dataesr/scientific-paragraphs-categorization huggingface dataset:
Taxonomy names and accession numbers are also clickable, allowing for fast retrieval of all taxonomic information or sequence entry data (see output_microseek. tar. xz file at https://doi. org/10. 5281/zenodo. 6937000, accessed on 28 July 2022). In its structure, Microseek consists in a pipeline specification, several scripts written in Python 3. 6. 9 (accessed date: 28 July 2022), and a provided Singularity container including all the needed dependency softwares. It requires a machine running Linux, x86_64 platform, and preferably runs on a cluster. User indicates in a yaml (formatted plain text) configuration file some parameters about the sample: path and name of the fastq files, sequencing type (paired or single ends), and name of the experiment. Snakemake manages the clusters options.
Characterization of Pre-Existing Unspiked Datasets and Negative Control To define the pre-existing content of viral sequences in real clinical matrices, the two datasets were analyzed with Microseek and CZ ID in the absence of additional in silico viral spike. In the unspiked pre-existing Tissues dataset, only one read wrongly attributed to Hepatitis C virus was reported by Microseek because of a wrong reference sequence in NCBI, while no background was visible in CZ ID output. By contrast, in the unspiked pre-existing Plasma dataset, a broad-spectrum of virus-related sequences was observed by both methods, including non-pathogenic viruses carried by human plasma (mostly bacteriophages) and viral nucleic acids introduced by reagents and contaminating the NGS libraries (Supplementary Table  S3 ).
Detection of Known Viral Sequences To evaluate the performances of Microseek in detecting known viral sequences, given amounts of viral reads were spiked in the datasets. The horizontal coverage of spiked reads along the genome of the six reference viruses ranged from 7. 5-99. 8% (dilution 1; d1), 2. 0-56. 9% (dilution 10; d10) and 0. 3-16. 1% (dilution 100; d100) for the Tissues experiments, and from 51. 1-99. 8% (d1), 6. 9-78. 7% (d10), 1. 0-20. 4% (d100) for the Plasma experiments (Table  1 ). We deliberately chose not to fully cover each virus with spiked sequences in order to better reflect real life metagenomic results. The 50 M reads datasets containing the spiked sequences were analyzed with Microseek and CZ ID, and the results obtained were compiled (Table  1 ). Almost all of the 6 viruses were detected by both Microseek and CZ ID at the species level in the Tissues and Plasma datasets at dilutions d1, d10 and d100.
Detection of Novel, Distant Viral Sequences To assess the ability of Microseek to detect distant viruses, we simulated the evolution of three new viruses, so-called Neopneumovirus-1, -2 and -3, derived from the genus Orthopneumovirus (Figure  3  and Supplementary Table  S2 ). Neopneumovirus reads were spiked to a number corresponding to dilution 1 (107 reads for Tissues, 67 reads for Plasma) and to dilution 10 (11 reads for Tissues, 7 reads for Plasma), following the same random generation process as done for known viruses (d1: reference horizontal coverages of 70. 1-81. 6% in Tissues and of 52. 2-56. 1% in Plasma; d10: reference horizontal coverages of 12. 1-12. 9% in Tissues and of 7. 6-8. 4% in Plasma). The 12 corresponding spiked datasets were analyzed with Microseek and CZ ID (Table  2  and Figure  4 ).
Discussion In this work, we introduced Microseek, a Snakemake-based pipeline for virus identification and discovery taking advantage of a reference viral protein database, RVDB-prot  [14]. The performances of Microseek were illustrated with clinical metagenomics datasets in which reads from known and new viruses were spiked at known concentrations. This experimental procedure has the advantage of testing realistic situations, while controlling parameters of interest such as the number of reads and genome coverage. The number of spiked sequences in the Tissues and Plasma datasets was calibrated to mimic actual infection as tested routinely in the laboratory. CZ ID, an open-source cloud-based pipeline developed by the Chan Zuckerberg Initiative  [12]  was used for comparison's sake. CZ ID has previously demonstrated very good performances compared to 22 analysis tools  [12]  and can therefore be considered as a best-in class in the field.
