dataesr/scientific-paragraphs-categorization huggingface dataset:
BRIEF DESCRIPTION OF SYLVIA SOFTWARE The SYLVIA software [25] is developed at the Institut de Radioprotection et de Sûreté Nucléaire (IRSN) to simulate a full ventilation network, fire scenarios in a highly confined and ventilated facilities, and airborne contamination transfers inside nuclear installations. This software is based on a zone approach which consists in calculating mass and energy balances in each of two zones, separated by an interface, which constitute the enclosure (fire room or not): the lower zone simulating the fresh gas and the upper zone simulating the combustion products and the gas entrained by the plume. In a zone approach, a plume feeds the upper zone of the fire room, whose volume increases, which has the effect of lowering the interface if the gas flow in the exhaust duct or at the level of openings of the fire room is not sufficient to remove all gases supplied by the plume.
Droplets size distribution The spray model implemented in the SYLVIA software is based on mass and heat transfers between droplets and surrounding gas during their fall  [1]. No direct interaction between spray droplets and the fire source is modeled. The rates of mass and heat transfers between surrounding gas and falling droplets during the evaporation process are strongly dependent on the droplet size. (1) w w d ) f(d n d d  In pratice, the droplets size distribution produced by most of spray nozzles are often well represented by a log-normal distribution function as: (2)                     2 2 w 0 ) (ln 2 ln exp ln d 2 ) ( g w g w CMD d n d f    The log-normal distribution function requires the knowledge of two parameters for determining it: the geometric standard deviation  g that describes the dispersion of the distribution function and the count median diameter (CMD) or the mass median diameter (MMD).
Results and discussion The experimental results obtained from the CARAIDAS device allow validating the modeling of dynamics of water droplets. During these tests, a wide range of experimental conditions was performed: pressure from 1 bar to 7 bar, ambiant temperature from 20°C to 160°C, and relative humidity from few percent till 95%. For each test, measurements of the mean droplet diameter and standard deviation of droplets are determined at the three measurement levels. Only experiments performed at atmospheric pressure and low relative humidity (summarized in Table  1 ) are used for the validation of the model, corresponding to gas conditions that could be met in the fire room prior the activation of a spray system. Comparison between experimental data and SYLVIA simulations of droplet diameter evolution is reported in Table  2.
Methodology for the calibration process A numerical method for determining droplet size distribution and droplet initial velocity of a water spray system using the two-zone model SYLVIA is developed. This method is based on a calibration of spray modeling using full-scale fire tests carried out in the DIVA facility of IRSN. More precisely, a parametric study is firstly performed with the SYLVIA software to adjust the droplet size distribution at injection and droplet initial velocity on a given fire test. Indeed, the numerical technique  [15]  consists to keep the best inputs for minimizing the difference between some appropriate outputs (or responses) from experiments and simulations. In a second hand, the validation of the method is performed on other fire tests of the campaign with different operating conditions (water flow rate, heat release rate for the fire source, ventilation flow rate) using the same water spray system.
SYLVIA modeling of the ventilation network A fully dataset of the ventilation network of the DIVA facility was used to simulate properly the experimental aeraulic resistivity of the ventilation network for the Propane gas fire tests. Indeed, the various nodes and branches of the modeled ventilation network (see Figure  3 ) correspond to aeraulic elements for which one or more experimental measurements were performed. In order to prevent some errors for the dataset of the ventilation network, the characteristic of the DIVA ventilation network is processed by means of the experimental data obtained from Propane gas fire tests in operating conditions, just before the fire ignition. Of course, the fire scenario definition is achived in SYLVIA by defining the compartment equipment (firebreak doors, fire-dampers,.
