dataesr/scientific-paragraphs-categorization huggingface dataset:
The requirement of having n = 30 matchups to calculate the weighted statistics has a large impact on the choice of optimal algorithms. By including this stipulation, some of the more recent salinity input datasets are effectively deselected because there are not enough contemporary measurements over their temporal range to allow their evaluation, particularly in the Congo region. For example, the SMAP satellite was only launched in 2015, and there were not enough in situ matchups in either region to be able to fully assess the RSS-SMAP dataset. Therefore, using ISAS for salinity in the optimal TA algorithm in the Congo (where data are available from 2002 onwards) increases the number of matchups by a factor of 20 compared with using RSS-SMAP (Table  2 ). Given time, the number of matchups with the satellite-only products will increase, allowing them to be fully evaluated.
Considering the better spatial coverage of the satellite-only products, it is likely that, in the near future, the best algorithm combinations to generate updates to this product will all use satellite-derived SSS and SST. The recent proliferation of certified reference materials when measuring TA  (Dickson et al. , 2003)  and DIC  (Dickson, 2001)  should also mean that newer in situ data will have lower uncertainties. The relatively low number of in situ data limits the potential use of neural-network-based approaches to estimate the carbonate system in these outflows, and significantly more in situ data would be needed (for training and testing) before these approaches could likely be applied. By comparing the two Type-A uncertainty estimates, the standard combined uncertainty estimate (accounting for measurement uncertainty, spatial uncertainty, depth uncertainty and algorithm uncertainty -aka top down) and the second Type-A uncertainty evaluation (propagated input uncertainties through the literature algorithm -aka bottom up), the contribution of different factors to the uncertainty can be dissected.
Land et al. (2019)  noted that reducing the uncertainty of the in situ measurements was just as important as all of the remaining uncertainties in their full methodology. Further reducing the standard combined uncertainty is challenging and would require either improvements in satellite SSS retrievals  (Vinogradova et al. , 2019)  or improvements in how uncertainty is quantified in the GLODAP data (a huge challenge give the different systems and protocols used by different laboratories) (Bockmon and  Dickson, 2015). The time series data demonstrate that there is low TA and DIC in the river outflows of both regions. The time series data clearly show that the discharge of the outflows and their zone of influence changes seasonally. Our results are consistent with previous observations that the Amazon plume extent is smallest between January and March  (Fournier et al.
Data availability The dataset described in this paper is freely available at PANGAEA (https://doi. org/10. 1594/PANGAEA. 946888,  Sims et al. , 2023). The OceanSODA-MDB data are available at https://doi. org/10. 12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931  (Land and Piollé, 2022). All of the remote sensing datasets needed to create the gridded products are freely available from their respective online repositories.
Code availability The code used to run this analysis and download the remote sensing datasets is provided in the Supplement and is freely available at GitHub (https://github. com/Richard-Sims/Sims_ 2023_OceanSODA-UNEXE, last access: 25 April 2023) and https://doi. org/10. 5194/essd-15-2499-2023 Earth Syst. Sci. Data, 15, 2499 Data, 15, -2516 Data, 15, , 2023  on Zenodo (https://doi. org/10. 5281/zenodo. 7863884,  Sims and Holding, 2023). The code can be run on a desktop computer and requires no specialist computing facilities. For example, a laptop with an Intel i7-4800MQ 2. 70 GHz CPU and 8 GB of memory can complete the algorithm evaluation in around 1 h. On the same machine, the remote sensing datasets take several days to download and reprocess; this is partially subject to local internet speeds and host server speeds.
Conclusions OceanSODA-UNEXE is a time series dataset of the carbonate system in the outflow regions of the Amazon and Congo rivers. Optimal TA and DIC data are generated with the optimal combination of published algorithms and input datasets, which is determined by an exhaustive round-robin inter-comparison evaluation. By using a specially designed matchup database for the algorithm evaluation, uncertainties due to spatial and depth variability in the in situ references have been minimized. TA, DIC, SST and SSS are used as inputs into PyCO2SYS to calculate the remaining carbonate system variables (pH and pCO 2 ). TA and DIC are provided with standard combined uncertainties from a Type-A uncertainty evaluation, whereas pH and pCO 2 are provided with propagated uncertainties from PyCO2SYS. The assessed uncertainties are lower than the natural variability within these regions, and the main features of both river outflows are evident in all of the carbonate system variable outputs.
