dataesr/scientific-paragraphs-categorization huggingface dataset:
All genome submissions are processed through several analysis workflows for the syntactic and functional annotation (a detailed list of software and databases integrated in the MicroScope workflows is given in  (8) ). Results of computational inferences, including the prediction of metabolic pathways from KEGG  (9)  or Meta-Cyc  (10) , are loaded into the MicroScope data warehouse and made accessible to biologists through a Web user interface (via authenticated or anonymous connections) providing a variety of analytical and visualization tools for comparative analysis and for the exploration of metabolic data. MicroScope supports collaborative expert annotation processes through the use of specific curation tools and graphical interfaces; it helps to develop hypotheses about specific genomes or sets of genes to be experimentally tested (examples are given in  (6) ).
Gene annotation editor The gene editor has also been improved to offer a more accurate form with functionalities for checking inconsistencies. The annotation fields are now dependent on the type of object being considered (e. g. CDS or RNA). The 'Note' field is editable and corresponds to the '/note' qualifier in GenBank/ENA files. Two new fields have been added: (i) 'Additional data' allows users to store multiple types of data such as: comments, interactions, phenotypes or SMILES formulas (ii) 'dbxref' is used to link external database identifiers related to the annotation of the gene. Before saving any new (or modified) gene annotation, the procedure allowing to detect annotation inconsistencies between the fields of the gene editor has been improved in order to pinpoint conflicts and/or mistakes (see the MicroScope annotation rules here: https://microscope.
Genome overview Following the modification of MicroScope PkGDB database schema, the management of assembly was improved by providing unique identifiers for each version which are linked to a consistent list of replicons, sequences and scaffolds/contigs that compose the assembly. The 'Genome Overview' functionality now summarizes information about the different replicons on the same page. Scaffolds/contigs and gene counts, curation status and other metrics are given for each replicon. The taxonomic lineage and the estimation of the genome completion and contamination with CheckM software (12) are also provided.
Update of the automatic annotation procedure In order to make the most of the spread of high-quality annotations, the MicroScope automatic annotation pipeline has been improved. The procedure triggers at the end of the functional annotation step, and automatically associates a functional annotation to each predicted CDS. It relies on different computational results such as sequence alignments with proteins from Swiss-Prot or selected reference genomes. To capitalize over new developments made in collaboration with the UniProt EMBL-EBI team, we integrated UniFIRE (The UniProt Functional annotation Inference Rule Engine, https://gitlab. ebi. ac. uk/uniprot-public/ unifire) in our automatic annotation procedure. UniFIRE is an open-source Java-based framework and tool to apply the UniProt annotation rules on given protein sequences  (13).
Two sets of rules are provided (i) the SAAS rules (Statistical Automatic Annotation System) which are generated automatically from expertly annotated entries in UniProtKB/Swiss-Prot, and (ii) the UniRules (The Unified Rule) which are devised and tested by experienced curators. In MicroScope, UniFIRE annotation results are available in the gene editor page in UniFIRE SAAS and UniFIRE UniRules datasets. Besides, in order to enhance the quality of information delivered to the users working on unfinished genome sequence, we have improved the procedure which maps annotated features of the earlier version of the genome assembly to the new one. The 'Annotation mapping' functionality now gives more precise information about the mapping between two versions of assembly. During this process, sequence alignments are performed to compare newly predicted genomic objects with those previously annotated.
Prediction of essential genes. Delineating a set of essential genomic elements that makes up a living organism helps to understand critical cellular processes sustaining life. A new 'essential gene' dataset from the Database of Essential Genes (DEG)  (16)  has been set up in MicroScope. It replaces the three original datasets of essential genes of the Nucleic Acids Research, 2020, Vol. 48, Database issue D583 model organisms Pseudomonas aeruginosa PAO1, E. coli K-12 and Bacillus subtilis 168. DEG contains records for bacterial and archaeal essential genes determined by genomewide screens. The experiments were performed under diverse conditions including those for survival, pathogenesis and antibiotic resistance. The DEG dataset has further been expanded with data from A. baylyi ADP1 and Neisseria meningitidis 8013, two highly curated genomes in MicroScope  (17, 18).
