dataesr/scientific-paragraphs-categorization huggingface dataset:
For bacterial identification, the spectrum of a strain of interest is compared to a spectrum library. The problem is that these spectra are usually made under stringent conditions and on reference collection strains (human pathogen mainly causing infectious disease) that do not reflect the strains in their natural environment. The analysis of 16S rDNA sequences is a widely used method to identify bacterial species because it allows to compare 16S rDNA sequences to differentiate the bacteria at the genus/species level. However, the quality of the 16S DNA sequence databases used is an important factor. The sequences present in the databases serve as a reference for taxonomic The identification of more Gram + bacteria could be due to the bacterial biodiversity particularly of nature samples and/or the difficulty of growing viable culturable bacteria from natural habitats  (Popovic, 2017).
We showed that 57. 8% of the strains from seafood were identified with a Bruker score higher than 2 and 21. 2% were identified with a Bruker score between 1. 7 and 2, which allowed identification to the species level as in the study of  Böhme et al. (2013)   However, in 15% (27/180 strains) of the cases, MALDI-TOF MS gives a misidentification of the species with Bruker scores close to 2. The MALDI-TOF MS identification spectrum therefore matched a reference spectrum of a close species for the majority of strains (23/27). The explanation was that reference spectra of bacterial species were not present in the Bruker BioTyper TM database, but the data in 16S rDNA sequence exist. For four others, the identification at the Bacillus genus level was identical but was divergent at the species level, while the spectra of these species were present in the MALDI-TOF BioTyper TM database.
The similarity of the protein structures of the species made the discrepancy of differentiation of the quality peak difficult and made it difficult for MALDI-TOF MS to correctly identify the strains. This has been described for both anaerobic bacteria and other genera, such as Bacillus spp. (Takahashi et al. , 2020) , Streptococcus spp. (Fan et al. , 2017) , Mycobacterium spp. (Zingue et al. , 2016) , Enterococcus spp. (Lallemand et al. , 2017) , and yeast  (Ling et al. , 2014). Therefore, updating the existing information and perfecting the database of difficultly identified organisms are useful to improve the identification accuracy of MALDI-TOF MS  (Li et al. , 2019). We also confirmed the unreliability of MALDI-TOF MS identifications at Bruker scores below 1. 7. The primary recommended method for obtaining bacterial IDs from colonies is the cell smear (direct transfer) method.
Different observations were made previously by  Schulthess et al. (2016) , which showed that after ethanol-formic acid extraction and analysis of MALDI-TOF MS identifications with scores below the threshold of 1. 7, 128 Gram-rods were identified at the genus level among 151 analyzed. Despite this low number of strains and low species diversity, studies by  Böhme et al. (2010 Böhme et al. ( , 2011 Böhme et al. ( , 2013) )  showed the interest of building a reference spectrum database specific to each pathway/domain/industry. In conclusion, we have shown that the MALDI-TOF MS in association with the Bruker BioTyper TM V7. 0. 0 database allowed a better strain identification to the species level contrary to the 16s rDNA sequencing, which allowed an identification mainly to the genus level. MALDI-TOF MS in association with the Bruker database and 16S rDNA sequencing are powerful tools to identify a wide variety of bacteria from seafood but require further identification by biochemical, molecular technique or other conventional tests.
Identification of isolates by MALDI-TOF method with Bruker score < 1. 7 and 16S rDNA technique. Yes, but no Genius sp. MALDI-TOF, matrix-assisted laser desorption ionization-time of flight. assignment. The richness and precision of these databases are therefore a determining factor for good bacterial identification. Sometimes, there are several different 16S rDNA databases that have their advantages and disadvantages. In our study, we queried the NCBI 16S ribosomal RNA database as well as RDP database.
753 (16) Total 27 MALDI-TOF, matrix-assisted laser desorption ionization-time of flight. reference spectrum of these species B. pumilus, B. safensis, B. nealsonii, and B. aquimaris were in the Bruker BioTyper TM database. Finally, 17. 4% (124/713 strains) obtained a Bruker score below 1. 7 by MALDI-TOF MS, which normally does not allow any reliability of identification (Table.
Acknowledgments. This article owes much to the inspiring suggestions and challenging comments of IJMES editor  Joel Gordon  and the anonymous reviewers. I also thank  Adam Mestyan ,  Mehdi Sakatni ,  Talal al-Rashud, Faisal al-Wazzan, Eid al-Yahya, Abdulrahman Alebrahim, and 'Ali al-Kandari  for discussing many of the topics dealt with in this article, and  Badr and Sami al-Bassam  for uncovering the family's history.
