dataesr/scientific-paragraphs-categorization huggingface dataset:
DisTAL Accurately Recreates the Phylogeny of In silico-evolved TAL Effectors To test the ability of DisTAL to decipher the phylogeny of TAL effectors, we designed a script to simulate the evolution of TAL effectors under the assumption of repeats acting as evolutionary units (Figure  3 ). For this, an initial hypothetical TAL effector is created by randomly selecting 10 repeats out of a set of 344 unique repeats found in Xoo TAL effectors in the public dataset. Two copies (descendants) are then generated from this TAL effector and each descendant undergoes 100 evolutionary cycles where in each cycle two different events can occur: (i) Replacement: a repeat is chosen at random and replaced by another one from the set of 344 repeats. This process is equivalent to mutating a series of amino acids in one repeat, the probability of this occurring in each cycle is designated α.
Next the resulting eight TAL effectors were fed into DisTAL (under default parameters with duplication and indel penalties equal to 10) and the resulting tree was compared to the expected tree. The topological distance between the trees was calculated using the Penny and Hendy method  (Penny and Hendy, 1985) , as implemented in the R package APE  (Paradis et al. , 2004). As shown in Figure  4 , this process was repeated 100 times for different combinations of α and β values, from 0 to 0. 1 with 0. 005 increments (40,000 trees total), to account for different evolution scenarios. DisTAL consistently produced trees that differed little from the expected tree (mean topological distance = 0. 09, median = 0). The program worked better when α and β were both higher than 0. 02 (at zero all the TAL effectors have the same distance and all the nodes are at the same distance), and slightly better when β was higher than α.
QueTAL: FuncTAL, A Program for Comparison of TAL Effectors Based on DNA Binding Specificities TAL effectors act as transcription factors and their binding sites can be predicted according to a code  (Boch et al. , 2009; Moscou and Bogdanove, 2009; Noel et al. , 2013). It is therefore feasible to compare the probable binding sites for TAL effectors using similar strategies as those devised to compare DNA motifs  (Heinz et al. , 2010). The program FuncTAL was designed to compare DNA binding capabilities for TAL effectors. Briefly, the program translates the RVD sequence of a TAL effector into a position weight matrix (PWM) stating the binding probabilities to nucleotides according to the TAL effector-DNA binding code  (Boch et al. , 2009; Moscou and Bogdanove, 2009). The PWMs are then compared using the strategy from the program HOMER  (Heinz et al.
Identification of RVDs and Creation of PWMs The program reads either a tabular file containing RVD sequences or, a FASTA file with nucleotide or amino acid sequences and then extracts RVDs. The sequence of RVDs for each TAL effector is then transformed into a position weight matrix according to a modified version of the RVDnucleotide association matrix used by the program Talvez  (Perez-Quintero et al. , 2013)  which was shown to perform well for the identification of known binding sites  (Perez-Quintero et al. , 2013). The matrix was modified to include updated RVD specificities according to the literature  (Streubel et al. , 2012; De Lange et al. , 2013; Deng et al. , 2014). The specificities are shown in Supplementary Table  3. The program also builds and outputs a consensus binding site for each TAL effector by identifying the most probable nucleotide for each position.
FuncTAL Accurately Represents Relations between Functionally Convergent TAL Effectors To show that FuncTAL can identify TAL effectors with unrelated RVD arrays but similar binding specificities, we decided to take advantage of three cases of experimentally observed functional convergence among TAL effectors. One of the best-studied cases of functional convergence among TAL effectors is that of the rice S susceptibility gene SWEET14 which is induced by multiple X. oryzae pv. oryzae TAL effectors targeting at least three different EBEs (Figure  6A ). AvrXa7 and PthXo3, from strains PXO86 and PXO61 respectively, target overlapping EBEs in the SWEET14 promoter  (Yang and White, 2004; Chu et al. , 2006; Antony et al. , 2010). Another TAL effector from Xoo strain KACC10331, with similar RVDs but different length than AvrXa7 and PthXo3, is predicted to target the same site (accession number AAW77509.
