dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, the GLACIOCLIM-SAMBA observations reveal very strong variability of accumulation at sub-kilometre scale in the area  (Agosta et al. , 2012) , clearly related to the distribution of blowing snow by the wind. Nevertheless, we used this data to compare results from a snow pack model in terms of variability (Sect. 4); we checked that the way the data are used here is not strongly affected by sub-annual changes of the elevation of the instruments above the surface. Such changes are thus neglected and the initial, annually readjusted instrument heights are used. Data at standard levels (2 m for temperature and moisture, 10 m for wind) are necessary to compare with other sources of meteorological information (Sect. 2. 2) and to force a snow model (Sect. 2. 3). Reports from the third mast level (256 cm) are used as proxy for 2 m meteorology.
Meteorological-analysis data The ECMWF (European Center for Medium-Range Weather Forecasts) operational meteorological analyses (ECMWF, 1989) 2  compare well with the observation for temperature and reasonably for wind (Fig. 3 ). The meteorological analyses are the results of the assimilation of in situ and satellite observations into a meteorological model. The daily radiosounding at Dumont d'Urville station, and reports from 2 Antarctic Meteorological Research Center (AMRC) AWSs 3  within less than 100 km of D17, are both transmitted to the global telecommunication system and thus, in principle, available in time for operational analysis at ECMWF. This probably contributes to the good agreement with observation. On the other hand, atmospheric moisture is underestimated, suggesting that it is not properly assimilated.
, 2013) , obviously including wind speed. For models that do not parameterize blowing snow, the most straightforward proxy for blowing-snow occurrence is probably wind speed. Figure  6  shows the distributions of RH wri values for wind speed above or below 12 m s -1 , an arbitrary blowing-snow proxy threshold, and for all values of wind. The distribution is plotted for the observations and the ECMWF analyses at D17, and for two climate models in the CMIP5 (Climate Model Intercomparison Project 5 4  ) archive. Their continental grid point closest to D17 is used. The data are sorted in 10 % wide RH bins, from 0-10 to 100-110 %, frequencies in the latter bin obviously being 0. A strong maximum of the distribution in the 90-100 % RH wri bin shows that conditions close to saturation occur frequently in the observations.
Relationship between atmospheric moisture and wind speed Even the dry values in the ECMWF analyses may be surprising considering that, although the moisture holding capacity of the katabatic air increases through adiabatic compression, the flow is a very turbulent one over an infinite source of potential sublimation at the surface. A number of AMRC AWSs report atmospheric moisture. AWSs D10, Gill and Bonaparte Point do. D10 is only ∼ 7 km from D17, in a very similar environment, although closer to the coast and the ocean. This is a proxy for D17 in the following intercomparison of data from AMRC AWSs. Station Gill (178. 59 It is located on an island on the western side of the Antarctic Peninsula. Temperature is about 10 • C higher, and the mean wind speed is about half that of D10. The three stations are near sea level.
Acknowledgements. We wish to thank  J. Lenaerts  and  M. Frezzotti  for their comments which helped to improve the paper. This work was supported by funding by the  ICE2SEA programme from the European Union 7 framework programme , grant number  226375. This paper is ICE2SEA contribution number  179. Additional support by  INSU  through the  LEFE/CLAPA  project and OSUG through the CENACLAM/GLACIOLCIM observatory is also acknowledged. Field observations would not have been possible without the logistical support and additional funding by the  French polar institute IPEV  ( programme CALVA/1013 ). Edited by:  M. van den Broeke.
5. Ape1 solution (10-20 mg ml À1 ) and well solution were mixed in a 1:1(v:v) ratio. Selenomethionine-labelled crystals of Ape1 were grown at 18 C by streak-seeding drops of 10-20 mg ml À1 Ape1 in 25 mM Tris pH 7. 6, 150 mM NaCl, 1 mM -mercaptoethanol (BME) equilibrated in a 1:1(v:v) ratio against a well solution consisting of 100 mM HEPES pH 7. 5 10-40% PEG 10 000. To generate reaction intermediates, the native crystals of Ape1 were soaked with a tenfold molar excess of muramyl dipeptide (MDP) for 5 and 30 min just before flash-cooling. Crystals were cryoprotected in a mixture of 50% Paratone and 50% paraffin oil and flash-cooled in liquid nitrogen. Single-wavelength anomalous dispersion diffraction data were collected on X06DA at SLS and native data sets was collected on PROXIMA1 at SOLEIL.
