dataesr/scientific-paragraphs-categorization huggingface dataset:
Purified PCR products were cloned, sequenced and edited as described  (Oni et al. , 2015). An in-house mcrA gene database was created by acquiring 1 and manually aligning long (>1000 bp) gene sequences of cultured and published methanogenic, methanotrophic and hydrocarbon degrading archaea in ARB 6. 02  (Ludwig et al. , 2004). Using the RAxML algorithm in ARB, a phylogenetic tree was constructed, to which shorter mcrA gene sequences were added using the ARB Parsimony tool. Edited FASTA sequences were imported and translated into their protein sequences in ARB. The protein sequences of clones were manually aligned and imported in the aforementioned mcrA gene database using the ARB Parsimony tool in order to determine their taxonomic affiliations (on DNA and protein level). Abundances of specific ANME phylotypes were determined from various sediment depths and from 13 CH 4 tracer experiments using quantitative PCR (qPCR).
OTU clustering and chimera removal was done using the UPARSE-OTU algorithm  (Edgar, 2013)  to create an OTU database. Chimeric sequences were checked and discarded by the UPARSE-OTU algorithm during this step. The truncated, non-dereplicated reads were mapped back to the OTU database to create an OTU table. OTUs were classified for their taxonomy using uclust and an in-house mcrA gene database as reference (see mcrA genes cloning section). The taxonomic assignment was done on the family level at a sequence identity of 0. 7  (Yang et al. , 2014). The OTU table and taxonomy assignment files were merged together using a set of "biom" commands  (McDonald et al. , 2011)  to obtain a tab-delimited text file useful for downstream analysis. A few modifications of the above pipeline were done to analyze 16S rRNA gene sequences.
Forward reads were used to analyze the community composition. After extraction of barcodes, forward reads were de-multiplexed, quality filtered and their lengths were truncated to 143 bp. Taxonomic assignment was done on clustered OTUs against the 16S rRNA gene SILVA database (Release 128 for QIIME)  (Quast et al. , 2012). (Schmidt and Eickhorst, 2014). Bacterial cells were targeted with a mixture of three probes namely, EUB338 (5 GCTGCCTCCCGTAGGAGT 3 )  (Amann et al. , 1990) , EUB338II (5 GCAGCCACCCGTAGGTGT 3 )  (Amann et al. , 1990)  and EUB338III (5 GCTGCCACCCGTAGGTGT 3 )  (Daims et al. , 1999). Archaeal cells were targeted with the ARC915 probe (5 GTGCTCCCCCGCCAATTCCT 3 )  (Amann et al. , 1990). The filters were mounted on a clean glass slide containing a drop of VectaShield H-1200 containing DAPI in order to counter-stain cellular DNA.
ACKNOWLEDGMENTS We thank the captain, crew and scientists of RV HEINCKE for the multi-year sampling campaigns to the  Helgoland Mud Area. We also thank  Jenny Wendt ,  Xavier Prieto , and  Mirja Meiners  for their technical support. Data from this original research was published online as a part of DA Ph. D. thesis (Aromokeye, 2018) and can be found online at https://elib. suub. uni-bremen. de/edocs/00106906-1. pdf.
DATA AVAILABILITY STATEMENT Raw sequence data used in this study can be accessed from GenBank Short Reads Archive with accession number SRP156177. Clone sequences used in this study for qPCR were deposited to GenBank and have been assigned the accession numbers MH917693-MH917696. Geochemical dataset was submitted to the PANGAEA data publisher for Earth & Environmental Sciences database under the following doi https: //doi. pangaea. de/10. 1594/PANGAEA. 893768.
We therefore investigate here the statistical relationship between morphology of convective systems and extreme precipitation by pooling data from all over the tropics. We quantify this relationship using recent satellitederived and well-curated databases of both daily accumulated precipitation and convective systems characteristics. We focus on the duration of the system as a physically sound and key metric to quantify convective organization. Despite being infrequent, longlasting convective systems do have an overwhelming contribution to total tropical precipitation amount  13. We show here that these infrequent, long-lasting systems are also largely responsible for the tropical extreme precipitation.
