dataesr/scientific-paragraphs-categorization huggingface dataset:
The CMIP6-PMIP4 Tier 2 lig127k-veg experiments will consider the sensitivity of the responses to prescribed boreal forests in the Arctic and shrub/savanna over the Sahara, separately and together. Incorporating these vegetation changes has been shown to impact the albedo and evapotranspiration on the surface energy and water budgets, reducing model and data mismatches at high latitudes  (Swann et al. , 2010)  and for the North African monsoon  (Pausata et al. , 2016). Recent results show large changes in hydrology, with, e. g. , the possible existence of an extensive river network across the Sahel and Sahara, therefore also pointing to the need to prescribe or model vegetation changes to capture regional feedbacks  (Scussolini et al. , 2020). Additionally, the CMIP6 models do not currently simulate changes to soil texture or color for different climate states.
The lig127k results can be used to force Greenland ice sheet models, both one-way as included in the ISMIP6 protocols  (Nowicki et al. , 2016)  and fully coupled to a climate model as is now being done by several modeling groups. With the availability of LIG ice and marine core records, LIG simulations with an evolving Greenland ice sheet will allow an assessment of the corresponding future projection simulations. Data availability. The necessary output variables from both the lig127k and piControl simulations are freely available from the Earth System Grid Federation at https://esgf-node. llnl. gov/search/ cmip6/ (last access: 22 October 2020; please see the Supplement and Table  S1  for further details, including digital object identifiers and references, of the precise datasets used in this analysis) AWI-ESM-2-1-LR, HadGEM3-GC31-LL, and MPI-ESM1-2-LR have committed to lodge their data as soon as practical.
We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF. The  Paleoclimate Modelling Intercomparison Project  is thanked for coordinating the lig127k protocol and making the model-model and model-data comparisons possible within  CMIP6. PMIP is endorsed by  WCRP  and  PAGES. Financial support. Funding of the publication has been supported by the  National Center for Atmospheric Research (NCAR) , which is a major facility sponsored by the  National Science Foundation  under cooperative agreement no. 1852977. Review statement. This paper was edited by  Johann Jungclaus  and reviewed by two anonymous referees.
American Arctic sites, in northern Finland (Sokli) and northeast Russia (El'gygytgyn) correlative dating provides continuous chronologies. The reported peak warmth at those sites occurred at ∼ 125 and 127-125 ka, respectively (Melles et al. , 2012;  Salonen et al. , 2018). Reconstructed temperature at Sokli at 127 ka was ∼ 1 • C lower than the peak temperature reported here from that site. The Greenland ice-core-derived temperature reconstruction from NEEM complements the Arctic terrestrial dataset, but it reflects annual rather than summer-specific climate. Despite an abundance of LIG pollen records from Eurasia and various attempts at pollen-based climate reconstructions (e. g. , compilations Velichko et al. , 2008; Turney and Jones, 2010) , chronological and methodological uncertainties continue to complicate comparisons with climate model outputs.
Pollacco et al. (2013b)  found an empirical relation between these two parameters for a set of soils from the UNSODA  ( Leij et al. , 1999 )  and HYPRES  ( Wösten et al. , 1999 )  databases and used it to reduce the non-uniqueness of the inverted Kosugi hydraulic parameters. This relationship was also used by  Fernández-Gálvez et al. (2019)  to reduce the non-uniqueness of the hydraulic parameters derived from infiltration tests:.
Experimental data used to validate the parsimonious bimodal 𝜃( 𝜓) and K( 𝜃) The model is validated with data collected across the Canterbury region of New Zealand  ( Pollacco et al. , 2020 ). The dataset has a sample size of n = 259 collected from soil profiles at 46 different sites. Sampled sites include irrigated and non-irrigated pastoral farming land. Rainfall across the sites varies from 550 to 800 mm per year. Soil parent material is sediments derived from quartzo-feldspathic hard sandstone, deposited as either river alluvium or windblown loess  ( Schmidt et al. , 2005 ). All soils in this study have at least 60 cm depth of fine earth soil material, with most sites classifying to the Pallic soil order in the New Zealand Soil Classification  ( Hewitt, 2010 ) , correlating in the Soil Taxonomy to Haplusteps and Humustepts great groups.
Objective function and goodness of fit The fitting process that estimates the parameters used an adaptive differential evolution algorithm  ( Qin et al. , 2009 ; Zhang and Sanderson, 2009 )  in the BlackBoxOptim. jl library (version 0. 5. 0, https: //github. com/robertfeldt/BlackBoxOptim. jl ), written in the Julia language. This procedure used an objective function and goodness-of-fit defined below. The minimization process between the observed and the fitted values used a weighted objective function, WOF , which includes information Fig. 3. Example of normalized bimodal hydraulic functions, S e ( ) and K Se ( ), for three different values of  (0. 75, 2. 38, and 4. 00) and the corresponding feasible range of  m (solid and dotted lines represent hydraulic functions with lower and upper limit respectively) derived from Eq.
Conclusions We present in this paper a procedure to derive physical and unique soil hydraulic parameters to describe the bimodal Kosugi hydraulic functions, ( ) and K ( ), from inverse modelling. The full methodology is only applicable to the Kosugi hydraulic model, where the representation of the ( ) and K ( ) functions is directly related to the pore size distribution. In the proposed approach,  s is derived from total porosity,  r is derived from ,  MacMat is considered constant, and  Mac and  mMac are derived from  MacMat. We also use a dynamic physical constraint in the relationship between  and  m to reduce non-uniqueness. The constraint for  r improves the goodness of fit, although the values used in the expression relating  r with  rely on the fit of experimental data from a specific New Zealand dataset, and it would require validation with data collected from other regions.
Acknowledgements This research was funded by the  Ministry of Business, Innovation and Employment's Endeavour Fund , through the  Manaaki Whenua-led  ' Next Generation S-map ' research programme,  C09 × 1612. We would like to thank from  Manaaki Whenua -Landcare Research John Dando for the laboratory physical determinations ,  Veronica Penny  for collecting soil cores and samples, and  John Drewry  for reviewing the manuscript. Mathieu Sellier  from  Civil Mechanical of the University of Canterbury (New Zealand ) is also thanks for reviewing the manuscript.
Hydrological and climate data The data consist of precipitation, temperature, and discharge time series located over 152 watersheds  (Figs. 1, 2a and b). Discharge time series were extracted from French reference hydrometric network compiled by  Giuntoli et al. (2013). This network of stations identifies near-natural watersheds (i. e. , with negligible anthropogenic modifications) with long-term, high-quality hydrometric data. According to  Giuntoli et al. (2013) , this subset of stations does not show abrupt changes and trends that could have resulted from anthropogenic influence. The period of 1968-2008 was chosen by  Giuntoli et al. (2013)  as being the best trade-off in terms of data availability over the different regions. Here, this database was further subset to 152 watersheds in order to select complete monthly time series (i.
Acknowledgements. The authors would like to thank  Nikola Jajcay , for his support with the adaptation of the Conditional Mutual Information algorithm (pyclits) to our context. The authors would also like to thank the  Centre Régional Informatique et d'Applications Numériques de Normandie (CRIANN) , for providing the HPC environment needed for all computations. This research work is part of a contribution to the EURO-Friend group 2. Review statement. This paper was edited by  Miriam Coenders-Gerrits  and reviewed by two anonymous referees.
Code availability. The code used for this study is available at https://doi. org/10. 5281/zenodo. 5638845 (Fossa, 2021). The pyclits Python code used for cross-scale interactions is available at https: //github. com/jajcayn/pyclits (last access: 17 March 2021;  Jajcay and Lavicka, 2018). Data availability. The Safran precipitation and temperature data set can be obtained from http://www. meteofrance. fr (last access: 1 October 2015). Discharge data are available from http://www. hydro. eaufrance. fr (last access: 15 October 2015 Ministère de la Transition Energetique, 2015). Supplement. The figures presented in this article are available in a colorblind-friendly palette in the Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/hess-25-5683-2021-supplement.
The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl 4 ) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but updated to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For this annual update, adjustments for the 43 new cruises were derived by comparing those data with the data from the 946 quality controlled cruises in the GLODAPv2. 2020 data product using crossover analysis. Comparisons to estimates of nutrients and ocean CO 2 chemistry based on empirical algorithms provided additional context for adjustment decisions in this version.
The adjustments are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent with to better than 0. 005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg -1 in dissolved inorganic carbon, 4 µmol kg -1 in total alkalinity, 0. 01-0. 02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO 2 fugacity (fCO 2 ), were not subjected to bias comparison or adjustments. The original data, their documentation, and DOI codes are available at the Ocean Carbon Data System of NOAA NCEI (https://www.
ncei. noaa. gov/access/ocean-carbon-data-system/oceans/GLODAPv2_2021/, last access: 7 July 2021). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones -the Arctic, Atlantic, Indian, and Pacific oceans -under https://doi. org/10. 25921/ttgq-n825 (Lauvset et al. , 2021). These bias-adjusted product files also include significant ancillary and approximated data and can be accessed via https://www. glodap. info (last access: 29 June 2021). These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2. 2021 methods and provides a broad overview of the secondary quality control procedures and results.
, 2010) , Pacific Ocean Interior Carbon (PACIFICA)  (Suzuki et al. , 2013) , and notably GLODAPv2  (Olsen et al. , 2016). GLO-DAPv1. 1 combined data from 115 cruises with biogeochemical measurements from the global ocean. The vast majority of these were the sections covered during the World Ocean Circulation Experiment and the Joint Global Ocean Flux Study (WOCE/JGOFS) in the 1990s, but data from important "historical" cruises were also included, such as from the Geochemical Ocean Sections Study (GEOSECS), Transient Traces in the Ocean (TTO), and South Atlantic Ventilation Experiment (SAVE). GLODAPv2 was released in 2016 with data from 724 scientific cruises, including those from GLO-DAPv1. 1, CARINA, and PACIFICA, as well as data from 168 additional cruises. A particularly important source of data was the cruises executed within the framework of the "repeat hydrography" program  (Talley et al.
The aim is to conduct a full analysis (i. e. , including an inversion) again after the third GO-SHIP survey has been completed. This completion is currently scheduled for 2023, and we anticipate that GLODAPv3 will become available a few years thereafter. In the interim, the third intermediate update is presented here, which adds data from 43 new cruises to the last update, GLODAPv2. 2020 (Olsen et al. , 2020). the period 1972-2019 for GLODAPv2. 2020 (Olsen et al. , 2020). Information on the 43 cruises added to this version is provided in Table  A1  in the Appendix. Cruise sampling locations are shown alongside those of GLODAPv2. 2020 in Fig. 1 , while the coverage in time is shown in Fig. 2. Not all cruises have data for all of the above-mentioned 12 core variables. For example, cruises with only seawater CO 2 chemistry or transient tracer data are still included even without accompanying nutrient data due to their value towards computation of, for example, carbon inventories.
The same procedure is applied to data flagged 8 (very few such data exist); WOCE flags of 1 (data not received) and 5 (data not reported) are also set to 9, while flags of 6 (mean of replicate measurements) and 7 (manual chromatographic peak measurement) are set to 2 if the data appear good. Also, in the merged product files a flag of 0 is used to indicate a value that could be measured but is approximated: for salinity, oxygen, phosphate, nitrate, and silicate, the approximation is conducted using vertical interpolation; for seawater CO 2 chemistry variables (TCO 2 , TAlk, pH, and fCO 2 ), the approximation is conducted using the calculation from two measured CO 2 chemistry variables (Sect. 3. 2. 2). Importantly, interpolation of CO 2 chemistry variables is never performed, and thus a flag value of 0 has a unique interpretation.
