dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016 (Leng et al. , , 2019)). The AxiSEM3D solver can account for a number of predefined mantle models, topography, and crustal model crust1. 0 (Laske et al. , 2013). In addition to these three options, custom waveform databases can be constructed by formatting results of wave propagation modeling as an HDF5 database usable by noisi.
Wavefield databases Green's functions are stored in one HDF5 file per seismic receiver component. The format is specified in the Appendix C. For the preparation of this database, routines are provided that take a seismic station list, the format of which is also specified in the Appendix B, as input. One may set up a database for analytic far-field surface wave Green's functions for 2-D homogeneous media (following  Fan and Snieder, 2009) ; obtaining Green's functions for PREM or other reference models additionally requires an instaseis database (e. g. , downloaded from Syngine). If a surface wavefield output from AxiSEM3D is provided, Green's functions can be extracted from this surface wavefield, allowing us to include 3-D lateral Earth structure. If run on multiple processors, the task of preparing the Green's function database is performed in embarrassingly parallel mode in that each receiver component is prepared on one core.
0035-0. 007 Hz. This hum model is interpolated on a dense global source grid for the noisi tool. To illustrate the use of Green's functions describing different Earth structures and obtained with different wave propagation solvers, the correlations are constructed with two different Green's function databases. Synthetics from anisotropic PREM with uniform crustal layer are contrasted with synthetics from S40RTS including attenuation, laterally varying crust2. 0, and ocean load. These have been computed with AxiSEM3D (in spherically symmetric mode) and SPECFEM3D_GLOBE, respectively. We illustrate a selection of the resulting correlations (selected to represent the variety of inter-station paths and distances) in Fig. 3. The map shows the averaged source model and station locations for the synthetic correlations.
Ambient noise source inversion Sensitivity kernels computed with noisi can be used to run gradient-based inversion for the distribution of ambient seismic sources from a dataset of observed ambient noise cross-correlations. To demonstrate the effectiveness of this approach, we conduct two synthetic inversions using two different functions to measure the misfit between observations and model. The sensitivity kernel of any misfit function for the zz component of cross-correlation can be expressed as K zz (x 1 , x 2 , ξ ) = ω Nyq ω=0 G * zz (x 1 , ξ , ω)G zz (x 2 , ξ , ω) • f zz (x 1 , x 2 , ω) dω, (8) where merely the function f zz is determined by the chosen misfit function and corresponds to the derivative of the misfit function with respect to the modeled cross-correlation. As the first misfit function, we use the L2 norm of the synthetic (C syn ) and observed (C obs ) correlation waveforms, i.
) will produce different optimal models of the noise source distribution. For example, provided adequate coverage, one can expect a higher resolution to result from using the L2 waveform misfit, which has more short-wavelength spatial features. This appears even more clearly once we conduct the inversion. We first construct a synthetic dataset by forward modeling cross-correlations from a source distribution shown in Fig. 6a , which has a low background level of sources in the left half of the domain, along with three strong Gaussianshaped sources marked by green crosses at varying distances outside the array, which is marked by red triangles. The right half of the domain is source free. The frequency content of the starting model is homogeneous for all sources (background and blobs), with Gaussian power spectral density S(ξ , ω) of Eq.
(  3 ) having a mean frequency of 0. 05 Hz and standard deviation of 0. 02 Hz. We compute cross-correlations through PREM at all station pairs of the array and add Gaussian noise with an amplitude of ± 5% of the average root mean square of all synthetic cross-correlation traces. To treat the inversions with different measurements consistently, we proceed in the same manner concerning filtering and smoothing. The inversion starts at a lower frequency, and a higher frequency band is added (taking two measurements after bandpass filtering in two different bands) after 20 iterations. Gaussian smoothing is applied in lieu of a more formal regularization, and smoothing length is decreased after 20 iterations. The optimization itself is performed with the L-BFGS algorithm of the SciPy minimize module  (Nocedal and Wright, 2006; Millman and Aivazis, 2011).
