dataesr/scientific-paragraphs-categorization huggingface dataset:
nutans  (Jafari et al. , 2020). The 111 gene sequences of S. paradoxa were blasted against the assemblies of S. nutans individuals and the best hit for each gene was extracted. For two genes, clpP and trnK-UUU, no hits were detected. Blast results were then filtered for a percentage of identity of 90% and a length of 30 bp to also get the small trn genes. After filtration, we conserved 103 genes out of the 111 annotated in S. paradoxa plastid genome. The gene ycf1 was also excluded because it was missing in more than 50% of our samples. With the program MACSE 2. 00  (Ranwez et al. , 2011) , MSAs of the blasted plastid genes were generated and aligned, containing the sequences of S. latifolia, S. paradoxa, and of the 47 sampled individuals of S. nutans. MSAs were manually cleaned and checked on SeaView 4.
7  (Gouy et al. , 2010). Seven genes were excluded following this step, depending on the quality of the alignment and the correct frame of the  ORFs (i. e. , accD, atpF, rpoC1, rps12, ycf2, and ycf3). Among the remaining 95 plastid genes, 23 were trn genes and four were rrn genes which were not analyzed here. A phylogenetic tree was generated with PHYML 3. 0  (Guindon et al. , 2010)  with the concatenated set of the 68 genes, using the GTR + GAMMA model of nucleotide substitution selected by SMS (Smart Model Selection)  (Lefort et al. , 2017). To compare the evolutionary trends of S. nutans plastid genomes, we constructed MSAs including 10 other angiosperm and Silene species for which plastid genome assemblies were available on NCBI: Arabidopsis thaliana (NC_000932), Nicotiana tabacum (NC_001879), Oryza sativa (NC_001320), and Zea mays (NC_001666), and for Silene species: S.
capitata (NC_035226. 1), S. chalcedonica (NC_023359. 1), S. conica (NC_016729. 1), S. conoidea (NC_023358. 1), S. noctiflora (NC_012728. 1), and S. vulgaris (NC_016727. 1). Plastid gene sequences of S. nutans individuals were blasted against plastome assemblies of these species (percentage of identity of 98%) and the best blast hit was extracted. To avoid bias due to the larger amount of data for S. nutans lineages, MSAs were constructed for the 68 plastid genes with only one sequence per S. nutans lineage. To choose which individual to keep for this analysis, we selected the individual with the least fragmented assembly within each lineage. MSAs with Silene and angiosperm species nucleotide sequences were aligned with MUSCLE and manually checked on SeaView. Concatenations were generated per plastid complex: photosystem I and II (PSI and PSII), ATP synthase, cytochrome b6/f, Rubisco, NDH, RNA polymerase, and large and small ribosomal subunits (LSU and SSU, respectively), except for three genes (ccsA, cemA and matK).
Nuclear data To identify nuclear genes potentially involved in plastid-nuclear incompatibilities (PNIs), we searched for nuclear genes encoding subunits of plastid complexes. We used the protein-protein interaction database cyMIRA  (Forsythe et al. , 2019) , the Protein Data Bank in Europe-Knowledge Base (PDBe-KB)  (Varadi et al. , 2020) , SUBA4  (Hooper et al. , 2017) , UniProt  (Bateman, 2019) , STRING 11  (Szklarczyk et al. , 2019) , and BioGrid  (Oughtred et al. , 2019)  to establish a list of putative nuclear interactors (Table  S3 ). A transcriptome assembly was already available as well as transcriptomic data for 22 individuals from the four genetic lineages of S. nutans (Table  S1 )  (Muyle et al. , 2021; PRJEB39526). RNAs were extracted from flower buds. An in-house BioPython script was used to align the reads of these 22 individuals on the transcriptome assembly.
We used BamBam (SAMtools package)  (Page et al. , 2014)  to generate one consensus sequence per individual from the reads. From this, we extracted the nuclear genes identified and annotated in the transcriptome, using their position in the transcriptome assembly and their TAIR identifiers. Only 106 out of the 139 nuclear genes were found in S. nutans transcriptomes and were subsequently analyzed. MSAs per gene containing S. nutans individuals were blasted on S. latifolia and S. paradoxa transcriptomes (PRJEB39526), using the same criteria as for plastid genes, extracting the best hits for both species. Three nuclear genes for which no hits were identified in S. latifolia or S. paradoxa were excluded. The MSAs for the 103 remaining nuclear genes were then aligned with MUSCLE and checked on SeaView.