The detection of known viral sequences was achieved with Microseek with an overall good sensitivity on a wide range of spike concentrations despite, however, a loss of sensitivity for the detection of HIV-1. In complex matrices with a high background of unspiked sequences such as plasma, as low as one read spiked in a 50 million reads dataset was detected for HRSV. Microseek succeeded in detecting the Parvovirus B19 starting from 5 spiked sequences, where CZ ID did not in limit dilution represented by the d100 condition, which reflects low viral loads found in some clinical situations. Overall, the detection performances of spiked sequences in Plasma were better with Microseek (slope: 0. 867; intercept: 0. 011) than with CZ ID (slope: 0. 852; intercept: 0. 107). This discrepancy may come from the random subsampling of 1 million sequences made by CZ ID among non-human sequences, which appears to be a handicap of CZ ID for samples made of a large fraction of non-human sequences even when viral loads are compatible with qPCR detection (10 2 -10 3 copies/mL).
For example, if a query has identical matches in the database with several viruses belonging to different families, the LCA will not be precise (for instance «Viruses») but will be more accurate regarding the robustness of the taxonomic assignation than random picking of one species. Conversely, the identification of a sequence at the virus species level would strengthen the taxonomic assignation of the species. Reporting the LCA thus prevents reporting false assignments and opens the way to characterizing novel viruses. An important feature of Microseek is its protein-based search, assuming viral genomes are compact and contain mainly coding sequences. If needed, after primary identification, the identification of non-coding sequences can be achieved by re-analyzing the raw nu-Viruses 2022,  14, 1990  16 of 20 cleotide data to identify sequences that do not code for proteins (regulatory sequences, packaging signals, intergenic sequences, untranslated RNA transcripts.
Due to evolutionary constraints, the functionally important regions in proteins tend to be relatively conserved  [35]. Therefore, protein analysis can allow the use of protein motif libraries such as Hidden Markov Model (HMM) profiles. Each release of RVDB-prot contains a comprehensive HMM database of viral proteins  [14]. In our experiments on human samples, the search for HMM patterns from unassigned contigs longer than 2000 nt did not bring any new information compared to the blast approach (Supplementary Table  S6 ). In the Rhipicephalus tick sample, a single HMM cluster attributable to Riboviria (double stranded RNA-binding domain) was document, which requires further investigation (Supplementary Table  S6 ). Indeed, the end-user can adjust the search parameters to match the nature of the samples and the purpose of his search.
Microseek was used to describe the diversity of viruses present in ticks as exemplified by the exploration of the Chuviridae, Phenuiviridae and Flaviviridae families identified from Caribbean Amblyomma and Rhipicephalus ticks [41], and was useful at increasing knowledge on the host range, genetic diversity, and geographical distribution of Jingmenviruses  [42]. Microseek was also used to detect viruses with potential relevance for public health found in ticks from Eastern Romania [43], in particular in the Danube Delta  [30] , and to track silent spillovers before emergence at the tick/human interface in Thailand  [44]. As of July 2022, and over the past year, RVDB-prot, the core database of Microseek, was downloaded more than 10,400 times, showing the interest of the metagenomics research community in having a comprehensive and curated viral protein database.
A smart feature of Microseek is its dynamic HTML interface allowing easy data mining by a biologist. The interplay between a graphical interface and a sortable data table provides both a synthesis view and a direct access to the sequences and their associated GenBank and PubMed entries. The standalone web-page format facilitates sharing of results. This interface thus favors viral discovery and collaborative activities. Nevertheless, a simplified graphical interface or a simplified B1 database retaining only viruses of known medical interest could be proposed to medical virology laboratories for diagnostic purposes. Microseek was developed in the context of an institutional cluster providing abundant resources and was therefore optimized for accuracy. In order to make it compatible with more modest computational facilities, including desktop computers, efforts could be made on reducing computation times for some steps.
For instance, B3 is the most CPU consuming step and since it aims at discarding non-coding sequences, it could be replaced by a less sensitive algorithm. Diamond, used in Microseek and integrated as a scalability upgrade in CZ ID pipeline Version 7. 0, has shown its huge gain in performance on benchmark studies, but other similar blast-like algorithms were also proposed such as MMseqs2  [45]  and claim the same level of precision. In addition, some scripts could be parallelized to take advantage of a DataBase Management System (DBMS) faster than SQLite (on which rely some databases for annotation), such as PostgreSQL. We plan to offer such upgrades in the future to facilitate the broad adoption of Microseek by the community.