'Essential genes' results (obtained from amino-acid sequence alignments) are available for each CDS of all genomes integrated into MicroScope. Prediction of resistome. For a given genome, genes potentially involved in resistance to antibiotics are listed in the 'Resistome' section of the 'Comparative Genomics' menu. This prediction relies on the use of the Comprehensive Antibiotic Resistance Database (CARD). CARD is a manually curated resource containing high quality reference data on the molecular basis of AntiMicrobial Resistance (AMR), with an emphasis on the genes, proteins and mutations involved in AMR  (19). It is built upon the Antibiotic Resistance Ontology, a custom built, interconnected and hierarchical controlled vocabulary allowing advanced data sharing and organization. Every genome integrated into Micro-Scope is scanned using the RGI (Resistance Gene Identifier) software, which is provided with the CARD resource, for prediction of resistome.
This software uses different models (CARD Proteins Homologs, CARD Proteins Variants or CARD overexpression) to detect the AMR from protein sequences. In MicroScope, a summary of the CARD results for each genome is available in the 'Resistome' section of the 'Comparative Genomics' menu. Prediction of virulome. Similarly, genes potentially involved in the virulence of a given organism (e. g. genes coding for bacterial adhesins, colonization factors, protein toxins like hemolysins) are listed in the 'Virulome' section of the 'Comparative Genomics' menu. The prediction is based on nucleotide and protein BLAST searches against a homemade virulence database. It was built using three datasets of virulence genes: (i) the core dataset from VFDB (setA), which is composed of genes associated with experimentally verified Virulence Factors (VFs) for 53 bacterial species (20) (ii) the VirulenceFinder dataset which includes virulence genes from Listeria, Staphylococcus aureus, E.
coli/Shigella and Enterococcus (https://cge. cbs. dtu. dk/ services/VirulenceFinder) (  21 ) and (iii) a manually curated dataset of reference virulence genes for E. coli (Coli Ref)  (22). In order to ease the functional interpretation of the results, the classification from VFDB is attributed to each VF of VirulenceFinder and Coli Ref as frequently as possible. VFs are divided into four main classes: offensive, defensive, non-specific and regulation of virulence-associated genes. A VF can be involved in many classes. For example, the gene kpsE from E. coli (that encodes a capsule polysaccharide export inner-membrane protein) is classified as an offensive and a defensive VF. Predictions can be filtered according to the taxonomy of the studied strain (i. e. results must be obtained with VFs of organisms of the same species or genus) or using different alignment identity thresholds.
New tools for the characterization of genomic regions The 'Secondary metabolites' functionality dedicated to secondary metabolite prediction (already described in (  7 )) has been updated using the version 5 of antiSMASH (antibiotics and Secondary Metabolite Analysis Shell) software, which enables rapid genome-wide identification, annotation and analysis of secondary metabolites  (23). The new version detects 52 types of Biosynthetic Gene Clusters (BGCs) covering a large range of known secondary metabolite compound classes that are produced by non-ribosomal peptide synthetases (NRPS), polyketide synthase (PKS) or ribosomally synthesized peptide products (RiPP). The program also offers extended and improved BGC detection and analysis capabilities particularly for regions containing multiple BGCs of one or several types.
To help users analyze these regions, the program introduced a new terminology to describe different biological options that lead to BGCs with notably 'protoclusters' and 'candidate clusters'. A protocluster describes a BGC (core enzymes and neighborhoods at both sides of the core) and is associated with one product type. Candidate clusters are designed to allow modeling of hybrid clusters, such as PKS/NRPS hybrids, which combine two or more different biosynthetic classes (as identified in antiSMASH detection rules), or cases where one class is used to biosynthesize a precursor for a second class. Thus, a candidate cluster contains one or several protoclusters. Hence, the MicroScope graphical interface for the visualization of predicted secondary metabolite clusters has been updated to show protoclusters and candidate clusters defined by antiSMASH (Figure  3 ).
