dataesr/scientific-paragraphs-categorization huggingface dataset:
For each of these taxa, we downloaded a representative set of either whole chloroplast or whole mitochondrial genome fasta sequences from NCBI Genbank  82  , including a single representative sequence from a recently diverged outgroup. For the Betula genus, we also included three chloroplast genomes from the PhyloNorway database  34, 83. We changed all ambiguous bases in the fasta files to N. We used MAFFT  84  to align each of these sets of reference sequences, and inspected multiple sequence alignments in NCBI MSAViewer to confirm quality  85. We trimmed mitochondrial alignments with insufficient quality due to highly variable control regions for Leporidae, Cricetidae and Anserinae by removing the d-loop in MegaX  86. The BEAST suite  49  was used with default parameters to create ultrametric phylogenetic trees for each of the five sets of taxa from the multiple sequence alignments (MSAs) of reference sequences, which were converted from Nexus to Newick format in Figtree (https://github.
com/ rambaut/figtree). We then passed the multiple sequence alignments to the Python module AlignIO from BioPython 87 to create a reference consensus fasta sequence for each set of taxa. Furthermore, we used SNPSites  88  to create a vcf file from each of the MSAs. Since SNPSites outputs a slightly different format for missing data than needed for downstream analysis, we used a custom R script to modify the vcf format appropriately. We also filtered out non-biallelic SNPs. From the damage filtered ngsLCA output, we extracted all readIDs uniquely classified to reference sequences within these respective taxa or assigned to any common ancestor inside the taxonomic group and converted these back to fastq files using seqtk (https://github. com/lh3/seqtk). We merged reads from all sites and layers to create a single read set for each respective taxon.
Next, since these extracted reads were mapped against a reference database including multiple sequences from each taxon, the output files were not on the same coordinate system. To circumvent this issue and avoid mapping bias, we re-mapped each read set to the consensus sequence generated above for that taxon using bwa  89  with ancient DNA parameters (bwa aln -n 0. 001). We converted these reads to bam files, removed unmapped reads, and filtered for mapping quality > 25 using samtools  90. This produced 103,042, 39,306, 91,272, 182 and 129 reads for Salix, Populus, Betula, Elephantidae and Capreolinae, respectively. We next used pathPhynder  62  , a phylogenetic placement algorithm that identifies informative markers on a phylogeny from a reference panel, evaluates SNPs in the ancient sample overlapping these markers, and traverses the tree to place the ancient sample according to its derived and ancestral SNPs on each branch.
Mammut americanum. We confirmed the phylogenetic placement of our sequence using a selection of Elephantidae mitochondrial reference sequences, GTR+G, strict clock, a birth-death substitution model, and ran the MCMC chain for 20,000,000 runs, sampling every 20,000 steps. Convergence was assessed using Tracer 91 v1. 7. 2 and an effective sample size (ESS) > 200. To determine the approximate age of our recovered mastodon mitogenome we performed a molecular dating analysis with BEAST 47 v1. 10. 4. We used two separate approaches when dating our mastodon mitogenome, as demonstrated in a recent publication  92. First, we determined the age of our sequence by comparing against a dataset of radiocarbon-dated specimens (n = 13) only. Secondly, we estimated the age of our sequence including both molecularly (n = 22) and radiocarbon-dated (n = 13) specimens using the molecular dates previously determined  92.
Because of this, we continued with only Betula. First, we downloaded 27 complete reference Betula chloroplast genome sequences and a single Alnus chloroplast genome sequence to use as an outgroup from the NCBI Genbank repository, and supplemented this with three Betula chloroplast sequences from the PhyloNorway database generated in a recent study  29  , for a total of 31 reference sequences. Since chloroplast sequences are circular, downloaded sequences may not always be in the same orientation or at the same starting point as is necessary for alignment, so we used custom code (https://github. com/miwipe/KapCopenhagen) that uses an anchor string to rotate the reference sequences to the same orientation and start them all from the same point. We created a MSA of these transformed reference sequences with Mafft  84  and checked the quality of our alignment by eye in Seqotron  94  and NCBI MsaViewer.
