dataesr/scientific-paragraphs-categorization huggingface dataset:
Bionano Genomics-based inversion discovery. We analyzed Bionano Genomics Optical Mapping data by using Saphyr 2 nd generation instruments (Part # 60325) and Instrument Control Software (ICS) version 4. 9. 19316. 1. De novo assemblies of each sample were obtained using the Bionano Solve v3. 5 De Novo Assembly pipeline with haplotype-aware arguments (optArguments_haplotype_DLE1_saphyr_human_downSampleLongestMole. xml) as described previously  (Ebert et al. 2021). Using the Overlap-Layout-Consensus paradigm, pairwise comparisons of DNA molecules at least 250 kbp in length, contributing to a coverage of 250X, were generated to create a layout overlap graph and produce initial consensus genome maps. By realigning molecules to the genome maps (alignment confidence cutoff of Bionano p-value < 1e-12)  (Anantharaman, Mysore, and Mishra 2004)  and by using only the best matching molecules, a refinement step was applied to label positions on the genome maps and to remove chimeric joins.
Such cells are informative and can be used for unambiguous phasing for a given inverted region, since the Crick and Watson reads are coming from different parental homologs. Inverted haplotypes were assigned to a respective parental homolog based on the phasing of reads from Strand-seq cells that inherited either Watson or Crick strands from each parent. We implemented the functionalities for correcting the phase of inverted sequenced in the R package  StrandPhaseR (v0. 99) , in a new function called 'correctInvertedRegionPhasing'. We supplied this function with the sample-specific inverted regions reported in this study, in order to correct each of them using the given function parameters (recall. phased = TRUE, het. genotype = 'lenient', pairedEndReads = TRUE, min. mapq = 10, background = 0.
We considered an inversion to be supported by ONT reads if there were at least three split-read mappings. We also required that the fraction of inverted base pairs inside an inverted region is at least 0. 5 higher than the fraction of inverted base pairs outside of the inverted region (Fig. S54 ). PCR validation. As a further line of validation for the callset, we subjected 10 randomly selected breakpoint-resolved inversions (length: 0. 8-366 kbp) to site and genotype validation via PCR. Primers were designed using a computational SV validation primer design pipeline, as previously described  (Sudmant et al. 2015) , which is in turn based on the Primer3 method  (Koressaar and Remm 2007 ) and available at https://github. com/zichner/primerDesign. Briefly, the pipeline uses an iterative approach to extract two uniquely mapping, inversely oriented primers pairs per inversion, with each pair mapping to opposite sides of one inversion breakpoint.
DAPI, Cy3, Cy5, and fluorescein fluorescence signals, detected with specific filters, were recorded separately as grayscale images. Pseudocoloring and merging of images were performed using Adobe Photoshop software. Since the tested inversions were >2 Mbp, two-color FISH on metaphase chromosomes was performed using two probes within the inverted region and the centromere as anchor. Excess in common inversion polymorphisms in the genome. Since we were evaluating inversions excluding misorients (found in all haplotypes), we also excluded variants found in all haplotypes from the SV and single-nucleotide variant (SNV) callsets to avoid biasing allele frequency and growth rate comparisons. Singletons are defined as variant calls with an allele count of 1. Statistics were computed per haplotype (two haplotypes per sample).
Pairwise differences between haplotypes were visualized and grouped based on inverted versus direct orientations. • Ancestral recombination graph reconstruction: We used the software Relate  (Speidel et al. 2019)  to reconstruct the local genealogy of an inversion locus. The method infers underlying coalescent events consistent with the observed data under the infinite-sites model, taking into account recombination, and thus, recapitulates the multi-locus genealogy of the genomic region. We used a mutation rate of 1. 25 × 10 !% per base per generation and set the haploid effective population sizes as 10,000  (Gutenkunst et al. 2009 ) and 6,900  (Veeramah et al. 2014)  for autosomal and X chromosome loci, respectively. We projected the inversion genotype onto the resulting trees and inferred the number of inversion events using Fitch's algorithm for homoplasy  (Fitch 1971).
