dataesr/scientific-paragraphs-categorization huggingface dataset:
Our PC-SAFT implementation in FORTRAN 2008 has been made also publicly available on Github 11 and it has been archived on Zenodo 12 under the DOI: https://doi. org/10. 5281/ zenodo. 5085305. thermal diffusion in a nonideal ternary mixture Our model is essentially based on the formalism described in  Ghorayeb & Firoozabadi (2000)  for nonideal multicomponent mixtures. In this work, for the purpose of clarity we mainly kept the notation adopted in the mentioned reference. Thus, the derivative of the logarithm of the fugacity f i of component i, with respect to the mole fraction x j of the species j, is f i j = ∂ln f i ∂x j x i j ,T,P. (B. 1) The fugacity f i is evaluated according to the PC-SAFT equation of state, already used in many Titan-related papers (e. g. ,  Tan et al. 2013; Cordier et al.
Acknowledgements. The present research was supported by the  Programme National de Planétologie (PNP)  of  CNRS-INSU  co-funded by  CNES , and also partially supported by the  HPC center of Champagne-Ardenne (ROMEO)  and the  French HPC center CINES  under project number  A0090711976. D. C. thanks the  University of Reims Champagne-Ardenne  for granting him with the " dispositif Mobilité Courte Entrante et Sortante " for its stay at the  Jet Propulsion Laboratory  in 2019. Part of this work was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration. This work has been done mainly using open-source softwares like Python, gfortran, kate,  Jupyter Notebook , L A T E X under GNU/Debian Linux operating system, and also GROMACS under Red Hat Linux.
Introduction Logic-based models have made significant contributions to our understanding of a wide range of biological processes in health and disease. Initially introduced in the 60s to describe gene regulatory circuits  [1] [2] [3] , logic-based models have evolved substantially over the past five decades to cover various biological processes, such as signalling cascades, ion channels, coregulation of transcription factors and even metabolism. With the growing body of data available due to technological breakthroughs, new methods are being developed to integrate different biological J o u r n a l P r e -p r o o f scales and expand the size and complexity of discrete models. Additionally, efforts to create formalised, large-scale representations of network "maps" open avenues for rapidly repurposing these datasets to serve as scaffolds for qualitative models  [4].
To build the regulatory graph, besides literature mining, the authors used proteomic data, pointing to novel SLP76 interactants identified for the first time in mastocytes  [13]. A combination of small-scale experiments, such as quantitative PCR, Western blots, EMSA, together with data from genome-wide assays, such as RNA-sequencing and ChIP-sequencing, was used to assemble a comprehensive regulatory network to study the reprogramming of pre-B cells into macrophages  [14]. An iteration of model predictions and in vitro validation led to the update of the model with new knowledge and a better understanding of B cell reprogramming mechanisms. In the same line, researchers developed a methodology that integrates several -omics datasets to identify candidate genes, serving as seeds for network modelling.
They analysed multi-omics data from the Consensus Molecular Subtypes  [15, 16]  study of colorectal cancer to expand a previously built generic cell-fate decision network  [17]. In many studies, omic data is used as a source of biomarker signatures compared against stable states to validate phenotypic outcomes. This requires discretizing the measured data, using statistical thresholds such as the p-value or fold change. In this case, the regulatory graph of the discrete model is usually built manually through curation of the literature, text mining, and pathway database interrogation. The logical formulae describing specific mechanisms of gene activation are derived from the results of small-scale experiments. The modeller curates the relevant literature and uses the experiments to infer causality and mechanistic details,.
Going one step further and focusing on the role of ion channels in cancer, an executable model of osmotic regulation and membrane transport was proposed predicting behaviour from expression data  [21] [22]. In addition to considering large datasets, this model expands the family of biological processes beyond just expression and gene activation, to include the coordinated activities of biomolecules (in this case ions) that are not under direct control by single genes. In a recent commentary, the need for personalised models and the challenges that lie in incorporating high-throughput data into mechanistic dynamic models were highlighted  [23]. An example of this is the framework developed to tailor logical models to a particular biological sample. The approach focuses on integrating mutation data, copy number alterations (CNA), and expression data (transcriptomics or proteomics) into logical models  [24].