Next, we called a consensus sequence from this MSA using the BioAlign consensus function  87  in Python, which is a majority rule consensus caller. We will use this consensus sequence to map the ancient Betula reads to, both to avoid reference bias and to get the ancient Betula sample on the same coordinates as the reference MSA. From the last common ancestor output in metaDMG  36  , we extracted read sets for all units, sites and levels that were uniquely classified to the taxonomic level of Betula or lower, with at a minimum sequence similarity of 90% or higher to any Betula sequence, using Seqtk  95. We mapped these read sets against the consensus Betula chloroplast genome using BWA  89  with ancient DNA parameters (-o 2 -n 0. 001 -t 20), then removed unmapped reads, quality filtered for read quality ≥25, and sorted the resulting bam files using samtools  89.
We downloaded the gff3 annotation file for the longest Betula reference sequence, MG386368. 1, from NCBI. Using custom R code  97  , we parsed this file and the associated fasta to label individual sites as protein-coding regions (in which we labelled the base with its position in the codon according to the phase and strand noted in the gff3 file), RNA, or neither coding nor RNA. We extracted the coding regions and checked in Seqotron  94  and R that they translated to a protein alignment well (for example, no premature stop codons), both in the reference sequence and the associated positions in the ancient sequence. Though the modern reference sequence's coding regions translated to a high-quality protein alignment, translating the associated positions in the ancient sequence with no depth cut-off leads to premature stop codons and an overall poor quality protein alignment.
For the overall tree prior, we selected the coalescent model. The age of the ancient sequence was estimated following the overall procedures of Shapiro et al. (2011)   98. To assess sensitivity to prior choice for this unknown date, we used two different priors, namely a gamma distribution metric towards a younger age (shape = 1, scale = 1. 7); and a uniform prior on the range (0, 10 Myr). We also compared two different models of rate variation among sites and substitution types within each partition, namely a GTR+G with four rate categories, and base frequencies estimated from the data, and the much simpler Jukes Cantor model, which assumed no variation between substitution types nor sites within each partition. All other priors were set at their defaults. Neither rate model nor prior choice had a qualitative effect on results (Extended Data Fig.
Phylogenetic placement of Elephantidae mitochondrial reads within mastodons (Mammut americanum), using Elephas maximus as outgroup, including transitions and transversion SNPs. (Please note that the NCBI taxonomy includes the Mammut genus within Elephantidae). The reference dataset consisted of mitochondria from mastodons (Mammut americanum) only and one Elephas maximus as an outgroup. Reads have been merged from all layers and sites. The green numbers on each edge represent the number of supporting (+) SNPs, whereas the red numbers indicate conflicting (-) SNPs in the ancient Elephantidae environmental mitochondrial genome overlapping the reference SNPs assigned to the respective edge. There is a placement for the ancient Elephantidae environmental mitochondrial genome on the edge marked +2/-1, identifying it as basal to the mastodon (Mammut americanum) clade, which contains most of all mastodon reference mitochondrial genomes.
Acknowledgements We acknowledge support from the  Carlsberg Foundation  for logistics to carry out two expeditions to Kap København in 2006 and 2012 ( S. Funder , principal investigator for Carlsberg foundation grant to LongTerm and Kap København-the age). The fieldwork in 2016 was supported by a grant to N. K. L. from the  Villum Foundation. We highly appreciate the collaborative support by  Illumina Inc. that was crucial for the success of the project. E. W. and K. H. K. thank the  Danish National Research Foundation (DNRF)  and the  Lundbeck Foundation  ( R302-2018-2155 ) for providing long-term funds to develop the necessary DNA technology that eventually made it possible to retrieve environmental DNA from these ancient deposits in the Kap København Formation. E. W. also acknowledges the  Wellcome Trust  ( UNS69906 ), the  Carlsberg Foundation  ( CF18-0024 ),  Novo Foundation  ( NNF18SA0035006 ),  Leverhume  ( RPG-2016-235 ) and  GRF EXC CRS Chair -Cluster of Excellence (44113220)  for their support.
Extended Data Fig. 4 | 4 Phylogenetic placement of Elephantidae mitochondrial reads within mastodons (Mammut americanum), using Elephas maximus as outgroup, including transitions and transversion SNPs. (Please note that the NCBI taxonomy includes the Mammut genus within Elephantidae). The reference dataset consisted of mitochondria from mastodons (Mammut americanum) only and one Elephas maximus as an outgroup. Reads have been merged from all layers and sites. The green numbers on each edge represent the number of supporting (+) SNPs, whereas the red numbers indicate conflicting (-) SNPs in the ancient Elephantidae environmental mitochondrial genome overlapping the reference SNPs assigned to the respective edge. There is a placement for the ancient Elephantidae environmental mitochondrial genome on the edge marked +2/-1, identifying it as basal to the mastodon (Mammut americanum) clade, which contains most of all mastodon reference mitochondrial genomes.
Proxy compilation Here we compile paleobotanical MAP estimates for the late Paleocene (59. 2 to 56 Ma; Thanetian) to early Eocene (56. 0 to 47. 8 Ma; Ypresian). Our compilation builds upon previous EECO-  (Carmichael et al. , 2016)  and Paleocene-Eocene Thermal Maximum (PETM; 56 Ma)-aged  (Carmichael et al. , 2017 ) compilations. We supplement this with i) published MAP estimates generated since, and ii) newly generated MAP estimates using CLAMP and NLR on published palynological and macrofloral (predominantly leaf-based) datasets. Our new proxy synthesis (n = 322) contains 133 MAP estimates (41%) from  Carmichael et al. (2016) , 106 data points (33%) from other published sources, and 83 new data points (26%) (Figure  1 ; Table  S1 ; Supplementary Data). The new data in the compilation helps to improve geographical coverage in previously data-poor regions, including central west coast and eastern Africa (e.
g. ,  Eisawi and Schrank, 2008; Adeonipekun et al. , 2012; Cantrill et al. , 2013)  (also recently presented in  Williams et al. , 2022) ; the coal and lignite bearing deposits of northeastern India and southern Pakistan  (Frederiksen, 1994; Tripathi et al. , 2000; Verma et al. , 2019) ; the Tibetan plateau and sedimentary basins of southern China (e. g. ,  Aleksandrova et al. , 2015; Su et al. , 2020; Xie et al. , 2020) ; and the South American (e. g. ,  Quattrocchio and Volkheimer, 2000; Pardo-Trujillo et al. , 2003; Jaramillo et al. , 2007)  and North American continent and Caribbean islands (e. g. ,  Graham et al. , 2000; Jarzen and Klug, 2010; Smith et al. , 2020)  (Figure  1 ; Supplementary Data). Most of these use the NLR approach based on palynological datasets, as plant macrofossils from the late Paleoceneearly Eocene low latitudes are more rarely preserved, although some exceptions are known  (Wing et al.
, 2009; Shukla et al. , 2014; Herman et al. , 2017). We also incorporate data from the mid and high latitudes, e. g. , southern South America, North America, Australia and New Zealand, and high Siberia (Supplementary Data). For regions with exceptionally poor data coverage (e. g. , tropical and subtropical latitudes, Antarctica), we also compile and generate MAP estimates from the early middle Eocene (47. 8 to ~45 Ma; first half of the Lutetian). Published CLAMP and NLR data were re-analysed following recent recommendations, so that there is no bias as a result of discrepant methodology. Specifically, 1) CLAMP-scored fossil leaf assemblages were re-analysed using up-to-date geographically appropriate calibration datasets  (Kennedy et al. , 2014; Yang et al. , 2015; Reichgelt et al. , 2019) , 2) for both CLAMP and NLR reconstructions, gridded climate datasets from the R package dismo were employed  (Hijmans et al.
For proxy estimates, we use the proxy error and error type as reported in the original study. Typically, this is a minimum-maximum range or confidence interval (e. g. , 95%) for NLR approaches (e. g. ,  Willard et al. , 2019; West et al. , 2020) , and standard error (SE) or standard deviation (SD) derived from calibration dataset residuals for leaf physiognomy methods (e. g. ,  Teodoridis et al. , 2011). For our newly generated values, uncertainties are reported as 95% confidence interval for NLR and ±1 SD for CLAMP. The subsequent overlap between the model and proxy uncertainty range is assessed following the MioMIP methodology  (Burls et al. , 2021). Any overlap between the proxy and model uncertainty ranges is defined as "no bias" (Figure  S1  in  Burls et al. , 2021).
