dataesr/scientific-paragraphs-categorization huggingface dataset:
Consensus or meta-predictors have been used successfully for many years to improve protein structure predictions accuracy  (Mariani et al. , 2011)  and we have shown that consensus predictions may be used to increase the accuracy of subcellular localisation prediction also. Alternative predictors have implemented different training methods (e. g. neural network-based methods such as SignalP or SCL-Epred, or methods trained using support vector machines (SVM) such as LocTree2), different training datasets, prediction into different locations or number of locations. Accordingly, some predictors are more accurate than others at prediction into any one class. This variability among methods can be exploited to lead to more accurate overall consensus predictions. We have shown that a simple consensus predictor built using predictions from SCL-Epred, LocTree2 and SignalP leads to improved prediction of secreted versus non-secreted proteins over any of the individual methods with a increase in GC from 0.
79 to 0. 84. Similarly, a consensus predictor of SCL-Epred and LocTree2 showed improvement in three class prediction accuracy from a GC of 0. 7 to 0. 72. There is very little difference in terms of accuracy, ease of use and speed between SCL-Epred and LocTree2. As SCL-Epred and LocTree2 have both been implemented as web servers there is no need to install software locally, predictions are fast and are returned to the user via email. However, as we have shown a consensus of both predictors is better than either individually, and the end user should keep this in mind. Both methods are significantly more accurate than the other predictors tested (with the exception of two class predictions for SignalP). Importantly, we have shown that SCL-Epred performs better than any other predictor in the SAR-Excavates group and if this is the area of interest for the end user then SCL-Epred should be the predictor of choice, however, there are many other predictors available which are specialised for animal, plant and fungi sequences.
Abstract. This article investigates the potential impact of future ground-based lidar networks on analysis and short-term forecasts of particulate matter with a diameter smaller than 10 µm (PM 10 ). To do so, an Observing System Simulation Experiment (OSSE) is built for PM 10 data assimilation (DA) using optimal interpolation (OI) over Europe for one month from 15 July to 15 August 2001. First, using a lidar network with 12 stations and representing the "true" atmosphere by a simulation called "nature run", we estimate the efficiency of assimilating the lidar network measurements in improving PM 10 concentration for analysis and forecast. It is compared to the efficiency of assimilating concentration measurements from the AirBase ground network, which includes about 500 stations in western Europe.
In meteorology, DA has been traditionally applied to improve forecasts  (Kalnay et al. , 2003; Lahoz et al. , 2010). In air quality,  Zhang et al. (2012)  review chemical DA techniques developed to improve regional real-time air quality forecasting model performance for ozone, PM 10 , and dust. However, applications of DA to PM 10 forecasts are still sparse. They include  Tombette et al. (2009)  and  Denby et al. (2008)  over Europe and  Pagowski et al. (2010)  over the United States of America. They demonstrated the feasibility and the usefulness of DA for aerosol forecasts. As in  Tombette et al. (2009) , in situ surface measurements are often assimilated, e. g. AirBase, BDQA (Base de Données de la Qualité de l'Air) or EMEP. However, they do not provide information on vertical profiles. Niu et al.
Observational data In this paper, as in  Sartelet et al. (2007)  and  Tombette et al. (2009) , we use the locations of stations of two ground databases for the comparisons to ground data measurements: -the EMEP database, available on the EMEP Chemical Co-ordinating Centre (EMEP/CCC) web site at http:// www. emep. int/; -the AirBase database, available on the European Environment Agency (EEA) web site at http://air-climate. eionet. europa. eu/databases/airbase/. Note that the traffic and industrial stations are not used, because the simulation horizontal scale (0. 5 • × 0. 5 • ) can not be representative of these station types. In 2001, PM 10 concentrations are provided on a daily basis at EMEP stations, against an hourly basis at most AirBase stations. Moreover, data are provided at only 27 EMEP stations, against 509 AirBase stations.
Conclusions In order to investigate the potential impact of a ground-based lidar network on short-term forecasts of PM 10 , an OSSE has been implemented. Because the AirBase network covers well western Europe and provides in situ surface measurements and because AirBase measurements have been used for DA of PM 10 , we took AirBase as an assimilation reference network. We have compared the impacts of assimilating groundbased lidar network data to assimilating the AirBase surface network data. Because we made several simplifying assumptions: we used an identical twin scenario (perfect model) and assumed uncorrelated observational errors, the PM 10 improvements from assimilating lidar and ground observations may be over optimistic. Compared to the RMSE for one-day forecasts without DA, the RMSE between one-day forecasts and the truth states is improved on average over the summer month from 15 July to 15 August 2001 by 54 % by the lidar DA with 12 lidars, and by 59 % by the AirBase DA.