Data-driven discrete model inference Whilst high-throughput datasets offer new ways to build and analyse models following bottom-up approaches; reverse engineering methods can also be applied to infer models from experimental data. Different algorithms have been developed to reconstruct logic-based models, and specifically BNs, from high-throughput data. There exist two broad categories; combinatorial optimisation methods, which include integer or answer set programming (ASP) and allow for full exploration of the search space to identify the model that best explains the experimental data, and methods that J o u r n a l P r e -p r o o f implement heuristic approaches. The first category has the drawback of not scaling well due to computational explosion, while the second one tends to focus on specific conditions and stable states to ease the calculation burden.
In broad terms, automated inference of Boolean networks and functions from data, can be a daunting task due to the uncertainty of the data itself and also to the large number of unknowns regarding structure and functions that need estimation. Moreover, identifying the most suitable data type and available datasets for model training adds to the task, as they need to be different from the data used for inference. It should be noted that the experimental ability to resolve biologically important expression or concentration differences will impact the results; datasets that are prone to noise, or that concern low-expressed genes, may introduce bias by excluding important pathways. Recently, the caspo time series (caspo-ts) method  [28, 29] , which allows learning of BNs from phosphoproteomic time series data given a Prior Knowledge Network (PKN), was applied to data from four breast cancer cell lines (BT20, BT549, MCF7, UACC812)  [28].
Based on ASP and model-checking, the method could handle a large PKN with 64 nodes and 170 edges  [30]. Another popular software for building logic-based models of signalling networks using prior knowledge and phosphoproteomic data is CellNOptR. CellNOptR supports multiple formalisms, from BNs to differential equations, in a common framework  [31, 32]. GABNI (Genetic Algorithm-based Boolean Network Inference) is a method that searches for an optimal Boolean regulatory function by exploiting a mutual information-based Boolean network inference (MIBNI). If this step fails to find an optimal solution, then a genetic algorithm (GA) is applied to search an optimal set of regulatory genes on a broader solution space  [33]. BONITA (Boolean Omics Network Invariant-Time Analysis (BONITA)) is a new algorithm for signal propagation, signal integration, and pathway analysis capable of modelling heterogeneity in transcriptomic data.
Commonly used software such as GINsim  [38]  can handle Boolean and multivariate logic-based models; however, the attractor's search can be challenging when scaling up, relying on model reduction techniques to deal with large systems. Several platforms offer different approaches to dealing with large complex systems, focused on different problem areas. Cell Collective  [39]  efficiently handles large-scale BNs for simulations but does not offer attractors search. In contrast, BoolNet, an R/ Bioconductor package, offers a collection of options for the analysis of BNs and a set of heuristics for attractors search when the size and the complexity of the model is considerably large  [40]. These heuristics focus on retrieving stable states in lieu of searching the whole state space and significantly reducing the calculation burden, though the results are limited to analysing stable states.
BMA  [41, 42]  focuses on analysing stable states and, more particularly, fixed points, offering several highly scalable algorithms for model analysis, including stability proof, cycle searching, and linear temporal logic  [43] [44] [45]. The specialisation of tools emphasises the importance of commonly agreed standards for model storage. In parallel, progress has been made in developing hybrid and multi-scale integrative modelling frameworks, connecting different formalisms, and generating new insights from the emergent, combined properties. FlexFlux, an open-source java software, combines metabolic and regulatory networks based on the identification of steady states. These steady states are further used as constraints for metabolic flux analyses using Flux Balance Analysis (FBA)  [46]. A multi-scale framework that couples cell cycle and metabolic networks in yeast was proposed, integrating BNs of a minimal yeast cell cycle with a constraint-based model of metabolism.
