dataesr/scientific-paragraphs-categorization huggingface dataset:
We evaluated the feasibility of this novel approach by using simulated data, and by applying it to an illustrative MEG data set obtained from a measurement in which the subject was exposed to a repeated auditory stimulation with sinusoidal 1-kHz tones. We focused on a time window around the most prominent late auditory evoked magnetic field (AEF), the M100, with a peak latency of approximately 100 ms after stimulus onset. Furthermore, we assessed the robustness of the proposed method by studying its performance on several smaller subsets of trials, for different signal-to-noise ratios (SNRs), and for up to 10 ECDs. In addition, we compared the performance of STMP with that of a well-established source localization method, the RAP-MUSIC algorithm  (Mosher and Leahy 1999).
Distinct dipole locations were obtained by a triangulation of the cortex using the hybrid watershed and deformable surface algorithm  (Ségonne et al. 2004 ) of the FreeSurfer software (http://surfer. nmr. mgh. harvard. edu). Hence, the centre of each triangle determined the location of a single current dipole whose orientation was normal to the surface of the triangle. The temporal subdictionary G consists of complexvalued chirplets (chirp functions modulated in amplitude by Gaussian envelopes of different scales) defined as g γ (t) = L(γ ) exp -π t -τ σ 2 + i ω (t -τ ) + κ 2 (t -τ ) 2 , ( 1 ) where t is time, γ = (τ, ω, σ, κ), and L(γ ) is such that ||g γ || = 1. Here, τ denotes translation, i. e. the position of the centre of the Gaussian envelope in time, ω is the angular frequency, σ is the scale corresponding to the temporal span of the Gaussian envelope, κ is the angular chirp rate, i.
Extension to subdictionaries with very large numbers of elements Typically, N d ≈ 15,000 to 25,000 different dipole locations per hemisphere are used in the source analysis of evoked magnetic fields. In the present STMP implementation, we worked with values of N d covering the range from ∼15,000 up to ∼600,000. Our intention behind the use of a very large number of locations has been guided by the default value of the FreeSurfer software package which is 300,000 per hemisphere, corresponding to a 1-mm resolution in MRI. However, along with the large number of chirplets used in our computations, N g ≈ 9 × 10 4 , and with N c = 148 MEG channels and N t = 613 time samples (length of analysed time interval: 601. 62 ms; sampling rate: 1017. 25 Hz), the number of operations required to compute P (n) for the largest number of N d = 6 × 10 5 is then of the order of N c N g (N d + N t ) ≈ 8 × 10 12.
Application of STMP to simulated and real MEG data 3. 1 Simulations To examine the efficiency of the STMP algorithm, we carried out simulations. We tested its performance in two scenarios, where STMP was compared with (1) two other multichannel-MP (MMP) approaches (Sect. 3. 1. 1), and (2) a non-MMP approach (Sect. 3. 1. 2). In both cases, ECDs were seeded at distinct locations of the brain, and the resulting magnetic fields were superimposed with noise from real MEG data. Furthermore, in order to test a specific localization property of the algorithm-i. e. its capability to separate two ECDs with different waveforms but identical location-we performed additional simulations without noise (Sect. 3. 1. 3). Note that we did not apply noise in the latter simulations on purpose, since only then one is able to evaluate the properties of the algorithm itself as opposed to assess its performance at, for example, several SNRs.
1 ) for 28 different SNRs, where we defined SNR as the square of the ratio of the largest singular values-one for the signal in the nominator and one for the noise in the denominator-of a trial-averaged multichannel time course arranged as channels × samples. Noise was taken from the pre-stimulus baseline of a real MEG data set comprising 190 single trials (see also Sect. 3. 2. 1). In order to obtain a realistic scenario, two subsets of baseline epochs (noise) were generated, one used to generate simulated noise in both approaches (STMP and RAP-MUSIC), and one to produce the noise estimate additionally required by RAP-MUSIC only. These two subsets had to differ from each other, since otherwise RAP-MUSIC would have 100% information about the noise. Thus, from the entire set of baseline epochs, we randomly selected and averaged 95 for each of 1000 realizations of the simu- STMP outperforms the two MMP approaches, i.
