dataesr/scientific-paragraphs-categorization huggingface dataset:
The proportion of loci for which He was greater than Heq (prophet-exc) was then quantified for all of the mutation models. To quantify consistency of the measure across mutation models, we calculated the repeatability of prophet-exc using the rptR package  51  in R with 1000 bootstraps while adjusting for the mutation model as a fixed effect. Although the relative pattern across species was very consistent across mutation models (repeatability = 0. 81, CI = [0. 71, 0. 89],), absolute values of prophet-exc within species decreased with lower proportions of multistep mutations (means for the TPM70, 80, 90 and SMM were 0. 63, 0. 58, 0. 49 and 0. 27, respectively). Based on our posterior estimates (Supplementary Fig. 2 ) and in line with previous studies, we therefore based our subsequent analyses on prophet-exc from the intermediate TPM80 model.
Hence, the bottleneck time priors encompassed the last four centuries for all species, as their estimated generation times vary between approximately 7 and 19 years (Supplementary Table  1 ). The microsatellite mutation rate µ was refined after initial exploration and drawn from a uniform prior with µ ~ U  [10 -5 , 10 -4  ] which lies within the range of current empirical estimates  40, 58    ABC analysis. We simulated a total of 2 x 10. datasets of 40 individuals and 10 microsatellite loci each under the two demographic scenarios using the fastsimcoal function in strataG  59  as an R interface to fastsimcoal2 60 , a continuous-time coalescent simulator. For both the simulated and empirical data, we used five different summary statistics for the ABC inference, all calculated as the mean across loci.
Allelic richness (number of alleles), allelic range, expected heterozygosity (i. e. Nei's gene diversity  61  ), the M-ratio  62  and the proportion of low frequency alleles (i. e. with frequencies < 5%). The summary statistics for the empirical datasets were computed by repeatedly re-sampling 40 individuals with replacement from the full datasets and calculating the mean across 1000 subsamples (for the Ladoga ringed seal and the Baltic ringed seal which had sample sizes smaller than 40, the full datasets were taken). As a small number of loci in the empirical data exhibited slight deviations from constant repeat patterns (i. e. not all of the alleles within a locus conformed to a perfect two, three or four bp periodicity), we calculated the M-ratio as an approximation using the most common repeat pattern of a locus to calculate the range of the allele size r and subsequently the M-ratio with M = k/(r + 1) where k is the number of alleles.
All statistics were calculated using a combination of functions from the strataG package and self-written code. For the ABC analysis, we used a tolerance threshold of 5 x 10 -4 , thereby retaining 5000 simulations with summary statistics closest to those of each empirical dataset. For estimating the posterior probability for each scenario and each species, we used the multinomial regression method  20, 63  as implemented in the function postpr in the abc package  26  where the model indicator is the response variable of a polychotomous regression and the accepted summary statistics are the predictors. To construct posterior distributions from the accepted summary statistics for the model parameters, we a local linear regression approach  20  as implemented in the abc function of the abc package.
Evaluation of model specification and model fit via cross-validation. We evaluated whether ABC can distinguish between our two models by performing a leave-one-out cross validation implemented by the cv4postpr function of the abc package. Here, the summary statistics of one of the existing 2 x 10. simulations were considered as pseudo-observed data and classified into either the bottleneck or the neutral model using all of the remaining simulations. If the summary statistics can discriminate between the models, a large posterior probability should be assigned to the model that generated the pseudo-observed dataset. This was repeated 100 times and the resulting posterior probabilities for a given model were averaged to derive the rate of misclassification. We furthermore checked for each species that the preferred model for each species provided a good fit to the empirical data by conducting a formal hypothesis test using the gfit function in abc.
Specifically, we used the median distance between the accepted and observed summary statistics as a test statistic, whereby the null distribution was generated using summary statistics from the pseudo-observed datasets. Hence, a non-significant p-value indicates that the distance between the observed summary statistics and the accepted summary statistics is not larger than the expectation based on pseudo-observed data sets, i. e. the assigned model provides a good fit to the observed data. Evaluation of the accuracy of parameter estimates via cross-validation. In order to determine which parameters (i. e. population sizes, times and mutation rates and models) could be reliably estimated, we used leave-one-out cross validation implemented in the cv4abc function from the abc package to determine the accuracy of our ABC parameter estimates.
