dataesr/scientific-paragraphs-categorization huggingface dataset:
On the contrary, REFLOOD requires minimal overhead thanks to its reactive approach and does not present scalability issues thanks to its fully distributed nature. Another proactive approach for routing WuS is presented in  [35] , in the context of CTP-WuR  [37]. This protocol is a link-state routing protocol and is composed of two phases: Discovery and Exploration. In Discovery, each node broadcasts its ID over the main radio and the WuR to construct a list of 1-hop neighbors on both channels. After a certain delay, the nodes move to Exploration in which they  exchange link-state information over the main radio. However, linkstate routing protocols are not suited for IoT networks because they can have a large convergence time, can be energy-hungry due to the construction of the link-state database, and memory consuming to store such database on each node  [38].
Sea level In this study we used a sea level daily gridded dataset for the global ocean  (Taburet et al. , 2019; Legeais et al. , 2021)  that is distributed by the Copernicus Climate Service (C3S) and contains the sea level anomalies around a mean sea surface above the reference mean sea surface computed over 1993-2012, also referred to as the total sea level change. Data are available over the entire altimetry area from January 1993 onward. They are provided on a daily basis at a F. Marti et al. :  OHC change and EEI from space spatial resolution of 0. 25 • × 0. 25 •. Thanks to rigorous processing of altimetry measurements based on a two-satellite altimetry constellation, homogeneous altimetry standards applied over time (e. g. geophysical corrections, orbit solutions) and solid validation activities carried out upstream, C3S sea level products are dedicated to the monitoring of the longterm sea level variations.
g. Chen et al. , 2020; Landerer et al. , 2020). Ocean mass variations observed by GRACE are mainly due to freshwater exchanges with the continents (including ice melting and water cycle) at the global scale, and also to the ocean circulation at the regional scale. However, estimating the rates of global and regional ocean mass change with GRACE data remains a challenging task due to numerous processing choices that can strongly affect the results and lead to a large variety of solutions with significant uncertainty  (Uebbing et al. , 2019). In this study, we considered the GRACE LEGOS ensemble V1. 4 (http://ftp. legos. obs-mip. fr/ pub/soa/gravimetrie/grace_legos/V1. 4, last access: 19 January 2022) updated from  Blazquez et al. (2018). This ensemble version includes 216 solutions, based on fully normalised spherical harmonic solutions from six different centres and a large variety of choices for post-processing corrections including the corrections of the geocentre motion, the oblateness of the Earth, the atmosphere ocean dealiasing, the filtering of the noise responsible for the characteristic stripes of GRACE gravity data, the leakage correction, and the GIA.
Ancillary data For comparison purposes, OHC change and EEI are also estimated from in situ ocean temperature and salinity from Argo datasets covering the first 2000 m depth range. We considered the IAP, IFREMER, IPRC, ISHII, EN4, JAMSTEC, NOAA, and SIO datasets. Differences in ocean temperature among these products are due to the different strategies in data editing, temporal and spatial data gap filling, and instrument bias corrections (see  Boyer et al. , 2016). All Argo products are post-processed homogeneously in the framework of this study for integration of temperature and salinity to derive the ocean heat content (e. g. one single integration scheme, climatology computed over the same period 2005-2015). Regional OHC change is retrieved relying on the thermodynamic equation of seawater  (McDougall and Barker, 2011).
In this case we find a result (+0. 61 ± 0. 18 W m -2 ) that is closer by less than 0. 05 W m -2 to the mascon-based estimate. This precise consistency at the level of 0. 05 W m -2 gives confidence in our estimate. The residual difference could be due to sources of errors that were omitted in the calculation of the spherical harmonic ensemble, such as in- spherical harmonic (c) 0-bottom approach (detailed in this paper) Ensemble mean of three +0. 56 ± 0. 21 4 +0. 57 ± 0. 18 5 solutions based on mascon approach (JPL, CSR, GSFC) 1 List of Argo international groups: EN4 dataset from the Met Office Hadley Centre  (Good et al. , 2013) , including MBT and XBT data corrected by  Gouretski and Reseghetti (2010)  and  Levitus et al. (2012) ; IAP (Institute of Atmospheric Physics of the Chinese Academy of Sciences), including MBT and XBT data corrected by  Gouretski and Reseghetti (2010)  and  Levitus et al.
