dataesr/scientific-paragraphs-categorization huggingface dataset:
It converts SWATH-MS data from XML standard formats to mzDB, (supporting also the variable m/z window isolation width setup, recently introduced for SWATH-MS acquisitions). The "mzDB-swath" library provides the methods to perform classical targeted data extraction of SWATH-MS data using the R*Tree index, enabling efficient high-throughput XIC extraction of fragment ions. The built-in query access method returns a peak list, compliant to the mzXML/mzML spectra representation. The Cϩϩ implementation for DDA files (pwiz-mzDB) is available under the Apache 2. 0 license (which also applies to ProteoWizard), whereas the Java counterpart (mzDB-access) is distributed under the CeCILL-C license. The Java library for DIA files (mzDB-swath) is licensed under GPL 3. 0 license. The software packages can be downloaded from a dedicated web-site (https://github.
This leads to a reduced loss of information compared with the centroided mode. This fitted mode is optional and the user can choose how to encode each MS level: profile, centroided, or fitted. For SWATH-MS data, the conversion to mzDB is simply reproducing the same data stored in the XML format. Benchmarks-Performance of mzML, mz5, and mzDB file formats was assessed through a rigorous benchmark setup, using either DDA or SWATH-MS data. We first compared the file size obtained after converting a raw file into the different formats. We toggled on/off the compression option of the Proteowizard "MSconvert" tool when creating the mzML and mz5 formats, and generated the mzDB format either in profile or fitted mode using the "raw2mzDB. exe" tool. Results are shown in supplemental Fig. S3  for DDA data, and indicate that mzML file, depending on whether it is compressed or not, need two to three times more space compared with the raw file.
Compressing spectra using zlib considerably reduces the size of mzML and mz5 data sets, with a gain around a factor of two. SQLite offers compression as well (e. g. using the Compressed and Encrypted Read-Only Database commercial extension), however, we deem that the fitted encoding mode introduced by mzDB is a valuable option for data size reduction, where a parameterized model of the data is saved. Finally, another option to compress the data set is to use algorithms compressing the whole file, as recently proposed for XML formats as well (44). Regarding conversion time, we have observed that this represents around 1 min per gigabyte (relative to the size of the native raw file). Of course, any workflow that would be based on the use of mzDB will have to include this additional conversion time, as opposed to pipelines that can directly access the raw files by making use of proprietary constructor libraries (e.
It can be quantified several times, after changing for example the list of targeted peptides in SWATH data sets, changing the experimental design and including new compared conditions in label-free DDA experiments, or simply changing the tuning parameters of the quantitative software. This last point may not be underestimated, as very often, the duration of the bioinformatics processing hinders any optimization by the users of the default parameters in many software tools. Finally, there are examples of processes that can be shorter than the conversion time itself, but that will be repeatedly performed by the user and will benefit strongly from the conversion, such as extraction of one particular XIC for visualization of the data in a graphical interface. Once the conversion is performed into mzDB, the data can be accessed multiple times very quickly, and for example, the XIC of any peptide of interest can be retrieved and displayed in a small fraction of a second.
This would offer the possibility for the user of a very efficient visualization and interaction with the data, which is not always achieved with current tools dedicated to the display and exploration of raw data. The mzDB format is currently implemented in a fully integrated, open-access label-free quantitative proteomic pipeline, based on algorithms that typically take advantage of the indexing of the data to retrieve MS signal (unpublished results). This new software will thus offer to users an optimal solution for the management of DDA and DIA data sets. Besides this forthcoming implementation, it is important to stress that the mzDB format could easily be used as input for many current label-free proteomic tools. To that aim, we provided here multiple software libraries for mzDB file creation and usage, e.
g. pwiz-mzDB, mzDB-access, and mzDBswath. The availability of these Cϩϩ and Java tools should simplify the handling of this new format for programmers, as they can choose the library more suited to the programming language in which their application is developed. Because mzDB is based on the standard SQLite technology, an additional advantage is that it can be intuitively browsed by nonspecialized users by means of existing dedicated SQLite graphical user interfaces (supplemental Fig. S4 ). In addition, SQLite adoption enables the use of the "Structured Query Language" (SQL) through the embedded SQLite engine, for example, it is possible to execute simple queries to retrieve in a few seconds any subset of the data file or some metadata information such as acquisition parameters, ion injection times, precursor ions m/z and charge, etc.
It must be noted that mzDB, as a hybrid combination of an SQL model and some few XML schema definitions, is very flexible to use and easily extendable: indeed, the XML strings are particularly suited for the agile representation of the metadata. The semantic of this XML model has been inspired by the mzML format, in order to simplify the format conversion procedure. Moreover, it ensures metadata sustainability because of their compliance to the Ontology developed by the HUPO Proteomics Standards Initiative. Finally, the widespread adoption of the SQLite technology by all programming languages assures that mzDB can be easily implemented in any existing mass spectrometry data analysis software (e. g. MaxQuant, OpenMS, ProteoWizard, and Skyline). In conclusion, we have shown that, in comparison to existing file formats, mzDB has strong advantages both in terms of performance, compactness, sustainability, and usability.
Acknowledgments-We thank  Matthew Chambers  for his support in the usage of ProteoWizard and the fruitful discussions with  Francesco Silvestri , and  Piero De Gol  for informatics consultancy and careful reading of the manuscript. We are grateful to  Pierre-Alain Binz  for his advices about the integration of the  PSI-MS Controlled Vocabulary  into the mzDB format. We would like to thank the  French Ministry of Research  with the " Investissement d'Avenir Infrastructures Nationales en Biologie et Sante" program  ( ProFI, Proteomics French Infrastructure  project,  ANR-10-INBS-08 , to BM) for support of the work.
The Cϩϩ implementation is available under the Apache 2. 0 license (which also applies to ProteoWizard), the Java counterpart is distributed under the CeCILL-C license and the Java library for SWATH-MS is licensed under GPL 3. 0 license. The software packages can be downloaded from a dedicated website (https://github. com/mzdb). For any enquiry about the mzDB project please do not hesitate to contact: mzDB. developers@gmail. com.
erk1/ERK1 and erk2/ERK2 phylogenies are indicated by red and blue branches respectively. Key taxonomic clades for this manuscript (tetrapods and Chondrichthyes) are indicated by brackets. Accession numbers and common names are listed in Additional file 7. Branch length is indicative of degree of sequence divergences. The order of species in the derived trees is noted to vary slightly between different topological methods and whether it is calculated using protein or nucleotide sequences. Given that all vertebrate ERK sequences are highly conserved, minor variations in tree placement can be expected Fig. 1  ERK1 and ERK2 protein appear first in bony vertebrates (Bichir). Western-blot analysis of samples from brain, spinal cord or cultured cells: immunoblots were incubated with polyclonal anti-ERK antibody (ERK1 and ERK2; panels a, b, c) or anti-phosphoERK antibody (pERK1 and pERK2; panels d, e, f).
The search of exons in fully sequenced genomes combined with the screening of EST databases reveals that the erk1 gene is absent from the genomes of Xenopus tropicalis (amphibian), chicken (Gallus gallus, a neognathae)  [27]  and 4 other recently published genomes of neo-gnathae (Ensembl release 75). Furthermore, ERK1 protein expression is not detectable in the brain of the paleo-gnathae nandu (Rhea. americana, Additional file 2C), and no erk1 gene was identified in the genome of the paleo-gnathae ostrich (Struthio camelus). These observations suggest that erk1 has been lost in all bird lineages and in some amphibians. As summarized in Table  1 , only erk1 gene has been lost in some tetrapods and all chondrichthyes. In reptiles either one or both ERKs are expressed Both ERK proteins are expressed in turtle brain, however crocodiles appear to express only ERK2 protein in the same organ (Fig.
DNA sequences erk coding sequences were gathered from genomic sequencing by searching the Ensembl database or by exon search by BLAST in NCBI database or ambystoma. org database. Hagfish erk sequence was obtained from transcriptomic sequencing. For animals with unknown erk sequences, tissues fragments were dissolved into 1 ml TRIzol reagent (# 15596-018 Life Technologies), aqueous phase containing RNA was separated by adding 20 % chloroform, then RNA isolation was performed with aqueous solution with RNeasy Mini kit (Qiagen # 74124) as per manufacturer specifications. cDNA was generated by oligo-dT or random hexamer priming with either omniscript enzyme (Qiagen # 205111 ), primescript enzyme (Clonetech # 2680A) or revert aid enzyme (Thermo-scientific EP0441) according to manufacturers' specifications.
Phylogeny ProtTest  [39]  was used to evaluate the best model to use with the MaximumLikelihood method and the protein sequences for Fig. 2b. The tree obtained with the best model showed little difference with the tree resulting from the consensus of all model. In addition sequences were analyzed using SeaView  [40]. Sequences were aligned as amino-acid sequences and then back converted to nucleotide sequences (as implemented in SeaView) and alignments were manually checked. Trees were then built using the BioNJ (Kimura correction), Parsimony and Maximum likelyhood methods as implemented in SeaView. For computations of Shannon entropy, an ad-hoc Python program was written. For nucleotide sequences, indels were treated as a 5 th character. Since erk1 and erk2 differ in length, positions that are all indels in one of the gene have an energy of 0; this can be clearly misleading, but it allowed direct comparison of other positions.
For the identification of Site-Specific Positive or Purifying selections, we used the Selecton program, locally compiled under Linux and with default values  [41]. For the calculation of global Ka/Ks ratios of Table  2 , the mean nucleotide substitution rate, amino acid substitution rate, nonsynonymous substitution rate (Ka), synonymous substitution rate (Ks), and nonsynonymous to synonymous substitution rate ratio (Ka/Ks) were calculated for ERK1 and ERK2 CDS alignments in MEGA 6  [42]. The shark ERK sequences were excluded in this calculation to ensure our calculation for Erk1 and Erk2 covered exactly the same species sampling. 94 mamalian sequences and 28 vertebrate sequences corresponding to the species' list of Fig. 2  were studied. The Jukes-Cantor correction model  [43]  was used when calculating nucleotide substitution rate; the Poisson model  [44]  was used when calculating amino acid substitution rate; and the Nei-Gojobori model  [45]  was used when calculating Ka and Ks.
The mean Ka/Ks value was calculated by calculating Ka/Ks for each pairwise comparison first and then taking the average. 3D structures used to generate still images 3D structures solved from ERK crystals were recovered from Protein Data Bank (PDB): 1ERK (rat ERK2)  [46] ; 4QTB and 4QTA (respectively human ERK1 and ERK2 bound to inhibitor SCH772984)  [47]. Sequences were opened with the freely available 3D software Cn3D (from NCBI), and amino-acids to be highlighted were colored with the annotation tool. (http://www. ncbi. nlm. nih. gov/ Structure/CN3D/cn3d. shtml). Still images were generated when 3D structure was rotating in the software Cn3D. For all structures, threonine and tyrosine phosphorylated upon activation by MEK are colored in white; the amino-acids interacting with substrates forming the DEJL(KIM) motif are colored in blue, light blue for docking groove and darker blue for acidic patch; the amino-acids interacting with substrates via the DEF domain (FXFP) are colored in green.
ERK1 protein sequences (top) and ERK2 protein sequences (bottom) from M. musculus and A. carolinensis are aligned side by side with the software multalin (http://multalin. toulouse. inra. fr/multalin/). Amino-acids in red are identical between mouse and anolis sequences, whereas amino-acids in black differ between mouse and anolis sequences. The percentage identity between sequences is calculated with the sequences after the arrow indicated in the figure (the N-terminal end of the molecules which is extremely variable in all vertebrates is not taken into account). C: 3D structures of human ERK1 and human ERK2 are highly similar, especially at the "face of the kinase". Human ERK1 and human ERK2 were aligned by multalin program, and amino-acids that diverge between ERK1 and ERK2 are highlighted in yellow on 3D human ERK1 structure (PDB #4QTB).
