dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Continuous variables are presented as medians with interquartile ranges (IQR), categorical parameters as numbers and frequencies. Factors associated with presence of PCOM were tested using a multivariable binary logistic regression model. For this model, odds ratios (OR) with their 95% confidence intervals (95%CI) and p-values are provided. Using the IBM Statistical Package for Social Science software 25. 0 (level of significance: p < 0. 05), groups were compared with the analysis of variance (ANOVA) and the Fisher's exact test.
, 2015). The backscatter data were gridded at the same resolution as the multibeam bathymetry. The data were supplemented by the bathymetric compilation of  Gutscher et al. (2017). The  Gutscher et al. (2017)  bathymetric map is gridded at 50 m and covers the entire western Ionian Basin (~36. 000 km 2 ) (Figs. 1, 2A ). Specifications on the acquisition are available in e. g. ,  Krastel et al. (2014) ,  Gutscher et al. (2013) , and  Urlaub et al. (2022). Multibeam bathymetry and backscatter data were used to characterise the morphology of the western Ionian Basin and to define different conduit systems and source areas for the 1908 gravity flow (Fig. 4 ). ArcMap was used to map geomorphological features such as the channel floor and thalweg, basins, interchannel heights, escarpments and bedforms.
5 • , which allows data acquisition with fewer side-echoes when compared to conventional systems (e. g. , boomer, chirp) (Teledyne  Marine, 2017). The minimum horizontal resolution is 70 to 280 m between 1000 and 4000 mwd  (Spieß, 1993). Detailed information about specifications of the systems used are available in the cruise reports (e. g. ,  Krastel et al. , 2014; Gutscher et al. , 2013). Sub-bottom profiler data were used to map the distribution and relationships of acoustic facies (e. g. , high/low amplitude sub-bottom echoes, onlapping) according to  Damuth (1975 Damuth ( , 1980) )  and  Damuth and Olson (2015). This information was used to infer sediment grain size, erosion, and long-term deposition in order to reconstruct erosional and depositional processes along the upper sediment strata as a consequence of recent gravity flow activity.
C. Overflow of the turbidity current that also caused the first two cable breaks via the external CAW (Fig. 15A ). Sub-bottom data (Facies D) and turbidites found in KCIR-02 provide evidence that past turbidity currents deposited sediment along large portions of the accretionary wedge and likely also the 1908 turbidite (see 5. 3. 1, Figs. 11, 13B ). The question remains whether a turbidity current crossing the external CAW with its basin-ridge morphology would have still been powerful enough to cause the third cable break (Fig. 15A ). D. The timing of breaks along the Malta-Zante telegraph cable are wrong. This possibility cannot be ignored as the only information about the timing of cable breaks is provided by  Ryan and Heezen (1965)  with the source about the cable repair stated as "unpublished cable repair records".
Acknowledgements This work is part of  MARGRAF , which is supported by the  European Union Widening  Fellowship  WF-03-2020  of the  Horizon 2020 Marie Sklodowska-Curie Action  (project number  101038070 ). A. M. was funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement  677898  [MARCAN]). M. U. has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No. 948797 ). Special thanks also to  Nathalie Babonneau  and  Vincent Coussin  from the  IUEM (Brest, France ) for their help and constructive feedback on the CIRCEE sediment core analysis. We further thank  IHS Kingdom Suite  for providing their software and  Christian Berndt  for providing the SO277 bathymetry data.
Data availability Metadata of the research expeditions (M86-2, CIRCEE-HR, M111, SO277) can be accessed online via Pangaea (https://www. pangaea. de/). The bathymetry data were previously published in  Gutscher et al. (2017)  and  Urlaub et al. (2022). The sub-bottom profiles were acquired during research expedition M86-2 and CIRCEE -HR  (Gutscher et al. , 2013; Krastel et al. , 2014). Sub-bottom profiles and multibeam echosounder data are available through A. M. , M. A. G. , S. K. , M. U. and H. K. The raw data can be accessed via the following links: https://webapp-srv1a. awi. de/eBathy/batdataset_info. php?idf=803 for M86-2 data (the sub-bottom profiles will be uploaded soon to https://www. pangaea. de/); https://campagnes. flotteoceanographique. fr/campagnes/13020060/ for CIRCEE-HR data; https://www.