Fourteen genes for which alignment with a correct ORF was not possible were excluded. A concatenated set of 89 nuclear genes was generated to construct a phylogenetic tree using PHYML 3. 0 with the GTR model of nucleotide substitution selected by SMS. As with the plastid data, we constructed nuclear gene concatenation per plastid complex. To compare evolutionary patterns of nuclear genes encoding subunits of the plastid ribosome with the nuclear genes encoding subunits of the cytosolic ribosome (i. e. , gene products not targeted to the plastid), RNAseq data for these genes were also extracted and aligned. We took the 204 nuclear genes used in  Sloan et al. (2014a)  and their available TAIR identifiers. The same steps as described above were followed, and MSAs containing the sequences of the 22 S.
Variant detection We used an in-house BioPython script to identify the SNPs (single nucleotide polymorphisms) differing between the four lineages in the MSAs of the 95 plastid genes. As RNAseq data of S. nutans were available  (Muyle et al. , 2021) , we also checked whether the identified nonsynonymous substitutions were "true non-synonymous substitutions" or RNA editing sites (T to C substitutions) using RNAseq data and confirmed that they were non-edited (see Appendix S1). For the nuclear genes, the diploid individual genotypes were determined at each position in the individual alignment with the program reads2spn  (Gayral et al. , 2013)  and used to identify the nuclear variants. An in-house BioPython script was used to identify the SNPs only present in one lineage. Only SNPs for which at least two individuals per lineage were genotyped were kept.
Conservation status analysis First, to improve the accuracy of the analysis, additional outgroups were added to the existing nuclear MSAs. We used the transcriptome assemblies of Dianthus chinensis, S. diclinis, S. dioica, S. heuffelli, S. latifolia, S. marizii, S. otites, S. paradoxa, S. pseudotites, S. viscosa, and S. vulgaris  (Muyle et al. , 2021). Nuclear gene sequence data were extracted from these transcriptomes and new MSAs were constructed, integrating the sequences of those species. For the plastid genes, the analysis was run on the 12 species used for the plastid genetic diversity analysis. Then, for both nuclear and plastid data, a phylogenetic tree was constructed using the concatenated set of the nuclear and plastid genes, respectively, with PHYML -SMS, which assessed the best model of nucleotide substitutions to use.
We then estimated the conservation degree of the mutated amino acids as a proxy for the functional constraint acting on the mutated sites of the plastid and nuclear genes using the PHAST program  (Hubisz et al. , 2011). For this purpose, we ran PhyloFit analyses to fit a phylogenetic model to the MSA by maximum likelihood  (Siepel and Haussler, 2004) , using HKY85 as a model of nucleotide substitutions and the EM option. With the phylogenetic model output file of PhyloFit, we ran PhastCons a first time as a training step to estimate PhastCons free parameters as suggested in  Hubisz et al. (2011) (optionno-post-probs). Then, we ran PhastCons a second time with the formerly estimated parameters, to compute the conservation scores for each nucleotide site in a given MSA, varying from 0 = poorly conserved to 1 = strongly conserved.
Analysis of the pattern of selection For assessing the evolutionary dynamics and selection patterns on nuclear and plastid genes in S. nutans, we estimated the d N /d S ratios, with d N = non-synonymous substitution rate and d S = synonymous substitution rate using codeml implemented in PAML 4. 9  (Yang, 2007). We ran different analyses with several models. For nuclear genes, as transit peptides of the proteins targeted to the plastid are highly variable and thus could increase d N /d S ratios  (Christian et al. , 2020) , we used the TargetP software  (Juan et al. , 2019)  to identify their locations in the nuclear sequences and remove them from the nuclear alignments. Two types of analyses have been conducted with codeml, varying in terms of their assumptions about how d N /d S varies across the sequence (site models) or across branches of the phylogeny (branch models).
e. , not targeted to the plastid, later called NuCyto) (Appendix S2). For each analysis, the d N /d S values of the best models were kept. To test for differences in d N /d S values on plastid vs. nuclear genes and on nuclear genes encoding the plastid ribosome vs. NuCyto, we performed Mann-Whitney U tests on R 1. 3. 1093 (package stats4). A second analysis of selection with RELAX  (Wertheim et al. , 2015)  was performed to assess selective pressures acting on nuclear and plastid genes in S. nutans. RELAX allows to distinguish positive selection from relaxed purifying selection in case of elevated d N /d S ratios. We used the program RELAX with default parameters values. We used the same dataset as for the codeml analyses: first the plastid gene concatenations with the angiosperm and Silene species and then the plastid and nuclear gene alignments, with only S.