For a randomly selected pseudo-observed dataset, parameters were estimated via ABC based on the remaining simulations using the rejection algorithm and a prediction error was calculated. This is possible because we know the "true" parameter values from which a given pseudoobserved dataset was simulated. This procedure was repeated 1000 times and a mean prediction error ranging between 0 and 1 was calculated, where 0 reflects perfect estimation and 1 means that the posterior estimate does not contain any information about the true parameter value  26. Bayesian phylogenetic mixed models. Finally, we used Bayesian phylogenetic mixed models in MCMCglmm  64  to evaluate the ecological and life-history variables affecting bottleneck strength and genetic diversity, and to test whether bottleneck history and genetic diversity are predictive of contemporary conservation status.
Convergence was checked visually and by applying the Gelman-Rubin criterion to three independent chains. All of the upper 95% confidence limits of the potential scale inflation factors were below 1. 05. Data and code availability. All data wrangling steps and statistical analyses except for the heterozygosity-excess tests  53  were implemented in R  67. The documented analysis pipeline along with the raw data can be accessed via GitHub (https://github. com/mastoffel/pinniped_bottlenecks) and is fully reproducible.
Acknowledgements This research was supported by standard grants ( HO 5122/3-1  and  HO 5122/5-1 ) from the  German Research Foundation (DFG)  and as part of the SFB TRR 212 (NC³) together with a dual PhD studentship from  Liverpool John Moores University. We are grateful to  John Arnould ,  David Coltman ,  Corey Davis ,  Larissa Rosa de Oliveira ,  Tom Gelatt  and  NOAA ,  Melissa Gladstone ,  Roger Kirkwood ,  Melanie Lancaster ,  Tim Malloy ,  Rolf Ream ,  Garry Stenson  and  Rob Stewart  for providing access to published microsatellite datasets. We also thank  Matthias Galipaud  for advice on statistical analysis,  Kevin Arbuckle  for advice on Bayesian phylogenetic mixed models and  Luke Eberhart-Phillips  for helpful comments on the figures. Finally, we are very grateful to  Rebecca Carter  (www.
Thus, the range of GH families depends on plant species, and each of them has to be studied separately. By bioinformatic analyses of amino acid sequences, it is possible to classify newly identified GHs into families. We have done it for the GHs identified in the sugarcane and the B. distachyon cell wall proteomes. However, assigning a GH to its family as defined in the CAZy database  [20]  does not necessarily provide a clear picture of its function, since proteins from a given GH family can display different roles. For example, GH1 family members can be involved in cell wall metabolism, lignification, signaling, or defense  [17]. Phylogenic analyses can help getting more information regarding the functions of specific plant GHs. Since monocots could be a major source of raw material for E2G production, it is interesting to study their cell wall metabolism and outline possible strategies to increase biomass production or lower cell wall recalcitrance to deconstruction  [27].
This is consistent with the fact that only type II grass cell walls present this kind of hemicellulose. After a survey of the cell wall proteomes described so far and collecting information regarding microorganism enzymes used for biomass deconstruction, we decided to focus this review on the GH1, GH3, GH17, GH27, GH35, and GH51 families. We have predicted functional and structural domains in newly identified CWPs using the PredictProtein bioinformatic software and grouping them in families  [35]. Since plant cells perform cell expansion themselves by involving cell wall polysaccharide rearrangements, the plant mechanisms could be mimicked by the enzymes used in cocktails. The comparison of plant and microorganisms enzymes presently used for biomass hydrolysis could contribute to determining their common characteristics and which specificities of plant enzymes could be copied in order to improve industrial cell wall deconstruction processes.
distachyon were also assumed to be AFases (EC 3. 2. 1. 55, GO:0046556). AFases catalyze the hydrolysis of α-L-arabinofuranoside in α-L-arabinosides. They act together with hemicellulases and pectinolytic enzymes to achieve hemicellulose and pectin hydrolysis. Several AFases used commercially belong to the GH51 family, generally originating from fungi. Such enzymes are of special interest for monocots Figure  2. Phylogenic tree of the GH1 subfamily comprising the GH1 identified in sugarcane and B. distachyon cell wall proteomes. All the plant sequences have been retrieved from Phytozome v12. 1 (phytozome. jgi. doe. gov), whereas the microorganism sequences originate from the NCBI website (www. ncbi. nlm. nih. gov/protein/). The MEGA6 software (www. megasoftware. net) was used to generate the tree with the following options: MUSCLE for amino acid sequence alignments and maximum likelihood tree with 500 bootstraps.
