dataesr/scientific-paragraphs-categorization huggingface dataset:
) during long recording time (some of them last up to 40 min). The 23 remaining chess players in our study were 9 experts and 14 intermediates level players (10-51 years, 2 female, age: M = 30. 0; SD = 12. 9). Expert players were all active players and with Elo ratings ( The Elo system is a method to calculate rating for players based on tournament performance. Ratings vary between 0 and approximately 2850. https://en. wikipedia. org/wiki/Elo_rating_system (last seen September 2017)) ranged from 1759 to 2150 (M = 1937. 3; SD = 123. 2). For the intermediate players, the Elo ratings ranged from 1100 to 1513 (M = 1380. 3; SD = 100. 5) and 6 among them were casual players who were not currently playing in club. We can also give some statistics on the recorded session: the average recording time per participant is 13:35 min (MI N = 4:54, MAX = 23:54, SD = 5:02) and the average compressed size of gathered data is 56.
Procedure Participants were tested individually in sessions lasting approximately 45 min. Each participant was asked to solve the 13 chess tasks and their behaviors were observed and recorded. To avoid biased behavior, no information was given about the recording equipment. Nevertheless, it was necessary to reveal the presence of the eye-tracker bar to participants in order perform a calibration step. After providing informed consent, the Lichess web platform was presented and participants could play a chess game against a weak opponent (Stockfish (Stockfish is an open-source game engine used in many chess software, including Lichess. https://en. wikipedia. org/wiki/Stockfish_(chess) (last seen September 2017)) algorithm level 1: lowest level) to gain familiarity with the computer interface.
No recording was made during this first game. Once familiar and comfortable with the platform, the eye-tracking calibration was performed using Tobii Studio software, in which subjects were instructed to sit between 60 and 80 cm from the computer screen and to follow a 9-point calibration grid. Participants were requested to avoid large head movement in order to assure good eye-tracking quality. Aside from this distance, no other constraints were instructed to participants. Each task to solve was individually presented, starting with the openings, followed by the N-Check-Mate tasks. Participants were instructed to solve the task by either playing a few moves from the opening or to check mate the opponent (played by Stockfish algorithm level 8: the highest level) in the required number of moves.
Facial Emotions Micro-expressions, as defined by Ekman and Fiesen  [10]  in 1969, are quick facial expressions of emotions that could last up to half a second. These involuntary expressions can provide information about cognitive state of chess players. In our pilot study, the Noldus FaceReader software  [23]  has been used to classify players' emotions in the form of six universal states proposed by Ekman: happiness, sadness, anger, fear, disgust and surprise (plus one neutral state). These emotional states are commonly defined as regions in a two-dimensional space whose axes are valence and arousal. Valence is commonly taken as an indication of pleasure, whereas arousal describes the degree to which the subject is calm or excited. In practice, the FaceReader software analyses video by first applying a face detector to identify a unique face followed by a detection of 20 Facial Action Units  [10].
• Valence: intensity of positive emotions (Happy) minus intensity of negatives emotions (sadness, anger, fear and disgust); • Arousal: computed accordingly to activation intensities of the 20 Action Units. Finally, besides giving us information about emotional responses, FaceReader software also provided a heart rate estimation. FaceReader was tested on two different datasets: the Radboud Faces Database  [24]  containing 59 different models and the Karolinska Directed Emotional Faces  [25]  which regroups 70 individuals. Both dataset display 7 different emotional expressions (plus neutral) on different angles. FaceReader algorithm correctly classified 90% of the 1197 images from Radboud Face Database  [26]  and 89% of the Karolinska Dataset (4900 images)  [23].
Discussion This research and primary results (see Section 4) show consistency results on unimodal features used to distinguish expert and intermediate chess players. Although these results appear promising, they are only preliminary: the number of participants  (23) ; the variation of recording duration (from seconds to a couple of minutes depending on the task and players' expertise); and the tasks must all be expanded and developed. Due to the size of our dataset, generalizing this preliminary results is not possible for the moment. Further experiments must be conducted to validate them. Our results showed that negative emotions carry significant information about participant's situation understanding. However, the semantic and class names used by the FaceReader software (i. e. , the 7 basics emotions) should be carefully interpreted.