STMP applied to evoked magnetic fields from an auditory MEG experiment To test the performance of STMP on real MEG data, we applied the algorithm to auditory evoked magnetic fields that originated from an MEG measurement with a subject who was exposed to a repeated binaural stimulation with a 500ms long 1-kHz sinusoidal tone presented at a sound pressure level of 90 dB. The tone was repeated 224 times, with a stimulus onset interval of 2 s. Magnetic fields were acquired using a 148-channel whole-head magnetometer MEG system (Magnes 2500, 4D-Neuroimaging), with a sampling rate of 1017. 25 Hz. Preprocessing of the data (notably artefact rejection due to, for example, eye blinks) reduced the number of trials used in the final analysis to 190. Figure  5a  displays the result of the STMP decomposition of the trial-averaged AEFs on the sensor level.
Iterations 3 and 4 both provide atoms with much smaller energy, i. e. 5. 9 and 6. 1%, respectively. We set the decomposition to stop as soon as the energy of an atom in the consecutive iteration would reach the value smaller than 5% of the energy of the original signal. Since this was the case for atom number 5 (3. 15%), we restrict the representation to the first four atoms. Figure  5c  shows the locations of the four ECDs represented by the spatial atoms of the decomposition. All panels show an inflated brain with the white matter surface computed using the FreeSurfer software, based on the subject's individual MRI scan and coded in grey scale according to the local curvature. The ECDs of iterations 1 and 4 were localized in the superior temporal gyrus (STG) of the right hemisphere, the ECD of iteration 2 in the STG of the left hemisphere.
STMP versus RAP-MUSIC on real data For a comparison of STMP with RAP-MUSIC on real data, we analysed the same MEG data introduced in Sect. 3. 2. 1 at  The trials with odd numbers were used to derive the signal of interest (the mean across those trials), whereas trials with even numbers served as basis for noise estimation. The noise estimate was obtained by subtracting the mean across odd trials from the even single trials. In this approach, noise is estimated within a time window whose length is equal to that of the signal to be analysed. In general, 95 trials are sufficient to provide a good SNR in a simple auditory experiment like the one considered here. Figure  6  shows the results from STMP on the subset of averaged 95 trials. They are very similar to those for the entire set of 190 trials.
1 and 2 in Supplementary material), STMP well described the AEFs in both hemispheres, whereas RAP-MUSIC failed to explain the left auditory-cortex activity. For 24 trials (Figs. 3  and 4  in Supplementary material), the STMP results remained consistent with those obtained Iteration 2 -0. 1 0 0. 1 0. 2 0. 3 t [s] Iteration 3 -0. 1 0 0. 1 0. 2 0. 3 t [s] Iteration 4 -0. 1 0 0. 1 0. 2 0. 3 t [s] Fig. 8  STMP for 12 trials, i. e. every 16th trial from the original dataset of 190 trials. Note that at this SNR the maximum absolute signal was found at slightly different channels compared to the data with larger SNRs from larger SNRs. RAP-MUSIC addressed the left auditory cortex well, yet the quality of source localization in the right hemisphere worsened. For as few as 12 trials, the STMP results still remained fairly stable (Fig.
8 ), whereas RAP-MUSIC overfitted heavily in the first iteration (Fig. 9 ) resulting in a large error introduced to iteration 2 and, thus, a spurious source. The third iteration, however, handled the left-hemisphere activity very well. The analysis for even smaller SNRs turned out to be pointless. The SNR was simply too small to provide sufficient information for the two algorithms. Overall, our analysis of real data shows the superiority of STMP compared to RAP-MUSIC especially at small SNRs. Moreover, another strong observation is the consistency of Iteration 2 -0. 1 0 0. 1 0. 2 0. 3 t [s] Iteration 3 -0. 1 0 0. 1 0. 2 0. 3 t [s] Iteration 4 -0. 1 0 0. 1 0. 2 0. 3 t [s] Fig. 9  RAP-MUSIC for 12 trials, i. e. every 16th trial from the original dataset of 190 trials. Note that at this SNR the maximum absolute signal was found at slightly different channels compared to the data with larger SNRs the STMP results across a wide range of SNRs, from 190 down to 12 trials.
