dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Systems biologists study interaction data to understand the behaviour of whole cell systems, and their environment, at a molecular level. In order to effectively achieve this goal, it is critical that researchers have high quality interaction datasets available to them, in a standard data format, and also a suite of tools with which to analyse such data and form experimentally testable hypotheses from them. The PSI-MI XML standard interchange format was initially published in 2004, and expanded in 2007 to enable the download and interchange of molecular interaction data. PSI-XML2. 5 was designed to describe experimental data and to date has fulfilled this basic requirement. However, new use cases have arisen that the format cannot properly accommodate. These include data abstracted from more than one publication such as allosteric/cooperative interactions and protein complexes, dynamic interactions and the need to link kinetic and affinity data to specific mutational changes.
Results: The Molecular Interaction workgroup of the HUPO-PSI has extended the existing, well-used XML interchange format for molecular interaction data to meet new use cases and enable the capture of new data types, following extensive community consultation. PSI-MI XML3. 0 expands the capabilities of the format beyond simple experimental data, with a concomitant update of the tool suite which serves this format. The format has been implemented by key data producers such as the International Molecular Exchange (IMEx) Consortium of protein interaction databases and the Complex Portal. Conclusions: PSI-MI XML3. 0 has been developed by the data producers, data users, tool developers and database providers who constitute the PSI-MI workgroup. This group now actively supports PSI-MI XML2. 5 as the main interchange format for experimental data, PSI-MI XML3.
Background Understanding the interaction networks that govern biological systems is essential to fully decipher the molecular mechanisms ensuring cellular biology and tissue homeostasis. Interactions between molecules result in both the assembly of stable functional protein complexes, which form the molecular machinery of the cell, and transient, often regulatory, networks of weakly associating molecules. Together these drive and regulate cellular processes, cell-cell interactions and cell-matrix interactions. The capture and curation of published interaction data has been the work of interaction databases for many years, and many of these resources have collaborated through the Molecular Interaction workgroup of the Human Proteome Organization Proteomics Standards Initiative (HUPO-PSI) to create and maintain community data formats and standards  [1].
These formats and standards have enabled the systematic capture, reuse and exchange of these data and the building of tools to enable network contextualization and analysis of -omics data. Version 1. 0 of PSI-MI XML was published in 2004 and enabled the description of simple protein interaction data  [2]. The format was widely implemented and supported by both software tool developers and data providers, but was soon found to be too limited in its scope. To facilitate rich, integrative analyses, many databases wished to describe and exchange the full wealth of data generated by interaction experiments, including a detailed description of experimental conditions and features such as binding sites or affinity tags on participating molecules. In order to make this possible, the Molecular Interactions working group of the HUPO-PSI further extended the XML schema to enable the annotation of a wider range of data.
PSI-MI XML2. 5 expanded the type of interactors to encompass any molecule or complex of molecules which can be described in the 'interactor type' branch of the accompanying controlled vocabulary (PSI-MI CV)  [3]. Sequence or positional features on a participant molecule that are relevant for the interaction can be described in a featureList, again using an appropriate controlled vocabulary term. The PSI-MI XML2. 5 schema allows two different representations of interactions. The compact format was designed for larger datasets. In this, the repetitive elements of a larger set of interactions, such as the interactors and experiments, are only described once, in the respective list elements, and subsequently referred to. The extended format groups all related data closely together and was designed to simplify parsing.
In these cases it may be necessary to describe a 'set' of molecules. This is not a new conceptit has been common practice in pathway databases such as Reactome  [16]  for some years, and indeed the required CV terms have been taken from the Reactome definition. However, this cannot be a simple addition to the Participant type CV as the ability to add a feature to a specific molecule within that set may be necessary. In PSI-MI XML3. 0, the participant element will now contain a choice between interactor, inter-actorRef, interactionRef and interactorCandidateList. The interactorCandidateList element would contain a molecule-SetType element (PSI-MI CV Type) followed by one to many interactorCandidate elements. The interactorCandidate node contains a required id attribute, a required interactor or interactorRef element to describe or reference an interactor and an optional featureList element with one to many features to describe binding features for each interactor candidate (Additional file 9).
