dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , long-branch attraction). Inaccurate taxonomy may result from two contrasting phenomena: oversplitting of species, or lumping of (often cryptic) species. We refer to all these nonbiological processes as sources of operational bias. Rapidly accumulating DNA barcode libraries, such as the Barcode of Life Data System (BOLD;  Ratnasingham and Hebert 2007) , are boosting the number of species VOL. 65 being described as new to science  (Olave et al. 2014). Typically, DNA barcode gene trees are used as an important source of information in species delimitation. Several algorithmic species delimitation tools have been developed, some of them specifically for use with DNA barcodes  (Pons et al. 2006; Puillandre et al. 2012; Fujisawa and Barraclough 2013; Ratnasingham and Hebert 2013; Zhang et al.
2013; Jones et al. 2015). However, these methods can misdiagnose species boundaries when gene trees are non-monophyletic and hence it is important to know the frequency of paraphyly and polyphyly. A benchmark review concluded that on average 23% of species express non-monophyly in mitochondrial DNA markers  (Funk and Omland 2003). In 146 studies of arthropods, the average percentage of non-monophyletic species was 26. 5% and in other invertebrates as high as 38. 6%  (Funk and Omland 2003). Recently, Ross (2014) estimated the incidence of paraphyly (including polyphyly) with an extensive, but not completely validated, data set of the animal COI barcodes accessed from BOLD, and concluded that 19% of species were non-monophyletic. For Lepidoptera, he reported the level of 17% of non-monophyly.
Here, we studied the incidence of para-and polyphyly in trees built using the standard COI barcode gene. The data set we analyzed included 41,583 specimens of 4977 species of European Lepidoptera, 50. 6% of all known species in this order from this continent. We developed a novel tool (named Monophylizer) that permits the automated identification of paraphyletic and polyphyletic lineages based on tree topology. Prior to carrying out the non-monophyly assessment, we paid particular attention to potential sources of operational bias, in particular by cross-checking identifications (based on current morphology-based taxonomy of all studied taxa) as carefully as possible. We then continued by assessing various other operational factors, particularly taxonomic uncertainties and identification difficulties.
Target Group The insect order Lepidoptera constitutes one of the most diverse animal groups; it contains approximately 157,500 described extant species  (van Nieukerken et al. 2011). Lepidoptera is likely the best-studied insect order, although with strong geographic and taxonomic biases. Families with large species are generally better known than those dominated by small species, and speciesrich tropical faunas are generally poorly investigated compared to those in temperate regions  (Lees et al. 2013). At a global scale, Lepidoptera is the best represented order in the International Barcode of Life Project (iBOL) with approximately 1 million sequences on BOLD associated with 100,000 species. By way of context, 9846 lepidopteran species have been reported from Europe up to 2011  (Karsholt and van Nieukerken 2013).
Material Collection and Delimitation of Sampling Area The data used in this study were largely collected within the framework of the iBOL as part of national or regional initiatives such as the Fauna Bavarica project (http://www. faunabavarica. de, last accessed June 2, 2016), the Finnish Barcode of Life project (http://www. finbol. org/, last accessed June 2, 2016), the "Lepidoptera of the Alps" campaign, the Norwegian Barcode of Life (http://www. norbol. org/, last accessed June 2, 2016) and the Nature of The Netherlands project, or as individual research projects. Our analysis focused on specimens from Europe as defined by current political boundaries but excluding European Turkey, Cyprus, and most of the Macaronesian islands. Most tissue samples analyzed were from identified pinned specimens collected in the past 15 years, because older material was less likely to generate sequence data.
Full taxonomic and collection information of specimens is available in BOLD through individual specimen pages within the public data set DS-MARKALL (dx. doi. org/10. 5883/DS-MARKALL) in the BOLD (www. boldsystems. org) barcode data repository. Collection localities are also available in. klm format (viewable with Google Earth) on Dryad at http://dx. doi. org/10. 5061/dryad. k3mr1.
