dataesr/scientific-paragraphs-categorization huggingface dataset:
Note that at K = 2, individuals from South Africa are not always assigned to cluster C with a high P value (Fig. 3 ). This is likely due to the strong genetic bottlenecks in invasive African olive populations (for instance, many alleles initially shared between europaea and cuspidata in the native range could have been lost after bottlenecks and may bias assignments). In contrast, at K = 5, no South African olive individuals are assigned to Mediterranean clusters with P > 0. 05, while admixture between E and C clusters is still detected in the Camden Park population, one individual from Bringelly (no. 21), three individuals from Mt Annan (no. 2, 8 and 17) and two individuals from Lonsdale (no 16 and 22). Pairwise F ST between invasive populations confirmed the main patterns observed with the Bayesian clustering approach (Table  S5 ).
Acknowledgements We thank  Stephen McLeod  for plant sampling,  Silvana del Vecchio and Ng LZ (Reuben)  for their help in the laboratory and  Jordi Salmona  for helpful discussion on the ABC analyses. This work was partially funded by the  Intra-European  fellowship  PIEF-GA-2008-220813  to GB. This work has been conducted at  Imperial College London (Silwood Park ) and the laboratory EDB, part of the LABEX entitled  TULIP  ( ANR-10-LABX-41 ). Herbarium specimens were provided by  Kew Gardens  and the  British Museum of Natural History. We thank two referees and  A. Estoup  for their suggestions which helped us to clarify and improve the manuscript.
The most expensive ingredient of the reaction (72% of the costs) is the mixture of double-labeled amino acids (1 mg/mL). With the high level of amino acid incorporation in our CFPS  [13] , the amount of protein produced in 1 mL of the reaction was sufficient for backbone assignment. This corresponds to a total material cost of about $20 for the NMR sample. Chemical shifts report on IDP structure and interactions  [5, 11]. For the sequential backbone assignment we use the following TA procedure: (i) a small fraction of data is acquired in NUS mode for each of the triple resonance BEST-TROSY experiments; (ii) the data are combined with the earlier collected measurements (if present) and processed using co-MDD  [25] ; (iii) signals are detected in the resulting one-dimensional spectral shapes and used for the automated assignment with the AutoAssign software  [26] ; (iv) scores on completeness of the peak lists and the achieved backbone assignment are evaluated, and, if needed, the procedure is repeated starting from step (i).
This should be compared with the traditional approach, where the automated assignment is performed on a single set of peaks observed in the spectra processed using all acquired data. For this input, residues E20, N23 are not assigned and residues E18, Y19, E22, M35, Y36, E37, R41 are assigned incorrectly, while the assignment validation suite from the AutoAssign package  [26]  only reports suspicious assignment for residues D21, E22, G42 and not unique mapping for residues 57-62. Thus, the new resampling-based validation procedure better reveals residues with problematic assignments and more efficiently finds the correct solution. Table  1  and Figure  S1  summarize samples and de novo backbone assignments obtained using the presented CFPS-TA approach. These include five out of six TCR and BCR cytosolic domains, as well as mutated and modified forms of the domains.
The experiments were performed in ''native'' (phosphate buffer), denaturing (6 M urea), and helix-promoting (20% trifluoroethanol, TFE) conditions. Depending on the sample, the TA measurement time required for the backbone assignment varied from 0. 8 to 47 hours. Values for TCRf are taken from our previous publication  [25]. doi:10. 1371/journal. pone. 0062947. t001 The lowest protein concentration, for which assignment was obtained, is 30 mM. It should be noted that apart from the relatively low protein concentrations the assignments were obtained despite of low signal dispersion and strong peak overlap, which is typical for IDPs. The signal separation in the spectra of our samples was significantly lower than the average of the BMRB database (Figure  S2 ) for the corresponding protein sizes.
