dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). There are R package interfaces for all of these programs, including R2OpenBUGS  (Sturtz et al. , 2005) , rjags  (Plummer, 2019) , and rstan (Stan Development Team, 2019), respectively. The R package nimble (de Valpine et al. , 2017) and its nimbleEcology extension for common HMMs in ecology  (Goldstein et al. , 2019)  use a statistical programming language similar to BUGS and can be used for Bayesian or maxmimum likelihood inference. The R package TMB  (Kristensen et al. , 2016)  generally has a steeper learning curve but can be advantageous for maximum likelihood inference (e. g. Benhaiem et al. , 2018; Marescot et al. , 2018) , particularly for mixed HMMs that include continuous-valued random effects  (Altman, 2007). From a computational point of view, neither maximum likelihood estimation nor MCMC sampling is vastly superior, and in practice users will typically adopt the approach they are most comfortable with (cf.
We consider the full analysis cycle, comprising the formulation of an HMM, the estimation of its parameters, the selection between different candidate models, checking how well the model fits the data, inferring the latent states, and interpreting the final results. The supplemental R (R Core Team, 2019) script caracaraExample. R can be used to completely reproduce and further explore this illustrative example using the package momentuHMM  (Mc-Clintock and Michelot, 2018).
It is common to consider the activity level of an animal to vary across different behavioural modes (see Section 3. 1. 3 in the main text). The model states are thus intended to correspond with these behaviours. Whereas in some applications of HMMs the choice of the number of states can be obvious, such as in the Cormack-Jolly-Seber model with the two states "alive" and "dead" (see Section 3. 1. 1 in the main text), there are also such where it is not clear a priori how many states there are. This is indeed the case in the caracara example considered here. In cases such as these the choice of N should be guided by expert knowledge on the subject matter. In the given VDBA example, the research biologists who collected the data expected N = 4 states, corresponding to resting behaviour, minimal activity such as preening, moderate activity such as walking or digging, and flight, to most adequately reflect the variation in activity.
For example, in the VDBA example, we simulated 100 datasets under the fitted model with N = 4 states. Figure  4  shows the quantile function and ACF estimated from these simulations. Overall, we find that the HMM fits the data fairly well, capturing the overall patterns in the data. However, we can also see the model's deficiencies: the quantile plot shows a minor lack of fit for very small observations, and the ACF shows that the correlation between neighbouring observations is higher than expected under the model, and that correlation at higher lags is also underestimated. The poorer fit of the ACF may indicate the limitation of the Markov assumption and the assumed geometric distribution in state dwell times, or alternatively a violation of the conditional independence assumption of the observations.
Numerical methodology In the present manuscript, we adopt the same numerical methodology used in  Fabre et al. (2020)  and described in  Fabre et al. (2019). The computation of the steady-state solutions, the resolution of the linear problems and the time stepping techniques are implemented using the open-source finite element software FreeFem++. Parametric studies and generation of figures are performed using Octave/Matlab thanks to the generic drivers of the StabFem project (see a presentation of these functionalities in  Fabre et al. 2019). According to the philosophy of this project, codes reproducing parts of the results of the present paper are available from the StabFem website (https://gitlab. com/stabfem/ StabFem). On a standard laptop, all the computations discussed below can be obtained in a few hours, except time stepping simulations which take longer.
The AIR serves as an open access knowledgebase, including a gateway to numerous public databases. It is furthermore possible for the user to map experimental data onto the molecular interaction maps of the AIR, providing the basis for bioinformatics analyses and systems biology approaches. By mapping experimental data onto the Atlas, it can be used to elucidate drug action as well as molecular mechanisms underlying different disease phenotypes. For the visualization and exploration of information, the AIR uses the Minerva platform, which is a well-established tool for the presentation of disease maps. The molecular details of the AIR are encoded using international standards. The Atlas of Inflammation Resolution was created as a freely accessible resource, supporting research and education in the fields of acute inflammation and inflammation resolution.