The rate of inversion at each inversion locus is defined as the estimated number of inversion events divided by the total tree length in generations. Note that for a given tree, a monophyletic inversion group indicates a single origin of these inverted haplotypes, while a polyphyletic group of inverted haplotypes suggests that these inverted haplotypes are derived from more than one common ancestor and, thus, recurrent. To evaluate the consistency of topology across inferred trees, we used generalized Robinson-Foulds distances implemented in the R package TreeDist (v2. 1. 1). • Phylogenetic tree reconstruction: For phylogenetic tree reconstruction, we applied the maximum likelihood-based software IQ-TREE (v2. 1. 3) using the following options: 'keep-ident -redo -bb 1000 -m MFPMERGE --date date.
Chromosome Y inversion genotyping. ArbiGent, at present, is not well tailored to genotype haploid chromosomes such as chromosome Y in males. Because of this caveat we decided to regenotype reported chromosome Y inversions (n=15) using Strand-seq data only based on the binomial distribution of Crick (plus) and Watson (minus) reads. For this purpose we used the R function 'genotypeRegions' implemented in R package primatR  (Porubsky et al. 2020). We required a minimum of five Strand-seq reads (min. reads = 5), in order to report a genotype and allowed for 10% of background reads (alpha = 0. 1). Genotypes for chromosome Y inversions are reported as a supplementary table (Table  S9 ).
A constant-sized coalescent tree prior, the HKY substitution model, accounting for site heterogeneity (gamma) and a strict clock with a substitution rate of 0. 76 × 10 -9 (95% confidence interval: 0. 67 × 10 -9 -0. 86 × 10 -9 ) singlenucleotide mutations per bp per year was used  (Fu et al. 2014). A prior with a normal distribution based on the 95% confidence interval of the substitution rate was applied. A summary tree was produced using TreeAnnotator (v1. 10. 4) and visualized using the FigTree software. In order to estimate the inversion rate, we counted the minimum number of inversion events that would explain the observed genotype patterns in the Y phylogeny. A total of 4,419 SNVs called in the set of 16 analyzed males and Y chromosomal substitution rate from above was used. A total of 126.
Out of these, we identified 29 inversions that predominantly (>90% of flipped SD pairs weighted by length) affect SD pairs in direct or in inverse orientation. We classify these inversions as 'potentially protective' (n=9) and 'potential pre-mutational state' (n=20), respectively. Morbid CNVs from the decipher database  (Bragin et al. 2014)  were additionally intersected with this set of 29 inversions, identifying 6 inversions overlapping with 8 unique morbid CNVs (Table  S11 ). RNA-seq data processing and mapping. Before RNA-seq (Data Availability) read mapping, adapters and low-quality reads and bases were removed using Trim Galore  (Andrews et al. 2015). The remaining reads were mapped to GRCh38 using STAR aligner in 2-pass mode  (Dobin et al. 2013). Leveraging sample-specific SNP-calls based on GRCh38 reported previously in  Ebert et al.
(2021) , alignment to this genome reference was performed with WASP filtering (van  de Geijn et al. 2015)  to mitigate allelic mapping bias. To prepare the data for haplotype-unaware eQTL analysis, reads were quantified with respect to GENCODE v35 genome annotation  (Frankish et al. 2019 ) using featureCounts  (Liao, Smyth, and Shi 2014). Read counts were normalized using the TMM normalization implemented in edgeR  (Robinson, McCarthy, and Smyth 2010)  and transformed to the transcripts-per-million (TPM) metric. eQTL mapping. The set of 399 inversions outside of L1 internal sequences was tested for association with expression in nearby genes together with a set of 41,833 deletions, 66,825 insertions, and 16. 4 million SNPs as previously reported in  Ebert et al. (2021). We first identified expressed genes (TPM > 0.
Enrichment analysis of inversions intersecting morbid CNV regions. We tested whether various subclasses of inversions outside of L1-internal sequences (n=399), namely balanced inversions (n=292), consensus single-inversion events (n=61), and consensus recurrent inversion events (n=32) are enriched in the vicinity of known pathogenic CNVs (redundant set, n=155)  (Cooper et al. 2011; Coe et al. 2014; Bragin et al. 2014). We used the R package regioneR  (Gel et al. 2016)  with its function 'permTEST' to perform permutation testing (n=10,000 permutations). At each permutation, we randomized the position of each inversion in every tested subgroup using regioneR's function 'circularRandomizeRegions'. This way the relative distance of each inversion is kept, as inversion occurrence on each chromosome is not completely random and highly depends on the underlying SD architecture.
