dataesr/scientific-paragraphs-categorization huggingface dataset:
Species (MASAE accession) Gram  ). The polished size of curated circular replicons is reported in the last column. Assembly notes are documented with the following indices: near circular molecule (N), presence of small missassemblies (M), tandem repeat length variation (T), large chromosome inversion (X), plasmid concatemer (R), presence of artifactual tandems (A). Note that the two replicons marked with an asterisk (*) (SF1671 and SAF3325) were reassembled with sorted LIG data for circularization and/or further confirmation of the chromosome scaffold. See methods and Additional file 4 Fig. S3  for further information on assembly assessment and replicon curation Finally, we found three leaked plasmids occurences in Illumina assemblies (in CD3406, MIP2422 and SF1671) that resulted from libraries run on the same Illumina flow cell at Microsynth (Balgach, Switzerland) (not shown).
This is because Flye/MetaFlye tended to misassemble plasmids into concatemers (see CD3406 Plasm. 4, MIP2602, and SAF3333, Table  2 ), a problem that others have reported prior as "doubled plasmids"  [7]. Interestingly, the shorter reads of the PCR library seemed to cause less issue in this regard (Table  2 ). Thus, this problem may only arise for small plasmids assembled with longer reads. We also noted that Flye/MetaFlye sometimes circularized small contigs made of repeats, which users may initially consider as plasmids when browsing the "assembly_info. txt" file, but these generally did not match any biological sequences online (e. g. on GenBank, not shown). A simple mapping/BLASTing of putative plasmids on themselves allows for the detection of concatemers or repeats. Finally, in exploring the genomic complexity of the different strains sequenced (Additional file 3 Table  S5  and Illumina assembly fragmentation, Table  1 ), we could not pinpoint at any specific genomic attributes or taxonomic basis (Gram ±) that may be responsible for assembly issues or incongruence observed between hybrid and nanopore approaches.
Insights As demonstrated by our results with different assemblers and library datasets (Table  2 ), users should generally not accept assembly results at face value without further checking. Indeed, for beginning researchers in nanopore (or long-read) sequencing, knowing how to characterize genomic variation between Mbp-scale contigs (i. e. chromosomes) can be daunting, particularly because no clear guidelines on how to best proceed are available. Tools like Trycycler  [17]  may save users a lot of time to build a consensus from multiple assembly files but also require human intervention (e. g. further checking of the read pile-up profile in IGV) and may not necessarily be useful in complicated cases as mentioned by its author  [7]. Moreover, Trycycler requires conducting multiple assemblies of the data with different assemblers, or using a single assembler with multiple subsets of the data, which may only be possible when coverage is in excess to insure circularization.
g. see structural heterogeneity discussion and related figures in  [7] ). We suspect that for two of our strains, SVs were present and responsible for Kbp-scale variation between the assembled chromosomes from different libraries (Additional file 3 Table  S3  for SF1671 and SAF3325, see TAG vs. LIG). Assemblers reconstruct SVs to some extent but these contigs generally contain misassembled segments. Indeed, prior to the development of specific tools (e. g. [19, 20] ), characterizing SVs required manual curation of contigs by comparison to raw nanopore reads (e. g. see SVs discovered in canu assemblies in  [3] ). Testing for, and characterizing SVs, should probably be part of assembly pipelines since their occurrence is likely underestimated and probably more than often responsible for incongruent assemblies between datasets and tools.
e. additional long read recovery to troubleshoot ambiguous assemblies). These three sequencing runs were all conducted for 72 h with flow cells harboring > 1400 pores at run start. The resulting datasets were named hereafter PCR, TAG, and LIG libraries, according to library preparation strategies (i, ii and iii above). Basecalling (i. e. translating the nanopore electronic raw signal of the sequencer into nucleotide bases) of the fast5 files was conducted with Guppy v6. 1. 1 (Oxford Nanopore Technologies) set with the « super accuracy» mode (i. e. the highest accuracy model -config dna_r9. 4. 1_450bps_ sup. cfg) and demultiplexing conducted by assessing barcodes on the front and rear of the reads. For simplicity and to examine read properties from different library strategies (see further below), no subsequent read filtering was applied.
