dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS In silico identification of miRNAs, pre-miRs and target candidates The global strategy for the in silico analysis is shown in Figure  1. We searched for miRNAs in two sets of nonprotein-coding RNA sequences: (i) 12 798 'intergenic' sequences, i. e. sequences that are neither in genes nor in rRNAs or tRNAs and (ii) 112 513 intron sequences. Each of these two sets of sequences was analyzed together with the 16 254 mRNAs of E. siliculosus, using the findMiRNA software  (31). The intergenic sequences were searched on both strands (2 Â 68 917 369 nt), while for introns, only the transcript strand was used (78 816 594 nt). As an output, we obtained 864 679 results from the intergenic sequences, and 403 761 from the introns. Each of these $1. 27 million potential miRNA candidates is associated to one folded precursor and one target sequence located within an mRNA.
As a comparison, we also included data from the exonic regions and tRNAs. The distribution of the expression levels of these various sets are shown on Figure  2. In both intergenic and intronic sequences, the expression level of the predicted pre-miRs is significantly higher than the expression level of the other sequences (Student t-test, a = 10 À2 ). The intronic predicted miRNAs have an expression level similar to that of exons. In addition, we observed that in both the intergenic and the intronic sequences, the predicted pre-miRs in the set MiR95 are expressed at an even higher level than those in the set MiR90 (not shown). These results showed that the sets of predicted miRNAs were strongly biased toward highly expressed and stable RNA sequences, which confirmed the statistical enrichment of these sets in actual miRNA sequences.
Among the 1488 miRNAs retained in Mir95 or Mir90, we extracted at random a subset of 36 sequences from each set, and quantified their expression by RT-qPCR (41) in E. siliculosus sporophyte filaments. We could detect a specific expression characterized by a unique dissociation curve with the expected half-dissociation temperature (Tm) for 22 different miRNAs (Table  2 ). Figure  3  shows the variable relative expression level (compared with tRNA-Leu) of the detected miRNAs. As a control, we attempted to amplify 13 randomly chosen non-retained potential miRNAs (i. e. instances filtered out from the structural filtering step). In agreement with the predictions, we could not detect any of them (not shown). Among the 22 validated miRNAs, 16 were from the set Mir95, and 6 from the set Mir90. Thus, these data show that increasing the threshold from the 90 th to the 95 th percentile enhances the ratio of experimental validation from $30 (22/72) to $44% (16/36).
We computed that the Hausdorff distance between Mir95+90 and the miRBase entries issued from Metazoa and Viridiplantae were 12 and 11, respectively. The Hausdorff distance The first part of the name, 'miR95' or 'miR90', indicates the set from which the candidate was drawn (see Table  1 ). The mature sequence of each miRNA is shown, together with the list of predicted target proteins, for which the Uniprot accession numbers are indicated. between Metazoa and Viridiplantae was 15. Again, the differences between these values corresponded to an expected bias, as bigger sets have a higher probability to contain at least one highly divergent sequence. Noticeably, the edition distances were high compared with the length of the sequences considered, showing that in each of these sets, there was at least one sequence displaying a high level of divergence with any miRNA in the other sets with which the comparison was being performed.
DISCUSSION This study presents the first genome-wide scale list of candidate miRNAs for an organism of the heterokont phylum. Our in silico search for new miRNA candidates in E. siliculosus was based on structural considerations, without any a priori on the sequence conservation or on the expression level of mature and/or precursor RNAs. Many features that are usually used to identify miRNA precursors in plants or animals had to be discarded because they were specific to one of these two kingdoms  (45). The initial search step, however, was performed using findmiRNA, a software designed to detect a nearly perfect complementarity between the miRNA candidates and their target(s), as it is usual in plants. The rationale behind this choice is that a less-constrained search would have allowed to detect one or more target(s) for nearly any oligonucleotide with a length in the range expected for an miRNA (data not shown).
