P Aeruginosa

Reuben Ramphal

Viviane Balloy

Jeevan Jyot

Amrisha Verma

Mustapha Si-Tahar

Michel Chignard

Control of Pseudomonas aeruginosa in the lung requires the recognition of either lipopolysaccharide or flagellin 1

Keywords: Bacterial infection, Lung, Inflammation, Lipopolysaccharide, Transgenic/Knockout Mice

Acute lung infection due to Pseudomonas aeruginosa is an increasingly serious problem which results in high mortality especially in the compromised host. In this report, we set out to ascertain what components of the toll-like receptor (TLR) system are most important for innate immunity to this microorganism. We previously demonstrated that TLR2,4 -/-mice were not hypersusceptible to infection by a wild type P. aeruginosa strain. However, we now find that TLR2,4 -/-mice are hypersusceptible to infection following challenge with a P. aeruginosa mutant devoid of flagellin production. We demonstrate that this hypersusceptibilty is largely due to a lack of innate defense by the host that fails to control bacterial replication in the lung. Further evidence that a response to flagellin is a key factor in the failure of TLR2,4 -/-mice to control the infection with the mutant strain was obtained by demonstrating that the intrapulmonary administration of flagellin over a 18 h period following infection, saved 100% of TLR2,4 -/-mice from death. We conclude that the interactions of either TLR4 with LPS or TLR5 with flagellin can effectively defend the lung from P. aeruginosa infection and the absence of a response by both results in hypersusceptibility to this infection.

Introduction

Infection of the lungs by Pseudomonas aeruginosa, a Gram-negative opportunistic pathogen, results in two distinct clinical syndromes. In ventilated patients, it causes an acute pneumonia with a high mortality rate and in the genetic disorder Cystic Fibrosis, it is the prime cause of chronic inflammation which is a key factor in the destructive lung disease that occurs in these patients [START_REF] Pier | Pseudomonas aeruginosa[END_REF]. The different clinical courses of these two lung diseases likely result from the interplay of different host and bacterial factors but inflammation is a common feature of both illnesses with bacterial toxins possibly playing a significant role in acute disease [START_REF] Berka | Studies of phospholipase C (heat-labile hemolysin) in Pseudomonas aeruginosa[END_REF][START_REF] Tang | Contribution of specific Pseudomonas aeruginosa virulence factors to pathogenesis of pneumonia in a neonatal mouse model of infection[END_REF][START_REF] Shaver | Relative contributions of Pseudomonas aeruginosa ExoU, ExoS, and ExoT to virulence in the lung[END_REF]. The inflammatory response in both cases is most likely due to the recognition of the lipid A portion of lipopolysaccharide (LPS) moieties on the cell wall [START_REF] Hajjar | Human Toll-like receptor 4 recognizes host-specific LPS modifications[END_REF] and the flagellin of the microorganism [START_REF] Feldman | Role of flagella in pathogenesis of Pseudomonas aeruginosa pulmonary infection[END_REF][START_REF] Honko | Mucosal administration of flagellin induces innate immunity in the mouse lung[END_REF][START_REF] Balloy | Role flagellin versus motility in acute lung disease due to Pseudomonas aeruginosa[END_REF] but there are no data that can definitively exclude other pathogenassociated molecular patterns (PAMPs) of P. aeruginosa.

Host recognition of PAMPs may have two entirely different consequences. An appropriate response leads to the eradication of a microorganism [START_REF] Medzhitov | Innate immunity[END_REF] but an exaggerated inflammatory response may lead to illnesses such as sepsis and shock [START_REF] Girardin | Tumor necrosis factor and interleukin-1 in the serum of children with severe infectious purpura[END_REF]. The corollary of the former response is that failure to recognize a microorganism results in failure to eradicate it and in susceptibility to disease. Indeed, animal models of infection have demonstrated that the susceptibility to a number of Gram-negative pathogens is linked to the lack of recognition of LPS [START_REF] Cross | Pretreatment with recombinant murine tumor necrosis factor alpha/cachectin and murine interleukin 1 alpha protects mice from lethal bacterial infection[END_REF][START_REF] Branger | Role of Toll-like receptor 4 in gram-positive and gram-negative pneumonia in mice[END_REF] in which case a defective response leads to extreme susceptibility. These experimental findings are supported by clinical observations that polymorphisms in human genes that encode pattern recognition receptors result in greater susceptibility to certain infections [START_REF] Turvey | Towards subtlety: understanding the role of Toll-like receptor signaling in susceptibility to human infections[END_REF]. However, in the case of P. aeruginosa, whose LPS is recognized by toll-like receptor (TLR) 4 [START_REF] Hajjar | Human Toll-like receptor 4 recognizes host-specific LPS modifications[END_REF] and possibly by TLR2 [START_REF] Hajjar | Human Toll-like receptor 4 recognizes host-specific LPS modifications[END_REF][START_REF] Erridge | Lipopolysaccharides of Bacteroides fragilis, Chlamydia trachomatis and Pseudomonas aeruginosa signal via tolllike receptor 2[END_REF], mice lacking both TLR2 and 4 (TLR2,4 -/-) are not hypersusceptible to this bacterium and mount an effective innate response that clears the microorganism [START_REF] Ramphal | TLRs 2 and 4 are not involved in hypersusceptibility to acute Pseudomonas aeruginosa lung infections[END_REF][START_REF] Skerrett | Redundant Toll-like receptor signaling in the pulmonary host response to Pseudomonas aeruginosa[END_REF]. In contrast MyD88 -/-mice are extremely susceptible with 100% of mice dying within 48 h with a low dose of P. aeruginosa [START_REF] Ramphal | TLRs 2 and 4 are not involved in hypersusceptibility to acute Pseudomonas aeruginosa lung infections[END_REF][START_REF] Skerrett | Redundant Toll-like receptor signaling in the pulmonary host response to Pseudomonas aeruginosa[END_REF], indicating the potential involvement of TLR-ligand interactions in host defense. In P. aeruginosa, one other possible TLR ligand is flagellin, the known TLR5 ligand, which has been implicated in a pathogenic role in acute pneumonia [START_REF] Feldman | Role of flagella in pathogenesis of Pseudomonas aeruginosa pulmonary infection[END_REF][START_REF] Ramphal | TLRs 2 and 4 are not involved in hypersusceptibility to acute Pseudomonas aeruginosa lung infections[END_REF] and which has been demonstrated to cause inflammation when instilled into the lungs [START_REF] Feldman | Role of flagella in pathogenesis of Pseudomonas aeruginosa pulmonary infection[END_REF][START_REF] Honko | Mucosal administration of flagellin induces innate immunity in the mouse lung[END_REF][START_REF] Balloy | Role flagellin versus motility in acute lung disease due to Pseudomonas aeruginosa[END_REF]. However, in a lung model of acute infection using a P. aeruginosa mutant devoid of flagellin production (a ΔfliC mutant), we recently demonstrated that the absence of flagellin does not significantly alter the LD 50 but results in slower clearance of this microorganism from the lungs and a delay in the time to death [START_REF] Balloy | Role flagellin versus motility in acute lung disease due to Pseudomonas aeruginosa[END_REF]. Thus, neither of the Pseudomonas interactions, LPS-TLR2,4 or flagellin-TLR5 by themselves, play an essential role although they both may participate in the innate immune response.

Two recent studies have shed more light on the role played by these interactions in defense of the lungs but the reported data are not entirely consistent on whether these TLRs are critical for the control of a Pseudomonas lung infection and survival of infected mice. Feuillet et al. [START_REF] Feuillet | Involvement of Toll-like receptor 5 in the recognition of flagellated bacteria[END_REF] demonstrated that TLR4,5 -/-mice are hypersusceptible to infection by wild type P. aeruginosa strain PAK but the innate immune response to the strain and bacterial clearance were not measured. A second study by Skerrett et al. ( 16) using TLR2 -/-, TLR4 -/-or TLR2,4 -/- mice and a P. aeruginosa strain PAK lacking flagellin, in effect abrogating the TLR5 response, concluded that none of the mutant mouse strains were hypersusceptible to this strain that lacked flagellin and that bacterial clearance was not affected in any of these mutant mice but was defective in MyD88 -/-mice. Thus, the results of these studies have not been consistent in regard to control of bacterial replication and mortality. This may be due to the fact that the outcome of infection by P. aeruginosa is much more complex and may involve inflammatory injury in response to the microorganism, the initiation of an effective innate immune response to clear the organism, as well as the pathogenic effects of secreted and injected toxins. As evidence for this, strains of P. aeruginosa defective in the production of various exoenzymes have been shown to be less virulent in the mouse model of pneumonia [START_REF] Berka | Studies of phospholipase C (heat-labile hemolysin) in Pseudomonas aeruginosa[END_REF][START_REF] Tang | Contribution of specific Pseudomonas aeruginosa virulence factors to pathogenesis of pneumonia in a neonatal mouse model of infection[END_REF][START_REF] Shaver | Relative contributions of Pseudomonas aeruginosa ExoU, ExoS, and ExoT to virulence in the lung[END_REF]. With respect to the role of inflammation, the inflammatory response to the inhalation of Pseudomonas LPS leads to lung injury [START_REF] Skerrett | Role of the type 1 TNF receptor in lung inflammation after inhalation of endotoxin or Pseudomonas aeruginosa[END_REF] and the inflammatory response to strains of P. aeruginosa that produce an excess of flagellin results in rapid death when compared to a wild type strain [START_REF] Balloy | Role flagellin versus motility in acute lung disease due to Pseudomonas aeruginosa[END_REF].

