dataesr/scientific-paragraphs-categorization huggingface dataset:
Viscoplastic self-consistent modeling (VPSC) In this paper, we explore the possibilities of the viscoplastic selfconsistent approach  (Lebensohn and Tomé, 1993)  using the software VPSC7 compiled for Mac OS X. Through this approach, the overall response of the aggregate is averaged from known properties of the constituent grains, and the effective medium assumption is considered to calculate the interactions between each grain with the surrounding ones. In the VPSC approach, strain compatibility and stress equilibrium are averaged at the aggregate scale, and the microscopic stress and strain rate may differ significantly from the macroscopic values. The plastic anisotropy is also considered in this approach. Although this factor has a minor influence in quartz because of the relatively large number of slip systems, it is significant in materials with few slip systems.
Orientation distribution functions and pole figures were calculated using the software PFvpsc  (Mainprice, 1990) , whereas inverse pole figures were plotted using the MTEX toolbox for Matlab  (Hielscher and Schäben, 2008). Prior to the simulations with combined slip systems, we performed a series of models to illustrate the development of preferred orientations when the slip systems act alone (Fig. 2 ). The relative CRSS values for the simulations with multiple slip systems are represented as simple ratios. For example (see the x-axis in Fig. 3 ), if baN(c)/baN{r/z} = 1/10, the CRSS for b aN{r/z} slip is 10 times stronger than the CRSS for baN(c) slip, such that the latter system dominates the CPO. In contrast, if baN(c)/b aN{r/z} = 1/1, the values of CRSS are identical for both systems. Obviously, if b aN(c)/baN{r/z} = 10/1, then the CRSS for basal ba N slip is ten times higher than that for rhomb b aN slip.
The whole merger sample is built using three distinct idealised disk models G1, G2, and G3 that are also simulated in isolation with stellar masses respectively of log M = 9. 8, 10. 2, and 10. 6. These stellar masses were selected to sample the stellar mass histogramme of a representative spectroscopic sample of 1 < z < 2 galaxies, namely the MASSIV sample  (Contini et al. 2012). Each model is composed of a stellar disk, a gaseous disk, a stellar bulge, and a dark matter halo. The two disk components are built using an exponential density profile, while the stellar bulge and the dark matter halo are following a common  Hernquist (1990)  profile. The initial gas fraction of the three disk models is f g = 0. 65 which is in agreement with the most gas-rich 1 < z < 2 population observed  (Daddi et al.
2010a ). These simulations were carried out using the adaptive mesh refinement code RAMSES  (Teyssier 2002) , allowing to reach a physical resolution of 7 pc in the most refined cells. The modelled gas physics in the simulations includes metal lines cooling, star formation using a local Schmidt law, metallicity advection as well as a recent physically-motivated implementation of stellar feedback  (Renaud et al. 2013 ) that encompasses OB-type stars radiative pressure, photo-ionization heating and supernovae. The SFH of each simulation has been obtained by computing a mass-weighted histogramme of the age of the stars particles using a bin of 1 Myr over about 800 Myr. Because of the high gas fraction of the galaxy models, Jeans instabilities arise in the first dynamical times of the simulations and trigger very high SFR, more than 2σ away from the normal star formation regime of the simulations.
Simulating observations For each of the 23 MIRAGE simulations, we compute the attenuation-free spectra every 1 Myr from the SFH, foregoing all spatial information. To do so, we use the cigale SED modelling code  (Boquien et al. , Burgarella et al. in prep. ). We adopt the stellar population models of  Bruzual & Charlot (2003)  with a Chabrier (2003) IMF (initial mass function) between 0. 1 M and 100 M and a metallicity Z = 0. 008, consistent with the subsolar metallicity of galaxies at the considered redshifts (e. g. Yuan et al. 2013 ). We will discuss the impact of the metallicity on our results in Sect. 4. 2. The emission from H ii regions includes recombination lines and the nebular continuum via freefree, free-bound, and two-photon mechanisms. The luminosity in individual bands is computed by integrating the spectrum in the corresponding passbands except for the number of Lyman continuum photons which is an output parameter of the model, and for the TIR luminosity, which is computed by integrating over the entire spectrum, based on the assumption that the galaxy is entirely buried, following  Kennicutt (1998).
