dataesr/scientific-paragraphs-categorization huggingface dataset:
5 mm gap; TR = 10 s (1. 680 s for acquisition); TE = 12 ms; flip angle = 90°; acquisition matrix 80 × 58; spatial resolution 2. 5 mm × 2. 5 mm; 37 volumes and 1 dummy scan). During the acquisition, the trainer and the owner remained inside the scanner room. We used as the exclusion criterion of movement, a maximum of 3 mm in any direction and less than 1° rotation in any direction (mean framewise displacement (FD)  31  ) across all dogs and runs = 0. 25 mm). Data analysis. Image pre-processing and statistical analysis were performed using FSL  32  version 5. 0. 11, and PyMVPA software package  33. Pre-processing. Each run was motion-corrected using MCFLIRT, spatially smoothed using a Gaussian kernel (FWHM 5 mm) and filtered using a high-pass filter to remove low-frequency signals. For each dog, the acquired images were manually skull-stripped and spatially aligned to the first volume of the first run.
Acknowledgements We thank  Isabell Mougeot  and  Emira Mehinagic  for their valuable help in reviewing and commenting on this manuscript. We thank the  Department of Neuroradiology, Medical Imaging Centre, Semmelweis University, Budapest  for the ongoing support of our dog fMRI studies. We thank  Barbara Csibra , the trainer of some of our subjects. We thank all participating dogs and caregivers for volunteering for our studies!.
Funding Open access funding provided by  Eötvös Loránd University. The research project has been funded by  Symrise Pet Food  (then  Diana Pet Food)-Spécialités Pet Food SAS, Elven, France , the  Hungarian Academy of Sciences  via a grant to the  MTA-ELTE 'Lendület' Neuroethology of Communication Research Group  (grant no. LP2017-13/2017 ), the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement No. 950159 ), the  Mexican National Council of Science and Technology (CONACYT ,  407590  and  409258 ), the  National Brain Program  3. 0 of the  Hungarian Academy of Sciences , the  ELKH-ELTE Comparative Ethology Research Group  ( 01 031 ) and the  VEKOP-2. 3. 3-15-2017-00019  fund provided by the  NKFI (National Research Development and Innovation Office).
Data The literature review was conducted, and the results were reported following the PRISMA guidelines  [23]. A structured literature search was carried out on 16 June 2020, covering the literature published up to this date using the Embase literature database. A search string (see Supplementary Files-Figure  S1 ) was developed based on previous work  [22]  using Emtree terms. The search was designed to cover farm animal and wildlife species intended for human consumption, as well as feline species, sampled within Europe. Forty-one countries, including the 27 European Union member states, were included in the search strategy. The selection of included animal species was based on the FoodEx2 database  [24]  by including all animal species with consumption data from national food consumption surveys from the years 1997-2018.
Each publication was screened by two randomly chosen scientists from the group, first at the level of the title and abstract, and after a consensus on inclusion between the two scientists was reached, full-text screening was performed on the remaining publications. Publications that met the inclusion criteria after full-text screening proceeded to data extraction, which was conducted by a smaller group of nine scientists. For each publication, the data were extracted by one of the nine scientists, and inputs were checked by another scientist. Inconsistencies and disagreements were discussed until an agreement was reached. A data extraction template file (see Supplementary Files-Data extraction template. xlsx) was created in a spreadsheet (Microsoft Excel) to record the required data. For each study, data describing the study design, sampling, and testing methods were collected as follows: Country or the region covered in the epidemiological screening, animal species, total number of farms or herds sampled, the total number of animals sampled, animal age or age group estimates, first and last years of the sampling period, and the sample type used.
Data on sample testing encompassed the type of diagnostic assay, the commercial test name (when applicable), and cut-off values. Extracted data were harmonized and categorized for modelling. Firstly, animal species were sorted into thirteen categories (Table  1 ) based on their common physiological traits and phenotype. As grouping on a coarser level is more suitable for hierarchical modelling, the countries present in the dataset were assigned to one of five European regions-Western, Northern, Eastern, Southeastern, and Southwestern (Figure  1 , Supplementary Files-Table  S1 ), as described previously  [26]. As grouping on a coarser level is more suitable for hierarchical modelling, the countries present in the dataset were assigned to one of five European regions-Western, Northern, Eastern, Southeastern, and Southwestern (Figure  1 , Supplementary Files-Table S1), as described previously  [26].
Indoor animals were defined as those kept strictly in an enclosed environment (e. g. , pigs kept under controlled housing) and outdoor-held animals as animals commonly kept in the open, with potential contact with the outside environment and free-roaming felids. Indoor and outdoor access were based on details from the publication or the typical holding and living conditions (e. g. , strictly outdoor access for wildlife). For buffalo, cattle, equids, sheep, and game species (wild boar, wild birds, and wild ruminants), outdoor access was assumed by default. For publications that included data on animals with and without outdoor access, these data were extracted on separate rows and given a different population identifier. Since the ages of individual animals at the time of sampling were not given, we define an uncertainty distribution based on estimates of the minimum, maximum, and most probable age at sampling.
Data Collection A total of 1985 publications were retrieved, with more than 50 animal species included (Table  1 ). Twenty-four articles were excluded as duplicates, 1599 publications did not meet the inclusion criteria during the title and abstract screening, and a further 86 were excluded during the full-text screening (see Supplementary Files-Figure  S2 ). Following the screening process, 276 publications were considered eligible for data extraction, out of which 226 publications with a complete set of data were included. Relevant data on at least one of the animal species of interest were recovered from 29 out of the 41 countries included in the search string. The number of articles on each of the individual species, including the number of animals and the type of detection method used, is summarized in the supplementary data (see Supplementary Files-Table  S3 ).
Animal Prevalence Results of Direct Methods The highest prevalence based on direct detection data was 68. 4% (95% CI: 54. 8-80. 1%) observed in the brains of lagomorphs in the Western region (other tissues, the United Kingdom) (Figure  2 , Supplementary Files-Table  S4 ). The lowest T. gondii prevalence was observed in the hearts of equids from the Eastern region (n = 82, other tissues, Romania) at 0. 0% (95% CI: 0. 0-0. 4%) (Figure  2 , Supplementary Files-Table  S4 ). A low prevalence, equal to 0. 0% when rounded up to a single decimal space, was also recorded in buffaloes from the Eastern region (n = 74, 95% CI: 0. 0-4. 9%), lagomorphs from the Southeastern region (n = 52, 95% CI: 0. 0-6. 9%), pigs from the Southwestern region (n = 44, 95% CI: 0. 0-8. 0%), and wild birds from the Southwestern region (n = 5, 95% CI: 0.
A low prevalence, equal to 0. 0% when rounded up to a single decimal space, was also recorded in buffaloes from the Eastern region (n = 74, 95% CI: 0. 0-4. 9%), lagomorphs from the Southeastern region (n = 52, 95% CI: 0. 0-6. 9%), pigs from the Southwestern region (n = 44, 95% CI: 0. 0-8. 0%), and wild birds from the Southwestern region (n = 5, 95% CI: 0. 0-52. 2%). A consistently low T. gondii prevalence was observed in fecal samples of felids, especially in the Northern region at 0. 2% (n = 598, 95% CI: 0. 0-0. 9%) and the Western region at 0. 2% (n = 104309, 95% CI: 0. 1-0. 2%). Average weighted prevalence estimates for included animal species reported separately for different sample matrices can be found in supplementary files (Supplementary Files-Table  S4 ). No eligible direct detection data were available for ducks and geese and poultry.
Discussion An extensive literature review on the prevalence of T. gondii infection in animal species that can be relevant as sources of human infection was carried out. Although the prevalence of T. gondii in animals does not give a direct indication of the risk of human infection, the prevalence in combination with exposure data is important to estimate the relative contribution of different sources of T. gondii human infection by quantitative risk assessment. Performing the literature search within the Embase database allowed us to utilize its advanced guided mapping of keywords to Emtree, conveniently covering a broad The grey dots represent seroprevalence data points at the best estimate of age in the data for the studied populations. The size of the dots indicates number of animals, in categories from 10,000 to 50,000.
