dataesr/scientific-paragraphs-categorization huggingface dataset:
[36] : for T < 11 GeV we assume a(T ) = 0. 147 m 2 sr, for larger kinetic energies we derive an energy dependence by fitting the curve in Fig. 8  of Ref. [73]. Finally, we assume that the statistical error of the mock data in each energy bin is poissonian, and we allow for a 5% systematic uncertainty. The generated AMS mock data, together with the theoretical uncertainty bands of 40%, 20% and 5% sizes, are reported in Fig. 11. Due to geomagnetic effects, the efficiency will drop starting from energies of about 30 GeV, down to sub-GeV energies where the detection efficiencies (or, alternatively, the effective area) will be reduced to few percent of its nominal value  [74]. For this reason, we include in the analysis of AMS mock data only the energy range above T min = 1 GeV. Results are shown in Fig.
Consolider Ingenio 2010 Programme under grant MULTIDARK CSD2009-00064. LM acknowledges support from the Alexander von Humboldt foundation and partial support from the European Union FP7 ITN INVISIBLES (Marie Curie Actions, PITN-GA-2011-289442). This work is supported in part by the European Research Council (ERC) under the EU Seventh Framework Programme (FP7/2007-2013)/ERC Starting Grant (agreement n. 278234 -'NewDark' project).
Acknowledgments N. F. and A. V. acknowledge the  Research Grant  funded by the  Istituto Nazionale di Fisica Nucleare  within the  Astroparticle Physics Project  ( INFN grant code :  FA51 ). N. F. and A. V. acknowledge support of the  Strategic Research Grant  jointly funded by the  University of Torino and Compagnia di San Paolo  (grant Unito-CSP 2010-2012 entitled  Origin and Detection of Galactic and Extragalactic Cosmic Rays ). N. F. acknowledges support of the spanish.
Background: Tuberculosis is an infectious bacterial disease caused by Mycobacterium tuberculosis. It remains a major health threat, killing over one million people every year worldwide. An early antibiotic therapy is the basis of the treatment, and the emergence and spread of multidrug and extensively drug-resistant mutant strains raise significant challenges. As these bacteria grow very slowly, drug resistance mutations are currently detected using molecular biology techniques. Resistance mutations are identified by sequencing the resistance-linked genes followed by a comparison with the literature data. The only online database is the TB Drug Resistance Mutation database (TBDReaM database); however, it requires mutation detection before use, and its interrogation is complex due to its loose syntax and grammar.
Description: The MUBII-TB-DB database is a simple, highly structured text-based database that contains a set of Mycobacterium tuberculosis mutations (DNA and proteins) occurring at seven loci: rpoB, pncA, katG; mabA(fabG1)-inhA, gyrA, gyrB, and rrs. Resistance mutation data were extracted after the systematic review of MEDLINE referenced publications before March 2013. MUBII analyzes the query sequence obtained by PCR-sequencing using two parallel strategies: i) a BLAST search against a set of previously reconstructed mutated sequences and ii) the alignment of the query sequences (DNA and its protein translation) with the wild-type sequences. The post-treatment includes the extraction of the aligned sequences together with their descriptors (position and nature of mutations). The whole procedure is performed using the internet.
The results are graphs (alignments) and text (description of the mutation, therapeutic significance). The system is quick and easy to use, even for technicians without bioinformatics training. Conclusion: MUBII-TB-DB is a structured database of the mutations occurring at seven loci of major therapeutic value in tuberculosis management. Moreover, the system provides interpretation of the mutations in biological and therapeutic terms and can evolve by the addition of newly described mutations. Its goal is to provide easy and comprehensive access through a client-server model over the Web to an up-to-date database of mutations that lead to the resistance of M. tuberculosis to antibiotics.
These kits, unfortunately, detect only the most frequent mutations  [18, 19]. An alternative approach that allows for the exhaustive detection of mutations consists of the PCR amplification and sequencing of resistance-linked genes  [20, 21]  and, more experimentally, of complete genome sequencing  [22]. It is, however, time consuming to compare the sequences with the reference genome and the mutation identity with the literature data. Recently, Sandgren et al. [23]  established a comprehensive database that gathers information on the mutations associated with TB drug resistance and the frequency of the most common mutations associated with resistance to specific drugs. The TBDReaM database is a free online resource that allows for the molecular diagnosis of resistant TB after the processing of the amplified sequences by any method.
