dataesr/scientific-paragraphs-categorization huggingface dataset:
research communications Figure  4  IR (a) and Raman (b) spectra of (I). Computer programs: CrysAlis PRO  (Rigaku OD, 2015) , SHELXS  (Sheldrick, 2008) , SHELXL  (Sheldrick, 2015)  and OLEX2  (Dolomanov et al. , 2009). Data collection: CrysAlis PRO  (Rigaku OD, 2015) ; cell refinement: CrysAlis PRO  (Rigaku OD, 2015) ; data reduction: CrysAlis PRO  (Rigaku OD, 2015) ; program(s) used to solve structure: SHELXS  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL  (Sheldrick, 2015) ; molecular graphics: OLEX2  (Dolomanov et al. , 2009) ; software used to prepare material for publication: OLEX2  (Dolomanov et al. , 2009).
Acknowledgements The authors thank the  UniversiteCheikh Anta Diop, Dakar, Senegal , the  Laboratoire de Chimie et Physique des Materiaux ( LCPM) de l'UniversiteAssane Seck de Ziguinchor, Senegal , the  CNRS and Universitede Strasbourg, France , the  Aix Marseille Univ, CNRS ,  Centrale Marseille ,  FSCM, Spectropole, Marseille, France  and the  ICMUB-UMR 6302, Dijon, France  for financial support.
These parameters represent the linear effects, the 161 quadratic effects and the interactional effects, respectively. The first coefficient a 0 is required 162 for fitting the mathematical model. The p-values from F-tests (95 % confidence) and the 163 coefficient of determination R 2 were used to evaluate the fitness of the model. The experiment 164 was designed and evaluated using the software STATGRAPHICS Centurion XVI Version 165 16. 1. 03 (©StatPoint Technologies Inc. ). 166 The reactors were incubated in an Automated Methane Potential Testing System (AMPTSII) 167 (Bioprocess Control, Sweden). Twelve reactors from the AMPTSII system, with a total 168 volume of 600 ml, were used. According to the manufacturer instructions, they were 169 connected to CO 2 traps (NaOH solutions) and to gas flow meters to determine continuously 170 the methane flow rates.
The next stage is the development of a program implementation plan, i. e. the distribution of projects over time, given the limited means for periods. As a rule, loss of profit is the criterion of efficient allocation of resources. To that end, tools of project management can be applied. The application of scheduling methods such as critical path method is also possible, and in case of large uncertainties in terms of performance of works and provision of resources PERT method can be used. For effective management of programs and projects the use of modern information technologies, implemented by software systems Microsoft Project, Primavera, Spider, Open Plan can be recommended. Thus, all suggested measures allow to increase the efficiency of the management of environmental safety programs and to guarantee high reliability of their implementation.
Sequence pre-processing Paired-end Illumina sequence data was processed with a custom pipeline (Supplementary Information) using mainly the USEARCH-64 version 8 package (Drive5; Tiburon, CA, USA) with some commands from QIIME version 1. 9. 1  (Caporaso et al. , 2010)  and mothur version 1. 33. 3  (Schloss et al. , 2009). Analysis of the Mock data set first allowed us to calibrate the different steps of the sequence processing pipeline ensuring that the assigned OTUs reflected as closely as possible the defined composition of the sample and allowed us to reveal potential sequencing artifacts (errors, contamination). To minimise errors and reduce over-inflation of diversity, the following criteria were chosen in the pre-processing steps; (a) zero mismatches allowed in the overlapping region when merging the paired end reads, (b) quality filtering was carried out after merging using a stringent expected error of 1.
0  (Edgar & Flyvbjerg, 2015) , (c) zero mismatches allowed in the barcode when demultiplexing, (d) exact matches to both primers required, (e) removal of singleton sequences that are expected to have errors and that increase the number of spurious OTUs  (Edgar, 2013). Sequence data was submitted to the European Nucleotide Archive under the study number: PRJEB23513.
Sequence data analysis OTUs were defined with the UPARSE algorithm, part of the USEARCH-64 package, which clusters sequences with >97% identity to OTU centroid sequences (representative sequences that were selected based on their rank of read abundance after dereplication) and that simultaneously removes chimeras  (Edgar, 2013). Subsequent analysis steps were performed in QIIME or MACQIIME version 1. 9. 1. OTU taxonomy was assigned with the RDP classifier using the GreenGenes OTU database (gg_13_8_otus) defined at 97% identity. For the lichen dataset, the OTU table was filtered to remove eukaryotic, archaeal and organelle sequences (chloroplast, mitochondria). Due to the presence of the lichen photobiont, a large number of cyanobacterial sequences were recovered. The OTU table was filtered to separate non-cyanobacteria sequences from the cyanobacteria sequences.