STMP: an advanced approach in the analysis of MEG data We developed a new MP-based algorithm that enables simultaneously, within each iteration, finding a solution to the inverse problem in space and parametrizing the characteristics of multichannel MEG waveforms in the time-frequency domain, with the important advantage of a sparse signal representation both in space and time. The core of the STMP algorithm-and the novelty of this approach-is a dictionary composed of a spatial and a temporal subdictionary, with dipolar fields reflecting the spatial and chirplets reflecting the temporal characteristics of the components of the MEG signal. Note that the choice of a particular dictionary is only a matter of the assumptions behind the experiment. The algorithm itself, and our Matlab implementation in particular, do not impose any constraints in this matter.
The three pivotal assumptions of the STMP approach are: (1) the underlying brain activity is focal, which justifies the use of the ECD source model, (2) the time-varying signal of an ECD can be well described by a chirplet, and (3) spatiotemporal characteristics of ECDs underlying the measured data can be identified sequentially, one after another, in an iterative manner. We have demonstrated superiority of this algorithm over two previous MP-based approaches  (Durka et al. 2005; Matysiak et al. 2005 ) by means of simulations. When applied to illustrative MEG data acquired with a simple auditory paradigm, we found that STMP outperforms RAP-MUSIC especially at small SNRs. The decomposition provided convincing temporal atoms to represent the MEG time courses and plausible loci for the ECDs underlying the M100 waveforms.
However, the size of this effect may potentially stem from the constraints imposed by the size of the temporal dictionary, an aspect which is not applicable to RAP-MUSIC; hence, we would refrain from overemphasising its significance. Another positive feature of STMP is that it is capable of efficiently disentangling multiple ECDs, both in terms of locations and the corresponding time courses (Fig. 4 ). We would also like to emphasize here, that, when real data are parametrized, every method introduces a certain bias because no model describes the real world perfectly. This is exactly the reason to opt for maximum consistency in simulations. The fact that we simulated the ECD activity with functions from the MP dictionary is not a bias, but, on the contrary, it is mere consistency which enables avoiding bias.
Outlook: decomposition of induced signals As an outlook we would like to mention that we have recently conceptualized an STMP-based approach to the estimation of induced rather than evoked activity by extending the selection criterion from Eq. (  2  where k = 1. K denotes trial index. Because of the increased dimensionality, this approach implies huge computational costs. However, early analyses with the data from a complex experimental paradigm to study gamma activity are promising. Yet, since they are part of a currently realized project, they are beyond the scope of this paper. Here we only want to signal the possibility of applying STMP to the estimation of the sources of induced activity. Details of technical implementation will be revealed in a future publication. The Matlab code used for the simulations and real-data analysis, as well as the illustrative MEG data set presented in this work, is available on reader's request.
Acknowledgements We would like to thank  Prof. Piotr J. Durka ,  Prof. Wojciech Kordecki , the BC editor, and the reviewer for their constructive criticism on an earlier version of the manuscript. The authors acknowledge support from the  German Academic Exchange Service (DAAD)  within its  Project Based Personnel Exchange Programme with Poland  ( PPP 2013-2014 56269824 ). P. Kordowski  has been supported by the  Foundation for Polish Science : International PhD Projects Programme co-financed by the  EU European Regional Development Fund  ( MPD/2009-3/6/styp15 ). A. Matysiak is supported by the  German Research Foundation  ( DFG; He 1721/10-1 & 10-2 ). R. König acknowledges support from a  Humboldt Honorary Fellowship of the Foundation for Polish Science. C. Sielużycki  was supported by the  Fondation pour la Recherche Médicale  (FRM; ING 20130526750) and by the  European Commission  under the 7th  Framework Programme, Coordination and Support Action, ENGINE-European research centre of Network intelliGence for INnovation Enhancement  ( 316097 ).
robusta by miRNA-seq and microarray data  [31, 32]. Some C. robusta miRNAs control development processes  [19]. For example, miR-124 promotes neuronal development via the inhibition of Notch signaling  [34, 35] , while miR-1 and miR-133 have muscle-specific functions, as in vertebrates. In this study, we performed a comprehensive search for miRNA in H. roretzi, an ascidian of a different order, the stolidobranchia, using a recently sequenced and annotated genome draft  [19]. Three major approaches were used to predict miRNAs: conservation to miRNA described in miRBase, de novo miRNAs prediction, and similarity to Ciona small RNA-seq reads. A total of 319 miRNA genes were discovered, whose evolutionary conservation was studied. This study thus advances our understanding of the complex gene regulatory network of ascidian embryos and will facilitate future developmental biology studies.
