 2c -d reveals that electrons of different energies are not focused to the same extend. This is shown more clearly in Fig. 4 , which displays the reduction factor of the beam divergence as a function of both the electron energy and the distance between the two stages.
The first has an exit diameter of 3 mm and a Mach number of 3, the second has a diameter of 0. 8 mm and a Mach number of 1. 6. The laser was fired at 1. 6 mm from the nozzle exits. The electron density profiles of both gas jets were characterized by interferometry, and analyzed using the image processing program Neutrino. The density profile in the acceleration stage had a plateau of 2. 4 ± 0. 1 mm surrounded by 600 ± 100 µm gradients. The peak electron density was about 9. 2 ± 0. 5 × 10 18 cm -3. In the second gas jet, the density profile was triangular with 1±0. 1 mm gradients. The distance between the two gas jets was measured at half maximum. Electron diagnostics The electron divergence was measured, in the vertical direction, from electron spectra. The electron spectrometer consisted of a permanent bending magnet (1.
Acknowledgments This work was supported by the  European Research Council  through the  ERC projects PARIS  (Contract No. 226424 ) and  X-Five  (Contract No. 339128 ), and by the  Agence Nationale pour la recherche  through the project  LUCEL-X  ( ANR-13-BS04-0011 ). A. D. acknowledges  LA3NET , which is funded by the  European Commission  under Grant Agreement Number  GA-ITN-2011-289191. C. T. thanks  A. Loulergues  for fruitful discussions regarding the emittance growth in beam transport lines.
In these areas, observations hinted at the possibility that the topological distribution of these chromosomal regions inside the nucleus could determine HIV-1 integration. By applying three-dimensional immuno-DNA fluorescence in situ hybridization (FISH), we assessed the position of RIGs and hotter zones in primary CD4 1 T cells from healthy donors. Selected FISH probes, listed in the Supplementary Information, provided topological information for a total of 169 RIGs and other integration sites located within 10 megabases (Mb) from the centre of the probe (Extended Data Fig. 2 ). When the radial positions of the RIG FISH signals were binned into three zones of equal area  10, 11  (Fig. 1a ), a clear gradient in signal localization was observed, which decreased from the nuclear envelope towards the interior (images of 14 RIGs in Fig.
Microscopy. Three-dimensional-stacks of slides with fixed cells were captured on a Zeiss LSM 510 META confocal microscope (Carl Zeiss Microimaging) with a 363 numerical aperture 1. 4 Plan-Apochromat oil objective. The pinhole of the microscope was adjusted to obtain an optical slice of less than 1. 0 mm for any wavelength acquired. Distances observed between the FISH signals and the nuclear envelope were measured using LSM 510 Image Examiner Software (Zeiss) and Volocity (Perkin Elmer); measurements were normalized over nuclear radius (defined as half of the middle of the mAb414-TRITC ring), and then binned into three classes of equal surface area  11. Measurements were acquired for the alleles of the following genes in activated CD4 1 T cells: NPLOC4 (n 5 90), FKBP5 (n 5 128), NFATC3 (n 5 68), HEATR7A (n 5 78), RPTOR (n 5 94), SPTAN (n 5 72), SMG1 (n 5 80), GRB2 (n 5 132), KDM2A (n 5 102), DNMT1 (n 5 136), hotter zone 1 (n 5 118), hotter zone 2 (n 5 100), hotter zone 3 (n 5 62), hotter zone 4 (n 5 154), GAPDH (n 5 52), ACTN1 (n 5 126), CD4 (n 5 100), CD28 (n 5 52), HEATR6 (n 5 44), KDM2B (n 5 106), PACS2 (n 5 42), IKZF3 (n 5 52), TAP2 (n 5 54), CNTN4 (n 5 56), GPC5 (n 5 30), PTPRD (n 5 30).
M. (Extended Data Table  1 ). HIV-1 RIGs (n 5 156) were genes found in more than one list (Supplementary Information and Extended Data Table  1 ); their genomic position was plotted onto the chromosome map using the Idiographica webtool (http://www. ncrna. org/idiographica (ref. 9); Extended Data Fig. 2 ). Genomic coordinates of eight selected hotter zones, into which HIV-1 integration density was found higher than expected, were downloaded from the Bushman Lab website (http://www. bushmanlab. org/tutorials/ucsc)  1  and reported in the Supplementary Information. The calculation of the probability of finding 156 genes present in more than one list by chance was performed by computer simulation. A program was written to randomly draw, from 25,000 genes, 265, 329, 294, 32, 158 and 58 genes, and to count the genes drawn more than once.
The simulation was repeated 1 3 10 9 times. The distribution obtained is shown in Extended Data Fig. 1. Calculations were performed using the Matlab 2011R software (http://www. mathworks. com/). Expression of HIV RIGs and control genes was derived from published transcriptomic data in CD4 1 T cells, using biogps. org (as in refs 12, 13, 22), and was compared with a random sets of genes. Using the non-parametric Mann-Whitney-Wilcoxon test, it was concluded that that HIV RIGs and control genes are more transcribed than a random sets of genes (P , 0. 005). LAD coordinates were obtained from ref. 5, whereas genes inside LADs were derived from BioMart Ensembl and named LAD genes. Then, the P value of the common genes in the LAD genes, HIV RIGs, the six integration lists and cold genes were calculated by pairwise comparison of each combination, followed by hypergeometric test (Fig.
