dataesr/scientific-paragraphs-categorization huggingface dataset:
These proteins act via their cellular adhesion properties, e. g. CLEC14A, CD44, CD99, and the integrins ␣3, ␣6, ␣V, and ␤3, or via their functions as co-receptors; e. g. CD105 is a co-receptor for TGF␤1 and TGF␤3 and is a crucial molecule in vascular development and angiogenesis, and neuropilin-1 is a key player in angiogenesis  (28). Among these proteins, the characterization of the regulation of CLEC14A under hypoxia brings new information to the previously published data (29). CLEC14A was described as a key target in angiogenesis, and our results suggest that this role could be triggered by hypoxic conditions. Regarding other adhesion molecules like CD44, the receptor for hyaluronic acid, and the integrins ␣3 and ␣6, we conclude that this up-regulation is a consequence of endothelial activation.
Erosion models, such as the Universal Soil Loss Equation (USLE), depict a proportional relationship between rainfall and soil erosion. As radiocesium is tightly bound to fine soil and sediment particles, characterizing the rainfall regime of the falloutimpacted region is fundamental to modelling and predicting radiocesium migration. Accordingly, monthly and annual rainfall data from ∼ 60 meteorological stations within a 100 km radius of the FDNPP were analysed. Monthly rainfall erosivity maps were developed for the Fukushima coastal catchments illustrating the spatial heterogeneity of rainfall erosivity in the region. The mean average rainfall in the Fukushima region was 1387 mm yr -1 (σ 230) with the mean rainfall erosivity being 2785 MJ mm ha -1 yr -1 (σ 1359). The results indicate that the majority of rainfall (60 %) and rainfall erosivity (86 %) occurs between June and October.
Rainfall monitoring stations All rainfall stations within a 100 km radius of the FDNPP were selected along with two additional long-term stations within 115 km (Fig. 1 ). From this initial selection, all rainfall stations that recorded pluviometric data consistently (e. g. throughout the entire year) for more than five years were selected for preliminary analyses (Table  1 ). There were 14 stations omitted within the 100 km radius of the FDNPP, five stations because they had less than five years of data, and another nine stations that were not operational during winter. Up to 19 years was available for the 10 min rainfall data  (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013)  and 38 years for daily rainfall data  for the majority of the rainfall stations.
The minimum duration of the long-term stations was 60 years with a maximum of 126 years. These long-term stations were included in the analysis of daily rainfall starting from 1977. Further, all daily rainfall data from the long-term stations was also analyzed separately to investigate potential long-term rainfall trends. For the 10 min data, there were on average 38 stations operating consistently between 1995 and 2013. As the R factor is a parameter of long-term rainfall erosivity (e. g. ∼ 20 years), only stations with 10 min rainfall data for over 10 years were included in the initial analyses. The main difference between number of stations utilized in the daily and 10 min analyses was that the long-term stations did not start recording 10 min Figures.
R factor calculations The Rainfall Intensity Summarisation Tool (RIST) software (USDA, 2013) was used to calculate the R factor, which is a product of the kinetic energy (KE) of an individual rainfall event (E ) and its maximum 30 min intensity (I 30 )  (Renard and Freimund, 1994) : R = 1 n n j =1 mj k=1 (E I 30 ) k (1) where R equals the annual average rainfall erosivity in MJ mm ha -1 h -1 yr -1 , n represents the number of years of rainfall data utilized, m j is the number of erosive rainfall events in year j , and E I 30 was calculated as: E I 30 = 0 r=1 e r v r I 30 (2) with e r representing the unit rainfall energy in MJ mm ha -1 h -1 , v r is the volume of rainfall (mm) during a given time interval (r), and I 30 is the maximum rainfall intensity over a 30 min period of the rainfall event (mm h -1 ).
R factor spatial analyses Annual and monthly R factor data were used to derive maps of annual and monthly rainfall erosivity over the eastern part of the Fukushima Prefecture. A regression approach based on relationships established between rainfall erosivity and spatially-distributed covariates was used to produce these maps. The covariates used for the spatial interpolation of annual and monthly R factors over the study area were: - and Tourism (http://www. gsi. go. jp/). This DEM was derived from LIDAR airborne monitoring surveys. Different derivatives were computed based on this DEM: slope, aspect along with eastness and northness, which represent the degree to which aspect is close to the east and the north, respectively (Zar, 2010). -Climatic data, i. e. monthly mean rainfall and temperature, from the WorldClim database reported for the period 1950-2000 (30 arcsec resolution, equivalent to 1 km resolution)  (Hijmans et al.
