dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction of Candidate Horizontal Gene Transfer Putative HGT events in X. index and L. elongatus were detected by the Alienness software  [44, 45]  which calculates an Alien Index (AI) as initially described in  [46]. Briefly, all predicted proteins were compared against the National Center for Biotechnology Information (NCBI) non-redundant library (nr) using BLASTp  [32]  with an E-value threshold of 1E -3 and no low-complexity filtering. BLAST results were parsed to retrieve associated taxonomic information, using the NCBI's taxonomy as a reference. For every protein returning at least one hit in either a metazoan or non-metazoan species, an AI was calculated according to the following formula: AI = ln best metazoan E -value + 1E -200 -ln best non_metazoan E -value + 1E -200. When neither a metazoan nor non-metazoan BLAST result was found, a penalty E-value of 1 was automatically assigned as the best metazoan or non-metazoan E-value, respectively.
GH12 Cellulase Cloning and Functional Characterization Part of a GH12 cellulase sequence was originally identified in two expressed sequence tags (ESTs) from a cDNA library made from mixed stages of X. index  [10]. Analysis of these sequences (accession numbers CV508600. 1 and CV127906) suggested that the sequenced part of the gene was incomplete at the 5 end. Rapid amplification of cDNA ends (RACE) was therefore used to clone the remaining portion of this gene using standard protocols. For biochemical testing, the full-length sequence, with the signal peptide removed, was cloned into a bacterial plasmid expression vector and cellulase activity of the proteins produced was tested using carboxymethylcellulose as a substrate as previously described  [49].
elongatus (440 proteins) and 66 X. index (171 proteins) orthogroups were species-specific multicopy proteins. Species-specific multicopy proteins are those represented by more than one sequence in the transcriptome and does not distinguish between isoforms and gene families. We identified 1660 orthogroups that were unique to the Clade 2 nematodes (T. spiralis, L. elongatus and X. index), of which 187 orthogroups were conserved in the three species and could represent core Clade 2-specific proteins. We also identified 1108 orthogroups that were unique to L. elongatus and X. index and conserved in these two PPN. These orthogroups covered 2664 and 1549 proteins from L. elongatus and X. index, respectively. This dataset represents an interesting group of proteins that may contain genes specifically associated with the evolution of plant parasitism in Clade 2 nematodes.
elongatus and X. index, but not the Clade 2 animal parasite T. spiralis. Nine of these were restricted to the available X. index and L. elongatus transcriptome data, and may indicate either transfer events ancestral to the separation of L. elongatus and X. index but after their divergence from T. spiralis, or early acquisition in Clade 2 and secondary loss in T. spiralis. The remaining four orthogroups correspond to more complex possible acquisitions. One case encompassed one X. index and two L. elongatus proteins bearing a GH32 'Glyco_hydro_32N' domain (PF00251. 18). These Clade 2 GH32 proteins were clustered together with GH32 of Clade 12 plant-parasitic nematodes, root-knot and cyst nematodes, for which an invertase activity has been biochemically characterised and an acquisition via HGT supported by a phylogenetic analysis  [28].
Finally, in the cellular component category, only one term was significantly enriched in HGT candidates and it was the same in both species (DNA-directed RNA polymerase II, core complex). Among the HGT-specific Pfam domains that we found in only one of the two Clade 2 PPNs, we identified two of particular interest regarding plant parasitism. One, in the L. elongatus transcriptome is pantoate ligase (PF02569. 13), a domain usually found in bacterial enzymes involved in biosynthesis and salvage pathway of vitamin B5. This vitamin is essential to all life and most animals have lost the capacity to synthesise this and other essential vitamins and so must take them up from their diet. Interestingly, cyst nematodes (Clade 12) have acquired enzymes from bacteria for the salvage and biosynthesis of essential vitamins, including an enzyme bearing a pantoate_ligase domain for vitamin B5  [59, 60].
