dataesr/scientific-paragraphs-categorization huggingface dataset:
Models' Performance Supplementary Table  3  reports models' performances for the main analysis, when fitting to 80% of the dataset and testing within the remaining 20%. AUC varies between approximately 91% and 97%; Brier scores between 4% and 9%. The nonlinear XGBoost models tend to achieve better discrimination than the linear LogReg patients. When looking at a subset of 5,172 patients being admitted to an ICU, they found that African Americans patients were still as likely to receive guideline-concordant antibiotics as White patients but had lower 30day mortality (OR 0. 82, 95% CI 0. 68 -0. 99). But their analysis only adjusted for 18 covariates which were all collected for billing purposes and had no information on clinical covariates such as laboratory values or vital signs. It is important to note that high-resolution databases like MIMIC-IV are prone to errors in data extraction or data capture, but these errors should be random and equally distributed across patient groups in theory  [44].
These data sources do not provide adequate information when it comes to important clinical covariates  [18] , and thus have intrinsic limitations and mis-classification biases  [41] , which can be further amplified by model misspecifications  [42]. For instance, Frei et al. analyzed data from 35,706 patients on general wards with community-acquired pneumonia from the Veterans Health Administration database  [43]. They found that African American patients were equally likely to receive guideline-concordant antibiotics and have the same 30-day mortality as White healthcare facilities limited our ability to fully control for all potential SDH confounders. This constraint may impact the interpretation of observed disparities, underscoring the importance of considering these factors in future research.
Data analysis was performed by SM, TS, MD, and JM. All authors contributed to the review of results. SM, TS, and JM drafted the first version of the manuscript, which was reviewed by all authors. TS takes ultimate responsibility for the content of the manuscript, While we found different treatment likelihoods for IMV and VP, the absence of disparity in RRT administration warrants further investigation. It may be attributed to better established guidelines for RRT initiation  [30, 31] , reducing the influence of personal biases. In comparison to RRT and VP, there are even fewer clear-cut recommendations on the initiation of IMV  [28, 29]. Given the low adherence of physicians to the surviving sepsis campaign's recommendations  [45] , it is not surprising that this leaves much room for subjective decisions.
Limitations While our study brings significant insights into the discussion of racial-ethnic disparities in critical care, we recognize limitations of our analysis. Firstly, the MIM-IC-IV database does not include patients with sepsis who were admitted to the regular ward. Selection bias is introduced if there is difference in triaging decisions as regards who are admitted to the ICU  [48] , and the disparities may be more pronounced than the estimates in our models. Secondly, while MIMIC-IV is highly granular on clinical data, it lacks specific information on certain SDH, mostly to protect the confidentiality of the patients. Numerous studies as highlighted in a review by Sheikh et al. [49] , have affirmed the significant role of SDH as confounding factors in healthcare outcomes. Our study adjusted for insurance type and English language proficiency as proxies for SDH.
X, J1X. X, J85. X, or J86. X if department listed among top 3 diagnoses by billing N/A Variable Treatment Urinary tract infection on admission IMV (any invasive ventilation) Biliary tract infection on admission Skin infection on admission Description ICD codes N30. 0X, or N39. 0X if listed among top 3 diagnoses by billing department Within the eligibility window ICD codes K81. X, K83. 0X, or K85. 1X if paO2/FiO2 ratio ≤ 300 (average of the day) or listed among top 3 diagnoses by billing Respiratory rate ≥ 20 bpm (average of the department day) as indication for hypoxemic respiratory failure paCO2 ≥ 60 mmHg (minimum of the day) ICD codes L0X. X if listed among top 3 diagnoses by billing department Handling of missing values N/A N/A N/A N/A indication for hypercapnic respiratory failure RRT (any acute continuous renal N/A replacement method) persistent metabolic acidosis VP Mean blood pressure ≤ 65 mmHg (average of ((nor-) epinephrine, phenylephrine, or the day) or lactate ≥ 2 mmol/L (minimum of the day) indication according to Sepsis-3 N/A vasopressin) guidelines Controls Patients not receiving IMV, RRT, or VP within the eligibility window N/A Outcomes Primary: Treatment initiation As provided by dataset N/A Covariates Age At admission N/A Sex As provided by dataset N/A Race-Ethnicity 0 if Racial-ethnic group (self-reported), 1 if White Exclusion of Group "Other" Insurance As provided by dataset, Medicare/Medicaid or other N/A English Proficiency As provided by dataset, English proficiency or limited N/A Year group As provided by dataset, bi-yearly bins N/A 3.