This new feature is particularly useful for the analysis of complex regions composed of multiple BGCs. Moreover, corresponding result tables have been split into three sections to describe (i) similarities with known clusters from the MIBiG database, (ii) the list of genomic objects that compose the region and (iii) candidate cluster and protocluster coordinates. In addition to the update of antiSMASH, the comparative genomics functionalities of the platform have been enriched with new tools dedicated to functional annotation of genomic regions. First, we have integrated IntegronFinder which is able to detect, with high accuracy, integrons in DNA sequences  (24). Integrons are major genetic elements, notorious for their major implication in the spread of antibiotic resistance genes. More generally, integrons are gene-capturing platforms, whose broader evolutionary role remains poorly understood.
MicroScope Genome Clusters We have designed a new workflow to compute MICGCs which aims to group genomes belonging to the same species into clusters (Figure  4 ). The classification method relies on pairwise genomic distances estimated with the Mash software  (28). We consider only distances below or equal to 0. 06, which corresponds to a 94% Average Nucleotide Identity (ANI) cutoff that is a usual value to define species  (29). Then, the Louvain community detection method (  30 ) is applied to define species clusters, called MICGCs. To avoid bias in the classification, incomplete or contaminated genomes are excluded based on CheckM results (i. e. genomes with estimated contamination >5% or completion <90%). In addition, a specific web page has been developed to explore MICGCs and is available in the 'Genome Clustering' section of the 'Comparative Genomics' menu.
Pangenomes and region of genomic plasticity For each MICGC containing at least 15 genomes, a pangenome is computed using the PPanGGOLiN software (https://github. com/labgem/PPanGGOLiN, Gautreau et al. , in preparation). It uses a graph model to represent pangenomes in which nodes and edges represent gene families and genomic neighborhood information, respectively. The pangenome is then partitioned by evaluating, through an Expectation-Maximization (EM) algorithm, the best parameters of a multivariate Bernoulli Mixture Model smoothed using a Markov Random Field. This approach takes into account both graph topology and occurrences of genes to classify gene families into three partitions: (i) persistent genome, equivalent to a relaxed core genome (genes conserved in almost all genomes) (ii) shell genome corresponding to moderately conserved genes potentially associated with environmental adaptation capabilities (iii) cloud genome, genes found at a low frequency.
First genomic distances between all MicroScope genomes are computed using Mash software with kmer and sketch sizes equal to 18 and 5000, respectively. Then, a weighted graph is made from these distances removing edges corresponding to distances higher than 0. 06 (which corresponds to a 94% ANI). Using CheckM results, genomes with estimated contamination >5% or completion <90% are removed from the graph. At last, the Louvain community detection method is applied to define species clusters, called MICGCs. Mash genomic distances are also used to compute neighbor joining trees to classify strains (this functionality is available on the 'Genome Clustering' page of MicroScope). their experience on using MicroScope for their research projects. Six live demos were presented to highlight recent developments of the platform (i.
e. transcriptomics tools, new comparative genomics tools, the Genome Clusters functionality, curation of metabolic pathways, etc. ). We also got valuable feedback for future developments of the platform. Since 2 years, an average of 160 genomes per month are requested for integration in the platform (this includes bins/MAGs from metagenomic samples). The Mi-croScope resource contains today data for >11 800 genomes of which ∼4400 are available (38%). At the request of the project coordinators, sequences and annotation data are prepared for submission to INSDC databanks; they can also easily be downloaded via the web interface ('Search/Export→Download Data' functionality). Two export modes are implemented into MicroScope: (i) the 'Replicon mode' to retrieve various data for only one replicon (i.
e. chromosomes, plasmids or the whole contigs if the genome is not finished) (ii) the 'Organism mode' to download genomic annotation files (EMBL, GenBank, Fasta, GFF3 file formats) in batches of up to 20 genomes. In both modes, genomic data can be split by contig or scaffold entries. At last, MicroScope is one of the resources listed in the catalog of the ELIXIR bioinformatics infrastructure (https://elixir-europe. org/services). In this context, we implemented an export of MicroScope data based on semantic web technology using the Resource Description Framework (RDF). The functionality is available in the 'Organism mode' of the export page from which users can download RDF files in Turtle format. These files contain all genomic and metabolic information dynamically extracted from the MicroScope PkGDB database.
This chapter presents the modeling and simulation results of variable speed wind turbine driven by doubly fed induction generator (DFIG). The feeding of the generator is ensured through its stator directly connected to the electrical grid and by its rotor connected to the grid through two power converters, which are controlled by the pulse width modulation (PWM) technique. This configuration is the most used in the wind power generation systems. For the variable speed operation of the studied system, the maximum power point tracking strategy is applied for the turbine, and the stator flux-oriented vector control is used for the generator. The MATLAB/Simulink software is used for the system modeling and simulation. For the wind velocity model, a random wind profile is simulated, and the turbine and the generator parameters are extracted from an existing wind turbine system in the literature.
Moreover, the economic model is used in the case of manufacturing and installing wind turbines with the purpose to evaluate the cost-effectiveness. In addition, there are models which predict the weather conditions and the power output of wind farms. Furthermore, there are models, which have the objective of evaluating the impact of wind turbines on the environment such as the evaluation of noise produced by the wind turbine operation. Finally, the general-purpose models concerned with the electrical properties of wind turbines are widely used. The speed variable wind turbine (SVWT) model, developed and simulated in this work, is concerned with providing time simulation signals that can be exploited for designing fault diagnosis approaches based on models; the software tool used for simulation is the MATLAB/Simulink environment.
Simulation results The variable speed wind turbine model based on DFIG with a power of 3 Kw has been developed and simulated using MATLAB/Simulink software. The  turbine and DFIG parameters are extracted from  [30]. Some simulation results of the wind system modeled in this study are presented in the figures below. Figure  14  shows the random wind speed profile applied to the turbine. The Figures  18  and 19  show respectively the variation of the power coefficient Cp and the variation of the speed ratio λ, which coincide with the maximum power coefficient and with the optimal speed ratio. The simulation results of the wind system electrical part, including the electrical characteristics of the DFIG, the power converters, and the capacitive bus, are presented in Figures  20 21 22 23 24 25 26 27.
Conclusion This chapter presents the modeling and simulation results of the most commonly used speed variable wind turbine by a doubly fed induction generator. In order to generate efficient and quick electrical power, the control techniques are applied, such as the MPPT control for wind turbine and the stator flux-oriented vector control are used for the generator. The wind turbine system and its control methods are established in the MATLAB/Simulink environment. The obtained signals are used for the design of fault diagnostic methods in future works.
Optimization of the enzyme immobilization A homogeneous mixture containing XOD solution and PVA/AWP was prepared and 3 μL of this mixture was carefully spread on the WE surface. The electrodes were exposed to neon light at +4°C to allow polymerization. The following parameters: XOD charge per electrode (Cxod), XOD:PVA/PWA ratio (RXP), and polymerization time under UV exposition (Tp) were optimized for biosensor construction through Response Surface Methodology (RSM) on the Statistica software. Box-Behnken Design was used with 3 factors and 3 levels: Cxod (5, 8, and 10 mU/WE), RXP (1:2, 1:1, 2:1), and Tp (48, 24, and 0. 5 h). The statistic program constructed by 15 sets of fractional factorial experiments.
Box-Behnken design was used applying high, central, and low values for CXOD as 5. 0, 8. 0, and 10. 0 mU per electrode, for RXP as 1:2 (0. 33), 1:1 (0. 50), and 2:1 (0. 66) representing XOD-enzyme:PVA/AWP ratios, and for Tp as 48, 24, and 0. 5 hours, respectively. 15 of the working set was generated by Statistica program and thus 15 electrodes were constructed. Each electrode was tested in 50 mM K-PBS buffer containing 10 mM KCl (pH 7. 5) at a working potential of -100 mV until steady state currents were obtained, then the net current values in 50 µM HX solution were measured. For fitting of experimental data, linear (L) and quadratic (Q) models were tested. Significant p-value (< 0. 05) was found in all factors to a linear model (Table S-2,), which showed 0. 99887 predicted R-squared value in reasonable agreement with the adjusted Rsquared value (0.