Pantoate ligase proteins in L. elongatus resemble those found in the betaproteobacteria genus Burkholderia (the 100 best BLAST results are all sequences of Burkholderia species) while those from Clade 12 nematodes resemble enzymes from Actinobacteria  [59]. This suggests that two independent acquisitions of these enzymes putatively involved in biosynthesis of vitamin B5 occurred in Clade 2 and Clade 12 nematodes (similar to the GH32). One X. index predicted protein contains a Glyco_hydro_12 (PF01670) domain. This domain is present in Archaea, Bacteria and Fungi but not in Metazoa (including nematodes). Enzymes bearing a Glyco_hydro_12 (or GH12) domain can have cellulase activity. Cellulases acquired via horizontal gene transfers in Clade 12 nematodes are thought to be involved in the degradation of plant cell walls and thus are directly linked to their plant-parasitic lifestyle  [25, 26, 61].
Characterisation of a GH12 Cellulase from X. index The X. index and L. elongatus GH12 proteins have AIs of 38. 05 and 114. 03, respectively, confirming their similarity to non-metazoan cellulases and suggesting acquisition via HGT. Part of a GH12 cellulase sequence was previously identified in two ESTs from a cDNA library made from mixed stages of X. index  [10]. Analysis of these sequences (accession numbers CV508600. 1 and CV127906) suggested that they are incomplete at the 5 end, and have a poly A tail at the 3 end. The corresponding full-length nucleotide sequence was cloned by 5 RACE and encoded a 256 amino acid protein with a 19 amino acid eukaryotic signal peptide at its N-terminus  [62]. Taken together, these attributes suggest that the GH12 identified is a nematode gene, not a contaminant transcript.
Analysis of the BLAST results shows that the X. index, X. americanum and L. elongatus GH12s have between 38% and 42% identity to bacterial GH12s, with a query coverage ranging between 87% and 98% of the length. Overall, the phylogenetic analyses confirm a likely acquisition via HGT of bacterial origin, although identification of the precise bacterial group of the putative donor is not currently possible. GH12 cellulase sequence was previously identified in two ESTs from a cDNA library made from mixed stages of X. index  [10]. Analysis of these sequences (accession numbers CV508600. 1 and CV127906) suggested that they are incomplete at the 5′ end, and have a poly A tail at the 3′ end. The corresponding full-length nucleotide sequence was cloned by 5′ RACE and encoded a 256 amino acid protein with a 19 amino acid eukaryotic signal peptide at its N-terminus  [62].
Acknowledgments: Work described in this article was partly funded by the  Rural & Environment Science & Analytical Services Division of the Scottish Government  and by two grants from the  French National Research Agency (ANR)  under the  Genoplante program  (project  ANR-PCS-08-GENO-166 NEMATARGETS ) and the  JCJC program  (project  ANR-13-JSV7-0006-ASEXEVOL ). This work was facilitated by interactions funded through  COST action   FA1208  and  Royal Society International Exchange  award  IE130707. Sebastian Eves-van den Akker is supported by  Biotechnology and Biological Sciences Research Council (BBSRC)  grant  BB/M014207/1. The authors are grateful to the  GenoToul bioinformatics platform Toulouse Midi-Pyrenees  for providing computing resources.
Biological background and CFSE Understanding cell proliferation in general, and immune cell dynamics in particular is a great challenge for biologists. Even if tremendous discoveries have been made in the past decades, many mechanisms remain unclear. Our aim here is to focus our attention at the cell population level and more specifically to get the best estimates of the few key parameters able to describe in vitro proliferation of immune cells stimulated by an antigen. To obtain good parameter estimates for cell population dynamics, it is necessary to have time series of experimental data. A good way to get them is to use cell markers. In this work, we study data obtained with carboxyfluorescein diacetate succinimidyl ester (CFSE). It has been shown that CFSE labels resting and proliferating cells regardless of their stage in the division cycle  [1, 2].
This model is written as a set of integral equations. A general cyton solver (GCytS)  [8] , coded in Matlab, has been developed for parameter estimation. However, CFSE data are generally not rich enough to correctly estimate the nine parameters in the model. Hyrien and Zand proposed a branching process model in order to describe CFSE data  [10, 11]. This model has been improved by Miao et al. [12]. Cells are classified into four subtypes according to the events that occur at the end of a cycle time (death, rest, division or differentiation). This model is a mathematical tool representing cell behaviour and it can predict the average number of cells in different generations as well as the probability to have a certain number of cells in a given generation. Fitting this model to CFSE data provides satisfactory results.