Ensemble of precipitation products. A sub-ensemble of precipitation products is built from a new database of 1°× 1°× 1 day gridded precipitation datasets that permits to investigate the robustness of results to the selection of the products  31. The data used are for the 30°S-30°N region. Owing to the availability of the database of convective systems (see next section), a 5-year period spanning 2012-2016 is used for our analysis. Statistics are computed for each member of the ensemble and the resulting ensemble mean and standard deviation reported. Twelve « flagship » products from various international groups, reflecting a large variety of estimation techniques and data sources are used initially (Table  S1 ). Over land, the analysis of the probability of exceedance (Fig. S1  top) shows that the satellite products are relatively well clustered with very few outliers.
Products mainly relying on IR are on the lower end of the probability scale for the highest daily precipitation accumulation. The most-recent version of the GPCC dataset which makes use of rain-gauge data only, exhibits the highest probability among the 12 products, over all range of precipitation. This version indeed proposes quite a different representation of the tropical precipitation from earlier versions of this product. This is likely an artifact of the update of the Kriging algorithm used  42. Note that the land sub-ensemble product agrees well with GPCC v1 distribution. Over the ocean, the situation is similar although the spread among the cluster of products is larger than for land. The TAPR product exhibits a significant underestimation of the probability of the largest rain accumulation compared to other microwave-based products.
This is likely due to the relative lack of light rain situations (Table  S2 ). Based on our analysis and previous studies on extreme precipitation using an ensemble of products  43  , a land ensemble is built using: TAPR  44  , TMPA  45  , GSMArtg  46  , CMORg 47 , MSWE  48  , and IMFC  49. In essence, it includes all datasets using satellite microwave observations from multiple platforms (as well as IR satellite data and rain gauges except in the case of TAPR). Over ocean, the ensemble includes TMPA, GSMArtg, CMORg, MSWE, HOAP, and IMFC; thus, it is restricted to datasets using microwave observations from multiple satellites. The TAPR product is not considered over ocean, as it exhibits limited skills in depicting the tail of the precipitation distribution (Fig. S1 ). The sensitivity of results to the selection of a period of 5 years is assessed using four products (TMPA, GSMArtg, CMOR, and MSWE) and comparing the probability of exceedance from 2012 to 2016 to that of a larger period, namely 2001-2016.
An issue that the TOOCAN approach has overcome  33. This algorithm first performs a multi-threshold multi-step screening on the space and time infrared image volume to detect convective seeds. Then, it grows them towards the edges of their stratiform extension (defined by a 235 K threshold), as done in previous studies. The outputs of the algorithm are various morphological parameters of the cold cloud shield (geolocation, size, brightness temperature distribution) as a function of the MCS life cycle, with a temporal resolution of 30 minutes. From these raw outputs, many integrated MCS parameters are built: duration, distance of propagation, average speed of propagation, etc. In this study we focus on the duration parameter. Earlier versions of this MCS database have been derived from various geostationary archives, and used in a number of studies to investigate the contribution of MCS to the distribution of tropical rainfall  13  , the radiative properties of the MCS  52  , their life cycle behavior  22  and intraseasonal variability  53.
Here, the algorithm is applied to a recently developed homogenized multi geostationary infrared imagery archive covering all of tropics for the period 2012-2016  54  , thus providing the best estimate to date of the MCS distribution in the tropics. The quality control of the MCS dataset is stringent. The tracking algorithm indeed nominally requires 30-min image sampling. During the MTSAT-1 and 2 operations (from 2012 to May 2015) over the Western Pacific, the acquisition scheme only provids northern hemisphere imagery at a 30-minute rate; the full hemisphere images were available only once every hour. As a result, no tracking is performed for the southern hemisphere between 100 W and 100E. After June 2015 and the shift to HIMAWARI-8, the required temporal sampling is available for both hemispheres and MCS statistics are computed accordingly.
In order to avoid such artifacts, only those days with all images available have been retained in our analysis. This results in less availability than the precipitation datasets. For each product, this sub-sampling corresponds to ~75% of the original valid precipitation data. The effect of sub-sampling on the distribution of extreme precipitation is either within (up to ~100 mm/d) or much smaller (for precipitation above ~100 mm/d) than the ensemble spread (Fig. S2 ). The filtering of precipitation products on the sampling of the MCS database does not impact our results. The morphological parameters of convective systems have been gridded on the same 1°× 1°daily grid as the precipitation estimates, so as to enable simultaneous analysis of storm characteristics and precipitation amount. In this Eulerian framework, the properties of systems are viewed from a grid box perspective.
