dataesr/scientific-paragraphs-categorization huggingface dataset:
The task of epitope discovery and vaccine design is increasingly reliant on bioinformatics analytic tools and access to depositories of curated data relevant to immune reactions and specific pathogens. The Immune Epitope Database and Analysis Resource (IEDB) was indeed created to assist biomedical researchers in the development of new vaccines, diagnostics, and therapeutics. The Analysis Resource is freely available to all researchers and provides access to a variety of epitope analysis and prediction tools. The tools include validated and benchmarked methods to predict MHC class I and class II binding. The predictions from these tools can be combined with tools predicting antigen processing, TCR recognition, and B cell epitope prediction. In addition, the resource contains a variety of secondary analysis tools that allow the researcher to calculate epitope conservation, population coverage, and other relevant analytic variables.
iNTRODUCTiON The Immune Epitope Database and Analysis Resource (IEDB) is a freely available resource that contains an extensive collection of experimentally measured immune epitopes and a suite of tools for predicting and analyzing epitopes (Figure  1 ). The IEDB includes antibody and T cell epitopes for infectious diseases, allergens, autoimmune diseases, and transplant/alloantigens studied in humans, non-human primates, mice, and other animal species. Life science researchers can use the IEDB to develop new vaccines, diagnostics, and therapeutics. The database is populated using information captured or curated from peer-reviewed scientific literature and from data submitted by researchers. As of December 2016, over 18,000 references have been curated, and the database contains over 260,000 epitopes and over 1,200,000 B cell, T cell, MHC binding, and MHC ligand elution assays (positive and negative).
Because the database is continually being updated with new literature and data submissions, the IEDB provides researchers designing vaccines with a comprehensive collection of experimental data in a single data repository that can be used to query for known epitopes and their immunogenic responses. The tools in the Analysis Resource 1 (1, 2) fall into two general categories: prediction and analysis tools. Prediction tools predict the outcome of experiments, such as MHC class I or class II binding, MHC class I processing and immunogenicity, and for predicting linear and discontinuous (conformational) B cell epitopes. The epitope prediction tools are valuable resources for vaccine developers when experimentally measured epitopes are not available in the IEDB. In this review, we briefly describe the basic principles of machine learning algorithms, on which the prediction tools are based, and some basic principles of tool evaluation.
Next, we describe the MHC class I and class II binding prediction tools hosted in the analysis resource. The tools for class I cover a broad range of alleles, including 83 human, 8 chimpanzee, 18 macaque, and other non-primates. The accuracy of these predictions is very high, with AUC values greater than 0. 9. For MHC class II binding, the breadth of allele data is less extensive, involving 24 human alleles and 3 mouse alleles. There are also pan predictions that extrapolate from these alleles to predict binding for other alleles. The class II binding predictions are being retrained with new data as of the end of 2016. The accuracy of class II binding predictions has improved significantly over the past 10 years, from AUC of 0. 76 to 0. 87, but it is lower than class I. Subsequently, we describe T cell processing predictions which combine MHC binding with other parts of the MHC class I cellular pathway, namely proteasomal cleavage and TAP transport, generated from independent experimental datasets.
The calculation of a scoring matrix offers a relatively simple example. With a scoring matrix, the binding affinity for the sequence is computed based on the amino acid and its position in the binding groove. The values for each residue in the sequence are summed to yield the overall binding for the entire sequence. The position-specific scoring matrix is derived by varying the values of the matrix until the sums for known, measured peptides approximate the measured affinities. To evaluate how well this function works for predicting MHC class I peptide binding, objective methods to evaluate prediction quality are necessary. Peptide binding datasets, far larger than any previously assembled, were originally compiled in 2006  (6). The dataset covered 48 MHC class I alleles from 88 different datasets with a variety of peptide lengths with a total of 50,000 IC50 values.
