dataesr/scientific-paragraphs-categorization huggingface dataset:
graminearum, FgrGalOx (Genbank AAA16228. 1), including the absence of the N-terminal CBM32 of FgrGalOx. Amino-acid sequence alignment reveals that CgrAlcOx and CglAlcOx share 82% sequence identity with each other, but only 46% and 48% identity, respectively, to the AA5 module of GalOx from F. graminearum (Supplementary Fig. 1 ). All AA5 sequences known to date contain the key activesite residues of FgrGalOx, namely, C228 and Y272 that combine to form the unique crosslinked thioether-tyrosyl cofactor, and Y495, H496 and H581 that also coordinate to the copper ion  34  (see also Fig. 1  and Supplementary Fig. 1 ). Production of CgrAlcOx and CglAlcOx by Pichia pastoris. The methylotrophic yeast P. pastoris was chosen as the recombinant production host for both AA5 members, based on previous work with FgrGalOx (refs 28,29).
A similar trend is observed for the homologous CglAlcOx (Table  1 ). Indeed, plots of initial rate kinetics versus substrate concentration are classically Michaelian for all competent substrates, except for those with apparently high K m values, which appear as strictly linear plots (representative data for CgrAlcOx are shown in Supplementary Fig. 3 ; similar data for CglAlcOx are not shown). 1-Butanol was selected as the benchmark substrate and was used to determine the pH-rate profile and temperature stability of CgrAlcOx and CglAlcOx. Both enzymes exhibit bell-shaped pH-rate profiles, consistent with two ionizable groups involved in catalysis as proposed for FgrGalOx (ref. 11), with basic pH optima (Supplementary Fig. 4 ). Both enzymes are significantly stable below 30 °C, but rapidly lose activity above 50 °C, in keeping with the mesophilic nature of their natural host (Supplementary Fig.
k cat (s À 1 ) K m (mM) k cat /K m (s À 1 M À 1 ) k cat (s À 1 ) K m (mM) k cat /K m (s À 1 M À 1 ) apical His423 that is one of the copper coordination residues (described below). As expected for proteins with B40-50% sequence identity, the two common domains of CgrAlcOx and FgrGalOx are extremely similar with an r. m. s. d of 0. 98 Å over 465 equivalent Ca atoms (48% identical amino acids); the best match was with the FgrGalOx W290F variant (PDB code 2EIC (ref. 35), PDBeFOLD Q score 0. 64). The most obvious structural difference between FgrGalOx and CgrAlcOx is that the latter lacks the N-terminal CBM32 (ref. 34; Fig. 4c ). In other enzymes, CBM32 members are generally known to bind galactosyl residues  36  , and indeed galactose binding was revealed in the seminal crystal structure of FgrGalOx (ref.
Indeed, the classification of CgrAlcOx and CglAlcOx in the same subfamily as FgrGalOx may result in the erroneous extrapolation of functional annotation as galactose 6-oxidases to these enzymes. For example, CglAlcOx is currently listed under the NCBI GenBank ID ELA25906. 1 as a 'galactose oxidase precursor. ' Although our enzymological data clearly demonstrate that CgrAlcOx and CglAlcOx are not galactose 6-oxidases, they do share a common Kelch-repeat b-propeller fold, conserved copper and crosslinked Cys-Tyr cofactor active site and mechanistic commonality with their closest AA5_2 relative, the archetypal FgrGalOx. Caution is therefore warranted in the annotation of enzyme function on the basis of primary and tertiary structural data alone. CgrAlcOx and CglAlcOx exhibit a broad substrate range, resulting in the oxidation of saturated and unsaturated primary alcohols to the corresponding aldehydes with the concomitant production of hydrogen peroxide from molecular oxygen.
Proteomics-grade trypsin was purchased from Promega (Madison, WI, USA). Galactose 6-oxidase from F. graminearum was expressed in P. pastoris (Invitrogen, Groningen, the Netherlands) and purified using pre-packed Ni-NTA  29. Bovine serum albumin and pre-cast 4-20% SDS-PAGE TGX were purchased from Bio-Rad (Hercules, CA, USA). Sequence alignment and phylogenetic analysis. The gene models of locus GLRG_05590. 1 of C. graminicola M1. 001 (encoding UniProt E3QHV8; GenBank EFQ30446) and locus CGGC5_13025 of C. gloeosporioides Nara gc5 (encoding GenBank ELA25906) were downloaded from their respective genome databases  [31] [32] [33]  (accessible via http://www. colletotrichum. org/genomics/) and translated to the corresponding protein sequences, herein referred to as CgrAlcOx and CglAlcOx, respectively.
