dataesr/scientific-paragraphs-categorization huggingface dataset:
The rabbit polyclonal antibody was used to detect C7  (Chen et al, 1997). Tubulin immunodetection served as a loading control. Signals were revealed with ECL-plus reagent (Pierce) using ImageLab software (Bio-Rad Laboratories). For detection of secreted C7, keratinocytes were grown for 48 hours in the presence of 20 ng/ml TGFb2 and 50 mg/ml ascorbic acid in a serum-free keratinocyte growth medium (Gibco). Similarly, fibroblasts were grown for 48 hours in the presence of 20 ng/ml TGFb2 and 50 mg/ml ascorbic acid in a serum-free fibroblast growth medium (Gibco). The medium was collected, precipitated, and analyzed using NC1-specific antibody as described earlier  (Chen et al, 1997). Intensity-based quantification of C7 was performed using ImageJ software. The percentage of C7 rescue was calculated by considering the mean intensity in control cells (normal human keratinocytes or normal human fibroblasts) as 100%.
IncuCyte live-cell imaging Gene-corrected RDEB fibroblast morphology and proliferation were investigated using an IncuCyte live-cell imaging device. Briefly, gene-corrected and -untreated RDEB fibroblasts as well as primary normal human fibroblasts were seeded at 10 4 cells per well in 12-well plates and monitored for 8 days for confluence and proliferation. Cells were seeded in triplicates. Five independent regions were scanned for each well every 6 hours for 8 days with the live-cell imaging system (phase-contrast, objective Â4). Data were analyzed using the IncuCyte S3 basic analyzer software.
J o u r n a l P r e -p r o o f In T1DM, insulin therapy is not without risk. This has pushed the need for the development of alternative therapies/drugs by which to improve glycemic levels, whilst reducing the risk of hypoglycemia. The beneficial impacts of SGLT2is in T2DM patients have led to a growing number of clinical studies investigating the latter's potential in T1DM subjects  21, 28, 30, 36. Both DAPA and SOTA have been considered in the management of T1DM and have thus shown promising results in controlled clinical trials; the administration of these drugs resulted in a reduction in HbA1c without the increased risk of hypoglycemia  30, 37. Moreover, two pivotal clinical trials, namely inTandem 1 (ClinicalTrials. gov, NCT02384941) and inTandem 2 (ClinicalTrials. gov, NCT02421510), have notably contributed to unraveling SOTA's efficacy and safety for patients with T1DM  27, 30.
SGLT2 inhibitors attenuate reverse cardiac remodeling: Basic and clinical evidence Evidence regarding whether SGLT2is attenuate adverse CR and improve the prognosis of patients with failing hearts is being increasingly investigated  63. Numerous clinical trials have J o u r n a l P r e -p r o o f explored the beneficial effects of SGLT2is on the heart and the management of its pathological remodeling. It has been shown that EMPA treatment reduced left ventricular volume and showed improvement of left ventricular functional capacity among non-diabetic patients with HF and preserved ejection fraction (EMPA-TROPISM; NCT03485222) 139 , as well as patients with both HF/preserved ejection fraction and T2DM (SUGAR-DM-HF; NCT03485092). The data from this trial highlighted the concept of reverse CR as a plausible mechanism underlying the cardiac benefits associated with SGLT2is 140.
Using cardiac magnetic resonance, the EMPA-TROPISM study has found that regardless of the diabetic status of the patients, EMPA mediates regression of interstitial myocardial fibrosis in HF with preserved ejection fraction  49. On the other hand, the Empire-HF study (NCT03198585) showed that EMPA was associated with a modest but significant reduction in left ventricular and left atrial volumes; however, there was no association with ejection fraction reported by this study 141. Moreover, the EMPA-HEART study (NCT02998970), a phase IV double-blind, randomized, placebo-controlled trial has also assessed the effect of EMPA on cardiac structure and function in patients with T2DM. Data from this clinical investigation illustrated that EMPA reduced extracellular myocardial volume in contrast to the placebo.
