dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion Ralstonia solanacearum T3E database Inventory and re-annotation of T3E genes in the RSSC Our goal is to provide a comprehensive and an as exhaustive as possible inventory of T3E in the RSSC as a public database from which curated information can be retrieved. To this end, we manually curated and compiled the T3E genes from eleven sequenced strains representative of the genetic diversity of the RSSC (see Methods). The workflow of the retrieval and annotation of the T3E genes from the RSSC genomes as well as the main outputs of this analysis are shown in Figure  1. The inventory of T3E in the published RSSC genome sequences was primarily based on homology searches with the established repertoires of strains GMI1000  [7]  and RS1000  [6]. Identification of additional T3E was conducted using criteria defined previously  [5]  to mine the GMI1000 genome: (i) homology to known T3E in other bacterial species (ii) presence of a hrp II box in the promoter region since 52/70 T3E gene promoters harbor this cis-regulatory element in GMI1000  [24] , (iii) existence of specific amino acid distribution biases in the 50 N-terminal domain  [24].
These two latter criteria were hampered by the fact that many T3E genes have wrongly annotated start codons. Hence all the genes possessing a putative hrp II box were inspected for potential start codon errors before being included in the T3E annotation workflow (see Figure  1 ). This process led to the "discovery" of twenty new T3E genes (generating 42 new gene accessions), and the re-annotation of 34% of the existing RSSC T3E genes. Altogether these changes affect 39% of the RSSC T3E dataset (841 individual entries) submitted or already present in GenBank to date.
Identification of T3E candidate genes in RSSC strains. A mining of the genome of nine RSSC strains from phylotypes 2, 3 and 4 for previously undescribed T3E gene families was performed based on the criteria listed above  [5]. In this process, we only kept the T3E candidates strictly fitting with both criteria (ii) and (iii) described above. This search yielded 16 RSSC T3E candidates, for which T3SS-dependent translocation is not yet demonstrated. These 16 hypothetical T3E gene families are listed in the Additional file 1 as well as in the RSSC-T3E database. Most of the corresponding genes did not display homology to any other known proteins, except for families RSSC-T3E-Hyp5, Hyp6 and Hyp7 having homologues only in Acidovorax spp or Xanthomonas spp, which are both plant pathogenic bacteria.
In some other cases, genome sequence gaps resulted in incomplete T3E gene prediction. Many genes encoding T3E with internal repeats are often predicted as truncated or incomplete, probably due to the difficulty to assemble repeat-containing short sequence reads (Next Generation Sequencing techniques). Frameshift-mutated and incomplete T3E genes were included in the RSSC-T3E database and are distinguished by the prefix fs ('frameshift') before the gene name. Future re-sequencing should verify the current pseudogene status of these genes. Probable non-functional pseudogenes are also listed in the RSSC-T3E database (with the "pg" prefix, for pseudogene). These pseudogenes correspond to genes or gene fragments which are either gene remnants, open reading frames disrupted by a transposable element insertion or frameshift mutated genes confirmed after re-sequencing.
The RSSC-T3E database interface The dataset corresponding to the lists and expert annotation of validated and candidate T3E in the 11 sequenced strains representative of the 4 RSSC phylotypes were compiled in a web interface named "Ralstonia T3E" (https://iant. toulouse. inra. fr/T3E) designed to provide the user with a convenient and straightforward access to all the underlying data. The home page provides a synthetic table displaying the distribution of the 94 T3E gene families in the RSSC strains under the proposed nomenclature (see below). This table summarises for each strain whether a gene member is present (in single or multiple copies), absent, or is predicted as being not functional (pseudogene). A specific colour code also indicates genes with putative frameshift mutations. This information is also available as a table in the Additional file 2.