These spectra are created with the cigale SED modelling code  (Boquien et al. , Burgarella et al. in prep. ). In the present case we assume a Chabrier (  2003 ) IMF and a metallicity Z = 0. 008. Nebular emission is also included. It takes into account recombination lines and the nebular continuum from free-free, free-bound, and two-photon processes. The continuum contributes most in the red part of the shown spectra. We have computed the spectra for all 23 MIRAGE simulations (3 isolated galaxies and 20 interacting galaxies) every 1 Myr. Fig. 3. Left: SFR(true) (black), SFR(Ly) (magenta), SFR(FUV) (blue), SFR(NUV) (cyan), SFR(U) (green), and SFR(TIR) (red) for the MIRAGE G2 G2 90 90 90 simulation  (Perret et al. 2014). Note that SFR(true) and SFR(Ly) are nearly blended. Right: zoom between 350 Myr and 450 Myr.
Conclusion In this paper we have investigated the impact of the SFH properties on the measurement of the SFR in galaxies through classical estimators. To do so, we have used the state-of-the-art MIRAGE sample which consists of a set of 23 detailed hydrodynamical simulations of gas-rich mergers and isolated galaxies, including highly refined star formation prescriptions. It is designed to reproduce the characteristics of MASSIV  (Contini et al. 2012) , a spectroscopic sample of 1 < z < 2 galaxies. Passed the initial relaxation, the SFH show little to no long term evolution but have rapid variations of the SFR with an amplitude of σ SFR 0. 34 ± 0. 06 × SFR. Peak-to-peak variations are of the order of a few. We have then combined the simulated SFH with the cigale SED modelling code  (Boquien et al.
Fig. 3 ). Rasmussen and Kellis (2012)  implemented this model into a program that can reconcile a gene tree with a species tree, given rates of duplication and loss, a dated species tree, and effective population sizes. The DTLSR  (Tofigh 2009; Tofigh et al. 2011; Sjöstrand 2013)  and ODT models combine a model of gene transfer with a model of gene duplication and loss. Both are natural extensions of the DL models, and do not employ an additional object like the locus tree in the DLCoal model. Instead, the birth-death process is modified to accommodate two types of birth events, gene duplications and gene transfers. The ODT model has been applied to biological datasets, and has been extended to account for gene transfers that involve species that have gone extinct or are otherwise unrepresented in the tree under consideration  (Szöllősi et al.
Second, the inferred gene trees were then used to simulate alignments. Third, these alignments were fed back into ALE+ex-ODT to assess its reconstruction accuracy, comparing both the reconstructed gene trees and the associated duplication, transfer and loss events to those used in the simulation. This approach has the advantage of circumventing potentially complex simulations while at the same time retaining otherwise hard to reproduce properties of biological datasets, such as the distribution of gene family sizes and the variation of evolutionary rates within and among gene families  Szöllősi and Daubin (2012). Computing the conditional probability of a gene tree. -By the joint conditional probability of a gene tree, we mean the probability of a gene tree given a gene alignment and a species tree.
The use of a species tree in addition to a gene alignment yields better gene trees than methods that only consider the gene alignment. Akerborg et al. (2009)  studied a dataset of about 180 gene families in 17 yeast genomes with two methods, their own method that uses the sequence alignment and a species tree, and mrBayes, that only uses the alignment  (Ronquist et al. 2012). Several of these yeast species descend from a species whose genome has been duplicated. As a consequence, all gene trees in the data set must show a duplication event in the branch containing this species. They found that their method detects a branch corresponding to a whole genome duplication in 66% of the gene families, when mrBayes only detects this branch in 35% of the cases. Rasmussen and Kellis (2010)  obtained a similar result by comparing the inferred orthologs from gene trees obtained using 11 methods to orthologs inferred from synteny information, on a data set of 16 fungi.
