dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Lp ST23 are a well-known cause of both sporadic and epidemic LD cases, representing 4. 4% of the ST found in Italy and 6. 6% of the ST reported to the SBT database, although reporting countries are only France and Italy, with France having the most abundant number of ST23 cases (S ánchez-Busó et al, 2016;  Zanella et al, 2018). In this study, the ST23 collection investigated was found to be quite preserved which is in contrast to the genomic plasticity and variation observed with other L. pneumophila and among diverse Legionella species  (Cazalet et al, 2004 (Cazalet et al, , 2008)) , mostly due to the presence of mobile genetic elements such as transposons and insertion sequences, recombination regions, and conjugative elements. The year and geographic area of isolation for all Italian ST23 showed highly similar cgMLST and a very restricted number of SNP differences, which suggests that ST23 has undergone a very low selective pressure, according to a recent appearance and an adjustment to a specific environmental niche  (David et al, 2016a).
On the contrary, French ST23, mostly from national communityacquired cases, were a good example of a genomic similarity from a large, single geographical area. In the Italian ST23 LD cases, southern Italy was not represented and this is could be probably due to a lower notification rate compared with the North of the country. Therefore, the Italian ST23 are from an even smaller geographic region of a country with high ST23 incidence and high genomic similarity. Looking at the ST23 isolated in Italy and those entered the SBT database, it is noteworthy that they are prevalently associated to travel-and community-acquired LD cases. The evidence that the ST23 strains are able to cause disease in a population not at high risk of infection deserves more in-depth studies, with particular regard to its virulence features.
All genomes were submitted to GenBank (Bioproject PRJNA606275; accession numbers listed in Table  S8 ). To better understand the phylogenies of ST23 isolated in Italy, further 41 ST23 isolates already published, sequenced elsewhere or as part of this study were included. In addition, 14 genomes, ST1 (n = 5), ST37 (n = 4), and ST62 (n = 5) were also included (Table  S10 ).
WGS and assembly Sequencing libraries were prepared using the NextEra XT library prep kit (Illumina) and then run on two different Illumina platforms: a 150-bp paired-end sequencing run was performed on the NextSeq-500 (Illumina) using a Mid output Kit v2, whereas a 250-bp paired-end sequencing run was performed on the Miseq (Illumina). The fastq files were trimmed and assembled using the fully automated pipeline INNUca by Galaxy ARIES platform (https:// aries. iss. it/; Mitchell & Simner, 2019). Eleven ST23 from Danish patients were sequenced at the Statens Serum Institut, as described above, and the reads were de novo assembled using SPAdes v3. 12. 1 with default parameters  (Bankevich et al, 2012 ) (Bioproject PRJEB48072). A further seven ST23, originated from Scottish patients who had travelled to the Southern Europe, were sequenced at the University of Birmingham by MicrobesNG, using Illumina 2 × 250 bp paired-end short read sequencing and deposited in the SRA database (Submission ERA1765603: Bioproject PRJEB31628).
cgMLST analysis chewBBACA software (24-25) was used to convert the cgMLST scheme defined by  Moran-Gilad et al (2015) , which included 1,521 core genes. The allelic profiles were then corrected to include only the subset of genes that were present in all the strains. The allelic profile output was then used to create minimum spanning trees with PHYLOViZ Online software  (Ribeiro-Gonçalves et al, 2016; Silva et al, 2018; Knijn et al, 2020 Preprint). Geneious 9. 1. 8 (Biomatters Ltd. ) was used for nucleotide sequence alignment of loci to identify differences against corresponding nucleotide reference sequences.
SNP analysis The kSNP program, based on k-mer analysis, was used to identify core genome SNP on WGS data for each isolate. After the kSNP analysis, the core SNP matrix output file, containing only SNP loci common to all isolates, was used to construct minimum spanning tree by PHYLOViZ online software. To investigate if SNP occurrence was due to recombination events, sequence reads were mapped to the LT632615. 1 ST23 reference genome using Snippy v4. 5. 0, and horizontally transmitted SNP were removed by Gubbins analysis (https://aries. iss. it/; Mitchell & Simner, 2019). Tree was inferred using the Neighbor-Joining method  (Saitou & Nei, 1987)  and the evolutionary distances were computed using the Maximum Composite Likelihood method  (Tamura et al, 2004 ) that were in the units of the number of base substitutions per site.