Acknowledgments: This research has been be funded by the  French ANR  project  CEEGE  ( ANR-15-CE23-0005 ), and was made possible by the use of equipment provided by  ANR Equipement for Excellence Amiqual4Home  ( ANR-11-EQPX-0002 ). Access to the facility of the  MSH-Alpes SCREEN  platform for conducting the research is gratefully acknowledged. We are grateful to all of the volunteers who generously gave their time to participate in this study and to Lichess webmasters for their help and approval to use their platform for this scientific experience. We would like to thank  Isabelle Billard , current chairman of the chess club of  Grenoble "L'Échiquier Grenoblois " and all members who participated actively in our experiments.
If local transmission dominates the dynamics, then mass vaccination could be sufficient for control. However, if the repeated importations dominate the dynamics, restricting these and focusing on finding and vaccinating the external 'source(s)' of infections would be more efficient. Relying on dates of onset, geographic locations, and viral genetic sequences, we characterize the epidemiological situation using our method and compare the results to previous, more complex and computationally intensive approaches  [39]. We also test the performance of our method by applying it to a range of simulated datasets. Results suggest our approach provides a fast, flexible, and efficient tool for detecting clusters of cases of an infectious disease linked by transmission.
Results 151 dogs infected with rabies were reported in Bangui, the capital of the Central African Republic between the 6th January 2003 and the 6th March 2012. Of these, 123 cases had a date of report available, as well as a geographical location and viral genome sequences (  Fig  3  shows, overlaid onto the observed pairwise distances, the distributions for the expected distances (in time, space or genetic) between a case and its closest observed ancestor based on the literature and given an assumed reporting rate of 20%  [39]. The equivalent distributions with no underreporting, i. e. the distributions of expected distances between a case of rabies and its infector are shown in S1 Fig. Based  on these input distributions, we defined cutoffs for each data stream corresponding to the 95% quantiles (90 and 98% quantiles were also considered in sensitivity analyses, see S1 Text).
Using all data streams together, a total of 23 distinct clusters were identified, including 16 singletons, two pairs and 5 larger clusters containing respectively 5, 6, 8, 9, and 75 observed cases. These clusters are overlaid on the original data in Fig 2. Importantly, the fact that spatial data alone did not identify any cluster does not mean that this data contained no information on potential clusters. In fact, spatial data was useful in combination with temporal and genetic information, allowing to disentangle outbreak clusters which were otherwise seen as a single outbreak when considering temporal and genetic data only. For a given reporting rate, the size of outbreak clusters contains information on transmissibility, as measured by the reproduction number (R). Intuitively, low values of R will result in many sporadic cases, imported from outside of the reporting area but without subsequent transmission, while higher values of R would result in a smaller number of larger clusters.
In S1 Text, we present a sensitivity analysis where our method is applied to the rabies dataset at two intermediate time points, as well as at the end. We show that our method is robust to application in real-time, with very few differences between the allocation of cases to different clusters in real-time and retrospectively (see S1 Text for detailed results). We also performed a simulation study to assess the extent to which our method was able to correctly identify clusters of cases linked by transmission and accurately estimate the underlying reproduction number and reporting rate. Results are presented in Fig 5. Our method performed generally very well, but the cutoff choice affected the results dramatically. In our main analysis of simulated datasets aiming to reproduce the Bangui epidemiological situation, the mean sensitivity (measured as the True Positive Rate, TPR, see materials and method) was 99.
On the other hand, non-parametric approaches such as that described in Ypma et al. [38] , which make no assumption about the underlying pathogen, may be better suited to the analysis of data on emerging, poorly characterised pathogens. Note however that the performance of the method described in Ypma et al. [38]  has only been tested on data simulated using an extremely simple and unrealistic evolutionary model, with at least 80% reported cases. In contrast, our approach explicitly accounts for underreporting, and was tested on more realistic epidemic scenarios, with reporting rates as low as 10%. This makes our method more widely applicable in real-life epidemic investigations, in which fully observed transmission chains are rare. We used our approach to analyse a dataset of dog rabies in Central African Republic, previously studied using a combination of methods, including a much more complex approach involving particle Monte Carlo Markov chain inference  [39].
Our simulation study confirmed that our method runs fast (S14 Fig) and is able to correctly identify clusters of cases linked by transmission, and to subsequently accurately estimate the underlying reproduction number and importation rate. Importantly, our simulations also highlighted the importance of adequately choosing the cutoffs used at the pruning step. In our rabies application, we used cutoffs corresponding to the 95% quantiles, identified in our simulation study as maximising the average between sensitivity and specificity (S12 and S13 Figs). In practice, optimal cutoffs will likely vary across datasets, diseases and epidemic contexts. Different strategies may be employed to address the issue. First, sensitivity analyses should be performed to study the impact of changing the cutoffs and reporting rates, as has been done here.
Beyond its computational efficiency, our approach is also extensible to most other infectious diseases, and can be used to achieve epidemic evidence synthesis from any data. For instance, contact data can readily be incorporated in the form of binary distances, where the distance between cases with a reported contact is zero, and one otherwise. Our approach could use any measure of social  [43] [44] [45]  or ecological distances  [46] , as well as measures of dissimilarity based on reported symptoms (e. g. proportion of different symptoms reported by a pair of cases). Whenever deep sequencing data is available, existing measures of the genetic differentiation between populations can also readily be applied to describe genetic distances between individuals whilst accounting for within-host diversity  [47] [48] [49] [50].
Evidence synthesis using a graph approach Rationale. The rationale for our approach is illustrated in Fig  1 with  a schematic example including two data streams: geographical and genetic data. For each stream of data, pairwise distances between cases are computed and used to define pruned graphs where only potentially related cases are connected. These graphs are then merged into a single graph whose connected components represent likely outbreak clusters. The distribution of the sizes of these clusters is finally used for estimating transmissibility, as measured by the reproduction number R. We first consider each data stream independently. For each data stream n we define a dissimilarity measure d, used to compute distances between all pairs of cases (Fig 1A  and 1D ). We denote d n i,j the distance between cases i and j with respect to data stream n.
For the input distributions f n , we consider specific parametric distributions that are particularly appropriate to describe these three data streams and allow us to derive an analytical formulation for f n,π , in the presence of underreporting (S1 Text). We use Gamma, Rayleigh, and Poisson distributions for f n for temporal, spatial and genetic distances respectively. For each of these cases, we provide analytical formulas for f n,π , the input probability density function or probability mass function of the distance between two cases, accounting for underreporting. These can then be used to derive corresponding quantiles, and hence cutoffs to be used at the pruning step. Availability. The method is implemented and documented in the package vimes for the R software  [52] , available on github (http://github.
com/reconhub/vimes) and documented in a dedicated website (http://www. repidemicsconsortium. org/vimes). The analysis of one of our simulations is entirely reproduced in a dedicated vignette, which can be accessed in R by typing the three following commands: devtools::install_github("reconhub/vimes", build_vignettes = TRUE) vignette(package = "vimes") # list available vignettes vignette("rabies", package = "vimes") # open rabies vignette.
Importantly, this method also accounts for the possibility for some, typically small outbreaks, to go entirely unobserved. The method estimates R, as well as the probability of observing an outbreak, P obs. An estimate of the number of unobserved outbreaks may then be obtained assuming that it follows a negative binomial distribution characterised by P obs and the number of observed outbreaks. A full description of the inference on both the reproduction number and the number of importations can be found in S1 Text. The method is implemented and documented in the package branchr for the R software  [52] , available on github (http://github. com/reconhub/branchr). In summary, given a set of observed outbreak cluster sizes, we obtain a maximum likelihood estimate of R (and associated confidence intervals), as well as estimates of the total number of imported cases accounting for under-reporting.