To ascertain what factors are involved in mortality and control of bacterial replication, we examined different combinations of wild type, TLR2,4 -/-, TLR2 -/-and TLR4 -/-mice infected by P. aeruginosa or its ΔfliC mutant. We demonstrate that TLR4 and TLR5 play major but redundant roles in controlling bacterial replication and in host defense against P. aeruginosa pneumonia.

Materials and Methods.

Mice. Males of several mouse strains were used for the experiments. TLR2 -/-and TLR4 -/- mice were obtained from S. Akira, Osaka University, Japan and were backcrossed eight times with C57BL/6 to ensure similar genetic backgrounds. TLR2,4 -/-mice were generated by breeding TLR2 -/-mice and TLR4 -/-mice. C57BL/6 mice used as control mice were supplied by the Centre d'Elevage R. Janvier, Le Genest Saint-Isle, France and used at about 8 weeks of age. Mice were fed normal mouse chow and water ad libitum and were reared and housed under standard conditions with air filtration. Mice were cared for in accordance with Pasteur Institute guidelines in compliance with the European Animal Welfare regulations.

Bacterial strains. The wild type strain, PAK, a commonly studied P. aeruginosa strain was obtained from Stephen Lory, originally isolated by D. Bradley. This strain of P. aeruginosa is known to contain and express a full complement of virulence factors, including pili, flagella, the type II secreted enzymes, exotoxin A, elastases and phospholipases and the type III secreted exoenzymes S, T and Y. PAK ΔfliC and PAK-L88 are mutants of the parent strain where the fliC gene has been deleted [START_REF] Dasgupta | A four-tiered transcriptional regulatory circuit controls flagellar biogenesis in Pseudomonas aeruginosa[END_REF], and where there is an L88A substitution in the TLR5 binding site of Pseudomonas flagellin [START_REF] Verma | Roles of specific amino acids in the N terminus of Pseudomonas aeruginosa flagellin and of flagellin glycosylation in the innate immune response[END_REF], respectively. PAO1 ΔfliC is a fliC mutant of strain PAO1 [START_REF] Fleiszig | FlhA, a component of the flagellum assembly apparatus of Pseudomonas aeruginosa, plays a role in internalization by corneal epithelial cells[END_REF], another well studied strain of P. aeruginosa. Luminescent strains of PAK and its ΔfliC mutant, were constructed by inserting luxAB into the neutral att site of the chromosome of strain PAK and its derivative PAK-C using a mini-Tn7-lux plasmid provided by Microbiotix, Worcester, Massachusetts, where luxAB is driven by the lac promoter [START_REF] Moir | A High-Throughput, Homogeneous, Bioluminescent Assay for Pseudomonas aeruginosa Gyrase Inhibitors and Other DNA Damaging Agents[END_REF].

Bacteria were grown overnight in Luria-Bertani (LB) broth then transferred to fresh medium and grown for 4-5 hours to midlog phase. The cultures were centrifuged at 4,000 g for 15 min and the cell pellets washed twice with phosphate buffered saline (PBS). The bacterial pellet was diluted in its original volume and the optical density adjusted to give the approximate desired inocula. The inocula were verified by serial 10 fold dilutions of the bacterial suspensions and plating on LB agar.

Animal infections.

Mice were anesthetized by intraperitoneal injection of a mixture of ketamine-xylazine and were placed supine. A plastic catheter (diameter of 0.86 mm) was inserted into the trachea via the oropharynx. The proper insertion was verified by checking the formation of mist due to expiration on a mirror placed in front of the external end. A 50 μl bacterial suspension was laid down at the internal end of the catheter with a micropipette using a sterile disposable tip for gel loading, introduced into the catheter. Mice were then immediately held upright in order to facilitate bacterial inhalation and until normal breathing resumed. A series of experiments were done to ascertain the effects of the TLR mutations in bacterial clearance of the fliC mutant of P. aeruginosa. Groups of mice were infected by the intratracheal route using inocula of bacteria that were known to be less the LD 50 for wild type mice from previous experiments [START_REF] Balloy | Role flagellin versus motility in acute lung disease due to Pseudomonas aeruginosa[END_REF][START_REF] Ramphal | TLRs 2 and 4 are not involved in hypersusceptibility to acute Pseudomonas aeruginosa lung infections[END_REF]. In one series of experiments recombinant P. aeruginosa flagellin prepared as described [START_REF] Verma | Roles of specific amino acids in the N terminus of Pseudomonas aeruginosa flagellin and of flagellin glycosylation in the innate immune response[END_REF] was mixed with the bacterial inoculum prior to intratracheal instillation and at several times after infection.

Broncho-alveolar lavages (BAL) were performed on some mice at 6 hours after infection following pentobarbital euthanasia. The lavage fluids (4 ml) were diluted and plated on LB agar plates, to obtain viable bacterial counts in the lavage fluid. Total cell counts were measured in the BAL fluids with a Coulter Counter (Coulter Electronics, Margency, France) and cell differential counts were determined after cytospin centrifugation and staining with Diff-Quick products. Murine cytokine concentrations in BAL fluid were determined using DuoSet ELISA kits obtained from R&D systems (Minneapolis, MN).

Luminescence measurements. Photon emission of the luminescent bacteria in the lungs of infected mice was measured using the IVIS system (Xenogen Biosciences, Cranbury, NJ). In preliminary experiments we ascertained that the system could detect 10 6 cfu of luminescent P. aeruginosa given intratracheally. To achieve detection in black C57BL/6 mice, it was necessary to remove hair from their chests with a depilatory agent used for this purpose in humans. After infection, analysis of photons was done in mice under isoflurane inhalation anesthesia in an IVIS CCD (charge-coupled device) camera coupled to the LivingImage software package (Xenogen). A digital false-color photon emission image of the mouse was generated and photons were counted within a constant defined area corresponding to the surface of the chest encompassing the whole lung region. Photon emission was measured as photons/second emitted.

Statistical calculations.

Cytokine levels, myeloperoxidase (MPO) concentrations, polymorphonuclear neutrophil (PMN) counts, and pathogen counts were expressed as mean ± SEM. Differences between groups were assessed for statistical significance using the analysis of variance test followed by Fischer's test. A value of p < 0.05 was considered statistically significant.

Results

Effect of TLR2,4 mutations on survival of mice following infection by a ΔfliC mutant of

P. aeruginosa.

Survival of C57BL/6 mice and TLR2,4 -/-was examined using wild type strain PAK, and its flagellin deficient mutant PAKΔfliC. The dose of bacteria (5x10 6 cfu/mouse) was chosen after preliminary experiments showed that the mutant mice were dying rapidly when given ½ the LD 50 of the ΔfliC mutant for wild type mice as measured in a previous study [START_REF] Balloy | Role flagellin versus motility in acute lung disease due to Pseudomonas aeruginosa[END_REF]. The dose chosen was expected to cause little or no mortality among wild type mice, but should uncover any hypersusceptibility among knockout mice.

As previously demonstrated with wild type strain PAK [START_REF] Ramphal | TLRs 2 and 4 are not involved in hypersusceptibility to acute Pseudomonas aeruginosa lung infections[END_REF], there were no differences in the survival of the control and the TLR2,4 -/-mice (Fig. 1). In contrast, the survival studies with the ΔfliC mutant demonstrated extreme susceptibility of the TLR2,4 -/-mice, with all mice dying within 2 days of challenge with a dose of this strain that caused no mortality among the control mice. These results suggested that the extreme susceptibility was likely due to the absence of a response to both LPS and flagellin consequent to the mutations in TLR2,4 and the concomitant absence of flagellin from the bacterium.

Effect of the TLR2,4 mutations on bacterial proliferation in the lungs.

We have previously shown that proliferation of wild type strain PAK was controlled in TLR2,4 -/-mice (15). Skerrett et al [START_REF] Skerrett | Redundant Toll-like receptor signaling in the pulmonary host response to Pseudomonas aeruginosa[END_REF] recently demonstrated that proliferation of a ΔfliC mutant of this same strain of P. aeruginosa is also controlled in TLR2,4 -/-mice even though host innate immune responses are severely blunted. These recently published data did not however explain the hypersusceptibility that we observed (Fig. 1). We therefore examined bacterial proliferation to ascertain whether hypersusceptibility was accompanied by failure to control bacterial proliferation as occurs in MyD88 deficient mice [START_REF] Ramphal | TLRs 2 and 4 are not involved in hypersusceptibility to acute Pseudomonas aeruginosa lung infections[END_REF][START_REF] Skerrett | Redundant Toll-like receptor signaling in the pulmonary host response to Pseudomonas aeruginosa[END_REF]. When examined at 6 h post infection (Fig. 2A), TLR2,4 -/-mice failed to control replication of the ΔfliC mutant, with bacterial counts reaching 10 times the challenge dose in these mice whereas it was down to less than 20% of the inoculum in the control mice. We also measured proliferation in vivo at 6 and 20 h by detecting the growth of luminescent bacteria directly in the lungs of living anesthetized mice. Representative pictures of bacterial luminescence are shown in Fig. 2B.