Both Argonaute (AGO1) and DICER (DCL1) genes and some of the pre-miRs tested were induced in response to a modification in salt concentration. These changes in AGO1 and DCL1 expression on stress distinguish E. siliculosus from other organisms. In animals, various stress conditions result in a decrease in DCL1 expression  (57). In plants, the expression pattern of miRNA-related proteins is often complex, as it involves multi-copy genes, with divergent expression patterns within each family  (58). For instance, in A. thaliana, all four DCL transcript levels are depleted on a salt stress, but each gene exhibits a distinct time course and intensity for this regulation  (59). In Oryza sativa, only one AGO gene among 19 is induced in stress conditions, while none of the eight DCL genes is affected  (60).
ACKNOWLEDGEMENTS The Interproscan analysis was run on the ABIMS platform ( Station Biologique de Roscoff ). The authors wish to thank  Marie-He´le`ne Mucchielli-Giorgi  for her expertise in SVM analysis,  Martine Boccara  ( ENS, Paris ),  Martin Crespi ,  Christine Lelandais-Briere and  Florian Frugier  ( ISV, CNRS Gif-sur-Yvette ) for fruitful discussion and critical reading of the manuscript.
92 2. 55E-03 Methyltransferase Pfam PF00066 31 9 3. 11 1. 58E-03 LNR domain Prosite PS00120 13 5 4. 12 4. 76E-03 Lipases, serine active site Gene3D G3DSA:2. 160. 20. 10 98 17 1. 86 8. 82E-03 Pectin lyase-like.
EduGATE is a collection of basic examples to introduce students to the fundamental physical aspects of medical imaging devices. It is based on the GATE platform, which has received a wide acceptance in the field of simulating medical imaging devices including SPECT, PET, CT and also applications in radiation therapy. GATE can be configured by commands, which are, for the sake of simplicity, listed in a collection of one or more macro files to set up phantoms, multiple types of sources, detection device, and acquisition parameters. The aim of the EduGATE is to use all these helpful features of GATE to provide insights into the physics of medical imaging by means of a collection of very basic and simple GATE macros in connection with analysis programs based on ROOT, a framework for data processing.
It was exactly this feature of GATE that led us to use this powerful simulation package for educational purposes during basic, but also advanced lectures in medical physics and medical imaging. A preliminary version of "EduGATE", as we named the collection of educational examples, has been successfully used for demonstrations during lectures in medical physics. This encouraged us to make our collection available and prepare a contribution for potential users interested in applying such a utility during education and also as a starting point for practical exercises. Here we provide details about the current collection of examples, describing the basic options, which come with the examples, starting with configuring very simple setups. The collection of examples is available via the official GATE web site and as such freely available as is the GATE package.
Methods and Materials EduGATE is fully integrated in the GATE environment; hence, it is operational whenever GATE and GEANT4 are correctly implemented. Besides the macros, which are required to run an experimental configuration within GATE, there are data analysis modules provided and applicable within the ROOT environment  [4]. ROOT is a framework for data processing, developed at CERN for the purpose of saving, accessing, processing and visualizing large amount of data, as often happens in high-energy physics. The utilities provided by ROOT are also available within the GATE-framework. It is possible to store a history of hits, indicating the path of a particle through a detector, its interaction, including all major coordinates and parameters, essential for later analysis like energy, volumes of interaction, starting position (x,y,z) within a source volume and position (x,y,z) of the final interaction within a scintillator crystal.
In general, the output generated by GATE is stored as binary data ("Root-File") readable by the accompanying ROOT macro, written in C++. Such ROOT macros provide with basic histogram options, which can easily be extended and modified by the user. The GATE macros (text-files containing commands interpreted by GATE) are subdivided into major parts, describing the phantom(s), the selection of sources, the imaging device/camera, the digitizer, the selection of physics models and finally visualization options. This general structure has been maintained throughout all examples provided within the EduGATE project. To simplify the selection of parameters like source activity, phantom materials or type of source, a single configuration macro has been created, which lets the user control the simulation by setting the respective values, that are replacing aliases in the other macro files.
