dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported in part by The  Graduate University for Advanced Studies, SOKENDAI , and  JSPS KAKENHI  grant No. 22J13587. Data analysis was carried out on the Multi-wavelength Data Analysis System operated by the  Astronomy Data Center (ADC) ,  National Astronomical Observatory of Japan. This work was carried out in part at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with  NASA  ( 80NM0018D0004 ). N. S. acknowledges support from the project  GENESIS  ( ANR-16-CE92-0035-01/DFG1591/2-1 ), the project  FEED-BACK  ( DLR ,  50 OR 1916 ), and the  SFB 956   (DFG). P. S. was partially supported by a  Grant-in-Aid for Scientific Research (KAKENHI Number   18H01259 ) of the  Japan Society for the Promotion of Science (JSPS). P. P. acknowledges support by the grant  PID2020-115892GB-I00  funded by  MCIN/AEI/10.
Results In this study, we characterized the gut mucosal microbial communities of three Scorpaena sp (n = 125) by using a comprehensive comparative dataset including 16S rRNA gene amplicon data from four different locations in the Mediterranean Sea. We report that in this sh group, geographical origin in uences the diversity and the composition of the gut microbial communities more than host's phylogenetic relatedness. Moreover, we observe a positive correlation between the composition of the gut microbiota and the phylogenetic distance between the hosts (i. e. phylosymbiosis). Finally, the core microbiota of each species was described both regionally and across the Mediterranean Sea. When investigated regionally, the core microbiota was characterized by bacterial genera holding important functions for the health and homeostasis of the host.
Retrieval of Mediterranean scorpion sh gut microbiota data from the literature Only two other studies investigated the gut microbiota of Mediterranean scorpion shes in the past using the 16S rRNA sequencing technique (Ruiz-Rodríguez et al. , 2020; K. Kormas et al. , 2022). The amplicon data they reported were integrated with those obtained in our study to extend our dataset. The study by  Kormas et al. (2022)  reported data from the mucosal gut microbiota of four Scorpanea porcus and four Scorpanea scrofa sampled in the Greek Island of Gyaros (GY), while the study by Ruiz-Rodríguez et al. (2020) reported that of four individuals of Scorpaena notata sampled in BA. The raw FASTQ les from both studies were downloaded from the NCBI Sequence Read Archive (SRA) using the publicly available accession numbers.
, 2015)  (rm. phix = TRUE); select only the reads with a number of error equal or lower than 2 (maxEE = 2) and truncate them at the rst instance of a quality score equal to or lower than 2 (truncQ = 2; the value 2 is used by Illumina as read end quality indicator). After dereplicating, the Amplicon Sequence Variants (ASVs) were inferred by the dada2 algorithm (Divisive Amplicon Denoising Algorithm). Contaminant reads found in the negative controls of the sequencing runs were removed from the ASVs tables. The ASVs tables obtained (three tables from this study, one table for the reads from K. Kormas et al. , 2022 and one for Ruiz-Rodríguez et al. , 2020) were merged together (using the dada2 mergeSequenceTables() function) before assigning the taxonomy that was performed with the naive Bayesian RDP classi er implemented in dada2 by using the SILVA rRNA database (release v138)  (Quast et al.
, 2013). Each ASV was assigned at the taxonomical level of Kingdom, Phylum, Class, Order, Family and Genus. A maximum likelihood tree was generated using the DECIPHER package in R  (Wright et al. , 2012)  and FastTree  (Price et al. , 2009). A few following ltration steps were performed on the ASVs table: the ASVs assigned to Mitochondria, Chloroplasts and Archaea and those unclassi ed at the Class level were removed from the dataset; in addition, the samples with less than 10,000 reads were removed from the dataset. Among all the samples, three out of the four BA samples of S. notata from the study by Ruiz-Rodríguez et al. (2020) were excluded from further analyses due to their low number of reads. The nal dataset (Extended-dataset) comprised 125 samples belonging to S. porcus, S. notata and S.
scrofa from four different locations (BA; CR; BO; GY) and three different studies (this study, K. Kormas et al. , 2022 and Ruiz-Rodríguez et al. 2020 ). Before starting to analyze the bacterial communities of the scorpion sh, two additional adjustments were made on the Extended-dataset. First, it was used to generate a reduced dataset (Species-dataset) which comprised only the samples belonging to the three different species of scorpion sh collected in Corsica (BO) (N = 47 samples): this Species-dataset was created for investigating the effect of host's phylogeny on the gut microbiota of scorpion shes at a regional scale. Secondly, all the unique sequences (ASVs) present in the Extended-dataset were agglomerated at the Genus level to override the differences among the three studies in: the section of the intestine analyzed (midgut and hindgut in this study and Ruiz-Rodríguez et al.