Acknowledgements We thank  S. Cloché  for her help with the handling of the multiple datasets used here. This study benefited from the  IPSL mesocenter ESPRI facility , which is supported by  CNRS ,  UPMC ,  Labex  L-IPSL, CNES, and  Ecole Polytechnique. This study was supported by  CNES  and  CNRS  under the  Megha-Tropiques program.
Data availability The precipitation datasets analyzed during the current study are identified under the https://doi. org/10. 14768/06337394-73A9-407C-9997-0E380DAC5598 and are available at http://frogs. ipsl. fr. The MSWEP data were acquired directly from H. Beck and are available at http://www. gloh2o. org/. The MCS database is identified under the https://doi. org/10. 14768/20191112002. 1 and is available at http://toocan. ipsl. fr.
3% of identity were assembled. Based on these sequences and on the 54 coat protein sequences available from GenBank, a new forward primer, named BanMMV CP9, compatible with Poty1, an oligodT reverse primer already used in diagnostics, was designed. A retrospective analysis of 110 different germplasm accessions from diverse origins was conducted, comparing BanMMCP2 and BanMMV CP9 primers. Of these 110 accessions, 16 tested positive with both BanMMCP2 and BanMMV CP9, 3 were positive with only BanMMCP2 and 2 tested positive with only BanMMV CP9. Otherwise, 89 were negative with the two primers and free of flexuous virions. Sanger sequencing was performed from purified PCR products in order to confirm the amplification of the BanMMV sequence for the five accessions with contrasting results. It is highly recommended to use the two primers successively to improve the inclusiveness of the protocol.
Currently, they are accelerating the identification of viruses associated or not with a disease of unknown etiology  [19] [20] [21]. This allows unbiased and hypothesis-free testing in particular for symptomless plant material  [22]. This study aimed to describe the identification and genome sequencing of two isolates of BanMMV, and, based on the virus sequences available in GenBank, to design and test a new diagnostic primer for a routine indexing use. A retrospective analysis of 110 accessions from the international banana germplasm collection was conducted using this newly designed primer to compare its performance with that of an existing diagnostic primer.
Bioinformatics Analysis The Geneious software v10. 2. 6 (Biomatters Ltd. , Auckland, New Zealand)  [26]  was used for sequence analysis. The RNA-seq reads were paired, merged using BBMerge from the BBtools suite and the duplicates eliminated. They were then de novo assembled into contigs for genome reconstruction, using the SPAdes software embedded in Geneious with default parameters and a k-mer of 55  [27]. De novo contigs were annotated using the tBLASTx module with the refseq database of viral nucleotides sequences downloaded from GenBank on 15 November, 2019. All the non-redundant reads (merged and unmerged) from the sample were mapped to selected contigs using the Geneious mapper with the following parameters: minimum mapping quality 30, word length 17, maximum mismatches per read 2%, do not allow gaps, minimum overlap identity 98%, index word length 9, maximum ambiguity 16, search more thoroughly for poor matching reads set to yes  [28].
Whole genome alignments between our contigs and the refseq sequence of other Betaflexiviridae species were done using multiple sequence comparison by log-expectation (MUSCLE) alignment embedded in Geneious with a maximum number of 8 iterations. SNP calling was performed using Geneious software v10. 2. 6 with default parameters (Minimum frequency (%) = 0. 25, Minimum coverage = 1) in order to determine in which positions there are polymorphisms. To remove possible sequencing artifacts, only the SNPs that appeared at a frequency higher than 1% were retained for further analyses  [29].
Phylogenetic and Sequence Analysis The RdRp and CP genomic regions of the sequenced genomes in this study were retrieved using the ORF finder online tool from GenBank (https://www. ncbi. nlm. nih. gov/orffinder/). In addition to these sequences, the complete genomes of the BanMMV and RdRp nucleotide sequences were aligned and used for phylogenetic analyses with the MEGA software package version 7. 0. In addition to these sequences, full nucleotide genomes of BanMMV and other Betaflexiviridae members, RdRp aa sequences of BanMMV and CP nucleotide sequences of the virus were separately aligned using MUSCLE implemented in MEGA, then used for phylogenetic analyses with the MEGA software package version 7. 0. [30]. The phylogenetic relationships were inferred using UPGMA embedded in the same version of the MEGA software.