The coincidence channel is the basic setup, consisting of two detector modules shown as red boxes. The users, however, can extent this system by setting the number of detectors to a higher number like 8, 16, 32, or even 64, ending up with a fully equipped PET system (see Figure  2b ). The green lines shown in Figure  2b  again represent gammas, unscattered and scattered, whereas the red line represents an electron (Colors predefined in GEANT/GATE as green for neutral, red for negative and blue for positive particles). The material of the crystal used for detecting the gammas can be varied among various types. An analysis program based on ROOT and provided with the EduGATE-distribution yields a short statistical evaluation:  the center is shown. Despite the fact that the two gammas are emitted in a narrow angular range, a number of coincidences are detected remote from the detector modules these gammas were initially directed to.
g. Elmegreen & Scalo 2004; Scalo & Elmegreen 2004 ) because they affect both the particle density as well as the emission, absorption, and propagation of radiation. Most of the observations about large-scale Galactic magnetic fields rely on Faraday rotation measures (RMs), where the imprints of magnetic fields and thermal electron density fluctuations are mixed; therefore, RM data allow direct study of fluctuations in the Galactic magnetic field only with a reliable electron density model. But the radio synchrotron continuum of our Galaxy should also contain imprints of the magnetised turbulence in the ISM (see e. g. Eilek 1989a,b; Waelkens et al. 2009; Junklewitz et al. 2011; Lazarian & Pogosyan 2012). Below ν 1 GHz, Galactic CR electrons involved in synchrotron emission can be assumed to be uniformly distributed over the scales of magnetic field inhomogeneities (see e.
A small field in the Fan region, which contains a conspicuous circular polarized feature  (Bingham & Shakeshaft 1967; Verschuur 1968; Haverkorn et al. 2003b) , was recently studied in detail both in total  (Bernardi et al. 2009 ) and polarized  (Iacobelli et al. 2013)  intensity. We used this field to probe the relationship of Galactic magnetic field and turbulence by studying the Galactic radio synchrotron foreground. Moreover, we had the advantage that there exists a previous observation of this field with the Westerbork telescope (WSRT) at comparable frequencies  (Bernardi et al. 2009) , which enables a comparison with the new LOFAR results. In this paper we summarize results obtained from a 12-hour LOFAR observation of part of the Fan region. In Sect. 2 we describe the data processing.
Observations and data reduction The target field was observed with LOFAR in the framework of commissioning activities. The observation was performed on 2012 January 07-08th for 12 hours (mostly during night time), using the LOFAR high band antennas (HBAs) arranged into 57 stations. The array configuration consisted of 48 core stations (CS) and 9 remote stations (RS). The phase centre was set at right ascension α=03:10:00. 00 and declination δ=+65:30:00. 0 (J2000), and no flux calibrator was observed for the adopted single-beam observing mode. Data were recorded over the frequency range 110-174 MHz with an integration time of 2 s. This frequency range was divided into 244 subbands (each with a bandwidth of about 0. 18 MHz). The longest and shortest base- lines recorded correspond to ∼ 81 km and ∼ 36 m respectively, although we used baselines only up to about 12 km for better calibratability, resulting in a resolution of about 60 -80.
g. Pizzo et al. 2010; Heald et al. 2010). Both the subtraction of A-team visibilities and the single direction calibration were performed with the BlackBoard Self-calibration (BBS) package  (Pandey et al. 2009) , which is based on the measurement equation (see e. g. Hamaker et al. 1996). In order to solve and correct for directional dependent effects we used the SAGEcal software  (Kazemi et al. 2011). We now discuss each of these steps individually.
However, estimates of differential Faraday rotation in the HBA indicated phase variations of about ten degrees for baselines comparable to or larger than ours (Wucknitz, priv. comm. on LOFAR Users Forum). Moreover, we found no signal from point sources in Stokes V maps, which indicates a very limited role of differential Faraday rotation. Therefore, we performed corrections of the visibility phases for differential phase delays using BBS and decided to not apply Faraday rotation corrections in this analysis. A modelled estimate of the station beam was taken into account when calculating the model visibilities. We applied the calibration solutions and corrected the data for each station beam response in the phase centre.