Potential uses for these data could include evaluating the riverine carbon flux from the land into the ocean resulting from the Amazon and Congo rivers or evaluating the extent that riverdriven episodic changes in the carbonate system may be having on sensitive coral reefs that interact with the outflows. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-15-2499-2023-supplement. Author contributions. RPS led the writing of the manuscript, with inputs from TMH and JDS, and all co-authors made contributions to the final paper. TMH wrote the majority of the code used for the algorithm evaluation and the creation of the dataset, and RPS made additional changes to the code for the purpose of generating the final dataset. HLG assisted with debugging and testing the code and outputs.
PEL and JFP provided essential updates to the matchup database. TMH performed the literature search to identify relevant algorithms from those available in the literature. RPS produced the final figures, which were partially based on earlier versions produced by TMH. JDS secured project funding and oversaw completion of the work. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. We would like to thank our OceanSODA colleges,  Helen Findlay ,  Luke Gregor  and  Nicolas Gruber , for their helpful discussions and feedback on this work. Financial support. This research has been supported by the  European Space Agency  (grant no. 4000112091/14/I-LG ). Hannah L. Green  was supported by a PhD studentship funded by an  AXA   XL Ocean Risk Scholarship  that was awarded to  Helen Findlay ,  Jamie D. Shutler  and  Peter E. Land. Review statement. This paper was edited by  François G. Schmitt  and reviewed by two anonymous referees.
These values are much lower than those from the top-down uncertainty evaluation, as this uncertainty evaluation does not fully account for spatial and depth variability. RMSD values from the literature do not fully capture all the measurement variability in the bottom-up uncertainty evaluation. The bottom-up evaluation does account for uncertainty in the input SST and SSS datasets, suggesting that spatial and depth variabilities and differences between the in situ data (used to evaluate the uncertainties) are likely dominating the uncertainty budget. Variable TA DIC Using SSS and SST from the TA algorithm Using SSS and SST from the DIC algorithm pCO 2 pH converted pH cal arag pCO 2 pH converted pH cal arag from H + from H + Combined 34. 74 44. 34 85. 19 0. 08 relative to 0. 07 relative to 0.
U1 regulates IPA isoforms in several genes involved in signaling pathways downstream of RTKs To identify on a genome-wide scale the set of IPA events that are regulated by U1-ASO in 4T1 cells, we used high-throughput sequencing of 3' ends of polyadenylated transcripts (3'-Seq) (Supplementary Fig. S3A ) and our bioinformatics pipeline called 3'-SMART (3'-Seq Mapping Annotation and Regulation Tool) (Supplementary Fig. S3B ). We applied this approach to 4T1 cells treated or not with the U1-ASO. The vast majority of sequencing reads were found in the last exon (LE) of coding genes and in the vicinity of a pA signal (AAUAAA), as expected, while about 10% of the reads were found in introns, and intronic pA sites were found in 60% of detected genes (Supplementary Fig. S3D-S3F ). 3'-Seq analysis indicated that the U1-ASO led to increased usage of 1113 IPA sites in 841 genes (relative to the sum of pA sites present in the LE of matched genes), and to decreased usage of only 271 IPA sites in 208 genes (p<0.
05 and FDR<10%; Fig. 5A  and Supplementary Table  S4 ). The majority of regulated IPA sites were annotated in databases of pA sites  40, 41  , were significantly expressed (above a threshold of 5% reads relative to the LE) and/or had an AATAAA-like hexamer  42  (Fig. 5B  and Supplementary Fig. S3G-S3H ). However, compared to down-regulated IPA sites, up-regulated ones were less often annotated (49% versus 66%), abundant (53% versus 77%) and associated with a canonical hexameric pA signal, AA/TTAAA (29% versus 60%). These data are consistent with previous studies in other cell lines showing that U1-ASO up-regulates IPA isoforms and derepresses cryptic IPA sites in many genes  4, 6. Interestingly, IPA up-regulation by U1-ASO was enriched in genes involved in specific processes, including cell adhesion and MAPK signaling (Fig.