7. 3. 1 (Agilent Technologies), using the GE1_107_Sep09 protocol. All the protocols used can be obtained by contacting the CRB GADIE facility  1. The transcriptomic data from the chicken immune subsets were obtained from a previous study  (31). All microarray data have been deposited in the Gene Expression Omnibus (GEO) database under reference numbers GSE9810, GSE53500, GSE55642 which have already been released and GSE66311 which is under embargo until publication of the present study. computational Pipeline to assess cell subset homology across species We have designed a computational pipeline in order to define cell subset homology across species, based on the analysis of gene-expression microarray data. In the current study, it has been applied to identify homology relationships between mononuclear phagocyte cells in mammalian species and then extended to the comparison with a more distant species (chicken).
However, it can be applied to any cell type and to any species, provided that the annotations of the genes for each species are sufficiently well documented to allow the retrieval of the orthologous genes. In order to perform the comparison of expression profiles of cells coming from different species, thus from different platforms, we have designed two independent procedures. The first procedure (Figure  S2A  in Supplementary Material) is based on the assessment of the conservation of cell-specific fingerprints/signatures, as assessed by performing gene set enrichment analyses (GSEA, see below) between pairs of cell types. The second procedure (Figure  S2B  in Supplementary Material, see below) consists in cross-normalizing the expression datasets coming from the different species, in order to simultaneously examine the relationships between all cell types together.
cross-species Transcriptome comparison by Pairwise gene set enrichment analyses The methodological pipeline is depicted in Figure  S2A  in Supplementary Material, based on an example with comparison of three different species (A, B, and C). Species A is the reference species, i. e. , the species for which the cell types are the most accurately described and generally also for which gene orthologous relationships can be retrieved from (mouse or human here). Species B and C are the test species, i. e. , the species for which the identity of the cell types has to be established. Coming from three different platforms, the expression datasets have different numbers of probes, illustrated by boxes of different size. In brief, the strategy is to examine by GSEA whether the transcriptomic fingerprint of a given cell type (X) from the referent species A is enriched in one cell type (Y) of a test species (B for example) as compared to all other cell types of the same species.
If this is the case, this would support the hypothesis that the cell type Y from the test species B is homologous to the cell type X of the referent species A. To perform these high-throughput GSEA in a processive way that could be easily reproduced and interpreted by other researchers devoid of bio-informatics expertise, we designed and implemented a dedicated software, called Bubble GUM (manuscript in preparation in which an extensive description of the software will be provided) 2. Bubble GUM encompasses two main modules, GeneSign and BubbleMap, respectively, dedicated to the generation of gene sets and to their use for GSEA applied to multiple pairwise comparisons of samples integrated together into a simple graphical output that helps in the interpretation of the results. The first step consists in extracting from the reference species the transcriptomic fingerprints of each cell type.
A cell-specific transcriptomic fingerprint can be defined as the list of genes that 2 http://www. ciml. univ-mrs. fr/applications/BubbleGUM/index. html are more highly expressed in the cell type of interest than in all other cell types. These fingerprints were extracted using the "Min (test) vs. Max (ref)" method [(minimum expression among all replicates for all samples for which the transcriptomic fingerprint is defined/maximum expression among all replicates used as reference) ≥1. 5-fold] (15, 32), using the GeneSign module of Bubble GUM. These transcriptomic fingerprints, in gene symbol format, will be assessed for enrichment on the expression datasets of species B and C. Thus, it is necessary to convert the probe annotations from the arrays of species B and C into the gene symbol of their orthologous counterparts in species A.
For this purpose, we used the orthology relationships defined by the Sigenae pipeline which annotated the pig and sheep genes with their human and mouse orthologous gene symbols  (30). The genes present on the gene chips of species B and C that were not associated to an orthologous counterpart in species A remained annotated with the gene symbol corresponding to their species of origin. The statistical enrichment of the cell-specific transcriptomic fingerprints extracted from the reference species A were then calculated between pairwise comparisons of cell types from species B or C with the GSEA methodology, using gene set permutations for computing the p-values and false-discovery rates (FDR)  (33). This was achieved, and the results graphically represented, by using the BubbleMap module of Bubble GUM.
cross-normalization of the species-specific expression Datasets Using the same starting expression datasets, this is an alternative strategy which is complementary to the pairwise GSEA of the species-specific expression datasets, since it allows clustering all cell types together based on the overall evaluation of the proximity of their expression patterns of hundreds to thousands of orthologous genes. The first step (Orthology Filter) consists in aligning the genes across the species (A, B, and C). It requires retaining only one representative probe per gene for each species/platform. This is needed since, in microarray designs, many genes are often each represented several times by a number of individual probes having each a different signal-to-noise ratio. However, probes have no equivalence across species, whereas genes do.