dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The FEWS prototype exhibits good prediction skills and relies on a simple interface satisfying the local users. However, we report two main issues. First, regarding the prediction skills of the metamodels, to address the lower skills of the GP-based metamodels (Y 7:18 ) close to 0, we adopt a pragmatic approach of using the classification metamodel (Y 1 ) outputs and combining them with the GP-based metamodel outputs. However, from a theoretical point of view, this is not fully satisfactory. One way to improve the indicators relying on the GP-based metamodels close to 0 is to increase the number of learning scenarios providing Y = 0 values. We tested this strategy by adding 40 non-flood event scenarios to the existing 174 scenarios used in the learning dataset; no significant improvement was found.
Recent efforts to build historical flood databases at national scales (see, e. g. ,  [75, 76] ) may support this work. In addition, users' needs and the selected targets have to be defined together with local stakeholders even if there are similarities with the Gâvres site. Once a model is chosen according to the previously mentioned parameters (the site dimensions and flooding processes), the model can be fed the DEM of the area (including the coastal defences) and validated on at least one reference flooding event. A key issue is the availability of sufficiently precise topo-bathymetric data; moreover, especially if significant coastal changes have recently occurred, these data should be relatively fresh. At least for the topography, the vertical precision should be on the order of a few centimetres, especially if high local prediction skills are to be expected.
Acknowledgments: The following data providers are acknowledged: LEGOS, NOAA, LOPS-IF REMER, SHOM, and  Météo-France. The authors are also grateful to the  Lorient Agglomeration  for providing lidar survey data, to  D. Paradis  for providing VVS information, to  S. Le Roy  for his participation to the user's workshops, to  G. Le Cozannet  for his contribution to the revision of the present manuscript, and to the user committee and advisory committee members of the  RISCOPE  project. Finally, the reviewers thank the two reviewers of this manuscript for their constructive comments.
The script with ovine data resulted in 4761 microRNA genetic variants in 106 different microRNAs. Of them, 33 variants were located in the precursor of 19 microRNAs, with six of them in five mature microRNAs and one variant in the seed region of oar-miR-539 (Table  1 ). In addition, the microRNA miR-539 presented a genetic variant in bovine species in a 100 bp-flanking region of this microRNA. In these analyses, five microRNAs (miR-93, miR-215, miR-671, miR-874, and miR-1307) had variants in their precursor in both bovine and caprine species (Fig. 1 ). These five microRNAs are very similar in the two species, with the same seeds and, therefore, the same predicted mRNA targets. They differ by only one nucleotide at the 3′ end of the mature sequence (Fig. 2a ). Four microRNAs, miR-30b, miR-148a, miR-150, and miR-654, had variants in both bovine and ovine species (Fig.
Discussion MicroRNA genetic variants are well studied in humans to diagnose or provide a prognosis for diseases such as cancer or cardiovascular disease  [44] [45] [46] [47]. MicroRNA databases, in particular the MSDD (MicroRNA SNP Disease Database)  48  and the miRSNP database  49  , have been developed in this context. Other databases, such as EpimiR-Base or miRCancer, provide access to the microRNAs linked with human databases  50, 51. Approximately 10% of human microRNA precursors contain SNPs, with less than 1% located in seed regions  52. In the present study, microRNA genetic variants were studied in the context of dairy traits and in three different dairy ruminant species: bovine, caprine, and ovine species. A total of 59,124, 13,427, and 4761 genetic variants in microRNAs expressed in the mammary gland or present in the milk were identified in bovine, caprine, and ovine species, respectively.
Material and methods In silico detection of genetic variants. VCF files. Similar processes were used for the three bovine, caprine and ovine species. The genomic variation SNPs and small InDels, defined as insertions or deletions shorter than 60 bp located in microRNA genes, were identified using three INRAE databases (all the available data were from the three INRAE databases used), as described in Table  4   65, 66. Small genomic variations located in bovine microRNA genes were identified using a bovine database containing approximately 28 million SNPs and InDels using whole-genome sequence data from 396 animals corresponding to 18 different breeds (Supplementary Table  S2 )  65. Annotation of this database was performed using Variant Effect Predictor v81 (VEP)  67. For caprine species, the microRNA small genomic variations were identified using VCF files containing approximately 87 million SNPs and InDels produced from whole genome sequences for 1159 animals, corresponding to 102 Capra hircus breeds as well as 8 other capra species for 35 of the animals: Capra aegagrus, Capra caucasica, Capra pyerenaica, Capra falconeri, Capra ibex, Capra nubiana, and Capra siberica.