Acknowledgements This research was funded by the  CNRS ,  Curie Institute  and  Ligue Nationale contre le Cancer (Equipe labellisée)  to SV. High-throughput sequencing was supported by the grant  ANR-10-EQPX-03  from the  Agence Nationale de la Recherche (investissements d'avenir). We thank the  Institut Curie Next Generation Sequencing  ( Thomas   Rio-Frio  and  Sylvain Baulande ) and Genomics ( Audrey Rapinat  and  David Gentien ) platforms for high-throughput sequencing and Nanostring measurements, respectively. Acquisition of the Nanostring platform was initiated and supported by  ANR-10-IDEX-0001-02 PSL ,  ANR-11-LBX-0044 , and Grant  « INCa-DGOS-4654 » SIRIC11-002. MV was supported by a post-doctoral fellowship from the  "Association pour la Recherche sur le Cancer " (ARC,  n°PDF20140600985 ).
Both LTA and FC approaches are time consuming, difficult to standardise, and require the processing of fresh citrated blood samples soon after venepuncture. This has limited the sample sizes of previous genetic meta-analyses  [9] [10] [11] [12] [13] [14] [15]  (Supplemental Table  1 ). To address the limitations of PR and LTA, we explored the possibility of imputing PR phenotypes from measurements made by an alternative technology in widespread clinical use. Sysmex XN haematology analyzers are sophisticated, high-throughput, clinically standardised instruments containing a miniaturised flow cytometer, a device to measure cellular impedance and a spectrophotometer. The primary function of a Sysmex analyser is to generate a complete blood count (CBC) from the data measured by these instruments.
Secondly, we applied the trained models to predict PR to ADP (only PR to ADP could be predicted with useful accuracy) from Sysmex data in 29,806 genotyped participants in the INTERVAL cohort of blood donors  16. Thirdly, we performed a GWAS of the predicted PR (PPR) phenotype in INTERVAL. Fourthly, we estimated the effects of the variants identified by the GWAS on the means of (i) FC-derived PR phenotypes, and (ii) a phenotype of in vitro thrombus formation. Fifthly, we built a genetic score using the variants identified by the PPR GWAS. Sixthly, we computed the genetic score for 384,059 British ancestry participants in the UK Biobank study and tested for associations between the score and 524 health outcomes. Finally, we conducted Mendelian randomisation analyses using large GWAS summary statistics to test for association between PR and the risks of coronary artery disease (CAD), stroke and venous thromboembolism (VTE).
We fitted a Lasso regression  18, 19  model to predict PR in response to a given agonist from the 15 scattergram-derived features and five standard CBC platelet traits (20 features in total)  18, 19  using data from 533 participants in the PFC. We optimised the penalty parameter of the Lasso to maximise the mean (over repetitions and folds) R 2 of the regression in held-out data. In the case of ADP, the optimal value of the penalty parameter yielded a mean prediction R 2 =0. 26 (Supplemental Figure  2 ). Finally, we fitted a linear regression model of the ADP response on the 14 covariates with non-zero coefficients in the cross-validation, using the entire dataset.
Acknowledgements This research has been conducted using the  UK Biobank Resource  under Application Number  13745. We are grateful to the  Lowy Foundation USA  for supporting this work. We are grateful to  Mr Stephen Garner  for generating part of the Cambridge PFC FC dataset and for his invaluable advice on the operation of Sysmex haematology analysers. We thank  Drs. Jarob Saker  and  Joachim Linssen  of  Sysmex Europe  and  Rob Gillions  of  UK Biobank  for invaluable technical assistance and advice. We gratefully acknowledge the participation of all  UK Biobank ,  NIHR Cambridge BioResource , and INTERVAL volunteers. We thank members of the  Cambridge BioResource Scientific Advisory Board and Management Committee  for their support of our study and the  NIHR Cambridge Biomedical Research Centre  for funding ( RG64219 ).
Participants in the INTERVAL randomised controlled trial were recruited with the active collaboration of  NHS Blood and Transplant England , which has supported field work and other elements of the trial. DNA extraction and genotyping were co-funded by the  National Institute for Health and Care Research (NIHR) , the  NIHR BioResource  and the  NIHR Cambridge Biomedical Research Centre  ( BRC-1215-20014 ). The academic coordinating centre for INTERVAL was supported by core funding from the:  NIHR Blood and Transplant Research Unit in Donor Health and Genomics  ( NIHR BTRU-2014-10024 ),  NIHR Blood and Transplant Research Unit in Donor Health and Behaviour  ( NIHR203337 ),  UK Medical Research Council  ( MR/L003120/1 ),  British Heart Foundation  ( SP/09/002 ;  RG/13/13/30194 ;  RG/18/13/33946 ) and  NIHR Cambridge BRC  ( BRC-1215-20014 ;  NIHR203312 ).