Results All data resources using the IntAct database as their data storage repository, i. e. , members of the IMEx Consortium  [17]  including IntAct, IID, InnateDB, MINT, DIP, MatrixDB, HPIDB routinely make their data availin PSI-MI XML3. 0 in addition to the existing PSI-MI XML2. 5 and MITAB 2. 7 formats. Manually curated protein complexes from the Complex Portal are also made available in PSI-MI XML3. 0. The PSI-MI maker software (https://github. com/MICommunity/psimi-maker-flattener), a desktop application that helps users to create PSI-MI XML documents and extract data from them, has been updated to support PSI-MI XML3. 0. In addition, the new features included in PSI-MI XML 3. 0 are currently being used to extend an existing tool suite, the MI Bundle, that integrates molecular, structural and genomics data and that already relies on the PSI-MI standard  [18].
Conclusion PSI-MI XML3. 0 will enable the molecular interaction community to meet the demands of new data types and increase our ability to systematically describe important biological events such as the composition, topology and stoichiometry of protein complexes, the cooperative binding of molecules to form new binding sites, and to modulate the activity of enzymes through allosteric binding. The accompanying PSI-MI controlled vocabulary used to populate this schema is also constantly being updated and expanded to more fully describe new ways of measuring molecular interactions and meet the needs of novel data types. We have developed a Java library, JAMI  [19] , The PSICQUIC web service  [20] , that is capable of both reading and writing all the PSI-MI formats, PSI-MI XML, MI-JSON and MITAB, to ensure that software developers are not faced with having to create multiple version of a program to address all versions of the interchange formats.
Funding MD, MK, AS, JS, JH and YY were funded by  BBSRC   MIDAS grant  ( BB/L024179/1 ), this grant provided the funds for the design of  PSI-MI XML3. 0  and its implementation by the IntAct database. KVR was funded by  European Commission  ( FP7-HEALTH-2009-242129  SyBoSS), LL by  ELIXIR-IIB , the Italian Node of the European ELIXIR infrastructure, IJ was funded by  Ontario Research Fund  ( GL2-01-030 , # 34876 ) and  Canada Research Chair Program  (# 225404 ), DJL by  EMBL Australia  and  FP7-HEALTH-2011-278568 , SRB and NTM by  Fondation pour la Recherche Médicale  (grant n°D  BI20141231336 ) and by the  French Institute of Bioinformatics  (2015 call), NHC and RCL by  British Heart Foundation  ( RG/13/5/30112 ), GC by the  European Research Council  (Grant Agreement  32274 ), CC was funded by the  Wellcome Trust  (grant numbers  103139 ,  063412 ,  203149 ) and LS by  National Institutes of Health.
Availability and requirements Project name: PSI-MI XML3. 0. Project home page: e. g. http://psidev. info/groups/molecular-interactions GitHub source:https://github. com/HUPO-PSI/miXML/tree/master/3. 0 Operating system(s): Platform independent. Programming language: XML. Other requirements: License: Apache2. 0. Any restrictions to use by non-academics: None. Availability: All example files are available in both Supplementary Materials and in GitHub, as listed in the article. The data used in the example files is also freely available from the IntAct or Complex Portal databases, as appropriate, with the exception of the cooperative interaction described in Additional file 8, which is not available in any public repository. Availability of data and materials Not applicable.
Data processing Analysis was performed using the Spinal Cord Toolbox (SCT) version 2. 2. 3  [54]. The four datasets were first co-registered, then metrics were calculated. For extracting metrics within specific pathways in the white matter (dorsal column, DC, lateral funiculi, LF, ventral funiculi, VF), data were registered to the MNI-Poly-AMU template  [55] , which includes an atlas of WM tracts  [56]. For sake of clarity, details about the processing pipeline are included in the supplementary material (see S1 File in section 8. Supporting information).
