dataesr/scientific-paragraphs-categorization huggingface dataset:
Software implementation The methods outlined in this manuscript are available within the cellfinder software, part of the BrainGlobe suite of computational neuroanatomy tools. The software is open-source, written in Python 3 and runs on standard desktop computing hardware (although a CUDA compatible GPU allows for a considerable reduction in processing time). Source code is available at github. com/brainglobe/cellfinder-core and pre-built wheels at pypi. org/project/cellfindercore. To aid adoption of the method, a plugin for napari  [47]  is available at github. com/ brainglobe/cellfinder-napari, and an integrated pipeline for the analysis of whole-brain microscopy data (including e. g. atlas registration) is available at github. com/brainglobe/cellfinder. Documentation, tutorials, and the data underlying.
Material and Methods To assess the environmental performance of a system composed of wind turbines combined with P2G storage, the following elements need to be modeled and quantified: 1. The environmental impacts of wind turbines. 2. The environmental impacts of the components of a P2G system. 3. The need and use of storage. Environmental impacts were calculated using the Python library Brightway2  [16]  dedicated to LCA and using the cutoff version of ecoinvent 3. 4 for background life cycle inventories.
Environmental Impacts of the Wind Turbine Environmental impacts of wind turbines are assessed making use of the parametric LCA model developed and presented in detail in  [5, 17]. This parametric model uses the LCA-specific Python library Brightway2 and can be accessed online at https:// github. com/romainsacchi/LCA_WIND_DK. It enables to create tailor-made life cycle inventories of wind turbines considering their specific technological characteristics and fitting their spatiotemporal context. Onshore and offshore wind turbines have been selected to have an environmental performance representative of the fleet. Their nominal power is 3. 6 MW, and rotor diameter is 120 m corresponding to a power density ratio of 310 W/m 2. The onshore wind turbine has a 95 m hub height, while the offshore turbine has an 85 m hub height.
The power electronics is modeled using the existing inverter LCI originally created for photovoltaic systems. The additional equipment, which mainly consists of pipes and reservoirs, are modeled by the ecoinvent stainless steel pipe dataset. The methanation reactor is also modeled with stainless steel in accordance with the previous work from Zhang et al. [14]. Carbon dioxide is required for methanation reaction, so its extraction from the flue gas of an industrial chimney is modeled using inventories from Koornneef et al. [19]. Finally, the power plant used to burn the synthesized gas and generate electricity is modeled using the ecoinvent combined cycle gas turbine substituting the fossil gas by the synthesized gas. The weight, lifetime, and efficiency of all the devices are based on data from industrial reports and scientific literature  [18, 20].
Assessment of the Need and Use of Storage The need and the use of storage are assessed from the comparison of energy production time series and consumption time series. The approach is represented in Fig. 1. Firstly, wind power production time series are determined from wind speed time series and the wind turbine power curve. Wind speed data can come from on-site measurements or weather reanalysis data. MERRA-2 wind speeds have been downloaded from the online platform Renewables. ninja (https://www. renewables. ninja/) and are used in this study. Power curve gives the relationship between the wind speed the turbine is exposed to and the corresponding power output. Manufacturer power curves or modeled power curves can be used. A model able to generate a wind turbine power curve based on the nominal power, the rotor dimension and the wind turbulence intensity is used, as well as a wake loss coefficient when wind turbines are grouped into wind farms  [5].
We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https:// apps. cytos cape. org/ apps/ TimeN exus. Time-series gene or protein expression data can give invaluable insight into the temporal dynamics of biological processes. It informs about the changes in activity of molecular pathways and key players upon a cellular stimulus or helps characterize molecular activity in cyclic processes, such as the cell cycle or the circadian rhythm. Methods and protocols exist to analyze time-series expression data and extract the dynamically expressed genes from a temporal dataset, some of which have been reviewed and compared in 1.
