dataesr/scientific-paragraphs-categorization huggingface dataset:
Satellite instruments, with a very high spectral resolution (1 nm and better, originally designed for atmospheric applications), provide additional opportunities for distinguishing multiple PT based on their optical properties. The capability to retrieve quantitatively major PT groups based on their optical signature has been clearly shown with the PhytoDOAS method  (Bracher et al. , 2009; Sadeghi et al. , 2012a)  in the open ocean using hyperspectral satellite data from the atmospheric sensor SCIAMACHY. However, the exploitation of hyperspectral satellite data for ocean color has been so far very limited because hyperspectral sensors like SCIAMACHY (spectral resolution <0. 5 nm) do not provide operational water-leaving radiance products and have very large foot-prints (30 by 60 km per pixel) and low global coverage (6 days).
This provides a major constraint on assessing the retrieval's accuracy with in situ point measurements. It also limits the application of such PT satellite data sets. The difficulty of working with SCIAMACHY data is that one has to handle strong atmospheric absorbers (true for all hyperspectral satellite data) and the heterogeneity of big pixels; hence, the PhytoDOAS algorithm was designed to retrieve three PT directly from top of atmosphere radiances, by separating their high frequency absorptions from each other and relevant atmospheric absorbers, while accounting for broad band effects by using a low order polynomial. This method requires high spectral resolution (<1 nm). SCIAMACHY data acquisition ended with the lost contact to the ENVISAT satellite (April 2012). First results from adapting PhytoDOAS to the Ozone Monitoring Instrument (OMI) sensor (measuring since 2004) are very promising  (Oelker et al.
, 2016)  and will enable the extension of the spectrally derived PT data into the future with much improved global coverage (daily) and smaller foot print (13 × 24 km). OMI is also the precursor instrument to the in 2017 launched Sentinel-5-Precursor (S-5-P) with TROPOspheric Monitoring Instrument (TROPOMI) and in the 2020s launched Ultra-violet/Visible/Near-Infrared Instrument (UVN) instruments on Sentinel-4 and Sentinel-5 (all with a pixel size of 3. 5 × 7 km). The Hyperspectral Imager for the Coastal Ocean (HICO) provided data with high spatial (100 m) and spectral (∼6 nm) resolution and limited coverage (only a restricted number of scenes globally). However, so far lack of robust atmospheric correction for HICO (see current implementation in http:// seadas. gsfc. nasa. gov/) has prevented the exploitation of the full spectrum.
Eventually, not much more than standard phytoplankton information (chlorophyll, fluorescence line height) as for multispectral data was derived  (Ryan et al. , 2014). It is a big challenge to provide spectrally consistent high quality atmospheric correction for PG retrievals. The new ocean-color sensor OLCI on Sentinel-3 already provides two more bands in the visible range than its predecessor MERIS. It is anticipated that the number of bands will further increase for future multispectral ocean-color sensors. In addition, hyperspectral missions like Pre-Aerosol, Clouds, and ocean Ecosystem (PACE; global, high coverage, 1 km pixels, launch 2022) and Environmental Mapping and Analysis Program mission (EnMAP; regional, low coverage, 30 m pixels, launch 2019) are planned for operating in the near future.
However, hyperspectral instruments like ENMAP or PACE with 5 nm resolution are still very different from atmospheric instruments like SCIAMACHY. Hence, algorithms will have to be developed (or adapted) to retrieve the PT from these new instruments. To monitor marine ecosystem and assess their vulnerability to future anthropogenic and climate change, beyond a good spatial-temporal resolution of existing data long-term time series data are needed to monitor trends in phytoplankton community structure, and its variability on inter-annual to decadal time scales. The average cloud-free repeat time per pixel for an ocean color sensor is only 100 observations per year for the temperate and tropical zones  (Werdell et al. , 2007) , while it is much lower for high latitudes (e. g. , 12 per year for the East Greenland Sea,  Cherkasheva et al.