Parameter estimates Our goal here is to obtain parameter estimates with which we can reproduce the observed cell population dynamics for a given time series in a consistent way. To reach this objective, we first derive parameter estimates for a given time of the experiment. We claim that a parameter set estimated using only the last time point provides an accurate initial guess to start an estimation algorithm. In this work, we choose to use the Levenberg-Marquardt nonlinear least squares algorithm  [26, 27]  implemented in the software MATLAB 1 (Release 2014b, The MathWorks, Inc, Natick, MA, US). In the next two sections, we provide formulae to estimate parameters of the two-phase and the three-phase models. Similar work has been performed in  [6]  for an ODE model.
Application to immune cells 4. 1 Data We use previously published data  [28]  as a benchmark to estimate model parameters. This dataset was also used in  [12]  to compare different models. We apply our model to the same dataset, ensuring a consistent comparison with  [12]. Data consists of heparinized blood samples collected from anonymous healthy human donors. CD4+ and CD8+ T cells were isolated from these samples and labelled with CFSE. The evolution of these cells was observed after stimulation with a polyclonal agent, PHA, which is a T-cell activator. The number of cells in each generation (from 0 to 6) was measured at 0, 24, 48, 72, 96, 108 and 120 hours using CFSE profiles.
Comparison with the branching process models and the Cyton model We then compare our results to the ones published by Miao et al. in  [12] , where the same dataset is used. It appears that Miao et al. use data in log 10 scale to estimate model parameters. In a first step, we recomputed the sum of the squares of residuals (LSS) and the AICc value, using data and simulations in log 10 scale. However, our simulation results had been obtained with a parameter set based on raw data. Consequently, this parameter set may not be the optimal one to fit data in log 10 scale. Therefore, in a second step, we estimated new model parameters using data in log 10 scale. Values of LSS and AICc for the different models are presented in Table  8. We observe that for both CD4+ and CD8+ cells, our results are similar with the ones reported in  [12] , in terms of AICc values.
The logscale is more appropriate for fitting time series with exponential kinetics. Recent evidence suggests that duration of both G 1 phase and S/G 2 /M phases is highly variable  [34]. In our model, we assume that only the resting phase has a probabilistic duration. Although this phase is often thought to contain G 0 and G 1 phases, this distinction is not required and it can therefore includes a part of S/G 2 /M phases. In that case, phases S/G 2 /M can have a variable duration. To go further, one could assume that the parameter τ is randomly distributed, but CFSE data may not be rich enough to estimate the part of variability from resting and active phases. Applying this model to experimental CFSE data allows us to compare CD4+ and CD8+ cell dynamics. Both type of cells are better described with the three-phase model, assuming a positive loss of non-divided cells, and death rates depending on the number of divisions for the other generations.
It therefore benefits from all groupware functionality offered by this environment, such as the system to share and publish a workspace. It also provides the possibility of running analytic software installed in the Galaxy instance on selected genomic regions. But this strong link with Galaxy also has the disadvantage of forcing system administrators to install and maintain the full package even if their users use other software to analyze their data. The second solution proposed by WebGBrowse  (Podicheti et al. , 2009)  is to automatically deploy genome browser from annotation files. However, this solution only allows users to view a single file type (GFF3), which is no longer sufficient as BAM, VCF, BED and bigWig formats are now also widely used as standards. In addition, the tool does not offer built-in data privacy, therefore requiring the system to be reinforced in order to add a layer of authentication.