Measured in photons/second, luminescence increased at least 10 fold in a 24 h period in the TLR2,4 -/-mice infected with the ΔfliC mutant whereas it decreased in wild type mice (Fig. 2C). In agreement with our previous report [START_REF] Ramphal | TLRs 2 and 4 are not involved in hypersusceptibility to acute Pseudomonas aeruginosa lung infections[END_REF], luminescence decreased as a function of time in wild type and TLR2,4 -/-mice challenged with the wild type strain PAK (data not shown).

Evaluation of TNF-α, IL-6, KC and G-CSF production in response to infection of

TLR2,4 -/-mice by a ΔfliC mutant of P. aeruginosa.

We have previously noted that when TLR2,4 -/-mice are infected with the wild type P. aeruginosa strain, the TNF-α response is severely attenuated but the IL-6 response is intact [START_REF] Ramphal | TLRs 2 and 4 are not involved in hypersusceptibility to acute Pseudomonas aeruginosa lung infections[END_REF] suggesting that a PAMP other than LPS may also stimulate the IL-6 response. We thus examined the production of these two and other inflammatory mediators under the different experimental combinations used in the present study. Studies were conducted at 6 h post infection, as mutant mice were often dead at 18-24 h following challenge with the ΔfliC mutant. We confirmed the earlier data that the TNF-α response is lost in the TLR2,4 -/-mice (Fig. 3A) and that the IL-6 (Fig. 3B) response is preserved. The most striking observation was that infection of TLR2,4 -/-mice with the ΔfliC mutant led to a complete suppression of host response in terms of both TNF-α and IL-6 formation. Given the complete loss of TNF-α synthesis in the TLR2,4 -/-mice infected with the flagellated strain and the additional loss of IL-6 in these mice only when flagellin is absent, it is possible that the TNF-α response is mainly due to a PAMP recognized by TLR2,4, and that the IL-6 response is a sum of the effects of a PAMP recognized by TLR2/4 and the activity of flagellin. The KC and G-CSF responses (Fig. 3C andD) to the absence of flagellin were similar to those of IL-6 in these mice. Thus absence of IL-6, G-CSF and KC responses, was correlated with hypersusceptibility but the absence of a TNF-α response was not.

Evaluation of PMN recruitment and activation.

In agreement with the above data, PMN recruitment in the airspaces and their activation as measured by the release of free MPO, were similar under the different experimental combinations, except in the case of TLR2,4 -/-mice infected with the ΔfliC mutant (Fig. 4).

Under the latter, the values for both parameters were not different from those of uninfected control mice suggesting a failure of recruitment and activation by PMN in these mice.

Role of flagellin in the innate immune response.

To examine whether an innate immune response to flagellin as opposed to LPS (TLR2 or 4) was critical to the survival of these mice, one group of TLR2,4 mutant mice was given 0.1 µg flagellin mixed with the challenge bacteria, followed by another dose of flagellin 6 h later by the intranasal route. A second group was given the same dose at the time of infection then at 6, 12 and 18 h post infection by the intranasal route. A control group of TLR2,4 -/-mice received PBS instead of flagellin but did not survive beyond 24 h. Survival of the mice that received two doses of flagellin was prolonged by about a day but all were dead by 48 h post bacterial challenge, whereas the administration of four doses of flagellin resulted in the survival of 100% of the mice even as long as 2 weeks post bacterial challenge (Fig 5).

To further implicate the requirement for TLR5 recognition of flagellin in TLR2,4-/-mice, we challenged these mice with P. aeruginosa strain PAK-L88 (8) which is a PAK mutant with an L88A amino acid change in the putative TLR5 binding site of Pseudomonas flagellin [START_REF] Verma | Roles of specific amino acids in the N terminus of Pseudomonas aeruginosa flagellin and of flagellin glycosylation in the innate immune response[END_REF]. This single amino acid change results in a significant reduction in the inflammatory response of the mouse lung when recombinant L88 flagellin is instilled into the lungs [START_REF] Balloy | Role flagellin versus motility in acute lung disease due to Pseudomonas aeruginosa[END_REF]. Using different concentrations of the L88 mutant (between 1-2 X 10 7 ) we observed greater mortality than that seen with the use of the wild type strain but not as great as that seen with the ΔfliC mutant. The differences in survival observed within the two first days of infection of the TLR2,4-/-mice challenged with wild type strain PAK or PAK-L88 were statistically significant (p < 0.05; n = 3), with mean survivals at 48 h of 78% with the wild type strain and 35% with the L88 mutant. Under the same experimental conditions, the survival of the mice was 0% for the ΔfliC mutant (Fig 6). Some possible reasons for the lack of total susceptibility of TLR2,4-/-mice to infection with the PAK-L88 mutant are discussed below.

Role of TLR2 versus TLR4 in innate immunity to a ΔfliC mutant of P. aeruginosa.

Since TLR2,4 -/-were hypersusceptible to a ΔfliC mutant of strain PAK, we sought to examine which of these two TLRs was instrumental in defense against this organism in the absence of a response to flagellin. TLR2 -/-and TLR4 -/-mice were infected with the ΔfliC mutant of strain PAK at a dose of 5x10 6 cfu which does not kill wild type C57BL/6 mice. All TLR4 -/-mice died within 48 h of infection whereas all TLR2 -/-mice survived (Fig. 7). To confirm that this hypersusceptibility was more general to P. aeruginosa strains, a ΔfliC mutant of strain PAO1 was used to infect wild type and TLR4 -/-mice. In preliminary experiments, an inoculum of

Discussion

The outcome of an acute pulmonary infection due to P. aeruginosa is a balance between an appropriate innate immune response and bacterial virulence. The pathway to an appropriate response is through the recognition of PAMPs by TLRs and possibly other nonTLR systems such as the NOD-like receptor system and the inflammasome [START_REF] Akira | Pathogen recognition and innate immunity[END_REF][START_REF] Sutterwala | The inflammasome in pathogen recognition and inflammation[END_REF]. However, although it has been shown that P. aeruginosa triggers the NOD system [START_REF] Travassos | Nod1 participates in the innate immune response to Pseudomonas aeruginosa[END_REF] and its flagellin is recognized by the inflammasome [START_REF] Franchi | Critical role for Ipaf in Pseudomonas aeruginosa-induced caspase-1 activation[END_REF], a role for host defense by these systems in vivo has not been demonstrated to date. The data from this study and that of Feuilllet et al. [START_REF] Feuillet | Involvement of Toll-like receptor 5 in the recognition of flagellated bacteria[END_REF] suggests that the most important systems may be the TLR system and that TLR4 and TLR5 are redundant for the recognition of P. aeruginosa and possibly other flagellated bacteria.

However, there have been reported instances where defense against Gram-negative bacteria required only TLR4 [START_REF] Cross | Pretreatment with recombinant murine tumor necrosis factor alpha/cachectin and murine interleukin 1 alpha protects mice from lethal bacterial infection[END_REF][START_REF] Branger | Role of Toll-like receptor 4 in gram-positive and gram-negative pneumonia in mice[END_REF][START_REF] Wang | Toll-like receptor 4 mediates innate immune responses to Haemophilus influenzae infection in mouse lung[END_REF][START_REF] Mann | Toll-like receptor 4 is critical to innate host defense in a murine model of bordetellosis[END_REF]. On closer examination of those studies, some of the microorganisms studied do not possess a flagellum or do not express flagellin in vivo. For example Klebsiella pneumoniae and Hemophilus influenzae are not flagellated bacteria and Bordetella down regulates flagellin production in vivo [START_REF] Akerley | The bvgAS locus negatively controls motility and synthesis of flagella in Bordetella bronchiseptica[END_REF]. One study of P. aeruginosa that showed TLR4 alone to be essential [START_REF] Faure | TLR4 signaling is essential for survival in acute lung injury induced by virulent Pseudomonas aeruginosa secreting type III secretory toxins[END_REF], used strain PA103, which is known to be a nonflagellated strain [START_REF] Liu | The roles of various fractions of Pseudomonas aeruginosa in its pathogenesis. II. Effects of lecithinase and protease[END_REF]. Other studies have alternatively shown TLR4 mutant mice not to be hypersusceptible to Escherichia coli strains [START_REF] Evans | Differences in cytokine response and induction of nitric oxide synthase in endotoxin-resistant and endotoxin-sensitive mice after intravenous gram-negative infection[END_REF][START_REF] Haziot | Induction of a novel mechanism of accelerated bacterial clearance by lipopolysaccharide in CD14-deficient and Toll-like receptor 4-deficient mice[END_REF][START_REF] Lee | TLR-4 pathway mediates the inflammatory response but not bacterial elimination in E. coli pneumonia[END_REF], however whether these strains or clones of a given strain expressed flagellin and made a flagellum, is unknown. It is possible that the lack of flagellin expression in the strains or clones of a given strain that was used in the studies that showed hypersusceptibility to E. coli was responsible for this observation, as TLR4 mutant mice would be able to not mount an effective response if flagellin were absent.