Although very helpful, the TBDReaM database has not been updated since April 2010, and its usage remains time consuming because it demands prior processing of the TB nucleotide sequences to detect mutations and cannot be easily interrogated due to its relaxed grammatical conception. Here, we describe MUBII-TB-DB, a database of the mutations of M. tuberculosis linked to resistance to firstline and second-line antibiotics that can be used to identify the mutations from a submitted sequence. This database and its related software, MUBII, have been developed to satisfy the need of clinical microbiology labs to easily analyze M. tuberculosis sequences and to link the results to a potential therapeutic failure. MUBII combines the use of reconstructed mutated gene sequences that can be searched by BLAST, aligned against the wild-type gene sequence, and compared with the mutation database.
Mutations database Data The database was constructed based on a systematic review of the literature, as described below. We focused on publications reporting an association between specific mutations in clinical isolates of M. tuberculosis and phenotypic resistance to INH, RIF, PZA, FQs, and AMK. The genes studied were rpoB for RIF; inhA, katG, and the promoter region of the mabA(FabG1)-inhA operon for INH; pncA for PZA; gyrA and gyrB for FQs; and rrs for AMK. As a starting point, we used the TBDReaM database because it constitutes a comprehensive resource on drug resistance mutations in M. tuberculosis based on studies published from January 1966 to December 2009  [23]. Additional publications on M. tuberculosis resistance mutations recorded in MEDLINE from December 2009 to March 2013 were included in the analysis (see infra).
Strategy of the systematic literature review All studies reporting an association between specific mutations in clinical isolates of M. tuberculosis and phenotypic resistance to INH, RIF, PZA, FQs and AMK that were previously selected for the construction of the TBDReaM database have been included again in our survey. Moreover, we searched the MEDLINE database for similar works issued since the TBDReaM database release (Dec 2009 -Mar 2013). We also searched for additional references in the bibliographies of the reports and reviews. Only English language articles were considered. Combinations of the following search terms were used: Tuberculosis AND (mutation OR mutations) AND (isoniazid OR rifampin OR fluoroquinolones OR amikacin OR pyrazinamide OR katg OR maba OR fabg1 OR inha OR rpob OR rrs OR pnca OR gyra OR gyrb).
Data retrieval We extracted and recorded information on the following: 1) the gene in which putative resistance mutations were found and the nucleotide and/or codon position of the mutation and 2) nucleotide and/or amino acid changes. Studies that did not allow the extraction of the above data or showed discrepancies in the wild-type sequences compared with published M. tuberculosis genomes sequences were excluded. The workflow of the literature review is summarized in Figure  1. The extracted mutations and original citations are supplied as Additional file 1.
~wildXPmutatedX[~Notes[~RULE:: type[,action][~Remarks]]]/~Remarks] where P is the position on the nucleotide or protein chain; N is a nucleotide (or short sequence); and X is an amino-acid or a short peptide sequence. Notes are written freely and may contain alphanumeric characters and punctuation, except the tilde sign. The specific tag "Rules::" identifies the actions to apply to clearly differentiate the rules from the remarks. Rules modify the computed result, for example, to suppress the peptide sequence if a stop codon is created or to correct the result in the case of ambiguous positions of indels that occur within repeated features. Remarks are not used by the programs but are information, such as references and dates, linked to the database entry. Other data are also recorded in the database: the position of the first codon of the nucleotide chain, the status of the final main product (DNA/protein), and the DNA sequence of the wild strain (the reference sequence).
Implementation MUBII is the analysis and interpretation engine and is entirely written in Python 2. 7. The external programs that are called from Python are BLAST2seq  [25] , BLASTN, BLASTX  [26]  and the alignment tools MUSCLE  [27]  and MAFFT  [28]. The routines transeq and showalign from EMBOSS  [29]  are also used. The scheme of the global organization is shown in Figure  2. The Web interface exclusively uses CGI and HTML. Cascading Style Sheets are used to color the alignments. These decisions were made to achieve simplicity and reliability on every browser. The Web server runs under the APACHE MPM Prefork http server on a LINUX Debian server.
