dataesr/scientific-paragraphs-categorization huggingface dataset:
Regarding genetic structure, our simulations showed a slight decrease of F ST only for a clonal rate of 0. 9: this agrees well with theoretical expectations  (Balloux et al. , 2003). The correction of datasets for repetitions of clonal lineages changed the estimated F ST values but this did not change our main conclusions: an important differentiation for the All datasets, and low to no genetic structure in Oman and Polynesia. In some cases such correction for clonal diversity can lead to very different conclusions, from genetic differentiation without correction to near panmixia in Pocillopora in French Polynesia  (Adjeroud et al. , 2014). Therefore, one should not rely on a single analysis strategy (e. g. all individuals or single genotypes for example), but consider the different results thus obtained  (De Meeûs et al.
Genomic analysis of species hypotheses When considering the All datasets, we observed a marked differentiation between Polynesia and Oman which was superimposed on a differentiation between PSH05 and other species hypotheses (with one exception in Oman). This was well visible on the network, PCA and snmf analyses, both with the 97_75 and the 75_75 filtering strategies. The mitochondrial lineage 5a corresponds to the PSH05, and is phylogenetically well separated from lineages 3e -3g and 7a which correspond to SSH13a and PSH12  (Gélin et al. , 2017). The distinction of PSH05 from PSH 12 and SSH13a was previously confirmed with microsatellite loci  (Gélin et al. , 2017). Our results confirm the distinction of PSH05, but our sampling scheme did not allow to test the delimitation and possibility of hybridization of this species with other species in sympatry.
(e. g. (Nelson et al. , 2020) , and if possible with the use of high-quality genome  (Manel et al. , 2016). It would also be necessary to include additional PSHs to better estimate the range of genomic divergence among species in this genus. In Polynesia, both datasets did not show evidence of genetic structure among sites, which were distributed in the two islands of Moorea and Tahiti. Though limited by a reduced number of individuals, these results are in line with previous studies on the genetic structure of Pocillopora:  (Adjeroud et al. , 2014; Magalon et al. , 2004; Oury et al. , 2021). One can note that in Pocillopora corals the patterns of genetic structure are evidently dependent on the species hypothesis and location considered  (Oury et al. , 2020).
Conclusions and perspectives: Because of unequal success in the production of RAD sequences, we obtained a dataset with very different levels of missing data among samples, and potentially low number of loci shared among samples depending on the level of analysis. We therefore had to explore different filtering strategies to get informative datasets. The trade-off between the number of loci and individuals retained in the datasets led to different estimates of genetic diversity. Nevertheless the main patterns of genetic structure were conserved among the different datasets, which shows the interest of testing the impact of data filtering on the obtained results. Interestingly, we can conclude that the main signal of genetic structure was visible with only slightly more than 300 SNPs. Of course, depending on the objectives of the study, higher number of loci may be necessary.
number of SNPs and individuals for the different datasets. The locus threshold corresponds to the minimum proportion of available data among individuals to retain a locus. The individual threshold corresponds to the minimum proportion of available data among loci to retain an individual. The mean depth indicates the mean depth per individual averaged over all individuals in the dataset. The last column indicates the number of retained individuals after correction for the presence of multiple MLL (see text for details). All corresponds to all samples (Polynesia and Oman).
presents the characteristics of the six final datasets: a total of 140 samples were distributed among the different datasets. The highest numbers of SNPs, with one SNP per RAD locus, were obtained for the 75 % filtering on loci missing data in the All (194 370 SNPs) and Oman datasets (134 307 SNPs). The separate assembly of Oman and Polynesia allowed the recovery of more SNPs than the All assembly with the 95_75 strategy, and a reverse result was obtained with the 75_75 strategy. The All_95_75 dataset had the highest number of individuals (132) and the lowest number of SNPs (320; Table.
Acknowledgements: This work is a contribution to the  Labex OT-Med  (n°  ANR-11-LABX-0061 ) funded by the  French Government "Investissements d'Avenir" program  of the  French National Research Agency (ANR)  through the  A*MIDEX project  (n°  ANR-11-IDEX-0001-02 ). This project has been funded by the  ADACNI program  of the  French National Research Agency (ANR)  (project n°ANR-12-ADAP-0016; http://adacni. imbe. fr). The project leading to this publication has received funding from  European FEDER Fund  under project  1166-39417. We thank  Nicolas Fernandez  and  Béatrice Loriod  from the Marseille TGML platform for their invaluable help and advice with the preparation of the RAD libraries; the team of the MGX platform for the sequencing of the RAD libraries. The authors thank the  UMR 8199 LIGAN-PM Genomics platform (Lille, France , especially  Véronique Dhennin ) which belongs to the ' Federation de Recherche' 3508 Labex EGID (European Genomics Institute for Diabetes ;  ANR-10-LABX-46 ) and was supported by the  ANR   Equipex 2010 session  ( ANR-10-EQPX-07-01 ; ' LIGAN-PM ').
