dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2011). Visual ERPs (P1, N170 and P2) latencies were measured for each subject at the electrodes sites of interest. ERPs amplitudes were measured at this latency on both hemispheres within a 40 or 60 ms time window around the peak of the grand average waveform, as recommended in international ERP guidelines  (Picton et al. , 2000). P1 and P2 were measured at O1, O2 (between 80-120 ms and 190-250 ms, respectively); N170 was measured at T5 and T6 (between 120 and 180 ms). Data were analysed using repeated-measures analysis of variance (ANOVA) with sex (2; men, women) as the between-subject factor and emotions (7; sadness, fear, disgust, anger, surprise and happiness plus neutral) and hemisphere (2; right and left) as the within-subject factors. Results were corrected by Huynh-Feldt test. Significant interactions were followed by Newman-Keuls post hoc comparisons to determine where the differences lay.
Pearson correlation: effect of SSI emotional subscores To meet the study's aims, correlations were used to investigate how early visual ERPs were related to the subjects' emotional abilities. Pearson correlations were computed between amplitude and latency of N170, P1 and P2 components averaged for all emotions and the EE, ES, EC and EI scores of the SSI. The mean SSI scores obtained Significant effects of emotion revealed by ANOVA analyses were followed by Newman-Keuls post hoc comparisons to determine where the differences lay. ERP data were submitted to repeated-measures of ANOVA according to emotion (7), sex (2) and hemisphere (2). Pearson correlations were performed between the ERP characteristics (amplitude and latency) averaged for all emotions and the SSI emotion subscores (EE, ES, EC, EI).
, 2008; Palla et al. , 2009 Palla et al. , , 2012 ) have used the HYDRUS software  (Simunek et al. , 1994 (Simunek et al. , , 2005)) , whose most recent version also takes into account the heat transfer  (Yu and Zheng, 2010). This calibrated model relies on the Richards equation and on the hydraulic functions of  Van Genuchten (1980)  and  Mualem (1976)  to simulate the processes of infiltration, lateral flow and surface runoff and predict moisture content profiles. Although very detailed, this model needs user-input evapotranspiration rates, which must therefore be estimated by other means. For numerical reasons (finite element method), HYDRUS works at very fine spatial resolutions. The work of  Palla et al. (1999)  is a good example of this resolution: they used 3695 grid elements to simulate a roof of 215 cm long by 46 cm thick.
Consequently, a model such as HYDRUS is used to evaluate the hydrological performance of individual green roofs at small temporal scales such as a hydrological event (generally a few hours), but its computing cost due to its fine spatial resolution is not appropriate for city-scale applications. In summary, even if most models are able to capture the dominant processes involved in green roof soil and structural compartments, none of them currently couple a combined modelling of thermal and hydrological processes that could C. S. de Munck et al. : The GREENROOF module for TEB be used on the scale of a city and for long time periods. The presence and impact of drainage or retention layers are also not simulated, even though these layers play a major role, not only with regard to hydrological performance but also in terms of thermal performance, as evidenced in the work of  Jim and Tsang (2011).
Acknowledgements. The results presented in this publication are the outcome of two research projects:  MUSCADE (Modélisation Urbaine et Stratégie d'adaptation au Changement climatique pour Anticiper la Demande et la production Énergétique ,  ANR-09-VILL-0003 ) and  VegDUD (Rôle du végétal dans le Développement Urbain Durable ,  ANR-09-VILL-0007 ). We are grateful for the expertise and support provided by  Aaron Boone and Bertrand Decharme (CNRM-GAME)  on the ISBA-DF model. Edited by: D. Lunt The publication of this article is financed by  CNRS-INSU.
