dataesr/scientific-paragraphs-categorization huggingface dataset:
Thermodynamics-enriched network analysis The constraining capacity of metabolites is not uniform, and depends on their connectivity in the network  [20, 46]. To further constrain the model, a priority list of metabolites to be quantified should be considered when designing the metabolomics protocol. In this study, the suitability of the selected dataset for this purpose was analysed (S1 Dataset). The importance of each metabolite in the network was measured by means of PageRank as implemented in MATLAB. This algorithm was developed by Google  [47] and has been recently applied to metabolic networks  [48]. In this sense, the presence of over-represented metabolites (e. g. proton donor) biases centrality measures  [48]. Therefore, a removal of these currency  [49] , side  [48] or pool  [50] metabolites from the network was performed (S1 Appendix).
Non-redundant flux distributions from TFA were selected and subjected to network simplification and correction. Briefly, only active metabolites and reactions were kept, and stoichiometric coefficients were corrected so that they reflected the flux direction of each reaction. Centrality measures require a graph G, defined as a pair G = (V, E), where the vertices (or nodes) V are the metabolites, and the edges E the reactions connecting them. The stoichiometric matrix was converted into an adjacency matrix using an in-house script (S1 Appendix), which was later used to generate a G ready for the PageRank analysis. The final lists of metabolites were ranked by their centrality score, and the top 50% compared against the list of available experimental values.
In order to discern among tests, focus on highly variable flux values should be promoted, but the variance among them was low (S2 Dataset). In fact, only 36/1679 showed a variance greater than zero, where 6 reactions had an experimental counterpart to compare against. Optimal solutions for all tests were similar (reducing the discerning capacity), which explained the overall high correlation coefficients for all tests. Therefore, results from the comparison of predicted and experimental metabolite concentration values are paramount to better understand the impact of varying the physicochemical parameters.
TFA (full) TFA (match Regarding the metabolomics level, the 9 non-redundant solutions were subjected to a similar analysis. Likewise, only 46/972 metabolites had a variance among tests greater than zero (S3 Dataset), out of which 7 were quantified: L-aspartate, phosphoenolpyruvate, ATP, L-valine, pyruvate, NADP + , and FAD. Reliable quantitation of energy-carrying molecules and redox cofactors is not easily achievable, given the inherent cell dynamics (e. g. cell cycle and cell size variations) and degradation during extraction  [55] [56] [57] [58] [59] [60] [61] [62] [63]. Since the correlation coefficients were calculated using a dataset blind to highly variable metabolites (e. g. 3-phosphohydroxypyruvate ranged four orders of magnitude), resulting values were similar for different factor t (˚C) (0 = 25, 1 = 37) 1 1 1 1 1 1 1 1 1 1 0 I (M) (0 = 0, 1 = 0.
Identification of central metabolites to further constrain the model Successful tests converged into 5 solutions at the fluxomics level (S4 Table ), which are structurally equivalent. Therefore, a single stoichiometric matrix was considered for further analysis. After the simplification step (removal of inactive metabolites and reactions, as well as side compounds) 622/1805 metabolites were left in the network. The experimental dataset included information about 44 metabolites (S1 Dataset), out of which 34 were also considered in the simplified network, and the rest was discarded as side compounds. PageRank scores were calculated, allowing to identify metabolites in the top 50% for which experimental data was available (Table  5 ). Overall, 18/34 quantified metabolites were in the top 50%, with only 7 in the top 10%.
The lack of high centrality for most metabolites explains the aforementioned result, where tests only differing in the set of concentrations values used as a constraint (default ATP/ADP/AMP or experimental) led to the same optimal solution (e. g. tests 20 and 52, Table  3 ). Table  5. Quantified metabolites in the top 50% of PageRank (PR) based analysis. The last position in the ranking (#622) was L-Tyrosine (PR score = 0. 0004), which had been quantified. The full list can be found in (S4 Dataset). The priority list is led by L-glutamate, pyruvate, 2-oxoglutarate (not quantified), D-fructose-6-phosphate and glyceraldehyde 3-phosphate (not quantified). Both L-glutamate and 2-oxoglutarate participate in the assimilation of nitrogen in E. coli, where the former also plays a role as nitrogen donor in the biosynthesis of nucleic acids  [66].
