dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting quality of the sea surface height estimate is improved. In this paper we introduce DT2014, the latest version of the Aviso SLA product, and evaluate its improvements with respect to the previous version. The paper is organized as follows: details of the L3/L4 altimeter data processing used for the generation of the DT2014 products are presented in Sect. 2. In Sect. 3, results obtained from the DT2014 SLA reprocessed products are compared with equivalent DT2010 results, focusing on the mesoscales and coastal areas. In the same section, for the first time, we make an estimate of the L4 SLA product errors. Finally, a summary of the key results obtained is given in Sect. 4. 2 Data processing.
Acquisition Sixty-plus cumulative years of different data sets were acquired over the 21-year period. They include measurements from 10 different altimeters: ERS-1 (repeat 35-day and geodetic 168-day period orbits), ERS-2, EN (repeat track and geodetic orbits), TP (historical repeat orbit and new interleaved orbit, i. e. , on the midway of its historical ground tracks), J1 (repeat track orbit, interleaved and geodetic end of life orbit), J2, GFO, C2, AL and HY-2A. The different periods covered by the different altimeters are summarized in Fig. 1. The main differences from DT2010 are the introduction of the year 2011 for C2 and the first cycles of the J1 geodetic orbit (cycle 500 to 505, May to mid-June 2012).
Input data quality control The detection of invalid measurements involves various algorithms, from the simplest, such as threshold selection for the different parameters, to more complex (e. g. , SLA selection with splines). It was based on the same approach developed for DT2010, detailed in  Dibarboure et al. (2011). Details of threshold editing can be found in the handbook of each altimeter mission (e. g. ,  Aviso/SALP, 2013 , 2015b)  as well as Cal/Val reports and publications (e. g. , Aviso/SALP, 2015a;  Ablain et al. , 2010). For the DT2014 processing, a specific procedure was established specifically for non-repeat track and new repeat track orbit missions, inducing a more restrictive data selection. As these new missions are able to sample the ocean surface in areas never reached before by older altimeters, their data are usually contaminated by the reduced quality of the mean sea surface (MSS) in these specific areas.
Multi-mission homogenization and cross-calibration The first homogenization step consists of acquiring altimeter and ancillary data from the different altimeters that are a priori as homogeneous as possible. The data should include the most recent standards recommended for altimeter products by the different agencies and expert groups such as OSTST, ESA Quality Working groups or the ESA SL_cci project. The up-to-date standards used for DT2014 are described and discussed in Sect. 2. 1. Although the raw input L2 GDR data sets are properly homogenized and edited (see Sect. 2. 2. 2), they are not always coherent due to various sources of geographically correlated errors (instrumental, processing, orbit residual errors). Consequently, the multi-mission cross-calibration algorithm aims to reduce these errors in order to generate a global, consistent and accurate data set for all altimeter constellations.
They reach 4. 1 % of the error variance observed in the DT2010 products. However, local degradations are observed, such as along the Philippine coasts. The comparison between gridded SLA products and monthly mean tide gauge (TG) measurements from the PSMSL (Permanent Service for Mean Sea Level) database  (Holgate et al. , 2013; PSMSL, 2016)  also emphasizes a global improvement in the DT2014 products in coastal areas. TGs with a long lifetime (> 4 years) were used. The TG data processing is described by  Valladeau et al. (2012)  and  Prandi et al. (2015). The sea surface height measured by the TGs is compared to the monthly mean SLA field given by altimeter gridded products merging all the altimeters available (i. e. , "all-sat-merged" products). Data collocation is based on a maximum correlation criterion.
Acknowledgements. The DT2014 reprocessing exercise has been supported by the  French SALP/CNES  project with co-funding from the European MyOcean-2 and  MyOcean Follow On  projects. Edited by:  E. J. M. Delhez Reviewed  by:  D. Chelton ,  G. Quartly ,  A. Pascual , and two anonymous referees.
The frequency of GC/AG introns in G. rostochiensis was 3. 46 %, the highest reported for any nematode. In addition to the GT or GC dinucleotide, 5′ donor sites are characterised by a nine-base consensus sequence, CAGG[T|C]AAGT (where the initial CAG is in the preceding exon  [27] ). Although variations in the 5′ donor site sequence were found, G. rostochiensis GC/ AG introns conformed equally well, if not better, to this consensus as did GT/AG introns (Fig. 1a  and b ). We derived a revised 5′ donor consensus for the predicted introns for both GC and GT 5′ sites and found both intron classes to use AAGG[T|C]AAGT (where the first AAG is in the preceding exon). We identified a similarly high frequency of GC/AG introns in G. pallida (3. 53 %), and Rotylenchulus reniformis (2. 36 %) (PRJNA214681, Showmaker et al.
