dataesr/scientific-paragraphs-categorization huggingface dataset:
Images of the coculture system were acquired using Spinning Disk microscopy (Zeiss ® ) with the 20X and 40X objectives and a z-stack plan. For quantification of the AChR clustering, neuritic network and myotube area, each image taken was processed as a maximum projection and analysed by using Fiji Software. The following quantifications were performed semimanually. The total area and mean size of AChR clusters were determined by an algorithm developed in-house in Fiji (supporting information Material and Method 1). The neurite outgrowth was measured by applying an intensity threshold and a background correction to detect the neurites stained by TUJ1, class III beta-Tubulin shown to be expressed in the soma and the neuritic processes of neurons. The number of branches was measured as intersections between neurites from the cluster of soma (supporting information Material and Method 2).
Islet1 quantification was determined by automatically counting the number of Islet1-positive cells by micropattern using an intensity threshold on Fiji software. The myotube area was quantified by delimiting the cells stained by MF20 (MHC) antibody and application of an intensity threshold (supporting information Material and Method 3). From day 4 in coculture, visual contractions were recorded in phase contrast using Spinning Disk (Zeiss ® ) under a controlled atmosphere (37 C, 5% CO 2 ). Movie acquisition lasted 2 min, and they were analysed using the detection of pixel variations on Fiji Software and then visual contractions were analysed by the kymograph tool.
In contrast, the activation of synaptic activity was evaluated after treatment with L-Glutamic acid (100 uM-Sigma-Aldrich ® ). Placebo experiments were conducted by adding drugs to myotubes alone without hiPSC-derived MNs or by treating cocultures with a buffer solution that did not contain any of the chemical reagents. The calcium flux assay analysis was performed semi-automatically using Fiji Software, and the exported raw data were processed on Microsoft Excel to generate the ΔF/F 0 curve (supporting information Material and Method 4). Briefly, for each condition, two fields per well were analysed. For each field, one ROI (Region of interest) determined by using the ROI manager on Fiji Software has been determined for each micropattern (one ROI was also generated for the background signal to calculate the corrected fluorescence).
The final library was quantified using an Agilent High Sensitivity DNA kit before the samples were pooled in sets of six for sequencing. About 2 nM of pooled libraries were denatured, and the quantity of 1. 8 pM was used for cluster generation before paired-end sequencing on an Illumina Nextseq550 (Hight output 2*150pb run). Samples were sequenced with an average of 89,532,366 reads (paired-end) per sample. The quality control of the sequencing data was evaluated using FastQC (v0. 11. 2). The reads were trimmed using Prinseq (v0. 20. 4)  [31]  (--trim-right 20) and filtered by average quality score (--trim-qual 20) and cutadapt (v1. 16). Reads were mapped EnsEMBL GRCh37. 87 human reference using rna-STAR (v2. 7. 3)  [32]. Reads below a mapping score of 10 or multimapped were filtered using samtools (v0.
1. 19). The gene expression level in each sample was quantified with HTSeq-count (v0. 8. 0). The differential gene expression (DEG) between conditions was calculated with DESeq2 (v1. 26. 0 using R v3. 6. 3). We consider that genes are differentially expressed when their BaseMean is greater than 20, their p-value adjusted is less than 0. 05 and jlog2FoldChangej is upper than 0. 4 (options: lfcThreshold = 0. 4, altHypothesis = 'greaterAbs'). For the alternative splicing analysis, the raw reads were annotated on GRCh37. 75 EnsEMBL reference annotation by Tophat2 (v2. 0. 13) and analysed using only reads on exomic junctions with FaRLine pipeline and FasterDB database. Exons were considered differentially spliced when their adjusted p-value was under 0. 05 and their DeltaPSI (per cent spliced-in) value was upper than 0.
Nuclei were detected by Hoechst (blue). Scale  Bar   (C) Representative diagram of calcium waves recorded for 100 s with (w/) or without (w/o) human-induced pluripotent stem cell (hiPSC)-derived MNs. ΔF/F0 representation after background correction. On the right, the quantification of the number of calcium waves per minute and per pattern in the absence or presence of hiPSC-derived MNs. Data are presented as the mean ± SD values from three independent experiments in technical triplicate (at least 100 micropatterns/condition/experiment) and were analysed with Student's t-test (**: p < 0. 005). (D) Graphical representation of calcium waves recorded in basal condition or 4 and 8 h after the addition of a solution of 5 nM botulinum neurotoxin type-A (BoNT/A). ΔF/F0 representation after background correction.