T is temperature in K. There is a 'default matTFA' constraint regarding set concentrations values for cofactors (AMP, ADP and ATP) as included in the original matTFA code. 'Experimental data' refers to the use of published metabolomics data (S2 Dataset), setting the lower and upper bound for the simulation as 90-110% of the concentration values. https://doi. org/10. 1371/journal. pcbi. 1007694. t002.
� T is temperature in K. There is a 'default matTFA' constraint regarding set concentrations values for cofactors (AMP, ADP and ATP) as included in the original matTFA code. 'Experimental data' refers to the use of published metabolomics data (S2 Dataset), setting the lower and upper bound for the simulation as 90-110% of the concentration values. https://doi. org/10. 1371/journal. pcbi. 1007694. t002.
Run #3 represents the analytical conditions from the original matTFA, added here as a reference. There is a 'default matTFA' constraint regarding set concentrations values for AMP, ADP and ATP, as included in the original matTFA script. 'Experimental values' refers to the use of published metabolomics data (S1 Dataset). Correlation coefficient values were rounded to the closest integer for ranking purposes. Davies equation; DH, extended Debye-Hu ¨ckel equation; [met], metabolite concentration values. Values of 0 and 1 in the headers refer to the binary codification from the full factorial design (S3Table). � https://doi. org/10. 1371/journal. pcbi. 1007694. t004.
g. negligible, medium and high). Logical models are usually derived from a careful manual curation of relevant scientific literature, but they can also be enriched with other sources of information, such as high-throughput sequencing data  [17]. GINsim is a computational software dedicated to the building and analysis of logical models of cellular networks  [16]. GINsim includes specific tools to perform model simulations, as well as efficient algorithms to identify the attractors of the system (stable states and/or oscillatory behaviour), for wild-type or mutant conditions  [16]. The resulting model can be further analysed using the CoLoMoTo suite, an interactive toolbox integrating several logical modelling software tools, with a uniform interface to perform complementary analyses, which are easy to share and reproduce through the use of notebooks  [18].
Consequently, to complete this preliminary network, we decided to take advantage of the public epigenome and transcriptome datasets to infer novel regulatory interactions and integrate them into our logical model (a proof of concept of this approach can be found in  [17] ). The genomic region coding for IRF4 is displayed in the UCSC genome browser. For each cell type, the ChromHMM analysis generates a specific segmentation, where each colour represents a chromatin state defined in the heatmap shown above. The annotation of chromatin state was derived from biological knowledge. Note that for this example, active gene marks (in green) are present only in the moDC track.
These regulatory regions were used to predict binding sites for the TFs known to be activated by the CSF2 and IL4 pathways (figure  1 ), using position-weight matrices collected from the Jaspar database  [19] , with the pattern-matching tool matrix-scan  [20]  from the RSAT suite  [23]. This led us to predict novel transcription factor binding sites presumably involved in the regulation of specific gene markers for moDCs, monocytes and macrophages (table  1 ), thereby enabling us to complete the regulatory network controlling monocyte to moDC differentiation. For 13 out of 19 genes related to moDC phenotype, we detected putative binding sites for IRF4 (figure  3a ) into putative regulatory regions (cf. ChromHMM analysis) located near to the TSS of the corresponding genes. Interestingly, this led us to corroborate the central role of IRF4 in moDC differentiation.
[6]. To validate the different expression signatures, we analysed RNA-seq data from monocytes, moDCs and macrophages. Figure  5  displays the differential expression of the genes included in the model. Interestingly, we found two main clusters of genes highly expressed in moDCs, but downregulated in macrophages. These moDC differentially expressed genes include those coding for the TFs STAT3, STAT6, CEBPA and IRF4, which participate in moDC differentiation, as well as those for CD209, MAOA and SLAMF1, which are specific markers for moDCs. Additionally, monocytes display high expressions of the genes coding for KLF4, IRF8, SELL and CD14. In the next step, we took advantage of the CoLoMoTo toolbox to recapitulate documented cellular commitment experiments.
Model simulations recapitulate the main aspects of cellular commitment to differentiation We imported our model into the CoLoMoTo environment to ease further analyses with complementary software tools such as Pint, BioLQM and MaBoSS  [34]. The integration of all analyses can be found in a Jupiter (python) notebook (available at http://ginsim. org/model/monocytes-to-dc) further ensures reproducibility. We used the tool BioLQM to compute the trap-spaces for the wild-type situations  [35]. As trap-spaces provide approximations of cyclic attractors, we could thereby verify that the model does not generate any cyclic attractors. We continued to use BioLQM  [35]  to assess the behaviour of the model for nine single gene losses-of-function (affecting IRF4, STAT6, PU1, IRF8, MAFB, NCOR2, AHR, JAK3 and CEBPB, respectively) that have been reported in the literature royalsocietypublishing.
Model implementation and simulations Using the software GINsim  [16] , we integrated previously described signalling pathways activated when monocytes are cultured with CSF2 and IL4 (all the corresponding studies are referenced in the GINsim model as node annotations, and further listed in electronic supplementary material, table  S3 ). We also reviewed the literature on the differentiation of monocytes into moDCs. The logical model was built using GINsim v. 3. 0  [41] , where nodes represent genes or proteins, and edges represent regulatory interactions between them, which can be negative or positive (or sometimes dual). In general, each node can take two values, zero or one, but in special cases, one may need to consider different qualitative levels of activation (e. g. STAT3 expression is activated by JAK1, but the presence of LnC-DC leads to a further increase of STAT3 expression).
ChIP-seq data analysis Raw fastq files from ChIP-seq experiments were retrieved from the Blueprint Consortium  [31]  data access portal (http://dcc. blueprint-epigenome. eu/#/datasets) with dataset identifiers EGAD00001001552, EGAD00001002484, EGAD00001002485, EGAD00001001576 and EGAD00001002504. We processed datasets for six histone marks (H3K4me1, H3K4me3, H3K27ac, H3K36me3, H3K9me3 and H3K27me3), in triplicates, from human monocytes, macrophages and moDCs. We performed quality control of read sequences with FastQC/0. 11. 3 tool  [42] , used Trimmomatic/0. 33  [43]  to improve their quality, and then mapped them with bowtie 2-2. 2. 6  [44]  to the human hg38 reference genome with default parameters (-sensitive -phred33). A second quality control was performed after alignment, using ENCODE QC (electronic supplementary material, table  S4 ), which consists of three major tests: NRF (non-redundant fraction) > 0.
Chromatin states definition We used ChIP-seq data for six histone marks (H3K4me1, H3K4me3, H3K27ac, H3K36me3, H3K9me3 and H3K27me3) for each cell type (monocytes, macrophages and moDCs), with their respective input control. Chromatin states were defined using ChromHMM  [18]  v. 1. 12  [46]  with the recommended parameters (BinarizeBed -b 200, assembly hg38), and specifying 10 hidden states for the hidden Markov model. Each chromatin state was annotated based on the probability of appearance of the different marks (e. g. H3K27ac-Enhancers, H3Kme1-Enhancers, H3K4me3-Promoters, H3K27me3-Repressive, H3K9me3-Repressive and H3K36me3-Transcribed  [47] ). We then assessed.
Search for transcription factor binding site using matrix-scan Based on the literature, we identified 22 TFs participating in the differentiation of monocytes to macrophages or moDCs. We retrieved one Position-Specific Scoring Matrix (PSSM) for each of these 22 TFs (electronic supplementary material, table  S5 ) from the JASPAR 2018 database human collection  [19]. We performed pattern-matching searches for TF motif instances using the 22 PSSMs in the selected chromatin regulatory regions, active TSS, repressed TSS, active gene/enhancer and Poised regulation in the vicinity of the TSS annotated in RefSeqTSS (see ChromHMM analysis above). For this task, we used the tool matrix-scan  [20]  from the RSAT suite  [23]  with the following main parameters: background model of Markov order 1, and stringent thresholds of p ≤ 10 -5 , and score 1 (-markov 1 -lth score 1 -uth pval 1 × 10 -5 ).
