dataesr/scientific-paragraphs-categorization huggingface dataset:
To this end, C(X) was considered as a dataset in the context of a regression problem, where the variable with the missing data (Xmiss) was set as the class variable and the other q variables (X1, X2, …, Xq) as predictors. We also applied the RReliefF algorithm [3] The set was, therefore, filtered to select a subset Cs(X) ⊂ C(X) where (X1, X2, …, Xs) ⊂ (X1, X2, …, Xq) and s < q. In the present context, we set the number of neighbors for RReliefF equal to 10 and set s as 10 %, 20 % or 30 % of q. As C(X) is invariant to Xi, the filtering step was performed only once before the NN imputation step that, on the contrary was performed separately for each Xi. Supplemental References:.
, 2005; Yu et al. , 2006 Yu et al. , , 2003)). In this regard, inverse modeling can be used to reduce large aerosol simulation uncertainties. For instance, several studies  (Chen et al. , 2018; Dubovik et al. , 2008; Henze et al. , 2007)  showed the ability to retrieve global aerosol sources with inverse models that rely upon satellite observations. Therefore, the practice of satellite data fusion into models provides a possibility of improving aerosol simulations of the preand post-satellite eras. However, besides the knowledge of amounts (concentrations) and locations of aerosol emissions, an accurate modeling of atmospheric aerosols and their effects also requires information about particle composition. The lack of comprehensive datasets providing multiple constraints for the key parameters employed in the models has hindered the improvement of model simulation.
Refractive indices at other wavelengths are spectrally interpolated utilizing measured data  (Downing and Williams, 1975; Gosse et al. , 1997; Hale and Querry, 1973; Kou et al. , 1993; Palmer and Williams, 1974; Tang, 1996; Tang and Munkelwitz, 1991). A detailed description and FORTRAN subroutines for calculating the host complex refractive index is accessible at the website of GACP (Global Aerosol Climatology Project, https://gacp. giss. nasa. gov/data_sets/). Once the refractive index of the host is determined, the refractive index of the mixture is computed using the Maxwell-Garnett equations. The Maxwell-Garnett effective medium approximation allows computation of the average dielectric function based upon the average electric fields and polarizations of a host matrix with embedded inclusions, and can model insoluble particles suspended in a solution  (Bohren and Huffman, 1983; Lesins et al.
Composition retrieval from AERONET AERONET provides measurements that are among the most sensitive data to the aerosol refractive index. In addition, the AOT in AERONET is result of direct measurements and not retrieved as in the case of satellite observations. The GRASP aerosol composition retrieval concept was therefore first tested with the real AERONET data to check if the retrieved optical characteristics are consistent with the results of standard AERONET product. Figure  9  presents the AERONET measured AOT and Ångström Exponent (870 nm/440 nm) and retrieved Single-Scattering Albedo (SSA) at 675 nm versus those retrieved using the GRASP/Composition approach. Namely, the operational AERONET product is presented versus the derived from GRASP/Composition for 3 sites in the African continent: Banizoumbou (data for April 2007), Skukuza (data for September 2007), and Ilorin (data for January 2007), representing according to the sites location and the considered seasons the dust, the biomass burning and the mixture of dust and biomass burning cases, respectively.
POLDER/PARASOL satellite observations After testing the aerosol composition retrieval approach with the AERONET measurements, the algorithm was applied to the POLDER/PARASOL satellite observations. Figures  11, 12  and Table  3  summarize an inter-comparison of aerosol optical characteristics derived by the GRASP/Composition approach applied for POLDER/PARASOL and those of the operational AERONET product. The intercomparison is presented for six sites in Africa and Middle East (Fig. 11 ) and for all available AERONET data (Fig. 12 ) representing performance for different aerosol types and on the global scale. Because of a limited sensitivity to absorption when the aerosol loading is low, the SSA product is filtered for AOT at 0. 440 µm equal or higher than 0. 4  (Dubovik et al. , 2002a; Dubovik and King, 2000).
Illustration of global scale satellite aerosol composition retrieval We processed the POLDER/PARASOL observations globally using the aerosol composition retrieval algorithm. The results of this processing present the first attempt to assess the measurement-based global distribution and seasonal variability of aerosol composition. The data were processed for the year 2008, which provides a notable variety of different aerosol types, including volcanic aerosols from a Hawaiian eruption. The results are further presented as seasonal means. It should be mentioned however that any interpretation of the statistical values should take into account also the number of available observations. Therefore, it is worth presenting the global maps of the number of available cloud-free pixels. Figure  13  shows that the number of the cloud free pixels over land is significantly higher than over ocean, which can produce a difference in the mean values and create some artificial spatial patterns.