Acknowledgements The authors thank  Simone Köhler  and  Tonia Brown  for valuable comments on this manuscript as well as editing, and  Marc Jan Bonder  for providing valuable advice on eQTL mapping. We thank the  Genomics Core Facility  and the  IT Services at the EMBL  for valuable technical assistance. Funding for this research project came from the following grants:  National Institutes of Health (NIH)   U24HG007497  (to C. L. , E. E. E. , J. O. K. , T. M. ),  U01HG010973  (to T. M. , E. E. E. , and J. O. K. ), as well as  R01HG002385  and  R01HG010169  (to E. E. E. ), the  German Federal Ministry for Research and Education (BMBF   031L0184  to J. O. K. and T. M. ), and the  European Research Council  ( ERC Consolidator  grant  773026  to J. O. K. ). Support for the work also came from the  European Molecular Biology Laboratory  (J.
Data availability Additional materials, including supplementary tables and figures, are not part of this preprint and will be released ensuing formal peer-review. All data generated has been made publicly available via the International Genome Sample Resource (IGSR); see www. internationalgenome. org/dataportal/data-collection/hgsvc2. The full inversion callset is available in Table  S2. Raw genomic datasets are available at the International Nucleotide Sequence Database Collaboration (INSDC) under the following accessions and project IDs: Illumina WGS (PRJEB37677), RNA-seq (ERP123231), Bionano Genomics (ERP124807), PacBio (PRJEB36100), and Strand-seq (PRJEB12849, PRJEB39750). Cell lines/DNA samples were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research.
All phased assemblies used in this study were obtained from their original resources as described in Ebert et al. 2021 (PGAS  v12 assemblies)  and in Ebler et al. 2020 (PGAS v13 hifiasm assemblies). TEMPORARY SOURCE: since the PanGenie paper  (Ebler et al. 2020)  has not yet been formally published, we state the source for the PGAS v13 hifiasm phased assemblies here in explicit form 10. 5281/zenodo. 5119259. Publicly available data (PacBio and ONT) used in this study are reported in Table  S13.
Moreover, numerical studies allow accessing information difficult to obtain experimentally, such as the bending moment in the pile. However, no numerical study of pile subjected to lateral multidirectional cyclic loading has been reported in the literature to date. The objective of this work was to develop a numerical approach to acquire knowledge concerning the system behaviour and governing parameters, in the case of multi-directional and cyclic lateral loading as encountered for mutualized anchoring piles. The aim was to be able to estimate the accumulated pile displacements, in particular at the mudline, and the accumulated pile rotation and/or bending, in such complex loading situations. To achieve these objectives, a conceptual 3D numerical model using the commercial finite difference software Flac3D  [22]  was developed, simulating the pile, the surrounding ground mass and their interface.
The aim was to be able to estimate the accumulated pile displacements, in particular at the mudline, and the accumulated pile rotation and/or bending, in such complex loading situations. To achieve these objectives, a conceptual 3D numerical model using the commercial finite difference software Flac3D  [22]  was developed, simulating the pile, the surrounding ground mass and their interface. Specific loading procedures are implemented to apply mono-directional and then multi-directional cyclic loadings. The multi-directional loading consists of varying the direction of the lateral load during the unloading-reloading process.
Numerical Model The issue addressed in this study is a typical soil-structure interaction problem. It is handled here by implementing a numerical method termed a "direct method", since the soil mass and the structure elements are part of the same model, here in a continuum. The numerical model is developed with the commercial finite difference program FLAC3D (Fast Lagrangian Analysis of Continua) v. 5. 01  [22]. The code uses an explicit numerical scheme that solves the dynamic equations of motion, even for static problems, in conjunction with an incremental constitutive law over a small time step, at discrete points in space (grid-points). This method is particularly well adapted for analysing the nonlinear behaviour of soils and soil-structure interactions. Because no matrices are formed, large three-dimensional calculations can be made without excessive memory requirements.