The loss in echinoderms is not clear, since the number of species is only three. Twenty one families were found in the distantly related C. robusta and H. roretzi ascidians but not in other animals (labeled in green on Fig. 1 ), and may thus correspond to ascidian or tunicate-specific miRNAs. These miRNAs are all represented by at least 10 reads in the Ciona small RNA sequencing dataset  [32]  (BLASTN, word size of 15, and E-value ≤1000), suggesting that they are expressed during C. robusta embryogenesis and are therefore likely to be genuine miRNAs. Finally, three miRNA families, miR-3182, miR-3876 and miR-7238 were only found in H. roretzi and a single non-tunicate species (Fig. 1 , right most three columns). Our confidence in the predictions of these miRNAs is more limited. To test whether the small number of candidate Halocynthia miRNAs conserved across metazoa reflected a low sensitivity in our identification method, or the overall weak conservation of ascidian miRNAs, we checked the situation in C.
robusta. A total of 348 C. robusta miR-NAs, belonging to 285 families, were previously identified and deposited in miRBase. These miRNAs were predicted from miRNA-seq data collected from Ciona embryos at the gastrula and larval stages, using the miRTRAP computational program  [32] , a method that makes no hypothesis on the evolutionary conservation of these candidates. Of these 348 miRNAs, only 47 miRNAs, belonging to 36 families, were widely conserved in many non-tunicate metazoan species (Additional file 3). An additional six miRNAs (Cirobu-mir-1473, Cirobu-mir-1497, Cirobumir-1502a, Cirobu-mir-1502b, Cirobu-mir-1502c, Cirobumir-1502d) belonging to three families were found in at least one tunicate species other than C. robusta (miRNA data had so far been described in three tunicate species, C.
3 , Additional file 1, miR-5000 to -5257). These novel candidates showed no significant hit by BLASTX (E-value ≤ 1e-3) with proteins in nr and UniProtKB/Swiss-Prot database confirming that they are non-coding RNAs. miRNA precursors are transcribed from only one of the double-stranded DNA template. Which strand the miRNA precursors originated from was inferred in 291 cases from published transcript information (see Method section). Prediction of the mature miRNA sequences for de novo predicted miRNAs is difficult and moderately reliable as the precise mechanism by which Dicer cuts miRNAs from the hairpin structure remains unknown. Li et al  [7]  reported an inference method for mature miRNA locations by supposing that Dicer precisely cuts the mature miRNA at the loop-stem junction. We note, however, that the majority of miRBase miRNAs do not obey this rule.
Data Preparation & Pre-processing The H. roretzi genome sequences were assembled by us and are publicly available through the Aniseed database (http://www. aniseed. cnrs. fr/) [19]. Prior to miRNA predication, we masked genomic regions with less miRNA-cording possibility. This includes protein coding and non-coding RNA generating regions that are rRNAs, tRNAs, snoRNAs and lncRNAs. To be specific, coding sequences and repeat regions were masked firstly. Coding regions were obtained from the predicted gene models in the Aniseed, and repeat regions were generated by RepeatMasker and RepeatModeler (http://repeatmasker. org). Tandem repeats were masked by Tandem Repeats Finder  [46]. Other RNAs, including rRNAs, tRNAs, snRNAs and lncRNAs were filtered using Rfam database (Release 11. 0)  [47]  by cmsearch in INFERNAL  [48]  (E-value threshold: 1e-3).
Identification of widely conserved miRNAs and precursors To reduce the search space, we firstly queried all metazoan mature miRNA seeds deposited in miRBase database (Release 21)  [44]  against the masked H. roretzi genome sequences using blastn with a word size of 7 and E-value of 10. The 110 bp sequences of matched genomic regions were retrieved separately and extended for additional 20 bp from both 5′ and 3′ ends. Potential miRNAs with no more than two mismatches with known metazoan miRNAs were identified by patscan  [13, 41, 42] , and the candidates were folded by RNAfold  [50, 51]  subsequently. Unmatched sequences were trimmed. If more than one miRNA were mapped to the overlapping position, only the best one with the minimum folding free energy (MFE) was kept. In addition, Rfam (Release 11.