4. 0 (Broad Institute, http:// picard. sourc eforge. net). Only properly paired reads with a mapping quality of at least 30 (-q = 30) were retained. The resulting BAM files were subsequently used to the variant calling, using the Genome Analysis Tool Kit 2. 4-9 (GATK) version and GATK-UnifiedGenotyper  71. All the identified variants were then annotated using the Ensembl Variant Effect Predictor (VEP) v81 tool  67  based on the Ensembl version 81 transcript set and using dbSNP build 143. The SnpEff v4. 3 tool permits variant annotation and prediction of their effects in caprine and ovine species  72. In caprine species, the process differed in the calling variants step: the SAMtools tool version 1. 6 was used at this step as well as the GATK version 3. 6 tool  71. BCFtools version 1. 6 and Freebayes version 1.
A total of 1019 QTL data were listed for the three breeds and the different traits, corresponding to 118 distinct QTLs. In each study, confidence intervals of the QTLs were defined by the positions of the SNPs included in the upper third of the GWAS peak. As a result, the regions spanned a total length of almost 401 megabases, with a mean length of 815 kb (Table  5 ). In caprine studies, 82 QTLs for five different traits (fat content, protein content, fat yield, protein yield, and milk yield) were studied in two French alpine and Saanen dairy breeds  27. Due to different reference genomes (Assembly CHIR_1. 0 versus ARS1 (GenBank accession GCA_001704415. 1)), the positions of the 82 QTLs were transposed to the ARS1 goat assembly. This step was carried out by selecting the 100 first and the 100 last nucleotides in each confidence interval of the QTLs using the Genome Data Viewer tool on NCBI  74.
These 107 regions corresponded to a total length of almost 77 megabases, with a mean length of 1197 kb. miRNomes. To characterize the microRNAs expressed in the mammary gland and/or present in milk, dairy miRNomes were used. The miRNomes of bovine mammary glands with or without mastitis and the miRNomes of milk or different fractions of milk were used. A total of 1063 microRNAs are listed in these data sets  11, [13] [14] [15] [16]. For each listed microRNA, its genomic locations in the UMD3. 1 bovine genome and its name were indicated. In caprine species, the mammary gland miRNome data were available from Mobuchon et al. 12. A total of 239 microRNAs are listed, and their genomic locations in the ARS1 genome version were used. In ovine species, no mammary gland or milk miRNome data were available when the script was developed.
Therefore, to prioritize ovine microRNAs detection, bovine and caprine miRNome data were used for the ovine species. Data filtering. Files containing genomic variants, QTLs and miRNomes were combined and further analyzed using an in-house Python script. This script consisted of a cascade of several filtering steps allowing the selection of genetic variants located in dairy and/or mastitis QTLs, within microRNA gene regions expressed in the mammary gland and/or present in milk. The script used the microRNA, miRNome and frequency data as filters added in this script. First, only biallelic variants affecting microRNAs were further filtered from the annotated VCF files. Second, we selected genetic variants that impact microRNAs present in the dairy miRNome. Finally, the variants with a frequency higher than 0.
01 were selected and corresponded to our miRNA genetic variant panel (Fig. 5a ). Then, the QTL data filter was added to this whole script to be more precise and more stringent. Only genetic variants located in dairy or mastitis QTLs were selected. Then, the microRNA, miRNome and frequency filters were processed (Fig. 5b ). This panel also contained different features from the original three files, such as their genomic position, nature of reference, alternative alleles and their frequencies, the impacted microRNA, the breed in which the variant is polymorphic, and the QTL traits in which the variant was located. Additional filters were applied to the variants to classify them as follows: (i) variants located in microRNA precursors (in the seed, the mature microRNA out of the seed or in the precursor microRNA out of the mature) or (ii) in flanking regions of different sizes (50, 100, 500 or 1000 base pairs upstream or downstream of the microRNA precursor), which were predefined according to the literature data  [77] [78] [79] [80] [81] [82]  , and (iii) in bovine species, CpG island or transcription factor binding site (TFBS) elements that could have an impact on gene expression were added Table  5.
