dataesr/scientific-paragraphs-categorization huggingface dataset:
e. groupings of plant species with distinctive branch architecture and/or stem wood density. There is evidence that such an approach negates the need to explicitly account for stem wood density in allometric models of biomass  (Paul et al. 2016). The groups used were: Generic allometrics 8 i. FShrub&Ac. Shrubs and small multi-stemmed trees. This group included the common Australian genus of Acacia (36% of the FShrub&Ac data set), which comprised both shrub  (31%, e. g. A. hemiteles, A. murrayana and A. victoriae)  and small tree (69%, e. g. A. saligna, A. acuminata and A. aneura) forms. The group also included another 18 genera of shrubs (generally < 2 m height), with the most common genera being Eremophila, Dodonaea and Melaleuca.
ii. FMallee. Multi-stemmed (mallee) trees from the genus Eucalyptus, and which commonly have a lignotuber and relatively high wood density: mean 0. 88 ± 0. 08 g cm -3 standard deviation, largely (93%) based on estimates from global stem density database  (Chave et al. , 2009; Zanne et al. , 2009) , with the remainder being directly measured. Australia. There was a large diversity of genera sampled from the tropical ecoregion, with the most common being species from the genera Argyrodendron.
iv. FRadiata. An introduced tree species Pinus radiata, of relatively low stem woody density: mean 0. 40 ± 0. 04 g cm -3 standard deviation, largely (86%) based on estimates from the Generic allometrics 9 global stem density database, with the remainder being directly measured. This species is the most common species in softwood plantations within high rainfall regions of temperate Australia. The FShrub&Ac, FMallee, FTree, and FRadiata groupings comprise about 20, 30, 40 and 10% of the data set respectively. The geographical extent of the Australian terrestrial ecoregions is shown in Fig. 1 , and the representation of these ecoregions in the sampling for each plant functional type is shown in Fig. 2. The 'Mediterranean forests, woodlands and scrub' was the most well represented ecoregion (66%).
Harmonisation of BGB data estimates Because measurement of BGB is resource-intensive and challenging, among the 38 studies used to build the data set  (Paul et al. , 2017b)   (Wildy & Pate, 2002; Saint-Andre et al. , 2005). Three other protocols were used for the remaining 49% of the data set. For 20% of the data set, the stump was included with the AGB. In these cases, stump biomass was estimated using empirical data as described by  Paul et al. (2014) , and added to the BGB. For 15% of the data set, BGB was excavated in a set area (generally 4 m 2 around a tree base). Based on empirical evidence described by  Paul et al. (2014) , it was assumed that only 70. 2% of BGB was excavated and so an additional 29. 8% was added to the mass sampled. Finally, for 14% of the data set, excavation of the BGB of an individual tree or shrub was not possible due to the close spatial association of the target individual with nearby individuals (known as 'clustering').
In such instances, relatively large areas (50-200 m 2 ) were excavated and the BGB of the entire vegetation 'cluster' was provided. Although root stumps belonging to each individual could be identified, the remaining coarse roots were allocated to each individual within the 'cluster' in accordance with its proportional contribution to the total AGB measured for that 'cluster'. As quantified by  Paul et al. (2014) , uncertainties in allometry-predicted BGB result from assumptions required to harmonise the BGB data sets derived from alternative protocols. These uncertainties may be reduced as additional data becomes available to inform the adjustment factors applied, e. g. varying the adjustment factor for set area excavation based on the size of the tree and/or the stand density.
Ancillary stand and site data Data about the stand and site from which an individual was sampled were also collated (Table  1 ). Stand variables included whether the site was 'natural' (i. e. naturally-regenerated shrubland, woodland or forest) or managed (i. e. human-induced establishment from planting of nursery stock, direct seeding or human-induced natural regeneration). It was noted if a stand was younger than 20 years. There was insufficient replication, and/or confidence in exact ages, to facilitate further age-class groupings. Site factors included long-term mean annual rainfall (MAR, mm yr -1 ) and mean annual temperature (MAT, o C)  (BoM, 2015; 1970 -2015, 2. 5 km resolution).
