dataesr/scientific-paragraphs-categorization huggingface dataset:
Two additional locations in the Mediterranean were sampled (VAC and PZU; Table  S1 ) and combined with 35 existing sampling locations in the Mediterranean Sea described by  Mokhtar-Jamaï et al. (2011). Overall, the dataset covered 46 locations, nine from the Atlantic and 37 from the Mediterranean, and included 1597 individuals (448 newly sampled; Figure  1  and Table  S1 ). After quality filtering, the microsatellite dataset kept for downstream analyses included 1223 samples from 46 sites (filters: 23% of samples excluded due to missing data, sites with <5 samples and identical multilocus genotypes).
The K value corresponding to the "upper most hierarchical level of structure" was determined using the likelihood of observing the data (lnP(D)). CLUMPP 1. 1  (Jakobsson & Rosenberg, 2007)  and DISTRUCT 1. 1  (Rosenberg, 2004)  were used for graphical output. A discriminant analysis of principal components (DAPC;  Jombart et al. , 2010)  was conducted in ADEGENET  (Jombart, 2008). Data were transformed into principal components and discriminant analyses were used to maximize variation among-groups while minimizing within group variation. Population locations were used as a group prior. Based on the a-score method, the number of principal components was set to 80, while we retained two discriminant functions. Hierarchical genetic subdivision (i. e. , among lineages, among population and among individuals) was analyzed with an analysis of molecular variance (AMOVA;  Excoffier et al.
Allele frequencies were bootstrapped 100 times in SEQBOOT in  PHYLIP 3. 6 (Felsenstein, 2005). The consensus tree was obtained using GENDIST, NEIGHBOR and CONSENSUS in PHYLIP and drawn with FigTree v. 1. 4. 4 (https://github. com/ramba ut/figtree). S2 ). Quality assessment of the four de novo transcriptome assemblies obtained for each population showed that those built for P. cf. grayi (Baleeira, Portugal) and the Italian population of P. clavata (Altare) were superior to those obtained for the other two P. clavata populations (La Vaca and Balun; Table  S3 ). Transrate quality metrics for the two top assemblies were generally similar (average Transrate contig score = 0. 36 for P. cf. grayi and 0. 33 for P. clavata; Table  S3 ). BUSCO analysis of metazoan single-copy orthologues indicated that the curated assemblies (i.
| Species delimitation Species delimitation based on the ASTRAL-III species tree and performed with the SODA software supported the clades formed by samples of P. cf. grayi and P. clavata as separate species (Figure  2 ). The same split was also evident with the barcode marker mt-mutS for which our analyses recovered two distinct haplotypes, one including all P. clavata samples from the Mediterranean (96% bootstrap support) and another including the samples of P. cf. grayi sequenced here together with P. grayi from the northern Iberian Peninsula (100% bootstrap support; Figure  S1 ). Furthermore, within the Atlantic, the purple and yellow morphotypes of P. cf. grayi were all recovered as two distinct species in the SODA analysis (with the caveat that our dataset only included two purple individuals, although they were sympatric with one individual of the yellow morph -site P39).
cf. grayi, and the Mediterranean P. clavata; as well as between two segregating lineages within the P. cf. grayi clade that are coincident with coloration of the colonies, thus casting doubts about the taxonomic status of these two entities. While the conservation status of P. grayi remains unassessed, P. clavata has been listed as vulnerable in the IUCN red list of anthozoans in the Mediterranean since 2016  (Otero et al. , 2017). Although the broader species limits between P. cf. grayi and P. clavata could be resolved with a single mitochondrial barcode (mt-mutS), this study emphasizes the need for genome-wide multi-locus datasets to resolve recent lineage splitting events in the face of, for example, ILS and/or introgression. The marked spatial genetic structure and differentiation observed between the P.
2. 6. 2 | DNA extraction, amplification and genotypingGenomic DNA of the Atlantic samples was extracted with a cetyltrimethylammonium bromide protocol (CTAB; Winnepenninckx et al. , 1993) , with purification by standard chloroform:isoamyl alcohol (24:1) followed by DNA precipitation. All samples (N = 384 colonies) were genotyped at six microsatellite loci: Parcla_09, Parcla_10, Parcla_12, Parcla_14, Parcla_17 (Molecular Ecology Resources Primer Development Consortium, 2010) and Par_d (Agell et al. , 2009). The dataset from Mokhtar-Jamaï et al. (2011) and the additional Mediterranean sites (N = 1243 samples) were extracted using a salting out procedure (Mokhtar-Jamaï et al. , 2011). All samples were genotyped as in Mokhtar-Jamaï et al. (2011) with minor modifications of the PCR protocol (for more details see Appendix S1).
20457758, 2023, 1, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/ece3. 9740 by Cochrane France, Wiley Online Library on [30/01/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3 | RE SULTS 3. 1 | Phylogenomic inference and species delimitation using RNA-seq data 3. 1. 1 | Transcriptome sequencing and quality of the assemblies RNA-sequencing generated a global dataset of 143 Gb high-quality paired-end reads (28. 04 ± 0. 91 M reads per sample [mean ± SE]), 38 and 105 Gb for P. cf. grayi and P. clavata, respectively (Table.
Joaquim Garrabou: Funding acquisition (equal); resources (equal); writing -review and editing (equal). Ester A. Serrao: Conceptualization (equal); funding acquisition (equal); writing -review and editing (equal). Jean-Baptiste Ledoux: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); resources (equal); validation (equal); visualization (equal); writing -original draft (equal); writing -review and editing (equal). ACK N O WLE D G E M ENTS This work was funded by program BIOMARES, a Pew Marine Fellowship, and FCT -Foundation for Science and Technology UIDB/04326/2020, UIDP/04326/2020, LA/P/0101/2020, PTDC/ BIA-CBI/6515/2020; EU-BiodivERsA BiodivRestore-253 (FCT: DivRestore/0013/2020); EU Horizon 2020 research and innovation program (MERCES: Grant no.
DATA AVA I L A B I L I T Y S TAT E M E N T Raw RNA-seq data and mt-mutS sequences have been uploaded to NCBI's SRA (BioProject ID: PRJNA847883) and GenBank databases (Accessions numbers: ON804207-ON804214), respectively. Final sequence alignments and microsatellite data are available on DRYAD (https://doi. org/10. 5061/dryad. r7sqv 9sfm).
Since this observation is from observation cycle 2 in 2014, only eight international stations are included: five in Germany (Effelsberg, Unterweilenbach, Tautenburg, Jülich, and Potsdam) and one each in the UK (Chilbolton), France (Nançay), and Sweden (Onsala). This is unlike the present day, where LOFAR routinely observes with 14 international stations, with more new stations being planned. The longest baseline length for this work is therefore 1297 km between the stations in France and Sweden (which yields an angular resolution of 0 329 at 145 MHz). The observation bandwidth is the standard survey frequency range of 120-168 MHz that avoids radio frequency interference above this frequency range. The data were recorded with 2 s sampling and in channels of 24. 41 kHz width (8 channels per subband, 244 subbands).
The first part of the data reduction involves the use of PREFACTOR (https://github. com/lofar-astron/prefactor; Shimwell et al. 2019) for direction-independent calibration of the Dutch stations (core and remote). A model with suitably high resolution was used for 3C 196 10  in the PREFACTOR pipeline with CS001 as the reference station. All stations produced good solutions for total electron content and bandpass for both the calibrator and target. The data were analyzed with v4. 0. 0 of the LOFAR-VLBI pipeline  (Morabito et al. 2022  introduces the pipeline and describes v3. 0. 0). The latest version of the pipeline uses selfcalibration optimized for LOFAR data (van Weeren et al. 2021) using NDPPP  (van Diepen et al. 2018)  and WSClean  (Offringa et al. 2014). The pipeline uses the LoTSS and Long Baseline Calibrator Survey (LBCS;  Jackson et al.
2016 Jackson et al. , 2022 ) catalog servers for useful information about the sources in our target field and nearby phase calibrators, respectively. The LBCS source L332164 (ILT J132703. 36+470543. 5), 0°. 50803 away, was used for an initial in-field correction of the phases and complex gains. The pipeline forms a single superstation ST001 combining all of the core stations, which have already been phasecorrected using the PREFACTOR solutions. Unlike the pipeline described by  Morabito et al. (2022) , in the latest version, the delay calibration part of the pipeline is done by the facet self-calibration script in the form of three types of solutions done with the delay calibrator, which are then transferred onto the nearby target source (scalarphasediff, scalarphase, and scalarcomplexgain).