The under-pressure peak at the start of the spray period is correctly simulated by the SYLVIA software and the difference of mean gas temperature between experimental data and prediction during the stationary phase of the fire is kept less than 4 °C for both tests. With the reduction of the spray volume flow rate in C5 test, the adjustments discussed previouly in this paper have been performed on droplet size distribution and droplet initial velocity. According to Eq. 15 and Eq. 16, the predicted droplets size distribution produced by the deluge spray nozzle for a volume flowrate of 40 l/min is characterized by a mass median diameter of 1140 µm and a geometric standard deviation of 1. 5 (see Figure  6 ). The droplets initial velocity is estimated at 5. 1 m/s. Using these spray characteristics in the simulation of C5 test, time evolutions of pressure and mean gas temperature in the fire room are well predicted by the SYLVIA software as seen in Figure  7 -e and Figure  7 -f.
CONCLUSION A numerical method for determining droplet size distributions and droplets initial velocity of spray nozzles using a two-zone model has been developed. This method is based on a calibration of spray modeling on full-scale fire tests carried out in the DIVA facility of IRSN for which the fire was perfectly controlled. More precisely, a parametric study was performed with the SYLVIA software to fit the droplet size distribution at injection and the droplets initial velocity on a given test of the Propane gas fire test campaign. Since the uncertainty associated with each considered parameter is summarized by a range of variation, a probability distribution was chosen to represent this information and the propagation through SYLVIA modeling was achieved by Monte Carlo simulations. The validation of the method was then performed on other tests of the Propane gas fire test campaign, with other operating conditions.
Main features The SYLVIA software [25] is designed to predict the behaviors of mechanical/natural ventilation, growth fire, hot gas and smoke propagation, and airborne contamination transfer in confined and mechanically ventilated enclosures. Indeed, it is a simulation tool for calculating the consequences of multi-compartment fires in industrial and nuclear facilities, which are equipped with a full ventilation network (including ducts, room leaks, a pers, fans, horizonta /vertica openings, fi ters…). The fire design of the SYLVIA software belongs to the well-known two-zone fire modeling (  [17] ,  [18] ). In such software, each compartment is described with two Lagrangian control volumes, i. e. one lower and one upper gas layer separated by a thermal interface changing with time. The features of ambient quantities (pressure, temperature, gas concentration, etc.
) in each control volume are assumed to be uniform at a given time. These control volumes can be connected by various ventilation components representing either natural or mechanical ventilation. The convective/conductive/radiative heat transfers inside the compartments and in the ventilation network (walls, gas phase) are fully modelled and play a major role for predicting temperature and pressure, especially in rooms and ventilation ducts near the fire. The fire itself is described by several correlations (plume, fire height, source point approach, chemical reaction, etc. ) available in the literature (  [17] ,  [18] ,  [19] ). Furthermore, the SYLVIA software has additional specific models for simulating a water spray system, the condensation/adsorption of gaseous species (as water vapour) on walls, and the transport and deposition of aerosol particles (soot, dust) on solid surfaces (as ventilation ducts, room walls, High Efficiency Particulate Air filters).
). A mass flow rate is assumed to be uniform in 0D branches and is a function of the pressure difference at its boundaries (nodes)). A given branch is always connected to two nodes, but there is no limit on the number of branches connected to a given node. Pure aeraulic simulations in connection with the ventilation requirements (design of ventilation networks, aeraulic balancing, studies of the wind effect on airborne contamination transfer and their release to the environment…  [28] ) is possible with the SYLVIA software.
Technical review of SYLVIA software The fire and ventilation network modeling in SYLVIA considers an initial value problem based on usual Ordinary Differential Equations (ODEs). The mathematical system of equations is derived mainly from the conservations of mass, species (gas and particles) and temperature (or enthalpy), and from the ideal gas law. Each equation can be written for a control volume as:  (18)  t t t (l = index on the source terms) The source term t is positive for increasing the term t with time (i. e. production term) and negative otherwise (i. e. sink term). The outputs ( t as functions of time) are mainly pressure, layer height, temperatures and mass fraction of gas/particle species in the two (or one) layers for the fire modeling and for the node modeling of the ventilation network.