The area under this ROC curve is the AUC value and that has a number of important statistical properties  (9). It is independent of the predicted scale because it compares the rank of your matrices and it is independent of the composition of the dataset, such as having different proportions of binders and non-binders. The AUC value is essentially capturing the probability that given two peptides, one a binder and the other a non-binder, the predicted score will be higher for the binder compared to the non-binder. An AUC value of 0. 5 is equivalent to a random prediction and a value of 1. 0 is equivalent to a perfect prediction. In evaluating the different web server predictors, we discovered that it was difficult to separate the performance of the algorithms from the datasets used to train them.
The peptide binding specificity is also very broad, and a given MHC can bind and present a number of different peptides  (12). Given the large number of variants possible and this broad specificity, experimental characterization of all peptide-MHC interactions is experimentally challenging. Binding prediction methods facilitate the selection of potential epitopes. The methods are developed using experimental peptide binding data for different MHC alleles to train machine learning algorithms that in turn can be used to predict the binding for any arbitrary peptide. The IEDB database resource houses binding data for 173 MHC class I molecules, which includes 119 human alleles for HLA-A, B, and C. It also has data for macaque, chimpanzee, mouse, cattle, pig, and rat  (13). The machine learning methods are periodically retrained when sufficient amount of new data become available in the IEDB.
The web interface for MHC Class i Binding Predictive Tools The primary interface for epitope prediction tools is through a web interface, which is described below. Users can access the MHC class I binding prediction tool from the IEDB home page or directly at http://tools. iedb. org/mhci/. The class I home page has numerous tabs to assist users, including a Help tab for detailed explanations on inputs and outputs, an Example tab with specific examples and a Reference tab with publications related to the methods. From the Download tab, the user can download the scoring matrices for the various methods and a link to the dataset used for retraining the class I binding prediction tools in 2013  (16). Tool developers are encouraged to make use of these data. In addition, users can download a standalone version of the binding prediction tool that can be hosted on the user's own server.
Standalone version and Application Program interface (APi) The MHC class I binding tools are also available in a standalone version that runs on a Linux operating system and can be downloaded from the Download tab. Because the standalone version is hosted locally on hardware of the user's choosing, an internet connection is not needed and users do not need to worry about their web browser timing out. Instructions for installing the software are provided in the accompanying README file. All users can also access the MHC binding prediction tools using the API. The API uses a Representational State Transfer, an application that uses HTTP requests to GET, PUT, POST, or DELETE data from web servers. In this manner, users can send parameters over the internet directly to the IEDB web servers. No special software needs to be installed locally to use the API, and users will always access the latest version of the tools, unlike the standalone versions that need to be updated on local servers with each new release of the prediction tools.
The MHC score is the -log10 of the IC50 value, so in this case a higher MHC score indicates a better binder. The total score is the sum of these three scores. There are several caveats associated with the combined predictor and the other processing prediction tools. Based on our evaluation of the performance of prediction methods, we found that the processing predictions were better than the MHC binding predictions alone when predicting eluted peptides  (38). However, eluted peptides are typically identified by mass spectrometry, which requires the peptide to be reasonably abundant to allow for detection. In actuality, an immune response could occur with few peptides on a cell. So there is a potential bias that eluted data overrepresents the "best possible" ligands and the difference between the processing predictions and the binding-only predictions may not be relevant in practice.
THe T CeLL iMMUNOGeNiCiTY PReDiCTOR The Analysis Resource has a T cell immunogenicity predictor tool that predicts the relative ability of a given set of peptides bound in an MHC complex to be recognized by a T cell. To develop the tool, we assembled datasets of peptides that have similar MHC binding affinity and then separated the ones that are recognized by T cells from the ones that are not recognized by T cells. We then observed that certain amino acids, such as tryptophan, phenylalanine, and isoleucine, are enriched in immunogenic peptides while other residues, such as serine, methylamine, and lysine, are depleted. Studies suggest that tryptophan and phenylalanine have long side chains that have a greater ability to make contact with the T cell receptor, possibly making them more immunogenic than other residues  (39).