Primer Design Two hundred and ten nucleotide sequences of BanMMV were publicly available in the Genbank database on 16 March 2020. Among them, 54 sequences correspond to the CP sequences of the virus (40 partial and 14 complete sequences), and 154 correspond to partial RdRp sequences. A single complete genome of BanMMV was available before the identification of the two novel genomes from this study. The new forward primer (BanMMV CP9) was designed via the Geneious v10. 2. 6. software based on the Muscle Alignment of the 54 CP region sequences, the two novel isolates from this study and the single complete BanMMV genome (GenBank accession NC_002729). The candidate primer was evaluated for its Tm and self-or heterodimer formation using the IDT OligoAnalyzer 3. 1 software (https://eu. idtdna.
High-Throughput Sequencing and Bioinformatics Analysis The HTS yielded a total number of 8,683,460 sequence reads ranging from 50 to 151 nucleotides. After removing duplicates and de novo assembly, a total of 101,160 contigs was obtained, summing 33,003,893 nucleotides. The maximum, minimum and mean lengths were 16,883 bp, 86 bp and 386 bp, respectively. Two contigs, contig1 and contig 2, each approximately 5 kb in length, had high homology (E-value of 0) to the reference Banana mild mosaic virus (BanMMV) sequence (GenBank accession NC_002729). The contigs were further extended by iterative mapping to lengths of 7336 and 7311 nt, respectively. The genome coverage of both isolates reached almost 100%. The two new nucleotide sequences were deposited on GenBank. They are hereafter called MT872724 and MT872725 with their GenBank accessions numbers, respectively, for contig 1 and contig 2.
They shared a pairwise identity of 75. 3% at the nucleotide level, and had pairwise identities of 75. 2% and 75. 4%, respectively, with the BanMMV reference genome (GenBank accession NC_002729). At the amino acid level, MT872724 and MT872725 shared a pairwise identity of 40%. They had pairwise identities of 76. 5% and 75. 9%, respectively, with the translated amino acid sequence of the BanMMV reference genome (GenBank accession NC_002729). MT872724 and MT872725 shared 80% nt identity (91. 1% aa identity) between their respective CPs. They shared 76% nt identity (84. 3% aa identity) between their respective RdRps. All percentages of identity between BanMMV (GenBank accession NC_002729) and the two isolates regarding CP and polymerase genes, either at the nt or the aa level, are detailed in Table  1.
Phylogenetic Analysis The whole genome sequences of MT872724 and MT872725 (7336 nt and 7311 nt, respectively) were compared and aligned together with the single complete genome of BanMMV (GenBank accession NC_002729; 7352 nt), and with other members from the different genera of the Betaflexiviridae family. The phylogenetic tree reconstructed from the full genome sequences (Figure  2 ) showed the clustering of the new genomes into a group along with BanMMV, confirming their taxonomical placement as novel isolates of this unassigned member of the Betaflexiviridae family. They are also close to the other genera of the same family, but the closest one seems to be the Foveavirus, as it is the genus of Peach chlorotic mottle virus. The phylogenetic tree reconstructed from CP nucleotide sequences (Supplementary File S5) had a similar topology to the one inferred from full genomes, and also showed that the new genomes clustered together into a group with the single BanMMV genome (GenBank accession NC_002729).
Further analysis was carried out in order to assess how the new genomes of BanMMV related to the currently known viral diversity. Phylogenetic analysis inferred from RdRp nucleotide sequences showed that several monophyletic groups were generated, which confirms that BanMMV is a genetically diverse virus species (Figure  3 ). Similarly, Meena et al. [35]  reported the extreme variability of the RdRp genomic region of flexiviruses. In the case of the novel genomes characterized during this investigation, each genome clustered in a different group. The phylogenetic tree reconstructed from CP nucleotide sequences (Supplementary File S5) had a similar topology to the one inferred from full genomes, and also showed that the new genomes clustered together into a group with the single BanMMV genome (GenBank accession NC_002729).
A new primer, called BanMMV CP9, was designed to amplify, with Poty-1, a fragment of BanMMV coat protein. In silico evaluation has been carried out in order to highlight potential mismatches for the 54 CP sequences of BanMMV available on GenBank, the single complete genome of the virus and the new isolates from this study. Data are shown in Supplementary File S7. According to these results, BanMMV CP9 could be able to detect all these isolates since it presents only one mismatch at the fourth (JX183725. 1) or fifth base (AY730743. 1) in the 3 end of the primer.
