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MALDI-TOF MS monitoring of PBMC activation status in sepsis
Aurélie Daumas1,2,
Julie Alingrin1,3,
Richard Ouedraogo1,
Patrick Villani2,
Marc Leone1,3 &
Jean-Louis Mege1
MALDI-TOF mass spectrometry (MS) on whole cells enables the detection of different cell types and cell activation. Here, we wondered whether this approach would be useful to investigate the host response in sepsis.
Peripheral blood mononuclear cells (PBMCs) from patients with severe sepsis and healthy donors were analyzed with MALDI-TOF MS. PBMCs from healthy donors were also stimulated with lipopolysaccharide, peptidoglycan, CpG oligonucleotides, polyinosinic polycytidylic acid, and with heat-inactivated bacteria. Averaged spectra of PBMCs stimulated in vitro by different agonists were generated from the database using the Biotyper software and matching scores between each spectrum from patients and averaged spectra from the database were calculated.
We show that the MALDI-TOF MS signature of PBMCs from septic patients was specific, compared with healthy controls. As the fingerprints observed in patients may be related to PBMC activation, PBMCs from healthy controls were stimulated with cytokines, soluble Pathogen-Associated Molecular Patterns (PAMPs) and heat-killed bacteria, and we created a database of reference spectra. The MALDI-TOF MS profiles of PBMCs from septic patients were then compared with the database. No differences were found between patients with documented infection (n = 6) and those without bacteriological documentation (n = 6). The spectra of PBMCs from septic patients matched with those of interferon-γ- and interleukin-10-stimulated PBMCs, confirming that sepsis is characterized by both inflammatory and immunoregulatory features. Interestingly, the spectra of PBMCs from septic patients without documented infection matched with the reference spectrum of PBMCs stimulated by CpG oligonucleotides, suggesting a bacterial etiology in these patients.
Despite the limits of this preliminary study, these results indicate that the monitoring of functional status of PBMCs in peripheral blood by whole cell MALDI-TOF MS could provide unique opportunities to assess disease progression or resolution in clinical settings.
Sepsis is the combination of a systemic inflammatory response syndrome (SIRS) and infection [1]. Sepsis can progress to severe sepsis and septic shock, with mortality rates of 25 to 30% and 40 to 70%, respectively [2, 3]. The clinical manifestations of the early stages of sepsis are often similar to those of a patient with SIRS caused by sterile inflammation [3], leading to frequent underappreciation of sepsis in clinical practice. The traditional approach to sepsis diagnosis is based on the clinical signs and symptoms of sepsis, supported by relevant microbiological data. Unfortunately, up to 40% of the infections suspected in patients with sepsis are not microbiologically documented [4,5,6]. Consequently, physicians often use empiric antibiotic therapy, which has three major drawbacks: increased antibiotic resistance, patient toxicity, and elevated costs [7].
Two important challenges for physicians are to determine if the patient is infected or not in the absence of microbiological documentation, and when to begin antimicrobial therapy [8,9,10]. Numerous molecules, such as procalcitonin, are unable to discriminate between sepsis and SIRS [11, 12]. Cytokine imbalance has been thought to be useful for defining sepsis. Indeed, the recognition of soluble Pathogen-Associated Molecular Patterns (PAMPs) such as lipopolysaccharide (LPS), peptidoglycan (PGN), CpG oligonucleotides (CpG-ODN), and polyinosinic polycytidylic acid (poly I:C) by pathogen recognition receptors (PRRs) induces the release of inflammatory cytokines, such as gamma interferon (IFN-γ) and the concomitant liberation of immunoregulatory cytokines, including interleukin (IL)-10 and IL-4. However, cytokine profiles are also unable to discriminate sepsis, and seem more related to SIRS severity than to sepsis [13].
MALDI-TOF mass spectrometry (MS) has emerged as a fast, reliable and inexpensive tool for bacterial identification and diagnosis [14, 15]. Interestingly, bacterial identification does not require previous fractionation steps [16]. Recently, we and others have applied the whole-cell MALDI-TOF MS technique to identify eukaryotic cells, including circulating cells [17,18,19]. Our team has also shown that whole-cell MALDI-TOF MS detects the multifaceted activation of monocyte-derived macrophages in response to various cytokines and bacterial pathogens [20]. Portevin et al. [21] demonstrated recently that MALDI-TOF MS fingerprints distinguish human monocyte sub-populations activated by distinct microbial ligands.
Our goal was to analyze peripheral blood mononuclear cells (PBMCs) in septic patients through whole-cell MALDI-TOF MS. This approach enabled the detection of a specific PBMC signature in septic patients. The analysis of the signature of healthy PBMCs stimulated with cytokines, soluble PAMPs and bacteria, frequently involved in sepsis, showed that the spectra of PBMCs from septic patients matched with those of PBMCs stimulated by IFN-γ, IL-10 and CpG-ODN. These findings evoked an infectious activation in septic patients regardless of documented or undocumented infection. Despite the limits of this preliminary study, this is the first report describing the use of a whole-cell MALDI-TOF MS approach to identify PBMC activation in septic patients.
The study was approved by the Ethics Committee of the Assistance Publique-Hôpitaux de Marseille, France. Blood was collected after informed and written consent of healthy donors and septic patients.
Patients and healthy controls
Patient recruitment was provided from an ancillary study to the project "De-escalation of Empirical Antibiotics in Severe Sepsis" (Comité de Protection des Personnes Sud Méditerranée No. 2011–002297-22). Twelve patients (aged 18 and over) were enrolled in the polyvalent intensive care unit (North Hospital, Marseille, France). A single blood sample was collected at the time of empirical antibiotic initiation. Eligibility criteria were the presence of severe sepsis requiring empirical antimicrobial treatment. Severe sepsis was defined as the criteria for SIRS and suspected infection with at least one organ failure. SIRS was defined by two or more of the following conditions: temperature > 38 °C or < 36 °C, heart rate > 90 beats per minute, respiratory rate > 20 breaths per minute or PaCO2 < 32 mmHg, white blood cell count >12G/l, < 4G/l, or > 10% immature cells. Two patients with SIRS had Staphylococcus aureus bacteremia, 4 had gram-negative bacillus bacteremia and 6 had a strongly suspected infection clinically but not microbiologically documented. PBMCs from healthy donors were isolated from leukopacks (Etablissement Français du Sang).
Isolation and in vitro activation of PBMCs
Blood was collected in EDTA-containing tubes and PBMCs were isolated using Ficoll cushions (MSL, Eurobio). After centrifugation, PBMCs were washed in sterile phosphate buffered saline (PBS) without Ca2+ and Mg2+, and 1 × 106 cells were suspended in 10 μL of PBS and frozen at − 80 °C for 2 to 3 days before analysis. In some experiments, PBMCs from healthy donors (1 × 106 cells in 6-well plates) were incubated in 2 mL of RPMI 1640 containing 10% fetal calf serum (FCS) and stimulated with 20 ng/mL IFN-γ (PeproTech), 20 ng/mL IL-4 or IL-10 (R&D Systems) for 18 h [22]. PBMCs were also stimulated with LPS from Escherichia coli (1 μg/mL, Sigma-Aldrich), peptidoglycan (PGN) from Bacillus subtilis (10 μg/mL, Sigma-Aldrich), CpG-ODN (2 μg/mL, InvivoGen) and poly I:C (25 μg/mL, InvivoGen). Finally, PBMCs were stimulated with heat-inactivated bacteria (10 bacteria per cell). Bacteria included oxacillin-sensitive S. aureus, community strain group B streptococcus, Pseudomonas aeruginosa (ATCC 27853) and E. coli (ATCC 25922). PBMCs stimulated for 18 h were pelleted in 10 μl of PBS and frozen as unstimulated PBMCs for MALDI-TOF MS analysis.
MALDI-TOF MS analysis
After thawing of the PBMCs, 1 μL of cell suspension was added to 1 μL of matrix solution (saturated solution of α-cyano-4-hydroxy-cynnamic acid in a mixture of 50% acetonitrile, 25% trifluoroacetic acid and water) as previously described [17, 20, 23]. The mixture was deposited on the MALDI target. The evaporation that gradually took place at room temperature allowed the formation of α-cyano-4-hydroxy-cynnamic acid crystals containing the dispersed samples. Measurements were performed using an Autoflex II mass spectrometer (Bruker Daltonics, Wissembourg, France) equipped with a 337-nm nitrogen laser. Each sample was irradiated with a laser for desorption and ionization. Each spectrum resulted from the sum of positive ions obtained after 525 laser shots in different regions of the analyzed spot (automatic mode). All the positive-ion mass spectra were acquired in the linear mode at an acceleration voltage of 20 kV in the delayed extraction mode. A signal-to-noise of 3.0 was selected to define peaks, with a maximum of 100 peaks per spectrum. Spectra were automatically acquired with a mass/charge (m/z) ranging from 2000 to 20,000 Da using FlexControl and FlexAnalysis 2.4 software (Bruker Daltonics). The x-axis of spectra represented the m/z ratio (in daltons) of ionized molecules, and the y-axis indicated the intensity (relative abundance) of these ions.
Analyses and graphical outputs were performed using the free and open source statistical analysis software R (version 2.13), along with specific analysis libraries (MALDIquant) as previously described [20]. The gel view representation indicates the reproducibility of the spectra. A hierarchical clustering with a ward algorithm for agglomeration and a dissimilarity matrix based on the Jaccard distance were used to classify the spectra. The MALDI Biotyper 3 software (Bruker Daltonics) was used to create an average reference spectrum for each PBMC sample, corresponding to at least 10 individual spectra. The Biotyper software realigns acquired spectra and automatically creates an average spectrum using default Biotyper software settings provided by the manufacturer, and we created a database as previously described [20]. The Biotyper software also allows the identification of unknown spectra as shown in clinical samples by comparison with reference spectra, for the identification and classification of microorganisms [14]. The score values proposed by the manufacturer have been used for microorganism identification. The score values between 0.000 and 1.699 do not allow reliable microbe identification; the values between 1.700 and 1.999 allow probable cell identification and score values higher than 2.0 are considered statistically significant; they allow the confident identification of different microbe species. We extended this method to assess PBMC activation status in septic patients. As the score values provided by Bruker Daltonics ranged from 0.000 to 2.000 when we used control samples and reference spectra, we considered that medians of matching scores higher than 1.5 allowed reliable identification of the activation profile of patient PBMCs and could be clinically relevant. Differences between conditions were tested with the Mann-Whitney U test and a cutoff value of 0.05 was chosen to consider a difference statistically significant.
Signatures of PBMCs
As compared to phenotype studies that require panels of antibodies to characterize circulating cells, MALDI-TOF MS enables simple and rapid measurement of cell status. First, we wondered if PBMCs from healthy controls were characterized by a specific whole-cell MALDI-TOF MS signature. The spectra of PBMCs from ten healthy donors were composed of numerous peaks ranging from 2000 to 15,000 Da, with a major peak at 4961 Da. Note that the spectra of the ten donors were highly reproducible (Fig. 1a). Second, we found that the signatures of PBMCs from ten septic patients were similar but they were distinct from those of PBMCs from healthy controls. Because MALDI-TOF MS profiles were specific for PBMCs from healthy controls and septic patients, the dendrogram constructed by Ouedraogo et al. [17] was implemented with reference spectra of PBMCs from two healthy donors and two septic patients. As expected, the PBMCs from the two healthy donors were similar and clustered with T lymphocytes and, to a lesser extent, with monocytes and polymorphonuclear cells (PMNs). Likewise, the PBMCs from the two septic patients were similar and formed a specific cluster. This cluster was close to that of monocytes and PMNs but was away from the T lymphocyte cluster (Fig. 2). This specific pattern underlines the prominent role of the innate immune response in sepsis. Taken together, these results demonstrated that PBMC fingerprints distinguished patients with sepsis from healthy controls.
MALDI-TOF MS spectra of PBMCs. The PBMCs (1 × 106 cells) from ten healthy donors (a) and ten septic patients (b) were suspended in 10 μL of PBS, and 1 μL was deposited onto the MALDI target. Representative MALDI-TOF MS spectra are shown. MALDI-TOF MS spectra were analyzed using statistical analysis software R (version 2.13)
Dendrogram representation of PBMCs. The dendrogram constructed by Ouedraogo et al. [17] was implemented with reference spectra of PBMCs from two healthy donors and two septic patients. The Biotyper (Bruker Daltonics) software was used to create an averaged spectrum for each patient, corresponding to at least 10 individual spectra. The averaged spectra were added to the database using the Biotyper software and the dendrogram creation method. Peripheral blood mononuclear cells (PBMCs); polymorphonuclear cells (PMNs); dendritic cells (DCs); monocyte-derived macrophages (MDMs); bone marrow-derived macrophages (BMDMs); red blood cells (RBCs)
Peak characteristics of PBMCs in sepsis
