dataesr/scientific-paragraphs-categorization huggingface dataset:
Uncertainty Analysis In order to study the effect of several parameters on the accuracy of the proposed method, simulations on synthetic data were carried out using MATLAB. Sets of 3D points are randomly generated following a uniform random distribution within a cubic work space in front of the camera-radar system. The projected pixel and the polar coordinates of each 3D point are computed using the pinhole model of the camera and the geometric model of the radar sensor, as explained in Section 2. First, the calibration and reconstruction algorithms were tested with exact data input, and the obtained errors are around 10 -6 on translation, 10 -12 on rotation and around 10 -14 for the reconstruction method. Afterwards, the simulations were extended emulating realistic cases in order to test the efficiency and the robustness of the methods with respect to several parameters, such as the number of matches, the noise level, the baseline length and the elevation of the target.
The segmentation and matching of the data provided by the sensors are done interactively. Polygons are extracted from the images covering the targets of interest, and then their vertices are matched by pairs. This step is to be automated in future work. The matched points are then reconstructed and the polygons are plotted using the Delaunay triangulation algorithm, as shown in Figures  22e  and 23e. Finally, texture mapping is done in order to enhance the representation of the reconstructed map. In fact, the interest of this sensor fusion is shown in the example in Figure  22 , as the radar provides no information about the elevation of the bridge, this later being detected as a barrier. The elevation and vertical occupation of the bridge are extracted from the image of the camera. Therefore, this ambiguity is eliminated after the reconstruction process.
The dependent variable was the survival rate defined as the number of surviving aphids among the fifteen for each aphid type. For each experimental treatment, the aphid survival rate was tested against the aphid type, defined as a fixed effect, and their genotype, defined as a random factor. The ladybird was also included in the statistical modelling as a random factor. For both statistical analyses, we fitted a generalized linear mixed model (GLMM) assuming a Binomial error and a logit-link function. These GLMMs were fitted using the lme4 package  [24]  in R 3. 1. 1 [25]. To assess the significance of the fixed model term, we used a likelihood ratio test. The pairwise contrasts were performed with the function esticon in the doBy package. Given the objectives of our study, only the effect of the fixed factor (i.
Clay content (fraction < 2 µm) is one of the most important soil properties. It controls soil hydraulic properties like wilting point, field capacity and saturated hydraulic conductivity, which in turn control the various fluxes of water in the unsaturated zone. In our study site, the Kairouan plain in central Tunisia, existing soil maps are neither exhaustive nor sufficiently precise for water balance modeling or thematic mapping. The aim of this work was to produce a clay-content map at fine spatial resolution over the Kairouan plain using a time series of Landsat Thematic Mapper images and to validate the produced map using independent soil samples, existing soil map and clay content produced by TerraSAR-X radar data. Our study was based on 100 soil samples and on a dataset of four Landsat TM data acquired during the summer season.
Relationships between textural indices (MID-Infrared) and topsoil clay content were studied for each selected image and were used to produce clay content maps at a spatial resolution of 30 m. Cokriging was used to fill in the gaps created by green vegetation and crop residues masks and to predict clay content of each pixel of the image at 100 m grid spatial resolution. Results showed that mapping clay content using a OPEN ACCESS Remote Sens. 2015, 7 6060 time series of Landsat TM data is possible and that the produced clay content map presents a reasonable accuracy (R 2 = 0. 65, RMSE = 100 g/kg). The produced clay content map is consistent with existing soil map of the studied region. Comparison with clay content map generated from TerraSAR-X radar data on a small area with no calibration point revealed similarities in topsoil clay content over the largest part of this extract, but significant differences for several areas.
2 µm). The use of such bands requires dry bare soil, which makes this method more suited to mapping semi-arid lands. All these studies indicate that the electromagnetic energy interacts with some soil properties in specific wavelengths. An analysis of the behavior of these wavelengths can be utilized in the modeling process to determine and map some soil attributes such as clay content. In our case, we used the MID-infrared index to produce clay content maps over semi-arid lands using time series of Landsat TM data. A similar index as the STI (Soil Tillage Index) was used to detect and quantify crop residue out of the soil baseline  [28] [29] [30]. The aim of this research was to map topsoil clay content at fine spatial resolution over the Kairouan plain. This study used time series of Landsat Thematic Mapper (TM5) images and a dataset of 100 soil samples collected over our study area.