Effectors in G. rostochiensis are sequence diverse between pathotypes Effectors play central roles in both pathogenicity and virulence. The evolution of virulence on a particular host or variety can involve both gain and loss of effector function. Effectors may become specialised to function in a new host  [35] , while effector gene loss (or loss of expression) may allow a pathogen to evade recognition  [36]. We identified G. rostochiensis effectors by sequence similarity to effectors with experimentally verified gland cell expression in related taxa (Heterodera, Globodera). Many effectors in plant-parasitic nematodes are members of large multi-gene families, only a subset of which are effectors  [10, 13, 37]. For example, in G. pallida there are ~300 SPRY (PF00622) domain containing proteins, fewer than 10 % of which are deployed as effectors  [13].
Raw data were analysed using the Illumina RTA1. 8 analysis pipelines. An initial assembly was produced from a combination of short-fragment paired-end and mate-pair Illumina libraries (Additional file 23: Table  S11 ). Short paired-end sequence reads were first corrected and initially assembled using SGA v0. 9. 7 30 [53]. This draft assembly was then used to calculate the distribution of k-mers for all odd values of k between 41 and 81, using GenomeTools v. 1. 3. 7  [54]. The k-mer length for which the maximum number of unique kmers were present in the SGA assembly (k = 63) was then used as the k-mer setting for de Bruijn graph construction in a second assembly with Velvet v1. 2. 03 32  [55]. The mate-pair library was then used to further scaffold this Velvet assembly using SSPACE  [56]  with an iterative approach, in which the number of read-pair links required to scaffold two contigs was initially set to 50, then reduced to 30, 20 and finally set to 10 for two final iterations of SSPACE to produce assembly nGr.
v0. 9. The three whole genome sequencing libraries were subsequently used to gap fill the assembly (GapFiller v1. 10 [57] , 10 iterations and default values for extension parameters), producing the final assembly nGr. v1. 0. A BlobDB (Blobtools v0. 9. 9 (https://drl. github. io/blobtools/) [58, 59]  was constructed using: (1) the assembly; (2) similarity search results against the NCBI Nucleotide database (BLASTn 2. 3. 0+ [60]  megablast, E-value cutoff 1e -65 ), Uniref90 (Diamond v0. 7. 12  [61] , blastx, using the options -sensitive, -k 25 and -c 1) and the G. pallida.
Functional annotation was performed using InterProScan5 (v5. 7-48. 0 [64] ) to identify motifs and domains in the proteins by comparing them against databases Gene3D, PRINTS, Pfam, Phobius, ProSitePatterns, ProSiteProfiles, SMART, SUPERFAMILY, Signal-P_EUK, TIGRFAM, TMHMM, Annot8r with KEGG, GO, EC, tRNAscan and rfam. GO-Term annotation and GO-enrichment analysis was carried out using Blast2GO 3. 1. 3 [65].
Splicing Splice sites were extracted from the genomes and GFF3 files present on WormBase for the species in Additional file 4: Figure  S2 , using custom script extractRegionFromCoordinates. py (https://github. com/ DRL/GenomeBiology2016_globodera_rostochiensis/GNU GENERAL PUBLIC LICENSE). Four base pairs up and downstream of the 5′ donor site, and 6 bp upstream of the 3′ acceptor site were used to construct a consensus sequence for all GC/AG introns, and an identical sized sample of randomly selected GT/AG introns, using MEME SUITE v4. 9. 1  [66].
Transcriptome sequencing and differential expression RNA from two life stages (hatched second-stage juvenile and 14 dpi female) was sequenced, each in biological duplicate, with Illumina Hiseq 100 bp paired-end reads (SRA accessions ERR202479, ERR202487 and PRJEB 12075). These were compared with two additional life stages (dormant cysts and hydrated eggs), similarly sequenced in biological duplicate (Genbank accessions SAMN03393004 and SAMN03393005). All RNA-seq was carried out on pathotype Ro1. Normalized gene expression values and differentially expressed genes were identified as previously described  [50]. In brief, raw reads were trimmed of adapter sequences and low quality bases (Phred <22, Trimmomatic  [67] ), mapped to the genome (Tophat2,  [68] ), counted on a per gene basis (bedtools v2.
