dataesr/scientific-paragraphs-categorization huggingface dataset:
After the network has been constructed and the modules identified, the next step in the workflow, as suggested by Langfelder et al  [10] , is to link the identified modules to external information as a mean to sort out modules that are biologically relevant for the disease. To link a particular module to meta-data such as disease status, the strength (and the significance) of the correlation between the Module Eigengenes (MEs) and the trait variable is typically used, which in this case is the binary variable CD. The ME is defined as the first principal component of each module's protein expression matrix, thus reducing all co-expressed proteins within a module to one variable that can be used for correlating to the trait variable. Using this approach, we applied WGCNA to the three datasets separately.
We thus identified the modules associated with CD by correlating the generated MEs (within each of the three datasets) to CD (see Supporting Information for details). For this purpose, only proteins quantified by TMT-based approach in all samples of the given dataset were used and their log 2 -transformed reporter intensities were used for analysis. Analyses were done within the R environment (R version 4. 1. 1) using the WGCNA v1. 70-3 package  [10]. Among the 3258 proteins in the CD45 dataset (3258 of 3260 proteins were quantified in all seven samples), 32 modules (each identified by color) with sizes ranging from 32 to 455 proteins per module were identified (Figure  S2A-B ; Table  S5 ). Four of these CD45 modules had a significant correlation to CD (Figure  2A , indicated with asterisks).
In the EpCAM dataset, the network analysis of the 4185 proteins (4185 of 4188 proteins were quantified in all seven samples) resulted in 32 modules ranging between 30 to 835 proteins per module (Figure  S2C-D ; Table  S5 ). We identified three EpCAM modules that significantly correlated to CD (Figure  3A , asterisks). Lastly, analysis of the stroma cell dataset resulted in 22 modules (Table  S5 ) with a few slightly above the significance threshold. However, none were significantly correlated to CD and were thus not investigated further. As several disease-related modules correlated either positively or negatively within each dataset, we focused on the most significant module with positive correlation and the most significant module with negative correlation in each dataset (for the MExCD correlation) (Table (A) (B) (C) F I G U R E 1 Overview of the experimental setup.
The plots in A-G were generated using the following R packages: WGCNA  [10] [(v1. 70-3; dendrogram in A), pheatmap (v1. 0. 12; heatmap in A), ggplot2 (v3. 3. 5; boxplots), enrichplot  [38]  (v1. 10. 2; barplots and upset plots) additional modules in the datasets also correlated to CD, albeit to a lesser significance than these four modules, and were not pursued in detail. Within CD45 dataset, these were CD45 White, which positively correlated to CD and contained 41 proteins (Figure  S3A ) and CD45 (A) (B) (C) (D) (E) (F) (G) F I G U R E 3 Orange that negatively correlated to CD and contained 44 proteins (Figure  S3D ). Similarly, in the EpCAM dataset, EpCAM Pale turquoise positively correlated to CD and contained 30 proteins (Figure  S3E ). To decipher the function of the modules that significantly correlated with CD in a cell type-specific manner, we continued with over-representation tests of GO Cellular Component  [11, 12]  and Reactome Pathway terms  [13].
Analyses were conducted R using the clusterProfiler (v4. 0. 5;  [14] ) and ReactomePA (v1. 36;  [15] ) packages, respectively, with our respective datasets as background. Over-representation analysis of the CD45 Turquoise module, which correlated positively to CD, showed that many of the CD45 Turquoise module proteins were components of the mitochondrial membrane or matrix (Figure  2C , Table  S6 ). The module was highly enriched for proteins involved in mitochondria-related pathways such as mitochondrial translation, citric acid cycle and respiratory electron transport (Figure  2D , Table  S6 ). This may reflect a process aiming to sustain the increased demand for energy in immune cells during active inflammation. However, different immune cell subtypes require or adapt their energy demand in different ways  [16] , and the alterations on the "global" CD45 level in our dataset might as well reflect changes in the composition of immune cell populations that occur in the inflamed environment  [17, 18]  rather than a true increase in mitochondrial proteins within a given cell.