SN 2011dh Ancillary Data Object SN 2011dh is detected in both the LoTSS and ILT images. The ILT observation is discussed in Section 2, and the PYBDSF fit for SN 2011dh from the ILT image can be found in Table  1  (source 2). Both the LoTSS and ILT flux densities for the SN are shown in Figure  2. To complement the LOFAR data of SN 2011dh, we consider other late-time radio observations of the SN here. GMRT observations. The GMRT data from 2012 July published in  Yadav et al. (2016)  are used in this work to complement the VLA data at a similar epoch from 2012 August. Roy & Manna (2021)  studied seven nearby galaxies with the GMRT, including M51. The flux for SN 2011dh, clearly visible in the radio map of the galaxy, is also used in this work (S. Roy 2021, private communication). This gives an idea of the SN at a much later epoch at lower frequencies.
SN 2011dh Modeling The Type IIb SN 2011dh was first detected by  Griga et al. (2011). The LoTSS data point (5. 2 ± 0. 9 mJy) is also plotted for comparison with the ILT data point, showing a large discrepancy in flux densities with the LoTSS value more than five times higher. This arises from the larger beam size of LoTSS that does not include the international stations. The higher-resolution observation with a lower beam size is not as contaminated by diffuse emission from the surrounding region, and this translates directly into estimating parameters such as the mass-loss rates of the progenitor of SN 2011dh. We note here that the LoTSS catalog has a PYBDSF fit for SN 2011dh that is bigger than its beam size of 6″ (8 05 × 7 51) and shows a peak flux of 3. 1 ± 0. 4 mJy. It is likely then that the bias due to the diffuse flux from the background would be lower when considering the peak flux, rather than the total flux from the LoTSS catalog, which is fit for a region much larger than the beam size.
CXOU J132954. 9+470922 Source 11 in Table  1  is a high-mass X-ray binary. The source is not detected by LoTSS but has a high flux density similar to SN 2011dh and can be cross-matched with the NASA/IPAC Extragalactic Database. Maddox et al. (2007)  found a spectral index of -0. 3 for this source (listed as 65 in Table  2  of their work). The 20 cm flux density in their work can be extrapolated to 145 MHz (817 μJy), which matches very well with our finding of 830 μJy from PYBDSF.
Conclusion This paper presents the first subarcsecond-resolution ILT image of the nearby galaxy M51 at 145 MHz. We discuss the compact sources in the galaxy, the center of M51, and the supernovae (SNe) in the galaxy. We detect SN 2011dh with the ILT, which is also seen in the LoTSS catalog. Importantly, we find that the LoTSS flux density for the SN is about five times higher than the flux density with our ILT image, which would affect the absorption scenario for the SN and, consequently, the mass-loss rates and other parameters. Using VLA and GMRT data at two different epochs combined with the ILT flux density, we derive the radii of the forward shock, magnetic field strengths of the shocked circumstellar medium, and the corresponding mass-loss rates for the progenitor at these two epochs.
Acknowledgments This paper is based on data obtained with the  International LOFAR Telescope (ILT)  under project code LC2_038. LOFAR data products were provided by the  LOFAR Surveys Key Science  project (LSKSP; https://lofar-surveys. org/) and were derived from observations with the International LOFAR Telescope (ILT). LOFAR (van Haarlem et al. 2013) is the Low Frequency Array designed and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources) and collectively operated by the  ILT foundation  under a joint scientific policy. The efforts of the LSKSP have benefited from funding from the  European Research Council, NOVA ,  NWO ,  CNRS-INSU , the  SURF Co- operative, the  UK Science and Technology Funding Council , and the  Jülich Supercomputing Centre.
This research has made use of the  NASA /IPAC Extragalactic Database (NED), which is funded by the  National Aeronautics and Space Administration  and operated by the  California Institute of Technology. This work made use of published GMRT data. We thank the staff of the GMRT that made these observations possible. The GMRT is run by the  National Centre for Radio Astrophysics  of the  Tata Institute of Fundamental Research. P. L. acknowledges support from the  Swedish Research Council. D. V. acknowledges support from the  Onsala Space Observatory  for the provisioning of its facilities support. The  Onsala Space Observatory  national research infrastructure is funded through  Swedish Research Council  grant No. 2017-00648. M. P. T. and J. M. acknowledge financial support through grants  CEX2021-001131-S  and  PID2020-117404GB-C21  funded by the  Spanish MCIN /AEI/10.