To produce clay content map, we proceeded in three steps. The first step consisted in studying relationships between the MID-infrared index and observed clay content from calibration data. The second step was to combine multi-temporal Landsat TM data through cokriging to predict topsoil clay content within the Kairouan plain. The final step was to validate the produced clay content map by independent soil samples and by comparison with existing soil map and with topsoil clay content map produced from TerraSAR-X radar data.
Ground Measurements The dataset used in this study includes 100 samples. A first dataset of 30 samples was collected in March 2010  [12]  over a limited area within the plain of Kairouan. An additional campaign was conducted during the summer season of 2013 after cereal harvesting over a larger part of the plain within a 670 km 2 area (Figure  1 ). During this campaign, 70 samples with a large spatial range of soil textures were selected along main and agricultural roads of the study area. Sample points were selected based on visual assessment of topsoil textural changes. These samples were located in bare soil areas and cereals fields  [12]  corresponding to homogeneous areas extracted from MID-Infrared index maps, and over some olive fields with a vegetation fraction coverage lower than 10%.
These plots are of sufficiently representative size (3 × 3 pixels) and easily accessible. The samples were collected from the surface to a depth of 5 cm. About 20 g were extracted for topsoil texture analysis. The initial samples were air-dried and sieved up to 2 mm. Topsoil texture measurements were carried out over the selected fields to measure clay content (soil fraction <2 µm), fine silt content (between 2 and 20 µm), coarse silt content (between 20 and 50 µm) and sand content (between 0. 05 and 2 mm) by means of a classical laboratory analysis (Robinson pipette). To divide the dataset into calibration set and validation set, we kept one sample out of two. We considered 50 samples for calibration and 50 samples to validate the clay content map produced in this study. For clay content, the mean and standard deviation were 325.
Remote Sensing Data To produce the topsoil texture map, time series of Landsat TM5 (2009) archival images were used. The Landsat TM5 was launched on 1 March 1984 and decommissioned on 5 June 2013. These images were downloaded from http://earthexplorer. usgs. gov/. Landsat TM5 data are acquired at 30 m spatial resolution. These images were geometrically and atmospherically corrected. All images are projected in the Universal Transverse Mercator system (UTM/Zone 32/North). The root mean square error of ortho-rectification is less than 1/2 pixel for each image. Visible, near-and mid-infrared bands calibration were carried out as developed in  [34]. The method is based on a multi-temporal algorithm for detecting clouds, cloud shadows, free water and for estimating aerosol optical thickness (AOT).
Subsequent preprocessing included Cl: Clay SiCl: Silty clay SaCl: Sandy clay CILo: Clay loam SiClLo: Silty clay loam SaClLo: Sandy clay loam Lo: loam SiLo: Silty loam SaLo: sandy loam Si: Silt LoSa: Loamy sand Sa: Sand conversion of digital numbers to radiance, radiance to top-of-atmosphere reflectance and top of atmosphere reflectance to surface reflectance. The detailed method for correcting these images is illustrated in  [35, 36]. From the time series of Landsat TM data, we selected images during the seasons that minimize the influence of green vegetation (cereals mainly) and soil moisture due to rain. The latter is observed at the meteorological station. Calibration data were located on Landsat TM data. A polygon with 3 × 3 pixels was digitized to extract statistical data from Landsat TM images around each measurement.
Legacy Data The 1:50,000 previous soil map used in our study was produced in 1975 by the Ministry of Agriculture, whose extent is shown in Figure  1. This map covers an area of about 13 km 2. It includes four soil type units: Fluvisols, Vertisols or Vertic soils, Calcisols and Isohumic soils. We also used an existing topsoil clay content map derived from two TerraSAR-X radar images acquired over the Kairouan plain with a ground pixel spacing of 1 m  [12]. The first TerraSAR-X radar image was acquired on 8 March 2010, just after a period of a heavy rain. The second one was acquired on 30 March 2010, when the soil was dry. The method used to produce clay content map from radar data was based on analyzing the difference in soil moisture content between the two images. The largest decrease in topsoil moisture is statistically related to the coarsest soil material based on a soil sample dataset of 15 samples  [12, 37].
