dataesr/scientific-paragraphs-categorization huggingface dataset:
This region was delimited from the saturation with hydrogen atoms using the Austin model1 (AM1) (base of the software spartan20' of Wavefunction acquired license, https://www. wavefun. com/corporate/more_spartan. html (accessed on 22 June 2022)) to measure, from the electronic correlation, changes of bond distances and angles, as well as energetic changes  [48] [49] [50]. The QM region also undergoes a molecular mechanics (MM) QM approach with the MMFF aq forcefield to better understand the second solvation sphere, given that the surrounding targeted region is a polyatomic system. The rest of the structure undergoes an approach with classical physics from molecular mechanics (MM) with the MMFF aq base, which favors systems that can be measured in terms of the properties from the electronic correlation at the atomic level, i.
The 3D Reconstruction of the PURAwt Structure Showed Significant Defects Caused by the Phe233 Mutation The prediction model generated by the AlphaFold resulted in a very high per-residue estimate of its confidence (pLDDT) score > 90 within the Phe233 area (β-sheet), as presented in the β-sheet composition with three Phenylalanine's expansion (Figure  1b ). The PyMol software showed the composition of the β-sheet containing the Phe233. In agreement with protein-DNA binding interactions, protein structures, such as the protein region containing the deletion, may induce a deformation of the DNA due to binding in specific regions of the DNA  [52]. For instance, it is binding to the minor groove that, in addition to electrostatic interactions and other noncovalent interactions, may also play an essential role in generating such deformation.
This will allow the transcription initiation to generate mRNA, i. e. , AlphaFold analysis of the region harboring the Phe233 change showed its hydrogens in a 3D conformation (Figure  1c ) and the Phe233 polar contacts measurements were less than 4 Å (Figure  1d ). Table  S2  of the Supplementary Material summarizes the evaluation parameters and scores obtained by different software on the available models. Additionally, a global alignment with the Needleman-Wunsch algorithm and a standard deviation value (RMSD = 0. 595) was performed by zooming in on the region near position 233 of the wild-type R245-D234 protein region (translated from the PURA gene) to evaluate the effect of the deletion and potential changes in the angle of the β-sheets of PURAwt once the PURA-Phe233del occurred (Figure  S1a , Supplementary Material).
Energetic and Stereochemical Characterization of PURA Molecular Models Indicate Protein Damage The structures at the energetic level have QMEAN6 values within the normalized ranges of (0-1), presenting larger energetic values for PURAwt of 0. 61 compared to the structure with the deletion of 0. 59. Likewise, they have Z-score values close to the scattering values of the structure plot, contemplating experimentally solved structures with values of -2. 41 (PURAwt) and -3. 60 (PURA Phe233del). Additional data is shown in Figure  S2  of the Supplementary Material. Considering the statistical values and the structure of amino acids for the local groupings of nonbonding interactions, the Errat software estimated high scores, close to 93. 89% for PURAwt and 89. 66% for PURA Phe233del. At the stereochemical level, measurements of the peptide bonds alpha-carbons-dihedral angles (Cα) using the Ramachandran plots showed, for the PURAwt, 219 amino acids in the favored region (84.
These parameters generated a high global value for the two structures in terms of stability. Likewise, the Z-score parameter is a term that allows a comparison of the predicted hypothetical models to those experimentally obtained by X-ray diffraction techniques with a monochromator, NMR nuclear magnetic resonance, and CryoEM cryogenic imaging microscopy. We can observe that both structures leave the model located in the trend of the graph, as shown in Figure  S1  of the Supplementary Material. The Errat software has a statistical estimation algorithm that allows for the grouping of nine amino acids and performs a characterization, following along the whole primary sequence, to generate the summation of a global value of the proteins, producing a very high score for the two hypothetical models obtained.