SAD data sets were collected at the selenium absorption peak. The highest resolution selenomethionine-labelled crystals diffracted to 2. 3 A resolution with anomalous signal to 2. 8 A ˚resolution (Table  1 ). Initial phases were found using SHELXC/D/E and the initial model was built using phenix. autobuild with 22 selenium sites and a figure of merit of 0. 59  (Adams et al. , 2010; Sheldrick, 2008). The refined SAD structure was used to determine the phases for the native data set. Data processing for all structures was performed using XDS  (Kabsch, 2010). Phasing by SAD or molecular replacement was performed using PHENIX  (Adams et al. , 2010). Model building was performed using Coot  (Emsley & Cowtan, 2004 ) and restrained refinement was carried out using a combination of the PHENIX and CCP4 software suites  (Winn et al.
Acknowledgements We are indebted to  Mrs. Alli Tallqvist  and  Mrs. Kaija Järvinen  for their skilful technical assistance. We thank  Asla Ryynänen , CM, for summarizing and editing patient data. We are also deeply grateful for  Dr. Harri Alenius  for providing us with some Taqman reagents. We thank the  Groupe d'Etude Français des Lymphomes Cutanés, Paris, France , for access to some patient samples and clinical data (provided by author SF).
Data. Sea level data For the altimetry-based sea level data, we use two di erent products : (1) the GMSL time series from AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic Data,www. aviso. oceanobs. com/en/data/products/seasurface-height-products/global/msla/, AVISO hereafter) and (2) the Colorado University GMSL (http://sealevel. colorado. edu/, CU hereinafter). Both data sets are based on Topex/Poseidon, Jason-1 and Jason-2 data. The two GMSL time series (AVISO and Colorado University) are based on di erent processing approaches, in particular the geographical averaging process. Moreover some of the geophysical corrections applied to the data are slightly di erent as well as the editing procedure (see  Masters et al. , 2012 and Henry et al. , 2014 , for a discussion on these di erences).
The two GMSL time series cover the period 1993-2013. But for the purpose of the present study that focuses on GRACE and Argo periods, we limit the study time span to January 2005 to December 2011. In the following, we average the two data sets to produce a single GMSL time series, as no preferred product has been identi ed so far. The corresponding curve and associated uncertainty (based on the dispersion around the mean) is shown in Fig. 1  for the 2005-2011 time span. For the regional analysis presented in section 6, we also use the gridded  AVISO data over 1993 AVISO data over -2012 (www. aviso. oceanobs. com) (www. aviso. oceanobs. com). The gridded data are based on a larger set of altimetry missions merged together: in addition to the Topex/Poseidon and Jason-1&2-data, ERS-1&2, Envisat and Geosat follow-on data are also used.
(2011). Error bars represent one standard error, accounting for reduced degrees of freedom in the mapping and uncertainty in the reference climatology as described in von  Schuckmann and Le Traon (2011). The Argo based steric sea level time series covers the period 2005-2011. In the following, we consider both the thermosteric and halosteric components. 2. For comparisons between the recent La Niña events and former El Niño events (in particular the 1997 -1998 El Niño), we also consider the recent update (V6. 12) of the  Ishii and Kimoto (2009)  ocean temperature data (covering the 0-700 m depth range). These data are vertically integrated to estimate the thermosteric sea level from 1993 onwards at monthly interval. This thermosteric product is called IK12.
GRACE-based space gravimetry data To estimate the ocean mass variation, we use the GRACE Release 2 products from the Groupe de Recherche en Geodesie Spatiale (GRGS) (http://www. grgs. obs-mip. fr/grace/variable-models-grace-lageos/grace-solutionsrelease-02). The degree 2 coe cients -poorly determined by GRACE-are those derived from satellite laser ranging to Lageos 1 and 2. These data are provided at 10-day interval on 1°x1°grids. However the real spatial resolution of this data set is coarser, on the order of 400 km (see below). No Gaussian ltering nor destripping are applied to the GRGS data. Such post-processing is developed by other groups for removing the various errors a ecting the GRACE data, in particular the north-south noise (stripes) due to systematic correlated errors of GRACE data within a particular spectral order or the leakage of nearby signals onto the study area due to the coarse GRACE resolution (see  Velicogna and Wahr, 2013  for a discussion on errors impacting the GRACE data).