Discussion and conclusions The noisi tool allows users to create correlations for a variety of source models without the burden of costly numerical wave propagation simulations by utilizing instaseis or to run noise source inversion at reduced cost with precalculated Green's function databases from AxiSEM3D or other wave propagation solvers. Due to its implementation in Python, the tool can be easily modified and integrated into a rapidly growing ecosystem of seismologic applications in Python (e. g. ,  Beyreuther et al. , 2010; Hosseini and Sigloch, 2017; van Driel et al. , 2015; Heimann et al. , 2019; Lecocq et al. , 2014). Disadvantages compared to implementations integrated into spectral element solvers, such as the ones by  Tromp et al. (2010)  and  Sager et al. (2018a) , are the rigid setting of the source grid and the approximation of spatial integrals.
These are evaluated by weighted sums which can lead to approximation artifacts (see Fig. 2 ). Tromp et al. (2010 ), Basini et al. (2013 ), and Sager et al. (2018a)  evaluate the spatial integrals using the spectral element basis, which is expected to approximate the integral better at a comparable spatial resolution. However, this is not a conceptual but rather a current practical limitation of the tool and could thus be overcome by adapting the wavefield storage and spatial integration. While the errors in Fig. 2c  and d may appear large, they may often be negligible in comparison to data noise and can be further diminished by increased spatial sampling. The storage burden of the Green's function database may be regarded as another disadvantage. However, wavefields at the surface of the modeling domain have to be temporarily stored in either type of implementation to allow the application of the ambient source spectra, and thus the choice to reuse them appears intuitive.
Acknowledgements. The authors thank  Kees Wapenaar  and  Erdinc Saygin  for their thoughtful, constructive reviews on the paper, and the topical and executive editors of  Solid Earth ,  Michal Malinowski  and  Charlotte Krawczyk , for its efficient handling. Laura Ermert gratefully acknowledges support from the  Swiss National Science Foundation  under grant  P2EZP2_175124  and thanks  Naiara Korta Martiartu  for improvement suggestions to the paper. A warm thank you also to  Kuangdai Leng  for valuable help with AxiSEM3D. Korbinian Sager  thanks the  Swiss National Science Foundation  for support under grant  P2EZP2_184379. Computations were run on the UK national supercomputer ARCHER under Laura Ermert's driving test allocation and  Tarje Nissen-Meyer 's  NERC  remit. Observational data were obtained from the  IRIS Data Management Center.
IRIS Data Services are funded through the  Seismological Facilities for the Advancement of Geoscience and EarthScope (SAGE)  proposal of the  National Science Foundation  under Cooperative Agreement  EAR-1261681. Financial support. This research has been supported by the  Swiss National Science Foundation  (grant no. 175124 , http://p3. snf. ch/project-175124, last access:  26 August 2020 , and grant no. 184379 , http://p3. snf. ch/project-184379, last access:  26 August 2020 ). Review statement. This paper was edited by  Michal Malinowski  and reviewed by  Erdinc Saygin  and  Cornelis Wapenaar.
L. Ermert et al. : Noise modeling and inversion Code and data availability. The Python code can be downloaded from GitHub (https://github. com/lermert/noisi, Ermert and Igel, 2020). A tutorial in the form of a Jupyter Notebook is provided as the main item of documentation and details each step for the computation of cross-correlations and sensitivity kernels. The GitHub repository contains a set of basic test cases to be passed by further developments. It also provides a numerical test for the consistency of forward model and gradient, which can be employed for the development of additional misfit functions. All observed seismic data used to prepare this paper were downloaded from IRIS Data Management Center. Supplement. The supplement related to this article is available online at: https://doi.
Study Population From November 2014 to February 2018, MSM attendees of the Sexually Transmitted Infections (STI)/HIV Unit of the San Gallicano Dermatological Institute IRCCS (Rome, Italy) were enrolled to participate in the Oral/Oropharyngeal HPV in Men At Risk (OHMAR) study. The enrollment criteria for this study are described elsewhere  [21]. Information regarding sociodemographic and sexual behaviors was collected through face-to-face interviews. HIV-related data were retrieved from medical records. Written informed consent was obtained from the participants. The study was cleared by the institutional Ethics Committee, I. F. O. Section-Fondazione Bietti (CE/417/14, 13-05-2014). All procedures were performed in accordance with the Declaration of Helsinki.