Calibrating MID Infrared Index and Topsoil Clay Content The MID-infrared index is defined in Equation (  1 ) and represents a normalized index between TM band 5 and TM band 7. Applying this index over the driest bare soils allows separating sandy soils from clayey ones. This index was applied over bare soil for each selected image in order to produce mid-infrared index maps at 30 m spatial resolution. One out of two samples from the 100 samples was selected in order to build the calibration dataset. We applied green vegetation and crop residue mask for each selected image: 20 samples corresponding to fields with either green vegetation or crop residue were thus masked and not available for calibration. The remaining 30 samples were used for calibration with topsoil clay content values ranging from 50 g/kg to 684 g/kg with a mean value of about 323 g/kg.
These values span the whole range of observed topsoil clay content with a satisfying distribution amongst dominant texture classes. The use of these 30 samples seems therefore sufficient to represent topsoil clay content variability within the study area. They were distributed in the different textural classes in our study area. A mean value of MID-infrared index was extracted from each test field and used with clay content. This step allowed establishing a statistical relationship (one for each selected image) between the mid-infrared index and the soil clay content using a simple linear regression model. These relationships were used to produce clay content maps at 30 m spatial resolution over our study area and for each selected image. The resulting maps contain two types of pixels: pixels with clay content estimates and others with no data over the masked areas that differ from one image to another, except for orchards.
, 2 0 ) ( 1        (6) The cokriging variance can be estimated from the covariance C11 and the cross-covariances Ci1 through Equation (  7 ):             u u C u u C u i i i i v i u n OCK N i OCK OCK               1 1 1 1 11 2 0 ( 7 ) where μ is the Lagrange multiplier of the cokriging system and (uαi-u) denotes the distance between uαi and u. From each clay content image, a point vector layer at 100 m with clay content estimates was extracted and used for interpolating clay content over masked areas. For each date, an experimental semi-varioram was calculated. The Geostatistical operations were performed with ArcMap Software (version 10. 1) using the Geostatistical wizard.
Validation Method Clay content predicted by cokriging at each point of the validation data was extracted and compared to the measured clay content at the same location. The performance of the prediction models was evaluated with the following statistical criteria: the coefficient of determination (R 2 ) (Equation (  8 )), and the Root Mean Square Error (RMSE) (Equation (  9 )). The coefficient R 2 measures the effectiveness of a variable to predict another variable, whereas RMSE measures the average error of prediction. The indices were calculated according to  [45]. To perform the calibration model estimation, we used only the R 2 and the RMSE. In addition, to independent soil samples, validation was performed using existing soil maps. The validation consists on comparing topsoil clay content and the soil typology extracted from existing soil maps.
Validation of Clay Content Map from Landsat TM Data Using Independent Soil Samples and Existing Soil Maps The resulting clay content map was validated by comparison with 50 samples over the study area (Figure  1 ). RMSE is about 10%, corresponding to 100 g/kg of clay. The R 2 between predicted and observed values is close to 0. 65. Comparison between validation data set and clay content values predicted by cokriging is illustrated in Figure  8. The model used to predict topsoil clay content overestimates low observed clay content. Another validation of the clay content map was achieved by comparison with existing soil maps, produced in 1975 by the Ministry of Agriculture at 1:50,000 scale. To validate our map, we compared those existing soil type maps and the soil clay map derived from Landsat TM data.