We wondered if the analysis of individual peaks enables stratification of patients with sepsis, microbiologically documented or not. When the spectra were analyzed by comparing the presence or absence of peaks, we found that 10 peaks were present only in controls, 12 peaks were common to healthy donors and septic patients and 18 peaks were specific in septic patients (Table 1). Taking into account the limitations related to the limited number of patients in this preliminary study, among these 18 specific peaks, 15 were common to all septic patients independently of microbiological documentation. Concerning the three other peaks, the peak at 5415 Da was found in patients with gram-negative bacillus bacteremia (4/4) and one patient without microbiological documentation. The peak at 3329 Da was present in 3/4 patients with gram-negative bacillus bacteremia and the same patient without bacteriological documentation, suggesting that these two peaks could be associated with gram-negative bacillus bacteremia and that this patient without microbiological documentation could have gram-negative bacillus sepsis. Finally, the peak at 2942 Da was found in the two patients with S. aureus bacteremia and in one patient without microbiological documentation, suggesting that this peak could be related to S. aureus bacteremia (Table 1). Despite the limits of the study, these results show that the signature of septic patients is very similar, regardless of documented or undocumented infection.
Table 1 Peak characteristics of PBMCs
Signatures of PBMCs activated in vitro
Because the differences between the signatures of healthy and septic PBMCs may be related to their activation state, we stimulated PBMCs with various agonists to determine if PBMC activation results in specific profiles. The stimulation of PBMCs with IFN-γ, LPS, IL-4 and IL-10 led to specific and reproducible responses, as shown using a virtual gel view representation (Fig. 3). The spectra from all stimulated samples were clearly separated from those of unstimulated PBMCs. Each type of stimulation led to specific fingerprints. The spectra from the PBMCs stimulated with inflammatory molecules (IFN-γ/LPS) clustered together. Similarly, immunoregulatory cytokines induced the clustering of PBMCs. The existence of these three major clusters (unstimulated, IFN-γ/LPS-, IL-4/IL-10-stimulated PBMCs) suggests that MALDI-TOF MS may be used to analyze the inflammatory response of PBMCs in the clinical setting.
Gel view representation of activated PBMCs. The PBMCs from healthy donors were stimulated with 20 ng/ml IFN-γ, IL-4, IL-10 or 1 μg/mL LPS for 18 h. The spectra were arranged in a pseudo-gel format using a gel view representation. Vertical axis refers to the m/z ratio. Spectra are classified according to the presence/absence of peaks. Unstimulated PBMCs are presented in white
We also investigated the responses of PBMCs to PAMPs and bacteria often found in sepsis. For that purpose, we used unstimulated PBMCs as controls and we compared the fingerprints induced by PAMPs and bacteria to these controls. Two very different clusters were clearly identified: one including unstimulated and poly I:C-stimulated PBMCs and the other with PBMCs stimulated with bacteria and other PAMPs (Fig. 4). Among the bacterial signatures, we observed that the signatures induced by P. aeruginosa and E. coli (Gram negative bacteria) were coupled to the signature induced by LPS from E. coli. Similarly, the signatures induced by S. agalactiae group B and S. aureus (gram-positive bacteria) were associated and close to the signature induced by PGN from B. subtilis. These results suggest that MALDI-TOF MS may be useful to assess a series of specific responses of PBMCs to varied agonists.
Hierarchical clustering of activated PBMCs. PBMCs were stimulated with different agonists for 18 h. The results are shown as hierarchical clustering. Vertical axis refers to the m/z ratio. Spectra are classified according to the presence/absence of peaks. PBMCs stimulated with LPS from Escherichia coli are presented in yellow, with Pseudomonas aeruginosa (P.a) in red, Escherichia coli (E.c) in orange, CpG ODN in dark green, PGN in green, Staphylococcus aureus (S.a) in blue, Streptococcus agalactiae (S.ag) in turquoise, unstimulated PBMCs in purple and PBMCs stimulated with poly I:C in dark blue
Finally, we used matching scores to compare the different modes of PBMC activation after having created averaged spectra of stimulated PBMCs. The MALDI-TOF MS profiles of PBMCs from septic patients were then compared with the database. None of the septic patients matched with the averaged spectrum of healthy donors, while the spectra of septic patients significantly matched with IFN-γ and IL-10 spectra regardless of whether the infection was documented (n = 6) or not (n = 6), confirming that sepsis is characterized by both inflammatory and immunoregulatory features (Fig. 5 and Additional file 1: Figure S1). It is noteworthy that the scores obtained by comparing the spectra of E. coli- and S. aureus-infected patients with the reference spectrum of E. coli- and S. aureus-stimulated PBMCs were significantly lower (p < 0.05) than those obtained with IL-10- and IFN-γ-specific spectra, respectively (see Fig. 5a, b), suggesting the prominence of the activation profile as a specific response to pathogens. Importantly, we found that the spectra of PBMCs from septic patients significantly (p < 0.05) matched with CpG-ODN, independently of a documented infection. Clearly, the activation profile found in the patients with unknown infection was similar to that of the patients with documented infection (see Fig. 5c), suggesting that these patients had a bacterial infection. Taken together, these results are consistent with that septic PBMCs were activated in a specific way and suggest a bacterial infection in septic patients without documented infection.
Comparison between in vitro and in vivo data. Averaged spectra of PBMCs stimulated in vitro by different agonists were generated from the database using the Biotyper software. The spectra (n = 16) from four patients with E. coli bacteremia, two patients with S. aureus bacteremia and six patients with undocumented infection were then compared with the averaged spectra of the database. Scatter plots of one representative septic patient with E. coli infection (a), S. aureus infection (b) or without microbiological documentation (c) are presented. Matching scores between each spectrum from patients and averaged spectra from the database are represented with circles. Horizontal lines represent the medians of matching scores; a value higher than 1.5 was considered significant and allowed confident identification of the activation status of PBMCs. The nonparametric Mann-Whitney U test was used to compare scores with the averaged spectra of the database
Sepsis is a frequent and serious complication in intensive care unit patients. Despite many years of active research to find effective and specific therapies, the only true treatment still relies on organ system support and effective antimicrobial eradication with antibiotics and/or surgical intervention. An important factor in optimizing survival rates in these patients is the speed of diagnosis [2,3,4,5]. However, diagnosing sepsis is not always straightforward, particularly in patients who have complex ongoing disease processes. In addition, many of these patients received antimicrobial therapy that rendered microbial cultures negative. Hence, 30–40% of intensive care unit patients with severe sepsis had negative bacterial cultures [4,5,6]. Even when cultures are positive, results take several days to become available, thus slowing the diagnostic process. Many biomarkers have been proposed over the years, but the diagnostic value of these molecules remains uncertain [24, 25].
We reasoned that a more integrated approach such as MALDI-TOF MS may be used to detect sepsis specifically in patients without microbiological documentation. As expected, we found that the signature of PBMCs from different healthy subjects was highly reproducible. It clustered with T lymphocytes but was largely different from the signature of numerous non-circulating cells. The signatures of PBMCs from septic patients largely differed from that of PBMCs from healthy controls. Interestingly, they clustered with the signature of monocytes and PMNs but not with T lymphocytes. The absence of clustering with T lymphocytes may be related to the lymphopenia associated with systemic inflammation syndromes [26]. This specific pattern underlines the prominent role of the innate immune response in sepsis.
We postulated that the fingerprints of septic patients may be related to specific activation of PBMCs. To assess the activation of PBMCs, we stimulated PBMCs from healthy controls with cytokines, PAMPs and bacteria. First, we identified both inflammatory (IFN-γ and LPS) and immunoregulatory (IL-4 or IL-10) signatures in PBMCs. Second, we also found that gram-negative bacteria and LPS induced specific signatures compared to those induced by gram-positive bacteria and PGN. Interestingly, the signatures induced by bacterial PAMPs were separate from a poly I:C, a PAMP known to strongly stimulate type 1 interferon as do most of viruses. This result might be useful in discriminating bacterial and viral infections, such as in pneumonia, for which no clinical, radiological and laboratory data differentiate bacterial from viral pneumonia [27].
Finally, we attempted to relate in vitro data and the fingerprints of septic PBMCs. We clearly identified IFN-γ and IL-10 signatures in septic patients. This result is consistent with the natural history of sepsis, where both inflammatory and immunoregulatory responses are observed [28]. We did not detect the signals delivered in vitro by heat-inactivated bacteria and bacterial ligands such as LPS and PGN, even when microbiological infection was documented. We can hypothesize that the lack of LPS and PGN signatures in sepsis may be related to anergy (endotoxin tolerance) [29]. In contrast, we found an intense CpG-ODN signature in septic patients, even in patients without microbiological documentation, suggesting the prominence of the activation profile as a specific response to pathogens. The Biotyper score cutoffs of 1.5 can be considered as unacceptably low for functions such as microbial identification. However, no comparison is possible because no score has so far been validated to discriminate specific responses of PBMCs to varied agonists. This is the first report describing the use of a whole-cell MALDI-TOF MS approach to identify PBMC activation in septic patients. As the score values provided by Bruker Daltonics ranged from 0.000 to 2.000 when we used control samples and reference spectra, we considered that medians of matching scores higher than 1.5 allowed reliable identification of the activation profile of patient PBMCs. Despite the choice of this score cutoff, we did not detect the signals delivered in vitro by heat-inactivated bacteria or bacterial ligands such as LPS and PGN in septic patients even with documented microbiological infection. Obviously, we observed a distinct and reproductible IFN-γ, IL-10 and CpG-ODN. The preliminary nature of the findings requires nevertheless confirmation of results.
To our knowledge, this is the first report describing the use of whole-cell MALDI-TOF MS analysis to identify mass spectra that discriminate specific responses of PBMCs to varied agonists and to study functional and activation status of septic patients with and without documented bacterial infection.
This study shows that MALDI-TOF MS of patient PBMCs is easy and fast to perform and may be considered as a routine method for the detection of sepsis. The reproducibility and accuracy of this approach enables the analysis of several types of PBMC activation and shows a similar activation signature for septic patients with and without documented bacterial infection. Consequently, this innovative approach may be promising in helping physicians in the identification and prognosis of septic patients and/or their treatment. This proof of concept could easily be translated to clinical studies to monitor the functional status of PBMCs from patients under treatment and to study the activation status of PBMCs from patients suffering from systemic and chronic inflammatory disorders. However, the preliminary nature of the findings requires confirmation of results. As a next step, larger studies would confirm whether this new technique can improve the medical management of patients. High throughput monitoring of functional status of PBMCs in peripheral blood based on whole cell MALDI-TOF MS could provide unique opportunities to monitor disease progression and resolution in clinical settings.
CpG-ODN:
CpG oligonucleotides
E. coli :
IFN-γ:
Gamma interferon
Interleukin
LPS:
MS:
PBMCs:
PGN:
Poly I:C:
Polyinosinic polycytidylic acid
PRRs:
Pathogen recognition receptors
S. aureus :
SIRS:
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We thank Christian Capo for his attentive reading of our manuscript. RO was financially supported by "Infectiopôle Sud" foundation.
Aix-Marseille Université, URMITE, IHU Méditerranée Infection, UMR CNR 7278, IRD 198, INSERM 1095, Marseille, France
Aurélie Daumas, Julie Alingrin, Richard Ouedraogo, Marc Leone & Jean-Louis Mege
Service de Médecine Interne et Thérapeutique, Hôpital de la Timone, Assistance Publique-Hôpitaux de Marseille, Marseille, France
Aurélie Daumas & Patrick Villani
Service d'Anesthésie et Réanimation polyvalente, Hôpital Nord, Assistance Publique-Hôpitaux de Marseille, Marseille, France
Julie Alingrin & Marc Leone
Aurélie Daumas
Julie Alingrin
Richard Ouedraogo
Patrick Villani
Marc Leone
Jean-Louis Mege
AD, RO and JLM designed the study. AD, RO and JA carried out the experiments. AD, PV, ML and JLM carried out the statistical analysis and drafted the manuscript. All authors read and approved the final version of the manuscript.
Correspondence to Aurélie Daumas.
Patient recruitment was provided from an ancillary study to the project "De-escalation of Empirical Antibiotics in Severe Sepsis". Written informed consent was obtained from the participants or their parents. The study was approved by the Ethics Committee Sud Méditerranée (No. 2011–002297-22). Confidentiality of the participants' details was assured.
Figure S1. Comparison between in vitro and in vivo data. Averaged spectra of PBMCs stimulated in vitro by different agonists were generated from the database using the Biotyper software. The spectra (n = 16) from 2 other patients with gram-negative bacillus bacteremia, the second patients with S. aureus bacteremia and three other patients with undocumented infection were then compared with the averaged spectra of the database. Matching scores between each spectrum from patients and averaged spectra from the database are represented with circles. Horizontal lines represent the medians of matching scores; a value higher than 1.5 was considered significant and allowed confident identification of the activation status of PBMCs. (JPG 320 kb)
Daumas, A., Alingrin, J., Ouedraogo, R. et al. MALDI-TOF MS monitoring of PBMC activation status in sepsis. BMC Infect Dis 18, 355 (2018). https://doi.org/10.1186/s12879-018-3266-7
MALDI-TOF
Mononuclear cells
IFN-γ
Interleukin-10


text:
\section{Introduction}

 Galaxies form and evolve on the cosmic web: a complex network of intricately woven filaments and sheets of matter which intersect at the nodes, aka the clusters of galaxies \citep{1996bond}. The distribution of all matter on such a web-like structure is expected from the theory of structure formation \citep{zeldovich82} even before it was observed. However,
 the impact of the cosmic web on the properties of galaxies through their interactions with the large-scale environment is yet to be understood fully.

 The role of the large-scale environment in modulating the evolution of galaxies has been well established in the literature
 \citep{1997Dressler,1988Binggeli,2004Kauffmann,2009Weinmann,2012Peng,2016Boselli}.
 While interactions with the ambient hot gas in dense environments such as clusters and groups can lead to a loss of cold gas from galaxies through ram-pressure stripping \citep{1972Gunn},
 gravitational interactions between galaxies \citep{2006Kewley,2015Sobral,2015Stroe}, or harassment of low-mass satellite galaxies by a more massive counterpart \citep{1996Moore} can quench
 formation of stars in intermediate and low-density environments.

 Simulations have confirmed that even though there are more galaxies in clusters, a large fraction of all galaxies resides on large-scale filaments \citep[e.g. see table~2 of][]{libeskind18}.
 So in order to understand the role played by the cosmic web in the evolution of galaxies, it becomes critical to study the large-scale filaments and their influence on the
 properties of galaxies. The intra-filament medium (IFM) is expected to be at $10^5-10^7$ K, and over-dense by a factor of $\sim 20$ relative to the mean of the Universe \citep[e.g.][]{cen99,croft01}.
 Hydrodynamical simulations show that the Bremsstrahlung brightness in the cosmic web reaches at most $10^{-16}$ erg s$^{-1}$ cm$^{-2}$ arcmin$^{-2}$ \citep{dolag06}. Hence, while we
 overcome the technical challenges to observe the IFM directly, it is essential to study its impact on the galaxies residing therein by studying their properties.

 There is plenty of evidence in the literature to suggest that the galaxies on filaments have higher star formation rate relative to their counterparts in clusters and voids
 \citep{porter07,fadda08,porter08,mahajan12,2018Smriti,vulcani19}. Furthermore, galaxies on filaments are found to be more metal-rich \citep{2015Darvish}, as well as host more satellite galaxies
 \citep{guo15}. Studies of galaxy pairs on filaments indicate a significant alignment between the pair axis and the axis of the filament on which they reside
 \citep{lambas88,donoso06,mesa18}. In particular, \citet{mesa18} found that the alignment for pairs of elliptical galaxies is stronger than the pairs comprising one or both spiral galaxies, especially
 within $200~h^{-1}$ kpc of the spine of the filament.

 Several recent studies suggest that the properties of galaxies on filaments change as a function of their distance from the cylindrical axis of the filament. Using optical spectroscopic and
 photometric data from the Galaxy And Mass Assembly (GAMA) survey and the Horizon-AGN simulation suite, \citet{2018Kraljic} found that galaxies tend to become passive closer to the
 filament axis. Multi-wavelength data from the GAMA survey were also used by \citet{alpaslan16} to show that galaxies ($M_{*} > 10^9; z < 0.09$) become less star forming and more massive
 with decreasing distance from the filaments' axis.
 \citet{2018Smriti} studied the Coma supercluster ($z\sim 0.023$) using similar optical data from the Sloan Digital Sky Survey (SDSS) and ultraviolet (UV) data from the Galaxy Evolution
 Explorer ({\it Galex}) mission. One of their major results is that the colours of galaxies become redder and emission in the H$\alpha$ line decreases on approaching the spine
 of the filament. Similar results have also been obtained for galaxies at higher redshift \citep{chen17,malavasi17,laigle18,luber19}.

 In the last few years, state-of-the-art simulations have been employed to understand the symbiotic relationship between the large-scale structure and its galaxy constituents. Large cosmological
 simulations not just serve as a test bed for validating various theories of structure formation and evolution, but also serve as a laboratory to predict the properties of the yet to be observed
 constituents of the Universe. For instance, \citet{2018Martizzi} used the IllustrisTNG simulations to show that the filaments contain more star-forming gas, as well as higher mass
 fraction of such gas relative to clusters. In a study based on the Horizon MareNostrum simulation, \citet{2010Gay} explored the properties of galaxies at redshift of $z = 1.5$ (box size of
 $50 h^{-1}$ Mpc), and found that the {\it G-K} colour of galaxies becomes redder on approaching the spine of the filaments.

 Even though the present day simulations are able to produce global trends in the properties of galaxies, they depend upon recipes to mimic effects of various sub-grid physical processes at play.
 Since these parameters can not be found using first principles, it is critical to understand the variation in properties of galaxies as a continuous function of their environment in order to
 validate different sub-grid recipes. The Evolution and Assembly of GaLaxies and their Environment \citep[{\sc eagle};][]{EAGLE_paper,2015Crain,2016McAlpine,2016Tray,2017Camila} is one such suite of simulations
 which has been used to study various aspects of evolution of galaxies in recent years \citep{2015Tray,Furlong2015,2017Rossi}. A very useful aspect of the {\sc eagle}~simulations is that the
 calibration of the feedback parameters is performed by broadly matching the galaxy stellar mass function (GSMF) at $z \sim 0$, maintaining reasonable galaxy sizes. Through this new
 implementation, {\sc eagle}~can reasonably match and predict multi-wavelength observations at large scales, making them an ideal resource to study galaxies in different environments.

 We describe our methodology in the next section, followed by the results of our analysis in Sec.~\ref{sec:Results}, which are discussed in the
 context of the existing literature in Sec.~\ref{sec:Discussion}. We finally conclude in Sec.~\ref{sec:Conclusion}.

\section{The {\sc eagle}~Simulations}
\label{sec:EAGLE}

 In this work we employ data produced by the {\sc eagle}~simulation \citep{EAGLE_paper,2016McAlpine}, which is a suite of hydrodynamical simulations run with modified version of the Gadget3 code.
 The  simulation  uses  a  flat  $\Lambda$CDM  cosmology  with  parameters  taken  from  the Planck mission \citep{2014Planck} results: $\Omega_\Lambda$ = 0.693, $\Omega_m$ = 0.307, $\Omega_{b}$ = 0.04825, $\sigma_8$ = 0.8288, $\mathrm{n_s}$ = 0.9611,Y = 0.248 and $\mathrm{H_0}$ = 67.77 $\mathrm{km \ s^{-1} \ Mpc^{-1}}$(i.e. $h$ = 0.6777). Specifically, we use the reference
 model RefL0100N1504 with co-moving box length of 100 $h^{-1}$ Mpc. The total number of particles in the RefL0100N1504 run is $2 \times 1504^3$, giving an initial gas particle mass ($ m_{\rm g}$) of
 $1.81 \times 10^{6} M_{\odot}$, and a dark matter particle mass of $9.70 \times 10^{6} M_{\odot}$, respectively. By using state-of-the-art numerical techniques, {\sc eagle}~ differs from other simulations in its implementation of energy feedback from stars, i.e. it does not distinguish between core-collapse supernovae, stellar winds and radiation pressure. In this study we have used the snapshot at $z = 0.1$ to create mock observational data.

\subsubsection{The physical parameters}

 Star formation in the {\sc eagle}~simulations is implemented using the Kennicutt-Schmidt relation \citep{1998Kennicutt} between the gas mass density and the star formation density \citep{2008Schaye}.
 In particular, the star formation rate decreases with increasing metallicity above the density threshold given by \citet{2004Schaye} as:
\begin{equation}
n_{\rm H}^* = 10^{-1} {\rm cm}^{-3} \big(\frac{Z}{0.002}\big)^{-0.64}
\end{equation}
where, {\it Z} is the gas metallicity. The simple stellar populations represented by these stellar particles follow the Chabrier initial mass function (IMF). Furthermore, in order to prevent
artificial fragmentation, a density dependent temperature floor ($T_{\rm eos} ( {\rho}_{\rm g} )$) is applied following the equation of state : $P_{\rm eos} \propto {\rho}_{\rm g}^{{\gamma}_{\rm eos}}$
 with ${\gamma}_{\rm eos} = 4/3$, where $P$ is the pressure, $\rho_{g}$ is the gas density and $\gamma$ is the ratio of the heat capacities. The temperature floor is normalised to $T_{\rm eos}=8000$ K at $n_{H}=0.1 \ \mathrm{cm^{-3}}$ to mimic gas in cold phase. When the gas density exceeds $n_{\rm H}^*$,
 and the temperature $\log_{10} (T/{K}) < \log_{10} (T_{\rm eos}/{K}) + 0.5$, the gas particles are assigned a star formation rate (SFR):

\begin{equation}
\label{eq:sfr}
{\dot m}_* = m_{\rm g} \ A \ (1 {\rm M}_{\odot} {\rm pc}^{-2})^{-n} \ (\frac{\gamma}{G}
f_{\rm g} P)^{(n-1)/2} ,
\end{equation}
 where, $G$ is the gravitational constant, $\gamma = 5/3$ is ratio of specific heats and  $f_{\rm g}$ is the fraction of mass in the gas, and is kept at 1.0 for the RefL100N1504 model.
 Parameters $n=1.4$ and $A= 1.515 \times 10^{-4} \ M_{\odot} \ {\rm yr}^{-1} \ {\rm kpc}^{-2}$ are estimated from the fit to observational data \citep{1998Kennicutt}.

 The rates of radiative cooling and heating are computed on an element-to-element basis in the presence of \citet*{2001Haardt} ultraviolet or x-ray ionizing background, and the cosmic microwave
 background. Eleven elements (H, He, C, N, O, Ne, Mg, Si, S, Ca and Fe) which are important for calculating cooling rates at $T>10^4K$ \citep{2009Wiersma} along with the total metallicity are
 tracked in the simulations.

 The simulations incorporate various sub-grid feedback mechanisms including stellar winds from asymptotic giant branch (AGB) stars, supernovae \citep[both core collapse and type IA;][]{2009Wiersma1} and active galactic nuclei \citep[AGN;][]{EAGLE_paper}. The prompt stellar feedback is implemented in a manner similar to \citet{2012Dalla}. In this `stochastic feedback' model, the fraction of energy ($f_{\rm th}$) released by the core collapse supernovae is given by:
\begin{equation}
\label{eq:fth}
f_{\rm th} = f_{\rm th,min} +
\frac{f_{\rm th,max}-f_{\rm th,min}}{1 + \big( \frac{Z}{0.1 Z_{\odot}} \big) ^{n_Z}
    \big( \frac{n_{\rm H, birth}}{n_{\rm H,0}} \big) ^{-n_n}}  ,
\end{equation}
where, $f_{\rm th,max}$ and $f_{\rm th,min}$ are the asymptotic values of $f_{\rm th}$ and $n_{\rm H, birth}$ is the density of the parent gas particle. In this model, $n_n$, $n_Z$, and $n_{\rm H,0}$ are free parameters. This fraction is injected into the neighbouring cells 30 Myr after the birth of the stellar particle. The thermal energy ejected by a star is distributed stochastically isotropically, resulting in temperature change of $\Delta T = 10^{7.5}$K.

When the mass of a halo reaches $10^{10}M_{\odot}$, a gas particle at the centre of the potential well is converted into a seed black hole with a mass of $10^5$ ${\it{h^{-1}}}M_{\odot}$ \citep{2005SringelDiMatteo}. The accretion of matter on the central black hole is based on a modified Bondi-Hoyle model \citep{EAGLE_paper}. A fraction of 0.015 of the rest mass energy
 of the matter accreted on the central black hole is returned to the surrounding thermally as a jump of $\Delta T = 10^{8.5}$K.

All the free parameters related to physical processes are calibrated to reproduce the GSMF and galaxy sizes at $z = 0.0$.

\begin{figure}
    \includegraphics[scale=0.55]{setup.png}
    \caption{The setup employed for creating the slices for mock observations. A slice is cut from the simulation box around each pair of clusters ($M_{200}/M_{\odot} > 10^{14}$), by translating the original coordinate system ($O$), such that the centre of the modified coordinate system coincides with the centre of the line joining the two clusters. The system is then rotated about this axis, and angles $\alpha$ and $\beta$ are randomly generated. This new coordinate system is referred in the text as $O^{\prime}$. Throughout this paper, environment of galaxies is characterised based on the 2D projection of 36 such slices in the $O^{\prime}$ frame of reference.}
    \label{plot:setup}
\end{figure}

\begin{figure}
    \centering
    {\includegraphics[scale=0.6]{No_color_with_filament_combined.png}}
    \caption{(a)The projected position of galaxies in one of our slices.
    (b) Same as (a), with {\it red lines} overplotted to  represent the filaments identified by {\sc disperse}.}
    \label{proplot}
\end{figure}

\begin{figure}
    \includegraphics[scale=0.4]{Color_without_filament.png}
    \caption{The projected position of galaxies in the same slice shown in Fig.~\ref{proplot}, with each point labeled with the environment it belongs to. The radius for the filaments is assumed to be 2 Mpc (see text for details). The {\it black, red}, and {\it grey points} represent galaxies in groups and clusters, filaments, and voids, respectively.}
    \label{finalexslice}
\end{figure}

\subsubsection{Properties of galaxies}
\label{Propsec}

In {\sc eagle}~simulations, the dark matter haloes are identified using the friends-of-friends (FOF) algorithm \citep{1984Einasto}, such that the dark matter particles separated by less than 0.2 times the mean
inter-particle separation are bound into a single halo. The individual galaxies are identified using the SUBFIND algorithm \citep{2001Springel,2009Dolag}, which labels the galaxy
 closest to the centre of the parent halo as the central galaxy, and other haloes inside the parent as satellite galaxies.

It is difficult to quantify the mass or luminosity of galaxies in simulations using the SUBFIND algorithm, because it assigns the mass of unbound particles to the central galaxy. This leads to an extended stellar distribution for such galaxies. Hence, in this work we use the stellar mass of the subhalo obtained within an aperture of 30 kpc in physical units (pkpc, henceforth), centred at the minima of the subhalo potential \citep{2015Tray,EAGLE_paper}. The choice of aperture is motivated by the fact that 3-D aperture of 30 kpc matches the results obtained using 2-D Petrosian aperture \citep{EAGLE_paper}.

The {\sc eagle}~simulation uses the \cite{2003Bruzal} population synthesis models for each particle to obtain the spectral energy distribution (SED) of galaxies. The spectra convolved with the filter response function within 30 pkpc aperture gives the broadband colours of the galaxies in optical and near infrared wavebands \citep{2010Doi,2006Hew}.
We employ some of these rest-frame colours to study the properties of galaxies. A full list of all the variables used in our analysis is provided in Table~\ref{proptable}. In this work, we use the SFR inside 30 kpc in physical units centred at minima of subhalo potential to explore such trends. The SFR has been measured using the mass of dense gas in the subhalo \citep{2008Schaye}.

 Studies of large-scale filaments based on observations mainly focus on the outskirts of
 massive clusters \citep[e.g.][]{dolag06,porter08,mahajan12}, or superclusters\footnote{In this work a filament or a network of filaments bound by clusters of galaxies on opposite ends is referred to as a `supercluster'.}
 \citep[e.g.][]{porter07,haines11,2018Smriti}, which often comprise two or more massive clusters and groups with large-scale filaments passing through them. In order to mimic such superclusters using the {\sc eagle}~simulations, in this work we identify systems where two massive clusters may be connected via filaments.
 The {\sc eagle}~simulations however have a small box size (the largest box is 100 $\rm h^{-1}$ Mpc on each side), hence not many superclusters are expected to have formed in it \citep{2005Bagla,2009Bagla}. Therefore, in order to make the best use of the available data, we generated mock observations by identifying the positions of haloes with mass $\sim 10^{14} \ \rm M_{\odot}$. Nine such clusters were found, resulting in 36 ($\Comb{9}{2}$) potential slices comprising superclusters. Table~\ref{cluster_table} lists the properties of these haloes ($z=0.1$), which are analysed
 further. To find the underlying structure of the cosmic web, we restricted ourselves to galaxies with $M_*/M_\odot \geq 10^9$ (within 30 pkpc aperture), which gives us a sample of
 294,780 galaxies. This choice of stellar mass limit is motivated by earlier work which showed that for the RefL0100N1504 run, simulations are well matched to the observations for $M_{*}/M_\odot \geq 10^{9}$ \citep{EAGLE_paper,2016Tray}.

\begin{table}
    \caption{The physical parameters from the {\sc eagle}~simulation used to study the properties of galaxies. }
    \label{proptable}
    \begin{tabular}{lc}
        \hline
        Parameter & Description \\
        \hline
        $M_{*}$ & Total stellar mass within the aperture ($M_{\odot}$). \\
        $M_{gas}$ & Total gas mass within the aperture ($M_{\odot}$).\\
        $M_{tot}$ & Total subhalo Mass ($M_{\odot}$)\\
        x,y,z& Position of subhalo \\
        SFR & Star formation rate within the aperture.\\
        $u$ & u band without dust attenuation.\\
        $g$ & g band without dust attenuation.\\
        $r$ & r band without dust attenuation.\\
        \hline
    \end{tabular}
\end{table}

 \begin{table*}
     \caption{Clusters used for analysis. The columns are: (i) grpid: group id of the group detected by the friends-of-friends algorithm. (ii)  $M_{\rm tot}$: mass ($M_{200}$) of the group.
     (iii) R: radius of the group halo. (iv) x, (v) y and (vi) z  are the respective coordinates of group halo, and, (vii) number of subhaloes identified by the SUBFIND algorithm. }
         \label{cluster_table}
    \begin{tabular}{|c|c|c|c|c|c|c|}
        \hline
        grpid & $M_{tot}/M_\odot$ & R (pkpc) & x (Mpc) & y (Mpc) & z (Mpc) & Number of subhaloes \\
        \hline
        27000000000000&$3.4 \times 10^{14}$&1426.70&5.72&75.61&47.48&17101\\
        27000000000001&$1.7\times 10^{14}$&1135.12&18.12&79.88&53.51&9942\\
        27000000000002&$2.89\times 10^{14}$&1349.73&9.12&35.03&54.52&9152\\
        27000000000003&$2.9\times 10^{14}$&1356.48&52.43&5.00&20.12&7430\\
        27000000000004&$1.85\times 10^{14}$&1162.48&10.96&81.29&54.89&7365\\
        27000000000005&$1.38\times 10^{14}$&1054.56&77.85&76.97&42.28&6909\\
        27000000000006&$1.38\times 10^{14}$&1055.64&76.13&89.23&41.89&6191\\
        27000000000007&$1.11\times 10^{14}$&981.45&61.25&33.08&20.23&6183\\
        27000000000008&$1.00\times 10^{14}$&948.02&84.83&47.33&8.14&3190\\
        \hline
    \end{tabular}
      \end{table*}

\begin{figure}
    \includegraphics[scale=0.5]{Length_compare.png}
    \caption{Comparison between the distribution of length of filaments in a study based on the SDSS data \citep{2014Tempel}, the Coma supercluster \citep{2018Smriti} and our sample. The vertical lines represent the median of the respective distributions. The filaments identified in both the observational datasets are found to be longer relative to the filaments from the {\sc eagle}~simulations in this work.}
    \label{plot:Lengthplot}
\end{figure}

\subsection{Mock Observations}
\label{Obsersec}

 Although simulations contain 3-dimensional (3D, henceforth) information, we created 2D projections of 3D slices in order to compare our results with the observations. Furthermore,
 the 2D analysis allows us to artificially expand our dataset by rotating the central axis of the supercluster systems found in the simulation box as described below.
 Motivated by observational studies of supercluster filaments crossing clusters of galaxies \citep[e.g.][]{dolag06,porter08,mahajan12,2018Smriti}, we begin by considering each cluster
 pair system as a possible supercluster candidate.

 In order to generate a slice of these data for making mock observations, the coordinate system is changed by translating the origin to the centre of the line joining the
 two clusters in the system. The system is then rotated by keeping one axis fixed with the line joining the two clusters, and choosing the orientation of the remaining two axes randomly.
 \citet{2018Laigle} have shown that a 60--200 Mpc thick slice can capture real 3D filaments even in their 2D projection. Therefore,
 in order to characterise the environment in 2D projections, we choose a slice of co-moving width 60 Mpc with the normal perpendicular to the line joining the cluster pair as
 shown in Fig. \ref{plot:setup}. This choice of width also compliments the width of the Coma supercluster region studied by \cite{mahajan12,2018Smriti}.
 In the following, we refer to this transformed frame as $\mathrm{O^\prime}$.

\subsection{Classification of Environment}
\label{sec:Class}

The cosmic web can be broadly segregated into different components based on the density of galaxies. Characterisation of these environments requires identifying the local topology and clustering
of the constituents. In this study, we follow an approach similar to that used in \citet[][hereafter SM18]{2018Smriti} to characterise environment into groups and clusters, filaments and voids,
respectively.

 In order to identify filaments in the 2D projected slices, we employ the Discrete Persistent Structures Extractor \citep[{\sc disperse};][]{2011Sousbie} algorithm. {\sc disperse} uses the
 Morse theory and persistence theory to map the persistence of critical points obtained by measuring density at discrete points to the underlying topology. It identifies features such as nodes, filaments, and saddle points using just one parameter. This parameter
 called the persistence threshold crudely corresponds to the level of complexity to be retained while identifying structures in a distribution. We direct the reader to SM18 and \citet{2011Sousbie} for detailed application and working of the algorithm, and choice of parameters.

  The persistence threshold above which all the critical points are retained for analysis is usually taken to be 3-5 $\sigma$, where $\sigma$ is the standard deviation. It suppresses the selection noise and picks relatively denser and longer filaments \citep{2011Sousbie}. In this work, we choose the significance threshold of $3\sigma$ which segregates the noise
  from the points used for analysis in this work for each mock observation.

 Filaments are obtained by applying {\sc disperse} on the distribution of galaxies in the $\mathrm{O^\prime}$ projection. In Fig.~\ref{proplot}(b) we show the filaments identified
 for a representative slice. We identify filament galaxies as those which (i) do not belong to clusters or groups, and (ii) are $\leq 2$ Mpc from the spine of the filament. The choice of
 filament radius is driven by the fact that $\sim 75\%$ of all galaxies found within $5$ Mpc of the spine of the filaments lie $\leq 2$ Mpc from the central axis (also see Sec.~\ref{s:colour}).

 In order to identify the galaxies in each slice, we use {\sc eagle}'s group catalogue to identify the friends-of-friends groups with total mass,
 $M_{\rm halo} > 10^{12.5} \ \rm M_{\odot}$, and comprising at least four galaxies with $M_*/M_\odot > 10^9$ \citep{EAGLE_paper}.
The galaxies which are not linked to groups, clusters or filaments were classified as void galaxies. In Fig.~\ref{finalexslice} we show the distribution of galaxies in different
 environments in one of the slices.

\begin{figure}
    \centering
    \includegraphics[width=\columnwidth]{dvscolfrac.png}
    \caption{The (a) $g-r$ colour of
 galaxies, (b) median absolute deviation (MAD) of $g-r$ colour shown in (a), and (c) fraction of passive galaxies ($sSFR < 0.01$ Gyr) on filaments as a function of their distance from the spine of the filament. Galaxies become redder and the fraction of passive galaxies increases with decreasing distance from the spine of the filaments. }
\label{plot:dvscol}
\end{figure}

\section{Properties of galaxies on filaments}
\label{sec:Results}

 In order to study the properties of galaxies in different environments in the {\sc eagle}~simulations, we stack all the 36 slices obtained from mock observations. In the following we
 analyse the trends in the properties of galaxies, in particular the galaxies on filaments with respect to their counterparts residing in denser and rarer regions of the space.

\subsection{The large-scale filaments}
\label{dpercolsec}

 In Fig. \ref{plot:Lengthplot}
 we compare the distribution of length of filaments from our stacked sample generated from the {\sc eagle}~simulations, with the filaments in the Coma supercluster (SM18),
 as well as another distribution of filaments obtained from the SDSS dataset \citep[see fig.~11 of][]{2014Tempel}. While SM18 identified the filaments in 2D projections, just like this work,
 the filaments of \citet{2014Tempel} were identified from the 3D distribution of galaxies.

 To compare these distributions, we use the Welch's t-test \citep{1947Welch}, which tests for the hypothesis that two distributions have different means. We find that the distribution of lengths
 of filaments from {\sc eagle}~simulation when compared with the filaments from SM18 and \citet{2014Tempel} gives p-values $< 0.01$ in both cases.
 The filaments found in both the observational datasets are found to be relatively longer than the filaments found in the {\sc eagle}~simulations. However, this result can only be considered
 qualitatively because of the different redshifts of the samples, and the fact that the procedure for finding the filaments is dependent on the algorithms with free parameters which
 can influence the distribution of length of filaments \citep{libeskind18}.

\subsubsection{Colours of galaxies}
\label{s:colour}

 Broadband optical colours are often a good representative of the age of the most dominant stellar population, and hence, the age of galaxies. Therefore, in order to study the impact of the filament
 environment on the evolution of galaxies we analyse their colour as a function of increasing environmental density. In Fig. \ref{plot:dvscol} we show the running median of the $g-r$ colour of galaxies, median absolute deviation (MAD) of $g-r$ colour, and the fraction of passive galaxies \citep[sSFR $< 0.01$ Gyr$^{-1}$;][]{EAGLE_paper}, as a function of their perpendicular distance from the spine of filaments ($d_{\rm per}$). We find that the galaxies become redder and more passive closer to the spine of filaments within a radius of $2$ Mpc from the filament axis. Specifically, the passive fraction increases from 8\% at a distance of $\sim 2$ Mpc from the spine of the filaments to 11.5\% at the centre of the filaments. Welch's t-test statistic
 shows that the colour of galaxies at $d_{\rm per} < 1$ Mpc is statistically different from their counterparts farther away from the spine of the filaments
 (Table~\ref{stats}). The MAD quantifies the width of the distribution at different $d_{\rm per}$, essentially showing that the distributions are broader closer to the spine of the
 filaments where most of the galaxies lie.
This result is in tune with the findings from observations \citep{alpaslan16,2017Kuutma,2018Kraljic,2019Bonjean}, as well as other simulations \citep{2010Gay}. Such trends with $d_{\rm per}$ have helped in constraining the radius of filaments to $1-2$ Mpc \citep{2018Bonjean,2018Smriti,2019Tanimura}.

 The stellar mass of galaxies is well known to correlate with the environmental density. Hence in order to break this degeneracy between $M^*$ and environment, in Fig. \ref{plot:fracapp} we show the cumulative fraction for the active and passive galaxies in filaments separately in four mass bins: $10^{9}<M/M_{\odot} \leq 10^{9.22}$, (b) $10^{9.22}<M/M_{\odot} \leq 10^{9.52}$, (c) $10^{9.52}<M/M_{\odot} \leq 10^{9.98}$ and (d) $10^{9.98} < M/M_{\odot} \leq 10^{11.2}$. The mass bins are chosen to include the same number of galaxies in each bin. For the three low stellar mass bins ((a)-(c)), the median $d_{\rm per}$ for the active galaxies' distributions have values close to 1 Mpc, i.e. the radius of filaments comprising $\gtrsim 50\%$ of the galaxies on the cosmic web. We further tested for statistical differences among the distributions in each panel using the Kruskal-Wallis (KW) test \citep{1952Kruskal}. The KW statistic is a non-parametric rank-based test which assumes independent observations, continuous variability and same shape for the two distributions.
 For the four panels in Fig.~\ref{plot:fracapp} the KW test yields a probability of 5.67e-82, 1.10e-52, 2.85e-06, and 0.31 in favour of the hypothesis that the two distributions had the same medians. Based on this analysis we also conclude that except for high mass galaxies, at fixed stellar mass, passive galaxies lie closer to the spine of the filament than their active counterparts.

\begin{figure}
    \includegraphics[width=1.0\columnwidth]{Fraction_approach.png}
    \caption{The cumulative distributions of active and passive galaxies are shown as a function of $d_{\rm per}$ for different mass bins. The {\it vertical dashed lines} correspond to median value for $d_{\rm per}$ for the passive ({\it dashed red line}) and the active ({\it solid blue line}), respectively. The four mass bins are chosen to have  the same number of galaxies in each bin. The median of the distribution for the active galaxies in the three low stellar mass bins (a)-(c) have value close to 1 Mpc, i.e. the radius of filaments comprising majority of the
    galaxies on the cosmic web. The distributions for the active and passive galaxies in these three mass bins are also statistically distinct. On the other hand, the galaxies in
     the highest mass bin are statistically similar to each other. These distributions evidently show that
    at fixed stellar mass, passive galaxies lie closer to the spine of the filament than their active counterparts.}
    \label{plot:fracapp}
\end{figure}

 \begin{table}
 \caption{The Welch test statistical probability for the likelihood that the galaxies in bin x are the same as bin y. The bins are chosen in ascending order of the distance
 of galaxies from the spine of the filament such that, bin 1: $0 \leq d_{\rm per}/{\rm Mpc} \leq 1$, bin 2: $1 < d_{\rm per}/{\rm Mpc} \leq 2$ and bin 3: $2 < d_{\rm per}/{\rm Mpc} < 5$. }
 \begin{center}
 \begin{tabular}{c|c|c|c}
 \hline
Parameter 	&    bin1-bin2   	& bin2-bin3   		& bin1-bin3  \\  \hline
$g-r$                 &  $1.497~\times~10^{-23}$    & $1.554~\times~10^{-27}$   & $3.059~\times~10^{-82}$ \\
sSFR 		&    0.310      	& $8.711~\times~10^{-25}$  		& $2.871~\times~10^{-44}$ \\
  $M_{gas}/M_{tot}$		&    0.058      	& $5.401~\times~10^{-14}$  		& $1.341~\times~10^{-31}$ \\
$M_*/M_{tot}$ 		&    $9.859\times~10^{-108}$ 	& 0.021      		& $5.094\times~10^{-158}$ \\
$Z_{NSF}$ 	&    $3.845~\times~10^{-127}$ 	& $2.321~\times~10^{-49}$  		& $\lll 0.0$ \\
$Z_{SF}$  	&    0.871     	& $1.216~\times~10^{-26}$  		& $5.194~\times~10^{-44}$ \\

\hline

\end{tabular}
\end{center}
\label{stats}
\end{table}

\subsubsection{Star formation in filament galaxies}

 The star formation rate of filament galaxies is enhanced relative to their counterparts in clusters as well as the voids \citep{fadda08,mahajan12}. SM18 showed that while the optical
 and UV colours becomes bluer, the equivalent width of the H$\alpha$ line increases for the filament galaxies moving away from the spine of the filaments.

 \begin{figure}
	\centering
	\includegraphics[width=\columnwidth]{d_vs_SFR_gas_frac}
	\caption{The (a) median sSFR, (c) gas mass fraction, and (e) stellar mass fraction as a function of $d_{\rm per}$. Plots (b), (d) and (f) show the MAD for sSFR, gas mass and stellar mass respectively. $M_{tot}$ is the sum of $M_{gas}$, $M_*$ and $M_{dm}$ inside the 30pkpc aperture of each galaxy. Outside a radius of 0.5 Mpc, the sSFR and the gas mass fraction decrease with decreasing distance from the spine of the filaments, while the stellar mass function remains constant. Within a radius of $\sim 0.5$ Mpc, the trend seems to reverse, and all three quantities exhibit enhancement with decreasing $d_{\rm per}$, indicating that the intrafilamentary medium condensing closer to the spine of the filament may be responsible for increasing the SFR of filament galaxies with $d_{\rm per} < 0.5$ Mpc. }
	\label{plot:dvsSFR}
\end{figure}

 In Fig. \ref{plot:dvsSFR}(a)-(b) we show the variation of the running median of the specific star formation rate (sSFR) as a function of the perpendicular distance ($d_{\rm per}$) to the spine of the filaments and the MAD for the same. On approaching the filaments, the sSFR decreases slowly until $\sim 1$ Mpc from the spine of the filaments, and thereafter increases again closer to the central axis.
 Table~\ref{stats} confirms the observed trends by showing that the sSFR distribution for galaxies within $\lesssim 2$ Mpc is different from their counterparts further away from the
 spine of the filaments.
 This result correlates well with the $g-r$ colour of galaxies becoming bluer in Fig. \ref{plot:dvscol}(a). Furthermore, these results support the findings of \citet{2019Liao}, who have suggested that the galaxies on filaments can accrete intrafilamentary gas to feed star formation, and the density of this gas should also increase with decreasing distance from the spine of the filaments.

 In order to test our hypothesis and test if the increase in star formation activity near the spine of the filaments is correlated with an increase in the availability of gas,
 we study the distribution of mass in stars and gas in the filaments as a function of $d_{\rm per}$.
 In Fig. \ref{plot:dvsSFR}(c)--(f) we show the variation in the fraction of stellar mass and gas mass as a function of $d_{\rm per}$ for $\sim 96\%$ of the galaxies in our sample with non-zero gas mass fraction, and the respective MAD for the same. The denominator for the fractions, $M_{tot}$ is the total mass of the galaxy, which includes the gas ($M_{gas}$), stars ($M_{*}$) and the dark matter mass ($M_{dm}$) inside a 30 pkpc aperture.

 The median stellar mass fraction (SMF) shows a mild increase only within $\lesssim 0.5$ Mpc from the central axis of the filaments. This trend is reciprocated by the gas mass fraction (GMF) as well. However, at $d_{\rm per}>0.5$ Mpc, the median GMF rises smoothly, but the median SMF remains constant. The observed trends are also confirmed statistically in
 Table~\ref{stats}. Furthermore,
 we note that all the trends seen in Fig. \ref{plot:dvsSFR} are primarily driven by low mass ($10^{9} \leq M/M_{\odot} \leq 10^{10}$) which dominate our sample. Galaxies with stellar
 masses in the range $10^{10}-10^{10.5}$ also show trends similar to their less massive counterparts with minor deviations. But the most massive galaxies show an
 almost constant distribution for sSFR and the gas mass fraction, but an incline in the stellar mass fraction distribution with decreasing $d_{\rm per}$ at small
 distances ($d_{\rm per} \lesssim 0.5$ Mpc) from the spine of the filament.

 These trends suggest that the intrafilamentary gas condenses into the galaxies on filaments and fuel star formation, supporting the results of \citet{2019Liao}.
 However, these small changes (2\%--5\%) in the GMF and the sSFR are not enough to turn a passively evolving galaxy to an active one as seen here in Fig.~\ref{plot:dvscol}, and in
 other studies based on observations \citep{2019Bonjean}. Also because of the small magnitude of these changes it is not surprising that such trends have yet not been observed in real data. The increase in the passive fraction of galaxies is likely caused by increased gravitational interactions amongst filament galaxies, likely due to an increased number density of galaxies closer to the spine of the filament. As a result of these interactions, these galaxies not only exhaust their reservoir of gas, but will also show momentary increase in their star formation activity, therefore contributing to the trend observed for the sSFR. Similar conclusions have also been drawn by \cite{2019Bonjean}, who modelled the passive fraction of
 filament galaxies to find a positive gradient in their SFR which they attributed to mergers.

\begin{figure}
    \includegraphics[width=\columnwidth]{Z_plot2.png}
    \caption{The median metallicity of (a) the star forming gas ($Z_{SF}$) and, (c) the non star-forming gas ($Z_{NSF}$) as a function of $d_{\rm per}$. Plots (b) and (d) show MAD for $Z_{SF}$ and $Z_{NSF}$ respectively.
    The $Z_{SF}$ within $d_{\rm per} \lesssim 1$ Mpc is elevated relative to gas further away. The  $Z_{NSF}$, on the other hand increases smoothly with decreasing $d_{\rm per}$, indicating accretion of non-star-forming gas on the filament galaxies.}
    \label{plot:dpervsZ}
\end{figure}

\subsubsection{Metallicity}
\label{subsec:Metallicity}

 Metallicity, the fraction of mass in elements heavier than Helium is dependent on the complex interplay of gas accretion on galaxies and outflows from different feedback processes. For our analysis of metallicity, we use the smoothed metallicity provided for
 each subhalo by \citet{2009Wiersma}. The gas metallicity is divided into the star-forming ($Z_{SF}$) and non-star-forming ($Z_{NSF}$) metallicity, respectively. The star-forming gas is cold ($\sim 10^{4} K$) enough to form stars, with the SFR dependent upon the metallicity \citep{2008Schaye}. On the other hand, the non-star forming gas comprises hot and
 under-dense gas which can not form stars. We refer the reader to \citet{2004Schaye} for a detailed discussion on how star-formation is implemented in the {\sc eagle}~simulation.

 Our prior results indicate that galaxies are likely to accrete gas as they move towards the spine of the filament. Therefore, in order to test this hypothesis using
 $Z$ we show the running median of $Z_{SF}$ as a function of their perpendicular distance from the spine of the filaments, $d_{\rm per}$ in Fig.~\ref{plot:dpervsZ}. Although with significant scatter, the median
 $Z_{SF}$ within $\lesssim 2$ Mpc of the spine of the filaments is clearly elevated relative to the galaxies further away. On the other hand, $Z_{NSF}$ is found to increase smoothly with decreasing distance from the filament axis\footnote{Just like Fig. \ref{plot:dvsSFR}, these trends in metallicity are also driven by the low-mass
 galaxies ($10^{9} \leq M/M_{\odot} \leq 10^{10.5}$). The more massive galaxies show noisy, almost constant distribution of both $Z_{SF}$ and $Z_{NSF}$ except an
 apparent decline within $d_{\rm per} \lesssim 1$.}  (Fig. \ref{plot:dpervsZ}(c)).
 This result, together with Fig. \ref{plot:dvsSFR}(c) indicates that the non-star forming gas is accreted
 onto the galaxies. This result also supports the findings of \citet{2019Liao} who used hydrodynamical simulations to show that at high redshifts ($z = 4.0$ and $2.5$) around
 30\% of the gas accreted on the galaxy halos on filaments is the intrafilamentary medium.

\section{Discussion}
\label{sec:Discussion}
We have used data products from the EAGLE simulations to show that the intermediate density environment prevalent on the filaments influence the properties of galaxies on them. In the following, we discuss our results in the context of the filament galaxies studied in the literature, and also analyse the properties of filament galaxies relative to their counterparts in other environments.

\subsection{Filaments in the literature}

\cite{2017Kleiner} used the 6 degrees Field Galaxy Survey \citep{2009Jones} and the Parkes all-sky survey \citep{2000Stavenley} to study the HI fraction of galaxies on nearby filaments identified by {\sc disperse}. They found that massive galaxies ($M_{*}/M_{\odot} >= 10^{11}$) closer ($< 0.7$ Mpc) to the spine of the filaments have higher HI-to-stellar mass ratio
 (HI fraction), where as the HI fraction among lower mass galaxies show no difference with respect to the control sample of galaxies $> 5$ Mpc away from the spine of the filaments.
 \cite{2017Kleiner} attributed the higher HI fraction in massive filament galaxies to cold gas accretion from the intrafilamentary medium.

 On the other hand, \cite{2018Crone} used data from the ALFALFA survey \citep{2005Giovanelli} to study the HI reservoir in galaxies. Complimentary to the results presented here,
 \cite{2018Crone} observed that at fixed stellar mass and colour, galaxies show a deficiency in HI on approaching the filaments. Furthermore, they also observed that at fixed local
 density, the HI fraction of lower mass galaxies ($10^{8.5}<M_{*}/M_{\odot}<10^{10.5}$) decreases on approaching the filaments.

\begin{figure}
	\includegraphics[width=\columnwidth]{Temp2.png}
	\caption{The projected temperature for the sample slice plotted in Fig. \ref{finalexslice}. This plot makes use of the particle dataset at $z=0.1$. The colours represent the mean of $log T$. The filaments clearly show concentration of gas at $10^{4}<T(K)<10^{5}$. This gas can be accreted by the filament galaxies.}
	\label{plot:temp}
\end{figure}

 Even though the lower mass limit of our sample is 0.5 dex greater than the sample used by \cite{2018Crone}, we can make use of the particle dataset to study the trends observed in the {\sc eagle}~simulation. In Fig.~\ref{plot:dvsSFR} we show that the gas mass fraction decreases with $d_{\rm per}$ until $~ 0.5$ Mpc from the spine of the filament, but at $d_{\rm per} \lesssim 0.5$ Mpc, the trend seems to reverse. It is worth noting that the stellar mass of galaxies also seems to increase within the same radius around the spine of filaments. These results, together with enhanced sSFR within $0.5$ Mpc from the spine from the filament (Fig.~\ref{plot:dvsSFR} (a)), indicate that the enhanced gas condensing in galaxies closer to the spine of the filaments may lead to an increase in the rate of formation of stars. This hypothesis is in tune with the results of \citet{2017Kleiner} as well as Fig. \ref{plot:temp}, where we have used the particle dataset from the {\sc eagle}~simulation to show the projected temperature map for the gas in the example slice shown in Fig. \ref{finalexslice}. It is evident that the filaments in the {\sc eagle}~simulation comprise gas at $10^{4}<T(K)<10^{5}$, which can fuel star formation in galaxies, especially closer to the spine of the filaments \citep{2019Bonjean}.

\cite{2015Darvish} studied 28 galaxies on filaments against 30 galaxies in voids from the COSMOS survey \citep[$z\sim0.53$;][]{2007Scoville}.
They found that the star-forming galaxies on filaments have higher metallicity than their counterparts in the voids. \cite{2015Darvish} attributed this difference to the accretion of pre-enriched filamentary gas on to the filament galaxies. In a complimentary study of filaments at $z=1.5$ from the Horizon hydrodynamical simulation, \cite{2010Gay} also found that the
 gas-phase metallicity of galaxies increases with decreasing distance from the spine of the filaments.
Our analysis shows similar trends in Fig. \ref{plot:dpervsZ}. However, we also note that the {\sc eagle}~simulations' reference model used here (RefL0100N1504) is known to over-predict the metallicity relative to runs with smaller box lengths and observations \citep[fig. 1;][]{2017DeRossi}. But since this over-prediction is consistent throughout the box, it should not influence the trends observed in the metallicity as a function of environment.

\cite{2017Kuutma} used the SDSS data to study the statistical variation in the properties of galaxies on approaching the spine of the filaments.
 \cite{2017Kuutma} found that the galaxies become more elliptical and redder in the $g-i$ colour with decreasing distance from the spine of the filaments. Using a similar dataset, SM18 studied the Coma supercluster to find a similar increase in the $g-r$ colour index of galaxies with decreasing $d_{\rm per}$. A similar trend is reflected in the analysis presented here in Fig.~\ref{plot:dvscol}. We find that the median $g-r$ colour of galaxies becomes redder with decreasing distance from the spine of filaments.

 \citet{alpaslan16} analysed 1799 star-forming spiral galaxies ($z\leq0.09$) within $3.79~h^{-1}$ Mpc of the centre of filaments using data from the Galaxy and Mass Assembly
 (GAMA) survey. In agreement with the results presented in this work as well as other literature \citep[e.g.][]{2018Kraljic,laigle18}, they found that galaxies closer to the spine
 of the filaments are more massive, and at fixed {$M_{*}$}, galaxies are quenched closer to the spine of the filaments. Based on our own results and those presented in the
 literature we therefore suggest that along with stellar mass and large-scale environment, anisotropic tides along filaments impact the assembly history of galaxy halos, and
 hence the properties of galaxies.

\subsection{Filaments vs groups and voids}
\label{sec:compare}

In this section, we compare the properties of galaxies on filaments to their counterparts in groups and clusters, and the voids.

\begin{figure}
    \centering
    \includegraphics[width=0.9\columnwidth]{Red_Blue.png}
    \caption{The (a) $g-r$ colour of  galaxies in groups and clusters, and (b) filaments as a function of their stellar mass. The black line shows the red sequence fitted to the galaxies in clusters. In this work all galaxies bluer than $2\sigma$ from the red sequence are considered as blue, and the complimentary population is considered red. (c) This figure shows the kernel density estimates of stellar mass for the red and the blue populations on filaments. The Welch's t-test gives a p-value of $2.25e-175$, indicating that the two populations are statistically distinct. It is also evident that although majority of blue galaxies have masses $< 10^{10} M_{\odot}$, a significant fraction of red galaxies follow suit. }
    \label{plot:BlueRed}
\end{figure}

In Fig.~\ref{plot:BlueRed} we show the distribution of galaxies in the colour-$M_*$ plane for the groups and filaments, respectively. In order to compare the two colour distributions, we fit the red sequence to the distribution of cluster galaxies. In this work, all galaxies with $g-r$ colour more than $2\sigma$ bluer than the red sequence are classified as blue galaxies, where $\sigma$ is the standard deviation (0.055 mag) obtained by iteratively fitting the red sequence. It is evident from
Fig.~\ref{plot:BlueRed} (b) that there are fewer red galaxies on filaments, relative to the clusters. Specifically, while $\sim 54\%$ of the cluster galaxies are red, only $13\%$ of the filament galaxies follow suit.

In Fig.~\ref{plot:BlueRed}(c) we plot the kernel density estimates (KDE) of stellar mass for galaxies in both the colour selected populations of filament galaxies. KDE are closely related to histograms but are continuous and smooth. KDE is a non-parametric methods for calculating probability distribution of a variable using a non-negative function called kernel. We used Gaussian kernel and scott estimator \citep{2015Scott} for bandwidth used in the kernel. A large fraction of the blue galaxies (77\%) are found to have stellar mass, $M_{*}/M_{\odot}\le 10^{10}$. On the other hand, while most of the red galaxies on filaments are massive, around $63\%$ of them have $M_{*}/M_{\odot}\le 10^{10}$.
The Welch's t-test applied to the two distribution yields a p-value of $2.25e-175$, inferring that the two populations are statistically distinct. This result suggests that some environmental mechanism(s) may be responsible for quenching star formation in lower mass galaxies on filaments.

\begin{figure}
    \centering
    {\includegraphics[width=0.8\columnwidth]{MvsZfrac.png}}
    \caption{The (a) median metallicity of star forming gas ($Z_{SF}$) and (c) non star-forming gas ($Z_{NSF}$)as a function of stellar mass ($M_{*}$) of galaxies in groups and clusters ({\it red}), filaments ({\it blue}) and voids ({\it green}), respectively. MAD for $Z_{SF}$ and
    $Z_{NSF}$ is shown in (b) and (d) respectively. (e) The fraction of passive galaxies as a function of $M_{*}$ in the three environments. In groups the fraction of passive galaxies increases with decreasing $M_{*}$ for galaxies with $M_{*} < 10^{10} M_{\odot}$. While an opposite trend is observed for the filaments and voids. }
    \label{plot:EnvMstar}
\end{figure}

 The metallicity of a galaxy is related to its stellar mass $M_{*}$, such that more massive galaxies also have higher gas as well as stellar metallicities \citep{1968McClure,1979Lequeux,2002Garnett,2004Temonti,2006Lee,2010Mannucci,2019Cresci}. In Fig.~\ref{plot:EnvMstar}(a and c) we show the median metallicity of the star-forming gas ($Z_{\rm SF}$), and the non-star forming (NSF) gas ($Z_{\rm NSF}$) as a function of $M_*$ in different environments. In agreement with the previous findings \citep{2017DeRossi}, the NSF gas is found to be less metal rich relative to the star-forming gas. While the galaxies on filaments and voids show almost constant $Z_{\rm SF}$ and $Z_{\rm NSF}$, their counterparts in groups show
 marginally different trends on either side of $M_{*}/M_{\odot} \sim 10^{10}$, such that the Welch t-test probability is 0.045 in favour of the hypothesis that the distribution of $Z_{\rm SF}$
 for the lower mass galaxies ($M_{*}/M_{\odot} \lesssim 10^{10}$) is statistically similar to their massive counterparts. The respective probability for $Z_{\rm NSF}$ for group
 galaxies is found to be $\ll 0$.
 Such trends of lower $Z$ in the most massive galaxies have also been observed in the Coma cluster \citep{pogg01}, and the Coma supercluster \citep{tiwari20}.

Fig.~\ref{plot:EnvMstar} can be compared to fig.~1 of \cite{2017DeRossi}, who have also made use of the {\sc eagle}~simulation to compare the mass-metallicity relation obtained in simulations with various observational datasets and found them to agree within uncertainties. Our analysis indicates that the slope of the mass-metallicity relation is primarily determined by the galaxies in dense environments.

