dataesr/scientific-paragraphs-categorization huggingface dataset:
5% and 49. 0%, and 28. 3% and 33. 6% of the transcripts from the initial Cufflinks and StringTie assemblies for Datasets 1 and 2, respectively (Supplementary Figure  S1A ). The high numbers of discarded transcript models, containing at least one intron with non-canonical splice sites or which was poorly supported by splice junction reads, correlate well with previous observations that Cufflinks and StringTie generate 35-50% false positives  (43). The prediction of such 'introns' by these programs highlights the need to quality control assembled transcripts prior to quantification of transcript isoforms. Following the splice junction quality filter, antisense transcripts that were completely contained within an annotated gene in TAIR10 were removed along with transcripts from unknown genes and transcripts with no or very low expression (Supplementary Figure  S1A ).
The latter were identified by analyzing the RNA-seq read data using Salmon and were removed on the basis that they likely represent mis-assembled transcripts. Only the transcripts which had a TPM (transcripts per million) >1 in at least three samples were kept. At this stage, transcriptomes of around 50k transcripts were generated by Cufflinks and StringTie from the RNAseq data of Datasets 1 and 2 (Supplementary Figure  S2A  and B ). In each case, 71-79% of the transcripts were identical between the Cufflinks and StringTie assemblies. To generate the final AtRTD2, these transcriptomes were merged together and with AtRTD1 and Araport11 in a step-wise manner (Figure  1 ), and a second set of splicing and redundancy filters was applied after each merge (Supplementary Figure  S1B , and Supplementary Methods).
The stringent filters will result in the loss of some transcripts with alternative transcription start sites or polyadenylation sites and of some bona fide transcripts which may be only expressed at very low levels or in a small number of specific cells. However, they have been removed currently for the purpose of generating a robust core dataset for transcript level quantification and AS analyses. The final AtRTD2 contains 82 190 unique transcript models. To show that the introns in the AtRTD2 transcripts predicted by the splice junction reads were bona fide introns, we searched for sequence signatures of plant U2 and U12 introns using PWMs  (8, 51). The majority of predicted introns (>99. 8%) have signatures of typical plant introns with PWM values of >60 at both 5 and 3 splice sites (74.
Transcript diversity of AtRTD2 The 82 190 unique transcripts in AtRTD2 are made up of 7518 and 10 460 novel transcripts from Datasets 1 and 2, respectively, 33 673 transcripts from AtRTD1, of which 14 872 were from Marquez et al. (8)  and 18 801 from TAIR10, and 30 538 transcripts from Araport11 reflecting the extended 5 and 3 UTR sequences generated in Araport11 compared to TAIR10. AtRTD2 contains transcripts from non-coding (nc) RNA genes such as microRNA (miRNA), spliceosomal small nuclear RNA, small nucleolar RNA, transfer RNA genes etc. The majority of these genes do not undergo AS and will have a single transcript isoform. However, although some miRNA and long ncRNA precursors are alternatively spliced  (60) (61) (62) , our main focus is on alternatively spliced protein-coding genes.
For the 27 667 proteincoding genes currently annotated in Araport11, AtRTD2 has a total of 74 197 unique transcript isoforms. Thus, there is an average of 2. 68 transcripts per protein-coding gene in AtRTD2 reflecting higher transcript isoform complexity than found previously (Table  1 ). The increased transcript complexity in AtRTD2 compared to TAIR10 and AtRTD1 is also shown by the increased number of genes with higher numbers of transcripts (Supplementary Figure  S3 ). Thus, AtRTD2 represents a non-redundant transcript dataset highly enriched in AS transcripts. The transcripts in AtRTD2 contain simple and complex AS events and combinations of AS events. The types of AS events were determined using AStalavista (v4. 0. 1) (63) to allow a direct comparison to our earlier analysis of the AS landscape in Arabidopsis  (8).
AtRTD2 contained 37 137 events and those which occurred at least 50 times made up 95. 24% of all AS events (Figure  2  and Supplementary Ta- ble S4). Intron retention is the most common event (∼40% across the different AS types) followed by alternative 3 splice site selection, alternative 5 splice site selection and exon skipping agreeing with previous RNA-seq AS analyses  (8, 64). We also examined the AtRTD2 transcripts for the presence of exitrons in the coding sequences  (28)  and identified 2459 exitrons making up 6. 6% of the AS events identified above.