(2012) ; IPRC (combined to altimetry data); IFREMER  (Gaillard et al. , 2016; Kolodziejczyk et al. , 2017) ;  Ishii et al. (2017) ; JAMSTEC (Japan Agency for Marine-Earth Science and Technology) MILA GPV (Mixed Layer dataset of Argo, Grid Point Value) product dataset  (Hosoda et al. , 2010) ; NOAA (National Oceanic and Atmospheric Administration) data  (Huang et al. , 2017) ; and SIO (Scripps Institution of Oceanography) climatology monthly gridded 1 • × 1 • data  (Roemmich and Gilson, 2009 ). 2 Uncertainty given by the dispersion of the ensemble and uncertainty on deep ocean contribution. 3 Uncertainty given by the dispersion of the ensemble. 4 Uncertainty derived from the approach described in this study (gravimetry data uncertainty is simply computed as the standard deviation between the three mascon solutions).
GOHC trends obtained with each mascon dataset -JPL: 0. 60 W m -2 , CSR: 0. 55 W m -2 , GSFC: 0. 54 W m -2. 5 Uncertainty derived from the approach described in this study (gravimetry data uncertainty is simply computed as the standard deviation between the three mascon solutions). GOHC trends obtained with each mascon dataset -JPL: 0. 61 W m -2 , CSR: 0. 56 W m -2 , GSFC: 0. 54 W m -2. F. Marti et al. :  OHC change and EEI from space complete leakage errors or differences in the regularisation process of the mascon solutions and the spherical solutions. The space geodetic GOHC interannual variations of 5 × 10 7 J m -2 are presented in Fig. 5. We find the interannual variations in GOHC change to be in agreement with Argobased estimates for timescales greater than 3 years, low during the period from 2006 to 2011, and high during the period from 2011 to 2015 (Fig.
5 ). At shorter timescales (lower than 3 years), variations in GOHC change are poorly correlated. At these timescales, part of the signal is due to the internal variability of climate (e. g. ENSO) that may not be detected in the same way by both space geodetic and Argo-based estimates because of their different time and space resolution. In addition, GOHC variations depicted by all datasets suffer from a lack of accuracy at these timescales to analyse any differences in a significant way (see the large uncertainty envelope at sub-annual timescales shown in Fig. 5 ). At the regional scale, over the period 2005-2015, space geodetic and Argo-based OHC trends are similar (Fig. 6 ). Overall there is a fairly good spatial coherence of the observed spatial structures as in the equatorial Pacific Ocean and in the North Atlantic, but the amplitude of the signals is systematically higher in the space geodetic OHC trend.
In addition some discrepancies are observed in the Indian Ocean where space geodetic OHC trends are about 2 times the Argo-based estimates. Although input data are similar, the OHC trends based on the various Argo datasets also show differences at regional scales up to 2. 6×10 -3 W m -2 among different Argo products. This is the same order of magnitude as the difference with the regional MOHeaCAN trends (Fig. 6 ). These analyses on a regional scale provide insights into the regional structure of the signal. They remain preliminary and present several limitations. On the one hand, the contribution of the regional halosteric signal is not taken into account here in the calculation of the space geodetic OHC change. Ocean salinity change may have a significant impact in some local regions (as in the southeast Indian Ocean  (Llovel and Lee, 2015) , in the northwest Indian Ocean, or close to the Arctic Ocean).
77 ± 0. 27 W m -2 over the period  2005 -2015 (Hakuba et al. , 2021)). This result agrees very well with ours (+0. 77 ± 0. 24 W m -2 ) despite significant differences in the input data, in particular the EEH and the ocean mass. Our space geodetic EEI is also compared at interannual timescales with Argo-based and CERES-based EEI estimates (Fig. 7 ). Signals lower than 3 years are filtered out in all EEI time series. EEI means and trends are also removed beforehand from each dataset to compare EEI variations at interannual scales. The interannual signals are better correlated between the time series from space geodetic and CERES data than with the Argo-based data. Although the amplitude of the space geodetic EEI signal is slightly higher (up to 0. 8 W m -2 ), they appear to be fairly well phased between 2006 and 2013 (same phase within a few months).
