dataesr/scientific-paragraphs-categorization huggingface dataset:
tc. cornell. edu/), 43 SUBA3 (Subcellular location database for Arabidopsis proteins http://suba. plantenergy. uwa. edu. au/),  5  and AT_CHLORO (http://www. grenoble. prabi. fr/at_chloro/). 42 ubcellular localization of proteins. The first 100 codons or the whole PPR ORFs were PCR amplified from Arabidopsis thaliana (ecotype Columbia-0) genomic DNA or cDNA using iProof DNA polymerase (Bio-Rad), specific primers (listed in Table  S1 ) and a two-step amplification protocol as described previously. 11 PCR products were recombined into pDONR207 (Invitrogen) using Gateway® BP Clonase® II Enzyme mix (Invitrogen) as described. 11 For microscopic investigation, LR recombination reactions were performed using Gateway® LR Clonase® Enzyme Mix (Invitrogen) in order to transfer PPR sequences from Entry vectors to the pGREENII-derived destination vector p0229-RFP2 11 allowing C-terminal translational fusion with the RFP protein under the control of the 2X35S promoter.
Acknowledgments We thank  Etienne Delannoy  ( Unité de Recherche en Génomique Végétale ) for valuable discussions and comments on the article. We thank  Olivier Grandjean  ( Institut Jean Pierre Bourgin ), and  Lionel Gissot  ( Institut Jean Pierre Bourgin ) for their help with confocal microscopy. We thank  Andeol Falcon de Longevialle  ( Genopole Plant Process Innovation ) for providing us with beautiful healthy Nicotiana benthamiana plants. We thank  Jennifer Yansouni  for English editing. This research was supported a PhD fellowship to ML-O from the  Université d'Evry Val-d'Essonne  and the  French Ministry of Research and Higher Education.
Genes were ordered in the alpha blocks based on their orders in the paralogous segments. Singlet genes that were localized between duplicated genes in the paralogous segments were equitably distributed between the duplicated genes in the blocks. These steps were automated using in-house python scripts. The 13 beta regions were identified by performing an all-by-all comparison of the alpha blocks using BlastP and an e-value cutoff of 1eÀ05 (fig. 2  and Musa beta dot plot in supplementary fig. S2 , Supplementary Material online).
Patterns of Gene Expression and Genome Dominance Gene expression quantifications were measured using RNAseq data and performed as described in supplementary information S1, Supplementary Material online. For Musa, two sets of mRNA-seq data were used (supplementary table  S4 , Supplementary Material online) and for all other species without previously published descriptions of genome dominance, mRNA reads here downloaded from NCBI's (National Center for Biotechnology Information) Sequence Read Archive. For all species, genome dominance was tested by comparing the expression levels within each pair of duplicated genes along pairs of paralogous regions. Two methods were used: first, a "horserace experiment" was performed, in which we considered that within a duplicate gene pair, a gene was more expressed than its duplicated copy if it had a higher expression level.
Second, a "2-fold change method" was used in which a gene was considered more expressed than its duplicated copy when it was expressed at least 2-fold higher. For Musa, genome dominance was measured for all alpha blocks by comparing the expression levels of duplicate genes shared between paralogous regions (supplementary table S1, Supplementary Material online). A binomial test (P value, 0. 05) was used to test whether the differential expression patterns were significant. For other species, the analysis was done using an in-house python script, allowing the identification of duplicated regions containing different total gene counts. For gene pairs present in both regions the total number of cases where the more expressed gene was present in a region with fewer total genes was compared with the total number of cases where the less expressed gene was present in a region with fewer total genes (supplementary table  S3 , Supplementary Material online).
Synonymous Substitution Rates (Ks) and "Dating" of Tetraploidy Events To identify relative sequence divergence (a proxy for age) among multiple WGDs examined, self-self comparisons of genomes were run using SynMap with default settings  (Lyons et al. 2008 ) and the optional QuotaAlign filter  (Tang et al. 2011). A quota of 1:1 was used for tetraploidies (Me. trunculata, sorghum, arabidopsis, maize, poplar, soybean, banana) or 2:2 for hexaploidies (B. rapa). Synonymous substitution rates were taken from SynMap, where values are calculated by aligning the protein sequences of a gene pair using the package nwalign (http://pypi. python. org/pypi/nwalign/, last accessed December 11, 2013), translating that alignment back to DNA codons, and calculating Ks using CodeML  (Yang 2007).
