dataesr/scientific-paragraphs-categorization huggingface dataset:
The information available when the assay was submitted in November 2012 to OECD TGs program was used to fill the ReadEDTest. As expected, the assay got an overall high score for almost all criteria (Fig. 4A ), except the data management section that was beyond the limit score. This section is not essential for acceptance of a test method by the working program but must be considered carefully after approval in order to ensure the traceability of the data collected between the different laboratories and a correct reporting of the data to the validation expert group. The ERα CALUX transactivation assay is one of the three methods included in the OECD TG No. 455 "Performance-Based Test Guideline for Stably Transfected Transactivation In Vitro Assays to Detect Estrogen Receptor Agonists and Antagonists".
Discussion and conclusion One of the reasons why validation of a method takes many years is the lack of readiness of the method. The ReadEDTest online tool, presented here, aims to guide developers regarding the technical/scientific optimization of their method. It allows researchers to become aware of the lacks within the different items. In particular, the importance of historical data and of positive and negative controls, the demonstration of the stability of the test, the need for a detailed characterization of the test system, or a fixed and precise operating procedure, are examples of often retrieved missing information when tests are developed. Indeed, method developers are often unaware of the necessary requirements and  important aspects to consider when engaging a test into a validation process.
The advantage of the ReadEDTest is the compilation in one place of all these aspects informing the developers on the strengths and weaknesses of their methods. The in vitro case studies demonstrated that the researchers usually have sufficient data for 9 sections that concern the characterization of the test methods. These sections have good scores (over the score limit). Even in these sections, ReadEDTest was helpful as it reveals some missing data such as historical data. However, the sections "Data management", "reproducibility and transferability", and "SOP" (except for the StAZ assay) present low scores as there were missing data. It is worth noticing that data management has the worst score. That justifies the generic effort of research organizations to enhance "research data management" (https://dmp.
Overview of transcriptomes in low-and high-acetate conditions The RNA-Seq datasets contained a total of 2,882 genes out of the 3,013 predicted in the RefSeq database (i. e. , over 95%). Of these, 1,161 genes were found to be differentially expressed (DE) (log 2 fold change (FC) ≥ |2| and FDR-adjusted p-value ≤ 0. 01) between conditions (Table  S1  in Additional le 2). All DE genes are presented in Additional le 2 (Tables  S2-S5 ). Eight of these genes were differentially expressed in early exponential cultures, compared to 542 in the late stationary phase (340 and 202 transcripts up-and down-regulated, respectively, Fig. 2A ), which is consistent with the different patterns of growth kinetics. This result clearly demonstrates that, under our experimental conditions, acetate concentration (3 vs.
When comparing the DE transcript lists in low-and high-acetate conditions (Sa/Ea compared to S/E, Fig. 2C ), it clearly appeared that the acetate-limiting condition triggered a larger and more-speci c adaptive transcriptional response at the onset of the stationary growth phase compared to the acetate-saturated conditions. Using the COG and PATRIC databases, we classi ed DE genes into 12 functional categories (Table  S6 , Additional le 2). As shown in Figure  3A , for all transcriptomes (high-acetate, low-acetate, and early stationary), the largest category (27. 2% to 58. 8% of genes) was the superclass "Poorly characterized protein-genes", which included the categories "Hypothetical proteins" and "General function prediction". It is likely that these unknown DE genes contribute to the adaptive responses of F.
For this, we analyzed a metaproteomic dataset obtained from eight healthy individuals, which included 123 425 peptides from the envelope fraction of the gut microbiota (Fig. 5 )  33. The metaproteomic data are presented in Additional le 4. Among these peptides, 236 matched with 10 of the 42 transporter genes that we had found to be upregulated in high-acetate conditions (Table  S7  in additional le 2). Remarkably, the second highest degree of protein coverage (43. 6%, 51 peptides) was found for the FeoB transporter. Of those 51 peptides, 9 were speci c to a single protein in the metaproteomic dataset ('a5. b59. a1', sheet 'CG447_12750 FeoB' in Additional le 4), which was identi ed as FeoB of the A2-165 strain (K04759 in the KEGG orthology database). This demonstrated that the FeoB protein from A2-165 is expressed (i.
