dataesr/scientific-paragraphs-categorization huggingface dataset:
S12 ). Highly hydrophobic pockets, like the site in which ligand EKM binds the Mango-II Fluorescent RNA Aptamer structure (PDB 6c64, Fig. 4B ), and less hydrophobic pockets, such as the binding site of ligand UG4 in Fusibacterium ulcerans ZTP riboswitch (PDB 6wzs, Fig. 4C ), were found to be equally ligandable. However, druggable pockets have a tendency to be more hydrophobic (Fig. S13 ). In agreement with previous studies  [40] , RNA-SM pockets span a broad range of volumes, independently from the software and dataset used for pocket identification and volume calculation (Fig. 3H ). The size of the most druggable and ligandable pocket in HARIBOSS, corresponding to the binding site of 747 to the Corn RNA aptamer (PDB 5bjp and 5bjo, Fig. 4D ), is equal to ~230 Å 3. However, cavities larger than 300 Å 3 were also classified as ligandable (Fig.
Conclusions Here, we presented HARIBOSS, a curated database of structures of RNA-small molecule complexes, built to aid the development of computational drug design pipelines. For each HARIBOSS entry, we provided general structural information and we analyzed the physico-chemical properties of the ligands bound to RNA, and of the respective pockets. For the majority of structures in our database, the experimentally-resolved pockets were confirmed as ligandable. Only one third of all pockets was ranked as good for drug design purposes, the remainder part being a difficult target or undruggable. Our analysis indicates that low druggability is due to the fact that RNA pockets are less hydrophobic and more exposed to solvent than protein pockets. In line with these findings, RNA binders in the HARIBOSS database were mostly highly polar and water-soluble ligands.
Characterizing the dynamic properties of RNA molecules is particularly important to identify selective and specific compounds using structure-based approaches that target individual members of RNA conformational ensembles  [60]. HARIBOSS can be accessed via a web interface available at https://hariboss. pasteur. cloud and explored using a compound or pocket perspective. Our database will facilitate: i) assessing the accuracy of existing protein-oriented drug design computational tools, identifying areas of improvement, and optimizing them for RNA molecules; ii) investigating the nature of RNA-SM interactions; iii) defining the chemical space of RNA binders and their potential to be used as drugs; iv) identifying new potential RNA targets based on pocket druggability or starting from a specific compound.
Acknowledgments We would like to thank  Arnaud Blondel  and  Guillaume Bouvier  ( Institut Pasteur, Paris ) for assistance with mkgridXf and watershed calculations. The development of the HARIBOSS website built on the web infrastructure developed for the iPPI-DB database, a project led by  Olivier Sperandio  ( Institut Pasteur, Paris ). This work used the Kubernetes cluster provided by the  IT department at Institut Pasteur, Paris. We thank  Thomas Ménard  for his help with the Kubernetes configuration,  Fabien Mareuil  for his help with the use of the NGL javascript library.
The proposed new algorithm is implemented in a combination of R and C computer codes which is particularly efficient with large data sets: a whole image with more than 3 million pixels and covering more than 10,000 km 2 can be analysed in a few minutes. Four applications with hyperspectral and multispectral data are presented. For the data sets with ground truth data, the overall accuracy of the probabilistic k-means was substantially improved compared to the standard k-means. One of these data sets includes more than 120 million pixels, demonstrating the scalability of the proposed approach. These developments open new perspectives for the large scale analysis of remote sensing data. All computer code are available in an open-source package called sentinel.
In particular, satellites can cover the whole surface of the Earth with a frequency of a few days or weeks  (Li and Roy, 2017; Wulder et al. , 2018). Furthermore, the most recent satellites are equipped with high-resolution sensors which are able to record a wide range of information such as reflectance at different wavelengths, altitude, or temperature  (Fu et al. , 2020). During the last decade, there has been a remarkable increase in the resolution of these sensors. To illustrate this progress, the University of Twente maintains a database listing 334 satellites (some being out of service) and 396 sensors with a number of bands ranging between 1 and 16,921 (URL: https://webapps. itc. utwente. nl/sensor/; accessed 2021-08-31). Among these sensors, 43 (11%) are indicated to have a resolution of one meter or less (until 1.