1 or YP_202894. 1) (Perez-Quintero et al. , 2013). Tal5 from Xoo strain MAI1 binds to another EBE in this promoter with minor overlap to that of AvrXa7/PthXo3  (Streubel et al. , 2013) , and TalC from Xoo strain BAI3 binds to an EBE with no overlap to the two other target sites  (Yu et al. , 2011). From this we expect that when fed to FuncTAL, TAL effectors that target completely overlapping sequences (AvrXa7, PthXo3 and the predicted AAW77509. 1) will group together. As an outgroup, we included the TAL effector PthXo1 from Xoo strain PXO99 A known to target SWEET11 which is another SWEET member acting as an S gene in rice  (Yang et al. , 2006). Indeed, using FuncTAL on these TAL effectors results in a tree where, AvrXa7, PthXo3, and YP_202894. 1 are grouped together (Figure  6D ). And although the EBE targeted by this group and that of Tal5 EBE overlap by 3 nucleotides, this is not enough for the program to consider them as functionally similar.
We thus fed the RVD sequences for these TAL effectors into FuncTAL. As an outgroup we included PthA3 which is a TAL effector from Xcc strain IAPAR 306 that fails to induce either CsLOB1 or CsSWEET1. The resulting tree reflected the expected relations (Figure  6F ). In these analyses the maximum pairwise distances for TAL effectors binding overlapping EBEs were 0. 70 in the X. citri TAL effectors (between PthC and PthA w ), 0. 67 in the X. oryzae TAL effectors (between AAW77509. 1 and AvrXa7) and 0. 44 in the Bs3-targeting TAL effectors (between AvrHah1 and AvrBs3 rep109). Ideally, this data would serve to establish thresholds to group TAL effectors with functional convergence. However, these values might be too variable to make accurate recommendations. More experimental data will be needed to accurately define these thresholds.
FuncTAL and DisTAL Show Different Groupings of TAL Effectors To assess how the results from DisTAL and FuncTAL differ from each other based on different settings we followed an approach based on the comparison of topological distances. For this, a set of n complete TAL amino acid sequences was selected at random from our dataset and five trees were created for that set with the following methods (Figure  7A ): • DisTAL using default parameters (using the full repeat), • DisTAL excluding RVDs, • DisTAL using only RVDs, • FuncTAL using default parameters, • ClustalW alignment and neighbor-joining phylogenetic tree using only the N-terminal region, to be used as a reference. The topological distance was calculated as before between each tree and the one obtained with the N-terminal region using ClustalW.
Discussion In order to understand how TAL effectors or other related proteins differ from each other within and between strains of one or several pathovars, current approaches mainly rely on the evaluation of genetic distances through the alignment of the Nterminal and/or C-terminal regions, thus excluding the central region due its repetitive nature. To fill this gap, the first aim of this work was to adapt existing methods to compare the sequences of TAL effectors repeats and infer evolutionary scenarios. The program DisTAL, used to calculate phylogenetic distances, relies on the hypothesis that one of the major driving forces of evolution of TAL effectors is probably through recombination between repeats or slipped strand mispairing during DNA replication, resulting in duplication, deletion or reorganization of one or several repeats.
This hypothesis is supported by the fact that in several strains the TALome appears to be the result of numerous duplications (e. g. , in Xoo strain PXO99 A  (Bogdanove et al. , 2011) , that deletions occur in nature  (Vera Cruz et al. , 2000) , and that internal recombination events were detected upon experimental evolution assays  (Yang et al. , 2005)  and for TALEN systems in vitro  (Lau et al. , 2014). Since the structure of the genes and the mechanisms of evolution are expected to be similar to that of microsatellites, we chose to adapt an algorithm and program designed to compare coded "maps" representing tandem repeats  (Abouelhoda et al. , 2010). DisTAL considers repeats as evolutionary units and finds similarities between arrays of repeats. Using simulated data we showed that the program can accurately infer relationships The "marine metagenome" TAL effectors correspond to TAL effector sequences from an unidentified organism  (Juillerat et al.
, 2014). These types of experiments will also help determine the recombination points on these proteins. So far, DisTAL uses amino acid sequences for all of the comparisons instead of nucleotides because the former are shorter, reducing greatly the computational time to calculate distances. This could represent a loss of information since synonymous mutations are not taken into account. However, this loss may be minor since, for example, a set of 169 complete and unique nucleotide TAL repeat sequences (from public available databases) corresponds to 168 unique amino acid sequences. A robust scientific framework to understand and anticipate TAL effector diversity, evolution and dynamics is essential to assess the value of control strategies based on manipulation of their host targets  (Boch et al.