Finally, we question if force matching is an adequate benchmark to train CG FFs and if these FFs will only sample from "physically plausible" regions in the FES. To study these research problems, we selected four (mini)proteins based on structural properties  [27]  -1) a folded protein: P-Element somatic inhibitor miniprotein (PDB ID:2BN6)  [28]  2) a half folded protein: Miniature Esterase (PDB ID: 1V1D)  [29]  3) a small fast-folding protein near its melting point: Trp-cage (PDB ID: 2JOF)  [30]  and, 4) a disordered protein: 𝛽-amyloid peptide residues 10-35 (PDB ID: 1HZ3)  [31]  First, we conducted atomistic simulations for these miniproteins with GROMACS software and mapped the output trajectories to generate the input data for training of NN CG FFs. Each residue was represented by only the 𝛼-Carbons of the backbone in the CG mapping.
Mapping atomistic simulations: To map atomistic trajectories to the CG representations, MDAnalysis software  [44, 45]  and "fast_forward" software tool  [46]  were used. The latter is a specific CG mapping tool designed for MARTINI modeling. The MDAnalysis software was used to select the CG sites (specified heavy atoms) from the all-atom to correspond with CGSchNet, TorchMD-Net and OpenAWSEM CG simulations.
To perform NVT CG simulations with MARTINI3 FF, we employed Gō-like models in combination with the Martini model following the standard protocol. [47]  Each CG simulation was run for 10 ns with a timestep of 20 fs using GROMACS software at constant temperatures 300K, 290K, 350K and 310K for 2BN6, 1V1D, 2JOF, 1HZ3 proteins respectively. Explicit water was used to solvate the protein system; see SI further details. For the CG simulations with OpenAWSEM FF, we used Langevin dynamics at constant temperatures 300K, 290K, 350K and 310K for 2BN6, 1V1D, 2JOF, 1HZ3 miniproteins respectively. Each CG simulation was run for 1 ns with a timestep of 2 fs. A standard simulation protocol was followed. [48]  The CG mappings used in MARTINI and OpenAWSEM analyses were the default mappings of MARTINI  [49]  and AWSEM  [50]  FFs.
Training data Firstly, the all-atom trajectories of 2BN6,  [28]  1V1D,  [29]  2JOF  [30]  and, 1HZ3  [31]  miniproteins were mapped into a CG representation, where each residue was represented with its 𝛼-carbon atom. MDAnalysis  [44, 45]  software was used for the mapping. Then each mapped trajectory was clustered into four meta stable states based on a Hidden-Markov State Model (HMSM)  [51, 52]  using the PyEMMA python library as described in the following text. [53, 54]  Finally, configurations (snapshots from the trajectory) from various subsets of the meta states were used for training separate FFs. Figure  1 : Meta stable clusters of the miniproteins in the low dimensional space projected using the TICA method. [55]  P-Element somatic inhibitor miniprotein (PDB ID:2BN6), Miniature Esterase (PDB ID: 1V1D), Trp-Cage miniprotein (PDB ID:2JOF) and, 𝛽-amyloid peptide residues 10-35 (PDB ID: 1HZ3) were used in this study.
Training forcefields and running CG simulations In this study, we selected two NN-based CG FFs; CGSchNet  [9]  and TorchMD-Net. [25]  CGSchNet  [9]  is a modified version of the CGNet model  [16]  which learns the CG PES based on the force matching approach. In CGNet model, the inputs are hand-selected features such as bond distances, angles and dihedrals. However, in the CGSchNet model, the features are "learned" during training by leveraging the SchNet model. [70, 71]  Additionally, TorchMD-Net  [25]  provide state-of-the-art graph neural network (GNN) and equivariant transformer (ET) based NN potentials for molecular simulations. In this work, we used TorchMD-Net's GNN model and TorchMD for training CG FFs and for conducting CG simulations respectively. Note that we used TorchMD  [72]  which is the Python API for performing CG MD simulations using the TorchMD-Net trained FFs.
CGSchNet tool is equipped with its own Python script for performing the simulations. The key objective of this work is to investigate if NN CG FFs are able to extrapolate to sample from unseen regions of the FES. Therefore, during training, we subsampled different sets of meta-stable states and trained multiple independent FFs per miniprotein (listed in Table  2. 3). For example, to train "FF1" we used 75% of clusters, those labeled as 2,3 and 4 -data from cluster 1 were withheld. Note that the labels were generated randomly. See table 2. 3. We followed the same nomenclature for all four proteins and both CGSchNet  [9]  and TorchMD-Net FFs. The number of frames from each cluster were kept constant through downsampling -each cluster contained only a fixed number of frames. Other hyperparameters used in training and train-validation error plots can be found in SI.