25 cm), and 90 (23%) others are listed with a resolution between 1 m and 10 m. Spectral imaging sensors record electromagnetic waves and provide data in two broad categories: hyperspectral imaging (HSI) where reflectance is recorded for several hundreds of narrow bands (typically a few nanometres wide), and multispectral imaging (MSI) where reflectance is recorded for a few bands (usually less than 20) each with a width of few tens or hundreds nanometres. Both HSI and MSI usually record wavelengths beyond visible light (e. g. , ultraviolet, infrared). During the last decade, a range of open-access remote sensing data have been made available (e. g. , URL: https://developers. google. com/earth-engine/; see also  Guo et al. , 2020). As an example of these developments, the Sentinel program is made of seven satellites currently in orbit around the Earth (URL: https://sentinel.
esa. int/). Two of them, Sentinel-2 A and Sentinel-2 B, are equipped with an MSI sensor which records reflectance in thirteen bands from ultraviolet (UV) to infrared (IR) including three bands in visible light  (Gascon et al. , 2017). The Sentinel program stands apart from other similar programs because the data are available publicly in near-real time through the Copernicus datahub (URL: https://scihub. copernicus. eu/). Each satellite covers the same location every two weeks, so the same location is potentially covered every week giving the opportunity to monitor environmental and land use changes at relatively high temporal resolution  (Li and Roy, 2017). One of the applications of spectral imaging data is to infer land cover and land use. Two types of approaches are used for this objective.
Furthermore, a specific aim was to perform analysis of images with several millions of pixels in reasonable times. For instance, an image (or product) of Sentinel-2 (about 10,000 km 2 ) at a 10-m resolution has more than 120 million pixels. The method is available in a computer package called sentinel (which also includes functions to query, manage, and download data from the Copernicus datahub). Before detailing the proposed methodological development, the next section presents a review of the recent literature on the applications of the k-means method to the analysis of remote sensing data. Four applications are then presented with two HSI data sets and two MSI data sets. The discussion gives further comparisons with previous contributions on unsupervised classification. The perspectives of current and future developments are also discussed.
Literature review This review focuses on developments and applications of the k-means method in remote sensing data analysis published during the last ten years. Where possible, the sizes of the imaging data and the software used have been noted. Several papers attempted to develop methods aimed to improve the properties of the k-means method. Galluccio et al. (2012)  developed a method which assumes there are modes (areas of highest densities of observations) in the distribution of reflectance. These modes are found in the multivariate density space using the link lengths of a minimum spanning tree. Basically, the goal of their method is to initialise the centres of the k-means algorithm. They applied it to image data from Paris (512 × 521 pixels, 7 bands) and from Mars (300 × 120 pixels, 256 bands).
Another study found that the standard k-means algorithm usually performs poorly on HSI data  (Zhang et al. , 2013). These last authors define the pure neighbourhood index (PNI) to perform neighbourhoodconstrained k-means which adds steps to the iterations of the standard kmeans with a weight function defined with the PNI. They applied this method to a 200 × 200 pixels image with 80 bands. Haut et al. (2017)  used the MapReduce computational framework to analyse two images from Indian Pines (145 × 145 pixels and 2678 × 614 pixels, both with 220 bands). They programmed their analyses with Apache Spark for distributed computing and Python Scikit for the k-means. However, they did not assess the effect of different numbers of groups. He et al. (2014)  showed that support vector machine (SVM), a supervised classification method, performs very well even with a small training data set.
On the other hand, fuzzy k-means (FKM) was found to have a reduced usefulness with large data sets. These authors proposed a fusion of the two methods where the entropy is used to find the appropriate number of groups (see below for details about the use of entropy). They applied their method on two SPOT6 images (1982 × 1630 pixels and 2113 × 2151 pixels) each with six reference classes. Their analyses were implemented in  ENVI and IDL (ver. 4. 8). Zhang et al. (2016)  used an object-based approach defining a hierarchy from the pixels up to the scene. Their analyses used a combination of principal component analysis (PCA) on HSI images, k-means with drop-out, and SVM. The code was implemented in LIBSVM. They applied their approach to the Indian Pines data (145 × 145 pixels, 220 bands) and the University of Pavia data (610 × 340 pixels, 103 bands).
They concluded that the drop-out k-means improves efficiency of the standard k-means with a small computational burden. They also demonstrated that the spatial information contained in the neighbourhood of pixels is useful, although their results did not relate this improvement with the identification of physical objects on the ground. Similarly, in another study  Kavzoglu and Tonbul (2018)  used k-means to perform image segmentation in a framework of object-based image analysis. They applied their approach to an image with 5000 × 3700 pixels and 8 bands. They found that k-means generally performs well for image segmentation using different specific algorithms. They implemented their computations with ENVI and MATLAB. Image matching and indexing are also applications of k-means. Cao et al.
(2013)  used k-means to perform image indexing based on the Kullback-Leibler discrepancy. They provided code in C++ and Matlab. Sedaghat and Ebadi (2015)  performed image matching using k-means in a second step to classify images into groups. They used MATLAB to implement their method. Several papers used k-means to perform fine-scale spatial structure analyses. Kuo et al. (2019)  analysed canopy structure by quantifying leaf angle distribution using a combination of k-means and an octree data structure: they analysed point cloud data (PCD), a kind of LiDAR (light detection and ranging) data which can reconstruct 3-D structures. The PCD were first split into octree subspaces so that each single octree unit contained no more than 1500 points. Each octree unit was then analysed with a standard k-means.
Computational details and implementation The overall workflow is summarised on Fig. 2. The whole procedure was implemented in code written in the R and in C computer languages. The PCA was performed by singular value decomposition (SVD) which is faster and numerically more stable than the usual eigendecomposition  (Venables and Ripley, 2002). With HSI data, it was observed that a relatively substantial number of PCs had nearly zero variance so that keeping all PCs made the computations much slower for no benefit: the number of PCs selected was set to keep at least 99% of the overall variance. For MSI data, all PCs are kept. The coordinates on the p PCs are first analysed with a standard k-means using  Hartigan and Wong's (1979)  algorithm which is particularly efficient and fast. The means and SDs are calculated for the p PCs and each group.
g. , if this proportion is zero, then iterations are stopped when no pixel is reclassified). This probabilistic k-means has been coded in a C routine called from R. All code is available in a package named sentinel distributed on GitHub (URL: https://github. com/emmanuelparadis/sentinel). Some code is also provided to use the standard k-means method in order to ease comparisons with the present method. This package includes code to query the SciHub repository where the Sentinel data are stored.
Hyperspectral data: Pavia University and Okavango Two hyperspectral data sets were considered. The Pavia University and Okavango data are two HSI data sets that have been preprocessed (URL: http://www. ehu. eus/ccwintco/index. php/Hyperspectral_Remote _Sensing_Scenes; accessed 2021-07-07). Both data sets are associated with reference data defined as "ground truth" with 9 and 14 classes of land cover, respectively (Tables  S1-S2 ). Two analyses were performed with both data sets. First, the ground truth data only were analysed with the standard and the probabilistic k-means with K set equal to the known number of classes of land cover for each data set. The classification performance of each method was quantified with the overall accuracy as defined by  Olofsson et al. (2014). Because both k-means algorithms are unsupervised, the reference and inferred land cover values were matched with the Hungarian algorithm, a method which aims to maximise the values on the diagonal of a matrix, as implemented in the package RcppHungarian  (Silverman, 2019) ; the diagonal values of the matrix output were used to calculate the accuracy.
