dataesr/scientific-paragraphs-categorization huggingface dataset:
096) for salinity, and 0. 28 (or a RMS difference of 0. 16 kg m -3 ) for density. The biases present the same trends as those observed with in situ data: a slight overestimation of temperature in our simulation (positive biases between 0. 053 • C during spring and 0. 33 • C during winter), while salinity and density fields are slightly underestimated (negative biases between -0. 043 during spring and -0. 0063 during winter, -0. 069 kg m -3 during winter and -0. 017 kg m -3 in autumn, respectively) (Table  3 ). These different statistical metrics giving satisfying results, we illustrate in the following paragraph two types of model/data distributions: in space (Fig. 5 ) and in time (Fig. 6 ). The simulated 8 yr mean of temperature, salinity, and density is compared to the annual climatology from CARS database (2009) along a vertical section (west-east direction) at 23 • S (Fig.
3  for location) at 23 • S  (Monteiro and van der Plas, 2006 ). An underestimation of salinity over the first 100 m depth can be noticed for the simulated field as explained previously. Despite a lower standard deviation as compared to CARS database (2009), these statistics show that the model is able to simulate the annual mean and seasonal cycle for temperature, salinity, and density fields over the Namibian upwelling system.
Oxygen and nutrients Simulated oxygen and nitrate fields are first compared to in situ data. As for the previous evaluated fields (Sect. 3. 1), the different statistical metrics highlight an important variability between the in situ data and the modeled fields. For oxygen and nitrate, the correlation coefficient is above 0. 8 for all comparisons, except for AHAB1 data in January 2004 (Fig. 4d  and e ). The normalized centered pattern RMS difference is usually lower than 0. 6 and the normalized standard deviation stands between 0. 4 and 1. 1, except for AHAB1 data. This standard deviation stays lower than 1 for the oxygen concentrations. The bias between the simulated and in situ oxygen concentrations is usually positive, with higher bias for AHAB1 cruise up to 67. 7 mmol O 2 m -3 (Table  3 ).
Except these extreme events, the seasonal cycle and annual mean of simulated oxygen and nitrate concentrations give satisfying results as compared to CARS database (2006) (Fig. 4d  and e ). The correlation coefficient is above 0. 9 and 0. 95 for oxygen and nitrates, respectively, for all comparisons with CARS. The normalized standard de-viation is between 0. 84 and 0. 89 for oxygen, and between 0. 92 and 1. 04 for nitrates. The biases vary between -5. 56 and 2. 95 mmol O 2 m -3 for oxygen, and between -1. 89 mmol N m -3 and 1. 01 mmol N m -3 for nitrates and are found in austral winter and summer, respectively (Table 3). The vertical section at 23 • S (Fig. 5d  and e ) and the time series (Fig. 6 ) confirm the good statistics for oxygen and nitrate fields as compared to CARS database  (2006).
4 and 8 mmol N m -3 for nitrite and ammonium concentrations, respectively. Note that nitrite and ammonium concentrations respectively above 1 and 4 mmol N m -3 only correspond to extremely low oxygen concentrations (O 2 < 4. 5 mmol O 2 m -3 ) on the bottom shelf waters (water depth usually below 130 m). As explained above, these extreme conditions are not represented by our climatological simulation. More in situ measurements of nitrite and ammonium concentrations are needed to conclude. However, simulated fields seem to have the same order of magnitude as in situ data, possibly at the lower limit of the measured concentrations. The comparison between simulated fields and the CARS database (  2006 ) confirms that our model correctly represents the annual mean vertical distribution and seasonal cycle of oxygen and nitrate concentrations.
19 mg Chl m -3 for the last 10 yr  (Demarcq, 2009). The annual and seasonal mean fields present a correlation coefficient between 0. 8 and 0. 9 compared with SeaWiFS climatology, corresponding to the same decade as the wind used to force the model. The normalized standard deviation lower than 1 highlights an underestimation of the simulated Chl a variance in an annual, seasonal, and spatial way, and the normalized centered pattern RMS difference is less than 0. 6 (or a RMS difference less than 0. 22 mg Chl m -3 ). Satellite Chl a seasonal cycle is well depicted by the model (Fig. 7c, d ), with statistical metrics for seasonal distribution close to those obtained for the annual mean pattern (Fig. 7e ). To complete our model/data comparison, the simulated distribution of Chl a is now compared with in situ data from the AMT 6 cruise in May 1998, where vertical profiles of Chl a were estimated from four different sources (E.