Acknowledgements The authors are grateful to all health professionals (microbiologists, doctors, nurses, health assistants, laboratory technicians, etc. ) who have collaborated by sending the surveillance forms, respiratory samples and isolated Legionella strains. The authors thank  Antonella Fortunato  for technical support. This work was supported by the  Ministry of Health  (Centro per il controllo delle malattie, 2019-2020; Grant  ISS N. R934 ). We thank  Anna Maria Marella  for her technical support.
Data Availability All raw and processed sequencing data generated in this study have been submitted to the National Center for Biotechnology Information (NCBI; https://www. ncbi. nlm. nih. gov/) Bioproject database PRJNA606275 and accession numbers are also listed in Table  S10  in Supplemental material. All the other isolates, sequenced in this study, were submitted to bioprojects PRJEB48072 and PRJEB31628.
Harmonization can be prospective (studies share the same study design and measurement instruments from their inception), ex-ante retrospective (studies were not originally designed to be comparable but use standard collection tools and operating procedures) or ex-post retrospective (when no common standard formats or protocols are used across studies)  (14). Regarding the analyses, three options are available: pooled analysis combines all the data from individual participants into a single analysis by aggregating them into a common data infrastructure; meta-analysis is a twostage analysis that combines the results of inferences on each separate cohort dataset  (15) ; federated analysis performs the analysis of individual level data in a central infrastructure while participant-level data remain on their local infrastructure.
Database search in MEDLINE The search was intended to produce a representative, albeit non-exhaustive, list of cohort integration initiatives. An initial set of search terms was agreed upon by the consortium partners. In order to accomplish a manageable number of hits, these terms were tested and the ones that yielded more than 500 hits were discarded. (To reduce the number of hits, the term "cohort" was added to some searches before the discarding). New terms were extracted from abstracts of relevant retrieved papers and tested again. To obtain the most recent scientific evidence, the search was limited to studies published in 2000 or later. The search was conducted in July 2019. The final search query was: (cohort OR "prospective study" OR "longitudinal study" OR "individual meta-analysis"[All Fields] OR "individual participant data meta-analysis"[All Fields] OR "individual patient data meta-analysis"[All Fields] OR "individual meta analysis"[All Fields] OR "individual participant data meta analysis"[All Fields] OR "individual patient data meta analysis"[All Fields] OR "meta analysis using individual"[All Fields] OR "meta-analysis using individual"[All Fields] OR "meta analysis of individual"[All Fields] OR "meta-analysis of individual"[All Fields] OR "mega-analysis"[All Fields] OR "mega analysis"[All Fields]) AND ("harmonization study" OR "integration study" OR "integration initiative" OR "integrated study" OR "merged cohort" OR "data pooling" OR "pooled sample" OR "combined data" OR "combining data" OR "harmonized data" OR "harmonized data" OR "harmonizing data" OR "data harmonization" OR "data harmonization" OR "data sharing" OR "common database" OR "multiple cohorts" OR "multiple longitudinal studies" OR "international consortium" OR "collaborative effort").
From the full-text review, 155 articles were excluded due to one or more of the following reasons: one-time efforts (i. e. , data merged adhoc for the specific analysis supporting the paper), initiatives already submitted by partners, articles with cross-sectional harmonization only, multisite cohorts/harmonization of waves, meta-analyses, reviews, case-control studies, patients' cohorts, clinical trial cohorts, and others. From the systematic review, 11 articles were included. Additionally, from the partners' and officers' suggestions and the descendent searches, 189 potential initiatives were obtained. After screening for eligibility, 141 projects were excluded for one or more of the following reasons: initiatives published before the year 2000, projects that did not integrate cohorts, one initiative with only cross-sectional cohorts, and one initiative that integrated patient and clinical trial cohorts.