, 2014). DisTAL is the first program to allow classification of TAL effectors in a manner which includes the possibility of repeat rearrangement and duplication as a major determinant of TAL effectors evolution. The program includes pre-processing of any TAL sequence, and alignment of repeat sequences based on the ARLEM program  (Abouelhoda et al. , 2010). We believe this tool not only is more reliable at comparing and classifying TAL effectors according to their phylogeny but will also offer precious help for future experimental and modeling works on TAL effector evolution. An important feature of TAL effectors is that their function can, to some extent, be predicted from their RVDs sequence thanks to their modular and specific interaction with DNA  (Boch et al. , 2009; Moscou and Bogdanove, 2009).
Indeed tools already exist to predict candidate EBEs in plant genomes  (Doyle et al. , 2012; Grau et al. , 2013; Perez-Quintero et al. , 2013). As more TAL effectors are discovered, notably through sequencing of entire TALomes (e. g. ,  Wilkins et al. , 2015) , it is essential to classify them according to what can be hypothesized about their function. The second main output of this study is the design of a tool for comparing TAL effectors through their EBEs, which will facilitate the identification of cases of functional convergences and therefore candidate susceptibility hubs. FuncTAL calculates correlations between potential TAL effector binding sites by translating RVD sequences into PWMs according to the RVD-DNA code. The program successfully inferred functional relations for known cases of functional convergence among TAL effectors targeting overlapping EBEs.
As for the analysis of the full TAL effectors dataset corresponding to 18 different taxonomic groups, the associations found by FuncTAL are more likely reflective of RVD usage and general binding preference than actual potential for convergent gene induction. We expect that to be the case for the well-defined group found for R. solanacearum and X. translucens. Indeed, the effectors in this group may have different targets, but their association reflects a preferential targeting for a certain type of sequences (GC rich regions), which may be of biological relevance. Developing methods to predict true evolutionary and functional convergence is still needed, particularly since TAL effectors tend to preferentially target specific genes or gene families that are crucial for disease development [Reviewed in  Hutin et al.
Acknowledgments We wish to thank  Alexis Derepeer  for his help on the construction of the web version and to  Jonathan Jacobs  for fruitful discussions. We are grateful to  Adam Bogdanove ,  Adriana Bernal ,  Carlos Zarate ,  Celine Pesce ,  Daniela Osorio ,  Katherine Wilkins ,  Laurent Noel ,  Li Wang ,  Nicolas Denancé ,  Nicholas J. Booher ,  Niklas Schandry ,  Orlando De Lange ,  Ralf Koebnik ,  Ricardo Oliva ,  Thomas Lahaye , and  Tran Tuan Tu , for contributing with TAL effector sequences for this project. AP is supported by a doctoral fellowships awarded by the  Erasmus Mundus Action 2 PRECIOSA program  of the  European Community. This project was supported by a grant from  Agence Nationale de la Recherche  ( ANR-14-CE19-0002 ) and from  Fondation Agropolis  (# 1403-073 ). JG received funding from the  European Union 's  Seventh Framework Programme  ( [FP7/2007([FP7/ -2013]])  under grant agreement no.
If sequences of images are available, time series based techniques can be applied, for instance Kalman filters have been used to identify anomalous radiance values  (Van Den Bergh & Frost, 2005; Van Den Bergh, Udahemuka, & Van Wyk, 2009; Zakšek, Shirzaei, & Hort, 2013). Geostationary imagers are particularly suited to the time series approach due to their consistent acquisition geometry, which excludes complicating factors such as changes in view angle, pixel size and irregular acquisition intervals which make analysing data from Low Earth Orbit (LEO) imagers more difficult. Given this regular sampling in space, time and wavelength by geostationary sensors, the sensor can be thought of as generating a consistently sampled hypercube of data. Mining this hypercube for signals of interest can be considered a 'big data' problem (e.