Standardising diameter estimates, and outlier checking For many individuals in the data set, D was measured at multiple heights, thereby allowing derivation of generic relationships for prediction of D at a given height based on D measured at another height (see Table  S2  of  Paul et al. , 2016). These relationships were used to 'gap-fill' D estimates where required, with D130 or D10 being estimated for 28% of the 2 054 individuals within the data set. Very small individuals (D10 < 0. 6 cm and D130 < 1. 1 cm) were not included in the database because they were considered unlikely to conform to biomass scaling laws typical of woody plants as they had relatively little secondary thickening  (Niklas, 2004; Enquist et al. , 2007). Data for a further 38 individuals from 10 sites (and six sources) were also excluded as outliers.
Here, individuals were defined as outliers if their measured BGB fell outside the 99. 9% confidence interval of prediction of the appropriate plant functional type model. Although the BGB of these outliers were assumed to come from a normally-distributed population and had no major influence on model fit, they were nonetheless removed on the basis that they were highly unlikely values of BGB for the measured dimensions, and were most likely due to errors in data entry. Table  1  Characteristics collated for the entire data set (AllUniversal), or for each of the four categories of plant functional types. (FShrub&Ac, FMallee, FTree, and FRadiata) Abbreviations as follows: 'N', total number of individuals; 'D10' and 'D130', mean stem diameter measured over bark at 10 cm and 130 cm respectively; 'N stand', number of stands from which the trees or shrubs were harvested; 'N spp.
Allometric model There are alternative approaches for developing statistical models of the allometric scaling relationships of biomass. Traditionally, logarithmic transformations have been used in order to apply linear regression, with back-transformation required. An alternative is the application of nonlinear statistical procedures. There is a lack of consensus in the literature over the preferred approach (e. g. Packard et al. 2009 cf. Kerkhoff and Enquist 2009). We elected to use the traditional approach given: (i) critical reviews or tests of different approaches support the validity of this approach (e. g. Xiao et al. 2011; Ballantyne 2013; Sileshi 2014) , and; (ii) it provides consistency with a complementary study (based on similar datasets) of AGB allometry  (Paul et al. 2017). Bayesian approaches to parameter estimation have also been successfully applied and they offer great promise, particularly for combining predictions from multiple alternative allometric models (e.
( 2012 )): BGB = a × X b × ε' (1) lnBGB= lna + b×lnX + ε (2) Eqn. 2 was applied to model the entire dataset (universal model, AllUniversal), and to the data sets of the four plant functional types: FShrub&Ac, FMallee, FTree and FRadiata. The simplest form of Eqn. 2 had Therefore, although the MM CF is recommended, we also report the Baskerville CF for reference. X = D,.
Effect of level of generalisation on BGB prediction accuracy The impact of the level of generalisation of allometric models on the accuracy of BGB prediction was also explored. Data from the seven tree species with N > 100 (E. polybractea, E. loxophleba, and E. kochii, E. globulus, E. occidentalis, P. pinaster and P. radiata) were used to assess the improvement in fit in BGB predictions as specificity in the allometric models increased (i. e. Alluniversal model cf. functional-type model cf. species-specific model). Large samples sizes were required to target a 5% coefficient of variation prediction of biomass when applying allometric models of relatively high variability, as anticipated for BGB (i. e. residual standard deviations 0. 47-0. 50; Roxburgh et al. , 2015). Although the shrub species Dodonaea viscosa subsp.
Model validation using whole plot root excavation To test the accuracy of allometric models, we utilised data from 11 stands of varying structure and contrasting environments (Table  2 ) where whole plots were excavated to obtain 'true' and direct measurements of stand-scale BGB as described by  Paul et al. (2014). The generic FShrub&Ac, FMallee and FTree models of best fit we identified (Eqn. 2, using D as the predictor variable) were applied to inventories of D obtained from each of these 11 stands. The allometry-predicted BGB of all individuals within the stand was then summed to provide a predicted BGB at the standlevel. We calculated the resulting prediction quality statistics of EF, MAPE and RMSE. To determine whether there was any improvement in model performance when less generalised models are applied, this analysis was repeated with the use of species-specific models when they were available (Table  S1 ).
