dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, the average identity and similarity (based on pairwise alignments) between the set of 307 sequences to classify and the set of the 240 sequences generating the models are 26. 35% and 36. 73%, respectively. In order to capture conserved motifs likely to be of functional relevance for domain sequences, we built highly specific clade-centered models. Indeed, we are not concerned with improving domain annotation of divergent sequences  [4]  but, instead, we aim at classifying proteins sharing their domain architecture from the perspective of their function and expect their functional motifs to be represented by positions in the sequence that remain conserved on representative subsets of homologs. In the analysis of CPF sequences, we focused on the FAD binding domain of DNA photolyase from Pfam version 31 (accession number PF03441), due to its functional importance for CPF activity.
More in detail, we selected all 4615 sequences which belong to the FULL alignment in Pfam. For each one of them, a CCM has been constructed with the command mentioned above. Finally, our model library M FAD for the FAD-binding domain comprises 3735 CCMs, because for some sequences we could not collect a minimum of 20 homologs. The pipeline for M FAD construction is depicted in Fig. 1 , top. For the analysis of WW domains, we constructed models from the set of sequences in Pfam version 32 (accession number PF00397). They have been extracted from the reference proteome RP15 comprising 5634 sequences. We could construct 3733 models based on at least 20 homologs.
To reduce the number of models in the library, we selected the 5 best matching models for each of the 346 sequences and obtained a total of 1244 CCMs considered for the construction of the ProfileView space of WW domain sequences. The number of best matching models was increased from 3 (value used for the CPF analysis) to 5 to consider more WW domain models, that is 1244 versus the 845 obtained with k=3. The idea being that when the dataset of sequences to be classified is very diversified, as in the case of the WW domain family, the number of models should be large (> 1000) to explain diversity, eventually through non linear effects captured by multiple conserved motifs defining the ProfileView space.
It is important to notice that given two models and a position, the score assigned to that position in the hhblits pairwise alignment of the models depends on the reliability of the querytemplate alignment (https://github. com/soedinglab/hh-suite/wiki). Depending on which one of the models is considered as a template, the scores assigned to the same position might vary (the confidence values are obtained from the posterior probabilities calculated in the Forward-Backward algorithm of hhblits). In particular, hhblits is warning that the confidence score in CRY Pro. While the CRY Pro motif records the coloured dot for a matching with NCRY, this is not true for the NCRY motif. Indeed, while the two positions align together with a confidence score of 0. 8 for the CRY Pro model taken as a template, they also align together when the NCRY model is taken as the template but with a confidence score dropping at 0.
Output files of ProfileView ProfileView produces several output datasets: the model library, the ProfileView tree, the list of representative models associated to internal nodes of the tree. Also, ProfileView provides to the user the possibility to choose a list of representative models to be compared. The first model of this list is considered as a reference model. A first output describes and provides the logo reporting all conserved positions together with a list of coloured dots (possibly circled) obtained after a pairwise comparison of a model in the list with the reference model (see Methods above; see for example Fig. 3AB ). A second output describes and provides the logo reporting an intermediate representation of the positions in the reference model, namely reporting all conserved positions in the associated motif and all positions that are not conserved in the reference model but that are conserved in some other model in the list (see for example Fig.
Implementation and software availability ProfileView has been developed and tested under a UNIX operating system, using Bash, Python, and R scripts. It exploits GNU parallel  [53] , if available on the system, in order to perform some jobs in parallel. It is implemented in three main parts carrying out the following modules of the pipeline: the construction of a single-domain model library, the generation of the ProfileView tree along with its representative models, the comparison of selected representative models and the identification of conserved positions/motifs. ProfileView is available at http://www. lcqb. upmc. fr/profileview/ under the version 2. 1 of the CeCILL Free Software License. A restrained access is momentarily set. Once the article is accepted, all information will be freely accessible.