The  LIGAN-PM Genomics platform (Lille, France)  is also supported by the  FEDER  and the  Region Nord-Pas-de-Calais-Picardie. This study was set within the framework of the  Laboratoire d'Excellence (LABEX) TULIP  ( ANR-10-LABX-41 ) We acknowledge the staff of the  "Cluster de calcul intensif HPC" Platform of the OSU Institut Pythéas (Aix-Marseille Université, INSU-CNRS)  for providing the computing facilities. We gratefully acknowledge  Julien Lecubin  and  Christophe Yohia  from the  Informatic Service of Pythéas Institute (SIP)  for their technical assistance. We acknolwedge  Stéphanie Rialle ,  Marine Maurine Bonabaud  from the MGX sequencing platform ( CNRS, Montpellier, France ) for the help with data production and quality controls. The authors acknowledge the financial support from the  France Génomique National Infrastructure , funded as part of " Investissement d'Avenir" program  managed by the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ).
489 The mean F ST estimates among loci were generally lower for the datasets corrected for MLLs compared to the non-corrected datasets (Table4), except for the comparison among mitochondrial lineages with All_95_75, and the comparison among populations with All_75_75. The mean F ST between Polynesia and Oman (by grouping samples from each region) was 0. 105 and 0. 352 for the All_95_75 and the All_75_75 datasets respectively. The distributions of F ST among loci for these Oman / Polynesia comparison are presented in Figure.
METHODS. We combined individual level data from 13,888 COVID-19 patients (n = 7185 hospitalized) from 17 cohorts in 9 countries to assess the association of the major common COVID-19 genetic risk factor (chromosome 3 locus tagged by rs10490770) with mortality, COVID-19-related complications, and laboratory values. We next performed metaanalyses using FinnGen and the Columbia University COVID-19 Biobank.
While we included information from participants who were of non-European ancestry, ongoing efforts should enable larger sample sizes to better define the importance of the chromosome 3 risk locus in these ancestries. This further emphasizes the importance of developing genomics-enabled studies in individuals of non-European ancestry. Since the beginning of the pandemic, we aimed to aggregate and harmonize individual-level clinical and genotype data from multiple cohorts from diverse countries. Due to the nature of the heterogeneity of health care systems, our data from multiple countries substantially increases the generalizability of our research findings  (38). Moreover, we deposited a subset of this harmonized data to the European Genome-Phenome Archive (EGAS00001005304) for future use by all bona fide researchers to further improve our ability to understand the COVID-19 pandemic.
Methods Study participants. We gathered clinical and genomic data from 13,888 COVID-19 cases (7185 of whom were hospitalized) with genetic information available, harmonizing individual-level data from 17 studies. COVID-19 cases were defined as individuals having at least 1 confirmed SARS-CoV-2 viral nucleic acid amplification test from relevant biologic fluids or whose SARS-CoV-2 status was confirmed by ICD-10 codes, using codes U071 and/or U072. We combined data from hospital-based studies that recruited participants after COVID-19 outbreak and a population-based biobank in which recruitment was not dependent upon COVID-19 status. Data were centrally collected at the Institute for Molecular Medicine Finland and harmonized through a standardized data dictionary (https://docs. google. com/spreadsheets/ d/1hwBeqckB3_qC8nnavT0kLLntOh3GrmWRJQHeO9zwG8w/ edit#gid=665246845).
Detailed information for data collection in each individual study is described in Supplemental Methods. Genotyping and ancestry assignment. In order to tag the chromosome 3 locus, we selected the SNP rs10490770, which was most significantly associated with hospitalization in the COVID-  19    reduce the risk of collinearity (details are described in Supplemental Methods. We further estimated the frequency of rs10490770 risk allele carrier status from population frequencies reported in an external database (the Genome Aggregation Database, version 3. 1 [gnomAD]; ref. 19) , assuming this variant follows Hardy-Weinberg equilibrium. Association with mortality. The HR for all-cause mortality was estimated by Cox's proportional hazard models using the coxme version 2. 2-16 R package (https://cran.