Visualization was done with FigTree (tree. bio. ed. ac. uk/software/figtree/). biomass hydrolysis, since this material is particularly rich in arabinoxylans, which need to be degraded with AFases in addition to endoglucanases  [51]. To compare plant and microorganism GH1 and GH3, we have performed phylogenic analyses. Some GH1 and GH3 identified in cell wall proteomes of sugarcane and B. distachyon have been selected. For plants, we have chosen species at critical evolutionary nodes since terrestrialization like a moss (Physcomitrella patens), the common ancestor to higher plants (Amborella trichopoda), Sorghum bicolor as the closest plant to sugarcane having a sequenced genome, and two dicots (Lycopersicon esculentum and A. thaliana). The B. distachyon and sugarcane sequences of several GHs identified in cell wall proteomes have been included in this analysis.
This experimental procedure was also applied in the laboratory with 18 participants while continuously recording their EEG activity (Fig. 1b, c ). A canonical computational model of choice  38  was used in both studies to analyze the weight attributed by participants to public information during post-test trustworthiness ratings (see Supplementary Information). Multivariate decoding was used to analyze the EEG data recorded in the laboratory  39. A temporal generalization method  40  was further employed to track the dynamics of the neural 'coding' of public information and face processing. Finally, inter-individual variations were tracked by regressing the behavioural and neural correlates of susceptibility to social influence against scores of perceived vulnerability to extrinsic morbidity risks.
Results In the following paragraphs, the analyses of data collected online and in the laboratory are presented in a sequential order. The section dedicated to the online study contains a description of the analyses performed on the behavioural data only. The section dedicated to the laboratory study contains a description of the analyses performed on the behavioural data, followed by a description of the results obtained from the multivariate decoding of EEG data. Analyses of behavioural data acquired online and in the laboratory were performed using Matlab version R2014b and R. Participants' performance was first analyzed in terms of mean rating change (see Supplementary Figure  S1  of the Supplementary Information). The mean rating change is the mean difference between the participant's ratings collected after watching the public information and the participant's ratings collected before watching the public information.
The effect of public information (i. e. , disagreements between the group ratings and the participant's ratings) and the effect of indicators of perceived vulnerability to extrinsic morbidity risks (Perceived Infectability and Germ Aversion scores) on social influence scores were analyzed with linear mixed models using the lme function of the nlme R package  41. All linear mixed models used a maximum likelihood fitting method and had random intercepts. Bayes factors (BF 10 ) with default Jeffreys-Zellner-Siow (JZS) priors were also calculated using the lmBF function of the BayesFactor R package to compare the predictive power of the linear mixed models  42. Comparing models using Bayes factors allowed us to determine whether indicators of perceived vulnerability to extrinsic morbidity risks have an effect on social influence scores and, if yes, whether this effect was greater than the effect of potentially confounding factors like the participants' age or gender.
01 (*p is the true proportion of clusters obtained from random data permutations whose AUC sums are greater than the AUC sum calculated from the real dataset, see Materials and Methods section for details). Figure  3a  depicts the time course of the decoder's sensitivity (area under the curve -AUC) averaged across participants. Two successive clusters were identified (cluster 1: mean AUC = 0. 58, *p < 0. 001; cluster 2: mean AUC = 0. 55, *p < 0. 001). These two clusters remarkably covered an important portion of the epoch: the decoding sensitivity differed from chance level 205 ms after the onset of public information, and turned back to chance level 645 ms later. To determine whether distinct processing stages could be isolated from the dynamics of the decoder performance, we used the 'temporal generalization' methods  40  which consists in testing whether a classifier trained at a certain time point t is able to generalize to other time points t'.