Conclusions This manuscript presents a first report of a de novo mutation underpinning a PURA syndrome in a Latin American patient. We predicted the molecular effects in the protein structure using artificial intelligence algorithms with AlphaFold, a novel neural network. The p. Phe233del mutation produces a distortion in the PURA structural conformation in the ~5 Å surrounding area of the repeat III, where PURA interacts with DNA and RNA helix unwinding. Further to Protein-DNA docking analysis, we used energetic and stereochemical and the hybrid Quantum Mechanics-Molecular Mechanics (QM-MM) analyses to achieve an atomic resolution of the specific surrounding region affected by the mutation. These analyses are essential in understanding the molecular effects in protein structure and the efficiency of artificial intelligence algorithms and molecular and atomic resolution stereochemical analyses.
For that reason, allowing this possibility in the MadAnalysis 5 package  [31] [32] [33]  has recently become a priority in the development of the program. The first steps in this direction date from 2018, when MadAnalysis 5 version 1. 6 was released. This consisted of the first version of the code that allowed for handling of displaced leptons  [34] , and it was the first time that one of the public recasting codes was made suitable for the recasting of an LHC analysis involving displaced objects. In practice, the recasting module of the code had been updated in a twofold manner. First, it was linked to a modified version of Delphes 3  [35]  for the simulation of the detector response. These modifications, which were merged shortly later with the official version of the Delphes 3 detector simulator package, allowed the user to go beyond the default behaviour in which all particles are assumed to decay within the tracker volume of the detector.
Second, information about the impact parameter and the position from which a lepton originated was incorporated into the internal data format of the code. As a proof of concept, the CMS-EXO-16-022 analysis  [36]  was implemented in MadAnalysis 5  [37]. This implementation was validated in a satisfactory manner, so that for most signal regions, MadAnalysis 5 reproduced the expected signal for the model given within 20%. However, for long decay lengths (≥ 100 cm), the difference between the MadAnalysis 5 result and the expected signal was greater than 100%. Subsequently, due to the intense experimental and theoretical interest in this area, additional public codes became available. Firstly, a collaborative collection of independent standalone codes, called the LLP Recasting Repository, was established on the GitHub platform  [38].
It has recently been augmented by two sophisticated disappearing track searches  [39, 40]. Secondly, an LLP version of Check-MATE 2  [41] , which includes five (classes of) LLP searches, and an updated version of SModelS  [42] , which includes eight LLP searches, were released. Last year, the simplified fast detector simulation (SFS) framework of MadAnalysis 5 was introduced. It consists of a new simplified fast detector simulator relying on efficiency and smearing functions  [43]. Additional commands were supplemented to the Python command-line interface of MadAnalysis 5 , so that users could design and tune a detector simulator directly from a set of intuitive commands, and the C++ core of the program was accordingly modified to deal with such a detector simulator. Proof-of-concept examples were presented both for simple phenomenological analyses and in the context of the reinterpretation of the results of existing LHC analyses in Refs.
In Sect. 4  we describe the recast of a new run 2 analysis  [4]  from the ATLAS Collaboration searching for displaced vertices consisting of two (visible) leptons. Section 5 describes the implementation in MadAnalysis 5 SFS of the full run 2 CMS disappearing track search  [23, 24]. We summarise and conclude in Sect. 6. All our implementations are publicly available on the MadAnalysis 5 dataverse  [51] [52] [53]  and its Public Analysis Database  [54]. 1  MADANALYSIS 5 as a tool for detector simulation and LHC recasting.
Generalities MadAnalysis 5  [31] [32] [33]  is a general framework dedicated to different aspects of BSM phenomenology. First, it offers various means for users to design a new analysis targeting any given collider signal. To this aim, the code is equipped with a user-friendly Python-based command-line interface and a developer-friendly C++ core. Any user can then either implement their analysis through predefined functionalities available from the interpreter of the program, relying on the normal mode of running, or implement their needs directly in C++ in the expert mode of the code. Second, MadAnalysis 5 allows users to automatically reinterpret the results of various LHC searches for new physics to estimate the sensitivity of the LHC to an arbitrary BSM signal. The list of available searches and details about the validation of their implementation can be obtained through the Public Analysis Database (PAD) of MadAnalysis 5.
