dataesr/scientific-paragraphs-categorization huggingface dataset:
Several advantages have been considered with this choice: (i) Java code can run on all Java-supporting platforms without the need of recompilation; (ii) the software tool can be installed with a single executable file, which can even include the appropriate Java version; (iii) the application can be launched from a USB stick and, as such, is easily delivered to the users; (iv) Java includes a large number of scientific class libraries, which allows the easy development of numerical calculations adapted to our needs; (v) ImageJ libraries provide the basic image visualization and processing tools. The MMX-I workflow is presented in Fig. 2. The data processing chain is divided into four modules: data importa- 2. 2. Implemented modules 2. 2. 1. Data importation. At SOLEIL, raw data including metadata (such as scan parameters, sample identifier, beamline parameters and timestamp), obtained during an experiment, are recorded in an HDF5  (The HDF Group, 2015a ) file following the NeXus convention  (Ko ¨nnecke et al.
, 2015). Fig. 3  presents a file tree of a single NeXus file of data taken during a multi-technique X-ray 2D scanning experiment at Nanoscopium. Being an HDF5 file it is opened using the HDFView software  (The HDF Group, 2015b ). The hierarchical HDF5 format facilitates data processing and interpretation, because the data from each subsystem (sensors, motors, etc. ) are recorded in a single HDF5 file following the order of the acquisition process. The HDF5 is a binary file indexed in Btree (tree data structure). In addition to being self-describing, the HDF5 format is quasi-unlimited in size (i. e. limited by the file system). HDF5 is well supported and continuously improved by the HDF Group in order to stay at the cutting edge of innovation. Reading and processing large HDF5 files of 0. 5-1 TB volumes, produced regularly by Flyscan at Nanoscopium, requires high computing performances, which is not available by standard PC or laptop computers.
As such, the optimization of the computing resources was a crucial part of the MMX-I project in order to cope with the handling of big data files even by standard user-PCs. For this purpose, a dedicated readout API, named Hdf5Opener, has been developed. Indeed, reducing data takes only a small fraction of the computation power compared with file readout. The bottlenecks of the  MMX-I workflow. Raw data are imported from an HDF5 file via Hdf5Opener and then reduced to absorption, dark-field, phase-contrast (DPC) and elemental maps. After this step data can be exported as tiff files or existing maps can be imported. This is followed by data correction and then data reconstruction. reduction procedure are the memory available for the readout of the complete dataset contained in the file and the time necessary for data transfer from the disk to the memory.
In order to address these problems, Hdf5Opener owns its own dedicated memory and execution thread, which allows working in parallel with the other processing threads of MMX-I. The strategy is to partially read the Hdf5 file and to separate data reading and processing. In detail, as shown in Fig. 4 , a stream object sends to the API a list of read requests, each containing the selection of the partial dataset to be sequentially read out. After the readout of the first request of the list, the block of data is stored in the storage array and queued up to be processed. The operation is performed continuously for all elements of the list, according to the available memory size. Once one of the parallel-processing tasks on a given block of data is terminated, the corresponding memory space in the storage array is released.
By default, the size of the block of data is set to be equal to the size of the first dimension of the scan. As an example, for a 2D scan, the block of data will contain the elements of one line. This strategy is detailed in x2. 2. 2. The data contained in each block may not be necessarily adjacent in the pixel map. Indeed, for rapid overview of very large datasets, the API can read each second pixel of the map in all dimensions. This mode is called 'low resolution' in MMX-I and is described below. Moreover, it can be decided not to process parts of the dataset which are considered to be uninteresting (region outside of the sample or corresponding to beam loss etc. ). 2. 2. 2. Data reduction. Data reduction consists of processing X-ray transmission and XRF raw data to obtain: (i) images from the transmitted signal; (ii) horizontal and vertical differential phase contrast images; (iii) dark-field images; (iv) the orientation of the scattering (to be implemented in the near future); (v) elemental distribution maps.