Acknowledgments LabEx CALSIMLAB (public grant  ANR-11-LABX-0037-01  constituting a part of the  "Investissements d'Avenir" program  -reference :  ANR-11-IDEX-0004-02 ) (RV); the  Institut Universitaire de France  (AC); access to the  HPC resources of the Institute for Scientific Computing and Simulation (Equip@ Meso  project  -ANR-10-EQPX-29-01 ,  Excellence Program  " Investissement d'Avenir "). We thank  Simona Cocco  and  Jérôme Tubiana  for sending us the dataset of WW sequences used in their study.
Simulation results One hazardous task in an airship mission is the loading and unloading of the heavy load by means of motorized winches while the airship hovers above the ground. In this section, we test the proposed framework in numerical simulations that have been developed using MATLAB/Simulink. Simulation experiments are conducted on a suspended mobile CDPM prototype having the following characteristics. We use n c ¼ 8 cables to manipulate a suspended platform in the full n ¼ 6 dimensional task space, thus resulting in two redundant cables that can be used to better distribute the tensions. The suspended platform is a cube with a total mass of m ¼ 100 kg. Its inertia moment matrix is I p ¼ diagð78; 78; 27Þ expressed in the platform body-fixed frame R P.
Imaging spectroscopy holds great promise to map canopy species by air, as has already been proven by a series of seminal studies  [12] [13] [14] [15]. However, to date only few case studies have been published, and to our knowledge there is no clear evidence that airborne imaging spectroscopy is sufficiently reliable and transferable to achieve operational tree species mapping in the absence of a large ground sampling effort to build a training data set for every new site to be mapped. Practical solutions for enhanced reliability and operationality of airborne tree species mapping include the collection of spectral libraries focusing on hyperspectral signatures from different tree species, which could be stored and organized in shared spectral databases like the Specchio database  [16, 17].
For the purpose of this study, a field survey was conducted to build a large ground truth dataset. Easily discernible crowns were first delineated manually on the Canopy Height Model derived from LiDAR (Light Detection And Ranging) data with the help of a high resolution (10 cm) RGB (Red, Green, Blue) mosaic. The correct delineation of these Individual Tree Crowns (ITC) was then validated in the field and the corresponding species ascertained. The 20 most represented species i. e. , those with at least 24 individuals tagged, were selected for the purpose of the present study, totalling 1297 trees (Table  1 , Figure  1 ). On the first flight date, all crowns were imaged twice (once each of two neighbouring flight lines) and the cumulated crown area considered for the single date analysis was 112,313 m 2.
Hyperspectral Data Imaging spectroscopy was acquired with a Hyspex VNIR-1600 (Hyspex NEO, Skedsmokorset, Norway) sensor mounted alongside a Riegl LMSQ780 laser scanner (See Appendix D). The 160 spectral bands cover the range from 414 nm to 994 nm (i. e. , visible to near infrared) with a spectral sampling distance of 3. 64 nm. The entire spectral range was used in this study (no spectral masking). The King Air B200 airplane flew at an average altitude of 920 m. The two flights took place on cloudless days on the 19 th (from 15:00 to 17:00, solar time) and the 20 th (from 16:00 to 17:00, solar time) September 2016. The second flight covered approximately one third of the area imaged on the first date. Images were orthorectified and georeferenced (level L1b see below) at 1 m spatial resolution with the PARGE software  [27]  using the canopy DSM (Digital Surface Model) produced from the LiDAR point cloud.
Impact of Flight Line Overlap Individual flight lines were not mosaicked prior to crown extraction. In this way the full spectral variability of the crowns species in the images was preserved (and notably variability among flight lines induced by changes in the atmospheric properties and solar angle). Hence, due to a 50% overlap between flight lines on date 1, most of the crowns were extracted from two neighbouring flight lines. On date 2, only about 40% of the crowns were imaged twice. The effect of this lower redundancy on date 2 imagery is later considered. We specifically evaluated the effect of prior mosaicking for an arbitrarily selected processing pathway (L1c Spa. F, SHAOT). To this end, we mosaicked the strips using center cropped method in the PARGE software. Hence, mosaicking was done by selecting the most central pixel of any two overlapping flight lines and not by averaging reflectance values of co-occuring pixels.