The same operator (ES) measured all the external and internal pelvic variables. Correlation analyses were performed with SPSS Statistics 17. 0. A correlation was significant with a p-value<0. 05. The correlations were weak, moderate or strongly positive for a B value (coefficient of correlation) between 0-0. 2, 0. 2-0. 5 and 0. 5-0. 8 respectively  (Zhou et al. , 2003). For statistics, we used the following method: (1) A simple correlation analysis using the Pearson correlation coefficient permitted investigation of the association between birth canal and external pelvimetry variables. (2) Potential confounding effect of birth order and BMI in the association of birth canal and external pelvimetry variables were tested by ANOVA. (3) Multiple regression models were used to find associations between birth canal and external pelvimetry variables.
Context. The Ch-type asteroid (130) Elektra is orbited by three moons, making it the first quadruple system in the main asteroid belt. Aims. We aim to characterise the irregular shape of Elektra and construct a complete orbital model of its unique moon system. Methods. We applied the All-Data Asteroid Modelling (ADAM) algorithm to 60 light curves of Elektra, including our new measurements, 46 adaptive-optics (AO) images obtained by the VLT/SPHERE and Keck/Nirc2 instruments, and two stellar occultation profiles. For the orbital model, we used an advanced N-body integrator, which includes a multipole expansion of the central body (with terms up to the order ℓ = 6), mutual perturbations, internal tides, and the external tide of the Sun acting on the orbits. We fitted the astrometry measured with respect to the central body and also relatively, with respect to the moons themselves.
Light curves, AO images, and stellar occultations of Elektra For the shape reconstruction of Elektra, we prepared a data set consisting of 60 light curves, 46 AO images, and two stellar occultations. Most of the light curves (55 out of 60) were taken from the Database of Asteroid Models from Inversion Techniques (DAMIT 1  ;  Ďurech et al. 2010 ), which contains light curves and other input parameters in addition to shape models. The observational data covered the time span from 1980 to 2016. In March 2022, we expanded the set by measuring five new light curves (Table  1 ) using the BlueEye600 robotic observatory  ( Ďurech et al. 2018 ) located in Ondřejov. These light curves were obtained exclusively for this study and are now available in DAMIT together with the new shape and spin state solution.
Moreover, we also uploaded them to the ACLDEF 2  database. All of the light curves used in this study, including the new measurements, can be found in Appendix D. The set of AO images consists of two subsets. The first one from  Hanuš et al. (2017)  consists of 14 images captured by the Keck/Nirc2 instrument during the years 2002-2012 and two high-resolution infrared images were taken by the AO system VLT/SPHERE in 2014. The InfraRed Differential Imager and Spectrograph (IRDIS;  Dohlen et al. 2008 ) and the Integral Field Spectrograph (IFS;  Claudi et al. 2008)  were used simultaneously to cover a more extended spectral range. The second subset of 30 images is taken from  Vernazza et al. (2021). It was obtained by the Zurich IMaging POLarimeter  (ZIMPOL; Schmid et al. 2018)  during the summer of 2019.
The two stellar occultations were taken from the Asteroidal Occultation Observers in Europe 3  database. The first event occurred on 21 April 2018 and was observed by 48 observers in total. After excluding 4 far misses and 6 chords with incorrect time indications, we were left with 38 usable chords. The second event occurred on 21 February 2021 and was observed by 17 observers. However, eight of the chords were non-detections. Although such chords are generally useful as 'strict' bounds for the asteroid's shape, we did not use them because the shape is already well constrained. Both occultation events are shown in Fig. 1.
Astrometric measurements of the moons For the process of fitting the orbital parameters of the moons, it is crucial to have a sufficient set of positions, usually related to the central body or its photocentre. These positions were measured on the AO images of Elektra -with various methods -whenever some of its moons were visible. The first data set was taken from  Berdeu et al. (2022) , where the process of reduction, halo removal, and astrometry is explained in detail. Their data set is based on individual AO images of Elektra from December 2014 and consists of 120 positions of S/2014 (130) 1, 120 positions of S/2014 (130) 2, and 150 positions of S/2003 (130) 1. However, it is important to note that the individual positions are not mutually independent. Indeed, they correspond to a linear fit (in time) of 40 or 50 unique measurements.
The second data set is based on 30 AO images from  Vernazza et al. (2021). The images were reduced using the eclipse data reduction package  (Devillard 1997). After reduction, the images were separately processed to remove the bright halo surrounding the asteroid, which was a necessary step to improve the detectability of the moons. To obtain the astrometric positions of the satellites, we used a specialised algorithm  (Hanuš et al. 2013 ) that extracted the primary's contour and determined its photocentre. By fitting a Moffat-Gauss source profile to the moons and measuring from the photocentre, we obtained the 20, 20, and 12 new positions of the respective moons. Moreover, we computed the astrometric positions of the moons with respect to the other moons, which is handy to avoid any systematic related to the photocentre of the central body, or possibly centre-of-mass corrections.