A second group of paralogous rip genes is characterised by a smaller number (2-3) of paralogous sequences in a given strain. Phylogenetic analyses were used to estimate the evolutionary relationships between paralogues using sequence data from the 11 RSSC representative strains. We defined eight additional rip genes (RipC2, RipE2, RipF2, RipO2, RipV2, RipAF2, RipAX2 and RipAZ2) (Table  1  and Additional file 4). Several of these paralogous genes, such as ripC2 or ripO2, seem to differ significantly from RipC1 and RipO2 respectively and could have originated through lateral gene transfer (see below) since homologous genes exist in other bacterial species. For the gene families present in most of the RSSC strains (ripE2 and ripV2), the genes are located in each strain in a similar genomic context, an observation which also supports a common evolutionary origin.
Several rip genes underwent selection and recombination After excluding from the datasets the likely pseudogenes, all Rip genes with more than 3 orthologs (75 out of 93 Rip genes) were analysed for traces of recurrent diversifying positive selection. The analysis performed here was carried out like described previously  [17] , except that gene phylogenies were inferred using one-ratio codon model M0  [51]. The full results are displayed in the Additional file 7. Considering that some of the datasets were rather small we concentrated on identifying Rip genes with strong indications of positive selection. This was the case for the nine following Rip genes: RipAA, RipAJ, RipAT, RipAW, RipBD, RipD, RipG7, RipH3 and RipS7 with three out of three likelihood ration tests (LRTs) for positive selection being significant (Table  2 ).
Comparative genomics and functional implications The RSSC T3E core set: a large group of conserved effectors. The establishment of a near-complete T3E repertoire in strains representative of the large phylogenetic diversity of the RSCC allows a more specific and accurate comparison than those based on comparative genomic hybridizations  [12]. We performed T3E repertoire comparisons using the following criteria: (i) rip genes listed as pseudogenes in the database were considered non-functional but those listed as containing frameshifts were considered as functional genes. The assumption that all the frameshifts are due to sequencing errors is probably an overestimation. Since we can't validate this experimentally, and considering that the number of frameshifts identified is inversely correlated with the genomic sequence quality, we will keep this assumption.
Conclusion T3E are essential to R. solanacearum pathogenesis but progress in understanding of their relative contribution to disease through reverse genetic approaches has been hampered by the evidence of functional redundancies, due to the existence of large T3E repertoires. In this study, we have undertaken groundwork for a global inventory of R. solanacearum T3E at the species level in order to provide to the community a curated dataset, tools and a rationalized nomenclature that should pave the way for future work on RSSC effectomics. We conducted a large scale approach aimed at the identification, expert annotation and phylogenetic analyses of T3E from the RSSC, a species complex showing considerable genomic diversity  [10, 11]  and responsible for one of the most devastating bacterial disease of plants worldwide  [2].
Prediction of T3E start codon. We observed a great heterogeneity among the predicted start codons for many T3E families in the RSSC annotated genomes deposited at GenBank. When possible, multiple sequence alignments of the regions located downstream the hrp II box element were performed to predict the most probable start codon which was defined as the more distal 5′ initiator codon conserved among the different strain sequences. Frameshift and pseudogene prediction T3E genes were annotated as frameshift in two cases: (i) when several contiguous open reading frames displayed homology to a defined Rip gene sequence (thus resulting in the annotation of two or multiple gene fragments), and (ii) when the T3E gene sequence was located on a contig border (thus resulting in the annotation of a T3E gene fragment).
Phylogeny Rip sequences were aligned using the ProGraphMSA program, which implements the evolution-aware alignment  [65, 66]. This program performs well with indel rich data as well as with variation in tandem repeats such as leucine rich repeats, as is often the case here. All phylogenies were reconstructed using fast maximum likelihood (ML) heuristic search. For all individual Rip genes we captured information from both nonsynonymous and synonymous sites by using tree searches under codon model M0  [67]  using CodonPhyML  [51]. Since phylogenies for paralogous gene families described much more diverse datasets, they were reconstructed under amino acid model LG  [68]  with C-rate variation among sites  [69] , as implemented in PhyMLv3. 0  [70]. Branch supports were estimated using the aBayes method, which is fast, accurate and has performance comparable with the Bayesian method  [71].