The procedure will involve review by legal departments at both institutions, and the process will typically take about 2 months from initial contact. The BQC19 is an open science biobank. Instructions on how to access data for individuals from the BQC19 at the Jewish General Hospital site are available here: https://www. mcgill. ca/genepi/ mcg-covid-19-biobank. Instructions on how to access data from other sites of the BQC19 are available here: https://www. bqc19. ca/en/ access-data-samples. For the COVID-19 Kohortenstudie am Klinikum München Rechts der Isar (COMRI) cohort, data protection legislation does not allow for deposition of individual level data in public repositories. Upon direct contact with Christoph Spinner (christoph. spinner@tum. de), an institutional data transfer agreement can be established and data will be shared if the aims of data use are covered by ethical approval and patient consent.
The procedure will involve an update to the ethical approval as well as review by legal departments at both institutions, and the process will typically take 2 to 3 months from initial contact. Regarding the Fondazione IRCCS Milan data (FOGS study), institutional data privacy regulations prohibit deposition of individual level data to public repositories without specific consent. Participant written consent also does not cover public sharing of data for use for unknown purposes. Upon contact with Luca Valenti (luca. valenti@ unimi. it), an institutional data transfer agreement can be established and data shared if the aims of data use are covered by ethical approval and patient consent. The procedure will involve the request for an amendment to the ethical approval as well as review by legal departments at both institutions, and the process will typically take 1 to 2 months from initial contact.
16. 2 (https://cran. r-project. org/web/packages/pROC/index. html), and PredictABEL, version 1. 2-4 R packages (https://cran. r-project. org/ web/packages/PredictABEL/index. html). Metaanalyses. As secondary analyses, we metaanalyzed the results for non-European ancestries and 2 external cohorts for which we did not have access to individual-level data: FinnGen and Columbia University COVID-19 Biobank (CUB). This resulted in a total study population of 15,064 individuals with COVID-19. Inversevariance weighted metaanalyses were performed under a fixed effect and random effects model using the meta version 4. 16-1 R package when the appropriate phenotypes were available and case counts, control counts, and the rs10490770 risk allele carrier counts were larger than 10 in each cohort. Sensitivity analysis.
We did not apply a multiple-testing correction, and a P value of less than 0. 05 was considered significant, since all the outcomes tested were related to COVID-19 severity and not independent of each other. Data and materials availability. All code for data management and analysis is archived online at https://github. com/tomoconaka/ COVID19-chr3 (commit 183ddb7) for review and reuse. The harmonized individual-level data of some participating cohorts from Belgium (BeLCovid_2), Brazil (BRACOVID), Italy (COVID19-Host.
Acknowledgments We thank the patients who volunteered to contribute to all of the participating studies in such difficult times and the research staffs of every cohort who recruited patients at personal risk. FinnGen is detailed in the Supplemental Methods. The COVID- 19 HGI is Clin Invest. 2021;131(23): e152386  https://doi. org/10. 1172/ JCI152386  Humanitas per la Ricerca). The generous contribution of  Banca Intesa San Paolo  and of the  Dolce & Gabbana Fashion Firm  is gratefully acknowledged. Data acquisition and sample processing were supported by the  COVID-19 Biobank ,  Fondazione IRCCS Cà Granda Milano. The LV group was supported by  MyFirst Grant AIRC  n. 16888 ,  Ricerca Finalizzata Ministero della Salute   RF-2016-02364358 ,  Ricerca Corrente Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico , the  European Union (EU)   Programme Horizon 2020  (under grant agreement no.
777377 ) for the project  LIT-MUS-Liver Investigation: Testing Marker Utility in Steatohepatitis, Programme Photonics  under grant agreement  101016726  for the project  REVEAL: Neuronal Microscopy for Cell Behavioural Examination and Manipulation, Fondazione Patrimonio Ca' Granda Liver Bible   PR-0361. DP was supported by  Ricerca Corrente Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico ,  CV PREVITAL Strategie di Prevenzione Primaria Nella Popolazione Italiana Ministero della Salute , and  Associazione Italiana per la Prevenzione dell'Epatite Virale (COPEV). DB was supported by  Programa Estratégico Instituto de Biología y Genética Molecular (IBGM) Junta de Castilla y León  ( CCVC8485 ),  Proyectos COVID-19 de la Junta de Castilla y León  ( 07. 04. 467B04. 74011. 0 ),  Consejo Superior de Investigaciones Científicas  ( CSIC-COV19-016/202020E155 ), and  NextGenerationEU.