Technology and usages AIR allows users to visualize detailed molecular level events underlying biological processes, pathways and cell/tissue-related phenotypes associated with acute inflammation initiation, transition, resolution and finally return to homeostasis. All the submaps are prepared using standard Systems Biology Markup Language (SBML)  29  to ensure their reusability and are accessible through the MINERVA platform 30 using a web-browser. The AIR is a portal which enables connection to state-of-the-art publicly available databases, providing information about genes, proteins, lipids, miRNAs, lncRNAs, chemicals and drugs. It is furthermore a tool to highlight research gaps and supports the formulation of new hypotheses to address those gaps.
In case of the bottom-up approach, we first reviewed published literature to identify molecular processes, cell and tissue specific phenotypes associated with one of the four acute inflammatory phases i. e. inflammation initiation, transition, resolution and return to homeostasis (Figure  1 ). For each of the processes/phenotypes, we manually screened literature and databases (Reactome (https://reactome. org), KEGG (https://www. genome. jp/kegg/), InnateDB (https://innatedb. com)) to extract experimentally validated signaling and regulatory events. These are finally represented by a process diagram using the CellDesigner software (http://www. celldesigner. org) in standard systems biology graphics notation (SBGN) representation. The top-down approach to the acute inflammation response started with the collection of key molecular signatures (seed molecules).
These molecular signatures are then extended with known interacting and regulatory partners. As acute inflammation can be triggered by a variety of etiological agents, from which we considered three sets of seed molecules for the construction of AIR, these are 1) damage associated molecular patterns (DAMPs); 2) receptors recognizing pathogen associated molecular patterns (PAMPs); and 3) disease genes from selected acute inflammatory clinical phenotypes. While DAMPS and PAMPs-recognizing receptors were mainly identified through research articles, key molecules associated with acute inflammatory clinical indications were screened through disease-gene association databases. To this end, we mainly used DisGeNET (https://disgenet. org), eDGAR (http://edgar. biocomp. unibo. it), KEGG disease (https://www.
genome. jp/kegg/disease) databases. As many of the disease-gene association databases are based on text mining algorithms, we manually cross checked the disease-gene association from associated research papers. For each of the seed molecules, we identified interacting molecular components from literature and databases to prepare a molecular interaction network. We also used the Bisogenet 3. 0. 0 app 34 available on Cytoscape 3. 7. 0 which connects large number of biological databases (e. g. DIP (https://dip. doe-mbi. ucla. edu/), BioGRID (https://thebiogrid. org), HPRD (https://hprd. org), IntAct (https://www. ebi. ac. uk/intact/), MINT (https://mint. bio. uniroma2. it)) to first create biological networks around each of the seed molecule. We extracted only experimentally validated interactions.
mirbase. org), miRTarBase (http://mirtarbase. mbc. nctu. edu. tw), TriplexRNA (https://triplexrna. org); transcription factors from TRNSFAC (http://genexplain. com/transfac), TRRUST (https://www. grnpedia. org/trrust) and HTRIdb (http://www. lbbc. ibb. unesp. br/htri); long noncoding RNAs from EVLncRNAs (http://biophy. dzu. edu. cn/EVLncRNAs), lncRNADisease (http://www. cuilab. cn/lncrnadisease) databases are also integrated with the AIR using an inhouse script. The overall workflow for the construction of AIR is described in Figure  2.
AIR as a directed graph Providing direction (e. g. activation, inhibition) to the edges connecting various nodes in the network is a crucial step for network topological analyses and for initiating dynamic systems biological models for the prediction of biomarkers and therapeutic candidates. Only a directed graph will provide mechanistic insights through the study of network motifs and system dynamics. Giving direction to interactions is however a manual effort. Databases using text mining approaches are prone to include false positive information about regulatory directions. We therefore manually cross-checked associated publications before providing directions to the connected edges. More than 80% of the total interactions in the network are directed as of December 2019. The AIR is not just an addition to the existing knowledgebase around acute inflammation but also partially parameterized which can be easily customized for context-specific (e.