Processing of the NUS spectra is performed using recursive co-MDD  [25, 43] , which allows high quality spectra reconstruction from a very small number of measurements. Further time saving is obtained by replacing lengthy manual work with the spectra by the automated hyperdimensional signal detection  [25]  and automated backbone assignment  [26]. TANSY software consists of two separate modules: the acquisition module (AM) and the processing and analysis module (PAM). The former is running on an NMR spectrometer and organizes the stepwise incremental data collection. AM is implemented using macro and graphical interface features of VnmrJ (Agilent Technologies, Inc. ) and TopSpin (Bruker Biospin) spectrometer software. AM runs a set of NMR experiments using a NUS schedule. The acquired data are continuously saved to computer disk and become immediately available for the downstream processing by PAM.
The PAM can operate in realtime with the AM or can be used as an off-line processing and analysis tool. PAM is implemented in the Python programming language (http://python. org/) with QT (http://qt. nokia. com/) based graphical interface. PAM uses MDDNMR  [15]  and NMRPipe  [44]  programs for the spectra processing, its own routines for signal detection in one-dimensional MDD shapes, and AutoAssign  [26]  for the automated backbone assignment. The output of the PAM module comprises a set of processed spectra in an NMRPipe format, peak lists, assignment tables, and a set of progress and quality scores. The output can be easily imported into various programs, including CCPN Analysis  [45]  and CARA (http://cara. nmr. ch/) for manual inspection of the assignment and further analysis. TANSY software is available from the authors upon request.
Spectral Acquisition The acquisition module introduced in the previous section performs iterative incremental acquisition of NUS experimental sets  [46]. The most sensitive experiment from the set, HNCO is recorded first using NUS with 10-30% sparse sampling. Subsequently, at each TA step, every experiment from the suite has a short run over a small fraction (e. g. 0. 5-1. 0%) of points from the NUS schedule. The number of points differs for individual experiments and can be adjusted during the course of the TA procedure in order to balance the experiment time allocations. Thus, AM performs incremental acquisition for all experiments in an interleaved manner and provides input amenable for the realtime analysis by PAM.
Processing and Analysis Module First, the HNCO experiment is processed with recursive MDD  [47]  or compressed sensing  [48]. A 3D peak list is produced with program pkFindROI from the NMRPipe package  [44]. At this stage, the peak list can be manually verified to add missing and remove occasional false peaks. Although ideally MDD components have a one-to-one relation to peaks in the spectrum, in practice it is not always so. In the case of mixing  [49] , one component may describe several peaks. Some of the components may correspond to spectral artifacts or strong noise features. ''Clean'' MDD components, i. e. those unambiguously related to peaks, are obtained by fixing 15 N and 13 CO frequencies to the values in the peak list. Namely, time domain shapes for the constant time 15 N dimension are analytically calculated as F N F N = cos(v N t)+I sin(v N t).
The work was supported by the  Swedish Research Council  ( 621-2005-2951 ,  2011-5994 ),  Wenner-Gren Foundation (Stockholm) , and the  European Commis -sion ( FP7-I3-Bio-NMR  project No. 261863  and FP7-ITN-IDPbyNMR contract No. 264257 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
SEPServer is a three-year collaborative project funded by the seventh framework programme (FP7-SPACE) of the European Union. The objective of the project is to provide access to state-of-the-art observations and analysis tools for the scientific community on solar energetic particle (SEP) events and related electromagnetic (EM) emissions. The project will eventually lead to better understanding of the particle acceleration and transport processes at the Sun and in the inner heliosphere. These processes lead to SEP events that form one of the key elements of space weather. In this paper we present the first results from the systematic analysis work performed on the following datasets: SOHO/ERNE, SOHO/EPHIN, ACE/EPAM, Wind/WAVES and GOES X-rays. A catalogue of SEP events at 1 AU, with complete coverage over solar cycle 23, based on high-energy (~68-MeV) protons from SOHO/ERNE and electron recordings of the events by SOHO/EPHIN and ACE/EPAM are presented.