The branch modeling allows predicting the mass flow rate between two successive nodes. The great challenge for simulating properly a compartment fire coupled with a ventilation network consists in describing all the source term sub-models of each equation of conservation (  [17] ,  [18] ). For the numerical aspect, the SYLVIA software solves a set of ODEs to calculate the thermodynamic states versus time in each compartment and in the ventilation network. The numerical algorithms to compute the ODE system is based on a BDF (Backward Differential Formula) method, which is an implicit, multistep method, well-suited to stiffness problems (  [21] ). The time step is managed automatically according to both the previous time step and the maximum number of iterations necessary to converge on the previous time step.
Modeling of the ventilation network The SYLVIA software is able to model a comprehensive ventilation network of a nuclear facility by using the node/branch method (usually adopted by ventilation computer codes  [6] ,  [20] ). This approach is based on the analogy of the ventilation network with an electrical one. Hence, at each node (as described in Table  4 ), the conservative equations of mass, enthalpy and species (gas/particles) are solved and the mass flow rate between two nodes is computed by means of the generalized Bernoulli equation taking into account the effect of branch length L on the mechanical inertia, which can be significant for a very long duct for instance. This latter relation can be written as: -- The function h() is the branch's characteristic function depending on the type of branch considered.
Fire modeling The fire modeling in SYLVIA software involves several sub-models (fire source, plume, combustion, radiative heat transfer, horizontal/vertical hot smoke propagation…) for simulating the fire compartments as summarized in Figure  8. The usual fire sources (gas burner, pool fire, fixed heat release rate) are modeled and some complex fuels can also be taking into account in fire engineering purposes as electrical cabinet (with open or closed door) and horizontal cable trays. As the heat conduction can be calculated by a 1D approach in walls and targets, the ignition of fire source is possible based on a temperature criteria. The fire sources can be limited due to the oxygen depletion (Peatross approach  [24] , Low Oxygen Limit law  [27] ) in the fire compartment until the possible fire extinction based on mass loss rate criterion  [19] , adiabatic flame temperature criterion (  [22] ,  [23] ) or simply by lack of fuel.
Enhanced myelopoiesis correlates with increased parasite burden To further investigate the impact of HSPC expansion on downstream myeloid differentiation during infection, we evaluated the acquisition of regulatory markers by Fzd6 -/-Ly6C hi monocytes. There was no difference in the proportion of granulocytes (SSC hi GR1 hi /Ly6G + ), mature Ly6C hi monocytes, or Ly6C lo monocytes/macrophages at steady state (S7 Ccr2 and Ly6C downregulation are generally associated with monocyte differentiation into macrophages  [28] [29] [30] , while Cxcr4 expression has been recently shown to correspond to a transitional stage between cMoPs and mature monocytes  [31]. Together, these data suggest Thus, Fzd6 -/-Ly6C hi monocytes are produced in lower numbers, presumably due to decreased expansion of upstream progenitors, but they do not significantly differ in the expression of activation markers, such as MHCII, Sca1, or iNOS.
Foxp3 staining kit (eBioscience, San Diego, CA) and then incubated with appropriate antibodies. For cell cycle analysis, cells were first incubated for 30 min at 37˚C with Hoechst #33342 (Sigma-Aldrich, Oakville, ON, Canada) in DMEM supplemented with 10% Premium FBS (Wisent Bioproducts, St-Bruno, QC, Canada) and 1 mM HEPES (Life Technologies, Burlington, ON, Canada), followed by staining with surface antibodies and intracellular anti-Ki67 as described above. Samples were acquired with a four-laser LSR Fortessa flow cytometer (BD Biosciences, Mountain View, CA) and analyzed using BD FACS Diva software (BD Biosciences) or FlowJo (for histogram overlays; Tree Star).
Acknowledgments We are grateful to  Dr. Claude Perreault  ( University of Montreal, QC ) for the gift of Fzd6 -/- mice; to  Jessy Tremblay  at the  INRS-IAF Imaging facility  for advice on flow cytometry; and to  Tania Charpentier ,  Roxann Hetu-Arbour , and the staff of the CNBE animal care facility for technical assistance.