, 2000). Integrated over 200 m depth, in situ data between 2000 and 2007 present an important interannual variability, with biomass from 21. 5 to 150 mmol N m -2. However, the maximum biomass is usually observed during the first part of the year (summer-early autumn) and is located between 10 (14. 2 • E) and 50 nautical miles (13. 5 • E) off the coast  (Kreiner and Ayon, 2008). For the same transect (23 • S), our coupled model simulates a copepod biomass varying from 21. 5 to 120 mmol N m -2 , with a maximum biomass during summer-early autumn, situated at the same distance from the coast as in situ data. Seasonal cycle of zooplankton follows that of Chl a with a lag time of 3 to 8 weeks necessary for zooplankton to react to the phytoplanktonic biomass increase  (Postel et al. , 1995). During AMT 6 cruise, zooplankton biomass was estimated at three stations (St.
15, 12, and 7; see Fig. 3  for location). Data from different size classes (200-500 µm, 500-1000 µm, 1000-2000 µm, > 2000 µm) were integrated over the first 200 m depth of the water column. To compare with our simulation (Table  4 ), we only take into account the fraction between 200 and 2000 µm, usually considered as mesozooplankton (copepods in the model). For the same climatological month (May), the monthly mean concentrations are within the range of in situ data but do not reach the high in situ values; they still differ by a factor of 2. However, using the 3-day average outputs for May, the simulated copepod concentrations can reach 107 mmol N m -2 , closer to the maximum concentration measured during AMT 6 cruise. Regarding the full analyzed period and domain, mesozooplankton regularly reach concentrations of about 130-145 mmol N m -2 at the beginning of the year.
So, simulated copepod distribution seems to corroborate in situ measurements from  Kreiner and Ayon (2008)  and AMT 6 cruise, with slightly lower concentrations. In situ measurements of DON in the Namibian upwelling system are not available, so the simulated semi-labile DON is compared with data from the AMT 17 cruise that crossed the offshore region of the southern Benguela upwelling system in November 2005  (Holligan, 2005). To estimate the semilabile part of in situ DON measurements, we assumed the deepest value for each station as refractory DON, and we removed this refractory part (assumed constant with depth) of the vertical profile of DON. For the same climatological month, simulated vertical profiles of semi-labile DON concentrations were taken in the offshore region of the Namibian upwelling system.
Sensitivity analyses of key parameters Due to the different modifications and additions made from the original biogeochemical model of  Koné et al. (2005)  (see Sect. 2. 2), we had to adapt the BioEBUS model, especially some of the parameter values within the range of values found in the literature (Table  2 ). A first adjustment was made using BioEBUS in zero spatial dimensions (time dependent only) as the coupled high-resolution model requires a lot of computer time for a simulation. For the adjustments we used the main features for the different state variables in the coastal area and in the open ocean, deduced from the data (Sect. 2. 4) or from the literature. As a more detailed representation of the nitrification was introduced in BioEBUS as compared to  Koné et al. (2005) , some of the parameter values associated to phytoplankton nutrient uptake (half-saturation constants K NO3 P S and K NH4 P L ) were adjusted in order to obtain better agreement for the Chl a and nitrate concentrations between the simulated field and data.
(  2012 ) using β = 0. 01 mol N 2 O mol N -1 and the formulation from  Nevison et al. (2003)  give satisfying results in terms of N 2 O concentrations and vertical distribution as compared to the compiled database  (Suntharalingam et al. , 2012). Nitrification is the main process affecting the marine N 2 O cycle at global scale, while denitrification can play an important role at local scale. For this reason the β coefficient accounting for the N 2 O source in suboxic waters will also be tested here as in  Suntharalingam et al. (2012). Using β = 0. 02 to 0. 04 mol N 2 O mol N -1 , results represent an upper limit for the global N 2 O source -still reasonable as compared to the recent estimates  (Suntharalingam et al. , 2012). These results can then be more realistic under specific biogeochemical conditions such as those found in EBUS and associated OMZs.