The representative sequences were aligned using the PYNAST aligner in QIIME and the 13_8 GreenGenes alignment (Supplementary Information), and the OTU table was further pruned by removing non-aligning OTUs. Data exploration and analysis was carried out with QIIME (see Supplementary Information) and with the packages Phyloseq  (McMurdie & Holmes, 2013)  and vegan  (Oksanen et al. , 2013)  in R (  R Core Team, 2015). One-way ANOSIM tests were performed using the vegan package in R to determine significant differences between groups. To examine the patterns of phylogenetic relatedness of the lichen-associated bacterial communities, the mean pairwise distance (MPD) and the mean nearest taxon distance (MNTD) were calculated using the Picante package in R  (Kembel et al. , 2010). Whereas MPD calculates the mean pairwise distance between all OTUs in each community, MNTD calculates the mean distance between each OTU in the community and its closest relative.
Comparison of sequences with cultured species, and the NBCI database The 16S rRNA gene sequences of 247 bacterial strains previously isolated from the lichens L. pygmaea, L. confinis, and L. auriforme  (Parrot et al. , 2015)  were compared to the sequences from this study by blastn  (Altschul et al. , 1997)  queries against a database of the representative OTU sequences in the current study using the standalone version of NCBI blast. The bacterial strains were isolated from the lichens in France and Austria 18 months and 12 months respectively before those sampled in this study, but they were sampled at exactly the same sites. Note that in the previous study  (Parrot et al. , 2015) , the lichen L. auriforme was cited with its previous name; C. auriforme (Collema auriforme). In addition, the sequences of the top 30 OTUS from the strict marine or maritime-inland terrestrial lichens were subjected to blastn queries against the NCBI Genbank nt database.
Control of sequence quality and data treatment The mock community sequences were parsed out and analysed separately from the other sequences obtained from the lichen and seawater samples. Analysis of the 20-species mock community allowed us to check for potential contamination and also to refine our bioinformatic analysis pipeline to obtain the expected number and identity of OTUs. Using the criteria described in the methods and the algorithm UPARSE of the USEARCH package for clustering OTUs at 97% identity, we obtained 19 OTUs (identical to the reference sequence  Edgar, 2013)  which perfectly corresponded to the mock community (the two Staphylococcus sp. strains cluster together) plus 17 other OTUs. The 19 mock OTUs accounted for 99. 5% of the sequences. Of the remaining 17 OTUs, four were affiliated to the Cyanobacteria class and three to chloroplasts that were the dominant OTUs found respectively in the lichen samples (this study) and in samples from the offshore bacterioplankton study loaded in the same MiSeq run.
A comparison of the lichen bacterial community beta diversity as assessed by Bray-Curtis dissimilarity of a rarefied OTU table and hierarchical clustering is shown in Fig. 4A. The dendrogram shows a clear separation of the marine cyanolichens from the seawater samples and also from the maritime lichens whose bacterial communities formed a separate cluster with the inland terrestrial cyanolichens. The data were then reanalysed, focusing on the most abundant OTUs associated with the lichens. This involved removing the seawater samples from the dataset and filtering out low variance OTUs thus reducing the OTU number from 2,519 to 367. Despite this filtering step, the clustering of the lichen communities of the reduced dataset (Fig. S2 ) was similar to that of the full dataset (Fig. 4A ). ANOSIM tests confirmed that the separation of marine versus the other lichen groups, and also the separation of the L.
This was indicated by beta-diversity analyses both on the whole or filtered dataset which included the most abundant OTUs and which revealed a lack of clustering of the replicates according to site. A lack of geographical influence on the lichen associated bacterial communities was also observed in other studies where sampling was carried out on a small spatial scale  (Grube et al. , 2009; Bates et al. , 2011). In a study where lichen sampling was carried out at a much greater spatial scale, across the North American continent, a significant effect of geographical location on bacterial communities associated with a specific species was observed  (Hodkinson et al. , 2012).
