dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets Regarding the datasets, for the RA macrophage map, we used GSE97779, a publically available microarray dataset from the GEO database  (Edgar, 2002). The dataset contains nine RA synovial macrophages sample from nine patients and five peripheral blood monocyte-derived macrophages samples from five healthy donors. Gene expression was quantile normalized using the preprocessCore package  (Bostald, 2021). GSE164498, a RNA-seq single-cell dataset from the GEO database, was used to provide M1 and M2 macrophage signatures. It contains 1766 M1 macrophage cells and 2063 M2 macrophage cells. M1 macrophages were polarized using LPS, and IFN-Gamma and M2 macrophage cells were polarized using IL-4 and IL-13. We used GSE109449, a RNA-seq single cell dataset available in the GEO database for the RA fibroblast map.
It contains 384 freshly isolated fibroblasts in two RA and two osteoarthritis patients. For the RA Th1 map, we used SDY998, a single-cell RNA-seq dataset from the Immport database  (Bhattacharya et al. , 2018)  containing 19 samples from RA patients and two synovial samples from osteoarthritis patients with three cell types: 1142 B cells, 1844 fibroblasts, 750 monocytes, and 1529 T cells. The gene expression was normalized using log2 (CPM). For the GSE analysis, we used four datasets: SDY998, GSE32901, GSE107011 and GSE135390. The first dataset is the same one used for DEA. The second is a microarray gene expression dataset containing PBMC samples from 5 healthy donors. It includes four CD4+ T cells subtypes: naive CD4+ T cells, Th1, Th17 rich, and Th17 poor memory CD4+ T cells. Gene expression was normalized using the log2 RMA method.
The third dataset is bulk RNA-seq transcriptome profiling of 29 immune cell types, including Th1 and extracted from PBMC sorted from 4 healthy individuals. The counts were normalized using the Transcripts Per Million (TPM) method. The last dataset is RNA-seq gene expression profiling. It contains PBMC samples coming from 3 healthy donors. Several subtypes of CD4+ T cells are in the samples: Th1, TH2, TH22, Treg, naive CD4+ and Th17. Count data were normalized using TMM. In addition, we used GSE172188, a microarray gene expression dataset of synovial samples from ten patients with active RA treated with Abatacept. For each patient, synovial biopsies were obtained from the same affected joint before (W0) and 16 weeks after (W16) starting treatment with Abatacept.
Macrophage Map Integration The macrophage map was constructed as a PD graph on CellDesigner by integrating three maps as a base. The initial map was the RA-specific macrophage map from IPA including 44 molecules and 47 interactions associated with PMIDs. The second and third macrophage-specific maps (but not RA specific) were retrieved from the published literature  (Oda et al. , 2004; Wentker et al. , 2017). As the second and third macrophage maps were not RA-specific, we used the RA-specific marker gene lists from IPA as an overlay in MINERVA to identify disease-specific molecules and submodules. The Stream export plugin  (Hoksza et al. , 2019)  in MINERVA allowed us to export these submodules of interest from the macrophages maps. We identified corresponding signalling pathways in the extracted submodules and completed them by adding molecules from the literature and pathway databases like KEGG.
M1 and M2 Macrophages Map Enrichment Differential Expression Analysis (DEA) was performed using the preprocessCore and Limma packages  (Ritchie et al. , 2015)  in R on the GSE97779 dataset (cf. dataset section). The list of DEG was used for enrichment with IPA and MetaCore. To assign the newly identified pathways to the M1 or the M2 phenotype, DEA was performed on GSE164498 (cf. dataset section) using Bioturing software  (Le et al. , 2020)  with the Venice method  (Vuong et al. , 2020)  and M1 versus M2 macrophage cells to identify signature genes for both M1 and M2 phenotypes. The percentage of shared genes between each newly identified signalling pathway and M1 and M2 signatures was calculated. Based on these criteria, new pathways were assigned to the M1, M2 maps, or both.