De novo constructed BLASTN database Only a few mutated sequences are available in GenBank; therefore, the construction of a database containing the sequences of all the described mutations is necessary. A sequence database is built containing a unique mutation event for each of the sequences. It contains the descriptions of the mutations present in MUBII-TB-DB, the reference sequence and the position of the promoter if required. The mutated sequences are written in the FASTA format at the nucleotide level with the description of the mutation as the descriptor. This database is rebuilt when changes occur within MUBII-TB-DB and is compiled as a BLAST database.
Alignment to the reference and extraction of the core of the aligned sequences The query sequence is aligned to the reference sequence by MAFFT. An algorithm identifies the core of the alignment by eliminating the trailing short sequences that are not perfectly aligned at the extremities. The of the alignment is further used for mutation detection. The alignment was initially produced using MUSCLE, but as the program tries to align the whole length of the reference sequence to a query that is often a partial sequence, the ends of the query were frequently assigned to positions near the ends of the reference. This problem was observed especially in the low quality-extremities of     the query and has been found to occur less frequently when using MAFFT.
Extraction of the mutations from the alignment and identification For the core section of the alignment, a program extracts the non-matching sections and constructs a Python dictionary of the results. Each entry of this dictionary is compared with the entries of a mutation dictionary for the given gene constructed from the MUBII-TB-DB database. The resulting table contains a description of each point mutation, insertion or deletion and indicates its presence or absence in the database. In the case of deletion or insertion, the possibility of frameshift creation is also checked, and the result is added in an "alert" section. Moreover, if the mutation is known and modifies the encoded protein, this information is included. In the case of mutations usually identified by their positions in the Escherichia coli gene (some sections of the rpoB gene), the E.
Output page construction An HTML output file is computed for the whole alignment (DNA and protein levels) that highlights mutations using colored tags. The information gathered in the various results files is returned (detected mutations and frameshifts, mutation identification, BLAST result on the reconstructed sequence database, position within the M. tuberculosis wild-type gene, and, if possible, position within the E. coli gene). The results, alerts and alignment are placed in iframes that enable horizontal browsing and searching to inspect sequences for mutations. A specific output adapted for printing is constructed. It provides information about the detected mutations along with alignments using the showalign EMBOSS routine.
Data analysis The result appears in a new html page embedding the presentation of the DNA and protein alignments along with the sequence of the wild-type strain. The first section of the result pages concerns the nucleotide sequence (Figure  3 ). This section shows the DNA alignment of the query against the wild-type sequence and highlights any mutations. When mutations are detected, alerts are printed to note the position and the type of the mutation, its status (already described or unknown, therapeutic relevance), and situations such as frameshifts. At the end of this section, the name of the best matching sequence in the BLAST of the query against the reconstructed database of mutated sequences is shown. Access to the whole BLAST result is also possible. Because only a few strains of M.
tuberculosis carry double mutations, the database contains only singly mutated sequences. The second section concerns the protein corresponding to the core nucleotide sequence. This section shows the peptide-level alignment of the query against the wild-type sequence and the position of mutations. The alignment is deduced from the nucleotide alignment and highlights the mutations and observed changes in the case of frameshift. In this last case, as the gaps are not introduced, the picture shows the actual changes observed in the query. This presentation is more informative, for the biologist, of the real changes that occur (Figure  4 ). When a stop-codon mutation is created, a specific alert is highlighted, and the shortened version of the protein is shown.
Conclusions The growing incidence and spread of antibiotic-resistant M. tuberculosis has led to efforts to identify the mutations in clinical isolates. The mutations linked to therapy failure are a particularly serious concern for physicians managing TB patients. The reference method is to sequence the gene of interest and then to detect and identify possible mutations. This process is time consuming, as there is no mutation database directly coupled to an analysis tool that allows for the submission of a sequence, its comparison to the database and the immediate analysis of the results. MUBII-TB-DB is a structured database collecting the description and information on modifications to seven loci of major therapeutic value in tuberculosis. The simple structure and grammar of MUBII-TB-DB contribute to the evolving capacity of the database, which can be easily updated with new entries (both mutations and resistance genes).