Acknowledgements. This work was supported by  CEA  ( Commissariat à l'Energie Atomique ) and  CEREA, joint laboratory Ecole des Ponts ParisTech -EDF R & D. We thank our colleague  Youngseob Kim  for his help to use the air-quality platform POLYPHEMUS and  Lin Wu  for his geostatistical algorithms. Edited by:  W. Lahoz The publication of this article is financed by  CNRS-INSU.
Acknowledgments We thank the  Functional Genomics Division of the Department of Plant Systems Biology  at the  VIB-Ghent University  for providing plasmids pB7FWG2, pB7RWG2 and pB7WGF2,  M. M. Goodin  for providing the pGD vector, and  EW Kitajima  for the attempts to immunolocalize 54K CPsV using TEM. We would like to thank  Dr. Fernando Bravo Almonacid ,  Dr. Mariana del Vas  and  Dr. Vanesa Mongelli  for their help with the particle bombardment assays, and  Celina Castuma  for her help in the manuscript revision. M. L. García  and  Gabriel Robles Luna  belong to the staff of  Departamento de Ciencias Biológicas ,  Facultad de Ciencias Exactas (UNLP) , and  Maria Laura Garcia  is researcher of  CONICET. G. Robles Luna  is a fellow of  CONICET. This work was performed with financial support of  ANPCyT  and  CONICET  (grants PICT  11714 ,  PICT 32359 ,  PICT 1094  and  PIP 6108 ) in Argentina, and by the  CNRS  and the  ANR  (grant  ANR-08-BLAN-0244 ) in France.
In terms of total production flux, the CH 3 + C 2 H 3 + M and CH 3 + C 2 H 5 + M termolecular reactions are the most efficient. In the present work, several rate constants have been updated and some reactions have been added. Our new chemical scheme, derived from the chemical scheme presented in  Hébrard et al. (2006 Hébrard et al. ( , 2009 Hébrard et al. ( , 2012)) , includes 143 compounds and 944 reactions (102 photodissociation processes, 2 dissociation processes of N 2 by cosmic rays, 737 bimolecular reactions and 103 termolecular reactions). A major change in this update is the introduction of radiative association reactions, following the work of  Vuitton et al. (2012). The complete list of reactions is available upon request and can be downloaded from the KIDA database  (Wakelam et al.
Methodology for the chemical review The reactions and rate constants were updated following the same procedure used to complete the photochemistry of HCN and HNC  (Hébrard et al. 2012). First, a systematic literature search was performed (particularly for the reactions of C, CH and 1 CH 2 reactions with C 2 H 2 and C 2 H 4 ). When no information was found, we performed density functional theory (DFT) calculations at the M06-2X/cc-pVTZ level using the Gaussian09 software package  (Frisch et al. 2009 ) to evaluate the presence and the values of any energetic barriers to the entrance valley for the important reactions (in terms of production and/or loss rate). For some critical reactions or when DFT results were ambiguous, we also performed MRCI+Q/cc-pVTZ calculations using the Molpro software package  (Werner et al.
(2010). Tables  2  and 3  give RCCs for the main C 3 -species. The RCC threshold has been arbitrarily set to 0. 2. This choice gives for each compound a reasonable set of reactions allowing us to pinpoint only the most significant ones. Thirty key reactions were identified for four representative altitudes. We see that association reactions in general are predicted to be significant in all parts of the atmosphere. The termolecular reaction H + C 2 H 2 → C 2 H 3 in particular is a key reaction for all but one of the C 3 -compounds, listed in Tables  2  and 3. Comments on some of these reactions are given in Appendix C. The reaction N( 2 D) + HCN → CH + N 2 is also a main key reaction for HCN (see  Hébrard et al. 2012). A discussion about the reaction H + CH → C + H 2 can be found as a datasheet in the KIDA database  (Wakelam et al.