Repeatability Fig  1  shows test and retest multi-parametric maps by vertebral levels, for one single young and one single elderly subject, as well as for the group average (n = 33). The single subject data look noisy, however the average map shows clear distinction between WM and GM. Moreover, the symmetry that can be observed on the group average maps suggests no apparent differences in myelin content between left and right cord. In all metrics, the heterogeneity of values across WM regions suggests different microstructural compositions. For example, the fasciculus cuneatus shows higher MTV than the fasciculus gracilis, suggesting higher myelin content in agreement with previous histology studies  [1, 67]. Apart from MTR, all metrics show fairly stable values across vertebral levels. A guide for reading (and understanding) figures and tables in the paper.
Sensitivity to myelin content This section deals with the larger sample (n = 33 subjects). d  ) in order to allow the reader to assess whether differences between vertebral levels or WM regions can be distinguished from measurement errors or not. Individual subjects data are also plotted to see if differences between subjects can be carried out despite the measurement error. However, for individual comparison, measurement errors are assessed by the MDC, which is much larger than the CI d (as negative and positive errors do not compensate for each other). Only T 1 and MTV seem to allow the comparison between some healthy subjects. The differences that are distinguishable from measurement errors were sum up in Table  3 , along with the results of the one-way repeated ANOVAs. One can observe that some cases show significant differences but those differences are too small to be distinguished from measurement errors.
Data sharing Due to IRB restrictions, all data used here could not be publicly shared. However, we obtained specific consent for sharing MRI data from four young volunteers. Three of them were part of the tested and retested group. Along with those datasets, we provide the batch scripts used to produce the myelin-sensitive metric maps and to register them to spinal cord template and white matter atlas. Also available is a Microsoft Excel spreadsheet gathering all results of the metric estimations within each region of interest for every scan session and every volunteer of the cohort. The 1 st tab of the sheet corresponds to the tested and retested cohort only (n = 16), and the 2 nd tab corresponds to the whole cohort (n = 33). Finally, also shared are the scripts to extract these metrics values, to compute the statistical indices for reliability assessment and to produce the figures presented in this work.
Matlab Middle Layer (MML)  [3] [4] [5] [6]  is the framework chosen to develop the high level applications. MML consists of a library of Matlab files that enable the development of control applications for particle accelerators control. An operator can access Process Variables (PVs) on * giovanna. campogiani@uniroma1. it the machine control system I/O Controllers (IOC) or on a machine model. MML software development is aimed to allow accelerator physicists to develop more easily the required high level functions for a proper operation of the machine. The most basic MML functions are used to communicate either with the online machine or with the virtual one. The two main functions for data access are getpv and setpv. Access to the accelerator hardware is handled through machine-specific routines to acquire and control database parameters csuch as BPMs, Magnets, RF systems.
eleMML Architecture The integration of the physics engine of elegant  [7]  with the power of MATLAB enables to develop and test applications on the ELI-GBS virtual machine, through the Self Describing Data Sets (SDDS) file protocol  [8]. The result is eleMML i. e. a set of software tools developed to interface MML with elegant and to use the latter as a physics engine for the "model" operation of High Level Applications. An overview of the architecture of eleMML is presented in Fig. 1. The core of eleMML tool-set is the development of a software function called sddsReadAny which imports data from sdds-type files into the Matlab workspace, linking each column or parameter in the input sdds file into a column array. SDDS Toolkit provides a basic link between Matlab and elegant through the SDDSMatlab library.
This contains the sddsload function, which imports an sdds file into a Matlab structure-type variable. The sddsReadAny function goes one step further in providing the user directly with the simulation output data. When a setpv call is made, the simulation data is updated and element parameters changed. In order to write data to the different elements in the simulation mode, we exploit the -macro option of elegant call. The -macro option allows performing text substitutions in the command stream. Multiple -macro options may be given. A system call is made from Matlab when setpv is invoked. In preparation for the commissioning and operations of ELI-NP GBS, a dedicated "virtual accelerator" (VA) or "Model Server" has been created. This virtual test platform simulates the LINAC response to HLA commands.
