dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to  N Konstantinides ,  A Pavlopoulos ,  E Kabrani ,  Z Kontarakis  and  M Handberg-Thorsager  for sharing constructs and transgenic lines;  N Konstantinides  for advice on live imaging;  J Schinko  for help with cloning;  N Konstantinides ,  K Echeverri ,  J Casanova ,  M Strigini ,  P Ramos ,  A Gilles  and  M Grillo  for comments on the manuscript. This work was supported by a  Chaire d'Excellence grant  from the  Agence Nationale de la Recherche  ( ANR-12-CHEX-0001-01 ).
The faecal specimen was preserved at -80°C after collection. Strain SIT10 was isolated on Columbia agar supplemented with 5% sheep's blood (bioMérieux, Marcy-l'Étoile, France) in aerobic and anaerobic condition using GasPak EZ Anaerobe Container System Sachets (Becton Dickinson (BD), San Diego, CA, USA) at 37°C. Strain SIT10 exhibited a 97. 6% 16S rRNA sequence identity with Bacillus massiliogorillae strain G2 (NZ_CAVL000000000. 2), the phylogenetically closest bacterial species withstanding in nomenclature (Fig. 1 ). Its 16S rRNA sequence was deposited in GenBank under accession number LN827531. This value was lower than the 98. 7% 16S rRNA gene sequence threshold recommended by Stackebrandt and Ebers  [5]  to delineate a new species without carrying out DNA-DNA hybridization. The spectrum from SIT10 was added to our MALDI-TOF MS database.
Extended Features Descriptions MALDI-TOF MS  [6]  protein analysis was carried out as previously described. The SIT10 spectra were imported into the MALDI BioTyper software (version 3. 0; Bruker Daltonics, Leipzig, Germany) and analysed by standard pattern matching (with default parameter settings) against 7765 spectra of bacteria, including 231 spectra from Bacillus genus. The method of identification included the m/z from 3000 to 15 000 Da. A maximum of 100 peaks were compared with spectra in the database for every spectrum. The resulting score enabled the identification (or not) of tested species: a score of 2 with a validly published species enabled identification at the species  NMNI level, a score of 1. 7 but <2 enabled identification at the genus level, and a score of <1. 7 did not enable any identification.
No significant MALDI-TOF MS score was obtained for strain SIT10 against the Bruker database, suggesting that our isolate was not a member of a known species. We added the spectrum from strain SIT10 to our database Genome sequencing and assembly Genomic DNA of Bacillus testis strain SIT10 was sequenced on the MiSeq Technology (Illumina, San Diego, CA, USA) with the mate pair strategy. The assembly was performed using Soap De Novo with 179% coverage, and 1 910 663 paired reads were filtered according to the read qualities. Its leads to scaffolds and large contigs (>1500 bp) generated a genome size of 3. 99 Mb.
Genome annotation and comparison Genome was annotated by Rapid Annotation using Subsytem Technology (RAST) bioserver  [7]. The predicted bacterial protein sequences were searched against the GenBank database and the Clusters of Orthologous Groups (COGs) databases using BLASTP (E value 1e-03, coverage 0. 7 and identity percentage 30%). The tRNAScanSE tool was used to find tRNA genes  [8] , whereas ribosomal RNAs were found by using RNAmmer and BLASTn against the GenBank database  [9]. The resistome was analysed with ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation) database  [10]. The exhaustive search for bacteriocin was performed using the database available in our laboratories (Bacteriocins of the URMITE database BUR; http://drissifatima. wix. com/bacteriocins). Protein sequences from this database allowed putative bacteriocins from human gut microbiota to be identified using BLASTp methodology  [11].
Analysis of the presence of polyketide synthase and nonribosomal peptide synthase was performed by discriminating the gene with large size using a database realized in our laboratory, and predicted proteins were compared against the nonredundant (nr) GenBank database using BLASTp. Phylogenetic relationships with closely related species were determined by MEGA6. The evolutionary history was inferred by using the maximum likelihood method based on the JTT matrix-based model. We compared the genome sequence of Bacillus testis strain SIT10 with those of Bacillus massiliogorillae strain G2b (NZ_CAVL000000000. 2), Bacillus cereus ATCC 14579 (NC_004722. 1) and Bacillus psychrosaccharolyticus ATCC 23296 (NZ_AJTN00000000. 2).