In Fig.~\ref{plot:EnvMstar}(c) we show the fraction of passive galaxies ($sSFR < 0.01 \ \rm Gyr^{-1}$) as a function of stellar mass in different environments. It is evident that in the groups' environment, the fraction of low-mass ($M_{*}/M_{\odot}<10^{10}$) passive galaxies increases significantly with decreasing $M_{*}$. On the other hand, the galaxies in filaments and voids show identical trends in the opposite direction, i.e. the fraction of passive galaxies increases with $M_{*}$, particularly for galaxies with $M_{*}/M_{\odot}>10^{10}$. This result is in broad agreement with the literature \citep[e.g.][]{haines06} where the dwarf galaxies are found to be passive only if they are a satellite to a massive galaxy. SM18 have also shown that the $NUV-r$ colour distribution of galaxies changes from voids to clusters, mainly because of a higher fraction of massive galaxies turning redder in dense environments.

\begin{figure}
	\includegraphics[width=\columnwidth]{Ellipticity_plot_all.png}
	\caption{The distribution of ellipticity (e) of galaxies in the red population in different environments. Galaxies in clusters show a wider range of ellipticities relative to their counterparts in filaments and voids. The Welch's t-test for the distribution of $e$ of galaxies in all but one pair of populations is $\sim 0$. A p-value of 0.116 for the galaxies in voids and filaments indicate that the morphology of these two populations is statistically identical.}
	\label{plot:ellip}
\end{figure}

The morphology of galaxies can provide important clues to their evolution. In this work, we use the morphology of resolved galaxies having more than 300 stellar particles as per the procedure described by \cite{2019Thob}. The shape parameters are derived using the reduced iterative inertia tensor in ellipsoidal apertures. Ellipticity is defined as:
\begin{equation}
e=1-\frac{c}{a}
\end{equation}
where, $c$ and $a$ are the length of semi-minor and semi-major axis, respectively. In Fig.~\ref{plot:ellip} we plot the distribution of $e$ for galaxies in different environments. The Welch's t-test for all but one pairs of environments yield p-value of $\sim 0$. The distribution of $e$ for the filament and void galaxies has the Welch's t-test probability of 0.116 in favour of the hypothesis that the two distributions are identical, suggesting that the ellipticity of the void and filament galaxies are statistically indistinguishable. This result is in agreement with the observations \citep{2015Alpaslan} where the GAMA survey data was used to show that the galaxies in filaments and voids have similar distributions of ellipticity, such that the maximum of the distribution lies in the range $0.2-0.6$.
On the other hand, the groups and clusters are dominated by low $e$, i.e. more elliptical galaxies.

\section{Conclusion}
\label{sec:Conclusion}

We employ data from the {\sc eagle}~simulations to explore properties of galaxies in different environments, in particular the large-scale filaments. We summarize the major findings of our analysis below:
\begin{itemize}
    \item Galaxies become redder and more passive closer to the central axis of the filaments. Furthermore, this trend is likely observed due to an increase in the relative fraction of passively-evolving galaxies having $M_*/M_\odot \lesssim 10^{10}$ (Figs.~\ref{plot:fracapp}, \ref{plot:BlueRed}(c)) closer to the centre of the filaments. In agreement with the results from various observations, this trend is observed within a radius of 2 Mpc around the spine of the filament, thus providing an upper limit for the filaments' radius.

    \item Both, the gas and stellar mass fractions of galaxies rise closer to the spine of the filaments within $d_{per}<0.5$ Mpc. But at $d_{per}>0.5$ Mpc the SMF remains constant while the GMF rises smoothly with increasing distance from the spine of the filaments. Together with the findings from the literature, these results lead us to conclude that the observed trends are a consequence of increased gravitational interactions between filament galaxies closer to the spine of the filament. This enhancement in the interaction rate is caused by an increased number density of galaxies closer to the centre of the filaments.

    \item The morphology of galaxies on filaments is similar to their counterparts in voids, but statistically distinct from the ones in clusters. However, fewer red galaxies are found on the filaments relative to clusters.
\end{itemize}

 To conclude, our results indicate that many properties of galaxies on filaments are different from their counterparts in clusters and voids. The colour, star formation, metallicity and gas fraction of filament galaxies change as a function of their distance from the central axis of the filament. This could be an impact of the IFM as well as the number density of galaxies, both of which increase with decreasing $d_{\rm per}$. High resolution spectroscopic and HI data for galaxies covering all environments and a range of stellar masses are required to confirm our results. We hope the upcoming facilities such as the Vera Rubin Observatory (VRO) would be crucial for the same. At shorter wavelengths, the Advanced Telescope for High ENergy Astrophysics (ATHENA) x-ray observatory is likely to unravel the properties of the warm hot intergalactic medium (WHIM) in the 0.5--12 kev range.

\section{Data availability}

The data underlying this article were derived from sources in the public domain: \url{http://icc.dur.ac.uk/Eagle/database.php}

\section{Acknowledgement}
We are grateful to the reviewer for their suggestions which have greatly improved this manuscript.
The authors would like to acknowledge the high-performance computing facility at IISER Mohali. We acknowledge
the Virgo Consortium for making their simulation data available. The {\sc eagle}~simulations were performed using the DiRAC-2 facility at Durham, managed by the ICC, and the
PRACE facility Curie based in France at TGCC, CEA, Bruy\`{e}res-le-Ch\^{a}tel. Singh thanks Pooja  Munjal, IISER Mohali, for her help in making Fig.~\ref{plot:setup}.
 Mahajan is funded by the INSPIRE Faculty award (DST/INSPIRE/04/2015/002311), Department of Science and Technology (DST), Government of India.

\bibliographystyle{mnras}

redpajama_set_name: RedPajamaArXiv


Lori Culwell is making observations. : Technology Gone Wild!
First, Happy 4th of July (early)! I am frantically trying to get to "inbox zero" status before the holiday begins, and failing somewhat because it seems like half of all people are already out of the office. This email just came to me, though, and I thought I would tell you about it. Because here is, in my opinion, a totally hilarious example of technology that is not quite as sophisticated as it should be.
I don't know if you're a member of Rite Aid's ambitious "Plenti Points" program—it is a co-branded system that they rolled out last year that is overcomplicated to the point of absurdism, and I predict that it is not going to last. Here is one example of how they are trying so hard, but somehow failing.
Let me now say that it is NOT THAT HARD to write a simple script that eliminates a zero value parameter and would eliminate the possibility of an email like this going out. The administrators of this autoresponder would simply need to code in a parameter indicating that people with ZERO points would not receive this email, since (as you will see), including me in this distribution produced a totally absurd result.


Is there I way I can see your slides without flash?
The flash applet failed for me too, dunno why. A PDF fallback version would be appreciated.
unfortunately, i think it's not possible. There is a conference video+audio recording upcoming, HTH.


While Los Gatos is named after the furry felines that inhabit the Santa Cruz Mountains, the town has definitely gone to the dogs, thanks to the canine-friendly rules adopted by the establishments lining the streets in downtown.
While Los Gatos is named after the furry felines that inhabit the Santa Cruz Mountains, the town has definitely gone to the dogs, thanks to the canine-friendly rules adopted by the establishments lining the streets in downtown. Advancing the agenda for man's best friend is Society Dog, a social club for dogs and their owners founded in 1999 by Terri Festa.
Every Tuesday evening in the Town Plaza (at the corner of North Santa Cruz Avenue and Main Street), Society Dog provides an alternative to the usual walking route by strolling through the downtown district for approximately one hour. (Dogs must have non-retractable leashes.) Every walk's final destination is a woof-welcoming restaurant for— what Society Dog refers to—"Yappy Hour." It's a tremendous opportunity to get acclimated with peers and socialize. The same goes for dogs.
October 22nd marks the 10th anniversary of Howling Halloween, Society Dog's special event dedicated to trick-or-treating in over 30 shops and putting people and pooches, alike, in the most adorable (or spookiest) costumes imaginable. Pre-registration is available online for $15 ($20 on event day), and all proceeds benefit the K9 Cancer Campaign, an effort to research and eradicate cancer from dogs once and for all.
Check-in and registration on the day of the event is from 4pm to 7pm at US Bank (127 N. Santa Cruz Ave), and trick-or-treating runs from 6pm to 7:30pm. At 7:30pm, extraordinary prizes will be awarded in several categories: Best Individual Dog Best Small Team (requirements are a maximum group of four, minimum of one human and one dog, and all must be in costume), Best Large Team (minimum group of five with at least two dogs, and all must be in costume) and Best "Green" Individual Dog (a dog costume made entirely of recycled materials). Non-retractable leashes between 4-6 feet are required.


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text:
Seasonal Hurricane Predictions Can Only Forecast So Much
MIT Professor Kerry Emanuel describes the limitations of hurricane predictions.
By Ingrid Adamow·	 8/9/2013, 10:02 p.m.
Hurricane Sandy damage via Shutterstock
On August 8—at the cusp of peak hurricane season—the National Oceanic and Atmospheric Administration issued an update to its 2013 hurricane predictions, originally released in May. The good news is that the estimated numbers of named storms, hurricanes, and major hurricanes (think Sandy and Irene) were lowered in the updated outlook. The bad news is that the NOAA's predictions still call for a 70% chance of an "above-normal season" in terms of hurricane activity, which means that New England may well still suffer at the hands of Mother Nature. MIT professor of meteorology and hurricanes expert Kerry Emanuel, however, says this forecast should be taken with a grain of salt. In fact, he disagrees with the creation of these predictions, entirely.
"I'm actually opposed to the government doing seasonal hurricane forecasts, and let me tell you why I am: the only thing that anyone has ever shown to be somewhat mathematically skilled at is forecasting the number of storms," he said.
Emanuel offers his reasons why, though predictions like that of the NOAA can be helpful guides, they only offer so much in terms of forecasting what really matters when peak season rolls around.
These predictions don't exactly have the best track record for accuracy.
In August 1992, Hurricane Andrew became the then-costliest American hurricane, and resulted in 65 fatalities. Up until Andrew's arrival, Emanuel said, 1992 "was one of the finest years we've had in the Atlantic in terms of there being some accuracy in the hurricane forecast."
"Everybody would've been blown away" had they known Andrew was coming. "It was a very, very quiet year."
The small but mighty Andrew blew away not only meteorologists and the folks at the NOAA, but the homes of people in Louisiana, Florida, and other parts of the southern United States.
Some say we can make inferences about climate change based on an increasingly intense hurricane season, but that may not be the case.
Emanuel, who authored a book called What We Know About Climate Change, says our observations about one season don't give us much, if any insight.
"One year isn't nearly enough; it's almost insignificant. With predicting global climate change, you'd have to look over decades," he said.
Perhaps most importantly, a hurricane season forecast will never be able to predict what we really want and need to know: risks, damages, loss.
"We'd like to be able to predict any damage that could come from a hurricane, that's meaningful. Or, how many people it would put at risk—that's meaningful. But we can't do that." Estimates of the number of storms, like the one put out by the NOAA, don't give a sense of the risk.
Even with ample time to prepare for a rough season ahead, some damage may be inevitable
"The main problem that we have in the United States, if you talk to anybody in my field, is that we have an awful lot of property and people at risk during hurricanes, much more so than any other country. There's policy that encourages people to build and live in risky places, and not to build particularly well."
"That's why people really want to believe the seasonal hurricane forecast," he said. "They want to see how much potential for risk is out there." Unfortunately, he says, forecasting doesn't really provide a great assessment of the risk.
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