, 2020, and only midgut in K. Kormas et al. , 2022) , the DNA extraction method implemented, and the region of the 16S rRNA gene ampli ed (V4 region for this study and Ruiz-Rodríguez et al. , 2020; V3-V4 for the study by K. Kormas et al. , 2022). The Extended-dataset was then used to generalize the effect of host's species on a broader geographical scale, and to determine the effect of geographic location on the gut microbiota of the Mediterranean scorpion shes. All statistical analyses were implemented in R studio (R Core Team, 2021) on the two datasets in parallel.
Diversity analyses of the gut microbiota of Mediterranean Scorpaena shes The two datasets were normalized by rarefaction: the Species-dataset was rare ed to 20,000 reads per sample, and the Extended-dataset was rare ed to a lower number of reads per sample (18,025) to avoid the removal of one of the four S. porcus samples from GY. The alpha diversity of the bacterial communities was assessed in the rare ed datasets using the total number of ASVs (or genera) observed in the samples (Observed index), the evenness and richness of ASVs (or genera) (Shannon's diversity index), and the phylogenetic diversity of ASVs (or genera) (Faith's diversity index). In order to determine the level of variation in bacterial communities diversity across Scorpaena species and geographic locations, a one-way ANOVA (or Kruskal-Wallis test, if the requirement of normal distribution and/ or homogeneity of variance of the data were not met) was performed on the alpha diversity indices; in case of a signi cant outcome, the Tukey's HSD test (or the non-parametric Dunn's test) was then performed for multiple pairwise post-hoc comparisons among the groups (Scorpaena species or geographic locations).
Spearman's rank correlations were calculated to examine the relationship between sh size (i. e. total length) and the alpha diversity indices. A compositional approach was implemented as suggested in  Gloor et al. , 2017  to calculate the interindividual dissimilarities in terms of gut microbiota composition (beta diversity): the zeros in the unrare ed ASV tables were replaced with near-zero counts by the cmultRepl() function of the R package "zComposition" (Palarea-Albaladejo & Martín-Fernández, 2015) through the Bayesian multiplicative treatment (Martín-Fernández et al. , 2014). Then, the ASV tables were transformed by the centered log-ratio transformation (clr) and pairwise dissimilarities were calculated among the Scorpaena individuals using the Aitchinson's distance  (Aitchison et al.
, 2000). To test whether the inter-individual bacterial dissimilarity was greater between the groups (i. e. the three Scorpaena species and the different geographic locations) than within them, the PERMDISP test  (Anderson, 2001)  through the betadispers() function from the vegan package in R  (Oksanen et al. , 2013)  was performed. In case of absence of homogeneity of multivariate variances (i. e. signi cant PERMDISP test) or unbalanced sample size, the multivariate Welch MANOVA (Wd Test)  (Hamidi et al. , 2019 )performed by the MicEco package in R (Russel, 2020) -was used to test the effect of variables (i. e. host's species and geographic locations) on the bacterial composition. Otherwise, the permutational multivariate analysis of variance (PERMANOVA)  (Anderson, 2014)  was used. In case of signi cant outcome, the pairwise.
adonis() function from the pairwiseAdonis package in R (Martinez Arbizu, 2020) was applied to perform a multilevel pairwise comparison of the composition of the bacterial community in each category (i. e. the host species and the geographic locations). Because the samples for this study were collected at different locations along the coast in BA, CR and BO, the effect of shing location on the composition of bacterial community was investigated for the two datasets. Additionally, given the size range displayed by the samples collected in this study, the effect of sh size on the gut microbiota composition was also tested for both datasets. The specimens not collected for this study were excluded from these additional analyses. Due to the difference in sample size both in the Species-dataset (S.
