dataesr/scientific-paragraphs-categorization huggingface dataset:
However, while chl a is an indicator of biological activity, it is also a very good tracer of ocean circulation, so, depending on the water masses origin, f CO 2 and chl a are not expected to be systematically negatively correlated. Waters rich in detrital material tend to limit the phytoplankton growth, and microbial respiration of riverine material on the continental shelf likely dominates  (Aller and Blair, 2006; Medeiros et al. , 2015; Mu et al. , 2021). Even considering the extent of the EUREC 4 A-OA atomic cruise, the dataset is still sparse and cannot fully represent the small-scale variability it highlights. In order to understand the fluxes at regional scale the need for a good spatial resolution arises. For that, the surface T (temperature), S (salinity) and chl a diagram computed from the ship measurements (and co-located satellite chl a) is interpolated using a linear 3D interpolation on a grid of SST, SSS and chl a.
The grid has a resolution of 0. 01 • C in SST, 0. 1 in SSS and 0. 01 in log(chl a (mg m -3 )). Using a 3D linear interpolation to map the f CO 2 data over a grid is a simple yet effective solution for a dataset that is still relatively sparse. The method is presented in more details in the Supplement (Text S1 in the Supplement). Using a linear fit prevents oscillations between two data points and yields good results. Along the ship track, the standard deviation between the measured and reconstructed f CO 2 is ∼ 4 µatm. Each triplet of surface T , S and log(chl a) in the range of the values measured by the ship is therefore associated with a value of f CO 2 based on the 3D linear interpolation of the in situ values. In order to cover the whole range of T -S-log(chl a) present in the region, we extrapolate to lower temperatures and lower salinities than the ones measured by the ship.
In order to do so, we add four points to the T -S-chl a diagram at lower salinities and lower temperatures based on previous knowledge of the region. For the low-salinity domain (SSS < 30), f CO 2 is strongly dominated by salinity, and the influence of temperature is weak  (Lefèvre et al. , 2010). The SSS-f CO 2   2010 ) is in good agreement with the SSS-f CO 2 relationship computed from this study data (Fig. S1  in the Supplement) in the common range, and we therefore use it to compute f CO 2 at a salinity of 26 (f CO 2 (S = 26) = 251. 4 µatm). The lower temperature is mostly located in the northern part of the domain, which is the least variable and where the variations in f CO 2 are dominated by the ones in temperature. From this dataset we compute a variation of 15 µatm • C -1 , which is consistent with the 4.
Extension to other years and inter-annual variability Few tropical Atlantic measurements of biogeochemical tracers are available, in particular in the northwestern tropical Atlantic. The EUREC 4 A-OA/ATOMIC campaign provides the first in situ comprehensive measurements of f CO 2 in this region for the boreal winter season. The reconstruction of f CO 2 maps likely provides a good understanding of the spatial evolution of f CO 2 and air-sea CO 2 fluxes and is fitted for the months of January-February 2020. Although the processes described here are specific to winter and thus cannot be extended to other seasons, they will be useful to understand the winter variability in other years. Only a few cruises crossed the region according to the SO-CAT database between 2010 and 2019 (period with satellite SSS data), and investigating inter-annual variability is not possible.
However, we can test the relation developed for 2020 for other years by using selected cruises from the SOCAT database. We thus first use the relationship to reconstruct f CO 2 along the ship tracks (using in situ SSS and SST and colocalized chl a) and then over the whole region based on satellite products (OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis) SST, GlobColour chl a, CCI+SSS (Climate Change Initiative Sea Surface Salinity), detailed in Appendix A). A comparison between the measured and reconstructed f CO 2 for the water masses sampled by the SOCAT cruises (NASW, fresh plume, NBC retroflection, modified NBC) is presented in the appendix (Table  A1 ). Good agreement is found between the f CO 2 from the SO-CAT database and the one reconstructed from the in situ temperature and salinity, as well as co-localized chl a for the four water masses (averaged difference of 5.
12 ) but to a much smaller extent than south of Barbados. For example, the strong sink observed in March 2014 is caused by cold SST anomalies over the whole domain. Some small-scale variability in the northern part of the domain is sometimes correlated to SSS anomalies, as in 2019. The freshwater plume sampled during EUREC 4 A-OA is a common feature in February. During the 2010-2019 period, events of freshwater reaching the open ocean were observed each year, and freshwater plumes similar to the one described in this paper were observed during 7 out of 10 years of satellite salinity data  (Reverdin et al. , 2021). Two of the main mechanisms driving the occurrence of the plume are the winds near the Amazon estuary that can induce along-shelf transport to the Guyana plateau and the presence of NBC rings.