Seventy-three other amino-acid sequences of fungal AA5 members, from both subfamily 1 (AA5_2) and subfamily 2 (AA5_2), were extracted from the public version of the CAZy database (http://www. cazy. org/AA5. html, ref. 16). Where present, signal sequences and additional modules, such as carbohydrate-binding modules, were removed to isolate the catalytic modules for bioinformatics analysis. A multiple sequence alignment was produced using Muscle  52. A distance matrix based on maximum likelihood was then produced using the BLOSUM62 substitution matrix  53  and transformed into a phylogenetic tree using BioNJ  54. Subfamilies were inferred with the assistance of Secator  55  , and the tree was formatted using Dendroscope  56. DNA cloning and strain production. cDNA encoding CgrAlcOx without the predicted native signal peptide (MPTLRSALRNLPAALLALAAACEA)  57  , and including a C-terminal His 6 tag-encoding sequence, was commercially synthesized (BioBasic, Markham, Canada), cloned into pUC57 and subcloned into the P.
3 GHz. Spectral simulation was carried out using the Easyspin 4. 0. 0 software. To investigate redox properties, samples of CgrAlcOx were incubated with 20 equivalents of sodium dithionite under N 2 atmosphere or 20 equivalents of potassium ferricyanide in air for 1 h, with EPR spectra collected before the addition, after 10 min and after 1 h of incubation. Raw data files in support of this work can be accessed via the library at the University of York (DOI:10. 15124/d1278660-b889-4932-8430-401884176d75). Crystallography of CgrAlcOx. Before crystallization, 5 mg of purified CgrAlcOx were incubated with 10 ml of EndoH f (New England Biolabs) in a total volume of 5 ml in 50 mM phosphate buffer, pH 7. 5 for 2 h at 20 °C to reduce sample complexity because of N-glycosylation. The sample was then loaded on to a 1-ml Histrap HP column (GE Healthcare), previously equilibrated in the same incubation buffer.
Diffraction data (Table  2 ) were collected at the Diamond Light Source (UK), beamline I04, at a wavelength of 0. 979 Å. Crystals of the protein, in complex with copper, and of tetragonal appearance were obtained after optimization and microseeding in 50% tacsimate buffer (Hampton Research, Aliso Viejo, CA, USA), at pH 6. 5, followed by soaking. The crystal was soaked in a solution of 52% tacsimate buffer, pH 6. 0, with the addition of copper sulfate powder to the drop. After 15 days, the crystals were harvested using 52% tacsimate buffer, pH 6. 5, 25% (v/v) ethylene glycol as cryoprotectant and immediately fast-frozen in liquid nitrogen. Diffraction data (Table  2 ) were collected at the Diamond Light Source, beamline I03, at a wavelength of 0. 979 Å. Structure solution and refinement. All diffraction data were indexed, integrated and scaled with XDS  61  , and the following processing steps were performed with the CCP4i software suite  62.
Acknowledgements We thank  Diamond Light Source (DLS)  for granting access to beamlines I03 and I04 (mx-9948) that contributed to the results presented here, and their staff for the onsite assistance. This work is funded, in part, by  BBSRC  grant  BB/L021633/1. Work at the  University of British Columbia  was generously supported by the  Natural Sciences and Engineering Research Council of Canada  ( Discovery Grant and Strategic Network Grant  for the ' NSERC Industrial Biocatalysis Network '), the  Canada Foundation for Innovation , the  British Columbia Knowledge Development Fund and UBC  faculty funding. We thank  Ryan Barrett  ( Brumer laboratory, UBC ) and  Anthony Levasseur  ( INRA, Luminy, Marseille ) for assistance with sequence analysis and  Nicolas Lenfant  ( AFMB, Luminy, Marseille ) for assistance with phylogeny and tree generation.
The distribution of the chlorophyll a concentration ([Chl a]) in the Mediterranean Sea, mainly obtained from satellite surface observations or from scattered in situ experiments, is updated by analyzing a database of fluorescence profiles converted into [Chl a]. The database, which includes 6790 fluorescence profiles from various origins, was processed with a specific quality control procedure. To ensure homogeneity between the different data sources, 65 % of fluorescence profiles have been intercalibrated on the basis of their concomitant satellite [Chl a] estimation. The climatological pattern of [Chl a] vertical profiles in four key sites of the Mediterranean Sea has been analyzed. Climatological results confirm previous findings over the range of existing [Chl a] values and throughout the principal Mediterranean trophic regimes.
