dataesr/scientific-paragraphs-categorization huggingface dataset:
Leaves should be measured within seconds, to a few minutes at most, after detachment, and tests of intact v. detached foliage should be made for a subsample, to ensure similar rates are observed. If possible, the leaf material inside the chamber should be collected (see Section 3. 1), measured for LDMC and SLA, and stored for any subsequent chemical analyses. References on theory, significance and large datasets:  Reich et al. (1992 Reich et al. ( , 1997 Reich et al. ( , 1999)) ;  Wright et al. (2004). More on methods:  Wong et al. (1979 Wong et al. ( , 1985a Wong et al. ( , 1985b Wong et al. ( , 1985c)) ;  Reich et al. (1991a Reich et al. ( , 1991b)) ;  Ellsworth and Reich (1992).
Photomicrographs of freshly prepared sections are an alternative way to keep records for later assessment. References on theory, significance and large datasets: O'  Leary (1981) ;  Farquhar et al. (1989) ;  Earnshaw et al. (1990) ;  Ehleringer et al. (1997) ;  Lüttge (1997) ;  Zotz and Ziegler (1997) ;  Wand et al. (1999) ;  Pyankov et al. (2000) ;  Sage (2001) ;  Hibberd and Quick (2002). More on methods:  Farquhar et al. (1989) ;  Ehleringer (1991) ;  Hattersley and Watson (1992) ;  Mohr and Schopfer (1995) ;  Belea et al. (1998) ;  Pierce et al. (2002) ;  Taiz and Zeiger (2010).
It is also important to note that, because of their different biochemistry, the equation given for D does not apply to C 4 or CAM plants, and in these groups, C-isotope composition is not useful for estimating intrinsic WUE. References on theory, significance and large datasets:  Farquhar et al. (1989) ;  Cernusak et al. (2009). More on methods:  Ehleringer and Osmond (2000) ;  Seibt et al. (2008) ;  Diefendorf et al. (2010).
A chilling-sensitive tissue would leak electrolytes after such incubation, whereas a chilling-tolerant tissue should not. References on theory, significance and large datasets:  Levitt (1980) ;  Blum (1988) ;  Earnshaw et al. (1990) ;  Gurvich et al. (2002). More on methods:  Earnshaw et al. (1990) ;  Gurvich et al. (2002).
Assuming that the xylem osmotic potential is very low, the balance pressure represents the equilibrium water potential of the plant material in the chamber, multiplied by -1. Leaf water potential is conventionally expressed in MPa. Minimum leaf water potentials usually vary from near 0 to À5 MPa, but can be lower in (semi-)arid ecosystems. Extreme care should be taken when pressure chambers are under high pressures. References on theory, significance and large datasets:  Hinckley et al. (1978) ;  Ackerly (2004) ;  Bucci et al. (2004) ;  Bhaskar and Ackerly (2006) ;  Lenz et al. (2006) ;  Jacobsen et al. (2008) ;  Bartlett et al. (2012). References on methods:  Scholander (1966) ;  Turner (1988).
accessibility, following the same theoretical background on which palatability tests are based. For example, by offering whole shoots with and without spines to different animals (in this case, model herbivores should be bigger than snails or grasshoppers) and recording how much biomass is consumed per unit time. References on theory, significance and large datasets:  Grime et al. (1970 Grime et al. ( , 1996)) ;  Southwood et al. (1986) ;  Coley (1987) ;  Hartley and Jones (1997) ;  Cornelissen et al. (1999); Singer (2000). More on methods: Pérez-Harguindeguy et al. (2003). Grime et al. 1996; Cornelissen et al. 1999 ). If the model herbivores are snails, (a) the arena can be constructed on polystyrene and (b) the numbered grid should be wrapped in plastic, which is a substrate that snails like for crawling on.
SSD as described in the present protocol is called 'oven dry weight' (ODW). ADW can be transformed to ODW by using the formula ODW = 0. 800 ADW + 0. 0134 (R 2 = 0. 99) (this formula cannot be correct for 'dry weights', but only for densities). We suggest that data for ODW, directly measured or derived from ADW, can safely be used as SSD. This value ignores the contribution of the bark of a tree to its stem density; however, because bark usually makes up only a very small fraction of the mass of a large tree trunk, this error is probably unimportant, except as noted below.