RNA-seq analyses Raw fastq files from RNA-seq experiments were retrieved from the Blueprint Consortium  [40]  data access portal (http://dcc. blueprint-epigenome. eu/#/datasets) with dataset identifiers: EGAD00001002308, EGAD00001001506, EGAD00001002526, EGAD00001002507 and EGAD00001001582. Blueprint monocytes samples were obtained from the same laboratory from healthy volunteers using positive selection, then the same samples were treated with CSF2 and IL4 for 6 days (monocyte purification: https://www. blueprint-epigenome. eu/UserFiles/ File/Protocols/UCAM_BluePrint_Monocyte. pdf ). For this analysis, we used the methods described in Law et al. [48]. In brief, we performed quality control with FastQC/0. 11. 3  [49] , pseudo-alignment and count determination with Kallisto 0. 43. 1  [50]  using the release-90 from Ensembl (ftp://ftp.
Colomoto analysis In order to foster reproducibility, we used the CoLoMoTo toolbox  [18]  that integrates several logical modelling tools, including GINsim, bioLQM, Pint and MaBoSS. We used GINsim to compute the stable states and bioLQM to identify trap-spaces approximating cyclic attractors. The computation of mean stochastic temporal trajectories was performed using MaBoSS  [39]. The GINsim model and the CoLoMoTo notebook are available on the GINsim repository (http://ginsim. org/model/monocytes-to-dc), as well as a dedicated git repository (https://github. com/karenunez/ moDC_model_differentiation). The results can be replicated with the CoLoMoTo Docker image following the instructions provided at http://colomoto. org/notebook.
Acknowledgements. Karen Julia Nuñez Reza  is a doctoral student from  Programa de Doctorado en Ciencias Biomédicas ,  Universidad Nacional Autónoma de México (UNAM) , and was supported by a fellowship from  Consejo Nacional de Ciencia y Tecnología  ( CONA-CYT, Mexico; CVU/Becario :  634764/331823 ). This work received support from  Luis Aguilar ,  Alejandro León , and  Jair García  of the  Laboratorio Nacional de Visualización Científica Avanzada. We thank  Carina Uribe Díaz , and  Alejandra Castillo Carbajal  for their technical support. We want to thank  Cei Abreu-Goodger ,  Claudine Liliane Irles-Machuca ,  Marc Dalod ,  Thien Phong Vu Manh  and  Samuel Collombet  for your valuable feedback on our model.
P1 is the first station of our FMS and models stock production. The two transitions (t1 and t2) represent the events of pallet input to the assembly stations, while t4 and t5 model the output events through their annex conveyors. Otherwise, the product or the pallet can continue its way to the main conveyor by t3. In an optimization context, our station 2 can at most work twice the rate compared to the subcontractor (station 3). This constraint can be expressed using the GMEC  [23] : M(P 2 ) + 2M(P 3 ) ≤ 3 By using CPN Tools software, one can generate the state space. This reachability graph contains 13 markings (i. e. , Figure  7 ). The application of our algorithm can allow us to determine the specific zones (SZs) with a minimal number of markings. In each SZi, the theory of regions is applied to calculate the corresponding Pci offline.
Introduction The website https://www. n2yo. com/ tracks objects that fly in outer space. As of October 28, 2020, its database counts 21676 objects. The size of space objects ranges from the International Space Station (ISS), through the Hubble Space Telescope to very small satellites. Such very small satellites can be classified 1 according to their weights into different categories. One possible classification distinguishes: minisatellite (100 kg to 180 kg), microsatellite (10 kg to 100 kg), nanosatellite (1 kg to 10 kg), picosatellite (0. 01 kg to 1 kg) and femtosatellite (0. 001 kg to 0. 01 kg)  [23]. In order to visualise the difference in weight and size, Figure  1  depicts the mass of a satellite as a function of its volume for several satellites. In this figure, we also plot three ellipses encompassing different implementations of fault tolerance.
Nonetheless, it may also happen that there is no communication due to CubeSat trajectory. The data used in our experimental framework are based on real CubeSat data provided by the Auckland Program for Space Systems (APSS)  5  , based on Cortex-M3 processors, and by the Space Systems Design Lab (SSDL)  6  , using Atmel AVR32 microcontrollers. These data were gathered by functionality and generalised in order to generate more data for our simulations. They are respectively called Scenario APSS and Scenario RANGE and summarised in Tables  1  and 2 , where U denotes a uniform distribution and one hyperperiod is the least common multiple of task periods. In order to further analyse the algorithm performance (see Section 6), we also modified Scenario APSS. This scenario is called Scenario APSS-modified.