Thus, total of seven food categories was considered in the analyses. The relative numerical (N%) and mass (M%) abundance were calculated for each functional food category for each individual, along with the relative contribution of each food category to the whole stomach content. An index of relative importance (IRI) was then calculated following the equation: where O% is the frequency of occurrence of each prey category (in other words, the proportion of stomachs containing at least one item of the designated food category;  Martin, Twigg, & Robinson, 1996; Pinkas, 1971; Twigg, How, Hatherly, & Dell, 1996 ). Since it is not possible to calculate means for the frequency data, the IRI was determined for each prey category, for each sex, and for each site. Based on all measured and calculated variables, four diet subdatasets are retained and considered separately for the statistical analyses: the first three describe the numerical and mass abundance, and IRI for each of the seven food categories, and the last subdataset includes five prey dimensions.
| Statistical analyses All statistical analyses were carried out using R (v3. 5. 1, R Core Team, 2018). The relative numerical and mass abundance data, which values ranged between 0 and 1, were arcsin-transformed to normality. Values of prey dimensions and of head dimensions were log 10 -transformed. Prior to any analysis, the normality and the homogeneity of variances in each subdataset were tested by a Shapiro test and a Bartlett's test.
| Variability in diet A three-way MANOVA was performed on the whole diet dataset with sex, species, and site as factors as well as for a possible interaction between these factors to test for their effect on diet. Next, MANOVAs were computed for each sex and species separately, with the site as factor (function "MANOVA" in the package "stats"). Tukey's post hoc tests (function "TukeyHSD" in the package "stats") were used to explore which islands differed from one another.
test" from the "stats" package). For the latter analysis, the index was averaged by population or subpopulation to avoid pseudo replication. The disparity index calculates the hypervolume occupied by a subset in ecological space. Based on the two numerical and mass abundance datasets, it was computed using the function "disparity. per. group" from the "dispRity" package  (Guillerme, 2018)  in R. The comparison of the disparity of subsets allowed us to test for differences between species, sexes, and islands. This was done using the "test. disparity" function which uses a Mann-Whitney-Wilcoxon test of comparison of the medians, coupled with a Bonferroni correction multiple comparisons when required. We also tested for a relationship between disparity and island size. To do so, the disparity metric "sum" was extracted for each site, sex, and species, log 10 -transformed and regressed against island area.
All linear regressions involving island area exclude mainland sites and the island Korčula. Whereas continental sites have no area associated, Korčula is a very large island close to the mainland and thus its community is expected to behave similar to a mainland one. were performed on the datasets describing numeric and volumetric prey consumption, prey dimensions, and head dimensions including all individuals using the function "prcomp" from the "stats" package. H � = - S ∑ i=1 p i log p i p i = n i N TA B L E 2 This allowed for a reduction in dimensionality. The contribution of each specimen along the three first principal components (PC) was extracted and used to calculate the mean contribution of each sex of each population on these axes. The sexual dimorphism (SD) for each site was determined as follows: where m i and f i refer respectively to the mean contribution of the males and the females of the population of interest along with the PC i.
| Quantification of the diet variability The three-way MANOVA performed on the whole dataset reveals a strong species (F 14,482 = 17. 69, p <. 001), sex (F 14,482 = 2. 32, p =. 004), and site (F 252,6930 = 2. 57, p <. 001) effect on diet composition. However, no interaction is detected between these three factors (F 266,6930 = 1. 01, p =. 411). When considering the dataset describing the numerical abundance of the seven food categories, the diet of both species is significantly different (F The analyses on the mass proportion data show similar results, differences between islands being strongly driven by the volumetric proportion of plants, soft, medium, and evasive prey, and poorly influenced by hard prey, except again in males of P. melisellensis (F 11,164 = 2. 11, p =. 016).