The volumetric fractions of cell-wall material, water and gas ('air') in the stem can be calculated as follows. The volume fraction of water is simply the decrease in weight (in g) on drying, divided by the original volume (in cm 3 ) of the sample. The volume fraction that is cell-wall material equals the dry mass : fresh volume divided by the density of dry cellwall material (1. 53 g cm -3 for cell-wall material in dry wood); if an appreciable fraction of the stem volume is bark, using this number may involve an error because the density of bark cell-wall material is not necessarily the same as that for wood. The fraction of the original volume that was gas is simply 1 minus the foregoing two volume fractions. References on theory, significance and large datasets:  Putz et al. (1983) ;  Loehle (1988) ;  Reyes et al.
This type of chamber is commercially available from the PMS Instrument Co. , Albany, Oregon, USA (http://pmsinstrument. com, accessed 15 February 2013). References on theory, significance and large datasets: Tyree and Sperry (1989);  Davis et al. (2002) ;  Brodribb et al. (2003) ;  Maherali et al. (2004) ;  Holbrook and Zwieniecki (2005) ;  Choat et al. (2007) ;  Feild and Balun (2008) ;  Sperry et al. (2008a). References on methods:  Cochard et al. (1992) ;  Sperry et al. (1988 Sperry et al. ( , 2008b)) ;  Alder et al. (1997) ;  Pammenter and Vander Willigen (1998).
For a small number of roots, this can be carried out with image-analysis software (see Section 3. 1 for free software). For a large number of samples or root length, the commercially available application, WinRhizo (Régent Instruments, Quebec, Canada), is recommended. The software will automatically determine the length, diameter and root volume distribution of a sample of root length, enabling easy calculations of SRL, average root diameter and root tissue density (root dry mass over volume, the latter being derived from length and radius). Although the software is expensive for occasional use, roots can be scanned independent of analysis software, saved in the JPEG format and analysed later by someone who owns the software. See under Special cases and extras in the present Section for manual methods when none of the above facilities is available.
How to classify? This is a categorical trait. Record all categories that are assumed to give significant potential dispersal (see  Box 4) , in order of decreasing importance. In the case of similar potential contributions, prioritise the one with the presumed longerdistance dispersal; e. g. wind dispersal takes priority over ant dispersal. It is important to realise that dispersules may (occasionally) get transported by one of the above modes even though they have no obvious adaptation for it. This is particularly true for endozoochory and exo-zoochory. Note that there is ample literature (e. g. in Floras) for dispersal mode of many plant taxa. References on theory, significance and large datasets:  Howe and Smallwood (1982) ;  Van der Pijl (1982) ;  Bakker et al. (1996) ;  Howe and Westley (1997) ; Hulme (1998);  Poschlod et al.
Special cases or extras We recommend complementing this trait with other direct or indirect assessment of banks of seeds or seedlings for future regeneration of a species. For seed-bank assessment, there are good methods to follow (see More on methods below in the present Section); however, (above-ground!) canopy seeds banks of serotinous species of fire-prone ecosystems (e. g. Pinus and Proteaceae such as Banksia, Hakea and Protea) and long-lived seedling banks of woody species in the shaded understorey of woodlands and forests may also make important contributions. Vivipary as in some mangroves could also be part included in such assessments. References on theory, significance and large datasets: Hendry and Grime (1993);  Thompson et al. (1993 Thompson et al. ( , 1997)) ;  Leishman and Westoby (1998) ;  Funes et al.
Special cases or extras (1) Within individual variation. Be aware that seed size may vary more within an individual than among individuals of the same species. Make sure to collect 'average-sized' seeds from each individual, and not the exceptionally small or large ones. (2) Available databases. Be aware that a considerable amount of published data are already available in the literature, and some of the large, unpublished databases may be accessible under certain conditions. Many of these data can probably be added to the database; however, make sure the methodology used is compatible. (3) Seed volume. There are also many large datasets for seed volume, often measured as p/6 Â L1 Â L2 Â L3 (i. e. assuming an ellipsoidal shape). Most of these databases actually include both seed mass and volume.
Special cases or extras (1) Chlorophyll in fleshy cotyledons. In many cases fleshy cotyledons do contain chlorophyll; however, they are still considered reserve organs (e. g. Aspidosperma spp. ). References on theory, significance and large datasets:  Ng (1978) ; De  Vogel (1980) ;  Hladik and Miquel (1990) ;  Garwood (1996) ;  Kitajima (1996) ;  Wright et al. (2000) ;  Ibarra-Manriquez et al. (2001) ;  Zanne et al. (2005) ;  Leck et al. (2008).