Raw sequencing data, de novo assembly and quality control We obtained a total of about 121 million paired-end reads using Illumina RNA sequencing. After trimming and quality filtering, biological replicates produced between 5. 2 and 28 million paired-end reads (Table  1 ). The number of reads in each group was well balanced with 5. 5 million in the endurant group and 6. 6 million in the burst-performant group. The BUSCO analysis resulted in 65. 4% gene identification (54. 9% completeness and 10. 5% of fragmented genes), which is relatively good as only one muscle tissue was sampled. Next, we evaluated the Trinity de novo assemblies by mapping the trimmed reads. We obtained an overall alignment rate of > 97% percent identity and > 89% of reads aligned as proper pairs (Table  1 ). The de novo assembly consisted of 218,541 transcripts and 163,981 'genes' with an E90N50 value (i.
Differentially expressed transcripts We identified 109 transcripts with a significant differential expression between endurant and burst performant individuals (Fig. 3 ). Six of those transcripts yielded no similarities to either the Uniprot or the NCBI databases. The blast searches resulted in 103 protein-coding genes (Table  S1 ) matching either Xenopus laevis (n = 94) or Xenopus tropicalis (n = 9) proteins. Due to alternative splicing, some transcripts blasted to the same gene, therefore we identified 90 unique protein-coding genes. Using the human STRING database, we generated nine networks involving 46 differentially expressed proteincoding genes (Fig. 4 ). We highlighted differentially expressed protein-coding genes involved in the structural organization and functioning of muscle cells, such as actin cytoskeleton and microtubule composition, conformation, mitochondrial activity, and cellular trafficking.
In our study, we find ptk2 and other protein-coding genes (gca, lmo7) involved in focal adhesion and the signal transduction cascade through the activation of Rho-GTPases (e. g. RhoG, rac1, cdc42) to be up-regulated in burstperformant individuals. Also, kinectin 1 (ktn1), a receptor for kinesin that accumulates in integrin-based adhesion complexes, is up-regulated in endurant individuals, whereas mef2a, a DNA-binding transcription factor of Fig. 2  Geographic range of some Xenopus species in Africa and maximum-likelihood phylogenetic tree of the eight studied Xenopus males captured in Cameroon in 2009 (represented by a red cross). Geographic ranges were downloaded from the IUCN 2020 red list  [29]  and the map was created with QGIS v. 3. 14 (https://www. qgis. org/). The unrooted tree shows the phylogeny built with PhyML  [30]  based on mitogenomes assembled de novo (Sample A to H correspond to the reconstructed mitochondrial sequence based on each individual data whereas Sample ABCDEFGH corresponds to the reconstructed mitochondrial sequence from all individual data combined) and from mitogenomes of other Xenopus species previously published (corresponding GenBank accession numbers are presented in Table  S2 ).
The phylogenetic tree was designed using Figtree v. 1. 4. 4 (http://tree. bio. ed. ac. uk/software/figtree/). The branch lengths are proportional to the number of substitutions per site with the scale indicated under the tree. The Shimoidara-Hasegawa (SH)-like branch support test is represented by node colors (p-value > 0. 95 in green, p-value > 0. 80 in orange, p-value < 0. 80 in red) ktn1, is up-regulated in burst-performant individuals. Remarkably, kinectin interacts with RhoG to activate rac1 and cdc42 through a microtubule-dependent pathway  [39]. Indeed, kinesins are major microtubule motor proteins that have different functional properties depending on the 'cargo' (i. e. vesicle) they transport. We found several genes involved in microtubule composition and elongation, such as tubg1 and ckap5, to be upregulated in burst-performant individuals.
Lipid metabolism, mitochondrial activity and stress response Almost all differentially expressed transcripts related to lipid metabolism, energy production, mitochondrial activity (mfn1, esrra, atp5b, dgat2, gls2, nfs1) are upregulated in endurant individuals compared to burstperformant individuals. Yet, one protein-coding gene, a A-kinase anchor protein 1 (akap1), has 2 transcript isoforms, one being up-regulated in burst-performant and Fig. 4  Gene interaction networks that contain 46/109 differentially expressed transcripts between endurant and burst-performant individuals. Differentially expressed transcripts were analyzed using STRING  [31]  using gene symbols of human orthologous genes for analysis (see the supplementary table to find corresponding X. allofraseri annotated transcripts), and visual inspection was finalized using Cytoscape  [32].