To test TimeNexus, we used our app together with PathLinker and ANAT on a yeast cell cycle temporal study, following gene expression dynamics of the yeast cell division cycle in synchronized cells  34. We extracted active subnetworks of the cell cycle from a temporal multilayer network comprised of 16 temporal layers of one full cycle. We scored these active subnetworks for relevance to the process under study by looking for enriched GO-terms related to cell cycle. We also applied TimeNexus to our own data from an injury induced pain assay in mouse, following mechanosensitivity and associated transcriptional changes over 30 days. We predicted pathways relevant for this process, including immune response, stress response, apoptosis regulation and axonal growth. Although TimeNexus has been optimized for temporal and multiplex networks, it is also applicable to all other forms of multilayer networks.
TimeNexus is freely available from the Cytoscape App store (https:// apps. cytos cape. org/ apps/ TimeN exus). The source code is also available on GitLab (https:// gitlab. com/ haber mann_ lab/ tempo ral-netwo rk-proje ct). work structure in the Cytoscape app TimeNexus, whereby we assign the differential expression data from each time point to the layer representing this time point in the form of node weights. We refer to this network model as a temporal multilayer network (tMLN, Fig. 1 ). A priori, the network is the same on all layers. Therefore, the tMLN created by TimeNexus is a multiplex network. We refer to a node on an individual layer as a layer-node, as opposed to a node of a single-layer static network. Edges connecting nodes within one layer (A 1 , B 1 , C 1 ) are termed intra-layer edges; those connecting the same node between two different layers (A 1 and A 2 ) are called inter-layer edges.
Yeast and mouse datasets used. Yeast cell cycle dataset. The yeast dataset from Kelliher et al. 34  was retrieved from the NCBI GEO database (GSE80474). We reprocessed the raw fastq files corresponding to the 36 first samples of the wild-type S. cerevisiae cultures from 0 to 175 min by mapping the reads to the S. cerevisiae S288C genome R64-1-1 with STAR aligner  36  with default parameters. Raw read counts were determined using featureCounts  37. For all following steps, we selected 16 time points representing the first complete cell division cycle. These start at time point 25 min and last until time point 100 min as described by Kelliher according to the expression profiles of key cell cycle regulators. We renumbered these time points in our dataset to start at 0 min of the first full cycle (corresponding to 25 min in the original dataset) until 75 min (corresponding to 100 min in the original dataset).
Using edgeR  38  , lowly expressed genes were removed by the automatic function filterByExpr and the read counts were normalized by the Trimmed Mean of M-values (TMM normalization), resulting in normalized log-counts per million (logCPM). Then, we calculated the log2FC for each gene at time point i (t i ) versus its mean over the entire first cycle as follows: where logCPM is the log-counts per million given by edgeR and < logCPM > is the average logCPM over time for a given gene. Genes with a |log2FC| higher than or equal to 0. 25 were considered differentially expressed and defined as query nodes at the respective layer where this cut-off criterion was met. As no replicates were available, we did not consider statistical significance for this dataset. Time-resolved assay and RNA-sequencing dataset of a mouse pain assay.
RNA sequencing. DRG RNAs were extracted in experimental duplicates from 2-3 mice each (2 pooled replicates). RNA-seq libraries were prepared using the TruSeq RNA Sample Preparation Kit (Illumina). All libraries were validated for concentration and fragment size using Agilent DNA1000 chips. Sequencing was performed on a HiSeq 2000 (Illumina), base calling performed using RTA (Illumina). Data processing of RNA-seq datasets. Mouse sequencing data were quality controlled using FastQC (https:// www. bioin forma tics. babra ham. ac. uk/ proje cts/ fastqc/). We used cutadapt (https:// cutad apt. readt hedocs. io/ 40 ,) to trim adapter sequences. Resulting trimmed reads were mapped to the M. musculus genome version 10 (mm10) using STAR aligner with default parameters. Mapped data were re-analyzed with MultiQC  41.
Raw read counts were determined and filtered as described above for the yeast dataset. Differential expression analysis was done using edgeR, comparing the time points 1-day post injection (PI), 3 days PI and 30 days PI always against the 0 day control prior to injection. Finally, we showed the evolution of gene expression for the significantly differentially expressed genes of the mouse dataset by first computing the z-score of log counts per million and then splitting the significantly differentially expressed genes according to the time of their significant differential expression. Raw fastq files were submitted to the Gene Expression Omnibus database under the accession number GSE161764. We defined layer-nodes as query nodes if the associated gene had an adjusted p-value lower than 0. 05 for that given time point (layer) versus the 0d control.
Thus, we merged multi-edges of a given node pair by taking the mean of their confidence scores. For this, we assumed that a PPI is equal to 2 directed edges and set the confidence score of each PDI to 1. The final edge lists gave the undirected intra-layer edge tables. The nodes of the tMLN represent both, the genes and the proteins as the same entities, in case a node is both, a protein in a PPI or a regulated gene in a PDI. Nodes of genes that were not detected in the RNA-seq datasets were removed. Consequently, edges where one partner was removed were also filtered out. The weight for each individual layer-node was computed as follows: where p_adj is the adjusted p-value and log2FC the log2 fold change for the time point represented by that layer. As no replicates were available for the yeast cell cycle data, the p_adj term was ignored for this dataset.
The node weight was then standardized between 0. 01 to 1 (a lower bound of 0. 01 was chosen to avoid rejection of nodes with weight 0 by extracting apps). Moreover, a layer-node was tagged as a query for a layer if this layer-node had a |log2FC|≥ 0. 25 for the yeast cell cycle dataset; or if it had been defined as significantly differentially expressed with an adjusted p-value < 0. 05 for the mouse dataset. Node names, node weights, as well as the information whether a node is a query node were contained in the node tables, enabling TimeNexus to set the correct attributes to the layer-nodes. Extraction of active subnetworks. Active subnetworks were extracted with TimeNexus in combination with either ANAT or PathLinker from the yeast cell cycle time-series dataset. The algorithm "anchored network" with the sub-algorithm "approximation" was applied for ANAT.
The network was set as "undirected" for Path-Linker. For performance tests with PathLinker, we selected the optimal parameter K = 750 by testing PathLinker with K-values from K = 50 to K = 2000 and optimizing for the F1-score (Supplementary Table  S4 ; see below for w node = -log10 p_adj * log2FC calculating performance measures). PathLinker with a K = 50 was used to extract an active subnetwork for the mouse dataset, as the network size and the number of queries were both smaller. All other parameters were chosen by default.
genome. jp/ pathw ay/ sce04 111)  46. In each extracted active subnetwork from the yeast cell cycle dataset, we counted the number of nodes in this active subnetwork (# subnetwork nodes) and the number of active subnetwork nodes overlapping with the 130 KEGG cell cycle genes (True Positives (TPs)). We then calculated the percentage of the active subnetwork size, the False Positives (FPs, as subnetwork size minus TPs), as well as the false negatives (FNs, as # KEGG cell cycle genes minus TPs). From these values, we computed a set of scores: the ratio of extracted nodes and the interactome size, as well as Recall, Precision, and F1-score as follows: In addition, we performed GO enrichment analysis of active subnetworks to test for relevance of extracted nodes for the biological process 'cell cycle'.
Results Core functions of the TimeNexus app. TimeNexus was developed with the idea to create a versatile framework for working with temporal multilayer networks in the Cytoscape environment (Fig. 2 ). This included a function to easily create tMLNs given tabular information on the structure of the network and its temporal dynamic -realized in the TimeNexus Converter. We wanted to enable users to visualize tMLNs in different cerevisiae cell cycle pathway, containing core components of the yeast cell cycle as defined by KEGG. Yellow nodes are differentially expressed query nodes in the first three time points (0 min, 5 min, 10 min) of the first full cycle in the time-series expression dataset  34  , blue ones are differentially expressed query nodes in the late time points 60 min -70 min; those with a gradient from yellow to blue are differentially regulated and therefore query nodes in both, early and late time points.
Blue lines (intra-layer edges) represent protein-protein interactions, red ones protein-DNA interactions. Dotted lines represent inter-layer edges. The interaction data were extracted from HitPredict and the YEASTRACT + databases, respectively. (b) An active subnetwork was extracted from the first three time points of the yeast cell cycle (0-10 min), containing genes differentially expressed in early phases of the cell cycle. Layer 1 (0 min): 28 nodes and 58 intra-layer edges; layer 2 (5 min): 41 nodes and 134 intra-layer edges; layer 3 (10 min: 35 nodes ad 95 intra-layer edges. (c) Enrichment analysis with genes in the early active subnetwork identified processes related to replication and active transcription. Enrichment results from the modEnrichR server for yeast were sorted according to the EnrichR combined score  48.
The functionality for active subnetwork extraction was realized in the TimeNexus extractor. We wanted to demonstrate and test the usability of TimeNexus by extracting active subnetworks from two temporal gene expression datasets: 1) yeast cell cycle expression data which offer highly resolved temporal information; and 2) mouse temporal gene expression data following pain response after injury with low temporal resolution. Active subnetwork extraction using TimeNexus and PathLinker identifies relevant processes involved in early and late cell cycle events in S. cerevisiae. We wanted to test TimeNexus using a well-described, temporal biological system. We chose the budding yeast cell cycle as our model system. During the cell cycle, cells duplicate their content, replicate their DNA and at the end of the cycle faithfully divide into two identical cells.
We created a tMLN of the first full cell cycle, representing time points 25-100 min as described in the original publication  34. For demonstration purposes, we focused on three early time points of the cell cycle, which are characterized by cell growth and DNA replication (time points 0 min, 5 min and 10 min representing time points 25 min, 30 min and 35 min of the original dataset); and three late time points, which fall into the mitotic phase (60 min, 65 min and 70 min, representing the time points 85 min, 90 min and 95 min of the original dataset; see Supplementary Table  S5 ). We also created a cell cycle interactome by adding HitPredict and YEASTRACT + interactions to the 130 cell cycle genes as defined by KEGG (KEGG pathway sce04111, https:// www. genome. jp/ pathw ay/ sce04 111), resulting in a network of 130 nodes (genes/proteins) and 390 intra-layer edges (interactions, see node table and intra-layer edge table in Supplementary Table  S5 ).
genome. jp/ pathw ay/ sce04 111) from the entire yeast interactome using the time-resolved cell-cycle expression data  34. Log2FC was calculated as described above. The absolute log2FC was used as node weight, to compute inter-layer edge weights and to define layer-nodes as queries when their weight was equal to or higher than 0. 25 (Supplementary Tables  S6, S7 ). We built a high-quality interaction network for S. cerevisiae which included protein-protein, as well as protein-DNA interactions and contained 5785 nodes and 110 000 intra-layer edges in its aggregated state (see Methods and Supplementary Table  S6 ). We constructed the tMLN using the TimeNexus Converter and extracted active subnetworks using PathLinker or ANAT. For PathLinker, the parameter K was set to 750 after optimization (Supplementary Table  S4 ).
whether we could use TimeNexus on other systems, other model organisms and with less dense time-resolved data on differential gene expression. We used our own data from a time-resolved study of recovery from acute pain in mouse. In this experiment, Carrageenan is injected in the mouse hindpaw, inducing inflammation and mechanical hypersensitivity (Fig. 4a ). The onset and the recovery from hypersensitivity can be measured by testing the ability of mice to respond to Von Frey filaments with increasing caliber. In this pain model, one day after Carrageenan injection, mice exhibit a significant decrease in their mechanical thresholds, which is a sign of inflammation-induced mechanical hypersensitivity. At day 3 post-inflammation (PI) mice recover normal mechanical sensitivity which remains steady at day 30 PI and beyond (Fig.
4 b ). In conclusion, acute pain induced a temporary significant differential expression of genes in DRG and the vast majority of genes returned to basal expression levels after full recovery of the mouse at day 30. We next were interested whether we could extract active subnetworks relevant for this process from expression data integrated with the mouse PPI interactome using tMLNs. We built a high-quality mouse interactome using HitPredict, containing 3994 nodes and 7296 intra-layer edges in its aggregated form. We created a pain node table from the differential expression data for the three time points 1d, 3d and 30d PI compared to the 0d time point prior to injection. Query nodes were defined as having an adjusted p-value lower than 0. 05 (Supplementary Table  S9 ). Using TimeNexus, we generated the tMLN for these data.
These objects are created either globally over the entire tMLN by combining all layers in a single-layer network with layer-nodes as separate entities and by ignoring the differences between intra-and inter-layer edges; pairwise by creating a single-layer network from two consecutive layers similar to the global method, over the entire tMLN structure; or one-by-one by creating a single-layer network for each individual layer. The global method has the drawback that the network to be analyzed increases drastically, as the initial interactome is multiplied by the number of layers. Active subnetwork extraction is therefore computeintense. Moreover, only nodes from the first and last layers will be used as source and target nodes and active subnetworks will only be extracted if they span the entire dataset.
Figure3. TimeNexus extracts active subnetworks from the yeast cell cycle interactome enriched in relevant biological terms related to cell cycle from early and late cell cycle stages. (a) Aggregated network of the S. cerevisiae cell cycle pathway, containing core components of the yeast cell cycle as defined by KEGG. Yellow nodes are differentially expressed query nodes in the first three time points (0 min, 5 min, 10 min) of the first full cycle in the time-series expression dataset 34  , blue ones are differentially expressed query nodes in the late time points 60 min -70 min; those with a gradient from yellow to blue are differentially regulated and therefore query nodes in both, early and late time points. Blue lines (intra-layer edges) represent protein-protein interactions, red ones protein-DNA interactions.
Dotted lines represent inter-layer edges. The interaction data were extracted from HitPredict and the YEASTRACT + databases, respectively. (b) An active subnetwork was extracted from the first three time points of the yeast cell cycle (0-10 min), containing genes differentially expressed in early phases of the cell cycle. Layer 1 (0 min): 28 nodes and 58 intra-layer edges; layer 2 (5 min): 41 nodes and 134 intra-layer edges; layer 3 (10 min: 35 nodes ad 95 intra-layer edges. (c) Enrichment analysis with genes in the early active subnetwork identified processes related to replication and active transcription. (d) An active subnetwork of late time points in the cell cycle (60-70 min) was extracted. Layer 1 (60 min): 22 nodes and 72 intra-layer edges; Layer 2 (65 min): 27 nodes and 101 intra-layer edges; Layer 3 (70 min): 21 nodes and 70 intra-layer edges.
Acknowledgements Mouse and yeast interaction data from HitPredict, as well as YEASTRACT+ interactions were kindly provided by the respective authors of the resources. We thank  Andy Saurin  for support in RNA-sequencing of mouse pain assay samples. We thank  Anais Baudot ,  Maxime Lucas ,  Friedhelm Pfeiffer  and all members of the  IBDM Computational Biology Team  for critical reading of the manuscript. This work was supported by  ANR -grant  17-CE16-0020-02  (Myochronic) awarded to AM and BHH,  Aix-Marseille University  and the  French National Centre for Scientific Research (CNRS).
Nevertheless, none of the available methods provides complete coverage of isotopic space, even for small metabolites. Integrating measurements from different approaches should expand the range of quantifiable isotopic species  7  , thus improving the coverage of isotopic space. This is exploited in 13 C-fluxomics studies, where different datasets are frequently integrated using isotopic models of metabolic networks to improve flux quantification  4, [15] [16]. However, a major drawback of model-based integrative approaches is their strong dependence on the assumptions and simplifications of the model (e. g. the topology of the metabolic network as defined in the model) and the fact that labeling has to be quantified in several metabolites. As an alternative approach, intuitive reasoning has proven useful in improving the coverage of isotopic space by defining relationships between isotopic measurements directly at the level of the molecule.
EXPERIMENTAL SECTION 13 C-labeled standard of amino acids. The reference material we used to evaluate the proposed methodology was a biologically produced sample containing 13 C-labeled amino acids with a controlled and predictable isotopic composition: the isotopic species of each amino acid are forced to all be present at the same concentration. The nature, production, and qualification of this standard sample have been described in detail previously  9, 19. Isotopic analyses. We analyzed the 13 C-labeled reference material by GC-MS  20  , and by HSQC  15  and ZQF-TOCSY  15  NMR experiments, as detailed in the corresponding publications. We also gathered additional (HNCA, HACO-DIPSY, LC-MS) datasets for this reference material from the literature  11, 19. Implementation of the computational framework.