latifolia and S. paradoxa. For both datasets, the outgroups were annotated as references branches and Silene nutans individuals were again annotated as test branches, for both nuclear and plastid genes. Finally, to test for the parallel pattern of mutation accumulation between plastid and nuclear genes of S. nutans lineages, we performed a Spearman's rank correlation analysis between p N /p S ratios of plastid and nuclear gene concatenations per complex on S. nutans dataset. We used DNAsp 6  (Rozas et al. , 2017)  to calculate p N and p S values, i. e. , nonsynonymous and synonymous divergences, respectively, between S. nutans lineages.
Identification of contact positions in plastid complexes To assess whether some non-synonymous substitutions identified in plastid and nuclear genes were located at contact positions in the plastid complexes, we conducted a residue contact analysis, using crystallographic structures of the different plastid complexes. The PDBe-KB references of the plastid complexes were extracted from cyMIRA database  (Forsythe et al. , 2019). When the crystallographic structure of the plastid complex was available, we extracted the protein sequences of the organism with which the crystallographic structures had been defined and aligned them with the protein sequences of the S. nutans individuals. Data were not available for RNA polymerase and Rubisco.
, 2021). In fact, we found SNPs at 500 bp upstream of coding sequences (data not shown), but further analyses will be needed to survey gene expression. To do so, non-viable chlorotic hybrids should be rescued, possibly using media supplemented by sugar  (Kühn et al. , 2015). We acknowledge that some plastid genes that were excluded from our analyses could play a role in PNI (see Material and Methods), especially if the fact that we could not analyze them was due to their fast-evolutionary rate. For example, it was observed that CLP and ACCase complexes with fast-evolving plastid genes exhibited a strong pattern of coevolution with their nuclear counterparts, which could result in PNI  (Forsythe et al. , 2021; Rockenbach et al. , 2016; Williams et al. , 2019).
Acknowledgements This research was funded by the  Agence Nationale de la Recherche  ( ANR-11-BSV7-013-03 , TRANS), the  Région Hauts-de-France , and the  Ministère de l'Enseignement Supérieur et de la Recherche  ( CPER Climibio ), and the  European Fund for Regional Economic Development , and the  Région Hauts-de-France  and the  Ministère de l'Enseignement Supérieur et de la Recherche  for ZP's PhD grant. We wish to thank  Camille Roux  for his help in the elaboration of the scripts and  Dan Sloan  for discussion about transit peptide analyses.
We performed whole-mount immunostaining of the entire limb to stain axons (neurofilament) and blood vessels (CD31). Limbs were then cleared using an adapted iDISCO+ protocol  (Renier et al. 2014 ) and the areas of interest were imaged in 3dimensions. Using Imaris software, we observed the sciatic nerve inside the limb, thus keeping the physical integrity of the nerve and its surrounding (Figure  6F  and 6F' ). We also found that, whereas sciatic nerves of Ntn1 lacZ/lacZ embryos had the same size as control littermates (Figure 6G), they were less vascularized. This was confirmed by quantification of CD31 positive area density inside the nerve (Figure  6H ), with a similar ratio and in agreement with our findings on dissected nerves. This data suggests that Netrin-1 is likely to be one of the guidance molecules responsible for the attraction and angiogenesis of the blood vessels inside the embryonic nerve.
Tissue clearing. We used the iDisco + clearing method  (Renier et al. 2014). Samples were first dehydrated with Methanol/H2O series (20%, 40%, 60%, 80% and 100%; 1 hour each) at room temperature and then incubated in 66% dichloromethane (DCM, Sigma Aldrich)/33% Methanol during 3 hours. This was followed by an incubation with 100% DCM 15 minutes, twice. Samples were then cleared with dibenzyl ether (DBE, Sigma Aldrich), overnight. Imaging and images processing. The images of sections were taken using Zeiss Axiozoom apotome (and associated Zen software) and Leica confocal microscope. Maximum projections of the acquired stacks were obtained with Fiji. The cleared tissues images were acquired using a light-sheet microscope and Inspector pro software (Lavision biotec). The 3D reconstruction of nerves and limbs were visualized with Imaris software (Bitplane).