Accuracy of quantification of AS with Salmon and AtRTD2 To demonstrate the utility of the AtRTD2, we used it to quantify changes in AS transcript ratios with the Salmon quantification tool  (46). For experimental validation, we used High Resolution (HR) RT-PCR on the same RNA samples. Previously, HR RT-PCR (52) validated >92% of 586 assembled AS transcripts for 256 genes in Arabidopsis  (8). Here, AtRTD2 transcript structures were compared to the amplicons in HR RT-PCR and the TPMs of individual transcripts used to calculate splicing ratios for each of the AS events or event combinations in that region (see Supplementary Figures  S4  and S5 ). To verify the output from Salmon with HR RT-PCR, we analyzed 127 AS events from 62 genes and three biological replicates of the two timepoints (T1 and T2, see Materials and Methods), a total of 762 data points.
e. not full-length) and that modifying transcripts so they had the same start and end coordinates should improve quantification of isoforms. Therefore, to demonstrate that UTR length variation impacted quantification of such genes, we took two approaches: (i) we trimmed transcripts from the 5 and 3 ends to the coordinates of the transcript(s) that covered the smallest region of the gene (Supplementary Figure  S6A  and B ) or (ii) we padded genomic sequence from the ends of the shorter transcripts up to the co-ordinates of the end of the transcript(s) that covered the biggest region of the gene (Supplementary Figure  S7A  and B ). These modifications were performed transcriptome-wide on AtRTD2 to generate AtRTD2-trimmed and AtRTD2-padded versions, and the RNA-seq data were re-analysed with Salmon and the modified AtRTD2 datasets.
These sequences were supplemented with 2055 Arabidopsis protein isoform sequences from UniPro-tKB and we refer to the combined UniProt reference as UniProt+. A total of 8693 and 8749 peptides were identified using AtRTD2 and UniProt+, respectively. Over 96% of the peptides ≥7 amino acids (not including peptides with missed cleavages) were identified by both protein isoform sets, with only 115 peptides unique to AtRTD2 and only 171 peptides unique to UniProt+. Thus, the vast majority of peptides are detected by both AtRTD2 and UniProt+. Comparing the two datasets, AtRTD2 contained 57 227 non-redundant protein isoforms while UniProt+ contained 32 539. Similarly, AtRTD2 had 8056 genes with unique peptide support for at least two isoforms UniProt+ had 2927 such genes. Therefore, although AtRTD2 has greater protein diversity, this is not reflected in the proteomic analysis and probably reflects the detection of proteins from only 2732 genes.
Despite Arabidopsis being arguably the best-studied plant species with the most advanced genome and transcriptome annotation, AtRTD2 contains a significantly higher number of transcript isoforms than other collections such as TAIR10 and Araport11. Importantly, we demonstrate the significance of an extensive reference transcript dataset by showing experimentally the drastic effects of missing transcripts on quantification at the individual transcript and gene levels, and at reference transcriptome levels. Although AtRTD2 contains over 82k unique transcripts, it is unlikely to be complete because despite using a range of different sources of transcripts, all possible developmental stages and environmental conditions are not yet covered. Similarly, although deep RNA-seq data and multiple quality control filters have been employed in the pipeline, there are still likely to be misassembled and missed transcripts due to the issues with assembly of short read data  (42, 43).
New releases of AtRTD will be generated as other high quality RNA-seq and single molecule sequencing data becomes available. In assessing the quality of AtRTD2 in analysing RNAseq data, extensive validation of transcript isoforms and their abundance has been critical. Previously, we developed HR RT-PCR for analysis of changes in AS  (5, 22, 25, 52, 68, 69)  and have used it to validate transcript assemblies from RNA-seq data  (8, 48). Here, we showed generally good correlation of splicing ratios calculated from TPMs from RNA-seq data with Salmon/AtRTD2 and the HR RT-PCR. However, this detailed analysis identified discrepancies in the accuracy of quantification of transcripts from the same gene having different 5 and/or 3 UTRs and that correction functions within quantification programmes do not appear to deal effectively with this problem.