All-Data Asteroid Modelling algorithm The All-Data Asteroid Modelling (ADAM) algorithm  (Viikinkoski et al. 2015 ) is a versatile inversion technique for the shape reconstruction of asteroids from various data types (AO images, light curves, occultations, radar, etc. ). ADAM minimises the objective function, which is a measure of the difference between the Fourier-transformed image and the projected shape: χ 2 = w AO i N i j=1 F D i (u i j , v i j ) -e 2πi(o x i u i j +o y i v i j )+s i F M i (u i j , v i j ) 2 + w LC χ 2 LC + w OC χ 2 OC + i w i γ 2 i , (1) where F D i (u i j , v i j ) is the Fourier transform of the data image D i and F M i (u i j , v i j ) is the Fourier transform of the projected shape M i , corresponding to the ith image and evaluated at the jth frequency point (u i j , v i j ).
The scale s i and offset (o x i , o y i ) are free parameters resolved during the optimisation. The additional term χ 2 LC is the standard square norm of the light-curve fit, while the term χ 2 OC is the model fit to stellar occultation chords. The implementation of the latter term in ADAM is described in  Hanuš et al. (2017). The first three terms are multiplied by their corresponding weights w AO , w LC , and w OC and the last term is a sum of regularisation functions γ i multiplied by the weights w i  (Viikinkoski et al. 2015; Hanuš et al. 2017). The data weights have an impact on the initial convergence of χ 2 and subjectively depend on the dataset; they are chosen to balance the individual χ 2 values and enable good convergence. Regularisation weights then help guide the convergence during the process.
Best-fit and alternative shape models We took a two-step approach. First, we aimed to get just a coarse shape model. Second, the shape resulting from the first optimisation was used as an initial model, and we then doubled the number of facets and the spherical harmonics degree in order to capture surface details. The initial conditions for the convergence, which are needed for the period and the pole, were taken from  Vernazza et al. (2021). Figure  2  and Table  2  present the best-fit and alternative shape models and their parameters. The best-fit model is a revised version of the shape model published in  Vernazza et al. (2021) , which is based on a larger dataset. Its pole coordinates were allowed to converge along with the shape to best fit the data. The alternative model is a shape model that converged with the pole fixed according to the coordinates inferred from our orbital model (Sect.
4). The χ 2 map in Fig. 3  shows why the pole solution from ADAM remains unconstrained. Due to the close absolute distance of coordinates, there are many viable models. The best-fit model, denoted by a black tile, is in the upper left area of the map, while the alternative model is in a different local minimum    in the bottom right area of the map. We propose that this alternative local minimum is where the true orbital pole lies. In Sect. 4, we even present a counterexample model based on the shape pole solution to support this. We plotted a distribution of volume values from the dataset of models obtained from the χ 2 mapping (Fig. 4 ). By looking at the 16%, 50%, and 84% percentiles of the distribution we determined the uncertainties of D eq in Table  2. The comparison of these two models with several AO images from the 2019 data set is given in Fig.
5. In the comparison and Fig. 2 , we see that the overall shape of the alternative model is more rounded, with fewer surface features in some areas. However, this is in the range of expected differences dependent on regularisations. Both models are statistically and visually almost identical. In the last two columns of Fig. 5 , we see that the local topography present in the two AO images is not captured by either of the models. We tried using the subdivision shape representation and locally forcing those features, but it seems that they are not supported by the rest of the dataset, and forcing them just makes the overall fit worse. For both models, we used the following weights: w LC = 1. 5, w AO = 0. 85, w OC = 0. 019, and standard values for the regularisation weights. In Appendix D, we compare the best-fit model to all the observed light curves.
Multipole coefficients Our orbital model has to be evolved over approximately 1000 days. On such a timescale, approximating the central body as a point mass is insufficient. Therefore, we computed a multipole expansion of the gravitational field up to the order of ℓ = 10. The expansion is based on the Delaunay triangulation of the ADAM shape model made using the TetGen program  (Si 2006). The respective definitions of the multipole coefficients are as follows: C ℓ0 = 1 MR ℓ V ρ|r| ℓ P ℓ (cos θ) dV, (2) C ℓm = 2 MR ℓ (ℓ -m)! (ℓ + m)! V ρ|r| ℓ P ℓm (cos θ) cos (mϕ) dV, (3) S ℓm = 2 MR ℓ (ℓ -m)! (ℓ + m)! V ρ|r| ℓ P ℓm (cos θ) sin (mϕ) dV, (4) where r, θ, ϕ are the body-frozen spherical coordinates, M the mass of the body, R the reference radius of the gravitational model, P ℓ , P ℓm the Legendre and associated Legendre polynomials, and C ℓm , S ℓm are the respective coefficients.