One is the capacity for energy generation via increased expression of mitochondrial proteins. This seems reasonable, given that many immune cell functions typically upregulated during active inflammation include chemotaxis, phagocytosis, and cytokine secretion, which are highly energy-demanding processes. The other is proteins associated with neutrophil degranulation. This is also consistent with CD pathophysiology, as influx of neutrophils contributes to perpetuating inflammation and mucosal damage, although they also may have beneficial effects  [19]. String protein-protein interaction network analysis further supported the data of the CD45 Turquoise model presented above. In particular, the core of an intramodular network is formed by the proteins with the highest connectivity to all other module proteins  [10]  such that the centrally located core proteins (hub proteins) might represent drivers of higher biological importance.
To address which type of functional sets of hub proteins were represented in the CD45 Turquoise module core, and still had a high correlation to CD, we ranked the module proteins by the average of the absolute values of Module Membership (MM; correlation Protein x ME) and Gene Significance (GS; correlation Protein x CD). The top 30 proteins based on this ranking (corresponding to the dots farthest away along the scatter in Figure  S4B ) were visualized in a STRING protein-protein interaction network  [20]  (Figure  S4C ). Consistent with the CD45 Turquoise module data presented above, mitochondrial proteins (red and green nodes in Figure  S4C ), as well as several neutrophil degranulation proteins (blue nodes in Figure  S4C ), were also identified in the core of this module. Given the proinflammatory effects of neutrophil granules, it was not surprising that chemotactic proteins such as S100A12 and Azurocidin were identified among the hub proteins (Figure  S4C ).
In addition to recruiting other immune cells, S100A12 also serves as a damageassociated molecular pattern which, through binding its receptor on cells, produces proinflammatory responses  [21]. Moreover, S100A8 and S100A9, the two heterodimers that make up calprotectin that is abundant in neutrophils, were among the 20% top ranked proteins (based on the average MM-GS measure described above) of the CD45 Turquoise module proteins (Figure  S4D ). Intriguingly, the proteomics study of Pierre et al using biopsies from ulcer edges found  [22] , as we did in the CD45 Turquoise module, increased neutrophil degranulation markers, including S100A8 and S100A9, in CD compared to controls. Indeed, the presence of relevant neutrophil granule proteins including calprotectin, which is used clinically as a (fecal) marker for gastrointestinal inflammation and is routinely assessed in patients to monitor disease activity, found by us and others  [22] , strengthens the coherence of the proteomics approach used and the quality of the dataset.
We next turned to the module in the CD45 dataset that negatively correlated to CD, CD45 Blue. This module was highly enriched for cellular components related to the nucleus (e. g. chromosome, nuclear body, nucleolus) as well as cytoskeleton and catalytic complexes (Figure  2F , Table  S6 ). One major arm of this module's functional enrichment concerned gene expression and related regulatory processes such as enzymatic chromatin modification (Figure  2G , Table  S6 ). The other major arm of CD45 Blue enrichment involved two highly related processes, namely, DNA repair and cell cycle. Indeed, many of the top highly connected module proteins were related to gene expression (Figure  S4E-F ). The proteins in the CD45 Blue module negatively correlated with CD, suggesting that immune cells in the inflamed intestinal mucosa of CD patients are associated with decreased gene expression.
These include increased proteins related to mitochondrial metabolic capacity and neutrophil degranulation as elucidated in the CD45 Turquoise module, as well as reduced proteins associated with gene expression and DNA repair identified in the CD45 Blue module. Given the fundamental roles these processes play in cell function, shifts can have profound effects on cells, their surroundings and potentially disease pathogenesis. Next, we assessed the CD-associated modules in the EpCAM dataset. As mentioned above, the EpCAM Turquoise module positively correlated to CD. About half of the proteins were related to the GO cellular components endoplasmic reticulum, ribonucleoprotein complex (including the ribosome), nucleolus, or chromosome (Figure  3F , Table  S6 ). The Reactome analysis result was exhaustive (240 pathways with p.