Moreover, recent developments allow for automated projections at higher luminosities and the inclusion of theoretical uncertainties  [55] , and leave the choice to simulate the response of the detector with either the external Delphes 3 package  [35]  or the built-in SFS framework  [43]. The SFS module of MadAnalysis 5 has been designed to emulate the response of a detector through a joint usage of the FastJet software  [56]  and a set of user-defined smearing and efficiency functions. In this way, the code can effectively mimic typical detector effects such as the impact of resolution degradations and object reconstruction and identification. In practice, MadAnalysis 5 post-processes the set of objects resulting from the application of a jet algorithm to the hadron clusters in a Monte Carlo event.
The SFS framework in a nutshell The detector effects described in the previous subsection are implemented through dedicated MadAnalysis 5 commands that are extensively described in the SFS manual  [43]. We therefore only provide below a brief description of the extant features, emphasising instead the new developments that are documented for the first time in this work. Instructions related to how to run the code are available from Ref. [33]. In order to turn on the SFS module, users have to start MadAnalysis 5 in the reconstructed mode (. /bin/ma5 -R) and switch on reconstruction by means of FastJet. This is achieved by typing in  2 set main. fastsim. package = fastjet By default, objects will be reconstructed by making use of the anti-k T algorithm  [57] , with a radius parameter set to R = 0.
According to the above syntax, all event samples are collected into a single dataset defined by the label <set>, although multiple datasets (defined through different labels) can obviously be used as well. We refer to Refs. [33, 43]  for detailed information about all available options. In addition, users must indicate to the code the set of objects that are invisible and leave no track or hit in a detector, and the set of objects that participate in the hadronic activity in the event. This is achieved by typing in define invisible = invisible <pdg-code> define hadronic = hadronic <pdg-code> where, in the above examples, one adds a new state defined through its Particle Data Group (PDG) identifier  [59]  (pdg-code) to the invisible and hadronic containers. These containers include by default the relevant Standard Model particles and hadrons, as well as the supersymmetric lightest neutralino and gravitino for what concerns invisible.
define reco_efficiency <obj> <fnc> [<dom>] define smearer <obj> with <obs> <fnc> [<dom>] define tagger <obj> as <reco> <fnc> [<dom>] The first of these commands indicates to the code that the reconstructed object <obj> can only be reconstructed with a given efficiency. The latter is provided as a function <fnc> that can be either a constant or depend on the properties of the object (its transverse momentum p T , its pseudorapidity η, etc. ). If the optional argument <dom> is not provided, this function applies to the entire kinematic regime. Otherwise, the efficiency is a piecewise function that is defined by issuing the above command several times, each occurrence coming with a different function <fnc> and a different domain of definition <dom> (specified through inequalities). The second command instructs MadAnalysis 5 that the property <obs> of the object <obj> has to be smeared in a Gaussian way, the resolution being provided as a function <fnc> of the properties of the object.
No significant excess over background was observed, so the results were interpreted as limits on the displaced supersymmetry model  [61]. This model contains R-parity-violating stop decays t1 → b with = e, μ, τ which happen with equal branching ratio of 1  3 for all lepton flavours once lepton universality is assumed (i. e. if the vector μ L has three identical components). Since the stops are only produced in pairs, each signal event would have two displaced leptons. An implementation of this analysis  [34, 37]  is already part of the Delphes 3-based Public Analysis Database  [54]  of MadAnalysis 5. It was subsequently implemented in the LLP extension of the CheckMATE 2 program  [41]. It is therefore a logical choice for the first LLP analysis to implement in the MadAnalysis 5 SFS framework, which we present here, along with several improvements regarding the previous work.