Fibroblast Map To build the RA fibroblast-specific map, we filtered non-fibroblast-specific components from the initial RA-map based using the up-to-date RA fibroblast overlay available in MINERVA (cf. overlay section). Non-fibroblast-specific components were only kept if necessary to transduce the signal. We used the BioTuring software and the Venice method to perform DEA on GSE109449 (cf. dataset section). The filtered DEG list was used as an overlay in MINERVA to retrieve more RA fibroblastspecific components from the RA global map.
Th1 Map Regarding the Th1 map, we did not integrate any pre-existing cell-specific map. Alternatively, we first searched the literature for the main signalling pathways expressed in the Th1 subtype and involved in RA's pathogenesis (using queries like "Th1" and "rheumatoid arthritis"). In addition, we used the multicellular map of inflammation  (Serhan et al. , 2020)  and its not-RA-specific Th1 submap to identify transcription factors and cell-specific receptors involved in RA pathogenesis. We integrated the SDY998 gene expression dataset (cf. dataset section) to enrich the map with new pathways. Clustering was performed on the T cells using the Seurat package  (Hao et al. , 2021)  in R (Find_neighbors and Find_clusters functions). Based on the expression of the marker genes CXCR3, CCR7 and CCR6, the Th1 cluster was identified.
We performed DEA using CXCR3+ CCR7-CCR6-Th1 cluster versus CCR7+ naive cells from RA samples using Seurat and Find_marker_genes function. The filtered DEG list was used to identify new pathways using IPA and MetaCore tools described above. To expand the Th1 map, we also performed Gene Set Enrichment Analysis (GSEA) using GSEA software  (Subramanian et al. , 2005) , IPA gene sets and the SDY998 gene expression dataset. The list of enriched gene sets was filtered with a False Discovery Rate (FDR) less or equal to 5% to keep only the statistically significant ones. To ensure that the results are Th1 specific, we performed GSEA on three other datasets: GSE32901, GSE107011 and GSE135390 (cf. dataset section), and filtered the enriched gene sets with FDR less or equal to 5%. Only gene sets identified with the SDY998 dataset shared at least by one of the GSE32901, GSE107011 or GSE135390 datasets were kept.
M1/M2 macrophages map enrichment with gene expression datasets section), and the 44 molecules present in the IPA RAspecific macrophage map (cf. Macrophage maps integration. It consists of 648 RA macrophagespecific molecules. The Th1 overlay consists of 523 molecules, including the list of DEG obtained previously, the core enrichment genes from the GSEA (cf. Th1 map section), and information found in the literature. Finally, the serum overlay was created to account for a new source of information used in the maps.
RA Macrophage Maps We obtained the global macrophage map by converting the IPA cell and disease-specific map into an SBGN PD compliant map in CellDesigner. New pathways were added from the RA-specific submodules exported from the previously published maps  (Oda et al. , 2004; Wentker et al. , 2017)  like TLRs pathway, NFKB pathway, PI3K-AKT pathway, MAPK signalling pathway, and IL-18. Other pathways were identified from the literature, such as the IFN pathway, Notch pathway, IL10 and IL23 pathways. KEGG, MSig database and literature screening allowed to add 8 phenotypes to the map. The phenotypes include inflammation, apoptosis, angiogenesis, extracellular matrix degradation, cell chemotaxis/migration, T cells activation, proliferation/survival and osteoclastogenesis.
RA Macrophage M1 Map The RA macrophage M1-specific map includes all the signalling pathways from the RA macrophage map leading to the macrophage polarization into the pro-inflammatory M1 phenotype. It was enriched using gene expression dataset integration to identify new RA M1 macrophage-specific pathways using IPA and MetaCore. The top 5 identified signalling pathways are shown in Table  2. The RA macrophage M1-specific map includes 640 species: 278 proteins, 76 genes, 76 RNAs, 114 molecular complexes, 8 simple molecules and the same phenotypes as the RA macrophage map. These components interact together via 448 reactions. 61% of the components of this map are RA macrophage-specific (Figure  5 ). In addition, all the receptors and secreted components are RA macrophage-specific. Although less literature is available compared to the RA Fibroblast map, gene expression datasets integration enabled us to keep a high RA macrophage specificity in the map.