The results are graphs (alignments) and text (description of the mutation, previous description, therapeutic signification). MUBII-TB-DB is fast and easy to use, even by technicians without bioinformatics training. The system provides access to an interpretation in biological and therapeutic terms. It enables the automation of repetitive and somewhat technically challenging tasks in the tuberculosis laboratory. Its interest is to provide easy and comprehensive access to an up-to-date antibiotic resistance database concerning M. tuberculosis. Moreover, our algorithm is applicable to any mutation database and to any microorganism, making MUBII a useful tool for the surveillance and control of multidrug-resistant microorganisms.
We can do this by storing each of the phase-space points, along with the matrix elements and other information, in data files. In order to compress these sizeable volumes of data, we store them as Root n-tuple files, or simply 'n-tuple files' for short. We produce n-tuple files using the BlackHat one-loop library  [5]  in conjuction with SHERPA  [6, 7, 8, 9, 10]. In this paper we describe in more detail an implementation of this strategy for a class of interesting multi-jet final states at the Large Hadron Collider (LHC). We document n-tuple files for processes producing a single electroweak vector boson (W ± or Z) in association with 1, 2, 3, or 4 jets, and files for pure-QCD processes producing 2, 3, or 4 jets. In addition, we supply a C ++ library that provides an interface to the files.
The paper is organized as follows. In section 2, we discuss the organization of an NLO calculation. In section 3, we present the content of event files. In section 4 we explain how to compute differential cross sections using the event files. In section 5, we discuss additional details needed to change scales or parton distribution functions from those in the original calculation. In section 6, we present a software library for reading event files. We summarize in section 7.
This approach started with applications to specific processes  [12, 13, 14, 15]  and was later generalized by Catani and Seymour  [16, 17]  to a process-independent method. We use an implementation of the Catani-Seymour dipole method within the AMEGIC++  [8, 9]  and Comix  [10]  tree-level matrix-element package (both parts of SHERPA  [6, 7] , with Comix used for the highest-multiplicity processes). Other subtraction methods have also been applied, especially in NNLO calculations  [15, 18] , and related methods are under development  [19]. Some of these have also been automated  [20]. With a subtraction term, we obtain the following formula for a differential cross section computed to NLO, dσ NLO dv = dσ Born n δ v + dσ virt n + dσ int n δ v + dσ real n+1 -dσ sub n+1 δ v , (3) where σint n is the integral of σsub n+1 over the unresolved phase space.
Each integral in eq. (  3 ) is now finite as ǫ → 0. (The observable must be infrared and collinear safe, that is O n+1 must approach O n in every singular limit, for this to be true. ) As the divergent terms in σvirt n cancel those in σint n , we can drop them all, retaining only the finite contributions, dσ NLO dv = dσ Born n δ v + dσ fin. virt n δ v + dσ fin. int n δ v + dσ real n+1 -dσ sub n+1 δ v. (4) As in an LO computation, the integrals in eq. (  4 ) are most easily computed by Monte-Carlo sampling. (In our setup, using SHERPA  [6, 7] , the code first adapts an integration grid to the integrand, and many independent integrations are done using these grids. ) We generate events separately for each of the four types of contributions, over n-particle phase space for the Born (B), virtual (V), and integrated-subtraction (I) contributions, and over (n + 1)-particle phase space for the subtracted real-emission (R) contributions.
Coordinates and units We take the coordinate axes such that the beams are directed along the z axis, with the initial-state parton with momentum fraction x1 and Particle Data Group (PDG) code id1 moving in the positive z direction. The transverse directions are labeled by x and y, with (x, y, z) forming a right-handed coordinate system. The energies of particles are denoted by E and the spatial momenta by (p x , p y , p z ). We use natural units ( = c = 1) and GeV for energies, masses and momenta. Weights are normalized to yield cross sections in picobarns.