To do so, assemblies (Illumina vs. hybrid vs. nanopore) were scored for circular molecules by checking Unicycler's « unicycler. log» file (molecules indicated as « complete», which are also flagged as « circular = true» within the fastA titles of the assembled contig file) and Flye/ MetaFlye's « assembly_info. txt» file (circular molecule indicated as yes « Y»). When molecules were incomplete (i. e. not circular), BLASTn  [29]  was used to identify the longest assembled contig in individual assembly files. Contigs were compared to the curated genomes (see below) to document any missassemblies and/or observed genomic variation (Table  2 , see superscript indices).
Replicon assessment Complete circular replicons (chromosomes or plasmids) for a given strain and across libraries and/or assemblers were rotated from a common single-copy gene or nonrepeated intergenic region with perl script fasta_shift. pl from the FASTA-tools package  [30]. Rotated molecules were then aligned in a pairwise manner via BLASTn or Minimap2 to produce alignment reports and dotplots  [31] , which were examined for the presence of breaks revealing assembly differences. Large breaks, i. e. those comprising large physical distances (i. e. nucleotide stretch), were investigated via mapping with BWA-MEM and the resulting bam file visualized in IGV  [32]. Smaller breaks were verified for tandem repeat variation with TRF  [9]  (command line as in next section) or for homology against raw reads (or Genbank's nr) after extraction of the corresponding genomic region with fasta_sub.
pl from the FASTA-tools package  [30]. BLASTn results were also downloaded as xml file for visualization of eventual genomic segment(s) rearrangement(s) or presence of large interspersed repeats (Kbp repeated segments) with Kablammo  [33]. A summary table (Additional file 3 Table  S5 ) was also prepared to report overall genome complexity for each strain by documenting the number of 16S copies per chromosome, the number of long repeated segments per chromosome (> 1,000 bp in BLASTn of the chromosomes on themselves), detected tandem repeats using TRF and number of plasmids. Insertion sequences, prophages and CRISPR (clustered regularly interspaced short palindromic repeats), which represent additional small repetitive regions that may occur in bacterial genomes and may participate to genome complexity (i.
Read attributes Reads were mapped to the established curated genomes using Minimap2. The resulting sam/bam mapping files were then post-processed with samtools to extract mapped vs. unmapped reads using functions view and fastq (with flags -F 4 or -f 4, respectively). The mapping files were also processed with msamtools with the summary function to obtain reads' taxonomic identity (i. e. which genome a read mapped to). This allowed for estimation of sample-to-read misassignment (i. e. barcode leakage) by comparing read's mapped taxonomy to its barcode identity reported by Guppy. Tandem repeats were also computed for each of the read of the three libraries (PCR, TAG, and LIG) with TRF v. 4. 09. 1 set with a maximum 2000 bp pattern detection (option '2 5 7 80 10 50 2000 -d -h',  [9] ). The TRF report was then parsed to join overlapping tandems and compute total tandem length per read as well as their location with a custom script written in R  [43]  named TROP (Tandem Repeat Overlap Parser  [10] ).
All of the above information, a read mapped vs unmapped status, its mapped taxonomy and tandem content, was then merged via read labels to Guppy's « sequencing_summary. txt» file in order to create a synthesis file (available at  [44] ) from which to access all associated read attributes (e. g. read's length, mean Qscore, barcode identity) and compute different summary statistics (Tables  3, 4 , 5 and 6, Figs. 2, 3 and 4, Additional file 2, 3 and 4). A second synthesis file includes the location of the non-overlapping tandems along the reads (data displayed in Fig. 5  and available at  [44] ).
Computing Data analyses, assemblies and polishing tools were run on Ifremer's supercomputer DATARMOR located within the institute's bioinformatics core facility called Sebimer. Basecalling of nanopore's raw signal with Guppy was conducted on an NVIDIA Tesla V100 (PCIe 32GB) GPU Accelerator. Plots and tables were produced in R with package ggplot2  [45]  and data. table  [46].
