dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Data sets. To remove representative bias regarding protein resolution or sequence identity, non-redundant datasets were used. These datasets were generated using the PISCES database  60. As previously performed in  12, 61  , 10 sets of proteins were defined. Each contained no more than x% pairwise sequence identity (with x ranging from 20 to 90%). The selected chains had X-ray crystallographic resolutions less than 1. 6 Å or 2. 5 Å and R-factors less than 0. 25 or 1. 0. They comprised between 2,542 and 23,943 protein chains. Each chain was automatically examined with geometric criteria to avoid bias from zones with missing density. The main purpose of such diversity was to examine (i) the poorly populated turns and (ii) the stability of the clustering approach (see below). Secondary structure assignment.
The PBs were obtained with an unsupervised classifier similar to Kohonen Maps  54  and hidden Markov models  64. The PBs m and d are prototypes for the central regions of α -helix and β -strands respectively. PBs a through c primarily represent the N-cap of a β -strand, whereas e and f correspond to the C-caps; PBs g through j are specific to coils, PBs k and l correspond to the N cap of an α -helix, and PBs n through p correspond to C-caps. PBs were assigned by using in-house Python software, although similar assignment can be performed through the PBE web server  65  or PBxplore (https:// github. com/pierrepo/PBxplore 66 ).
Specific clustering approach. A specific clustering approach was designed to cluster type IV β -turns by using the classical rule, allowing + /-30° for all angles, with the exception of one at + /-45° for the defined values. The clustering derived from Self-Organizing Maps (SOM, without diffusion between the clusters  53, 54  ). The training was carried out in 2 successive parts; the first one limited the potential bias of initialization, and the second refined the clustering by using the specific rules for β -turn types. The type IV β -turns were selected from a dataset D. Thus, each dataset was associated with T type IV β -turns. Step one: 1. k clusters were created and were vectors v of length 2M = 4, representing the dihedral angles (ϕ i+1 , ψ i+1 , ϕ i+2 , and ψ i+2 ). k type IV β -turns were taken randomly to initialize the clusters.
2. One of the T type IV β -turns was randomly selected from the dataset D (denoted V 2 ) and compared with each of the k clusters. The dissimilarity measure between two vectors V 1 (representing the clusters) and V 2 of dihedral angles was defined as the Euclidean distance among the M links, the RMSDA (root mean square deviations on angular values  67  ): = ∑ Φ -Φ + Ψ -Ψ = = RMSDA M (V, V ) ([ (V) (V )] [ (V) (V )] ) 2 ( 1 ) i i i i i i 1 2 1 2 1 2 2 1 2 2 where {Φ i (V 1 ), Ψ i (V 1 )}(resp. Ψ i (V 2 ), Ψ i (V 2 ) ) denotes the series of the (2M) dihedral angles for V 1 (resp. V 2 ). The angle differences were computed modulo 360°. Thus, in the training, this distance was used for assessing the dissimilarity of any fragment in the database with the different clusters. 3. The minimal RMSDA value was used to define the winning cluster W, i.
e. , the closest to the observation. W values were modified according to the learning coefficient α : α Φ =Φ + Φ -Φ × + ( ) (V ) ( V ) (V ) (V ) (2) j w t j w t j j w t t 1 2 α Ψ =Ψ + Ψ -Ψ × + ( ) (V ) ( V ) (V ) (V ) (3) j w t j w t j j w t t 1 2 where {Φ j (V w )} and Ψ j (V w ) are the values of the winner at time t, with j ranging from 1 to 2, similar to the values of the real data (i. e. , dihedral angles i+ 1 and i+ 2, modulo 360°). α α = + 1 (4) t t T 0 The decrease of α was performed similarly to that for SOM  53, 54  , T represents the total amount of data to learn (here the number of type IV β -turns). t represents the number of β -turns already used. The process goes back to step 2. One cycle of training corresponds to the learning of the whole dataset α 0 , which is then equal to α 0 /2; after 5 cycles, it is equal to α 0 /5, etc.