In the case of the GRGS data, this truncation is at degree 50, which corresponds to a spatial resolution of 400 km. No gain factor is applied. When computing the ocean mass, we use a mask that ignores data within 400 km of the continents to avoid leakage from continental hydrology and ice sheet mass loss. Finally, as we only consider the interannual variability, we do not correct the GRACE data for Glacial Isostatic Adjustment (i. e. , the visco-elastic response of the solid Earth to last deglaciation) because this e ect is a purely linear trend.
Atmospheric water vapor To estimate change in atmospheric water vapor mass, we used three di erent products : (1) atmospheric surface pressure grids from the European Centre for Medium-Range Forecast (ECMWF) ERA-Interim data (//data-portal. ecmwf. int/data/d/interim_moda/), (2) the vertically integrated water vapor grids, also from ERA Interim, and (3) vertically integrated water vapor based on AMSRE remote sensing data (kindly provided to us by R. Allan). As shown by  Trenberth and Smith (2005) , seasonal and interannual variations in atmospheric surface pressure essentially result from changes in atmospheric water vapor content because of dry air mass conservation. Thus atmospheric surface pressure data can be used to estimate change in water mass of the atmosphere. Data from ERA Interim are provided as 1.
Comparison between interannual GMSL and GRACE-based ocean mass & sum of other mass components In this section we compare the interannual GMSL with the interannual ocean mass component estimated from the GRACE GRGS data as well as with the sum of other mass components (as described by eq. 1): land waters plus water vapor plus land ice. We neglect the interannual variability of the glaciers as no data are available to estimate it, but account for that of the ice sheets. The latter has been estimated from the GRGS GRACE data averaged over Greenland and Antarctica. It is generally very small, of at most 0. 3 -0. 4 mm ESL on interannual time scale (see below). Fig. 3a  compares the interannual GMSL with the GRACE-based ocean mass and the sum of 'land water storage + water vapor + interannual ice sheet component'.
Error bars are not shown on this plot. The GRACE GRGS data are not provided with error bars. However from discussions with the GRGS processing group, it comes out that the uncertainty of a single global mean monthly value is on the order of 0. 6 mm (1-sigma) (see also  Wahr et. al. , 2006). Uncertainty of the sum 'land water storage + water vapor + interannual ice sheet component' is also not known. We assume that it is of the same order of magnitude as for the GRACE-based ocean mass. Fig. 3a  indicates that the mass component (either from GRACE or from the sum 'land water storage + water vapor + interannual ice sheet component') has a signi cant contribution to the interannual GMSL, especially during the 2011 La Niña. But clearly not all interannual GMSL signal is of mass origin. In Fig. 3b , we have superimposed the Argo-based steric sea level to the interannual GMSL.
While the two curves do not exactly coincide, we nd nevertheless good agreement, suggesting that as for El Niño, the La Niña ocean mass anomaly originates in the north Paci c. To investigate in more detail the spatio-temporal variation of the negative ocean mass anomaly during the 2011 La Niña, we constructed a longitude-time diagram of the ocean mass averaging the data in latitude over the north Paci c (same area as indicated above). The diagram is shown in Fig. 6  for the 1996 -2012 time span (we extended back in time the analysis in order to include the effect of the 1997 -1998 El Niño). To do this, we computed the ocean mass from the di erence between the mean sea level data and IK12 steric data. In e ect, for this longer period, neither Argo nor GRACE data can be used (note that IK12 data cover the 0-700 m depth range only, instead of 0 -1500 m for Argo).
e. , a steric contribution from below 700 m), we computed an ocean mass longitude/time diagram (same procedure as above) by subtracting to the sea level data the Argo data down to 1500 m (but as of 2005 only). Corresponding Argobased ocean mass diagram is shown in Fig. 8. We note that over their overlapping time span, Fig. 6  and Fig. 8  are very similar. In particular, the west-east negative mass anomalies related to the 2008 and 2011 La Niña events are well reproduced and is still signi cant when using Argo data down to 1500 instead of IK12 data down to 700 m (local errors in the Argo dataset for the North Paci c reach 15 mm which gives a level of local error of 15 mm for the mass signal). While we cannot exclude that the resulting map contains some steric signal from the deep ocean (below 1500 m), more likely, this results suggests that an ocean mass component is also involved during La Niña, with a very similar geographical pattern as the thermal and sea level anomalies.