Special cases or extras (1) Data from literature. Useful and legitimate data may be obtained from the literature or by talking to local people (e. g. foresters, farmers, rangers). Make sure that the same conditions of major destruction of above-ground biomass have been met. In such cases, assign subjective numbers for resprouting capacity after major disturbance as follows: 0, never resprouting; 20, very poor resprouting; 40, moderate resprouting; 60, substantial resprouting; 80, abundant resprouting; and 100, very abundant resprouting. The same crude estimates may also be used for species for which the more quantitative assessment is not feasible, e. g. because the non-resprouting individuals are hard to find after disturbance, as is common in some herbs. (2) Strongly clonal plants. In the case of strongly clonal plants, it is important to assess whether damaged ramets can resprout from below-ground reserves and not from the foliage of a connected ramet.
Therefore, in such species, resprouting should be recorded only if most above-ground biomass has been destroyed for all ramets in the vicinity. (3) Resprouting of young plants. Additional recording of resprouting ability of young plants may reveal important insights into population persistence, although this could also be seen as a component of recruitment. Thus, data on the age or size limits for resprouting ability may reveal important insights into population dynamics. It is known that some resprouting species cannot resprout before a certain age or size, and others may lose their resprouting capacity when they attain a certain age or size. (4) Resprouting after smaller biomass destruction. Additional recording of resprouting after less severe biomass destruction may provide useful insights into plant response to disturbances.
Acknowledgements This contribution was funded, in part, by the  Inter-American Institute for Global Change Research (IAI)   CRN 2015  and  SGP-CRA2015 , which were supported by  US National Science Foundation  Grants  GEO-0452325  and  GEO-1138881  The work was also funded by  FONCyT  ( PICT 20441  and  365 ),  CONICET  ( PIP 2006-2011 11220080101532 ),  SECYT-Universidad Nacional de Córdoba -Argentina , and the  US NSF  ( DEB-0620652 ). The authors particularly thank  Phil Grime  and the  UCPE-University of Sheffield , and  Mark Westoby  and the  ARC-NZ Network of Vegetation Function-Macquarie University , for inspiring discussion and development of trait measurement. The authors also thank  Marcelo Cabido ,  William Bond ,  Paula Tecco ,  Fernando Casanoves  and  Bryan Finegan  for valuable discussions.
The cell wall composition of wheat grain endosperm is very specific, essentially composed of arabinoxylan (AX) (70%) and (1-3) (1-4) β-D-glucan or mixed-linked glucan (MLG) (20%) with minor amounts of cellulose (4%) and mannan (7%)  [2, 3]. The synthesis of cell wall polysaccharides is activated during wheat grain development. Drastic changes in cell wall composition and structure were observed in the developing endosperm  [4] [5] [6]. The metabolism of cell wall polysaccharides requires the intervention of numerous enzymes. Enzymes acting on carbohydrates are listed and classified on the basis of their sequence similarities and activities in the Carbohydrate-Active enZYmes database (CAZy, http://www. cazy. org/) [7]. Plants are underrepresented in this database, essentially because there are less fully-sequenced genomes of plant species compared to other kingdoms such as bacteria.
Database search and interpretation Raw data collected during LC-MS/MS analyses were processed into mgf format files and further searched against databanks using X!Tandem Spectrum Modeler. Protein identification was achieved by confronting mass data (MS and MS/MS spectra) against four databases: (i) UniProt Knowledgebase (http://www. uniprot. org/): restricted to Viridiplantae (version 2011_03), (ii) Gene Index Databases Wheat release 12. 0, August 2010 (Wheat GI) (http://compbio. dfci. harvard. edu/ tgi/), (iii) an in-house glycosyltransferases databank (GTIDB) (http://wwwappli. nantes. inra. fr:8180/GTIDB) [28] , and (iv) a contaminant database including keratins and trypsin. A database search was performed with X!Tandem 2008. 02. 01 (http://www. thegpm. org/TANDEM/) via the X!Tandem pipeline available at http://pappso.
inra. fr/bioinfo/xtandempipeline/. Enzymatic cleavage was declared as a trypsin digestion with one possible miscleavage event. Fixed modifications of cysteine residues by iodoacetamide as well as oxidized methionins were considered. Precursor mass and fragment mass tolerance were set at 5 ppm and 0. 8 Da, respectively. One missed trypsin cleavage was allowed. Identified proteins were filtered according to the following specifications: at least two peptides with an E value below 0. 001 and a protein E value below 10 -4. To take redundancy into account, proteins with at least one peptide in common were grouped. Protein identifications were compared in the three databanks. In the event of identification in the three databanks, the identifier from UniProt will be preferentially reported, followed by the one from Wheat GI and, finally, the one from GTIDB.