A role for TLR2 in defense against P. aeruginosa infections is more difficult to substantiate, although interactions of TLR2 with both LPS and flagellin have been described [START_REF] Hajjar | Human Toll-like receptor 4 recognizes host-specific LPS modifications[END_REF][START_REF] Erridge | Lipopolysaccharides of Bacteroides fragilis, Chlamydia trachomatis and Pseudomonas aeruginosa signal via tolllike receptor 2[END_REF][START_REF] Adamo | Pseudomonas aeruginosa flagella activate airway epithelial cells through asialoGM1 and toll-like receptor 2 as well as toll-like receptor 5[END_REF]. It has been reported that Pseudomonas LPS may be recognized by either TLR2 or TLR4 depending on its structure [START_REF] Hajjar | Human Toll-like receptor 4 recognizes host-specific LPS modifications[END_REF], thus one cannot entirely rule out a role for TLR2. However, Feuillet et al. [START_REF] Feuillet | Involvement of Toll-like receptor 5 in the recognition of flagellated bacteria[END_REF] as well as the current study, could not however measure a contribution of TLR2 in lung defense against P. aeruginosa. Since TLR2 does recognize P. aeruginosa, the suggestion by Skerrett et al. ( 16) that it may play a counter-regulatory role seems feasible but it does not appear to have a direct role in defense. The response of strains of P. aeruginosa that have been adapted to the airways as well as most common laboratory strains examined appear to be TLR4 dependent rather than to TLR2 [START_REF] Hajjar | Human Toll-like receptor 4 recognizes host-specific LPS modifications[END_REF]. Strain PAK used by Feuillet et al. ( 17), Skerrett et al. [START_REF] Skerrett | Redundant Toll-like receptor signaling in the pulmonary host response to Pseudomonas aeruginosa[END_REF] as well as this study fall into this group. Moreover, we see the same TLR4 dependency in PAO1, another P. aeruginosa strain.

Other features of this study that are worthy of commentary are the differences in deaths and bacterial clearance that was noted between us and Skerrett et al. [START_REF] Skerrett | Redundant Toll-like receptor signaling in the pulmonary host response to Pseudomonas aeruginosa[END_REF] for TLR4 -/-and TLR2,4 - /-mice, when a ΔfliC mutant of strain PAK was used. Skerrett et al. [START_REF] Skerrett | Redundant Toll-like receptor signaling in the pulmonary host response to Pseudomonas aeruginosa[END_REF] reported one death in TLR2,4 mutant mice and none in TLR2 or TLR4 mutant mice (data in text, page L317) while we noted hypersusceptibility. Bacterial clearance in TLR2,4 -/-mice was observed by Skerrett et al. ( 16) but was markedly reduced in our studies with actual proliferation noted at both 6 and 24 h following lung challenge. The use of different doses of bacteria could be an explanation since mortality appears to be dose dependent [START_REF] Feuillet | Involvement of Toll-like receptor 5 in the recognition of flagellated bacteria[END_REF]. Indeed, it can be deduced from the data reported by Skerrett et al. [START_REF] Skerrett | Redundant Toll-like receptor signaling in the pulmonary host response to Pseudomonas aeruginosa[END_REF] that the bacterial load of their mice fluctuates around 10 5 cfu/mouse while we challenged mice with 5x10 6 cfu. In agreement, Feuillet et al [START_REF] Feuillet | Involvement of Toll-like receptor 5 in the recognition of flagellated bacteria[END_REF] observed a dramatic death rate of the mice with a challenge of 6x10 6 cfu/mouse. Due to the unavailability of TL2,4,5-/-mice, we used a ΔfliC mutant of P. aeruginosa in most of these studies. However, having a mutant in the flagellin binding site for TLR5 (strain PAK-L88) allowed us to examine the role of TLR5 in our studies. TLR2,4-/-mice proved not to be as hypersusceptible to this strain as the ΔfliC mutant, where we saw 100% mortality after 2 days. However, infection of these mice with PAK-L88 did result in higher mortality than infection with the wild type strain.

We offer several explanations for this reduced hypersusceptibility. The L88 flagellin on the bacterium is glycosylated and the Pseudomonas flagellin glycosyl moiety stimulates an inflammatory response [START_REF] Verma | Roles of specific amino acids in the N terminus of Pseudomonas aeruginosa flagellin and of flagellin glycosylation in the innate immune response[END_REF]. The ΔfliC mutant has no glycosyl groups, as it is a flagellin deletion mutant. Pseudomonas flagellin activates caspase-1 through a mechanism that is TLR5 independent [START_REF] Franchi | Critical role for Ipaf in Pseudomonas aeruginosa-induced caspase-1 activation[END_REF], thus the L88 mutation in the TLR5 binding site still stimulates IL1β secretion which may offer some protection [START_REF] Franchi | Critical role for Ipaf in Pseudomonas aeruginosa-induced caspase-1 activation[END_REF]. Our own previous experiments (20) also demonstrate a low level of IL-8 release (10% of wild type flagellin), which may be enough to prevent the hypersusceptibility seen with a ΔfliC mutant. Thus there may be three possible explanations for the lack of extreme hypersusceptibility as seen with the ΔfliC mutant.

The current study also points to important roles for flagellin in the generation of IL-6 and G-CSF, since loss of these responses occurred only in TLR2,4 -/-mice infected with the flagellin mutant. We have previously reported the loss of the IL-6 response in MyD88 deficient mice and its preservation in TLR2,4 -/-mutant mice, when these mice are infected with a wild type P. aeruginosa strain [START_REF] Ramphal | TLRs 2 and 4 are not involved in hypersusceptibility to acute Pseudomonas aeruginosa lung infections[END_REF]. Their loss when a flagellin mutant was used, suggests the existence of a specific pathway that is flagellin dependent. Similar observations, that an IL-6 response was lost in response to an intranasal challenge of TLR5 -/-mice with flagellin protein were made by Feuillet et al [START_REF] Feuillet | Involvement of Toll-like receptor 5 in the recognition of flagellated bacteria[END_REF] suggesting that the IL-6 response may be TLR5 and flagellin dependent. Our study expands this by showing the lack of response is solely due to the loss of flagellin. How this response to flagellin is mediated remains to be determined.

Our observations on TNF-α are also of interest. We have noted in the past [START_REF] Ramphal | TLRs 2 and 4 are not involved in hypersusceptibility to acute Pseudomonas aeruginosa lung infections[END_REF] and find once more that animals incapable of mounting a TNF-α response are not susceptible to Pseudomonas lung infection and do not demonstrate a defect in Pseudomonas clearance.

However, most studies, including one that used a TNF-α knockout mouse [START_REF] Gosselin | Role of tumor necrosis factor alpha in innate resistance to mouse pulmonary infection with Pseudomonas aeruginosa[END_REF][START_REF] Lee | Modulation of bacterial growth by tumor necrosis factor-alpha in vitro and in vivo[END_REF] show a defect in protection against such an infection. Thus, the role of TNF-α is not as clear as described since it has also been shown that mice deficient in TNF-α receptors do not have diminished resistance to a Pseudomonas lung infection [START_REF] Skerrett | Role of the type 1 TNF receptor in lung inflammation after inhalation of endotoxin or Pseudomonas aeruginosa[END_REF], a result which is consistent with our findings.