In the current study, two divergent isolates of this virus were fully sequenced, adding two full genomes of BanMMV to the unique genome in GenBank. A new degenerate, inosine-containing diagnostic primer was designed to improve the inclusiveness of the detection protocol, which failed to detect these novel isolates. In fact, this kind of primer could help to overcome the high genetic diversity of the virus, even if a lower sensitivity could sometimes result from the degeneracy and inosine contents of the primers  [9, 13, 14]. The new primer detected BanMMV infection in two samples that tested negative with the existing primer, but failed to detect three infections among the 110 tested accessions. Therefore, as the very high genetic diversity of BanMMV is a strong challenge for the development of a single protocol with appropriate inclusiveness, our recommended approach would be to continue to use one of these primers for routine indexing and to confirm negative results with the other primer.
Pathogens 2020, 9 , 15 Figure 3. 9153 Figure 3. UPGMA phylogenetic tree inferred from the RdRp aa sequences of BanMMV and isolates from this study. The cyan dot denotes the single complete genome of the virus (GenBank accession NC_002729). The purple triangles denote the BanMMV isolates characterized during this study. Branches were bootstrapped with 1000 replications. The scale bar indicates the number of substitutions per site.
This is reminiscent of the parent complexes and confirms that the two oxidation processes are ligand centered. Enantiopure R or S complexes of 1 and 3 can be synthesized from enantiopure R or S forms of 2, as attested by circular dichroism and chiral HPLC analysis (Figures  S3  and S5 ). Attempts to grow crystals of 3 led to very weakly diffracting samples, with maximum resolution of 1. 29 A (see Supplemental Information), which indicates that the copper center is in coordinence 4 (N 2 O 2 of BINap). Crystals suitable for X-ray diffraction were obtained on racemate and enantiopure samples of complex 1 and revealed that the coordination sphere is completed by an aquo ligand to provide a square pyramidal environment in which an oxygen atom of the BINap occupies the axial position and not the water molecule (from a CCDC database search, of 126 structures of 5-coordinated Cu complexes bearing {(NCCO) 2 + OH 2 } coordination sphere, only five do not display a square pyramidal geometry with water as apical ligand) (Figure  1D ).
X-Ray crystal diffraction studies Single crystals of Ligand 2 were grown by acetonitrile. Using a mixture of hexane and DCM single crystals of 1 both as a racemate (C2/C space group) and in the pure (S) form (P1 space group) have been obtained. Attempt to grow crystals of 3 by a mixture of acetonitrile and DCM lead to low quality crystals (vide infra). For complex rac -1 and ligand 2, a single crystal of each compound was selected, mounted onto a cryoloop and transferred into a cold nitrogen gas stream. Intensity data were collected with a Bruker Kappa APEX-II CCD diffractometer using a micro-focused Cu-Kα radiation (λ = 1. 54178 Å). Data collections were performed at 200K and 150K respectively, with the Bruker APEXIII suite. Unitcell parameters determinations, integrations and data reductions were carried out with SAINT program.
SADABS was used for scaling and absorption corrections. The structures were solved with SHELXT 1 and refined by full-matrix least-squares methods with SHELXL 2 using Olex2 software package 3 or WinGX suite 4 respectively. All non-hydrogen atoms were refined anisotropically. These structures were deposited at the Cambridge Crystallographic Data Centre with numbers CCDC 1906978 and 1908272 respectively and can be obtained free of charge via www. ccdc. cam. ac. uk. For complex S-1, a single crystal was selected, mounted onto a cryoloop and transferred into a cold nitrogen gas stream. Intensity data were collected with a Bruker Kappa APEX-II CCD diffractometer using a graphite-monochromated Mo-Kα radiation (λ = 0. 71073 Å). Data collection was performed at 200K with the Bruker APEXII suite. Unit-cell parameters determination, integration and data reduction were carried out with SAINT program.
SADABS was used for scaling and absorption correction. The structure was solved with SHELXT 1 and refined by full-matrix least-squares methods with SHELXL 2 using Olex2 software package 3. All non-hydrogen atoms were refined anisotropically. This structure was deposited at the Cambridge Crystallographic Data Centre with number CCDC 1906977 and can be obtained free of charge via www. ccdc. cam. ac. uk.