notata = 11, S. scrofa = 10 and S. porcus = 26) and in the Extended-dataset (BA = 37, CR = 33, BO = 11, GY = 8) the above mentioned statistical analyses were repeated on sub-datasets created by the random combination of samples evenly distributed across the categories analyzed (10 samples for each Species, 11 samples for each geographic location of S. notata and four samples of S. porcus and of S. scrofa for each geographic location ). The statistical analyses on alpha (one-way ANOVA or Kruskal-Wallis test) and beta (PERMANOVA or Welch MANOVA) diversity indexes were performed on 1000 different sub-datasets, and the results were compared with those obtained using the full datasets. Finally, to highlight which speci c taxa (ASVs or genera) were signi cantly different in the gut microbiome of the three species in BO and of each species in the different geographic locations, the microbiome differential abundance method DESeq2 (Differential Expression analysis for Sequence count data)  (Love et al.
Investigating phylosymbiosis in Scorpaena shes Phylogenetic information about the three scorpion sh species were gathered from the study by  Turan et al. , 2009  which compared the mitochondrial 16S rDNA sequence across different species of Scorpaena in the Mediterranean sea. Total nucleotide distances for S. notata, S. porcus and S. scrofa were used to generate a genetic dissimilarity matrix of the individuals collected in BO only. The Mantel test from the vegan package in R  (Oksanen et al. , 2013)  was applied to detect the presence of a positive or negative relationship between the host phylogenetic dissimilarity matrix and the bacterial dissimilarity matrix generated by the Aitchinson's distance on the Species-dataset. This is recognized as the most appropriate test for the detection of phylosymbiosis  (Lim & Bordenstein, 2020).
Classi cation of the core microbiota of Mediterranean Scorpaena shes The core microbiota is de ned as the set of bacterial taxa that are characteristic of a host's species or an environment  (Neu et al. , 2021). The frequency of appearance of a taxon in a sample pool (i. e. occurrence) and its relative abundance in the bacterial community were combined to identify the core microbiota of Mediterranean scorpion sh. Using the Species-dataset, the core microbiota was identi ed in order to determine which bacterial taxa were common and unique to each scorpion sh species. In addition, the core microbiota of S. notata, S. porcus and S. scrofa were also identi ed using the Extended-dataset because the de nition of the symbiotic core microbiota of a host species is considered more accurate when analyzed across a spatial gradient  (Neu et al.
, 2021). These core bacterial taxa, found for each species in the different geographic locations, were then combined to detect those common in the genus Scorpaena and those unique to each species across the Mediterranean Sea. The identi cation of the core microbiota was performed using the core_members() function of the R package microbiome  (Lahti, 2023)  on the rare ed datasets agglomerated at the Genus level. For each Scorpaena species, the bacterial genera occurring in at least 75% of individuals and displaying a relative abundance in the community higher than 0. 01% were considered members of the core microbiota. In order to understand the contribution of the core members to the whole bacterial community, the average relative abundance of the core taxa was calculated across the samples of the same group (i.
Sequencing outcome The three Illumina MiSeq sequencing runs of the 16S rRNA gene (V4 region) produced together 9,375,424 uncontaminated reads with a median number of reads per sample equal to 68,840. The dada2 pipeline generated a total of 14,188 ASVs that were merged with the additional 546 ASVs obtained for the data retrieved from the literature. Then, 18. 3% of the ASVs were removed by the ltration steps resulting in a nal dataset of 12,042 ASVs (included in 1,202 different bacterial Genera). Three S. notata samples from the study by Ruiz-Rodríguez et al. (2020) were discarded after ltering to a minimum of 10,000 reads (Fig  S3 ) per sample. Three additional samples (one individual of S. porcus collected in BO and two individuals of S. notata collected in BA and in CR) were also removed from the Species-dataset after rarefying to 20,000 reads.
The effect of host phylogeny and geographic location on the gut microbiota The sh specimens included in this study were observed to vary in their total length (see Methods section). Among the three species collected in BO, S. notata displayed signi cantly lower sizes compared to the other two species (TukeyHSD test, P-value < 0. 0001 for both comparisons). The individuals of S. notata collected in BA were signi cantly bigger than those collected in CR and BO (Dunn's test: P-value = 0. 001 between BA and BO; P-value < 0. 0001 between BA and CR). In light of this, the relationship between the shes' size and the alpha diversity of their gut microbiota was investigated prior to checking for the effect of other factors: a not signi cant Spearman's correlation of Shannon (Fig S4  and S5 ) and Faith indices and total number of Observed ASVs was found (data available upon request).