Simplifying system administration myGenomeBrowser is provided with an installation script based on the Docker container management system  (Merkel, 2014). Configuration is limited to setting environment variables to define the directory that contains user data. This automatic, simplified configuration allows a bioinformatics platform to quickly deploy an instance of myGenomeBrowser. The default authentication system is based on an 'htpasswd' Apache file. User accounts can be created at shell level by the administrator or online by following a hyperlink received by email at the user's request. For platforms providing a LDAP or Shibboleth-based authentication, a so-called 'expert' configuration protocol is provided. To monitor the use of myGenomeBrowser, the administrator interface provides access to a statistics page that lists all users and the number and disk space occupied by their browsers.
Visualization Once the reference sequences have been loaded into the system by the user, that person can add perennial tracks, stored on the server side. myGenomeBrowser can display the various results (annotations, alignments, quantitative data, polymorphisms) produced in the standard GFF3, BAM, BED, bigWig and VCF formats and BLAST tabulated output. The software checks data integrity and consistency before adding these new tracks to the genome browser instance without any additional user configuration. Track rendering is ensured via custom JBrowse configuration templates in order to modify the color of the different biological objects or to identify the impact of polymorphic sites on the reference sequence. The default configuration offers context menus to extract sequences of biological objects and visualize the InterPro protein domain content  (Jones et al.
Result mining The upload of reference sequences and annotation tracks automatically triggers extraction and creates indices of sequence databases for all identified biological genomic features (genome sequence, genes, mRNA, ncRNA, CDS and proteins). This offers the possibility to search by keyword or accession number or from a list of identifiers. The results are presented with direct links to the genomic context. In addition, the user can search for similar sequences using an integrated BLAST server. Like for keyword searches, the similarity results show sequence alignments cross-linked to the target genomic regions directly on the genome browser. Finally, myGenomeBrowser provides a form to extract a set of sequences of different genomic features using their identifiers or their genomic coordinates and using the same syntax as JBrowse.
Managing and sharing data With myGenomeBrowser, users can share genome browsers in readonly mode by simply filling out a web form with the email addresses of their colleagues. The access control combines an e-mail address, a key and a password. A JSON file containing emails and keys manages the 'authorization' layer. An 'htpasswd' file containing emails and passwords manages the 'authentication' layer. Recipients receive a link (encoding the key) and a password that gives them personalized access to the genome browser and associated mining tools. The owner of the original data may at any time revoke access by removing the corresponding email address from the same web form. In addition, the owner may at any time remove tracks or entire genome browsers and all associated indices via a management form.
Conclusion myGenomeBrowser does not replace institutional genome browsers [UCSC, TAIR  (Lamesch et al. , 2012) , Ensembl  (Yates et al. , 2016) , WormBase  (Howe et al. , 2016) ]. myGenomeBrowser is a complementary solution to meet user needs in autonomously viewing, analyzing, querying and sharing results obtained on their organism of interest, via a range of tools. The environment can be used by everyone, but is particularly designed for platforms or laboratories wishing to increase the autonomy of their biologists working on many organisms, while simplifying system administration of multiple genome browsers. myGenomeBrowser does not require important computational resources. A large dataset like the human genome sequence and its annotation can be indexed in less than one hour on a standard workstation (Intel i7-4600U CPU @ 2.
We describe ColliderBit, a new code for the calculation of high energy collider observables in theories of physics beyond the Standard Model (BSM). Collider-Bit features a generic interface to BSM models, a unique parallelised Monte Carlo event generation scheme suitable for large-scale supercomputer applications, and a number of LHC analyses, covering a reasonable range of the BSM signatures currently sought by ATLAS and CMS. ColliderBit also calculates likelihoods for Higgs sector observables, and LEP searches for BSM particles. These features are provided by a combination of new code unique to ColliderBit, and interfaces to existing state-of-the-art public codes. ColliderBit is both an important part of the GAMBIT framework for BSM inference, and a standalone tool for efficiently applying collider constraints to theories of new physics.
This problem is addressed in GAMBIT (the Global And Modular Beyond-the-Standard-Model Inference Tool)  [1] , which combines calculations of observables and likelihoods in collider, flavour, dark matter and precision physics with a model database, a flexible system for interfacing to external codes, and a wide selection of different statistical methods and parameter scanning algorithms that can be applied to the models  [1]. In this paper, we introduce ColliderBit, a GAMBIT module for the application of high-energy collider constraints to BSM physics theories. The ATLAS and CMS experiments  [2, 3]  have made great progress in the search for evidence of BSM physics at high energies, but applying these constraints to a generic theory of such physics remains challenging. Searches for new particles at the LHC are typically presented either in specific planes of a restrictive high scale physics hypothesis, e.