Classification We used a LDA classification algorithm that is well adapted to classification problems in high dimension feature spaces provided that the number of observation is larger than the number of feature in the least abundant class  [36]. LDA also has the advantage of not being affected by the possible inconsistent relative abundance of classes in training set and test set, provided prior probabilities of class membership are set to 1 for all classes as done in the present case. This method maximizes the ratio of between-class variance to the within-class variance. The "Mass" package version 7. 3-50  [37]  of R language  [38]  was used to compute LDA method. Good performance of LDA has been reported for tropical tree species classification from hyperspectral data in various studies  [14, 15, 20].
In the first approach, the LDA classifier was trained using a random selection of 70% of the tree crowns (Table  1 ) using hyperspectral data from the 19 September for all processing levels. For every tree included in the training set both image segments (from neighbouring flight lines) were selected if available. The remaining 30% of crown segments were used to evaluate the performance of the classifier. This procedure was repeated 20 times to limit the impact of the random draw of the crowns used in training and testing sets. Classification accuracy was evaluated for all image-processing pathways using the same random sampling. The choice of using LDA can be questioned, as many alternative machine learning algorithms can be used as well. These state of the art classification algorithms include Random Forest (RF)  [39] , Support Vector Machine (SVM)  [40]  or neural network.
The goal of our study was not to compare of the performances of the different algorithms available for tree species discrimination, or to identify the most suitable as proposed by  [15, 20, 41]. We compared the performances of LDA with a "state of the art" machine learning algorithm, RF. The RF classification algorithm was applied, following the exact same training and validation scheme as defined for LDA. The RandomForest R package  [42]  was used for this task, and 1000 trees were defined in the forest during the training stage. The results obtained with the RF algorithm are described in Appendix C Table  A2. In a second approach (cross date validation), the classifier was trained using hyperspectral data from 19 September, excluding any crown imaged on 20 September and the prediction was applied to crowns imaged on 19 September (single date case) and on 20 September (multidate case).
First Setting (Single Date) Analysis of the impact of atmospheric correction was evaluated on all processing levels for a single data set to check the consistency of the methods. Standard deviation of accuracy at pixel level computed for the 20 runs was typically around 1-1. 5% giving a standard deviation of the reported mean accuracy of 0. 2-0. 3%. The pre-processing pathways that yielded the most accurate classifications (both at pixel and pixel-majority level) were L1b Spa. F, norm. , Sha. R followed by L1c Spa. F, SHAOT, norm. ,Sha. R (Table  4 ). Figure  4  shows a scatter plot of R 2 from the analysis of variance against the LDA accuracy. A slight positive correlation can be observed. The majority vote approach systematically outperformed the pixel classification. The majority vote was more efficient to retrieve species identity than a classifier based on a mean crown spectrum (see Appendix A, Table  A1 ).
The SHAOT method for AOT recovery had not been previously tested for tree species classification purposes. SHAOT correction did not increase ANOVA R 2 and notably reduced the variance explained by species identity in the 400-550 nm range (Figure  3 , red and orange lines). However, the SHAOT method included in the atmospheric correction slightly improved tree species discrimination achieved by the LDA, at pixel (0. 5%) and crown level (1. 0%). Overall, the results of this study are very encouraging as a number of improvements are foreseen for the near future. First, improved matching of tree crowns (mostly delineated on the LiDAR derived canopy height) and pixels may be achieved by better data co-alignment between lidar and spectral data as shown in  [55]. Second, only VNIR data (400 nm to 1000 nm) were used.
Ferreira et al. [41]  reported that the addition of SWIR (Short-Wave Infrared) data improved the identification of species of interest by 13% in their study. SWIR data was acquired simultaneously and will be used to enrich species spectral characterization. Third, LiDAR derived features such as tree height [56], crown density and crown shape may constitute complementary features to include in the species classification  [57]. Additional information related to pulse distribution or other features extracted from the lidar wave form have also proven to be useful  [58]. Ultimately, more advanced classification methods such as SVM or Convolutional Neural Networks may provide some additional improvement in classification accuracy. Discriminating trees species becomes more difficult as the number of species increases  [45].