Sequencing A 500-658 bp long amplicon of the 5' terminus of the mitochondrial COI gene (the standard DNA barcode region for animals) was sequenced for all specimens. A single codon deletion occurs in three species of Scardiinae (Tineidae), but otherwise the target gene region does not show length variation in European Lepidoptera. Sequencing was predominantly conducted at the Canadian Centre for DNA Barcoding (CCDB), but also at Naturalis Biodiversity Center (the Netherlands) and laboratories of the authors' research organizations. The CCDB's sequencing protocol is described in detail in deWaard et al. (2008). The primer pair LepF1 and LepF2  (Brower and Egan 1997)  was primarily used to amplify the barcode region, but, in cases of failure, other primer sets were also attempted. Full primer details, laboratory reports, trace files, sequences, and GenBank accession numbers can be retrieved from the sequence page of each record in BOLD and can be downloaded at dx.
We, therefore, harmonized names throughout the complete data set following the nomenclature of Fauna Europaea (http://www. fauna-eu. org/, last accessed June 2, 2016). This revealed hundreds of cases where two or more names had been applied to a single species, but also cases of homonymy, (the application of a single name to several species). Despite careful cross-checking of identifications, it is likely that some misidentifications remain in the data because of identification problems or taxonomic uncertainties in several species groups. We attempt to estimate the effect of this below.
2009 ). Since NJ is computationally less demanding than ML and permits rapid construction of trees with thousands of specimens, NJ trees were constructed without the exclusion of redundant (identical) haplotypes, which is expected to have minimal effect on the tree topology estimated by this method. In contrast, haplotype collapsing was done for the more demanding ML analyses to increase computational efficiency. However, redundant haplotypes were not removed when they occurred between different species (barcode-sharing). Haplotype collapsing was conducted using ALTER  (Glez-Peña et al. 2010). Distance matrices for NJ trees were calculated under both the Kimura 2-parameter (K2P)  (Kimura 1980)  and P-distance model, using the BOLD alignment of VOL. 65 sequences (amino acid based HMM). To estimate nonmonophyly in NJ trees, we applied K2P because it is usually used for DNA barcode data, although it is not necessarily the best-fit model of nucleotide evolution of the COI gene  (Srivathsan and Meier 2012).
Detection of Non-Monophyly Non-monophyly can be detected by eye in a graphical representation but is prone to human error. In trees with hundreds or thousands of terminals, internal branches are often short and detection by eye can be very difficult. Also, polyphyletic species dispersed among many other species might remain undetected. For these reasons, we developed a web service called "Monophylizer" that detects cases of non-monophyly. The service accepts Newick, Nexus, NeXML, and PhyloXML trees. The Monophylizer was designed to be rather permissive in the Newick syntax it allows because BOLD can omit syntactically invalid Newick tree descriptions. However, some of the database fields that BOLD includes allow text fields that may contain parentheses or commas, which file readers cannot distinguish from the commas and parentheses used by the Newick syntax.
7. If there is more than one distinct root-to-tip path (i. e. , group), the taxon is considered polyphyletic, otherwise paraphyletic. 8. For each first (i. e. , most recent) node in each group, all subtended species are collected. The union of these sets across groups forms the set of entangled species. The web service can be accessed at http://monophylizer. naturalis. nl/, last accessed June 2, 2016 and the source code is freely available at https://github. com/ naturalis/monophylizer, last accessed June 2, 2016. The output of the web service can be configured to be either a table in a web page, or a tab-separated spreadsheet for high-throughput applications, for example, when combined with automated web clients. We used this web service to analyze the topologies of our gene trees. Estimating the Effects of Sampling Effort and Intra-and Interspecific Divergence The frequency of observed species monophyly is strongly influenced by intraspecific genetic variation represented in the data and the genetic distance to related species.
Both measures are affected by sampling intensity. Higher sampling effort will reveal more intraspecific variation and will tend to identify more closely related species. We explored the effect of both these factors and their interaction on the frequency of monophyly as determined by Monophylizer. The measures we used of maximum intraspecific divergence and minimum genetic distance to the nearest neighbor were based on the K2P model of nucleotide substitution and were calculated using the "barcode gap analysis" tool of BOLD using pairwise deletion setting for missing nucleotides. Sequences were aligned using the BOLD sequence aligner (amino acid based HMM). The analysis was carried out with a slightly reduced data set of 4921 species (56 species excluded) since Barcode Gap Analysis currently treats records with infraspecific names as different species.
We fitted a nonlinear function, y = (1-e -(x-1)e ) ( 1 ) to the data with the R function "nls. " This function is constrained to go through the point (1,0) hence implicitly assuming the absence of non-monophyly when only a single specimen is available. The starting values for and were the estimated values of and from the model y = (1-e -x•exp() ), fitted with the self-starting function "SSasympOrig. " When estimating the parameters of model 1, we weighted the observed proportions of nonmonophyletic species by the inverses of their bootstrap standard errors (the weight for the class including single specimens was arbitrarily set to 200 as the standard error is zero though this choice did not affect the parameter estimates). Finally, we derived 95% confidence intervals for and by using the R function "confint"  (Venables and Ripley 2002)  Barcode-Sharing If a species shares its barcode with another species, and both show no intraspecific variation, they are treated as monophyletic by Monophylizer.
However, these species could equally be considered para-or polyphyletic. Furthermore, such species pairs would no longer be reciprocally monophyletic if even a single nucleotide substitution were to occur. We investigated the frequency of this phenomenon by searching for species that showed sequence identity to their nearest neighbor but had been ranked monophyletic. As the same issues can arise when VOL. 65 neighboring species are very similar but not identical, we also searched for monophyletic species differing by less than 1% from their nearest neighbor. Searches were performed using the "barcode gap analysis" tool in BOLD.
2001 ). Three species (0. 6%) showed phylogenetic "tangles" involving 5 (Glacies coracina, Geometridae), 36 (Dryobotodes monochroma, Noctuidae), and 71 (Deltote incognita, Noctuidae) genera, but these were very likely misidentification artifacts (see Supplementary Table  1  available on Dryad). Non-monophyly was not observed among species in different subfamilies or higher-level ranks. Para-and polyphyly are presumed to be more prevalent in young, recently diverged species than in older species. Although mitochondrial introgression may obscure the gene history, genetic distance to the closest relative is likely a good proxy for species' coalescence time. We found that non-monophyletic species often showed a low genetic distance to their nearest neighbor, and that this pattern was more pronounced in polyphyletic than paraphyletic species, although the difference was not statistically significant.
Monophyletic species show an average K2P minimum distance of 5. 66 (95% BCa CI 5. 57-5. 76) to their nearest neighbor, whereas the corresponding values for paraphyletic and polyphyletic species are 0. 89 (95% BCa CI 0. 751. 07) and 0. 74 (95% BCa CI 0. 50-1. 11), respectively. Singletons were excluded as they cannot exhibit non-monophyly and a further 56 monophyletic species were omitted because their nearest neighbor is a subspecies of the same species in the BOLD database. Of monophyletic species (singletons excluded), 7. 0% show less than 0. 5% minimum K2P distance to their nearest neighbor, whereas the equivalent numbers for paraphyletic and polyphyletic species are 63. 4% and 78. 7% (Fig. 3 ). However, many monophyletic species have low genetic distances to their nearest neighbor, including those represented by singletons which could become non-monophyletic with increased sampling (Fig.
4 ). In both para-and polyphyletic species, low K2P distance to the nearest neighbor is frequently caused by one or more operational factors, such as overlooked synonymies or potential cryptic species (Supplementary Table  1  available on Dryad). High intraspecific variability is associated with the presence of non-monophyly. Of 3807 monophyletic species represented by more than one individual, the mean K2P intraspecific maximum variability is 0. 99% (95% BCa CI 0. 95-1. 04; mean n = 8. 99). In paraphyletic species, the mean maximum intraspecific variation is Monophyletic species (215 in total) showing less than 0. 01 minimum K2P distance (or less than 7 nucleotide substitutions difference) to their closest species. The number of nucleotide substitutions to the nearest neighbors are indicated with arrows.