Conclusions Microseek is part of the metagenomic community's effort to develop accurate and easy-to-use tools for viral diagnostic and virus discovery. Its protein-based approach showed performance comparable to that of the state-of-the-art and cloud-based CZ ID software for the detection of known viruses. Microseek also facilitates the detection of distant pseudo-viral sequences in matrices with a high non-human content, such as plasma, by limiting the reporting of non-relevant hits. Microseek has demonstrated its value for medical diagnosis and is at the heart of research works on viral emergence. The software is supported through regular updates of its container and of the RVDB-prot database.
Supplementary Materials: The following supporting information can be downloaded at: https://www. mdpi. com/article/10. 3390/v14091990/s1, Figure  S1 : Mapping of spiked and detected sequences. Table  S1 : Rules followed for the generation of spiked datasets; Table  S2 : Id matrices between Neopneumoviruses-1 -2 -3 and HRSV-A; Table  S3 : Datasets analyzed in the absence of additional in-silico spike; Table  S4 : Residual hits following the analysis of the Plasma datasets spiked with known viral sequences; Table  S5 : Residual hits following the analysis of the Plasma datasets spiked with Neopneumoviruses sequences; Table  S6 : HMM results; Table  S7 : Results of classifiers based on kmer composition (Kraken2 and Centrifuge) or on predefined markers (Metaphlan 2).
I N T RO D U C T I O N Seismic imaging is essential for Earth Science research. The most widely used imaging approach is seismic tomography. It is based on integrated data trajectories, which means traveltime, amplitude, or waveform data are inverted along the wave propagation path to find models of seismic parameters that are in some sense optimal  (Liu & Gu 2012). A basic feature of tomography and other seismic imaging methods is the analysis of waves that are recorded away from a source. This focus on far-field observations can be considered to be inherited from times when seismic instruments were sparsely distributed and offered relatively poor sampling of the wavefield. Here we study a surface wave imaging method that is based on wavefield features resolved at subwavelength distances.
Green's functions in 1-D layered media are computed using the AXITRA solver  (Cotton & Coutant 1997). We use Python wrapper functions for the simulation set up, for the generation of AXITRA input files, and to control the multicore execution of AXITRA. The Python scientific computing ecosystem is also used for the data management, post-processing and analysis. The parallel processing on High Performance Computers of the Finnish Grid and Cloud Infrastructure exploits the independence of the Green's function computation between the carrier grid sources and the mirror elements. The Green's function forward computation of a typical experiment that consists of 6400 sources and M = 72 mirror elements at the surface and synthesizes 512 samples long three-component seismograms is completed on one node with 14 hyperthreading 2 × 2.
Time-reversal computation and narrow-band surface wave refocusing Wavefields are synthesized by correlating time-reversed Green's functions  (Derode et al. 2003 ) computed with AXITRA. The program is used in the ZNE vertical-north-east coordinate system with Z being positive downward for the exciting force and positive upwards for seismograms. In this work, we exclusively consider sources at the surface. We start the discussion by ignoring the small body wave amplitudes in the corresponding Green's function and assume the receivers-or mirror elements-at the surface in the far-field record surface wave motion only. This is to reproduce the set of equations that describe the associated Rayleigh and Love wave focal spot shapes. The positions of a carrier grid source and a mirror receiver element m are denoted r and r m.
Alternatively, estimating c R from ZZ, ZR and RZ data suggests the possibility to constrain c L from RR and TT data that contain both Rayleigh and Love wave energy. In this work we focus on the φ ZZ and φ ZR component focal spots to study fundamental mode Rayleigh wave properties because these components depend on c R only, and because the φ ZR and φ RZ synthetics are interchangeable. As discussed below wavefield separation is a central aspect in data applications for these components, too, if surface waves and body waves are present. The term focal spot is applied collectively to the amplitude fields at short distances around the origin including the vanishing amplitudes of the ZR and RZ components. AXITRA forward simulations and the correlations are performed for a broad frequency range.
