dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of clusters, first chosen on the basis of the explained inertia rate, was consolidated through the calculation of the optimal number of clusters using K-Means partitioning. The description of clusters was, first, based on chi2 correlation tests. A modality was linked to a cluster if its pvalue < 0. 05. Second, a TO modality was described as a cluster's character if i) it was present in more than 50% of the cluster's TMRs, or ii) if more than 50% of all TMRs of the dataset were present in the cluster's TMRs in the case of significant p-value. A rule was considered as linked with a cluster if S ≥ Sm, C=1 and Sa ≥ 0. 5. An Sa of 0. 5 meant that 50% of individuals in the cluster should contain the rule. For each cluster, the four longest rules were represented and compared to MCA and AHC descriptions.
In addition, the data-mining also describes clusters' similarity and homogeneity, as these increase with number and length of rules. The combined MCA, AHC and datamining analysis led to a complete description of TMRs for each cluster. These typical TMRs were finally compared to the most representative individual of each cluster, called the paragon, obtained in the clustering phase, to assess in what extend they match with actually existing TMRs. The statistical analyses were performed with the R software (R Core Team, 2019). MCA and data-mining were computed using the FactoMineR  (Husson et al. , 2020)  and Arules  (Hornik et al. , 2005)  packages, respectively.
Identifying farm types associated to each cluster This final step consisted in confronting the typical TMRs to farm structural information, in order to qualitatively link the respective TMRs and their potential environmental impact to farm-level characteristics. A contingency table of plots and farm types was obtained, by cross-tabulating the TMRs of each cluster with the characteristics of the farms with which they were associated. The structural data of farms were used to classify them regarding ten criteria, representing farm characteristics, typical of the case-study-site  (Azizi et al. , 2017; Poussin et al. , 2008). These pertained to i) access to water ("individual drilling", "collective network"), ii) access to land ("direct access" for land owners, 'indirect access" for tenants, "indirect & indirect access" for tenants who also owned some land), and iii) types of production ("grain farming", "cucurbitaceae", "leguminous", "arboriculture" and "livestock).
Chili-pepper could be associated to leguminous, watermelon or melon and olive groves. Data concerning overall water consumption, unavailable, were not taken into account. Two irrigation strategies were however observed, with the use -or not -of water stress periods, ranging from 10 to 30 days, which « allow good root development », according to farmers. Farmers have access to a wide range of plant protection products, applied on the basis of trade names rather than active substances. As far as fertilization is concerned, nutrient supply was mainly by fertigation. Foliar and soil applications were also common, depending on the strategies and product. Further, the harvest was very dependent on workforce and sale prices. The latter determined the dates and color of fruit at harvest. nitrogen inputs.
Fruits were harvested red. They are moderately environmental-friendly. Cluster 7 presented an atypical farming practice. Indeed, it described management routes that were part of a two-season growing cycle, during which the residues of the first growth season were cut to initiate a second cycle by regrowth. This cluster 7 which described the second cycle, was characterized by rare interventions of the farmer, with no hoeing nor application of bottom manure at the beginning of the growth cycle. It was also characterized by a total absence of pest management. This cluster included only two individuals and is also considered as moderately environmental-friendly. This thorough description highlights that at the territorial scale, farming practices were very diversified and contrasted. TMRs of cluster 3 and 7 were less common in the dataset.
Acknowledgements This work was supported by the  VIANA  project funded through the ARIMNet2 2018 Joint Call by the following funding agencies:  ANR (France , grant agreement no. ANR-17-ARM2-0004 ),  SEESRS (Morocco) ,  FNRSDT/DGRSDT (Algeria). ARIMNet2  ( ERA-NET ) has received funding from the  European Union 's  Seventh Framework Program  for research, technological development and demonstration under grant agreement no. 618127. We thank all the farmers involved in the study.