Comparison between Clay Content Maps Produced from Landsat TM and TerraSAR-X Radar Data The accuracy of our clay content prediction model (R 2 = 0. 64 and a RMSE of about 10% corresponding to 100 g/kg) is in agreement with an existing study  [12]  who obtained an R 2 = 0. 6 and an RMSE about 12% (120g/kg). A comparison was therefore carried out with this study based on TerraSAR-X radar data on a smaller area (Figure  9a, b ) where neither calibration nor validation points were present. Before comparing these two maps, the same mask of green vegetation is applied over the clay content map extracted from Landsat TM data. The clay map produced from TerraSAR-X radar data displays sharp contrasts over short distances compared to the smoother fields of Landsat TM. This is due to the effect of residual speckle.
Cokriging and the rough resolution of Landsat TM lead to more continuous fields and therefore a smoother variation from one spatial element of similar texture to the other. Both maps show similar features such as the wadi bed of Merguellil with low clay content values. The other regions show similarities and differences in clay content values. Additional field observations were made without analysis to differentiate coarse and fine textural classes over some areas. Within region (I), we obtained high clay content value of about 60% when using Landsat TM data and low clay content value of about 15% using TerraSAR-X radar data. Field observation shows that the fields were not cultivated and this situation corresponds to bare soil areas with fine topsoil texture. Landsat TM, therefore, estimates clay content better than TerraSAR-X radar data within this area.
Within region (II), the use of TerraSAR-X data overestimates the clay content compared to Landsat TM data. It can be due to the presence of olive trees, which cover 5% of the soil surface. Concerning the third region, we predicted high clay content when using Landsat TM data. This is in agreement with our in-situ observations, but only in partial agreement according to radar data. In these areas, the dominant crops are cereals, beans, chilies and tomatoes; all of them being irrigated and over fine topsoil texture. For region (IV), more consistent outputs were provided when using Landsat TM data than when using TerraSAR-X radar data. We predicted fine topsoil texture using TM data with clay content ranging from 35% to 45%. The difference between both approaches can be due to soil moisture variation and to green vegetation at the date of image acquisition and to the erroneously non-masked crop residue areas.
The difference between both approaches is validated by the density plot of clay content values obtained from Landsat TM data and TerraSAR-X radar data (Figure  10 ). In this area, the bias is about 5% for radar data. The central part of the scatter plot is occupied by clay content values ranging from 10% to 25% for Landsat TM data and from 5% to 10% for TerraSAR-X radar data. The average clay content value from TerraSAR-X radar data is significantly lower than the average clay content estimated using Landsat TM data. Disagreement between Landsat TM and TerraSAR-X products could be particularly explained by the presence of irrigation over some agricultural fields during experimental campaigns, which induce large errors for radar methodology.
Conclusions The purpose of this paper was to develop a multi-temporal approach using Landsat TM data in order to map topsoil clay content at a fine spatial resolution. To predict topsoil clay content spatial interpolation can be performed with ordinary cokriging using more than one variable. The results show that remote sensing is an efficient and low-priced tool that enables generating a topsoil clay content map. We can predict clay content for each pixel of the image, thus avoiding sampling a large number of fields. The resulting topsoil clay content map is at 100 m spatial resolution and is reasonably accurate with an R 2 about 0. 64 and a RMSE of 10% (100 g/kg). Cokriging made it possible to fill in the information gaps created by the vegetation mask and to validate model predictions. Improvement of relationships between MID-infrared index and clay content need more sample sites.
The method needs to be tested with other satellites e. g. , the new Landsat-8 and the future Sentinel2 (ESA, launch scheduled in April 2015). The ground resolution of the latter one is more accurate (20 m) to detect homogenous surface. The revisit frequency of this sensor is less than five days. With this type of data, we can improve mapping of bare soils in dry conditions. Comparisons with radar show differences. Moreover, these two approaches are complementary if we consider that visible and middle infrared reflectances are limited to surface characteristics, and radar data are limited to the drying speed of the first centimeters. Such topsoil clay content maps will be used to calculate soil hydrodynamic properties with pedotransfer functions. The surfaces, detected in the visible and MID-infrared bands, represent the agronomic horizon.