Secondary quality control The aim of the secondary QC was to identify and correct any significant biases in the data from the 43 new cruises relative to GLODAPv2. 2020 while retaining any signal due to temporal changes. To this end, secondary QC in the form of consistency analyses was conducted to identify offsets in the data. All identified offsets were scrutinized by the GLODAP reference group through a series of teleconferences during April 2021 in order to decide the adjustments to be applied to correct for the offset (if any). To guide this process, a set of initial minimum adjustment limits was used (Table  3 ). These represent the minimum bias that can be confidently established relative to the measurement precision for the variables and cruises considered and are the same as those used for GLODAPv2.
pH scale conversion and quality control Altogether 13 of the 43 new cruises included measured pH data, and none required adjustment (Sect. 4. 2). All new pH data were reported on the total scale and at 25  details on scale and temperature conversions in previous versions of GLODAPv2, we refer the reader to  Olsen et al. (2020). In contrast to past quality control of GLODAP pH data, evaluation of the internal consistency of CO 2 system variables was not used for the secondary quality control of the pH data of the 13 new cruises; only crossover analysis was used, supplemented by CONTENT and CANYON-B comparisons (Sect. 3. 2. 5). Recent literature has demonstrated that internal consistency evaluation procedures are subject to errors owing to incomplete understanding of the thermodynamic constants, major ion concentrations, measurement biases, and potential contribution of organic compounds or other unknown protolytes to alkalinity.
For calculations involving TCO 2 , TAlk, and pH, if less than a third of the total number of values, measured and calculated combined, for a specific cruise were measured, then all these were replaced by calculated values. The reason for this is that secondary QC of the few measured values was often not possible in such cases, for example, due to a limited amount of deep data available. Such replacements were not done for calculations involving fCO 2 , as this would either overwrite all measured fCO 2 values or would entail replacing a measured variable that has been subhttps://doi. org/10. 5194/essd-13-5565-2021 Earth Syst. Sci. Data, 13, 5565-5589, 2021 jected to secondary QC (i. e. , TCO 2 , TAlk, or pH) with one calculated from a variable that has not been subjected to secondary QC (i.
Now, we perform a thermodynamic analysis to understand why non-oxide precipitates are present in our LMD 316LSS walls. The first step involves analyzing the most stable (i. e. , lowest Gibbs free energy) equilibrium phases that could exist in the liquid and solid states of 316LSS. This analysis is performed using the ThermoCalc software and the results are presented in section S4 of the supplementary material for several different temperatures in both the solid and liquid states. It reveals that the main phase in the solid 316LSS is γ-austenite and the main phase in the liquid is molten 316LSS, henceforth called Liquid A (LA). Amongst all the possible oxides, MnSiO3 is the most stable equilibrium phase in both the liquid and solid states; in the liquid state it exists in the form of Liquid B (LB), which is an Mn-Si-O rich liquid with composition close to MnSiO3 having trace amounts of Fe and S.
Mesh The simulated geometry shown is created based on the experimental setup (Fig. 1 ). It consists of a baseplate (not shown in Fig. 1  but present during the experiment), the hot-rolled 316LSS substrate, and the 3-layer LMD 316LSS wall. The baseplate, substrate and wall are created as independent volumes and merged to form a single material volume for the simulations. The geometry is then decomposed into subdomains as shown in Fig. S5  and meshed using the open-source gmsh 36 software. The structured regions of the substrate and baseplate were meshed using 6 mm seeds and the structured region of wall was meshed using 0. 1 mm seeds. An unstructured region was necessary to transition from the 0. 1 mm seeds of the wall to the 6 mm seeds of the substrate and baseplate. This unstructured region starts around the wall and goes to a distance of 15 mm in all directions to ensure a uniform increase in mesh element size.
Thermodynamic calculations The phase stability and elemental solubility analysis is performed using the Thermo-Calc software version 2019b with the TCFE9 database. A mean field precipitate kinetics model based on the classical nucleation and growth theories  [37] [38] [39] [40] [41]  is used to predict the nucleation, growth and coarsening kinetics of M-S precipitates on MnSiO3 precipitates already present in γ-austenite/LA. M-S precipitates are assumed to spherical and their growth is assumed to be controlled by the diffusion in γ-austenite/LA. Their growth rate is calculated following Zener's approximate solution for spherical precipitates  42. The Gibbs-Thomson effect  43  is also taken into account. γaustenite/LA is assumed to be an ideal supersaturated solid/liquid solution containing both interstitial and substitutional elements.