In some circumstances, ICA can also be used to correct for instrument saturation. In this study, we apply ICA to time series of images from the geostationary SEVIRI imager, and test the procedure on a number of simulated and observed volcanic eruptions. In the next section we describe the SEVIRI instrument, the source of the data used in the real world case studies. In Section 3 we present an overview of ICA and our application of it to the retrieval of HTE radiance and correcting for saturation. In Section 4 we present the results for the simulations and each case study and in Section 5 we assess the effectiveness of the technique.
Datasets In this section we present an overview of the geostationary imager used to test the technique. We chose the SEVIRI imager as it is currently the most advanced geostationary multispectral imager with a field of view that includes numerous active volcanoes, and selected two volcanic events with spatial and temporal characteristics that lend themselves to the application of ICA (spatially localised, effectively a point source on the scale of a SEVIRI pixel, some saturation, little to no plume and cloud cover) to test the algorithm as a proof of concept. The SEVIRI instrument is a multispectral imager aboard the Meteosat 2nd generation geostationary meteorological satellites operated by EUMETSAT. The instruments provide coverage of Africa, Europe and the Middle East at intervals of 15 min or less in 12 channels across the visible, short wave and thermal infrared.
Ideally, the number of principal components to be retained would be determined by the data dimensionality, but this can be hard to determine in natural datasets, so a trial and error approach is usedtoo few PCs retained results in useful information being discarded but too many can result in sources that are isolated spikes due to overfitting  (Hyvärinen, Särelä, & Vigário, 1999). The iteration stage then finds an orthogonal rotation matrix that further rotates the signals in data space such that they are oriented in directions that are nonGaussianity maxima. Each unmixing vector, which is a row of W, thus defines a projection of the whitened data along a direction that is a nonGaussian maximumthese are the sources. The relationship between the sources and the signals is given by S ¼ W Á P Á X C ð2Þ where X C is the matrix of centred signals, P is the whitening matrix, W is the unmixing matrix and S is the source matrix.
In this study we automatically classify the sources as HTE/not HTE by means of a 'HTE index', calculated by taking the product of the magnitude of the skewness of the image and time series vectors. HTE sources tend to be very localised in space and time giving a high skewness for the time series and image, and we take the magnitude since sources are only specified up to sign, and so can give a positive or negative skew value. In this study, we identify the HTE source by choosing the one with the highest HTE index as all of our modelled and observed HTEs are effectively point sources on the scale of the SEVIRI pixel and are accounted for by a single source, however a threshold could be used where more than one HTE source is expected. This approach effectively has five free parameters, the width and height in pixels and the length in images of the image cube to process, an estimate of the dimensionality of the data (how many PCs to retain), and in more complicated cases some HTE index threshold for distinguishing between HTE and nonHTE sources.
Saturation correction Extracting the sources using Independent Component Analysis permits for saturation correction. In this approach, we treat the saturated pixels as missing data (for this study, we assume that the down scan 'after image' effect mentioned earlier is negligible). Due to the large size of the instrument Point Spread Function (PSF), any given point source emitting radiance will be sampled by a number of pixels, weighted by the magnitude of the PSF of each pixel above that point. If the radiance is intense enough to saturate a given pixel, a neighbouring pixel with a lower sensitivity at that geographic location may record the full time series unsaturated. We illustrate this with an example from the May 2010 Manda Hararo fissure eruption in Fig. 2. The HTE source is a fissure and advancing lava flow that is small on the scale of a SEVIRI pixel, however given the large size of the PSF, the signal is registered in many neighbouring pixels.
Simulated volcanic eruptions It is difficult to assess the effectiveness of the technique as measurements of remote, hazardous volcanic events at the scale of a SEVIRI pixel are nearly impossible to ground truth. However we can test the procedure on a synthetic dataset. We use synthetic datasets chosen to resemble the actual examples presented in this study to check the validity of those results as a proof of concept; we do not attempt to exhaustively check the procedure's effectiveness on a wide range of different types of HTE, nor the effects of varying algorithm parameters. We produce a 'synthetic volcanic eruption' radiance curve described by a plausible functional form (and hopefully therefore a plausible statistical distribution of values, which is what the ICA algorithm acts upon) by convolving an area effusion rate function with a cooling function to model the radiance emitted due to the progressive emplacement of infinitesimal area elements of cooling material in a manner similar to that of  Carr (1986)  and  Davies (1996).
For the real HTE events, the original, decomposed and recombined datasets are large, so presenting an overview of all extracted sources and HTE signals for each dataset is difficult. As a result, to give an overview of the decomposition into sources stage, we present a selection of the sources extracted by the FastICA algorithm showing the range of HTE, diurnal cycle and cloud process dominated sources found. For the recombination to give the HTE signal per pixel, we then show examples of the recovered HTE signals for a representative selection of pixels, as well as the original signals, and their residuals after subtracting the HTE signal. Finally, we show the extracted total HTE signal for the given event, showing the total radiant output in that band from the HTE event integrated over all pixels, which is typically the parameter of interest.
We implemented the FastICA algorithm in Mathematica, based on the code of  Langlois, Chartier, and Gosselin (2010) , but with modifications for symmetric orthogonalisation and matrix operations to increase speed. The contributions of each source to each pixel is then given by Y ¼ P þ Á W -1 ðA10Þ where Y is the spatial contribution matrix, of columns that give the size of the contribution of a given source to each pixel, P + is the pseudoinverse of P and W -1 is the inverse of W. Alternatively, Y can be found from Eq. (A11): Y ¼ X C Á S þ ðA11Þ where S + is the pseudoinverse of the recovered sources, S. This is useful in the case where signals with one or more saturated observations have been excluded for finding the sources. In this case matrices P and W which give the sources in terms of the reduced number of signals cannot be used to estimate Y; instead we wish to estimate Y from the centred signals with any saturated values removed.
3D stacks and isosurface reconstruction were obtained using the Imaris 7. 2. 3 software, allowing the quantification of object volume and number. OVA delivery to endolysosomes in migrating DCs. AlexaFluor-488 OVA was coupled to CypHer5E, which fluoresces below pH ¼ 6. 4. Coupling was performed following the manufacturer's manual (GE Healthcare). pH sensitivity of the dye was checked by measuring the optical density at pH ¼ 7. 4 in PBS compared with pH ¼ 5. 5 in CH 3 COOH using a NanoDrop (Labtech). Fluorescence of CypHer5E was only detected at acidic pH. The microfluidic device used for this experiment was equivalent to the one used in macropinocytosis assays. In all, 10 5 cells per well were loaded and left overnight to allow their spontaneous entry into microchannels. Just before image acquisition, OVA coupled to AlexaFluor-488 and CypHer5E was added, and cells were immediately imaged using an epifluorescence video-microscope Nikon TiE microscope equipped with a cooled CCD camera (HQ2, Photometrics) with a Â 10 or Â 20 objective during 90 min.
After two washes with culture medium, they were loaded in microchannels. After 12 h, cells were imaged at low resolution with an epifluorescence video-microscope Nikon TiE microscope equipped with a cooled CCD camera (HQ2, Photometrics) with a Â 20 objective. When indicated, AlexaFluor647-OVA at 0. 5 mg ml À 1 was added to the channels just before acquisition. Data collection for cross-correlation analysis was carried out using the Metamorph software (Molecular Devices). Front and back regions were determined using myosin IIA-GFP signal relative to the position of the nucleus. Front and back myosin IIA relative intensities correspond to the average intensity of each region divided by the average intensity of the entire cell after background subtraction. Accumulation of AlexaFluor647-OVA in endolysosomes was measured in the AlexaFluor555-WGA mask.
Semiautomatic analysis of myosin IIA-GFP enrichments. Myosin IIA enrichments were analysed using a custom routine written in Matlab (Mathworks) as following. First, to avoid any noisy detection, a low-pass filter with a cutoff frequency of three sampling steps was applied to the raw data (myosin IIA mean intensity at the front). The initiation of myosin IIA-GFP enrichment events was detected when the derivative signal became positive. Enrichment amplitude was calculated as the difference in grey levels between the highest value and the lowest value of the myosin IIA-GFP mean front intensity. To exclude enrichments due to steady-state fluctuations of myosin IIA at the front, an amplitude threshold of 10 grey levels was applied. All enrichments automatically detected with this routine were ultimately confirmed by eye.