Furthermore, no error on the fraction of energy entering the ocean α is in-cluded in the EEI uncertainty. Finally, the approach depends on the knowledge of the GMSL and GMOM error budget. These error budgets can be improved further. In particular, an effort must be made to better describe the errors in spatial gravity measurements, especially to include the uncertainties related to the differences in the harmonic and mascon approaches in the error budget. The consistency between the processing of altimetry and gravimetry data could still be improved for instance by homogenising the GIA datasets used to correct the gravimetry signals and the sea level from altimetry. Also, atmospheric effects should be harmonised. Indeed, altimetry data are currently processed with the dynamical atmospheric correction  (Carrère and Lyard, 2003)  while only the inverse barometer correction is applied in gravimetry processing  (Blazquez et al.
, 2018). Another area for improvement is the extension of the spatial and temporal scales of the OHC change estimation. While altimetry and GRACE data are available together since August 2002, the datasets provided in this study are limited in time (August 2002-August 2016) and space (Argo mask) as the objective of this study is to demonstrate the feasibility of such an approach (proof of concept) using reliable GRACE measurements and EEH/IEEH data over the Argo geographical mask. However, in the future, the OHC change and EEI time series could be extended in time using the GRACE-FO data already available from August 2018. This requires managing issues related to the 11-month gap between GRACE and GRACE-FO data (July 2017-June 2018) and the degradation of GRACE data quality after August 2016.
The OHC change could also be estimated outside the current Argo mask by extrapolating the EEH coefficient grid to the full ocean using ocean reanalyses. At this stage of the study, OHC changes and EEI are retrieved in a conservative way. Altimetry and gravimetry grids are resampled with a 3 • × 3 • spatial resolution, and the GOHC time series is filtered at 3 years with the aim of mitigating the impact of high frequencies from the input geodetic datasets and reducing signals related to internal variability on the EEI. Additional studies are necessary to better apprehend how geodetic data can be combined on both temporal and spatial dimensions so as to investigate regional OHC changes. This study emphasises that the synergy between spatial data (altimetry and gravity) and in situ data (Argo network) is essential to obtain accurate estimates of OHC change.
Acknowledgements. We would like to thank all contributors of the MOHeaCAN product, and in particular  Françoise Mertz ,  Laurent Soudarin , and  Caroline Mercier  for making the data available on the ODATIS portal and AVISO. Financial support. This work has been supported by the  ESA  in the framework of the  MOHeaCAN  project ( Monitoring Ocean Heat Content and Earth Energy ImbalANce from Space ): https://eo4society. esa. int/projects/moheacan/(last access: 19 Jan-uary 2022). This work is also supported by the  CNES  for the dissemination of the products. Julia Pfeffer  and  Anne Barnoud  are supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (GRACEFUL Synergy  (grant no. 855677 )). Jonathan Chenal was supported by the  French Ministry of Ecological Transition.
X-ray crystal analysis Structural characterization of the colchicine derivatives is helpful in order to elucidate their antiproliferative properties related to their interaction with tubulin as well as to enable structure-activity relationship analysis (SAR). Therefore, structural analyses of all crystals that were suitable for X-ray analysis of single crystals, were performed. Crystals of 7, 8, 14, and 17 suitable for the X-ray single crystal analysis were obtained by recrystallization of the respective colchicine derivatives from acetonitrile  (7 and 14)  or ethyl acetate  (8)  and crystals 17 from acetonitrile with the addition of dichloromethane. All crystals were measured at room (295 K) and low (100 K) temperature. Details of the data collection parameters, crystallographic data and the final agreement parameters are listed in Supplementary Materials (Table  S1 ).
Molecular electrostatic potential map analysis The molecular electrostatic potential map (MESP) provides information regarding the electronic density in a molecule and is a powerful tool for analysis of interactions, between molecules and also for analysis or the interaction in guest-host complexes in biological models  [43e45]. The roles of ring A as well as that of ring C of colchicine in tubulin binding have been studied in great details. The tropolonoic ring C of the colchicine molecule is found to be crucial for the interaction with tubulin  [46, 47]. Therefore, the molecular electrostatic potential map was calculated for all structurally characterized colchicine derivatives 7, 8, 14 and 17. Additionally, the gas-phase structures of all molecules were determined using the DFT optimization with the Gaussian09 program package  [48].