Study of genetic modifiers for COVID-19-related illness (BeLCovid_1) was supported by  Fonds Erasme. The Host Genetics and Immune Response in  SARS-CoV-2 infection (BelCovid_2) study  was supported by grants from  Fondation Léon Fredericq , from the  Walloon region , and from  FNRS. The  INMUNGEN-CoV2 study  was funded by  CSIC  (grant n. 202020E086 ), and used samples received from the COVIDBANK of the  HCB-IDIBAPS Biobank  funded in part by  Fundació Glòria Soler. KUL is supported by the  German Research Foundation  ( LU 1944/3-1 ). SweCovid  is funded by the  SciLifeLab/KAW national COVID-19 research program  project grant to  Michael Hultström  ( KAW 2020. 0182 ) and the  Swedish Research Council  ( 2014-02569  and  2014-07606 ) as well as The  Swedish Kidney Foundation  ( F2020-0054 ) to RF.
HZ is supported by  Jeansson Stiftelser, Magnus Bergvalls Stiftelse. The  COMRI  cohort is funded by the  Technical University of Munich. Genotyping for the COMRI cohort was performed and funded by the  Genotyping Laboratory of the Institute for Molecular Medicine Finland FIMM Technology Centre, University of Helsinki. ECS received support through the  Munich Clinician Scientist Program   (MCSP)  and the  German Research Foundation  (DFG)  SCHU 2914/2-1 ). The  Norwegian SARS-CoV-2 study  was supported by grants from the  Research Council of Norway  (no. 312780 ) and a philanthropic donation from  Vivaldi Invest  A/S, owned by  Jon Stephenson von Tetzchner. These funding agencies had no role in the design, implementation, or interpretation of this study. Address correspondence to:  Andrea Ganna , Institute for Molecular Medicine Finland,  University of Helsinki, Helsinki, Finland.
The  COVID19-Host (a)ge study was supported by the  German Federal Ministry of Education and Research (BMBF)  within the framework of the  Computational Life Sciences funding concept  ( CompLS  grant  031L0165 ). Genotyping in COVID19-Host(a)ge was supported by a philanthropic donation from  Stein Erik Hagen  and the  BMBF  (grant  01KI20197 ). The  COVID GWAS ,  Premed COVID- 19  study ( COVID19-Host (a)ge_3) was supported by  Grupo de Trabajo en Medicina Personalizada contra el COVID-19 de Andalucia  and also by  ISCIII (CIBERehd and CIBERER). Funding comes from  COVID-19-GWAS ,  COVID-PREMED  initiatives. Both of them are supported by  Consejeria de Salud y Familias of the Andalusian Government. DMM is currently funded by the  Andalussian government  ( Proyectos Estratégicos-Fondos Feder   PE-0451-2018 ).
The  Columbia University Biobank  was supported by  Columbia University  and the  National Center for Advancing Translational Sciences ,  NIH , through grant number  UL1TR001873. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH  or Columbia University. AK was supported by  K25 ( K25DK128563 ) from the  NIH National Institute of Diabetes and Digestive and Kidney Diseases (NID-DK)  and  TL1 ( UL1TR001873 ) from the  NIH/National Center for Advancing Translational Sciences (NCATS). The SPGRX study was supported by the  Consejería de Economía, Conocimiento, Empresas y Universidad  no. CV20-10150. The  GEN-COVID  study was funded by  Italian Ministry of Education, University and Research (MIUR)  grant  Dipartimenti di Eccellenza   2018-2020  to the  Department of Medical Biotechnologies University of Siena, Italy ; the  Intesa San Paolo 2020  charity fund dedicated to project  NB/2020/0119 ; and philanthropic donations to the  Department of Medical Biotechnologies, University of Siena , for the  COVID-19  host genetics research project (DL n.