Previous to data reduction, raw data have to be pre-treated. For instance, the signal type (absorption, phase, dark field and fluorescence) contained in the treated data matrix has to be identified, denoised and calibrated. Pre-treatment. Usually, the largest part of the total data volume is represented by the transmission images. In order to save computing resources and time, only those image-fractions [image region of interest (ROI)] of the detected raw transmission images will be read by Hdf5Opener, which contains 'significant' information for calculating the transmission modalities. To this end, when the raw data file is loaded for the first time, MMX-I will automatically read 10% of the whole transmission dataset, uniformly distributed in the coordinates of the scan. From this step, the ROI containing the whole transmitted and scattered beam is determined (see Fig.
5 ). In a second step, a mask of the illumination pattern is defined, which is especially important for the calculation of the darkfield images as described later. The ROI images are thresholded with a minimal value, transformed to binary images and then summed. The resulting 'illumination mask' represents for each pixel the number of times the pixel was illuminated by X-rays (above the threshold value) during the whole scan. To obtain a reasonable transmitted beam shape we only consider the pixels which are illuminated within the 95-100% range of the maximum value of the illumination mask (see Fig. 5 ). This ROI and illumination mask are then proposed to the user as the default ones (see x2. 5). The default ROI and illumination mask can be customized by the user. Hot pixels are removed from each 2D detector image before further data processing.
The image is displayed on a log scale. Pixels used for data reduction are inside the default ROI marked with a white square. The white circle represents the outer limit of the default 'illumination mask' used for determining the pixels belonging to the dark field. X-ray beam with sufficient energy, characteristic X-ray fluorescence radiation will be emitted with discrete energies that are specific of the excited elements. By measuring their energies and intensities, it is possible to determine the concentration of the measured elements. This information for a given element is obtained by measuring, for each pixel of the map, the area of the corresponding peak. Energy calibration of the XRF spectra is a crucial step for the identification of the elements contained in the sample and the reconstruction of the elements maps.
By default, MMX-I calculates the sum spectra separately for each X-ray fluorescence detector. The energy calibration of the spectrum is performed in the standard way  (Grieken & Markowicz, 2001) , by selecting at least two known X-ray lines. Then, the user can interactively select ROIs, containing the characteristic X-ray peaks of interest, which will be used during the further data reduction process. XRF spectrum deconvolution and background subtraction have not been integrated in MMX-I, since the strategy was not to re-create existing functions, which have been optimized and widely used in excellent software for several years, e. g. PyMCA  (Soleet al. , 2007). Instead, MMX-I provides the possibility to export sum spectra in 'mca' file format in order to be processed by PyMCA. The resulting 'fit' file can be imported into MMX-I in order to create ROI images.
The refractions angles are determined by calculating the centroid of the transmitted beam, inside the ROI, along the horizontal and vertical directions. DPC images are used for phase map reconstructions (described below). It has to be noted that the scanning DPC technique allows the phase to be measured beyond 2 and therefore does not suffer from any phase-wrapping effects (de  Jonge et al. , 2008). The dark-field data represent the intensity of the scattering signal. Its amplitude is linked to the density fluctuation on a length-scale smaller than the illuminated area  (Menzel et al. , 2010)  and is evaluated by integrating the scattered intensity outside of the transmitted beam, i. e. outside the illumination mask shown in Fig. 5. The anisotropy of the scattering can also be exploited and provides contrast information on the orientation of structures in the sample.
2. 2. 4. 1. Phase contrast reconstruction. Two methods have been implemented in MMX-I for quantitative phase reconstruction. The first is the Fourier integration method  (Kottler et al. , 2007) , which applies a Fourier derivative technique to integrate directional phase gradients. The second procedure is the finite-difference-based least-squares integration methods with Southwell  (Southwell, 1980)  configuration. The two methods and their performance on quantitative reconstruction of scanning X-ray phase contrast data are described and discussed below. The Fourier technique is based on the properties of the Fourier transform of a derivative function. A 2D Fourier transform is performed on the complex image formed by the vertical and horizontal phase gradient images as real and imaginary parts, respectively.
In order to illustrate this, XRF tomography datasets acquired on a test sample at an energy of 14 keV  (Medjoubi et al. , 2013a)  were processed with MMX-I. This test sample consists of a glass capillary of 500 mm diameter containing three nylon fibres (two with a diameter of 100 mm and a 50 mm-diameter one) and a copper wire (40 mm in diameter). A virtual slice of the sample was measured through 500 angular projections equally distributed over 360. One projection consisted of 500 pixels of 4 mm step size. FBP and SIRT tomographic reconstructions have been performed using in one case all the 500 projections and in the other case a sub-dataset of only 30 projections. Results are presented in Fig. 8. As expected, tomograms reconstructed with the SIRT method present a better SNR compared with FBP and are presenting fewer artefacts [e.
g. missing star-like artefacts in Fig. 8(d ) compared with Fig. 8(b) ]. The signal in the XRF sinograms is usually low compared with transmission modalities. Under such conditions, even using the full dataset, the FBP reconstructed tomogram is noisy, as can be seen in Fig. 8 (a), which is less the case for the  SIRT reconstructed tomogram [cf. Figs. 8(c) and 8(d) ]. With a large angular sampling interval, i. e. 12 between each projection (30 projections dataset), FBP produces significant star-like artefacts which superimpose on the useful signals. Such artefacts are not observed with SIRT. Moreover, the SNR of the SIRT tomogram is above 5, i. e. better than the Rose criteria  (Rose, 1948). The spatial resolution is worse in the case of treating the 30 projections dataset, due to the limited angular projections.
The GUI is based on the Swing platformindependent Java widget toolkit and ImageJ, which has been used to display images and provide image interaction with user input. The panel of the GUI corresponding to the raw data importation and visualization parts is presented in Fig. 9. In this panel the raw data of the HDF5 file, e. g. measured transmission images (as actually shown in the upper part of the figure), are displayed in the top right of the interface and the XRF spectrum panel in the bottom right. The current ROI for the 2D detector images is automatically shown and can be modified manually by the user. The shown data are updated while navigating with a scroll bar through the measured pixels of the scan. As an option, the sum spectrum of each XRF detector for the whole dataset can also be obtained by selecting the 'mean function' as shown in Fig.
We measured 23 virtual slices in 1 mm steps between the slices. The acquisition was accomplished slice by slice in the x (scanning line), Â (rotation in the axis perpendicular to the scanning line) and z (axis perpendicular to the scanning line) sequence, by scanning a full horizontal line with Áx = 1 mm steps and then performing a rotational step (ÁÂ). The measurement time was 3 ms per pixel. The total measurement time of the 500 Â 200 Â 23 voxels, each containing the XRF, the transmission data, data coordinate etc. as described above, was 3 h. It resulted in a 500 GB data file. This dataset was analysed and the tomograms of the different modalities were reconstructed by MMX-I. In Fig. 11  the volume rendering of the reconstructed phase, dark field and the distributions of Fe and Ca are shown.
Discussion and conclusion The MMX-I software developed at the Nanoscopium beamline of SOLEIL is, to our knowledge, the first software tool that aims at treating all modalities (except ptychography) of scanning multi-technique imaging and tomography experiments. MMX-I includes different data-correction and reconstruction methods both for 2D imaging and tomography. For each included contrast modality, which up to now is scanning XRF, differential phase contrast, absorption contrast and dark field, the software offers a dedicated default data-treatment and tomographic reconstruction method which is, according to our experience, the best suited to a given modality for most sample types. As such, users unfamiliar with X-ray imaging can readily use the software. This is well illustrated by the examples of the foraminifera and the standard structure, where the default ROI, 'illumination mask', reconstruction methods and variables were used for data treatment.