Low culturability of marine lichen associated bacteria Culturing of bacterial isolates from novel environments is essential for natural product discovery. The harsh environments, in which the Lichina lichen species are found, could provide a wealth of bioactive products, particularly from the potentially thermophilic/radiation resistant bacteria identified in our study. However, only 34 OTUs showed significant similarity to the 254 previously isolated cultured isolates  (Parrot et al. , 2015)  even when lowering the similarity threshold to 97%. Moreover, three OTUs that were 100% identical to isolates were present at low abundances in the Illumina dataset. Whereas the Bacteroidetes phylum accounted for around 53% of the 16S rRNA sequences from the marine lichens, only four strains from this phylum were cultured.
ACKNOWLEDGEMENTS We thank the BIO2MAR platform (http://bio2mar. obs-banyuls. fr)for providing technical support and access to instrumentation. The following reagent was obtained through  BEI Resources, NIAID, NIH  as part of the  Human Microbiome Project: Genomic DNA from Microbial Mock Community B (Even, Low Concentration ), v5. 1L, for 16S rRNA  Gene Sequencing ,  HM-782D. We would also like to thank  Dr. Nicolas Magain  for his thorough and thoughtful review, particularly on the lichen taxonomy, which greatly helped to improve this paper.
Oceanographic and ecological setting Long-term monitoring of water mass properties at stations near Anholt (Kattegat) and in Hanö Bay (Baltic Proper) is maintained by the Swedish Agency for Marine and Water Management (SWAM), through the Swedish Meteorological and Hydrological Institute (SMHI) and available via the SHARK hydrographic database (http://www. smhi. se, last access: August 2016). Monitoring data were used from the stations Anholt-E (56 • 40 N, 12 • 07 E) and Hanöbukten (Hanö Bay; 55 • 37 N, 14 • 52 E). Bottom water conditions (30-60 m water depth) in the Kattegat are very similar to open marine conditions, although relatively large variations can occur. Since the 1980s bottom water salinity varied between 32 and 34. 5 (values similar to the Skagerrak and North Sea), and temperatures varied between 4 and 14 • C.
Foraminifer assemblages Samples were processed according to standard laboratory methods using a 63 µm sieve for wet sieving of the samples, before drying and dry-picking (without splitting) the foraminifera from the size fraction > 125 µm (Tables  S1, S2  in the Supplement). Taxonomic identifications followed Loe-  blich and  Tappan (1964)  and  Tappan and Loeblich (1988)  for the genus level, and  Feyling-Hanssen (1964 ), Feyling-Hanssen et al. (1971) , and  Murray and Alve (2011)  for the species level. For original descriptions of the species, see  Ellis and Messina (1940, and supplements up to 2013). To quantify the difference in diversity between Anholt and Hanö Bay the Shannon-Wiener index was calculated using the PAST software package  (Hammer et al. , 2001). Despite recent advances in the taxonomic identification of the elphidiids by  Pillet et al.
78±0. 066 mmol mol -1. Analytical precision based on three repetitions for each analysis for all species combined was 1. 77 %, 0. 12 %, and 3. 58 % for Mn/Ca, Mg/Ca, and Ba/Ca, respectively. Stable oxygen (δ 18 O) and carbon (δ 13 C) isotope analyses on foraminiferal samples were performed on a Finnigan MAT 252 mass spectrometer equipped with an automatic carbonate preparation device at the MARUM,University of Bremen. The stable isotopic data are reported relative to the Vienna Peedee belemnite (VPDB) using the NBS19 standard. The standard deviation of the internal laboratory standard was 0. 04 ‰ for δ 18 O and 0. 02 ‰ for δ 13 C.
, 2016). In the study of  Hayward et al. (2004) , Ammonia genetic types were defined by large subunit (LSU) rDNA sequencing and in the studies of  Schweizer et al. (2011a, b)  by both LSU and SSU rDNA sequencing. For Elphidium, the morphological profile descriptions of the genetic types were used as the basis for their taxonomic designations  (Darling et al. , 2016). Sequences were edited in ChromasPro v1. 5 (Technelysium Pty Ltd. ) and compared with the Basic Local Alignment Search Tool (BLAST;  Madden, 2002)  to identify the genetic types present in the studied material. Our sequences were then manually aligned in BioEdit v7. 0. 9. 0  (Hall, 1999)  with sequences of the studied genera (i. e. , Ammonia, Bulimina, Cassidulina, Elphidium and Nonionellina) previously deposited in the GenBank database (http://www.
Foraminifer genetic types Molecular characterization was carried out on foraminifera collected from sampling stations at the Anholt (C-An-1 and DAn-1), Arkona Basin (DBY2-1), and Hanö Bay (C-Ha-1 and C-Ha-2) sites (Fig. 1 ). From a total of 249 live specimens picked and SEM imaged, 131 specimens were successfully genetically characterized (Tables  2, S3 ) and their sequences deposited in the GenBank database (accession numbers KX962726-KX962802 and KX963042-KX963062, previously published in  Darling et al. , 2016; and MF170098-MF170141, this study). Sequences were identified using BLAST and manually aligned against sequences of the same genera available in GenBank (see the Methods section). Genetic type designations for Elphidium were based on Darling www. j-micropalaeontol. net//37/403/2018/.
marginata (n = 13), C. laevigata (n = 13), and N. labradorica (n = 1; Table  2 ). For the genus Bulimina, the partial SSU rDNA sequences of 13 individuals morphologically identified as B. marginata in our sample set had 98 %-99 % identity using BLAST to GenBank sequences from specimens identified as B. marginata from the Skagerrak and New Zealand  (Schweizer et al. , 2005; Holzmann and Pawlowski, 2017). For the genus Cassidulina, partial SSU rDNA sequences from 13 individuals sampled at Anholt had 98 %-99 % identity using BLAST to GenBank sequences from specimens collected in the Oslo Fjord, which were identified as C. laevigata  (Schweizer et al. , 2005 (Schweizer et al. , , 2009)). However, they differ from the GenBank sequences HG966253-HG966296, retrieved from specimens collected in the Faroe Islands, which were also identified as C.
laevigata  (Weber and Pawlowski, 2014). This implies that there may be cryptic diversity within this group. A more in-depth study including morphometric analysis and comparison with type material, as advocated by  Roberts et al. (2016) , would be needed to investigate the different Cassidulina found in northern Europe and link the DNA sequences to taxonomic names. The sequence belonging to the Nonionellina specimen BA37 is very short (189 bp) and only of medium quality. Using BLAST, it is 91 % similar to sequences from specimens of N. labradorica collected in the Skagerrak, Svalbard  (Schweizer et al. , 2005 (Schweizer et al. , , 2008 (Schweizer et al. , , 2009) )  and the White Sea  (Holzmann and Pawlowski, 2017). Nevertheless, N. labradorica is still the closest known taxon identified in GenBank and will be retained here because it corresponds to the morphological identification of the sequenced specimen BA37 (Fig.
Acknowledgements. We thank the captain and the crew of the  R/V Skagerak ,  Silvana Pape ,  Henning Kuhnert , and  Lisa Schönborn  for laboratory assistance;  Kath Evans  for her contribution towards the molecular project;  Karen-Luise Knudsen  and  Laurie Charrieau  for discussing foraminifer identification issues;  Kirsty Edgar  (editor),  Manuel Weinkauf  and an anonymous reviewer for their constructive comments. We acknowledge funding through the  Swedish Research Council (VR)  (project no. 621-2011-5090 ), the  German Research Council  (project  GR 3528/3-1 ), the  Lamm Foundation , the  Centre for Environmental and Climate Research at Lund University for Jeroen Groeneveld 's guest research stay,  NERC  grants  NE4/G018502/1  and  NE/G020310/1  to  William E. N. Austin , and the  University of Bremen  for covering the article processing costs for this open-access publication.
Here we will take the standpoint of evaluating long-read errorcorrection tools on RNA-seq data, most of which were designed to process DNA sequencing data only. We evaluate the following DNA hybrid correction tools: HALC  Bao and Lan [2017] , LoRDEC  Salmela and Rivals [2014] , NaS  Madoui et al. [2015] , PBcR  Koren et al. [2012] , proovread  Hackl et al. [2014] ; and the following DNA selfcorrection tools: Canu  Koren et al. [2017] , daccord  Tischler and Myers [2017] , LoRMA  Salmela et al. [2016] ,  MECAT Xiao et al. [2017] , pbdagcon  Chin et al. [2013]. We also evaluate an additional hybrid tool, LSC  Au et al. [2012] , the only one specifically designed to error correct (PacBio) RNA-seq long reads. i i "output" -2019/3/14 -15:16 -page 3 -#3 i i i i i i Comparative assessment of long-read error-correction software applied to RNA-sequencing data A majority of hybrid correction methods employ mapping strategies to place short fragments on long reads and correct long read regions using the related short read sequences.
g. HG-Color  Morisse et al. [2018] ,  HECIL Choudhury et al. [2018] , MIRCA  Kchouk and Elloumi [2016] , Jabba  Miclotte et al. [2016] , nanocorr  Goodwin et al. [2015], and Racon Vaser et al. [2017]. Other works have evaluated error correction tools in the context of DNA sequencing. LRCstats  La et al. [2017] , and more recently ELECTOR  Marchet et al. [2019] , provide automated evaluations of genomic long read correction using a simulated framework. A technical report from  Bouri and Lavenier [2017]  provides an extensive evaluation of PacBio/Nanopore error-correction tools, in the context of de novo assembly. This analysis is completed with more recent results in  Fu et al. [2019]  on hybrid correction methods. Perhaps the closest work to ours is the AlignQC software  Weirather et al. [2017] , which provides a set of metrics for the evaluation of RNA-sequencing long-read dataset quality.
In  Weirather et al. [2017]  a comparison is provided between Nanopore and PacBio RNA-sequencing datasets in terms of error patterns, isoform identification and quantification. While  Weirather et al. [2017]  did not compare error-correction tools, we will use and extend AlignQC metrics for that purpose. In this article, we will focus on the qualitative and quantitative measurements of error-corrected long reads, with transcriptomic features in mind. First we examine basic metrics of error-correction, e. g. mean length, base accuracy, homopolymers errors, and performance (running time, memory) of the tools. Then we ask several questions that are specific to transcriptome applications: (i) how is the number of detected genes, and more precisely the number of genes within a gene family, impacted by read error correction? (ii) can error correction significantly change the number of reads mapping to genes or transcripts, possibly affecting downstream analysis based on these metrics? (iii) do error-correction tools perturb isoform diversity, e.
4, available at https://github. com/ nanopore-wgs-consortium/NA12878/blob/master/ nanopore-human-transcriptome/fastq_fast5_  bulk. md). We concatenated the fail and pass RNA-direct reads from the aforementioned dataset, obtaining 894,289 reads. Further, to correctly run all tools, we transformed bases U into T. 2. 3 Error-correction improves base accuracy and splits, trims, or entirely removes reads Table  2  shows an evaluation of error-correction based on AlignQC results, for the hybrid and non-hybrid tools. The error rate is 13. 72% in raw reads, 0. 33-5. 45% for reads corrected using hybrid methods and 2. 91-6. 43% with self-correctors. Notably, the hybrid tools NaS(µ), Proovread(t), and HALC(s) output micro-assembled, trimmed and split error-corrected reads, respectively, with the lowest error rates (¡0.
Minor isoforms are corrected towards major isoforms We define a major isoform of a gene as the isoform with the highest coverage of that gene in the raw dataset, all other isoforms are considered to be minor. To follow-up on the previous subsection, we investigate whether correctors tend to correct minor isoforms towards major isoforms. We do so by comparing the difference i i Fig. 4. Histogram of isoforms that are lost after correction, in relation to their relative transcript coverage, in genes that have 2 or more isoforms. The y axis reflects the percentage of isoforms lost in each bin. Absolute values can be found in the Supplementary Material Figure  S3. of coverage of the major and the minor isoforms before and after correction. In Figure  5 , we observe that the coverage of the major isoform generally slightly increases after correction.
The exceptions are tools that split reads (HALC(s), LoRDEC(s), PBcR(s), and LoRMA(s)), where the coverage is increased significantly, and MECAT, where the coverage decreases significantly, likely due to a feature of MECAT's own correction algorithm. Since these 5 tools seem to heavily distort the coverage of isoforms due to aggressive splitting or filtering steps, we will focus now on the 14 other results. The slight increase of a transcript coverage after correction is expected, as already discussed in Section 2. 6: uncorrected reads that were unmapped can become mappable after correction. Therefore, the effect presented in Figure  5  could be simply due to reads being corrected to their original respective isoforms, instead of correction inducing a switch from a minor isoform to the major isoform.
2. 8. 4 Correction towards the major isoform is more prevalent when the alternative exon is small In order to observe if particular features of alternative splicing have an impact on error-correction methods, we designed a simulation over two controlled parameters: skipped exon length and isoform relative expression ratio. Using a single gene, we created a mixture of two simulated alternative transcripts: one constitutive, one exon-skipping. Several simulated read datasets were created with various relative abundances between major and minor isoform (in order to model a local differential in splicing isoform expression), and sizes of the skipped exon. Due to the artificial nature and small size of the datasets, many of the errorcorrection methods could not be run. We thus tested these scenarii on a subset of the correction methods.