, 2014 ). Yet, merged oceancolor products significantly increase this temporal coverage  (Maritorena et al. , 2010; Racault et al. , 2015). The development of long time series of PG satellite products, covering more than a decade, has just started (e. g. , references given in  Mouw et al. , 2017). Such data sets are necessary to respond to user needs. Efforts have been taken to apply the multispectral PG algorithms not only to SeaWiFS but also to MODIS and MERIS. Synergistic use of multiple sensors will enable creating long-term time series moving from monthly to daily resolution, but also provides an opportunity to improve performance of individual retrievals. The ESA project SynSenPFT is an example for that where an algorithm was developed by synergistically using PT information from SCIAMACHY-PhytoDOAS and Ocean Color Climate Change Initiative chl-a-OC-PFT retrievals.
Improving Match between Satellite PG and Users' Needs A mechanistic framework needs to be developed which draws the complementary use of the various PG data and links them to PFT (Figure  1 ). This will assure that users are aware of the actual specific groups in the different satellite products and how they compare to the groups they require in their specific application. Such a framework requires an international effort and funding, including experts in in situ measurements (HPLC, microscopy, flow cytometry, genetics, bio-optics), algorithm developers and representatives of the user communities (modeling, marine services). Certain medium-term actions should be taken: -Regular workshops to improve communications between users (biogeochemical-, ecosystem-, RT-modelers, taxonomists, ecologists, fishery managers, HAB, and water quality experts) and algorithm developers should be held to achieve a common understanding and a consistent and comprehensive definition of the "groups" in PG algorithms, but also of their metrics  [% vs.
chl-a (or carbon)   -Specific comprehensive datasets should be selected that include coincident IOPs, AOPs, and phytoplankton composition that serve as a resource for PG algorithm development, refinement, and validation, and improve the ability to inter-compare validation metrics. -Standardized data quality, nomenclature, and format among different data bases (e. g. , SEABASS, LTER, PANGAEA, BODC,. ) should be assured to enable easy compilation and expansion. -Pigment databases used for testing, validating and refining PT algorithms should be archived along with complete information on all detected pigments to allow rigorous intercomparison studies and sensitivity analysis. -Methods to convert from in situ data to PG biomass or fractions should be assessed and protocols for merging different datasets (e.
-Methodological errors associated with different approaches of measuring phytoplankton absorption (filter pad method and methods measuring directly a water sample) need to be identified and quantified which also include the intercomparison of in situ and benchtop techniques over a range of environmental settings (optically complex to oligotrophic open ocean). For that universal protocols for data collection and processing need to be established, such as those currently prepared by NASA. -Differences in various particle imaging and identification technologies (e. g. , holography, flow cytometry, flow cam, etc. ) need to be assessed. -Following the launch of new ocean color sensors (particularly now OLCI on S-3), there is a need for validation activities to be funded across all AOPs and IOPs to PG products.
Sustaining Long-Term PG Satellite Data As outlined in Section Gap 3: Missing Capabilities of Current Ocean Color Satellite Measurements, long-term data sets of sufficient spatial and temporal resolution are needed to be established which are also adequate for regional applications (see Section Gap 4: Lack of Regional Capability of PG Algorithms). At first, the exploitation of hyperspectral data needs to be intensified in order to base those data on deriving the spectral imprints of phytoplankton groups in ocean color: -In preparation for the exploitation of future hyperspectral ocean color sensor [PACE, EnMAP or Hyperspectral InfraRred Imager (HyspIRI) and hopefully more] missions, much more effort needs to be put into the development of atmospheric correction for hyperspectral satellite data; methods should be developed over open ocean and complex waters, with the help of RTM, and considering multispectral atmospheric correction methods.
Also current hyperspectral satellite data sets, such as SCIAMACHY, HICO, OMI (and from 2017 also TROPOMI), should be explored further. -Hyperspectral and quantitative spectral PT and PSC algorithms should be adapted, extended and applied at global and regional scales to data with high spectral resolution from former, current and near-future "atmospheric" satellite sensors such as SCIAMACHY, OMI, TROPOMI and also, where possible, to the HICO data to setup time series of hyperspectral PG data from 2002 onwards. -Hyperspectral and quantitative spectral PT and PSC algorithms should be adapted, extended and applied at global and regional scales to data with high spectral resolution from former, current and near-future "atmospheric" satellite sensors such as SCIAMACHY, OMI, TROPOMI, and also, where possible, to the HICO data to set-up time series of hyperspectral PG data from 2002 onwards.