BGB:AGB The allometric models developed for BGB, and those developed by  Paul et al. (2016)  for AGB, were applied to predict the BGB:AGB ratio for the different sub-categories of plant functional types (Fig. 3 ). A comparison of this predicted BGB:AGB ratio with that observed was possible for the 1 990 individuals in the data set where both AGB and BGB were measured. We then compared the average (± standard deviation) allometry-predicted BGB:AGB between the different species groups. We also explored the relationship between allometry-predicted Generic allometrics 19 BGB:AGB and the D of an individual tree or shrub.
Allometric models Results confirmed that across a wide range of individuals, BGB can be predicted using generalised plant functional type allometric models with reasonable accuracy and efficiencies of 72-93% (Fig. 4 ). Significantly, this is achieved using the easily measured predictor variable of D. Although the BGB allometric models here were based on datasets covering a broader range of vegetation types and site characteristics than have previously been collated for Australia (including the previously under-represented tropical ecoregions), the fit statistics obtained were comparable to those for generalised allometric models previously developed for much smaller datasets covering smaller stem diameters  (Paul et al. , 2014). Hence, increasing the domain of application of generalised allometric models does not substantially reduce their prediction accuracy.
Acknowledgements Funding for this work was largely provided by the  Department of the Environment and Energy ,  Australia. Debbie Crawford  is thanked for their assistance in preparing Fig. 1. Anthony Fitzgerald ,  Geoff McAuthur ,  Bruce Brand ,  Peter Snowdon ,  S. Theiveyanathan , and  Jenny Carter  are thanked for data contributions. We are indebted to the landowners who gave us permission to excavate roots on their properties. Assistance with excavation of roots on these properties was provided by  Micah Davies ,  Laura Kmoch ,  Simon Murphy ,  Craig Neumann ,  Tim Fairman ,  Rob Law ,  Ben Finn ,  Mark Brammar ,  Jaymie Norris ,  Katelyn Ryan ,  Mervyn Tucker ,  Zoe Read ,  Alex Drew ,  Gordon McLachlan ,  Byron Yeo ,  Alex Winter ,  Mike Cully ,  Len Norris ,  Bob Hingston ,  Katelyn Ryan ,  Mervyn Tucker ,  Alie Fairlamb ,  Gary Bastin ,  Dailiang Peng ,  Mark Edginton , Dan Keolman,  Ron Booth  and  Doug Mohr.
| Database The Anaphylaxis Registry is a real-life database that collects data regarding moderate and severe anaphylactic reactions. 1] [12]  One hundred and thirty-seven specialized tertiary allergy centers from eleven countries (Germany, Austria, Switzerland, Poland, Italy, Spain, Ireland, Greece, France, Bulgaria, and Brazil) contribute currently to the registry. Pseudonymized data of patients with anaphylaxis in the previous year are locally captured by trained health professionals through a web interface. Elicitor, symptoms, course, and treatment of the reaction, along with diagnostic procedures and preventive measures, are the focus of the registry. Furthermore, patients' demographic and medical data are collected. The registry was established in 2007 (initially in German-speaking countries and in other countries since 2011), and the questionnaire evolved over time, including additional topics (current version 7.
| Patients We included cases reported between June 2011 and March 2018 fulfilling the modified National Institute of Allergy and Infectious Diseases/Food Allergy and Anaphylaxis Network (NIAID/FAAN) criteria. 13 Cases reported prior to this time period were excluded, because the previous questionnaire versions did not contain all items required for this analysis. The scope of the registry is moderate and severe anaphylactic reactions; however, it contains a small proportion of cases with mild anaphylactic symptoms, which were excluded from the analysis (defined as skin/mucosa and/or gastrointestinal symptoms only). Reactions with skin/mucosal and severe gastrointestinal symptoms (vomiting/incontinence) caused by a parenteral elicitor were defined as moderate reactions and remained in the dataset.