Discussion An extensive literature review on the prevalence of T. gondii infection in animal species that can be relevant as sources of human infection was carried out. Although the prevalence of T. gondii in animals does not give a direct indication of the risk of human infection, the prevalence in combination with exposure data is important to estimate the relative contribution of different sources of T. gondii human infection by quantitative risk assessment. Performing the literature search within the Embase database allowed us to utilize its advanced guided mapping of keywords to Emtree, conveniently covering a broad range of animals and countries in the literature search. In the present study, the animal prevalence was modelled using a Bayesian approach, which is increasingly being used in epidemiological studies because of the ability to quantify uncertainty.
As a result, the biochemical prior information that they encode will fit a specific dataset of interest only approximately. If the dataset of interest happens to be sufficiently large, re-estimation of the complete general time-reversible model may give a more accurate model than the one proposed by any available empirical matrix. The problem that model selection has to solve in this context is whether one can afford this re-estimation or whether falling back onto the prior biochemical knowledge encoded into an empirical matrix represents a safer option. The answer to this question will fundamentally depend on data size, but also, on how well the biochemical information encoded into currently available amino-acid replacement matrices generalizes to the specific dataset of interest. As another example, accounting for pattern heterogeneity across sites is usually done using mixture models  (Pagel & Meade, 2004; Koshi & Goldstein, 2001; Lartillot & Philippe, 2004; Quang et al.
The simplest and original approach is leave-one-out cross-validation (LOO-CV), whereby each observation is successively taken out of the sample and reserved for subsequent validation of the model, while training is done on the remaining data  (Stone, 1974). Alternatively, in k-fold cross validation (k-fold CV), the dataset is split into k equal sized subsets, then each subset is set aside for validation and the remaining k -1 subsets are used for training  (Breiman et al. , 1984; Zhang, 1993). A variant of k-fold CV is based on repeated random sub-sampling of a fixed fraction f = 1/k of the data that are set aside for validation. In all cases, direct implementation of cross-validation is expensive, owing to the total number of replicates to consider. Brute-force k-fold cross-validation and its random subsampling version have previously been used in a phylogenetic context  (Lartillot et al.
, 2007; Lartillot & Philippe, 2008) , sometimes in combination with strict subsampling, i. e. using training and validation sets that together represent a subset of the data  (Pisani et al. , 2015). Strict subsampling was motivated by the need to reduce the computational cost. A downside, however, is that the models are then under a regime of data size that does not correspond to the effective regime in which subsequent inference is conducted. Yet the relative fit of alternative models with differing dimensions depends on data size, since higher-dimensional models typically require more data to learn their parameters. For all these reasons, indirect approaches to cross-validation, which would avoid the explicit resampling and fitting procedure, would be particularly useful. In this direction, and in the specific case of leave-one-out, it is in fact possible to get an estimate of the cross-validation score based only on a standard MCMC run conditioned on the full dataset  (Gelfand et al.
, 1992; Chen et al. , 2012; Lewis et al. , 2014). This clever importance sampling approach, called cross-predictive ordinates (CPO), makes leave-one-out cross-validation particularly attractive, practically and computationally. In a more theoretical spirit, and starting with  Akaike (1974) , a long series of information criteria have been proposed, based on information-theoretic considerations. The fundamental idea behind these information criteria is to identify the model which, once trained on the dataset of interest, induces a distribution over the data that is closest to the true distribution of the population. Mathematically, the distance between model and truth is measured by the information loss (i. e. the Kullback-Leibler divergence). Importantly, this distance is measured under the effective conditions of use of the model, that is, under the current data size.
As a result, it accounts for the two different reasons why the model might not be so close to the true distribution in practice: because of model mis-specification, but also, because of stochastic error in parameter estimation due to finite sample size. This last point will critically depend on both the size of the dataset and the model dimension. The original criterion proposed by Akaike, the AIC, has a particularly simple expression. However, its derivation relies on the assumption that the models being considered are not far from the true distribution. It is thus not valid under strong model violation, a situation often encountered in practice. The AIC was revisited by Takeuchi (in an original contribution in Japanese, as reported in  Konishi & Kitagawa, 1996) , who proposed a criterion, the TIC, which is valid even in the presence of strong model violation.