Prediction of mRNA targeted by microRNAs. The TargetScan release 7. 2 bioinformatic tool was used to determine putative mRNA targets of microRNAs of interest (http:// www. targe tscan/) in bovine, caprine and ovine species based on the bovine database. This tool predicts and lists mRNAs that are potential targets of each microRNA. The target prediction is based on the complementarity between the microRNA and mRNAs in the seed sequence corresponding to nucleotides 2 to 7  84  , which is the recognition site in the microRNA. Additionally, the transcripts from in silico prediction were compared to the transcriptome data of the bovine mammary gland during lactation and the dry period  43. 3 ) For each species, microRNA genes containing the filtered variants were subsequently compared to the species corresponding miRNomes of mammary gland or milk.
Data concerning each snow leopard, namely time, location of observation (camera trap number, position, GPS coordinates), sex and if accompanied, are compiled into a database. This allowed for a growing number of pictures of each individual over the years which in turn made possible a posteriori identification. Sex determination was made only when we could observe the presence or absence of genitals on pictures  (Alexander et al. , 2015)  or in case of a mother with cubs, as they stay together during breeding  (Fox and Chundawat, 2016; Novikov et al. , 1962). We consider one "crossing" or "event" when a snow leopard, a couple or a mother and its litter, is pictured by a camera trap. We consider that two different events have occurred, when more than 15 min have passed between two pictures of a same individual or group.
Acknowledgments We would like to acknowledge  Naryn State Nature Reserve director Mirtemir Turdaliev , vice-director  Joldoshbek Kyrbashev  and rangers for supporting us during our studies in the reserve,  Maksatbek Anarbaev  from the  Kaiberen Wildlife Research and Conservation Project of the Shinshu University and Naryn State Nature Reserve  for sharing some of their camera trap data from 2016 to 2019, our local partners, all the  OSI-Panthera expedition volunteers from Objectif Sciences International NGO  and staff members of  Objectif Sciences International NGO. We would also like to acknowledge  Fréderic Salgues from Piege Photographique  for providing us the camera traps at reduced prices,  Rosina Karavia  for spell checking and  Koustubh Sharma  for his helpful insights.
Analysis of Clonality of the Strains The genetic relationship between the E. coli strains was evaluated by repetitive sequence-based PCR (rep-PCR) using the DiversiLab ® strain typing system (bioMérieux). Results were interpreted with DiversiLab web-based software (bioMérieux) using the Pearson correlation and the modified Kullback-Leibler method. Isolates with identical strain patterns were considered indistinguishable if the similarity percentage was ≥95%. Multi-Locus Sequence Typing (MLST) analysis was performed using the Achtman MLST scheme (pubmlst/org/mlst). Seven housekeeping genes (edk, fumH, gyrB, icd, mdh, recA and purA) were amplified according to this protocol.
Whole-Genome Analysis and Single-Nucleotide Polymorphism Identification E. coli strains (n = 4) isolated from same patients at different periods were sequenced. Whole Genome Sequencing (WGS) was performed with an Illumina MiSeq sequencing sys-tem (Illumina, San Diego, CA, USA) using paired-end (PE) read libraries (PE250) prepared by Nextera XT DNA Library Prep Kit (Illumina) following the manufacturer's protocol. Rax reads were processed using FastQC (v. 0. 11. 7) to assess data quality. The Cutadapter tool (v. 1. 16 ) implemented in Python (v. 3. 5. 2) was used to remove residual PCR primers and to filter low quality bases (Q_score <30) and short reads (<150 bp). The filtered trimmed reads were included in the downstream analysis. Obtained reads were mapped against E. coli IAI39 genome (GenBank accession number: GCA_000026345.