The study revealed significant differences in extracellular compartment volume, indexed-extracellular compartment volume, and left ventricular mass indexed to body surface area  142. In support of this, other investigations have shown that short-term EMPA treatment significantly regresses left ventricular mass, promotes reverse remodeling, and improves diastolic function in diabetic patients with cardiovascular diseases  143. Additional clinical trials are presently being conducted, and the outcomes of these investigations may help to thoroughly establish the influence of SGLT2is on CR. For instance, the CARDIA-STIFF study (NCT04739215) aims to investigate the underlying mechanisms behind SGLT2i-associated effects on patients with HF and preserved ejection fraction. This study will ultimately investigate the impact on J o u r n a l P r e -p r o o f myocardial remodeling by monitoring mass, volume, and ventricular function using cardiac magnetic resonance.
The correlation of myocardial remodeling with intrinsic diastolic properties is also one of the objectives of this clinical trial. Similarly, another phase IV study launched in 2021: PRESTIGE-AMI (NCT04899479) is investigating the effectiveness of SGLT2is in reducing the size of infarction and myocardial remodeling in patients with acute myocardial infarction and high risk of HF using cardiac magnetic resonance. Of note, previous research has reported an association between EMPA and improvement of ischemiareperfusion injury, reduction in myocardial infarction size, and microvascular obstruction, in a porcine model  128. Cardiac magnetic resonance is also being used in the SOTA-P-CARDIA study (NCT05562063) to investigate the adverse CR of SOTA in non-diabetic HF patients. This ongoing study focuses primarily on the changes in left ventricular mass, myocardial mechanics, and interstitial myocardial fibrosis.
We introduce Moonbase, a flexible pipeline that can be used to build targeted, project-specific, databases in order to improve the accuracy of taxonomic detection and quantification in complex microbial communities. Moonbase was evaluated using synthetic metagenomic samples and compared against existing methods including MetaPhlAn3 and generalized Kraken2 databases. Our results indicate that Moonbase significantly improves species precision and quantification, outperforming marker genes and generalized databases. Construction of a phylogenetic tree from 16S data of genomes in Moonbase allows for the incorporation of UniFrac-type phylogenetic information into diversity calculations of samples. We demonstrate the resulting analysis has increased statistical power in distinguishing microbial communities.
By contrast, low diversity is considered to be the optimal composition of the vaginal microbiota. Here, the single genus of Lactobacillus serves in its protector role primarily by maintaining a low pH that is inhospitable to other organisms including potential pathogens  [4]. The introduction of high-throughput sequencing technologies revolutionized the study of the human microbiota, moving beyond traditional culture-based techniques to describe the full diversity of microorganisms in the microbiota  [5]. Early surveys of digestive tract microorganisms revealed that a large proportion of the organisms present, especially strict anaerobes, were poorly represented in databases owing to difficulties in isolation and culturing. Large groundbreaking studies, including MetaHIT and HMP, were initiated in order to gain a better understanding of the diversity of the human microbiota as well as to generate a comprehensive catalog of organisms and genes from this environment  [6, 7].
A multitude of data from subsequent studies have been reported, leading to more comprehensive coverage in public databases. The development of advanced culturing techniques has also improved isolation and sequencing of many species from the human gut  [8]. Lower costs and improved technology have increased our ability to generate vast amounts of sequence data in a single experiment. These advances have facilitated large-scale comparative studies, allowing researchers to explore variations in the microbiota across individuals, populations, and diseases. Coupled with increased sequencing data, bioinformatic tools have revolutionized the analysis of microbiota data by enabling the processing, classification, and interpretation of vast amounts of sequencing information. These tools play a pivotal role in translating raw sequencing data into meaningful biological insights, allowing researchers to discern microbial taxa, infer phylogenetic relationships, and estimate species abundances.
However, the results generated by these tools are ultimately dependent on their robustness and accuracy. Cautionary examples include reports of a placental microbiota that have more recently been recognized as artifacts of contamination or database biases affecting low-bacterial-biomass environments  [9] [10] [11]. Indeed, one of the key challenges in microbiota research lies in accurately assessing the diversity and quantifying the abundance of microbial species within these communities. Enumeration of the number of unique taxa in a given sample is called α-diversity. Many of the current tools used to assess the α-diversity of the human microbiota have been adapted from ecology studies including Shannon and Simpson indexes  [12, 13]. Another important aspect is how individuals or population change, often in the context of some change, such as pregnancy, or treatment, including antibiotics.