Physical performance Performance traits were measured for the eight males at a fixed temperature of 24ºC. Maximal exertion capacity was measured by chasing each individual down a threemeter long circular terrestrial track until exhaustion, as indicated by the lack of a righting response. The floor of the track was covered with moistened cork to improve traction and prevent dehydration. For each individual, we recorded both the total distance covered and time spent moving until exhaustion. Burst performance capacity was quantified by measuring maximal instantaneous swimming speed and acceleration (see additional information about materials and measurement protocol in  [8] ). From the four physical performance traits, we performed a Principal Component Analysis (PCA) followed by an agglomerative hierarchical clustering using respectively the ade4 and FactoMineR packages of the R software.
Pooled libraries were 100 paired-end sequenced using an Illumina HiSeq 2500 located at the University of Exeter Sequencing Service facility. Paired-end sequence reads were pooled together to generate a de novo transcriptome assembly. The raw sequence reads were trimmed and Illumina adapters emoved using Trimmomatic  [97]  with the following parameters: leading:5 trailing:5 slidingwindow:4:15 minlen:36. Transcriptome assembly was then performed de novo with the program Trinity  [98]. We assessed the completeness of our transcriptome assembly by searching for a tetrapod set of 3950 orthologs using BUSCO version 4. 0. 2 [99]. To obtain assembly quality statistics, paired-end reads were aligned back to the assembly with Bowtie2  [100]. A high-quality transcriptome assembly is expected to have strong representation of the reads input to the assembler and specifically for a trinity transcriptome assembly at least 80% of reads should mapped back to the assembly and exist as proper pairs.
Phylogenetic analysis To evaluate the phylogenetic position of the eight Xenopus males, we implemented a phylogenetic analysis with previously published mitogenomes of most known Xenopus species downloaded from GenBank  [102]  (Table  S2 ). We retrieved reads mapping to the mitochondrial genome and performed a reference-based assembly of the complete mitochondrial genome for each sample using Geneious (www. geneious. com) and the Xenopus allofraseri mtDNA sequence  [98]. We used MAFFT v. 7 to compute a multiple alignment of all Xenopus mtDNA  [103]. From this multiple alignment, we discarded the control region and all positions with too many gaps or misalignments using Gblocks. The phylogeny was then constructed using PhyML v. 3. 0  [30]  including the Shimodaira-Hasegawa (SH) statistic test.
Differential expression analysis To detect differentially expressed transcripts, we ran edgeR  [105] , integrated in the R Bioconductor suite  [106] , using as input the TPM expression values that were cross-sample normalized using the Trimmed Mean of M-values (TMM) method. We chose edgeR rather than DESeq2 as it can detect differentially expressed transcripts between our two conditions even for transcripts that are expressed at low levels, and in cases where there is high variability between the biological replicates, as we observed during data exploration. Differentially expressed transcripts were defined by a log 2 fold change (log 2 FC) of 2 between burst-performant and endurant groups, and a false discovery rate (FDR) of 0. 05. Expression data analysis using Volcano and heatmap plots relied on transcripts identified using the Trinity analysis framework.
Transcript annotation We compared the significantly differentially expressed transcript sequences to the combined proteomes of Xenopus laevis and Xenopus tropicalis extracted from the Uniprot database using BLAST. We manually annotated the transcript sequences lacking detectable protein homologies by comparing them to the nucleotide (nr/nt) database using BLASTN with an e-value threshold of 1e -5. Differentially expressed transcripts were analyzed using STRING  [107]. The STRING database allows the construction of protein-protein interaction networks, ranging from direct protein-protein interactions to indirect interactions (such as coexpression and text mining). The human database was selected for the network analysis as it contained substantially more information than the one for Xenopus tropicalis (the STRING database does not include data for Xenopus laevis).