The origin of this ocean mass decrease during La Niña events is possibly related to the net precipitation over the area, but the exact origin of the observed pattern remains to be investigated. This will be the object of a forthcoming study that should also analyze the relationship between ocean mass anomaly and surface salinity, in particular using data from the SMOS and Aquarius satellite missions. It will be also of interest to investigate whether numerical ocean models are able to reproduce the observed ocean mass decrease and its spatial pattern. This should help better understanding the physical cause of the observed pattern.
Background: Pods of the vanilla orchid (Vanilla planifolia) accumulate large amounts of the flavor compound vanillin (3-methoxy, 4-hydroxy-benzaldehyde) as a glucoside during the later stages of their development. At earlier stages, the developing seeds within the pod synthesize a novel lignin polymer, catechyl (C) lignin, in their coats. Genomic resources for determining the biosynthetic routes to these compounds and other flavor components in V. planifolia are currently limited. Results: Using next-generation sequencing technologies, we have generated very large gene sequence datasets from vanilla pods at different times of development, and representing different tissue types, including the seeds, hairs, placental and mesocarp tissues. This developmental series was chosen as being the most informative for interrogation of pathways of vanillin and C-lignin biosynthesis in the pod and seed, respectively.
The combined 454/Illumina RNA-seq platforms provide both deep sequence coverage and high quality de novo transcriptome assembly for this non-model crop species. Conclusions: The annotated sequence data provide a foundation for understanding multiple aspects of the biochemistry and development of the vanilla bean, as exemplified by the identification of candidate genes involved in lignin biosynthesis. Our transcriptome data indicate that C-lignin formation in the seed coat involves coordinate expression of monolignol biosynthetic genes with the exception of those encoding the caffeoyl coenzyme A 3-O-methyltransferase for conversion of caffeoyl to feruloyl moieties. This database provides a general resource for further studies on this important flavor species.
Although its short read length limits de novo contig assembly efficiency  [9, 10] , various strategies, algorithms and software have been developed for short-read assembly, especially for de novo assembly in the absence of a reference genome  [11]. Using the above technologies, we have generated very large gene sequence datasets from vanilla pods at different times of development, and representing different tissue types, including the seeds, hairs, and placental and mesocarp tissues within the pod. Based on previous studies  [5, 12, 13] , this developmental series was chosen as reflecting the times of synthesis and accumulation of vanillin and catechyl lignin in the pod and seed, respectively. The annotated sequence data provide a foundation for understanding multiple aspects of the biochemistry and development of the vanilla bean, as exemplified by the identification of candidate genes involved in lignin biosynthesis and interrogation of their expression patterns in the developing pod and seed in relation to the accumulation of the C-lignin polymer in the seed coat  [4].
An often used combination of current sequencing technologies is to mix de-novo 454 assembly and Illumina mapping assemblies; the 454 approach allows the building of long contigs, and the Illumina approach reduces problematic 454-generated homopolymer sequences  [16]. Therefore, to produce high quality vanilla transcripts, we employed an optimized two-step strategy. First, a combined assembly of all 26 Illumina samples  (3, 439, 193 ,362 reads total) was produced using Velvet 1. 2. 03 with 27 k-mer length, producing 6,371,646 contig sequences. Then, Illumina contigs and qualified 454 reads were assembled together with MIRA 3. 2. 1  [16, 17]. This resulted in a total of 301,459 contigs which were considered as vanilla-expressed transcripts for further annotation and analysis. The size distribution of the total 301,459 contigs is shown in Figure  2.
The contig N50 is the length of the  smallest contig in the set that contains the largest contigs whose combined length represents at least 50% of the contig assembly; this parameter is generally used as a standard metric for assembly size  [18]. Here size distribution at N50 was 1960 bp in length and average contig size was 1256 bp. All short reads obtained in this study have been submitted to the NCBI Sequence Read Archive (SRA) [BioProject: PRJNA253813]. Accession numbers for each library are listed at "Availability of supporting data" section. All assembled data can be searched and retrieved at http://www. sc. noble. org/vanilla/blast/blast. php. For quality control, we mapped our results to another recently produced V. planifolia sequence dataset that did not include multiple tissue types.