The acceleration, compression and transport of the electron beams occupies approximately the first 300 m of the machine. The machine operates in single bunch mode. The bunch charge was 700 pC and the energy at the starting point of the correction was 1. 2 GeV. We used 5 horizontal and vertical correctors on a section of the LINAC (red box in Fig. 4 ) comprising the last accelerating section and the transport line to the undulators' hall. The relevant beam parameters are summarized in Our HLA tool were ported to the resident TANGO control system through existing Tango-MATLAB binding. Moreover, specific utilities were developed online in order to perform the measurement, and coordination with preexisting measurement tools was achieved. In particular the Fermi LINAC has an ongoing Fast Trajectory Feedback always active.
Here, we used a spatially explicit stochastic demogenetic model, implemented in the R package landsepi, to assess the epidemiological and evolutionary outcomes of these deployment strategies when two major resistance genes are present. We varied parameters related to pathogen evolutionary potential (mutation probability and associated fitness costs) and landscape organization (mostly the relative proportion of each cultivar in the landscape and levels of spatial or temporal aggregation). Our results, broadly focused on qualitative resistance to rust fungi of cereal crops, show that evolutionary and epidemiological control are not necessarily correlated and that no deployment strategy is universally optimal. Pyramiding two major genes offered the highest durability, but at high mutation probabilities, mosaics, mixtures and rotations can perform better in delaying the establishment of a universally infective superpathogen.
3. Under what conditions it is possible to achieve both evolutionary and epidemiological control of pathogens (i. e. , resistance that is both durable and efficient)? To motivate this work, we focus on crop resistance to rust pathogens (fungi of the genus Puccinia), although our general conclusions are likely to have broader implications. Many major resistance genes against rust pathogens have been described, but also quickly overcome after deployment in the field  (Boyd, 2005; Park, 2008; Thompson & Burdon, 1992). We investigate the questions above using a generic spatially explicit stochastic model, which simulates the spread of epidemics across an agricultural landscape and the evolution of a pathogen in response to the deployment of host resistance (Figure  1 ). This model, described in a previous study  (Rimbaud, Papaïx, Rey, Barrett, & Thrall, 2018)  and implemented in the R package landsepi, is flexible enough to vary resistance sources, deployment categories and epidemiological, evolutionary and landscape parameters (see Supporting information Videos S1-S4 for examples).
We analyse interferometric data obtained for Regulus with AMBER (Astronomical Multi-BEam combineR) at high spectral resolution (λ/δλ ≈ 12000) across the Brγ spectral line. The study of the photocentre displacement allows us to constrain a large number of stellar parameters -equatorial radius R eq , equatorial velocity V eq , inclination i, rotation-axis position angle P A rot , and flattening -with an estimation of gravity-darkening coefficient β using previously published theoretical results. We use the Simulation Code of Interferometric-observations for ROtators and CirCumstellar Objects (SCIROCCO), a semi-analytical algorithm dedicated to fast rotators. We chose Regulus because it is a very well-known edge-on star, for which an alternative approach is needed to check the previously published results.
& van Belle Based on observations performed at the European Southern Observatory, Chile under ESO AMBER Visitor mode program IDs 092. D-0342 † E-mail: Massinissa. Hadjara@oca. eu (2012). The extreme stellar flattening induced by the rotation was measured by interferometry by Domiciano de  Souza et al. (2003)  on Achernar (Req/R pol = 1. 56 ± 0. 05), using VLTI/VINCI (Very Large Telescope Interferometer/VLT INterferometer Commissioning Instrument) with a dense (u, v) coverage. The first image reconstruction of the surface of a fast rotator, showing the gravity darkening effect  (von Zeipel 1924a,b)  was on Altair  (Monnier et al. 2007 ) from CHARA (Center for High Angular Resolution Astronomy) observations, inferring several fundamental parameters: inclination; position angle; effective temperature; and polar and equatorial radii.