Phosphorylation sites, threonine, and tyrosine are indicated by arrows and are highlighted in yellow. ERK1 list in red, ERK2 list in blue, and single ERK list in black. Species names, sequences, accession numbers and common names are presented in that order. Note: yeast sequences of the two ERK orthologous genes, Fus3 and Kss1 are presented to illustrate that the phospho-ERK epitope is not fully conserved in yeast, unlike in sea anemone for example. B: Three distinct antibodies recognize phosphorylated ERK in extracts from mouse, lizard and hagfish. 10 % acrylamide gels were loaded with protein samples from mouse fibroblasts NIH3T3 or lizard embryo fibroblasts (A. sageii and A. carolinensis) or hagfish brain extracts as indicated in the figure (top). Mouse fibroblasts were pre-treated for one hour with 20 μM UO126 prior cell lysis to block activation of the ERK signaling cascade (lane1), or were stimulated with 10 % serum in presence of 1 mM sodium orthovanadate (NaVO 4-) for one hour to block phosphatases and activate maximally the ERK signaling cascade (lanes 2).
Acknowledgments We are grateful for the very generous gifts of animal material to:  Dr. T. Cabana  ( Opossum ),  Dr. E. Houliston  (Xenopus),  Drs. M. Avella  and  P. Pierson  (European seabass),  Dr. J-Y Sire  ( Bichir and African jewelfish ),  Dr. Y. Andéol  (Axolotl),  Dr. P. Fafournoux  ( Greater Rhea, Chicken, Cattle, Rabbit ),  Dr. G. Anderson  ( Winter skate ),  Drs. R Caldwel  and  M. Erdmann  (Coelacanth),  Dr. Cagnol  ( Mallard Duck , Phaesant),  Dr. S. Martin -La  ferme aux crocodiles-( Crocodile, Turtles, Common agama ),  Mr. Daoues -La  ferme tropicale-( Carolina Anole ,  Brown Anole ,  Corn Snake , Bullsnake, Eastern milksnake, Western terrestrial garter snake),  Restaurant"la maison baron lefevre" (Lamprey) ,  Mrs F. Latrache  (Dogfish). We also thank  F. Paput ,  J. Paput ,  Z.
ACID Properties A transaction is the execution of a program containing a sequence of operations (e. g. , reads and writes) performed on resources (e. g. , databases, objects, components, services, etc. ). The execution of a transaction ends with either commit or abort. The commit of a transaction makes the effects of its operations permanent while its abort cancels them  (Bernstein, 1997). To control the adverse effects of concurrent modifications in a data system, the transaction defines four properties identified by the acronym ACID (Atomicity, Consistency, Isolation and Durability). The transaction model generally associated with the ACID properties is the flat transaction model  (Gray and Reuter, 1993). This one is particularly suited to transactions running in parallel, short and with a limited data handling.
The package fitdistrplus provides functions for fitting univariate distributions to different types of data (continuous censored or non-censored data and discrete data) and allowing different estimation methods (maximum likelihood, moment matching, quantile matching and maximum goodness-of-fit estimation). Outputs of fitdist and fitdistcens functions are S3 objects, for which specific methods are provided, including summary, plot and quantile. This package also provides various functions to compare the fit of several distributions to the same data set and can handle to bootstrap parameter estimates. Detailed examples are given in food risk assessment, ecotoxicology and insurance contexts.
Moreover, the fitdistrplus package offers the possibility to specify a user-supplied function for optimization, useful in cases where classical optimization techniques, not included in optim, are more adequate. In applied statistics, it is frequent to have to fit distributions to censored data  (Klein and Moeschberger 2003; Helsel 2005; Busschaert, Geeraerd, Uyttendaele, and VanImpe 2010; Leha, Beissbarth, and Jung 2011; Commeau, Parent, Delignette-Muller, and Cornu 2012). The fitdistr function in the MASS package does not enable maximum likelihood estimation with this type of data. Some packages can be used to work with censored data, especially survival data  (Therneau 2014; Hirano, Clayton, and Upper 1994; Jordan 2005 ), but those packages generally focus on specific models, enabling the fit of a restricted set of distributions.