De novo miRNAs prediction To detect potential miRNA precursors, srnaloop  [38]  was employed to identify hairpin structures from masked H. roretzi genome sequence. We used the parameter "-st d -Gs -1. 5 -gu 1 -sml 100000000 -am 990000 -t 15 -lml 5". "-st d" means that we used the DNA sequences, "-Gs -1. 5" means that gap start score is -1. 5, "-gu 1" means that GU base pairs score is 1, "-t 15" means that alignment score threshold is 15, "-lml 5" means that minimum size of hairpin loop is 5. The sequence length parameter "-sml" and the maximum hairpin alignments "-am" parameters were set at their maximal possible value (100000000 and 900000). In srnaloop software, the predicated hairpin lengths were less than and tend to be close to the length parameter that is set, while the miRNA precursors length generally ranges from 60 to120 (Fig.
6a ). Therefore, the maximum length of hairpin sequence parameter "-l" was set from 60 to 130 with an interval of 10, and chose the shortest hairpin that passed the folding free energy evaluation. Potential precursors were further blasted against nr database to remove protein coding genes (E-value threshold of 10). miRNAs prediction using C. robusta small RNA-Seq data The C. robusta small RNA-Seq reads (SRR038843, SRR038844, 26 nt) were mapped onto the repeatsequence-masked H. roretzi reference genome using BLASTN (E-value of ≤1000, word size of 10). Only the best hits with a high-scoring segment pair (HSP) identity of ≥90% and an alignment length of ≥20 and ≤2 mismatches were kept. Then, we examined possible stem-loop structures in the sequences and the flanking 100 bp sequences using srnaloop  [38].
Inference of strand of miRNA precursors The strands of the miRNAs were determined according to the miRBase database (Release 21)  [44]  (best BLASTN hit), Halocynthia ESTs (http://magest. hgc. jp) (E-value ≤ 1e-5) and Ciona small RNA-Seq data (best BLASTN hit). In miRNAs without a significant hit, the sequences of them are tentatively represented by those in plus strand of the reference genome.
The difference imaging method measured an upper bound on the wavefront position in the radiographs and the wavefront position error was set to the system spatial resolution of 6 pixels (71 μ m). It could not be assumed that waves were steady in the powder bed. Accordingly, wavefront velocity was calculated differently for the first and second radiographs recorded on each shot. In the first frame, the wavefront distance was calculated by subtracting the static driver/powder interface position from the wavefront position. This was converted to wavefront velocity (U wf ) by dividing the result by the calculated transit time in the powder bed. The calculation of this transit time required impedance matching at the flyer/driver and driver/powder interfaces. Hugoniot data for polycarbonate and copper were obtained from the LASL Shock Hugoniot Data Compendium  27.
For the second frame, the wavefront transit distance was calculated as the difference in wavefront positions between the two radiographs and the transit time was equal to the interframe time. Driver/powder interface velocities (U dp ) were calculated similarly from the measured driver/powder interface positions. Figure  4  shows on-axis wavefront velocity (U wf ), driver/powder interface velocity (U dp ) states measured directly from the radiographs. To obtain these values the wave velocities in centred 10-chondrule-diameter regions of the radiographs were binned according to Scott's rule  28  , which assumed the data were normally distributed. The size of these regions was 1. 960 mm and 4. 250 mm for the small and large chondrules, respectively. The data sets were well-fitted by a normal distribution, from which a mean velocity and standard deviation were derived.
Acknowledgements The authors would like to firstly thank the technical staff at  Imperial College  for their machining efforts. We would also like to extend a sincere thank you to the team at  Beamline ID19  for their engineering and analytical assistance. Finally, the authors would like to thank  AWE ,  EPSRC ,  ESRF  and  Imperial College London  for the continued support of this research. J. G. D. was funded by an  EPSRC  studentship. G. S. C. was funded by  STFC  grant  ST/N000803/1.
The Stellar Seismic Indices database (hereafter SSI) has been developed within the SPACEInn project funded under the UE Seventh Framework Programme (FP7). This data base aims at providing stellar seismic indices of solar-like oscillating stars as well as other stellar indices such as the main characteristics of stellar granulation. These indices are extracted using an automatic method, which is able to derive simultaneously the seismic and granulation parameters. This method was applied to almost all Kepler long-cadence light-curves and to almost all CoRoT targets observed in the faint fields. A total of about 320 000 targets have been analyzed, among which seismic indices and granulation parameters have been extracted for about 18 000 redgiants. In this article, we present the data base content, its interface, as well as some illustrative results.