Removal of bad data Due to limited receiver synchronization at the time of the observation, the performance of some stations was not optimal, causing decorrelation of signals and, especially around 100 MHz, Table  1. Observational properties of our LOFAR data set. Phase centre a (J2000) α: 03:10:00. 0 (± 0. 2) δ: +65:30:00. 0 (± 0. 1) Start date (UTC) 07-Jan-2012/14:00:10. 0 End date (UTC) 08-Jan-2012/02:00:10. 0 Frequency range 110-174 MHz Wavelength range 172-273 cm CS primary beam FWHM at 160 MHz 4. 3 • RS primary beam FWHM at 160 MHz 2. 8 • beam-shape deformation. These effects were visible in the solutions of the calibration step in these faulty stations, showing up as systematically lower gains or noisier phase patterns. This occurred in 15 stations (12 CS and 3 RS), which were subsequently flagged.
Finally, a flagging of the corrected data was done. The final imaging step of the self-calibrated dataset was performed using both the CASA and AW imagers. The sky maps for each subband were imaged with uniform weighting, allowing high resolution. Again, the CASA imager provided us with a wide-field mapping, while the AW imager  (Tasse et al. 2013) , which is part of the LOFAR software, provided us with primary beam-corrected sky maps to be used when comparing the LOFAR and WSRT fluxes. Moreover, the AW imager is tailored to perform corrections for direction-dependent effects (e. g. the LOFAR beam and the ionosphere) that vary in time and frequency. Finally, each clean model sky map was convolved with a nominal Gaussian beam, and the SAGEcal solutions were applied to the residual sky maps in order to properly restore the fluxes.
At low the power spectrum shows a power law behaviour, while at high multipoles (i. e. > 10 3 ) a flat power spectrum is seen. Bernardi et al. (2009)  interpreted this power law as the large-scale foreground emission from the Galaxy toward the Fan region and the flattening of the spectrum as the rms confusion noise (σ c ) due to the point source contamination. As in  Brown (2011) , we can estimate σ c as σ c mJy beam -1 ≈ 0. 2 × ν GHz -0. 7 × θ arcmin 2 , ( 3 ) where ν is the reference frequency and θ is the FWHM of the Gaussian beam. The confusion noise level for the LOFAR data is σ c ∼ 0. 85 mJy beam -1 , which corresponds to a power of C c ∼ 4 × 10 -5 K 2. For comparison, the WSRT power spectrum obtained by  Bernardi et al. (2009)  is overplotted in Fig. 9. The shapes of the power spectra from the LOFAR and WSRT data match very well, indicating that the diffuse emission in the LOFAR data probes the same total intensity fluctuations as the WSRT.
However, the amplitude of the LOFAR data is a factor 3 or so lower, a plausible reason being the applied weighting scheme. In order to clarify this point we performed an imaging step with a natural weighting scheme, thus not changing the power distribution at different angular scales. Now we obtain an amount of power in LOFAR profile that was consistent with the WSRT one, with no change of the spectral shape in the multipoles range of interest (i. e. 1300). However, the beam is about three times worse and more diffuse emission is recovered, thus implying a less accurate source subtraction step with PyBDSM. Because of the higher rms confusion noise, a lower maximum value characterizing the power law is obtained and we decided to use the uniform weighting scheme.
For subsonic turbulence, the fluctuations in synchrotron emission are likely predominantly caused by magnetic field fluctuations, with only slightly varying relativistic electron and positron densities, so that (σ 2 S I ) 1/2 / S I ≈ B 2 r /(B 2 r + B 2 o ). However, for transonic turbulence, density fluctuations will also be important and the source function behaviour can be approximately represented as S ∝ B 4  (Eilek 1989b) , so that (σ 2 S I ) 1/2 / S I ≈ B 4 r /(B 2 r + B 2 o ) 2. A major dependence by density fluctuations is also expected for supersonic turbulence, but this case is not treated by  Eilek (1989b). (σ 2 S I ) 1/2 S I L max L out σ I I , (5) We compute the ratio of random to ordered magnetic field B o /B r for our LOFAR data set using the prescriptions above. Since this is an interferometric measurement, short spacing information is missing and we cannot estimate I from our data.