Detecting targeted species within a large set of species, many of which not being identified, is a slightly different problem from the one addressed in this study, in which a predetermined subset of species had to be sorted out. The former problem is particularly challenging in the case of hyperdiverse canopies  [13]. Yet it is an unavoidable step to be taken before a reference hyperspectral database for the vast number of tropical species becomes available. The extreme species richness of tropical forests makes recognition of all species in the canopy a very challenging enterprise, even if those species constitute only a subset of the tree species diversity. Futher complication comes from the fact that species abundance is extremely variable and that some species are globally or locally rare  [1].
However mapping diversity patterns does not necessarily rely on species identification. Spatial patterns of species diversity can correlate to patterns of spectral diversity  [59, 60]. The majority filter per crown used here requires a pre-segmentation of individual crowns. In the present case, crown segmentation was performed manually on high-resolution images and validated in the field. However, automatic segmentation of tree crowns from point clouds is becoming effective even in dense tropical forests  [61]  and can be further refined using hyperspectral data  [62]. Therefore high throughput mapping of selected species in complex tropical forest canopies seems to be a realistic goal for the near future. One limitation to automatic detection of targeted species may stem from locally abundant lianas and epiphytes, which can strongly affect or even mask out the tree's specific spectral signature.
We, therefore, recommend to systematically conduct atmospheric compensation, in order to gradually build databases of standardized species spectral signatures. Such databases will be required to effectively move towards high throughput species mapping of tropical forest canopies, by allowing to circumvent or significantly alleviate the site specific calibration stage of the classifier. Finally, our results showed that the use of raw flight lines images improved both training and classifying over the use of a mosaic image. We recommend to systematically examine the benefit of using overlapping images for species recognition.
2014 , Willmott et al. 2017) , wing colour pattern evolution  (Jiggins et al. 2006) , and biogeography  (Elias et al. 2009 , Dasmahapatra et al. 2010 , De-Silva et al. 2016 , 2017 , Chazot et al. 2016b , Chazot et al. 2018). In this study, we generate the first species-level molecular phylogeny of the entire tribe, providing a large and densely sampled (340 species included out of 393 currently recognized) phylogenetic dataset for a Neotropical insect clade that underwent diversification during the last ~30 million years  (Wahlberg et al. 2009 , De-Silva et al. 2017). We investigate the dynamics of diversification and dispersal rates in Ithomiini, focusing on the turnover of ecological conditions in Western Amazonia and the concomitant Andean uplift during the Miocene. Specifically, an important role for Andean uplift and the Pebas wetland would be supported if: (1) During the Pebas period: (a) Andean diversification largely exceeded Amazonian diversification, due to increased diversification in the Andes driven by the evolving ecological gradient and uplift dynamics and/or a reduced diversification rate in Amazonia accompanying the loss of terrestrial habitats; (b) interchanges between the Andes and Amazonia were reduced; (c) interchanges between the Central and the Northern Andes were reduced, because of the existence of the WAP.
Molecular data We compiled sequences of 1474 Ithomiini individuals (see Appendix S1. 1 in Supporting Information) that included 718 sequences newly generated for this study and 3147 previously published sequences  (Whinnett et al. 2005 , Mallarino et al. 2005 , Brower et al. 2006 , Elias et al. 2009 , De-Silva et al. 2010 , Chazot et al. 2014 , De-Silva et al. 2016 , Chazot et al. 2016b , De-Silva et al. 2017 , Chazot et al. 2018). We used a concatenation of nine gene fragments, a mitochondrial fragment spanning genes COI-tRNA-COII, and fragments of nuclear genes EFIα, Tektin, CAD, RPS2, MDH, GAPDH, representing a total of 7083 bp  (Wahlberg & Wheat 2008). Primers and PCR conditions followed  Mallarino et al. (2005)  and  Wahlberg & Wheat (2008). We obtained at least one gene fragment for 340 species out of 393 currently known in the group, which represents 87% of the known species richness of the tribe.