OBSERVATIONS AND DATA REDUCTION Regulus was observed with the AMBER/VLTI instrument located at Cerro Paranal, Chile, with the Auxiliary Telescopes. The fringes were stabilized using FINITO (Fringetracking Instrument of NIce and TOrino)  (Mérand et al. 2012 ) as a fringe tracker, allowing us to use a Detector Integration Time DIT=3sec for 20 exposures. The observations have been performed using the high spectral resolution mode of AMBER (λ/δλ ≈ 12000). Table  2  provides the observation log of Regulus. The differential phase φ diff obtained from the data reduction algorithm is related to the object's Fourier phase φ obj by (e. g. ,  Millour et al. 2006 Millour et al. , 2011)) : φ diff (u, v) = φ obj (u, v) -a(u, v) -b(u, v)/λ, (1) where the spatial frequency coordinates u and v depend on the wavelength λ, the projected baseline length Bproj and the baseline position angle P A (from north to east; u = Bproj sin(P A)/λ and v = Bproj cos(P A)/λ).
The parameters a and b correspond to an offset and a slope, given in appropriate units. The corresponding (u, v) coverage is shown in Fig. 1 , where the (u, v) plane is spanned over ∼1. 5 h/night. Note the rather poor sampling of the Fourier space. According to the Table 2, the (u, v) points for the date 2014-03-10 are represented by red circles and for the date 2014-03-12 by blue crosses. Data have been reduced using version 3. 0. 9 of the amdlib software  (Chelli et al. 2009; Tatulli et al. 2007 ). We adopt a mild frame selection based on fringe signal to noise (S/N) and geometric flux to noise thresholds greater than unity. Our dataset includes stellar spectrum; differential visibilities; differential phases; and closure phases.
Spectrum The high spectral resolution mode of AMBER leads to a velocity resolution of 25km. s - 1. The projected equatorial rotational velocities Veq sin i above ∼ 150km. s -1 of Regulus ensures that the Brγ line, is sampled by 6 spectral channels. Thus rotation effects should be taken into account when modeling phase signatures. Figure  2  shows the normalized observed flux of Regulus as observed (dashed red curve) and after correction (in blue line). The smooth black curves superimposed on the observations is our best model that is discussed in section 5. 1. The normalized observed flux was corrected by removing the both strong tellurics and doing a wavelength calibration. We converted the wavelength scale to the laboratory frame using Reguluss heliocentric velocity of 5. 90 ± 2. 40km. s -  measured by  Anderson & Francis (2012)  and the IRAF package 1 , in order to convert the observed velocity to the heliocentric frame (RV = -10.
SCIROCCO To interpret the φ diff observations, we use the semianalytical model for fast rotators: SCIROCCO Simulation Code of Interferometric-observations for ROtators and Cir-Cumstellar Objects. This code written in Matlab 2 , allows to compute monochromatic intensity maps of uniformly rotating, flattened, and gravity-darkened stars using semianalytical approach. SCIROCCO, which is a parametric description of the velocity field, the intensity map and line profile modelisation, in each point (latitude, longitude), allow us to obtain directly from the modeled specific intensity maps, on the photospheric lines, the spectro-interferometric observables as spectra and photocentres, and by using Fourier transformations, the visibility amplitudes, phases, and closure phases. SCIROCCO is described in detail in  Hadjara et al.
To fix this problem we deduce the MCMC best parameters by fitting the differential phase and the spectrum data together. MCMC explores the full posterior distribution using a set of random simulations of SCIROCCO with a frequency of adaptation. The result of the simulations is used for the next step in order to optimize the maximum likelihood. We run MCMC with 600 numbers of simulations and 50 points of adaptation frequency. We do 3 successive runs, starting from the values of the previous run, because we started from non optimized values and the chain needs some time to find the location of the posterior. At the last run, all the simulations are around the maximum likelihood and their average represents well the best solution, while the standard deviation provides the uncertainties. Fig. A1  (in appendix) shows, in addition of the covariance matrix, pairs of parameters, with their histogram.
Che et al. (2011);association PSTJ 5 for its official host agreement, the Lagrange and OCA for computer server support. This research made use of the Jean-Marie Mariotti Center SearchCal service 6 codeveloped by Lagrange and IPAG, and of the CDS Astronomical Databases SIMBAD and VIZIER 7. This research made use of the AMBER data reduction package of the Jean-Marie Mariotti Center 8. Special thanks go to the project's grant ALMA-CONICYT N • 31150002 and the PI, Keiichi Ohnaka, who supported this work.