Bioinformatic Analysis of NGS Data Bioinformatic analysis was performed as previously described  [26, 27]. Details of the bioinformatic pipeline named "PVAmpliconFinder" and the parameters used can be found at https:// github. com/ IARCbioinfo/ PVAmpliconFinder. All the results in this study were based on the identification of the sequences following the homology-based classification using the evolutionary placement algorithm (EPA) in Randomized Axelerated Maximum Likelihood (RAxML)  [28, 29] , hereafter referred to as RAxML-EPA. The initial tree used to infer the classification of the PV sequence was built based on 517 L1 sequences available in the PaVe database  [30]. Only the longest sequence was considered for RAxML-EPA classification, where several singlets or contigs were clustered together by a PVAmpliconFinder.
Conclusions In conclusion, our study evidenced that a wide spectrum of HPV types can be detected in oral samples of MSM and that beta HPVs represent the most abundant types, exceeding the prevalence of both alpha and gamma HPVs. Our findings confirm that cutaneous HPVs are frequently observed in mucosal epithelia. A robust NGS strategy for PV analysis confirmed the high diversity of HPVs in oral infection, with the alpha genus being the least diverse compared to the cutaneous genera. Through this strategy, a number of putative novel PVs were also identified. Further studies are necessary to clarify the natural history and transmission modalities of cutaneous HPVs in the oral cavity and, more importantly, to investigate their possible pathogenic role at this site. 180), γ-species 11 (126, 169, 171, and 202), γ-species 12 (127, 132, 148, 165, and 199), γ-species 13 (128), γ-species 14 (131), γ-species 15 (179), γ-species 18 (156), γ-species 19 (161, 162 and 166), γ-species 20 (163), γ-species 21 (167), γ-species 22 (172), γ-species 23 (175), γ-species 24 (178 and 197), γ-species 25 (184), γ-species 27 (201), and SD2 (Genbank accession id: KC113191) were detected using type-specific multiplex genotyping (TS-MPG) assays (IARC, Lyon, France).
In order to define the scope for improving survival outcomes in NTSCI by adopting etiology-specific clinical management and health policy, population-based studies are needed that separate the mortality that is attributable to NTSCI etiology from lesion-related or demographicattributable mortality. Such studies are timely in face of the growth in incidence and prevalence of NTSCI  [2, 7]. We therefore used longitudinal data from a Swiss populationbased cohort study  [8]  and applied formal causal inference methodology  [9] [10] [11]  to infer the strength of support for the hypothesis that mortality differentials in NTSCI causally relate to etiology group. Using the causal estimates for mortality differentials, we also estimated the disparity in survival that was directly attributable to etiology-specific differences.
Study description and vital status The present study used medical records data collected from the Swiss Spinal Cord cohort study  (SwiSCI) , which includes all SCI-specific rehabilitation centers in Switzerland  [8]. Individuals aged 16 years or older that initiated first SCI-rehabilitation between 1990 and 2011 were included in the present study. SwiSCI does not include disorders such as multiple sclerosis. Vital status data were obtained with high reliability (85. 5%) through probabilistic data linkage with the Swiss National Cohort (SNC) using birthdate, date of death (when available), age, sex, and geocoded address, further described in-depth elsewhere  [4]. The present study uses vital status data that was updated until December 31, 2018.
Data management International Spinal Cord Society (ISCoS) guidelines were followed in the grouping of all variables  [12, 13]. NTSCI etiologies were grouped according to the International SCI core datasets for non-traumatic spinal cord injury  [13]. The etiological categories included were identified as "acquired abnormalities" within this core dataset, mainly from the second level of classification, which included: 'degenerative disc disorder,' 'infection,' 'vascular disorder,' 'benign tumor,' and 'malignant tumor. ' NTSCIs originating from tumors 'benign' and 'malignant' were split at the third level of classification, following previous research that has identified strong survival differences between these groups  [3, 5, 6]. NTSCIs that originated from metabolic disorders, inflammatory disease, radiation-related causes, and those of an unclear origin were grouped into an 'other' category (n = 98).