There also exists the possibility that other systems may play a role in defense against this organism but we have not been able to substantiate this. It has recently been demonstrated that flagellin from two intracellular bacterial species Salmonella and Legionella [START_REF] Franchi | Cytosolic flagellin requires Ipaf for activation of caspase-1 and interleukin 1beta in salmonella-infected macrophages[END_REF][START_REF] Miao | Cytoplasmic flagellin activates caspase-1 and secretion of interleukin 1beta via Ipaf[END_REF] is recognized by Ipaf which activates caspase-1 [START_REF] Amer | Regulation of Legionella phagosome maturation and infection through flagellin and host Ipaf[END_REF] leading to the formation of IL-1β and IL-18 which are proinflammatory cytokines. This system also recognizes Pseudomonas flagellin [START_REF] Franchi | Critical role for Ipaf in Pseudomonas aeruginosa-induced caspase-1 activation[END_REF], however, its role in infection by an extracellular pathogen such as P. aeruginosa may not be as critical and be supplemental. Alternatively, this system may be functional but is overwhelmed by high inocula of bacteria such as those used in experimental in vivo studies or since the action of the inflammasome requires the formation of pro-IL-1β which in turn requires TLR activation [START_REF] Amer | Regulation of Legionella phagosome maturation and infection through flagellin and host Ipaf[END_REF], the inflammasome may not be functional. Thus, we conclude that the activation of inflammatory pathways through TLR4 or TLR5 is sufficient to control an acute P. aeruginosa lung infection. While the studies of TLR-Pseudomonas interactions have been limited to acute infections, one may conclude that these are equally important in chronic infection in Cystic Fibrosis, however in this case these interactions may fail to control the infection because of host and microbial factors such as microcolony formation [START_REF] Costerton | The role of the microcolony mode of growth in the pathogenesis of Pseudomonas aeruginosa infections[END_REF], defective phagocytosis in the presence of mucins [START_REF] Vishwanath | Respiratory-mucin inhibition of the opsonophagocytic killing of Pseudomonas aeruginosa[END_REF] and instead, result in cyclical bouts of uncontrolled inflammation. TLR2,4 -/-mice are unable to control replication of a flagellin mutant of P. aeruginosa in the lungs. A. Bacterial replication is shown as a percentage of the initial inoculum (2x10 6 cfu) administered intratracheally. TLR2,4 -/-mice were unable to control replication of the flagellin mutant of strain PAK (n = 3) when compared to the flagellin positive strain (n = 6) when measured a 6 h post infection (p < 0.05). Clearance of the flagellin mutant by the wild type mice (n = 4) was less than that of the flagellin positive bacterium (n = 6), but did not reach statistical significance. A minimum of three mice were used for each assay. B. Bacterial luminescence in the lungs of wild type and TLR2,4 -/-mice measured by injecting luminescent P. aeruginosa intratracheally (5x10 6 cfu/mouse) and capturing photon emission from the chest at different times post infection, using the IVIS system. TLR2,4 -/-mice are unable to control the replication of the ΔfliC mutant of strain PAK as opposed to wild type mice. C.

Plots of the photon emission as a function of time. Clearance of the same bacterial strains by wild type and TLR2,4 -/-mice was recorded at 2, 6 and 20 h post challenge. TLR2,4 -/-mice show a significant increase in the luminescent ΔfliC mutant whereas the wild type mice control replication of this strain (p < 0.05; n=7 and 9, respectively). TLR2,4 -/-mice infected with a P. aeruginosa mutant devoid of flagellin production are defective in their TNF-α, IL-6, G-CSF and KC responses. A. TNF-α responses are blunted in the TLR2,4 -/-mice in response to infection by both wild type and ΔfliC mutant. B. By contrast, the IL-6 response is only reduced and by approximately 50% when the ΔfliC mutant is used to infect wild type mice (p < 0.05 compared to infection by wild type bacteria) but is drastically reduced when this mutant is used to infect TLR2,4 -/-(p < 0.01 compared to infection of these same mice by the wild type strain). Similarly, the KC (C) and G-CSF (D) responses are dramatically lost only when the ΔfliC mutant is used to infect TLR2,4 -/-mice, (p < 0.05 and p < 0.01, respectively when compared to infection of these same mice with the flagellin positive strain). The basal values for non infected mice were at maximum 0.7, 7, 16 and 0 pg/ml for TNF-α, IL-6, KC and G-CSF, respectively. Each histogram is the mean ± sem obtained with a minimum of 4 mice. An innate immune response to flagellin alone is sufficient to defend the lungs against P. aeruginosa. TLR2,4 -/-mice were infected with the ΔfliC mutant of strain PAK and treated with different regimens of recombinant P. aeruginosa flagellin. One group (black squares, n = 6) was given 0.1 µg flagellin mixed with the challenge bacteria, followed by another dose of flagellin 6 h later by the intranasal route (2 doses). A second group (white triangles, n = 7) was given the same dose at the time of infection then at 6, 12 and 18 h post infection (4 doses). A control group of TLR2,4 -/-mice received PBS (black circles, n = 10) instead of flagellin. Susceptibility of TLR2,4 -/-mice to infection by a P. aeruginosa mutant devoid of flagellin production is due to the TLR4 mutation and is not specific to one strain of P. aeruginosa. TLR2 -/-mice (white triangles, n = 10) or TLR4 -/-mice (black squares, n = 10) were infected with the ΔfliC mutant of strain PAK (5x10 6 cfu). All TLR2 -/-mice survived the infection, 90% TLR4 -/-mice died rapidly. Similarly all TLR4 -/-mice infected with a flagellin mutant of strain PAO1 (black circles, n = 5) died when infected with an inoculum of 1x10 6 cfu.

Figure 1 .

 1 Figure 1. TLR2,4 -/-mice are hypersusceptible to lung infection by a P. aeruginosa mutant devoid of flagellin production. Survival of wild type mice (WT) (n = 10) and TLR2,4 -/-mice (n = 10)

Figure 2 .

 2 Figure 2.

Figure 3 .

 3 Figure 3.

Figure 4 .Figure 5 .

 45 Figure 4.PMN influx and activation are defective when TLR24 -/-mice are infected with a P.

Figure 6 .

 6 Figure 6.

Figure 7 .

 7 Figure 7.

Hal-Pasteur author manuscript pasteur-00277620, version 1 Hal-Pasteur author manuscript pasteur-00277620, version 1

this mutant (Fig.7).Hal-Pasteur author manuscript pasteur-00277620, version 1 Hal-Pasteur author manuscript pasteur-00277620, version 1

Acknowledgements. We thank Herbert Schweizer (Colorado State University, Fort Collins, Colorado) for his advice on use of the lux reporter system in P. aeruginosa, and Marie-Anne Nicola (Plate-Forme d'Imagerie Dynamique, Imagopole, Institut Pasteur, Paris) for her advice on the use of the IVIS system.

This work was supported by grants from NIH to RR (AI 45014)

Hal-Pasteur author manuscript pasteur-00277620,


Sabah Al-Fedaghi

Jassim Al-Fadhli

Thinging-Oriented Modeling of Unmanned Aerial Vehicles

Keywords: Unmanned aerial vehicle (UAV), drone, conceptual modeling, diagrammatic representation, system architecture

In recent years, there has been a dramatic increase in both practical and research applications of unmanned aerial vehicles (UAVs). According to the literature, there is a need in this area to develop a more refined model of UAV system architecture-in other words, a conceptual model that defines the system's structure and behavior. The existing models mostly are fractional and do not account for the entire important dynamic attributes. Progress in this area could reduce ambiguity and increase reliability in the design of such systems. This paper aims to advance the modeling of UAV system architecture by adopting a conceptual model called a thinging (abstract) machine in which all of the UAV's software and hardware components are viewed in terms of the flow of things and five generic operations. We apply this model to a real case study of a drone. The resultsan integrated conceptual representation of the drone-support the viability of this approach.

I. INTRODUCTION

Individuals are becoming more reliant on automated systems for a wide range of applications. According to Culus Schellekens, and Smeets [START_REF] Culus | A drone's eye view[END_REF], "It looks like our 21st century will be the century of robots, with a lot of buzz concerning a fast-growing subfamily of these machines, namely drones." The recent drone attacks on Saudi Arabia's oil installations highlight the importance of giving this technology high research priority. The introduction of unmanned aerial vehicles (UAVs) has raised profound questions around the world with regard to issues such as accountability, transparency, privacy, legality, use of force, and safety [START_REF] Smith | Ethics on the fly: toward a drone-specific code of conduct for law enforcement[END_REF]. In recent years, although UAVs have been an in-demand research topic, "there still exist many unanswered questions" [START_REF] Akhtar | The use of modern tools for modelling and simulation of UAV with haptic[END_REF].

From a technical standpoint, unmanned airplanes can be categorized as UAVs, remotely piloted vehicles, or drones. These types differ mainly in the type of the mission, the size, and (importantly) the level of autonomy in their operation [START_REF] Fahlstrom | Introduction to UAV Systems[END_REF]. The term "drone," which is in general use among both the media and the public, refers to all types of UAVs. Nowadays, the number of possible uses of UAVs is large and increasing [START_REF] Kardasz | Drones and possibilities of their using[END_REF]. One planned development in this direction is the use of a centimeter-scale quadcopter with a driving application over vast regions. An immense number of such vehicles are used for various purposes (e.g., providing climatic and meteorological data) [START_REF] Akhtar | The use of modern tools for modelling and simulation of UAV with haptic[END_REF]. One especially important application of UAVs is carrying out so-called D missions [START_REF] Pastor | A hardware/software architecture for UAV payload and mission control[END_REF]: those that are dangerous, dirty, or dull. This paper focuses on the high-level modeling and control of UAVs. Its general objective is to offer a schematic language for UAVs so as to enhance the understanding of their functionalities. The understanding of technology is a constitutive part of human life and helps to address issues of survival and improve the use and practice of technology. According to Sellars [START_REF] Sellars | Philosophy and the scientific image of man[END_REF], contemporary society hangs together largely through technology. This context requires understanding "both the practice of designing and creating artifacts (in a wide sense, including artificial processes and systems) and the nature of the things so created" [START_REF] Franssen | Philosophy of technology[END_REF]. Accordingly, there is a need to develop a language that ensures good technical specification. Such a specification is similar to a script for performing a task in that it allows stakeholders (engineers, team members, legislators, technocrats, managers, officials, etc.) to understand the roles they need to play and that helps them to avoid either stepping on one another's toes or overlooking a critical piece of information [9]. This paper's specific objective is thus to provide a modeling language that can be used to specify the system architecture as an integral phase of the UAV development process. The system architecture is the conceptual model that defines the structural, behavioral, and other views of a system [START_REF] Jaakkola | Architecture-driven modelling methodologies[END_REF]. Indeed, it is necessary to develop, for UAVs, both "architecture generation and assessment models. Architecture assessment models that presently exist tend to be fractional and do not account for all dynamic attributes that should be considered in the architecture assessment" [START_REF] Renaul | A model for assessing UAV system architectures[END_REF]. Further development in this area can reduces systems' ambiguity and increases their tangibility.