Given the ever-growing list of constraints on BSM physics from experiments at the LHC, the need to rigorously test those limits against various models is ever more pressing. Partial solutions to each of these issues exist, but there is as yet no comprehensive tool that tackles all of them. The package SModelS applies constraints to supersymmetric (SUSY) models based on a combination of simplified model results  [18]. FastLim provides similar functionality for SUSY models, but is extendible (in principle) to non-SUSY models through the use of user-supplied efficiency tables  [19]. Both of these tools will provide limits that are much more conservative than a more rigorous calculation, due to the limitations of simplified models. SUSY-AI  [20]  provides a random forest classifier for SUSY models based on LHC exclusions, but as seen in earlier applications of machine learning to this problem  [21] [22] [23] [24] [25] , accuracy concerns exist when applying the method to large-volume parameter spaces, due to the relative sparsity of the training data  [26]  in the model parameter space.
As ColliderBit is designed within the GAMBIT framework  [1] , it offers seamless integration with modules that provide statistical fitting  [1, 39] , the ability to impose constraints from electroweak precision data  [40] , flavour physics  [41]  and a large range of astrophysical observations  [42]. For LHC physics, we use a combination of parallelised Monte Carlo (MC) simulation and fast detector simulation to recast LHC limits without the approximations of the simplified model approach. The first release of the code comes with a list of AT-LAS and CMS analyses that collectively present strong constraints on supersymmetry and dark matter scenarios  [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53]. It contains interfaces to the Pythia 8 MC event generator  [54, 55] , to the Delphes detector simulation  [30, 31] , and a customised detector simulation based on four-vector smearing (BuckFast).
In this paper we show that that BuckFast gives comparable results to Delphes, but at a dramatically lower CPU cost. We also supply custom routines for re-evaluating LEP limits on supersymmetric particle production, and include interfaces to HiggsBounds and HiggsSignals for calculating Higgs observables. ColliderBit follows the modular design of GAMBIT, thus enabling the user to easily swap components (e. g. choose a different detector simulation without affecting the LHC analysis framework), add new collider analyses, or provide interfaces to standard particle physics tools. This paper serves as both a description of the physics and design strategy of ColliderBit, and a user manual for the first code release. In Appendix A, we provide a quick start guide for users keen to compile and use the software out of the box.
Sec. 2 describes the physics and implementation of the ColliderBit software. The Col-liderBit user interface is outlined in Sec. 3. In Sec. 4  we cover two use cases: First, we point to an annotated GAMBIT input file that details the application of collider constraints in a scan of the constrained minimal supersymmetric standard model (CMSSM). Second, we provide a detailed example of how the user can add their own model to the ColliderBit code. The second of these examples shows the flexibility of ColliderBit in tackling generic theories supplied by the user, using existing codes for automatic generation of matrix elements. After summarising in Sec. 5, we also provide Appendices B and C, where we detail the C++ classes defined by ColliderBit, and a glossary of common GAMBIT terms, respectively.
Physics and implementation To perform any calculations, ColliderBit requires numerical values for the free parameters of a theory for new physics. If ColliderBit is run with other GAMBIT modules, these will come from a scanning algorithm implemented in the ScannerBit  [39]  module, and other GAMBIT modules will then perform the necessary spectrum generation and decay rate calculations. The user may also run ColliderBit as a standalone code, in which case the parameters can be supplied via a model description, such as an SLHA file for supersymmetric models  [59, 60]. In this case, the user must supply spectrum and/or decay calculations as appropriate. The ColliderBit output is a series of signal event rate predictions and likelihood terms derived from BSM searches at the LHC, as well as likelihood terms from SUSY searches at LEP and Higgs searches at LEP, the Tevatron and the LHC.