The effect of host phylogeny on the scorpion sh gut microbiota The inter-individual dissimilarity of the gut microbiota composition (beta diversity, Fig. 1A ) was calculated by the Aitchinson's distance on the clr transformed ASVs counts. The dissimilarity of the bacterial gut communities of the three Scorpaena species distributed homogenously (PERMDISP test = 0. 12). However, the uneven samples size of the three sh categories in the Species-dataset justi ed the application of the Welch MANOVA test. This analysis revealed the in uence of host species on the gut microbiota composition in Scorpaena shes (WdTest; W*d statistic = 1. 29, P value = 0. 006); however, the W*d statistics reported by the test suggests that other variables should be taken into account to fully understand the reason behind this difference.
notata specimen from BA, 4 S. porcus and 4 S. scrofa from GY) were included in the Extended-dataset (n = 125). This dataset was used to generalize the effect of host species through a broader geographical range and to investigate the variation of the gut microbiota of the three Scorpaena species across the Mediterranean sea. The ASVs in this dataset were agglomerated to the Genus level prior to performing the diversity analyses (see Methods section). In contrast to the observation in BO (at the ASVs and Genus level, respectively in Fig. 1B  and Fig S7A  ), a  signi cant effect of the host species on the levels of richness and evenness (Shannon's index), phylogenetic diversity (Faith's index) and total number of observed genera of the gut microbiota was found when testing the Extended-dataset (Kruskal-Wallis: Shannon's index, P-value = 0.
02; Faith's and Observed ASVs, P-value < 0. 001). Differently, geographic location had no signi cant effect on the alpha diversity values of the S. porcus and S. scrofa microbiota (Fig. 3A. Mann-Whitney test for S. porcus: Shannon's, P-values = 0. 3; Faith's, Pvalues = 0. 051 and Observed ASVs, P-value = 0. 07; Mann-Whitney test for S. scrofa: P-value > 0. 1 for the three indexes). Due to the effect of geographic location on the alpha diversity observed for the individuals of S. notata, it was not possible to con rm the effect of host species on the alpha diversity of the gut microbiota in the extended dataset (Fig  S7B ). In fact, to disentangle the effect of host species from that of geographic location on the alpha diversity, similar values of alpha diversity should have been observed among the individuals of each species from the different geographic locations.
001). However, the pairwise comparisons performed to detect which shing locations signi cantly differed in BA and BO for the two species respectively, revealed that this effect was not generalizable. In fact, the signi cancy reported by the Welch MANOVA for S. notata in BA and for S. porcus in BO was driven by the dissimilarity among the samples collected only from two shing locations (S. notata, pairwiseAdonis between shing location 4 and 11 in BA: Bonferroni adjusted P-value = 0. 03; S. porcus, pairwiseAdonis between shing location 2-3 and 11 in BO: Bonferroni adjusted P-value = 0. 02). DeSEQ2 and ANCOM II were implemented on each species from the Extended-dataset parallelly to determine the bacterial genera mainly responsible for the variation in the gut microbiota composition across the geographical range.
Differently, in BO, the genera Aliivibrio and Enterovibrio were found to be more abundant in S. porcus and Photobacterium in S. scrofa. Some of these taxa accounted for a large portion of the whole bacterial community: for example, the two genera Diaphorobacter and Cutibacterium made up to 19% of the whole gut bacterial community of the individuals of S. scrofa from the GY while Photobacterium -alonerepresented 18. 6% of the gut bacterial community of those from BO. The different sample size among the three species investigated appeared to not in uence the outcome of the statistical tests performed to assess the effect of the host species or of the geographic location. Indeed, the test repeated on the 1000 different sub-datasets displaying even sample size across the three phylogenetic and geographical categories provided results that con rmed those obtained using the full dataset.