16. 2  [69] ), TMM normalised and differential expression analysis and clustering were performed using a Trinity wrapper pipeline and associated scripts for RSEM  [70]  and EdgeR  [71]  (FDR <0. 001, minimum foldchange 4,  [72] ). Expression clusters were grouped based on the tree height parameter (12 %) and manually assigned to expression super-clusters. resource/doi:10. 5061/dryad. 5qs11). Alignment was carried out using clustal-omega 1. 2. 0  [73]  and alignment was trimmed to only include the core PIWI PAZ domain section of argonautes. The WAG + G + F model of amino acid sequence evolution was selected under AICC using Prottest 3. 4  [74]  and phylogenetic trees were inferred using RAxML 8. 1. 20 [75]  (ML search + 100 rapid bootstraps).
Horizontal gene transfer Candidate horizontal gene transfers (HGT) were detected as previously for plant-parasitic nematodes  [37]  by calculating AIs as described in  [32, 33]  using Alienness  [31]. Briefly, AIs were calculated for each G. rostochiensis protein returning at least one similar sequence in either a metazoan or non-metazoan species (E-value threshold of 1e -3 ) present in NCBI's non-redundant (nr) database, according to the following formula: AI ¼ log best metazoan e value þ e -200 À Á -log best non metazoan e value þ e -200 À Á Sequences derived from species under NCBI Taxonomy's 'Tylenchida' (TaxID: 6300, equivalent to Tylenchomorpha) were not included in this calculation to allow detection of HGT events which took place in an ancestor of cyst nematodes and their tylenchomorph relatives.
No AI value could be calculated for proteins returning no similar sequences in the nr database. An AI >0 indicates a better hit to a non-metazoan species than to a metazoan species and thus a possible acquisition via HGT. An AI >30 corresponds to a difference of magnitude e 10 between the best non-metazoan and best metazoan E-values and is estimated to be a strong indication of a HGT event  [32]. Proteins with an AI >0 and ≥70 % identity to a non-metazoan protein were considered putative contaminants and not included in further analysis.
Variant analysis Sequence reads (Bioproject PRJNA305631) were mapped against the assembly using bwa mem v0. 7. 12-r1044  [78]. Duplicated read pairs were removed using Picard (http:// broadinstitute. github. io/picard). Variants were called using freebayes v0. 9. 20-16-g3e35e72  [79]. Haplotypes and other complex variants were decomposed using vcflib vcfallelicprimitives v1. 0. 0-rc0 (https://github. com/ekg/vcflib/releases/tag/v1. 0. 0-rc0) followed by normalisation using vt normalize v0. 57 [80]. The resulting VCF file was filtered with the following parameters: DP > 10 & MQM > 30 & QUAL > 1 & QUAL/AO > 10 & SAF > 2 & SAR > 2 & RPR > 1 & RPL > 1 using vcffilter from vcflib. Variants were annotated using SnpEff v4. 1 L  [81]. The resulting VCF file was analysed using vt peek, RTG Tools  [82]  and parse_snpeff.
py. Variants (vcf file) were filtered to retain only SNPs (TYPE = snp) with no missing data, 730,705 loci were found from whole genome data. Allele frequencies at each locus was computed by dividing the reference allele observation count (RO) by the read depth (DP). In the same manner, allele frequencies for SNPs present in non-coding regions (n = 619,886) were computed. Seqboot module in PHYLIP v3. 695  [83]  was used to make 100 bootstrapped datasets. Maximum likelihood phylogenetic trees of the nine populations of G. rostochiensis were calculated with the Contml module based on genome-wide SNP allele frequencies and a majority rule consensus tree was constructed using Consense. Principal component analysis (PCA) were calculated with the prcomp() function from the stats package in R based on genome-wide allele frequencies at these 730,705 loci.
Protein clustering Putative one-to-one orthologues between G. pallida and G. rostochiensis were identified by the reciprocal best BLAST hit method. Both proteomes were compared against each other using BLASTp (v2. 2. 30+) and the resulting files were processed using the script rbbh. py (https://github. com/DRL/GenomeBiology2016_globoder-a_rostochiensis GNU GENERAL PUBLIC LICENSE, Evalue ≤1e -25 and reciprocal-query coverage >75 %). Protein clustering analysis was performed on the proteomes (retrieved from Wormbase WS248) of B. xylophilus, C. elegans, M. hapla, M. incognita, G. pallida (retrieved from WormBase ParaSite WBPS2) and G. rostochiensis (nGr. v1. 0) using OrthoMCL (v2. 0. 9  [84] ) (with an inflation value of 1. 5) and following the guidelines specified in  [84]. Phylogenetically informative sets of clusters were plotted using UpSetR (Release v1.