g. , cable elements or nonlinear springs). However, potential slack in the keel lines have not been observed during the testing  (Borg, 2019)  and any of the above proposed approaches should provide similar results. NREL used two numerical models with the only difference being the structural approach adopted. Similar to OC6 Phase II  (Bergua et al. , 2022a) , NREL1 models the tower in the ElastoDyn module and the substructure (i. e. , floater) in the SubDyn module while NREL2 models the tower and substructure in the SubDyn module. The SubDyn module of OpenFAST makes use of a modal reduction. When performing a modal reduction by means of the Craig-Bampton approach for a fixed-bottom system (e. g. , system studied in the OC6 Phase II project) in SubDyn, the Guyan modes (also known as boundary modes) provide information about the static deflection of the body and the Craig-Bampton modes (also known as internal or normal modes) provide information about the inherent dynamics.
e. , rigid body motion and elastic deflections in the substructure). For the above reasons, when modeling floating systems in OpenFAST, it is recommended to avoid including in SubDyn bodies that experience significant elastic deflections. Accordingly, the NREL2 model can be considered of lower fidelity than NREL1. Other participants using the OpenFAST code (e. g. , DUT1 and SHELL) adopted a similar approach to NREL1. W&UG included the structural part in Flexcom rather than OpenFAST. Flexcom does not perform a modal reduction, and instead computes the solution in the time domain using a direct integration method. Most participants used a dynamic approach to account for the mooring lines in their numerical models (Table  1 ). The mooring line dynamic approach is usually based on a lumped mass approach where the lines are discretized into concentrated masses connected by massless springs with dampers in parallel.
Conclusions In the frame of the OC6 Phase IV project, participants modeled a 3. 6-MW wind turbine atop the TetraSpar floating support structure designed by Stiesdal Offshore Technologies. This configuration is representative of the demonstration project installed in Norway in 2021. Numerical results from participants in the project were compared against measurement data from a 1:43 scale test performed by the University of Maine. The system response was studied under wind, wave, and combined wind and wave conditions. Participants used numerical models of different fidelity levels for the aerodynamics, hydrodynamics, and structural dynamics. During the testing of the floating system, the sensor umbilical used to transfer measurement data and power had an impact in the system equilibrium position and dynamics.
Acknowledgements The authors would like to thank  Stiesdal Offshore Technologies, Shell ,  RWE Renewables ,  TEPCO RP , and the  University of Maine  for providing the data to characterize the numerical models and the measurements from the experimental campaign. MARIN would like to acknowledge that their contribution to the project was partly funded by the  Dutch Ministry of Economic Affairs. This work was authored in part by the  National Renewable Energy Laboratory , operated by  Alliance for Sustainable Energy, LLC , for the  U. S. Department of Energy (DOE)  under Contract No. DE-AC36-08GO28308. Funding was provided by the  U. S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the  DOE  or the  U.
Identification of tRNAs with dual identity We previously determined that tRNA ProA genes are annotated as alanine tRNAs (tRNA Ala ) in genomic databases due to automated annotation algorithms' reliance on the anticodon sequence to establish the identity of tRNA genes. Consequently, the AGC anticodon of tRNA ProA causes its annotation as tRNA Ala , which hindered a complete assessment of the phylogenetic distribution of tRNA ProA genes. To circumvent this, we performed searches based on sequence homology using the Streptomyces turgidiscabies tRNA ProA as a query. The search identified 18 new genomes containing a region with a predicted tRNA gene and high homology to tRNA ProA. While all the identified tRNA genes contained the signature C1:G72 base pair of tRNA ProA and tRNA Pro , only six tRNAs had the AGC anticodon of tRNA ProA.
Phylogenetic analysis and distribution of ProRSx and tRNA ProX A total of 121 sequences corresponding to ProRSx (45), Bacterial-type ProRS  (42) , and Archaeal/Eukaryotic-type ProRS  (34)  were aligned using Clustal Omega  (65). The Bacterial-and Archaeal/Eukaryotic-type ProRS sequences are from the organisms encoding ProRSx (Table  S1 ). The sequence alignment was used to build the phylogenetic tree using MEGAX  (66)  with the Maximum Likelihood method with 100 bootstraps. The tree was visualized with iTOL  (67). Multilocus sequence analysis alignments were generated with protein sequences of housekeeping genes from Streptomyces scabiei 87. 22 (strepdb. streptomyces. org. uk) for atpD (SCO5373), gyrB (SCO3874), recA (SCO5769), rpoB (SCO4654), and trpB (SCO2037) against a local database of 84 genomes using automlsa2  (68, 69).