27 Nucleotide transport and metabolism H 89 2. 35 Coenzyme transport and metabolism I 124 3. 28 Lipid transport and metabolism P 203 5. 38 Inorganic ion transport and metabolism Q 71 1. 88 Secondary metabolites biosynthesis, transport and catabolism R 423 11. 21 General function prediction only S 264 6. 99 Function unknown - 1256 33. 29 Not in COGs COGs, Clusters of Orthologous Groups database.
C. Analysis The evolution under the scalar coupling of each point in the resulting spectra was fit using either I τ j = I 0 × C τ j × exp  - 2τ j T ′ 2  (10) 055008-6 Guerry, Brown, and Smith AIP Advances 6, 055008 (2016) for experiment A, or I τ j = I 0 × ( S 2 τ C τ j + S τ C τ S τ j ) × exp  - 2τ j T ′ 2  (11) for experiments B and C, after normalizing the data and adding Gaussian noise (σ = 0. 05) and an exponential decay (T ′ 2 = 15 or 45 ms). The results obtained using custom-written Python 2. 7 scripts were confirmed by using the fit command in Gnuplot 4. 6. 6. for a subset of the data. Fitting errors and the correlations between the fitting parameters were obtained from the associated covariance matrix. Equation (  9 ) was evaluated for the values of τ max j listed in Table  II , by calculating the values of the numerator and denominator for 10,000 points equally spaced from τ j = 0 to τ max j.
Abstract. As formaldehyde (HCHO) is a high-yield product in the oxidation of most volatile organic compounds (VOCs) emitted by fires, vegetation, and anthropogenic activities, satellite observations of HCHO are well-suited to inform us on the spatial and temporal variability of the underlying VOC sources. The long record of space-based HCHO column observations from the Ozone Monitoring Instrument (OMI) is used to infer emission flux estimates from pyrogenic and biogenic volatile organic compounds (VOCs) on the global scale over 2005-2013. This is realized through the method of source inverse modeling, which consists in the optimization of emissions in a chemistry-transport model (CTM) in order to minimize the discrepancy between the observed and modeled HCHO columns. The top-down fluxes are derived in the global CTM IMAGESv2 by an iterative minimization algorithm based on the full adjoint of IMAGESv2, starting from a priori emission estimates provided by the newly released GFED4s (Global Fire Emission Database, version 4s) inventory for fires, and by the MEGAN-MOHYCAN inventory for isoprene emissions.
php), the OMI rows presenting higher levels of noise and fitting residuals than the average were systematically removed from the dataset  (De Smedt et al. , 2015). Although this filtering leads to a loss of coverage, the resulting dataset is more appropriate for addressing trend studies, as explained in De  Smedt et al. (2015). The IMAGESv2 global model calculates the concentrations of 131 transported and 41 short-lived trace gases with a time step of 6 h at 2 • × 2. 5 • resolution between the surface and the lower stratosphere. The effect of diurnal variations is accounted for through correction factors on the photolysis and kinetic rates obtained from model simulations with a time step of 20 min, which are also used to calculate the diurnal shapes of formaldehyde columns required for the comparison with satellite data.
A detailed model description is provided in  Stavrakou et al. (2013). Meteorological fields are obtained from ERA-Interim analyses of the European Centre for Medium-range Weather Forecasts (ECMWF). The model uses anthropogenic NO x , CO, SO 2 , NH 3 , and total NMVOC emissions from the Emission Database for Global Atmospheric Research (EDGAR4. 2, http://edgar. jrc. ec. europa. eu), which is overwritten by the EMEP inventory (http://www. ceip. at/ms) over Europe and by the REASv2 inventory  (Kurokawa et al. , 2013)  over Asia. The NMVOC speciation is obtained from REASv2 over Asia and from the RETRO inventory  (Schultz et al. , 2007)  elsewhere. The emissions over the US are scaled according to the NEI national totals for all years between 2005 and 2013 (http://www. epa. gov/air-emissions-inventories/ air-pollutant-emissions-trends-data).
Biomass burning emissions are taken from the latest version of the Global Fire Emissions Database, GFED4s (July 2015), which includes the contribution of small fires based on active fire detections  (Randerson et al. , 2012; Giglio et al. , 2013). The GFED data are available on a daily basis at 0. 25 • × 0. 25 • resolution from 1997 through the present at http://www. globalfiredata. org. Those emissions are distributed vertically according to  Sofiev et al. (2013). A priori isoprene emissions are obtained from the MEGAN-MOHYCAN model  (Müller et al. , 2008; Stavrakou et al. , 2014)  for all years of the study period at a resolution of 0. 5 • × 0. 5 • (http://tropo. aeronomie. be/models/isoprene. htm). Besides the emission dependence on leaf temperature, photosynthetically active radiation (PAR), leaf area, and leaf age, the model accounts for the inhibition of isoprene emissions in very dry soil conditions through a dimensionless soil moisture activity factor (γ SM ) expressed as a function of volumetric soil moisture content  (Guenther et al.