Cross-validation, on the other hand, measures the average loglikelihood of the validation data points, under the parameter value estimated on the training set. Thus, cross-validation can be seen as an operational estimate of the information loss, with the slight nuance that the model is trained on a subset of the data, rather than on the complete dataset. For large datasets, this difference is relatively minor, however, and particularly so for LOO-CV. As a result, LOO-CV is asymptotically equivalent to information criteria of the Akaike family  (Stone, 1977; Watanabe, 2010) , or equivalently, information criteria of the AIC family are just asymptotic expressions for the leave-one-out cross-validation score, each being valid in a different context and under different specific assumptions. This result is important, as it emphasizes the operational meaning of information criteria.
The computational challenges raised by the numerical evaluation of marginal likelihoods  (Lartillot & Philippe, 2006; Xie et al. , 2011; Baele et al. , 2012a ) also represent a clear limitation, preventing a broader and more systematic application of this paradigm to current empirical problems based on large datasets. Cross-validation was used in Bayesian phylogenetics primarily for computational reasons  (Lartillot et al. , 2007; Lartillot & Philippe, 2008) , although without any correct evaluation of its numerical accuracy and its theoretical validity in that context. The implementation of LOO-CV offered by CPO appears to be attractive, and has already been introduced specifically in phylogenetics  (Lewis et al. , 2014) , but has thus far not been broadly used in this context. Finally, the wAIC has never been applied to phylogenetic model selection.
Results The alternative measures of model fit that are considered in this work are marginal likelihoods, or equivalently Bayes factors, leave-one-out CV (LOO-CV) and k-fold CV (with k = 5 and based on independent randomizations of the dataset), the latter in two versions: joint and site-wise. In joint k-fold CV, the joint likelihood of all data points of the validation set is averaged over the posterior distribution under the training set, while in site-wise k-fold CV, the likelihood of each data point of the validation set is averaged over the posterior distribution separately, and then the resulting marginal likelihoods for all data points of the validation set are combined multiplicatively. In order to be measured on the same scale, all scores are log-transformed and normalized so as to be expressed on a per-site basis (see methods for details).
n , which are iid from a multivariate normal distribution of mean θ * (also a p-vector) and of covariance matrix Σ = σ 2 I p , where I p is the identity matrix. The true mean θ * used for simulation is chosen to be close to, but not equal to 0. Inference on these simulated data is conducted under two models. In both models, the variance parameter σ is assumed known. Under model M 1 , the vector of means θ is fixed a priori to θ 0 = 0. Under model M 2 , on the other hand, θ is estimated, assuming a normal prior of mean 0 and of covariance Σ 0 = σ 2 0 I p. The hyper-parameter σ 0 is chosen to be large, so as to implement a vague prior on θ. Of note, when σ 0 → ∞, the prior becomes improper but the posterior reaches a well-defined limit. We wish to evaluate the relative fit of model M 2 against M 1 on a dataset of size n.
Importantly, the simulation experiment is designed so as to represent a situation where model comparison is recruited for selecting the best approximating model, not the true model. Thus, what we want to formalize is a situation where the fixed parameter value θ 0 = 0 defined by model M 1 is never exactly true. Instead, θ 0 may be viewed as a reasonably good proxy for the unknown true value θ * , and the question is just whether we can hope to get closer to θ * by re-estimating θ on the dataset of interest, thus by using M 2 rather than using M 1. Data were more specifically simulated under the following settings: p = 300, σ 2 = 10, θ * = 0. 1, and n varying from 100 to 10000. For model M 2 , two values were considered for the prior width, σ 2 0 = 10 and σ 2 0 = 1000. Then, the alternative measures of model fit were computed: marginal likelihood (Bayes factor), 5-fold cross-validation, both joint and site-wise, and leave-one-out cross validation.