Users experienced in the treatment of X-ray imaging have the possibility to modify and optimize all the parameters. This diversity and flexibility makes MMX-I a software tool well adapted to data treatment of fast multimodal imaging. Multimodal imaging opens the additional possibility of the combination of all modalities in order to obtain complete information for quantification. For light elements in strongly absorbing sample matrices, self- absorption correction of XRF tomographic data is crucial, which will be developed as a further option in MMX-I. The modular construction of MMX-I makes the integration of other imaging modalities or reconstruction algorithms  (Gu ¨rsoy et al. , 2015)  easy and readily available. For example, it is possible to integrate other methods, e. g. full-field tomography, as a new modality which could be useful for correlative imaging.
However, MMX-I does not intend to replace the already existing powerful full-field tomographic reconstruction software tools. In addition, one of the important features of the software is the dedicated data read-in API, named Hdf5Opener, which makes the read-in and treatment of large, several hundred GB, data files possible also with standard and laptop user PCs. This is a crucial criterion for its utilization by a large user community. Moreover, fast data read-in provided by the beamline data-treatment server makes data pre-treatment and tomographic reconstruction possible during user experiments. The standalone Hdf5Opener API can be readily used by other software. The MMX-I freeware is available for data treatment of scanning multimodal imaging both for users at the Nanoscopium beamline and for other user communities.
tRNA molecules contain numerous chemically altered nucleosides, which are formed by enzymatic modification of the primary transcripts during the complex tRNA maturation process. Some of the modifications are introduced by single reactions, while other require complex series of reactions carried out by several different enzymes. The location and distribution of various types of modifications vary greatly between different tRNA molecules, organisms and organelles. We have developed a computational method tRNAmodpred, for predicting modifications in tRNA sequences. Briefly, our method takes as an input one or more unmodified tRNA sequences and a set of protein sequences corresponding to a proteome of a cell. Subsequently it identifies homologs of known tRNA modification enzymes in the proteome, predicts tRNA modification activities and maps them onto known pathways of RNA modification from the MODOMICS database.
Preparation of the enzyme profile-HMM database in tRNAmodpred- Sequences of all known RNA modification enzymes were obtained from the MODOMICS database in August 2014. For each enzyme, similar sequences were collected with PSI-BLAST [14] (3 iterations, E-value threshold = 0. 001) by querying the non-redundant sequence database obtained from the NCBI (ftp://ftp. ncbi. nih. gov/blast/db/FASTA/) on 15 th January 2014. Before the PSI-BLAST searches the database was clustered at 90 % sequence identity using CD-HIT  [15]  to reduce bias due to the presence of nearly identical sequences. Homologs of each enzyme from MODOMICS were separately clustered using CD-HIT with sequence identity threshold set to 0. 4. Clusters that contained the query enzymes from MODOMICS were chosen and compared to each other.
Sequences present in multiple clusters corresponding to different queries were removed. For each chosen cluster, sequences retained were aligned using MAFFT with the FFT-NS-i algorithm  [16]. Profile hidden Markov models (HMMs) were built using the hhmake program  [17, 18]  based on the alignments. As a result, we obtained a set of HMMs, each corresponding to a family of homologous proteins containing a single MODOMICS RNA modification enzyme that catalyzes a known reaction (or a set of reactions) in a defined position (or positions) in tRNA sequence. The obtained library of HMMs was merged with a library of HMMs representing all protein families from the Pfam database [19] downloaded from ftp:// toolkit. genzentrum. lmu. de/pub/HH-suite/databases/ (version 27. 0). A mapping between Pfam and RNA modification enzymes from MODOMICS was made to identify Pfam families representing known RNA modification enzymes.
Searches of the enzyme profile-HMM database with user-defined sequences-Input protein sequences provided by the user are used as queries in sequenceprofile search of the HMMs library, performed using the HHsearch program from the HHsuite package. The matches with probability below 70 % or E-value above 3. 16e-18 are discarded (the 3. 16e-18 E-value threshold gave best results in the preliminary benchmark of the method). The E-values of the remaining matches (in ascending order) are analyzed to identify the first biggest drop of E-value between consecutive matches. The number of matches reported before the E-value drop is compared to the number of matches reported with the highest probability. The bigger of these two sets of matches is chosen for further analysis. This approach was applied to prevent rejection of matches to several homologous modification enzymes, which may exhibit slightly different enzymatic activities.
Benchmarking tRNAmodpred The performance of the tRNAmodpred server was assessed by performing predictions for six species: Escherichia coli K12 substr DH10B, Bacillus subtilis 168, Lactococcus lactis subsp. cremoris MG1363, Mycoplasma capricolum ATCC 27343, Haloferax volcanii DS2, and Saccharomyces cerevisiae. Fasta-formatted files with complete proteomes were downloaded from ftp://ftp. ncbi. nlm. nih. gov/genomes/Bacteria/ (Please note that NCBI has changed the FTP recently. Currently these data are available at: ftp://ftp. ncbi. nlm. nih. gov/ genomes/archive/old_genbank/Bacteria/). tRNA sequences from E. coli, M. capricolum, H. volcanii, B. subtilis and S. cerevisiae were obtained from the MODOMICS database (version from September 2015). tRNA sequences from L. lactis were obtained from  [20].
In these cases the fn number was calculated as the minimal number of intermediate modifications missing in any of the alternative pathways. For further explanation and examples see Figure S 9 in Supplementary File 3. Random sets of predicted modifications were obtained by randomly selecting (with replacement) tRNA modification enzymatic activities from MODOMICS and taking the products of these activities. These products (modifications) were then randomly assigned to tRNA sequences from the species under consideration, assuring that a given tRNA sequence contains appropriate base type in the position related to the activity associated with the modification. The number of selected activities was equal to the number of predicted activities in the prediction to which the random dataset was compared.
tRNAmodpred -a server for computational prediction of posttranscriptional modifications in tRNAs 3. 1. 1. Method overview-tRNAmodpred is a program for the prediction of position and type of modified residues in tRNA molecules. The prediction is performed based on input protein and tRNA sequences, using information about tRNA modification pathways and location of modifications in experimentally studied tRNA sequences deposited in the MODOMICS database  [3, 4]. It is assumed that the presence of a modification in tRNA depends on the presence of appropriate tRNA modification enzymes. The workflow of the predictions is presented in Figure  1. In the first step, the presence of possible homologs of known tRNA modification enzymes among the input protein sequences is detected. This step is performed using remote protein homology detection method HHsearch [17] and a library of HMMs that contains profiles representing known tRNA modification enzymes.
subtilis and L. lactis, Fig. 5 ). However, when no data from close relatives are available, tRNAmodpred rather than mapping from distant organism should be used. Apart from tRNAmodpred there is another computational tool that deals with the problem of the prediction of tRNA modifications. tRNAmod is a machine-learning based program which predicts modifications of uridine based on tRNA sequence  [28]. The reported performance of the method is very good -AUC (area under the curve) of about 0. 9. However, it must be noted that tRNAmod has limited application: it can only predict localization of three most common uridine modifications (pseudouridine, dihydrouridine and 5-methyluridine) or report that a particular uridine residue is modified or not. We checked whether combining tRNAmod and tRNAmodpred results improves the accuracy of predictions, but unfortunately we have not observed significant improvement compared to predictions done by tRNAmodpred alone (Tables S 5-8 in Supplementary File 3).
Acknowledgements We would like to thank  Dr Lukasz Kozlowski ,  Dr Jo Marie Bacusmo  and  Marcin Magnus  for valuable discussions, help in the webserver development and testing tRNAmodpred. This analysis was initially supported by the  Foundation for Polish Science (FNP , grant  TEAM/2009-4/2  to J. M. B. ) and subsequently by the  Polish National Science Center (NCN , grant  2012/04/A/NZ2/00455  to J. M. B. ). M. A. M. was additionally supported by funds from the  European Union (European Social Fund  -stipend for Mazovian PhD students, Human Capital Operational Program 2007-2013). S. D. H. was supported by  Polish National Science Centre (NCN)  (grant  2011/03/D/NZ8/03011  to S. D. H. ) and by a fellowship for outstanding young scientists from the  Polish Ministry of Science and Higher Education.