Southern France An image data taken by the satellite SPOT6 was analysed. The image was taken on 2019-06-27 above the South of France and had no cloud cover. The area of the image was estimated to be 3207 km 2. A preliminary analysis of the CORINE land cover database over this area found that it is covered by 34 distinct land classes (as defined by the CORINE database). Out of these 34 classes, 17 were represented by less than 0. 5% of the area, whereas 14 classes were represented by at least 1% of the area (Tables  S3 ). The data were analysed with the probabilistic k-means with increasing values of K: the value of H was computed for each value of K and the final maps were drawn for both standard and probabilistic k-means using the value of K giving the smallest value of H.
Computational efficiency With 3,348,900 pixels and 13 variables, each iteration with K = 12 took around 2. 4 s. Therefore, 200 iterations (the default limit set in the code of sentinel) took 8 min. Furthermore, it was observed that in all cases, with either real or simulated data, there was convergence to a stable classification with no further reassignment. In all cases reassignment was around 0. 001% of the pixels after 200 iterations.
Discussion The present work has contributed a new k-means method which appears as an improvement compared to currently available implementations with respect to three points: better accuracy, possibility to With both HSI data sets for which ground truth data were available, the method proposed here showed better overall accuracy compared to the standard k-means. The improvement was particularly substantial for the Okavango data set. Although no ground data were available for the two MSI data sets analysed here, the maps suggest improved classification with the probabilistic method compared to the standard k-means. These results clearly suggest that the proposed probabilistic method has improved performance compared to the standard k-means for land cover classification of spectral imaging data.
More recently,  (Liu et al. , 2021)  proposed a method, with a name similar to that presented here, which is based on a probabilistic modelling of the standard k-means which is solved by numerical optimisation (see also  Li et al. , 2020). However, similarly to the standard k-means, and by contrast to the present method, they assumed homogeneous variance among groups. These authors implemented their method in MATLAB and presented several applications with artificial and real data sets of modest sizes (several thousands observations or less) with known number of groups. Richards et al. (2010)  present a method that shares some similarities with the present one as it is based on the multivariate normal distribution. However, these authors proposed to maximise the log-likelihood function by expectation-maximisation  (Dempster et al.
, 1977). Besides, they weight the contributions of the pixels to the likelihood function with respect to their spatial contiguity, which was not considered in the present work (but see the perspectives below). In terms of running times, the probabilistic k-means has an attractive feature. As reported above, the analysis of a complete Sentinel-2 data set covering more than 12,000 km 2 at a resolution of 60 m with 13 bands takes around 8 min. Additionally, three analyses with several million pixels (including one with more than 10 8 pixels) are reported showing how the method presented here is scalable to very large data sets. In addition to the developments on k-means, the package sentinel presented in this paper adds to the software tools available for the analysis of Sentinel-2 data. Ranghetti et al.
(2020)  presented another package written in R, sen2r, to handle and manage Sentinel-2 data. By comparison, sentinel makes it possible to search, download, and manage products and data from all Sentinel satellites. Besides, the R environment makes it possible to read the different file formats used in Sentinel products thanks to the packages rgdal  (Bivand et al. , 2018)  and ncdf4  (Pierce, 2019). These integrated tools have the potential to contribute to a software environment for time-series analysis of remote sensing data  (Gray and Song, 2013; Cai et al. , 2014; Gómez et al. , 2016). The present paper aims at developing and implementing a fast unsupervised classification method to analyse multispectral data. The ultimate goal of this work is to be able to analyse large scale remote sensing data to infer changes in forest cover over large areas (see e.
Snapshot of Microbial Community Variation We performed a set of field experiments that included natural inbred lines of Arabidopsis thaliana (hereafter "accessions") originally collected throughout Sweden, mainly in two climatically contrasted regions of the country (Dataset S1); A. thaliana in the north of Sweden experiences long, snowy winters, and, as a consequence, plants are typically found on south-facing slopes of rocky cliffs. Arabidopsis populations in the south of Sweden, on the other hand, tend to be associated with agricultural or disturbed fields that experience highly variable snow cover over the winter months. We used replicate experiments in four representative Arabidopsis sites, two each in the north (sites NM and NA) and south (sites SU and SR) of Sweden. Experiments were repeated across 2 years, for a total of eight experiments.