When used for clustering, the SOMs have the following main advantages. Firstly, starting with a sufficient number of neurons, the SOM procedure is able to identify features in the data even when these features are only typical of a small number of input vectors. Secondly, the resulting layer of neurons is arranged according to the similarity of these prototypes in the original data space. This 'topology-preserving' property is generally not available in other data-reduction techniques, such as independent component analysis (ICA) or k-means clustering. This is one desirable property that SOMs share with multidimensional scaling. This topological structure facilitates the merging of nodes in order to form 'superclusters', which provide a way to visualize and compare highdimensional fMRI data sets.
In this paper, we will use a variant of the quantization error in order to identify the output units, which explain the largest amount of between-subject 'variance' in the data. However, the quantization error does not allow the computation of the distance between two given SOMs. Kaski and Lagus (1996)  have proposed a measure of dissimilarity between two SOMs. They proceeded by comparing the shortest path on each SOM after matching a given pair of input vectors. This dissimilarity measure is computed by comparing the distances between all pairs of data samples on the feature maps. This method, however, is not computationally efficient, and would be especially challenging when considering fMRI data sets, where neuroscientists are commonly handling about 100,000 input vectors -that is, the voxel-specific time series-for every subject.
Spatial SMD One may also be interested in quantifying the amount of 'spatial overlap' between two given SOMs. This question is especially pertinent when analyzing SOMs based on fMRI data sets. Here, we therefore wish to evaluate whether the units in two different maps correspond to similar subsets of voxels in the original images. Such a distance can be quantified through a slight modification of the aforementioned SMD metric, where the Hamming distance is used in the place of the Euclidean distance.
Subjects and Experimental Design This data set consists of fMRI data from 16 right-handed males with a mean age of 23 years. All participants had normal eyesight and no history of neurological or psychiatric disorders, and gave written informed consent to participate in the study, in accordance with the local ethics    2. Significance results of all pairwise comparisons for the three conditions of interest, where subjects were exposed to pleasant, unpleasant and neutral stimuli. These results are reported independently for three different metrics, denoted T-SMD, and S-SMD and ST-SMD, which stand for temporal SMD, spatial SMD and spatio-temporal SMD, respectively. Observe that since three tests were conducted for each pair of conditions and we therefore necessitate the application of a Bonferroni correction for testing for these three pairwise comparisons.
Hence, only p-values below 0. 016 should be regarded as statistically significant. committee of the University of North Carolina (see  Mourao-Miranda et al. , 2006). Data were acquired using an experimental block design, composed of three different conditions: (i) exposure to unpleasant visual stimuli (i. e. photos of dermatological diseases), (ii) exposure to neutral visual stimuli (i. e. photos of neutral day-to-day scenes including human actors) and (iii) exposure to male-relevant pleasant visual stimuli (i. e. scantly dressed women or women in swimsuits). The entire experimental design consisted of six blocks, where each block contained seven images, which were each presented to the subjects for three seconds. Each block was followed by a resting block period where subjects were solely exposed to a fixation cross.
Data Acquisition and Pre-Processing Blood-oxygenation-level-dependent (BOLD) signal was measured using a 3-Tesla Allegra headonly MRI System at the Magnetic Resonance Imaging Research Center in the University of North Carolina. The scanning parameters were specified as follows. Voxel size was 3 × 3 × 3mm 3 , TR was 3s, TE was 30ms, FA was 80, FOV was 192 × 192mm and each MRI volume had dimensions 64 × 64 × 49. In each experiment, a total of 254 functional volumes were acquired for each participant. Data were pre-processed using the FSL Software suite  (Smith et al. , 2004a) ; through the use of the Nipype Python Library  (Gorgolewski et al. , 2011). All fMRI volumes were first motioncorrected and the skulls were removed, after tissue segmentation. The voxel time series were detrended and filtered in time and space: that is, low-frequency (drift) fluctuations were reduced using a band-pass temporal filter comprised between 0.