The 46 genomes of strains that encode ProRSx or tRNA ProX were complemented with 38 additional genomes selected to represent the diversity of Streptomyces, Streptacidiphilus, and Kitasatospora (39-41, 70). The S. turgidiscabies gene sequence (STRTU-CAR8_03650) was used as the query against the local sequence database with a cutoff threshold of 1e -10 to confirm that ProRSx was not present in the other genomes. The ML tree was constructed using default parameters for iqtree2. 2 and RAxML embedded in automlsa2 with 1000 bootstrap iterations. The R package ggtree was used to plot the phylogeny (71) and covariates. Bacterial strains and plasmids E. coli Stellar cells (Takara) were used for cloning, and E. coli MG1655 was used for in vivo experiments. The HiFi DNA assembly kit (New England Biolabs) was used for molecular cloning.
Peptide mixtures were loaded into a PEPMAP100 C18 5uM trap column (Thermo Fisher) at a constant flow of 30 uL/min, holding at 60 C. Peptides were eluted at a rate of 0. 25 μl/min and separated using a PepSep Reprosil c18, 1. 9 μm, 15 cm ID 75 μM analytical column (Bruker) throughout a 60-min gradient: 0 to 50 min, 4% -32% acetonitrile + 0. 1% formic acid; 50 to 55 min, 95% acetonitrile + 0. 1% formic acid; and 55 to 60 min, 4% acetonitrile + 0. 1% formic acid. Mass spectrometry data were acquired using the datadependent mode with a cycle time of 3 s. MS1 scan was performed in orbitrap within a range of 400 to 1600 m/z, at resolution 120,000 and autoinjection time with a standard AGC target. RF lens was set to 30%. Isolation for MS2 scans was performed in the quadrupole, with an isolation window of 0.
7. MS2 scans were done in the linear ion trap at turbo scan rate, with an auto maximum injection time and a standard AGC target level. HCD was used for generating the MS2 spectrum as fixed normalized collision energy of 30%. MS2 spectra were acquired at a 15,000 resolution. All LC-MS/MS was performed at Bioinformatics Solutions Inc. in Waterloo, Ontario, Canada. MS Raw Files were processed using PEAKS XPro (v10. 6, Bioinformatics Solutions Inc). The data were searched against a custom database containing the sfGFP sequence in the E. coli K12 UniProt-reviewed database. Precursor ion mass error tolerance was set to 10 ppm, and fragment ion mass error tolerance was set to 0. 02 Da. Semispecific cleavage with trypsin or chymotrypsin was selected with a maximum of three missed cleavages. A fixed modification of carbamidomethylation (+57.
02 Da) on cysteine residues was specified. Variable modifications of deamidation (+0. 98 Da) on asparagine and glutamine, as well as oxidation (+15. 99 Da) on methionine, were specified. A maximum of two variable modifications per peptide was allowed. The peptide false discovery rate was set to 1% for the database search. Only confident mutations with an A-Score of 20 or higher and minimum mutation ion intensities of 1% were considered. To ensure the accuracy in identifying mutations, we applied a false discovery rate filter of <1% for peptide identification and minimum fragment ion intensity of 1% for each mutation.
Acknowledgments-We thank  Dr Natalie Krahn  for enlightening discussions and technical support and  Drs Noah Reynolds  and  Jeffery Tharp  for critically reading the manuscript. Author contributions-D. S. and O. V. -R. conceptualization; D. B. S. , K. S. H. , C. R. C. , and O. V. -R. methodology; D. B. S. , K. S. H. , and O. V. -R. validation; D. B. S. and O. V. -R. formal analysis; D. B. S. , J. T. F. , J. B. , S. A. G. , B. A. S. , A. A. , and O. V. -R. investigation; M. J. , D. S. , C. R. C. , and O. V. -R. resources; D. B. S. , J. T. F. , S. A. G. , and O. V. -R. data curation; D. B. S. and O. V. -R. writingoriginal draft; D. B. S. , J. T. F. , B. A. S. , M. J. , K. S. H. , E. W. , D. S. , C. R. C. , and O. V. -R. writingreview and editing; D. B. S. , B. A. S. , K. S. H. , E. W. , and O.
| DNA sequencing and sequence analyses DNA libraries were enriched for the mitochondrial genome using home-made baits (described in the SI) and the protocol from Horn  (2012). Enriched libraries were pooled, quantified using NEBNext® Library Quant Kit for Illumina® (NEB) and sequenced on NextSeq 500 (Illumina) using three NextSeq 500/550 Mid Output v2. 5 (2 × 75 cycles) kits. Raw reads were trimmed, filtered and collapsed using Adapter Removal v. 2 (options -minlength 30 -trimns -trimqualities -collapse)  (Schubert et al. , 2016). Following these QC procedures, reads were mapped to the CanFam3 dog reference genome using bwa aln and filtered by samtools (command samtools view -q 30)  (Li et al. , 2009). PCR duplicates were removed using Picard MarkDuplicates (https:// broad insti tute. github.
| Phylogenetic and population genetic analyses To evaluate the phylogenetic position of the newly sequenced French wolves, we carried out a phylogenetic analysis, which included previously published wolf and dog mitogenome sequences (Table  S3 , Figure  1a ). After excluding sequences with more than 20% of missing data (after control region trimming), we aligned the remaining sequences with the sequences obtained in this study using  MAFFT v. 7 (Katoh et al. , 2019). We removed sites in the alignment that were represented in less than 90% of samples as well as two codon positions adjacent to the excluded sites. The resulting alignment of a length 15,378 bp was named 'dataset A'. Phylogenetic analysis was performed in MrBayes  (Ronquist & Huelsenbeck, 2003)  using substitution models defined by PartitionFinder2  (Lanfear et al.
, 2017 ; Table  S4 ) and 15 mLn generations. The coyote sequence (Canis latrans, NC008093) was used as an outgroup. The first 25% of samples were discarded as burn-in. This analysis allowed us to reconstruct phylogenetic relationships of the French wolves studied with other wolf populations worldwide and identify individuals belonging to maternal lineages of domestic dogs. To reconstruct the population dynamics of European wolves, we performed a coalescence analysis in BEAST v. 1. 10. 4 (Drummond et al. , 2012). For this purpose, we created a dataset (named 'dataset B') that excluded sequences from dog haplogroups and wolf sequences outside Europe and outside the Modern Holarctic Wolf clade (MHW, see Results). We used six substitution models estimated by PartitionFinder2 (Table  S4 ), a strict clock with tip calibration and a Skyline model that was chosen as the best model by marginal likelihood estimation (MLE).
We ran two MCMC simulations through 100 mLn generations with a sampling frequency of 10,000 steps. MLE analysis was run using path/stepping-stone sampling with chain length of 1 mLn. For each parameter, we checked mixing and convergence by accessing if the ESS values were above 100 and visually inspected the trace plots using TRACER v. 1. 6 (Rambaut et al. , 2014). The phylogenetic trees were summarized in TreeAnnotator 1. 10. 4. with 10% burn-in. Skygrid analysis was performed in TRACER. We also performed separate analyses in BEAST that included either sequences from Haplogroup W1 (dataset C) or W2 (dataset D). We used six substitution models defined by PartitionFinder2 (Table  S4 ), strict (dataset C) or uncorrelated relaxed (dataset D) clock, 50-100 mLn generations and Skygrid population models as chosen by MLE analysis.
The first 25% of samples were discarded as burn-in. To assess phylogenetic relationships between the historical French wolf population and other historical European populations, we carried out a separate analysis using a shorter D-loop fragment (201 bp) available from an earlier study  (Dufresnes et al. , 2018). The dataset included 41 haplotypes found in historical populations across Europe  (Dufresnes et al. , 2018)  and 67 D-loop sequences obtained in this study, collapsed into 22 haplotypes. We created a haplotype network in PopArt v. 1. 7  (Leigh & Bryant, 2015)  using the median-joining model. Standard genetic diversity indices for individuals from historical European populations were calculated in DNASP  (Librado & Rozas, 2009). To assess genetic differentiation in historical French wolves, we divided them into three geographic regions (Southern, North-Eastern and North-Western; Figure  1b ).