G R A P H I C A L A B S T R A C T Data from the Anaphylaxis Registry were analyzed to identify secondary prevention measures offered to patients who experienced anaphylaxis. In specialized allergy centers 84% of patients were prescribed adrenaline autoinjectors following EAACI guidelines and only 37% outside the centers. The secondary prevention measures offered to the anaphylaxis patients varied with the elicitor, and to some extent with other factors such as patients' age, comorbidities, and reaction severity. Abbreviation: AAI, adrenaline autoinjector 2. What kind of emergency drugs were prescribed following the recovery of the reaction? Here, the multiple selections of the following medication were offered: "Adrenaline autoinjector," "Adrenaline inhaler," "Antihistamines," "β2-mimetics," "Corticosteroids," and "Other" (free text possible).
026 a Only patients with absolute indication for an adrenaline autoinjector according to EAACI guidelines were included. b Reactions grade I was excluded from the analysis during database adjustment (Figure  S1 ). c  We assumed that patients are residents of the country where their specialized center is localized. d As there were no cases from Brazil in which adrenaline autoinjector was prescribed before visit in the specialized center, this variable was omitted in this model.
| The majority of patients with anaphylaxis were offered adequate prophylaxis measures for the first time in a specialized center As these data show the great majority of patients were offered adequate secondary prevention measures at some point, we aimed to further examine whether these measures were introduced during a visit to a specialized allergy center or previously in a primary care setting/emergency department. For this analysis, the patients who directly received emergency treatment in a specialized center or were visited by a center member during hospitalization immediately after the reaction had to be excluded to avoid bias. Thus, only patients who presented in the center for the first time at least 2 days after the reaction were included in this part of the analysis. Therefore, the total number of cases decreased from n = 7788 to n = 6354.
| Eighty-four percent of the patients with an absolute indication for an adrenaline autoinjector according to EAACI guidelines were prescribed one The EAACI guidelines defined six groups of patients with an absolute indication for an adrenaline autoinjector: history of food, latex, or aeroallergens anaphylaxis (a); exercise-induced anaphylaxis (b); idiopathic anaphylaxis (c); coexisting asthma and food allergy (d); venom allergy without receiving SIT (e); and venom allergy and mast cell disorder (f). In our dataset, 4032 cases fulfilled one of these criteria. The information on adrenaline autoinjector prescription was provided in 3817 cases. Of these, 84% were prescribed an adrenaline autoinjector (before or during a consultation in a specialized center; Figure  2A ). Guideline adherence was very good in patients with venom allergy (91%), followed by food-induced anaphylaxis (85%; Figure  2C ).
| D ISCUSS I ON Our data provide basically a positive outcome regarding the range of preventive measures offered to anaphylaxis patients. In most cases, adequate measures were offered, and EAACI guideline adherence regarding autoinjector prescription (84%) was satisfying. However, this perspective is strongly influenced by the structure of the registry; the cases entered into the database were recorded by allergists in a cooperating center, and we had no data regarding the patients who never reach these healthcare facilities (which may be depending on the country/region the majority of the patients). The second main source of bias may be the definition of particular measures, such as "counseling about avoidance of the trigger" or "training in emergency management plan, including drug training" that can strongly vary with regard to their extent, as no minimal standards for those measures were defined in the questionnaire.
Another population-based study conducted in Canada reported that 45%-55% of patients with food allergy have an autoinjector. 17 terestingly, the elicitor of the reaction appears to be an important factor, influencing physicians' decision regarding emergency drug recommendation; patients with food allergy were prescribed emergency medication less frequent than those with venom allergy. This difference was substantial, particularly with regard to autoinjector prescription outside an allergy center (48% vs 27%). As this effect remained significant in the multivariate analysis, it cannot be explained by the difference in the patients' age or reaction severity. Most cases in the registry are from high GDP European countries, and no data on the individual socioeconomic background are collected. Therefore, the Anaphylaxis Registry is not the appropriate database to analyze the differences regarding this issue.
Characterization Scanning electron micrographs were obtained using a Hitachi S4800 operating at 10 kV. Raman characterization of the VA-CNTs was performed at a laser wavelength of 532 nm using a Horiba Jobin-Yvon T64000 spectrometer equipped with a silicon camera cooled with liquid nitrogen and a microscope. The focused laser spot on the sample was typically 1 µm. Atomic force micrographs were obtained using a Brucker AFM D3100 in tapping mode using a NCH point probe with a tip radius of 5 nm at F = 242. 9 kHz, Q = 460 and k = 18 N. m -1. X-Ray Photoelectron spectroscopy (XPS) measurements were performed on Mepitel®. The excitation source produced a monochromatic Al Kα line (1486. 6 eV) and worked at 100 W of power. The analyzed surface had a 400-µm diameter. All XPS components were assigned from the analysis of the values reported for reference compounds and referenced in the NIST database  [34].