MCMC analysis The Markov chain Monte Carlo (MCMC;  Robert & Casella 2011; Tierney 1994 ) is a method for randomly sampling probability distributions. To sample the distribution functions of the parameters of our orbital model we used the Python package emcee  (Foreman-Mackey et al. 2013) , which is an implementation of the MCMC. We let all parameters of the model be free, including the rotational pole of Elektra, the total mass of the system, the mass ratios of the moons q 1 , q 2 , and q 3 , and 18 orbital parameters, six for each orbit of the three moons. To initialise the chains, we started with the parameters of the best-fit model and set some a priori limits to keep the walkers around the local minimum of our preferred solution. The limits were an order of magnitude higher and lower for q 1 , q 2 and q 3 to test if they are constrained, and a range of a few hours for the orbital periods.
Acknowledgements. The  Czech Science Foundation  supported this work through grants  GA22-17783S  (M. F. , J. H. ) and  GA21-11058S  (M. B. ). In this work, measurements from the BlueEye600 telescope, supported by  Charles University , were used. A189 , page 10 of 19 Fuksa, M. , et al. : A&A, 677, A189 (2023) A189, page 12 of Fuksa, M. , et al. : A&A, 677, A189 (2023).
[2]  Several commonly described peptides include the tumor homing peptides such as the RGD sequence and its cyclic derivatives (cRGD) that target the integrin αvβ3 which are overly expressed in endothelial cells. Abbreviations: ADMET: Absorption, Distribution, Metabolism, Excretion, and Toxicity; Boc, tert-ButOxyCarbonyl; tBu, Tert-butyl; BSA, Bovine Serum Albumin; DMF, DiMethylFormamide; DMSO, DiMethylSulfOxide; FEB, Free Energy of Binding; Fmoc, Fluorenylmethoxycarbonyl; HBTU, 2-(1H-Benzotriazol-1-yl)-1,1,3,3-TetramethylUronium hexafluorophosphate; HPLC, High Performance Liquid Chromatography; HRMS, High Resolution Mass Spectrometry; LBA, Ligand Binding Assay; NMM, N-MethylMorpholine; NMP, N-MethylPyrrolidine; NRP-1, Neuropilin-1; Pbf, Pentamethyl-2,3-dihydrobenzofuran-5-sulfonyl; PBS, Phosphate Buffer Saline; PDB, Protein Data Bank; PDT, Photodynamic Therapy; PS, Photosensitizer; RMSD, Root-Mean-Square Deviation; TFA, TriFluoroacetic Acid; TIPS, TriIsoPropylSilane; Trt, Triphenylmethyl; VEGF, Vascular Endothelial Growth Factor; VEGFR, Vascular Endothelial Growth Factor Receptor.
[13]  Very recently, a molecular modelling study of cyclic Lyp-1 in complex with its receptor gC1qR/p32 was performed by using a crystal structure of gC1qR/p32 from Protein Data Bank (PDB code: 1P32). It was shown that Asn3, Lys4, Arg5 and Arg7 were important amino acids for the specific binding of gC1qR/p32. The authors also supported their finding with molecular dynamic study, to further determine the interaction pattern between Lyp-1 and p32. [14]. As the truncated sequence of Lyp-1 produces tLyp-1 with a sequence of CGNKRTR that follows the CendR, we are of the opinion that it could be a useful candidate in the targeting of NRP-1 receptor. As far as we know, until now there is no docking simulation study being reported on the binding of tLyp-1 on NRP-1. Previously in 2016, we published a molecular docking study of several peptides on NRP-1 receptor in combination with in vitro binding study, to determine the affinity of the tested peptides on this receptor and to explain the binding through in-depth simulation analysis.
Molecular docking simulation and ADMET predictions The crystal structure of human NRP-1 (PDB ID: 6TKK) was retrieved from the Protein Data Bank database. [16] [17]  Using Biovia Discovery Studio Visualizer, all water and heteroatoms were removed. [18]  Additional treatment on the crystal structure, such as reconstructing missing atoms, assigning atomic charges, and radii using the SWANSON force field (AMBER ff99 charges with optimised radii) was performed on the PDB2PQR web service (https://pdb2pqr. poissonboltzmann. or g/pdb2pqr, accessed on June 14th, 2021). [19]  Protonation at pH 7. 4 was carried out using (PROPKA3). [20] [21]  The MolProbity web service (http://molprobity. biochem. duke. edu/, accessed on June 14th, 2021) was used for correction of bad contacts, the addition of missing hydrogen atoms and flipping of HIS, GLU, and ASN residues.