In an independent study, we analyzed peptide datasets with measured immune responses from house dust mite, Timothy grass, Mycobacterium tuberculosis, cockroach allergens, and Pertussis  (44). The aim of the study was to devise prediction strategies not at the level of single alleles, but rather at the level of the general population. After extensive experimentation, we discovered that a combined prediction for a set of seven alleles, representative of prototypic binding supertypes, could capture 50% immune response with 20% of the peptides. Users can generate 15-mers overlapping by 10 residues from their protein of interest, predict binding for these seven alleles, compute the median consensus percentile rank of the seven values for each peptide, and select all peptides with a median consensus percentile rank less than or equal to 20.
STRUCTURe-BASeD ePiTOPe PReDiCTiONS The IEDB Analysis Resource has two structure-based methods for predicting discontinuous epitopes-DiscoTope (64, 65) and ElliPro  (66). Both make use of the proteins' geometrical properties. There are also protein-protein docking algorithms that can be used for antigen-antibody interaction prediction. To use structure based prediction tools, the user must provide a three-dimensional (3D) structure of the antigen-antibody complex such as the ones provided by the Protein Data Bank. 5  The search feature on the home page of the PDB allows the user to search 3D structures of a protein by key work, PDB ID, or a sequence, while an advanced sequence search feature allows one to run BLAST on an input sequence to find relevant structures in the PDB database. Clicking on the desired structure will open a structure specific web page from which the structure coordinates can be downloaded in PDB format for use in the prediction tools.
FASTA files are also available for download. PDB ID or a PDB format file is a required input for the DiscoTope epitope prediction method. If a 3D structure is not available in the PDB, there are homology modeling or comparative modeling methods, servers, and databases that can accessed, as described below. The performance of ElliPro was evaluated in two separate studies in 2007  (67)  and 2012  (65). The 2007 benchmark used 42 X-ray structures of antibody-antigen complexes and ElliPro obtained an AUC value of 0. 73, a score better than several other predictors, including Epitopia  (68) , PEPITO  (69) , and DiscoTope 1  (64). Two antibody-protein docking tools were benchmarked and had AUC values less than 0. 60. The 2012 benchmark used 52 X-ray structures of antibody-antigen complexes. In this study, ElliPro had an AUC value of 0.
The DiscoTope Method for B Cell epitope Prediction DiscoTope 2. 0 (65) was trained on 75 X-ray structures of antibody-protein complexes and it takes into account multiple epitopes of an antigen. It assigns each residue a score calculated as a linear combination of normalized values from Parker's hydrophilicity scale, amino acid propensity, the number of contacts within 10 Å for each atom, and the area of relative solvent accessibility. The DiscoTope web page requires three inputs: the PDB ID, a PDB chain ID, and the version of DiscoTope to use. If a user has generated a PDB file from homology modeling, that file can be specified and uploaded as well. The IEDB team recommends the use of DiscoTope 2. 0, but it involves a more complex calculation than version 1. 1 and subsequently takes longer to run.
Each row in the table has a button labeled CPK, which when clicked will highlight the residue in the structure model. elliPro Like DiscoTope, ElliPro can be accessed from the IEDB home page from the Analysis Resource pull-down menu or via the link in the right-hand panel. ElliPro  (67)  predicts epitopes in three steps. It first approximates the protein shape with an ellipsoid. It next calculates a protrusion index (PI)  (70)  for each and every residue. The PI is determined by constructing an ellipsoid that encompasses as many residues as possible but excludes that particular residue. Once the ellipsoid is constructed, it computes the ratio of the number of residues contained in the ellipsoid to the total number of residues to produce the PI. In the third step, the program clusters neighboring residues based on PI values to predict epitopes.
Homology Modeling for B Cell epitope Predictions If the user has an amino acid sequence for their protein of interest but a PDB structure is not available, the user needs to perform homology modeling to generate a PDB file. In this case, we recommend using Protein Model Portal (PMP). 6 Users can enter their protein sequence in the search field and hit the Search button. PMP will search major protein databases, such as UniProt, Swiss-Prot, and NCBI, and display results of the query. Users can then select a record and obtain a PDB-formatted file. If no models are found, the user can click the Submit button at the bottom of the query result page to submit their target protein sequence to one of PMP's registered homology modeling services. The subsequent page displays a list of the protein modeling servers along with a brief description of their server policy.