The search against the decoy database (Reverse Uniprot restricted to Viridiplantae) gave a mean false-discovery rate of 0. 3% for each query when using the p<0. 01 significance level. Tentative functional annotations were assessed by running a BLAST homology search of sequences identified from mass spectrometry against the NCBI nr database using Blast2GO (http://www. blast2go. org). The cut-off stringency for BLAST was E = 10 -10  [29]. For each protein, the best hit from each species was collected if present. The two sets of proteins (protein sequences obtained from spectrometry analysis and their best homologs) were analyzed by applying the Phobius (http://phobius. sbc. su. se/) and TMHMM (http:// www. cbs. dtu. dk/services/TMHMM-2. 0/) programs to predict transmembrane domains (TM) and signal peptides  [30].
A refined search enabling protein identification from single peptide assignment was performed to obtain more putative GTs. This extended protein list was then blasted against the entire non-redundant sequences of the CAZy database using sequence similarity or protein domains (Pfam) association rules  [31]. Those for which an ortholog was found in the CAZy database (e-value < 0. 01) were reported. Proteins with homology to GTs and GHs were listed, and when identified with only one peptide in MS, the quality of the peptide was checked and validated manually. For a subset of GTs and GHs, the best homolog (best Blast hit) in the rice and Brachypodium distachyon genomes were searched using the Blast tool of the Rice Genome Annotation website (http://rice. plantbiology. msu. edu/analyses_search_blast.
Transcriptomic analysis A subset of GT and GH sequences were blasted against the sequence of the wheat Affymetrix probe set using the PLEXdb Blast tool (http://www. plexdb. org/modules/tools/plexdb_blast. php)  [32]. The expression pattern of the corresponding "genes" was retrieved from expression studies covering wheat (whole) grain development  [33]  and different organs and tissues (PlaNet platform WheatNet http://aranet. mpimpgolm. mpg. de/wheatnet) [34]. These analyses may not discriminate all possible homologous copies within the wheat genome such as highly similar paralogs and homoeologous forms of genes from the three genomes of hexaploid wheat. Therefore, we will consider one "gene/transcript" when possibly several forms co-exist.
Confocal laser scanning microscopy Confocal microscopy experiments were performed at the BIBS platform at INRA Nantes with an inverted Nikon A1 confocal laser scanning microscope. The samples were examined with a water-immersion ×40 objective. GFP and mRFP channels were acquired by simultaneous scanning using 488-561 nm laser lines for excitation: emissions were collected via a photomultiplier through band-pass filters, a 500-530 nm filter and a 570-620 nm filter. Images were processed using NIS-Element Software (Nikon) and Adobe Photoshop 6. 0.
Global analysis First, a global analysis of all spectra generated from the two fractions described in Fig. 3  was performed and a total of 1135 proteins were identified and validated with at least two peptides. All proteins with at least one common peptide were grouped to avoid redundancy. With these criteria, 668 proteins were selected (Supplemental Table  2 ), and the vast majority of them were identified from the Wheat GI database. Their best homologous proteins were then searched against the GenBank non-redundant protein database (NCBI), and with the exception of one sequence, homologous proteins were found with an e value < 10 -10. Only 20% of the sequences were identified in wheat. The others were identified by best homologs mainly found in Hordeum vulgare (54%), Oryza sativa (15%), followed by other less represented species.
In a second analysis the spectra were grouped on the basis of the fraction they came from, membranes (MPG) or associated (APG) Golgi proteins (Table  1 ). The sub-datasets were further inspected for the presence of transmembrane domains (TMD) and of signal peptides (SP) (Table  1 ). Among the 262 sequences identified from the membrane fractions, 43% (112) had at least one predicted TMD. This proportion increased to 59% when the analysis was performed on their best homologs. In the case of sequences identified exclusively in the APG fractions, the proportion of protein predicted with at least one TMD drastically decreased to 4%.
