dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Descriptive analyses included frequency tables, number of observations, means, standard deviation, and 95% confidence intervals. A linear mixed model was used to analyze repeated measures data such as the VAS score and the SF-12-derived PCS-12 and MCS-12  (20). Age (continuous variable), group (presence or absence of an underlying condition), and time (visit number) were included in the model, as well as an interaction term between time and group. To account for individual differences in VAS score and PCS-12/ MCS-12, patients were included as a random effect. Patients were excluded from the analysis for one visit, if it occurred outside of the time window, but included in the analysis for a subsequent visit, occurring within the time window. All analyses were performed in SAS version 9.
Acknowledgments The authors thank  Audrey Souverain  and  Stéphane Thomas  ( SPMSD employees ) for precious support in statistical analysis;  Xavier Cornen  ( SPMSD employee ) for study database management;  Marc Baay  and  Sally Jackson ,  P95  Epidemiology and  Pharmacovigilance Consulting  and Services (supported by  Sanofi Pasteur MSD ) for editorial assistance.
Results Low affinity Immunoglobulin epsilon Fc receptor appear in immunized and non-mmunized sea star genomes. Result with nonmmunized animals is given: One contig (Contig10847) could be annotated via BLASTX to Mus musculus "Isoform 3 of Low affinity immunoglobulin epsilon Fc receptor" from the Swissprot database (FCER2_MOUSE), with an e-value of 1. 49e-11. On an aligned region of 118 amino acids, 63 positive and 40 identical amino acids were found.
Effects on NMDA receptor mediated currents for receptors expressed in Xenopus oocytes We evaluated MPX-004 and MPX-007 inhibition of NMDA receptor-mediated currents in Xenopus oocytes expressing human GluN1 + GluN2A. IC 50 s were 198 ± 17 nM (n = 12) for MPX-004 and 143 ± 10 nM (n = 4) for MPX-007 (IC 50 s ± SEM;  Fig 4). These IC 50 s are slightly higher than those determined for GluN2A in the HEK cell assays. Slight reduction in potency in oocyte expression systems relative to mammalian expression systems is not uncommon, as discussed by others  [28]. At concentrations up to 10 μM, MPX-004 only weakly (up to 8%) inhibited currents in oocytes expressing GluN2B, C, or D receptors  (Fig 4)  or in control oocytes (data not shown). MPX-007 at 10 μM was similarly ineffective for inhibition of GluN2C or D currents (Fig  4 ) and had no effect in control oocytes (data not shown).
cRNAs were mixed together at a ratio of 1 GluN1:2 GluN2. Each oocyte was injected with 50 nL of cRNA (~2 ng, GluN2A and GluN2B; ~38 ng, GluN2C; ~11 ng, GluN2D), and stored in ND96 (96 mM NaCl, 2 mM KCl, 1. 8 mM CaCl 2 , 1 mM MgCl 2 , 5 mM HEPES, 50 ng/L gentamycin, 100 units/ml penicillin and 100 μg/ml streptomycin; pH 7. 6) at 16°C for 72 h prior to recording. Currents were recorded using a two-electrode voltage-clamp amplifier (Oocyte Clamp OC-725C; Warner Instrument Corp, Hamden, CT), and digitized using Digidata 1550A and pClamp (v. 10) software (Molecular Devices, Sunnyvale, CA). Electrodes were filled with 3 M KCl and had resistances that ranged from 0. 2-1 MO. All measurements were made at a holding potential of -40 mV. Cells were continuously perfused with oocyte recording solution containing 90 mM NaCl, 1 mM KCl, 0.
The MEA was quickly transferred to the amplifier stage on an inverted Motic microscope, and the slice continuously perfused with oxygenated ACSF (3 ml/min at 37°C) of the following composition (in mM): NaCl 127, KCl 3. 5, NaH 2 PO 4 1. 2, MgCl 2 0. 1, CaCl 2 2, NaHCO 3 25 and Glucose 11. NBQX (10 μM) was added to inhibit AMPA receptors. The bath was ground connected with a 1 M NaCl-Agar bridge. All data were recorded with a MEA set-up from Multi Channel Systems MCS GmbH (Reutlingen, Germany) composed of a 4-channel stimulus generator and a 60-channel amplifier head-stage connected to a 60-channel A/D card. Softwares for stimulation, recording and analysis were the ones commercially available from Multi Channel Systems: MC Stim (2. 0. 3. 0 release) and MC Rack (3. 2. 1. 0 release). All of the experiments were carried out with MEA from MCS that consist of 60 electrodes spaced by 200 μM.