Vertex fit: The ATLAS analysis requires a good fit for the vertex; similarly to the primary vertex cut, we cannot reproduce the algorithm employed in the analysis. However, the HEPData entry provides per-decay acceptances, intended to be applied after all previous cuts. In our MadAnalysis 5 SFS implementation we use them as vertex reconstruction efficiencies, as discussed further in Sect. 4. 3. Transverse displacement: Prompt decays and decays after a small displacement are avoided by considering only DVs at positions x DV = (x DV , y DV , z DV ) for which the transverse displacement from the proton collision axis is larger than 2 mm. As in any given simulated Monte Carlo event protons are assumed to collide at the origin, this gives r xy ≡ x 2 DV + y 2 DV > 2 mm. (4. 8) Fiducial volume: Only vertices in a restricted detector volume, where track and vertex reconstruction are expected to be reliable, are taken into account.
The above model is a relatively simple extension of the SM with only two additional parameters, namely the VLL mass M τ (which is equal for the charged and neutral states at leading order), and the mixing parameter. We implemented the model in the Sarah package  [81, 82] , which we used to generate a custom SPheno code  [83]  to compute the particle spectrum and the decay tables. Provided the mixing parameter is sufficiently small, the new physical leptons τ and ν are both long-lived and can be produced at colliders via the processes pp → ν ν , ν τ + , ν τ -. (4. 11) The neutral vector-like lepton (VLL) ν then decays to an electron and a W -boson, where the latter can further promptly decay to an electron or a muon and a neutrino. This decay chain generates a displaced vertex from which two leptons and a neutrino originate, exactly as in the displaced χ 0 1 decay studied in the RPV SUSY model.
The latter includes a pseudocode, and efficiencies for "strong" and "electroweak" LLP production scenarios that convolve a great deal of information about tracklet reconstruction and selection cuts. This formed the basis of a code published in  [39]  and available on the LLP Recasting Repository  [38] , and has also been incorporated into CheckMATE 2  [41]. Recently a conference note  [22]  analysing the full run 2 dataset of 136 fb -1 appeared, followed by the full paper  [22] , which was released while this paper was under review. It would be very interesting in the future to recast that search and compare to the one presented here. On the other hand, the final CMS analysis of the entire LHC run 2 dataset was already extant  [23, 24]  in early 2020, where the first analysis  [23]  comprised 38.
Conclusions We have discussed three classes of new developments of the SFS framework, the simplified fast detector simulator shipped with MadAnalysis 5. First, new options have been added to treat lepton, photon and track isolation, that can now be enforced on the basis of the activity inside a cone centred on the object considered. Second, energy scaling, and in particular jet energy scaling, can be implemented in the same way as any other detector effect that is included in the SFS framework. Finally, we have augmented the code with a particle propagator module that simulates the impact of the magnetic field lying inside the detector. The trajectories of all charged objects and their decay products are hence mod-ified appropriately, in contrast with the previous implementation where they were assumed to propagate along straight lines.
While previous attempts have been made to recast the two CMS analyses, the validation here shows the best agreement with the experiments. Moreover, there was no previous recast of the ATLAS search. The codes, available online from the MadAnalysis 5 Public Analysis Database 11  and as entries in the MadAnalysis 5 dataverse  [51] [52] [53] , can hence now be safely used to reinterpret the results of these analyses in the context of any model of physics beyond the SM. As an illustration, we have considered a BSM model in which the SM is extended by a long-lived vector-like lepton doublet, and we have extracted the constraints on its parameter space by recasting the results of the ATLAS-SUSY-2017-04 analysis. Whereas we should in principle account for synchrotron radiation effects that reduce the kinetic energy of the particle with time, such a phenomenon is neglected.