Quantifying anisotropic incidence from 2-D spatial Fourier transform The coefficients w m used in Section 2. 4 to implement anisotropic incidence cannot be inferred from observations. For a consistent anisotropic surface wave incidence estimate that can also be applied to focal spots obtained from data, we use an anisotropy ratio that is estimated from the 2-D spatial discrete Fourier transform (2DFT) of ZZ component focal spots. In general the 2DFT is applied to data from a circular window with radius equal to the largest inner circle of the rectangular carrier grid. We apply zero-padding to 1024 samples in both directions. We present the 2DFT results in the slowness instead of the wavenumber domain. The maximum energy in the slowness domain is detected as well as the corresponding azimuth.
Implementation of incoherent noise To study the effect of incoherent fluctuations on the Rayleigh wave speed estimates, a noise term n(t) is added to the broadband Green's functions G ij between sources and mirror elements. We use Gaussian noise centred on amplitude zero and with a variance set as a fraction of the maximum amplitude of each considered time series. The pseudo-random numbers are generated with Python's random package. Green's functions calculated by AXITRA do not have a constant spectrum across the simulated frequency range, the spectrum is meant to be flat once convolved with a source step function. However, in our case, the source introduced in the convolution is a Dirac delta function. This leads to spectral amplitudes that are proportional to the angular frequency ω, which implies a frequency dependent effect of the Gaussian white noise.
Parameter estimation for local imaging Rayleigh wave speed c R = ω c /k is estimated from the comparison between simulated focal spots and the theoretical Bessel functions models using a non-linear least squares fitting method from the SciPy package that uses the Levenberg-Marquardt algorithm. We use the default termination tolerances. The quality of the regression is assessed using the scaled difference between the estimated wave speed c R and the target Rayleigh wave speed c input. The error estimate is E = c R -c input c input × 100. ( 16 ) The square grid implies that the number of data points increases with distance r from the origin, which can influence the fitting results. To study this scaling effect, we perform regressions with a 1/r data weight. We find that the differences are generally negligible and therefore show results obtained without the 1/r weights, except for the noisy wavefields in Section 3.
Isotropic distribution The first case uses the complete azimuthal distribution of mirror elements to simulate P waves impinging symmetrically from below on the array, as for example in the San Jacinto fault zone case studied by  Hillers et al. (2016). Fig. 6 (a) presents an example of nine-component focal spots and Fig. 6 (b) the associated 2DFT for the ZZ and ZR components obtained under these conditions. The energy ratio ζ (eq. 11) between P waves and Rayleigh waves is 25 per cent. The space and slowness domain results both show that the focal spot is symmetric around the origin, which is expected from the mirror geometry. A comparison of the amplitude distributions in Fig. 6 (a) to the reference data in Fig. 3(a)  shows that the ZR focal spots and the other cross-component fields are unaffected.
The ZZ and ZR focal spot data are fitted using the isotropic assumption N k = 0 for the non-linear regression (eq. 17). Fig. 7 (a) shows the regression results obtained from the complete 2-D amplitude distribution at all azimuths using different fitting distances r fit. The ZR component data are well fitted for every distance range, confirming that the ZR and RZ results are not biased. However, the ZZ component results strongly depend on the fitting range. For short distances smaller than 0. 5λ the focal spot seems sufficiently well approximated, although the obtained function fails to follow the biased shape at larger r. For larger r fit , the local maxima appear better resolved but the overall amplitude level is underestimated as the sinc-function contribution is neglected. Figs 6(a) and 7(a) use a P to Rayleigh wave energy ratio of ζ = 25 per cent (eq.
Differences in Downloaded from https://academic. oup. com/gji/article/232/1/201/6618556 by guest on 04 November 2022 the ZZ component energy ratio dependent error between the isotropic P-wave incidence (Fig. 8a ) and the angular P-wave incidence (Fig. 8b ) are negligible. Together with the consistent small ZR errors in the 1 per cent range, this similarity demonstrates again the stabilizing averaging effects of the isotropic model assumption. To conclude, steeply arriving P waves interfere with the refocusing surface wavefield and distort the focal spots. The Rayleigh wave speed is best estimated using ZR component data which is less affected by the P waves compared to ZZ data. Results from the vertical and oblique incidence scenarios are essentially identical, which leads to the recommendation to rely on ZR component data in case the configuration allows azimuthal averaging.