The NMDA fEPSP amplitude was measured as the difference between the baseline (before stimulation) and the maximal peak amplitude. Background noise at individual electrodes level was subtracted to the NMDA fEPSP amplitude. NMDA fEPSP amplitude was normalized as a function of the meanaveraged amplitude recorded over the 10-minute control period. The normalized NMDA fEPSP values of all the validated electrodes of a single slice were first averaged. Next data from the slices in the same experimental conditions were averaged. The NMDA fEPSP mean values (± SEM) were plotted versus time. Dose-inhibition curve of MPX-004 (mean inhibition of NMDA EPSP after 40 minutes) was fitted with an empirical Hill equation, yielding its IC 50. For MPX-004 concentrations from 0. 1 to 30 μM, data was collected from 4 to 8 slices prepared from 2 or 3 different rats.
2 mM ethylene glycol tetraacetic acid (EGTA), 10 mM 4-(2-Hydroxyethyl)piperazine-1-ethanesulfonic acid (HEPES), 2 mM adenosine triphosphate-magnesium, 0. 3 mM guanosine triphosphate, 5 mM QX-314, and 5 mg/mL biocytin (pH 7. 2). Excitatory postsynaptic currents (EPSCs) in layer 2/3 pyramidal neurons were evoked by extracellular stimulation (0. 2 msec, 16-100 μA) in layer 4 of the visual cortex using a bipolar stimulating electrode pulled from standard wall borosilicate tubing and filled with artificial cerebrospinal fluid. Voltage-clamp recordings were acquired at a 10 kHz sampling rate with an AxoPatch-1D (Axon Instruments, Union City, California) and an ITC-18 AD board (Instrutech, Mineola, New York). All data were acquired and analyzed using custom-made procedures in Igor-Pro Software (Wave-Metrics, Lake Oswego, Oregon).
Results of the elsA software package, the ONERA multipurpose tool for applied aerodynamics and multiphysics, are presented for a well-known NACA0012 configuration proposed by the turbulence modeling resource website of NASA Langley Research Center and on the ONERA-M6 wing. ONERA proposed new computer-aided design models for the turbulence modeling resources to make ONERA-M6 a reference test case. Results and solver performance are presented and discussed.
S INCE 1997, the elsA software package [1,2] is the ONERA multipurpose tool for applied aerodynamics and multiphysics, which capitalizes on the innovative results of computational fluid dynamics (CFD) research over time. The elsA CFD simulation platform solves the compressible Navier-Stokes equations and deals with internal and external aerodynamics from the low-subsonic to the high-supersonic flow regime. A very wide range of aerospace applications is covered by elsA: aircraft, helicopters, tilt rotors, turbomachinery, counter-rotating open rotors, missiles, unmanned aerial vehicles, launchers, etc. The elsA software is also used for configurations outside the aerospace field, such as car turbochargers and steam and wind turbines. The development and validation of the elsA software benefit by input from research partners and feedback from industry users  [2].
In particular, elsA is used as a reliable CFD tool by Airbus for transport aircraft configurations, by the Safran group for turbomachinery flow simulations, and by Airbus Helicopter for helicopter applications. A description of elsA and an illustration of the wide range of applications covered by elsA were shown by Cambier et al. [1]. In this previous work, the Reynolds-averaged Navier-Stokes (RANS) capabilities of the software have been assessed on largely used configurations from basic test cases provided by the NASA turbulence modeling resource (TMR) website  [3]  to transport aircraft, turbomachinery, and helicopter well-known applications often encountered in international community workshops. Nevertheless, the effort on the elsA validation has to be pursued and intensified, especially in collaboration with other major CFD software research centers, to build a reliable database allowing researchers to compare and highlight the most effective and accurate methods among those already implemented in each software.