21 10 3 km 3 very close to CARS database (2. 32 × 10 3 km 3 ) for the same O 2 threshold. The tested biogeochemical parameter values have the same impact on the suboxic water volume or OMZ volume (O 2 < 25 mmol O 2 m -3 ) as the hypoxic water volume -however with a higher intensity. For example, the OMZ was almost inexistent when starting the sensitivity analyses (Test 1 in Fig. 13 ) and became noticeable in the "standard simulation" (0. 8 × 10 2 km 3 ; see Test no. 4 in Fig. 13 ). The OMZ volume is still doubled at the end of sensitivity analyses (1. 7 × 10 2 km 3 ; see Test no. 15 in Fig. 13 ) but differs by a factor of 2. 3 from the CARS database (3. 9 × 10 2 km 3 ). However, this volume difference comes from the area shoreward of 130 m isobath. Excluding this area both estimations are very close (1.
Indeed, between the "standard simulation" using the  Nevison et al. (2003)  parameterization (Test 4) and the "reference simulation" using the one from Suntharalingam et al. (  2012 ) with β = 0. 03 mol N 2 O mol N -1 (Test 15), 45 % of N 2 O emission increase comes from the parameterization used in BioEBUS and about 55 % due to the improvement of the parameter values. So, both improvements are crucial to better simulate N 2 O emissions using oceanic biogeochemical models. However, in situ measurements are needed to refine the α and β coefficients used in the parameterization of  Suntharalingam et al. (2000 Suntharalingam et al. ( , 2012) )  and to improve the N 2 O vertical distribution and emissions to the atmosphere. Recently,  Zamora et al. (2012)  suggested that N 2 O production increases linearly with decreasing oxygen, rather than exponentially, in the eastern tropical Pacific, based on MEMENTO database.
Conclusion We developed a biogeochemical model (BioEBUS) taking into account the main processes linked with EBUS and associated OMZs. We implemented a 3-D realistic coupled physical/biogeochemical configuration for the Namibian upwelling system using the high-resolution hydrodynamic ROMS model. A detailed validation using in situ and satellite data as well as "metrics" is presented in this paper. Sensitivity analyses were performed on key parameters and different N 2 O parameterizations to adjust the coupled model, and relevant simulations are discussed in order to improve the spatio-temporal distribution of oxygen, nitrate, Chl a, and N 2 O concentrations, as well as key processes of the microbial loop. The "reference simulation" gives satisfying results as compared to in situ and satellite data as well as microbial rates available in the studied area.
Acknowledgements. We would like to thank the  CNES  and the  Midi-Pyrénées region  for the financial PhD support and the  Paul Sabatier University  for the ATER support attributed to  E. Gutknecht  as well as the  CNES support (TOSCA CNES program ) of  I. Dadou  and  V. Garc  ¸on. We thank the European  MEECE  project ( EU  grant agreement number  212085 ) for providing funding to develop this study. The  COST Action 735  is thanked for the financial grant for E. Gutknecht's scientific mission performed at the  National Environmental Research Institute (Aarhus University, Roskilde, Denmark ), and  L. L. Sørensen  ( NERI, Denmark ). The  Max Planck Gesellschaft  by  M. Kuypers  funded the nutrient analysis, and  G. Klockgether  ( MPI-Bremen ) the analytical support. We also thank the  IOW  scientists ( V.
Mohrholz , and  H. Lass ) for kindly letting us use their data in the Benguela upwelling system for both cruises in 2003 and 2004. For the AMT data, we are grateful the  BODC (British Oceanographic Data Center, UK) ,  C. Robinson  ( PML/UEA, UK ),  R. Harris  ( PML, UK ),  T. Bale  ( PML, UK ),  M. Woodward  ( PML, UK ),  E. Fernandez  ( UV, Spain ),  R. Barlow  ( MCM, South Africa ),  P. Holligan  ( PML, UK ), and  R. Sanders  ( NOCS, UK ) for providing their data. Ocean color data were produced by the  SeaWiFS  project at  GSFC  and obtained from the  DAAC. We thank the  CSIRO Marine and Atmospheric Research  for the development and distribution of CARS databases (2006 and 2009) and the  German Federal Ministry of Education and Research for supporting GENUS  ( 03F0497D ). We express our special thanks to  P.