The solubility of each element in γ-austenite, MnSiO3, M-S, LA and LB is taken from the ThermoCalc software generated data used to plot Fig. 8. An important step in the precipitation kinetics simulations is to estimate the number of nucleation sites for M-S. In order to understand how these sites are chosen, the TEM results for L1 -L5 are reconsulted (Fig. 3 , S3 and 6). These results show that 72. 22% of all the mixed precipitates contain oxide inclusions at their core and non-oxide inclusions surround them. These numbers strongly suggest that non-oxide inclusions mainly nucleate on oxide inclusions, a phenomenon that has already been observed in other studies performed at slower cooling rates  15, 16. Meanwhile, Fig. 6  shows that (i) the average size of oxide precipitates is larger than average size of oxide inclusions in mixed precipitates and (ii) with the exception of 2 oxide inclusions in mixed precipitates, the remaining oxide inclusions are either in the size range of oxide precipitates or smaller than the smallest oxide precipitate.
Acknowledgement: M. V. U. , M. B. H. S. and E. H. are grateful to the  Fédération Francielliene de Mécanique (F2M)  for their support through the  Coup de Pouce 2019 grant. M. V. U. and S. G. are grateful to the  European Research Council (ERC)  for their support through the  European Union 's  Horizon 2020 research and innovation programme  (Grant agreement No. 946959 ). N. M. is grateful to the doctoral school of  Ecole Polytechnique  for their support through the  PhD scholarship program. FIB-SEM and TEM work was carried out using the facilities available at the  MSSMat laboratory within the MATMECA consortium , which is supported by the  Agence National de la Recherche (ANR)  under the contract number  ANR-10-EQPX-37. Finally, the authors would like to thank  Y. Balit  for performing the 3D printing of the LMD sample used in this work.
Immuno-Informatics and Immune Simulation Studies In addition to the experimental studies, we have extended our work to bio-computational approaches to investigate the immunogenicity of FSAg, development of a vaccine construct using the epitopes of FSAg and in silico prediction of the immune response. Before designing the experiment, we retrieve the amino acid sequence of W. bancrofti FSAg (Accession number: YCEL_CAEEL) from Swiss Prot database (www. uniprot. org/uniprot/ (accessed on 6 December 2020)) in FASTA format and further experiments were conducted subsequently.
Prediction of Linear B-Cell Epitopes Prediction of the presence of linear B-cell epitope in a given antigen is one of the crucial steps in analyzing immunization and antibody production in silico  [10]. Herein, ABCpred web server (http://crdd. osdd. net/raghava/abcpred/ (accessed on 6 December 2020)) that runs on recurrent neural network technique was primarily used to identify potential B-cell epitopes in FSAg  [11]. Furthermore, BCPREDS (http://ailab-projects1. ist. psu. edu: 8080/bcpred/ (accessed on 6 December 2020)), SVMTriP (http://sysbio. unl. edu/SVMTriP/ (accessed on 6 December 2020)) and Immune Epitope Database (IEDB) (https://www. iedb. org/ (accessed on 6 December 2020)) servers were also used to verify and screen actual B-cell epitopes  [12] [13] [14].
Acknowledgments: We are grateful to  M. Mukherjee ,  ex-physician, Arambag Sub-divisional Hospital, West Bengal, India ;  S. Dey ,  Physician & Surgeon ,  ESI Hospital Asansol, India , and  S. Mitra ,  Epidemiologist (Bankura), India  for their kind help in this study. We express sincere thanks to the volunteers for giving the blood samples. Graphical abstract was created by using support from  BioRender. com.
The α-helix at the N-terminus of P, V, and W corresponds to the region shown to adopt a stable α-helical conformation upon binding of P to N  [9]  or upon binding of V to host cellular transporters  [33]. The crystal structure of HeV XD (PDB code 4HEO)  [15]  and a model of HeV PMD  [13]  are shown. The W CTD is represented as disordered according to predictions  [21]. Known interaction sites with human cell partners are shown. Beyond their ability to antagonize IFN signaling, the V and W proteins also inhibit the production of chemokines in vitro and modulate the inflammatory response in vivo  [34]. In addition, the Henipavirus W proteins bind to 14-3-3 proteins via their CTD, with this interaction resulting in the modulation of host gene exppression and apoptosis  [35]. Although the Henipavirus P protein has an anti-IFN function as well, its IFN antagonist activity is moderate compared to V or W.