, 2006)  obtained from the ERA-Interim reanalysis. The parameterization of γ SM bears large uncertainties, as it is based on scarce (and sometimes contradictory) field data, and its implementation can lead to very different results depending on the choice of database for soil moisture data  (Müller et al. , 2008; Sindelarova et al. , 2014; Marais et al. , 2012). It reduces the emissions by ca. 20 % globally according to MEGAN-MOHYCAN, with strongest effects (up to factor of 3 or more) over Australia and southern Africa and to a lesser extent over northern Africa (Sahel), the western US, and the Middle East. The chemical degradation mechanism of pyrogenic NMVOCs is largely described in  Stavrakou et al. (2009a) , with only minor modifications. This mechanism includes an explicit treatment for 16 pyrogenic formaldehyde precursors.
sedoo. fr). The mean isoprene emission amounts to 452 and to 570 Tg year -1 , according to the GUESS-ES and to the MEGAN-MACC in-ventory, respectively (Table  1 ), and both lie much higher than the a priori MEGAN-MOHYCAN inventory (343 Tg year -1 average over  [2005] [2006] [2007] [2008] [2009] [2010] [2011] [2012] [2013]. The large discrepancy between MEGAN-MACC and MEGAN-MOHYCAN datasets, both relying on the MEGAN emission model  (Guenther et al. , 2006)  and the same version of basal emission factors  (version 2011) , can be explained to a large extent by (i) the neglect of soil moisture stress effects in MEGAN-MACC, (ii) a reduction by a factor of 4. 1 of the basal emission factors for forests in Asia in MEGAN-MOHYCAN  (Stavrakou et al. , 2014)  as suggested by field observations in Borneo  (Langford et al.
, 2010) , and (iii) the use of the crop distribution database of  Ramankutty and Foley (1999)  in MEGAN-MOHYCAN, along with the necessary adjustment of the other plant functional type distributions, leading overall to larger crop extent and lower total emissions. The average global fire flux, expressed as burned biomass, is reduced from 2028 Tg C year -1 (GFED4s) to 1653 Tg C year -1 after optimization (Table  1 ). Note that the inversion provides updated VOC emissions of HCHO precursors. However, to ease the comparison with other inventories, VOC emissions are converted to carbon emissions through the use of emission factors obtained from the compilation of  Andreae and Merlet (2001)  (with 2011 updates). It should be acknowledged that the top-down estimates given here for fuel consumption may be affected by errors in the emission factors as well as by errors in the formaldehyde yields per VOC.
16 ). More importantly, it would imply a general underestimation of our top-down emissions in low-NO x environments and tropical forests in particular. In south China, the negative summertime trend (-0. 7 % year -1 ) in HCHO columns drives a change in the sign of the 2005-2013 isoprene trend: from 0. 1 % year -1 in the a priori to -1. 6 % year -1 in the OMI-based fluxes. The very small a priori emission trend results from a combination of compensating effects: on the one hand, declining trends in the ERA-Interim photochemically active radiation (PAR) (-0. 33 % year -1 ) and temperature (-0. 03 K year -1 ), and on the other hand, an increasing trend in leaf area index (1 % year -1 , cf. Fig S5 ) and a decline in crop extent in south China, suggested by the land use database of  Ramankutty and Foley (1999)  and supported by MODIS land cover data  (Friedl et al.
, 2010). However, a recent land cover database suggests that the extent of crops has increased in eastern China in the last 30 years  (Hurtt et al. , 2011). In addition, the declining trend in PAR was also derived from ERA-Interim data complemented by surface radiation measurements  (Weedon et al. , 2014). The crop expansion and declining PAR were proposed to cause a negative isoprene trend in south China  (Yue et al. , 2015)  and likely explain the observed negative trend in HCHO.