Faith's phylogenetic diversity (PD) addressed this problem by considering the phylogenetic distances between members of a population in its calculation  [15]. A similar problem is found in β-diversity with Bray-Curtis dissimilarity. Here, another solution linked to phylogenetic distance was proposed by Lozupone and Knight, Unifrac, which uses the distances on a phylogenetic tree generated from 16s rDNA sequences  [16]. Here we describe a hybrid use of MetaPhlAn3  [17]  and Kraken2  [18]  to improve accuracy of bacterial species detection and quantification in complex communities. Taxonomic diversity was found to be particularly important in studying the perinatal vaginal microbiota and we also show application of Weighted UniFrac  [16]  to shotgun metagenomic datasets.
Programing environment and python libraries The workflow, Moonbase, was coded using Snakemake (v. 7. 16. 1). Code, documentation and required files are available as GitLab Repository (https://gitlab. pasteur. fr/metagenomics/moonbase). Figures were generated using the plotly python library. Analyses were performed in the Jupyter notebook environment  [19]. External packages MetaPh-lAn3  [17] , Kraken2  [18]  and Python3 (v. 3. 8), along with their dependencies, are required. Moonbase requires downloading additional files from the repository: A Kraken2 taxonomy directory, a multi-FASTA file, as well as a correspondence file between the headers from the multi-FASTA file and their associated taxID.
Building Prerequisite Files For the root multi-FASTA file, we searched the archaea, bacterial, fungi, protozoa and virus Kraken2 libraries for species from genera present in the MetaPhlAn3 markers database. Species not found in any Kraken2 libraries were downloaded as assemblies from NCBI (in assembly_summary_genbank. txt or assembly_summary_refseq. txt files available at ftp. ncbi. nlm. nih. gov/genomes/genbank/). Assemblies were chosen using parameters in the summary files: assemblies tagged as "excluded_from_refseq" were excluded from our files as well. If multiple assemblies were found RefSeq Category ("reference genome" > "representative genome" > then "NA") then Assembly Level ("Complete Genome" > "Chromosome" > "Scaffold" > "Contig") were used for selection. In cases of equivalence the most recent assembly was used.
Phylogenetic Tree Sequences for 10 202 16S rRNA gene sequences (of 13 610 in the multi-FASTA file) were download from SILVA  [21]  and NCBI nucleotide databases. Sequences were aligned using MAFFT  [22] , with the option -auto. RAxML  [23]  was used to construct the phylogenetic tree with the options: random seed (-p 1234), nucleotide substitution (-m GTR-GAMMA) and 100 bootstraps (-N 100). The 3 408 remaining species, without a curated 16s sequence, were added as follows: The closest higher taxID of the one containing all target children was identified (i. e. , family taxID of a genus). Where the taxID contained only one species, a new inner node is added midway between the leaf node and its parent node, and the missing species is added at the same distance as the original leaf is from the newly added inner node.
Diversity Calculations The calculation α-diversity was performed using Shannon diversity and the Faith's phylogenetic distance  [12, 15]. β-diversity was computed using both Bray-Curtis dissimilarity and the weighted UniFrac methods  [14, 16]. α-diversities were calculated using downsized counts of sample reads (10k each) classified using Kraken2 with the genus-level custom database. β-diversities were computed upon all classified reads and normalized by geometric means as in DESeq2  [24]. All calculations were performed at the species level. Significance of the differences was evaluated using Mann-Whitney -U statistical tests and p-values adjusted by False Discovery Rate correction with the Benjamini-Hochberg method.