Availability of data and materials Raw (fastQ) nanopore and Illumina data are available under ENA's Bioproject PRJEB60605. Raw (fast5) nanopore signal data, curated genomes and syntheses files with read properties are available on Ifremer's repository  [44]. The curated chromosome and plasmids of strain Serratia proteomaculens (CD3406, previously published  [24] ) were updated under NCBI's Bioproject PRJNA686006 and available under Genbank's accessions CP117168-CP117175.
We update results from the Mycotic Infections in COVID-19 (MUNCO) Registry, May-September 2021. Data collection from May to September 2021 yielded 728 cases from India, Nepal, Bangladesh, Thailand, and the United States. The cases consisted of mostly mucormycosis (97. 6%), primarily rhinocerebral, and were analyzed to investigate clinical characteristics associated with negative outcomes. Patients were mostly diabetic (85%) and male (76%), with significant mortality (11. 7%). All patients received treatment of coronavirus disease 2019 (COVID-19) as well as antifungal treatment. The crude mortality rate was 11. 3% for mucormycosis and 22. 7% formixed infections. This study demonstrates the utility of online databases in the collection of high-caliber data.
METHODS As previously described,  2  cases were solicited through social media and contacts at participating hospitals and entered into a deidentified, secure electronic database at https://www. covidmucor. com using REDCap. 3  Mycoses were based on the judgment of the clinician entering the data and included all patients with histopathologically confirmed infection. The primary outcome was in-hospital mortality. Secondary outcomes were measured at the time of COVID-19 diagnosis and included hospital admission, length of admission, intensive care unit (ICU) admission, oxygen requirement, incomplete recovery, and vision loss. Incomplete recovery was defined as continued treatment at day 42, interrupted treatment, palatal perforation, stroke, or paralysis.
The use of zebrafish as a model organism is gaining evidence in the field of epilepsy as it may help to understand the mechanisms underlying epileptic seizures. As zebrafish assays became popular, the heterogeneity between protocols increased, making it hard to choose a standard protocol to conduct research while also impairing the comparison of results between studies. We conducted a systematic review to comprehensively profile the chemically-induced seizure models in zebrafish. Literature searches were performed in PubMed, Scopus and Web of Science, followed by a two-step screening process based on inclusion/exclusion criteria. Qualitative data were extracted and a sample of 100 studies was randomly selected for risk of bias assessment. Out of the 1058 studies identified after removing duplicates, 201 met the inclusion criteria.
Search Strategy Studies were identified through a scientific literature search in three different databases: PubMed, SCOPUS, and Web of Science. Search strategies were designed to suit the characteristics of each database. Varied terms were used for the searches that describe the intervention (epileptic seizure induction protocol) combined with terms referring to the population of interest (zebrafish), in order to carry out a sensitive search. The complete query for each database can be found at https://osf. io/2njhw  [16]. The searches were performed without restrictions for language or year of publication, on November 5 th , 2021. The bibliographic data acquired were imported into Rayyan software  [18] , where duplicates were detected and removed by one of the investigators (RC). The reference lists of the included studies were also screened to detect additional relevant articles.
Eligibility screening After the removal of duplicates, the selection of eligible studies was conducted using Rayyan software. The records underwent a pre-selection based on their title and abstract. For records that did not fit the determined exclusion criteria, an analysis of the full text was carried out. In both stages (title/abstract and full-text analysis) two independent reviewers analyzed each study (RC and MGL or RB or CGR). Disagreements between reviewers' decisions were resolved by a third reviewer (MEC or APH or AP). Experimental studies using chemicals to induce seizures in zebrafish on the following outcomes were included: behavioral parameters during epileptic seizures (distance traveled, immobility, frequency, and duration of epileptic seizures, latency for stages of epileptic seizures), neurochemical outcomes (e.
Data extraction Data extraction from the included studies was conducted by two independent investigators (RC and MGL or RB or CGR or TSB) and disagreements were resolved by a discussion between the two investigators. Whenever available, the exact information was extracted directly from text or table. When information was not found, we used unclear. The following characteristics were extracted: (1) study characteristics: study title, digital object identifier (DOI), first and last authors, last author's institutional affiliation, and year of publication; (2) animal model characteristics: strain, sex, developmental stage during exposure to the chemical inducer; (3) protocol characteristics: seizure inducer, administration route, frequency of exposure, duration of exposure, dose or concentration, interval between inducer exposure and test, antiepileptic treatment; (4) test characteristics: type of test, test duration, category of measured variable, measured variable and main findings of the study.
Search results From the search in the selected databases, 2130 records were retrieved altogether (Pubmed = 807; Scopus = 710; Web of Science = 613). Following the removal of duplicates, 1. 058 records were screened for eligibility based on title and abstract. After the first screening phase, 283 studies remained to be assessed based on full-text, and 201 met the criteria and were included in the review (Fig. 1 ). In the two screening phases (title/abstract and full text), 299 did not use a chemical inducer of epileptic seizures as an intervention, 277 articles were excluded due to the study design, 268 did not meet the population criteria and 10 did not report any of the outcomes of interest. In addition, 3 studies were not retrieved and were therefore excluded from the final analysis. This resulted in a total of 201 studies being included in the qualitative synthesis.
io/bgcdv. Fig. 3. Risk of bias assessment of included studies. The risk of bias assessment was performed by two independent investigators based on the SYRCLE's risk of bias assessment tool. Items 1 to 5 (Allocation sequence, Baseline, Blinding, Incomplete data and Selective reporting) account for methodological quality and were scored as presenting a high, unclear or low risk of bias. Sample size estimation and outlier exclusion criteria evaluate the reporting quality of the studies and were scored as "Yes" or "No", meaning satisfactory or unsatisfactory reporting quality, respectively. Classification is given as the percentage of assessed studies (n = 100) presenting each score.
Risk of bias and reporting quality To assess risk of bias and reporting quality we randomly chose a sample of 100 articles included in this review. The majority of studies were rated as having a low risk of bias for selective reporting, suggesting that most studies included in the review reported all mentioned outcomes. This, however, should be interpreted with caution as protocol preregistration is not standard in preclinical literature, and we thus cannot rule out that other outcomes were collected and not reported. In addition, the results indicate that more than half of the studies have a low risk of bias for baseline characteristics of animals and blinding, but these points still could be potential biases in some studies. The fact that randomization procedures and incomplete data were frequently rated as unclear is also a concern, as this suggests that some studies have not adequately reported on these important aspects of study design.
ACKNOWLEDGMENTS We thank the  Conselho Nacional de Desenvolvimento Científico e Tecnológico  (CNPq, proc. 303343/2020-6 ),  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) , and  Pró-Reitoria de Pesquisa (PROPESQ) at Universidade Federal do Rio Grande do Sul (UFRGS)  for funding and support.
As dynamic population models with interactions between species are data-hungry, with S 2 interaction parameters for S species, ecological statistics searches for improved ways to infer such population-level interaction strengths. A recently developed technique consists in combining data sources in multispecies Integrated Populations Models (IPMs) including those interspecific interactions  (Péron & Koons, 2012; Barraquand & Gimenez, 2019; Quéroué et al. , 2021). Because Integrated Population Models (IPMs,  Besbeas et al. , 2002)  combine data on demographic rates (e. g. , capture recapture, breeding data) with data on population size (typically from counts), they allow: (a) estimating both demographic rates and population size (and hence their inter-dependencies) in a joint analysis, (b) a improved precision in parameter estimates, compared to separate analyses of component datasets, since the information contained in several datasets combine into estimated parameters (e.
e. , with/without interactions × with/without stochasticity on interactions; see Supplementary Table  2  and Figures  S1  and S3 ). For each of these four combinations of parameter sets, we simulated 100 datasets using the Nimble package (de Valpine et al. , 2017, 2022, version 0. 12. 2) in R (R Core Team, 2022, version 4. 2. 1).
, 2017 de Valpine et al. , , 2022 , version 0. 12. 2). To improve their mixing and minimize their posterior correlations, intercepts, slopes and temporal SD were block sampled using automated factor slice samplers  (Tibbits et al. , 2014; Ponisio et al. , 2020). For each simulated dataset, we fitted the same multispecies IPM that was used to generate the data (e. g. , no random temporal noise estimated on data without temporal noise), except in that species interactions were estimated even in absence of such interactions. Two MCMC chains were run for 40000 iterations and we sampled the last 20000 iterations every 20 th iteration leading to 2000 posterior samples saved per dataset. Real parameter values were used as initial values to minimise time to convergence (see Appendix Section C for an evaluation of the influence of initial values on parameter estimation).
We assess convergence and mixing of the chains for all α i by calculating the potential scale reduction factor ( R,  Brooks & Gelman 1998; Gelman & Rubin 1992)  and effective sample size (n ef f. )using the "gelman. diag()" and the "effectiveSize()" functions of the coda package  (Plummer et al. , 2006, version 0. 19-4). We only used outputs from models for which all α i had R < 1. 1 and n ef f. > 50, that is, 96/100 models for the scenario without random temporal variation and 73/100 models for the scenario with random temporal variation. The computer code is provided at https://github. com/MatthieuPaquet/ multi_species.
An important additional outcome of our study pertains to the sample size needed to be able to estimate intra and interspecific interactions, which is likely higher than previously anticipated. By adding observation error on count data (σ 2 obs = 0. 1 on the log scale) to allow for identifiability of stage-specific abundances, we had to increase the sample size of the capture-mark-recapture dataset used in  Barraquand & Gimenez (2019)  up to 100 prey individuals and 100 predator individuals newly marked every years for 30 years, in order to reach practical identifiability of the density dependence parameters and satisfactory convergence of the MCMC chains for a large majority of the datasets (i. e. , 98/100). Estimation was still unbiased with the data designs considered in  Barraquand & Gimenez (2019)  but failing for a number of datasets.
When adding unexplained environmental stochasticity (i. e. , random time variation on the link functions), the proportion of converging MCMC chains went down to 73/100 datasets, which means that density dependence parameters could be satisfactorily estimated for about three quarters of the datasets. These proportions were highly similar when interspecific interactions were present (i. e. , 97/100 and 74/100 respectively). This raises the question of how common such large datasets are for (suspected) predator-prey population pairs. That being said, parameter estimation was still possible with smaller sample sizes in many cases (i. e. , 44/100 datasets in the worse case studied, Figure  S8 ), and the only predator-prey IPM using real data so far used 22 years of capture recapture histories for 318 predators and 1210 prey individuals (i.
The extension to spatially explicit IPMs  (Chandler & Clark, 2014; Zhao, 2020)  for interacting populations represents a promising way forward for the estimation of species interactions. Although our results are encouraging, the efficiency of multispecies IPMs in estimating species interactions may also depend on the parameter set, and thus on the ecological features of the populations studied. For example, the parameters considered here correspond well to vertebrate predator-prey systems with a stable equilibrium in absence of environmental perturbations. Faster life histories, different stage or age structure, and multiple factors contributing to altering the quantity of information encapsulated in the various data streams may alter the sample sizes required for efficient inferences. When applying these models to new systems with different life history parameters and density-dependent structures, simulated datasets with plausible ecological features for the empirical system considered (and similar data designs), will help confirm that parameter values can be recovered without bias and with sufficient precision.
Statistical Analyses Data analysis was performed using GraphPad Prism software v. 9. 3. 1 (GraphPad Software, LLC, San Diego, CA, USA). The coefficient of variation (CV) was calculated as the ratio of the standard deviation to the mean multiplied by 100. Intra-assay CV ≤ 10% and inter-assay CV ≤ 15% were considered acceptable  [44]. A Pearson correlation coefficient was calculated for ELISA-positive serum samples that were further analyzed by sVNT.