Applications Several options are available in MINERVA for the user to dive into the different RA-Atlas maps. Users can explore the maps using the search bar and get the locations of their compounds of interest and their associated references (Figure  6A ) or visualize experimental data and cell-specific components using the appropriate overlays (Figure  6B ). Users can exploit online queries for drug targets via DrugBank  (Wishart et al. , 2008)  and CHEMBL  (Gaulton et al. , 2017). Users can also use plugins available in MINERVA, such as the Stream-export, to export parts of the network upstream and downstream of a node. GSEA plugin can be used to calculate enrichment for uploaded user-provided data overlays. We performed GSEA by using a list of DEG from the GSE172188 dataset (cf. dataset section) between the paired post-and pre-treatment samples as an overlay to identify enriched signalling pathways from the fibroblast map in RA patients after treatment with Abatacept compared to untreated RA patients.
Data Availability The RA-Atlas is freely accessible at https://ramap. uni. lu/minerva/. All PMIDs of scientific articles used for the map construction are available in the annotation section of the maps. All datasets used for the map construction are publicly available (cf. dataset section). All data and code used to generate results, including XML files of the maps, cell-specificity and confidence score calculations, overlays, and gene expression analysis are available on a GitLab repository at https://gitlab. com/genhotel/a-mechanistic-multicellular-atlas-of-the-rheumatic-joint (accessed on June, 8 th 2022).
Generic mathematical model of the microtubule tyrosination cycle The schematic outline of our modeling pipeline (Fig  1 ) illustrates our model building method. We first design a Chemical Reaction Network (CRN) focusing on the main molecular species and parameters governing the microtubule tyrosination cycle, without taking into account all the other known microtubule-regulating proteins with their associated signaling pathways, due to their high number and unknown kinetics  [43, 49]. We focus on tubulin and microtubule in their detyrosinated and tyrosinated forms and on the TCP and TTL enzymes as main regulators (Fig  1A ). This CRN is formatted in the Systems Biology Markup Language (SBML)  [50]  and implemented in the Biochemical Abstract Machine (BIOCHAM) software, a modeling environment for systems biology and synthetic biology  [51, 52].
CDT P model obtained by fitting the CDT N model to experimental data We performed HCI experiments to quantify the tyrosination status in hTERT RPE-1 and MEF cells (Figs 2A, 2B, S1 and S1 Data). Tubulin and microtubule are found to be predominantly tyrosinated  (Fig 2B and S1 Data). The observed ratio of fluorescence of tyrosinated over detyrosinated species is greater by a tenfold ratio in those cellular models  (Fig 2B and S1 Data). Our goal is thus to find the minimal changes in the CDT N model that make the system stabilize with a tenfold ratio of tyrosinated over detyrosinated species no later than five minutes knowing that the tyrosinated microtubule half-life is of the order of minutes  [58] [59] [60]. We formalized that constraint in quantitative temporal logic and used BIOCHAM software to search for parameter values to reproduce that observed behavior by continuous optimization (see Parameter search procedure in Materials and Methods).
Image acquisition was performed using either the Cell Voyager 8000 (Yokogawa Inc. ) or the Opera Phenix (PerkinElmer, Waltham, MA, USA) high content screening systems. Images were captured using a 40X or 20X water immersion objective depending of the cell line and the cell morphologies. Confocal images acquired by the Opera Phenix were then analyzed using the Harmony software (PerkinElmer) and those acquired by the Cell Voyager 8000 were first transferred to the Columbus Image Data Storage and analyzed using the Columbus Analysis System (PerkinElmer). Since we used proliferative cells and neurons as cellular models, we had to optimize the image segmentation parameters to perform a consistent detection of the cellular compartments (nucleus and the cytoplasm) in these cellular models. To improve the nuclear segmentation, the nuclear staining channel image is first filtered using a regular Gaussian convolution kernel.