0 Å in protein-protein docking. Once a job is submitted, PatchDock typically returns results within a few minutes. The results are ranked by the geometric shape complementarity score (Score), where the higher score indicates a better result. Values are also presented for the approximate interface area of the complex (Area) and the atomic contact energy. The page has a link to visualize the candidate with a Jmol applet and a link to download the results. Users can improve the solutions using the FireDock (78, 79) link at the bottom of the page. PatchDock performs a rigid-body computation so the backbone does not move. FireDock is an efficient method for refining the rigid-body docking solutions of PatchDock. ClusPro 9  is another well-known docking server and was the first web-based antibody-antigen docking program publicly available.
MPTRAC is a rather new model and our study also serves the purpose of validating the model. However, the results are transferable to other Lagrangian models for the stratosphere as well, e. g. , the Chemical Lagrangian Model of the Stratosphere (CLaMS)  (McKenna et al. , 2002a, b)  or the Alfred Wegener Institute Lagrangian Chemistry/Transport System (ATLAS)  (Wohltmann and Rex, 2009). The results of the direct validation of the temperature and wind data of the meteorological analyses are of interest for studies using chemistry-transport models to assess polar ozone loss in the stratosphere (e. g. ,  Chipperfield, 1999; Grooß et al. , 2002 Grooß et al. , , 2005;; Wohltmann et al. , 2013). The results of the trajectory validation are of particular interest for studies applying the 'Match' technique (von der  Gathen et al.
, 1995; Rex et al. , 1997)  to assess polar ozone loss. In order to distinguish between chemically and transport-induced changes of ozone abundance, the Match approach uses trajectory calculations to relate ozone observations within the same air mass at different locations to each other. In Sect. 2 we introduce the superpressure balloon observations during the Concordiasi campaign. We also describe the four meteorological data sets and discuss the meteorological conditions during the campaign. Furthermore, we introduce the Lagrangian particle dispersion model MPTRAC and the approach used for trajectory validation. The results of our study are provided in Sect. 3. In the first part we compare temperatures and horizontal winds of the different meteorological data sets directly at the position of the balloon measurements.
Note that technical issues aboard the scientific gondola caused a few data gaps in the TSEN data set, but most of them were shorter than 15 min. In order to quantify the coverage of the balloon observations during the free-flying phases, we independently calculated the 5% and 95% quantiles of various parameter distributions. All statistics presented in this paper are most representative for the parameter ranges reported below. Any findings for parameters outside these ranges need to be considered carefully, because only few measurements are available to support them. We found that most of the measurements (i. e. , more than 90%) took place between 25 September and 22 December 2010, at an altitude range of 17. 0 -18. 5 km, and within a latitude range of 59 -84 • S. The pressure measurements are mostly within a range of 58.
LHC searches at 1TeV Studying the pair-production of Higgs bosons at the LHC is an important topic of research. There exist a large number of dedicated ATLAS and CMS analyses at a centre-of-mass energy of √ s = 8 and 13 TeV, and they differ by the final state they focus on. Various signatures have been considered, which include final states with two h → bb decays  [8] [9] [10] [11] [12] , as well as those with one h → bb and one h → τ + τ -decay  [13, 14] , one h → bb and one h → γγ decay  [15, 16] , one h → bb and one h → W + W -decay  [17] , as well as one h → W + W -and one h → γγ decay  [18]. Here we focus on the ATLAS and CMS analyses using 13 TeV data in the four-b-jet final state and estimate the LHC sensitivity to the model proposed in Section 2. 1  This final state has the advantage of arising from Higgs boson decays with the largest branching fraction.