Quantifications. The density of intra-nervous blood vessels on sciatic nerve cross sections, at different ages, was quantified using the same semi-automatic method on Fiji for all the sections. After thresholding the images and creating a mask for the channel representing the blood vessels' staining, the total area contained in the section was quantified. This value was reported to the area of the section. Quantification of the length and number of branch points of the vasculature was done using "Surface" and "Filament" tools of Imaris software. For Ntn1 +/+ and Ntn1 LacZ/LacZ embryos, quantification of the nerve area (neurofilament staining) and intra-nervous blood vessel density (CD31 staining) were made using Fiji software.
Candidate Set Some eviction set construction algorithms require an initial set of virtual addresses, which is called a candidate set. A candidate set is a set of virtual addresses that has a good chance of falling into the target cache set. An unprivileged process does not have full control over the cache placement, especially in a large last-level cache. Indeed, as we show in Figure  4 , the upper address bits are used to determine the set and the slice that are selected by the operating system through its page allocation mechanism. On Linux, it has been shown that the buddy allocator can be tricked into allocating continuous chunks of memory  [54] , but page allocation can usually be viewed as a random oracle. Therefore, a candidate set can be built by fixing the controlled bits of the set index to the correct value and selecting many multiples of this address.
Participants Forty-one participants (M/F: 15/25; age: 26±7) took part in the study. Participants were recruited through a mailing list they previously registered in and received a monetary incentive to participate in the study. All participants had normal or corrected-to-normal vision and were not informed about the purpose of the experiment. All the participants gave their informed written consent. The experiment was conducted under the ethical standards (Declaration of Helsinki, 1964) and approved by the local Ethical Committee (Comitato Etico Regione Liguria). The data of one participant have been excluded because they did not complete the experiment. Therefore, data of forty participants were included in the final analysis.
Apparatus Participants were seated facing two 22'' LCD monitors. The first screen displayed the virtual environment for the decision task running on a computer with an AMD Ryzen Threadripper 2950X 16-core 3. 5 GHz CPU, 128 GB of RAM and a NVIDIA GeForce GTX 1060 3GB video card. The 3D-animated virtual environment including avatars with the appearance of the iCub robot  [30]  was developed using Unreal Engine (Epic Games: www. unrealengine. com). An adhoc Python program (version 3. 9. 5) handled stimulus presentation and data collection. Participants responded by pressing the 'a' and 'd' keys (left and right respectively) on the QWERTY keyboard. The second monitor was used to display the trust ratings and questionnaires, which were administered through SoSci (https://www. soscisurvey. de).
Trust ratings were averaged for each type of block within each participant and then submitted to a mixed ANOVA with type of feedback as a within-subject factor and type of avatar as a between-subject factor. The relation between Trust ratings and MDMT was measured through correlation analysis. Throughout the paper, multiple comparisons were corrected and p-values were reported according to Tukey's correction. Cohen's d and eta-squared equations were used to calculate effect sizes respectively for t-test and ANOVA. Behavioral analysis were examined using R (version 4. 0. 2. (RStudio Team (2010): www. rstudio. com)). Plot were created using ggplot2 package in R (https://ggplot2. tidyverse. org/).
Genetic Findings Whole-exome sequencing analysis allowed us to prioritize two missense variants in the SCN5A gene. One heterozygous rare variant shared between the index case and her daughter (II-2) NM_000335:exon12:c. 1715G>T; p. (Ala572Asp) (MAF = 0. 0051 in the gnomAD database; MAF = 0. 0032 in the European non-Finnish population). The variant is located in the DI-DII interdomain linker of Nav1. 5 channel. The SCN5A:p. (Ala572Asp) is predicted as probably pathogenic by UMD-predictor and has a CADD-phred score = 19. 44 (GRCh37-v1. 6). The second variant, NM_000335:exon12:c. 1673T>C; p. (His558Arg) is found in all the family members. Thus, the father (I-1) and the oldest daughter (II-1) with prodromal MVP carried only the modulatory SCN5A:p. (His558Arg) polymorphism (Figure  3 ). Both symptomatic family members (I-2 and II-2) carried the two variants.