Discussion In this study the bacterial communities associated with the mucosa of the mid and lower intestinal tract of three species of commercially important scorpion shes (Scorpaena scrofa, Scorpaena notata and Scorpaena porcus) were investigated. By using a comprehensive comparative dataset including 16S rRNA gene amplicon data from four different locations in the Mediterranean Sea (n = 125), it was possible to describe an important in uence of the geographic location on the composition of the gut microbiota of the three species. In addition, the presence of a distinct microbiota in the individuals of S. scrofa from that of the other two species highlighted the existence of a relation between the composition of the gut microbiota and the phylogenetic relation between the hosts (i. e. phylosymbiosis).
| Data collection Medical records were reviewed for clinical and surgical details. ECGs were analyzed to measure QRS duration. Rhythm assessments were based on ECGs, Holter monitors, and ICD interrogations, when applicable. Cardiac magnetic resonance (CMR) and transthoracic echocardiography (TTE) studies were reviewed to determine biventricular function and volumes. The presence of established risk factors for VA were assessed, including QRS duration >180 ms, left ventricular ejection fraction (LVEF) <45%, right ventricular ejection fraction (RVEF) <35%, prior nonsustained ventricular tachycardia (NSVT), history of syncope, and prior palliative shunts. 13 ese factors were considered along with the results of EPS and acute success of ablation, if applicable, in decisions regarding ICD implantation.
| Cardiac magnetic resonance CMR was performed during the electrophysiological study (EPS) with chamber volume assessments as well as a high-resolution late gadolinium enhancement (LGE) sequence. The processing of highresolution LGE was performed using MUSIC software (IHU Liryc, University of Bordeaux and Inria Sophia Antipolis, France). In-plane and through-plane interpolation were applied to resample the imaging volume at a voxel size of 0. 625 × 0. 625 × 1 mm. Semiautomated tools were used to segment endocardial and epicardial contours of the right ventricle. Iterative histogram thresholding was applied to segment myocardial scar, the threshold being set three standard deviations above the mean intensity of normal myocardium, as measured in a normal septal area. Endocardial segmentation was used to compute a 3D surface mesh at high density ( >50 000 triangles), onto which myocardial scars were projected and scar areas were measured.
| Follow-up Patients were assessed by members of our adult congenital heart disease and/or electrophysiology departments at their first outpatient visit. Subsequent visits could be either at other tertiary centers or with patients' cardiologists. Missing data was retrieved by phone calls and mail exchange. For the purpose of this study, followup started at the time of EPS and ended at last patient's contact. 24 h Holter monitor recordings, ICD interrogations, or 12 lead ECG documentations were used to asses VAs. Mortality was evaluated from hospital records. The primary outcome of interest was a combined event including VT, appropriate ICD therapy, and SCD.
| Jurkat T-cell KO generation Two Jurkat KO models, a Jurkat cell line lacking LF-Kv1. 3 (expressing only the SF-Kv1. 3 isoform) and a total Kv1. 3 KO cell line, were generated with CRISPR-Cas9 tool. Guide RNAs (gRNAs) were designed using the CRISPOR software (primers F16/R16 to F18/R18 in Supporting Information: Table  I ). Two gRNAs located at the beginning and at the end of the KCNA3 were selected to eliminate the complete exon (total KO), whereas the SF-Kv1. 3expressing clone was generated by choosing two gRNAs located both at the 5′-end of the KCNA3 gene. All gRNAs were generated using GeneArt Precision Synthesis Kit. 3FLAG-2MYC (10 6 ) homozygous-tagged Jurkat cells were Nucleofected with 5 μg of Cas9 and 500 ng of each gRNA, and a limiting dilution was performed to establish individual clones, which were confirmed by western blot analysis.
| Protein determinations Western blotting protocols were performed as described elsewhere  (Cidad et al. , 2020). Briefly, 10 6 cells were lysed in 50 µL of radioimmunoprecipitation assay buffer (20 mM Tris pH 7. 4, 150 mM NaCl, 1 mM EDTA, 0. 1% sodium dodecyl sulfate [SDS], 1% sodium deoxycolate, 1% Nonidet P-40, protease inhibitor cocktail  [Sigma-Aldrich] ). 50 µg of protein lysates were loaded into 10% SDS-polyacrylamide gel electrophoresis, transferred into a polyvinylidene difluoride membrane and blocked with 5% milk plus 5% bovine serum albumin (BSA) TTBS (Tween-Tris Buffered Saline) for 1 h. Primary and secondary antibodies used are listed in Table  2  (Supporting Information). Images were acquired with a VersaDoc 4000 Image System (Bio-Rad) and quantified using ImageJ software. For western blot analysis of phosphorylated proteins, a flatbottom 96-well plate was coated with 10 μg/mL of mouse antihuman CD3 and 10 μg/mL of mouse antihuman CD28 overnight.