Modeling software biochemical abstract machine (BIOCHAM) The Biochemical Abstract Machine (BIOCHAM) is a modelling software for cell systems biology based on Chemical Reaction Networks (CRN), with some unique features for static analysis, and dynamic analyses using temporal logic constraints. BIOCHAM is compatible with the Systems Biology Markup Language (SBML). BIOCHAM is a free software protected by the GNU General Public License GPL version 2 (http://lifeware. inria. fr/biocham4/). The online version 4. 5. 17 of BIOCHAM was used in this study.
BIOCHAM software uses the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), a state-of-the-art black-box continuous non-linear optimization algorithm  [76] , to infer parameter sets satisfying such FO-LTL(Rlin) constraints  [51, 52]. In our study, we optimized the kinetic parameters mc 1 and mc 2 to parameterize the CDT N model using the following BIOCHAM command: The two different kinetic parameter values of the CDT P model have been obtained by BIOCHAM's optimization procedures for fitting the CDT N model to experimental properties observed in proliferative cells and formalized in quantitative temporal logic  [51, 52]. The parameter search procedure aims at finding the minimal changes in the CDT N model to make the system stabilize before five minutes and with a tenfold ratio of tyrosinated over detyrosinated species in accordance to our experimental and literature data.
Multistability analysis BIOCHAM software can check necessary conditions for the existence of multiple non-degenerate steady states in the differential dynamics of a reaction model, by checking the existence of positive circuits in a labeled influence graph associated to the structure of the reaction network  [78]. When applied to our reaction model, the BIOCHAM command: check_multistability. returns the absence of such positive circuits. This proves the absence of multiple non degenerate steady states in the ODE system and ensure the uniqueness of the stable state reached from different initial conditions.
Unperturbed numerical simulation Unperturbed numerical simulations correspond to the numerical simulations of the computational models CDT N and CDT P as parameterized. Numerical simulations of the computational models CDT N and CDT P were carried out within BIOCHAM. BIOCHAM reaction models can be interpreted in multiple ways. In the differential semantics, we can run the software command numerical_simulation. That command performs a numerical integration from time 0 up to a given time x specified with the command option (time:x). By default, BIOCHAM uses the numerical solver bsimp (Implicit Bulirsch-Stoer method of Bader and Deuflhard) of the GNU Scientific Library to perform numerical simulations. BIOC-HAM offers other numerical solvers for continuous, stochastic and boolean models not used in this study.
Data analysis The data processed in the study come from numerical simulations, parameter search and sensitivity analysis using BIOCHAM software (unperturbed and perturbed numerical simulations), raw data from the image analysis which were processed by software Colombus and Harmony. The data from numerical simulations, sensitivity analysis and parameter scan were uploaded and processed within GraphPad 8. 3. 0 software for the creation of figures. For all datasets exported from image analysis from Columbus and Harmony, python scripts have been developed to extract the HCI descriptors of interest in order to export the data in csv format file to be used by GraphPad 8. 3. 0 software. The HCI descriptors of interest are those representing the quantification of Tyr, Detyr and the Tyr/Detyr ratio in the respective experiments.
Introduction The Internet of Things (IoT) is becoming more popular as many "things" are getting more intelligent and connected, e. g. , smartphones, smart cars, smart energy grids, smart cities. The IEEE Standards Association defines an IoT system as "a system of entities (including cyber-physical devices, information resources, and people) that exchange information and interact with the physical world by sensing, processing information, and actuating" (IEEE SA 2018). In 2019, the International Data Corporation (IDC) made a forecast that there will be 41. 6 billion IoT devices in the field by 2025. 1 Most of the critical infrastructures pointed in the EU's Directive on security of network and information systems 2 such as for energy, water, transport, and healthcare are or will be IoT-based.
For instance, smart cities are integrating IoT sensors with analytic to streamline spending, improve infrastructural efficiency. 3 Internet-connected pacemakers have been implanted for millions to help control their abnormal heart rhythms. The IoT will thus play a key role in the digitalization of the society and IoT security issues will "affect not only bits and bytes", but also "flesh and blood"  (Schneier 2017). Without solid security in place, attacks and malfunctions in IoT-based critical infrastructures may outweigh any of its benefits  (Roman et al. 2011). On the other hand, privacy is also very important in the IoT. Many "things" that people use in daily activities at work and at home are now connected to the Internet. This means that sensitive private data can be exposed via the Internet.