Initially, α 0 = 0. 35, as in  68, 69. The process was iterated for 20 cycles, i. e. , 20 times T; these steps were important to diminish the potential effect of the initialization. Step two: 1. The final values of the k clusters were used as initial values. α 0 was still equal to 0. 35. 2. One of the T type IV β -turns was randomly selected from the dataset D (denoted V 2 ) and compared with each of the k clusters. Instead of using only RMSDA, the β -turn rule was used: 3 angles can be at + /-30° and 1 angle at + /-45°. The winner positively applied this rule; otherwise no training was performed. 3. Modification of the winner weights was performed as in step one -3. 4. The process was iterated for 20 cycles. An important point is the choice of k. k was first set at 50 and then reduced. The obtained clusters were compared in the order of largest to smallest k values.
Z-score. The amino acid occurrences for each local structure conformation were normalized into a Z-score: = - Z n n n n ( ) (5) i j i j obs i j th i j th , , , , wheren i j obs , is the observed number of occurrences of amino acid i in position j for a given secondary structure, and n i j th , is the expected number. The product of the occurrences in position j with the frequency of amino acid i in the entire databank equals n i j th ,. Positive Z-scores (respectively negative) corresponded to overrepresented amino acids (respectively underrepresented); threshold values of 4. 42 and 1. 96 were chosen (probability less than 10 -5 and 5. 10 -2 , respectively). The same computation was also performed for the protein blocks. Analysis. Most of the quantitative analysis was performed using in-house Python scripts, and statistics and visualization were performed with R software (version 3.
Results and Discussion Protein structure dataset. The different amino acid datasets showed the expected amino acid and protein block occurrences, with no peculiarities in the rate of redundancy and the resolution quality (see Supplementary Information 2). As noted previously  27, 28  , the occurrence of β -turns is highly dependent on the way in which the assignment is performed. Following the work of Fuchs and Alix  27  , we assigned secondary structures to the different protein datasets by using DSSP  5. The DSSP provided 8 classes that were reduced to 3 classes (helix, strand and coil) or 4 classes (helix, strand, turn and coil, see Supplementary Information 3) for practicality. Helical structures represented more than 37. 3% of the residues and the β -sheets represented 22. 5%, whereas the remaining coil class covered 42.
In related research, Micheletti and collaborators have decided to take the largest cluster each time and iteratively repeat the clustering, each time removing the largest cluster  73. This clustering is slightly unstable because each repetition removes a large amount of data. Thus, it did not seem pertinent to use it here. Moreover, with a large initial number of clusters, determining the clusterability of the data was manageable. The training was performed with different datasets beginning with a large number of clusters (50 at first), which was progressively reduced (to 10). A notable feature of the learning was that four clusters appeared at the beginning and remained the most frequently occurring cluster for each of the different datasets. The deviation in the dihedral angle values between the different simulations (and different datasets) was never higher than 0.
3°, thus indicating that the clustering was reasonably stable (a more detailed description is provided in Supplementary Information 8). (d, h, l, p ) of residue i+ 2; red dots are the ideal values. The number of observations of both residues is strictly identical. The four new type IV β -turn sub-types were named IV 1 , IV 2 , IV 3 and IV 4. They represent half of the of type IV β -turns (see Table  2 ), composing 16. 1, 12. 4, 11. 2 and 8. 5% of the IV ori type, respectively. In regards to all of the  defined types, they were the 4 th , 6 th , 7 th and 8 th most frequent turns (5. 10%, 3. 9%, 3. 5% and 2. 7%, respectively). These numbers are reasonable because they were highly consistent across all of the datasets. Figure  3  shows these four new categories. The remaining clusters were not selected because (i) their occurrences were very low (largely less than those of type VI β -turns) and (ii) they were often dependent on the number of clusters (see Supplementary Information 9).
On the other hand, type IV 1 and II β -turns were very close in terms of dihedral angles but were distinct in terms of their amino acid content. Figure  5  clearly shows that type II β -turns were highly specific, whereas type IV 1 β -turns had more classical characteristics, being closer to type I' β -turns than type II β -turns. The two remaining β -turn types, IV 3 and IV 4 , were within bin 6 of the Ramachandran plot, close to type I β -turns  79. Although their amino acid profiles were highly similar, their local protein structure conformations were distinct. A classical question raised by any clustering methodology is the relevance of the results. Here, our results can be considered reliable, owing to their reproducibility and stability. The use of 10 different datasets ranging in quality and sequence identity highlighted the high stability of the four main clusters (i.