VOL. 64 FIGURE  7. species tree inferred by PHYLDOG, with ancestral genome contents reconstructed by different methods on selected nodes. Ancestral genomes reconstructed by PHYLDOG, in blue, have a size similar to that of extant genomes. Right: reconstruction of ancestral gene neighborhoods. Two genes are considered neighbors if there is no other gene between them in the dataset, so in a linear chromosome every gene (except two) should have two neighboring genes. So we expect from a method to recover exactly two neighbors for most ancestral genes. PHYLDOG is in blue. Although gene tree reconstruction benefits from the input of an accurate species tree, it can probably suffer from the use of an erroneous species tree. Unfortunately, especially as new genomes get sequenced, accurate and well-resolved species trees are not always available.
To address such situations and jointly infer the species tree and gene trees, we developed PHYLDOG  (Boussau et al. 2013)  and Fig. 7. We tested this program on both simulated and real data and found that both the species tree and the gene trees were reconstructed with good accuracy. Notably, as with the methods that use a given species tree to reconstruct gene trees, we found that our method improved upon methods that only use sequence alignments as input. From a statistical point of view, joint reconstruction of the species tree and gene trees is the most principled approach, but it is computationally very challenging. In  Szöllősi et al. (2013a) , we used an approach that is almost as accurate as methods that explicitly infer gene trees while being easier to put in practice. We considered as our data a distribution of gene trees for each gene family instead of just a single gene tree.
Using the ALE dynamic programming algorithm (see section "Simulation and inference"), this approach provides a fast but accurate approximation of the actual amount of phylogenetic information contained in the sequence alignment. We found that by not using a single gene tree as our data, the estimates of the number of gene transfers in the Cyanobacterial data set were 60% lower than when a single gene tree was used. This suggests that the claim that there have been too many transfers in Bacteria and Archaea for reconstructing the tree of life may have been a premature exaggeration. Szöllősi et al. (2013a)  provides a method to reconstruct a gene tree given a species tree and rates of duplication, transfer, and loss, which can be given or all be inferred provided enough information is given to the program.
estimation of parameters, and exploration of dated or ordered species trees combine intractable problems. In practice, optimizing a gene tree can necessitate up to a few hours for very large families. As there can be thousands of gene families in a typical dataset, the computations even for a fixed species tree can take a long time. However, models of gene family evolution as well as sequence-based models all make the assumption that genes evolve independently from each other. This assumption can be questioned (see below) and is also broken by evolutionary parameters shared among gene families. But it allows a trivial parallelization by the data. All genes trees can be computed independently, given a common species tree. Hence, a species tree exploration is mainly constrained by the largest multigene families.
Currently, every time a new comparative genomics project is undertaken, or every time a database of homologous sequences is updated, many inference tasks need to be redone from scratch. The computations of gene families, alignments, trees, and species tree are usually done as if there was no prior information obtained from previous analyses. This is obviously a huge waste of useful information, as these computations are often very demanding. Future approaches to comparative genomics will need to be not only integrative, but also incremental. There is a clear need for new developments, and the Systematic Biology community is well equipped to undertake them.
FUNDING This project was supported by the  French Agence Nationale de la Recherche (ANR)  through Grant  ANR-10-BINF-01-01  " Ancestrome. "G. J. S. was supported by the  Marie Curie CIG 618438 "Genestory"  and the  Albert Szent-Györgyi Call-Home Researcher Scholarship   A1-SZGYA-FOK-13-0005  supported by the  European Union  and the  State of Hungary , co-financed by the  European Social Fund  in the framework of  TÁMOP 4. 2. 4. A/1-11-1-2012-0001  " National Excellence Program. ".
However, the design of these assays remained mostly empirical, being notably based on the use of the 16S rRNA gene  [3] , until bacterial genome sequencing became a reality in the mid-1990s  [4]. Microbial genomics, enabling a rational design of most molecular assays by selecting molecular targets according to their objective, has now had a major impact on the diagnosis and prevention of infectious diseases, with detection and identification of pathogens being directly performed within specimens without the need for culture  [5]. Since 2005, the development of next-generation sequencing (NGS), together with decreasing costs for sequencers and reagents, has democratized genomics (Table  1 )  [6]. Currently, a bacterial genome sequence can be obtained within a few days for less than US$500  [6] , and more than 38,000 genome sequences are available in public databases  [7].