The composition retrieval algorithm demonstrated here using AERONET and POLDER/PARASOL data can also be used for interpreting other observations. That is, the composition approach is now incorporated in the GRASP algorithm, which has a generalized input and can be easily modified and adapted to other both passive and active remote sensing instruments, for example, the Directional Polarimetric Camera (DPC) launched onboard the GaoFen-5 Satellite in Chinese High-resolution Earth Observation Program, which is the first Chinese multi-angle polarized earth observation satellite sensor  (Dubovik et al. , 2019; Li et al. , 2018). Moreover, the proposed aerosol parameterization using components can be helpful not only for retrieving additional information about aerosol composition, but also for optimizing retrieval stability.
Additionally, we tested the volume-weighted mixing model, in addition to the Maxwell-Garnett EMA, to evaluate the sensitivity of our approach to the assumed aerosol EMA. We tested both approaches using our suite of aerosol species (i. e. BC, BrC, coarse mode absorbing insoluble, fine and coarse mode non-absorbing insoluble). The sensitivity tests revealed that implementation of the volume-weighted mixing rule also presents stable results that are consistent with the Maxwell-Garnett EMA. Thus, the volume-weighted model can also be employed in GRASP/composition retrieval, and may be preferable in some applications due to its simplicity. The results of the aerosol composition retrieval from AERONET and POLDER/PARASOL satellite measurements demonstrate a potential for constraining global and regional aerosol modeling that can be particularly valuable because no other aerosol composition data are often available on a large spatial and temporal scale.
Acknowledgments This work is supported by the  Labex CaPPA (Laboratory of Excellence -Chemical and Physical Properties of the Atmosphere)  project, which is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract " ANR-11-LABX-0005-01 ". This work is also financially supported by grant from  National Natural Science Foundation of China  ( 41875157 ),  National Key R&D Program of China  ( 2016YFA0601901 ). The authors thank CNES and ICARE data distribution center for POLDER/PARASOL data and the entire AERONET team, especially the principal investigators of the AERONET sites used in this study, for their long-term efforts to maintain AERONET observation. Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2019-208Manuscript under review for journal Atmos.
Statistical Significance All data was analysed using Graph pad prism version 6. 0 statistical software and the values are expressed as mean ± standard error of mean (SEM). Statistical significance was considered at p < 0. 05 using the Turkey Cramer multiple comparison test. The asterisk (*) (**) and (***) show p < 0. 05, p < 0. 01 and p < 0. 001, respectively.
Statistical Significance All data was analysed using Graph pad prism version 6. 0 statistical software and the values are expressed as mean ± standard error of mean (SEM). Statistical significance was considered at p ˂ 0. 05 using the Turkey Cramer multiple comparison test. The asterisk (*) (**) and (***) show p ˂ 0. 05, p ˂ 0. 01 and p ˂ 0. 001, respectively.
Variant ΔEx3 was only found in colon cancer cells. GAPDH (e) was used as the loading control. The images were captured using Chemidoc XRS image analyser (BioRad, USA). The band density differences of survivin splice variants (f). Shown are representative data of three independent experiments (percentage mean ± SEM). The difference were found to be statistically significant (* p < 0. 05, ** p < 0. 01 and *** p < 0. 001). The density was measured using Quanty-One software (BioRad, USA).
| RE SULTS AND D ISCUSS I ON S To examine the evolutionary relationships of bradyrhizobial strains, the nucleotide sequences of the 16s rRNA gene from various reference strains of Bradyrhizobium, Sinorhizobium, Mesorhizobium, and Rhodopseudomonas species were used to construct a phylogenetic tree. Cupriavidus taiwanensis LMG19424 was chosen as the outgroup strain to root the phylogenetic tree (Figure  A1 ). The DNA sequences were generated, and the most closely related sequences were obtained from the NCBI database. The nucleotide sequences were aligned using the ClustalW program, and the phylogenetic trees based on the 16S rRNA and T3SS gene sequences were constructed using the maximum-likelihood method with PhyML  (Guindon & Gascuel, 2003). Based on the 16S rRNA gene sequence similarity, the phylogenetic tree could be divided into two major clusters.
Cluster 1 included various groups of Bradyrhizobium species, including non-photosynthetic bradyrhizobia (non-PB), photosynthetic bradyrhizobia (PB), B. elkanii species, and Rhodopseudomonas members. The bradyrhizobial strains SUTN9-2, DOA1, DOA9, ORS3257 and B. diazoefficiens USDA110 belonged to the non-PB cluster. Moreover, strain SUTN9-2 was closely related to strain ORS3257. Both sinorhizobial and mesorhizobial species were located in major cluster 2 of the phylogenetic tree. A phylogenetic tree based on sequences of the T3SS gene rhcJ was also constructed (Figure  A2 ). The rhcJ genes of strains ORS3257, DOA1, DOA9, and SUTN9-2 were obviously distinct from those in the non-PB and PB clusters and the B. elkanii group. To more clearly understand the evolution of T3SSs among bradyrhizobial strains, the genomic arrangements of several T3SS clusters were determined using the program GenomeMatcher  (Ohtsubo, Ikeda-Ohtsubo, Nagata, & Tsuda, 2008)  at the amino acid level (Figure  1 ).
The annotated genome sequences of USDA110 (accession number BA000040), USDA61 (accession number FM162234. 1), NGR234 (accession number NC_000914. 2), and MAFF303099 (accession number NZ_CP016079. 1) were obtained from Genome Assembly/ Annotation Projects (NCBI database). The genome sequences of SUTN9-2 (accession number LAXE00000000), DOA1 (accession number JXJM01000000), and DOA9 (accession number DF820426) were available in the DDBJ/GenBank/EMBL database. The genome sequence of ORS3257 was received from the MicroScope platform  (Vallenet et al. , 2016). The T3SS gene clusters were separated into three clusters based on their T3SS structural components (Figure  1a ). The T3SS of bradyrhizobia displayed more notable differences compared with those of S. fredii NGR234 and Mesorhizobium loti MAFF303099.
Several putative T3Es could not be found in DOA9 and SUTN9-2. It seems that the variation in the T3Es was higher than that in the T3SS structural core components. Based on the phylogenetic tree data and the gene arrangements, the T3SSs of the Thai strains (SUTN9-2, DOA1, and DOA9) and ORS3257 may share the same origin. Thus, the bradyrhizobial strains SUTN9-2, DOA1, DOA9, and ORS3257 evolved their T3SSs independently of the other bradyrhizobia because of biological and/or geological events. If this scenario is true, then it could be hypothesized that horizontal gene transfer (HGT) plays an important role in bradyrhizobial evolution. The T3E genes nopP and nopL were present in all of the bradyrhizobial strains, whereas homologues of these genes are not present in phytopathogenic bacteria  (Ausmees et al.
Based on these phenomena, we hypothesized that Nod factor is the main factor required for V. radiata ssp. symbiosis; however, the T3SS is also important for host specificity. To better understand the relationship between the T3SS and the nodulation (nod) genes in V. radiata ssp. symbiosis, the Nod clusters were preliminary identified at the amino acid level using the GenomeMatcher program  (Ohtsubo et al. , 2008 ; Figure  A5 ). The Nod clusters of strains SUTN9-2 and ORS3257 were similar to that found in USDA110, whereas the Nod cluster of DOA9 was diverse. In addition, the nodulation-mutant strains SUTN9-2 (SUTN9-2 ΩnodABC) and DOA9 (DOA9 ΩnodB) lost their symbiotic properties with V. radiata ssp. (data not show). It seems that V. radiata ssp. were promiscuous plants with diverse Nod factors.
Observe that at each round of communication, the message sent by each of u, v only depends on its ID and the bits received from its neighbor at previous steps. At the first round, at least half of the IDs (call this set S 1 ) would send the same bit, say b 1 , to their neighbor. At the next round, at least half of the IDs of S 1 , upon receiving b 1 , would sent the same bit to their neighbor, say b 2. We continue this process by constructing sets S i and bits b i for any 1 i < log n as above (except that for the final round, we define b i as the bit output by the vertices, instead of the bit sent to the neighbor). If we use less than log n rounds of communication, we can find two distinct IDs in the last set S i such that if we assign these IDs to u and v, these two vertices will output the same bit b i , and therefore they will not be able to deterministically agree on an orientation of the edge uv.