Numerical Model The issue addressed in this study is a typical soil-structure interaction problem. It is handled here by implementing a numerical method termed a "direct method", since the soil mass and the structure elements are part of the same model, here in a continuum. The numerical model is developed with the commercial finite difference program FLAC3D (Fast Lagrangian Analysis of Continua) v. 5. 01  [22]. The code uses an explicit numerical scheme that solves the dynamic equations of motion, even for static problems, in conjunction with an incremental constitutive law over a small time step, at discrete points in space (grid-points). This method is particularly well adapted for analysing the non-linear behaviour of soils and soil-structure interactions. Because no matrices are formed, large three-dimensional calculations can be made without excessive memory requirements.
Loading Procedure Due to the formulation of the program Flac3D, a loading can be defined by applying a velocity to model grid-points. This procedure is especially appropriate as the loading (and loading direction) varies during the process. The applied velocity then corresponds to an increment in displacement (in the same direction) at each time-step (of duration ∆t) of the stepping process. In the case of a quasi-static study, the velocity has only a numerical signification, and should be sufficiently small in order to keep the model in a quasi-static equilibrium (i. e. , with a negligible unbalanced force ratio). The choice of the applied velocity is discussed later. Moreover, numerical damping is introduced in the computational process to reach a quasi-static equilibrium state (combined damping is used in this study).
Conclusions A numerical procedure has been implemented in Flac3D using the program built-in "FISH" language, to apply a multi-directional cyclic lateral loading at pile head. The specific feature lays in the fact that a displacement is applied on the pile head (through a velocity due to the program formulation), that is thus controlled and adjusted based on pile forces conditions to meet the loading direction requirement. The effect of multi-directional loading compared to mono-directional loading has been highlighted, leading to larger pile displacements. The main numerical work perspective is to perform the simulations with a more complex constitutive model, for instance with kinematic hardening shear mechanism or multi-surface, to account for ratcheting and soil stiffness evolution. Furthermore, the azimuth amplitude and the loading path followed during the cycles of the multi-directional loading certainly influence the results.
2016) , and the transits of three planet candidates were detected in the SPOC pipeline and promoted to TESS object of interest (TOI) status by the TESS science team. We started radial velocity (RV) observations to confirm and measure the mass of these transiting planets with ESPRESSO and with HARPS, on 9 February and 1 January 2019, respectively (see Section 2. 2). As these observations were ongoing,  Günther et al. (2019)  also reported on the validation of these planets, by performing a statistical analysis of the TESS observations, as well as obtaining ground-based seeing-limited photometry coordinated through the TESS Follow-up Observing Program (TFOP)  1. We downloaded the TESS photometry from the Mikulski Archive for Space Telescopes (MAST 2  ) and started our analysis using the presearch data conditioning (PDC) light curve reduced by SPOC.
ESPRESSO We obtained 26 high-resolution spectroscopic observations of L231-32 between 9 February and 22 March 2019 using ESPRESSO  (Pepe et al. 2014 (Pepe et al. , 2020) )  on the 8. 2 m Very Large Telescope (VLT; Paranal, Chile) as part of observing program 0102. C-0456. Four additional observations were obtained as part of observing programs 1102. C-0744 and 1102. C-0958. ESPRESSO is a relatively novel instrument at the VLT which was first offered to the community in October 2018. Each observation has an integration time of 1200 sec, a median resolving power of 140,000, and a wavelength range of 380-788 nm. We used the slow read-out mode, which uses a 2x1 spatial by spectral binning. Observations were taken in high-resolution (HR) mode. One of the observations was flagged as unreliable due to a detector restart just before the exposure, and we excluded this data point from the analysis as a restart can result in additional noise.
All required calibration frames are automatically associated with the raw science frames and were simultaneously downloaded from the ESO archive 6. We followed the standard pipeline routines for the ESPRESSO data reduction pipeline using ESO Reflex. The radial velocities were computed following the procedure described in  Astudillo-Defru et al. (2017b). A slight adaptation to ESPRESSO data was introduced to construct the stellar template by combining the two echelle orders covering a common spectral range. The template is then Doppler shifted and we maximised its likelihood with each 2-dimensional reduced spectrum. The mean RV precision is 0. 47 m s -1. The resulting RV observations are listed in Table  A1.