The three-dimensional MESP maps for colchicine derivatives and for colchicine itself were calculated on the basis of the DFT (B3LYP) optimized geometries of molecules and mapped onto the total electron density isosurface (0. 008 eÅ À3 ) for both molecules using the GaussView 5. 0 program (Fig. 6 ). The colour coding of MESP ranges from 0. 05 (red) to 0. 05 eÅ À1 (blue). For all colchicine derivatives, the regions of negative MESP are usually associated with the lone pair of electronegative atoms (O and N), whereas the regions of positive MESP are associated with the electropositive atoms (Fig. 6 ). The nucleophilic regions in colchicine derivatives are observed near oxygen atoms of all methoxy and carbonyl groups. In addition, significantly less The labelling of the atoms of colchicine derivatives is the same as in Figs.
Single crystal X-ray measurement The single crystals of colchicine derivatives  (7, 8, 14 and 17)  were used for data collection on a four-circle KUMA KM4 diffractometer equipped with two-dimensional CCD area detector at room (295 (1) K) and low temperature (100 (1) K). The graphite monochromatized Mo-Ka radiation (l ¼ 0. 71073 Å) and the u-scan technique (Du ¼ 1 ) were used for data collection. Data collection and reduction along with absorption correction were performed using the CrysAlis software package  [58]. The structures were solved by direct methods using SHELXT  [48]  giving positions of almost all non-hydrogen atoms. The structures were refined using SHELXL-2018  [59]  with the anisotropic thermal displacement parameters. Hydrogen atoms were refined as rigid. Visualizations of the structures were made with the Diamond 3.
0 program  [60]. Hirshfeld surface analyses and 2D fingerprint plots were calculated using the Crystal Explorer Ver. 3. 1 program package  [61]. Details of the data collection parameters, crystallographic data and final agreement parameters are collected in Table  S1  (in supplementary). The structures have been deposited with the Cambridge Crystallographic Data Centre in the CIF format, no. CCDC 2033754e2,033,761 for 7, 8, 14 and 17 at 100 K and RT, respectively. Copies of this information can be obtained free of charge from The Director, CCDC, 12 Union Road, Cambridge, CB2 1EZ, UK (fax: þ44 1223 336 033); email: deposit@ccdc. cam. ac. uk or www: http:// www. ccdc. cam. ac. uk).
DFT molecular modelling Molecular orbital calculations with full geometry optimization of colchicine derivatives (7, 8, 14 and 17) were performed with the Gaussian09 program package  [62]. All calculations were carried out with the DFT level using the Becke3-LeeeYangeParr correlation functional (B3LYP) [49e51] with the 6-31þG basis set, assuming the geometry resulting from the X-ray diffraction study as the starting structure. As convergence criteria, the threshold limits of 0. 00025 and 0. 0012 a. u. were applied for the maximum force and the displacement, respectively. The three-dimensional molecular electrostatic potential (3D MESP) maps were obtained on the basis of the DFT (B3LYP/6-31G) optimized. The calculated 3D MESP was mapped onto the total electron density isosurface (0. 008 eÅ À3 ) for each molecule.
Results are presented as mean IC 50 (concentration of the tested compound, that inhibits cell proliferation by 50%) ± standard deviation. IC 50 values were calculated in Cheburator 0. 4, Dmitry Nevozhay software (version 1. 2. 0 software by Dmitry Nevozhay, 2004e2014, http://www. cheburator. nevozhay. com, freely available) for each experiment  [64]. Compounds at each concentration were tested in triplicates in a single experiment and each experiment was repeated at least three times independently.
Tubulin model The 3D structures of the ab tubulin isotypes (bI (UniProtKb: P07437) were built by homology modelling using the crystallographic structure of the bovine protein complexed with colchicine as a template (PDB ID: 1SA0)  [66]. Molecular Operating Environment (MOE) software package [67] was used for homology modelling of the structures by setting the number of generated models to 10 and by selecting the final model based on MOE's generalized born/volume integral (GB/VI) scoring function. The sequence corresponding to the gene TUBA1A (UniProt ID: Q71U36) was chosen as a reference sequence for human tubulin, whereas the gene TUBB associated to bI isoform (UniProt ID: P07437) was chosen for human tubulin. The missing hydrogens for heavy atoms were added using the tLEAP module of AMBER 14 with the AMBER14SB force field  [68].