The main feature in this part of the domain is the fresh plume that contributes almost 20 % of the total sink. The processes described here highlight the high variability in air-sea CO 2 fluxes in winter that are quite different from the ones in summer. These features are relatively common in winter and can be used to better understand the inter-annual variability in air-sea CO 2 fluxes. The northern part of the domain is driven by the variability in SST, while the southern one is a combination of the inter-annual variability in temperature, salinity and chlorophyll. It is therefore linked to the year-to-year variability in the NBC rings and the Amazon outflow. This study is limited by the paucity of data in the region and for this time period. More f CO 2 data closer to the coast would help us to better quantify the influence of shelf water on the flux.
Merian and Ronald H. Brown. The measurements on the Ronald H. Brown were supported by the Global Ocean Monitoring and Observation (GOMO) programme (fund Ref. 100007298). support. This research has been supported by the Centre National d'Etudes Spatiales (grant no. 6146) and the European Research Council, H2020 European Research Council (grant no. EU-REC4A (694768)), and Global Ocean Monitoring and Observation (GOMO) programme (fund ref. 100007298).
Acknowledgements. This research has been supported by the  European Research Council (ERC)  advanced grant  EUREC 4  A (grant agreement no. 694768 ) under the European Union's  Horizon 2020 research and innovation programme  ( H2020 ), with additional support from  CNES  (the  French National Centre for Space Studies)  through the  TOSCA SMOS-Ocean, TOEddies , and  EUREC 4 A-OA proposals , the  French national programme LEFE INSU, IFREMER , the  French research fleet , the French research infrastructures AERIS and ODATIS, IPSL, the  Chaire Chanel programme of the Geosciences Department at ENS , and the  EUREC 4 A-OA JPI Ocean and Climate programme. Léa Olivier  was supported by a scholarship from  ENS  and  Sorbonne Université. We thank  Jonathan Fin  at the  Service National d'Analyse des paramètres Océaniques du CO 2  ( SNAPO-CO 2  ) at LOCEAN for the analysis of DIC and TA samples, as well as François Baurand at the  US IMAGO  for the nutrient analysis.
Code availability. Code used in this study can be made available upon reasonable request to the corresponding author. Data availability. We benefited from numerous data sets made freely available and listed here: the ADT produced by Ssalto/Duacs distributed by CMEMS (https://resources. marine. copernicus. eu), the chl a and SST maps produced by CLS (https: //datastore. cls. fr/catalogues/chlorophyll-high-resolution-daily urlhttps://datastore. cls. fr/catalogues/chlorophyll-highresolution-daily and https://datastore. cls. fr/catalogues/ sea-surface-temperature-infra-red-high-resolution-daily), the SMOS L2Q field produced by CATDS (CATDS,  2019)   (https://doi. org/10. 12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0), the SMAP maps produced by Remote Sensing System (RSS v4 40 km), the CCI+SSS maps produced in the frame of the ESA CCI+SSS project (https://doi.
org/10. 5285/5920a2c77e3c45339477acd31ce62c3c), and the OSTIA SST and Copernicus GlobColour chl a distributed by the CMEMS (SST_GLO_SST_L4_REP_OBSERVATIONS_ 010_011 and OCEANCOLOUR_GLO_CHL_L4_REP_ OBSERVATIONS_009_082). RV Atalante f CO 2 data are available on the SEANOE website: https://doi. org/10. 17882/83578. RV Ron Brown and RV Merian f CO 2 data can be found on the SOCAT database (expocodes 33RO20200106 and 06M220200117 respectively). The Surface Ocean CO 2 Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research programme, to deliver a uniformly quality-controlled surface ocean CO 2 database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SO-CAT.
Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-19-2969-2022-supplement. Author contributions. LO, JB, GR and NL conceptualized the project. LO carried out the measurements and data analysis. LO, JB, GR, NL and PL contributed to result interpretation. PL, MR and RW provided the crucial datasets. LO, MR, SS, JK, ML, TS and CN conducted field work. LO wrote the manuscript with input from all co-authors.
Merian and Ronald H. Brown. The measurements on the Ronald H. Brown were supported by the Global Ocean Monitoring and Observation (GOMO) programme (fund Ref. 100007298). support. This research has been supported by the Centre National d'Etudes Spatiales (grant no. 6146) and the European Research Council, H2020 European Research Council (grant no. EU-REC4A (694768)), and Global Ocean Monitoring and Observation (GOMO) programme (fund ref. 100007298). Fresh plume NBC waters Modified NBC NASW SOCAT f CO 2 316. 2 413. 4 385. 8 349 f CO 2 reconstructed from 310. 7 410. 7 392. 9 358. 7 MERIS, Financial SOCAT SST and SSS Transect date Ship name Expocode 2016/01/05 Colibri (France) 35MJ20151229 35MJ20160107 642B20151209 2016/01/08 2015/12/23 Colibri MSC Marianna Benguela Stream 2013/02/10 (France) (Panama) (the Netherlands) 33RO20130108.
Study Sites, Sampling and Environmental Descriptors An annual water sampling, once in a month, was carried out in two freshwater ecosystems for which long-term ecological surveys exist. The locations investigated were Lakes Geneva (46 •. 78 E). In Lake Geneva, the SHL2 point corresponds to the CIPEL monitoring site (established in 1962)  [19] , and in Lake Annecy, the GL site has been monitored by the SILA since 1966  [20]. The CIPEL and SILA mandate the OLA observatory for data collection and analysis  [21]. Sampling of Lakes Geneva and Annecy took place from February 2018 to January 2019. BALOs were mainly investigated in the epilimnion and hypolimnion. The epilimnion layer is known to be relatively homogeneous, warm, well lighted and rich with autotrophs, where the hypolimnion is almost deprived of light and rich with heterotrophic bacteria.
Bioinformatic Pipeline R1 and R2 fastq files were processed using Frederic Mahé's pipeline found at https:// github. com/frederic-mahe/swarm/wiki/Fred\T1\textquoterights-metabarcoding-pipeline (accessed on 2 January 2020). Briefly, the pipeline uses several programs such as Vsearch  [28] , Cutadapt  [29] , Swarm and Stampa  [30]. All default parameters of the pipeline were left unchanged unless stated otherwise. OTUs were created using Swarm with "d = 1". All OTUs were taxonomically assigned with the arb-SILVA database release number 138  [31]. OTU tables were filtered by removing chimera sequences, singletons, sequences with less than 90% identity to arb-SILVA database and sequences with a quality score inferior to 0. 0002. After the application of the aforementioned filters, the number of sequences for Bdellovibrionaceae dropped to 6830 in Lake Geneva (14% loss) and 1054 in Lake Annecy (26% loss).
Statistics For the abundance data (copy mL -1 ), the values were transformed using log(x). All graphs were built via ggplot2  [32]  using the transformed values. The "vegan" package  [33]  was used to analyze the link between BALOs and environmental variables. First, a DCA (detrended correspondence analysis) was performed on Lakes Geneva and Annecy data (DCA axis lengths < 3). The test determined that RDA (redundancy analysis) was the adequate choice for the analysis  [34]. First, "corrplot" package  [35]  was used to visually select relevant environmental variables, then variance inflation factors (VIF) were calculated and only variables with VIF < 10 were retained. The RDA significance was checked using a 999 permutations ANOVA test. For OTUs, non-metric multidimensional scaling (NMDS), permutational multivariate analysis of variance using distance matrices (Adonis), canonical correspondence analysis (CCA) and rarefaction curves were performed using the R vegan package  [33].
Acknowledgments: We are grateful to all colleagues (technicians and crew) who helped to obtain samples at field. We want to thank  Louis Jacas  and  Cécile Chardon  for their help in sample preparation for molecular analysis. We are also grateful to  Laura Crépin ,  Viet Tran-Khan Pascal Perney ,  Philippe Quetin  and  Jean-Christophe Hustache. This study and J. A. E. were funded through a grant from  INRAE  and  USMB.
Data Availability Statement: The raw files (R1 and R2), tags list, unfiltered and filtered OTUs table of the run datasets obtained in this study have been deposited at Zenodo's depository under https://doi. org/10. 5281/zenodo. 4293824, accessed on 23 march 2020. In addition, most of the OTU sequences used to construct the phylogenetic tree can be found at NCBI GenBank under accession numbers: MW299511:MW299708 and MW302902:MW302988.
41  ). This model builds on the formalism and terminology of the "PRA framework" (introduced by Pöschl, Rudich and Ammann 58 ). It is a combination of K2-SURF, kinetic double-layer surface model  59  and KM-SUB, kinetic multi-layer model of aerosol surface and bulk chemistry,  60  but has been adapted to a planar geometry (see Sebastiani et al. 41  ). KM-SUB and K2-SURF have been applied to describe a range of experimental datasets and conditions. 48, 61, 62 Both models describe the evolution of the kinetic parameters of an organic droplet exposed to oxidants. We have adapted the model to a monomolecular organic layer at the air-water interface for analysis and interpretation of the experimental data presented here. In the binary mixture, only the surface excess of the deuterated component is detected and hence the kinetic modelling describes the reaction of the deuterated surfactant.
e. remaining at the surface and directly measurable by NR, Z S ), volatile (i. e. escaping into the gas-phase, Z G ) and soluble (i. e. accumulating in the water bulk, Z B ) compounds. Since the surface-active products (Z S ) will remain at the airwater interface, the surface-bulk transport is negligible: d½Z S ss dt ¼ c S k surf;Y;NO 3 ½Y ss ½NO 3 s (9) where c S is the branching ratio for the surface-active products. The volatile products (Z G ) will leave the surface based on their vapour pressures. The soluble products (Z B ) will diffuse into the water bulk depending on the diffusion coefficient. All equations describing the evolution of the various species can be found in the ESI. † This system of equations cannot be solved analytically, hence the ODE solver of MATLAB® (2011) has been used for numerical solving.
Following the approach we used for the single component monolayer,  41  the accommodation coefficients for the gas-phase species were xed to one, and the desorption lifetimes were le free to vary in the range 10 À9 to 10 À7 s, which is in agreement with the values suggested by Shiraiwa et al. 78  For the rate coefficient, k surf , the range of variability was optimised through a sensitivity study performed by changing in the Matlab code the value of k surf. The suitable range of values found was (0. 7-8) Â 10 À8 cm 2 per molecule per s, which is signicantly higher than the best t value provided by Shiraiwa et al. 70  for abietic acid exposed to NO 3 (1. 5 Â 10 À9 cm 2 per molecule per s) and wider than the range used for the single component monolayer study. The optimisation of the kinetic parameters was performed systematically by the c 2 minimisation routine FMINUIT.
Acknowledgements The authors would like to thank  Dr Francesco Piscitelli ,  Dr Ernesto Scoppola  and  Dr Kunal Rastogi  for the help during the night shis on FIGARO. We would like to thank the Partnership for So Condensed Matter for access to the ellipsometer, and the  ILL (Grenoble, France)  for allocations of beam time on FIGARO. We are grateful for technical support at  ILL  from  Simon Wood. FS is grateful for support from the  ILL  and the  University of Reading  in the framework of the NEATNOx studentship. CP thanks  NERC  (grant number  NE/G000883/1 ) for support.
Data availability The data from the beamline experiments at FIGARO are available at Institut Laue-Langevin with DOIs: https://doi. org/ 10. 5291/ILL-DATA. 9-10-1286, https://doi. org/10. 5291/ILL-DATA. 9-10-1234 and https://doi. org/10. 5291/ILL-DATA. 9-10-1344.
rigor further, we developed a novel MPR calculator tool (MPRCT) for standardized, comprehensive collection of percentages of viable tumor, necrosis, and stroma in the tumor bed. In addition, tumor width and length in the tumor bed are measured and unweighted and weighted MPR averages are calculated, the latter to account for the varying proportions of tumor beds on slides. We propose sampling the entire visible tumor bed for tumors having pCR regardless of size, 100% of tumors less than or equal to 3 cm in diameter, and at least 50% of tumors more than 3 cm. We describe the uses of this tool, including potential formal analyses of MPRCT data to determine the optimum sampling strategy that balances sensitivity against excessive use of resources. Solutions to challenging scenarios in pathologic assessment are proposed.
In 2020, the International Association for the Study of Lung Cancer (IASLC) published uniform recommendations for pathologic assessment of NSCLC after neoadjuvant therapies, including chemotherapy, radiotherapy, molecular-targeted therapy, and immunotherapy. 10 The recommendations were formulated to provide guidance because data in this setting for lung cancer are limited, with methodologies for assessing pathologic response described at limited and varying extents in earlier NSCLC studies  4, 5, 11, 13, 14, 19  (Table  1 ), in contrast to those in osteosarcoma  20  and breast cancer. 21, 22 To address absent or partially described aspects of assessment, the IASLC made recommendations for macroscopic assessment, tumor bed sampling, and microscopic assessment of NSCLC specimens. Building on these recommendations and to further aid studies confirming MPR as a surrogate end point of survival, we developed a systematic approach to NSCLC pathologic assessment that is being used in an IASLC interobserver study following patients with NSCLC and to review an estimated 451 lung resections after neoadjuvant chemotherapy with or without checkpoint inhibitor therapy in a phase 3 study (IMpower030; NCT03456063).
MPR Calculator Tool Several studies that have evaluated pathologic response in NSCLC described the correlation between the percentage of residual viable tumor and outcomes, but the methods used to calculate the proportion of residual viable tumor have varied (Table  1 ). 4, 8, 12, 14, 19, 23 To address this shortcoming, we developed and implemented a novel MPR calculator tool (MPRCT) to facilitate methodical, standardized, comprehensive collection of microscopic data for the tumor bed, which consists of the following three main components: viable tumor, necrosis, and stroma (i. e. , fibrosis and inflammation). 10 When used in conjunction with information in synoptic pathology reports,  10  radiologic reviews, and clinical outcomes, the MPRCT can be used to reveal evidence-based correlations not previously explored and can thus more concretely and accurately establish pathologic response as a surrogate marker for survival.
1B ). The MPRCT has safeguards to detect errors (e. g. , cells with missing data) and prevent inadvertent mathematical miscalculations, such as a mean or sum greater than 100% for viable tumor plus necrosis plus stroma on each slide. For the computed total tumor bed area, the MPRCT can establish whether the entered values are within or outside an expected range for the specified gross dimensions. In addition to standardization, functionality, and ease of use, the MPRCT can be used to provide data for evidence-based determination of optimal sampling and microscopic assessment strategies and for the discovery of new potential surrogate end points.
g. , abundant viable tumor on the initial slides is unlikely to be offset by an absence of tumor in the remaining slides and reach MPR). Currently, no evidence-based studies investigating the ideal number of sections or percentage of tumor examined are available, although others in the field have also recommended submission of the entire tumor bed to provide robust data for informed decision-making. 24 ltimately, the proposed sampling strategy may exceed what is necessary to calculate MPR accurately. Until more data become available, this unbiased approach and data from the MPRCT can be used in formal analyses to determine the optimum sampling strategy that balances sensitivity against overuse of resources. Similarly, examination of the entire tumor bed to confirm pCR may seem excessive, but the ability to correlate accurately confirmed pCR with improved outcomes would provide robust evidence for pCR as a surrogate end point of survival, similar to that established for high-risk HER2-positive early-stage breast cancer.
19 In another small study of neoadjuvant nivolumab, a median 5% variability in histopathologic features (range: 0%-29%) was reported for each sample reviewed by four pathologists. 28 In a retrospective study that evaluated agreement regarding MPR assessment between two pathologists (one trained, one untrained), levels of agreement were high after in-person training by the experienced pathologist (R 2 ¼ 0. 994). 12 lthough these data are encouraging, larger studies are needed to validate levels of concordance among general and thoracic pathologists and to define an acceptable range of discordance. Data from the MPRCT can be used to develop the best methodology for achieving high interobserver agreement and correlation with outcomes (e. g. , unrestricted continuous values can be compared with those rounded to nearest multiples of 10).
Conceivably, the methodology used might vary by case: unrestricted continuous values might be used if the percentage of viable tumor is near the 10% cutoff for MPR, and rounded values might be used in other scenarios. In addition, the MPRCT data can be used to identify cases that have data points revealing significant discrepancies for adjudication. It can also be used to identify trends and biases among pathologists (e. g. , whether a given pathologist averages higher estimates) and sharpen criteria for the parameters assessed (e. g. , exactly what constitutes stroma to the individual assessor). The details provided by the MPRCT permit granular analysis at a case or slide level (e. g. , agreement among pathologists on slides that contain tumor bed).