In addition, early repolarization and QT dispersion have been documented in MVP cases and suggested as arrhythmogenic factors  [2, 37]. Hourdain et al. reported the largest cohort of resuscitated patients with SCD in whom MVP was the only detectable cause  [9]. MR has been demonstrated to be an independent arrhythmogenic risk factor. Nevertheless, polymorphic ventricular arrhythmias originating from the posterior papillary muscle with typical prolonged duration were reported as frequent and represented the main ventricular arrhythmia triggers. Long-term follow-up revealed the recurrence of life-threatening arrhythmias in those patients  [9]. In the present family study, WES data were analyzed with a primary focus on genes related to MVP such as DCHS1 and DZIP1 but also on genes implicated in valvular and aortic defects.
Whole Exome Sequencing (WES) Peripheral blood sample was collected after obtaining written informed consent from all family members included in this study or their guardians. Genomic DNA was extracted by standard techniques. Family based WES was performed using the NimbleGen SeqCap EZ MedExome kit according to the manufacturer's protocol (Roche Sequencing Solutions, Madison, MI, USA). Paired-end 150 bp reads from the DNA libraries were sequenced using Illumina NextSeq 500 platform (Illumina, San Diego, CA, USA). Raw fastQ files were aligned to the hg19 reference human genome (University of California Santa Cruz, UCSC) using the maximum exact matches algorithm in BWA software. Alignment quality was evaluated using Qualimap 2. 2. 1. Variant calling and annotation were performed using GATK and ANNOVAR best practices, respectively.
Both studies rely on the same computational approach to define the INT of resting-state fMRI time series. Specifically, an autocorrelation function was calculated for each voxel at incremental time lags until its value became negative. The positive autocorrelation values were then summed up and multiplied by the repetition time (TR), thus resulting in the INT index. Using resting-state data collected from both SZ and ASD in the same site with an identical protocol, we applied a ROI analysis to focus specifically on the areas highlighted by these two studies to explore INT differences between each patient group and controls. Moreover, because of the suggested overlap between ASD and SZ in symptoms  (Kästner et al. , 2015)  and underlying neuropathology  (Moreau et al. , 2021) , we directly compared the two patient groups.
Participants Participants were recruited via the Olin Neuropsychiatry Research Center (ONRC) at the Institute of Living, Hartford Hospital, and the Department of Psychiatry, Yale School of Medicine, and underwent resting-state fMRI scanning for the current study. After discarding datasets displaying head motion > 10 mm, our dataset contained 58 TD, 39 ASD, and 41 SZ. Of these, some were subsequently excluded due to incomplete phenotypic assessment information, thus resulting in the following final samples: 55 TD, 30 ASD, and 39 SZ. As this dataset has been previously used by  Hyatt et al. (2020 Hyatt et al. ( , 2021) )  and  Rabany et al. (2019) , the exclusion criteria were the same, namely: intellectual disability (i. e. , estimated IQ < 80), a neurological disorder (e. g. , epilepsy), current drug use as indicated by pre-scanning interview and urine test, incompatibility with MRI safety measures (e.
Means and standard deviations, as well as group comparison tests of the above-mentioned instruments, are given in Table  1. Table  1. Means and standard deviations (in parentheses) of demographics, phenotypic and clinical instrument scores for all three groups. FD = framewise displacement; est. IQ = estimated Intelligence Quotient; EQ = Empathy Quotient; ADOS = Autism Diagnostic Observation Schedule module 4; BVAQ = Bermond-Vorst Alexithymia Questionnaire. Group statistics are shown in the last four columns. Pairwise comparisons were performed using Welch's two-sample t-test. Both uncorrected (i. e. , p) and false discovery rate corrected (i. e. , pFDR) p values are shown. °, 48 slices, multiband (8), interleaved slice order, 3 mm 3 voxels. Pre-processing of structural MRI data was done using fMRIPrep v20.
Discussion In the current study, we sought to replicate previous findings regarding INT differences between TD and ASD, and TD and SZ independently. We also verified to which extent previously reported relationships between INT and clinical and phenotypic characteristics could be replicated. In addition, we made the novel contribution of directly comparing ASD to SZ. Uscătescu et al. (2021)  previously reported five ROIs that showed a trend of decreased INT in SZ compared to TD (right occipital fusiform gyrus/U_rOccFusG, left superior occipital gyrus/U_lSupOccG, right superior occipital gyrus/U_rSupOccG, left lateral occipital cortex/U_lLatOccC, right post-central gyrus/U_rPostCenG), two of which were replicated across three independent datasets, namely the U_rSupOccG and the U_rPostCenG. In the present study, we found a similar pattern of decreased INT in SZ compared to TD in all five ROIs, but this difference was significant only for U_rSupOccG and the U_rPostCenG.