, 2005). -Geographical coordinates (longitude and latitude) of the stations in order to include spatial autocorrelation. A spatial resolution of 40 m was chosen as a compromise between spatial resolution and computation time. Continuous variables were rescaled in ArcGIS10 (ESRI, 2011) with bilinear interpolation, which determined the output value of a cell based on a weighted distance average of the four nearest input cell centres. The spatial variation of monthly and annual R factors was modeled with Generalized Additive Models (GAM;  Hastie and Tibshirani, 1986) , implemented in the mgcv package in R  (Wood, 2001). The GAM regression technique is a generalization of linear regression models in which the coefficients may be a set of smoothing functions, accounting for the non-linearity that may exist between the dependent variable and covariates.
Hence, this station was rejected from the dataset used for the mapping procedure. GAM specifies a distribution for the conditional mean µ(Y ) and a link function, g, relating to an additive function of the covariates: g[µ(Y )] = α + f 1 X 1 + f 2 X 2 + • • • + f p X p ( 4 ) where Y is the dependent variable, X 1 , X 2 ,. X p represent the covariates and the f i 's the smooth (non-parametric) functions. A Gaussian error model incorporated the conditional mean µ (Y ) and, due to the predominant logarithmic distribution of the monthly and annual data, a log-linear link function g(µ) = log(µ) was incorporated. The GAM models were built using regression splines fitted by penalized Maximum Likelihood to avoid over-fitting. For each month and the year, a model including all the covariates was developed.
Back Close Full induced soil erosion and concomitant radiocesium transfers to downstream catchments and ultimately to the Pacific Ocean. The highest precipitation in these areas occurs during the typhoon season (July-September). So far, research on radiocesium and sediment transfers in the Fukushima region has focused on modelling and monitoring without a comprehensive examination of rainfall erosivity. Accordingly, up-to-date and Fukushima up-to-date R factor maps are provided online (database link to be provided) for use in on-going modelling work in the Fukushima region. Further, all the rainfall event data are included in the Supplement (Table  S1 ). Although there are many approaches to modelling the influence of rainfall on erosivity, we believe the provision of the R factor maps and a thorough quantification of the rainfall erosivity in the Fukushima region is beneficial to the research and management community.
This research also provides insight into the impact of typhoons on rainfall erosivity and the importance for the incorporation of this driver of soil erosion when examining sediment and particle-bound contaminant transfers in subtropical regions subject to cyclonic activity. A1. Dataset 1: R factor Maps in ArcGis Raster (grid) format (40 m) will be provided. We are currently searching for an archiving/database service online to host the 13 Map files (1 for each month and the annual maps) as the size of the maps is much greater than the ∼ 50 mb HESS limit. Dataset 2: Table of rainfall data results for all events included in the analyses (an example of the databased to be appended including 20 000 events over the study period for rainfall stations analyzed is provided below).
Modelling biomass production and the environmental impact of short rotation coppice (SRC) plantations is necessary for planning their deployment, as they are becoming increasingly important for global energy production. This paper describes the modification of the widely used land surface model ORCHIDEE for stand-scale simulations of SRC plantations. The model uses weather data, soil texture and speciesspecific parameters to predict the aboveground (harvestable) biomass production, as well as carbon and energy fluxes of an SRC plantation. Modifications to the model were made to the management, growth, and allocation modules of OR-CHIDEE. The modifications presented in this paper were evaluated using data from two Belgian poplar-based SRC sites, for which multiple measurements and meteorological data were available.
For this reason models are needed that can simulate the larger-scale effects of wide-spread SRC use, which are sufficiently general to allow application at larger scales while being specific in the essential details. The objective of this study is to further develop an existing land surface model called ORCHIDEE, to have the model simulate the C and water fluxes of SRC plantations over a range of site conditions. In the future we want to use this model to test a number of management scenarios across Europe to study the variation in the management effects on biomass production and CO 2 uptake. To this aim we made changes to the management, growth and allocation modules of ORCHIDEE, adjusted the parameterization and evaluated the performance of the adapted model against site-level information from two operationally managed SRC stands in Belgium.
Model description ORCHIDEE is a mechanistic land surface model that was designed to operate from regional to global scales. The model is composed of two components: (i) SECHIBA, which computes the energy and hydrology budget on a half-hourly basis, and (ii) STOMATE, which simulates the carbon cycle on a daily timescale. The equations used by ORCHIDEE are given in  Ducoudre et al. (1993) ,  Krinner et al. (2005)  and in the online documentation (http://forge. ipsl. jussieu. fr/ orchidee). The source code can be accessed at http://forge. ipsl. jussieu. fr/orchidee/browser/tags/ORCHIDEE_1_9_5. For these simulations, ORCHIDEE needs seven meteorological variables at a 30 min interval: wind speed, air pressure, short-wave radiation, long-wave radiation, air temperature, precipitation and specific air humidity.
The SRC simulations in this paper further develop the "temperate deciduous broadleaf forest" functional type. As an extension to the standard version of ORCHIDEE, ORCHIDEE-FM was developed to include a number of adaptations for forest management  (Bellassen et al. , 2010). These adaptations include an age-related limitation of leaf area index (LAI) in young stands, an age-related decline in NPP, self-thinning in unmanaged stands and anthropogenic thinning in managed stands. The source code for this extended version can be found at http://forge. ipsl. jussieu. fr/ orchidee/browser/perso/toon. degroote/orchidee_FM.
European biomass sites For the evaluation of aboveground standing woody biomass production across Europe, we used biomass measurements found in Njakou  Djomo et al. (2015). From their list of sites, we selected the 23 sites that were not irrigated and had poplar trees (Table  3 ). Because meteorological data of sufficient resolution and a detailed site description for these sites were not available, we could not perform a site-by-site comparison. Therefore, we collected meteorological data from 22 different European sites in a similar geographical range on the European Fluxes Database Cluster (http://gaia. agraria. unitus. it/, 1 September 2014) to run our simulations. This way we could compare the range and trend of aboveground woody biomass production along the latitudinal gradient, as well as along the annual precipitation gradient and the average annual temperature gradient, We selected sites with a public data access and open data use policy, for which data were available for a minimum of 5 years (Table  3 ).
Data processing On the POPFULL site, meteorological data for 2010 and 2011 were collected together with the eddy-covariance flux data. Since the measurements did not start until June 2010, this gap was filled using data from a nearby station (Melle) from the Royal Meteorological Institute (RMI). For the Boom site, meteorological data were used from a nearby field site (Brasschaat). For the POPFULL site, measured eddy-covariance fluxes (GPP, R eco , NEE, H and LE) were used to evaluate the model outputs. These data were not related to the data that were used to calibrate the model. NEE, H and LE were measured directly by the eddy-covariance technique, but for GPP and R eco an approximation had to be calculated using flux partitioning. Here, GPP and R eco were calculated using the online eddy-covariance gap-filling and flux-partitioning tool of the Max Planck Institute for Biogeochemistry (http://www.
Acknowledgements. The research leading to these results has received funding from the  European Research Council  under the  European Commission 's  Seventh Framework Programme  (FP7/2007-2013) as  ERC  grant agreement no. 233366  ( POP-FULL ), the  Flemish Hercules Foundation as Infrastructure  contract  ZW09-06 , the  Flemish Methusalem Programme , the  Research Council of the University of Antwerp , and the  Research Foundation -Flanders  ( FWO ;  DOFOCO  project). We gratefully acknowledge the excellent technical support of  Joris Cools  and the logistic support of  Kristof Mouton  at the field site. T. De Groote  is a PhD fellow of the  Research Foundation -Flanders (FWO)  and the  Flemish Institute for Technological Research (VITO). Edited by:  A. Ridgwell.
1995; Bejarano et al. 1996; Ashby et al. 1997; Murad et al. 2004 ). Although so-called endogenous geminivirus sequences are clearly apparent within the recently published genome sequences of Lactuca sativa (common lettuce, GenBank accession: PRJNA68025), Malus domestica (common apple tree, GenBank accession: PRJNA28845), Coffea canephora (common coffee, GenBank accessions: CDP12517, CDP12572, CDP16111, CDP16477, CDP18020, CDP18767, CDP19427, CDP21894, CDP18021, CDP18667, CDP20185, and CDP20985), and Populus trichocarpa (black cottonwood tree, GenBank accession: PRJNA17973)  (Liu et al. 2011; Martin et al. 2011) , the best studied of these integrated geminivirus sequences are the so-called 'geminivirus-related DNA' (GRD) elements within the genomes of various Nicotiana species  (Kenton et al.
In silico screening of publically available EST from yam A systematic search of assembled sequences (using the CAP3 sequence assembly program  (Huang and Madan 1999)  from publicly available D. alata EST resources (GenBank accession numbers: HO809681-HO825421, HO825422-HO840419, and HO850622-HO864016) was performed using the BlastN and BlastX methods implemented in the software KoriBlast 3. 1 (KoriLog) with a maximum E-value threshold of 10 À4.
Distribution of geminivirus-like sequences within the genomes of Dioscoreacea species DNA was extracted from a further forty yam species and screened for the presence of EGV1 and EGV2 using four broad spectrum primer pairs (Supplementary Table  S1 ), designed according to geminivirus-like sequences found in an unpublished D. alata and D. rotundata EST database produced by the Agropolis Resource Centre for Crop Conservation, Adaptation and Diversity (Montpellier, France).
Cloning and sequencing of DNA fragments Amplified DNA fragments obtained from PCR assays (EGV1 and EGV2 rep genes, rbcL and matK partial genes-see below) were gel purified (using the SV Gel and PCR Clean-Up System from Promega) and inserted into pGEM V R -T Easy vector (Promega) following the manufacturer's protocol. The universal T7 and SP6 primers were used for sequencing. Sequence data of all the amplified fragments were obtained by single-pass double-stranded analysis (Beckman Coulter Genomics) using a primer walking approach when needed and was further assembled using DNAMAN for windows (Lynnon Corporation).
Western blotting analysis One peptide, LEGRAQVTNNRFDL, putatively encoded by EGV1 rep gene from D. alata acc. 313 was selected because it did not display significant similarity (all obtained BlastX or tBlastN, E-values > 10 À1 ) to any Dioscoreales (NCBI Blast Dioscoreales taxid: 40548) proteins from the non-redundant protein sequence (nr), the metagenomic protein (env_nr), and the transcriptome shotgun assembly protein (tsa_nr) NCBI databases. However, BlastX comparisons between LEGRAQVTNNRFDL and all geminiviral sequences deposited in GenBank (NCBI Blast Geminiviridae taxid: 10811) indicated that the highest identity score was detected with the Sida micrantha mosaic virus Rep protein (accession number CAD89704. 1, highest percent identity ¼ 80%, E value ¼ 1. 10 À4 ). In addition, Tomato yellow leaf curl virus-Mld (TYLCV-Mld, accession number AJ865337) shared 47 per cent identity with the selected EGV1 peptide.
Phylogenetic analysis of geminivirus rep and ren coding regions We focused on the rep and ren coding regions to determine the evolutionary relationships of EGV1 and EGV2 to the various major geminivirus lineages. We assembled datasets consisting of sixty-nine Rep amino acid sequences representing the entire breadth of known geminivirus diversity. Besides the inferred EGV1 and EGV2 Rep amino acid sequences from D. alata, the Rep dataset contained twenty-four Mastrevirus sequences, eighteen Begomovirus sequences, eight Curtovirus sequences, two Becurtovirus sequences, one Eragrovirus sequence, one Turncurtovirus sequence, one Topocuvirus sequence, and one sequence each from the divergent geminivirus-like ssDNA viruses recently discovered infecting citrus plants (Citrus chlorotic dwarf-associated virus, CCDaV, genome accession: JQ920490), grapevines (Grapevine Cabernet Franc-associated virus, GCFaV, genome accession: JQ901105), Euphorbia caputmedusae (Euphorbia caput-medusae latent virus, EcmLV, genome accession: HF921459), and French bean (French bean severe leaf curl virus, FbSLCV, genome accession: NC_018453).