As the genomes from more than 37,000 bacterial strains are currently available, including those of all major human pathogens, it is now possible for clinical microbiologists to design specific PCR assays according to their needs by using the available tools. As examples, Marshall developed 'PerlPrimer' , a software enabling the design of target-specific PCR or RT-PCR primers  [15] , Pritchard and colleagues proposed an alignment-free method for designing strain-specific primers for Escherichia coli O104:H4  [18] , and Hung and associates designed a stepwise computational approach mixing several publicly available softwares to identify species-specific signatures in whole-genome sequences  [17]. Using Streptococcus pyogenes as a model, Hung and colleagues designed and tested the validity of 15-signature-derived primer sets, including nine that were highly species-specific in vitro  [17].
Fenollar and colleagues identified a seven-copy fragment in the genome from the bacterium Tropheryma whipplei and demonstrated that a RT-PCR assay targeting this repeated fragment was significantly more sensitive than assays targeting a single-copy fragment  [23]. By contrast, Drancourt and colleagues developed a strategy named 'suicide PCR' that is based on nested-PCR assays targeting genome fragments that had never been used as PCR targets previously and that will be targeted only once with single-use primers  [25]. These authors also demonstrated a higher sensitivity of their method over regular PCR. Targeting multicopy fragments was demonstrated to be highly sensitive for the detection of Q fever, Whipple's disease, brucellosis, Streptococcus pyogenes  [17]  Syndrome-based detection RT-PCR Febrile illness  [20]  Multiplex RT-PCR Tuberculosis  [8]  Microarray Pneumonia  [22]  Highly sensitive molecular detection PCR targeting multi-copy targets Whipple's disease  [23]  Suicide PCR Rickettsioses  [24]  Genotyping DNA banding methods Pulsed-field gel electrophoresis, PCR-RFLP Yersinia pestis  [9]  Presence/absence of genes RT-PCR Acinetobacter baumannii  [29]  Presence/absence of repeats MLVA Mycobacterium tuberculosis  [32]  Presence/absence of point mutations SNP detection Bacillus anthracis  [35]  Whole-genome typing Microarray Escherichia coli  [44]  Genome sequencing Staphylococcus aureus  [61]  Multiple gene sequencing MLST Escherichia coli  [49]  Multiple non-coding fragment sequencing MST Rickettsia species  [56]  Detection of virulence markers Comparison of virulent/avirulent strains Yersinia pestis  [10]  Identification of lateral gene transfer Salmonella Enterica  [73]  Search for known virulence factors in public databases Campylobacter species  [77]  Detection of antibiotic resistance Comparison of resistant/susceptible strains Streptococcus pneumoniae  [96]  Detection of antibiotic resistance markers in clinical isolates and specimens.
Non-sequence-based genotyping methods PFGE and PCR-RFLP have long been considered as 'gold standard' genotyping methods. These methods are DNAbanding-pattern-based methods that compare the electrophoretic profiles of restriction-enzyme-cut genomes or PCR-amplified genes from various strains. Initially, these methods relied on uncharacterized genomic differences or empirically selected target genes. By contrast, genome sequences, as was the case for M. tuberculosis or Y. pestis  [9] , can be used to rationally improve the sensitivity and specificity of PFGE or PCR-RFLP by enabling the 'in silico' prediction of the most appropriate restriction profiles of rare-cutter enzymes for a given bacterium. In an alternative approach, Yang and colleagues have used genomics to design the 'Pan-PCR' software, dedicated to the identification of strain-specific PCR targets in genome sequences in a 'presence/absence' mode, that is, the amplification of a series of unrelated genes that were differentially present in the genomes from the studied strains  [29].
Sequence-based genotyping methods By comparison with non-sequence-based methods, sequencebased genotyping has the major advantage of being highly reproducible because the sequence fragments on which it is based are stored in public databases. Sequence-based genotyping methods can rely on the selection of one or several genomic targets or on the whole genome sequence. Single-locus sequence-typing methods require the in silico identification of a highly variable gene, such as the coagulase-and protein-A-encoding genes that are the genomic targets of coa or spa typing, respectively, two broadly used tools for Staphylococcus aureus  [46, 47]. MLST, developed in 1998, is one of the most frequently used sequence-based genotyping methods. It is based on the combination of genotypes obtained from several individual genes, usually housekeeping genes, for characterizing bacterial strains  [48].