Studying the biogeochemistry of the Southern Ocean using remote sensing relies on accurate interpretation of ocean colour through bio-optical and biogeochemical relationships between quantities and properties of interest. During the Antarctic Circumnavigation Expedition of the 2016/2017 Austral Summer, we collected a spatially comprehensive dataset of phytoplankton pigment concentrations, particulate absorption and particle size distribution and compared simple bio-optical and particle property relationships as a function of chlorophyll a. Similar to previous studies we find that the chlorophyll-specific phytoplankton absorption coefficient is significantly lower than in other oceans at comparable chlorophyll concentrations. This appears to be driven in part by lower concentrations of accessory pigments per unit chlorophyll a as well as increased pigment packaging due to relatively larger sized phytoplankton at low chlorophyll a than is typically observed in other oceans.
Consequently, substantial field sampling and the collection of both in situ biological and bio-optical datasets are essential to the application of ocean color remote sensing approaches. An additional benefit of establishing robust relationships is that by measuring one observable variable-for example the chlorophyll a concentration at the surface which can be achieved with ocean color remote sensing-further information can be inferred about correlated parameters like phytoplankton community composition, average size, or vertical distribution  [10, 11] , all of which have implications for ecosystem function and the efficiency of the BCP. When sunlight enters the surface ocean, it is absorbed and scattered by particles (phytoplankton and other non-algal particles), coloured dissolved organic matter (CDOM) and water, and partially scattered back out of the surface ocean with a spectrum that is different to that of the incoming sunlight  [12].
The Antarctic Circumnavigation Expedition (ACE) presented an opportunity to collect the most spatially extensive SO dataset for investigating the relationship between phytoplankton particulate absorption, pigments and particle size. Specifically, we investigated the variations in phytoplankton pigments and community composition with chlorophyll a and pheopigment concentration and the impact on the phytoplankton absorption coefficients. We also looked into the changes in particle volume as a function of chlorophyll a. The results from this study provide a set of chlorophyll-specific particulate absorption coefficients for modelling phytoplankton abundance and particulate absorption in this region.
Study area and sampling Samples were collected during legs 1-3 of the ACE expedition (see Walton and Thomas  [42] ) aboard the RV Akademik Tryoshnikov during the Austral Summer from 20 December 2016-19 March 2017. Legs 1-3 of the expedition travelled east around the Southern Ocean from Cape Town, South Africa, through the Indian Ocean to Hobart, Australia (leg 1), via the Pacific Ocean to Punta Arenas, Chile (leg 2), and through the Atlantic Ocean back to Cape Town (leg 3). During legs 1 and 3, the ship's meridional position remained within approximately 40 to 60 • S, venturing further south to almost 80 • S during leg 2 (Fig. 1 ). In addition, a number of subantarctic islands and the Antarctic continent were visited. Most locations are highlighted in Fig. 1. The voyage GPS track and other datasets cited here are available at https://zenodo.
Physical oceanography A thermosalinograph (Chelsea Technologies AquaLine Ferrybox system) connected to the underway seawater supply collected a semi-continuous record of sea surface temperature (SST; • C) and salinity of the surface ocean (data available at  [43] ). The mixed layer depth (MLD; m) at each sampling point was retrieved by linearly interpolating the climatological monthly MLD means from de Boyer Montégut et al. [44]  to the sampling dates. The MLDs were derived using the density threshold method (depth where σ θ =σ θ 10m +/-0. 03 kg m -3 ;  [44] ; Fig. 2 ). The MLD was additionally calculated using density profiles derived from conductivity-temperature-depth (CTD;  [45] ) casts. However those measurements were sporadic and cannot be interpolated meaningfully along the complete ship transit.
Filter-pad absorption measurements Particulate absorption measurements were made (first) on the same filters analysed for pigments. A full description of the measurement protocols and the data are available at  [52]. Briefly, measurements of optical density from 350-750 nm (1 nm interval) were blank corrected and converted into particulate absorption (a p (λ); m -1 ). Unless specified, all chlorophyll-specific coefficients presented in this paper have been derived by dividing by [Tchla + Pheoa].
Decomposition of phytoplankton and non-algal particle absorption The phytoplankton (a φ (λ); m -1 ) and non-algal particle (a nap (λ); m -1 ) absorption spectra were numerically decomposed using a semi-analytical inversion of the particulate absorption spectra using Bricaud et al. [53]  and Bricaud et al. [54]  phytoplankton chlorophyll-specific spectral shapes as the eigenvectors. Equation (3) was fitted to the particulate absorption spectra with a least squares fit with trust-reflective algorithm  [55, 56]  in Matlab 2019b. a p (λ) = C 1 A ϕ (λ)⟨Chl Bricaud ⟩ E ϕ (λ)-1 + a nap (λ ref )e -S(λ-λ ref ) + Residual(λ) ( 3 ) where the A φ (λ) and E φ (λ) are the input coefficient and exponent for the power law describing phytoplankton absorption as a function of [Tchla + Pheoa] from Bricaud et al.
Pigment and packaging indices The contribution of three pigment-based sized classes  [57, 58] , microphytoplankton (f micro ; > 20 µm, unitless), nanophytoplankton (f nano ; 2-20 µm, unitless), and picophytoplankton (f pico ; < 2 µm, unitless) to the pigment biomass were calculated according to  [10]. Pigment packaging was quantified following the pigment reconstruction technique  [53]  to derive a sol (λ), the absorption coefficient of the same material dispersed into solution. The a sol (λ) is calculated as a sol (λ) = ΣC i a * sol,i (λ) (5) where a * sol,i coefficients are the weight-specific absorption spectra of individual pigments (m 2 mg -1 ) (from Clementson and Wojtasiewicz  [59] ), and C i are their concentrations in the medium (mg m -3 ). The Clementson and Wojtasiewicz  [59]  dataset was selected as it includes a wide variety of accessory carotenoid pigments, as well as chlorophyll a degradation pigments, chlorophyllide a, pheophorbide a, and pheophytin a.
The spectra from  [59]  were wavelength shifted by first correcting for the differences in refractive index between the solvent and water (i. e. multiply the wavelength by the ratio of the solvent refractive index and water refractive index) as in  [60, 61]. The spectra of all pigments were then shifted to in vivo positions to match the positions in Bricaud et al. [53]  and Bidigare et al. [62]. See Supplement 1 for the wavelength corrected spectra, refractive index multipliers, and wavelength shifting information for each pigment. The pigment packaging index (Q * a (λ)) is calculated as Q * a (λ) = a ϕ (λ)/a sol (λ) (6) The a miss (440) term from Bricaud et al. [53]  was not included in the Q * a (440) calculation here as the relationship from Bricaud et al. [53]  did not appear to be correct for this ACE dataset, and when applied, reduced the Q * a (440) by 50 %.
Comparison data For comparison, data from the Southern Ocean (south of 40 • S) within the NASA bio-Optical Marine Algorithm Dataset v2. a (NOMAD)  [63]  are presented as grey symbols in some figures. The locations of these samples are constrained primarily to Drake's Passage, Scotia Sea and Antarctic Peninsula waters and along parallels between 80-90 • E in the Indian Ocean sector (Fig. 1 ). The Bricaud et al. [53, 54, 64]  datasets are used herein as comparison with our dataset. They include data from the Mediterranean Sea, North Atlantic Ocean, Benguela Upwelling and Pacific Ocean. A significant fraction of the 1998 dataset, especially at high chlorophyll concentration are from the Gulf of St-Lawrence. The pigment and particulate absorption data from  [53]  are also included as mauve symbols in some figures for comparison.
For ease of interpretation, the following acronyms have been assigned to these datasets: B98 for Bricaud et al. [64] , B04 for Bricaud et al. [53]  and B10 for Bricaud et al. [54]. It is noted that many bio-optical papers presenting chlorophyll-specific parameterisations and statistics vary in their definition of total chlorophyll a and the abbreviations or symbols used to represent the inclusion of chlorophyll a derivatives and pheopigments. For example the chlorophyll-specific parameters and functions of B98, B04 and B10 include pheopigments in the calculations or normalisations, whereas Uitz et al. [10]  does not. As we show below, the concentration of pheopigments, pheophorbide a and pheophytin a ([Pheoa]) in our ACE dataset vary from 5 % to > 100 % of the  [Tchla]. In contrast pheopigments contributed < 5 % in the B04 dataset.
Physical and biological oceanography during sampling Sea surface temperature (SST) decreased with increasing latitude (Fig. 2 ) as expected  [34] , transitioning from warmer subtropical waters with a maximum of 22 • C north of the STF in the Atlantic Ocean sector, to -2 • C in Antarctic waters south of the SACCF. SST ranged from 5-16 • C in the SAZ, 4-10 • C in the PFZ, 2-4 • C in the AZ and subzero south of the SACCF (Fig. 2 ), typical of Austral summer conditions  [65]. The MLDs from in situ CTD casts and from climatological monthly means of de Boyer Montégut et al. [44]  are presented in Fig. 2. Overall the trends in the in situ and climatology datasets agree well (See section Physical Oceanography). Surface mixed layers ranged from 15 to 100 m in situ and 20 to 80 m in the climatology.
The MLDs are noticeably shallower in the Pacific sector of the Southern Ocean, typically < 40 m, where the ship predominately surveyed waters in the MIZ south of the SACCF. Increasing sea-ice meltwater in December is known to form shallow surface mixed layers of around 20 m in the Ross and Amundsen Seas  [66] , which can however deepen with changes in cross-shelf advection. The deepest MLDs were observed during the time the ship was near Kerguelen and Bouvetoya Islands (Fig. 2 ). At both islands, a combination of changes to the bathymetry and surface advection, which can also be influenced by nearby frontal boundaries, typically results in a deepening of the MLD relative to surrounding open waters  [67] [68] [69]. A deepening of the MLD at Siple Island in the climatological database were not observed in the CTD temperature profiles, which remained shallow before and after Siple Island.
Very high pheopigment concentrations at high latitude In our dataset, pheopigments (pheophorbide a and pheophytin a) make up a significant proportion of the total phytoplankton pigment concentrations, especially at latitudes in the MIZ (Fig. 3 ). At low latitudes 40-60 • S, pheopigments were on average 10% of the [Tchla], but at latitudes higher than 60 • S their contribution increased drastically (up to > 100 %, mean 27 % +/-18 %; Fig. 3 ). Pheopigment concentrations in the surface waters of oligotrophic ocean regions (or hyperoligotrophic ocean regions) have typically not been reported at levels higher than 5 % of [Tchla]  [53, 54, 82]  (See mauve symbols in Fig. 3 from B04 ). The studies of Mendes et al. [83]  and Wright et al. [84]  which conducted North to South transects of the Indian Ocean sector of the SO both reported increasing pheopigment concentrations with increasing latitude.
, prasinophytes, euglenoids, cryptophytes, dinoflagellates (both autotrophic and heterotrophic), small lightly silicified diatoms, and cyanobacteria  [36] [37] [38] 87]. South of the PF however, diatom species typically dominate the composition with haptophytes, especially Phaeocystis sp. , in high abundance  [28, 36, 38, 39]. Similar to Uitz et al. [10]  data from the SO, the picophytoplankton fraction (f pico ) in our dataset never exceeded 10 % of the total pigment biomass (Fig. 5 ), however, cyanobacteria Synechococcus sp. have been reported to contribute 20 % of cell counted biomass within Northern reaches of the SAZ (via flow cytometry)  [87]  and as such, their low contribution to the pigment biomass may simply be due to their low chlorophyll content  [88]. In Uitz et al. [10] , the authors suggest that the surface layer mixing regime may help explain the contribution of (f micro ) to total pigment biomass.
They found that in "mixed" waters of the Ross Sea (South of 60  dominant species or class of phytoplankton blooms varies between haptophyte species Phaeocystis sp. , coccolithophores like Emiliania huxleyi, diatoms, and cryptophytes  [92, 93]. In our ACE dataset, the mixing regime alone did not explain much of the variability in microphytoplankton abundance (Fig. 4 ; Z eu /MLD indicated by marker size), and the Z eu /MLD was strongly dependent on [Tchla] with the mixed vs stratified regime boundary occurring roughly at 0. 2-0. 3 mg m -3.
Surface [Tchla] does not relate well to the dominant phytoplankton size class In Uitz et al. [10] , the authors presented a simple relationship between [Tchla] and f micro for discrete trophic (chlorophyll a) categories or ranges as a means to infer community composition from the surface chlorophyll a, which are presented in Fig. 4  overlaying our ACE dataset. In addition to the relationship parameterised for well-mixed waters of the Ross Sea (Uitz "SO Mixed"), the relationships for well-mixed waters from 60   at low [Tchla]. However, the proportion of f micro within each chlorophyll a range (see Uitz et al. [10]  for the 7 stratified tropic categories) within stratified waters in the ACE dataset is highly variable, and cannot be parameterised as a function of [Tchla]. In our dataset, at [Tchla] between 0.
2-0. 8 mg m -3 the average f micro in mixed waters is close to that observed in the equivalent trophic categories in the Uitz et al. [10]  SO mixed dataset, but also highly variable. At [Tchla] > 0. 8, f micro in our dataset continues to increase, contrary to the Uitz et al. [10]  dataset. In fact at high [Tchla] > 0. 8, f micro is much higher than all of the statistics in Uitz et al. [10]. This may be a reflection of the greater importance of diatoms to Southern Ocean phytoplankton composition relative to other oceans. Interestingly the ACE samples with high [Tchla] and f micro in "well-mixed" waters were sampled within the SAZ which is typically co-limited by iron and light  [40, 72]. These samples were predominately found on the Kerguelen Plateau (and also near Bouvetoya Island) where subsurface pools of dissolved iron on the Kerguelen Plateau within the upper 100 m were observed during ACE by Janssen et al.
Similar to our study, the study by Hewes  [97]  in waters south of the ACC identified a low chlorophyll a scenario where phytoplankton in low-iron conditions in shoaled waters were dominated by microphytoplankton. Although Z eu /MLD was derived using a climatological MLD, this is not biasing the relationships between f micro and [Tchla]. We observed the same trends in [Tchla] vs f micro in a smaller sample set of n=28 using MLDs calculated from CTD density profiles, and corresponding pigment samples collected using niskin bottles on the same casts at surface depths < 10 m (data not shown). It is clear from this ACE dataset that the paradigm of phytoplankton size class increasing and decreasing proportionally with phytoplankton biomass (e. g. , chlorophyll a concentration)  [98, 99] , does not always apply to the SO, and as we show below has implications for the interpretation of bio-optical properties.
Particulate absorption is tightly linked to [Tchla] with significant background of non-algal particles The particulate absorption coefficient at 440 nm (a p (440), m -1 ) in the ACE and NOMAD datasets increased with [Tchla] in a similar manner to B04  [53]  and B98  [64]  [Fig. 7 (a);  ) that may arise from an increased proportions of non-algal particles (e. g. , heterotrophic bacteria, detritus, viruses, minerogenic particles; see also Fig. 16 ). This is reminiscent of the relationship between particulate backscattering and chlorophyll a seen in Brewin et al. [104]  which was attributed to an increased background of non-algal particles and the observation by Zhang et al. [105]  of a significant background signal from sub-micron particles in the particulate backscattering in low chlorophyll a waters of the North Pacific which become a more important part of the signal at low chlorophyll a compared to at high chlorophyll a.
Phytoplankton absorption is lower than in other oceans for the same [Tchla] The phytoplankton absorption coefficient at 440 nm [a φ (440)] as a function of [Tchla] [Fig. 7(b) ; Table  1 ] reflects the absorption by all photosynthetic and non-photosynthetic pigments in this waveband. It was 50% lower than the relationship derived by B04  [53]  for the first optical depth in tropical and temperate oceans and seas (ACE data a φ (440)=0. 036[Tchla] 0. 82 R 2 =0. 91). At [Tchla] greater than 0. 1 mg m -3 that ACE values were more similar to the NOMAD dataset and at >0. 3 mg m -3 the ACE values a φ (440) are more similar to the B98  [64]  dataset, which included data from all depths up to 200 m (i. e. , greater than 1 optical depth) where samples are strongly impacted by pigment packaging and hence substantially lower than the B04  [53]  coefficients.
[82]  (0. 0207 m 2 mg -1 ), Bidigare et al. [62]  (0. 0199 m 2 mg -1 ) and Clementson and Wojtasiewicz  [59]  (0. 0196 m 2 mg -1 ). However, a * φ (676) declined with [Tchla] (a * φ (676)=0. 020[Tchla] 0. 82-1 ) [and with increasing cell volume, see Fig. 8 (b)], and was substantially lower than the B04  [53]  and B04+B10  [54]  relationships at Tchla] > 0. 1, suggesting that this ACE dataset is more packaged than observed in other temperate and tropical oceans. The a * φ (676) was most similar to the NOMAD data from the SO, and B98 [64] data from temperate and tropical oceans which has also been described as being impacted by packaging. Comparing Figs. 8(a   1 ], it is clear that on a case by case basis, pheopigments may contribute substantially to a φ (676) and cannot be discounted as being an important part of the optical constituents in the Southern Ocean, especially at high latitudes.