Benchmark Comparisons We used Diamond  [25]  (v2. 0. 15) as a modern mapper to which to compare our custom database. Diamond was paired with the curated UniProt (release 2023_01, April 2023) database  [26]  to perform accurate protein-based mapping of reads. A complement to this approach used MEGAN (Metagenome Analyzer)  [27]  (v6. 21. 14) to generate taxonomic identification based on Diamond reads. We used AMBER (Assessment of Metagenome BinnERs)  [28]  (v2. 0. 3) to generate statistics and graphical outputs benchmarking mapping tools and the effects of database selection.
A project-specific database The accuracy of metagenomic analysis performed by comparison to either public or private databases is ultimately dependent on the quality and composition of these databases. Based on results from a number of human microbiota studies in reproductive-aged women, including our own group's work, we found the available tools for the analysis of metagenomic data from low-biomass samples to generate results that did not accurately reflect biological function or experimental results. Specifically, we were unable to accurately confirm the deleterious effects reported for a high proportion of Lactobacillus iners in the vaginal microbiota during pregnancy versus that of Lactobacillus crispatus  [29]. We also experienced difficulties in the correct identification of species in samples, including Lactobacilli, when using large inclusive databases such as those for Kraken2 (Figure  S1 ).
Further, when using MetaPhlAn3 in order to achieve more precise species identification, we were unable to confirm the presence of Klebsiella pneumoniae, an important pathogen which had been isolated and identified using clinical microbiological methods on the same samples. We therefore sought to address the shortcomings of existing methods and to improve the power and accuracy of our analyses of the human microbiota. Our goal was to build a flexible pipeline that would be capable of constructing a project-specific database that was adapted to any environment. We call this workflow Moonbase, and the overall workflow is described below and in Figure  1. Although MetaPh-lAn3 failed to correctly identify some Klebsiella species in our analysis, we found that the overall identification, especially at the taxonomic level of genus was on par or superior to other available tools  [30].
As a starting point all species from genera in MetaPhlAn3 were identified as a robust and inclusive set of genomes that could potentially be included in any custom database. As the foundation for building the custom database, we constructed a multi-FASTA from the list of 13 438 species and 3234 genera contains in MetaPHlAn3. The finalized multi-FASTA contains 752 507 sequences from 21 653 species = 13610 bacteria + 665 archaea + 123 eukaryotes + 7255 viruses, with the additional species coming from those already included in the Kraken2 microbial database whose genus was in the MetaPhlAn list. In order to guard against changes in taxonomic nomenclature  [31]  and potential inconsistencies introduced through incremental updates at NCBI, we choose not to include the building of these files on users' local machine.
Running the Moonbase Pipeline Moonbase has been designed to build a project-specific database. The pipe accepts a range of data, including raw read files. Input files are data files in a format accepted by MetaPhlAn, that is to say FASTA, FASTQ, bowtie2out or SAM formats. Generally, these files correspond to the collection of samples for a given project. The workflow first runs MetaPhlAn3 on the samples to produce a file called merged_taxonomic_profiles. txt. This file contains, for each sample, the relative abundance for each species detected. Secondly, the user chooses which taxonomic level they wish to use for constructing the custom database. If species is used, then only those species explicitly identified by MetaPhlAn3 will be added to the txids_list_to_keep. txt file which lists all the taxonomic identifications for species found across all input samples.
If genus is selected, then all species from the parent genera, identified from the input samples will be added to the list, even if the species in itself was not in the results of the MetaPhlAn 3. 0 classification. Then, it filters the multi-FASTA file to keep only sequences from taxIDs in the list. And finally, it builds the custom kraken2 database. 4 ) The finalized database is built using kraken2-build.
The customized database improves detection and quantification The performance of our project-specific database generated using the Moonbase pipeline was evaluated. We first generated a synthetic set of 100 samples using prevalent species found in the human microbiota, using the CAMISIM software. We tested taxonomic identification and quantification accuracy of MetaPhlAn3 as well as Kraken2 with generalized microbial 'fatfree' database. These results were compared against Kraken2 run on the same samples using our custom database generated using either the 'species' or 'genus' level taxa parameter. The results of the mapping comparison show that read identification with Kraken2 using a generalized database performs relatively poorly when compared to the ground truth. Performance of the generalized database is also poor compared to MetaPhlAn and with the Moonbase custom databases (Figure  2A ).