To accomplish these aims, we propose applying a diagrammatic modeling technique called a thinging machine (TM). This modeling apparatus is viable in the area of UAV systems architecture because it can provide a precise description of the total system. This claim is substantiated by contrasting TM models with the current UAV diagramming methods. Additionally, this paper shows the feasibility of the TM approach by describing a real case study.

Section II includes a partial survey of the works in the area of diagram-based modeling for UAVs. Section III provides a review of the TM modeling tool, and a detailed example is given in Section IV. Section V describes the case study, in which an actual drone is modeled. Sections VI clarify the TM www.ijacsa.thesai.org model itself and VII present the application of TM in the case study and describe the possible utilization of the resulting model in a simulation. VIII discuss the simulation aspects, and finally IX is the conclusion.

II. SOME RELATED WORKS

Extensive research has been conducted on the conceptual modeling of UAVs at all levels [START_REF] Li | Study on modeling of communication channel of UAV[END_REF]. Drones provide unprecedented levels of access to airspace, and such new access could fundamentally change business, shipment, and travel [START_REF]Drones: Benefits study: High level findings[END_REF]. However, we focus here on a few architectural models that facilitate contrasts with the TM approach. A comprehensive survey of the field can be found in Renaul [START_REF] Renaul | A model for assessing UAV system architectures[END_REF].

Pastor et al. [START_REF] Pastor | A hardware/software architecture for UAV payload and mission control[END_REF] presented a hardware/software architecture for UAVs, using block diagrams to provide a general view of the architecture of a mission-control computer and ad hoc diagrams to describe operational scenarios. This is an example of such a scenario: The mission control decides to take a georeferenced video. For this task, it will need the services provided by storage, the flight-computer system, and the camera and sensing modules.

Diem, Hien, and Khanh [START_REF] Diem | An object-oriented analysis and design model to implement controllers for quadrotor UAVs by specializing MDA's features with hybrid automata and real-time UML[END_REF] sought to analyze, design, and implement controllers of a standard UAV platform; they thus adopted a model-driven architecture with a real-time unified modeling language (UML). This architecture contains three models that are used to separate the specifications for a system's operation: the computation-independent, platformindependent, and platform-specific models. To capture the general requirements based on the object-oriented paradigm, Diem, Hien, and Khanh [START_REF] Diem | An object-oriented analysis and design model to implement controllers for quadrotor UAVs by specializing MDA's features with hybrid automata and real-time UML[END_REF] presented a model with abstract classes using UML stereotypes and a class diagram [15] in order to describe the main functional components of quadrotor UAVs (see Figs. 123). According to Diem, Hien, and Khanh [START_REF] Diem | An object-oriented analysis and design model to implement controllers for quadrotor UAVs by specializing MDA's features with hybrid automata and real-time UML[END_REF], in Fig. 1, "The Guidance System's class is responsible for giving the desired trajectory for a quadrotor UAV to follow. This responsibility is completed by taking the desired waypoints defining pre-mission with the possible inclusion of external environmental disturbances issued from the Air Environment's class; then, it generates a path for this quadrotor UAV to follow. This is the level of modeling and description that TM modeling targets. The style of diagramming in Figs. 123is included to provide a contrast with the TM diagrams for the model UAV, as developed later in this paper. Although nothing prevents a diagram from presenting multiple views of a system, these diagrams are heterogeneous and contain awkward symbols; thus, there is a need for more systematic depictions that, according to [START_REF] Wolf | Choice as a principle in network architecture[END_REF], help to meet the challenge of defining a single coherent architecture. TM both presents the totality of the system in a conceptual form and distinguishes between a model's static and dynamic aspects.

III. THINGING MACHINES

We adopt a conceptual model that is centered on a system's things and (abstract) machines. The philosophical foundation of this approach is Heidegger's notion of thinging [START_REF] Heidegger | The thing[END_REF]. According to Riemer, Johnston, Hovorka, and Indulska [START_REF] Riemer | Challenging the philosophical foundations of modeling organizational reality: The case of process modeling[END_REF], Heidegger's philosophy gives an alternative analysis of "(1) eliciting knowledge of routine activities, (2) capturing knowledge from domain experts and (3) representing organizational reality in authentic ways" [START_REF] Riemer | Challenging the philosophical foundations of modeling organizational reality: The case of process modeling[END_REF]. More information about TM's philosophical foundation can be found in Al-Fedaghi [START_REF] Al-Fedaghi | Five generic processes for behaviour description in software engineering[END_REF][START_REF] Al-Fedaghi | Thing/machine-s (thimacs) applied to structural description in software engineering[END_REF][START_REF] Al-Fedaghi | Toward maximum grip process modeling in software engineering[END_REF].

The simplest type of the thing/machine combination is a TM, which is a generalization of the known input-processoutput model. In a TM, the flow of things is the exclusive conceptual movement among the five operations (stages), as shown in Fig. 4. A thing is created, processed, released, transferred, and/or received in a machine. Accordingly, the stages of a TM can be described as operations that transform, modify, or otherwise affect things abstractly or concretely. They are as follows.

 Arrive: A thing flows to a new machine (e.g., packets arrive at a router's port).

 Accept: A thing enters a TM after arrival; on the assumption that all arriving things are accepted, arrive and accept can be combined as the Receiving stage.

 Release: A thing is marked as ready for transfer outside the machine (e.g., in an airport, passengers wait to board after passport clearance).

 Process: A thing's descriptions are changed (rather than the thing itself).

 Create: A new thing is created (e.g., a forward packet is generated in a machine).

 Transfer: A thing is input to or output from a machine.

TM also includes triggering (denoted by a dashed arrow), or the initiation of a new flow (e.g., electricity triggers a flow of air). TM modeling is used in many applications (e.g., see Al-Fedaghi [START_REF] Al-Fedaghi | Conceptual modeling of an IP phone communication system: A case study[END_REF][START_REF] Al-Fedaghi | Toward a conceptual foundation for physical security: Case study of an IT department[END_REF][START_REF] Al-Fedaghi | Thinging machine applied to information leakage[END_REF][START_REF] Al-Fedaghi | Modeling an unmanned aerial vehicle as a thinging machine[END_REF]).

IV. EXAMPLE

Transporting things is a main application area for UAVs [START_REF] Fandetti | Method of drone delivery using aircraft[END_REF][START_REF] Reyna | On blockchain and its integration with IoT. Challenges and opportunities[END_REF][START_REF] Fulfillment | The future of distribution[END_REF][START_REF]The future of distribution Part II: Product distribution in emerging markets[END_REF]; this includes delivering medicines and immunizations. For the delivery, a transaction message is sent to the UAV with the GPS coordinates and the identifier of the order's package docking device [START_REF] Shakhatreh | Unmanned aerial vehicles: a survey on civil applications and key research challenges[END_REF]. At the delivery location, the control unit checks to ensure that the identifier matches the one in the transaction message and then performs the package transfer [START_REF] Fulfillment | The future of distribution[END_REF][START_REF]The future of distribution Part II: Product distribution in emerging markets[END_REF][START_REF] Shakhatreh | Unmanned aerial vehicles: a survey on civil applications and key research challenges[END_REF]. Without loss of generality, we model just the delivery system's pickup, as shown in Fig. 5.

In the figure, a packet-transfer request is created (circle 1) and sent to the UAV system [START_REF] Smith | Ethics on the fly: toward a drone-specific code of conduct for law enforcement[END_REF], where it is processed (3) to extract the pickup-location address (4). This address-with proper processing (5)-flows [START_REF] Pastor | A hardware/software architecture for UAV payload and mission control[END_REF] to the tracking device (7, the antenna and communication) and is sent [START_REF] Franssen | Philosophy of technology[END_REF] to the satellites (9). The pickup location's GPS coordinates then flow from the satellites [START_REF] Jaakkola | Architecture-driven modelling methodologies[END_REF], through the tracking device, to the UAV [START_REF] Renaul | A model for assessing UAV system architectures[END_REF], which processes them [START_REF] Li | Study on modeling of communication channel of UAV[END_REF]. This process triggers the creation of control instructions [START_REF]Drones: Benefits study: High level findings[END_REF]. These instructions flow to the actuator [START_REF] Diem | An object-oriented analysis and design model to implement controllers for quadrotor UAVs by specializing MDA's features with hybrid automata and real-time UML[END_REF], which is responsible for moving and controlling the UAV's mechanism. Accordingly, the UAV moves to the pickup location (15).