1), using the CLC genomics workbench 7 (Qiagen Inc. , Valencia, CA, USA). The assembled contigs were processed by Prokka software for microbial genome annotation  [27]. The VirulenceFinder-1. 5 server (https://cge. cbs. dtu. dk/services/VirulenceFinder (accessed on 28 January 2021)) was used to infer virulence encoding genes from genomes sequences. Antimicrobial resistance genes and plasmid replicons were obtained from ABRIcate with the ResFinder database on assembled genomes  [28, 29]  and PlasmidFinder database  [30]. Toxin-antitoxin encoding ORF were extracted from genomes annotations. Targeted genomes were aligned binary against wild-type strains using the MAFFT software, as following NECS21/NECS50 and NECR70/NECR107  [31]. SNP calls were made from the PE library raw reads. The RE-ALPHY tool was used to identify the closest relationships between the strains and the different E.
coli reference genomes deposited in GenBank. For SNP analysis, we employed the following software: SNP-sites for variants calling  [32]  and SnpEff (v. 4. 3T) for SNP annotation in coding regions  [33]. SNP annotations of affected genes were searched within wild-type genomes and their effects were classified depending mutations impacts. Genes affected by stop gain mutations were searched for in the Uniprot database for virulence classification. Finally, PAIs were predicted within implicated genomes using IslandViewer 4 database  [34].
Conceptualization, J. -P. L. , A. S. and A. P. ; methodology, B. L. S. , J. -P. L. and A. P. ; software, M. H. ; validation, A. S. , B. L. S. , A. S. and A. P. ; formal analysis, A. L. , M. H. and J. J. ; investigation, S. S. , N. C. and A. S. ; resources, S. S. , N. C. and A. S. ; data curation, A. L. , M. H. , and J. J. ; writing-original draft preparation, A. L. , M. H. , J. -P. L. and A. P. ; writing-review and editing, J. J. , S. S. , N. C. , A. S. and B. L. S. ; supervision, B. L. S. , J. -P. L. and A. P. ; project administration, B. L. S. , J. -P. L. and A. P. ; funding acquisition, A. S. , B. L. S. and J. -P. L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Institute of Health and Medical Research (grant number U1047), and the French Government under the "Investissements d'avenir" (Investments for the Future) program managed by the Agence Nationale de la Recherche (ANR, French National Agency for Research) (grant number: Méditerranée Infection 10-IAHU-03).
Author Contributions: Conceptualization, J. -P. L. , A. S. and A. P. ; methodology, B. L. S. , J. -P. L. and A. P. ; software, M. H. ; validation, A. S. , B. L. S. , A. S. and A. P. ; formal analysis, A. L. , M. H. and J. J. ; investigation, S. S. , N. C. and A. S. ; resources, S. S. , N. C. and A. S. ; data curation, A. L. , M. H. , and J. J. ; writing-original draft preparation, A. L. , M. H. , J. -P. L. and A. P. ; writing-review and editing, J. J. , S. S. , N. C. , A. S. and B. L. S. ; supervision, B. L. S. , J. -P. L. and A. P. ; project administration, B. L. S. , J. -P. L. and A. P. ; funding acquisition, A. S. , B. L. S. and J. -P. L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the National Institute of Health and Medical Research (grant number U1047), and the French Government under the "Investissements d'avenir" (Investments for the Future) program managed by the Agence Nationale de la Recherche (ANR, French National Agency for Research) (grant number: Méditerranée Infection 10-IAHU-03).
More importantly, the reliability of the estimated network critically depends on the number of interactions to number of data samples ratio, which is in practice very low  (Uygun et al. , 2016). To overcome this limitation, we reconstruct the directed gene interactions associated with the inflammatory state of the human macrophages by adopting a novel ontology-based approach that integrates the available information from multiple datasets and results in the literature  (Henry et al. , 2017). Previous studies show that the number of driver nodes in biological networks is rather high because of their sparse and heterogeneous nature (Y. -Y. Liu et al. , 2011; Ruths & Ruths, 2014). Consistently, we find that a large percentage of genes (51%) can control Network Neuroscience at least one secreted molecule in the target set.
Cells were lysed after 24 hr and RNA was extracted with RNeasy Mini Kit (www. qiagen. com). Transcriptome sequencing cDNA libraries were prepared using a stranded mRNA polyA selection (Truseq stranded mRNA kit, www. illumina. com). For each sample, we performed 60 million single-end, 75 base reads on a NextSeq 500 sequencer (www. illumina. com). RNA-Seq data analyses were performed by GenoSplice technology (www. genosplice. com). Sequencing, data quality, reads repartition (e. g. , for potential ribosomal contamination), and insert size estimation were performed using FastQC  (Andrews, 2010) , Picard-Tools (http://broadinstitute. github. io/picard/), Samtools (H. Li et al. , 2009)  and rseqc  (Wang, Wang, & Li, 2012). Reads were mapped using STARv2. 4. 0  (Dobin et al. , 2013)  on the hg19 Human genome assembly.