A total of 115 energetic particle events have been identified and analysed using velocity dispersion analysis (VDA) for protons and time-shifting analysis (TSA) for electrons and protons in order to infer the SEP release times at the Sun. EM observations during the times of the SEP event onset have been gathered and compared to the release time estimates of particles. Data from those events that occurred during the European day-time, i. e. , those that also have observations from ground-based observatories included in SEPServer, are listed and a preliminary analysis of their associations is presented. We find that VDA results for protons can be a useful tool for the analysis of proton release times, but if the derived proton path length is out of a range of 1 AU < s [ 3 AU, the result of the analysis may be compromised, as indicated by the anti-correlation of the derived path length and release time delay from the associated X-ray flare.
Introduction Solar energetic particle (SEP) events are one of the most important elements of space weather  (Vainio et al. 2009) , being the defining component of solar radiation storms,  1  contributing to radio blackouts in polar regions and being related to many of the fastest coronal mass ejections (CMEs) driving major geomagnetic storms. In addition to CMEs, SEP events are also related to solar flares (e. g. ,  Reames 1999). The occurrence rate of large, space-weather relevant SEP events is about 100 per solar cycle, with a broad distribution in time that extends well into the declining phase of the activity cycle as traced by the sunspot index. Data Services and Analysis Tools for Solar Energetic Particle Events and Related Electromagnetic Emissions 2  (SEPServer) is a three-year collaborative project funded under the seventh framework programme (FP7-SPACE) of the European Union.
The project started in December 2010. It will construct an Internet server that will provide access to a large number of SEP datasets from different instruments on-board half-a-dozen missions, to electromagnetic (EM) observations related to the events indentified from the SEP data, and to state-of-the art analysis tools that can be used to infer the solar SEP emission time profiles and interplanetary transport conditions prevailing during the SEP events. All datasets will be accompanied by comprehensive metadata as well as assessments of data quality. To assess their validity, the developed data-analysis methods will be compared against each other and solar EM observations. Furthermore, the project will provide analysis results on the events in the form of comprehensive SEP event catalogues that list key properties of the events.
The server will be released to the community near the end of the project, in the fall of 2013. This paper presents the SEPServer project and the results from the first year: the first SEP event catalogue. A systematic scan of the space-weather relevant  3  proton intensities (55-80 MeV, or ~68 MeV) observed by SOHO/ERNE was performed over the years 1996-2010. A total of 115 proton events were identified and analysed using data from SOHO/ ERNE, SOHO/EPHIN and ACE/EPAM. We will present and discuss here the results of the analysis of these events. We concentrate on the onset analysis utilizing two different methods, velocity dispersion analysis (VDA) and time-shifting analysis (TSA), to get estimates of the particle release times close to the Sun. Furthermore, we analyse the EM associations of 44 of the events, i.
e. , those that occur during the day time in Europe and have the best coverage in terms of data available at SEPServer. The analysis is not meant to be comprehensive but spawn a number of further efforts by the community, utilizing the data and preliminary analysis results presented in this paper. Likewise, while we attempt to compile a comprehensive list of events for the years 1996-2010, the resulting catalogue is not complete due to several data gaps and since the methodology applied misses some of the events that occur in the aftermath of another. Nevertheless, the community is encouraged to perform further studies making use of the SEPServer event catalogue for which full access will be provided through the project website (http://server. sepserver. eu). The structure of the paper is the following.
The SEPServer project The SEPServer project aims at facilitating access to a number of complementary SEP datasets, allowing in-depth analyses of SEP events to be performed. The emphasis of the project is on the space-weather relevant SEP events, i. e. , events with proton spectra that extend to high enough energies to penetrate moderate shielding provided by spacecraft and constitute a space-weather hazard. The SEP datasets included in the project span over solar cycles 21-24 and the observations cover radial distances 0. 3-5. 4 AU. The SEP datasets distributed through SEPServer are listed in Table  1. In addition to SEP data, crucial ingredients in the analysis of SEP events are the solar EM observations that are related to the events. Besides from context observations of, e. g. , solar-wind plasma and magnetic field and CMEs related to the SEP event, important knowledge of the acceleration processes close to the Sun can be obtained from solar X-ray and gamma-ray observations as well as from solar radio observations that are generated by the accelerated particles, mostly electrons, that interact with the solar atmosphere.