Computational methods The structure of each studied cluster (i. e. [Pyrr][HSO4] + n H2O, with n = 0 to 18) was optimized by using the Turbomole 7. 0 program package,  [36]  as described by our group previously. [37]  Before their visualization using TmoleX (version 4. 1. 1), structures were optimized in the gas phase, with a convergence criterion of 10 -8 Hartree, using DFT calculations combining the Resolution of Identity (RI) approximation  [38, 39]  within the Turbomole 7. 0 program package using the B3LYP function with the def-TZVP basis set. [40] [41] [42]  The resulting optimized structures were then used as inputs in the COSMOconfX program (version 4. 0) to generate the conformers of each species able to be used then in the COSMOthermX software (version C30 17. 05, Mixture Option) to determine the lowest energy contact between two identical clusters to extract then an average of the cation -cation distances in solution.
One can note that, the collected ionic conductivity and viscosity values at 25 o C are close to (83 mS/cm and 8. 7 mPa. s), (178 mS/cm and 2. 7 mPa. s) and (109 mS/cm and 1. 3 mPa. s) while the following conductivity data 112. 80 mS/cm, 186. 60 mS/cm and 125. 60 mS/cm have been previously reported by Anouti et al. [44]  for the [Pyrr][HSO4]/water weight ratio close to 70/30, 41/59 and 14/86, respectively. The difference between data reported in literature and those determined during this work can be explained by the technique used (i. e. vibrating tube densitometer: this work and pycnometer: Anouti et al  [44] ), as well as the purity of pristine  [Pyrr] [HSO4] such as water level in each sample and/or the presence of unreacted material like the pyrrolidine. To avoid any further data analysis error associated to the used technique on transport properties of samples, the viscosity and the conductivity of the three selected electrolytes were both measured as a function of the temperature.
Introduction Soils carry out a number of key environmental functions that are essential for human subsistence (e. g. , food, fiber and timber production, water storage and redistribution, pollutant filtering and carbon storage). The understanding and modeling of physical, chemical and biological processes underlying these different functions have been the subject of many scientific works now that a number of models or indicators that represent these functions are available  (Sanchez et al. , 2009; Adhikari and Hartemink, 2016; Baveye et al. 2016). To be fully operational in facilitating decisions made at local, national and global levels, these models and indicators require the use of precise spatially referenced soil information as inputs. Given the lack of precision and extension of existing soil databases, Digital Soil Mapping  (Mc Bratney et al.
In addition, they also mapped Total Nitrogen, a soil property deprived of specific spectral features but, due to an associated chemical or physical structure, correlated to a soil property having such features (being SOC). DSM approaches are designed to employ this new source of soil data to allow for the exhaustive mapping of surface  (Walker et al. , 2017)  and subsurface  (Lagacherie et al. , 2013)  properties. Although hyperspectral VNIR/SWIR imagery has been proven to serve as a valuable tool for mapping key soil surface properties, it cannot be applied for large surface mapping or for temporal monitoring because it is expensive to apply and because hyperspectral VNIR/SWIR imaging data are not widely available. Indeed, only one hyperspectral VNIR/SWIR satellite sensor is currently operational.
Soil sampling This study gathered two soil data sets that were collected for the purpose of earlier studies (for Versailles,  Vaudour et al. 2013 Vaudour et al. , 2014a Vaudour et al. , 2016;; for La Peyne, Gomez et al, 2012). Despite some differences in chemical analysis, it was found useful to gather them in view of enlarging the range of the tested pedological situations. A total of 72 and 143 topsoil samples were collected from the Versailles Plain and Peyne Valley, respectively (Fig. 1 ). For the Versailles Plain, 69 topsoil samples were collected from 2010 to 2013, and 13 topsoil samples were collected in 2016. All of the samples were composed of roughly 10 sub-samples collected to a depth of 8 cm from random locations within a 2. 7 × 2. 7 m square area centered at the sampling plot as recorded at its center using a Trimble Pathfinder®Power DGPS of 50 cm precision  (Vaudour et al.