A prominent example of this idea is Omax  [2] , which learns the specific characteristics of the musician style in real-time, and then plays along with him from this learned model. Its generative strategy (step number two in Figure  2 ) is based on Factor Oracle automatons  [3] , allowing for generating stylistically coherent music using an online or offline audio database. In recent years, another family of software that provides musical control, while being style-sensitive has been proposed by Nika et al. [4]. In this work, the generative strategy is further improved through the concept of guidance. In this field of human-machine co-improvisation, the notion of guided music generation has two different meanings. On the one hand, guiding can be seen as a purely reactive and gradual process.
Guiding Step-by-Step The step-by-step process aims to produce automatic accompaniment by using purely reactive mechanisms without relying on prior knowledge. Hence, the input signal from the musician is analysed in real time and the system compares this information to its corpus, allowing for selecting the most relevant parts to generate accompaniments. For instance, SoMax  [5]  uses a pre-annotated corpus and extracts multimodal observations of the input musical stream in real time. Then, it retrieves the most relevant slices of music from the corpus to generate an accompaniment. Other software such as VirtualBand  [6]  or Reflexive Looper  [7]  also rely on feature extraction (e. g. , spectral centroid or chroma) to select the audio accompaniment from the database. Furthermore, improvisation software such as MASOM  [8]  uses specific listening modules to extract higher-features like eventfulness, pleasantness or timbre.
"Guiding" with Formal Temporal Structures Some research projects have introduced temporal specifications to guide the process of music generation. For example, Alexandraki  [9]  uses a real-time listening system to perform an audio-to-score alignment and then informs an accompaniment system which concatenates pre-recorded and automatically segmented audio units. Another approach to co-improvisation is to introduce different forms of guidance as constraints for the generation of musical sequences. Indeed, constraints can be used to preserve certain structural patterns already present in the database. In this line of thought, the authors of  [10]  built a system to generate bagana music, a traditional Ethiopian lyre based on a first-order Markov model. A similar approach using Markov models was proposed by Eigenfeldt  [11] , producing corpus-based generative electronic dance music.
g. , chord progression), to feed a scenariobased music generation system in real time would provide anticipatory improvisations from an inferred (and not just predefined) structure. The DYCI2 library  [1]  https://github. com/DYCI2/Dyci2Lib (accessed on 22 October 2021) has been developed to merge the "free", "reactive" and "scenario-based" music generation paradigms. It proposes an adaptive temporal guidance of generative models. Based on a listening module that extracts musical features from the musician's input or manually informed by an operator, the system will propose a scenario at each generation step. Each query is compared to previous ones and is dynamically merged, aborted and/or reordered, then launched in due time. The sound generation is then performed with a concatenative synthesis process so that the output sequences match the required short-term progression received from the listening module.
Therefore, the DYCI2 library opens a promising path for the use of short-term scenarios. Nevertheless, the short-term scenarios proposed by DYCI2 are currently generated from simple heuristics and from low-level musical features (e. g. , CQT, MFCC, Chromagram). Thus, using a module that infers a short-term scenario (i. e. , predicted chord sequence) from high-level feature discovery (i. e. , extracted chord sequence) would be a significant improvement. Based on this inference, music generation processes could fully combine the advantages of both forms of "guidance" mentioned above. Figure  3  illustrates the integration of our system with DYCI2. Hence, each chord sequence constitutes a "short-term scenario" for the agent, which generates a musical sequence in real time corresponding to this specification.
Nature and Functions of Musical Chords In CV, neural networks are commonly used for classification tasks, where a model is trained to identify specific objects, by providing a probability distribution over a set of predefined classes. For instance, we can train a model on a dataset containing images of ten classes of numbers. After training, this network can predict the probabilities over unseen images. Since a digit belongs to only one class, there is supposedly no ambiguity for the classification task. In the case of music, high levels of abstraction such as chords can be associated with multiple classes. C:Maj, C:Maj7, C:Maj9 are chords of 3, 4, and 5 notes that can be considered in some contexts as labels with different levels of precision defining the same chord. Indeed, one can choose to describe a chord with a different level of detail by deleting or adding a note and, thus, changing its precise qualities.