The explosion in connectivity created a larger attack surface area  (Covington and Carskadden 2013). Besides, the IoT fielddevices often operate under dynamic (physical) execution environments, involving dynamic actuation, but have limited data delivery and storage facilities. In other words, uncertainty is inherent in IoT systems. We are very much interested in examining the landscape of patterns and architectures being applied for the IoT domain, whose security (and privacy) challenges are huge. How have the existing security patterns been applied in tackling IoT security challenges? Are there any new security patterns that have been specifically introduced to address new security challenges in IoT? To make sense of the research landscape of methodologies around patterns for security and privacy in IoT, we have conducted a systematic literature review (SLR) following the most popular guidelines from  Kitchenham et al.
Our analysis results show the trend of an increasing number of published papers in this research area in three recent years. We have performed our analysis based on a taxonomy that we built for this research area. Our analysis sheds some light on the state of the art around patterns and architectures for IoT security and the current limitations. Based on our analysis, we provides some suggestions for a way forward of this research topic. Specifically, the contributions in this paper include: • We have an exhaustive database search process. Moreover, we manually conducted snowballing (backward and forward as suggested in  Wohlin 2014). We identified and included six new primary studies from this snowballing process. Therefore, our final set of primary studies reported in this paper is 36 (see "Our systematic literature review approach" section).
Database search Using online inquiry components of popular publication databases is the most notable approach to scan for essential primary studies when directing supplemental studies  (Kitchenham and Charters 2007). We used five of the popular publication databases IEEE Xplore, 5 ACM Digital Library, 6 ScienceDirect, 7 Web of Knowledge (ISI), 8 and Scopus 9 to search for potential primary studies. Scopus and ACM DL already index SpringerLink 10  (Tran et al. 2017). The five picked databases contain peerreviewed articles, which give advanced search capacities. Following the guidelines from  Kitchenham and Charters (2007) , based on the research questions and keywords utilized in some related articles, we have defined our search keywords. The search query was adopted to fit each of the search engines of the five publication databases.
Note that we did not include "misuse pattern" in the search query because misuse patterns (from the point of view of the attacker) are out of scope of this study. ("Internet of Things" OR "IoT" OR "Cyber Physical Systems" OR "Web of Things") AND ("Security Pattern" OR "Design Pattern" OR "Security Design Pattern" OR "Privacy Pattern" OR "Security Architecture" OR "Secure Architecture") During our database search process, we did conduct many rounds of testing the search query on the search engines. On the one hand, this testing process helped us to improve our search query and customize it for better fit the search features. On the other hand, we also saw very few hits returned by the search engines for the duration 2000-2010. Therefore, we mainly focused our review for the publications in the duration 2010-2020.
Manual search It is unrealistic to guarantee the database search results can cover all IoT security patterns and architectures in our study. We have, therefore, attempted to supplement the database search by doing a manual search. We started by manually searching through published papers from previous journals and conferences. The conferences and journals we went through to find papers were: The International Conference on the Internet of Things, 11 Pattern Languages of Programs (PLoP), 12 EuroPLoP, 13 IEEE ICIOT, 14 ACM Transactions on Internet of Things (TIOT) 15 and IEEE Internet of Things Journal. 16 We also manually did snowballing (backward and forward) on all the primary studies found as suggested in  Wohlin (2014). In the wake of looking through these journals and conferences as well as doing snowballing, we concluded that most of the relevant papers posted or found from our manual search were earlier discovered from the database search, or they did not satisfy our criteria.
Threats to validity We mainly found the primary studies of this work from the database search process. The search features provided by the five online publication databases are very different from each other. We had to adapt our search string to make use of the provided search features of the publication databases. We tried to use the keywords and built search strings that were not too strict to obtain as many relevant papers as possible. However, it would be impossible to have perfect search strings for the database search process. There is a possibility that we missed some studies that should have been included in the final set of primary studies. We have tried to mitigate possible missing primary studies of the database search process by the manual search process. While doing snowballing, we saw again some primary studies that we already found from the database search process.