1. Data collection 2. Database construction 3. Statistical analysis 4. TMR-farm type confrontation (individual drilling, collective network), and crop management (seed origin, chemical fertilizers, plant protection products). This allowed a confrontation of management routes to farms structures. Third, a series of detailed interviews were conducted on the pepper crop farming practices, using the TMR concept applied to both farmers' strategic and operational decisions. The strategic decisions are made at the beginning of the season and concern plot List of farm structural and functioning variables configuration, types of association, choice of crop varieties and planting date, while the factors List of farm diversity Interview operational decisions concern the technical steps of transplantation, tillage, irrigation, pest Selection Surveys farm management, fertilization and harvesting.
A list of TOs related to all the steps of a TMR was guides List of TMRs steps of farms and plot scale pre-established through expert-knowledge and was pilot tested with a few farmers. Classification of TMRs agroecological description Classification of farm variables of variables variables Matrix of TMRs and variables modalities Database of farms Multicriterial Analysis Descriptions Typology of farms Ascendant Hierarchical Classification Clusters number Data mining of TMRs Identification of the types of farms implementing each type of TMR.
Computing the snow-free/snow-covered pixels from satellite images is the most widespread approach for determining snow cover extent  [15] [16] [17]. This monitoring is achieved through passive sensors operating in the near-infrared and visible electromagnetic bands  [18] [19] [20]. Distinct satellite missions have sensors operating in these bands such as MODIS, AVHRR, Sentinel or Landsat. Nonetheless, there are important differences between these missions in terms of the product that they provide regarding the spatial and temporal resolution, but also the extent of the time series available  [17, 21]. Landsat Thematic Mapper imagery provides a long-lasting database (the first sensor carried on Landsat 4 was launched in 1982) with 30 m spatial resolution. Landsat has a revisit frequency of 16 days, which can be a major drawback for monitoring the snow cover in mountainous regions mainly due to cloud limitations and the rapid changes of snow extent  [17, 19].
In this study, a simple methodology is proposed to spatially downscale MODIS snow cover mapping. Through training with Sentinel-2 observations, two approaches for computing probabilities are proposed and evaluated. The methods rely on the high interannual repeatability of snow distribution patterns  [48, 49] , thanks to the strong topographic control of snow distribution at scales below 100-200 m, which remains similar throughout different snow seasons  [50] [51] [52] [53]. The two methods exploit the already available database of 20 m spatial resolution Sentinel-2 snow cover maps from the Theia snow collection  [18]. Both methods compute the probability of snow occurrence for each Sentinel-2 pixel over particular time periods (seasonal computation), but one of them calculates probabilities in Sentinel-2 pixels independently of each other and the other computes the snow occurrence inside every single MODIS pixel (500 m grid cell size).
Probabilistic Spatial Resolution Increase The methods described here exploit two satellite imageries: the Normalized Difference Snow Index (NDSI) of MODIS 6 and C6. 1 products  [23, 60]  and the binary snow occurrence maps of the Theia snow collection from Sentinel-2  [18]. Cloudy areas are masked in the NDSI imagery downloaded from the EathData NASA database  [60]. Unlike the NDSI of MODIS, which is a measure of the relative magnitude of the characteristic reflectance difference between the visible and shortwave infrared reflectance  [22] , Theia is a binary database rendering 1 for snow-covered pixels, 0 for snow-free pixels and NoData in case of cloud (or cloud shadow occurrence). From the available Theia imagery, the snow occurrence probabilities were derived as described below. To provide an easier interpretation of the snow-covered area fraction of MODIS pixels, the Fractional Snow Cover (FSC) was derived from the NDSI through a universal equation (Equation (2) in  [61] ).
The code with a complete example to compute ProbMODIS and to determine the snow occurrence distribution from daily MODIS FSC is provided in the repository https://github. com/ealonsogzl/ProbModis (accessed on 10 October 2021). All cloudcovered MODIS and Sentinel-2 pixels are considered as NoData and thus are not considered for computing probabilities, nor for deriving the spatial downscaled MODIS snow obser-vations. Nonetheless ProbMODIS routines allow the filling of missing Terra values due to cloud presence with Aqua observations of these are available for the same acquisition day, the option used here after in this research. The final output of the methodology consists of 20 m resolution binary (BinSnow 20m ) snow cover maps (Snow (1)/NoSnow(0) or NoData). First, 500 m MODIS pixels are resampled to the 20 m Sentinel-2 grid with these requirements; if the FSC of an original MODIS pixel exceeds or does not reach a given upper or lower threshold, all BinSnow 20m pixels inside will be respectively considered as snow-covered or snow-free.
As the only 20 m spatial resolution observation of snow distribution is the Sentinel-2 imagery, and this database is also exploited to generate the probabilities with the two methods proposed here, a leave-one-out cross-validation  [69]  was applied. This method involves the division of the original dataset into N subsets. The model is trained N times with the N -1 subsets and tested in the remaining N subset. In this case study, the Sentinel-2 imagery is divided into snow seasons (from early September to late July) and thus the evaluation of the ProbMODIS and ProbSentinel is performed with Sentinel-2 images not included in the probability training. In addition to the Kappa coefficient and Agree, the absolute snow-covered area error was computed to determine if the probabilistic spatial resolution increase of MODIS snow products (either ProbMODIS or ProbSentinel) shows a marked improvement when mapping at the regional scale.
Moreover, clouds' occurrence in mountain areas is highly impacted by the interaction between rugged terrain and the dominant wind direction  [83] , producing cloud patterns connected with the study area's topography  [84]. This irregular occurrence of clouds may have impacted the spatial evaluation of the probabilistic snow-mapping products, as some locations may have a much longer track record than others. Limitations from both cloud-cover occurrence and dissimilar lengths of snow occurrence in different zones of the study areas will become less important as Theia Sentinel-2 imagery continues to acquire images. Longer observation datasets will allow more reliable probabilities of snow occurrence to be generated, as more cases will be included in the training. For instance, Sentinel-2 pixels with the same Prob 20m values which now are sorted by elevation would be rarer as more snow seasons are available.
However, the improvement obtained in transition zones can have remarkable importance for monitoring snow hydrology as snow-dominated zones lead to rain-dominated zones  [86]. Other spatial downscaling methods of MODIS snow-cover products have also provided reliable and accurate snow maps  [45] , with even higher spatial resolution than that obtained in this study  [47]. These authors were able to downscale MODIS snow-cover information to very high spatial resolution (3 m) with very good results. Nonetheless this latter method required both a Digital Elevation Model (DEM) and a training/validation dataset with the same spatial resolution as that of the final product, and a specific sensitivity test to determine the weights of the terrain parameters and their search distances. The main benefits of the method proposed here when compared to previous works  [44] ,  [45, 47] , is that it only relies on space borne satellite observations of the snow cover without any further requirement on the computation of the spectral reflectance or the calculation of further topographic variables.
Hereby, as the spatial downscaling of MODIS snow cover observations with ProbMODIS and ProbSentinel, is rather simple and moreover it exploits already available snow cover observations (Theia snow collection and the NDSI from the NASA Earth Observation database), it has an easy and straight forward use. As the methodology obtains reliable and accurate snow cover patterns its simplicity and low computational requirements are the main benefits of its use. Although the methodology presented requires probabilistic training with the already available database of Sentinel-2 observations, the approach is suitable for a wide variety of applications requiring high spatial resolution information on snow occurrence before the Sentinel-2 mission started. For instance, studies aiming to understand the ecological cycles of mountain pastures require detailed observations of snow disappearance over a longer time period  [4, 87].
Another subject that can benefit from this MODIS spatial downscaling is the study of the habitat of some animal species, as snow affects the distribution of forage species  [91]. Lastly, the seasonal snow cover at low elevations in temperate mountain regions is predicted to cause important snowpack changes  [92]. This demonstrates the importance of high spatial resolution snow monitoring with a long-lasting database  [40]  as this would allow changes occurring in mountain areas to be understood.
Conclusions The methodology detailed here demonstrated that it is possible to spatially downscale MODIS snow cover observations by simply determining the snow occurrence probability from a database of higher spatial resolution, in this case study Theia snow distribution maps from Sentinel-2. The amelioration of the snow cover mapping is based on the strong topographic control of the snow distribution that characterizes mountain areas. Using several snow seasons to generate a snow occurrence probability for the original 500 m MODIS pixels (ProbMODIS) or an entire Sentinel-2 tile (ProbSentinel), the methodology is able to improve the snow cover mapping in partially snow-covered MODIS pixels generating snow occurrence maps at a 20 m spatial resolution. Nonetheless the snow occurrence probability computed for each MODIS pixel obtained more reliable snow cover maps and is thus recommended.
With the already available Sentinel-2 imagery it is possible to train snow probability occurrences for particular test areas, which after finding the FSC thresholds to classify snow pixels (snow-free, snow-covered or partially snowcovered) allows the generation of snow distribution maps at 20 m resolution from MODIS observations. Nonetheless, these snow observations may be impacted by the inherent limitations of optical satellite retrievals, and will have a lower confidence on the observed snow distribution in the early snow season. The simple computation of this method allows an easy application for a wide variety of research topics requiring a long-lasting database of high spatiotemporal resolution observations of snow distribution in mountain areas. Future work aims to derive ProbMODIS probabilities over the Iberian Peninsula mountain areas and then obtain a high spatial resolution database of snow distribution which may be of interest in other disciplines.
Acknowledgments: This work was supported by the  Spanish Ministry of Economy and Competitiveness  project " HIDROIBERNIEVE :  CGL2017-82216-R" , and the  "Organismo autónomo de Parques Nacionales " with the project " Cartografía de alta resolución especial del manto de nieve y su variabilidad reciente en los PPNN de montaña y los impactos del cambio climático para el horizonte 2050 ". J. Revuelto  is supported by the Grant  IJC2018-036260-I. S. Gascoin  is supported by the  ANR   TOP  project. The authors thanks the three anonymous reviewers and  Norah Wang  who edited this article.
(2020). To estimate the volume of putative flow features, we generate DEMs of Europa's surface using the AMES Stereo Pipeline (ASP)  (Beyer et al. , 2018). This open-source tool has been used previously by several authors to generate DEMs of Europa's surface: for instance,  Schenk (2004)  and  Schmidt et al. (2011)  have generated DEMs of chaotic terrains;  Dameron (2015)  proposed a statistical study of double ridge morphology; a large database that includes DEMs of putative cryolava domes is currently being built by  Nunez et al. (2019). Here we develop our own cryovolcanic features database based on two morphologic criteria (see sec. 2. 1): (i) a thin feature confined by the surrounding ridges and (ii) a smooth appearance with no blocks showing a ridged texture. We selected four images with smooth features that are compatible with liquid flows, and we calculate the volume of these features.
Selection of cryovolcanic features : criteria To obtain the volume of cryovolcanic eruptions putative products, we generate DEMs of features that might have possibly been put in place during the effusion of liquid water at the surface. These features, called hereafter "smooth features", are a few kilometers wide and have common characteristics with smooth plains previously defined by  Greeley et al. (2000). To select the most relevant features in the Galileo dataset and avoid confusion with other features, such as chaos, we choose two major criteria to define the putative cryovolcanic features: 1. Thin features occupying topographic lows. Because we are interested in features that may have been emplaced during the effusion of liquid onto the surface, and as modeled by  Miyamoto et al. (2005)  ("A-type" features in their study), the resulting features are expected to be thin, with no particular domal shape.
Discussion and conclusions We identified four images from Galileo SSI data presenting smooth features that may have formed during one or several eruptions of cryolava at Europa's surface. We produced DEMs of those smooth features using the Shape from Shading tool of the AMES Stereo Pipeline, and we measured their volumes and associated uncertainties. The major uncertainties on the DEM generation and volume measurement come from the user parameters of SfS, but their effect is relatively low for small scale features. We estimated the uncertainty on the volumes at around ±15% (see supplementary materials, section 2). Volume measurements of the four selected smooth features gave results ranging from 5×10 7 to 5×10 8 m 3. Shape from Shading tool allowed us to generate DEMs from single images, with high precision at low scales  (Nimmo and Schenk, 2008).
Acknowledgments We acknowledge support from the  "Institut National des Sciences de l'Univers" (INSU) , the  "Centre National de la Recherche Scientifique" (CNRS)  and " Centre National d'Etudes Spatiales" (CNES)  through the " Programme National de Planétologie ". We thank  Benoit Jabaud  for his analysis of the Galileo/SSI images. We also thank  Baptiste Journaux  for the interesting discussion we had. We gratefully acknowledge the two anonymous reviewers for their comments and suggestions that greatly improved this manuscript.
Biolayer Interferometry DENV2 NS1 binding to HDL and LDL particles was monitoreded by Biolayer Interferometry (BLI), using an Octet Red384 instrument (ForteBio). Streptavidin-coated biosensors (SA, ForteBio) were loaded with biotinylated anti-ApoA-I or anti Apo-B antibodies (Abcam), followed by HDL or LDL, respectively. Subsequently the biosensors were incubated in wells containing serial dilutions of NS1 protein (concentrations ranging from 6. 25 to 800nM for HDL, and from 36 to 2500nM for LDL) and the BLI association signals were recorded in real-time until they reached a plateau. Finally, the biosensors were incubated in wells containing buffer to monitor the dissociation of the complexes formed, before being Ultracentrifugation experiments were performed in PBS+/+ (Gibco). Sedimentation velocity data analysis was performed by continuous size distribution analysis c(s) using the Sedfit 15.
0 software  (82). All the c(s) distributions were calculated with a fitted fractional ratio f/f0 and a maximum entropy regularization procedure with a confidence level of 0. 95. Buffer viscosity and density as well as the extinction coefficient of NS1 were calculated using the sednterp software (http://sednterp. unh. edu/). Partial specific volume were measured for NS1 (0. 82 ml. g -1 , as defined on an iodixanol density gradient) and confirmed by multidetection AUC experiment for HDL (0. 94 ml. g -1 ). Extinction coefficients were extrapolated at 250 nm using the Utrascan II software. Deconvolution of the multi detectors signal into stoichiometric ratio was performed by integrating all the peaks on each detector to determine the contribution of each partner present (NS1, HDL or both).
Electron microscopy and image analysis Solutions of NS1 and NS1-HDL were spoted on glow discharged carbon grids, contrasted with 2% uranyl acetate and imaged with a Tecnai F20 microscope (Thermo Fisher, USA) in low dose conditions. Automated acquisitions were performed using EPU software (Thermo Fisher, USA) and images were acquired usind a Falcon II (Thermo Fisher, USA) direct detector. HDL and NS1-HDL images were CTF-corrected (phase flip) and sorted using the XMIP software  (83). Corrected images were imported in Relion  (84). The recommended strategy for particle picking was applied as follow: a manual selection of particles compatible with the HDL or NS1-HDL size was performed on a small number (about fifteen) of images. A 2D classification (40 classes) was performed and five representative well-defined classes were selected as template for the automatic picking, leading to about 30 000 particles.
The first visit was conducted at hospital admission. The day of onset of symptoms was defined as day 0 of the illness. The last visit was performed at the time of discharge for patients who recovered entirely, or as a follow-up visit for patients still in the critical phase. A clinical and biological follow-up including abdominal/chest ultrasound recording was conducted at each visit. DENV infection of hospitalized patients was confirmed by NS1 antigen detection using our NS1-capture ELISA  (47) (48) (49)  and/or RT-qPCR and/or virus isolation on Aedes albopictus C6/36 cells on the plasma sample obtained at admission  (85). Finally, the severity of the disease among confirmed dengue patients was assessed according to the WHO 2009 criteria using clinical and biological data recorded at admission (ADM), follow up visit (F-VIS) or discharge (DIS).