Remotely sensed images and geographic data Two SENTINEL-2A (S2A) images were acquired under nearly clear conditions on 12 and 19 March 2016 from the Versailles Plain and Peyne Valley, respectively (Table  2 ). The multispectral instrument aboard the S2A satellite has 13 spectral bands, including 4 bands of a 10 m resolution and 6 bands of a 20 m resolution (Table  3 ) retained for spectral modeling. Both images were atmospherically corrected using the ATCOR2/3 radiative transfer model  (Richter and Schläpfer, 2016). Both study sites are characterized by urban activity and by the presence of bare soil, water and vegetation. To mask no-soil pixels from the S2 data, the following approach was applied. First, urban and water areas were masked using Land Parcel Registry maps. In turn, only cropland and vineyards were maintained across the study areas.
e. , the ratio between the standard deviation of the calibration dataset to the RMSECV. According to  Chang et al. (2001)  and  Viscarra-Rossel et al. (2006) , values of 1. 4 to 1. 8 denote models exhibiting moderate levels of predictive capacity; values of between 1 and 1. 4 denote models exhibiting poor levels of predictive capacity, and values of <1 denote very poor models that should not be used. PLSR models were used through R version 3. 2. 1 (R Development Core  Team, 2015)  employing the "pls" package  (Mevik and Wehrens, 2007).
Simple omnidirectional variograms were calculated, and following  Gomez et al. (2012) , the spherical or exponential model used was fitted through a weighted least squares procedure  (Pebesma, 2004)  using the R statistical software program and the Gstats package. The universality condition (no drift) was assumed. For the Versailles Plain, it was not possible to obtain experimental variograms suitable for fitting a model due to the limited number of sites involved and the clustered sampling scheme employed. Variograms were therefore only examined for the Peyne Valley. To examine the robustness of spatial structures at the scale of the entire study region, variograms were calculated from all bare soil pixels.
Performance of the prediction models A PLSR model was built from S2A spectra and from each observed soil property associated with the available soil samples. No spectral outliers were removed from the calibration database, regardless of the study area concerned, and the number of latent variables was determined following the rule of the first local minimum of the RMSECV, which varied between 2 and 9 (Table  6 ). The predictive capacities of the PLSR models ranged from low to medium to high, with RPD values of between 1. 0 and 2. 0 and with R²cv of between 0. 02 and 0. 75 (Table  6 ). Three groups of variables can be identified from the cross-validation performance of the PLSR models (.
The highest levels of CaCO3 content (> 136 g. kg -1 ) show the highest levels of SOC residue prediction (Fig. 9 ). Of course, it cannot be inferred from Figs. 8 and 9 that coarse fragment content alone causes prediction errors, but presumably it contributes to such errors. In terms of vegetation features, the composition and adequacy of the dataset is conditioned by the NDVI threshold. Regarding dry vegetation, while the b12 band covers the 2,100 nm specific absorption wavelength for cellulose  (Daughtry 2001) , the MSI bands do not enable one to derive an index of dry vegetation such as the Cellulose Absorption Index (CAI = 0. 5 (R2000 nm+R2200 nm)-R2100 nm) elaborated by  Nagler et al. (2000). The CAI relies on two wavelengths covered by the b12 band (2,100 and 2,200 nm), but it requires the use of the 2,000 nm wavelength not covered by any MSI band.
However, other indexes, such as the Normalized Difference Tillage Index defined for Landsat Thematic Mapper (TM) bands (NDTI= (TM5-TM7)/(TM5+TM7))  (Van Deventer et al. , 1997) , could be adapted and tested for discriminating crop residue cover. The S2A data thus show limits in accounting for crop residue cover and vine wood vegetation more than the MSI spatial resolution does not enable one to discriminate between vine rows and vine interrows to remove the spectral influence of vine woods. In.
Conclusion S2A image spectra acquired from two French study areas representative of temperate and Mediterranean agroecosystems-the Versailles Plain and the Peyne Valley-were tested as input data of a chemometric model (PLSR) to predict 8 topsoil properties of bare soil areas: 8% and 39% of the Versailles Plain and the Peyne Valley, respectively. Six of these 8 soil properties (clay, SOC, iron, CaCO3, pH, and CEC levels) can be predicted with varying success depending on their intrinsic spectral properties, intra-regional variances, correlations and soil surface conditions. Some of the best predictions were also found to serve as good approximations of the spatial patterns of soil properties. Although a significant decline in the prediction performance of those obtained from hyperspectral data for the same study areas was observed, S2A soil predictions can serve as valuable inputs for Digital Soil Mapping.