0. 0, https://github. com/hms-dbmi/UpSetR/releases [85] ). For each of four orthologous gene cluster categories (all nematodes tested, all plant parasites tested, Globodera and Meloidogyne and Globodera alone), the percentage of genes present in each differential expression super-cluster was determined. This value was normalised by the total number of genes present in each given differential expression super-cluster, to return a relative measure of abundance used in Fig. 3.
Non-overlapping islands, delineated by furthest distance at which statistically significant enrichment was observed (±6, χ 2 goodness of fit, p <0. 001), were manually identified. Synteny between scaffolds of G. pallida and G. rostochiensis was assessed based on OrthoMCL-cluster membership of both sets of proteins using i-adhore-3. 0. 01 ((https:// github. com/widdowquinn/Teaching/tree/master/Compara-tive_Genomics_and_Visualisation/Part_2/i-ADHore) type = family, tandem_gap = 10, gap_size = 15, max_gaps_in_alignment = 20, cluster_gap = 20, q_value = 0. 9, alignment_ method = gg2, prob_cutoff = 0. 001, multiple_hypothesis_ correction = bonferroni, anchor_points = 5). Syntenic blocks were visualised as clusters in a graph using parse_iadhore. py. G. rostochiensis scaffold GROS_00007 (a member of the biggest syntenic cluster) was plotted with its homologous G.
pallida scaffolds using circos 0. 67-7, including GCcontent and BLASTn results at an E-value cutoff of 1e-65. To analyse putative enhancer elements, sequences 500 bp upstream of genes of interest (termed the promoter regions) were extracted from the genome using get_up-stream_regions. py (https://github. com/peterthorpe5 GNU GENERAL PUBLIC LICENSE). Enrichment of motifs between categories (DG versus all, DG versus SvG, etc. ) was calculated using HOMER  [49] , specifying max length of six nucleotides. Instances of the motif were identified in FASTA sequences of promoter regions using the FIMO web server  [86].
06 [92, 93] GROS_g04366 Pectate lyase GROS_g05398 GROS_g07968 Plant defense GH18 chitinase Chitin degradation PF00704 2. 30 [94] GROS_g11136 manipulation Glycosyl hydrolase family 18 Chorismate mutase Conversion of Chorismate PF01817 42. 36 [95] GROS_g02441 into SA Chorismate mutase type II GROS_g08190 Candidate Isochorismatase Conversion of Chorismate PF00857 66. 08 [96] GROS_g01640 into SA Isochorismatase family Detoxification Candidate Cyanate lyase PF02560 11. 51 [17, 24] GROS_g09531 Cyanate lyase C-terminal domain Nutrient GH32 invertase Degradation of sucrose in PF00251 241. 26 [22, 23] processing glucose and fructose Glycosyl hydrolases family 32 N-terminal domain.
00 [99] GROS_g07961 synthase Bacterial capsule synthesis protein PGA_cap Candidate GSI Glutamine synthase Nitrogen assimilation PF00120 29. 24 [100, 101] GROS_g02362 Glutamine synthetase, catalytic domain Feeding site NodL -like Candidate PF12464 13. 12 [100, 102] GROS_g11033 induction acetyltransferase Maltose acetyltransferase PF00132 Bacterial transferase hexapeptide (six repeats) Not known Candidate L-threonine aldolase ?? PF01212 164. 69 [100] GROS_g10421 Beta-eliminating lyase GROS_g10422 GROS_g10423 Candidate Phosphorybosyl ?? PF00156 198. 13 [100, 101] GROS_g04632 transferase Phosphoribosyl transferase domain GROS_g06735.
rostochiensis (n = 23), G. pallida (n = 18) and M. hapla (n = 18) WAGOs present in OrthoMCL clus- [30], which contained at least one G. rostochiensis protein with highest similarity to C. elegans WAGO1, were aligned to 545 WAGO sequences from Buck and Blaxter, 2013[30]. This comprised WAGOs from Clade I, Clade III, Clade IV and Clade V nematodes, as well as non-Nematode argonaute sequences (http://datadryad. org/ (.
ACKNOWLEDGEMENTS Funding from the  French National Institute for Cancer (Institut National du Cancer)  within the framework of  TWEEZ-RT Project of the 'Plan National Cancer 2013 ' is gratefully acknowledged. G. P. received a  Doctoral Scholarship  from the  Institut National du Cancer  and additional financial support provided by  CNRS. The photolithography masks were made with the 8 inch EB writer  F5112+VD01  donated by ADVANTEST Corporation to the  VLSI Design and Education Center (VDEC) ,  The University of Tokyo. We thank  Corinne Abbadie  and her team from  IBL, Lille, France , for the DNA preparation.
The GOMOS instrument on Envisat has successfully demonstrated that a UV-Vis-NIR spaceborne stellar occultation instrument is capable of delivering quality data on the gaseous and particulate composition of Earth's atmosphere. Still, some problems related to data inversion remained to be examined. In the past, it was found that the aerosol extinction profile retrievals in the upper troposphere and stratosphere are of good quality at a reference wavelength of 500 nm but suffer from anomalous, retrieval-related perturbations at other wavelengths. Identification of algorithmic problems and subsequent improvement was therefore necessary. This work has been carried out; the resulting AerGOM Level 2 retrieval algorithm together with the first data version AerGOMv1. 0 forms the subject of this paper.
The AerGOM algorithm differs from the standard GOMOS IPF processor in a number of important ways: more accurate physical laws have been implemented, all retrieval-related covariances are taken into account, and the aerosol extinction spectral model is strongly improved. Retrieval examples demonstrate that the previously observed profile perturbations have disappeared, and the obtained extinction spectra look in general more consistent. We present a detailed validation study in a companion paper; here, to give a first idea of the data quality, a worst-case comparison at 386 nm shows SAGE II-AerGOM correlation coefficients that are up to 1 order of magnitude larger than the ones obtained with the GOMOS IPFv6. 01 data set.
Stellar occultation largely resolves these problems (due to the abundance of stars), however at the cost of a reduced measurement S/N ratio. The GOMOS instrument and its 10-year quasi-continuous operation can be considered a success. An extensive body Published by Copernicus Publications on behalf of the European Geosciences Union. of papers have been published in the scientific literature that describe the instrument, data processing, and the obtained scientific results for the different atmospheric species; a good overview article has been published by  Bertaux et al. (2010). Notwithstanding this success, several problems regarding the instrument and the data processing posed difficulties. Most noteworthy, the imperfect correction of stellar scintillation due to isotropic turbulence  (Sofieva et al.
, 2010). Scientifically, good aerosol-cloud extinction observations in the UTLS and stratosphere are of main importance for atmospheric research, in two ways: (1) the Earth radiative budget depends on the optical properties of high-altitude clouds and volcanic sulfate aerosols, and (2) heterogeneous polar ozone chemistry is driven by the presence of polar stratospheric clouds (PSCs), stratospheric aerosols and high-altitude cirrus clouds. A good overview of the scientific significance of aerosols-clouds can be found in the SPARC report  (SPARC, 2006). Improvement of a long-term aerosol-cloud data record such as the one provided by GOMOS is therefore important. The European Space Agency (ESA) has acknowledged this by funding the AerGOM project (Aerosol profile retrieval prototype for GOMOS).
Within this project framework, data inversion problems have been studied, solutions have been found, and a new Level 2 algorithm has been developed. In this article, we summarize the essentials of the GO-MOS instrument and its operational data processing algorithm (currently IPF v6. 01). Subsequently, we present and justify the specific features that have been changed in the AerGOM code. Finally, the AerGOM aerosol-cloud extinction results are discussed in a qualitative way; a detailed data validation is presented in a companion paper  (Robert et al. , 2016). 2 GOMOS and its operational data processor.
The altitudinal distribution of atmospheric species can in principle be obtained from this sequence of measurements. The discrimination between different species is of course achieved by measuring in different spectral regions. The duration of a star occultation is deter- Finally, it should be mentioned that the measurement S/N ratio is largely determined by the apparent brightness and the temperature of the star; the catalogue used by GOMOS contains objects with visual magnitudes smaller than 4 and temperatures ranging from 3000-30 000 K (i. e. most stellar spectral types). The combined effects of varying obliquities and star characteristics lead to a GOMOS data set that has in a sense an inhomogeneous nature; during data analysis, the variation in altitudinal sampling and S/N ratio has to be taken into account.
General approach AerGOM shares with the GOMOS processor the same basic separation of the data processing in two distinct steps: a nonlinear spectral inversion, followed by a linear, regularized spatial inversion. There are however several significant differences. (1) To improve accuracy, better equations for the air refractive index and Rayleigh cross section have been used. (2) During spectral inversion, no differential (DOAS) method is applied to obtain NO 2 and NO 3 SGDs; all gases-aerosols are retrieved together, using their full absorption cross-section-spectral model. This is conceptually simpler; furthermore it ensures that all covariances are retained by the solution. (3) The spectral behaviour of aerosol SAOD is modelled in a better way. (4) The algorithm allows the use of SPB1 and SPB2 pixels, hereby increasing the spectral range and the information content of the solution.