Search for carbohydrate-active enzymes In order to identify all putative GTs, we ran BLAST search against the entire non-redundant sequences of the CAZy database by taking, as inputs, all identified protein sequences (1135). A set of 78 sequences with a pfam domain was retrieved (Supplemental Table  3 ). A second BLAST search was carried out against the CAZy database based on similarity. In order to avoid missing low abundant proteins we also examined the sequences identified with a single peptide. Our primary targets were the GTs and we identified 64 GTs in 17 GT families (Fig. 4 , Supplemental Table  3 ). Many hits were also found in the other classes of carbohydrate-active enzymes such as glycosyl hydrolases (GH), proteins with carbohydrate binding modules (CBM) and carbohydrate esterases (CE) (Supplemental Table  3 ).
Experimental validation of the Golgi localization for four chosen GTs To validate the Golgi localization of our candidates, we chose GT families known for their potential involvement in The anti-RGP1 results presented here were obtained on the associated protein fractions and those of anti-BiP2 on membrane protein fractions. cell wall biosynthesis. In particular, we selected GTs for which a complete nucleotide coding sequence was available at the time we did our analysis, and validated them in silico as described in Supplemental document 1. The coding sequences corresponding to the GT75/Wheat GI ID: TC386312, GT47/UniProt ID: E0ZPV1, GT61/UniProt ID: Q5QPY3 (GenBank ID AK333763. 1), and GT47/Wheat GI ID: TC390543 were cloned using cDNA obtained from the endosperm of wheat grain at 250°D. The intracellular localization of the GT proteins was determined by transiently expressing them as fluorescent protein fusions in tobacco leaf epidermal cells.
Some GH families identified here include enzymatic activities that could modify cell walls. The GH17 family includes β-1,3-glucanases (EC 3. 2. 1. 39) that specifically degrade callose  [67]. We identified several GH17 in our experiment that may be involved in the degradation of the callose transiently deposited in the grain cell wall  [5]. The expression of the gene encoding GenBank ID: BJ242908 seems to be specific to the grain endosperm and increases during grain development (Supplemental Fig. 2 ). We identified several GH9 sequences (Table  2 ) and best hit results by similarity search are endo-β-glucanase (cellulase) implicated in leaf and fruit abscission  [68]. In Arabidopsis thaliana, KORRIGAN is a GH9 endo-β-glucanase that was proposed to play a role in cellulose synthesis  [69] [70] [71].
The cell wall of wheat grain endosperm contains a minor amount of cellulose  [2]. The transcript corresponding to Wheat GI ID: TC382188 seems to be specific to the grain endosperm and its expression increases during grain development (Supplemental Fig. 2 ). Among the GH3 identified in our screen, Wheat GI ID: TC369308 is homolog to the barley β-D-glucan exohydrolase isoenzyme ExoII (UniProt ID: Q42835_HORVU), and Wheat GI ID: TC422719 and Wheat GI ID: TC384225 are homologs to a xylan β-D-xylosidase characterized in barley (Genbank ID: AAK38482). Transcripts encoding the putative xylosidases are well represented in the endosperm, and the abundance of Wheat GI ID: TC422719 increases considerably during early development (Supplemental Fig. 2 ). Genbank ID: CK196562 is homolog to the arabinofuranosidase/xylosidase identified in germinating barley  [72].
Acknowledgments This research was financed by the  AIC  and a CEPIA post-doc. We are grateful to  INRA Rennes  for providing the wheat used in this experiment, and to  G. Deshayes  for assistance with proteomics,  P. E. Bodet  and  A. Geairon  for assistance in mass spectrometry and  S. Tchoa  and  A. Bouder  for their technical assistance. We thank our colleagues of the  Cell Biology Unit of the Imaging platform (Imagif) of the Institut des Sciences du Végétal Gif sur Yvette , and of the  "plateforme de Recherche en Imagerie Cellulaire de Haute Normandie", Université de Rouen , in particular,  Laurence Chevalier  for their help and support with sample preparation for ultrastructural and immunocytochemistry TEM studies, respectively. We thank the " Qualité des produits animaux" unit of INRA Theix  for giving us access to their equipment, in particular P.
This was achieved using laser capture microdissection (LCM) to isolate oocytes and GCs during the earliest stages of folliculogenesis. Gene expression profiles were monitored at each follicle stage according to cell type using RNA-seq technology. We report the successful isolation and generation of global gene expression data sets of pure oocytes and GC populations at key stages of early follicular development. We describe the expression profile of 15 349 genes with 33. 4% of genes displaying differential expression between the two compartments. In addition, we identified oocyte and GCspecific gene expression and the pathways involved in oocyte-GC communication.