This is notably the case for S. aureus, in which the secretion of the Panton-Valentine leukocidin is induced by oxacillin or depressed by clindamycin  [63, 64] , for the Shiga-toxin production in E. coli that is stimulated by β-lactams, sulfonamides and fluoroquinolones  [65] , and for Rickettsia conorii, in which fluoroquinolones upregulate a toxin-antitoxin module  [66]. Therefore, determining the toxinic repertoire of strains of selected bacterial species can be crucial for effective clinical management. Genomics has played an important role in the identification of virulence factors in bacteria. Three main strategies are used to identify virulence-factor-encoding genes in genomes  [67] : first, comparison of genomes from strains or species exhibiting diverse degrees of virulence; second, identification of laterally transferred genomic islands, assuming that virulence genes are often acquired by this mechanism  [67] ; and, third, running the genome against databases of known virulence markers.
tuberculosis, in which a putative rRNA methyltransferase might explain its resistance to macrolide antibiotic drugs  [95]. Several PCR assays used in clinical practice derive from genomic sequences. The recent discovery of the mecC gene -a homolog of the mecA gene encoding methicillin resistance, responsible for false susceptibility testing results -in the genome of a methicillin-resistant S. aureus  [99]  elicited the design of specific PCR assays  [100]. The spread of carbapenemase-producing enterobacteriaceae also prompted the sequencing of genomes from various MDR strains, including an NDM-1-producing E. coli strain  [101]  and a bla KPC2 -producing K. pneumoniae  [102] , which in enabled the development of dedicated PCR assays  [103]. Therefore, although many genome-based molecular tests facilitating the management of infections have already been developed to date, there is no doubt that WGS data will be used extensively in future assay design.
In Germany, Mellmann and colleagues compared the genomes from two E. coli O104:H4 strains from two hemolytic uremic syndrome outbreaks and concluded that the strains had diverged from a common ancestor and that NGS was suitable for the characterization of a pathogen in the early stages of an outbreak  [115]. In both cases, genome sequences were obtained in a few days (five and three days, respectively). These findings demonstrated how rapid and precise genomic sequencing, although limited to a few clinical-microbiology laboratories currently, could transform patient management or improve hospital infection control in routine clinical practice. Although only a few studies to date have described a turnaround time sufficiently short to enable WGS data to influence the course of outbreaks  [119] , the increasing number of teams using WGS for epidemiological purposes (Table  3 ) leaves little doubt as to the likelihood of its systematic use as a first-line tool to track and understand epidemics in the near future.
However, classical methods (enquiries, surveys, soundings, diagnostics…) commonly used in Social and Human Sciences and Engineering Sciences are limited regarding to methodological (lake of reliability, insufficient answers rates…) and operational aspects (waste of time, duplications, lake of interest for the organizations investigated…). In this paper, we attempt to overcome these limitations by proposing WeKeyInnovation (WKI), an open interactive and collaborative support based on the crowdsourcing approach  [2]. Indeed, it can be used and enriched progressively by any individual or organization interested in innovation processes. Our open and collaborative platform is designed according to two main objectives:  The first is to give an online access to information related to existing methods, tools, software, funding, consulting… that can be used by companies to manage and support their creativity and innovation processes.
WeKeyInnovation Features The purpose is to develop a collaborative platform opening creativity and innovation processes to all individuals and companies with a special focus on SMEs. In this section, in order to illustrate and answer the key points we presented in the previous section, we will introduce our main proposal: the design of an interactive and collaborative opened platform (1) (2) (4) (10) designed to support SMEs in their operational daily tasks and enhance the creativity and innovation processes they would like to start, improve or implement. The idea is to develop a way toward a real "innovation culture" based on a broad creativity mindset within the WKI community we created (3) (10). One of the main key point we assert to answer implementing WKI is to better understand the practices of all the organizations by collecting raw data without having to resort to traditional methods.