Analysis of OneOff Figure  10  shows the rejection rate of Scenarios APSS and APSS-modified for both communication phases as a function of the number of processors. Scenario RANGE is not presented because the rejection rate is 0 regardless of ordering policy and communication phase. This is due to the task data set, which has rather low system load. We notice that the "Earliest Deadline" or "Earliest Arrival Time" techniques overall reject the least tasks. for both communication phases as a function of the number of processors. In these figures, we plot not only studied ordering policies but also a curve presenting the optimal solution provided by CPLEX solver. In general, the algorithm using the ordering policy achieving the lowest rejection rate has its competitive ratio of 2 or 3, which are rather good results taking into account that our search is not exhaustive compared to the search for optimal solution.
Comparison of Scheduling Time In this section, we compare the scheduling time of OneOff and OneOff&Cyclic when the algorithm executes on 2 Intel Xeon Processors E5640 running up to 2. 67 GHz. In order to show the data corresponding to the rejection rate presented in Section 6. 2, we do not make use of the buffer. First, we will analyse Scenario APSS and then Scenario APSS-modified. Scenario RANGE is not presented because the results of OneOff&Cyclic are qualitatively similar to the ones of Scenario APSS. As for the results of OneOff, there are several variations since the task queue length does not have significant differences for different ordering policies as for Scenario APSS. Figure  14  represents the scheduling time of Scenario APSS for OneOff and OneOff&Cyclic during the communication phase as a function of the number of processors.
The software package HEC-RAS (Hydrologic Engineering Center's River Analysis System) is widely used by the water engineering community to analyze hydraulic systems and perform development planning. Furthermore, it integrates a control module that allows implementing basic controllers. For more complex approaches, developers from the automatic control and artificial intelligence (AI) communities usually design, implement, and test new algorithms using dedicated software such as MATLAB. However, models of hydraulic systems employed in MATLAB are often very simple. The main objective of the paper is to design a simulation architecture by coupling HEC-RAS with MATLAB, thus improving the accuracy of the dynamics of the hydraulic systems considered in the control simulations. The main feature of the MATLAB HEC-RAS interface design is that it allows one to execute customized code at regular time intervals during the simulation.
Therefore, it seems logical to focus on the development of a methodology that is suitable for the needs of both communities and which can facilitate their interaction. In this regard, it might be of interest to develop an interface that allows combining two software tools of different nature. On the one hand, numerical computing environments with the capability to design advanced control strategies, e. g. , MATLAB, and on the other hand, dedicated hydraulic software packages that can provide accurate representations of the dynamics of hydrographical networks, e. g. , HEC-RAS. Research on the potential benefits of an interface between MATLAB and HEC-RAS has not attracted a lot of attention except for  [18]. The authors provide HEC-RAS specifications together with several useful scripts for basic operations, which are illustrated using a complete example.
Their interface allows MATLAB to control HEC-RAS by considering unique sequences, i. e. , hydrographs over long periods of time. Therefore, Monte Carlo simulations can be performed using HEC-RAS and MATLAB  [19, 20]. However, control algorithms that require a feedback loop cannot be implemented using their approach, as there exists no interface that enables it. Building on the existing results, this work tackles the design of a MATLAB HEC-RAS interface that provides both researchers and practitioners with a tool to test MATLAB control algorithms using the capabilities that HEC-RAS offers in terms of model accuracy. The developed interface is a ready-to-use solution that only requires using the MATLAB command window and editing some m-files to automate HEC-RAS computations. Moreover, it deals with any HEC-RAS project-related task, including writing, reading, and extracting results, thus allowing the user to focus on the control algorithm and the results.
Note that the main contribution with regard to the state-of-the-art is the set of codes employed by the interface to allow for the exchange of information between MATLAB and HEC-RAS. As stated before, these are offered as a ready-to-use solution and are accompanied by exhaustive supporting documentation that provide insight on the potential of the solution. Information on how to access codes and documentation is provided in the Data Availability Statement located at the end of the paper. Furthermore, the present paper describes the main features of the interface and presents some examples. The remainder of this paper is structured as follows. Section 2 presents some updates on the HEC-RAS controller discussed in  [18]  and provides an overview on the vast diversity of HEC-RAS versions. The MATLAB HEC-RAS interface is detailed in Section 3.