Interestingly, functional annotation of common DEGs also revealed that the most relevant GO terms concerned 'nervous system development', 'anterograde F I G U R E 6 Communication defect between hiPSC-derived motoneurons (MNs) and skeletal muscle cells in myotonic dystrophy type I (DM1) and muscleblind protein (MBNL)-depleted conditions. (A) Representative diagrams of calcium waves (time = 2 min) recorded in human primary skeletal muscle cells in different hybrid conditions (CTRL -control-in dark grey, DM1 in burgundy, DM1_3_ΔCTG in light grey and MBNL knockout conditions in blue). ΔF/F0 representation is indicated after background correction. (B) Quantification of the number of calcium waves recorded in human primary skeletal muscle cells per pattern and per min after 7 days of coculture. Data are presented as the mean ± SD values from at least three independent experiments in technical triplicate (at least 100 micropatterns/condition/experiment) and were analysed with an ordinary one-way analysis of variance (ANOVA), Tukey's multiple comparisons test compared with CTRL (*: P ≤ 0.
DISCUSSION The main findings of the present study are the anterograde neuronal contribution of spinal MNs to the NMJ defects in Myotonic Dystrophy type 1 and that this contribution is dependent on MBNL proteins. F I G U R E 8 Myotonic dystrophy type I (DM1) mutation and loss of muscleblind protein (MBNL1) and MBNL2 proteins lead to a large number of alternative splicing defects related to molecular motor activity and synaptic functions. (A) Genes set enrichment analysis using EnrichR on the 236 differentially spliced exons (DSE) in DM1 hiPSC-derived motoneurons (MNs) when compared with CTRL hiPSC-derived MNs (EnrichR analysis using GO Biological process 2018 database). The table on the right provides a list of specific genes differentially spliced-in DM1 hiPSCderived MNs. (B) Genes set enrichment analysis using EnrichR on the 232 differentially spliced exons (DSE) in DKO hiPSC-derived MNs when compared with CTRL hiPSC-derived MNs (EnrichR analysis using GO Molecular Function 2018 database).
The table on the right provides a list of specific genes differentially spliced-in DKO hiPSC-derived MNs. (C) Genes set enrichment analysis using EnrichR on the 48 common differentially spliced exons (DSE) between DM1 vs CTRL hiPSC-derived MNs and DKO vs CTRL hiPSC-derived MNs conditions (EnrichR analysis using GO Biological process 2018 database). On the right, list of specific misspliced genes in the panel of genes commonly identified between DM1 vs CTRL hiPSC-derived MNs and DKO vs CTRL hiPSC-derived MNs conditions. (D) Alternative splicing analysis for CAST exon 17 and COL13A1 exon 37 in hiPSC-derived MNs. Data represent the mean ± SD values from three independent experiments in technical triplicate and were analysed with an ordinary one-way analysis of variance (ANOVA), Tukey's multiple comparisons test compared with CTRL (***: P ≤ 0.
Myotonic dystrophy type I (DM1) mutation and muscleblind protein (MBNL) depletion affect the expression of genes related to synaptic functions. (A) Hierarchical clustering of differentially expressed genes (DEG) detected in DM1 and DKO human-induced pluripotent stem cell (hiPSC)-derived motoneurons (MNs) compared with control (CTRL) hiPSC-derived MNs. Gene expression is represented by a colour code ranging from green for underexpressed genes to red for overexpressed genes. (B) Schematic representation of the number of DEG in DM1 (red) and DKO (Green) hiPSC-derived MNs. (C) Genes set enrichment analysis using EnrichR on the 129 downregulated genes in DM1 hiPSC-derived MNs when compared with CTRL hiPSC-derived MNs (EnrichR analysis using GO Biological process 2018 database). (D) Genes set enrichment analysis using EnrichR on the 355 downregulated genes in DKO hiPSC-derived MNs when compared with CTRL hiPSC-derived MNs (EnrichR analysis using GO Biological process 2018 database).