The fluorescence technique therefore represents a noninvasive method to observe continuous vertical profiles of [Chl a]. Today, fluorimeters commonly equip CTD (conductivity-temperature-depth) sondes and can even be built into autonomous profilers. Indeed, an increasing number of profiling floats and gliders are equipped with a fluorimeter  (Johnson et al. , 2009) , while fluorescence is becoming the main source of data for [Chl a] vertical profiles. To date, more than 67 900 fluorescence profiles are available in the World Ocean Database 2013  (Boyer et al. , 2013). However, fluorescence is only a proxy for [Chl a], meaning that the fluorescence signal needs to be calibrated for a [Chl a] estimation. Calibration coefficients (α and β, see Eq. 1) provided by manufacturers are only indicative of the response of the sensor to a given Chl a concentration in an extract or in an algal suspension and cannot be applied to all in situ conditions.
Outlines This study aims at improving knowledge on the spatiotemporal variability in the vertical distribution of the [Chl a] in the Mediterranean Sea, focusing particularly on [Chl a] seasonality. For this, all the available proxies of [Chl a] are merged to build a new database. Special attention is paid to the shape of the [Chl a] profiles: different patterns can point to different processes controlling the phytoplankton distribution. The spatial and seasonal variability in the DCM, which is one of the most common features in Mediterranean [Chl a] vertical profiles, is also specifically investigated. The objective of this paper is the description of the variability of [Chl a] vertical profiles, as they result from the interactions between many factors that can be complex as well as poorly documented.
This variability is only discussed with regard to Mediterranean hydrology and light fields. In the following section, the fluorescence database is presented, including the quality control and calibration procedures that were applied. In the results section, the seasonal and spatial variability in climatological [Chl a] vertical profiles, derived from fluorescence-based reconstructed [Chl a] profiles, is presented. Climatological results are completed by the analysis of the shape of the [Chl a] profiles. Contrary to the climatology of [Chl a] vertical profiles, the shape analysis is based on normalized [Chl a] profiles and does not account for the [Chl a] values. The seasonal variability in occurrences in principal [Chl a] vertical shapes is also investigated here. In the fourth section, certain methodological points related to the production of climatological patterns are addressed.
Data set of fluorescence chlorophyll profiles More than 6000 chlorophyll fluorescence profiles and their corresponding temperature and salinity profiles from the Mediterranean Sea in areas where bathymetry exceeds 100m depth were collected from various data sources (Table  1 ). These comprise online databases (986 profiles), French cruises (2670 profiles), the MEDAR (228 profiles), the SESAME (Southern European Seas: Assessing and Modelling Ecosystem changes) programs database (1815 profiles) and, finally, fluorescence profiles derived from Bio-Argo floats (1091 profiles). The profiles cover the whole Mediterranean Basin, although some areas are better represented than others (Fig. 1 ). Many profiles are available in the northwestern Mediterranean Sea, whereas the southwestern Mediterranean Sea and the Levantine Sea are poorly represented.
Data processing and calibration Prior to calibration, a quality control procedure was applied to fluorescence profiles. This comprises a test of uniqueness (to eliminate repetitions of the same profile), the identification of spikes (see D  'Ortenzio et al. , 2010)  and the identification of the signs of fluorometer failure (a portion of profile with exactly the same value or jumps in the fluorescence profile). After this quality control step, 593 profiles were removed from the database. Then, incomplete profiles (i. e. , profiles for which the acquisition was not deep enough to display the whole fluorescence shape) were also removed. Profiles with a surface fluorescence value lower than the bottom value were removed from the database (202 profiles removed). In addition, the profiles obtained during the three "long-duration" stations of the BOUM (Biogeochemistry from the Oligotrophic to the Ultra-oligotrophic Mediterranean) cruise  (Moutin et al.
, 2012)  573 ALMOFRONT  (Claustre at al. , 2000)  1046 DYFAMED  (Marty et al. , 2002  et al. , 2012, for a comprehensive description and validation of the procedure), the method consists of (step 1) a correction for the NPQ effect, (step 2) the adjustment to a zero value of the fluorescence profile at depth and (step 3) the application of a calibration coefficient obtained from ocean color satellite matchups. The last step has only been applied to the fluorescence profiles available for the 1998-2014 period (i. e. , the time during which SeaWiFS or MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua data were available and could be used to calculate the matchups). Step 1 provides a systematic correction of the NPQ effect by extrapolating the maximum fluorescence value observed in the mixed layer up to the surface  (Xing et al.
Considering this result, the weak efficiency of the NPQ correction method in stratified conditions should not have major consequences for the present study. Only the analysis of the surface-to-integrated-content chlorophyll ratio (see Table  3 ) should be considered with caution. Step 2 corrects the systematic instrumental offset, which impacts the whole profile, although it can only be detected at depth. Except for very specific cases, [Chl a] is considered to reach a zero value at depths where there is no more light availability. If this is not the case, a correction factor (i. e. , β in Eq. 1) is subtracted from the whole fluorescence profile, considering that the median of the 10 deepest observations is equal to 0. Profiles in which the MLD was deeper than the deepest fluorescence observation were not processed but were not removed from the database either (1.
1 % of data set). After step 1 and step 2, 5571 profiles were successfully corrected and stored in the so-called "1994-2014 database". These fluorescence profiles were used later for the shape analysis (see Sect. 2. 3 and Sect. 3. 2). In step 3, fluorescence profiles collected after 1998 were converted into [Chl a] units using a transformation based on ocean color satellite observations  (Lavigne et al. , 2012). Eight-day Level 3 standard mapped images of SeaWiFS and MODIS Aqua surface chlorophyll at a 9 km resolution were obtained from the NASA web site (http://oceancolor. gsfc. nasa. gov/) for the  1998 -2014 period (1998 -2007 for SeaW-iFS and 2008 -2014  for MODIS Aqua). The use of NASA [Chl a] standard products allows for a good consistency between SeaWiFS and MODIS data sets, thus avoiding the introduction of any bias between the two time series  (Franz et al.
Determination of the shape of fluorescence profiles On the basis of a visual analysis of the whole database, five general types of fluorescence vertical shapes were identified. These five categories, which represent the most frequent shapes of vertical distribution observed in the Mediterranean, also reflect their conditioning by physical-biological processes. These categories are referred to as DCM, homogeneous, HSC (for high surface chlorophyll), complex and modified DCM on the basis of their general characteristics (Fig. 2 ). The DCM and homogeneous shapes have been commonly used to describe [Chl a] vertical profiles  (Morel and Berthon, 1989; Uitz et al. , 2006; Mignot et al. , 2011). They are referred to as "stratified" and "mixed", respectively, and are distinguished according to the relative position of Ze and the MLD.
The DCM shape is characterized by a subsurface DCM, and the homogeneous shape by a positive homogeneous [Chl a] in the mixed layer. After an examination of the database, three other standard shapes have been introduced (i. e. , HSC, modified DCM and complex shapes) to better describe the observed variability. The HSC standard shape was defined for profiles displaying a steady decrease in [Chl a] from surface to depth (∼ 100 m), as generally observed during phytoplankton blooms  (Chiswell, 2011). The modified DCM shape describes profiles with relatively high values in the mixed layer and with a peak of [Chl a] just below the MLD. It represents an intermediate condition between the DCM and homogeneous situations. Finally, profiles with a complex shape, often displaying several peaks and a relatively high surface [Chl a], were classed as standard complex shapes.
To automatically categorize each profile of the 1994-2014 database into one of the five shape classes, a simple algorithm has been used, computing the following metrics for each profile: the depth of fluorescence maxima (D max , see Fig. 2a  and d ), the MLD, the fluorescence-integrated content in a 20m layer centered on D max (F max , see Fig. 2a ), the fluorescence-integrated content in the 0-20 m surface layer (F surf , see Fig. 2a ), the fluorescence-integrated content in the mixed layer (F MLD , see Fig. 2d ) and the total fluorescence content (F T , see Fig. 2b ). The algorithm was applied to each profile. It first tests the HSC shape. The HSC shape is assigned to a profile if its fluorescence averaged over layers of a width of 10 m decreases from surface to 100 m. Secondly, the DCM shape is tested.
Some climatological behaviors Although the availability of the calibrated profiles (1998-2014 database) should allow us to generate interpolated products on a regular mesh grid (as, for example, the World Ocean Atlas;  Conkright et al. , 2002) , we preferred to avoid any large interpolation and only present Mediterranean patterns for locations well represented in our database. Hence, monthly climatologies of [Chl a] vertical profiles were computed for four geographical areas (i. e. , 4 • × 4 • boxes) where the data density was high. These locations were also placed in four main Mediterranean subbasins (i. e. , centered on 42 • N, 5 • E in the northwestern basin, 38 • N, 5 • E in the southwestern basin, 36 • N, 17 • E in the Ionian Sea and 34 • N, 30 • E in the Levantine Sea; see yellow diamonds in Fig.
Regional average values and standard deviations (numbers in brackets) for a set of parameters. The winter MLD was computed with January and February MLDs. The DCM depth and the PAR (photosynthetically active radiation) at DCM has been computed only for profiles belonging to the DCM standard shape category. PAR at DCM has been determined for each fully calibrated (i. e. , 1998-2014 database) [Chl a] vertical profiles. The vertical profile of the PAR attenuation coefficient was computed from [Chl a] vertical profiles and applied to surface PAR estimates derived from the monthly SeaWiFS PAR climatology. For the nitracline depth, the isoline 1 µM was computed on a large set of nitrates profiles derived from MEDAR and SESAME programs (see  Lavigne et al. , 2013 , for details about their database).
1 0. 2 0. 3 0. 4 summer q q q q q q q q q q q q q 0 0. C. Point: 36°N, 17°E / Ionian q q q q q q q q q q q q q 0 0. 1 0. 2 0. 3 winter q q q q q q q q q q q q q 0 0. 1 0. 2 0. 3 spring q q q q q q q q q q q q q 0 0. 1 0. 2 summer q q q q q q q q q q q q q 0 0. 1 0. 2 D. Point: 34°N, 30°E / Levantine q q q q q q q q q q q q q 0 0. 1 winter q q q q q q q q q q q q q 0 0. 1 0. 2 0. 3 0. 4 0. 5 spring q q q q q q q q q q q q q 0 0. In summary, the results of this comparison demonstrate that, although the MEDATLAS database is extremely valuable, the derived MEDATLAS fields for [Chl a] present serious limitations, and they need to be updated.
Methodological approaches In the present study, two different approaches have been used to describe the monthly variability in [Chl a] profiles. On one hand, the standard method consists of averaging [Chl a] values for a number of defined standard depths (i. e. ,  Conkright et al. , 2002; Sect. 3. 1). On the other hand, a "probabilistic" method (Sect. 3. 2), for which each [Chl a] profile is considered as a whole, focuses the analysis on each profile's general shape and on specific features (e. g. , DCM depth). The second approach requires an a priori knowledge of the different profile shapes found in the database as well as the definition of an efficient and automatic procedure to categorize the profiles. In this analysis, the main standard shapes and the classification procedure were defined after individual visualization of all the fluorescence profiles in the database and the determination of their characteristics (i.
Conclusions Since the initial work of the MEDAR/MEDATLAS group  (Maillard et al. , 2005; Manca et al. , 2004) , the proposed study represents the first attempt to analyze the seasonal variations in the [Chl a] vertical distribution over the Mediterranean Sea. The picture of the [Chl a] field in the basin has been updated here, as it had been mainly derived from surface satellite data or from limited and scarce in situ observations. Chlorophyll a fluorescence data (specifically calibrated and consistently processed with a dedicated method) provided a significantly larger database than the commonly The present study was often limited by the quantity of data, which did not allow for the analysis of each region of the Mediterranean Sea (e. g. , the Adriatic Sea). We regret the singular absence of fluorescence profiles in oceanographic databases compared to other parameters.
For instance, in the MEDAR database, there are 118 009 salinity profiles, 44 928 oxygen profiles and only 1984 chlorophyll a fluorescence profiles. Finally, in this study we were only able to present climatological behavior. Although it is a first and necessary step for a better understanding of processes which impact seasonal variability in [Chl a] vertical profiles, it would be interesting to further study certain particular cases showing, with a high-frequency, annual series of vertical [Chl a] profiles. These data have now become available with the development of Bio-Argo floats  (Johnson et al. , 2009)  and some studies have already demonstrated their potential for such applications  (Boss and Behrenfeld, 2010; Mignot et al. , 2014).
Acknowledgements. The authors would like to thank all people involved in the collection and distribution of the oceanographic data used in this paper. We thank PANGAEA, SISMER, the National Oceanographic Data Center and the  OGS data center  for making data freely available online. The programs LEFE-CYBER,  Bio-Argo , MEDAR and IP SESAME are thanked for their valuable contribution to Mediterranean databases. The  US NASA Agency  is thanked for the easy access to SeaWiFS and MODIS data. We acknowledge the support of the  European Commission  (Cofunded by the  European Union  under  FP7   -People -Co-funding of Regional, National and International Programmes , GA no. 600407 ) and of the  RITMARE Flagship Project. This work is also the contribution to the  PABO (Plateformes Autonomes and Biogeochimie Oceanique) , funded by the  ANR , to the  French "Equipement d'Avenir "  NAOS  project ( ANR J11R107-F ) and to the remotely Sensed Biogeochemical Cycles in the Ocean (remOcean) project, funded by the  European Research Council  (grant agreement  246777 ).