We showed in this study only the most significant key reactions for C 3 -compounds. The complete list of all main key reactions for Titan's atmosphere (concerning all species for which observational data are available) will be available from the KIDA database. Our model does not take into account ionic chemistry, which is known to have a noticeable impact on the abundances of neutral species (see  Plessis et al. 2012 ). As a consequence, it is important to keep in mind that the abundances of some compounds presented in our study might be significantly affected by the coupling of our neutral chemical scheme with a ionospheric model. This is the case in particular for C 3. Considering the Proton Affinity of C 3 (767 kJ mol -1 , Hunter & Lias 1998) and because exothermic proton transfer reactions generally do not present a barrier, C 3 will react with the most abundant ions in Titan's ionosphere (HCNH + , CH + 5 , C 2 H + 3 , C 2 H + 5 ), which will lead to C 3 H + formation.
The acquisition rate was 1 Hz corresponding to spectra summations of 5494. External calibration was performed with ESI-L Low Concentration Tuning Mix (Agilent Technologies). ElectroSpray Ionization -High Resolution Mass Spectrometry (ESI-HRMS) spectra were processed and charge-deconvoluted using DataAnalysis 3. 1 software (Bruker Daltonics) and the MaxEnt algorithm.
The LC-MS setup was controlled by the Bruker HyStar TM software version 3. 2. Phosphopeptides were preconcentrated online on a Dionex Acclaim Pepmap100, 100 mm Â 2 cm, C18 5 mm 100 A ˚reverse-phase precolumn and separated on a nanoscale Acclaim Pepmap100, 75 mm Â 15 cm, C18 2 mm 100 A column at a flow rate of 200 nl/min using a 5-45% gradient of acetonitrile in 0. 1% formic acid. Mass spectra were acquired in positive ion mode from m/z 100 to 2000 for MS, and from m/z 100 to 3000 for MS/MS. The MS acquisitions were carried out at enhanced resolution (8100 m/z units per second). Collision-induced dissociation (CIP) and electron-transfer dissociation (EDT) fragmentations were performed in separate nanoLC-IT-MS/MS runs.
Of the 12 methionines present in the sequence, seven are detected as partially oxidized, which could explain the mass difference of +49 Da between the expected and observed masses of the MBP-MOS1 protein. No mutation was identified, despite a sequence coverage of 85. 7%. While S170 has previously been predicted to be a phosphorylated residue  (9) , that is not the case for S2. Kinases capable of phosphorylating S2 and S170 were investigated using the GPS (Group-based Phosphorylation Scoring Method) software  (19)  (http:// csbl. bmb. uga. edu/$ffzhou/gps_web/predict. php). No kinase was predicted as capable of phosphorylating S2, whereas S170 could be phosphorylated by at least 10 different kinases. Among these, PKA had the best prediction score, and the sequence surrounding S170 (165-PKRKKSYV-172) perfectly overlapped the PKA Ten micrograms of sf21-expressed MBP-MOS1 was incubated without (A) or with (B) lambda phosphatase in the buffer recommended by the manufacturer (Sigma).
These data confirm a strong relationship between MOS1 transposition regulation by PKA and the S170 residue. We went on to identify which stage(s) of Mos1 transposition cycle are controlled by MOS1 phosphorylation, focusing first on single-end and PEC assembly. Electrophoretic mobility shift assays (EMSAs) were performed with MBP-MOS1 (±PKA) and the Mos1 3 0 ITR as a probe, using conditions appropriate for the detection of either SEC2 or PECs, as previously described (3,10). SEC2 assembly was done using UC-ITR, under noncatalytic conditions (EDTA and 4 C). Our data show that MBP-MOS1 phosphorylation did not prevent SEC2 assembly (Figure  4B , lanes 2-3), since we got the same signal regardless of the protein used (MBP-MOS1 ± PKA). Similarly, both S170A and S170D were able to promote SEC2 assembly (Figure  4B , lanes 5À6).
S170A treated or not by PKA (compared lanes 3 and 4), and the overall behavior of the protein was different from that of the wild type. S170A was mainly blocked in the initial dimer conformation, promoting SEC2 assembly instead of PEC. Little dimer reorganization was allowed, accounting for the SEC2/PEC ratio detected. This was consistent with the fact that the promotion of Mos1 transposition by S170A is less efficient than that produced by the wild-type protein. The inability of S170D to promote PEC assembly (Supplementary Figure  S1 ) is consistent with the data reported above. Hence, our data support the conclusion that the phosphorylation of the MOS1 S170 residue by PKA controls MOS1 activity by preventing PEC assembly. In the Mos1 transposition cycle, PEC assembly precedes the first and second strand cleavages that promote excision  (10).