Short-chain fatty acid production and consumption Gas chromatography (7890B, Agilent Technologies) was used to identify the short-chain fatty acids present in culture supernatants. The SCFAs of interest in this study were acetate and butyrate. An internal standard of 2-ethylbutyrate was used to normalize the data in each run. Measurements of SCFA concentrations in the bacterial samples were normalized using the basal SCFA levels of BHISAc and BHIS media from the same experiment.
Mapping and identi cation of differentially expressed genes Reads were mapped on the genome of F. duncaniae strain A2-165 (GenBank: CP022479. 1) with BWA 0. 6. 2-r126, and were counted using the subread feature of Counts v1. 5. 2. Analyses of differential expression were performed in R using DESeq2 (I2BC HTS platform, CNRS, Gif-sur-Yvettes, France). In total, 2,900 genes-including 2,819 protein-coding sequences, 63 tRNA genes, and 18 rRNA genes, were mapped. Only the rst two groups were analyzed in this study, so that a total of 2,882 genes were considered. These were further ltered to retain only ORFs that showed a four-fold change in expression (absolute value Log 2 FC >=2) (up/down) between treatment conditions. RNA-Seq datasets can be found on the database Omics Dataverse under the DOI accession number BDNCT2 (https://doi.
Protein sequence annotations, functional classi cation The manually curated and validated genome reconstruction iFpraus_v1. 0, which contains 602 genes of the A2-165 genome  37  , was used for annotation correction. Protein annotation data from the PATRIC database  42  (PATRIC genome ID 853. 173) were used for functional classi cation. As the functional subsystem coverage in PATRIC was low (29%), we manually generated COG-based functional classi cations using the COG system (https://www. ncbi. nlm. nih. gov/research/COG). We created a simpli ed classi cation scheme with 12 categories; the largest metabolism classes (protein synthesis, energy, and import system) were retained while smaller metabolism classes (cell wall metabolism, lipid metabolism, amino acid metabolism, amino sugar metabolism, nucleotide metabolism, vitamin & cofactor metabolism, bacterial cell division processes) were merged into an "Other metabolism" category.
The "Import system" and "Export system" classes encompassed all membrane transport systems involved in the uptake/export processes of nutrients, micronutrients, ions, and unknown compounds. Subclasses were de ned for import systems, transcriptional regulation, energy metabolism, defense mechanisms, and stress response. The TC-BLAST tool (TCD database https://www. tcdb. org/) was used for the determination of the number of TMS (putative TransMembrane Segment) and for the import system family assignment. To characterize gene similarity, homology, and gene context, NCBI BLAST was used, in particular for analysis of feo genes.
EEG acquisition and preprocessing The EEG was recorded from 64 active channels placed on a cap according to the international 10-20 reference system, using the BioSemi ActiveTwo system (www. biosemi. com). Continuous EEG data were amplified and digitized at 512 Hz, and bandpass filtered between 0. 5 and 45 Hz. To assess eye movements and blinks, 4 electrooculography channels (EOGs) were used; two placed above and beneath the left eye and one on the outside of each eye. The preprocessing of EEG was performed with the MNE-python software package  (Gramfort et al. , 2014) (www. mne. tools). EEG data was down-sampled to 250 Hz before being high-pass filtered at 0. 5 Hz and low-pass filtered at 45 Hz. Channels with excessively noisy signals were removed. Following Sitt et al. (Sitt et al. , 2014) , the EEG data was segmented into epochs spanning -200 ms to 600 ms relative to SART stimuli and baseline corrected with respect to the pre-stimulus period (-200 to 0 ms).
Noisy epochs were removed automatically with the Autoreject package (https://autoreject. github. io/). To correct for ocular and muscle artifacts, an ICA decomposition with the FastICA method was performed  (Hyvarinen, 1999)  and artifactual components were removed. Previously removed channels were interpolated using a spherical spline interpolation before re-referencing using a common average reference. Additionally, the ERP component of all epochs was subtracted for the computation of all non-evoked markers. From this point on, only the 5 epochs prior to a report (~10 s) were considered and labelled according to the category of thought reported.
The set of markers used here mirrors the selection by  Engemann et al. (2018)  with the addition of information theory and connectivity markers for all spectral bands, and excluding markers specific to their experimental methodology. For a detailed description and discussion of the markers see  Sitt et al. (2014). All markers were computed using the NICE library (available at (https://github. com/nice-tools/nice) for each of the 5 epochs immediately preceding a thought probe and linked to the condition corresponding to the self-report category. This time window of approximately 12 sec preceding thought-probes is broadly consistent with previous analyses  (Baird et al. , 2014; Baldwin et al. , 2017; Braboszcz & Delorme, 2011). The computation of each marker yielded multiple observations per channel, per epoch, per time point, and/or per frequency bins depending on the family of the marker.