Kraken2, when used with the generalized database, detects 32 of the 100 species present in the synthetic samples. Mapping with the custom species-level database, generated with the Moonbase pipeline, displays significantly improved sensitivity and is successful in detecting 75 of 100. Sensitivity here is similar to the performance of MetaPhlAn where 74 of 100 species are detected. The similarity is expected and serves as an effective control as the species in the custom database, defined at the species level, are those explicitly identified by MetaPhlAn. Interestingly, when using the genus-level custom database, which includes all members of genus if a single species from that genus was identified by MetaPhlAn, we see that 97 of 100 species are detected. Our results show that the most common species are identified in all tests.
Faecalibacterium prausnitzii, with 500+ genomes in the NCBI database (https://www. ncbi. nlm. nih. gov/datasets/genome/?taxon=853), and nearly 1000 publications since 2002, is detected in all cases. This hold true for other species that have been the subject of a relatively large number of studies and publications, including Bacteroides thetaiotaomicron, with 1000+ publications and 700+ genomes, and Bacteroides fragilis, with 6000+ publications and 800+ genomes. On the other hand, rarer taxa including Pseudoflavonifractor capillosus, where there are only six references and none of the nine genomes sequences in the database are complete, is only detected when using the genus-level custom database. Faecalibacterium duncaniae is a particularly instructive example in that it is a recently-described species  [32]  in an otherwise well establish genus.
The correct identification of closely related species takes on added importance when considering the quantification of those species and when making comparisons across samples. We observe that in many cases, the incorrect assignment of reads leads to large errors in the calculation of abundances. This effect is most visible in the generalized microbial database using kraken2 (Figure  2B ). Here we observe that, out of the ten Faecalibacterium species, only Faecalibacterium prausnitzii is detected with the generalized database and that this leads to an overestimation of abundance of this species compared to the known abundance in the synthetic samples. Indeed, this bias in the calculation of abundance for Faecalibacterium is evident in MetaPhlAn and the species-level custom database. In general, as fewer species are correctly identified, those that are found tend to be assigned more reads, leading to both an overestimation of some species and an underrepresentation of overall diversity.
Assessment of correct genome assignment Based on the results of species identification and quantification, and the observed benefits of proper species-level genome identification, we then benchmarked the ability of different mappers, and different databases, to correctly identify microbial species from complex mixed populations. We used AMBER (Assessment of Metagenome BinnERs) to investigate the performance of different tools, including our own custom database coupled to Kraken2 mapping. AM-BER is normally used to assess genome and taxonomic bins from metagenomic data. We harnessed its functionality in order to assess how well different methods and databases, including the Moonbase custom databases, performed at correctly classifying reads from 100 synthetic control samples. In this context, 'bins' are the 100 genomes used to construct the synthetic control samples with CAMISIM for which we know the ground truth sequence as well as quantification.
AMBER measures completeness, or the average amount each genome is covered by mapped reads. In our case, maximum theoretical completeness is less than 100% since the control samples were generated varying taxa abundances. AMBER also measures purity, or the percentage of correctly mapped reads to each genome. The results for each genome bin averaged over all samples (Figure  3A ) and the mean results of all bins for each method (Figures 3B) show that Kraken2 mapping using our custom databases is significantly more performant than mapping with the larger nonspecific microbial database. Mean completeness was 65. 0% for the Moonbase species-level DB 67. 3% for the genus-level DB. Purity was 84. 2% and 67. 2%, respectively. This compares with a mean completeness of 31. 7% and purity of 41. 0% for the general Kraken2 DB.
Interestingly, we found that our custom database performed as well as protein mapping with Diamond, using the curated UniProt database, in terms of purity and was able to recruit a higher number of reads that resulted in higher completeness (Figure  3C  & D ). Diamond-UniProt and MEGAN produced 79. 3% and 52. 9% purity scores coupled to 47. 3% and 35. 1% completeness scores, respectively. Although protein mapping experiences a built-in penalty due to the fact that not all genomic sequences are codes of proteins, the targeted Moonbase DBs still appear to outperform protein mapping with high-quality, but generalized, databases.