Interestingly, an increase in proteins involved in ER protein processing, which includes the protein secretion process, was identified in cells surrounding the ulcers of CD patients compared to controls  [22]. An interesting reflection, although speculative, regarding the suggested epithelial stress response revealed in the EpCAM Turquoise dataset, combined with the upregulated neutrophil proteins identified in the CD45 Turquoise dataset, is that neutrophils (and potentially other immune cells) accumulating in the mucosa of CD patients modify the behavior of epithelial cells. Many of the mediators inside the neutrophil granules can exert direct effects on epithelial cells. For instance, S100A12, one of the hub proteins in the CD45 Turquoise module (Figure  S4C ), is a danger-associated molecular pattern that engages receptors on epithelial cells, including TLRs and receptors for advanced glycation end products (RAGE)  [21].
Many of the module proteins belonged to the mitochondrial envelope or matrix (Figure  3C ), which are in turn functionally related to mitochondrial processes such as respiratory electron transport and mitochondrial protein import (Figure  3D ). As seen in the module core (Figure  S5C-D ), several of the hub proteins were involved in processes such as metabolism of fatty acids and amino acids. Together, this indicates that epithelial cells in inflamed mucosa of CD patients are associated with increased stress response and have a reduction in proteins involved in mitochondrial metabolism and cellular respiration. Alterations in mitochondrial processes was a theme permeating our CD45 and EpCAM data sets. Indeed, alterations in mitochondrial function are associated with inflammatory pathologies such as CD  [28, 29]  and genome-wide association studies have shown links between mitochondrial function-associated genes and CD  [30].
The central role mitochondria and the products they generate have on fundamental processes such as cell cycle, apoptosis and differentiation, highlight the impact alterations in mitochondrial function can have on disease pathogenesis. We found that asparagine N-linked glycosylation was upregulated in epithelial cells in the intestinal mucosa of CD patients (Table  S6 ). Glycosylation occurs in the endoplasmic reticulum and the Golgi apparatus and is one type of post-translation modification for proteins along the secretory pathway  [31, 32]. These secreted or membrane-bound glycosylated proteins can act as ligands for receptors on immune cells and have been proposed to be involved in integrating microbial and environmental cues to immune cells  [31]. Of particular interest, our dataset revealed that the top upregulated protein associated with asparagine N-linked glycosylation was RPN1 (Figure  S5B ).
RPN1 is part of the oligosaccharyl transferase enzyme complex that catalyzes the first and essential step in N-glycosylation  [33]. The presence of RPN1 in the module core (FigureS5B) implies the importance of this hub protein for the N-glycosylation process and potentially epithelial cell alterations during CD pathogenesis. Indeed, changes in glycosylation patterns are associated with intestinal inflammation and many glycosylation enzymes are considered CD risk genes  [31]. Our study using defined cell types purified by FACS allowed the opportunity to correlate alterations of pathways (overrepresented Reactome database terms within our modules) between the different cell types. To enable pathway correlation, we used the average normalized expression for significantly enriched terms within each module and included the most relevant pathways only using defined criteria (see Supporting information).
Since many proteasomal proteins were upregulated in combination with the β 2 -microglobulin component of MHC-I molecules, it's possible that epithelial cells display increased immunoproteasome activity in inflamed CD mucosa. However, of the five proteasomal protein subunits associated with immunoproteasomes, where all were present in the EpCAM dataset but distributed over several modules, only one, PSMD8 (in EpCAM Turquoise), was upregulated. Thus, it remains to be determined if the 22 upregulated proteasomal proteins alter proteasome function and whether the increased level is a matter of cause or effect. Overall, the data suggest that increased proteasome proteins in epithelial cells is associated with disease-associated changes in the epithelium of inflamed mucosa of CD patients and correlates strongly to alterations in immune cells.
Further investigation is needed to decipher the function of altered proteasome proteins in CD. In summary, we present datasets describing proteome changes in epithelial, immune and stromal cells sorted from intestinal mucosa of CD patients using TMT-based quantitative proteomics. A strength of our workflow was analyzing three distinct cell types from cellular compartments relevant to CD pathogenesis, which links concomitant changes occurring during the disease process to deepen our understanding of pathogenesis. Our approach differs from studies using biopsies that analyze whole tissue. While such studies provide valuable insight into "global" changes in healthy versus diseased tissue  [22, 34] , changes in subpopulations may be obscured due to analyzing a mix of cell types. Our approach using defined cell populations purified by FACS increases the precision in, and resolution of, exploring pathophysiological processes that take place locally in diseased tissue.