Annotation of AIR content We enriched every gene/protein in the AIR with specific UniProt-, HGNC-, RefSeq-, Ensembland NCBI-ID as well as common aliases with the full name of the encoding protein. In case of small molecules, the ChEBI-ID is provided. Every interaction in AIR is hyperlinked to the respective literature and database. With all the manual curation and annotation, we make AIR a reliable resource for studying processes related to acute inflammation and its resolution.
Entry to the AIR The AIR is a collection of molecular, subcellular, cellular, tissue and functional maps in the context of acute inflammation and inflammation resolution. Depending on the user's need, the AIR provides various entry points to access all the curated information. AIR is designed with the aim to serve diverse communities including clinicians, research scientists and drug developers from pharmaceutical industry. Clinicians generally rely on the higher-level organization, i. e. at the cellular and tissue level to understand the pathophysiology of disease. They are interested in either classifying patients into various risk groups or in therapy scheduling based on patient's pathological reports. Time-series transcriptomics profiling of patients is getting popular day-byday in hospitals which motivate clinicians to use the data in therapy personalization without involving themselves in a data analysis pipeline  36.
The AIR as a portal to connect public databases All biomolecules and chemicals present in the AIR are manually annotated with official names (official HGNC symbol for genes and proteins, ChEBI name for drug and chemicals) and reactions are manually annotated with PubMed IDs. The majority of the state-of-art databases can be linked out directly by selecting nodes or reactions present in the AIR (Figure  5 ). (A) (B) (C) Figure  5 : The AIR as a portal to connect public databases. (A) A node 'PTGS2' is selected. All the link outs to various state-of-the-art databases (HGNC, Entrez gene, gene, Pubmed, Reactome, Refseq, Uniprot etc. ) are directly available on the left panel. (B) A reaction is selected and the literature from where the reaction is derived is provided as Pubmed IDs. Wherever possible, we also summarize the reaction in the context of acute inflammation and inflammation resolution in the description section.
The AIR as a ready-to-use resource from structure analysis to dynamic models All the submaps available on the AIR are prepared in standard SBML notation along with complete annotation using CellDesigner tools to ensure their reusability. These submaps can be directly downloaded from the AIR and used for in silico simulation, perturbation experiments or network analysis. The majority of the proteins that have a 3D structure already resolved and available in Protein Data Bank can be directly visualized using MolArt visualization plugin  42  integrated in MINERVA environment (Figure  5C ). This plugin helps users in exploring both sequence and structural features (including protein variation data from large scale studies) associated with proteins. In addition to the sequence and structure level features of the protein, one can connect 3D structures of protein complexes, drugs and chemicals bound to their receptor proteins which can be directly integrated into structure-based drug development pipelines.
Visualization of time-series omics data AIR hosted on the MINERVA platform provides the user with an interactive interface to map timeseries omics data. Once uploaded on the AIR, these data can be visualized on all the interconnected submaps. Nodes are overlaid with multiple color bars depending on the number of associated time-points (Figure  6 ). By providing a visual representation of change in the node expression profile over time, this feature helps users in designing new hypotheses on the role of connected nodes regulating a phenotype. 48h ) and mapped onto the AIR when mice were challenged with higher titre E. coli (10 7 c. f. u. ) compared to self-resolving E. coli infection (10 5 c. f. u. )  43. The color gradient from red to blue indicates downregulation and upregulation. The color bars demonstrate that all the pro-inflammatory lipid mediators (PGE2, 5S-HETE and LTB4) are mostly upregulated from time point 0h to 48h due to high exposure of E.