-The potential of spectral PG quantitative satellite retrievals for future satellite sensor should be further explored based on assessment via RTM, hyperspectral satellite, and in situ data to retrieve as many as possible of the PG requested by users from hyper-(e. g. , like PACE, ENMAP) and multispectral data sets (e. g. , like OLCI bands and a few additional bands). In addition a framework is needed at an international level for integrating PG information from different sensors (hyper-/multispectral, global coverage/high spatial, and/or temporal resolution) to meet user requirements across different scales with special focus to regional applications (in order to fill Gap 4, see Section Gap 4: Lack of Regional Capability of PG Algorithms). -At first, a workshop with experts across the different disciplines (in situ, algorithm, modeling) is necessary to define round-robin exercises for the regional (thematic) assessment of algorithms (validation, uncertainties) under specified protocols.
When these regional algorithm intercomparisons and assessments with different kinds of in situ optical and PG data are executed, best practice to use different kinds of PG data to obtain synergistic PG information should also be developed. -PG data merged from all low spatial resolution hyperspectral available sources to date (incl. TROPOMI) should be used synergistically with multispectral derived PG products for building up high spatially resolved PG long-term global records from 1997 into the future. -In addition, PG retrievals should be optimized on trigonal scale based on other environmental and ecological information (e. g. , from remote sensing, climatology) such as sea surface temperature, available light, wind speed, mixed layer depth, nutrients fluorescence, and optically active water constituents (CDOM, total suspended matter) and better links to biogeography.
ACKNOWLEDGMENTS We thank  ESA/ESRIN  within the context of activities of the  ESA Scientific Exploitation of Operational Missions (SEOM)  for funding the " Phytoplankton Diversity at Global and Regional Scale" Session at the Colour and Light from Earth Observation (CLEO) workshop and International Satellite Functional Type Algorithm Intercomparison  Project (http://pft. ees. hokudai. ac. jp/ satellite/), with funding from  JAXA, UK National Centre for Earth Observation , and the  IOCCG  for facilitation of a series of meetings that led to the development of this manuscript.
IV. PLATFORM VALIDATION Isometric properties are linked to measurement noise resilience  [18]. We want to confirm our previous results in numerical simulation and infer that codes with good isometric properties are indeed robust to noise. All simulations are based on a MATLAB platform inspired by  [2]  and available upon request. Parameters choices are N = 127, f s = f p and Orthogonal Matching Pursuit (OMP)  [19]  as recovery algorithm. The input multiband signal is given by: x(t) = K/2 i=1 √ B sinc(B(t -τ i )) cos(2πf i (t -τ i )), (9) where K = 6, B = 78MHz, f max = 5GHz, τ i = {0. 4, 0. 6, 0. 8}T acq and f i are chosen uniformly at random. An accurracy graph showing the percentage of support fully recovered with respect to the compression rate M/N is represented on Fig. 5  for both a noisy (10 dB Input Signal to Noise Ratio (ISNR)  [20] ) and a noiseless environment.
Study Design This was a European and Australian double-blind, placebocontrolled, parallel-group study conducted at 51 centres (GSK116115/NCT01777269; initiation date: 18 February, 2013; completion date: 5 April, 2016) comparing DUT-TAM FDC therapy (dutasteride 0. 5 mg and tamsulosin 0. 4 mg; one capsule daily) with placebo. Patients were randomised (1:1) to DUT-TAM FDC therapy or placebo for 12 months following a 4-week placebo run-in period. Lifestyle advice, relevant to maintaining sexual function and improving LUTS, was provided at baseline to patients in both treatment groups. This study was approved by the appropriate regulatory and ethics committees, and performed in accordance with the Declaration of Helsinki of 2008 and Good Clinical Practice guidelines. Written informed consent was obtained from each patient before study participation.
96 TeV by the CDF Collaboration  [7]. Finally, observation of the t tγ process was reported by the ATLAS Collaboration in pp collisions at √ s = 7 TeV  [8]. This paper describes a measurement of the t tγ production cross section, based on a data set recorded with the ATLAS detector in 2012 at a centre-of-mass energy of √ s = 8 TeV and corresponding to an integrated luminosity of 20. 2 fb - 1. The cross section is measured with a maximum-likelihood fit using templates defined within a fiducial volume chosen to be close to the selection requirements implemented in the analysis. Only final states with exactly one reconstructed lepton (electron or muon), including those originating from τ lepton decays, are considered. These final states are referred to as the singlelepton channel in the following.