ACKNOWLEDGEMENT We are grateful to the research nurses at Sahlgrenska University Östra Hospital, Hillevi Björkqvist and Zunash Malik, for their invaluable help with patient inclusion and sample collection. The authors thank Anetta Härtlova for critical reading of the manuscript and we thank the Proteomics Core Facility at Sahlgrenska Academy, University of Gothenburg, Sweden, for LC-MS analysis of relative quantification.
The figure was created with BioRender. com. (B) The Venn diagram shows the total number of proteins identified and the number thereof quantified by TMT-based workflow in each of the three cell types as indicated in red (immune cells), blue (epithelial cells) and green (stromal cells). (C) Data-driven validation of cell sorting based on MS1 abundance of cell-specific markers in each of the three pooled samples (CD45, EpCAM, Stroma) depicted in rose plots. Proteins were quantified in the separate search in MaxQuant using MaxLFQ function and annotated by The Human Protein Atlas (Total Elevated category). Distributions of log 2 -transformed MaxLFQ intensities of cell-specific proteins across datasets were visualized by rose plots generated by R package ˈtriwiseˈ[37]. A rose plot is a polar variant of a histogram showing the radial distribution of the barycentric coordinates of, in this case, the relative expression of the indicated group of proteins (e.
g. B cell proteins) in the three cell types. Proteins expressed predominantly in one of the cell types result in a sector with the arc directly facing the cell type name, as seen in the B cell rose plot with the sector arc facing CD45. The length of the radii indicate the relative number of proteins Overview of the modules (clusters of coexpressed proteins) detected by WGCNA in the CD45 dataset, their relation to CD and characterization of the most significant CD-associated modules. (A) The eigengene dendrogram shows the hierarchical clustering of all modules (identified by the color boxes below the dendrogram) detected in the CD45 dataset based on their Eigenvectors. The heatmap below the color boxes shows the strength and direction of the correlation (Corr) between the ME and CD, as indicated in the inset to the right.
Significant correlations (p-value < 0. 05) are marked by an asterisk. In the CD45 dataset, four modules with significant correlation to CD were identified, with two being positively correlated (CD45 Turquoise and CD45 White) and two negatively correlated (CD45 Blue and CD45 Orange). The largest and most significant modules correlating to CD in each direction (CD45 Turquoise and CD45 Blue) were further characterized and are shown in B-G. The other two modules with significant correlation to CD, CD45 White (positive) and Orange (negative), are shown in Figure S3. (B) and (E): Boxplots of ME presented as groups (Controls vs CD; left) and boxplots of normalized expression values (z-scores) for individual module proteins presented by sample (each individual; right) of the CD45 Turquoise (B) and CD45 Blue (E) modules.
Overview of the modules (clusters of coexpressed proteins) detected by WGCNA in the EpCAM dataset, their relation to CD and characterization of the most significant CD-associated modules. (A) The eigengene dendrogram shows the hierarchical clustering of all modules in the EpCAM dataset based on their Eigenvectors. See the legend to Figure 2 for details. In the EpCAM dataset, three modules with a significant correlation to CD were identified, with two being positively correlated (EpCAM Turquoise and EpCAM Pale turquoise) and one negatively correlated (EpCAM Salmon). The modules with the largest and most significant correlation to CD in each direction (EpCAM Turquoise and EpCAM Salmon) were further characterized and are shown in B-G. The other module with a significant correlation to CD, EpCAM Pale turquoise (positive correlation), is shown in Figure S3E.
Within the CD45 dataset, this was CD45 Turquoise, which positively correlated to CD and contained 455 proteins (Figure 2B-D), and CD45 Blue that negatively correlated to CD and contained 437 proteins (Figure 2E-G). Similarly, for the EpCAM dataset, the modules with the most significant positive or negative correlation were EpCAM Turquoise, which positively correlated to CD and contained 835 proteins (Figure 3E-G) while EpCAM Salmon negatively correlated to CD and contained 89 proteins (Figure 3B-D). Three.