Data Access Committee at helpdesk@intervalstudy. org. uk. Access to the genotype, FC, Sysmex scattergram and microspotting data from PFC participants can be sought by emailing the corresponding author. The genotype data for the genotyped participants in UK Biobank are available via application at https://www. ukbiobank. ac. uk/enable-your-research/apply-for-access. The GWAS summary statistics for CAD, stroke and VTE are available from https://www. ebi. ac. uk/gwas/studies/GCST90132314, https://www. ebi. ac. uk/gwas/studies/GCST90104539 and https://download. decode. is/form/2022/vte_meta. txt. gz, respectively. The code for the analysis is available at https://github. com/hippover/sysmex2pf.
Provenance studies have revealed the antiquity and the extent of long-distance voyaging among Pacific Islands societies in the past, especially during the Lapita period and following the eastward expansion of the Polynesians  (30). The transfer of goods between very distant places is assumed to have played a role in maintaining social ties and political alliances between related communities, during phases of expansion and within newly settled regions  (31) (32) (33) (34). Here, we present a high-resolution geochemical fingerprinting of artifacts from multiple Polynesian Outliers and unambiguously identify the geological origins of these artifacts. Our study is geographically large scale and required the use of large databases to consider a massive area of our planet, much larger than other similar studies.
S1 and S2). In addition, we analyzed six geological samples from Emae because no data were available in the literature. From Taumako, we analyzed four complete basalt adzes and one flake associated with the resharpening of a polished adze (fig. S3 ). Among these, only two adzes were found in stratified deposits associated with the initial settlement of the Kahula site  (17) , which is dated to 1296 to 1472 CE (fig. S4 ). From the Touhou islet of Kapingamarangi, excavated by  Leach and Ward (37) , we analyzed three flakes of dense volcanic rocks and two cobbles of vesicular basalt identified as oven stones (fig. S5 ). On the basis of the established chronostratigraphic sequence, two artifacts were in association with deeper cultural deposits dated to 1258 to 1403 CE and 1433 to 1672 CE, respectively, and other artifacts were found in cultural deposits dated to the 17th century or later (text S1 and fig.
S6 ). Major elements were measured by inductively coupled plasma atomic emission spectrometry (ICP-AES), trace element by inductively coupled plasma mass spectrometry (ICP-MS), and isotopic ratios by multiple-collector ICP-MS (MC-ICP-MS) (text S2 and tables S2 to S4). The geochemical composition of each artifact was compared with data reported for geological samples from Southeast Asia and the Pacific Islands in the GEOROC and the Pofatu databases (texts S3 and S4)  (38) (39) (40) (41). Drawing on patterns of chemical variations in geological environments, we demonstrate that artifact compositions are compatible with specific source regions and archipelagos and, when possible, islands and specific quarries. First, we determine which samples are related to island arcs, with possible source islands located west of the andesite line, and which samples are related to intraplate contexts, with possible source islands located east of the andesite line (Fig.
Both OIBs and IABs have been documented as chemically and isotopically heterogeneous because of the dynamic evolution of the mantle over time  (42)  and processes such as the recycling of pelagic sediments in the source of subduction-related volcanic rocks  (43). By mapping the geochemical signatures of available source data from island volcanics in Southeast Asia and the Pacific islands, we can therefore assign a source to each artifact. Thanks to large geochemical datasets documenting a wide range of possible sources from both archeological and geological contexts  (38, 44) , we are able to ascertain the geological provenance of each artifact, including for islands where the extraction of raw material for artifact manufacture is not yet documented.