In addition to these sequences, the Rep dataset contained translated amino acid sequences representative of geminivirus-like Rep sequences that might be expressed from integrated geminivius-like sequences that are potentially found within various plant genomes: L. sativa (common lettuce, GenBank accession: PRJNA68025), M. domestica (common apple tree, GenBank accession: PRJNA28845), P. trichocarpa (black cottonwood tree, GenBank accession: PRJNA17973), Fraxinus angustifolia (narrowleafed ash, GenBank accession: AY760062), Camellia sinensis (tea plant, GenBank accession: HP764465), and Bituminaria bituminosa (Arabian pea, GenBank accession: JL856919). In addition, two divergent Rep sequences from ssDNA replicons closely related to geminiviruses were used to root the phylogeny: one derived from the witches' broom-associated phytoplasmal plasmid and the other from the geminivirus-like mycovirus Sclerotinia sclerotiorum hypovirulence-associated DNA virus (SsHADV, genome accession: NC_013116).
The estimated sample size was calculated using the program Tracer (http://tree. bio. ed. ac. uk/software/tracer/) and the combined estimated sample size in each case was greater than 200. The Ren sequence dataset contained, in addition to the forty-seven Ren sequences from D. inopinata, D. oryzetorum, D. alata, D. persimilis, D. nummularia, D. transversa, D. glabra, D. calcicola, D. hamiltonii, D. brevipetiolata, D. opposita, D. decipiens, D. cirrhosa, D. lanata, and D. schimperiana, fifteen begomovirus, one curtovirus, and one topocuvirus sequences. These sequences were aligned using MUSCLE method implemented in MEGA (with default settings, alignment file is provided as Supplementary Material) and were used to construct a maximum likelihood tree with PhyML3 using a JTT amino acid substitution model with branch supports being tested with 100 bootstrap replicates.
When testing for purifying selection within any partition, the parameters on the other two background partitions (five on each) remained unconstrained, so as not to bias the foreground partition tests. We also constructed an analogous test for positive selection (against a null model that allowed purifying and neutral selection), but omit it from discussion here because we found no evidence of positive selection in any of the three EGV1 and EGV2 partitions. The sequence datasets analysed by these methods included fifteen EGV1 and fourteen EGV2 Rep encoding sequences that contained no premature stop codons, frameshift mutations or evidence of recombination (tested with RDP4. 23;  (Martin et al. 2010) , alignment file is provided as Supplementary Material).
Inventory of viruses in D. alata Nine distinct virus-like sequences were detected through screening of the publically available D. alata EST database (dbEST Id: 71472229). In addition, one virus-like sequence was recovered using routine viral diseases diagnostic procedures (Supplementary Table  S3 ). These sequences displayed significant similarity (BlastN/BlastX, E-value < 10 À5 ) to members of the Secoviridae family (three sequences similar to viruses in the genus Sadwavirus and three sequences that cannot be classified within genera of the Secoviridae family), Geminiviridae (two sequences designated as EGV1 (209 bp) and EGV2 (316 bp) similar to viruses in the genus Begomovirus), Potyviridae (one sequence), and Caulimoviridae (one sequence).
The rep genes of EGV1 and EGV2 have likely been functionally transcribed post-integration Of the fifty-seven EGV rep gene copies derived from the various Dioscorea species, twenty-nine (fifteen from EGV1 sequences and fourteen from EGV2 sequences; Fig. 2 ) contained neither frameshift mutations nor premature stop codons. Canonical RCR motifs were present in twenty-one of twenty-nine of these genes, which suggests that they could possibly still be capable of encoding functional proteins. In addition, the fact that rep containing transcripts of EGV1 and EGV2 were identified in our screening of D. alata and D. rotundata EST datasets indicates that the rep genes of at least some of the EGV1 and EGV2 integrons are transcribed in at least some of the Dioscorea species. To further test the hypothesis that EGV sequences are being functionally transcribed, we analysed siRNA sequences from D.
It is noteworthy that this oligopeptide did not display any significant similarity to either (1) any translated yam sequences that are currently available within public sequence databases (2,781 nucleotide sequences in GenBank and 48,527 in the EST database) or (2) any eukaryotic or prokaryotic proteins in any of the publically available NCBI sequence databases: in both cases BlastX or tBlastN E-values that were obtained were uniformly >10 À1. An approximately 55-kDa protein was detected in D. bulbifera, D. dumetorum, D. nummularia, and the three D. alata seedling accessions. EGV1 Rep expression was also evident (albeit to a lesser degree) in D. togoensis, D. praehensilis, and D. rotundata (Fig. 4 ). In addition, this protein was not detected either in D. trifida and D. sansibarensis or in the two negative controls, including the tomato plant infected with TYLCV-mld (which was likely to contain both tomato and TYLCV-mld proteins; Fig.