As such, the subtropics are characterised by overall drier conditions in terms of P-E (Figure  8a ). Extrapolating from this, if early Eocene LTGs were weaker than suggested by these models  (Lunt et al. , 2021) , changes in strength of subtropical moisture circulation may outcompete changes in enhanced moisture divergence, leading to overall wetter subtropics (see  Burls & Federov, 2017). Although proxy-model bias is decreasing for certain models, overall, early Eocene proxy compilations suggest weaker global pole-to-equator LTGs (~14-22°C;  Gaskell et al. , 2022; Evans et al. , 2018; Cramwinckel et al. , 2018)  than predicted in the DeepMIP model ensemble (~18 to 25°C; Figure  1b  in  Lunt et al. , 2021). However, proxyderived LTG estimates remain associated with large uncertainties due to the use of different input datasets and/or the analysis of different time intervals (cf.
Acknowledgments G. N. I and M. C. were supported by a  Royal Society Dorothy Hodgkin Fellowship  ( DHF\R1\191178 ). N. B. was supported by the  National Science Foundation , via award  AGS-1844380. D. G was supported by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  through  Discovery Grants  ( DG 311934  and  2016-04337 ). C. K. W acknowledges funding from a private donor to the  Northern Climates Postdoctoral Fellowship  at the  University of Alberta. D. K. H acknowledges support from  Australian Research Council  grant  DE22010079  and the  Australian Centre for Excellence in Antarctic Science , project number  SR200100008. R. F is supported by  NSF-2114204. A. dB was supported by  Swedish Research Council  project  2020-04791. The GFDL simulations were performed by resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at the  National Supercomputer Centre (NSC) , partially funded by the  Swedish Research Council  through grant agreement no.
Data availability The data from a 3-month measurement campaign with devices installed on a meteorological mast of Boralex located at the wind farm of Pays d'Othe are presented in this paper. Raw data along with Python-formatted data with corresponding scripts are described. The Hydrology Meteorology and Complexity laboratory of École des Ponts Paris-Tech (HM&Co-ENPC) has made this data set available at https://doi. org/10. 5281/zenodo. 5801900  (Gires et al. , 2021). The following citation should be used for every use of the data: -For this paper, https://doi. org/10. 5194/essd-2021-463. -For the database, Gires, A. , Jose, J. , Tchiguirinskaia, I. , and Schertzer, D. : Data for: "Three months of combined high resolution rainfall and wind data collected on a wind farm", Zenodo [data set], https://doi.
Acknowledgements. The authors would like to thank  Boralex , and notably  Ernani Schnorenberger , for facilitating access to the meteorological mast they are operating on the Pays d'Othe wind farm. Financial support. This work has been supported by the  ANR   JCJC RW-Turb  project (grant no. ANR-19-CE05-0022 ). Review statement. This paper was edited by  David Carlson  and reviewed by two anonymous referees.
Structural Mechanisms PcTx1 primarily binds to the thumb domain through Trp7 and Trp24 that anchor the toxin to Phe351 of cASIC1, crucial for the specificity of PcTx1  [51, 103]  but also inserts an arginine-rich hairpin into the acidic pocket, making polar interactions mimicking local protonation of the acidic pocket (Figure  5A ). Together, these polar and non-polar interactions link the finger, β-ball, and thumb domains of one subunit and the palm domain of the adjacent subunit  [47, 51]  and lead to the collapsed conformation of the acidic pocket, which characterizes both the open and the desensitized states (Figure  3B, C ). Three PcTx1 molecules thus bind at three equivalent sites on one homotrimeric ASIC1. 1C ) in complex with PcTx1 (PDB ID: 3s3x)  [51]. (B), Structure of a single cASIC1 subunit in complex with MitTx (heterodimeric complex of MitTx-α and MitTx-β (PDB ID: 4NTY)  [60].
(C), Cryo-EM structure of a single hASIC1a subunit in complex with mambalgin-1 (Mamb) at pH 8. 0 (PDB ID: 7CFT)  [52]. (D), Model of a single rASIC3 subunit extrapolated from cASIC1 structure, along with APETx2 (PDB ID: 2MUB) at the same scale, with two potential binding sites (black arrows)  [129]. Designed with PyMOL software. A series of chimeras realized between rASIC1a and rASIC1b or between rASIC1a and rASIC2a showed that the thumb, the β-ball and the palm domains of the adjacent subunit are crucial in explaining the difference in sensitivity of PcTx1 between the different ASIC isoforms. When ASIC1a residues of the β-ball were exchanged with the ones belonging to the PcTx1-insensitive ASIC2a (mutant 1a-RDQ190,258,259KQE)  [49] ), PcTx1 cannot inhibit the channel and rather induces a strong potentiation, in good agreement with the fact that Arg190 was involved in the interface with PcTx1  [51].