To this end, we analyze in situ data taken in La Guajira sector by two Acoustic Doppler Current Profilers (ADCP) moored during years 2007 and 2008 by Petrobras Colombia (Brazilian Petroleum Corporation) in collaboration with Dimar (Dirección General Marítima, according to its Spanish initials), together with Chl-a satellite concentration images from MODIS/Aqua, ocean currents from the HYbrid Coordinate Ocean Model-HYCOM  (Chassignet et al. , 2007) , and Cross-Calibrated Multi-Platform (CCMP) surface winds  (Wentz, 2015). In order to elucidate how the CCC influences La Guajira upwelling system, data are analyzed from complementary Eulerian and Lagrangian perspectives. This article is structured into three main parts: first, the study area (section 2) an https://www. overleaf. com/projectd data and methods (section 3) are presented.
Chlorophyll-a Satellite-Based Data Chlorophyll-a (Chl-a) is a photosynthetic pigment commonly present in all phytoplankton species. It is used as a proxy of surface phytoplankton concentration. Chl-a concentration is a standard product from satellite-based optical sensors, usually retrieved through empirical algorithms that combine reflectance ratios at two or more wavebands. Daily MODIS/Aqua NIR-SWIR ocean color images, available from mid 2002, were obtained from https://oceandata. sci. gsfc. nasa. gov/MODIS-Aqua. This product provides surface Chl-a concentration at a spatial resolution of 1 km 2 by using the shortwave infrared (SWIR)-based atmospheric correction algorithm developed by NOAA/NESDIS. We use images of monthly mean sea surface Chl-a concentration (unit in mg•m -3 ) to characterize La Guajira area.
Self-Organizing Maps A Self-Organizing Map (SOM) is a visualization technique based on an unsupervised learning neural network especially suited to extract patterns in large datasets  (Kohonen, 1982 (Kohonen, , 2001)). SOM is a nonlinear mapping implementation method that reduces the high-dimensional feature space of the input data to a lowerdimensional (usually two dimensional) network of units called neurons. In this way, SOM is able to compress the information contained in a large amount of data into a single set of maps. The learning process algorithm consists of a presentation of the input data to a preselected neuronal network, which is modified during an iterative process. Each neuron (or unit) is represented by a weighted vector with a number of components equal to the dimension of the input sample data.
At the end of the training process, the probability density function of the input data is approximated by the SOM, and each unit is associated with a reference pattern that has a number of components equal to the number of variables in the dataset. Therefore, this process can be interpreted as a local summary or a generalization of similar observations. Compared to conventional statistical methods such as the Empirical Orthogonal Functions, SOM is able to introduce nonlinear correlations and it does not require any particular functional relationship or assumption on the data distribution, e. g. , distribution normality or equality of the variance  (Liu et al. , 2006 (Liu et al. , , 2016;; Hernández-Carrasco et al. , 2018). In this work we compute SOMs in the spatial domain with a map size of 4 × 4 (16 neurons) and a hexagonal map lattice to have equidistant neighbors and do not introduce anisotropic artifacts.
The signature of the CCC is more evident at the depth of maximum salinity (at around 100 m), as during these months subsurface waters can reach La Guajira peninsula transporting lighter waters from the western edge of the basin. A SOM analysis for the HYCOM model data reveals that from the surface up to 90 m depth, the CCC reaches La Guajira when the wind is weaker. This current advects waters from the continental shelf that are visible through the satellite-based color images likely thanks to the high concentration of nutrients and sand particles transported from the rivers to the sea. van der Boog et al. (  2019 ) found by combining model data and observations that most of the waters upwelled in the southern CCUS came from the subsurface CCC, which carries diluted waters. These authors hypothesize that waters seem to come from the Panama-Colombian Gyre traveling toward the east and reaching areas 4,400 km apart.
Strain isolation and phenotypic tests Mixta mediterraneensis strain Marseille-Q2057 T was initially isolated by direct seeding of 50 μL of sample on an Acinetobacter-specific medium  [6]  incubated in aerobiosis at 31°C, MALDI-TOF MS protein analysis was carried out using a Microflex spectrometer (Bruker Daltonics, Bremen, Germany)  [8]. Spectra from strain Marseille-Q2057T were imported into the MALDI BIOTYPER software (version 3. 0, Bruker) and analysed by standard pattern matching (with default parameter settings). The study was validated by the ethics committee Sud-Est IV under number ID-RCB: 2019-A01508-49. Different growth temperatures (30°C, 37°C, 45°C and 56°C), atmospheric conditions-anaerobic, aerobic and microaerophilic (CampyGEN, Oxoid, USA) and pH  (5, 6. 5, 7. 5, 8. 5)  were tested.