0  [34] [35] [36] , using the CT10 PDF and interfaced to Pythia 6. 427 for the parton shower, fragmentation and underlying-event modelling. Other settings are handled in the same way as for the signal t tγ sample. This sample is used exclusively to validate the data-driven backgrounds. All samples are processed either through the full ATLAS detector simulation  [37]  based on Geant 4  [38] , or through a faster simulation using parameterised showers in the calorimeters  [39]. The resulting simulated events are processed with the same reconstruction algorithms and analysis chain as the data. The measurement is based on data collected by the ATLAS experiment in pp collisions at √ s = 8 TeV in 2012. The corresponding integrated luminosity is 20. 2 fb -1. The absolute luminosity scale is derived from beam-separation scans performed in November 2012  [40].
Conclusions A measurement of the fiducial cross section of top-quark pair events in association with a photon, using 20. 2 fb -1 of 8 TeV pp collision data collected in 2012 by the ATLAS detector at the LHC is presented. The measurement is performed in the single-lepton channel with either one isolated electron or muon, at least four jets where at least one is b-tagged in the fiducial region p T (γ) > 15 GeV, |η(γ)| < 2. 37 and p T ( ) > 25 GeV, |η( )| < 2. 5, with angular separations ∆R( , γ) > 0. 7 and ∆R(jet, γ) > 0. 5. The result is based on the minimisation of a profile likelihood ratio, using the photon track isolation as the discriminating variable since it provides good rejection against the main background of hadronic jets misidentified as photons. The analysis uses data-driven measurements for the determination of the largest background contributions.
In addition, individual groups and members have received support from  BCKDF , the  Canada Council, CANARIE ,  CRC ,  Compute Canada, FQRNT , and the  Ontario Innovation Trust ,  Canada ; EPLANET,  ERC ,  ERDF ,  FP7 ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex and Idex ,  ANR ,  Région Auvergne  and  Fondation Partager le Savoir, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF; BSF, GIF  and Minerva, Israel; BRF, Norway;  CERCA Programme Generalitat de Catalunya, Generalitat Valenciana , Spain; the  Royal Society and Leverhulme Trust ,  United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from  CERN , the ATLAS Tier-1 facilities at  TRIUMF (Canada) ,  NDGF (Denmark, Norway, Sweden) ,  CC-IN2P3  (France),  KIT/GridKA (Germany) ,  INFN-CNAF (Italy) ,  NL-T1  (Netherlands),  PIC (Spain) ,  ASGC.
2016). Every strain characterized was Cu R and 20 were collected from the first disease focus in Morne Rouge. In addition to Cu R haplotypes (n = 115 identified among 219 strains), all known haplotypes corresponding to copper-susceptible (Cu S ) X. citri pv. citri strains sampled in Réunion (n = 524) from all the citrus producing areas were also included in the study to assess the genetic relatedness between Cu R and Cu S strains on the island. This extensive dataset was built from strain collections sampled over two  periods, 1978-1997 (historical strains) and 2009-2015 (contemporary epidemics). Finally, as Cu R xanthomonads were originally reported in strains causing bacterial spot of tomato and pepper and no genomic resources were available for such strains, reference Cu R strains from the four species causing this disease were included in this study.
2012). Categorical minimum spanning trees (MST) were built using the algorithm recommended for tandem repeat data combining global optimal EBURST (GOEBURST) and Euclidean distances in PHYLOVIZ v1. 0  (Francisco et al. 2012). The relatedness between strains was assessed at smaller spatio-temporal scales (i. e. at the country level) by examining the diversity of all X. citri pv. citri strains using a set of 14 microsatellites (MLVA-14), which represent the most discriminative X. citri pv. citri genotyping technique available for intrapathotype typing  (Bui Thi Ngoc et al. 2009). Population genetics analyses were performed on two different datasets, as explained above. Moreover, allelic richness was computed using a rarefaction method with the HIERFSTAT R package  (Goudet 2005). Population pairwise R ST values were computed for estimating genetic differentiation among Cu R populations at the country level.