Study of Cyt b amino acids variability:- Cyt b nucleotides sequences are transformed into amino acids sequences using to MEGA software version 6. 06 5  (Tamura et al. , 2013) with the best reading frame. The frequencies of amino acids were stand out for normal and cancerous tissue. Chi2 test was realized to see the amino acids that presented significant differences. Cyt b and FGB genetic structure:-Genetic distances between sane and cancerous tissues at intra and inter individual level for the two genes were explained by the genetic distance of Nei  (Nei,1978)  using MEGAsoftware version 6. 06 version software  (Tamura et al. , 2013).
Signature selection test:- We have made demo-genetic tests that compare the level of adjustment between the two genes diversity and theoretical expected values under the hypothesis of the evolution under a neutralist model (to mutation-derive 505 balance). We have, among these tests Tajima D  (Tajima, 1989) , Fs of Fu (Fu, 1997) and R2 of Ramos  (Ramos, 2002). These three tests were realized on FGB. These various estimators are obtained usingDnaSPversion  5. 10 (LibradoetRozas, 2009)  and Arlequin version 3. 5. 1. 3 (Excoffieret al. , 2010). As for Cyt b, the existence of any selection was apprehended by report dN/dS thanks to MEGA 6 softwarewith using Kimura model. dN is the substitution rate not synonymous and dS the substitution rate synonymous. The level of significance was held to 5% and a bootstrap value of 1000 replications.
While there is a great number of specific models of pattern formation or shape generation tailored to given species and particular embryonic events, only a handful of software tools propose a generic combination of mechanical and chemical rules in the global context of multicellular development (Supplementary Table  1  and Supplementary References). None of these systems, however, offers a fully integrated architecture relating molecular signalling, genetic regulation and structural changes to one another: Ingeneue 20 embeds gene networks in sheets of cells, but does not distinguish heterogeneous cell types or behaviours; VirtualLeaf  21  and Cellzilla  22  both simulate the transport of chemical signals across a polygonal mesh of elastic compartments, the latter also allowing the specification of complex chemical networks, but only for plant growth in 2D and without coupling between GRN and biomechanics; CompuCell3D (ref.
23), an on-lattice voxel-based model without freely moving components, provides a simulation environment merging the cellular Potts model with chemical fields and diffusion equations. Macroscopic cell behaviours are linked to intracellular molecular concentrations simulated using an external Systems Biology Markup Language (SMBL) library such as libRoadRunner; CellSys  24  focuses on multi-agent simulations of tumour growth and LBIBCell  25  couples viscoelastic cell mechanics with reactionadvection-diffusion solvers, but neither includes GRN specification. Our open-source modelling platform, MecaGen, links GRN dynamics to the control of individual cell behaviours in order to account for a wide range of morphogenetic events constitutive of animal early embryogenesis. Aiming for a trade-off between computational feasibility and physical realism, it relies on an agent-based model with one particle per cell obeying a set of ordinary and partial differential equations, both Newtonian dynamics and reaction kinetics.
2 ). Here, cells are classified into the three generic 'archetypes' observed during the early stages of embryogenesis across a wide variety of animal embryos: mesenchymal cells (M), epithelial cells (E) and 'idle' cells (I). The latter exhibit no active biomechanical behaviour but still offer resistance to deformation. Other cell archetypes appearing at later developmental stages through further differentiation are not specified here. Extraembryonic structures, such as the yolk and enveloping layer in the zebrafish, are handled by adding customized code to the MecaGen platform to model particles and forces similar to cells but not belonging to an archetype. Like gene expression, differentiation into M or E is controlled by two specific 'output' nodes of the GRN representing Boolean functions of proteins f(p) (Supplementary Note 4, 'Control of Archetype'), with a limited number of inputs under the simplified representation of molecular dynamics adopted here.
Their combination and distribution over a large assembly of self-organizing elements open up an infinite space of possible emergent phenotypes. The MecaGen framework was designed to be scalable to integrate new features, but also compatible with massive model exploration and parametric search software (Supplementary Note 3). In particular, combined with evolutionary methods such as genetic algorithms, the developmental machinery of MecaGen contributes to the foundations of a new kind of 'digital evo-devo' science. Beyond the few case studies presented here, exploitation of a generic model like MecaGen can be applied to a range of morphogenetic events in the embryonic development of different model organisms such as Xenopus, Echinoida, Drosophila, or other types of multicellular processes such as tumour formation, growth and spreading in 3D space and time.