Study Design and Population This study is an ancillary analysis of the prospective multicenter observational cohort of the French and euRopean Outcome reGistry in Intensive Care Units (FROG-ICU) study  (13). Details about design and data collection have been reported previously  (14). Briefly, FROG-ICU study aimed at understanding long-term outcomes after ICU discharge as well as risk factors for morbidity and mortality. All consecutive patients from 21 medical, surgical, or mixed ICUs in France and Belgium between August 2011 and June 2013 were included if they required invasive mechanical ventilation and/or vasoactive drug support for more than 24 h after ICU admission. Patients were followed until death or up to 1 year after ICU discharge. Clinical and biological data were recorded at admission and during ICU stay including severity scores such as Simplified Acute Physiology Score II (SAPS-II) and Sequential Organ Failure Assessment (SOFA) as well as the use of lifesustaining therapies such as vasopressors/inotropes, renal replacement therapy (RRT), or invasive mechanical ventilation.
Statistical Analyses Results were expressed as median [interquartile range (IQR)] or number (percentage) as appropriate. In the absence of a predefined cutoff for the two biomarkers in a population of critically ill patients, data were described after dichotomization according to the median value of each biomarker. Normal distribution of the biomarkers was evaluated and, if appropriate, log 10 transformation was performed. Differences between groups were assessed using the Mann-Whitney U-test or the Fischer exact test as appropriate. Rank correlation between IL-6 or CRP and other variables was assessed using Spearman's rank correlation coefficient (r s ). Survival curves plotted by the Kaplan-Meier method using median values of IL-6 and CRP were used for illustrative purposes, and differences between groups were tested with the log-rank test.
To explore the ability of these biomarkers to improve mortality prediction in comparison with classically used severity scores, category-free reclassification analyses were performed and net reclassification indexes (NRI) as well as integrative discrimination improvements (IDI) were calculated for each biomarker alone as well as in combination using previously described methodology  (15). SAPS-II and SOFA scores were used as the reference predictor of mortality  (16, 17). Areas under the receiver operating characteristic curves of the severity scores with or without addition of biomarkers values were compared using the DeLong test. All statistical analyses were performed using the R statistical software version 4. 0. 2 (The R Foundation for Statistical Computing, Vienna, Austria) with the ggplot2, survminer, survival, Hmisc, dplyr, pROC, and forestplot packages.
Our work also has several limitations. We used data and biological samples from a previous study with different objectives. The biological samples were expected to be taken within 24 h after ICU admission. Thus, concentrations obtained after a few hours of delay may be lower than those obtained immediately after admission considering the short half-life of IL-6; this may have influenced our results. Concentrations were consequently higher than the basal level. It is interesting to note, from a real-world perspective, that even a few hours of delay retains prognostic interest. Additionally, this instantaneous capture of the dynamic course of different conditions is susceptible to influence the respective prognostic value of each biomarker. Thus, for some conditions (e. g. , sepsis), the time between disease onset and hospital admission can be substantial.
The bipolar DC microgrid topology is characterized by three voltage levels and is able to transfer power more efficiently than a conventional DC microgrid. This paper proposes an advanced control strategy aiming to ensure the voltage balancing between the upper and lower terminals of a bipolar DC microgrid regardless of the distribution of loads. The proposed controller is based on the backstepping method, which is well known for its the robustness and the global asymptotic stability that can be guaranteed for the system. A particle swarm optimization algorithm has also been adopted for an optimal design of the proposed controller parameters. Simulation results in a Matlab/Simulink environment has been presented to verify the effectiveness and reliability of the proposed voltage-balancing controller.
Simulation Results In this section, simulation results are provided to validate the proposed controller for voltage balancing of a bipolar DC microgrid. Simulations are executed using MATLAB TM /Simulink. The parameters used in the simulation are listed in Table  2. Two main scenarios verifying the performance of the designed voltage-balancing controller and highlighting its importance in solving the imbalance issue in the bipolar DC microgrid caused by the asymmetrical loading were examined. Scenario1: First, the studied system was simulated without and with the voltage-balancing controller. In this test, resistive loads with different values were connected to the TLSC output (upper (positive to neutral) and lower (negative to neutral) terminals of the bipolar DC bus) to yield the voltage imbalance.