Significance was tested with 1000 permutations using ARLEQUIN 3. 5. 2. 2  (Excoffier et al. 2005 ). The first dataset, which consisted of all Cu R strains from the three resistance outbreaks, allowed us to decipher the genetic relationships between the three outbreaks. The second dataset, consisting of all Cu R and Cu S strains from Réunion (the only region where we have extensively sampled local populations), enabled us to examine the relationships between the Cu R and Cu S strains that were isolated both before and during the Cu R X. citri pv. citri epidemics. When comparing Cu R and Cu S strains from Réunion, the optimal number of clusters in the dataset was assessed by computing silhouette scores from multiple K-means runs using the BIOS2MDS R package.
Sequence annotations Sequence data obtained from the 13 strains were screened for known copper-resistance systems using BLASTN and BLASTP algorithms. As for most strains, Cu R genes were found on a 230 kb plasmid with strong homology. We arbitrarily selected strain LH201 as a reference. Hereafter, this plasmid is referred to as pLH201. 1. We carried out pLH201. 1 CDS prediction and automatic gene annotation using the MAGE genome annotation platform  (Vallenet et al. 2006 ) and then manual curation of the plasmid annotations using the extensive set of tools and databases available via the platform. Specific plasmid features were characterized in more detail. We conducted a search of plasmid toxin-antitoxin systems using the on-line databases TADB  (Shao et al. 2011 ) and RASTA  (Sevin & Barloy-Hubler 2007).
The conjugative apparatus of pLH201. 1 (encoded by 16 tra genes dispatched in three clusters) was blasted against amino-acid sequences of (i) the plasmid database of NCBI and (ii) the ICEberg database  (Wozniak & Waldor 2010). We only retained hits that displayed homology with at least one gene (on at least 70% of its length) from each of the three gene clusters involved in conjugation. Finally, ISfinder  (Siguier et al. 2006 ) was used to detect known insertion sequences. We used CIRCOS  (Krzywinski et al. 2009)  to plot a graphical representation of the plasmid. We computed GC and GC skew [(G + C)/(G - C)] using a custom perl script available at https://github. com/DamienFr/GC-content-in-sliding-window.
The search for homologous sequences In our search for sequences that are homologous to pLH201. 1 among all the available sequences, we queried the Whole Genome Shotgun (WGS) and Non-Redundant (NR) public nucleotide databases. Results were automatically retrieved using BioPerl modules (SEARCHIO, SEQIO and EUTILITIES). We first used CYTOSCAPE v. 3. 3. 0 to build a network of the gene sequences that were homologous with pLH201. 1. We retained all the sequences that had a hit with at least one of the pLH201. 1 genes (with 95% nucleotide identity over 95% of gene length). All the hits were blasted against each other to obtain the full network structure. The network was then plotted using an edge-weighted springembedded layout, which positions the nodes closer if the number of genes they share is higher.
We adopted a clustering strategy based on Average Nucleotide Identity (ANI) to determine the diversity of sequences related to pLH201. 1 and eliminate the redundancy of the hit dataset. Nucleotide sequences that shared more than 10 genes with pLH201. 1 (with 70% nucleotide identity over 70% of gene length) were clustered using a modified ANI algorithm (available at https://github. com/DamienFr/Clustering_with_ANI) that takes into account the identity of the homologous region in relation to the size of the complete sequence. For each sequence that was representative of a cluster, CDS prediction was performed using PRODIGAL  (Hyatt et al. 2010). Sequences were then compared using a combination of MAUVE  (Darling et al. 2004 ), MAFFT  (Katoh & Standley 2013 ) and the APE  (Popescu et al. 2012)  and GENOPLOTR  (Guy et al.
Relationships between copper-resistant and susceptible strains Microsatellite data (MLVA-14) were also used to decipher the genetic relatedness of the Cu R and Cu S strains from Réunion. Silhouette scores derived from multiple K-means runs suggested a lack of strong structure in the dataset. For K > 2, strain assignation probabilities to clusters were often low (i. e. < 0. 5). We decided to retain K = 2 because minimal strain assignation probabilities to clusters were > 0. 8. All but two Cu R strains were assigned to cluster A, together with Cu S strains. In contrast, only two Cu R strains were assigned to cluster B, including almost exclusively Cu S strains (Fig. 1B ). In a few cases, Cu R and Cu S strains shared the same haplotype. Most Cu R strains were genetically closely related. They were structured as five CCs and 27 singletons, i.