UAV system

Release

Transfer

Process

The physical UAV itself www.ijacsa.thesai.org Upon arrival at the pickup location, the transfer request is processed again [START_REF] Wolf | Choice as a principle in network architecture[END_REF] to extract the docking device's identifier [START_REF] Heidegger | The thing[END_REF] and send it to the local location [START_REF] Riemer | Challenging the philosophical foundations of modeling organizational reality: The case of process modeling[END_REF]. At the same time, the docking device's local-location identifier [START_REF] Al-Fedaghi | Five generic processes for behaviour description in software engineering[END_REF] flows [START_REF] Al-Fedaghi | Thing/machine-s (thimacs) applied to structural description in software engineering[END_REF] to the UAV, which compares the two identifiers [START_REF] Al-Fedaghi | Toward maximum grip process modeling in software engineering[END_REF][START_REF] Al-Fedaghi | Conceptual modeling of an IP phone communication system: A case study[END_REF]. If the identifiers' UAV [START_REF] Al-Fedaghi | Toward a conceptual foundation for physical security: Case study of an IT department[END_REF] and local location (24) match, confirmations are exchanged [START_REF] Al-Fedaghi | Modeling an unmanned aerial vehicle as a thinging machine[END_REF].

Upon confirming its identifier from both sides, the docking device [START_REF] Fandetti | Method of drone delivery using aircraft[END_REF] moves to the local location and transfers the involved package to the UAV [START_REF] Reyna | On blockchain and its integration with IoT. Challenges and opportunities[END_REF]. Lastly, the UAV leaves for the delivery location [START_REF] Fulfillment | The future of distribution[END_REF].

In a TM model, an event is a machine with at least three submachines: the time, the region, and the event itself. Accordingly, Fig. 6 shows each event, represented by its region, and Fig. 7 shows the UAV system's behavior in terms of the chronology of events (listed below).

 Event 1 (E 1 ): A package-transfer request is created.

 Event 2 (E 2 ):
The request arrives at the UAV, where the local-area address is extracted and sent to the tracking device, which sends the GPS coordinates.

 Event 3 (E 3 ): The GPS coordinates are received.

 Event 4 (E 4 ): The GPS coordinates are sent to the control, which issues instructions to the actuator.

 Event 5 (E 5 ): The UAV is processed (moved) according to the incoming coordinates.

 V. CASE STUDY This case study is from an actual project called RECON that involves a drone and its control unit, as initially reported in [START_REF] Al-Fedaghi | Modeling an unmanned aerial vehicle as a thinging machine[END_REF]. RECON was implemented to monitor, analyze, inspect, and intervene in data collection for traffic planners, safety managers, and commuters. RECON was originally built using non-TM notions. Figs. 8,9, and 10 show some of the diagrams used for the original drone (see [START_REF] Al-Fedaghi | Modeling an unmanned aerial vehicle as a thinging machine[END_REF]). No further details about RECON are given because this paper is focused on elaboration of the use of TM and not on description of a UAV project.

VI. MODELING UNMANNED AERIAL VEHICLES

Fig' 11 shows a TM-based static UAV model that consists of a user interface (UI) (circle 1), a server with a control panel (2), and the drone itself-comprising a controller (3) and a physical body (4). The UI contains several pointers (i.e., buttons; see Fig. 8), which are used to manage the flight.

If the drone is turned on, creating a click [START_REF] Kardasz | Drones and possibilities of their using[END_REF] in the UI causes one of the following signals to be sent ( 6): point-topoint, auto-landing, elevating signal, lateral balance, throttle signal, rudder signal, fail-safe, and follow-me.

A. Point-to-Point

This pointer causes the drone to move to a new point (position) from its current point in all directions. To accomplish this point-to-point movement, the UI process ( 6) creates a signal that is released and transferred [START_REF] Sellars | Philosophy and the scientific image of man[END_REF] to the server, where it is stored (8) in the database. The signal also flows to the drone controller (9), where it is processed to trigger the physical drone's movement [START_REF] Jaakkola | Architecture-driven modelling methodologies[END_REF].

The point-to-point movement (11, in the lower right corner of the figure) creates digital data ( 12) that flow to the server [START_REF]Drones: Benefits study: High level findings[END_REF], where they are stored. The data also flow to the UI [START_REF] Diem | An object-oriented analysis and design model to implement controllers for quadrotor UAVs by specializing MDA's features with hybrid automata and real-time UML[END_REF], where they are displayed on the user's screen (15).

B. Auto-Landing

This pointer causes the drone to land automatically in a given location. To accomplish this, a signal is created, released, and transferred [START_REF] Wolf | Choice as a principle in network architecture[END_REF] to the server, where it is stored [START_REF] Heidegger | The thing[END_REF]. The signal is also sent to the controller [START_REF] Riemer | Challenging the philosophical foundations of modeling organizational reality: The case of process modeling[END_REF], where it is processed, which triggers the physical drone's auto-landing operation [START_REF] Al-Fedaghi | Five generic processes for behaviour description in software engineering[END_REF]. In addition, the auto-landing operation [START_REF] Al-Fedaghi | Thing/machine-s (thimacs) applied to structural description in software engineering[END_REF] creates related data (21) that flow to the server [START_REF] Al-Fedaghi | Conceptual modeling of an IP phone communication system: A case study[END_REF], where they are stored. The data then flow to the UI [START_REF] Al-Fedaghi | Toward a conceptual foundation for physical security: Case study of an IT department[END_REF] for display on the user's screen [START_REF] Al-Fedaghi | Thinging machine applied to information leakage[END_REF].

C. Elevating Signal

This pointer causes the drone to move up or down. To accomplish this, a signal is created, released, and transferred (25) to the server, where it is stored [START_REF] Fandetti | Method of drone delivery using aircraft[END_REF]. The signal then flows to the drone controller [START_REF] Reyna | On blockchain and its integration with IoT. Challenges and opportunities[END_REF]. In the controller, it is processed to trigger the physical drone's movement [START_REF] Fulfillment | The future of distribution[END_REF]. Furthermore, this movement (29) creates related data (30) that flow to the server (31), where they are stored. The data then flow to the UI (32), where they are displayed on the user's screen (33).

D. Lateral Balancing

This pointer measures the rate of rotation and helps keep the drone balanced. Flying with unbalanced props can harm the drone's motors, reduce its flight quality, and affect its stability. Stabilization technology provides navigational information to the controller to enhance flight safety. The drone's lateral balance needs to work almost instantly to act against gravity, wind, and so on. To accomplish this, a signal is created, 34) to the server, where it is stored (35). The signal then flows to the drone controller (36). In the controller, it is processed to balance the physical drone (37). This stabilizing motion (38) creates related data (39) that flow to the server (40), where they are stored. The data then flow to the UI (41), where they are shown on the user's screen (42).

Physical Drone Feedback

Point-to= Point

Process: Display

Auto-Landing

Process: Display

Auto-Landing

E. Throttle Signal

This pointer affects the speed of the drone's electric motors (which increase and decrease its speed). Increasing the throttle generates more thrust. To accomplish this, a signal is created, released, and transferred (43) to the server, where it is stored (44). The signal then flows to the drone controller (45), where it is processed to increase or decrease the physical drone's speed (46). In addition, the change in movement speed (47) creates related data (48) that flow to the server (49), where they are stored. The data then flow to the UI (50), where they are displayed on the user's screen (51).

F. Rudder Signal

This pointer involves altitude adjustments. To accomplish this, a signal is created, released, and transferred (52) to the server, where it is stored (53). The signal then flows to the drone controller (54), where it is processed to increase or decrease the physical drone's speed (55). In addition, the change in altitude (56) creates related data (57) that flow to the server (58), where they are stored. The data then flow to the UI (59), where they are presented on the user's screen (60).

G. Fail-Safe

This procedure supports the drone in case of an error. This mode sets the conditions that the model's servos and motors revert to when it loses the transmitter's control signal. For instance, the fail-safe could automatically cause the drone to return home or to the nearest base station. To accomplish this task, a signal is created, released, and transferred (61) to the server, where it is stored (62). The signal then flows to the drone controller (63), where it is processed to move the physical drone (64). In addition, this movement (65) creates related data (66) that flow to the server (67), where they are stored. The data then flow to the UI (68), where they are displayed on the user's screen (69).

H. Follow Me

This pointer causes the activation of follow-me mode, which gives the drone the ability to autonomously track a target without piloting. For example, a drone can be programmed to automatically follow its operator around. To accomplish this, a signal is created, released, and transferred (70) to the server, where it is stored (71). The signal then flows to the drone controller (72), where it is processed to move the physical drone in follow-me mode (73). In addition, this movement (74) creates related data (75) that flow to the server (76), where they are stored. The data then flow to the UI (77), where they are shown on the user's screen (78).