These observation points were aggregated first in the time/frequency/sensor domain depending on marker type, before being averaged across all sensors. Finally, the last 5 epochs corresponding to one condition were aggregated via an average and the standard deviation, the latter being a measure of the variations between epochs (see Fig. 2 ). This yielded two different measures per epoch and marker, giving a total of 54 markers (27 average, and 27 variation measures). The markers were labeled according to their type and to the final processing step, i. e. , 'mean' or 'std' (e. g. , 𝛼 𝑚𝑒𝑎𝑛 or 𝛼 𝑠𝑡𝑑 ). All analysis scripts are publicly available in https://github. com/Nicobruno92/mw_markers_project. 3 ) Two features were extracted from each marker (indicated by red dots) by calculating both mean and standard deviation.
Multivariate pattern analysis (MVPA) In keeping with the methods applied in  Engemann et al. (2018) , we used an Extra-trees classifier  (Geurts et al. , 2006)  from the Scikit-learn python library  (Pedregosa, 2011)  for the MVPA. Extra-Trees classifiers are non-parametric models that perform robustly and are less sensitive to the scale of the input data  (Engemann et al. , 2018). This category of model is also more efficient at handling the type of dataset we obtained, called 'wide' because it contains more variables than observations. A further advantage of this type of model is the possibility of outputting feature importance scores which provide additional information on the discriminative capacity of individual features. In addition to the univariate correlation, this score also considers the interdependency with other variables.
Further demonstrating the discriminative ability of theta features, ERPs did not differentiate on-from off-task states, in opposition to our predictions based on prior studies that found reduced amplitudes of P1 and P3 components to be significant predictors of mind wandering  (Dong et al. , 2021; Groot et al. , 2021; Martel et al. , 2019). Nevertheless, our findings that oscillatory features were amongst the most discriminative for both contrasts are promising for the unobtrusive and continuous monitoring of EEG without the necessity for probing cognition to elicit and measure a brain response, as is the case for ERPs. Our work could be affected by several limitations. The first one concerns the possibility of overfitting of our models in the on-and off-task contrast. This is a common issue with wide datasets containing many features and a small set of observations.
22 The influence of tumor mutational burden status (TMBST) on the ORR was substantial for the EC subgroup and the whole dataset, according to multivariate logistic regression analysis. Similar associations between TMBST and ORR have previously been linked to inhibitors of immune checkpoint. 15 There are no reported interactions between drugs with dostarlimab because it is not a drug transporter or substrate for cytochrome P450, and it is not predicted to be a modulator of cytokine, but it is a monoclonal antibody. 16, 23, 24 sruption of adaptive immunity in cancer Cross-talk of dostarlimab with adaptive immunity Dostarlimab, an immunoglobulin G (IgG) 4 isotype humanized monoclonal antibody, blocks the interaction of PD-1 to its ligands PD-L1 and PD-L2. 23 It was produced from the monoclonal antibody of a mouse by grafting the heavy and light chain complementarity-determining sections on the variable region of the germline.
87 enerally, the proportion of individuals who encountered medical complications were modest and in line with assumptions for this type of treatment. In part 2 of the current phase I AMBER study, anti-TIM-3 antibody cobolimab and dostarlimab were investigated in patients with advanced NSCLC after receiving anti-PD-1 treatment. 88 ostarlimab 500 mg was given to patients once every 3 weeks in conjunction with cobolimab 100 mg. One of the 11 evaluable patients receiving dostarlimab with cobolimab 100 mg at the time of the data cutoff showed a confirmed partial response based on immune-related RECIST criterion, while 3 other patients had stable disease. Dostarlimab and cobolimab 300 mg were given to 20 evaluable patients: 3 of them had proven partial responses, while the remaining 8 had stable disease.
niraparib, doxorubicin, paclitaxel, gemcitabine, topotecan, NCT04679064 Fallopian tube, peritoneal, Phase III Recruiting bevacizumab ovarian Carboplatin, paclitaxel, dostarlimab NCT03981796 Endometrial Active, not recruiting Abbreviations: NSCLC, non-small cell lung cancer; SCC, squamous cell carcinoma.
py (https :// github. com / ucdavis -bioinformatics / proc 10 xG ) to extract the barcode sequence of each read pair and the number of read pairs associated to each barcode. The distribution of the number of read pairs per barcode was then analysed to identify rare barcodes potentially generated by sequencing errors and over-represented barcodes (Supplementary Figure  S1 ). A total of 143. 7 million paired-end reads carrying 1. 455 million retained barcodes were finally extracted using proc10xG scripts and used for linked-read-based de novo genome assembly using the Supernova-2. 1. 1 software package  (Weisenfeld et al. , 2017). Assembled scaffolds were outputted in pseudohap style, with a minimum size set to 1 kb. Assembly statistics of the Hgutt_V1 reference genome were computed and visualised with the BlobToolKit v3.
5. 2 software suite  (Challis et al. , 2020) , combined with an assessment of genome assembly completeness with BUSCO 5. 4. 4  (Manni et al. , 2021)  using the actinopterygii_odb10 fish dataset containing 3640 conserved genes. Whole-genome alignment was performed with Minimap2  (Li, 2018)  and visualised using D-GENIES  (Cabanettes & Klopp, 2018)   (Cabanettes and Klopp 2018)  to compare and anchor H. guttulatus scaffolds to the chromosome-scale assembly of the closely related H. erectus  (Li et al. , 2021) , provided by the authors. We also used the genome sequence of a north Atlantic long-snouted seahorse (UK), which was assembled by Iridian Genomes by ordering and orienting preassembled contigs based on other fish reference genomes (Accession PRJNA481552, hereafter called HguttRefA). We used RepeatModeler2  (Flynn et al.
Sequence processing and alignment To complement our dataset, WGS data for an additional 26 seahorse samples were obtained from  Barry et al. (2022)  (TruSeq DNA PCR-free libraries sequenced on Illumina NovaSeq6000, GenBank Sequence Read Archive, accession BioProject ID PRJNA777424). Therefore, our final combined dataset consisted of 112 samples (dataset #1, Fig. 1A , Supplementary Table  S1 ). Raw demultiplexed reads were processed using fastp (v0. 23. 1)  (Chen et al. , 2018)  with the " --merge" option, in order to stitch together paired-end reads with overlapping sections. Both merged and unmerged reads were aligned to our reference genome using BWA-MEM  (BWA v0. 7. 17; (H. Li, 2013). Picard (v2. 26. 8) (« Picard toolkit », 2019) was used for sorting read alignments, marking duplicates and adding read groups.
Variant calling of medium-to high-coverage samples Forty-eight samples with sufficient coverage (~5-40X) were selected for variant calling in such a way that this subset contained five individuals per location (dataset #2, Fig. 1A ). Variants were called using the GATK best practices workflow  (McKenna et al. , 2010; Van der Auwera et al. , 2013). Firstly, individual GVCF files were created from bam files with HaplotypeCaller  (GATK v. 4. 1. 8. 0). This information was then stored in a GVCF database using GenomicsDBImport, and VCF files (one file per scaffold) were generated with GenotypeGVCFs. After concatenation, the resulting VCF was filtered for indels, multiallelic SNPs and missing data ("--max-missing 0. 9"). Our detailed workflow and commands used are provided in the data availability section.
7 Population structure A Principal component analysis (PCA) was carried out on samples with sufficient coverage (>1X) from dataset #1 without calling genotypes. Genotype likelihoods were calculated using ANGSD (v0. 933)  (Korneliussen et al. , 2014)  with the GATK model ("-GL 2") and the following parameters: "-doMajorMinor 1 -minMapQ 30 -minQ 20 -doMaf 1 -doCounts 1 -minMaf 0. 05 -uniqueonly 1 -remove_bads 1 -C 50 -baq 1 -doCov 1 -doIBS 2 -makeMatrix 1 -ref referencegenome_Hgutt_V1. fa". In addition, the first 5 bp were trimmed off of reads (-trim 5) to account for DNA damage patterns due to cytosine deamination in historical samples (Supplementary Figure  S2 ). The analysis was restricted to sites with a strong probability of being SNPs ("-SNP_pval 1e-6"). Finally, sites were not considered if all samples had coverage higher than 30X ("-setMaxDepth"), in order to avoid artefacts in problematic regions that receive unexpectedly high coverage (low-complexity and duplicated regions), or if less than half of the individuals had data ("-minInd").
The same filters were used in subsequent analyses conducted with ANGSD, unless specified otherwise. To perform a PCA while minimising the effect of linkage disequilibrium, we used ANGSD with the "-sites" option and provided a file containing one randomly selected SNP per 10 kb window in every 500 kb interval. To complement this genotype likelihood-based approach, PCA was also performed on called genotypes from dataset #2 using the R package SNPRelate (v1. 28. 0)  (Zheng et al. , 2012). Lastly, we used local PCA to capture variation in population structure along the genome in order to identify outlying patterns indicating the presence of putative chromosomal inversions. To this end, local PCA was performed in non-overlapping windows of 5 kb using the R package lostruct (v0. 0. 0. 9, H. Li & Ralph, 2019).
Moreover, we followed the haplotagging library construction protocol described in  (Meier et al. , 2021)  to generate complementary linked-read sequencing data for alternate genotypes at the two inversions. Namely, we constructed haplotagging libraries for a DD/AA Mediterranean (Hgutt_MU_09) and a CC/AB Atlantic individual (Hgutt_GA_13), and sequenced them with 2*50 bp paired-end reads to an average coverage depth of 12X.
Genomic landscape of divergence and introgression We characterised the genomic landscape of divergence between localities and habitats using dataset #2. Genetic differentiation (FST), nucleotide diversity (π) and absolute genetic divergence (DXY) was calculated in 25 kb windows (with "-m 100") using the popgenWindows. py script  (Martin, 2018;  https :// github. com / simonhmartin / genomics _ general ). We sought to characterise two highly divergent regions on Chr2 and Chr12, which represented suspected inversions. We used BAMscorer v1. 4  (Ferrari et al. , 2022)  to assign inversion genotypes in all samples (including low-coverage samples). This firstly consisted of classifying all samples in dataset #2 with regards to their haplotypes, as based on PCA groupings and individual heterozygosity values calculated with VCFtools (v0.
1. 16)  (Danecek et al. , 2011). This reference database was then used to score alignments and to ascertain allelic state and haplotype for all samples. In order to test for introgression with other seahorse species and to determine evolutionary relationships across the genome, we obtained whole-genome resequencing data for H. erectus, H. hippocampus, H. zosterae, H. capensis and H. comes from Li et al. (2021)  (NCBI BioProjects accession code PRJNA612146), including data for one individual from each species. Reads were processed and aligned to the Hgutt_V1 reference genome as described above. ANGSD was used to call genotypes and to produce a VCF ("-doBcf") containing seven individuals (two H. guttulatus that were homozygous for the four alternate inversion haplotypes, i. e. AA-DD and BB-CC, and five individuals from other species).
This VCF was filtered to include only fixed sites (no heterozygous genotypes) and was used as input for performing topology weighting using the Twisst pipeline with an iterative sampling of subtrees  (Martin & Van Belleghem, 2017). We generated unrooted phylogenies using PhyML  (Guindon et al. , 2010)  as implemented in the script phyml_sliding_windows. py (https :// github. com / simonhmartin / twisst ) for windows of 50 SNPs, allowing a minimum of 25 non-missing genotypes per individual per window ("--minPerInd 25"). We produced a genome-wide consensus tree, as well as consensus trees for the two inversions 9 using the averageTree function from phytools  (Revell, 2012). Finally, topology weighting was performed using the twisst. py script.
erectus, H. kuda  (Camacho et al. , 2009). Flanking regions of 100 bp on either side of each SNP were blasted to the reference genome of each outgroup using blastn. The top hit was inspected to determine the state of the homologous position in each outgroup using a custom script. Thereafter, est-sfs (v2. 04)  (Keightley & Jackson, 2018)  was run using the Kimura 2-parameter model to infer ancestral state probabilities and phased VCFs were annotated with the most likely ancestral variant (bcftools v1. 9;  Danecek et al. , 2021). [Accession GCA_901007745. 1], H. comes [Accession GCA_001891065. 2]) using the BLAST+ package (v2. 2. 28) We reconstructed the ARG by running tsinfer on our phased, oriented VCF containing all chromosomes. Sample objects were created using the CYVCF2 library (v0. 30.