Acknowledgements We thank  D. Fabre  `ges for his help in testing the MecaGen software. This work was supported by a  CNRS-DGA  scholarship to J. D. , the  ZF-Health EC  project  HEALTH-F4-2010-242048 , the  France BioImaging infrastructure   ANR-10-INBS-04  and the  Equipex Morphoscope2   ANR-11-EQPX-0029.
Called peaks are further analyzed for functional, structural and biochemical characterizations of the RNA-protein interaction, including motif discovery, expression profile and gene ontology. Importantly, because recent advances in sequencing technologies and bioinformatics analyses enable us to handle many CLIP-seq samples simultaneously, it is important to optimize a bioinformatic pipeline that can facilitate the work of researchers in obtaining unbiased and high-quality data. Despite great efforts from researchers to streamline CLIP-seq analysis, much remains to be improved on both experimental (e. g. the quality of the antibody used for the immunoprecipitation) and computational procedures. In fact, because of the complexity of the large data set coming from high-throughput technologies such as CLIP-seq, a correct interpretation of the data is facilitated by a proper and an accurate data analysis with refined and optimized computational tools.
In this review, we describe the computational protocol for CLIP-seq analysis, discuss the latest bioinformatics developments for data processing and mining and provide advices for data analysis interpretation. We discuss the validity and the limitations of emerging programs for each step of the CLIP-seq analysis and the quality measurements currently available for specific tasks, by providing concrete examples on a case study: an inhouse Ago2 HITS-CLIP data set generated from stem cells  [24]  (GEO accession: GSE85219). For simplicity and limited space, we focus the scope of this review in providing a valuable computational guideline for the bioinformatics analysis of the three main variants of CLIP-seq analysis, namely, HITS-CLIP, PAR-CLIP and iCLIP. To help the readers, we provide in Supplementary Table  S1  the Web links to download all the programs cited in the present review.
Bioinformatics workflow for CLIP-seq analysis In Figure  1 , we have summarized the main computational steps for CLIP-seq analysis. Recently, few computational pipelines have been developed such as CIMS  [25] , CLIPSeqTools  [26] , CLIPZ  [27]  and PARCLIPsuite  [11] , which provide useful resources to deal with preprocessing steps and some of the main steps of the analysis, including peak-calling procedure. In the following sections, we describe step-by-step the bioinformatics workflow of the CLIP-seq analysis giving a quality overview of existing software and providing practical examples on an inhouse data set for Ago2 HITS-CLIP experiments on P19 stem cells  [24].
Preprocessing and read mapping onto the reference genome The first step of the analysis is the preprocessing that involves adapter removal, filtering raw data according to read quality scores and collapsing reads with the exact sequence. While for the adapter removal, specific programs have been developed such as cutadapt  [28]  or Trimmomatic  [29] , for the quality filtering, usually bioinformaticians develop ad hoc scripts. However, lately few programs have been developed, such as FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/) and PRINSEQ  [30]. To quantify the differences of the strategies used by different preprocessing programs to filter reads based on quality scores and collapse duplicated reads, we applied FASTX-Toolkit, PRINSEQ and the CIMS pipelines to a published in-house Ago2 CLIP-seq data set from mouse P19 stem cells  [24]  (GEO accession: GSE85219).
This analysis was run with the default tuning or the recommended parameters (see Supplemental Information). While some programs have tunable parameters, we forgo parameter optimization, which might have improved the results for some data sets, as this task may be beyond the ken of most users. The highest number of reads that survive the preprocessing step for the three replicates was obtained using PRINSEQ (Table  1 ). To inspect the quality of the reads after the preprocessing, we used the program FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/ developed by S. Andrews). As shown in Supplementary Figure  S1 , although FASTX-Toolkit yielded reads with best quality score per sequence and per base, CLIP-seq computational guideline | 1291 this program filtered out much more reads compared with the other two programs (Table  1 ).