To our knowledge, earlier angular power spectra of the Galactic radio diffuse synchrotron fluctuations at sub-degree angular scales and at a frequency of 150 MHz are only provided by  Bernardi et al. (2009) ,  Bernardi et al. (2010), and Ghosh et al. (2012) , obtained with the WSRT and Giant Metrewave Radio Telescope (GMRT) respectively. The LOFAR and WSRT  (Bernardi et al. 2009 ) studies both discuss the Fan region at low Galactic latitude b ≈ +7. 0 • , while  Ghosh et al. (2012)  focused on two sky patches at Galactic latitudes of b ≈ +25 • and b ≈ +30 • respectively. However, due to baselines corruptions the quality of the power spectra is not as good as for the Fan region field at lower Galactic latitude. Because the power law behaviour is not well sampled, we decide to not use these WSRT data for our analysis.
(2009)  within 2 sigma. 4. We estimate the outer scale (L out ) of ISM magnetic turbulence from theoretical arguments that a break in the power spectrum should be observed at a certain critical scale  (Cho & Lazarian 2002). We find a range of plausible values in the LOFAR and WSRT data sets and in a third low-frequency field with diffuse synchrotron fluctuations observed with the GMRT of 16 -29 pc. This value is in agreement with previous estimates of outer scales of turbulence in spiral arms, although it is a factor of a few too small to be consistent with outer scale values in the Galactic halo or interarm regions. This suggests that towards the Fan region observed fluctuations are at least in part due to synchrotron emission in the Perseus arm. 5. We constrain the ratio for the magnetic field components B o /B r from theoretical estimates of allowed magnetic field strength ratios based on the relative strength of the synchrotron fluctuations with respect to the mean total intensity  (Eilek 1989a,b).
Acknowledgements. The authors thank the anonymous referee for carefully reading the manuscript and providing helpful comments and suggestions in the preparation of the final manuscript. The authors wish to thank  M. Brentjens  for his help with the power spectral analysis of LOFAR data analysis. Chiara Ferrari  and  Giulia Macario  acknowledge financial support by the  Agence Nationale de la Recherche  through grant  ANR-09-JCJC-0001-01. LOFAR, the  LOw Frequency ARray designed  and constructed by  ASTRON , has facilities in several countries, which are owned by various parties (each with their own funding sources), and are collectively operated by the  International LOFAR Telescope (ILT)  foundation under a joint scientific policy. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement number  239490.
Protein structure alignment is a crucial step in protein structure-function analysis. Despite the advances in protein structure alignment algorithms, some of the local conformationally similar regions are mislabeled as structurally variable regions (SVRs). These regions are not well superimposed because of differences in their spatial orientations. The Database of Structural Alignments (DoSA) addresses this gap in identification of local structural similarities obscured in global protein structural alignments by realigning SVRs using an algorithm based on protein blocks. A set of protein blocks is a structural alphabet that abstracts protein structures into 16 unique local structural motifs. DoSA provides unique information about 159 780 conformationally similar and 56 140 conformationally dissimilar SVRs in 74 705 pairwise structural alignments of homologous proteins.
Introduction Protein structure comparison is an important step in improving our understanding of the mechanistic basis of function of a protein. Insights on function can be obtained by comparing the structures of proteins of a yet-unknown function with the structures of related proteins of known function  (1). Depending on their functional importance and structural roles, different regions of proteins have different levels of evolutionary pressure acting on them. Regions of high evolutionary constraints are usually implicated in maintaining structural or functional integrity of the protein. Such regions are identified in the Conserved Domain Database where protein structures are used to define domain boundaries and provide insights into sequence-structure-function relationships  (2). Regions that have low evolutionary constraints or undergo neutral mutations are usually flexible and are manifested as insertions, deletions and substitutions in the alignments  (3, 4).
Flexibility of these regions can vary from subtle local conformational variation to large changes in orientations of the regions to accommodate insertions  (5). These insertions may promote functional diversity by creating a new binding site or by changing a present binding site for ligands or macromolecules  (6, 7). Therefore, protein 3D structure comparison becomes an important tool to analyze structural divergence and in turn functional divergence. Alignment of protein 3D structures is much more complex than protein sequence alignment  (8). Many methods have been developed to circumvent the complexities in aligning protein 3D structures, e. g. DALI  (9) , CE  (10) , SSAP  (11) , MAMMOTH  (12) , COMPARER  (13) , FATCAT  (14)   (15)  and FlexProt  (16). Link outs for these structure alignment methods are provided on the database site under 'other resources' tab.