Acknowledgements I thank the editor and anonymous reviewers for their constructive comments, which helped me improve the manuscript. This work came from various trips and discussions I had during recent years in Bangalore, India, and I would like to dedicate this research to Indian protein pioneers G. N. Ramachandran ,  C. Ramakrishnan ,  C. M. Venkatachalam ,  P. Balaram ,  N. Srinivasan  and  R. Sowdhamini  and also to my colleagues C. Etchebest,  P. F. J. Fuchs ,  J. -C. Gelly , and especially  T. J. Narwani. This work was supported by grants from the  French Ministry of Research, University of Paris Diderot -Paris 7, French National Institute for Blood Transfusion (INTS) ,  French Institute for Health and Medical Research (INSERM). AdB also acknowledges the  Indo-French Centre for the Promotion of Advanced Research/CEFIPRA  for collaborative grants (numbers 3903-E and  5302-2 ).
This study was supported by grants from the  Laboratory of Excellence GR-Ex , reference  ANR-11-LABX-0051. The labex GR-Ex is funded by the  programme "Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. Calculations were performed on an SGI cluster granted by  Conseil Régional Ile de France  and  INTS  ( SESAME Grant ).
turn IV ori 92, type VI a1 by PDB id 1HBN90, type VI a2 by PDB id 1IQ691, and type VI b by PDB id 1YT392. IV 1 5. 10 16. 08 IV 2 3. 95 12. 44 IV 3 3. 53 11. 15 IV 4 2. 70 8. 50 IV misc 16. 44 51. 83 Sum 31. 72 100. 00 new β-turn (%) (%) of β-.
Recently, multiple trait-based approaches have been proposed to complement taxonomicbased assessment of streams and rivers using macroinvertebrates. However, the lack of specific trait information for Chironomidae prevents their use in the functional assessment of communities. Therefore, here, we aimed to: (1) develop a trait database for European Chironomidae genera that can be used in future bioassessment and ecological studies; (2) evaluate, by multivariate analyses, whether our new database provides additional information on Chironomidae compared to the trait information provided in the commonly used European trait database  (Tachet et al. , 2010) ; and (3) determine whether the new information on Eltonian traits (proxy to biological traits) translates the most accepted phylogenetic relationships among Chironomidae subfamilies.
We gathered information on 744 species and 178 genera, for 37 traits covering 186 trait categories, and found substantial differences between our database and the commonly used European trait database. In addition, available information on traits was not always in agreement with phylogenetic relationships among subfamilies. Orthocladiinae and Chironominae which are considered sister groups in evolutionary terms actually showed confident trait relatedness based on Eltonian traits tree while the remaining relationships between subfamilies are questionable. In addition, different traits can occur in closely related taxa depending on the environmental drivers operating on their habitats. Our study reveals that the usually accepted redundancy within the Chironomidae family and subfamilies must be a product of averaging the information from finer taxonomic resolution added to the substantial lack of information for this insect group.
, 2006). However, few researchers have attempted to quantify trait information for Chironomidae, with some of them achieving a trait database at the subfamily and tribe levels (see  Tachet et al. , 2010 for Europe and Poff et al. , 2006  for North America). Few works gathered information at the species or genus level considering a reduced number of traits and/or taxa (see  Franquet, 1996 for France; and Vieira et al. , 2006 for North America). Here, we had three objectives. First, we aimed to categorise the European Chironomidae genus characteristics into a set of 21 traits and 110 categories used in  Tachet et al. (2010)  for all aquatic macroinvertebrates and a set of 16 additional traits specific to Chironomidae. Secondly, we investigated the distribution and variability of trait patterns within Chironomidae subfamilies, using the new trait database.
Given the great variability reflected in trait heterogeneity within each Chironomidae subfamily, we expected that trait information gathered at higher or lower level of taxonomic resolution would determine differences in traits patterns gathered within each subfamily. To determine whether our database was actually providing additional assessment information, we contrasted trait patterns given by our database at the genus level with that obtained at the subfamily-level in the trait database of  Tachet et al. (2010) , which is commonly used in bioassessment studies. Finally, assuming that heritable traits (Eltonian) of organisms could disclose evolutionary processes operating among taxa, Chironomidae subfamily traits relatedness was expected to reflect their phylogenetic distances across subfamilies.