ACKNOWLEDGEMENTS We thank  Janet Laird  ( University of Glasgow ) and  Linda Milne ,  Micha Bayer  and  Iain Milne  ( The James Hutton Institute ) for technical assistance. FUNDING Biotechnology  and  Biological Sciences Research Council (BBSRC)  [ BB/K013661/1  and  BB/K006568/1  to J. B. ;  BB/K006835/1  to H. N. ];  Scottish Government Rural and Environment Science and Analytical Services division (RE-SAS)  [to J. B. and R. Z. ];  Austrian Science Fund (FWF)  [ P26333  to M. K. , DK  W1207  to A. B. ]; LABEX Saclay Plant Sciences [to H. H. ];  BBSRC EASTBIO  PhD studentships (to N. T. and J. C. );  European Alternative Splicing Network of Excellence (EURASNET)  [ LSHG-CT-2005-518238 ] for catalyzing important collaborations. Funding for open access charge:  University of Dundee.
The contrast factors for a range of elliptical loops with the major axis aligned along ½0001 were calculated in Ref. [53]  and the effects of ellipticity on calculated dislocation density are discussed later. The average contrast factors as a function of x for a and c loops, C a hkil and C ca hkil respectively, were calculated using ANIZC  [51]  for a range of x as displayed in Fig. 3. In principle, ANIZC can only calculate contrast factors for straight dislocations. Therefore, a program was written in C that can calculate the contrast factors for a dislocation loop with n straight sides, with each straight side's individual contrast factor being calculated using ANIZC. The contrast factors converged to within a relative error of 10 À6 when n ¼ 274, which was taken to be the contrast factor for a circular loop  [53].
Acknowledgements The majority of this work has been funded by an  EPSRC Leadership Fellowship  ( EP/I005420/1 ) with industrial financial and material contributions from  Westinghouse Electric  and  Studsvik Nuclear AB. G. R is grateful for the partial support of the  French State through the program  "Investment in the future" operated by the  National Research Agency (ANR)  and referenced as  ANR-11-LABX-0008-01 ,  LabEx-DAMAS. The authors would like to thank  Diamond Light Source  for beam time at beamline I11 ( EE8395  and  EE10006 ). The authors would also like to thank  Allan Harte ,  Matthew Topping ,  Elisabeth Francis  and  Christopher Race  for their assistance at experiments and for useful discussions. All data presented in this paper can be obtained on request.
Biogenic emission algorithms predict that oak forests account for ∼ 70 % of the total European isoprene budget. Yet the isoprene emission potentials (IEPs) that underpin these model estimates are calculated from a very limited number of leaf-level observations and hence are highly uncertain. Increasingly, micrometeorological techniques such as eddy covariance are used to measure whole-canopy fluxes directly, from which isoprene emission potentials can be calculated. Here, we review five observational datasets of isoprene fluxes from a range of oak forests in the UK, Italy and France. We outline procedures to correct the measured net fluxes for losses from deposition and chemical flux divergence, which were found to be on the order of 5-8 and 4-5 %, respectively. The corrected observational data were used to derive isoprene emission potentials at each site in a two-step process.
Currently, the largest source of uncertainty in global isoprene emission estimates is attributed to emission potentials  (Guenther et al. , 2012; Arneth et al. , 2008). Historically, emission potentials have been derived using enclosure measurements, where the emission rate of isoprene was measured from a single leaf or branch at standard conditions. Numerous laboratory and field studies have contributed to an extensive database of isoprene emission potentials (IEPs) from individual plant species which have been used to assign emission potentials to differing plant functional types (PFTs). Keenan et al. (2009)  compiled a database of leaf-level isoprene emission potentials for 80 European plant species which they used in conjunction with three separate BVOC (biogenic volatile organic compounds) emission models  (Niinemets et al.
Method In total, five datasets covering a total of 134 days of isoprene flux measurements made by (virtual disjunct) eddy covariance were analysed concurrently (i) to determine best practices for the processing of these data, (ii) to establish robust emission potentials suitable for use in atmospheric chemistry and transport models, and (iii) to compare the canopy-scale emission potentials with literature leaf-level emission potentials. These datasets comprise measurements above European oak forests in the UK, France and Italy which are summarised in Table  1. All emission rates are displayed in units of micrograms of isoprene per squared metre per hour, which is consistent with those used within the MEGAN model.
Observatoire de Haute Provence, France (O3HP) The oak observatory (O3HP) site is located at the Observatoire de Haute Provence (43. 937 • N, 5. 71 • E) in the heart of a 70-year-old deciduous oak forest in south-eastern France at approximately 650 m a. s. l. The 5 m tall forest canopy is dominated by two species, downy oak (Quercus pubescens) and Montpellier maple (Acer monspessulanum), which account for 75 and 25 % of the foliar biomass, respectively. The understory is dominated by European smoke bush (Cotinus coggygria Scop. ) and a multitude of herbaceous species and grasses. The average single-sided leaf area index is 2. 4 m 2 m -2. Measurements were made between 9 and 11 June 2012, and in total 8 days of isoprene flux data were collected at this site. A detailed description of the site and measurements are given by  Kalogridis et al.
, 1991; Guenther et al. , 1999). Implementation (b) uses measured air temperature and assumes this to be constant with height throughout the canopy, but still accounts for the influence of both current and previous light and temperature. The final implementation, (c), uses air temperature but does not account for the influence of previous light and temperature. In each of these three configurations we do not account for the effects of varying CO 2 concentrations, setting it to 400 ppm; nor do we consider the effects of soil moisture. In both cases this decision was motivated by a lack of the necessary observational data across all sites. Finally, a fixed site-specific leaf area index was used within the canopy environment model for each of the three MEGAN 2. 1 implementations.
This generates a time series of isoprene emission potentials, which typically shows systematic patterns, indicating that either the parametrisations imperfectly reflect the response of the emission to the meteorological drivers or that ε is subject to additional biological (e. g. circadian) control  (Hewitt et al. , 2011). Nevertheless, for the measurements to add to the emission potential database a single value needs to be chosen to represent that site. Various methods have been used to derive this single value, but there is currently no consensus in the literature as to which method is most appropriate. For example, both  Misztal et al. (2011 Misztal et al. ( , 2014 Misztal et al. ( , 2016) )  and  Langford et al. (2010)  chose to derive emission potentials as the average of midday emission potentials: IEP = F isoh 1.
An isoprene gas standard was not available during the Alice Holt field measurements. Instead, concentrations were derived on the basis of the instrument transmission curve which according to  Taipale et al. (2008)  gives an uncertainty of approximately ±25 %. The random uncertainty in derived emission potentials for each measurement site is taken as the average uncertainty of the individual flux measurements  (Langford et al. , 2015) : RE = N i=1 RE i 2 N , (11) where RE i represents the individual flux measurement uncertainty, and N is the total number of flux measurements being averaged. Additional uncertainties are associated with the oakspecific canopy emission potentials, which include the uncertainty in the species composition data and the change in LAI index that would result from assuming the canopy was comprised of only oak.
Wind rose analysis of isoprene emission potentials at the Alice Holt, Ispra and Bosco Fontana sites showed variation of 14, 19 and 28 %, respectively (see Supplement). The comparatively short time series of isoprene fluxes at the Castelporziano and O3HP sites meant that wind rose analysis was not possible for these locations, so an uncertainty of 20 % was assigned to the species composition data. Similarly, an uncertainty of 15 % was assigned to LAI data at each of the five sites. This value was then scaled based on the percentage of oak present at each site. For example, at Alice Holt the forest is 90 % oak so we multiply the estimated 15 % uncertainty by 1. 1 to give a final uncertainty of 16. 5 %. By contrast, at Bosco Fontana, where oak species only represent 27 % of the species present, an uncertainty of 26 % was derived by multiplying 15 % by 1.
Above-canopy flux measurements The time series of the five isoprene flux datasets used in this study are shown in Fig. 1. In total 2792 h of eddy covariance flux data were analysed and reviewed as part of the study. Isoprene fluxes were largest at the Ispra and Alice Holt forest sites, with average midday fluxes of ∼ 6500 and 2800 µg m -2 h -1 , respectively. The larger emission rates reflect the canopy composition, which in both cases was > 80 % oak, and the warm summer conditions. In contrast, emission rates at the Castelporziano site were comparatively small, typically below 150 µg m -2 h -1 despite the high temperature and high levels of solar radiation. This lower emission rate is attributable to not only a lower percentage of oak species present (27 %) within the canopy, but to the particular species of oak present.