Acknowledgement Research was supported by the  National Institutes of Health  through the award  R35GM137966  (for GPW and ADW) and  R35GM138312  (for GMH). This work was supported in part through the  NYU IT High  Performance Computing resources, services, and staff expertise, and simulations were partially executed on resources supported by the  Simons Center for Computational Physical Chemistry at NYU  (SF Grant No  839534 ). We would also like to thank  Fabian Grünewald  of  University of Groningen  for assistance with Martini simulations.
Patients The study population was obtained by pooling the data from 10 cohorts. The patients from the Angers (France) and Antwerp (Belgium) cohorts for the present work were enrolled after the end of recruitment for the MACK-3 development study. Thus, these 2 cohorts are independent of the originally published study. 8 0] [11] [12]  The last 3 were local cohorts of patients with biobanked samples from centers in Bern (Switzerland), Grenoble (France), and Perth (Australia). Each cohort received approval from its local ethics committee, and all patients gave written informed consent before their inclusion. All 10 cohorts included adult patients who underwent a liver biopsy for suspected NAFLD, after exclusion of concomitant steatosis-inducing drugs (such as corticosteroids, tamoxifen, amiodarone, or methotrexate), excessive alcohol consumption (>30 g/d in men or >20 g/d in women), chronic hepatitis B or C infection, and other causes of chronic liver disease.
Liver Elastography When available, liver elastography (FibroScan; Echosens, Paris, France) data were collected to calculate the FibroScan-AST score (FAST), an elastography-based score recently developed for the noninvasive diagnosis of fibrotic NASH. 15 FAST is a combination of serum AST with the 2 FibroScan results: liver stiffness as measured by vibration-controlled transient elastography and the controlled attenuation parameter, the latter being a surrogate of liver steatosis. In each center, FibroScan examinations were performed in fasting conditions, by experienced operators blinded to patient data, and according to the manufacturer's recommendations. FAST was calculated as per the previously published formula. The thresholds for ruling out and ruling in fibrotic NASH were 0. 35 or less and 0.
NIDA thus corresponds to the rate of correctly classified patients by the noninvasive test in the pooled rule-out and rule-in zones. NIDA was calculated as follows: ([true negatives in the rule-out zone] þ [true positives in the rule-in zone])/(all patients included in both rule-out and rule-in zones). Statistical analyses were performed using SPSS version 25. 0 software (IBM, Armonk, NY).
Consensus sequence was obtained after trimming of reads (reads with quality score < 0. 99 and length < 100 pb were removed, and the 30 first and 30 last nucleotides were removed from the reads) and mapping of the reads on a reference (OM909246, MN272404, MN018385, ON908232, MT261978 for inoculum strain and inoculum strain for samples) using CLC genomics workbench software v. 21. 0. 5 (Qiagen, Hilden, Germany). Parameters for reference-based assembly consisted of match score = 1, mismatch cost = 2, length fraction = 0. 5, similarity fraction = 0. 8, insertion cost = 3, and deletion cost = 3. A de novo contig was also produced to ensure that the consensus sequence was not affected by the reference sequence.
Recombination Study The occurrence of molecular recombination was investigated for each alignment of complete CDS using the Recombination Detection Program (RDP) version 4 software  [29]. RDP, GENCONV, and MAXCHI methods were used for primary screening, and BOOTSCAN and SISCAN methods were used to check for recombination signals  [30] [31] [32] [33]. For optimal recombination detection, the automask procedure was selected. Recombination events with an average p-value with RDP lower than E-10 were selected for downstream phylogenetic analyses.
Phylogenetic Analysis Whole-genome consensus sequences of each serotype were aligned with reference sequences downloaded from GenBank in May 2023, according to their isolation date and locality. They were then aligned using the Clustal W multiple sequence alignment software integrated into BioEdit 7. 2. 5 (Manchester, UK). The best-fit nucleotide substitution pattern was determined using MEGA 7. 0. 26  [34]  according to the corrected Akaike information criterion (AICc)  [35]. Maximum likelihood phylogenetic trees were then constructed with the GTR + G + I model, corresponding to the best-fit model, and 1000 bootstrap replicates were generated  [36]. A sequence identity matrix was also generated to determine the proportion of identical residues between the different sequences using BioEdit 7.