Acknowledgments We thank  N. Yahiaoui  for providing early access to Musa mRNAseq data and for critical reading of the manuscript,  J. F. Dufayard  for his help in phylogenetic analyses, and  G. Martin  for his help in the development of bioinformatics scripts. We thank the  SouthGreen Bioinformatics Platform  (http://southgreen. cirad. fr,http://banana-genome. cirad. fr/,last accessed December 11, 2013) for providing us with computational resources. This work was supported by funding from  NSF   IOS1248106  to M. F. and from  CIRAD (Centre de cooperation Internationale en Recherche Agronomique pour le Developpement).
Along with wave measurements, understanding where, when, and how, local acceleration occurs requires measuring where and when we observe the characteristic spectral and pitch angle changes discussed earlier. Over the course of the 2-year Van Allen Probes mission we expect to observe one or two relativistic electron events per month. We will analyze these events in-depth to understand the detailed processes that produce electron acceleration, transport, and loss. Statistical models for the global distribution of all relevant waves, electron spectra, pitch angle distributions, and radial phase space density will be constructed, as a function of MLT, L-shell, latitude, and geomagnetic activity. This resource will be made available to develop statistical, physical, empirical, and data assimilation based models.
However, the Van Allen Probes satellites along with the LANL-GEO, GOES, THEMIS and other satellites can significantly improve our understanding of magnetopause losses. The ECT particle suite will measure the near-equatorial pitch angle distributions that particle drift paths depend upon critically. At the same time advanced numerical models provide global magnetic field configurations for test particle simulations for both magnetopause compressions and storm-time adiabatic changes. Models such as DREAM that use data assimilation and relativistic electron phase space density matching techniques  (Chen et al. 2005; Reeves et al. 2012 ) are particularly powerful tools for combining the pitch angle and energy-resolved measurements from the ECT instruments with geosynchronous observations of magnetopause compressions.
Ideally this would be done with energetic ion (H + , He + , O + : E > 50 keV) measurements, plasma ion composition (H + , He + , O + : E < 50 keV) measurements, energetic neutral atom (ENA) images  (Jorgensen et al. 1997; 2001) , and self-consistent ring current and magnetic field models. Thermal (E < 50 keV) and energetic (E > 50 keV) ions in the inner magnetosphere each carry about half the total plasma pressure  (Spence et al. 1987 ); these pressure-bearing ions thereby control the magnetic field configuration. While Van Allen Probes' two-point data will be used to validate or act as input to global models, only models can provide the 3D magnetic field needed to track electron drift orbits. Comprehensive spectral and pitch angle coverage of electrons and ions provided by RBSP-ECT and by RBSPICE, along with magnetometer measurements on the Van Allen Probes spacecraft, will be used to specify μ for locally-mirroring particles and as an input into global field models.
Models such as DREAM will provide a data-driven, self-consistent, physical model of the storm-time field  (Zaharia et al. 2006 ) and a basis for calculating adiabatic invariants. We developed/validated a technique to test global fields using 2-satellite measurements similar to those of the Van Allen Probes mission  (Chen et al. 2005). This procedure identifies times when non-collocated satellites measure electrons with the same invariants. At these times, phase space densities should be identical. Discrepancies indicate magnetic field errors. Combining detailed measurements, improved magnetic field models, and accuracy tests will produce an unprecedented dataset. As with the current generation of models we will establish boundary conditions given by the Magnetospheric Plasma Analyzers (MPA) on the LANL geosynchronous satellites (e.
Science Requirements, Traceability, and Instrument Requirements In Sect. 1 we identified four overarching RBSP-ECT science objectives, derived from the eight underlying mission priorities that ECT observations will address. Within each objective, we identified one or more science questions that the ECT suite provides closure on, often in concert with other Van Allen Probes measurements, related data sets whether in space or ground-based, and/or with leveraged modeling activities. Within each question, we identified key measurements required to provide closure and discussed how the resultant data analysis answers the science questions. The RBSP-ECT Instrument Suite Traceability Matrix (see  Figs. 4a, 4b)  summarizes the Van Allen Probes mission science priorities and their mapping into our ECT measurement goals and science questions, as well as their flow down into science measurement objectives, and finally to instrument requirements.
ECT Suite Science Data Collection, Analysis, and Dissemination In accordance with the Van Allen Probes mission data policy, the RBSP-ECT instrument team ensures that calibrated high-quality science-grade data, software tools, and related documentation are made available as a public resource to a wide range of end-users including guest investigators (GIs) and members of the space weather community. Our overall plan and timelines for achieving these goals are described in detail below. We anticipate that in many cases science closure on the questions discussed above may be achieved with RBSP-ECT data in studies led by scientists that are not part of the ECT science team.
ECT SOC (Science Operations Center) Overview Figure  5  illustrates how the RBSP-ECT data flows from the spacecraft to the various end users. The JHU/APL-run Mission Operations Center (MOC) communicates with the spacecraft and provides command uplink and data downlink. In addition, Near Real Time (NRT) data feeds may also exist as a result of mission-level agreements with external downlink facility operators. The RBSP-ECT Science Operations Center (SOC) is split into two partsthe Command, Telemetry and Ground support part (CTG) and the Science data center part T 0 + <2 months L4 L3 + PSD units derived using B-field models, magnetic coordinates T 0 + <1 year (SDC). The SOC-CTG transmits instrument commands to the MOC and receives science data, state of health data, housekeeping data and ancillary data (S/C attitude, ephemeris, time offset and scale factor data) from the MOC.
Science data would also be received from NRT downlink facilities. The SOC-CTG retrieves and archives these Level-0 (L0) data products and transmits them together with ancillary data to the SOC-SDC where higher-level products are generated and detailed data analysis, dissemination, and archiving occurs. Since both the SOC-CTG and SOC-SDC reside at Los Alamos National Laboratory (LANL), data processing is highly integrated and a number of resources are effectively and efficiently shared. The RBSP-ECT suite maintains a stripped-down fully redundant emergency SOC capability in a separate facility to ensure reliable failsafe operations. The SOC-SDC creates higher-level data products (see Table  1 ) and makes them available for dissemination. Level-1 (L1) data products are raw instrument-specific, uncalibrated data files and are archived and made available to the ECT instrument teams.
Level-2 data (L2) products (instrument-specific, calibrated, and validated data) are then generated at the SDC with feedback and validation from the suite instrument teams. Higher level analysis of the particle data requires knowledge of the magnetic field vector at each spacecraft and these data are obtained in a preliminary format directly from the EMFISIS Fields and Waves team SOC (quicklook product). Higher quality magnetic field data (EMFISIS level 2) subsequently flows into the SDC when they become available. data are science data products based on these inputs (pitch angles, moments). To enable our science goals, data needs to be folded into adiabatic invariant space with the aid of magnetic field models (Level-4 or L4 products). The ECT SDC will also link to a number of other important and relevant ancillary datasets that will be accessible to the team and all interested users in a timely manner.
These include the LANL geosynchronous and GPS particle datasets that are being made accessible as they become available. The team will make available the RBSP-ECT data together with these additional datasets via the SOC-SDC website (http://www. rbsp-ect. lanl. gov). In addition, the ECT SOC has taken on a mission-wide responsibility of producing a set of value-added ephemeris files, called the "MagEphem" files. These files include the Van Allen Probes satellite position information together with magnetic field model derived coordinates that are useful for radiation belt analysis (such as L * as a function of pitch angle). These files are produced one month ahead based on static field models and orbit predictions, and on dynamic field models once definite orbit files become available. The predict files are also used by the SOC-CTG to plan ahead orbit-specific commanding.
All the MagEphem files are provided to the public on the ECT-SOC website. The SOC-SDC generates public-domain, science-quality data products available to various end-users in a timely manner: L1 data become available within hours of acquisition from the MOC; L2 data within two weeks; and, L3 data available no later than 2 months. L4 data is available within one year with early Phase-E priority given to intervals selected for community-wide investigation. Expected end-users include: RBSP-ECT co-investigators, Van Allen Probes investigator teams and guest investigators (GIs), the U. S. and interna-tional scientific community, virtual observatories, space weather users, archive operators, E/PO partners and the public.
High-quality pre-defined summary plots are generated at the SDC and made available via an efficient web-based browsing tool (Autoplot, www. autoplot. org). Software, documentation and data products served to end-users via the SDC are maintained under strict version control for quality control. This is accomplished with a version control system (e. g. , Git;  Loeliger and McCullough 2012)  and definition and adherence to strict file-naming conventions through an advanced database-driven processing chain. Information on the "providence" of each file (the data inputs, software used and their version) is maintained in a hierarchical SQL (Structured Query Language)-lite data base  (Date and Darwen 1997) , allowing for the full reconstruction of how each version of a given data product was produced.
Data Validation Primary data validation is the responsibility of the respective RBSP-ECT instrument teams and occurs during the L2 generation phase in the two-week period before public release. All L2 data and plots are marked "preliminary" until checked and cleared by the SOC-SDC lead. Secondary data validation involves the on-orbit cross-calibration of data between the Van Allen Probes spacecraft and auxiliary datasets. Proper inter-calibration of the two spacecraft is required in order to achieve closure on several of the mission scientific objectives (e. g. goals that require determination of PSD gradients).
Data Archiving The RBSP-ECT SOC archives raw telemetry, L0, and ancillary files. The SDC archive L1 and higher data products. Data, software and documentation generated and stored at the SDC will be prepared for archiving at a site and in a format determined by NASA. L0 to L3 datasets will be available for archiving within 2 months of data acquisition and L4 products within 1 year. Routine local archival operations at LANL will include migration of the datasets to a near-line mass storage facility. Following the mission prime phase, an additional year will be used to complete deep-archiving operations. LANL will also provide a long-term resident archive of the full datasets.
Acknowledgements We gratefully acknowledge the  Van Allen Probes mission team at the Johns Hopkins University (JHU)/Applied Physics Laboratory (APL)  and the Project Science team at  JHU/APL ,  NASA GSFC , and  NASA HQ  for their invaluable support during the design, development, testing, and early operations of the mission. This work was supported by  RBSP-ECT  funding provided by  JHU/APL  Contract No. 967399  under  NASA 's  Prime  Contract No. NAS5-01072. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
, 1999)  was touched with a microelectrode, and the TB fragmentation (as revealed by the small red or orange foci in E and F) and mixed-networks. The upper panels of (D-F) show confocal projections, and the lower panels show selected optical single sections. For heat shock (D), two individual sections representing a median section through each of the two encircled TBs are shown. In (E-F), the arrows indicate filamentous P2 labeling that is continuous with microtubule labeling, and the arrowheads point to small P2 aggregates in the vicinity of microtubules. Scale bars: 5 μm. The confocal single sections used to create the projections shown here can be found in Figure  2 -source data 1-6. See also response of TBs in epidermis cells was recorded using confocal time-lapse macroscopy. In the event of a fast entry of tubulin into TB, we should expect to observe a rapid appearance of fluorescent foci, corresponding to Tub + |TBs within these cells.
Original data can be found in. TB transformation correlates with enhanced transmission efficiency. (A) Azide enhances transmission from protoplasts. Aphids were allowed to acquire CaMV from infected protoplasts that displayed mixed-networks induced by azide. They were then transferred to healthy test plants for inoculation, and infected plants were counted 3 weeks later. The difference in transmission was highly significant (p<0. 0001, hierarchical GLM model, Table  3  and Figure  9 -source data 1). (B) Azide does not affect aphid behavior. To rule out an unwanted effect of azide on aphid viability and behavior, aphids were membrane-fed solutions containing purified virus particles, recombinant P2 and P3, in the presence or absence of azide, and then transferred to healthy test plants for inoculation.
Transmission rates were determined 3 weeks later by scoring infected plants. Data from one experiment are shown, using three different virus preparations as a virus source for each condition. See Figure  9 source data 2 for details. (C) Azide does not affect protoplast viability. Protoplasts were incubated for 1 h (the duration of a transmission test) in the presence or absence of 0. 02% azide, and then protoplast viability was determined with the FDA test  (Widholm, 1972). Data from one out of two experiments are shown. The difference in viability was insignificant in this experiment (p=0. 0658, n = 6, Mann-Whitney test) and also in the second experiment. See Figure  9 -source data 3 for all data. (D) Heat shock does not enhance CaMV transmission. Leaves from GFP-TUA6 Arabidopsis either received heat shock (+) or did not (-).
The presence of Tub + |TBs was verified by fluorescence microscopy and the leaves were then used in aphid transmission assays. No significant difference in transmission was observed in either of two independent experiments (p=0. 73 and p=0. 08, respectively, hierarchical GLM model, see Table  4  and Figure 9 -source data 4). (E) CO 2 enhances CaMV transmission. Leaves with mixed-networks induced by CO 2 were used in plant-to-plant aphid transmission experiments. CO 2 -treated leaves performed significantly better in transmission tests than controls (p=0. 0025, hierarchical GLM model, see Table  5  and Figure   annealed. This created XhoI-compatible restriction sites at the two extremities of the then doublestranded oligonucleotide that were used for insertion into the unique XhoI site in the Cabb B-JI genome cloned into the pCa24 plasmid  (Delseny and Hull, 1983).
Isolation of protoplasts Protoplasts were prepared from healthy or infected (14 dpi) leaves of turnip plants as described in  Martinière et al. (2009). Briefly, leaves were sterilized by submerging them in 20-fold diluted Domestos solution (http://www. unilever. com) for 3 min. The leaves were then washed three times with water, prior to overnight incubation in  We measured the transmission rate by aphids for each condition of treatment (-azide and +azide) for the five experiments. Since a non-significant interaction between experiments and treatments was found (GLM, df = 4, χ 2 = 6. 69, p=0. 15), the data was pooled in the line named 'global'. Azide induced a highly significant increase of the transmission rate compared to the control without azide (hierarchical GLM model using Firth's penalized likelihood, df = 1, χ 2 = 35.
DOI: 10. 7554/eLife. 00183. 056 were processed using LSM, ZEN or LAS software and final figures were prepared using GIMP 2. 6. 11 (http://www. gimp. org) and OpenOffice 3. 4 (http://www. openoffice. org). Quantification of TB phenotype was performed by counting 200-300 cells in 10 different, randomly chosen microscopy fields per treatment.
Figure9. TB transformation correlates with enhanced transmission efficiency. (A) Azide enhances transmission from protoplasts. Aphids were allowed to acquire CaMV from infected protoplasts that displayed mixed-networks induced by azide. They were then transferred to healthy test plants for inoculation, and infected plants were counted 3 weeks later. The difference in transmission was highly significant (p<0. 0001, hierarchical GLM model, Table3and Figure9-source data 1). (B) Azide does not affect aphid behavior. To rule out an unwanted effect of azide on aphid viability and behavior, aphids were membrane-fed solutions containing purified virus particles, recombinant P2 and P3, in the presence or absence of azide, and then transferred to healthy test plants for inoculation. Transmission rates were determined 3 weeks later by scoring infected plants.
Data from one experiment are shown, using three different virus preparations as a virus source for each condition. See Figure9source data 2 for details. (C) Azide does not affect protoplast viability. Protoplasts were incubated for 1 h (the duration of a transmission test) in the presence or absence of 0. 02% azide, and then protoplast viability was determined with the FDA test (Widholm, 1972). Data from one out of two experiments are shown. The difference in viability was insignificant in this experiment (p=0. 0658, n = 6, Mann-Whitney test) and also in the second experiment. See Figure9-source data 3 for all data. (D) Heat shock does not enhance CaMV transmission. Leaves from GFP-TUA6 Arabidopsis either received heat shock (+) or did not (-). The presence of Tub + |TBs was verified by fluorescence microscopy and the leaves were then used in aphid transmission assays.