Acknowledgements We thank  Brian Francis  for very helpful discussions about the CMS disappearing track analysis, and  Lakshmi Priya  for collaboration on its reinterpretation in [40]. We thank  Sabine Kraml ,  Humberto Reyes Gonzalez  and  Sophie Williamson  for collaboration on related topics;  Andre Lessa  for helpful discussions. We thank the organisers of the LLP workshops https://longlivedparticles. web. cern. ch/. We thank  Juliette Alimena  for correspondence about [64]. MDG acknowledges support from the grant "HiggsAutomator" of the  Agence Nationale de la Recherche (ANR)  ( ANR-15-CE31-0002 ). Opposite charge  2083. 2 2088. 4 2500. 3 2468. 4 2381. 4 2166. 0.
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Expected Performance To predict the optical performance of 4MOST, a full Top-Of-the-Atmosphere-to-Detector (TOAD) simulator was coded creating 2D detector images. The details of TOAD with all atmosphere and optical elements taken into account have been published earlier  [14]. 4MOST expected sensitivity for a number of typical science cases is depicted in Figure  7. TOAD is extensively used to generate artificial data for testing the data reduction and analysis pipelines (Section 5). A critical element in the performance of an instrument is to maximise observing efficiency and minimise observational overheads. The current aim and expectation that many of the observing overheads such as telescope slewing, guide star acquisitions, instrument configuration setting, etc. , can be hidden by performing them during the ~110 seconds it takes to read out the previous science exposure in unbinned, normal read speed mode.
SURVEY STRATEGY, OPERATIONS SCHEME, AND DATA ANALYSIS The operational model for 4MOST will differ in several respects from normal ESO operations. The 4MOST science time will be devoted to a coordinated set of Consortium and Community Surveys. A distinctive advantage of 4MOST lies in the capability to carry out many different science programs in parallel, thereby allowing not only unprecedented sample sizes, but also making it possible to observe programs that would otherwise be too expensive due to low target densities. In parallel mode, data for different Surveys are observed simultaneously at the telescope. In order to run the telescope efficiently in parallel mode, the 4MOST Consortium will take a service role for the user community of 4MOST, fulfilling some of the functions normally carried out by each individual programme PI.
Surveys therefore need to provide, next to their target catalogues, a number of other parameters such that target selections and observing schedules can be optimised to be both efficient and reproducible at least in a statistical sense. These parameters include: 1) spectral S/N or redshift success criteria to estimate needed exposure time and to signal target completion after the data is analysed, 2) a Small Scale Merit that defines the target completeness required for a sample on the scale of about a 4MOST Field of View, 3) a Large Scale Merit that describes a Survey's sky area preference, and 4) a total Figure of Merit that provides an overall measure of a Survey's scientific success. The 4FS (Section 2. 2) is used to tune the many parameters of the scheduling algorithms to maximise the total Figure of Merit of all Surveys as well as various other requirements from the Surveys (e.
Operations Scheme and Data Analysis Figure  9  shows the main actors and software modules of the 4MOST operations and data flow between them. The process starts with the Surveys submitting their target catalogues with their observing requirements and goals (e. g. , S/N, completeness) to the front-end Science Operations System (OpSys). OpSys ingests all input catalogue in a database, calculates the exposure times needed and plans the optimal tiling on the sky given the total target density and exposure time pressure. Using ESO's Designated Visitor Mode of remote observations, OpSys uses the current observing conditions (moon, seeing, wind, clouds, etc. ) and observing strategy parameters to select the next field to observe and assign fibres to objects in a fully probabilistic algorithm  [16].
An Observing Block (OB) with instrument instructions is created and send from MPE, Garching to Paranal Observatory to be executed on VISTA as soon as the previous OB is finished. The data of all executed OBs is transferred at the end of the night through ESO's Science Archive Facility (where all raw data is public immediately) to the 4MOST Data Processing Centre located at IoA, Cambridge. The Data Management System removes the instrument profile and extracts the science-ready, calibrated 1D spectra. Additional pipelines created by Infrastructure Working Groups then process the data further to perform machine learning object classifications, determine for instance radial velocities, stellar parameters and abundances for stars or redshifts and star formation histories for galaxies and AGN. Also, the target selection and observing completion probability is determined for all parameters of all targets.