Comparison with Standard GLM Maps The group-level SOM output units selected using the sample Jaccard index were compared with standard general linear model (GLM) z-score maps. Separate GLM analyses were conducted for each experimental condition, using the FEAT function provided in the FSL software suite  (Smith et al. , 2004b). The z-score maps were thresholded at p = 0. 05, for comparison purposes. These binarized z-score maps were compared with the maps of the three 'best' group-level output units, selected on the basis of their sample Jaccard indices, as described in the previous section. These  thresholded GLM maps have been overlaid in blue over the selected output units in figures 3, 4 and 5. In each experimental condition, we computed the percentage overlap between the maps obtained using these two different techniques.
We have hence described and evaluated several types of distance functions for SOMs based on fMRI data. In particular, we have considered variants of the classic SMD function, which has previously been used to compare pairs of SOMs. Our proposed variants distinguish between the temporal, spatial and spatio-temporal properties of the data under scrutiny. Our inferential framework and these metrics were tested on both synthetic and real data, Our analysis of the simulated data showed that the distance functions of interest were indeed capturing the aspects of the data that they were purported to measure. In addition, the findings of the re-analysis of an fMRI experiment has demonstrated the capacity of these methods to extract new information from existing data sets. In this paradigm, the differences of the restricted mean SOMs in the pleasant and unpleasant conditions were found to be smaller than the differences between the mean SOMs in any of these two conditions with respect to the one in the neutral condition.
This work was supported by a fellowship and core funds from the  UK National Institute for Health Research (NIHR) Biomedical Research Centre for Mental Health  ( BRC-MH ) at the  South London and Maudsley NHS Foundation Trust and King's College London. This work has also been funded by the  Guy's and St Thomas' Charitable Foundation  as well as the South London and  Maudsley Trustees. APF has received financial support from the  Region Rhône-Alpes  and the  Université Lumière Lyon 2  through an  ExploraDoc grant. We would also like to thank three anonymous reviewers for their valuable inputs.
Earthquake is one of the dominant triggering factors of landslides. Given the wide areas covered by mega earthquake-triggered landslides, their inventory requires development of automatic or semi-automatic methods applied to satellite imagery. A detection method is here proposed for this purpose, to fit with simple datasets; SPOT5 panchromatic images of 5 m resolution coupled with a freely and globally available DEM. The method takes advantage of multi-temporal images to detect changes based on radiometric variations after precise coregistration/orthorectification. Removal of false alarms is then undertaken using shape, orientation and radiometric properties of connected pixels defining objects. 80% of the landslides and 93% of the landslide area are detected indicating small omission errors but 50% of false alarms remain.
They are removed using expert based analysis of the inventory. The method is applied to realize the first comprehensive inventory of landslides triggered by the Pisco earthquake (Peru, 15/08/2007, Mw 8. 0) over an area of 27,000 km 2. 866 landslides larger than 100 m 2 are detected covering a total area of 1. 29 km 2. The area/number distribution follows a power-law with an exponent of 1. 63, showing a very particular regime of triggering in this arid environment compared to other areas in the world. This specific triggering can be explained by the little soil cover in the coastal and forearc regions of Peru. Analysis of this database finally shows a major control of the topography (both orientation and inclination) on the repartition of the Pisco-triggered landslides.
This topographic control on co-seismic landslides has also been pointed out through different parameters: convexity  [7] , slopes, proximity from ridges and crests  [6, 9]. The effects of all the parameters listed above are fairly well known qualitatively but not quantitatively due to the very few number of cases studied. Indeed, because of the tedious work of detection, the studies of earthquake-triggered landslides have been based on approximately 40 coseismic landslide inventories worldwide  [3, 13] , sampling only few different situations of magnitude, focal mechanism, climatic settings and land-surfaces. All these results emphasize the need for the generation of new datasets in order to draw robust conclusions on co-seismic landslides. Landslide inventories are, most of the time, based on field inventories and/or visual analysis of aerial or satellite images.