Number of Atoms process, the NVT ensemble was used. Next, the equilibration of the protein atoms and the surrounding solvent was performed in three steps for 2 ns each, and the SHAKE algorithm  [31]  was also utilized to constrain all bonds involving hydrogen. [21]  MD simulation was carried out until 70 ns. Trajectory analysis was done using CPPTRAJ to inspect the Root Mean Square Deviation and Fluctuation (RMSD and RMSF) values, radius of gyration (Rg) and hydrogen bond (H-bond) that were involved in the interaction between each ligand and the protein. QtGrace 0. 2. 6 was used to illustrate their graphs. Free binding energy and its energetic components for each system were collected for every 1 ps (total 1000 snapshots) from the last 1 ns of the trajectory using the MM-PBSA. py module in AMBER 18, and the data was calculated based on the "PBSA" model  [32]  with a salt concentration of 0.
Molecular docking and ADMET predictions At the time of initiating this study, there were 24 NRP-1 crystal structures (see Supporting Information, Table  S9 ) deposited in the RCSB Protein Data Bank (PDB). [17, [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46]  Of these structures, 20 were of homo sapiens (human origin), 2 were of rattus norvegicus (rat), and 2 mus musculus (mouse). The crystal structure of the NRP-1-b1 domain (PDB code: 6TKK) bound to the C-terminal peptide (Ace-Arg-Pro-Gln-Pro-Arg) was chosen for this study (Fig. 1 ). Despite not being the structure with the lowest resolution, it is however the most recent NRP-1 structure in the PDB bound with a peptide. Furthermore, the bound pentapeptide might give some insights on the binding of a peptide in NRP-1-b1 binding site.
In the crystal structure, Ace-RPQPR, which possesses a Cterminal CendR sequence, was found to interact with NRP-1-b1 key binding site residues such as Tyr297, Asp320, and Ile415 via ionic interactions and/or hydrogen bonds between the guanidine moiety of the C-terminal Arg and the carboxylic acid moiety of these residues. Also, Trp301 and Tyr353 created hydrophobic interactions with the alkyl chain of the C-terminal Arg. Proline (Pro) residues participated in the interactions through creating H-bonds and hydrophobic interactions with Tyr297. Fig. 1  shows the superimposition of the crystal structure and the redocked conformation of Ace-RPQPR on NRP-1 (PDB ID: 6TKK). redocking was done using molecular docking software AutoDock Crank Pep (ADCP). [26]  ADCP is a de novo method for protein-peptide docking that folds the peptide in the potential energy landscape created by a given receptor.
It combines CRANKITE's conformation sampling ability with the grid-based AutoDock representation of a rigid receptor. [26]  AutoDock, and its current version 4. 2 is a popular molecular docking program but is known to have limitations in docking peptides longer than 5 sequences with >32 rotatable bonds. [47] [48] [49]  ADCP on the other hand is purposely developed for protein-peptide docking and it can treat flexible peptides with up to 20 amino acids into rigid receptors,  [26]  thus overcoming the current limitation of peptide docking using Auto-Dock. From Fig. 1 , Ace-RPQPR is seen to docked at the VEGF 165 binding site in almost the same conformation as its crystal structure with an RMSD of 2. 7 Å. As the NRP-1 b1 binding pocket is shallow, it was reported that only the terminal Arg of a ligand will be able to directly enter and interact with the pocket.
Binding free energy (MM-PBSA). The binding free energy and its decompositional energies of the interactions between tLyp-1 (CGNKRTR, 1) and its Ala-scan analogues (2-8) towards NRP-1 (Table  5 ) were calculated for the last 1 ns of the MD simulation time using the module of the Molecular Mechanics-Poisson Boltzmann surface area (MM-PBSA) programme in AMBER software with a neglected entropic contribution. [32] [33]  The major contributions to this binding energy are the electrostatic, van der Waals as well as non-polar solvation terms. Table  5  shows that tLyp-1 (CGNKRTR, 1) and CGAKRTR (6) have the most favourable binding energies amongst all the peptides, with the ΔG bind * = -14. 63 ± 0. 20 kcal/mol and -14. 26 ± 0. 21 kcal/mol, respectively, followed by CANKRTR (7) (-12. 96 ± 0. 25 kcal/mol), CGNKRAR (3) (-9.