F I G U R E 2 2 The figure was created with BioRender. com. (B) The Venn diagram shows the total number of proteins identified and the number thereof quantified by TMT-based workflow in each of the three cell types as indicated in red (immune cells), blue (epithelial cells) and green (stromal cells). (C) Data-driven validation of cell sorting based on MS1 abundance of cell-specific markers in each of the three pooled samples (CD45, EpCAM, Stroma) depicted in rose plots. Proteins were quantified in the separate search in MaxQuant using MaxLFQ function and annotated by The Human Protein Atlas (Total Elevated category). Distributions of log 2 -transformed MaxLFQ intensities of cell-specific proteins across datasets were visualized by rose plots generated by R package ˈtriwiseˈ[37]. A rose plot is a polar variant of a histogram showing the radial distribution of the barycentric coordinates of, in this case, the relative expression of the indicated group of proteins (e.
Moreover, to supervise the data learning, we aim at providing an objective and dynamic task difficulty indicator. This article starts by a literature review that allows to present and choose the most relevant physiological data and classification methods for producing a supervised learning of OFS based on physiological indicators. Then the section "Materials and Methods" deals with two aspects: (i) experimental data acquisition is presented, by describing the use case carried out on a drone swarm simulator, and the dataset that will be used as input for OFS classification; (ii) the supervised classification approach is also described, by explaining how a task difficulty indicator was defined to label physiological data, and how the chosen tested classification methods were parameterized and trained.
Kotsiantis et al. (2007)  also shows that SVM and NN have similar operational profiles  (Kotsiantis et al. , 2007). These two methods are efficient for processing continuous data and are effective for managing multicollinearity. Both methods require having a large learning database to be effective, but SVMs are more tolerant of missing data, better manage over-learning, and generally have better reliability. Moreover, the synthesis on OFS classification (cf. Table  1 ) underlines that SVM is the most used algorithm. So, we choose to discard the NN for the benefit of the SVM. In addition, we seek to achieve a multiclass classification (more than two classes). kNN and Random Forest method have the advantage of being of a multi-class nature. It is therefore relevant to implement these two methods.
Equipment Participants performed the experiment in a room where brightness is controlled and constant, to avoid pupillary reflex  due to light variations. In this room a space has been created for running the scenario on the simulator, with fixed desk and chair (to avoid parasitical movements of participants). The system supporting SUSIE software is composed of a 24 screen and a mouse connected to a PC. Data recordings was done with a SeeingMachine FaceLAB5 R eye tracker for pupillary response, a Zephyr Bioharness R heart rate belt, and a log (text file) of scenario events and operator's mouse actions recorded on SUSIE. Subjective experience of participants was also collected by using questionnaires, popping up every 90 s on screen.
Data Selection and Processing The physiological data (Heart Rate Variability, HRV and Pupillary Diameter PD) were collected with the aid of the equipment described above. HRV was computed from the standard deviation of the NN interval on the last 300 heartbeats (SDNN method), following the formula sqrt N i=1 (RRi-mRR) 2 N-1. Pupil diameter was cleansed (diameter smaller than 2 mm and larger than 8 mm were excluded). All the physiological data were z-normalized to remove the interindividual differences. Moreover, as explained in section "Physiological and Contextual Indicators to Assess Operator Functional State, " these physiological data must be combined with contextual data. To have an accurate and dynamic indicator of task difficulty improving the OFS, we considered the following variables to characterize the dynamic task difficulty within SUSIE simulator: "N1" as the number of targets visible on the screen that must be processed, "N2" as the number of messages visible on the screen that must be processed, and "Entropy" as the spatial entropy of the distribution of targets displayed on the screen (by dividing the screen in 8 equal zones, and calculating Pi * Log(Pi), with Pi the proportion of the targets included in each zones on the total number of targets displayed on the screen).
Classification Approach We first define and validate a dynamic task difficulty indicator, to then supervise the classification of physiological data. Figure  2  summarizes the principle of this classification process that was implemented. We used MATLAB software  (Paluszek and Thomas, 2016)  and the machine learning libraries (fitcsvm and fitcecoc for SVM, treebagger for Random Forest, and Classification KNN for k-NN) to train and test the different machine learning methods.