A third objective is thus to provide R users with a function to estimate univariate distribution parameters from right-, left-and interval-censored data. Few packages on the Comprehensive R Archive Network (CRAN, http://CRAN. R-project. org) provide estimation procedures for any user-supplied parametric distribution and support different types of data. The distrMod package  (Kohl and Ruckdeschel 2010)  provides an object-oriented (S4) implementation of probability models and includes distribution fitting procedures for a given minimization criterion. This criterion is a user-supplied function which is sufficiently flexible to handle censored data, yet not in a trivial way, see Example M4 of the distrMod vignette. The fitting functions MLEstimator and MDEstimator return an S4 class for which a coercion method to class 'mle' is provided so that the respective functionalities (e.
g. , confint and logLik) from package stats4 are available, too. In fitdistrplus, we chose to use the standard S3 class system because of its ease of understanding by most R users. When designing the fitdistrplus package, we did not forget to implement method functions for the S3 classes. Finally, various other packages provide functions to estimate the mode, the moments or the L-moments of a distribution, see the reference manuals of the packages modeest  (Poncet 2012) , lmomco  (Asquith 2014)  and Lmoments  (Karvanen 2006 (Karvanen , 2014)). This paper reviews the various features of fitdistrplus  (Delignette-Muller et al. 2015). The package is available from the Comprehensive R Archive Network (CRAN) at http://CRAN. R-project. org/package=fitdistrplus. The development version of the package is located at R-Forge as one package of the project "Risk Assessment with R" (http://R-Forge.
It was used in a quantitative risk assessment published by Delignette-Muller, Cornu, and AFSSA-STEC-  Study-Group (2008). R> library("fitdistrplus") R> data("groundbeef", package = "fitdistrplus") R> str(groundbeef) 'data. frame': 254 obs. of 1 variable: $ serving: num 30 10 20 24 20 24 40 20 50 30. Before fitting one or more distributions to a data set, it is generally necessary to choose good candidates among a predefined set of distributions. This choice may be guided by the knowledge of stochastic processes governing the modeled variable, or, in the absence of knowledge regarding the underlying process, by the observation of its empirical distribution. To help the user in this choice, we developed functions to plot and characterize the empirical distribution. First of all, it is common to start with plots of the empirical distribution function and the histogram (or density plot), which can be obtained with the plotdist function of the fitdistrplus package.
Classical distributions are already defined in that way in the stats package, e. g. , dnorm, pnorm and qnorm for the normal distribution (see ?Distributions). Others may be found in various packages (see the CRAN Task View on Probability Distributions; Dutang 2014). Distributions not found in any package must be implemented by the user as d, p, q functions. In the call to fitdist, a distribution has to be specified via the argument dist either by the character string corresponding to its common root name used in the names of d, p, q functions (e. g. , "norm" for the normal distribution) or by the density function itself, from which the root name is extracted (e. g. , dnorm for the normal distribution). Numerical results returned by the fitdist function are (1) the parameter estimates, (2) the estimated standard errors (computed from the estimate of the Hessian matrix at the maximum likelihood solution), (3) the loglikelihood, (4) Akaike and Bayesian information criteria (the so-called AIC and BIC), and (5) the correlation matrix between parameter estimates.
Below is a call to the fitdist function to fit a Weibull distribution to the serving size from the groundbeef data set. R> fw <-fitdist(groundbeef$serving, "weibull") R> summary(fw) The plot of an object of class 'fitdist' provides four classical goodness-of-fit plots  (Cullen and Frey 1999)  presented in Figure  3:  a density plot representing the density function of the fitted distribution along with the histogram of the empirical distribution, a CDF plot of both the empirical distribution and the fitted distribution, a Q-Q plot representing the empirical quantiles (y-axis) against the theoretical quantiles (x-axis) a P-P plot representing the empirical distribution function evaluated at each data point (y-axis) against the fitted distribution function (x-axis). For CDF, Q-Q and P-P plots, the probability plotting position is defined by default using Hazen's rule, with probability points of the empirical distribution calculated as (1:n -0.
The two-parameter Pareto distribution and the three-parameter Burr distribution (which is an extension of both the loglogistic and the Pareto distributions) have been fitted. Pareto and Burr distributions are provided in the package actuar. So far, we did not have to define starting values (in the optimization process) as reasonable starting values are implicitly defined within the fitdist function for most of the distributions defined in R (see ?fitdist for details). For other distributions like the Pareto and the Burr distribution, initial values for the distribution parameters have to be supplied in the argument start, as a named list with initial values for each parameter (as they appear in the d, p, q functions). Having defined reasonable starting values 1 , various distributions can be fitted and graphically compared.