VII. THE DYNAMIC THINGING MACHINE MODEL

The space limitations and density of overlapping events do not permit us to diagram all the events for the processes in the UI. Accordingly, we show the events for only two of them: Point-to-Point and Follow Me, as shown in Fig. 12. Clicking on Point-to-Point causes the following events. Fig. 13 shows the chronology of these events. www.ijacsa.thesai.org

VIII. SIMULATION

The TM diagram can be used to simulate drone processes. TM events are fine-grained activities that result in the integration of a static description and a dynamic model of events. Without loss of generality, we focus on flowcharting using the simulation language Arena. In Arena, the flowchart plays an important role, and the success of a simulation depends on how well the flowchart projects the identification of events; the notion of an event is used informally. TM can assist with this process. By contrast, a TM diagram specifies all elementary events (create, process, release, transfer, and receive). For example, Fig. 14 shows the series of elementary events involved in elevating the drone (see Fig. 11, circle 5), including the click and the movement of the drone, as well as some more complex events that can be formed from the elementary events. Many possible selections of events are possible. For example, releasetransfer/transfer-receive can be considered one event in which a thing flows from one submachine to another. Alternatively, it can be considered two events: leaving (release-transfer) and arriving (transfer-receive). We start with a TM diagram and identify event boundaries from the elementary events until we attain the required level of granularity. We are experimenting with Arena flowcharts produced by an ad hoc method as well as those developed using TM.

IX. CONCLUSION

This paper addresses the development of conceptual modeling for UAVs. We propose a flow-based specification called TM as a good vehicle in this area and demonstrate the TM methodology through a case study involving the construction of a drone.

A shortcoming of TM regards its (visual) diagramming complexity, which originated in the various machines' and submachines' completeness. The TM diagram can be simplified to whatever level of granularity is required for the original TM description. For example, Fig. 15 was produced from a static TM representation of a UAV. Further research is needed to directly apply the TM methodology to more sophisticated UAV systems. Further investigation is also required to develop TM tools and supporting apparatus. Specifically, additional synchronization, constraints, and logical notation need to be superimposed on the base TM description. As mentioned previously, TM can be used as a basis for simulation.

Fig. 1 .

 1 Fig. 1. A UML Class Diagram for Presenting the main FunctionalComponents of Quadrotor UAVs (Redrawn and Adapted from[START_REF] Diem | An object-oriented analysis and design model to implement controllers for quadrotor UAVs by specializing MDA's features with hybrid automata and real-time UML[END_REF]).

Fig. 2 .

 2 Fig. 2. Local State Machine for the Drive use case. (Redrawn and Adaptedfrom[START_REF] Diem | An object-oriented analysis and design model to implement controllers for quadrotor UAVs by specializing MDA's features with hybrid automata and real-time UML[END_REF]).

Fig. 3 .

 3 Fig. 3. Main use-case Model for a Quadrotor UAV. (Redrawn and Adapted from [14]).

Fig. 4 .

 4 Fig. 4. A Thinging Machine.

Fig. 5 .

 5 Fig. 5. The TM Model of the UAV Delivery System's Pickup.

Fig. 7 .

 7 Fig. 7. The Chronology of Events in the TM Model of Part of the UAV Delivery System.

Fig. 8 .

 8 Fig. 8. The RECON user Interface.

Fig. 9 .

 9 Fig. 9. An Overview of RECON.

Fig. 11 .

 11 Fig. 11. The TM's Static UAV Model.



  Event 1 (E 1 ): Point-to-Point is clicked on the pointer.  Event 2 (E 2 ): A flow signal to the server is created.  Event 3 (E 3 ): The signal is stored in the database.  Event 4 (E 4 ): The signal flows to the drone controller.  Event 5 (E 5 ): The physical drone begins to move.  Event 6 (E 6 ): The movement operation takes its course.  Event 7 (E 7 ): The signal is processed to trigger up or down movement in the physical drone.  Event 8 (E 8 ): The signal is processed to balance (stabilize) the physical drone.  Event 9 (V 9 ): The signal is processed to accelerate or slow the physical drone's movement.  Event 10 (E 10 ): The signal is processed to increase or decrease the physical drone's speed.  Event 11 (E 11 ): The signal is processed to trigger the physical drone's movement; instructions are sent to the flight controller to execute to this mode.  Event 12 (E 12 ): The signal is processed to move the physical drone and thus follow the selected target.  Event 13 (E 13 ): The signal is processed to trigger flows to the controller and shift from analog to digital.  Event 14 (E 14 ): The signal flows to the control panel.  Event 15 (E 15 ): The signal is stored in the database.  Event 16 (E 16 ): The signal flows to the server.  Event 17 (E 17 ): The signal flows to the UI.

Fig. 12 .

 12 Fig. 12. Events in the TM Model of the UAV Delivery System.

Fig. 13 .

 13 Fig. 13. The Chronology of Events in the TM Model of the UAV.

Fig. 14 .

 14 Fig. 14. Some Combinations in a Series of Elementary Elevating Events.

Fig. 15 .

 15 Fig. 15. Simplification of the TM's Static UAV Model (Partial).

11 12 13 14 15 16 17 18 Create 20 21 19 23 22 24 25 27 26

	Transfer 2 Create Transfer Release	1	Transferring request Process Receive 3	Delivery location Transfer Receive Process Release Pick up location Pick up location address 4 5 address Receive Receive Receive Transfer Identifier of 6 docking device Transfer Transfer Transfer	Transfe r Receive	Release Address 8 9	Transfe r Satellites Data … 10
				pickup location Control GPS coordinates of	Release Create Process		Actuator Receive	Transfer	(Antenna and communication) Tracking Device Transf er Receiv e Releas e Transf er 7
									Transfer	Transfer	Receive	Process
	Process						Pick up location	Transfer
	Transfer		Identifier of docking device Release Receive	Transfer	Receive
												Process
		Transf	Recei er	ve Releas	e Transf	er	Process Match? Transfer Receive	Create Transfer Release	Receive Transfer	Transfer Release Create	Transfer Receive	Transfer Release
	Identifier of Releas e Transfer						Transfer Receive Confirmation Transfer Create Release	Match? Process Transfer Receive	device Transfer Docking Process Receive	e Transfer Packag Release
	docking										Receive
	device										Transfer

  Event 6 (E 6 ): The UAV moves to the pickup location. Event 10 (E 10 ): The package is picked up.  Event 11 (E 11 ): The UAV reaches the delivery location.

											 Event 7 (E 7 ): The docking-device identifier is extracted
											from the request and sent to the pickup location.
											 Event 8 (E 8 ): The docking-device identifier is received
											and checked at the pickup location.
											 Event 9 (E 9 ): Confirmations are exchanged between the
											UAV and the pickup location.
											 …
	Create Release Transfer		Transferring request E 1	Transfer Delivery location Receive	Transfer Pick up location address Receive	Transfer Receive	Identifier of docking device	Satellites Data Address
	Transfer	Receive		Process		Release E 2 Pick up location address Transfer Receive Process	Transfer	Transfer	Receive	Release	Transfer
			Pickup location GPS coordinates of UAV system Control	Process Create Transfer Release	Receive Actuator Transfer Receive E 4	Transfer Process	Tracking E 3 Device Process The physical Transfe r UAV itself E 5	Release	Receiv e Transfe r Release Transfer
	Process Transfer Identifier of docking device Releas e Receive	Pick up location Transfer E 7	Transfer Receive Process E 6
		Transf	Receiv er	e Releas	e Transf	Process Match? er Transfer Receive	Create Transfer Release	Receive Transfer	Create Release Transfer	Receive Transfer	Release Transfer E 11
	Identifie r of docking device Create e Transfer Releas	E 8			Receive Transfer E 9 Confirmation Create Release Transfer	Match? Process Transfer Receive Transfer Receive	Dockin Process Receive Transfer g device	Packag Release Transfer e E 10
											0

Fig. 6. The TM Model of Part of the UAV Delivery System. 0 www.ijacsa.thesai.org

Lateral Balance Auto Landing Point to Point Process Control Panel Database

								E5
								E6
			Click	E1	E2	E3	E4	E7 E8	E12	E13	E14	E15	E16	E17
								E9
								E10
								E11
	Pointer (on meters)		User
		Elevating		Interface
		Process				Event
						Event
			Create			Event	Event
			Release			Event
		Transfer				Event			Pointer (on meters)		User Interface
		Server Transfer			Event	Event			Elevating Signal	Mode Failsafe Rudder Signal Throttle Signal	Create	Follow Me Click
	Control Panel	Receive Release	Database	Event Event Event			Create	Create	Create Create Create Create Signal	Create Create	Movement of pointer
		Transfer			Event	Event						Server
	Drone	Transfer			Event
			Receive			Event
	Move Up/Down Process Create Controller Physical Drone		Event Event Event		Drone Controller Process Process Process Process Process Process Process Process
													Physical Drone
													Auto. Follow
													Create
													Create	Failsafe Mode
													Create	Rudder
													Speed Create: Inc/Dec
											Create	Stability
											Create		Move Up/Down
										Create		Auto Landing
									Create

Movement www.ijacsa.thesai.org