'Calendar period' defines a further potential confounding pathway, given that health survey and survival data from the Swiss general population indicate that the survival has steadily increased with calendar period, in addition to the declining risk of death following NTSCI  [3, 19, 21]. This DAG guided all statistical analysis. In order to estimate survival differences across NTSCI etiological groups, we included survival data from persons with a TSCI etiology as a baseline reference. This baseline reference was determined to estimate the additional effect of underlying health conditions on survival, therefore the main analysis implemented the baseline TSCI due to 'Transport' and Sports/Leisure,' as contemporary TSCI literature hypothesizes that TSCI due to 'Falls' or 'Other' may be linked to an underlying health condition  [14].
Determining conditional independences Directed-separation, also known as d-separation is a set of criteria that describe how to interpret the statistical associations or the conditional independences that are implied by the DAG  [22]. Testing these conditional independences provides an internal consistency check on whether the DAG is consistent with the effective dataset  [23]. The R package dagitty implements the DAGitty web application and was used to evaluate the DAG-dataset consistency  [23]. An internal check was applied to each conditional independence implied by the hypothesized DAG using logistic regression to calculate the root mean square error of approximation (RMSEA)  [24]. Values <0. 1 were considered consistent with our hypothesis  [25].
Statistical analysis Statistical analyses were implemented using Stata software version 16. 1 (College Station, TX). Descriptive analyses report raw numbers and percentages stratified by etiological group, for age and length of stay, the mean and interquartile range are additionally reported. Participant's time-at-risk began with date of SCI diagnosis, with study start time beginning on the date of admission to first rehabilitation. Follow-up time was censored on the date of death, or the end of study (December 31, 2018), whichever came first. Kaplan-Meier estimates were used to graphically display crude survival according to etiology and to provide non-parametric estimates of median survival of the NTSCI with respect to the TSCI population. Flexible parametric survival models were used to estimate hazard and survival functions between NTSCI and TSCI  [26].
Causal inference Figure  1  illustrates the directed acyclic graph developed to hypothesize the relationship between underlying health conditions that precede the onset of NTSCI and mortality. The hypothesized DAG specified six conditional independences, or d-separation criteria, namely, (1) 'Age at SCI is independent of sex', (2) 'Age is independent of SCI severity, given etiology', (3) 'Age is independent of calendar period of onset', (4) 'SCI severity is independent of sex, given etiology', (5) 'SCI severity is independent of calendar period of onset, given etiology', and (6) 'Sex is independent of calendar period of onset. ' RMSEA used for estimating DAG-dataset consistency was below 0. 1 for all d-separation criteria (Table  2 ). For example, the RMSEA for independence testing of the d-separation 'sex is independent of calendar period of onset' was 0.
Acknowledgements SwiSCI is hosted and funded by  Swiss Paraplegic Research. We thank the  SwiSCI Steering Committee  with its members  Xavier Jordan ,  Fabienne Reynard  ( Clinique Romande de Réadaptation, Sion );  Michael Baumberger ,  Hans Peter Gmünder  ( Swiss Paraplegic Center, Nottwil );  Armin Curt ,  Martin Schubert  ( University Clinic Balgrist, Zürich );  Margret Hund-Georgiadis ,  Kerstin Hug  ( REHAB Basel, Basel );  Urs Styger  ( Swiss Paraplegic Association, Nottwil );  Daniel Joggi  ( Swiss Paraplegic Foundation, Nottwil );  Nadja Münzel  (Parahelp, Nottwil);  Mirjam Brach ,  Gerold Stucki  ( Swiss Paraplegic Research, Nottwil );  Armin Gemperli  ( SwiSCI Coordination Group at Swiss Paraplegic Research, Nottwil). We thank the  Swiss Federal Statistical Office  for providing mortality and census data and for the support which made the SNC and this study possible.
Fig. 1b  and phyloP 100-way 8 score 9. 43), is extremely rare in public databases (not present in EVS6500  9  , MAF of 0. 0017% in ExAC  10  ) and is a good candidate according to in silico prediction scores (CADD score  11  of 33) (Suppl. Table  1 ). We then (i) screened the GENESIS  12  database for additional patients with recessive UGDH variants, (ii) contacted the EuroEPINOMICS RES Consortium, and (iii) searched with the help of Gene-Matcher  13  for additional families with germline UGDH mutations. We uncovered 27 additional patients from 24 families carrying either compound heterozygous or homozygous UGDH variants (Fig. 1a  and Suppl. Fig. 1a ). All variants were absent or had an extremely low frequency (<0. 01%) in the public databases ExAC/gnomAD  10  and EVS6500 (Suppl. Table  2 ).