Acknowledgement We thank  Michel Ramonda  and the  Near-Field Microscopy service (CTM)  of the  University of Montpellier  for AFM analyses. HRTEM studies were conducted at the  Laboratorio de Microscopias Avanzadas, Instituto de Nanociencia de Aragon ,  Universidad de Zaragoza, Spain. The Government of Aragon , and the  European Social Fund  are gratefully acknowledged. R. A. gratefully acknowledges the project " Construyendo Europa desde Aragon "  2014-2020  (grant number  E/26 ). R. A. gratefully acknowledges the support from the  Spanish Ministry of Economy and Competitiveness (MINECO)  through project grant  MAT2016-79776-P (AEI/FEDER , UE). Part of this work has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  642742.
The whole chloroplast genomes from the pooled 10-20 organelles were amplified by multiple displacement amplification (MDA) using the Qiagen RELPI-g kit (Qiagen, Hilden, Germany). Sequencing libraries of two samples from each species were constructed and paired-end sequenced on a partial Illumina MiSeq lane at the Department of Biology DNA sequencing facility in Lund (Sweden). The quality of the Illumina reads was checked using the software FastQC  [7]. Low quality sequences were trimmed or removed with the program Trimmomatic-0. 36  [8]  using leading and trailing cut-off values of 15 bp and a sliding-window approach with a window size of 10 bp and a quality threshold of 20. Reads from samples of the same species were pooled for assembly with IDBA-ud  [9] , SPAdes  [10]  and metaSPAdes  [11]  and the resulting assemblies were assessed with quast 4.
5  [12]. IDBA-ud settings included pre-correction of reads before assembly, a minimum k value of 30 and a maximum k value of 70, k-mer increments of 10 bp, and a seed kmer size of 30 bp. SPAdes and meta-SPAdes were run with default settings and k-mer sizes of 21, 33, 55, 77 and 99 bp. Assemblies were also repeated for reduced datasets (10%, 20%, 25% and 30% of reads) due to very high (>1000×) and uneven coverage of chloroplast contigs. After each assembly, the unconnected contigs were organized into biologically relevant units (bins) with Anvio (v2. 2. 2)  [13]  using information about GC content (approx. 30% GC expected based on H. akashiwo chloroplast genomes), coverage and sequence similarity. Thereby, contigs belonging to the chloroplasts were separated from potential nuclear DNA and bacterial contaminations.
All contigs in bins that potentially represented the chloroplast genomes were compared with the NCBI nucleotide database using blastn  [14]. To improve the assemblies, the coverage of the chloroplast contigs was homogenized by extracting the corresponding reads with the program Bowtie2  [15] , and python and perl scripts from Albertsen et al. [16]. The size of these contig-specific read sets was reduced to approximately 100× coverage by splitting them according to their original read depth. The remaining reads were re-assembled with SPAdes or metaSPAdes. To compare the contribution from replicated libraries of the same species to the chloroplast genome assemblies, trimmed raw reads were mapped with the software Bowtie2-2. 3. 4. 3  [17]  against the assembled contigs using default settings.
These alignments were analysed with samtools-1. 9  [18]. Phylosift  [19]  was used to identify marker genes in the chloroplast contigs from G. semen and V. virescens. The chloroplast contigs were afterwards translated into amino acid sequences using the program prodigal  [20]. These sequences were functionally annotated in KAAS (KEGG Automatic Annotation Server)  [21]  using the single-directional best hit method. To identify potentially unassembled fragments of light-independent protochlorophyllide oxidoreductase (LIPOR) genes (chlB, chlN, chlL) in the raw reads from G. semen, these reads were mapped with Bowtie2 against the chloroplast contigs of V. virescens that contained the genes of interest. Annotated amino acid sequences were aligned with MUSCLE  [22]  using default settings in the program Geneious 11.