Sampling and sample preparation We processed and analyzed samples from four artifacts from Emae (Shepherd Islands, Vanuatu), five artifacts from Taumako (Duff Islands, Solomon Islands), and five artifacts from Kapingamarangi (Caroline Islands, Federated States of Micronesia). Sampling of Emae artifacts was done through a research agreement provided by the Cultural Council of the Vanuatu Cultural Centre and with the support of traditional landowners on Emae Island. Sampling of Taumako and Kapingamarangi artifacts was done in agreement with the sampling policy of the Archaeology Laboratory of the Museum of New Zealand Te Papa Tongarewa and with the support of the archeologists who led the excavations in the two islands, J. Davidson and F. Leach. Because no geological data have yet been published for Emae, we also measured the geochemical composition of six samples collected in the vicinity of the archeological sites excavated on the island.
(95). Results of the ICP-MS analyses are presented in the Supplementary Materials (table  S3 ) and provide compositions of 37 trace elements. A description of the analytical procedure and details of analytical accuracy are presented in text S2. (39)  and in a GitHub repository: https://github. com/pofatu/pofatu-data. The GEOROC data, which are publicly available as csv-formatted files from the GEOROC website (https://georoc. eu/), were also loaded into the SQLite database for scalable and performant analysis, which was made available in the following GitHub repository: https://github. com/pofatu/georoc-data. The Python library and details on the programmatic access to GEOROC data and usage of the SQLite file are documented by R. F. and available online  (96). Both curated databases are open-access and are fully accessible to reproduce the study.
Acknowledgments: We thank the communities of Makatea, Tongamea,  Finongi , and  Sangava  on Emae Island as well as chiefs  Ti Makata  mata,  Ma Ti Tonga ,  D. Maribu ,  Sasamake ,  Ti Nambua  mata,  Ti Nambua roto , and  Ti Makura  mata for permission to undertake field research and for continuous support during fieldwork. We also thank  J. Davidson  and  F. Leach  for support and.
Code availability The computer code used to generate the Bayesian age models are provided in full in the Supplementary Materials, together with information about the program and version used. To ensure full reproducibility, source data and R scripts are available in a GitHub repository (https://github. com/tupuni/ polynesianoutliersvoyaging/releases/tag/v1. 0) and in the following archive: http://doi. org/10. 5281/zenodo. 7388196.
This precludes the reliance on publicly available databases for accurate curation of variants. Secondly, co-existing alterations that influence variant allele frequencies (VAF) [i. e. , copy number alterations, copy neutral loss of heterozygosity (CN-LOH)] commonly occur, such that this metric cannot confidently guide the determination of somatic vs germline origin. These challenges are especially compounded in patients with a history of allogeneic hematopoietic stem cell (HSC) transplant, where determination of somatic vs germline and the source (host or donor) is often not possible. Here, we show our experience addressing these unique challenges through the development and clinical experience of MSK-IMPACT Heme (Integrated Mutation Profiling of Actionable Cancer Targets for Hematologic malignancies), a comprehensive molecular profiling platform, utilizing hybridization capture and high coverage nextgeneration sequencing of paired tumor and normal tissues.
Definitive identification of somatic variants To highlight the importance of sequencing a matched germline comparator, we analyzed variant calls made in all targeted exonic regions of the MSK-IMPACT Heme panel resulting from 'unmatched' variant calling of these tumor samples against a pooled control sample composed of ten diploid blood samples (Supplementary Fig. 2 , Supplementary Table  2 ). This analysis resulted in 48,248 variants that were properly filtered by the matched tumor-normal analysis pipeline, but otherwise passed criteria for clinical reporting, namely minimum VAF (0. 05), variant sequence reads (10) and their absence from a panel of 25 curated normal samples, known to be lacking any hematologic malignancy. Of these, 27,611 (57%) were present in the gnomAD database with any population frequency >0.
01, the primary recommendation for population database filtering from the joint consensus of AMP, ASCO, and CAP  14  , and therefore annotated as putative germline variants that could be dropped in a tumor only analysis. Of the remaining 20,637 putative germline variants, 9,157 (44%) were present in COSMIC v94 database, and identified in 2,271 tumor samples, or 95% of our sequenced cohort with an average of 4 additional variants per sample. These represent germline variants that would have been incorrectly reported as somatic in an unmatched analysis, with potential adverse clinical implications. For instance, while specific mutations are not required in the FDA approval for hypomethylating agents (HMAs) in myeloid neoplasms, their presence has been associated with response to HMA treatment, and inaccurate reporting could alter choice of therapy  15.