This occurs in the chromosphere and low corona for hard X-rays (HXR) and gamma-rays, and between the low corona and interplanetary space for radio-emitting electrons. While the CME observations are relatively easily accessible through a number of sources (e. g. SOHO LASCO CME Catalog 4  , see  Gopalswamy et al. 2009 , and the automated CACTUS CME-detection facility 5  , see  Robbrecht et al. 2009) , energetic photon and radio observations relevant to SEP events have not so far been collected to a single access point. Table  2  presents the EM observations distributed through SEPServer. At present, these datasets provide a comprehensive coverage of the identified high-energy proton events of the 23rd solar cycle. In future, they will be extended back to cover key events at earlier times, in particular the Nancay radioheliograph (available as 1-D imaging data for  [1980] [1981] [1982] [1983] [1984] [1985] [1986] [1987] [1988] [1989] [1990] [1991] [1992] [1993] [1994] [1995]  and AIP/OSRA (to be recovered from microfilm archives) radio observations from the Helios era.
These OSRA data have not been available in digital format prior to SEPServer. In addition to SEP and EM observations, SEPServer will provide local magnetic field data from each spacecraft of Table  1  carrying a magnetometer and plasma data from near-Earth solar wind (ACE and Wind). In addition to a large number of well-documented datasets, SEPServer will provide the scientific community with state-ofthe-art analysis tools based on numerical simulations of particle transport in the interplanetary medium. These tools allow one to deconvolve the effects of interplanetary transport from the observations in order to assess the source function of particle injection close to the Sun. This, in turn, can be directly compared with EM observations to perform accurate comparisons of the temporal and spectral relation between the interacting and escaping particles.
The onset is defined as the time stamp of the first point above the threshold. As all these electron channels have mean speeds close to the speed of light (0. 73c, 0. 86c and 0. 98c, respectively), the data do not really allow for VDA to be performed. We determined the onset times of the electron channels for each event and then performed a back-shifting of the onset times to get the release times of the electrons at the Sun: t rel ðEÞ ¼ t onset ðEÞ À 8:33 min AU L b À1 ðEÞ: ð2Þ Rather than using the apparent path length obtained for protons, which is often severely affected by interplanetary scattering  (Lintunen & Vainio 2004; Saiz et al. 2005) , we used the nominal length of the Parker spiral L computed for the solar-wind speed u sw observed during the event, i. e. , L u sw ð Þ ¼ z r s=c À Á À z R ð Þ ð3Þ z r ð Þ ¼ a 2 ln r a þ ffiffiffiffiffiffiffiffiffiffiffiffi ffi 1 þ r 2 a 2 r ! þ r a ffiffiffiffiffiffiffiffiffiffiffiffi ffi 1 þ r 2 a 2 r " # ; ð4Þ where z(r) is the distance along an Archimedean spiral from the centre of the Sun, r s/c is the radial distance of the spacecraft from the Sun (here approximated as the position of ACE), R is the solar radius, a ¼ u sw =X and 2pX The results of the TSA are presented in Table  5.
Electromagnetic observations SEPServer will also provide a wealth of EM observations on SEP events. In Table  6 , we give the results of a preliminary analysis of SXR and radio observations for those 44 events that occur during the European day-time, i. e. , for the events that SEPServer will provide the most extensive EM data coverage including results also from the ground-based observatories. In this paper, we concentrate on deka-and hectometric emission, obtained from Wind/WAVES, but further data from groundbased observatories at metric wavelengths is provided at our website in the form of summary plots of electromagnetic emissions. The three first columns of the table give the event ID, the date and the solar release time (SRT) of protons, as deduced by the ERNE VDA. The fourth column shows the time of the impulsive flare phase.
The Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC) specification was created by the Human Proteome Organization Proteomics Standards Initiative (HUPO-PSI) to enable computational access to molecular-interaction data resources by means of a standard Web Service and query language. Currently providing >150 million binary interaction evidences from 28 servers globally, the PSICQUIC interface allows the concurrent search of multiple molecular-interaction information resources using a single query. Here, we present an extension of the PSICQUIC specification (version 1. 3), which has been released to be compliant with the enhanced standards in molecular interactions. The new release also includes a new reference implementation of the PSICQUIC server available to the data providers.
It offers augmented web service capabilities and improves the user experience. PSICQUIC has been running for almost 5 years, with a user base growing from only 4 data providers to 28 (April 2013) allowing access to 151 310 109 binary interactions. The power of this web service is shown in PSICQUIC View web application, an example of how to simultaneously query, browse and download results from the different PSICQUIC servers.
A number of public interaction databases have gone still further, forming the International Molecular Exchange consortium (IMEx)  (4, 5)  to facilitate assembly of a single non-redundant set of consistently curated protein-interaction data. This original XML model was later further refined and in 2007 was supplemented by a simple tab delimited format, PSI-MITAB  (6). These formats have been widely adopted by molecular-interaction databases  (7) , enabling the initial development of the Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC) service. The PSICQUIC  (8)  specification is defined by means of a Web Service, with a clean-cut set of methods that have as input a query in MIQL (Molecular Interactions Query Language). The initial release of PSICQUIC supported only the very limited set of 15 fields of PSI-MITAB 2.
5, which represented a simplistic description of molecularinteraction data. Following the development of extended PSI-MITAB formats (6) (version 2. 6 and more recently 2. 7), the number of fields has been increased to be fully compliant with MIMIx and enable presentation of IMExstandard curated data  (4). All these changes have resulted in the development of a new PSICQUIC specification encompassing extensions to the MIQL query language and a completely new implementation of the PSICQUIC reference server. Accompanying documentation helps data suppliers to easily migrate to the new, more efficient and feature-rich server.
The results are returned, as specified by the user, in one of the standard formats (PSI-XML or PSI-MITAB). The remaining SOAP methods do not directly use MIQL. A summary of the main methods is shown in Table  1  and Table  2 , and further information about their different options is available in the PSICQUIC specification for SOAP available in the PSICQUIC project web (http://code. google. com/p/psicquic/wiki/ PsicquicSpec_1_3_Soap). In addition to the SOAP-based protocol, PSICQUIC also implements a set of RESTful services to make it possible to retrieve data over HTTP using simple URLs. This protocol can also be used to access molecular interactions through scripting languages and supports other common output formats such as Resource Description Framework (RDF), Biological Pathway Exchange (BioPAX) and eXtensible Graph Markup and Modeling Language (XGMML).
MIQL The main input to the PSICQUIC web service is a query written in the MIQL query language. MIQL defines a set of standard fields to query molecular-interaction data, extending the syntax of the Apache Lucene query language on which it is based. MIQL has also been updated with the new data fields; these new fields allow users to filter (or query) molecular interactions with novel criteria that adjust the result to their needs and reduce post-processing steps. The retrieval of information that was previously unavailable has thus been enabled. For example, in the new PSI-MITAB 2. 6, the 'complex expansion' field is introduced. Thanks to this field, and with the new PSICQUIC service, the user now is able to distinguish the results that come from a original binary interaction or a binary pair resulting from the expansion of a n-ary interaction with one of the expansion methods available (spoke expansion, bipartite expansion Retrieve data using a list of participant identifiers.
Method name Description getSupportedReturn Types Returns the list of possible formats for the retrieved data getVersion Gets the version of the service getProperties Retrieve a list of the property objects defined in a service by the provider. Each property object have a key and a value getProperty Retrieve a property from the service or matrix expansion); distinguishing this information was previously impossible. Adding the 'stoichiometry' field (included in PSI-MITAB 2. 7) allows the retrieval of information about intra-molecular interactions and with the inclusion of the 'features' field, PSICQUIC is able to provide, for the first time, fully compliant MIMIx information. In the updated PSI-MITAB formats, some information has been reallocated to the new columns. This removed some previously existing inconsistencies and made the records more accurate and easier to access through a MIQL query.