[25]. Accuracy of the methods is generally reported using both the detection error (omission = real landslides that were not detected) and the false alarm rate (commission = "landslides" that were detected but did not exist). Unsupervised methods display errors of commission and omission of 56% and 24% respectively  [18, 19]. Supervised methods use training data to estimate the parameters of the image segmentation in object oriented algorithms. It allows to increase this accuracy with errors of commission between 12% and 27%, similar to omission errors  [20, 21, 23]. These 3 latter methods showing better accuracy used Very High Resolution (VHR, ≤1 m) sensors, whose past archives have small coverage on the globe. It therefore restrains the use of such datasets to very specific regions. The detection has been tested with different types of sensors.
Multi-spectral optical images are particularly well suited for object-oriented analysis  [16, 19]. Moreover, the use of Normalized Difference Vegetation Index (NDVI) coming from Landsat TM, SPOT or Quickbird images is of high interest to detect changes of vegetation and thus landslide in areas covered by vegetation. The high precision of Lidar (e. g. ,  [26] ) allows the detection of specific textures characterizing landslides, but the Lidar datasets do not cover extensive areas. On the contrary, radar images present the advantages of a good spatial and temporal coverage even in cloudy regions. In particular, polarimetric radar has been used to detect new landslides  [27, 28] , taking advantages of different scattering properties on the bare soil and forested ones. On bare soil area, the InSAR images also allows detection of slow moving landslides  [29, 30].
That is, for an Mw 8. 0 earthquake, landslides can be triggered up to 600 km away from the source. These large distances require imagery acquired with wide swath that only high to medium resolution satellite can provide. Moreover, for reduced areas, the detection can benefit from fine Digital Elevation Model (DEM) acquired by lidar or derived from a couple of optical images (e. g. ,  [19] ). However, for larger areas, fine DEM (typical resolution of 10 m) is generally not available and/or pricing can be high. In these cases, the 90-m posting SRTM or 30-m posting ASTER GDEM datasets are free alternatives. Finally, in most cases (especially when revisiting historical events), panchromatic images are the only optical dataset available. Landslide detection using panchromatic images  [15, 17, 33, 34]  is less accurate than using multi-spectral images.
We apply this method to pairs of co-seismic images, surrounding the Pisco earthquake (Mw 8. 0, Peru, 15 August 2007) over an area of 27,000 km 2 , where a field inventory (Figure  1 ) has been realized in the months following the earthquake  [36]. Using visual interpretation of satellite images, we show that the field inventory is not consistent with the satellite inventory and thus cannot be used to validate our method. The method is thus validated using a comparison with an expert-based inventory realized on two subsets of the satellite images. A first analysis of the topographical characteristics of the database is finally proposed. [37]. The white dots represent the landslides identified with the field inventory  [36].
Orthorectification Orthorectification of SPOT5 imagery is performed using the Cosi-Corr software  [41]. Each pre-earthquake image is first orthorectified using Ground Control Points (GCP) taken on the shaded DEM. GCPs are mostly located at the crossing between steep valleys or at the summit of the mountains. Around 30 GCPs are chosen for each image orthorectification. The ASTER Global DEM (GDEM-v2) is used, with a spatial posting of 1 arc-second (approximately 30 m in our area) and altitude errors (ǫ GDEM ) between ±7 and ±15 m  [42]. Then, the post-earthquake image is orthorectified on the same grid as the pre-earthquake one, using GCPs based on the picking of recognizable features (mostly man-made structures) on both images. The GCPs are first picked manually before running the Cosi-Corr automatic optimization in order to improve the quality of the co-registration  [41].
False Alarms Removal This thresholding method applied to our images leads to many false alarms, which can be classified in 3 classes: (1) river bed variations inside deep valleys; (2) anthropogenic changes of several types such as villages growth, new roads on slopes, agricultural fields on valleys and valley sides close to villages; (3) variations of solar reflections. These false alarms are usually removed using a combination of multispectral information, texture, shape and precise topography (e. g. ,  [19, 21] ). However, in our case not all of these information are available. The proposed methodology therefore mostly focuses on the development of methods for removing false alarms, adapted to the available datasets (SPOT5 panchromatic images of 5 m resolution, GDEM-v2, non-vegetated area).