The AIR in the approximation of aggregate influence on molecular processes / clinical phenotypes due to the change in the level of MIM components The AIR contains a large number of complexes and placeholders (gene / protein classes known to perform qualitatively similar biological function) regulating biological processes and phenotypes. These processes / phenotypes are described in the AIR from gene ontology databases e. g. Gene Ontology Resource available (http://geneontology. org), Mammalian Phenotype Ontology (http://informatics. jax. org). To facilitate a rapid assessment of the influence of change in the concentration/expression profile of regulatory components on the phenotype level (e. g. increased acute inflammation; increased vasodilation; decreased neutrophil numbers; efferocytosis etc.
) at various time-points and/or in various experimental conditions, the AIR implements various logic-based rules to first assign expression profiles to these complexes and place holders. All the submaps present in the AIR are accessible as standard SBML files, which can be analyzed using several state-of-art tools and algorithms to predict the influence on the phenotype. Example of an algorithm that gives a measure of how changes in the levels of components in the MIM contribute to a phenotype is provided in Figure  7. The influence of the MIM components on the phenotype levels may help clinicians in making quick decision on disease state after mapping of patient specific multi-omics data onto the AIR. for the analysis of phenotype level from multi-omics data mapped on the AIR. Left: An example where we measure the influence on the phenotype level (a.
Conclusions We have developed a comprehensive Atlas of Acute Inflammation and Resolution or AIR, covering more than 30 highly interconnected submaps at the molecular level. Key points summarizing the AIR are provided in Text Box 1. The AIR is designed with the aim to connect clinicians, biochemists, systems biologists, computer scientists and drug developers. Depending on the need of the end-user, the AIR provides various levels of representation and organization of data and models. Resources such as AIR need continuous improvement with the inclusion of missing links at the molecular level as soon as they are published. This can be realized only through community efforts. The AIR provides an interactive platform to connect the community; thus, we hope that the AIR will be sustained in future by the community.
Text Box1: The AIR at a glance: • The AIR is the first comprehensive collection of molecular interaction maps underlying acute inflammation initiation, transition, resolution, repair and return to homeostasis. • The AIR for acute inflammation and inflammation resolution is prepared by extending disease genes associated with primary clinical indications of acute inflammation and known DAMPs, PAMPs with experimentally validated interacting partners. • The AIR provides biosynthesis and down-stream signaling cascades of protein mediators (e. g. annexin-2, IL-10, TGF-b, INF-a) and lipid mediators (e. g. Prostaglandins, Leukotrienes, Lipoxins, Resolvins, Protectins and Maresins) along with time-series LC-MS-MS data from selected acute-inflammatory phenotypes which can be used for designing new therapeutics.
• The AIR is a portal to other databases (e. g. miRTarbase, UniProt, GenBank, PubMed etc). • Users can type in the name of protein / gene / regulatory molecule / lipid / biological processes to find the associated functional modules. • Connections to known drugs and chemicals can be searched directly from the AIR with linked databases. • Molecular interaction maps available on the AIR are both human and machine readable in a standardized SBGN, SBML format to allow the reproducibility. • The AIR can be visualized with several regulatory layers including transcription factors, miRNA, lncRNA, drug candidates. • The AIR itself becomes a knowledge-base to generate hypotheses around acute inflammation and inflammation resolution. • The AIR provides a scaffold to understand/hypothesize the drug mode of action in inflammation resolution.
Acknowledgements The  AIR uses Minerva , developed by the  Luxembourg Centre for Systems Biomedicine (LCSB). We are grateful for the Minerva support provided by  Piotr Gawron  and  Marek Ostaszeweski. The LCSB is also the elixir node hosting the AIR. We acknowledge  Tom Gebhardt  and  Martin Scharm  for providing IT support for the implementation and installation of the AIR. V. Chiurchiù was supported by grants  FISM 2017/R/08  and  GR-2016-02362380. S. Gupta  and  O. Wolkenhauer  acknowledge support from  Bundesministerium für Bildung und Forschung (BMBF)  grants [MelAutim ( 01ZX1905B ) and SASkit (012X1903B)] and funding received from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  765274. C. N. Serhan acknowledges support from  USA   NIH GM038765.