Mapping of transcripts to reference databases The 301,459 vanilla transcripts were compared to five reference databases (the non-redundant (NR) NCBI database; the Conserved Domains Database (CDD); and the Arabidopsis, rice, and sorghum proteomes) using BlastX with a cut-off E-value of 1E-5. Using this approach, over 52% of all vanilla transcripts (157,191 transcripts) returned an above cut-off BLAST result in at least one of these five reference databases (Additional file 6). The homology search against NR resulted in 130,550 annotated genes. Seventy eight thousand and ninety three genes were identified as encoding proteins with conserved domains, while 113,857 genes, 127,510 genes and 143894 genes were identified as encoding proteins homologous to proteins in the Arabidopsis, rice and sorghum proteomes, respectively.
An integrated digital gene expression database To estimate transcript expression levels in each of the 26 Illumina libraries, the reads were mapped back to the assembled transcript sequences using Bowtie2 2. 0. 0  [19]. The number of reads mapped to each transcript were then counted and used to calculate the reads per kilobase of transcript per million reads mapped (RPKM) for every transcript in each library (Additional files 7, 8 and 9). In the integrated gene expression database, the expression levels of a total of 301,459 transcripts are presented in 26 different tissues or organs. Most of these genes (183,724) showed above 1 RPKM value in at least one tissue or organ. Furthermore, 37,538 transcripts showed above 10 RPKM value in at least one tissue or organ, and 2,156 transcripts were above 100 RPKM value in at least one tissue or organ.
These stably expressed genes, listed in Additional file 10, can serve as reference genes for normalizing transcript levels of other vanilla genes prior to comparative gene expression analysis, as commonly used in gene expression atlas studies in other species  [20, 21]. For further validation of reference genes in Vanilla, two frequently used housekeeping genes, Actin1 and ubiquitin (Ubi), were selected from the stably expressed genes list, and their expression levels were evaluated in early stage seeds and in stem by quantitative real-time RT-PCR. Actin1 showed a similar constitutive expression pattern to that of Ubi, but with higher expression level (Additional file 11). The data are consistent with our digital gene expression database, and Actin1 was chosen as the reference gene for further gene expression analyses.
Annotation of tissue-specifically expressed genes Knowledge of genes that are expressed specifically in one organ or tissue, or genes that are expressed at substantially higher levels in some organs/tissues than in others can provide insights into specialized processes at work in these organs/tissues  [20]. To detect genes with tissue-restricted expression, all transcripts were filtered to obtain those above 10 RPKM value in at least one tissue or organ, then further analysed in R with the package "rsgcc"  [23] , setting the parameter value as "0. 75", which means that the expression level of a gene must be at least 3 times higher in one tissue than in any other tissue. We found 590 genes that were exclusively expressed in 6 week seeds, and 83 genes exclusively expressed in 8 week seeds. Eighty seven genes were more highly expressed in leaf than in any other organ; 119 genes were more highly expressed in aerial roots; and 93 genes were more highly expressed in the 1 year old stem (Additional file 12).
These data are summarized in the heatmap in Figure  6. After sorting the tissue-specifically expressed genes into Gene Ontology (GO) categories using Plant GOslim ancestor terms (http://www. geneontology. org/) [24]  and the KEGG pathway database (http://www. genome. jp/ kegg/)  [25, 26] , we found that genes involved in primary metabolic processes, biosynthesis processes and cellular metabolic processes were highly expressed in 6 week old seed (Additional file 13A). The 6 week seed specific gene set was rich in genes annotated as having involvement in starch and sucrose metabolism pathways in the KEGG database (Additional file 13, under KEGG enzyme code EC:2. 4. 1. 34, EC:2. 4. 1. 29, EC:3. 2. 1. 26, EC:2. 4. 1. 12 and EC:3. 2. 1. 48). Genes with transport, trans-membrane transport and response to stimulus functions in the GO categories were found to be more highly expressed in the aerial root-specific gene group (Additional file 13B).