Acknowledgements We would like to thank  Michel Baguette  and  Hervé Philippe  for providing valuable resources and discussion. We would like to thank  Eric Fokam  and  Legrand Nono Gonwouo  for their help in the field and without whom this study would not have been possible. We want to thank the Sequencing Service and Computational core facilities at the  University of Exeter  for producing quality RNA-seq data. We also want to thank the IUCN red list for making the spatial data publicly available.
Availability of data and materials The annotation of the 109 differentially expressed transcripts and the STRING TSV interaction file are available on Zenodo (https://doi. org/10. 5281/zenodo. 4028544). Processed and raw data files generated by this transcriptomic analysis have been deposited in NCBI's Gene Expression Omnibus  [108]  and are accessible through GEO Series accession number GSE157915 (https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE157915). The geographic ranges of Xenopus species are publicly available at the IUCN red list website (https://www. iucnredlist. org/). Species name with their corresponding accession numbers of mitochondrial sequences downloaded from GenBank and used in the phylogenetic analysis are indicated in Table  S2.
Funding Support for this work was provided by the Agence Nationale de la Recherche (ANR; France) MOBIGEN [ANR-09-PEXT-003]. RNA-seq data was produced by the Sequencing Service and Computational core facilities at the Declarations Ethics approval and consent to participate Animals were exported to France with the authorization for export from the Cameroonian Ministry of Forestry and Wildlife (MINFOF). All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. All experiments were approved by the Institutional ethics committee at the National Museum of Natural History in Paris (#. In accordance to the directive 2010/63/EU of the European Parliament and French legislation, our study did not require specific authorization because our observations did not cause any pain, suffering, distress, or lasting harm.
Over the last decade, several survey papers have been proposed on the use of machine learning approaches for agriculture mainly based on one of the different extracted data, such as IoT data, ground imagery or remote sensing imagery. Table  1  summarizes the review papers that have addressed different crop monitoring problems, covering acquisition techniques using wireless sensors, multispectral cameras, thermal cameras, hyperspectral cameras and satellite sensors. Nonetheless, as far as we know, there is no specific survey study covering IoT techniques, ground imagery, unmanned aerial vehicle (UAV) imagery and satellite imagery for disease detection using machine learning. The literature still lacks comprehensive insight on this field of study regarding the different data sources in the agriculture field and the fusion of these data for disease detection.
In this paper, we review crop disease detection methods based on unimodal data sources (wireless sensor networks, ground imagery, UAV imagery and satellite imagery) using machine learning algorithms. For each data source, these algorithms are broadly divided into two main categories: traditional machine learning and deep learning. We highlight the combination of multi-source data for agricultural applications and discuss data fusion approaches. The recent data fusion advances are presented. Current existing issues are also analyzed, particularly for disease detection. Table  1. Recent review paper in the agricultural domain using machine learning.
Statistical analysis of some indices using the principal component analysis (PCA) model successfully differentiated between healthy plants and infected golden potato disease progression  [50]. The authors first extracted the vegetation indices, simple ratio (SR) and Normalized Difference Vegetation Index (NDVI) from acquired hyperspectral images of infected potatoes at different disease development stages. The analysis results demonstrated the ability of spectral data to distinguish healthy from diseased plants. In the same way, the authors in  [51]  used K-nearest neighbor (KNN) and the decision tree-based classifier C5. 0 to classify grey mold infection severities on tomato leaves using hyperspectral images. Results indicate that the full range model can differentiate between healthy and infected leaves with an accuracy of 92.
86% and 85. 71% for KNN and C5. 0, respectively. In  [52] , the authors aimed to estimate the severity of three diseases on wheat using an artificial neural network composed of one hidden layer; the classification accuracy reached 81%. Deep learning models were then used to improve the prediction quality and address larger types of diseases and crops. This subsection presents deep learning models deployed on RGB images, multispectral images and hyperspectral images for early disease detection. In  [53] , the authors tested several deep learning models from scratch and with transfer learning. The tests were carried out on the PlantVillage dataset of plant leaves images  [54]. The ResNet34 model outperformed all other models by achieving an accuracy of 99. 67%. In  [42] , the authors proposed a conditional convolutional neural network derived from the ResNet50 topology.