| Approximate Bayesian Computation Analyses (ABC) To test the demographic scenarios that were detected using Skygrid or Skyline population models, we performed the ABC analyses for three datasets: (1) European samples from haplogroup W1, (2) European samples from haplogroup W2 and (3) European samples from both haplogroup W1 and W2. For each dataset, we compared three different scenarios: (A) constant population size, (B) population decline and (C) bottleneck. Analyses were performed in BaySICS v. 1. 9. 7. 9. 5 software  (Sandoval-Castellanos et al. , 2014). We set the generation time of the species to 3 years  (Koblmüller et al. , 2016)  and mutation rate to 2% per million years following  Aggarwal et al. (2003). Other parameters such as transition/transversion bias, gamma shape parameter and nucleotide frequency were set based on calculations in MEGA X  (Kumar et al.
| Phylogeographic analyses To investigate the geographic origin of two distinct wolf lineages found in Europe (W1 and W2), we carried out the phylogeographic analyses in RASP  (Yu et al. , 2015 (Yu et al. , , 2020) )  and BayesTraits  (Pagel & Meade, 2021). For the RASP analysis, we used the set of phylogenetic trees that were constructed using 235 wolf mitogenome sequences, excluding sequences from dog haplogroups (dataset E). Trees were constructed in BEAST using six partitions (Table  S4 ), uncorrelated relaxed clock, Skygrid model and 300 mLn generations. Sequences used in RASP analyses were divided into seven geographic regions: Beringia, North America, Japan, South Asia, Central North Asia, Middle East and Europe. We used 9000 trees after removing the first 1000 as a burn-in and performed S-DIVA reconstruction with maximum areas set to seven.
| RE SULTS Consensus mtDNA genome sequence successfully obtained from 78 (87%) samples was called with the average genome coverage of 195X by mapping NGS reads to the CanFam3 dog reference genome. The remaining 12 samples were removed from further analyses due to low coverage and missing data (Table  S1 ). The final dataset included only one sample from Switzerland; therefore, we refer to the analysed dataset as 'French wolves'. Analysis of damage patterns typical for aDNA showed that average C > T substitution on 5′ end was 15. 2% for ancient/medieval sequences (>700 years ago), and 4. 5% for historical samples (Figure  S3 ). One Bronze Age sample (A109) showed a very low C > T substitution rate (1. 5%), indicating either contamination or an incorrect sample age (which was obtained from the context rather than direct radiocarbon dating).
A similar pattern was inferred from the Skygrid analysis focussed on all wolves from Eurasia and North America (Figure  4b ). The analysis of this dataset also showed an increase in N e between 30 and 10 kya, which was not observed when analysing the European wolves only (Figure  4b ). Analyses focussed on European wolf haplotypes from haplogroup W1 and W2 separately showed less pronounced declines in N e (Figure  S7 ). For haplogroup W2, the signal of decline depended on the population model used. In the most supported Skygrid model, a scenario with constant population size could not be excluded due to wide 95% HPD intervals (Figure  S7B ). However, when the Skyline model was used, a more pronounced N e decline was observed (Figure  S7C ). It is worth mentioning that 95% HPD intervals of the reconstructed effective population size changes were generally wide in all analyses.
The model choice of the ABC analyses, regardless of the dataset and the number of statistical groups, showed that the most probable scenario is the constant population size (scenario A) with model likelihood >0. 7 for each analyses and BF of at least 7 but in one case as high as 309. These results indicate that we cannot exclude a constant population size model for the European wolf population. This is not entirely consistent with the results obtained by BEAST analyses (see Figures  4  and S7 ), which show the constant population size until about 1 kya followed by a decline. Therefore, the results from both methods should be treated with caution, and a constant population size model for the European wolf population cannot be excluded. S7 ). The lowest values of genetic diversity indices were observed in Italy, the Iberian Peninsula and Poland (Table  S7 ).
, 2022; Fan et al. , 2016; Freedman et al. , 2014) , while mitochondrial genomes point out that the decline in the global wolf population started around 50 kya  (Loog et al. , 2020). Differences in these estimates may be caused by the use of different datasets and differences in estimates of mutation rates and divergence times. Here, we reconstructed N e changes in the European population based on European wolves from the MHW clade only, while samples representing older lineages were excluded. We also excluded the sequences which were placed within the dog haplogroups (Figures 2, S4), for example TH14, TU14, TU11 and CGG16 from  Loog et al. (2020). Our BEAST analysis with Skygrid reconstruction for the global wolf population, including extinct lineages, showed an earlier onset of the decline at around 7-5 kya (Figure  4b ) and increase in N e (around 30-10 kya, Figure  4b ) not observed in the Skyline reconstruction for European wolves.