dataesr/scientific-paragraphs-categorization huggingface dataset:
Spatial integrated models as a promising tool for conservation When establishing species conservation status for large-scale environmental policies, discarding some datasets from the analysis can reduce the reliability of the ecological estimation  (Bischof, Brøseth, & Gimenez, 2016). Using multiple datasets into integrated spatial models helps to overcome some limitations present when using separated information sources (e. g. limited spatial or temporal survey coverage,  Zipkin & Saunders, 2018; Isaac et al. , 2019). However, caution should be taken as integrating data require additional modelling assumptions, for example, assuming population closure over a longer time period in our case  (Dupont et al. , 2019; Fletcher et al. , 2019; Farr et al. , 2021; Simmonds et al. , 2020). Integrated spatial models are flexible tools that can include more than two datasets  (Zipkin & Saunders, 2018) , and various types of data that enlarge the scope of usable information (presence-absence  (Santika et al.
Figure1Graphical description of the spatial integrated model (SIM) that combines spatial capture-recapture (SCR) and distance sampling (DS) of bottlenose dolphins. The SIM is a hierarchical model with three processes: i) latent population size E(N) and density λ informed by an inhomogeneous point process, ii) DS observation process that links the line transect dataset to the latent density surface and iii) SCR observation process that links the detection histories to the latent density. The observation process is stochastic according to detection probability. For DS model, the observed group size n obs is a binomial draw in the latent abundance N at the sample grid cell. For SCR model, observing an individual.
Acknowledgements The  French Ministry in charge of the environment (Ministère de la Transition Energetique et Solidaire)  and the  French Office for Biodiversity (OFB)  funded the project SAMM that performed the aerial line transects. The PELAGIS observatory, with the help of the OFB, designed, coordinated and conducted the survey:  Emeline Pettex ,  Ghislain Doremus  and  Olivier Van Canneyt. We thank all the observers:  Léa David  (cruise leader),  Eric Stéphan  (cruise leader),  Thomas Barreau ,  Ariane Blanchard ,  Vincent Bretille ,  Alexis Chevalier ,  Cécile Dars ,  Olivier Dian ,  Nathalie Di-Méglio ,  Emilie Durand ,  Marc Duvilla ,  Emmanuelle Levesque ,  Alessio Maglio ,  Marie Pellé ,  Morgane Perri  and  Sandrine Serre. We are indebted to all aircraft crew members of  Pixair Survey  and the logistic partnership SINAY.
SWAN and Data Pre-processing For the creation of a data set with matching LR and HR images, we compute various quantities including significant wave height H S , peak wave period T P and mean wave direction θ with the third-generation spectral wave model SWAN (Simulating WAves Nearshore)  (Booij et al. , 1999)  for a part of the coastal area near Biarritz (Fig. 1 ). The area of interest is nested inside a coarser grid that is forced by homogeneous spectral boundary conditions taken from the HOMERE hindcast database  (Edwige et al. , 2013) , at the location of the Donostia buoy (i. e. , around 35km from Biarritz in a water depth of about 450 m). The wave data set, which is considered for this work covers a two-year time period spanning from January 01, 2018 to December 31, 2019. For the HR nested grid, we chose a grid with 160 x 160 quadratic cells (8 km in x-and y-direction with ∆x = ∆y = 50 m), which is of sufficient resolution to capture small variations in the wave regime, while enabling down-scaling by multiples of two.
Consequently, the corresponding LR grids are of size 40 x 40, 20 x 20, and 10 x 10. The bathymetry data was obtained from the publicly-available 0. 001 • precision Digital Terrain Model "MNT bathymétrique de façade Atlantique" provided by the French Service Hydrographique et Océanographique de la Marine (SHOM) (SHOM, 2015) and interpolated with Octave  (Eaton et al. , 2020)  to the various grid sizes. With the objective to demonstrate the feasibility of the application of neural-networkbased super-resolution to ocean wave modeling, we use basic, but still realistic settings for the SWAN computations. In particular, wind forcing, wind growth, and white-capping are not taken into account. The water level was set constant and equal to the mean wa- ter level 2. 25 m. Depth-induced wave breaking was modeled with constant values α = 1 and γ = 0.
Data Augmentation Improvements of the predictions made by the neural network can be achieved though an artificial increase of the size of the data set by modifying the labeled data in a real--4-manuscript submitted to Geophysical Research Letters istic way. This is a common procedure, especially in the area of image recognition  (Perez & Wang, 2017). As an example, a picture of a recognizable object can be mirrored horizontally or the brightness and contrast of the image can be changed, but the object in the picture would still be recognizable. These artificial modifications help the neural network to generalize better and consequently enhance the results  (Shorten & Khoshgoftaar, 2019). In this study, we perform data augmentation on the significant wave height data set by adding a random uniformly distributed offset between zero and five meters to each data instance, to artificially account for wave heights in different regimes.
Summary and discussion The DSC/MS model proposed by  Fukami et al. (2019)  is able to reconstruct highresolution features of various sea state variables given a low-resolution image. Overall, this approach has the potential to reduce computation time for forecasts considerably. After a high, but only single computational effort of training the model, predictions can be obtained 50-times faster, compared to a standard high-resolution SWAN computation, with good accuracy. Furthermore, the speed and accuracy presented here are probably lower bound estimates, which can be ameliorated with a more elaborated pre-processing routine, an improved model architecture and careful training data sampling. Also, existing hindcast databases could be used to train the model extensively. Additionally, different loss equations and other neural network architectures like GANs, could improve the results substantially.
Data Availability For the creation of the low-and high-resolution sea state quantities we used the third-generation spectral wave model SWAN  (Booij et al. , 1999), version 41. 31. It was forced by data from the HOMERE hindcast database  (Edwige et al. , 2013)  at the location of the Donostia buoy covering the time range from January 1st 2018 to December 31st 2019. Bathymetry data from the 0. 001 • Digital Terrain Model "MNT bathy-métrique de façade Atlantique" provided by the French Service Hydrographique et Océano-graphique de la Marine (SHOM)  (SHOM, 2015)  and interpolated with Octave version 5. 2. 0  (Eaton et al. , 2020). The Neural Network in this paper is based on the code and the implementation of  Fukami et al. (2019). Pre-processing and training was done entirely in Python 3. 9.
7, using mostly the following libraries: Keras 2. 4. 3  (Chollet & Others, 2015)  with Tensorflow 2. 4. 1 as a backend  (Abadi et al. , 2015) , Pandas 1. 3. 4  (Reback et al. , 2021) , and Numpy 1. 19. 2  (Harris et al. , 2020). For the creation of the figures Matplotlib 3. 5  (Hunter, 2007)  was used. The SWAN, pre-processing and training scripts are available through GitHub under https://github. com/janfer95/SR on SWAN  (Kuehn et al. , 2022).
Acknowledgments This research was carried out under the framework of the joint laboratory KOSTARISK, which is part of the  E2S UPPA program  managed by the  French National Research Agency  ( ANR-16-IDEX-0002 ) and supported by the  French Government's "Investissements d'Avenir" (PIA). The  joint laboratory KOSTARISK  is co-funded by  E2S UPPA , the  AZTI Foun-.
This will enable to obtain more informative results and enhance the comparison with studies based on alternative methodologies. Morphological data inputs ought to be in NEXUS format  (Maddison et al. , 1997) , format used in most phylogenetic reconstruction analysis softwares (https://evolution. genetics. washington. edu/phylip/software. html#Parsimony) such as PAUP, PHYLIP, Mr Bayes and Mesquite. Note: Some authors use continuous variables such as for example craniodental measurements and angles between anatomical landmarks as proxy of characters. The coding of these pseudo-characters generally normalized is arguable for phylogenetic reconstruction. This type of continuous data is better treated with conventional morphometric multivariate statistical techniques  (Sokal and Rohlf, 1995) , or agglomerative techniques such as UPGMA or Neighbor-Joining  (Rohlf and Sokal, 1981; Saitou, 1991)  emphasizing overall phenetic similarities based on distance metrics  (Sokal and Sneath, 1963).
Execute 16 MP runs and 16 corresponding bootstrap runs With Data S1 and the PAUP software execute 16 MP runs corresponding to 16 different evolutionary tree scenarios with two settings for weights (equal weight and Rescaled Consistency Index RC reweight), four character types  (Fitch, Wagner, Camin-Sokal or Dollo) , and two settings for multistate taxa (uncertainty or polymorphism). The runs can be executed with a search algorithm in two phases: one phase with equal weights and one with characters reweighed. We show below the relevant dropdown menus used to execute the 2 phases. Note: With regard to the computational MP tree search methods, there exists three algorithms available in PAUP: Exhaustive Search: exact algorithm that evaluates every possible tree and can be used for a maximum of 12 terminal taxa.
Branch-and-Bound Search: also an exact search option that can be used for more than 12 taxa; however, this algorithm works computationally for data sets of a limited size. Heuristic search: when the processing with the branch-and-bound algorithm is not feasible with a large data set, the only alternative to process the data is by heuristic search that operates by hill-climbing methods, and is computationally effective. In  Caparros and Prat (2021)  all the runs were conducted by heuristic search with simple stepwise addition and TBR branch swapping. For further explanations of the search parameters consult the PAUP manual (http:// phylosolutions. com/paup-documentation/paupmanual. pdf). a. MP runs corresponding to 8 scenarios with initial equal character weights In a first phase, execute 8 equal weight runs with the appropriate search method and the following parameters: i.
d. Output After an MP run PAUP provides two types of output: i. Tree(s)-cladogram(s). The run may generate one most parsimonious tree or multiple trees that are also most parsimonious. A run producing more than one most parsimonious tree can be expressed for analytical purposes under the form of a consensus tree (see troubleshooting problem 2). We recommend to use the 50% majority rule consensus tree of the resulting minimal trees, the most robust commonly used method that allows one to retain all groups found in over half of the rival trees. ii. Analytical descriptive parameters. In addition to the most parsimonious tree(s) and eventual consensus tree resulting from the analysis, extensive descriptive output data are available for each run from the Describe Trees menu. The following information is available to conduct an in-depth analysis of the results: main result parameters (along with the cladogram, number of most parsimonious trees, number of steps, tree Retention Index and Consistency Index; these may be summarized in a table for all the 16 runs as for example in  (Caparros and Prat, 2021 , Table  1 ), branch lengths and linkages of terminal taxa and nodes (LCAs) to other nodes, possible character-state assignments to internal nodes, data matrix and reconstructed states for internal nodes on the basis of the optimization algorithm selected to resolve ambiguities (ACCTRAN or DELTRAN), character change lists showing at which nodes/terminal taxa each character changes, apomorphy lists showing for every branch which characters change and how, and character diagnostics list providing for each character the Minimum, Actual and Maximum number of steps (changes) allowing the computation of character consistency indices (ci), retention indices (ri) and rescaled consistency indices (rc).
c. Projection of individual characters on to the cladogram The PAUP software after conducting a run provides extensive detailed output data from the Describe Trees menu which are very useful in analyzing the anatomical features of individual character state changes and their relevant phylogenetic significance. However, PAUP does not provide an easy way to visualize synthetically this rich output material. In our opinion, the best software to visualize these results is MacClade which happens to be compatible with PAUP. Both the MacClade software  (Maddison and Maddison, 2005)  and the PAUP software  (Swofford, 2020)  use the NEXUS format, e. g. , one can set up a character data matrix in MacClade and execute the MP run in PAUP with this matrix, or vice-versa visualize with MacClade the tree output of a PAUP run from a given outsourced data set.
More generally, the question becomes how do we represent evolutionary relationships when the history of a set of taxa includes inheritance from multiple ancestors across hybridized taxa. Based on the analogy of hybrid species sharing multiple ancestral evolutionary histories, in  Caparros and Prat (2021)  we used a Phylogenetic Networks conceptual approach extensively employed in molecular biology to test the hypothesis of phenotypic hominin evolutionary reticulation. To establish a link between the MP and the PN analyses, we made two empirical assumptions: 1) apomorphous character-state changes represent analogous proxies of gene sequence changes evolving from common ancestors, and 2) the 3213 MP trees (MMPT) making up Data S3 (resulting from the parsimony PAUP run with apomorphies of Data S2) are methodologically analogous to gene trees from a set of species, and are thus used as input in the PN analysis.
Note: For a review of Phylogenetic Networks methods, we refer to the comprehensive syntheses of the field by  Huson et al. (2010)  along with the freely available application software SplitsTree  (Huson and Bryant, 2006), and Morrison (2011). 11. Steps used in  Caparros and Prat (2021)  as example of PN analysis With the MMPT Data S3 and using the SplitsTree software  (Huson and Bryant, 2006) , we computed the majority consensus tree, the consensus network and the reticulate network which we reproduce here in Figure  8. Read the MMPT Data S3 nexus file with the SplitsTree software and hit Apply. The sequence of steps with the dropdown menus to produce the 3 panels of Figure  8  are illustrated as follows: a. Consensus tree i. Screenshot for Consensus Tree run. ii. Screenshot of resulting Consensus Tree.
The ambiguity of the character at the level of H. antecessor shown in screenshot for trace show MPRs mode is resolved by DELTRAN. Note: A word of caution is required given that ACCTRAN or DELTRAN may result in numerous equivocal resolutions when there is substantial amount of missing data, as illustrated for example in Figure  S10  of  Caparros and Prat (2021). Problem 5 To conduct a proper Intermediate MP analysis, one way to measure the quality of an initial data set is to be able, in our opinion, to generate with Data S1 a most parsimonious tree RI after reweighing of at least 0. 80, and to get after this first run a number of apomorphies of at least twice the number of terminal taxa. Short of these two hurdles, it is very probable that the resolution of the most parsimonious scenario will be suboptimal to conduct a significant PN analysis given the poor quality of the initial data set.
(Dembo et al. , 2016) at Dryad Digital Repository https://doi. org/10. 5061/dryad. d7r4g. 2. The nexus matrix file Data S1 is made available here. Characters, character definitions, and character states are available from Supplementary Material(Dembo et al. , 2016)at https://doi. org/10. 1016/j. jhevol. 2016. 04. 008. Data S2 and S3 generated and analyzed in the study are available here: Data S2 in NEXUS format represents the 74 apomorphous characters selected from the most parsimonious scenario MPMAX executed with Data S1, while Data S3 represents the multiple MP trees (MMPT) that result from the intermediate MP run based on Dollo type characters and uncertainty setting for multistate taxa executed with the 74 apomorphies of Data S2.
ACKNOWLEDGMENTS We thank  Mana Dembo  and  Arne Mooers  for providing the workable Nexus file of the data set employed in the analyses of Caparros and Prat (2021), the findings of which are used as examples in the text. We thank  Josephine Herbelin /atelier Nous Travaillons Ensemble for her help in the graphic design of Figure 22. We express our thanks to professor Pascal Tassy from the  Museum national d'Histoire naturelle  for his teaching and numerous published works on cladistics which inspired our research in human Phylogenetic Systematics. We would also like to express our appreciation to the editor of iScience  Dr. Alessio Bolognesi  for his guidance during the review process of Caparros and Prat (2021). We thank  Dr. Shawnna Buttery  and her colleagues of  STAR Protocols  for guiding us through the editing and publishing process, and the three anonymous reviewers of the present paper for their comments, queries, and very helpful suggestions.
Bioinformatical analysis Verticillium dahliae PLL sequences were retrieved using available genome database (ftp. broadinstitute. org/). SignalP-5. 0 Server (http://www. cbs. dtu. dk/services/ SignalP/) was used for identifying putative signal peptide and putative glycosylation sites were predicted using NetNGlyc 1. 0 Server (http://www. cbs. dtu. dk/services/NetNGlyc/) and NetOGlyc 4. 0 Server (http://www. cbs. dtu. dk/services/NetOGlyc/). Sequences were aligned and phylogenetic analysis was carried out using MEGA multiple sequence alignment program (https://www. megasoftware. net/). Homology models were created using I-TASSER structure prediction software (https://zhanglab. ccmb. med. umich. edu/I-TASSER/) and UCSF Chimera (http://www. cgl. ucsf. edu/chimera/) was used for creation of graphics.
Cloning, heterologous expression and purification of VdPelB V. dahliae PelB coding sequence (VdPelB, UNIPROT: G2X3Y1, GenBank: EGY23280. 1), minus the signal peptide was amplified using cDNA and gene-specific primers. VdPelB mutants were generated using cDNA and specific primers carrying mutations (Table  S1 ). Cloning, heterologous expression in P. pastoris and purification of VdPelB was done as previously described in Safran et al. [35].
Crystallization of VdPelB VdPelB was concentrated at 10 mg. mL -1 in Tris-HCl pH 7. 5 buffer. Crystallization conditions were screened using the sitting-drop vapor-diffusion method. VdPelB (100 nL) was mixed with an equal volume of precipitant (1:1) using Mosquito robot (STP Labtech). The crystals that resulted in best diffraction data were obtained with 0. 1 M MIB (Malonic acid, Imidazole, Boric acid system) at pH 8. 0, with 25 % PEG 1500 as the precipitant (condition B5 from the PACT premier kit, Molecular Dimensions, Sheffield, UK) after 1 month. Optimization was realized using the hanging drop vapor-diffusion method forming the drop by mixing 1 µL of precipitant solution with 1 µL of the enzyme. The large beam-like crystals were cryoprotected by increasing PEG 1500 concentration to 35%, before mounting them in a loop and flash-cooling them in liquid nitrogen.