Data extraction The following technical information was extracted from the initiatives: name, principal investigator (PI), initiative partners, name of the leading institution, contact person, information source, whether the research team was active at the time of consultation, main objectives, criteria for the cohorts to be included, funding resources (public, private, or mixed public/private), and a brief description of the population addressed. When the information was not available through the website or published articles, the principal investigator and/or the contact person (i. e. , any person that could respond to the form requesting information about the initiative) of the project was consulted, by email first and, if no reply was received, by telephone or postal mail. The following information about the harmonization process was collected: harmonization strategy (prospective/retrospective ex-ante/retrospective ex-post), number of harmonized cohorts, whether more cohorts were foreseen to be harmonized, number of participants with harmonized data, maximum number of harmonized variables (including those where harmonization was not possible for all the cohorts), and setting of the harmonized cohorts (local-regional/national/international).
The following information was deemed relevant for the integration effort and thus was also collected for each initiative: total number of cohorts, total number of participants, age range of the sample, country/ies included, whether metadata and individual data were accessible to other researchers, whether any of the cohorts included biological (omics) samples, research topic(s) addressed, and type(s) of aggregated analyses performed on the integrated cohorts (pooled, federated, or meta-analysis). The initiatives are presented in Supplementary Table  1 , including a subset of their most representative information. The country where the leading institution was based and the countries included in the integrated cohorts were recoded into continents (Africa, Asia, Europe, Oceania, Latin America & Caribbean, North America).
Dataset descriptive statistics In a large number of initiatives the contact person was unreachable throughout the whole mapping process, so the proportion of missing data was as large as 70. 7% for some variables. At the time of consultation, 83. 3% of the research teams that had carried out the projects remained active (although this percentage drops to 69. 0% if we assume that those which did not respond were inactive). The majority of the leading institutions were based in The Netherlands (n = 9), followed by institutions from the USA (n = 8), the UK (n = 6), and Finland (n = 5). Of the initiatives, 82. 6% were funded by public institutions, while 17. 4% received funding from both private and public institutions. Interestingly, none of the three initiatives addressing the "Birth, infancy & childhood health" received funding from private institutions, while the two initiatives that reported studying cancer did receive funding from them.
Variable Level Median/n (N = 58) (Range/%)  (note that some initiatives used more than one type of integrated analysis). The targeted populations ranged in age from childbirth to death (the 150-year-old upper limit probably refers to a cohort with unbounded maximum age, rather than participants of that age). Aging was the most prevalent topic, addressed by 15. 5% of the initiatives, and 42. 1% (n = 8) of the initiatives included omics data (i. e. , biological samples). The cohorts were collected in as many as 43 countries, although there were also initiatives with cohorts from only one country. Most of them (51. 8%) were circumscribed to just one continent, with Europe being the most frequent (80. 4%), while only 3. 6% comprised cohorts from all six continents. Latin America and Africa were the most underrepresented continents, with only six (11.
Conclusion The purpose of this mapping was to gather knowledge about the state of the art of the initiatives that integrate population cohorts. The landscape of these initiatives has been revealed to quite disparate, with a high variability of populations and variables, as well as topics and regions addressed by the integrated cohorts. As foreseeable, the number of participants and variables integrated is generally larger than in initiatives integrating patient cohorts  (16). Taking the high volumes of data in some of the initiatives, their potential for future researchers is undeniable. Nevertheless, the number of integration initiatives seems rather low, compared to the overall and increasing number of very large cohort datasets worldwide. It is also worth stressing that a large proportion of the initiatives found are inactive nowadays, limiting the possibilities of data access and sharing.
Focus on environmental exposures was more prevalent than genetic and biological factors. Very few of the initiatives were actually found to harmonize and integrate biological sample data (although there was a high rate of missingness in this variable). Including them may help discover possible causal pathways among biological, behavioral, social, demographic, economic, and health outcomes  (17). Unfortunately, incorporating biomeasures in cohort research is not always feasible. Most of the initiatives were led by European and American institutions; as would be expected, most of the integrated cohorts were also collected in these two continents. Interestingly, very few of the initiatives included cohorts focused on African and Latin American & Caribbean countries. Previous studies emphasize the relative lack of health studies conducted in low-and middle-income countries [LMICs  (18) ].