We note that  Harmon et al. (2010, their eq. 2)  provide an expression for a θ -dependent amplitude distribution; see also  (Nakahara 2006)  and  Haney et al. (2012). However, although Fig. 11  shows disagreement between the model and data collected along the direction of strongest and weakest incidence, averaging over all azimuths yields a relatively accurate and precise c R estimate. Fig. 12  displays the error of the wave speed estimates as a function of the anisotropy ratio introduced in Section 2. 5 for the four fitting distances. Under the isotropic assumption, the accuracy is better than 1 per cent for the ZZ and ZR components, for the full range of the tested anisotropy amplitude, and for all fitting distances. This agrees with SPAC results from  Nakahara (2006)  who confirmed the original observation of  Aki (1957)  that artefacts associated with anisotropic incidence can be eliminated through azimuthal averaging.
Dispersion measurements In this section we study the feasibility of estimating the dispersion. The sources and mirror configuration is modified from the previous cases. Now the origin of the focal spot is not at the centre of the 632 × 632 m 2 carrier grid but close to a corner, which puts it still near the centre of the mirror circle considering the length scales illustrated in Fig. 1(a). This allows studying a larger distance range across the carrier grid for the low frequency estimates. We consider an isotropic situation. To generate dispersion we implement 1-D layered media shown in Fig. 15(a). In Case 1 the wave speed increases with depth, and in Case 2 the positive gradient is interrupted by a low-velocity layer. The lowermost last layer has an infinite thickness. This configuration sustains Love waves that are possibly not muted by the gate function filter, but since we analyse ZZ data this does not influence the c R measurements.
Homogeneous elastic half-space This section assesses focal spot imaging in the presence of noise. The first test is performed on the data from the homogeneous elastic half-space to compare the results with the reference case in Section 3. 1. 1. Figure  16. Focal spots at 10 Hz in a homogeneous elastic half-space obtained with 10 per cent variance Gaussian noise added to the source-mirror Green's functions G (eq. 1). The implementation is described in Section 2. 6. 10 per cent variance noise. Recall from Section 2. 6 that the noise is added to the Green's functions G between the carrier gridpoints and the mirror elements before correlation and filtering. While the autocorrelation assumes by definition unity, cross-correlation values even at small distances show a consistent amplitude reduction that is accommodated by the σ scaling factor in eq.
(  17 ), and that represents in general the level of coherency associated with wavefield properties and normalization choices in data applications. Due to this effect, the autocorrelation is usually not considered in data applications  (Hillers et al. 2016) , and we also neglect the autocorrelation for the results presented below. The observed 'wafting' effect in the time domain refocusing fields in Fig. 16  associated with the incoherent fluctuations potentially affects the azimuthal symmetry, and could thus also interfere with anisotropy estimates. For the 10 Hz synthetics Fig. 17 (a) explores the effect of the fitting distance on the ZZ and ZR regression results using the isotropic model N k = 0 (eq. 17    a)  shows that a fitting distance shorter than λ/2 leads to significant errors. For r fit > λ the error is in the 1 per cent range.
2012). The other components cannot readily be used because they are null-the other cross-terms-or because they involve a superposition of Rayleigh and Love waves-the RR and TT terms (eqs 8, 9)-which we do not attempt to separate here. We highlight five key results from our numerical studies. One, the isotropic model assumption provides remarkably good c R estimates  (Nakahara 2006 ) even if the surface wavefield incidence is strongly anisotropic (Fig. 9 ) and despite the apparent incompatibility between the resulting oval-shaped focal spots and the circular model (Fig. 10 ). Even for short fitting distances, the error is smaller than 1 per cent (Fig. 12 ). This applies to ZZ and ZR/RZ component results. Two, the isotropic model also provides very accurate c R estimates from the ZR/RZ data in case of impinging P-wave energy (Fig.