Each experiment was organized in a complete randomized block design including 24 replicates of 200 sequenced accessions  (15) , established as seedlings in a mixture of 10% native and 90% potting soil and timed to coincide with local germination flushes in late summer. Many of the microbiome members from our experiments were also found within the leaves of A. thaliana plants that we collected in the field in southern Sweden in 2017, suggesting that this percentage of native soil was sufficient to seed a representative microbiome (Dataset S2). Immediately upon snowmelt in early spring, we sampled and freeze dried five or six whole rosettes per accession. DNA was extracted from the freeze-dried rosettes, and both the ITS1 portion of the Internal Transcribed Spacer (ITS) and the V5 to V7 regions of the 16S RNA gene were sequenced to characterize the fungal and bacterial communities, respectively  (9, 12, 16).
Next, we asked whether heritable OTUs are more likely to be ecologically important hubs, because this could open the door to community-level impacts of host genetic variation. Across all eight experiments, we detected 23 OTUs that were both heritable and hubs at least once (SI Appendix, Table  S2  and Dataset S2). This represents a significant enrichment of hub OTUs among heritable OTUs (Wilcoxon rank sum test: n = 8, W = 57, P value = 0. 007), suggesting that host effects on the microbiota preferentially influence the relative abundance of ecologically important microbes. In fact, hub OTUs were often among the OTUs with the highest heritability within each experiment; these hub OTUs stand out in that we find no general relationships between heritability and either betweenness or degree (SI Appendix, Fig.
We reasoned that, if microbe-microbe interactions were largely responsible for the patterns of cooccurrence that we observed, then microbial cooccurrences would be diminished by our permutations of replicates within host genotypes. The same diminution would be evident if patterns of microbial cooccurrence were due to microenvironmental variation within experiments, independent of host genotype, although strong microenvironmental effects would have interfered with our ability to detect heritable OTUs. On the other hand, if OTUs tended to cooccur due to shared host genotype effects, then our permutations would have little impact. In the networks computed from the permuted datasets, on average, 91. 39% (ranging from 87. 67 to 95. 2% across our eight experiments) of all OTUs that previously cooccurred with at least one other OTU (with degree > 0) had fewer associations with other microbes.
Out of the 344 QTLs, only 27 included SNPs associated with multiple traits (Dataset S3). To investigate functions underlying these associations, we tested pathway and Gene Ontology (GO) term enrichment (biological processes only)  (28, 29). Each annotated gene was assigned the highest absolute SNP effect within 5 kb, and we used a combination of methods based on effect sizes accounting for multiple testing, overlapping gene lists, and the potential aggregation of functions and associations along the genome  (30) (31) (32) (33) ; we identified 29 enriched GO terms related to biological processes across 16 traits (Datasets S4 and S5), including genes involved in the response to virus (GO:0009615) and nematodes (GO:0009624), hypersensitive response (GO:0009626), and response to chitin (GO:0010200), all of which are related to interactions with other organisms.
Genes involved in the synthesis of glucosinolates from phenylalanine (like glucotropaeolin in ref. 36 , PWY-2821) and hexahomomethionine [specifically, 8-(methylsulfinyl)octyl-glucosinolate (36), PWYQT-4475] are also enriched in loci associated with B5 and F71, respectively. The functions highlighted by our analysis are in line with other studies suggesting the involvement of specialized metabolites, auxins, and the immune system in influencing the leaf microbial communities  (37, 38). Our analysis also highlights less obvious functions, like fatty acid and brassinosteroids biosynthesis (Dataset S5). This is especially true for beneficial members of the community. For example, loci associated with the relative abundance of the beneficial microbial hub B38 are enriched for transition metal ion transport (GO:0000041), response to carbohydrates (GO: 0009743), and fatty acid biosynthesis (PWY-4381).