Acknowledgments This study was funded by: the  ANR-Groundwater Arena  ( CEP S/11-09 ), the  PHC  Maghreb  14 MAG 22  ( Estimation spatialisée de l'utilisation de l'eau par l'agriculture pluviale et irriguée au Maghreb)  and the  ANR   AMETHYST-ANR-12-TMED-0006-01. We would like to thank the SICMED projects. The authors extend their thanks to  USGS for Landsat TM data, CESBIO/CNES  for applying kindly the processing of Landsat TM images used in this study (framework of VenµS and Sentinel-2 preparation). Agrocampus-Ouest Rennes,  INRA ,  UMR 1069 SAS  for their collaboration. Thanks To the  Institut National des Grandes Cultures (INGC, Chébika station) , the  Institut National Météorologique , the  Soil Science Department (DG/ACTA)  for providing us with soil studies, The  Commissariat Régional de Développement Agricole (CRDA) of Kairouan  for their assistance during the measurements on test fields.
The mixture of CALNN and CCALNN-PEG peptides (4:1 ratio) was selected to constitute the base of the ligand shell, with CALNN providing a dense and compact peptide monolayer  [50] , while poly(ethylene glycol) (PEG) helps to reduce non-specific interactions  [63, 64]. Indeed, increasing the proportion of PEG-terminated peptides in the protective monolayer leads to a reduced internalisation of gold nanoparticles (S3 Fig. , dataset available on figshare  [65] ). We hypothesized that limiting nanoparticle non-specific interactions with the plasma membrane should accentuate the difference in uptake between particles bearing functional peptides (TAT and/or HA2) and those that do not, consequently facilitating investigating the role of these functional peptides in the internalisation process. HeLa cells were incubated for 4h with the nanoparticles, fixed and imaged by photothermal microscopy.
The intensity of the photothermal signal scales with the quantity of gold nanoparticles internalised into the cells (Fig. 1B-E , dataset available on figshare  [66] ). The uptake of nanoparticles bearing 1% TAT peptide (Fig. 1D ) or 1% TAT and 10% HA2 in their monolayer (Fig. 1E ) is substantially higher than that of particles only coated with CALNN and CCALNN-PEG peptides (Fig. 1B ). Indeed, nanoparticles comprising 1% TAT peptides within their monolayer display a near six fold increased uptake (Fig. 1F ). Further study would be necessary to unambiguously determine the molecular basis for this increased uptake although both the literature  [67]   Noticeably, the presence of the fusion peptide HA2 in the nanoparticle monolayer also increased the uptake in the absence of TAT by more than two times as compared to nanoparticles with CALNN and CCALNN-PEG peptides only (Fig.
The gold cores diameter selected were of ~10nm to facilitate unambiguous TEM observation. As previously, HeLa cells were incubated for 4h with the nanoparticles (6nM). The cells were then fixed and processed for TEM observation. In both conditions, gold nanoparticles were mainly confined inside intact or partially ruptured endosomes (Fig. 2 ). Similarly, nanoparticles were interacting with the inner membrane of intact endosomes (white arrowheads in Fig. 2 ), regardless of the presence or absence of PEG-terminated peptides within their monolayer. Some particles escaped the endosomal compartments (black arrowhead in Fig. 2A ), but most of them remained in or around the disrupted membranes, apparently interacting with them. Only on rare occasions could one observe nanoparticles free in the cytosol (datasets available on figshare  [68, 69] ) irrespective of the presence or absence of PEG within the peptide monolayer.
The TEM images suggest that more nanoparticles are present in endosomes in case of particles functionalisation with 50% HA2 (Fig. 3A , dataset available on figshare  [70] ) or 100% HA2 (Fig. 3B , dataset available on figshare  [71] ), as compared to 10% HA2 (Fig. 2A-B ). However, it was not translated into a more efficient release of the particles from the endosomes. A quantification of the density of nanoparticles in endosomes was performed using the TEM images (Fig. 3C ), as described earlier for Fig. 2E. A nonparametric statistical analysis of the three population variances (Kruskal-Wallis test) demonstrated that they are significantly different (p = 3. 04 × 10 -3 ). Further series of two-sample nonparametric tests (Mann-Whitney U) showed a significant difference of the density of nanoparticle in endosomes between the 10% HA2 and 50% HA2 samples (p = 0.