This can be helpful for the choice of the best new features to be implemented. The first studied test case is the NACA0012 airfoil. A first campaign of computations was performed in a family zero grid with different turbulence models [Spalart-Allmaras, k-ω Menter shear stress transport (SST), explicit algebraic Reynolds stress modeling (EARSM) Hellsten] to analyze the turbulence model influence on the physics, then three families of grids with different relative grid densities at the trailing edge posted on the TMR website were used for analyzing numerical and performance aspects. These results are presented and compared with experimental data and CFL3D and FUN3D code solutions for the angle of attack equal to 10 deg. The discussion and conclusions focus on two main aspects: the behavior of the solution (in physical and numerical points of view) and the software performance.
In particular, the boundary condition on ω for  the Menter SST model is exactly the same as the one used in the calculations presented on the website with the CFL3D code developed at NASA Langley. Results given on the finest grid (Fig. 2 ) are compared with the CFL3D code, Gregory and O'Reilly  [6]  data, and Ladson data  [7]. On a family zero grid, elsA and CFL3D results are almost indistinguishable (Fig. 2 ). The emphasis of the next analysis is now placed on numerics. For this purpose, new families of extremely fine grids have been created to explore the influence of different grid streamwise spacing at the trailing edge, which greatly influences the convergence properties. The computational domain and the boundary conditions are shown in Fig. 1. Three families of grids are provided (Fig.
All computations were performed without a point vortex boundary condition and with a second-order turbulence advection. The pitching moment was computed with respect to the 0. 25c. Performance of elsA has been summarized in Table  1 , showing the CPU time by iteration and point for seven levels of grid size. It is influenced by the number of grids for the multigrid algorithm, the number of iterations in the coarse grid, the number of iterations for the turbulent system, and the number of processors used for computation. Table  1  gives an idea of the software scalability. As shown in Fig. 4 , families gave different results, particularly on lift, pitching moment, and the viscous part on the drag. The trailingedge streamwise spacing has an influence on lift and moment. The trailing-edge streamwise spacing on family 1 seems not to be sufficient.
The trailing-edge streamwise spacing for family II is the smallest, and so this grid family should give the most accurate result. The next results have been computed on this family on the finest grid. Table  2  shows the relative difference between elsA and CFL3D on the finest grid for each aerodynamic coefficient. Pitching moments have a relative difference 100 times more important than those on lift or total drag. This coefficient has historically been difficult to accurately predict with CFD. Large differences in computed results would suggest the need for further investigation. Figure  5  shows the pressure coefficients on the airfoil and the friction coefficient. The elsA software and CFL3D give consistent results compared with the experiment of Gregory and O'Reilly  [6]. Differences appear by zooming in on the leading (Fig.
6 , near x 0) and trailing edges (Fig. 7 , near x 1). These differences are only visible in extreme zooms of the suction peak and very near the trailing edge. The elsA software has difficulties obtaining a smooth solution very near the trailing edge. It explains the plate convergence in the last lower right view of Fig. 4  (Family1-7169x20489 grid); indeed it has been checked that residuals are the most high in this zone. Figure  8  shows eddy viscosity and streamwise velocity components near the trailing edge. Results between codes are indistinguishable. Figure  9  shows eddy viscosity and velocity near the trailing edge from the upper side of the airfoil and the lower part of the airfoil. Results are interesting because the trailing edge is a sensitive point. Results are almost indistinguishable also.
A. Brief History of ONERA-M6 The reputation of OM6 is largely due to Jameson  [8] [9] [10]. The first wing test case addressed in  [8]  (1977) with the Flo22 full potential equation solver is OM6. Several flow conditions taken from  [11]  (1973 and 1975) are considered, including those have been preferred over the years by the majority of the CFD community (M ∞ 0. 84, α 3. 06 deg). In 1979, Jameson and Caughey granted OM6 a fuselage to investigate fuselage effects on the flow past a wing  [9] , well aware that OM6 is not even a full wing, but is only "representative of the tip panel of a relatively simple wing of conventional high-speed section shape"  [8]. In 1982, the editor of the Proceedings of IMA Conference on Numerical Methods in Aeronautical Fluid Dynamics appended a paper entitled "Transonic Airfoil Calculations Using the Euler      Several years later, OM6 was still among the validation cases of the then-latest flow software, Flo3xx, designed for simulations in arbitrary meshes  [16]  (2005).
IV. ONERA-M6 Wing Test Case Proposal The purpose of the present document is to propose a nonambiguous definition of the OM6 wing test case and to reduce the data to what is strictly necessary for CFD. Fig. 12  Layout of the OM6 wing planform from  [17]. Fig. 13  ONERA-M6 wing model in the ONERA-S2MA wind tunnel (from  [17] ).
A. CAD Model with a Blunt Trailing Edge A CAD model based on the initial discrete data described in Sec. III was built. CATIA, Dassault System software, was used to generate the CAD model. The strategy adopted during the surface generation was to keep as many points possible from the initial discrete model and take some freedom concerning points that were responsible for oscillation in the shape to generate at the end a CAD model as smooth as possible. At the start of the root profile generation, a problem appeared and led to the conclusion that no B spline computed in CATIA and passing by all 72 points defining the half-section was able to represent smoothly the shape of the profile. Point no. 2 (in order of appearance in Table  3 ) was removed from the B-spline definition, which ensures to set properly the radius (set at 0.
B. CFD Mesh A structured mesh was generated using ICEM Hexa, Ansys software around the OM6 wing model. A C-H topology presented in Fig. 18  was built. The fine grid is made of 10. 2 million nodes. A coarser version of the mesh was also generated. Its size is 3. 8 million points. The coarsening process was made inside of ICEM Hexa and ensures a y on the wall cell close to one.
C. First Computation The two test cases initially retained and named after the AGARD report were test numbers 2308 and 2565. For both, the Mach number considered is 0. 84. The first one is an attached case with an angle of attack of 3. 06 deg and a Reynolds number of 11. 72 million. The computations of this test for the preceding meshes will be presented here. They are done with the ONERA elsA CFD code  [19]. The Jameson scheme with ki 2 at 0. 5 and ki 4 at 0. 016 and the Spalart-Allmaras turbulence model were used. A multigrid with one coarse level was used to obtain a convergence of seven orders of the residuals of the first conservative variable and six orders for the turbulent conservative variable on the finest mesh after 20,000 cycles. The coarser version gives a reduction of six orders of magnitude for the first conservative variable and five orders of magnitude reduction in the conservative turbulent variable after 10,000 cycles.
E. CFD Mesh of the ONERA-M6 Wing with Sharp Trailing Edge A structured mesh was generated using ICEM Hexa, Ansys software around the CAD of the OM6 wing with sharp trailing edge. The C-H topology presented in Fig. 26  was built. A grid of 3. 8 million points has been generated for the demonstration. In this section, we investigate effects of turbulence models (SA and k-ω Menter SST) with the proposed mesh with a sharp trailing edge. The Jameson scheme with ki 2 at 0. 5 and ki 4 at 0. 016 was used. A multigrid with one coarse level was used to obtain a convergence of seven orders of the residuals of the first conservative variable and six orders for the turbulent variable after 30,000 cycles. Evolution of the residuals is presented in Fig. 27  where ρ is the density and ρυ is the Spalart-Allmaras (SA) variable.
The accuracy of locations determined by aircraft tracking was estimated to be ±0⋅5 km and these locations were only used for calculations of large-scale movements. Fishes were captured from a 20 km river section and tagged between March 1994 and October 1996 and locations determined from April 1994 to December 1997. Flow and temperature data were collected from the gauges immediately downstream of the weir wall at Yarrawonga (Fig. 1 ).
An individual was defined as exhibiting site fidelity when three consecutive small movements for an individual were observed. The mean, quartiles and range for these small movements for each species were calculated. To calculate homing, the number of fishes showing location shifts where returns after large movements and continuous large movements were observed was calculated. To examine species differences and seasonality in large movements, mixed-effects binomial regression models were used to estimate probabilities of movement. All explanatory variables were categorical and included season, year and species as fixed effects and individual as a random effect. The model was implemented in R (R Core Team, 2014; www. r-project. org) using the GAMLSS package  (Rigby & Stasinopoulos, 2005). A stepwise AIC  (Akaike, 1973)  based procedure was used to select terms to model mean movement probability, , using the GAMLSS function stepGAIC.
Using this definition, the proportion of fishes exhibiting site fidelity was determined for each species for each month. To examine seasonality in movements, the proportion of individuals undertaking movements >1 km in each month was calculated. As the number of individuals per species was typically <10 in any month for both site fidelity and seasonality, data were pooled across years. Weighted logistic regressions were fitted to the proportion of fishes moving >1 km in each month for each species using the R statistical package. Month was categorized into season and was fitted as the predictor. The main effect and null (intercept only mode) were fitted and the most parsimonious of these models was selected to describe the effect of season on movement. The movements >1 km were also categorized into one of the four outcomes for all fishes with records >3 months: a shift to a new site with fidelity was demonstrated; a return to the original location (homing); continuous movements to new sites but where no site fidelity was shown; or no large movement was observed.
No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper.
We present here the development of a genome-scan method that exploits all single-cell values with no prior simplification of the cell population phenotype. Using simulations and existing single-cell data from yeast, we show that it can detect genetic effects that were missed by conventional linkage analysis. When applied to a novel experimental dataset, the method detected a locus of the yeast genome where natural polymorphism modifies cell-to-cell variability of the activation of the GAL regulon. This work shows how single-cell quantitative data can be exploited to detect probabilistic effects of DNA variants. Our approach is conceptually and methodologically novel in quantitative genetics. Although we validated it using a unicellular organism, it opens alternative ways to apprehend the genetic predisposition of multicellular organisms to certain complex traits.
This constitutes a major challenge that can be avoided by studying unicellular organisms. The yeast S. cerevisiae provides the opportunity to study individual cells that all belong to a single cell type, in the context of a powerful genetic experimental system. By analysing specific gene expression traits in this organism, we and others identified loci that meet the definition of scPTL but not of QTL  [20, 21]. This illustrated that, for some traits, scPTL mapping could complement classic quantitative genetics to identify the genetic sources of cellular trait variation. To estimate if non-QTL scPTL are frequent, we re-analysed an experimental dataset corresponding to the genetic segregation of many single-cell traits in a yeast cross  (Fig 2A). After a round of meiosis involving two unrelated natural backgrounds of S.
Detection of simulated scPTL We first evaluated if our method could detect scPTL from simulated datasets. To do this, we considered a probabilistic single-cell trait governed by a positive feedback of molecular regulations. This is representative of the expression level of a gene with positive autoregulation. As depicted in Fig  4A , the employed model is based on three parameters. For each individual, a set of parameter values was chosen and single-cell values of expression were generated by stochastic simulations. We chose to simulate a scPTL that modified the expected values of the parameters so that the skewness of cellular trait distribution is affected. To do so, we considered a panel of individuals and their genotype at 200 markers evenly spaced every 5cM. Parameter values of each individual were drawn from Gaussians and the mean of these Gaussians depended on the genotype at the central marker.
This defined two sets of phenotypes that are depicted by blue and red histograms in Fig 4B. A universal noise term η was added to introduce intra-genotype inter-individual variation which, in real datasets, could originate from limited precision of measurements or from non-genetic biological differences between individuals. For each of five increasing values of η, about 130 individuals were simulated. We first scanned the generated dataset by QTL mapping, treating either the mean trait or its variance as the phenotype of interest. This way, the central scPTL locus was detected only when intra-genotype noise was null or very low  (Fig 4C). This was anticipated because the mean and variance of the simulated trait values slightly differed between the two sets of individuals. In contrast, our new method allowed to robustly detect the scPTL locus even in the presence of high (up to 20%) intra-genotype noise (Fig 4D  and 4E ).