Analysis of the rabies data Data. The Institut Pasteur of Bangui, under the instruction of the State health department of the Central African Republic, conducts the collection of suspected rabies dogs and performs rabies laboratory diagnosis according to established national standardized protocols and WHO guidelines  [64, 65]. Over the period 2003-2012, a linelist of 151 rabies cases was collated, with information on the date of report, the spatial coordinates (for all the isolates originating from Bangui), and the genetic sequence of the isolated virus. Rabies diagnosis was performed by direct immunofluorescence on smears of suspected animals, and confirmed in parallel using RT-PCR  [64, 66]. Viral RNA from brain samples was extracted using Trizol reagent (Invitrogen) according to the manufacturer's instructions.
RNA extractions, RT-PCR and sequencing were performed as described previously  [39]. 42% of the viral genome was sequenced, including the N, P, M, G and intergenic G-L region, corresponding to positions 161 to 5,221 of the RABV genome  [67]. All 151 sequences were aligned using the MUSCLE  [68]  plugin for Geneious 5. 6. 6 (Biomatters). The dataset is available in the R package 'outbreaks', and can be accessed in R by typing the three following commands: devtools::install_github("reconhub/outbreaks") library(outbreaks) ?rabies_car_2003 Parameters. Information on the distributions of expected distances between rabies cases was retrieved from the literature to inform the cutoff choice used at the pruning step. Following Hampson et al. [69] , we assumed a Gamma distributed serial interval, with mean 23.
Simulations We performed a simulation study to assess the ability of our method to correctly identify clusters of cases linked by transmission and to accurately estimate the underlying reproduction number and importation rate. We considered six simulation scenarios mimicking the transmission of rabies among dogs. Our baseline scenario was designed to closely mirror the transmission characteristics underlying the transmission of rabies in our dataset from Bangui. We then considered five variations of this scenario: a 'low', 'high' and 'perfect' reporting scenarios similar to the baseline scenario but with varying levels of reporting, and a 'low' and 'high' diversity scenarios, similar to our baseline but where the imported cases had pathogen genetic sequences respectively much more similar and much more different to one another.
It is now known that Ellobiopsis groups phylogenetically within Dinozoa  (Gomez et al. 2009) , and Amoebophyra, which was affiliated with Neresheimeria in Blastuloidae by  Neresheimer (1904) , is a syndinian. Both of these are therefore dinozoan. Chatton (1920)  notes characteristics of paradinids that could not only indicate a relationship with syndinians but also identifies many differences between them. The first sequence data for Paradinium was published by  Skovgaard and Daugbjerg (2008) , showing moderate support for a sister relationship with haplosporidia. 18S rDNA sequences were generated for two Paradinium lineages: P. poucheti from Oithona similis (PaOi01) and an undescribed Paradinium sp. from Euterpina acutifrons (PaEu41) (i. e. , two different copepod host species). Two sequences from parasites of the spot prawn Pandalus platyceros  (Bower and Meyer 2002)  formed a weakly supported clade with Paradinium in the Bayesian phylogeny of  Skovgaard and Daugbjerg (2008).
Sequence processing and definition of OTUs Icelandic bivalve gut tissue and water samples: the 454 amplicons were processed following the 454 Standard Operating Procedure (SOP) for mothur (http://www. mot hur. org/wiki/454_SOP; accessed September 2012) using mothur Version 1. 27. 0  (Schloss et al. 2009 (Schloss et al. , 2011)). Quality control parameters were chosen according to the 454 SOP with a minimum amplicon length of 100 bp and using chimera. uchime for chimera detection. Alignment of the amplicons in mothur was done using the SILVA-compatible reference alignment for eukaryotes (http://www. mothur. org/wiki/Silva_reference_files) based on SILVA v102  (Pruesse et al. 2007; Quast et al. 2013). Taxonomic identification of amplicons used the classify. seqs command with default settings on a mothur-compatible dataset of 71787 eukaryotic sequences derived from SILVA release "SSURef 111" as reference (file available on demand from the authors).