Data availability The OMI HCHO column data are publicly accessible at http://h2co. aeronomie. be (De Smedt and Van Roozendael, 2016). The OMI-based inventories are freely available at the GlobEmission web portal (http://www. globemission. eu; Stavrakou et al. , 2016b)  and at the BIRA-IASB emission portal (http://emissions. aeronomie. be; Stavrakou et al. , 2016c). The MEGAN-MOHYCAN bottom-up inventory is also available at http://emissions. aeronomie. be. The Supplement related to this article is available online at doi:10. 5194/acp-16-10133-2016-supplement.
Acknowledgements. This research was supported by the  Belgian Science Policy Office  through the  PRODEX projects ACROSAT , by the  European Space Agency (ESA)  through the  GlobEmission DUE project  ( 2011-2016 ), and by the  MARCOPOLO  project ( 2014-2016 ) funded by the  European Commission  within the  Seventh Framework Programme  (grant agreement:  606593 ). Edited by: A. Pozzer Reviewed by: two anonymous referees.
2009 ) anomalies relative to the 1993-2014 mean. In the right panel an arbitrary offset has been introduced for more clarity. From top to bottom, the regions are NW Shelf, IBI, Med. Sea and Black Sea. No thermosteric contribution is shown for the Black Sea due to the scarcity of the in situ temperature observations in this region. In this figure, no Glacial Isostatic Adjustment (GIA) correction has been applied to the total MSL whereas a correction for the glacial isostatic adjustment was added for the MSL trends in Table  1. See Table  1  for the definition of the dataset. Table  1. Mean sea level trends during January 1993-December 2015 for the global ocean and different CMEMS regions for the total altimeter sea level (corrected from the Glacial Isostatic Adjustment -GIA, e. g. Tamisiea, 2011)  and the thermosteric sea level.
Durack & Wijffels 2010). No GIA correction has been applied on the altimeter data. See Table  1  for the definition of the dataset. The sea level anomaly (SLA) field for 2015 is dominated by the dipole (±) observed in the Equatorial Pacific Ocean associated with the El Niño event  (Schiermeier 2015)  with an anomalously high sea level in the Eastern Equatorial Pacific, and an anomalously low sea level in the western basin (Figure  13 , left). In the North Atlantic, an anomalous low sea level pattern occurs in the same area where the recent North Atlantic cooling event is reported (see Section 4. 2). In the Baltic Sea, the observed positive anomaly (Figure  13 , right) is related to a major inflow event that took place in late 2014 to early 2015 in connection with westerly winds and low air pressure  (Mohrholz et al.
The correspondence is remarkable, and the regional differences in the time series of ocean colour data are very clear. These regional responses to climate variability give us important clues on how phytoplankton might respond to long-term climate changes. The 2015-2016 ENSO event was the strongest observed since 1997, and in parallel a large reduction in phytoplankton chlorophyll concentration occurs in the Equatorial Pacific Ocean (Figure  17 ), which has not been seen since 1997 (Figure  16 ). In the tropical regions, large reductions in chlorophyll concentration were observed in the Indian Ocean, Equatorial Pacific, North-Eastern Pacific and Western North Atlantic, in 2015 (Figure  17 ). These reductions are associated with positive anomalies in SST and sea level (see  Sections 1. 2  and 1.
The September sea ice extent was less than the long-term mean along the Russian and western North American coast, while ice remained to the north of Greenland and in Eastern Canada. This is the same pattern that was seen during the summer minimum of 2012 (Figure  23(a) ). The 1993-2015 dataset confirms the decreasing trend of 76,300 km 2 /yr of Arctic sea ice extent (Figure  24(b) ). Both in the Arctic and the Baltic, the sea ice in 2015 was less than the mean (Figures  24  and 25 ). In the Baltic, the sea ice extent in 2015 was mostly more than one (up to two) standard deviation lower than the mean. Since the Arctic is a semi-enclosed ocean, almost completely surrounded by land while Antarctica is a land mass surrounded by an ocean, sea ice in the Antarctic differs from that in the Arctic in many ways.
2012; Rabe et al. 2014 ) and to estimate changes in freshwater storage requires knowledge of both sea ice volume and drift. While sea ice drift is observed and assimilated in the models, the uncertainty with regards to freshwater export is still present because of its dependency on ice thickness. Chapter 2: Large-scale changes Monitoring the state of the ocean is not only a question of having reliable observations and model products at hand. It also requires transforming these data into (useful) information. As stated in the introduction, there is a growing need for an ocean monitoring capability that captures the main ocean variability features and also aims to measure the direct impact of ocean changes on society. The purpose of this chapter is to deliver insight on large-scale changes of the physical state of the ocean which are known to contribute fundamentally to the role of the ocean in global and regional changes of the Earth's climate system.