Tree topology and time calibrations We used PartitionFinder v. 1. 1  (Lanfear et al. 2012 ) to find the best gene partitioning and substitution models (Appendix S2. 1, Table  S2. 1). Then we generated a phylogeny under maximum likelihood inference (ML), using IQ-Tree software as implemented in the W-ID-TREE server  (Trifinopoulos et al. 2016 , Nguyen et al. 2014 ) in order to obtain a tree topology (Appendix S2. 2). Finally, this topology was time-calibrated using BEAST v1. 8. 2  (Drummond et al. 2012). We used four secondary calibrations from  Wahlberg et al. (2009) 's phylogeny of Nymphalidae that were placed outside of Ithomiini, and six secondary calibrations consisting of maximum ages of hostplant (Solanaceae) lineages from  De-Silva et al. (2017) , and placed on Ithomiini lineages (Appendix S2.
Diversification in the Andes To investigate the pattern of diversification in the Andes with respect to the rest of the Neotropical region, we classified species as Andean or non-Andean, based on a combination of published and unpublished (i. e. , databases generated from museum collections and our own field collections) georeferenced distribution data and elevation ranges of species (Appendix S1. 2). We used models of trait-dependent diversification implemented in ClaSSE  (Goldberg & Igić 2012) , to compare the rates of speciation, of extinction and of transition between the Andean area and the non-Andean regions  (Chazot et al. 2016b , Beckman & Witt 2015, Appendix S3. 2). Ten models were fitted on the full tree and the core-group alone to account for the major diversification shift at the root of the core-group.
S2. 1 Gene partitioning and substitution models Prior to phylogenetic analyses, gene partitioning and substitution models were estimated using PartitionFinder v. 1. 1  (Lanfear et al. 2012). We divided all gene fragments into codon positions and allowed all partitions to be tested. Only substitution models available in BEAST were tested. The models of linked partitions had a better fit than unlinked partition schemes, hence the former was used. The best linked partition scheme contained 13 partitions (Table  S2. 1). We generated a phylogeny under maximum likelihood inference (ML), using IQ-Tree software as implemented in the W-ID-TREE server  (Trifinopoulos et al. 2016 , Nguyen et al. 2014 ) (Figure  S2. 1). The dataset was partitioned according to the results above (Table  S2. 1), and substitution rates modelled using GTR+I+G.
S3. 1. 3 Fit of TreePar diversification models We performed TreePar  (Stadler 2011)  analyses on the core-group and the backbone. For each case we used 100 trees randomly sampled from BEAST's posterior distribution. For the backbone we included the times of divergence at which the core-group and the Melinaea-group diverged from the backbone to keep track of these cladogenetic events. We split time into time bins of four million years, for which diversification rates was estimated. We used these estimates to obtain a second estimate of diversification rate "through time". We did not fit TreePar on the Melinaea-group since it is only 1 million years old. We allowed diversification rate to be negative but we did not allow mass-extinction events. Results are shown in the manuscript (Figure  2 ).
S3. 1. 4 Fit of BAMM diversification models We compared our estimates of diversification dynamics obtained with MEDUSA,  Morlon et al. 2011  and TreePar with results obtained from BAMM  (Rabosky et al. 2014)  We ran BAMM v. 2. 5. 0 on the whole tree of Ithomiini. We did not specify a global sampling fraction. Instead, we used a refined sampling fraction at the genus level. We ran BAMM for 30 million generations sampling every 3000 generations. The results of the MCMC were then analyzed in the R package BAMMTOOLS  (Rabosky et al. 2014). First, we checked for MCMC convergence using trace plots and ESS statistics using the R package CODA, after removing 10% of the chain as burnin. Second, we investigated the number and configurations of diversification rate shifts. We report here the set of credible shifts.