HARPS We used HARPS  (Mayor et al. 2003)  on the the La Silla 3. 6m telescope to gather 58 additional spectra (program id. 1102. C-0339). These high-resolution spectra, with a resolving power of 115,000, were obtained between 1 January 2019 and 17 April 2019, spanning 89 days. We fixed the exposure time to 1800 s, resulting in a total equivalent to 29 h of open shutter time. The readout speed was set to 104 kHz. To prevent possible contamination from the calibration lamp in the bluer zone of the spectral range, we elected to put the calibration fiber on the sky. Raw data were reduced with the dedicated HARPS Data Reduction Software  (Lovis & Pepe 2007). The resulting spectra have a signal-to-noise ratio ranging between 14 and 29 at 550 nm, with a median of 22. We then extracted RVs, again following Astudillo-Defru et al.
Stellar rotation We also investigated the activity indicators from the spectra (see Section 2. 2) to estimate the stellar rotation period. In doing so, we focused on the HARPS observations, which span a longer baseline than the ESPRESSO observations and which are therefore more suitable to determine the stellar rotation period. We computed the Generalised Lomb-Scargle periodogram (GLS; Zechmeister & Kürster 2009) of both   and Na D and found consistent periods of  = 54. 0 ± 2. 4 d and  = 61. 5 ± 4. 0 d, respectively. We finally also looked at the full-width at half maximum (FWHM) of the cross correlation function (CCF), which was extracted for HARPS observations directly by the Data Reduction Software 7  , and find  = 57. 5 ± 5. 7 days. We also searched the TESS light curve for rotational modulation.
The PDC pipeline  (Stumpe et al. 2012; Smith et al. 2012; Stumpe et al. 2014 ) produces high quality light curves well-suited for transit searches. However, stellar rotation signals can be removed by the PDC photometry pipeline, so we used the lightkurve package  (Barentsen et al. 2019)  to produce systematics-corrected light curves with intact stellar variability. lightkurve implements pixel-level decorrelation (PLD;  Deming et al. 2015)  to account for systematic noise induced by intra-pixel detector gain variations and pointing jitter. We normalized and concatenated the PLD-corrected light curves, then computed a GLS periodogram of the full time series. A sine-like signal is clearly visible, and GLS detects a significant ∼1. 1 ppt signal at 57. 90 ± 0. 23 days. We also analysed the full time series with a pipeline that combines three different methods (a time-period analysis based on wavelets, auto-correlation function, and composite spectrum) and that has been applied to tens of thousand of stars (e.
g. ,  Astudillo-Defru et al. 2017a ). However, near the Ca II H&K lines, the HARPS data set has an extremely low flux (with a median SNR of about 0. 6) that limits the precision of this approach to measure the stellar rotation rate. We find log(   ) = -5. 480 ± 0. 238, and estimate a rotation period of 88 ± 32 days from this approach. Based on the combination of RV stellar activity indicators, and the TESS photometry, it appears likely that the stellar rotation period is approximately 58 days, which is consistent with typically observed rotation periods for M dwarf stars in this mass range, i. e. ≈ 20 -60 days  (Newton et al. 2016).
ORBITAL AND PLANETARY PARAMETERS We modeled the TESS light curve and ESPRESSO RV data using the publicly available exoplanet code  (Foreman-Mackey et al. 2019). This tool can model both transit and RV observations using a Hamiltonian Monte Carlo (HMC) scheme implemented in Python in pymc3  (Salvatier et al. 2016) , and has been used to model photometric and spectroscopic observations of other exoplanets (e. g. Plavchan et al. 2020; Kanodia et al. 2020; Stefansson et al. 2020 ). Below we describe the ingredients of our model and the procedure for optimising and sampling the orbital planetary parameters.
Another model, in which we included a fourth 'planet' (without polynomial trend), once again provides fully consistent masses; the orbital period of this 'planet' was ∼63 days, consistent with the stellar rotation period found in Section 3. 2. We furthermore explored the specific choice of a GP kernel. For this, we used RadVel 9  (Fulton et al. 2018). With RadVel, we modeled the RV data using a quasi-periodic kernel, which is similar to the SHO kernel described in Section 4. 2. 2, but the SHO kernel has properties that make it significantly faster to calculate (see Section 4. 2. 2). This makes the SHO kernel more suitable for performing a joint 9 https://github. com/California-Planet-Search/radvel transit-RV fit. Comparing the best-fit masses using both kernels, we find that they are consistent to a fraction of.