We therefore confirmed our EMSA data using in vitro excision assays. Time-course analyses of the excision assays were performed using MBP-MOS1 (±PKA), and the pBC-3T3 as a transposon donor. Our data show that MOS1 phosphorylated by PKA is unable to promote excision, as shown by the continued presence of the supercoiled donor, and the absence of backbone release even after incubating for 24 h (Figure  5 ). The presence of linear products had previously been shown to be owing to nonspecific cleavages of pBC DNA by Mos1 transposase  (10). Under similar conditions, there was little difference between the activity of the MBP-S170A (±PKA), and this activity was much lower than that of the MBP-MOS1 without PKA (Supplementary Figure  S2 ). Taken together, these findings confirm that the phosphorylation of MOS1 by PKA prevents Mos1 transposition by inhibiting the assembly of PECs, and consequently further DNA cleavages.
The S2 residue As previously indicated, the kinase responsible for S2 phosphorylation is unknown, making it difficult to perform the same studies as that performed for p170. Nevertheless, the use of pseudo-phosphorylated residues (D for pS) was demonstrated to be convenient (see our data with S170D), we prepare the S2D mutant. We assume that this mutant may help to describe the consequences of S2 residue phosphorylation. In vitro genetic 'hop' experiments were done using either purified MBP-MOS1 transposase or the MBP-S2D mutant and the pBC-3T3 plasmid, as the transposon donor and the target plasmid. Our data show that the S2D mutation has no effect on the transposase activity since the MBP-S2D promotes the pseudo-Mos1 (3T3) transposition with a rate of 7. 10 À5 (for recall, the wild-type transposase promotes the 3T3 transposition with a rate of 10 À4 ).
ACKNOWLEDGEMENTS We would like to thank  Dr Marie-Ve´ronique Demattei  who kindly gave us the pCS2-GFP and pCS2-MOS1-GFP constructs, and some technical help. We also thank  Dr Guillaume Carpentier  for the sf21 expressing MBP-MOS construct and for the His-MBP-MOS1 protein. Many Nucleic Acids Research, 2014, Vol. 42, No. 2 1127 thanks to  Dr Patrick Gaudray  and  Sylvaine Renault  for helpful discussions. We would also like to thank the referees for valuable contributions to this work. Dr M. Ghosh  revised the English text. Autoradiograms have been recovered using the  PPF 'Genome' facilities [UFR Sciences & Techniques ,  University of Tours]. FUNDING Francois Rabelais University (Tours -France ); the  Centre National de la Recherche Scientifique (CNRS) ; the  French Ministe`re de l'Education Nationale, de la Recherche et de la Technologie ; the  Association Francaise contre les Myopathies [AFM #   11468  to CAG]; the  Agence Nationale de la Recherche  [ ANR  project  Elegineer , #  ANR 2010 BLAN 1618 02  to CAG];  Re´gion Centre/ FEDER  grant [#  2699-33931 , SyMBioMS] to the  CBM-ICOA Federation  (in part toward high-resolution spectrometry).
g. Sanders & Mirabel 1996; Farrah et al. 2001; 2002; Moustakas et al. 2004; Kartaltepe et al. 2010b ). Finally, they frequently harbour an active galactic nucleus (AGN), which is commonly considered a key player in the evolution of the system (e. g. Genzel et al. 1998; Ptak et al. 2003; Alexander et al. 2005; Page et al. 2012). Until recently our view of the IR-luminous galaxy population at high redshift has been based on data from the space observatories ISO and Spitzer, as well as ground-based submm/mm facilities such as JCMT/SCUBA, APEX/LABOCA, IRAM/MAMBO and SMA/AzTEC. Although huge advances have been made in our understanding of the nature and evolution of these sources, it has been challenging to reconcile the data from space observatories with comparable ground-based IR/mm datasets.
ACKNOWLEDGMENTS This paper uses data from Herschel 's photometers SPIRE and  PACS. SPIRE has been developed by a consortium of institutes led by  Cardiff Univ. (UK)  and including:  Univ. Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ. Padua (Italy) ;  IAC (Spain) ;  Stockholm Observatory (Sweden) ;  Imperial College London ,  RAL ,  UCL-MSSL ,  UKATC ,  Univ. Sussex (UK) ; and  Caltech ,  JPL ,  NHSC ,  Univ. Colorado (USA). This development has been supported by national funding agencies:  CSA (Canada) ;  NAOC (China) ;  CEA ,  CNES ,  CNRS (France) ;  ASI (Italy) ;  MCINN (Spain) ;  SNSB (Sweden) ;  STFC ,  UKSA (UK) ; and  NASA (USA). PACS has been developed by a consortium of institutes led by  MPE (Germany)  and including  UVIE (Austria) ;  KU Leuven, CSL, IMEC (Belgium) ;  CEA ,  LAM (France) ;  MPIA (Germany) ;  INAF-IFSI/OAA/OAP/OAT ,  LENS ,  SISSA (Italy) ;  IAC (Spain).
Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe datasets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil-fuel combustion and cement pro- 6, 2013    Global carbon budget.
duction (E FF ) are based on energy statistics, while emissions from Land-Use Change (E LUC ), including deforestation, are based on combined evidence from land-cover change data, fire activity in regions undergoing deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth (G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink (S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated for the first time in this budget with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink (S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of Dynamic Global Vegetation Models.
We adopt a range of ±1 standard deviation (sigma) to report the uncertainties in our annual estimates, representing a likelihood of 68 % that the true value lies within the provided range if the errors have a Gaussian distribution. This choice reflects the difficulty of characterising the uncertainty in the CO 2 fluxes between the atmosphere and the ocean and land reservoirs individually, particularly on an annual basis, as well as the difficulty to update the CO 2 emissions from LUC. A 68 % likelihood provides an indication of our current capability to quantify each term and its uncertainty given the available information. For comparison, the Fifth Assessment Report of the IPCC (AR5) generally reported 90 % likelihood for large datasets whose uncertainty is well characterised, or for long time intervals less affected by year-to-year variability.
Our 68 % uncertainty value is near the 66 % that the IPCC characterises as "likely" for values falling into the ±1σ interval. The uncertainties reported here combine statistical analysis of the underlying data and expert judgement of the likelihood of results lying outside this range. The limitations of current information are discussed in the paper. All units are presented in gigatonnes of carbon (GtC, 10 15 gC), which is the same as petagrams of carbon (PgC; Table  1 ). Units of gigatonnes of CO 2 (or billion tonnes of CO 2 ) used in policy are equal to 3. 664 multiplied by the value in units of GtC. This paper provides a detailed description of the datasets and methodology used to compute the global carbon budget estimates for the period preindustrial (1750) to 2012 and in more detail for the period 1959 to 2012.
Methods The original measurements and data used to complete the global carbon budget are generated by multiple organizations and research groups around the world. The effort presented here is thus mainly one of synthesis, where results from individual groups are collated, analysed and evaluated for consistency. We facilitate access to original data with the understanding that that primary datasets will be referenced in future work (see Table  2  for "How to cite" the datasets). Descriptions of the measurements, models, and methodologies follow below and in depth descriptions of each component are described elsewhere (e. g. Andres et al. , 2012; Houghton et al. , 2012). This is the second revised version of the "global carbon budget". It is an update of Le  Quéré et al. (2013) , including data until year 2012 and a projection for fossil-fuel emissions for year 2013.
, , 2012)) ; this makes the dataset a unique resource for research of the carbon cycle during the fossil-fuel era. During the period 1959-2010, the emissions are based primarily on energy data provided by the UN Statistics Division (   Peters et al. , 2013). When necessary, fuel masses/volumes are converted to fuel energy content using coefficients provided by the UN and then to CO 2 emissions using conversion factors that take into account the relationship between carbon content and heat content of the different fuel types (coal, oil, gas, gas flaring) and the combustion efficiency (to account, for example, for soot left in the combustor or fuel otherwise lost or discharged without oxidation). Most data on energy consumption and fuel quality are available at the level of countries. In general, CO 2 emissions for equivalent primary energy consumption are about 30 % higher for coal compared to oil, and 70 % higher for coal compared to natural gas  (Marland et al.