As the number of specimens increases, non-monophyly is predicted to occur in an increasing region of parameter space (Fig. 5 ). The statistical model performed very well according to the cross-validation analysis, which resulted in an area under the ROC curve of 0. 988 (Supplementary Fig. 1  available on Dryad). This result was not sensitive to the "leave-one-family-out" method as a 5-fold cross-validation analysis resulted in the same mean area under the ROC curve [0. 988; 95% CI estimated with the function "ci. cvAUC" (LeDell et al. 2014): 0. 984-0. 992) when the random division of species into the five groups was repeated 100 times. The proportion of non-monophyletic species tends to increase as more specimens are included in the analysis (Fig. 6 ). The asymptote of the regression function fitted to these data indicates that the proportion of non-monophyletic species approaches 0.
Effect of Taxonomic and Identification Uncertainty The taxonomic specialists among the authors estimate that 31. 8% of non-monophyly may not be valid, as they are likely to reflect "over-splitting" of species (Supplementary Table  1  available on Dryad). These include (i) cases where highly similar but allopatric populations have been considered distinct species, (ii) parapatric species pairs with little information on the extent of gene flow between populations, (iii) ecological (e. g. , altitudinal, latitudinal, habitat, or food-plant associated) forms or potentially polymorphic species, and (iv) sympatric pairs or groups of variable species separated by uncertain boundaries. In 15. 1% of non-monophyletic species, there was further independent evidence of cryptic diversity  (undersplitting), in most cases from morphological differences associated with barcode divergence.
DISCUSSION Our survey examined 41,583 specimens belonging to 4977 lepidopteran species, the largest data set of DNA barcodes so far published for any taxonomic group. Moreover, our data are of higher quality than previously published arthropod data sets due to the efforts made to resolve issues such as uncertainties in identification. For example, the benchmark review of species non-monophyly  (Funk and Omland 2003)  included 2319 species, whereas Ross (2014) examined 21,337 species. Both of these studies examined a broad range of taxa, but relied largely upon unpublished and poorly validated data, without examining the reliability of the identifications. Funk and Omland (2003)  reported the occurrence of non-monophyly to be 26. 5% in arthropods (figure for Lepidoptera not estimated separately), whereas Ross (2014) reported an 18% non-monophyly in Arthropoda and 17% in Lepidoptera (16% with interim operational names included).
We observed a considerably lower level of 12. 3% of non-monophyly in our "raw" data. This is not explained by differences in sampling effort, since Ross sampled an average of 8. 1 specimens per species among Arthropoda, whereas our average was 8. 4. Ross' Lepidoptera data are based partially on the same cleaned data used in our study, indicating that the level of non-monophyly in data not used in our study was likely higher than 17%. Since Funk and Omland's data were retrieved from GenBank while Ross's data came from BOLD, the higher incidence may be due to the higher proportion of misidentifications in GenBank  (Harris 2003; Bidartondo 2008; Groenenberg et al. 2011). A second possible cause is that before the advent of systematic species sampling through the iBOL, data sets were biased because of more concentrated efforts on difficult species groups, which in turn are likely to express unusually high levels of non-monophyly.
ACKNOWLEDGMENTS We are indebted to a large number of taxonomic experts and collectors who have contributed to the  Lepidoptera Barcode of Life campaign  in multiple ways, especially by providing material for DNA barcoding and by identifying specimens. We are grateful to the  ICT staff at Naturalis , and especially  David Heijkamp , for hosting the Monophylizer web service through their infrastructure. We are very grateful to staff at the  Biodiversity Institute of Ontario  for their key role in generating sequences, photographing specimens, entering data elements into BOLD, and aiding the curation of this information. We thank  Jess Johansson  and  Megan Milton  for help with GenBank submissions and BOLD data set. Finally, we thank  Frank Andersson ,  Karl Kjer , and an anonymous referee for their comments on an earlier version of the study.