Neuro-2a cells were pre-treated for 30 min with increasing concentrations of FGF1, FGF2, FGF9 (native ligands for FGFR3) or FGF10 (not a ligand for FGFR3, negative control) before treatment with 1 nM BoNT/A. In parallel, cells were incubated with rH C /A (positive control). SNAP25 cleavage, as a measure for BoNT/ A uptake and activity, was decreased when cells were pre-incubated with rH C /A, FGF1, FGF2, or FGF9. No decrease was observed after pre-incubation with FGF10. Data from a minimum of 4 experiments are included. (D) The data was fitted to a non-linear exponential decay model; Y = 100*e-CC*log(concentration). As a measure for competition, the Competition Constant (CC) and standard error (SE) for each competitor are shown. The three native ligands for FGFR3: FGF1, FGF2, and FGF9 competed equally well.
An interaction between FGFR3 and SV2 was detected using the Anti-SV2B (sc-28956) antibody, which recognizes SV2B and, to a lesser extent, SV2A and SV2C. No bands were detected when using antibodies for SV2A or SV2C (data not shown). The result suggests that FGFR3 and SV2B interact in differentiated Neuro-2a cells (Figure  2E-F ). To characterize the binding of FGFR3 and SV2C to rH C /A, the binding affinity of the two receptor surrogate peptides, FGFR3b Loop 2,3 and SV2C 529-579 to rH C /A was tested in a Surface Plasmon Resonance (SPR) binding assay. FGFR3b Loop 2,3 bound to rH C /A with an average K D = 15. 063 nM, n = 4, k a = 1. 77E+04 1/Ms, k d = 2. 40E-04 1/s (Figure  3B-C ). This is similar to what has been reported earlier upon binding of FGFR2b Loop 2,3 to FGF2, K D = 12. 860. 3 nM  [57].
The results showed slightly less uptake of rH C /A compared to FGF2, but similar kinetics (Figure  S1H ). The slightly higher uptake of FGF2 compared to rH C /A could be due to FGF2 having more receptor targets, since it is a general ligand for FGFRs. These data suggest that FGFR3 can mediate BoNT/A uptake independently of SV2. To explore the binding sites of FGFR3b Loop 2,3 and SV2C 529-579 on the binding domain of BoNT/A, we performed a series of dual binding experiments using the BIAcore. We tested if the peptides and anti-H C /A, the neutralizing monoclonal antibody previously used in the cell based inhibition assay (Figure  2C  & D), could bind to rH C /A simultaneously. rH C /A was captured by anti-H C /A monoclonal and FGFR3b Loop 2,3 or SV2C 529-579 were flowed across. The results show that binding of anti-H C /A monoclonal blocks binding of FGFR3b Loop 2,3, but not SV2C 529-579 to rH C /A in vitro (Figure  3F-G ), demonstrating that FGFR3 and SV2 bind to different sites on the BoNT/A binding domain.
The authors concluded that BoNT/A may exploit an alternative pathway(s), largely independent of stimulated synaptic endo-exocytosis, to enter neuronal cells in both resting and depolarizing conditions. Pre-incubation of BoNT/A with the FGFR3 and SV2C peptides before treatment of cells, blocked uptake in neuronal cells, presumably by interacting with the binding domain of BIAcore SPR Binding Affinity measurements of FGFR3b Loop 2,3 binding to FGF9. Shown here are sensorgrams from a single experiment, including the binding of increasing concentrations of FGFR3b Loop 2,3: 31. 25, 62. 5, 125, 250, 500, and 1000 nM respectively to a CM5 sensor chip covered with FGF9. The curves were fitted to a 1:1 kinetic-binding model (A + B « AB) with the BIAevaluation 3. 0 software and the binding constants; k a , k d , and K D, = K d /K a (at equilibrium) were estimated.
Secondary antibody was anti-rabbit IgG H+L HRP conjugate (81-6120; Invitrogen), anti-rabbit IgG veriBlot for IP secondary antibody (HRP) (ab131366, Abcam) (used for IP only), and anti-mouse IgG H+L HRP conjugate (62-6520; Invitrogen) diluted 1:5000 in TBS-T plus 2% blocking agent. Membranes were developed using ECL Plus Western Blotting System Detection Reagents (RPN2132; GE Healthcare). The Chemiluminescence was captured using a Typhoon 9140 (GE Healthcare) set to the following parameters: 455 nm excitation laser and detector set to all wavelengths below 520 nm emissions. The intensity of the gel bands were calculated using Image Quant software TL V2005 (GE Healthcare). The data was analyzed using PLA and SigmaPlot v 10. 0 (Systat Software Inc. ). Intensity values were plotted against concentration of BoNT/A in log scale and fitted to a 4-parameter logistics function (Y = Y 0 +a/[1+(X/X 0 ) b ]) without constraints.
5 (rH C /A) or 1-min injections at 30 ml/min of either; 10 mM Glycine, pH 1. 5 and 0. 125% SDS (FGFR3), 10 mM Glycine, pH 1. 5 and 20 mM CHAPS (Membrane extracts), or 10 mM NaOH (SV2C). The sensorgram curves were evaluated using the BIAevaluation 3. 0 software. The curves were fitted to a 1:1 Langmuir binding model (A + B « AB, where A is the analyte and B is the ligand immobilized on the sensor surface). Based on the fitted curves the association constant, k a , the dissociation constant, k d , and the equilibrium constant, K D (K D = k d /k a ) were determined. The FGFR3b/c peptide curves were also visually compared using the ''normalization'' wizard in the BIAevaluation 4. 1 software.
T-test To assess the significance of the differences from the BoNT/A cellbased competition/inhibition assays and the SPR Binding Analysis assays t-tests were performed using online Graphpad software; www. graphpad. com/quickcalcs/ttest1. cfm?Format = SEM (Graph-Pad Software Inc). ID numbers for genes and proteins mentioned in the text FGFR3 (ENSG00000068078), SV2A (ENSG00000159164), SV2B (ENSG00000185518), SV2C (ENSG00000122012), FGF1 (ENSG00000113578), FGF2 (ENSG00000138685), FGF9 (ENSG00000102678), and FGF10 (ENSG00000070193). labeled rH C /A (Hb) was measured, as an increase in intracellular fluorescence over time, using the Operetta High Content Imaging System. For comparison, internalization of Alexa Fluor 633 labeled FGF2 (Ha), a native ligand for FGFR3, was measured in parallel. The results show that rH C /A binds to and enters HEK 293 cells and that uptake of rH C /A is comparable to uptake of FGF2, even if slightly lower.
2 nM, k a = 1. 29E+03 1/Ms, k d = 1. 24E-04 1/s. (B) BIAcore SPR Binding Affinity measurements of FGFR3b Loop 2,3 binding to rH C /A. Shown here are sensorgrams from a single experiment, including the binding of increasing concentrations of FGFR3b Loop 2,3: 12, 37, 111, 333, and 1000 nM respectively to a CM5 sensor chip covered with rH C /A (,3000 RUs). The curves were fitted to a 1:1 kinetic-binding model (A + B « AB) with the BIAevaluation 3. 0 software and the binding constants; k a , k d , and K D, = K d /K a (at equilibrium) were estimated. The binding constants for the experiment shown were estimated to; K D = 15. 1 nM, k a = 1. 73E+04 1/Ms, k d = 2. 62E-04 1/s. (C) Table showing the binding constant averages for SV2C 529-579 upon binding to rH C /A (n = 3) and for FGFR3b Loop 2,3 upon binding to rH C /A (n = 4), FGF2 (n = 3), or FGF9 (n = 3).
(D) BIAcore SPR Binding Affinity measurements of FGFR3b Loop 2,3 binding to FGF2. Shown here are sensorgrams from a single experiment, including the binding of increasing concentrations of FGFR3b Loop 2,3: 31. 25, 62. 5, 125, 250, and 500 nM respectively to a CM5 sensor chip covered with FGF2. The curves were fitted to a 1:1 kinetic-binding model (A + B « AB) with the BIAevaluation 3. 0 software and the binding constants; k a , k d , and K D, = K d /K a (at equilibrium) were estimated. The binding constants for this experiment were estimated to; K D = 13. 5 nM, k a = 4. 98E+03 1/Ms, k d = 6. 69E-05 1/s. (E) BIAcore SPR Binding Affinity measurements of FGFR3b Loop 2,3 binding to FGF9. Shown here are sensorgrams from a single experiment, including the binding of increasing concentrations of FGFR3b Loop 2,3: 31.
25, 62. 5, 125, 250, 500, and 1000 nM respectively to a CM5 sensor chip covered with FGF9. The curves were fitted to a 1:1 kinetic-binding model (A + B « AB) with the BIAevaluation 3. 0 software and the binding constants; k a , k d , and K D, = K d /K a (at equilibrium) were estimated. The binding constants for this experiment were estimated to; K D = 29. 9 nM, k a = 2. 19E+03 1/Ms, k d = 6. 53E-05 1/s. (F-G) Dual binding experiment: 1000 nM FGFR3b Loop 2,3 (F) or 3000 nM SV2C 529-579 (G) were flowed across rH C /A (,1000 RU) captured by anti-H C /A 6B1 monoclonal antibody (3000 RU). The result show that SV2C 529-579 and anti-H C /A antibody can bind simultaneously to rH C /A, while FGFR3b Loop 2,3 binding is abolished. Background drift from dissociating rH C /A was subtracted. As a negative control, the peptides were flowed across anti-H C /A antibody before capture of rH C /A.
0489. There was no significant difference in IC value among the FGFR3c peptides. (D) Results from testing the eight FGFR3 deletion mutant peptides in a BIAcore SPR binding assay. Increasing concentrations, 0-4000 nM of each of the deletion mutant FGFR3 peptides were flowed across a CM5 sensor chip covered with rH C /A. The curves were fitted to a 1:1 kinetic-binding model (A + B « AB) with the BIAevaluation 3. 0 software. The table shows the calculated K D 's 6 SEM of all eight FGFR3 deletion mutant peptides upon binding to rH C /A. The average k a 's (association constants (1/Ms)) and k d 's (dissociation constants (1/s)) are shown in figureS3B. The K D values for the FGFR3b Loop 1,2,3 peptides are significantly lower than the K D values for the FGFR3c Loop 1,2,3 peptides, P-value#0. 0005 and the K D values for FGFR3b/c Loop 3 and FGFR3c Loop 2,3 are significantly higher than the K D values for the longer FGFR3b/c peptides, P-value#0.
This paper presents research results of the European  FP7  project  PEGASE  funded by the  European Commission. The work of F. Capitanescu was supported by the  National Research Fund Luxembourg  in the frame of the  CORE project  " Reliable and efficient distributed electricity generation in smart grids ",  C11/SR/1278568. The work of L. Wehenkel was supported by the  Belgian Network DYSCO (Dynamical Systems , Control, and Optimization), funded by the  Interuniversity Attraction Poles Programme , initiated by the  Belgian State, Science Policy Office. The scientific responsibility rests with the authors. Paper no. TPWRS-00774-2012. RTE, Versailles, France.
Schmallenberg Virus Infection in Dogs, France, 2012 To the Editor: In 2011, Schmallenberg virus (SBV) emerged in Europe  (1) ; the virus spread into France in January 2012  (2). During January-March 2012, a total of >1,000 cases were reported in France, mainly in stillborn and newborn lambs with congenital malformations. In March 2012, neurologic disorders were detected in five 15-day-old puppies (Belgian shepherd) from a dog breeding kennel in northwestern France (Orne). We report data suggesting that these puppies were infected with SBV. In June 2012, the kennel veterinarian contacted a veterinary school (Unité de Médecine de l'Elevage et du Sport Breeding and Sport Medicine Unit, Maisons-Alfort, France) after neurologic signs of ataxia, exotropia, a org/software/phylogenetics/ figtree).
The tree was generated by using the uncorrelated lognormal distribution relaxed molecular clock model and SRD06 substitution model in BEAST1. 74 (7). SEOV strain name/GenBank accession no/country: The location is shown in taxa. The posterior number is shown for each branch. Clades A and D were established as described (2) Scale bar represents number of nucleotide changes per site. head tilt, and stunted growth were observed in a litter of 5 puppies. Four of the puppies had died at 5-6 weeks of age. The veterinarian collected blood samples from the surviving puppy at 3 months of age, and the puppy was euthanized for necropsy. Severe torticollis was observed during the necropsy, but no other macroscopic signs were detected. The brain, including the cerebellum; a part of the spine; and cerebrospinal fluid (CSF) were collected for further investigation.
Data on individual confirmed human cases of influenza (H7N9) from February 19, 2013, through May 17, 2013, and of influenza (H5N1) from October 14, 2005, through May 17, 2013, were collected from the Chinese Center for Disease Control and Prevention. The definitions of these cases have been described  (3, 5). A total of 129 confirmed cases of influenza (H7N9) (male:female ratio 2. 39:1) and 40 confirmed cases of influenza (H5N1) (male:female ratio 0. 90:1) were included in the analysis. The median age of persons with influenza (H7N9) was higher than for persons with influenza (H5N1) (58 years vs. 27 years; z = -7. 73; p<0. 01). Most (75. 0%) persons with influenza (H5N1) had direct contact (e. g. , occupational contact) with poultry (including dead and live birds) or their excrement and urine, whereas most (64.
Figure. Phylogenetic tree, based on a 330-bp amplicon of the Seoul virus (SEOV) RNAdependent RNA polymerase gene, depicted in FigTree 1. 4. 0 (www. molecularevolution. org/software/phylogenetics/ figtree). The tree was generated by using the uncorrelated lognormal distribution relaxed molecular clock model and SRD06 substitution model in BEAST1. 74(7). SEOV strain name/GenBank accession no/country: The location is shown in taxa. The posterior number is shown for each branch. Clades A and D were established as described (2) Scale bar represents number of nucleotide changes per site.
Figure. Figure. Phylogenetic tree, based on a 330-bp amplicon of the Seoul virus (SEOV) RNAdependent RNA polymerase gene, depicted in FigTree 1. 4. 0 (www. molecularevolution. org/software/phylogenetics/ figtree). The tree was generated by using the uncorrelated lognormal distribution relaxed molecular clock model and SRD06 substitution model in BEAST1. 74(7). SEOV strain name/GenBank accession no/country: The location is shown in taxa. The posterior number is shown for each branch. Clades A and D were established as described (2) Scale bar represents number of nucleotide changes per site.
Africa's GHG emissions and removals as reported to UNFCCC We have estimated the greenhouse gases (GHG) emissions on the basis of the national communication to the United Nations Framework Convention on Climate Change (UN-FCCC) updated in December 2011 2. The results reveal a profile of emissions from the African continent that is remarkably different to that of other regions of the world. Firstly, the contribution of fossil fuel emissions is low (less than 4 % of total global CO 2 emissions;  Canadell et al. , 2009a)  but increasing rapidly. In terms of per capita emissions from 1 "C" indicates carbon from CO 2 only; when CH 4 and/or other non CO 2 GHG are included CO 2 -eq is used. 2 Data were processed from the latest (updated to December 2011) African countries national communication to the UNFCCC (http://unfccc.
, 2013). Biomass data for African forests remains insufficient for accurate estimation and comparison to other world regions  (Ciais et al. , 2011). The same is true for other important land cover classes in Africa, such as savannas, shrublands, crops and wetlands. In order to identify and visualize the pattern of aboveground biomass distribution, we integrated the land cover map of the European Space Agency (ESA),  GlobCover 2009 product (Globcover Land Cover v2. 3, 2009) , and the map of aboveground biomass developed by the Woods Hole Research Center (WHRC)  (Baccini et al. , 2008). Integration implied the resampling of data to higher available resolution and the calculation of biomass statistical values for each land cover class. Both the biomass and land cover map were based on remote sensed and field data collected in the period between 2000 and 2009 and are among the most recent public domain products.
All GIS analyses were conducted with data in sinusoidal projection, which is the original projection adopted in the WHRC biomass map. The WHRC biomass map is a 1 km raster data set expressed as Mg of biomass per hectare, saturating at 356 Mg ha -1. The GlobCover 2009 map is a 300 m raster data set (which spatial resolution was degraded to 1 km to match the WHRC map), developed according to the LCCS. It classifies vegetation using a pure physiognomic-structural method and is thus well suited for above ground biomass analysis. Forests in LCCS are by definition composed of trees with height > 5 m and a crown cover > 10 %. The different GlobCover 2009 land cover classes considered in this study are shown in Table  3 , together with the associated biomass values, their statistics and extent. After excluding the cells with zero biomass (assumed to be nonvegetated areas).
Terrestrial fluxes upscaled from in situ observations In recent years, there has been an expansion of the flux tower network in Africa  (Bombelli et al. , 2009). The total number of flux sites (see FLUXNET database) includes more than 15 sites with continuous carbon dioxide, water vapour, and energy fluxes, covering different plant functional types (from tropical forest, to transition forest and savannas) in different climatic regions. Some flux towers have been used in sporadic or seasonal campaign mode. The latest overview of the flux towers being active during CarboAfrica has been given in  Merbold et al. (2009)  and  Sjöström et al. (2013). The net annual carbon budget (NEE) of savanna sites ranges from a slight source of 0. 25 to a sink of -4. 53 t C ha -1 yr -1  (Bombelli et al. , 2009) , with a median value being a sink of -0.
, 2011). Uncertainties refer to one standard deviation of the spread of the 25 members of the model tree ensemble, and reflect the uncertainty in the structure of the model trees. Uncertainties related to FLUXNET data and global forcing data sets are not included in this measure. The NEE estimates are not consistent with the difference of TER and GPP because NEE was upscaled independently. The "La Thuile FLUXNET data set" was screened for the high quality data and the availability of required variables. 9 Because only one African flux data were available for training at the time, uncertainties due to extrapolation to African ecosystem are likely to be substantial. Jung et al. (2011)  inferred a systematic underestimation of 5 to 10 % of their global TER value, likely due to a biased sampling with enhanced representation of young and regrowing forests in the La Thuille database and an even larger bias when considering African ecosystems only.
e. 6. 3 Pg C yr -1 ), about 10 % of global terrestrial NPP, for the total forested zone. The net change in biomass of tropical forests has been better reported, thanks to the pioneering work of the AfriTRON forest plot network 10. Lewis et al. (2009)  collected data from repeated surveys of 79 forest plots in central and West Africa, and reported a net biomass carbon sink of 0. 63 (range 0. 22-0. 94) t C ha -1 yr -1. This is very similar to the value of 0. 45 (range 0. 33-0. 56) t C ha -1 yr -1 reported for 123 plots in Amazonia  (Phillips et al. , 2009) , suggesting that a common driver, such as atmospheric CO 2 fertilization, may be stimulating this biomass storage across the intact tropical forest biome. Scaling the data of  Lewis et al. (2009)  across the humid and dry forest biome, Malhi (2010) estimated a net biomass carbon sink of 0.
, 2009; Weber et al. , 2009). Global DGVM data was provided by the TRENDY multimodel evaluation project as part of the Global Carbon Project (GCP). 11 The DGVMs used in this study were CLM4CN  (Thornton et al. , 2007 (Thornton et al. , , 2009;; Bonan and Levis, 2010; Lawrence et al. , 2011) , Hyland  (Levy et al. , 2004) , LPJ  (Sitch et al. , 2003; Poulter et al. , 2011) , LPJ-Guess  (Smith et al. , 2001) , O-CN (v0. 74) (Zaehle and Friend, 2010), Orchidee  (Krinner et al. , 2005) , SDGVM  (Woodward et al. , 1995; Woodward and Lomas, 2004) , TRIFFID  (Cox, 2001) , and VEGAS  (Zeng, 2003; Zeng et al. , 2005). The models followed a common protocol  (Sitch et al. , 2013). 12 DGVMs were applied with the merged CRU-NCEP climatology over the period 1901-2009, and global atmospheric CO 2 was derived from ice core and NOAA monitoring station data, and provided at an annual resolution.