The protonation states of all ionizable residues were determined at pH ¼ 7 using the MOE program  [67]. Each protein model was solvated in a 12 A ̊box of TIP3P water. In order to bring the salt concentration to the physiological value of 0. 15 M, 93 Na þ ions and 57 Cl À ions were added. Minimization of the structure was carried out in two steps, using successively the steepest descent and conjugate gradient methods. At first, minimization was made in 2 ps on solvent atoms only, by restraining the protein-ligand complex. Next, minimization was run without the restraint in 10 ps. After minimization, the molecular dynamics (MD) simulations were carried out in three steps: heating, density equilibration, and production. At first, each solvated system was heated to 298 K for 50 ps, with weak restraints on all backbone atoms.
Next, density equilibration was carried out for 50 ps of constant pressure equilibration at 298 K, with weak restraints. Finally, MD production runs were performed for all systems for 70 ns. The root-mean-square deviation (RMSD) of both the entire tubulin structure and the colchicine-binding site were found to reach a plateau after 40 ns. Clustering analysis of the last 30 ns of the generated MD trajectory was carried out using the Amber's CPPTRAJ program  [69]  to identify representative conformations of the tubulin dimer. Clustering was made via the hierarchical agglomerative approach using the RMSD of atoms in the colchicinebinding site as a metric. An RMSD cutoff of 1. 0 Å was set to differentiate the clusters. On the basis of the clustering analysis, three representative structures of the tubulin dimer were found.
Docking simulations Docking simulations was performed using the Autodock, Auto-Dock Vina  [70]  and DOCK  [71]  (http://dock. compbio. ucsf. edu/) programs to predict the binding pose of the ligands under flexible ligand and rigid receptor conditions. The Dockbox computational package  [72]  was used to facilitate the use of multiple docking and scoring programs allowing their combination with different consensus strategies. The number of generated ligand poses was set to 10 for all the three docking programs, meaning that a maximum of 30 ligand poses was produced for every compound/protein structure pair. All the poses generated by the Autodcok, AutoDock Vina and DOCK programs were then rescored using the Vina scoring function. Docking simulations performed with a cubic box (size 30. 0 Å) were centred at the middle of binding pockets and the docking was run separately on the tubulin structure.
[73]. Pairwise free energy decomposition analysis was performed using the MM/GBSA decomposition process by the MM/GBSA program in AMBER 14 to compute the interaction between the inhibitors and each residue. For each of the tested compounds, the active residues estimated from MM/GBSA decomposition process and the best GB score out of the trajectories associated with the representative structures of the tubulin dimer were collected and are presented in Table  4  and Fig. Initially, the 3D structures of a library of 18 active compounds, with reported anticancer activity characterized by their IC  50  [nM] values, were selected for the development of QSAR models correlating structural properties with anticancer activity against two different human cancer cell lines, namely A549 and LoVo, respectively by ADMET predictor software (Simulations Plus, Inc.
QSAR model development In attempt to construction of a robust QSAR model, the experimental dataset was divided into the training set (12 compounds) and the test set  (6 compounds). Kohonen map, a technique that classifies sampling by structures, was used to divide the compounds into training and the test set. This technique typically gives good results for medium-sized data sets. Finally, the QSAR models were developed on the basis of the artificial neural network method  [75].
Whole-genome sequencing and phylogenetic analysis Whole-genomes of the 54 DENV-1 strains selected in this study were obtained by highthroughput sequencing of cell-culture supernatants  [38]. The resulting whole-genomes were deposited in GenBank (Supplementary Table  1 ). Additionally, five wholegenome sequences from Cambodia already available in GenBank (FJ639684, FJ639687, HM181944, FJ639685, and GU131893) were included in the analysis. Phylogenetic analysis of DENV-1 was conducted on whole-genome sequences from NC and Cambodia with reference sequences from GenBank representing the five genotypes of DENV-1. Alignment was done using the MAFFT software  [39]. The best substitution model for the alignment sequence was determined in W-IQ-Tree tool using the Model selection option, resulting in the GTR + G4 + I as the best model.
Statistical analysis Competition assays were analyzed statistically by detecting each genotype/lineage in individual mosquitoes and comparing their relative frequency for infection, dissemination, and transmission indices. The 2 x 2 contingency tables of presence/absence for both competing viruses were compared by McNemar's chisquared test for paired nominal data. All statistical analyses and data plotting of competition assays were performed with R v3. 6. 1 software  [41] , considering p-values < 0. 05 as statistically significant.