Strengths and weaknesses To our knowledge, this is the first effort to map and describe in detail all the initiatives integrating population cohorts. Given the difficulties reported by authors when integrating cohorts  (19) , we expect positive outcomes of our endeavor in three main aspects. First, the SYNCHROS repository is a resource where interested researchers can find integrated population cohort data or contribute their population cohorts. Second, several PIs and project managers have provided first-hand information on the barriers and solutions they have found when integrating cohorts. Finally, we expect all this information to be extremely relevant to designing a European strategy for cohort integration, such as is the aim of the SYNCHROS project. Although we must raise awareness of the non-exhaustive nature of this mapping, we should stress that this manuscript aims at representativeness rather than exhaustiveness.
Acknowledgements The work of AJ is supported in part by a  KIAS Individual  Grant No. QP084401  via the  Quantum Universe Center at Korea Institute for Advanced Study  and by the  Institute for Basic Science (IBS)  under the project code, IBS- R018-D1. The work of BF has been partly supported by the  French Agence Nationale de la Recherche (ANR)  under grant  ANR-21-CE31-0013  (project  DMwithLLPatLHC ). AK acknowledges the support and hospitality of the  CERN Theoretical Physics Department. The results of this study were obtained using.
Systematic and quantitative extraction of high resolution information on MABL properties in SAR measurements is still a challenge, due to overlapping geophysical processes which require complementary information. The aim of this paper is thus to present an innovative approach based on a combined use of SAR and geostationary satellite data to study the interplay between the MABL and convective organization. By doing so we (i) show how SAR clear-sky convection compares to in situ measurements and (ii) analyse the morphological and dynamical properties of cold pools, related to moist convection. The paper is organized as follows: the data and methods are presented in Section 2. Section 3 is devoted to the clear-sky turbulence and Section 4 concerns the cold pool detection and characterization.
Data We used C-band SAR images from the Sentinel-1 satellite, which probes the sea surface through clouds, in interferometric wide swath (IW) mode. Each image is 400km wide with effective resolution of 100 m, allowing multiscale sampling of cold pools. The C-band backscatter signal (σ 0 ) is sensitive to the slope distribution of centimetrescale wind-wave, with short adjustment timescales to changes in surface wind  (Kudryavtsev et al. , 2005). Backscatter contrasts can thus be related to surface signatures of MABL processes  (Ayet et al. , 2021). To obtain information on the cloud layer, we used infrared brightness temperature (T B ) provided by the Advanced Baseline Imager (ABI,  Schmit et al. , 2005)  onboard GOES-16 (Geostationary Operational Environmental Satellite). In the region of interest, the Atlantic trades, cloud cover mainly consists of low-level clouds which facilitates the interpretation of the satellite images can be detected with the brightness temperature despite their small temperature differences with the sea surface.
Open Research Sentinel-1 is part of the European space component of the Copernicus European program. level-1 GRD data are free of charge and available on the Copernicus Open Access Hub (https://sentinels. copernicus. eu/web/sentinel/sentinel-data-access). GOES-16 ABI Level 1b radiances are available at https://doi. org/10. 7289/V5BV7DSR.
WindSat data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the NASA Earth Science Physical Oceanography Program. RSS WindSat data are available at https://data. remss. com/ windsat/. The EUREC4A turbulence dataset derived from the SAFIRE ATR 42 aircraft is available on the AERIS database (https://doi. org/10. 25326/128).
Sentinel-1 is part of the European space component of the Copernicus European program. level-1 GRD data are free of charge and available on the Copernicus Open Access Hub (https://sentinels. copernicus. eu/web/sentinel/sentinel-data-access). GOES-16 ABI Level 1b radiances are available at https://doi. org/10. 7289/V5BV7DSR. WindSat data are produced by Remote Sensing Systems and sponsored by the NASA Earth Science MEaSUREs DISCOVER Project and the NASA Earth Science Physical Oceanography Program. RSS WindSat data are available at https://data. remss. com/ windsat/. The EUREC4A turbulence dataset derived from the SAFIRE ATR 42 aircraft is available on the AERIS database (https://doi. org/10. 25326/128). Vandemark, D. , Mourad, P. D. , Bailey, S. A. , Crawford, T. L. , Vogel, C. A. , Sun, J.
Acknowledgments The  EUREC4A  project provided a relevant framework for this study. PEB and AA were supported through a  Postdoctoral Grant  funded by the  Centre National d' Études Spatiales (CNES). Airborne data were obtained from the  ATR  aircraft operated by SAFIRE, the  French facility  for airborne research, an infrastructure of the CNRS,  Météo-France  and the  CNES. AM was supported by the  ESA Sentinel-1 Mission Performance Center  ( 4000107360/12/I-LG ). We also thanks the  CNES  which provide, through the TOSCA  PROCONUM  project (Pi. DB) the facilities for authors to collaborate.
Public virome data set A data set of 2,946 publicly available viromes was downloaded for this analysis, two of them being only available as assembled contigs. For the other 2,944 viromes, reads were first cleaned by removing potential adapters using Cutadapt v2. 8  (Martin 2011)  and trimmed using Trimmomatic v0. 36  (Bolger, Lohse, and Usadel 2014). Depending on the sequencing technology used to generate reads, the data sets were individually assembled into contigs using Newbler  (Silva et al. 2013) , IDBA_UD  (Peng et al. 2012 ), or MEGAHIT v1. 1. 3 (Li et al. 2015)  with default parameters. Details of these viromes (database source, ecosystem type, sampling location, associated publication, etc. ) are provided in Supplementary Table  S1. Circular contigs were detected based on identical 5 ′ and 3 ′ ends of at least ten nucleotides.
Identifying small circular contigs similar to Microviridae To identify new microvirus genomes, a data set of thirty-three reference viruses was generated that represent all known members of the Microviridae subfamilies described so far. The major Cap protein and Rep initiation protein of the viruses in this data set were aligned using MAFFT v7. 470 (-genafpair with 1,000 iterations;  Katoh et al. 2002) , and two hidden Markov model (HMM) profiles were generated. Three different sources were used to search for small potential microviruses. First, sequences described as complete genomes in GenBank (January 2020;  Clark et al. 2016)  were retrieved using BLASTp  (Altschul et al. 1990 ) with the Cap proteins of phiX174 (GenBank accession #AF299307) and Chlamydia phage Chp1 (GenBank accession #NC001741) as bait (bit-score >40).
The structure of the major Cap proteins was predicted using ColabFold  (Mirdita et al. 2022) , allowing to identify the position on the protein sequence of the eight beta strands assembled in two antiparallel sheets constituting the SJR fold, characteristic of such Cap proteins  (Liljas 1991; Koonin et al. 2020). Together with the proteins from the thirty-three references, a multiple alignment was computed independently for these two core proteins. Amino acid identity was calculated using these two alignments and plotted as a heatmap using the ggplot R package  (Wickham 2009). Three phylogenies were then computed, one for each of the two multiple alignments and one based on their concatenation, using RAxML v8. 2. 12  (Stamatakis 2014)  with 100 bootstrap replicates and automatic model selection (PROTGAMMAUTO) and visualized using iTOL  (Letunic and Bork 2021).
Potential host prediction and detection of dif-motifs To try and identify prophages closely related to the sixteen genomes that could help identifying their host, sequences similar to one of the major Cap proteins of all newly assembled microvirus genomes were searched in RefSeq Bacteria (tBLASTn). In addition, CRISPR spacer sequences similar to our sixteen microviruses, traces of a previous viral infection in bacterial genomes, were searched using BLASTn against the IMG spacer database  (Markowitz et al. 2012 ) and using SpacePHARER  (Zhang et al. 2021b). To assess the possibility of a lysogenic lifestyle, sequences recognized by the XerCD bacterial recombination machinery (dif-motifs) were searched in all newly found small complete microvirus genomes using nhmmer from HMMER v3. 1b2  (Wheeler and Eddy 2013).
2019 ) (version 2. 0. 16, cut-offs: probability ≥90% and coverage ≥50%). Protein singletons were also individually compared to all these HMM profiles. Using significant similarities, singletons and protein clusters were then again clustered using MCL (inflation 2. 0). This second step involving HMM comparisons was then performed twice, with no threshold on the coverage for the last iteration, to group clusters that were similar but not grouped yet. Finally, HMM profiles of the OGs were annotated by comparison to the Pfam database  (Finn et al. 2014).