Structure solution and refinement The structure of VdPelB was solved by molecular replacement using Phaser  [40]. The data were phased using pectate lyase BsPelA (PDB: 3VMV, Uniprot D0VP31), as a search model  [41]. Model was build using Autobuild and refined using Refine from PHENIX suite  [42]. The model was iteratively improved with Coot  [43]  and Refine. The final structure for VdPelB has been deposited in the Protein Data Bank (PDB) as entry 7BBV.
Separation of OGs was done as previously described using an ACQUITY UPLC Protein BEH SEC column (125Å, 1. 7 μm, 4. 6 mm x 300 mm)  [44]. The intensities were defined as the area under the curve, for each OG. Peak areas were clustered by hierarchical clustering with complete linkage on the euclidian distance matrix and visualized in the heatmap-package using R version 3. 6. 0.
Simulations were replicated 7 times for a total production run time of 1. 05 μs per complex. Replicates differed with respect to the random particle velocity sets computed under the nVT ensemble. For analysis, the first 50 ns of each production run were discarded as equilibration time. Inhouse Python 3 scripts implemented using Jupyter notebooks  [51]  were used to carry out analyses. Figures were created and rendered with Matplotlib  [52]  and VMD (Visual Molecular Dynamics)  [53].
VdPelB has a right-handed parallel β-helix fold VdPelB was crystallized and its 3D structure was determined using X-ray diffraction. VdPelB crystallized in monoclinic P 1 21 1 asymmetric unit. Four data sets, collected from the same crystal at 1. 2 Å resolution, were integrated, scaled and merged. There are two molecules in the asymmetric units: chains A and B are highly similar with a Cα root mean square deviation (rmsd) value of 0. 227 Å (Fig. S2A ). The VdPelB structure consists of 298 amino acids (aa) with 18 aa at the N-terminus and 27 aa at the C-terminus that were not resolved because of poor electron densities, while overall electron densities were well defined. While no N-glycosylation sites could be revealed on the VdPelB structure, six O-glycosylation sites carrying mannose are visible for each molecule: T22, T44, T45, T46, S48 and T54, in accordance with the shift in size previously observed (Fig.
Interestingly N-and C-terminus tails pack against PB2 (Fig. 2A ). There are only two Cys (C25 and C137) that do not form a disulphide bridge. Sequence and structural alignments show that VdPelB belong to the PL1 family. VdPelB shares the highest structural similarity with BsPelA (PDB: 3VMV), with 30. 06% sequence identity and Cα rmsd of 1. 202 Å. The second-best structural alignment was with DdPelC (PDB: 1AIR) with 24. 20% identity and Cα rmsd of 1. 453 Å  [41, 60]. Both of these structures lack the long T3 loop described in A. niger pectate lyase (AnPelA, PDB: 1IDJ, Fig. S2B  and S5 )  [15]. The putative active site is positioned between the T3 and T2 loops (Fig. 2A  and B ).
FECs are widespread across data types and methods, they can be used to evaluate the extent and context-specificity of functional annotations in a data-driven manner. For example, FECs suggest that B cell markers can be decomposed into shared primary markers (10-50 genes), and tissue-specific secondary markers (100-500genes). In addition, FECs suggest the existence of functional modules that span a wide range of the genome, with marker sets spanning at most 5% of the genome and data-driven extensions of Gene Ontology sets spanning up to 40% of the genome. Simple to assess visually and statistically, the identification of FECs in performance curves paves the way for novel functional characterization and increased robustness in the definition of functional gene sets.
Introduction Characterizing the functional properties of genes across conditions, species and other perturbations is a central challenge in post-genome biology. As datasets increase in size and complexity, exploiting methods from machine learning and AI research has become increasingly valuable to parse vast data collections for subtle convergent signals  (Berger et al. , 2013; Le, 2020; Libbrecht and Noble, 2015; Mahood et al. , 2020; Mostafavi et al. , 2008). However, the complexity and variety in formalism of these methods create interpretation problems of their own. Establishing a consensus framework to evaluate prediction accuracy and identify features driving prediction accuracy has been essential to progress, often using systematic data resources, and with well-defined performance metrics.