The getInfo program (see section 6. 4) provides useful information about the process, input parameters, cuts and jet algorithms for a specific n-tuple file. For processes containing a single electroweak boson, we fold in its decay into a massless lepton pair. We label the pair by e + ν for a W + boson, e -ν for a W -boson, and e + e -for a Z boson. Results for the corresponding muon channels are identical in the massless-lepton approximation. Off-shell effects are taken into account by distributing the lepton-pair invariant mass in a relativistic Breit-Wigner profile with width Γ V around the mass m V of the electroweak vector boson V. In the case of the Z boson (that is, e + e - final states), we include virtual photon γ * exchange as well. For all processes we neglect the contribution of a massive top quark in the quark loops.
Checks All n-tuple files contain several histograms which can be used to check their consistency and the implementation of the program reading them. The histograms are listed in Table  3. Each of the histograms listed in Table  3  comes in two copies, one in which the distributions are computed using the original weights, without change of renormalization or factorization scale, and a second in which both µ R and µ F have been changed to H all T , defined to be the scalar sum of the transverse momentum of all particles (including the neutrino) in the event  3. The histogram names have Orig and HTallp respectively appended to them. The program getInfo (see section 6. 4) can be used to obtain the cuts and jet algorithm used for the histograms.
Usage Your source code must include the header file nTupleReader. h to use the library. Your code may access information in the n-tuple files via an object of the nTupleReader class. We give an example of a C ++ program using the library in section 6. 8. The nTupleReader-config script returns the compiler and linker flags needed to compile a user program and link it to the library. The script is located in the bin subdirectory of the installation directory. Assuming this script is in your executable search path, you would compile and link a program as follows, CFLAGS='nTupleReader-config --include' g++ -c $CFLAGS -o NTRexample. o NTRexample. cpp LDFLAGS='nTupleReader-config --libs' g++ $LDFLAGS -o NTRexample NTRexample. o.
The getInfo program The make step creates a program called getInfo along with the library. This program takes one argument, the name of a n-tuple file. The program prints useful information about events in the file: • their center-of-mass energy; • their initial-state hadrons; • the process that generated them; • the part of the NLO calculation to which they contribute; • the jet algorithms and parameters allowed in an analysis; • the minimum jet transverse-momentum cut allowed; • additional generation-level cuts, such as the lepton invariant-mass cut for processes with a Z boson. ; • the electroweak parameters used; • the pdf set used in the n-tuple generation.
Example of the usage of the library in a C++ program The following listing shows an example of a C ++ program using the nTupleReader library. This example is included after installation in the directory share/ntuplereader/. #include "nTupleReader. h" using namespace std; int main(){ std::vector<std::string> fs; fs. push_back("@prefix@/share/ntuplereader/sample. root"); nTupleReader r; r. setPDF("cteq6ll. LHpdf"); r. addFiles(fs); while(r. nextEntry()){ int id=r. getID(); cout << "Checking momentum conservation for event ID: " << id << std::endl; double sumX=0; double sumY=0; double sumZ=0; int nbrP=r. getParticleNumber(); std::cout << "Number of particles: " << nbrP << std::endl; for (int i=0;i<nbrP;i++){ sumX+=r. getX(i); sumY+=r. getY(i); sumZ+=r. getZ(i); } std::cout << "Sum X: " << sumX << std::endl; std::cout << "Sum Y: " << sumY << std::endl; std::cout << "Sum Z: " << sumZ << " to be compared with: " << 3500 * (r.
Conclusions In this Article we described software tools for obtaining predictions to NLO in QCD using an n-tuple event-file format generated by SHERPA using the BlackHat software library. This framework offers a convenient means for evaluating cross-sections and distributions with different parton distribution functions, bypassing repeated and computationally costly evaluations of matrix elements. The set-up described here makes it straightforward to compute scale-variation bands and to obtain uncertainty estimates due to imprecise knowledge of the parton distribution functions. The n-tuple files also make it convenient to study the effects of varying jet algorithms and cuts, within a wide range of commonly-used ones. These tools have already proved useful in a number of theoretical  [31, 41, 34]  and experimental studies  [42, 43].