European freshwater Chironomidae traits We defined an a priori list of European species and genera. Due to a lack of consensus among European Chironomidae fauna taxa lists and guides (e. g. Illies, 1978; Andersen et al. , 2013; Soriano et al. , 1997; Cobo et al. , 2001; Vallenduuk and Pillot, 2007; Pillot, 2009 Pillot, , 2013) )  we followed the Fauna Europaea database (http:// www. faunaeur. org/see, Saether and Spies, 2013)  with 194 and 1261 genera and species entries distributed among eight subfamilies: Buchonomyiinae, Chironominae, Diamesinae, Orthocladiinae, Podonominae, Prodiamesinae, Tanypodinae, and Telmatogetoninae. Our database includes the most widespread European Chironomidae species, covering a wide geographic area, different categories of water bodies at different altitudes and latitudes.
Trait-affinity scores were further treated as frequency distributions and standardised to sum 1 for a given taxon-trait combination, to give the same weight to each taxon and to each trait in further analyses. This procedure is known as fuzzy coding  (Chevenet et al. , 1994). Total number of genera described per trait was estimated to define the best described traits, i. e. , with information gathered for more than 50% of the European genera. The genus trait database is provided as Supplementary data with the list of the references used to extract trait information and the list of species used to describe each genus.
Comparison between the two databases To determine whether our trait database built at the species and genus levels involved different distributions of taxa (subfamily, tribes) compared to the database of  Tachet et al. (2010) , we used Fuzzy Correspondence Analysis (FCA) that enables the joint ordination of taxa and trait categories  (Chevenet et al. , 1994). FCA uses a matrix (n × p) to interpret the relationships between trait categories (p) and resemblances among individual taxa (n). The affinity profile of each trait category among taxa enables the positioning of each trait category at the weighted average of taxa that uses this category. The variance of these positions corresponds to a correlation ratio (i. e. the highest the correlation ratio the highest the separation of taxa across trait categories) and FCA maximises the average correlation ratio across traits when FCA was performed separately on Grinnellian and Eltonian traits.
For comparison with the European trait database of  Tachet et al. (2010)  (hereafter TDB -Tachet DataBase), the fuzzy information of our database at the genus level (hereafter GDB -Genus DataBase) was averaged at the subfamily and tribe levels. Afterwards, these average affinity scores were rescaled so that their sum, for each of these coarser taxonomic groups for a given trait, equals one. Thereby, traits were described at the same scale for all different taxonomic levels of resolution. While the biological information in  Tachet et al. (2010)  describes only the Podonominae, Tanypodinae, and Orthocladiinae subfamilies and the Chironomini and Tanytarsini tribes, our database included  Finally, to assess the variability in community trait composition explained by the difference between GDB and TDB, we computed between-class variance (with class as type of database; see  Dolédec and Chessel, 1987; ter Braak, 1988)  and tested its significance against simulated values obtained after 999 permutations of the rows of the trait-composition arrays.
European freshwater Chironomidae trait database Our final list contained 178 Chironomidae genera and 744 species distributed among 8 subfamilies. Biological information on species and genera was found in the literature for ∼59% of the most widespread European species, and 92% of the European genera for 37 traits (Table  2  and see in the information supplied as Supplementary data). From all of the gathered trait information, 11 Grinnellian and 4 Eltonian traits had information for more than 50% of the European Chironomidae genera present in the database (indicated in Table  1 ). The best described Grinnellian traits were: transversal distribution in streams and general/gross habitat preferences (>90% of genera present in the database). Food types, pH tolerance, salinity preferences, longitudinal distribution along streams, altitude, substrate preferences, current velocity preferences, oxygen and depth preferences were described for 53-74% of the genera.
Comparison between the two databases FCA performed on Grinnellian traits showed low but significant differences between the two databases (15% of variance explained; simulated p-value = 0. 001; Fig. 3a , Table  3 ). Transversal distribution along the stream channel, pH, and to a lesser extent food type, were more important contributors for the difference among databases. Substrate preferences and transversal distribution each explained more than 30% of variance over first axis (32% and 40% respectively), whereas altitude preferences, food type and longitudinal distribution explained 13%, 14% and 18% of the variance, respectively. Transversal distribution also had a high contribution to explain distribution over the second axis (explaining 27% of the variance), whereas temperature and pH preferences were also relevant (13% and 11% variance explained, respectively).
FCA performed on Eltonian traits likewise revealed significant differences between databases (32. 7%, simulated p-value = 0. 002; Fig. 3b, Table 4 ) to which reproduction type, resistance form and,  to a lesser extent, life cycle duration had the highest contributions. Resistance forms and reproduction type explained 79% and 76% of the variance, respectively, considering the first axis, followed by life cycle duration, which explained 33% of the variation. The variance along axis 2 was explained by voltinism (43% variance explained) and to a lesser extent by the substrate relation, explaining 10% of the variance.
Here, we defend the hypothesis that Chironomidae could be appropriate indicators of environmental conditions, as the same taxonomic group includes tolerant (e. g. Chironomus) and sensitive (e. g. Diamesa spp. ) taxa to human impacts  (Armitage et al. , 1995; King and Richardson, 2002; Lencioni et al. , 2012). One main problem for bioassessment purposes arises from the difficulties of taxonomic identification and the poor knowledge on traits at the genus or species level, contrary to other groups of freshwater invertebrates (e. g. Poff et al. , 2006; Tachet et al. , 2010). Aiming to fill this gap, information on 37 Chironomidae traits and 184 trait categories was compiled in this study. The number of species and genera covered by our database (59% and 92% respectively) highlights the great effort that is still needed to understand the behaviour, physiology and ecological tolerances of Chironomidae species.
Considering the Eltonian traits only, the development of the database clearly showed the poor information available in the literature as only 4 of this biological type of traits were characterised for more than 50% of the European genera. Our database thus identifies the genera and species to which more attention should be given in future studies due to reduced available information. One of the reasons for the reduced and uneven information on Chironomidae traits is the fact that morphological and physiological studies typically focus on Chironomus species (e. g. C. tentans and C. riparius), because they are easy to keep in the laboratory and use in routine ecotoxicological tests  (Ankley et al. , 1994; Armitage et al. , 1995; Penttinen and Holopainen, 1995). A total of 16 European genera belonging to Diamesinae, Orthocladiinae and Chironominae subfamilies lacked information (Supplementary data).
The inability to characterise these genera may be due to their limited distribution so far (e. g. Molleriella, Neobrillia) and their small number of species in Europe (e. g. Baeotendipes noctivagus, Nilomyia aculeata) or by their relatively recent or very recent discover (e. g. Olecryptotendipes Zorina 2007; Arctosmittia Zelentsov 2006). One advantage of our database is that any additional information available on references not used in the original dataset can be simply added to the information provided in Supplementary data. Compared to pre-existing information (i. e. , from  Tachet et al. , 2010) , the data that we compiled at the genus level resulted in significant differences in the separation of Chironomidae subfamilies. This suggests that differences in specialisation among Chironomidae occur primarily at higher levels of taxonomic resolution (genus and species).
, 1983; Bitušík et al. , 2006; Lencioni et al. , 2007). Averaging trait affinities of Chironomidae subfamilies showed that they were distinct from each other considering some traits (e. g. maximal body size, food type); although the great trait diversity within each subfamily suggests that the subfamily level operating in the database of  Tachet et al. (2010)  is not appropriate. The latter database points out the ecological redundancy in the Chironomidae family and subfamilies, which may be simply due to the averaging operation, that masks the real trait diversity of Chironomids. Such false redundancy was highlighted by others (e. g. Lenat and Resh, 2001)  and may compromise the results of studies that attempt to recognise Chironomidae faunistic patterns using a lower taxonomic level. The differences between the subfamily/tribe trait patterns gathered at low and high levels of taxonomic resolution (TDB and GDB respectively) were clear for pH tolerance, transversal distribution in the river channel, reproduction types, resistance forms, and, to a lesser extent, food types and life-cycle duration.