Estimates of the OHC anomaly are obtained from integrated differences in the measured temperature and climatology along a vertical profile in the ocean. Ideally, this is integrated over the full depth of the ocean, but because of limitations in the observing system, it was typically done to a reduced depth such as the upper 700 m before the Argo era. From 1993 onwards, the global ocean in situ observing system (http://www. ioc-goos. org/) has undergone fundamental transitions mostly related to advances in measurement techniques. Before the year 2000, temperature measurements were most often made in the upper 700 m of the water column, and had uneven spatial coverage  (Levitus et al. 2012). Changes in measurement techniques and instrumentation, differences in data quality control and different statistical methods for spatially mapping all result in biases and large uncertainties in OHC estimates  (Abraham et al.
Since IBI reanalysis was only run until 2014, the monthly zonal velocity field was derived from the CMEMS IBI-MFC near-real-time model system. In order to monitor coastal upwelling conditions in the IBI area, two different upwelling indexes were computed. The first index, the CUI EK, is based on the estimation of Ekman transport perpendicular to the shoreline. It is derived from the wind stress forcing  (Bakun 1973). The surface wind data used to compute the CUI EK are those used as atmospheric forcing of the CMEMS IBI reanalysis (ERA-Interim). A second upwelling index based on SST (CUI SST ) was also derived from each latitudinal point computing the thermal differences between the shelf and the open ocean. The CUI SST computed at each latitude was defined as the temperature difference between the maximum and minimum temperatures in a range of distance from the coast up to 3.
2007). Ferrybox data from the CMEMS regional near-real-time product 23 in 2015 collected using the merchant vessel TransPaper on the route Lübeck-Kemi-Oulu-Lübeck are presented in Figure  54. So far, a full evaluation of such a data set has not been performed for the Baltic Sea. Satellite data on ocean colour have been used to analyse both the frequency and coverage of the summer cyanobacterial blooms (e. g. Öberg 2015). By using a multi-decadal time series of satellite data from multiple sensors  Kahru and Elmgren (2014)  showed that there was a significantly higher areal fraction of cyanobacteria during 1997-2013 compared to the earlier period 1979-1996. In most years, satellite observations of the blooms are made in July, while the cyanobacterial bloom in 2015 was mainly observed in August using satellite data.
Ferrybox data on phycocyanin fluorescence indicate that there were large amounts of cyanobacteria in July and August. Thus satellite data may underestimate the coverage of the bloom. To monitor the extent of cyanobacterial blooms the Swedish Meteorological and Hydrological Institute (SMHI) uses a combination of in situ data and satellite data. To monitor cyanobacterial blooms a map that shows the number of days with observation of blooms (see Figure  55 , Öberg 2015) is produced each year. These data show that 2015 is not a year with a strikingly high number of blooming days, but that blooming days were   High-biomass blooms of cyanobacteria may be detected using ocean colour data from satellites during cloud-free conditions. CMEMS currently offers a chlorophyll-a and an SST algorithm for satellite data, but does not offer ocean colour data for the Baltic Sea at present.
The data presented are from the SMHI-operated Baltic Algae Watch System and from a Ferrybox JER-ICO-NEXT project. The satellite data on cyanobacteria blooms are sensitive both to cloud cover and to wind conditions. Wind-induced turbulence causes surface accumulations of cyanobacteria which are mixed down in the water column. Ferrybox systems are useful for measuring optical properties of phytoplankton, in this case the fluorescence of the pigment phycocyanin. This is a very useful complement to the satellite measurement, both as a means to provide therminology for improving the ocean colour algorithm and to get more detailed knowledge on what type of plankton is in the water. Ferrybox systems are also used for automated collection of water samples that are analysed in the microscope or using imaging flow cytometry.
Since then, up until now, a return to average conditions is evident although in 2015 up to nine of these extreme storms were identified in this basin. In this section we analyze the variability of the surge component of the tide gauge data (tide extracted) available in CMEMS in situ data portals (see endnotes 24-26 for more details), for the period 1993-2015. The objective is to put 2015 storm surges in context and to explore possible trends on this surge component during the study period, while pointing out to the adequacy of sea level data available in CMEMS for this type of studies. A first review of the sea level time series available in CMEMS showed diverse data sampling, with only about 18% of the stations providing data spanning from 1993 to 2015 and very few time series from the Mediterranean basin.