In all pictures, MRI pulse sequences (T1, T2, and Flair) and image orientation (S: sagittal, A: axial and C: coronal) are indicated in the upper left corner. d Interface between the subunit A NAD-binding domain with the subunit C UDP-Glc-binding domain. In all the structures, residues carrying missense mutations in the patients are highlighted as 3D backbone. Residues Q110 and T325 known to interact together for dimer formation  15  ; and residue V132, which is important for hexamerization  15  are highlighted in black backbone. In all the structures, NAD-binding (blue), central (light/dark pink), and UDPbinding (orange) domains are shown. UDP-Glc (dark red) and NADH (midnight blue) are represented as colored carbon backbones. Adapted from PDB code 2Q3E 6 using the Swiss-Pdb Viewer software  67.
Exome sequencing. To unravel the molecular cause of the disease exome sequencing were performed at different genetic institutes using next-generation sequencing techniques according to local standard protocols. Variants were confirmed via Sanger sequencing using standard methods and chemicals (primer sequences are available on request). Family 1: Exome sequencing for two affected siblings was performed on a HiSeq2500 System (Illumina, CA) after enrichment with SureSelectXT Human All Exon V5 (Agilent, Santa Clara CA). FASTQ files were imported into GENESIS (http://thegenesisprojectfoundation. org/)  12  for further analysis using a pipeline build on BWA  41  , Picard, and FreeBayes. Variants were filtered for changes that segregated in an autosomal recessive fashion and passed the following filter criteria: (i) frequency in public databases (ExAC 10 minor allele frequency (MAF) <0.
Reads were aligned to human genome build GRCh37/UCSC hg19, and analyzed for sequence variants using a custom-developed analysis tool. Additional sequencing technology and variant interpretation protocol has been previously described  42. The general assertion criteria for variant classification are publicly available on the GeneDx ClinVar submission page (http://www. ncbi. nlm. nih. gov/clinvar/ submitters/26957/). After variant stratification based on population frequencies within an internal database and ExAC 10 , inheritance, in silico predictors such as Provean, Mutation Taster and CADD, GeneDX reported only the UGDH variants to be the best potentially pathogenic candidates and connected to this project via GeneMatcher entries. Family 3: Samples of the oldest sibling and both parents were sequenced in context of the EUROCORES project EuroEPINOMICS-RES, for which the technical details have been reported before  43.
Briefly, the trio underwent exome sequencing at the Wellcome Trust Sanger Institute (Hinxton/Cambridge, UK). Capturing of the exome was performed using the SureSelect Human All Exon 50 Mb exome kit (Agilent). The enriched exome libraries were then sequenced on a HiSeq2000 platform (Illumina) as 75 bp paired-end reads. BWA was used to align the sequenced reads to the reference genome (hg19). De novo analysis of these data did not reveal any variants. As the younger sib later developed a similar disorder, exome sequencing was also performed locally on the second sibling: for library preparation, genomic DNA was sheared to the average size 150 bp (Covaris) and the genome libraries prepared using KAPA HTP Lib Prep Kit Illumina 96 rxns (07138008001). Exome capturing was performed using the SeqCap EZ Human Exome v3.
0 capture system and the sample was sequenced on the NextSeq500 platform using NextSeq500 High-output V2 kit. Mapping to the human reference genome (Hg19) and variant calling were performed with the CLC Genomics Workbench. Subsequent annotation and filtering were executed with GenomeComb  44  (http://genomecomb. sourceforge. net/). Exome sequencing results of the trio and the second sibling were merged and reannotated and the family was reanalyzed as a quartet. Variants were filtered based on following quality parameters: coverage >7, quality >50 and not located in homopolymers >8 or tandem repeats. Only variants present with a frequency <0. 5% in control population databases ExAC 10 and Exome Variant Server, seen <3 times in the local exome sequencing database, and with predicted impact on the encoded protein (missense, nonsense, frameshift, deletions, insertions and (essential) splice site) were retained for further analysis.