Simulation setup For the simulation of the VLQ signal, we allow the VLQ mass M Q to vary and be equal to 500 GeV, 650 GeV, 800 GeV, 1 TeV and 2 TeV. Signal simulation is performed within the MG5 aMC@NLO framework  [38]  where the entire event generation process is automated  [39]. We make use of UFO model files  [40]  extracted from the Lagrangian presented in Ref. [20]  with the help of the FeynRules  [41] , NLOCT  [42]  and FeynArts  [43]  packages, as this Lagrangian embeds the model introduced in Section 2. Hard scattering events are generated at the NLO accuracy in QCD, the virtual one-loop contributions being evaluated with the MadLoop module  [44]  and then combined with the real contributions by means of the FKS subtraction method  [45]  as implemented in the MadFKS package  [46]. VLQ decays are then handled automatically by means of the MadSpin  [47]  and Mad-Width  [48]  programs, which allow for retaining both off-shell and spin correlation effects.
The fixed-order calculations, for which the NLO set of NNPDF 3. 0 parton densities  [49]  is used, are matched with the parton shower and hadronization infrastructure of the Pythia 8 package  [50] , and we simulate the response of an LHC-like detector by using the Delphes 3 program  [51]  (using a CMS or an ATLAS description where relevant) that internally relies on the FastJet package  [52]  for jet reconstruction.
We select three benchmark setups with VLQ masses of 500 GeV (blue), 800 GeV (red) and 1 TeV (grey) and with κT = 0. 07, and we overlay our predictions with the ATLAS  [9]  and CMS  [11]  data and background. VLQ pair production, as these two processes lead to a very different jet activity to which the boosted jet tagger is very sensitive to. We moreover observe smaller efficiencies for the ATLAS-like analysis due to the more severe selection strategy. These lower efficiencies are however not problematic as in the boosted regime, the background is also expected to be much more reduced than in a resolved context. The four analyses that we have reimplemented have not been designed to target VLQinduced di-Higgs production in the first place, as reflected in the comparison of the differential distributions in the analysis key observables with data illustrated in Figure  6  in the resolved case.
Although it may be challenging for a resolved analysis to be sensitive to VLQ-induced di-Higgs production for large VLQ masses, boosted analyses in principle offer extra handles for extending the sensitivity up to the TeV scale. The official numbers of data events and the predicted numbers of signal events for κ T = 0. 07 are given in Table  1  for various VLQ masses. Comparing the magnitude of the yields, it turns out that ATLAS and CMS are already sensitive to a large fraction of the model parameter space with present data. This prevents us from requiring a specific design of a VLQ-dedicated di-Higgs search. bosons after applying the boosted ATLAS-like analysis selection (left), and in the reduced mass resulting from the boosted CMS-like analysis (right). We select varied benchmark setups and overlay our predictions with the ATLAS  [9]  and CMS  [12]  data and backgrounds, respectively.
Acknowledgements We thank the organisers of the 2015 ' Les Houches -Physics at TeV  colliders' workshop where this work was initiated. GC, HC and AD acknowledge partial support from the  Labex-LIO (Lyon Institute of Origins)  under grant  ANR-10-LABX-66  and  FRAMA  ( FR3127 ,  Fédération de Recherche 'André Marie Ampère' ). The work of  HSS  and BF is supported in part by  French  state funds managed by the  Agence Nationale de la Recherche (ANR) , in the context of the  LABEX ILP  ( ANR-11-IDEX-0004-02 ,  ANR-10-LABX-63 ), and the one of TF by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  ministry of Education, Science and Technology  (No. 2013R1A1A1062597 ) and by  IBS  under the project  code IBS-R018-D1. AC has been supported by the  MIURFIRB RBFR12H1MW  grant.
Using this C-K-based cartography of solutions, interdisciplinary studies that mix human cognition with design theory have been able to develop smart lock-in methodologies to overcome fixation effects. These studies have demonstrated the stimulating role of expansive examples, i. e. , ideas and solutions that are outside fixation effects, in elevating the creative generation capacities of individuals  [8]. The authors utilized a classical creative ideation task that consists of proposing the maximum number of solutions to ensure that a hen's egg dropped from a height of ten meters does not break. Using an existing database of solutions created over the last five years  [8] , the authors revealed that 81% of the solutions belonged to three categories of "restrictive" solutions within the fixation path (i.