05 (*p is the true proportion of clusters obtained from random data permutations whose r sums are greater than the r sum calculated from the real dataset, see Materials and Methods section for details). We found that social influence score and decoding sensitivity positively correlated during both the first (200 ms-310 ms post-stimulus, *p = 0. 015; mean r = 0. 55) and the second processing stages (595 ms-890 ms post-stimulus, *p = 0. 01; mean r = 0. 52) (Fig. 3a ). Decoding analyses, temporal generalization and correlation analyses implying disagreement valence and disagreement strength are reported in the Supplementary Information (see also Supplementary Figure  S6 ).
In addition, classification of EEG data collected in the laboratory remarkably matched behavioural data. Our decoders revealed extended differences between EEG patterns coding for public agreement and EEG patterns coding for public disagreement. Notably, decoders had better performance for strong disagreement trials than moderate disagreement trials, and performed equally well for positive and negative disagreement trials. Temporal generalization analyses  40  showed that, overall, disagreement trials elicited two distinct processing stages. The first stage was characterized by a negative activity within the fronto-central sites of the scalp surface between 200 ms and 400 ms after stimulus onset. This is coherent with a previous study showing that the exposure to a disagreement with a group of peers elicited a negative prediction error coded by a feedback-related negativity (FRN)  30.
Stimuli and procedure. Stimuli consisted of emotionally neutral faces (N = 48; size = 477 × 400 pixels) obtained from the FaceGen Modeller 3. 1 (Singular Inversions, 2007) open database developed by Todorov and colleagues  [80] [81] [82]. Each face has a unique identity, although all of them were male, bald, Caucasian, front facing and with direct gaze. Contrasts were therefore approximately identical across faces. The experimental procedure was adapted from Klucharev and collaborators  29  , and was composed of two types of trials distributed into interleaved blocks (N = 6) of 8 trials each. In a block of test trial (Fig. 1a ), participants watched a series of 8 faces each presented for 400 ms, a duration that has been shown to minimize participants' ability to encode the identity of the faces but that provides enough time to consistently estimate social traits  [83] [84] [85].
Conversely, a negative score indicates that participants adjusted their ratings away from public information. Thus, the greater the social influence score, the greater the participant's susceptibility to social influence. Quality check and positive control test. All phases of the online study (the informed consent, the pilot task and questionnaires) were coded using Qualtrics (Qualtrics, Provo, UT, ©2016) and presented in a web browser. We subjected each participant data set to a series of quality control checks ensuring that (1) the participant had declared being 18 years old or more, (2) had entered the correct verification code (generated by Qualtrics at the end of the procedure), (3) did the task only once (by comparing the participants' IP addresses and the GPS coordinates provided by Qualtrics), (4) had valid data in each experimental condition, and (5) did not put into question the fact that the group rating came from real peers.
1 (Singular Inversions, 2007) according to the methods of Todorov and colleagues  [80] [81] [82]. Each face has a unique identity, although all of them were male, bald, Caucasian, front facing and with direct gaze. Contrasts were therefore approximately identical across faces. The number of faces (N = 480) and pairs of interleaved blocks (N = 60) were increased in order to match the standards of electroencephalographic recording studies (Fig. 1b ). As a consequence, the completion of the task lasted approximately 90 min (participants could make a pause after each block of post-test trials). Participants sat in a comfortable chair in a dimly lit sound attenuated room. The experiment was conducted using Matlab (MathWorks) with the psychophysics toolbox  88  , with face stimuli (size = 700 × 700 pixels) presented on a 21 in.
A inter-item reliability analysis performed on scores collected on the 17 participants showed that only the Perceived Infectability subscale had a satisfactory internal consistency (Cronbach's alpha = 0. 92). Internal consistency of the Germ aversion subscale was on the contrary unacceptable (Cronbach's alpha = 0. 52). We therefore maximized the scale reliability by sequentially removing the items which had a detrimental impact on the coherence of the construct  89. After the removal of 4 items (items 4, 9, 11, and 15 of the PVD questionnaire), the Cronbach's alpha reached a satisfactory value of 0. 77. The Germ Aversion score reported in the analyses of the laboratory data therefore represents the mean of the scores obtained at items 1, 3, 7, and 13 of the PVD questionnaire. The Perceived Infectability and the 'maximized' Germ Aversion scores were z-transformed and each of them was used as an independent predictor of social influence scores.