• S), the Galathea data in October 2006 (surface data, 5 stations, and triaxus data), the AMT 6 cruise in May 1998 (transect at 26. 7 • S and 4 stations), and the CARS databases (2006, 2009) (seasonal and annual climatologies). For the comparison with in situ data, simulated fields were monthly averaged (note there are 10 outputs by month) using the last 8 yr of simulation (Y12-Y19). For the comparison with CARS database, simulated fields were either seasonally or annually averaged. Biogeosciences, 10, 3559-3591, 2013 www. biogeosciences. net/10/3559/2013/.
Background: Conopeptides, often generically referred to as conotoxins, are small neurotoxins found in the venom of predatory marine cone snails. These molecules are highly stable and are able to efficiently and selectively interact with a wide variety of heterologous receptors and channels, making them valuable pharmacological probes and potential drug leads. Recent advances in next-generation RNA sequencing and high-throughput proteomics have led to the generation of large data sets that require purpose-built and dedicated bioinformatics tools for efficient data mining. Results: Here we describe ConoSorter, an algorithm that categorizes cDNA or protein sequences into conopeptide superfamilies and classes based on their signal, pro-and mature region sequence composition. ConoSorter also catalogues key sequence characteristics (including relative sequence frequency, length, number of cysteines, Nterminal hydrophobicity, sequence similarity score) and automatically searches the ConoServer database for known precursor sequences, facilitating identification of known and novel conopeptides.
When applied to ConoServer and UniProtKB/Swiss-Prot databases, ConoSorter is able to recognize 100% of known conotoxin superfamilies and classes with a minimum species specificity of 99%. As a proof of concept, we performed a reanalysis of Conus marmoreus venom duct transcriptome and (i) correctly classified all sequences previously annotated, (ii) identified 158 novel precursor conopeptide transcripts, 106 of which were confirmed by protein mass spectrometry, and (iii) identified another 13 novel conotoxin gene superfamilies. Conclusions: Taken together, these findings indicate that ConoSorter is not only capable of robust classification of known conopeptides from large RNA data sets, but can also facilitate de novo identification of conopeptides which may have pharmaceutical importance.
Advances in high-throughput sequencing technologies, combined with directed studies of venom producing cells  [36] [37] [38] [39] , have resulted in a data deluge which requires dedicated tools for the analysis and classification of conopeptide sequences. ConoServer, a specialized database dedicated to conopeptides  [22] , implemented a web-based tool (ConoPrec) that guides gene superfamily assignment of precursor toxins by the recognition of a limited number of known cleavage sites (or protease specificities) and a sequence similarity search based on existing conopeptide superfamilies  [16]. However, the limitations of this program include the restriction to known conopeptide motifs, as well as a requirement that the query precursor sequences contain the signal region, which is rarely the case as most conopeptide screening is conducted on milked venom or dissected venom gland that almost exclusively contains mature protein products.
Another web-based program, ConoDictor, overcomes the issue of missing signal regions by using three independent sets of models built from signal, pro-and mature regions of conopeptides respectively  [40, 41]. However, this tool only accepts selected amino acid sequences as input, only classifies conopeptides into the main superfamilies, does not provide any data quantitation, and perhaps most importantly, cannot facilitate the discovery of new conopeptide families. Both ConoPrec and ConoDictor are limited in their ability to handle large transcriptomic or proteomic datasets, and therefore are unlikely to fill the need for large-scale analysis of cone snail transcriptomes or proteomes. Here we describe ConoSorter, a program able to classify conopeptides into superfamilies and classes from either protein sequences or RNA sequencing data.
ConoSorter has been designed to recognize all currently annotated gene superfamilies and classes. Regular expression sequence searches are complemented by a profile Hidden Markov Model (pHMM) analysis allowing the classification of conotoxins that may be only distantly related to wellestablished conopeptide groups. ConoSorter also reports key sequence characteristics (including relative sequence frequency, length, number of cysteine residues, N-terminal hydrophobicity, sequence similarity score) and automatically searches the ConoServer database for known precursor sequences, which facilitates clear and precise identification of known and novel conopeptides and their associated families. ConoSorter allows an investigator to efficiently deal with the thousands of sequences produced by highthroughput sequencing methods in a rapid and accurate manner.