All sequences identified as belonging to the Ascetosporea were extracted from the whole dataset for further phylogenetic analyses. Endomyxa-biased V4 amplicons: the raw sequence files (SFF files) were processed using QIIME v 1. 8. 0  (Caporaso et al. 2010). The demultiplexing and quality filtering steps were done using default parameters except for minimum read length (150 bp instead of 200 bp) and maximum primer mismatches (three instead of zero) to allow for wobbles and ambiguous bases in the primers used (Table  1 ). Sequences were trimmed to 100 bp, then dereplicated and singletons were removed. OTU clustering of the remaining sequences was done with a threshold of 97% sequence similarity using USEARCH version 9  (Edgar 2013). Finally, taxonomy was assigned using the BLAST algorithm  (Altschul et al.
Phylogenetic analyses Three 18S alignments were produced (V3, V4 and V5-9) using the sequences generated as above aligned with all available basal ascetosporean, haplosporidian, gromiid and reticulosid and closely related environmental 18S sequences from GenBank, identified by blastn searches in January 2016. In each case sequences were aligned using the e-ins-I algorithm on the MAFFT server  (Katoh and Standley 2013) , terminal gaps were trimmed, the alignment was refined manually, and regions of ambiguous alignment and large indels were removed (masked) by eye. Bootstrapped Maximum Likelihood (ML) trees were then calculated via the Cipres Science Gateway server  (Miller et al. 2010)  using RAxML BlackBox version 8. 2. 9  (Stamatakis 2014; Stamatakis et al. 2008 ) (GTR + CAT; all parameters estimated from the data); bootstrap values were mapped onto the highest likelihood tree obtained.
ENDO-2 ENDO-2 was detected by both V5-9 and V4 primer sets, from water and sediment samples, DNA and cDNA, but not from any host-associated samples. No sequences within this clade were amplified using the V3 primer set. Although this lineage has been annotated as haplosporidian (DQ504354) on GenBank, all of our phylogenetic analyses show that it is not, and in fact forms a separate clade branching between ENDO-3 and Gromia, and so this mislabelling has been omitted from all figures. As is the case for ENDO-3b, there is no morphological evidence for this clade. ENDO-2 V4 amplicons cluster in three robust but weakly mutually related clades, two of which were unknown prior to this study (ENDO2a and ENDO2b). All sequence types within ENDO-2b were from the same library (Oslo sediment DNA), however, following completion of phylogenetic analyses further BLAST searches of these sequence types against the NCBI GenBank database recovered two environmental sequence types, from Adventfjorden in Norway, showing high sequence identity (98-99%) to V4 BIOM ENDO2b-01a (KT812216) and V4 BIOM ENDO2b-02 (KT810733).
DISCUSSION This study is further evidence that PCR primers targeted to defined phylogenetic ranges provide a powerful tool for revealing diversity that more broadly targeted primers either fail to amplify or only produce as a small proportion of large sequence datasets. Here, we designed a primer strategy to investigate the Paradinida, the ascetosporean order for which only a small amount of sequence data exist, and also to populate the region of the ascetosporean phylogeny between the free-living amoebae Gromia and Filoreta and basal haplosporidians. We reveal a major novel endomyxan clade, ENDO-3, robustly sister to Haplosporida. Morphological information is available for only two subclades of ENDO-3: two lineages whose morphology is entirely concordant with Paradinium (PaEu41 and PaOi01) within PARADIN-1, and the SPP.
The V4 dataset (also the largest in terms of sequence number and sample coverage) also contained ENDO-2b, so far only detected in sediments from Oslo, and the V5-V9 dataset contains ENDO-6, whose phylogenetic position within the Ascetosporea plus Gromia clade is unresolved (Fig. 1 ). ENDO-8 may be the closest relative to Gromia and Filoreta revealed by the study; therefore, we suggest it may resemble those or is a novel amoeboid form. Environmental OTU association analyses (e. g. interactome, Science; Lima-Mendez et al. 2015) may suggest potential hosts for ENDO-2 if it is parasitic, but direct evidence is required to prove such an association, for example, via a histological-molecular survey of invertebrates from habitats in which ENDO-2 is known or likely to occur. The novel deep-branching haploporids detected (Figs 2 and 3) expand the known ecological range of this order.