EEG recordings and preprocessing. EEG data collected on the 17 participants who complemented the task in the laboratory were recorded with 64 electrodes (actiCAP, Brain Products GmbH, Germany). The EEG signal was digitized at 500 Hz. EEG analysis was conducted using EEGLAB  90  and custom built Matlab scripts. The data were band-pass filtered online at 0. 01-100 Hz, and low-pass filtered offline at 20 Hz. The data were recomputed to average reference off line. Epochs corresponding to faces and public information presentation were generated from -200 to 1000 ms relative to stimulus onset, with a 200 ms pre-stimulus baseline correction. Ocular artifact correction was conducted in EEGLAB in Matlab using independent component analysis  90. Following removal of eye blinks and eye movements, artefact rejection was conducted in a semiautomatic manner (in EEGLAB) by rejecting epochs with activity above 100 mV or below -100 mV (mean % of discarded trials = 5.
Acknowledgements This work was supported by the  European Research Council  under the European Union's  Seventh Framework Programme  (Grant  FP7/2007-2013/ERC Grant 263067 ). We thank the  Paris Descartes Platform for Sensorimotor Studies (Université Paris Descartes ,  Centre National de la Recherche Scientifique, INSERM, Région Ile-de-France ) for supporting the experimental work presented here. Pierre O. Jacquet was supported by the  Fyssen Foundation  and by the  ANR-10-LABX-0087 IEC. Valentin Wyart  was also supported by a junior researcher grant from the  French National Research Agency   ANR-14-CE13-0028. We are very grateful to  Alex Todorov  and  Ron Dotsch  for their friendly assistance in generating the face stimuli, and to  Coralie Chevallier  and  Nicolas Baumard  for their fruitful comments.
Targeted Next Generation Sequencing (NGS). Initial DNA analysis was performed by a company (Blueprint Genetics, Helsinki, Finland). DNA was passed on an oligonucleotideselective sequencing (OS-Seq) panel, covering the coding regions of 17 genes known to be associated with CSNB. Putative disease causing variants were validated by Sanger sequencing. In addition, copy number variation (CNV) analysis was performed. Deletions and duplications were detected from the targeted next generation sequencing (NGS) data using a proprietary bioinformatics pipeline, which processes aligned sequence reads by the company OS-Seq variant calling pipeline. Identified deletions and duplications were compared to their in-house curated and maintained database and public databases (Database of Genomic Variants (DGV))  [32]  and DECIPHER (https://decipher.
Pathogenic Predictions Programs. To predict pathogenic mechanism, a software program (Alamut Visual 2. 7-1, Interactive Biosoftware) was used, combining different programs such as Sorting Intolerant From Tolerant (SIFT, http://sift. jcvi. org/), [35] , Polymorphism Phenotyping v2 (PolyPhen-2, http://genetics. bwh. harvard. edu/pph2/), [36] , and Mutation Taster (http://www. mutationtaster. org/) [37]. This analysis also delivers frequencies in known databases such as Database of Single Nucleotide Polymorphisms (dbSNP, https://www. ncbi. nlm. nih. gov/snp), Exome Aggregation Consortium (ExAC, http://exac. broadinstitute. org/), and Exome Variants Server (EVS, http://evs. gs. washington. edu/EVS/). In addition, the presence of the variant in common databases was investigated using 1000 Genomes (http://www.
Discussion Although the LRIT3 variant was not reported in common databases and is predicted to be disease causing, it cannot be responsible for the Riggs-form of CSNB observed in this family as the father does not show this variant. Furthermore, CSNB caused by LRIT3 mutations is inherited in an autosomal recessive manner and shows the Schubert-Bornschein rather than Riggs ERG phenotype  [47]. However, the patients also revealed that a novel GNAT1 variant (p. Ile52Asn), which cosegregated with the phenotype, was predicted to be pathogenic and was never reported in the general population. The mutations in GNAT1 adCSNB reported previously were p. Gly38Asp in the Nougaret family  [5, 48]  and p. Gln200Glu  [14]  in another family. Thus, our findings reveal a third GNAT1 mutation causing adCSNB.