When n > 1000, on the other hand, M 2 is more accurate than M 1. Second, by comparing the two panels of Figure  1 , one can see that Bayes factors are clearly conservative. For instance, when σ 2 0 = 10, it takes a dataset of at least 8000 observations for Bayes factors to show a preference for M 2. Thus, between n = 1000 and n = 8000, Bayes factors are choosing a simple model that can be up to 5 times less accurate than the more complex alternative. This conservativeness is more pronounced under a broader prior (i. e. for larger σ 0 ). For σ 2 0 = 1000, the cutoff at which Bayes factors switch to a preference for model M 2 is slightly above n = 10000. Importantly, the posterior distribution is virtually the same for these two values of σ 2 0 , which shows that the differences in Bayes factors induced by the choice of the value of σ 0 do not reflect any real-world difference, in terms of estimation.
This sIS approach, however, is expensive -even more expensive for CV than for marginal likelihood, since CV requires to run sIS ideally over a large number of randomized replicates of the original dataset, whereas only two runs on the original non-randomized alignment are needed for the marginal likelihood. Leave-one-out cross validation using cross-predictive ordinates An estimate of the LOO-CV score can be obtained very efficiently, based on a standard MCMC run under the posterior distribution, using the CPO approach  (Gelfand et al. , 1992; Chen et al. , 2012; Lewis et al. , 2014). The CPO method gives accurate estimates of the LOO-CV score (Table  1 ). Here again, the bias can be substantial for small sample size but is well estimated. Altogether, naive IS works well for site-wise k-fold CV, but does not work well for joint kfold CV.
The posterior estimate of the base distribution under the unconstrained model, however, is very far from uniform, which gives an additional argument, independent from the ESS, suggesting that nIS is giving a wrong answer in the present case. Second, concerning the alternative measures of model fit: k-fold CV and LOO-CV qualitatively agree with each other. On the other hand, they differ somewhat from the Bayes factor, which tends to be more conservative. In one case, BF and CV give a qualitatively different outcome, concerning the choice between GTR and empirical matrices: whereas all CV methods choose GTR, BF gives a higher score to the LG model on this EF2 dataset.
LOO-CV, BF and estimation accuracy in a phylogenetic context The experiment above on EF2 suggests that BF can sometimes disagree with cross-validation on real cases. To further investigate this point, another experiment was conducted, consisting of comparing LG and GTR on increasingly large subsets of an empirical supermatrix of 35 metazoan species  (Philippe et al. , 2005) , using either BF or LOO-CV. The k-fold CV approach was not considered, owing to its computational cost. Of note, here as above (Table  2 ), the prior on the renormalized exchangeabilities (constrained to sum to 1) of the GTR model is uniform, thus uninformative. The results of this experiment are summarized in Figure  2. For sufficiently large datasets, BF and LOO-CV both favor the GTR model over LG, while for smaller datasets, the LG model tends to be favored.
This point is expected, and confirms that, for sufficiently large data size, there is an opportunity for getting better estimates of the relative exchangeabilities than those proposed by LG. However, if both methods agree on this dichotomy between small versus large datasets, they substantially differ concerning the exact cutoff, in terms of data size, at which they switch from LG to GTR. Here again, as seen above in the case of the normal model, BF is generally more conservative than LOO-CV. As a consequence, there is an intermediate regime for which BF and CV qualitatively differ in their selection. In practice, this intermediate regime covers a non-negligible interval: whereas BF favors GTR over LG only starting from alignments made of more than 600 sites, LOO-CV does so for datasets as small as 200 sites.
In the case of the JTT model, this error is trivially constant (quadratic deviation between JTT and LG). For the GTR model, this error decreases with data size. On sufficiently small alignments, on the other hand (smaller than 200), the estimation error under GTR can be larger than the difference between JTT and the true exchange rates (LG). Thus, for small alignments, we are in a case where the most accurate model is in fact JTT, and this, in spite of the fact that JTT is not the true model. Finally, comparing RMSD with both BF and LOO-CV shows that LOO-CV provides a good predictor of which model is more accurate for parameter estimation, with a cutoff at around 200 aligned positions. BF in contrast, imposes a stronger penalty and still chooses JTT for datasets up to 600 sites, thus well within the regime of alignment size where GTR is in fact already returning a substantially more accurate estimation.