(E) Genes set enrichment analysis using EnrichR on the 137 common deregulated genes between DM1 vs CTRL hiPSC-derived MNs and DKO vs CTRL hiPSC-derived MNs conditions (EnrichR analysis using GO Biological process 2018 database). On the right, a list of specific deregulated genes in the panel of 137 genes commonly identified between DM1 vs CTRL hiPSC-derived MNs and DKO vs CTRL hiPSC-derived MNs conditions. (F) Quantification by RT-qPCR of CXCR4, DLK1 and WNT16 mRNA expression level in hiPSC-derived MNs after normalisation with Cyclophilin A, 18S and GAPDH levels. Data are presented as the mean ± SD values from three independent experiments in technical triplicate and were analysed with an ordinary one-way analysis of variance (ANOVA), Tukey's multiple comparisons test compared with CTRL (**: P ≤ 0.
ACKNOWLEDGEMENTS  I-Stem  is part of the  Biotherapies Institute for Rare Diseases (BIRD)  supported by the  Association Française contre les Myopathies (AFM-Téléthon ). This project was supported by grants from  Laboratoire d'Excellence Revive  ( Investissement d'Avenir ;  ANR-10-LABX-73 ), the  Region Ile-de-France  via the doctoral school « Innovation Thérapeutique, du fondamental à l'appliqué » (ED 569) from  Paris Saclay University and Agence Nationale de la Recherche (ANR). We thank  Lina El-Kassar  and  Karine Giraud-Triboult  for karyotyping the cell lines, the bioproduction platform for the large-scale production of control myoblasts. We also thank  Florine Roussange  for her help at the revision step. We gratefully acknowledge support from the  PSMN (Pôle Scientifique de Modélisation Numérique)  of the  ENS of Lyon  for the computing resource.
F I G U R E 7 7 Myotonic dystrophy type I (DM1) mutation and muscleblind protein (MBNL) depletion affect the expression of genes related to synaptic functions. (A) Hierarchical clustering of differentially expressed genes (DEG) detected in DM1 and DKO human-induced pluripotent stem cell (hiPSC)-derived motoneurons (MNs) compared with control (CTRL) hiPSC-derived MNs. Gene expression is represented by a colour code ranging from green for underexpressed genes to red for overexpressed genes. (B) Schematic representation of the number of DEG in DM1 (red) and DKO (Green) hiPSC-derived MNs. (C) Genes set enrichment analysis using EnrichR on the 129 downregulated genes in DM1 hiPSC-derived MNs when compared with CTRL hiPSC-derived MNs (EnrichR analysis using GO Biological process 2018 database). (D) Genes set enrichment analysis using EnrichR on the 355 downregulated genes in DKO hiPSC-derived MNs when compared with CTRL hiPSC-derived MNs (EnrichR analysis using GO Biological process 2018 database).
paper 'Close Kin Proximity in Yellowfin Tuna (Thunnus albacares) as a Driver of Population Genetic Structure in the Tropical Western and Central Pacific' provided observations of genetically related individuals among sampled yellowfin tuna. Resampling of some individuals from the original study produced very different sequencing results when compared with the original dataset, one possible explanation of which is cross-contamination among the original samples. Re-analyses produced no indication of strong population structure, including a very slightly and consistent heterozygosity deficit with only one statistically significant pairwise F ST value of 0. 002. We identified only one pair of genetically similar individuals that could still be an artefact of lingering contamination. These new results therefore support a highly connected population of yellowfin tuna in the study area, although we do not rule out the potential for local adaptation driven by non-observed loci or genetic sub-structure operating at more contemporary scales.
One example is Anderson et al. 's now-retracted 2019 paper 'Close Kin Proximity in Yellowfin Tuna (Thunnus albacares) as a Driver of Population Genetic Structure in the Tropical Western and Central Pacific', which reported the observation of individuals within sample groups that were genetically so similar that they were possibly siblings. These results reopened discussion about the life history of the species in the Western and Central Pacific Ocean (WCPO). However, a re-analysis of the  Anderson et al. (2019)  dataset indicates that the original samples was cross-contaminated, and that the sequencing results were consequently compromised. We present here a revised interpretation of the  Anderson et al. (2019)  study that incorporates new and higher-confidence sequencing data based on amended laboratory handling protocols and improved quality filtering protocols.
Specialized adaptors were ligated to digested DNA. Both PstI and SphI adapters included a PCR primer sequence and Illumina flowcell attachment sequence, and the PstI adaptor also included a unique, varying length barcode sequence for sample recognition within pooled libraries. PCR selectively amplified fragments capped with both adaptors, using the following protocol: 1 min denaturation at 94°C, 30 cycles of 20 s at 94°C, 30 s and 58°C and 45 s at 72°C, and a final extension step of 7 min at 72°C. Libraries were then further amplified using bridge PCR on the Illumina HiSeq 2500 platform and sequenced. The resulting data was submitted to an in-house software, DArTsoft, which interprets sequences from images of fluorescence taken during Illumina sequencing and produces FASTQ files. Files were quality controlled for sequences with 90% confidence at 50% of bases, and split by barcode into individual specimens.
Sequences were aligned de novo. A separate algorithm, DArTsoft14, called SNPs and further quality filtered for singletons and other suspected sequencing errors. The final output produced by DArT PL was a genotype report of all identified SNPs, their global call rate, polymorphic information content, and their co-dominant status in each sequenced specimen. The resulting data was published in the now retracted paper by  Anderson et al. (2019). The second stage of research began in 2021. As part of a larger exploration of genetic cross-contamination in the context of fisheries science, 94 samples from the  Anderson et al. (2019)  dataset were re-sequenced. The selected samples included all individuals that were identified as part of half-or full-sib pairs in the  Anderson et al. (2019)  paper, plus a random selection of other individuals to capture the impact of resampling on specimens that were not involved in the kin-versus-contamination discussion.
This time, subcutaneous subsampling was conducted by a staff member at SPC who sterilized cutting tools with 10% bleach followed by a Milli-Q water wash between incisions, rather than detergent wash followed by ethanol and flame exposure. This procedure has been validated as a method to recover contaminated samples using experimentally generated contaminated samples  (Anderson et al. , 2023). Samples were otherwise handled the same way as per  Anderson et al. (2019)  and processed using the same methods, except that DArT PL has since upgraded to an Illumina Novaseq platform. The new samples were co-analysed with the 2016 dataset during SNP calling. Data as returned by DArT PL is available at DOI 10. 17605/OSF. IO/YRFM3 (https://osf. io/yrfm3/). Returned data was further processed to replicate the  Anderson et al.
(2019)  methods, but subjected to additional tests for crosscontamination. First, the re-sequenced data was compared against the original data from the same specimens and the version with lower observed heterozygosity selected for further use, since elevated observed heterozygosity is a common indicator of crosscontamination (e. g. in  Petrou et al. , 2019). Subsequently, the dataset was filtered for low quality loci using the same thresholds as  Anderson et al. , 2019. All but the locus with the highest call rate per contig was discarded, plus loci with call rate below 99%, heterozygosity above 50%, read depth less than 7x, or minor allele frequency less than 5%. Further, loci were tested for compliance with Hardy Weinberg Expectations (HWE) at p > 0. 0001, linkage disequilibrium at < 60%, and departure from neutrality with respect to local selective pressures at a false discovery rate of 0.
1. Tests for HWE and linkage disequilibrium (LD) were conducted with PLINK 1. 9  (Chang et al. , 2015)  using the commands '-hardy' and '-r2', respectively. Loci under potential selection were identified using both OutFLANK v 0. 2  (Whitlock and Lotterhos, 2015)  and BayeScan v 2. 1  (Foll and Gaggiotti, 2008)  with a burn in of 50000 steps and prior odds of 10, followed by 20 pilot runs of 5000 iterations. Alternative filtering regimes were also explored with heterozygosity cutoffs reduced to 90%, read depth increased to 10x, and a minor allele frequency reduced to 1. 8% (equivalent to an allele being present 5 times in the dataset), but these datasets were less successful at flagging remaining contaminated individuals and did not significantly change downstream analyses. Basic results using these thresholds during final filtering are available in the Supplementary Materials.
It should be noted that methods exist to filter contaminated loci and individuals at this stage. The program ART-DeCo  (Fievet et al. , 2019)  interprets an unbalanced allelic ratio per heterozygote loci as contamination, and O'Leary et al. (  2018 ) encourages use of the same filter to address a number of potential data quality issues. The present dataset does not include the allelic balance raw data necessary for such analysis. We therefore proceeded with filters more in line with those described in  Guo et al. (2014) , which include heterozygosity rate and genetic similarity between individuals. Namely, after the filtering described in the previous paragraph, all specimens were again assessed for missing data and individual heterozygosity, the latter calculated as the mean heterozygosity for that individual across all the loci surveyed.
e. 0. 2649) compared with approaches using z-scores (0. 2884, stabilized after five iterations) or using the mean individual heterozygosity in the calculation instead of the median (0. 2869). After extraction, the remaining 247 individuals were filtered again using the same quality metrics and thresholds as during preliminary filtering. These steps produced a 'final' genotype dataset for further analysis. We again applied the Shapiro-Wilk test to test for normality of the heterozygosity distribution in this final dataset. Downstream population genetic analyses replicated those conducted by  Anderson et al. (2019). The inbreeding coefficient (F IS ) and adjusted expected and observed heterozygosity (H nb and H o ) values were obtained for each sample group using GENETIX v. 4. 05 (Belkhir et al.
, 2004) , while pairwise F ST  (Weir and Cockerham, 1984)  was calculated using Arlequin v 3. 5. 2. 2. ADMIXTURE v. 1. 3. 0  (Alexander et al. , 2015)  was used to recommend the number of independent genetic clusters (k) among all sampled individuals. Analyses were run with hypothetical k values ranging from 1 to 10, and the optimal k value was selected based on a low coefficient of variance. Discriminant Analyses of Principal Components (DAPC) in the 'adegenet' R package v 2. 1. 10 (Jombart and Ahmed, 2011) was first applied to infer cryptic structure within the dataset using kmeans clustering to determine the number of underlying groups. DAPC was also applied using the sampling units as a priori groups to visualize the heterogeneity among geographic populations and sampling years. In both analyses, cross validation was used to determine the number of principal components retained.
Additionally, in keeping with the original study, we explored relatedness using three software programs, RelateAdmix  (Moltke and Albrechtsen, 2014) , KING v 2. 3. 1  (Manichaikul et al. , 2010) and COANCESTRY v. 1. 0. 1. 7 (Wang, 2011). The algorithm employed in RelateAdmix maximizes its accuracy when working with admixed populations, which is one plausible population model for tuna. Meanwhile KING employs an algorithm that is robust to unknown population structure. COANCESTRY allows for the consideration of inbreeding, which was a relevant feature for some sample groups in the original study. RelateAdmix requires a priori information about general genetic clustering, which was recycled from ADMIXTURE. The output. P and. Q files from the selected ADMIXTURE analysis (k=1) were submitted to RelateAdmix, and output values used to calculate q = (k 1 /4) + (k 2 /2), where k 1 and k 2 are respectively the probabilities of sharing one or two alleles from a shared ancestor, and q is the expected fraction of two genomes that is identical by descent.
KING was run via command "related" and produces the kinship coefficient j, plus a synthesis of dyads that are related in the first, second and third degree. COANCESTRY was run using the TrioML algorithm, which was empirically demonstrated to best describe the relatedness coefficient (r) among the seven algorithms available in that software package after conducting preliminary simulations as described in the COANCESTRY user manual. RelateAdmix and COANCESTRY both produce relatedness coefficients, but no recommendation on how to differentiate full sibling pairs from half siblings and more distantly related individuals. To determine cut-off values for categorizing relatedness coefficients, we again used COANCESTRY to simulate 200 dyads each of full sibling, half siblings, first cousins, and unrelated individuals based on the same allele frequencies as the original population samples.
Results Of the 94 specimens that were resampled in 2021, 73 were selected to replace existing data in the original dataset. The updated dataset including all 279 non-duplicated individuals was filtered and retained 1,417 of 63,351 loci (Table  1 ). This dataset was used to assess individuals for missing data and high heterozygosity. The Shapiro-Wilk test conducted on the H o distribution at this stage produced W = 0. 519 and p < 2. 2e-16, and the associated skewness coefficient was 4. 77, indicating marked divergence from normality (Figure  S2 ). Four individuals were then removed due to missing data, and another 29 due to high heterozygosity. Subsequently, filtering again with the same thresholds but only 247 high quality individuals, 3,781 loci passed all quality checks (Table  1 ). The Shapiro-Wilk test on the H o distribution for these remaining 247 individuals produced W = 0.
940 and p = 1. 72e-8, with skewness coefficient 0. 50, (Figure  S2 ). Although the continued non-conformity to a normal distribution suggests that our final genotypic dataset is not entirely free of all contamination, based on the relationship between contamination and inflated heterozygosity demonstrated in  Petrou et al. (2019)  and  Anderson et al. (2023) , the substantial reduction in the amount of skewness suggests that crosscontamination is likewise much lessened. We therefore proceeded with a reassessment of the yellowfin tuna population structure results published in  Anderson et al. (2019). Observed and adjusted expected heterozygosity values (H o and H nb ) and inbreeding coefficients (F IS ) for each sample group are summarized in Table  2. All sample groups feature H nb values of 0.