ACKNOWLEDGMENTS We thank the anonymous referee for useful comments and suggestions. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  Grant Agreement n. 306476. This work is supported in part by the  Grant-in-Aid  from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan  ( 25287040 KO ).
Cognitive impairments associated with schizophrenia are very frequent. They concern both neurocognition and social cognition, including facial emotion recognition. These impairments have a negative impact on the daily functioning, in particular the social and vocational rehabilitation of people with schizophrenia. Previous studies in this area clearly demonstrated the interest of cognitive remediation to improve neurocognitive and social cognitive functioning in schizophrenia. They also established clear links between facial emotion recognition skills and attentional processes. The present study compares the GAÏA s-face program (GAÏA arm), which focuses on facial emotion recognition processes, with the RECOS program (RECOS arm), a neurocognitive remediation therapy focusing on selective attention.
According to these data, both neurocognitive remediation, focusing on attentional processes, and social cognition remediation, focusing on emotional perception, should increase facial affect recognition in people with schizophrenia. From a clinical perspective, it would be interesting to compare the effects of these strategies on symptoms, and cognitive and social functioning. The GAÏA s-face (Schizophrenia-Facial Affects recognition Cognitive Enhancement) program  (44, 45)  is an individual, computer-assisted cognitive remediation therapy focusing on facial emotion recognition. The present controlled efficacy study compares the GAÏA s-face program with the RECOS program, an individual, computer-assisted neurocognitive remediation therapy focusing on attentional processes  (46, 47).
Study Design Clinically stable people with schizophrenia, according to the DSM-IV-TR criteria  (48) , were recruited in four psychiatric centers in France. They were randomly distributed between two active treatment arms, namely GAÏA and RECOS. The assessments were blind to group allocation and carried out at baseline (T1 = week 0) and post-treatment (T2 = week 11). The main treatment outcome measure was the TREF (facial emotion recognition test) total score  (49). Secondary measures included clinical ratings, neurocognitive and social cognitive measures, and psychosocial evaluation. The study was approved by local ethics authority (CPP Lyon Sud-Est VI, project no. AU 940), and declared to the national authority (ANSM: project no. 2011-A00793-38) and on clinicaltrials. gov (project ID: NCT01607424).
GAÏA s-Face (Experimental Arm) GAÏA s-face  (44, 45)  is an individual, social cognitive remediation program focusing on facial emotion processing. The GAÏA program comes after a comprehensive assessment of neurocognition, social cognition, clinical symptoms, and social functioning. The therapy is spread over 30 sessions: three 1-h sessions per week for 10 weeks. One of these sessions (called the transfer session) is performed without the therapist in the participant's everyday environment. The GAÏA program is divided into three phases: (1) an exercise phase with photographs, aiming to develop strategies to recognize and discriminate joy, anger, and sadness. (2) A computerized exercise phase using videos, to adapt the strategies developed on photographs to dynamic situations with five levels of increasing difficulty.
No effect was, therefore, expected on neurocognitive assessment in the GAÏA arm. By contrast, RECOS participants receiving cognitive remediation targeting selective attention were expected to show improved performances at T2 in tasks targeting this process compared to GAÏA participants. Social cognition is defined as a set of separate processes  (5). A specific effect of the GAÏA s-face intervention on every test measuring emotional processing was expected at T2 vs. T1. No effect of the RECOS program was expected on social cognition tests, apart from facial emotion recognition, as described above.
The GAÏA program learns participants to focus on relevant facial features to recognize emotions; this specific training probably helped them directly reuse the strategies learned during cognitive remediation to perform the TREF test. As for the RECOS group, the ability to select and focus on relevant facial features may be considered as a transfer and generalization effect of the strategies they practiced during cognitive remediation. Previous research has shown that modifications the visual attention to faces were associated with facial emotion recognition deficit  (20, 21). The GAÏA program probably has a positive effect on the pattern of visual attention to faces, supported by an (unexpected) positive effect on visuospatial abilities on pre-post analysis. However, further studies should explore this hypothesis.