Comparison of averaging methods for emission potentials Measured eddy covariance flux data from each of the five sites were normalised to standard conditions using the G93 algorithm and the MEGAN 2. 1 (a) canopy-scale emission algorithm described in Sect. 2. 2. Normalising flux data in this way effectively produces a time series of isoprene emission potentials from which a single value can be chosen that is thought to best represent the canopy. We calculated this sitespecific emission potential using the LSR, ODR and several variations of the average method, each described in detail in Sect. 2. 3. For the latter approach, the time series of emission potentials were averaged over different time windows which included 08:00 to 18:00, 10:00 to 15:00, 11:00 to 13:00 LT and all hours. Figure  2a  and c  show an average diurnal cycle of the isoprene emission potentials (IEPs) calculated at the Ispra forest site using the simplistic G93 big-leaf emission algorithm (panel a) and the more sophisticated MEGAN model (V2.
Emission potentials calculated using the LSR and ODR methods agree closely at the majority of sites, but the ODR method appears very sensitive to the magnitude of the error weighting applied. We assumed a 25 % model error for the    (a, c ) show the average diurnal cycle in the isoprene emission potential (e. g. IEP = F iso γ ) calculated for the Ispra forest site, Italy, using the G93 (a) and MEGAN 2. 1 (b) algorithms. Superimposed on top of these are the isoprene emission potentials calculated using the least-squares regression, orthogonal distance regression and average (with several averaging lengths) methods -see text for detailed description. Panels (b, d) show the average diurnal cycle of the measured fluxes and the average diurnal cycle of the fluxes modelled using the seven different isoprene emission potentials calculated for this dataset.
G93 algorithm, which was consistent with sensitivities studies by  Guenther et al. (1993) , and 12. 5 % for the MEGAN model  (Situ et al. , 2014). For most sites these assumed model errors provide a fit and associated emission potential that is consistent with the other approaches. However, at some sites the ODR could only produce a sensible fit after adjusting the model uncertainty. For the Ispra data, for example, the MEGAN model error had to be reduced to 8 % in order to produce a viable fit. The fact that manual adjustment of errors may be required with some datasets means that the ODR is unlikely to produce the consistent results required for a standardised approach. Isoprene emission potentials were also calculated using the weighted average approach (Eq. 6). Using this method yielded emission potentials that, when used to simulate isoprene fluxes, matched the integrated flux measurements exactly.
, 1997). The full MEGAN 2. 1 uses an isoprene emission potential map that accounts for the fraction of isoprene emitters in each landscape based on the species composition. In our single-site version of MEGAN the detailed emission map is not used. Instead, 15 PFTs are used, covering land classes such as temper- ate and tropical forest, grasses, and crops  (Guenther et al. , 2012). Based on the species composition data reported by  Morani et al. (2014)  for this site, Castelporziano maps to a blend of three PFTs: 66 % "broadleaf evergreen temperate shrubs" (2000 µg m -2 h -1 ), 6. 8 % "needle leaf evergreen temperate tree" (600 µg m -2 h -1 ) and 27. 3 % "broadleaf evergreen temperate tree" (1727 µg m -2 h -1 ), which represents the evergreen oak. Combining these PFTs results in an overall emission potential of 1839 µg m -2 h -1 for the Castelporziano site.
Deviations from the temperature curve are rather modest by comparison, with the largest positive deviations seen for Bosco Fontana, followed by Ispra, Castelporziano and O3HP. By contrast, data from Alice Holt are generally lower than the standard temperature response curve, which is consistent with the previous 24 and 240 h temperature measurements at this site, being typically 7 K below the standard temperature. From these curves we can conclude that the inclusion of past light and temperature conditions in the G06 big-leaf algorithm, therefore, requires the standard response curves to increase (or decrease) depending upon the relative values of the previous light and temperature and has the potential to deviate significantly to values calculated using the G93 algorithm. In our analysis the largest difference was observed at the Bosco Fontana site, with the IEP calculated using the G06 algorithm over 2 times lower than that calculated using the G93 algorithm.