Identification and classification of known conopeptides To assess if ConoSorter can accurately classify conopeptides into superfamilies and classes we performed two initial control experiments -analysis of the ConoServer cone snail toxin database and an analysis of the universal UniProtKB/ Swiss-Prot protein database  [22, 42]. The ConoServer database contains 5,449 entries of complete or partial conopeptide sequences. We employed 36. 85% (2,008 sequences) of the ConoServer entries in the development of our training set, and here sought to assess the accuracy of both the regular expression and pHMM approaches described above to hierarchically classify the entire suite of ConoServer sequences into superfamily and class. We found that the regular expression analysis was able to classify 100% of well-defined ConoServer sequence regions (i.
60% for class (7 conflicts, plus 4 patent sequences with undetermined amino acids counted as false positives out of 2,750 sequences with known classes) using the HMMER hmmscan script with the default E-value cutoff at 10. This approach was also able to confidently assign 1,153 sequences into superfamilies and 32 into classes, which had previously lacked annotation. In the second experiment, the ability of ConoSorter to distinguish between Conus peptide toxins and other proteins from various organisms has been assessed by screening the entire UniProtKB/Swiss-Prot database. Using the version released on June 2013 we examined a total of 540,261 protein sequences isolated from 12,988 cellular and non-cellular species. Table  1  reports the specificity S calculated at 7 E-value cutoffs (10default threshold, 1, 0.
1, 0. 01, 10 -3 , 10 -4 and 10 -5 ) according to the following equation: S ¼ True Negatives True Negatives þ False Positives ð Þ where True Negatives = N -True Positives (with N = total number of input sequences, and True Positives = number of conopeptide matches), and False Positives = the number of non-Conus species matches plus the number of nonconopeptides Conus matches. At all E-value thresholds ConoSorter was able to confidently identify and classify conopeptides (Table  1 ). Of the 540,261 amino acid sequences referenced in the UniProtKB/ Swiss-Prot database, ConoSorter, using an E-value of 10 -5 , was able to classify 879 peptide toxins from the genus Conus with an annotated superfamily (specificity of 99. 94%) with only 345 false positives (p-value = 0. 06%) isolated from other organisms.
Analysis of Conus marmoreus venom duct transcriptome The results presented above indicate that ConoSorter is capable of identifying conopeptides at high specificity and sensitivity, and, even when the dataset being analyzed ostensibly includes all known proteins, accurately assigning the appropriate superfamily and class. We next sought to use ConoSorter's regular expression and pHMMs searches to ascertain if it was possible to identify novel conopeptides, superfamilies and classes in a previously interrogated RNAseq dataset. Dutertre et al. have recently performed an analysis of the C. marmoreus venom duct transcriptome, which principally relied on serial BLAST homology searches  [18]. They reported 30 full conopeptides precursor sequences (i. e. those beginning with a methionine residue and finishing by a stop codon) that had been previously characterized in this species.
1 and Mr11. 3 precursors, did not encode a methionine and thus were discarded). Moreover, 17 novel isoforms of known Conus marmoreus precursor conopeptides (Mr1. 1, conomarphin Mr1, conomarphin Mr2, contryphan M, cMrVIA, CMrX, and MrIA precursors) were identified and assigned the correct superfamily or class based on the signal, pro-, and mature regions. These conopeptides were confirmed by a tandem mass spectrometry (MS/MS) analysis of the milked venom, in which we were able to identify their corresponding mature sequences (Additional file 2: Table  S1 ). ConoSorter was also able to assign known gene superfamilies to 125 novel full-length precursor conopeptides (Figure  2 , Additional file 3: Figure  S2 , Additional file 4: Table  S2 ). Using the C. marmoreus milked venom mass spectrometry data, and employing the methods Dutertre et al.
For each known superfamily, the sequence alignment shows the amino acid conservation specifically in the N-terminal signal region (the deeper color the more conserved the amino acids are). Partial sequences in bold characters correspond to the peptide fragments isolated by MS/MS analysis of C. marmoreus milked venom. Numbers listed after conopeptide names indicate the sequence frequency in the input data. DNA Data Bank of Japan (DDBJ) accession numbers of full-length precursors, as well as post-translational modifications (PTMs) calculated by ProteinPilot 4. 0 are provided in Additional file 4: Table  S2. For example, H2 is 40% similar to superfamily H (Figure  3 , Additional file 5: Table  S3 ). The signal sequences of these 13 new groups of conopeptides show high intrinsic identity rates from 82.