To summarize, the implications of this new approach for the understanding of urban micro-climates are fourfold. Firstly, mobile measurements to acquire air temperature are used on the second French conurbation, which has never been thermally modelled, despite marked thermal discomfort. Then, this air temperature will be modelled with a very large sample of explanatory variables, including classic topo climatic variables (altitude, longitude, latitude, slope, exposure, and so on), variables derived from the characteristics of the urban morphology (sky view factor, variation in the height of buildings, etc. ), or variables linked to the occupation and nature of the soil (vegetation, moisture, water, bare soil, etc. ). One of the special features of this study for the acquisition of explanatory variables is the use of very diverse but complementary techniques, notably through the use of LiDAR or the analysis of data produced by the Landsat satellite.
Data Acquired by the Measuring Instruments and Selected Days Air temperature is the variable to be estimated at any point in the territory from several indicators. The training sample of this variable is obtained from mobile measurement transects using high-precision measuring devices, according to manufacturer's data. The first equipment used is the EL-USB-1-RGC (EasyLog From Lascar Electronic). It measures the air temperature continuously, with an accuracy of +/-1 °C (manufacturer's data) and a minimum recording interval of 1 second. The second equipment, the LOG 32 (from Dostmann electronic GmbH), records relative humidity and air.
Data Acquired by the Measuring Instruments and Selected Days Air temperature is the variable to be estimated at any point in the territory from several indicators. The training sample of this variable is obtained from mobile measurement transects using high-precision measuring devices, according to manufacturer's data. The first equipment used is the EL-USB-1-RGC (EasyLog From Lascar Electronic). It measures the air temperature continuously, with an accuracy of +/-1 • C (manufacturer's data) and a minimum recording interval of 1 second. The second equipment, the LOG 32 (from Dostmann electronic GmbH), records relative humidity and air temperature, with an accuracy of +/-0. 5 • C (manufacturer's data) and +/-3% (40 to 60%) and a minimum recording interval of 2 seconds. The measurement campaigns were associated with a precision GPS (from Garmin, with a high sensitivity GPS/GLONASS receiver and Quad Helix antenna) to record the geographical position of the measurement.
). In this study, thirty-eight explanatory variables contributed to the estimation of air temperature over the study area  [85] [86] [87] [88] [89]. They belong to various categories such as climate data from remote sensing, topographic variables, vegetation indices, the presence of water, moisture, bare soil, buildings, radiation, urban morphology, and proximity to various land uses (Table  4  and Appendix A). The acquisition sources were multiple and came from the Landsat 8 satellite (https://earthexplorer. usgs. gov/), LiDAR (https://data. grandlyon. com/jeux-de-donnees/nuage-points-lidar-2018-metropole-lyon-formatlaz/donnees) points and other cartographic products downloaded from the open data platform of the Greater Lyon. These morphological descriptors are acquired to a spatial precision that can go down to the centimeter scale.
Three Complementary Regression Methods in Modelling Use Three regression methods of air temperature modelling are compared in this study. These are two linear regressions: multiple  [42, 50, 56]  and partial least square  [90] , and one non-linear regression: random forest  [91, 92]. The aim is to select the best regression for this modelling. This evaluation is essentially carried out by comparing the coefficients of determination and the roots of the root mean square error (RMSE) obtained for the samples. The conditions of use for each of the regressions were also verified. Multiple linear regression (MLR) is a data modelling method that requires several statistical steps before its application  [93]. First, it is necessary to verify the normal distribution of the series in the dataset using the Shapiro-Wilk test (applies to samples of less than 5000 observations)  [94].
Single -cell transcriptomics offers unprecedented opportunities to infer the ligand-receptor (LR) interactions underlying cellular networks. We introduce a new, curated LR database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted LR interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entrylevel users and available from https://github. com/ SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways.