",isbn:"978-1-83969-234-5",printIsbn:"978-1-83969-233-8",pdfIsbn:"978-1-83969-235-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"a5f5277a1c0616ce6b35f4b44a4cac7a",bookSignature:"Dr. Basel I. Ismail",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10013.jpg",keywords:"Thermodynamics, Heat Transfer Analyses, Geothermal Power Generation, Economics, Geothermal Systems, Geothermal Heat Pump, Green Energy Buildings, Exploration Methods, Geologic Fundamentals, Geotechnical, Geothermal System Materials, Sustainability",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 29th 2020",dateEndSecondStepPublish:"November 26th 2020",dateEndThirdStepPublish:"January 25th 2021",dateEndFourthStepPublish:"April 15th 2021",dateEndFifthStepPublish:"June 14th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Leading research investigator in a collaborative project (2007-2010) with Goldcorp-Musselwhite Canada Ltd. and Engineering of Lakehead University, owner of a Ph.D. degree in Mechanical Engineering from McMaster University, Hamilton, Ontario, Canada and postdoctoral researcher (2004 to 2005) at McMaster University.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"62122",title:"Dr.",name:"Basel",middleName:"I.",surname:"Ismail",slug:"basel-ismail",fullName:"Basel Ismail",profilePictureURL:"https://mts.intechopen.com/storage/users/62122/images/system/62122.jpg",biography:"Dr. B. Ismail is currently an Associate Professor and Chairman of the Department of Mechanical Engineering, Lakehead University, Thunder Bay, Ontario, Canada. In 2004, Prof. Ismail earned his Ph.D. degree in Mechanical Engineering from McMaster University, Hamilton, Ontario, Canada. From 2004 to 2005, he worked as a Postdoctoral researcher at McMaster University. His specialty is in engineering heat transfer, engineering thermodynamics, and energy conversion and storage engineering. Dr. Ismail’s research activities are theoretical and applied in nature. Currently, his research areas of interest are focused on green engineering technologies related to alternative and renewable energy systems for power generation, heating and cooling. Dr. Ismail was the leading research investigator in a collaborative project (2007-2010) with Goldcorp-Musselwhite Canada Ltd. and Engineering of Lakehead University. 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1. Introduction
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Mast cells are key effector cells that clearly play both physiological and pathophysiological functions in the body [1]. In vertebrates, mast cells are widely distributed in the tissues, especially near surfaces exposed to the environment, where pathogens, allergens, and other environmental agents are frequently found. Due to this distribution pattern, they are some of the first cells involved in the immune response which interact with environmental antigens and allergens, invading pathogens or environmentally derived toxins [2]. Mast cells have been involved in the pathogenesis of a number of disorders including contact dermatitis, allergic rhinitis, asthma, atopic dermatitis, bullous pemphigoid, fibrotic lung disease, cancer, multiple sclerosis, neurofibromatosis, psoriasis, scleroderma, rheumatoid arthritis, interstitial cystitis, ulcerative colitis, peptic ulcer, and Crohn’s disease [1, 3, 4, 5, 6, 7]. Understanding mast cells is essential for the pathophysiological bases of this type of disorders since these cells release varied inflammatory mediators prompted by both immune and nonimmune causes. Among mast cell mediators, preformed molecules can be mentioned, for example, histamine and proteases, which are accumulated in secretory granules [7, 8, 9]. The immediate response upon mast cell activation to an appropriate stimulus is called degranulation, characterized by the extrusion of cytoplasmic granule contents into the extracellular space by a process called exocytosis [10]. In this context, the exploration of interactions of mast cells with molecules capable of modulating mediator release from cell granules is a promising field for the treatment of mast cell-mediated diseases.
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In vitro and in vivo studies have shown that several plant products with antioxidant properties inhibit mast cell activation induced by both immune and nonimmune secretagogues [11, 12, 13, 14]. Recent scientific evidence from preclinical studies and clinical trials including humans has highlighted different nutritional interventions, such as dietary polyphenols, as promising agents able to alleviate symptoms associated with mast cell activation [15]. Dietary polyphenols are a class of bioactive compounds found in abundance in plants and fruits which have been studied thoroughly in several disease models [15]. The pulp of olives contains these compounds, which are hydrophilic, and they are also found in the oil. The class of phenols includes numerous substances, such as simple phenolic compounds like hydroxytyrosol and more complex compounds like oleuropein [16]. Hydroxytyrosol and oleuropein, the major phenols found in olives, are used in disease prevention because they have important antioxidant and anti-inflammatory properties [17, 18]. Several in vitro and in vivo studies have shown that oleuropein and its derivate hydroxytyrosol possess a wide range of biochemical and pharmacological properties. However, no studies have been published on the effects of these molecules on mast cell degranulation.
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We carried out a series of experiments to determine the effects of both phenolic compounds on mast cell degranulation and thus explore the possibility that oleuropein and hydroxytyrosol might inhibit in vitro mast cell activation.
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The biochemical and morphological findings of the present study showed for the first time that hydroxytyrosol and oleuropein inhibit the degranulation of mast cells induced by both immune and nonimmune pathways.
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2. Material and methods
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2.1 Chemicals and reagents
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The compound molecules, hydroxytyrosol and oleuropein, were provided by Extrasynthèse (Lyon, France). Figure 1 shows the polyphenol chemical structure. A solution containing 6.7 mM Na2HPO4, 6.7 mM KH2PO4, 137 mM NaCl, 2.7 mM KCl, 0.8 mM CaCl2, 0.5 g/l albumin, and 1 g/l glucose was used for dissolving the polyphenols, which was adjusted to pH 7.2 and stored at −20°C. The same solution was used for diluting the stock solutions until the final concentration was reached. Bovine serum albumin (fraction V), concanavalin A, compound 48/80, calcium ionophore A23187, sodium cromoglycate, 4-nitrophenyl-N-acetyl-β-D-glucosaminide, toluidine blue, trypan blue, glutaraldehyde, formaldehyde, and osmium tetroxide were acquired from Sigma (St. Louis, MO, USA). Percoll was purchased from GE Healthcare (Munich, Germany). All other substances were provided by Merck (Darmstadt, Germany). The highest quality available is guaranteed in all the chemicals used for this study.
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Figure 1.
Structural formulas of polyphenols used in this study.
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2.2 Animals
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Adult male 300–500 g Wistar rats free from infections were used for the study. They were kept under a 12-h dark/light cycle in a room set at 24–25°C with free access to laboratory food and drinking water. Animals experiments were carried out according with the standards contained in the Guide for the Care and Use of Laboratory Animals, published by the National Academy of Sciences, National Academies Press, Washington, DC, and accepted by the Institutional Committee for Care and Use of Laboratory Animals (CICUAL, Facultad de Ciencias Médicas, Universidad Nacional de Cuyo, Mendoza, Argentina).
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2.3 Mast cell isolation and purification
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Isolation of mast cells was performed by means of peritoneal lavage as described before [19] with some modifications. Rats were killed by CO2 inhalation and then injected with 20 ml of a pH 7.2 solution containing 6.7 mM Na2HPO4, 6.7 mM KH2PO4, 137 mM NaCl, 2.7 mM KCl, 0.8 mM CaCl2, 0.5 g/l albumin, and 1 g/l glucose, into the peritoneal cavity. A gentle massage was applied on the abdomen for about 3 min. After the peritoneal cavity was opened with care, a Pasteur pipette was used for aspirating the fluid with peritoneal cells, which were then purified via centrifugation through a discontinuous gradient of Percoll per reports by MacGlashan and Guo [20]. It was easy to harvest the mast cells since they precipitated to the bottom of the tube and formed a layer, different from the other cells that formed a rather compact layer on top of the gradient and were simply removed by aspiration. Cell metachromatic staining was carried out with toluidine blue (0.1% w/v, pH 1.0), and quantification required a Neubauer hemocytometer under a Nikon microscope (magnification 200×). Mast cells were present in the crude peritoneal suspended content by 3%, and, after gradient centrifugation, their purity rose to over 95%. After washing purified mast cells, resuspension was performed in a balanced salt solution with 6.7 mM Na2HPO4, 6.7 mM KH2PO4, 137 mM NaCl, 2.7 mM KCl, 0.8 mM CaCl2, 0.5 g/l albumin, and 1 g/l glucose, adjusted to pH 7.2 (cell density of 1 × 106/ml) and kept at 4°C for 30 min at the most. Both the ability of mast cells to exclude trypan blue and the amount of β-hexosaminidase in the supernatant established mast cell viability. The trypan blue exclusion test determined that the mast cells were viable by over 95%. Basal β-Hexosaminidase release remained always below 4%.
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2.4 General protocol
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Purified peritoneal mast cells (cell density of 1 × 106/ml) were balanced at 37°C for 10 min. Preincubation of 30-μL aliquots of the balanced cells required polypropylene tubes at 37°C with hydroxytyrosol or oleuropein. Then, incubation was carried out at 37°C for 10 min using concanavalin A (final concentration was 200 μg/ml, with 50 μg/ml phosphatidylserine as a co-stimulator), compound 48/80 (concentrated at 10 g/ml), or calcium ionophore A23187 (final concentration 50 μg/ml). Positive and negative controls, that is, with and without mast cell secretagogues stimulation, respectively, were included. Studies on dose–response (hydroxytyrosol and oleuropein concentrations of 10, 50, 100, 200, and 400 M) and time-dependence (hydroxytyrosol and oleuropein preincubation for 5, 10, 20, and 45 min) were performed. Comparative studies were performed using sodium cromoglycate, a well-known mast cell stabilizer, with identical concentrations and time ranges. Each tube’s final incubation volume reached 100 l. During incubations, the average total number of mast cells was 4 × 104/ml per tube. Secretion was halted by cooling the tubes in an ice-cold water bath. Cells and supernatants were separated by centrifugation (180 g, 5 min, 4°C). The supernatants helped determine the β-hexosaminidase content by colorimetric reaction, which was a parameter to measure β-hexosaminidase release. The cell pellets were lysated with 1% Triton X-100 to release the remaining β-hexosaminidase, which was quantified by colorimetric reaction and taken as a measure of the residual β-hexosaminidase. Other cell pellet samples were studied for cell viability, by means of the trypan blue exclusion method, or analyzed with light and electron microscopy. The purpose of cell viability studies was to ascertain that changes in β-hexosaminidase release were not caused by cell death. The percentage of β-hexosaminidase obtained in each tube was measured. All the tests were performed at least five times in duplicate.
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2.5 β-Hexosaminidase assay
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β-Hexosaminidase release, as an index of mast cell degranulation, was assayed by a colorimetric assay as before explained [21] with some changes. In brief, 50 μL of the supernatant was combined with an equal volume of 2 mM substrate solution (p-nitrophenyl-N-acetyl-β-D-glucosaminide in 0.2 M citrate, pH 4.5) and then incubated for 3 h at 37°C. The reaction was halted by adding 250 μL of stopping buffer (0.4 M glycine in Na2CO3/NaHCO3, pH 9). Absorbance was studied with a microplate reader at 405 nm (Thermo Scientific Multiskan FC, Helsinki, Finland). Results were stated as the percentage of β-hexosaminidase activity released over the total (enzyme released plus intracellular enzyme).
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2.6 Light microscopy and morphometry
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Mast cells were fixed in 2% glutaraldehyde for 2 h. Then, the suspended cells were stained with toluidine blue (0.1% w/v, pH 3.0), put between slides and cover slides, and analyzed under a Nikon Optiphot 2 microscope. Using a magnification of 400×, percentage of degranulated mast cells was quantified. Mast cell was considered active due to the presence of extruded granules near the surface of the cell in question or a toluidine blue stain in half or less of the cell.
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2.7 Transmission electron microscopy
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Karnovsky’s fixative (2% formaldehyde, freshly prepared from paraformaldehyde, 2.5% glutaraldehyde, 0.025% CaCl2, 0.1 M cacodylate buffer, pH 7.4) was used for mast cell fixation. After 1 h in the fixative at 20°C, mast cells were rinsed in 0.2 M cacodylate buffer and postfixed in 1% OsO4 in 0.1 M cacodylate buffer for at least 2 h at room temperature and dehydrated in ethanol. Next, suspended cells were embedded in Spurr (Pelco, USA). An automatic ultramicrotome (Leica Ultracut R, Austria) was used to cut semithin transverse sections (1 μm), which were stained with filtered 1% toluidine blue. Ultrathin sections (60 nm) were cut with diamond knives, stained with uranyl acetate and lead citrate, mounted on grids (Pelco, USA), and examined in a transmission electron microscope (Zeiss EM 902, Germany).
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2.8 Statistical analysis
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Results obtained from biochemical and morphometric analyses are presented as ±SEM. Variance analysis was used to determine differences between groups, followed by Tukey-Kramer multiple comparisons test. P < 0.05 was considered statistically significant.
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3. Results and discussion
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Figure 2 shows the effect of varying concentrations of hydroxytyrosol and oleuropein on the mast cells β-hexosaminidase release, induced by concanavalin A, compound 48/80, or calcium ionophore A23187. β-Hexosaminidase release was significantly increased by incubating mast cells with 200 μg/ml concanavalin A or 10 μg/ml compound 48/80 or 50 μg/ml calcium ionophore A23187 solutions, as compared with the corresponding value from the basal group (basal release was always less than 4%). The concanavalin A-induced effects were inhibited by preincubation of mast cells with hydroxytyrosol (10, 50 and 100 μM) or oleuropein (100 μM). Mast cell activation induced by compound 48/80 was inhibited by hydroxytyrosol (100 μM) and oleuropein (100 μM). The calcium ionophore A23187-induced effects were inhibited by preincubation of mast cells with hydroxytyrosol (100 μM) and oleuropein (10, 50 and 100 μM). The inhibitory action of the polyphenols was not accompanied by changes in cell viability (the trypan blue exclusion test indicated a viability of greater than 80%), except for polyphenol concentrations higher than 100 μM (data not shown).
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Figure 2.
Effect of varying concentrations of hydroxytyrosol and oleuropein on the concanavalin A-, compound 48/80-, and calcium ionophore A23187-induced β-hexosaminidase release from peritoneal mast cells. Mast cells were preincubated with increasing concentrations of hydroxytyrosol or oleuropein for 10 min and then stimulated with 200 μg/ml concanavalin A, 10 μg/ml compound 48/80, or 50 μg/ml calcium ionophore A23187, for 10 min at 37°C. β-Hexosaminidase release was measured by colorimetric reaction with the chromogenic substrate p-nitrophenyl-N-acetyl-β-D-glucosaminide. Results are expressed as percentage release of β-hexosaminidase. Values are presented as means ± SEM +++P < 0.001 versus basal, *P < 0.05 versus secretagogue, **P < 0.01 versus secretagogue, and ***P < 0.001 versus secretagogue.
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Figure 3 shows a dose-response comparative study with sodium cromoglycate, a mast cell stabilizer, which was used to evaluate the potency of hydroxytyrosol and oleuropein. The inhibitory effect of hydroxytyrosol was higher than that obtained with sodium cromoglycate at the same concentration (100 μM) when mast cells were challenged with concanavalin A. No significant differences were observed when mast cells were challenged with compound 48/80. The oleuropein inhibitory effect was higher than the one obtained with sodium cromoglycate at the same concentrations (10, 50 and 100 μM) when the calcium ionophore A23187 was used to challenge mast cells.
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Figure 3.
Effect of hydroxytyrosol and oleuropein on β-hexosaminidase release from rat peritoneal mast cells, compared with a reference compound, the mast cell stabilizer sodium cromoglycate. Purified mast cells were preincubated with increasing concentrations of hydroxytyrosol, oleuropein, or sodium cromoglycate for 10 min and stimulated with 200 μg/ml concanavalin A, 10 μg/ml compound 48/80, or 50 μg/ml calcium ionophore A23187 for another 10 min at 37°C. β-Hexosaminidase release was measured by colorimetric reaction with the chromogenic substrate p-nitrophenyl-N-acetyl-β-D-glucosaminide. Results are expressed as percentage release of β-hexosaminidase. Values are presented as means ± SEM *P < 0.05 versus sodium cromoglycate and ***P < 0.001 versus sodium cromoglycate.
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The kinetic study results connected with the effect of hydroxytyrosol and oleuropein on β-hexosaminidase release from mast cell are described in Figure 4.
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Figure 4.
Kinetic study related to the effect of hydroxytyrosol or oleuropein preincubation on mast cell β-hexosaminidase release, induced by concanavalin A, compound 48/80, or calcium ionophore A23187. Peritoneal mast cells were preincubated for increasing times with 100 μM hydroxytyrosol or oleuropein and then stimulated with 200 μg/ml concanavalin A, 10 μg/ml compound 48/80, or 50 μg/ml calcium ionophore A23187 for 10 min at 37°C. β-Hexosaminidase release was measured by colorimetric reaction with the chromogenic substrate p-nitrophenyl-N-acetyl-β-D-glucosaminide. Results are expressed as percentage release of β-hexosaminidase. Values are presented as means ± SEM +++P < 0.001 versus basal, *P < 0.05 versus secretagogue, **P < 0.01 versus secretagogue, and ***P < 0.001 versus secretagogue.
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The concanavalin A-induced effect was inhibited by hydroxytyrosol (10 min), oleuropein (10 min), and sodium cromoglycate (10 min). Mast cell activation induced by compound 48/80 was only inhibited by sodium cromoglycate (5, 10, and 20 min). The ionophore A23187-induced effect was inhibited by hydroxytyrosol (10 min), oleuropein (5, 10, and 20 min), and sodium cromoglycate (10 and 20 min). The inhibitory action of the test compounds was not accompanied by changes in cell viability (the trypan blue exclusion test indicated a viability of greater than 80%), except for incubation times higher than 20 min (data not shown).
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As a matter of interest, the main findings of this study evidenced that hydroxytyrosol and oleuropein, at non-cytotoxic concentrations, inhibit β-hexosaminidase release from peritoneal mast cells stimulated by different triggers, acting then as mast cell stabilizers.
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Mast cell activation is inhibited by several dietary polyphenols, as shown in other in vitro studies [11, 15]. A well-studied immunological effect of polyphenols such as quercetin is their inhibitory action on degranulation, particularly histamine release from mast cells [11, 15, 22]. Quercetin is able to regulate the entry of calcium into mast cells; also the role of apple and grape quercetin and polyphenols in inhibiting mast cell degranulation has been thoroughly documented using in vitro systems such as the RBL-2H3 assay [11, 15]. Epigallocatechin gallate was found to be the active ingredient in green tea extracts for protection against cutaneous inflammation. This compound may also inhibit mast cell histamine release stimulated with both a calcium ionophore and an IgE-antigen complex [23]. Kaempferol, myricetin, phloretin, and luteolin also proved to be effective inhibitors of histamine release [11, 15]. However, hydroxytyrosol and oleuropein were never studied on mast cell mediator release in response to either immune or nonimmune triggers.
\n
In addition, we have also proven that the inhibitory effects by hydroxytyrosol were stronger than those of oleuropein or the reference compound sodium cromoglycate when mast cells were activated by concanavalin A. On the contrary, oleuropein resulted to be a more efficient inhibitor of mast cell degranulation than hydroxytyrosol or sodium cromoglycate when cells were challenged with the calcium ionophore A23187. Table 1 presents EC50 values for each compound. These results strongly suggest that the inhibitory activity might be defined by the nature of the stimulus for β-hexosaminidase release and the chemical structure of both polyphenols. Each of the secretagogues takes a different mast cell activation pathway, and these pathways may be differentially sensitive to the action of hydroxytyrosol or oleuropein. Mast cells may be activated via several different mechanisms, among which is the classical pathway known as immunological or IgE-mediated mast cell activation, triggered by the cross-linking of FcεRI receptors. Mast cell activation may also be completed in an IgE-independent manner using commercially available activators, such as basic secretagogues and calcium ionophores. Concanavalin A is a glucose-/mannose-specific lectin that activates mast cells by a mechanism similar to the antigen-antibody reaction. This lectin causes FcεRI receptors to cluster on cell membranes. In turn, this triggers a series of intracellular events leading to the secretion of mast cell mediators by exocytosis. Phosphatidylserine markedly enhances the release of mediators from rat mast cells by concanavalin A [24, 25, 26]. The synthetic compound 48/80, a basic secretagogue, activates heterotrimeric G proteins by enhancing the dissociation of GDP from Gα subunits, thus accelerating the event considered as a rate-limiting step in conventional G protein activation by receptors coupled to heterotrimeric G proteins [27]. Calcium ionophore A23187, a mobile carrier of divalent cations such as Ca2+, Mg2+, and double H+, may reduce the level of calcium stored in mitochondria or increase the inflow from the extracellular medium, resulting in a cytosolic calcium increase, which may induce mast cell exocytosis and preformed mediator release. Calcium release from internal stores has been shown to be related to some second messengers, including phospholipase C, phospholipase D, inositol 1,4,5-triphosphate, and diacylglycerol. Degranulation dependent on the influx of extracellular calcium may be associated to the members of the SNARE (soluble NSF attachment protein receptor) family, such as synaptosome-associated protein of 23 kDa (SNAP-23), syntaxin, synaptotagmin, and molecules of the vesicle-associated membrane protein (VAMP) family which regulate the granule-to-granule or granule-to-plasma membrane fusion process [28]. Sodium cromoglycate is a compound commonly used in the treatment of allergic diseases. The effect of sodium cromoglycate is due to its ability to stabilize the mast cell membrane and to prevent the release of histamine and inflammatory mediators [29]. Mechanisms of the action of sodium cromoglycate include blocking of the influx of calcium into mast cells, inhibition of phosphodiesterase, and regulation of phosphorylation of mast cell proteins [30].
\n
\n
\n
\n
\n
\n\n
\n
\n
Hydroxytyrosol
\n
Oleuropein
\n
Sodium cromoglycate
\n
\n\n\n
\n
Concanavalin A
\n
7.58 ± 4.9
\n
67 ± 5.1
\n
95 ± 8.9
\n
\n
\n
Compound 48/80
\n
61 ± 5.0
\n
59 ± 5.0
\n
97 ± 9.1
\n
\n
\n
Calcium ionophore A23187
\n
64 ± 5.0
\n
22 ± 1.8
\n
54 ± 5.1
\n
\n\n
Table 1.
EC50 (μM) ± SEM values for inhibitory activity of hydroxytyrosol, oleuropein, and sodium cromoglycate on concanavalin A-, compound 48/80-, and calcium ionophore A23187-induced mast cell activation.
\n
The findings of this study suggest that hydroxytyrosol and oleuropein act at different molecular sites. It seems that the hydroxytyrosol inhibitory mechanism may be related, at least partially, to inhibition of the cross-linking of high-affinity receptors for IgE (FcεRI) or to a probable interaction of the polyphenol with concanavalin A. Reports show that polyphenols form soluble and insoluble complexes with proteins and may cause them to become hypoallergenic by either modifying the structure of the allergenic protein or making it less bioavailable [15]. Moreover, two mechanisms are proposed for the mast cell inhibitory action of quercetin and other polyphenols: one where polyphenols impact allergen-IgE complex formation and another one where the polyphenols impact on the complex binding to their receptor (FcεRI) on mast cells [15].
\n
Our results also suggest that oleuropein seems to block signaling pathways downstream of cytosolic calcium increase. However, further research is needed in order to explain the exact molecular mechanisms of these actions.
\n
Despite the strong biochemical evidence, we considered a morphological evaluation necessary to reinforce the validity of our initial findings. Thus, a second set of experiments was designed to analyze the effect of the hydroxytyrosol and oleuropein on mast cell morphology by light and electron microscopy.
\n
Peritoneal mast cells were easily identified by the presence of a cytoplasm dominated by distinctive secretory granules which stain metachromatically (Figure 5A–J). A representative mast cell from the basal group is shown in Figure 5A. Tightly packed secretory granules dominate the cytoplasm. Characteristic mast cells stimulated with concanavalin A, compound 48/80, and calcium ionophore A23187 are shown in Figure 5B–D, respectively. Cell surface disruption, typical of degranulating mast cells, may be observed. Figure 5E–G show mast cells treated with 100 μM hydroxytyrosol, for 10 min, prior to the challenge with concanavalin A, compound 48/80, and calcium ionophore A23187, respectively. Figure 5H–J show mast cells treated with 100 μM oleuropein, for 10 min, prior to the challenge with concanavalin A, compound 48/80, and calcium ionophore A23187, respectively. The morphology of the cells treated with the polyphenols shows a lower degree of degranulation than that of secretagogue samples.
\n
Figure 5.
Light microscopic photographs of peritoneal mast cells (toluidine blue stain). Mast cells were preincubated with 100 μM hydroxytyrosol or oleuropein for 10 min and then stimulated with 200 μg/ml concanavalin A, 10 μg/ml compound 48/80, or 50 μg/ml calcium ionophore A23187, for 10 min at 37°C. After incubations, cells were fixed and stained for light microscopy. (A) Basal. The cytoplasm is dominated by closely packed secretory granules. (B–D) Concanavalin A (ConA), compound 48/80 (48/80), and calcium ionophore A23187 (A23187), respectively. Degranulating cells may be seen. (E–G) hydroxytyrosol + ConA, hydroxytyrosol + 48/80, and hydroxytyrosol + A23187, respectively. (H–J) Oleuropein + ConA, oleuropein + 48/80, and oleuropein + A23187, respectively. The morphology of the polyphenol-treated cells shows a lower degree of degranulation than that of secretagogue samples. 600×.
\n
Figure 6A–J shows mast cells observed under transmission electron microscope. Figure 6A shows a characteristic view of the basal mast cell population. These cells are characterized by a non-segmented, irregular nucleus with only moderate nuclear chromatin condensation, narrow surface folds, and numerous secretory granules regularly distributed throughout the cell cytoplasm. Almost all granules display either round or oval profiles and appear homogenously dense. Representative mast cells from the concanavalin A-, compound 48/80-, and calcium ionophore A23187-treated group are shown in Figure 6B–D, respectively. These cells display obvious morphological changes and show evidence of increased granule release by exocytosis compared to basal cells. Mast cells exhibit a cytoplasm with irregular secretory granules showing various degrees of electron densities. Perigranular dilated and electron-lucent spaces surround some granules. Some of these spaces look fused, forming multiple cavities and intracytoplasmic channels. Figure 6E–G show mast cells treated with 100 μM hydroxytyrosol, for 10 min, prior to the challenge with concanavalin A, compound 48/80, and calcium ionophore A23187, respectively. Figure 6H–J show mast cells treated with 100 μM oleuropein, for 10 min, prior to the challenge with concanavalin A, compound 48/80, and calcium ionophore A23187, respectively. The morphology of the polyphenol-treated cells shows a lower degree of degranulation than that of secretagogue samples.
\n
Figure 6.
Transmission electron micrographs showing peritoneal mast cells. Mast cells were preincubated with 100 μM hydroxytyrosol or oleuropein for 10 min and then stimulated with 200 μg/ml concanavalin A, 10 μg/ml compound 48/80, or 50 μg/ml calcium ionophore A23187, for 10 min at 37°C. After incubations, cells were processed for electron microscopy. (A) Basal. A non-segmented nucleus (N), narrow surface folds (red arrows), and numerous secretory granules (G) distributed throughout the cell cytoplasm are observed. (B–D) Concanavalin A (ConA), compound 48/80 (48/80), and calcium ionophore A23187 (A23187), respectively. Granules surrounded by perigranular dilated and electron-lucent spaces are evident. Some granules are seen out of mast cell cytoplasm, and others appear fused, forming cavities and intracytoplasmic channels (asterisk). (E–G) Hydroxytyrosol + ConA, hydroxytyrosol + 48/80, and hydroxytyrosol + A23187, respectively. (H–J) Oleuropein + ConA, oleuropein + 48/80, and oleuropein + A23187, respectively. Polyphenol-treated cells show minimal degranulation. 5000×.
\n
Our biochemical results about β-hexosaminidase release are consistent with those obtained by light and electron microscopy.
\n
\n
\n
4. Conclusions
\n
In conclusion, our present findings reveal for the first time that hydroxytyrosol and oleuropein, the major phenolic compounds in olive, inhibit mast cell degranulation triggered by both immune and nonimmune pathways. Our discoveries also suggest that olive polyphenols, especially hydroxytyrosol and oleuropein, may provide insights to develop useful tools to prevent and treat mast cell-mediated disorders. These findings may be of interest to the immunopharmacology industry or could even lead in determining the ideal concentrations of each component in virgin olive oils to be able to label them as healthy oils. However, further tests are needed in order to generate hypoallergenic products via polyphenol treatment.
\n
\n
Acknowledgments
\n
I thankfully acknowledge Dr. Fabio A. Persia, Dra. María Laura Mariani, Ing. Norberto Domizio, and Ing. Elisa Bocanegra for their technical support. Funding for this work was provided by grants from the Consejo Nacional de Investigaciones Científicas y Técnicas (PIP-CONICET 5128) and Secretaría de Ciencia, Técnica y Postgrado de la Universidad Nacional de Cuyo (Research Proyects SeCTyP UNCuyo 06/J268-06/J395 and Research Program CS-453-2010), Argentina.
\n
Conflict of interest
The author declares no conflict of interest.
Abbreviations
ConA
concanavalin A
48/80
compound 48/80
A23187
calcium ionophore A23187
FcεRI
high-affinity receptor for IgE
IgE
immunoglobulin E
\n',keywords:"β-hexosaminidase, degranulation, hydroxytyrosol, mast cell, oleuropein, olive",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/66111.pdf",chapterXML:"https://mts.intechopen.com/source/xml/66111.xml",downloadPdfUrl:"/chapter/pdf-download/66111",previewPdfUrl:"/chapter/pdf-preview/66111",totalDownloads:503,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,dateSubmitted:"June 12th 2018",dateReviewed:"January 21st 2019",datePrePublished:"March 12th 2019",datePublished:"October 2nd 2019",dateFinished:null,readingETA:"0",abstract:"The purpose of this study is to establish if hydroxytyrosol and oleuropein, the most significant phenols found in olive oil and olives, can inhibit the activation of mast cells induced by immune and nonimmune pathways. Preincubation of purified peritoneal mast cells was carried out in the presence of hydroxytyrosol or oleuropein compounds and, prior to incubation, with concanavalin A, compound 48/80, or calcium ionophore A23187. Dose-response and time-dependence were studied. Comparative studies were performed using sodium cromoglycate, a well-known mast cell stabilizer. The supernatants and pellets were analyzed for β-hexosaminidase content via colorimetric reaction after incubation. The percentage of β-hexosaminidase obtained in each tube was measured and taken as a referent mast cell activation indicator. Other cell pellet samples were studied for cell viability, by means of the trypan blue exclusion method, or analyzed with light and electron microscopy. For the first time, biochemical and morphological results have shown that hydroxytyrosol and oleuropein inhibit degranulation of mast cells triggered by both immune and nonimmune causes. These findings suggest that olive phenols, specifically hydroxytyrosol and oleuropein, may be set the bases for developing practical tools not only to prevent and treat mast cell-mediated disorders but also to improve olive oil industrialization.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/66111",risUrl:"/chapter/ris/66111",book:{slug:"technological-innovation-in-the-olive-oil-production-chain"},signatures:"Alicia Beatriz Penissi",authors:[{id:"262694",title:"Dr.",name:"Alicia",middleName:null,surname:"Penissi",fullName:"Alicia Penissi",slug:"alicia-penissi",email:"apenissi@yahoo.com.ar",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Material and methods",level:"1"},{id:"sec_2_2",title:"2.1 Chemicals and reagents",level:"2"},{id:"sec_3_2",title:"2.2 Animals",level:"2"},{id:"sec_4_2",title:"2.3 Mast cell isolation and purification",level:"2"},{id:"sec_5_2",title:"2.4 General protocol",level:"2"},{id:"sec_6_2",title:"2.5 β-Hexosaminidase assay",level:"2"},{id:"sec_7_2",title:"2.6 Light microscopy and morphometry",level:"2"},{id:"sec_8_2",title:"2.7 Transmission electron microscopy",level:"2"},{id:"sec_9_2",title:"2.8 Statistical analysis",level:"2"},{id:"sec_11",title:"3. Results and discussion",level:"1"},{id:"sec_12",title:"4. Conclusions",level:"1"},{id:"sec_13",title:"Acknowledgments",level:"1"},{id:"sec_16",title:"Conflict of interest",level:"1"},{id:"sec_15",title:"Abbreviations",level:"1"}],chapterReferences:[{id:"B1",body:'Bischoff S. Role of mast cells in allergic and non-allergic immune responses: Comparison of human and murine data. Nature Immunology. 2007;7:93-104\n'},{id:"B2",body:'Galli S, Borregaard N, Wynn T. Phenotypic and functional plasticity of cells of innate immunity: Macrophages, mast cells and neutrophils. Nature Immunology. 2011;12:1035-1044\n'},{id:"B3",body:'Sur R, Cavender D, Malaviya R. Different approaches to study mast cell functions. International Immunopharmacology. 2007;7:555-567\n'},{id:"B4",body:'Kumar V, Sharma A. Mast cells: Emerging sentinel innate immune cells with diverse role in immunity. Molecular Immunology. 2010;48:14-25\n'},{id:"B5",body:'Kraneveld A, Sagar S, Garssen J, Folkerts G. The two faces of mast cells in food allergy and allergic asthma: The possible concept of Yin Yang. Biochimica et Biophysica Acta. 2011;1822:93-99\n'},{id:"B6",body:'Weller C, Collington S, Williams T, Lamb J. Mast cells in health and disease. Clinical Science. 2011;120:473-484\n'},{id:"B7",body:'Theoharides T, Alysandratos K, Angelidou A, Delivanis D, Sismanopoulos N, Zhang B, et al. Mast cells and inflammation. Biochimica et Biophysica Acta. 2012;1822:21-33\n'},{id:"B8",body:'Yamada P, Zarrouk M, Kawasaki K, Isoda H. Inhibitory effect of various Tunisian olive oils on chemical mediator release and cytokine production by basophilic cells. Journal of Ethnopharmacology. 2008;116:279-287\n'},{id:"B9",body:'Tore F, Tuncel N. Mast cells: Target and source of neuropeptides. Current Pharmaceutical Design. 2009;15:3433-3445\n'},{id:"B10",body:'Kalesnikoff J, Galli S. New developments in mast cell biology. Nature Immunology. 2008;9:1215-1223\n'},{id:"B11",body:'Middleton E, Kandaswami C, Theoharides C. The effects of plant flavonoids on mammalian cells: Implications for inflammation, heart disease, and cancer. Pharmacological Reviews. 2000;52:673-751\n'},{id:"B12",body:'Penissi A, Vera M, Mariani L, Rudolph M, Ceñal P, de Rosas C, et al. Novel anti-ulcer α,β-unsaturated lactones inhibit compound 48/80-induced mast cell degranulation. European Journal of Pharmacology. 2009;612:122-130\n'},{id:"B13",body:'Sakai S, Sugawara T, Matsubara K, Hirata T. Inhibitory effect of carotenoids on the degranulation of mast cells via suppression of antigen-induced aggregation of high affinity IgE receptor. The Journal of Biological Chemistry. 2009;284:28172-28179\n'},{id:"B14",body:'Vera M, Persia A, Mariani L, Fogal T, Ceñal P, Tonn C, et al. Activation of the human mast cell line LAD2 is modulated by dehydroleucodine and xanthatin. Leukemia Lymphoma. 2012;53:1795-1803\n'},{id:"B15",body:'Singh A, Holvoet S, Mercenier A. Dietary polyphenols in the prevention and treatment of allergic diseases. Clinical and Experimental Allergy. 2011;41:1346-1359\n'},{id:"B16",body:'Tripoli A, Giammanco M, Tabacchi G, Di Majo D, Giammanco S, La Guardia M. The phenolic compounds of olive oil: Structure, biological activity and beneficial effects on human health. Nutrition Research Reviews. 2005;18:98-112\n'},{id:"B17",body:'Jemai H, Bouaziz M, Fki I, El Feki A, Sayadi S. Hypolipidimic and antioxidant activities of oleuropein and its hydrolysis derivative-rich extracts from Chemlali olive leaves. Chemico-Biological Interactions. 2008;176:88-98\n'},{id:"B18",body:'Hagiwara K, Goto T, Araki M, Miyazaki H, Hagiwara H. Olive polyphenol hydroxytyrosol prevents bone loss. European Journal of Pharmacology. 2011;662:78-84\n'},{id:"B19",body:'Mousli M, Bronner C, Bueb JL, Tschirhart E, Gies JP, Landry Y. Activation of rat peritoneal mast cells by substance P and mastoparan. The Journal of Pharmacology and Experimental Therapeutics. 1989;250:329-335\n'},{id:"B20",body:'MacGlashan D, Guo C. Oscillations in free cytosolic calcium during IgE-mediated stimulation distinguish human basophils from human mast cells. Journal of Immunology. 1991;147:2259-2263\n'},{id:"B21",body:'Puri N, Roche P. Mast cells possess distinct secretory granule subsets whose exocytosis is regulated by different SNARE isoforms. Proceedings of the National Academy of Sciences of the United States of America. 2008;105:2580-2585\n'},{id:"B22",body:'Weng Z, Zhang B, Asadi S, Sismanopoulos N, Butcher A, Fu X, et al. Quercetin is more effective than cromolyn in blocking human mast cell cytokine release and inhibits contact dermatitis and photosensitivity in humans. PLoS One. 2012;7:1-10\n'},{id:"B23",body:'Maeda-Yamamoto M, Ema K, Monobe M, Tokuda Y, Tachibana H. Epicatechin-3-O-(3″-O-methyl)-gallate content in various tea cultivars (Camellia sinensis L.) and its in vitro inhibitory effect on histamine release. Journal of Agricultural and Food Chemistry. 2012;60:2165-2170\n'},{id:"B24",body:'Hirata F, Axelrod J, Crews F. Concanavalin A stimulates phospholipid methylation and phosphatidylserine decarboxylation in rat mast cells. Proceedings of the National Academy of Sciences of the United States of America. 1979;76:4813-4816\n'},{id:"B25",body:'Hosono H, Aoki J, Nagai Y, Bandoh K, Ishida M, Taguchi R, et al. Phosphatidylserine-specific phospholipase A1 stimulates histamine release from rat peritoneal mast cells through production of 2-acyl-1-lysophosphatidylserine. The Journal of Biological Chemistry. 2001;276:29664-29670\n'},{id:"B26",body:'Lopes F, Cavada S, Pinto P, Sampaio H, Gomes C. Differential effect of plant lectins on mast cells of different origins. Brazilian Journal of Medical and Biological Research. 2005;38:935-941\n'},{id:"B27",body:'Palomäki V, Laitinen T. The basic secretagogue compound 48/80 activates G proteins indirectly via stimulation of phospholipase D-lysophosphatidic acid receptor axis and 5-HT1A receptors in rat brain sections. British Journal of Pharmacology. 2006;147:596-606\n'},{id:"B28",body:'Wang H, Wang HS, Liu P. Agents that induce pseudo-allergic reaction. Drug Discoveries & Therapeutics. 2011;5:211-219\n'},{id:"B29",body:'Shin H, Kim J, An H, Park K, Kim M. Effect of disodium cromoglycate on mast cell-mediated immediate-type allergic reactions. Life Sciences. 2004;74:2877-2887\n'},{id:"B30",body:'Nishibori M, Saeki K. Disodium cromoglycate inhibition of substance P-induced histamine secretion is calcium dependent. Japanese Journal of Pharmacology. 1983;33:1255-1261\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Alicia Beatriz Penissi",address:"apenissi@yahoo.com.ar",affiliation:'
Facultad de Ciencias Médicas, Instituto de Histología y Embriología “Dr. Mario H. Burgos” (IHEM-CCT Mendoza-CONICET), Universidad Nacional de Cuyo, Argentina
Facultad de Ciencias Médicas, Instituto de Investigaciones, Universidad del Aconcagua - Mendoza, Argentina
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Panseri, P.A. Biondi, D. Vigo, R. Communod and L. M. 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Seeds",slug:"moisture-dependent-engineering-properties-of-chia-salvia-hispanica-l-seeds",signatures:"Estefanía N. Guiotto, Vanesa Y. Ixtaina, Mabel C. Tomás and Susana M. Nolasco",authors:[{id:"90337",title:"Dr.",name:"Mabel",middleName:null,surname:"Tomás",fullName:"Mabel Tomás",slug:"mabel-tomas"},{id:"161905",title:"Mrs.",name:"Estefanía",middleName:null,surname:"Guiotto",fullName:"Estefanía Guiotto",slug:"estefania-guiotto"},{id:"162170",title:"MSc.",name:"Susana M.",middleName:null,surname:"Nolasco",fullName:"Susana M. Nolasco",slug:"susana-m.-nolasco"},{id:"162174",title:"Dr.",name:"Vanesa Y.",middleName:null,surname:"Ixtaina",fullName:"Vanesa Y. Ixtaina",slug:"vanesa-y.-ixtaina"}]},{id:"41673",title:"Scale Up of Polygalacturonase Production by Solid State Fermentation Process",slug:"scale-up-of-polygalacturonase-production-by-solid-state-fermentation-process",signatures:"Siumara R. Alcântara, Nathalya J. Leite and Flávio L. H. da Silva",authors:[{id:"161388",title:"Dr.",name:"Siumara",middleName:"Rodrigues",surname:"Alcântara",fullName:"Siumara Alcântara",slug:"siumara-alcantara"},{id:"161811",title:"Ms.",name:"Nathalya Janne",middleName:null,surname:"Leite",fullName:"Nathalya Janne Leite",slug:"nathalya-janne-leite"},{id:"161812",title:"Dr.",name:"Flávio Luiz Honorato Da",middleName:null,surname:"Silva",fullName:"Flávio Luiz Honorato Da Silva",slug:"flavio-luiz-honorato-da-silva"}]},{id:"41676",title:"Effect of Mucilage Extraction on the Functional Properties of Chia Meals",slug:"effect-of-mucilage-extraction-on-the-functional-properties-of-chia-meals",signatures:"Marianela I. Capitani, Susana M. Nolasco and Mabel C. Tomás",authors:[{id:"90337",title:"Dr.",name:"Mabel",middleName:null,surname:"Tomás",fullName:"Mabel Tomás",slug:"mabel-tomas"},{id:"162170",title:"MSc.",name:"Susana M.",middleName:null,surname:"Nolasco",fullName:"Susana M. Nolasco",slug:"susana-m.-nolasco"},{id:"162169",title:"BSc.",name:"Marianlea",middleName:null,surname:"Capitani",fullName:"Marianlea Capitani",slug:"marianlea-capitani"}]},{id:"41677",title:"The Redesign of Processes’ Development in Food Production Organizations Using Quality Engineering Methods and Tools",slug:"the-redesign-of-processes-development-in-food-production-organizations-using-quality-engineering-met",signatures:"Slavko Arsovski, Miladin Stefanović, Danijela Tadić and Ivan Savovic",authors:[{id:"160558",title:"Dr.",name:"Slavko",middleName:null,surname:"Arsovski",fullName:"Slavko Arsovski",slug:"slavko-arsovski"},{id:"160561",title:"Prof.",name:"Miladin",middleName:null,surname:"Stefanovic",fullName:"Miladin Stefanovic",slug:"miladin-stefanovic"},{id:"160565",title:"Mr.",name:"Ivan",middleName:null,surname:"Savovic",fullName:"Ivan Savovic",slug:"ivan-savovic"},{id:"160566",title:"Prof.",name:"Danijela",middleName:null,surname:"Tadic",fullName:"Danijela Tadic",slug:"danijela-tadic"}]},{id:"41679",title:"Calculus Elements for Mechanical Presses in Oil Industry",slug:"calculus-elements-for-mechanical-presses-in-oil-industry",signatures:"Biris Sorin-Stefan, Mariana Ionescu, Gheorghe Voicu, Nicoleta Ungureanu and Valentin Vladut",authors:[{id:"81249",title:"Dr.",name:"Sorin-Stefan",middleName:"I",surname:"Biris",fullName:"Sorin-Stefan Biris",slug:"sorin-stefan-biris"}]},{id:"41681",title:"Gastrointestinal Immunoregulation and the Challenges of Nanotechnology in Foods",slug:"gastrointestinal-immunoregulation-and-the-challenges-of-nanotechnology-in-foods",signatures:"MaryAnn Principato",authors:[{id:"141482",title:"Dr.",name:"MaryAnn",middleName:null,surname:"Principato",fullName:"MaryAnn Principato",slug:"maryann-principato"}]},{id:"41685",title:"Yeast: World’s Finest Chef",slug:"yeast-world-s-finest-chef",signatures:"Fábio Faria-Oliveira, Sónia Puga and Célia Ferreira",authors:[{id:"87309",title:"Prof.",name:"Célia",middleName:null,surname:"Ferreira",fullName:"Célia Ferreira",slug:"celia-ferreira"},{id:"95616",title:"Dr.",name:"Fabio",middleName:"Luis Silva",surname:"Faria-Oliveira",fullName:"Fabio Faria-Oliveira",slug:"fabio-faria-oliveira"},{id:"168280",title:"Dr.",name:"Sonia",middleName:null,surname:"Puga",fullName:"Sonia Puga",slug:"sonia-puga"}]},{id:"42000",title:"Valorisation of Cheese Whey, a By-Product from the Dairy Industry",slug:"valorisation-of-cheese-whey-a-by-product-from-the-dairy-industry",signatures:"Chiara Mollea, Luca Marmo and Francesca Bosco",authors:[{id:"93865",title:"Dr.",name:"Francesca",middleName:null,surname:"Bosco",fullName:"Francesca Bosco",slug:"francesca-bosco"},{id:"96159",title:"Dr.",name:"Chiara",middleName:null,surname:"Mollea",fullName:"Chiara Mollea",slug:"chiara-mollea"},{id:"166295",title:"Prof.",name:"Luca",middleName:null,surname:"Marmo",fullName:"Luca Marmo",slug:"luca-marmo"}]},{id:"41686",title:"Antioxidant and Emulsifying Properties of Modified Sunflower Lecithin by Fractionation with Ethanol-Water Mixtures",slug:"antioxidant-and-emulsifying-properties-of-modified-sunflower-lecithin-by-fractionation-with-ethanol-",signatures:"Dario M. Cabezas, Estefanía N. Guiotto, Bernd W. K. Diehl and Mabel C. 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José, Regina F. P. M. Moreira, Danielle B. Luiz, Elaine Virmond, Aziza K. Genena, Silvia L. F. Andersen, Rennio F. de Sena and Horst Fr. Schröder",authors:[{id:"89295",title:"Dr.",name:"Regina",middleName:null,surname:"Moreira",fullName:"Regina Moreira",slug:"regina-moreira"},{id:"90077",title:"Dr.",name:"Danielle",middleName:"Bem",surname:"Luiz",fullName:"Danielle Luiz",slug:"danielle-luiz"},{id:"95710",title:"Dr.",name:"Humberto",middleName:null,surname:"José",fullName:"Humberto José",slug:"humberto-jose"},{id:"159376",title:"Dr.",name:"Elaine",middleName:null,surname:"Virmond",fullName:"Elaine Virmond",slug:"elaine-virmond"},{id:"162077",title:"Prof.",name:"Rennio",middleName:null,surname:"F. De Sena",fullName:"Rennio F. De Sena",slug:"rennio-f.-de-sena"},{id:"162078",title:"Dr.",name:"Aziza",middleName:null,surname:"K. Genena",fullName:"Aziza K. Genena",slug:"aziza-k.-genena"},{id:"162080",title:"Dr.",name:"Horst",middleName:null,surname:"Fr. Schröder",fullName:"Horst Fr. Schröder",slug:"horst-fr.-schroder"},{id:"166341",title:"Dr.",name:"Silvia",middleName:null,surname:"Andersen",fullName:"Silvia Andersen",slug:"silvia-andersen"}]},{id:"41694",title:"Seaweeds for Food and Industrial Applications",slug:"seaweeds-for-food-and-industrial-applications",signatures:"Berna Kılınç, Semra Cirik, Gamze Turan, Hatice Tekogul and Edis Koru",authors:[{id:"88972",title:"Dr.",name:"Edis",middleName:null,surname:"Koru",fullName:"Edis Koru",slug:"edis-koru"},{id:"161688",title:"Dr.",name:"Berna",middleName:null,surname:"Kılınç",fullName:"Berna Kılınç",slug:"berna-kilinc"}]}]}]},onlineFirst:{chapter:{type:"chapter",id:"61855",title:"Real-Time Action Recognition Using Multi-level Action Descriptor and DNN",doi:"10.5772/intechopen.76086",slug:"real-time-action-recognition-using-multi-level-action-descriptor-and-dnn",body:'
1. Introduction
Visual action recognition—the detection and classification of spatiotemporal patterns of human motion from videos—is a challenging task, which finds applications in a variety of domains including intelligent surveillance system [1], pedestrian intention recognition for advanced driver assistance system (ADAS) [2], and video-guided human behavior research [3]. For delivering complete description about human actions, this work proposes a multilevel action descriptor (Figure 1) to solve the existing representation problem of an action. For instance, traditional methods give the action representation of phoning for one person who is phoning while running and the same action descriptor for another person who is phoning while sitting. The action semantics for these two cases should be substantially different. The first difference is posture: one person is standing, and the other is sitting. The second difference is locomotion: one person is running, and the other is stationary. The proposed multilevel action descriptor consists of three levels: posture, locomotion, and gesture, which describe different categories of human subactions in a single action to address the above problem. Each level of subaction can be recognized by a corresponding convolutional neural network (CNN)-based classifier, which captures different appearance-based temporal features to represent a human subaction.
Figure 1.
Conventional action representation and multilevel action descriptor: (a) texting for three different cases, (b) smoking for two different cases, and (c) structure of the multilevel action descriptor.
Most of the existing works [4, 5] have focused on video-based action recognition (“Is there a certain action in the video?”) trying to classify the video clip as a whole via globally pooled features. This global feature pooling method works well, however, fails to consider the difference in the actions of multiple individuals that are present at the same time. For instance, one person in the video is texting and, besides him, another person is smoking. In our work, the problem of action detection in video surveillance is addressed as: “is there a certain action in the video, and where is it spatially and temporally?” The rationale behind the action detection strategy is partly inspired by the technique used in a recent paper [6], where the regions of action are located and then classified to improve the representational power and classification accuracy.
This work aims to develop a real-time action recognition system with localizing and recognizing actions for multiple persons at the same time. Many works have been studied to estimate human pose [7, 8, 9, 10] and analyze motion information [11] in real time. However, to the best of our knowledge, the real-time multilevel action descriptor was first introduced by the authors in [12] and this work is the extended version by adding two new actions, bicycling and phoning, and the evaluation of the processing time.
Figure 2 shows the overall scheme of the proposed real-time action recognition model. Through background modeling, motion-detection, human-detection, and multiple-object tracking, the appearance-based temporal features of the regions of interest (ROIs) are fed into the three CNNs, which make predictions using the shape, the motion history, and their combined cues. In the training phase, the ROIs and the multilevel action annotations are acquired manually in each frame of the training videos, and three appearance-based temporal features, namely—binary difference image (BDI), motion history image (MHI), and weighted average image (WAI)—are computed from the ROIs. Every level of the subaction has its own CNN classifier denoted as PostureNet, LocomotionNet, and GestureNet, respectively.
Figure 2.
Overall process of the proposed real-time multilevel action recognition model.
In the testing phase, the prediction of each CNN in the multi-CNN model corresponds to the decision in one subaction level. A motion saliency region is generated using a Gaussian mixture model (GMM) to eliminate regions that are not likely to contain the motion. This leads to a big reduction in the number of regions to be processed. The conventional sliding window-based scheme is used on the motion saliency region as a mask. In the sliding window, a human-detection histogram of oriented gradient (HOG) descriptor [13] with a latent support vector machine (SVM) [14] is used to detect an initial human action in the ROIs. Then, the regions undergo Kalman filtering-based refinement of the locations in the image plane. Given the refined action in the ROI, the shape, the motion history, and their combined cues are used with the aid of the CNNs to predict three subaction categories. Finally, the postprocessing stage checks for any conflicts in the structure of the subaction descriptor and applies temporal smoothing according to the previous action history of each individual for the purpose of noise reduction.
The main contributions of this work can be summarized as follows:
The multilevel action descriptor is presented for the real-time action recognition. The multilevel action descriptor consists of three levels. The combination of subaction from three levels can describe many different types of actions precisely. Furthermore, new subactions or action-levels can be easily incorporated into the multilevel action descriptor.
A real-time action recognition model is developed on the basis of appearance-based temporal features with a multi-CNN classifier. Presented in this study is a model for action recognition that simultaneously localizes and recognizes multiple actions of individuals with both low computational cost and high accuracy.
2. Related works
Motion energy image (MEI) and motion history image (MHI) [15, 16] are the most pervasive appearance-based temporal features. The advantage of these methods is that they are simple, fast, and efficient in controlled environments, for instance, when the background of the surveillance video (from a top-view camera) is always static. The fatal flaw in MHI is that it cannot capture interior motions—it can only capture human shapes [12]. In our work, a novel method for encoding these temporal features is proposed, and a study of how many appearance-based temporal features affect performance is provided. Other appearance-based temporal methods are the active shape model, the learned dynamic prior model, and the motion prior model. In addition, the motion is consistent and easily characterized by a definite space-time trajectory in some feature spaces. Based on visual tracking, some approaches use motion trajectories (e.g., generic and parametric optical flow) of predefined human regions or body interest points to recognize actions [17, 18].
Over the past few years, local spatiotemporal feature-based algorithms are the most popular ones for recognizing human actions. Laptev [19] proposed space-time interest point (STIP) by extending the 2D Harris corner to a 3D spatiotemporal domain. Kim et al. [20] introduced a multiway feature pooling approach that uses unsupervised clustering of segment-level HoG3D [21] features. Li et al. [22] extracted spatiotemporal features that are a subset of improved dense trajectory (IDT) features [5, 23], namely, histogram of flow (HoF), motion boundary histogram (MBH), MBHx, and MBHy, by removing camera motion to recognize egocentric actions. However, the disadvantage of the local spatiotemporal algorithms is that it is computationally expensive.
Some alternative methods for action recognition have been proposed. Vahdat et al. [23] developed a temporal model consisting of key poses for recognizing higher level activities. Lan et al. [24] introduced a structure for a latent variable framework that encodes contextual information. Jiang et al. [6] proposed a unified tree-based framework for action localization and recognition based on an HoF descriptor and a defined initial action segmentation mask. Lan et al. [25] introduced a multiskip feature-stacking method for enhancing the learnability of action representations. In addition, hidden Markov models (HMMs), dynamic Bayesian networks (DBNs), and dynamic time warping (DTP) are well-studied methods for speed variation in actions. However, actions cannot be reliably estimated in real-world environments using these methods.
Computing handcrafted features from raw video frames and learning classifiers on the basis of the obtained features are a basic two-step approach used in most of the existing methods. In real-world applications, the design of the feature and the choice of the feature are the most difficult and highly problem-dependent issues. Especially for human action recognition, different action categories may look dramatically different according to their appearances and motion patterns. Deep CNNs make some impressive results for the task of action classification [26, 27]. Karpathy et al. [28] trained a deep CNN using 1 million videos for action classification. Gkioxari and Malik [29] built action detection models that select candidate regions using CNNs and then classify them using SVM. Using two-stream deep CNNs with optical flow, Simonyan and Zisserman [30] achieved a result that is comparable to IDT [5]. Ji et al. [31] built a 3D CNN model that extracts appearance and motion features from both spatial and temporal dimensions in multiple adjacent frames.
3. Proposed model for human action recognition
3.1. Multilevel action descriptor
Intraclass variation in the action category is ambiguous, as shown in Figure 1(a) and (b). Although the actions of the three persons are texting in Figure 1(a), they can be distinguished from a deeper aspect: the first is texting while sitting, the second texting while standing and is stationary, and the third is texting while walking. Assigning the same action label (texting) is insufficient in video surveillance because they are of different states either in posture or in locomotion for the same action. This is the same problem for the action of smoking in Figure 1(b).
The proposed multilevel action descriptor is depicted in Figure 1(c), where the subactions shown in each level are just examples that have been studied in this work and can be easily expanded by adding new subactions. Each of the three action levels, posture, locomotion, and gesture, has a corresponding CNN, and the total three CNNs work simultaneously. The first network, PostureNet, operates on a static cue and captures the shape of the subject of the motion. The second network, LocomotionNet, operates on a motion cue and captures the history of the motion of the subject. And, the third network, GestureNet, operates on a combination of static and motion cues and captures the patterns of a subtle action by the subject. In this descriptor, three levels can be combined to represent many different types of actions with a large degree of freedom.
3.2. Tracking by detection
For real-time applications, a processing time of 20–30 ms for each frame, a stable bounding box for the human action region, and a low false detection rate are the important factors for human detection and tracking. Therefore, we adapt existing methods to provide a stable human action region for subsequent action recognition.
The sliding window is the bottleneck in the processing time of the object detection because many windows, in general, contain no object. To this end, motion detection is performed before object detection to discard regions that are void of motion. The size of the mini motion map is computed with the following equation:
sizemni-map=sizeoriginal− sizedetectionstride.E1
The default value of sizedetection is (64, 128) and that of stride is (8, 8) in HOG [12]. Figure 3 shows the mini motion map. For instance, if the size of the original image is 640 × 360, then the size of the mini motion map is 77 × 34.
Figure 3.
Mini motion map for reducing the unnecessary computation in the HOG-based human detector: (a) original image with a size of 640 × 360 and (b) mini motion map with a size of 77 × 34, which was calculated from the GMM-based motion detection.
In object tracking, three cases exist in the data association problem: (1) adding a new track, (2) updating an existing track, and (3) deleting a track [32]. The procedure for handling multiple detections and tracks is shown in Figure 4. When a new track is added, it starts to count the number of frames that the track has updated without detection. If the number is larger than the threshold nskip, the track is considered being disappeared and is therefore deleted.
Figure 4.
Procedure for multiple detections and tracks.
3.3. Appearance-based temporal features
Appearance-based temporal features are very simple, fast, and work effectively in controlled environments, such as in surveillance systems where the cameras are installed on rooftops or high poles. Therefore, the view angles of the cameras are toward dominant ground planes. A video F is just a real function of three variables:
F=f(x,y,t).E2
The frame coordinate (x, y) and t is the index of the video frame. In a multilevel action descriptor, each level has one independent CNN that obtains different appearance-based temporal features. The BDI encodes the static shape information of the subject, denoted as b(x, y, t), and is given by Eq. (3):
It calculates the difference between the current frame f(x, y, t) and the background frame f(x, y, t0) and compares with a threshold ξthr. Examples are given in Figure 5 where BDIs are utilized for the posture level of the subaction descriptor, for example, sitting and standing.
Figure 5.
Examples of BDI for different subactions.
In a motion history image, pixel intensity is a function of the temporal history of motion at that point. MHI captures the motion history patterns of the actor, denoted as h(x, y, t), and is defined using a simple replacement and a decay operator in Eqs. (4)–(6) [14].
MHI is calculated from the difference between the current frame f(x, y, t) and the previous frame f(x, y, t-1) in Eq. (4). MHI is a vector image of motion, where more recently moving regions are brighter (see Figure 6). MHIs are used for the locomotion level of the multilevel action descriptor, which comprises stationary, walking, running, and bicycling. MHI captures the motion history cue of the subject, where more recently moving pixel regions are brighter. In Eq. (6), hyperparameter n is critical in defining the temporal range of an action. An MHI with a large n covers a long range of action history; however, it is insensitive to current actions. Similarly, MHI with a small n puts the focus on the recent actions and ignores past actions. Hence, choosing a good n can be fairly difficult.
Figure 6.
Examples of MHI for different subactions.
Weighted average images (WAIs) are applied at the gesture level of the multilevel action descriptor, which comprises nothing, texting, smoking, phoning, and others. For recognizing subtle actions, the easiest way would be to use the shape or motion history of the actor. It is constructed as a linear combination of BDI and MHI, which is given by Eq. (7):
s(x,y,t)=w1⋅b(x,y,t)+w2⋅h(x,y,t),s.t.w1+w2=1.E7
Here, w = {w1, w2}T is another hyperparameter. Figure 7 shows some examples of WAI for different subactions. WAIs were applied at the gesture level of the subaction descriptor, which comprises nothing, texting, smoking, phoning, and others. WAI obtained the combined cues of the shape and the motion history. Texting (frequently moving fingers) and smoking (repeated hand-to-mouth motion) were captured in WAIs.
Figure 7.
Examples of WAI for different subactions.
3.4. Multi-CNN action classifier
In order to reduce the computation time, a lightweight CNN architecture is devised for real-time human action recognition, as shown in Figure 8. The architectures of PostureNet, LocomotionNet, and GestureNet are identical with two convolutional layers, two subsampling layers, two fully connected layers, and one softmax regression layer. However, they need to be trained based on the different training data of multilevel action descriptor. The architecture of the network is as follows: Input-Convolution-ReLUs-Max pooling-Convolution-ReLUs-Max pooling-Fully connection-Dropout-Fully connection-Dropout-Fully connection-Softmax regression. The output layer consists of the same number of units as the number of subactions at the corresponding level of the descriptor. If the computational efficiency is not critical, one could use more complicated architectures [33, 34]. In our study, Adam optimizer [35] is used with a learning rate of 1e−3 and β1 = 0.5 with a batch size of 256 examples and a weight decay of 5e−4. The networks are trained for 1K iterations [36].
Figure 8.
Architecture of a CNN.
4. Experimental results
In this section, an ablation study of the appearance temporal features with the CNN-based approach is presented, and the results of the action recognition are shown with the ICVL dataset. The average processing time was computed based on the ICVL test videos. The experimental results showed that appearance-based temporal features with a multi-CNN classifier effectively recognize actions in surveillance videos.
4.1. Evaluation metrics
To quantify the results, we use the average precision at the frame-based frame-AP. The frame-AP was used in other approaches at the frame-based evaluation. Frame-AP: recognition is correct if the intersection-over-union (IOU) with the ground truth and detection area at that frame is greater than σ(σ = 0.5), and the action label is correctly predicted.
4.2. Action recognition on ICVL dataset
LocomotionNet encodes sequential frames as memory capacity to represent actions. However, deciding the number of frames n in Eq. (6) is a highly action-dependent issue. In this work, the number of frames in the MHI was defined by performing a grid search from 5 to 50 frames with an interval of 5. Figure 9 plots the classification accuracy (mAP) at the frame-based measurement for the subactions at the locomotion level of the multilevel action descriptor. The gray circles are drawn while training LocomotionNet from 100 to 1K iterations with an interval of 100. The circles lie over a 1.96 standard error of the mean and standard deviation in white. The baseline accuracy at n = 10 is given by encoding the temporal features. With n equal to 25 frames, LocomotionNet was able to get a performance boost from 1 to 2% of the mAP. This evidence indicates that correctly recognizing one action would need approximately 2 s (15 fps in the ICVL videos).
Figure 9.
Memory capacity in MHI for the locomotion level of the multilevel action descriptor.
Table 1 shows the results of each temporal feature with CNN. An ablation study of the proposed approach at the gesture level is presented by evaluating the performance of the two appearance-based temporal features, BDI and MHI, and their combination. Frame-AP is reported for PostureNet, LocomotionNet, and GestureNet. The leading scores of each label are displayed in bold font. As in Eq. (7), WAI is the weighted average of BDI and MHI. GestureNet performed significantly better than PostureNet and LocomotionNet, showing the significance of the combined cues for the task of gesture-level subaction recognition. The GestureNet combines the static and motion history cues to capture specific patterns of the action.
Frame-AP (%)
Nothing
Texting
Smoking
Phoning
mAP
PostureNet
51.3
42.0
11.2
37.9
35.6
LocomotionNet
62.4
53.5
14.7
49.2
45.0
GestureNet
53.4
83.3
26.7
57.9
55.3
Table 1.
Results of the ablation study on the gesture level of ICVL dataset.
Figure 10 shows the mAP across subactions at the gesture level of the multilevel action descriptor at the frame-based measurement with regard to varying weights on WAI and training iterations of the GestureNet. In the experiment, w1 = 0.6 and w2 = 0.4 show a significant improvement beyond w1 = 0.5 and w2 = 0.5. This implies that the shape cue is more important than the motion history cue in WAI and is quite different from the results in Table 1. One possible explanation for this finding is that the motion history cue is more informative than the shape cue if they are used individually. For the remainder of the experimental results, w1 = 0.6 and w2 = 0.4 were used in WAI.
Figure 10.
Recognition results with regard to varying weights of WAI and training iterations on GestureNet.
To evaluate the effectiveness of the action-recognition model, we included the full confusion matrixes as a source of additional insight. Figure 11 shows that the proposed approach achieved an mAP of 73.2% at the frame-based measurement. The horizontal rows are the ground truth, and the vertical columns are the predictions. Each row was normalized to a sum of 1. The proposed method was able to get most of the subaction categories correct, except for smoking. The results of the experiment show that a multilevel action descriptor can eliminate many misclassifications by dividing one action into many subactions that are not at the same levels.
Figure 11.
Confusion matrixes of the ICVL dataset at the frame-based measurement for the action-recognition task when using appearance-based temporal features with a multi-CNN classifier.
Figure 12 shows qualitative localization and recognition results using the proposed approach on the test set of the ICVL dataset. Each block corresponds to a video from a different camera. Two frames are shown from each video. The test platform has a PC with an Intel Core i7-4770 CPU at 3.49 GHz with 32 GB memory. The input video was resized to 640 × 480, and the processing time was tested on 72 videos shown in Table 2.
Figure 12.
Examples of action localization and recognition results from the ICVL dataset.
Module
Motion
Detection
Tracking
BDI
MHI
WAI
CNNs
Others
Overall
Processing time (ms)
11.33
11.60
0.28
0.26
0.83
0.12
4.66
12.16
42.93
Table 2.
Average processing time of the proposed action detection model.
5. Conclusions
This work introduced a new approach to real-time action recognition using multilevel action descriptor in video surveillance system. Experimental results demonstrated that a multilevel action descriptor delivers a complete set of information about human actions and significantly eliminates misclassifications by a large number of actions that are built by few independent subactions at different levels. An ablation study showed the effect of each temporal feature when considered separately. Shape and motion history cues are complementary, and the combination of both leads to a significant improvement in action recognition performance. In addition, the proposed action recognition model simultaneously localizes and recognizes the actions of multiple individuals at low computational cost with acceptable accuracy. The model ran at around 25 fps in 640 × 480 frame size, which is suitable for real-time surveillance applications. In future work, we will extend the approach to learn deep motion flow from original frame sequences and combine detecting and recognizing in one network for becoming an end-to-end human action detection framework.
Acknowledgments
This work was supported by the Industrial Technology Innovation Program, “10052982, Development of multiangle front camera system for intersection AEB,” funded by the Ministry of Trade, Industry, & Energy (MOTIE, Korea).
\n',keywords:"multilevel action descriptor, action recognition, video surveillance, deep neural networks",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/61855.pdf",chapterXML:"https://mts.intechopen.com/source/xml/61855.xml",downloadPdfUrl:"/chapter/pdf-download/61855",previewPdfUrl:"/chapter/pdf-preview/61855",totalDownloads:681,totalViews:377,totalCrossrefCites:0,dateSubmitted:"October 31st 2017",dateReviewed:"March 1st 2018",datePrePublished:"November 5th 2018",datePublished:"March 13th 2019",dateFinished:null,readingETA:"0",abstract:"This work presents a novel approach to the problem of real-time human action recognition in intelligent video surveillance. For more efficient and precise labeling of an action, this work proposes a multilevel action descriptor, which delivers complete information of human actions. The action descriptor consists of three levels: posture, locomotion, and gesture level; each of which corresponds to a different group of subactions describing a single human action, for example, smoking while walking. The proposed action recognition method is able to localize and recognize simultaneously the actions of multiple individuals using appearance-based temporal features with multiple convolutional neural networks (CNN). Although appearance cues have been successfully exploited for visual recognition problems, appearance, motion history, and their combined cues with multi-CNNs have not yet been explored. Additionally, the first systematic estimation of several hyperparameters for shape and motion history cues is investigated. The proposed approach achieves a mean average precision (mAP) of 73.2% in the frame-based evaluation over the newly collected large-scale ICVL video dataset. The action recognition model can run at around 25 frames per second, which is suitable for real-time surveillance applications.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/61855",risUrl:"/chapter/ris/61855",signatures:"Cheng-Bin Jin, Trung Dung Do, Mingjie Liu and Hakil Kim",book:{id:"6571",title:"Intelligent Video Surveillance",subtitle:null,fullTitle:"Intelligent Video Surveillance",slug:"intelligent-video-surveillance",publishedDate:"March 13th 2019",bookSignature:"António J. R. Neves",coverURL:"https://cdn.intechopen.com/books/images_new/6571.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"1177",title:"Prof.",name:"Antonio",middleName:"J. R.",surname:"Neves",slug:"antonio-neves",fullName:"Antonio Neves"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"231409",title:"Prof.",name:"Hakil",middleName:null,surname:"Kim",fullName:"Hakil Kim",slug:"hakil-kim",email:"hikim@inha.ac.kr",position:null,institution:null},{id:"231419",title:"MSc.",name:"Chengbin",middleName:null,surname:"Jin",fullName:"Chengbin Jin",slug:"chengbin-jin",email:"chengbinjin@inha.edu",position:null,institution:null},{id:"240573",title:"MSc.",name:"Trung-Dung",middleName:null,surname:"Do",fullName:"Trung-Dung Do",slug:"trung-dung-do",email:"dotd@inha.edu",position:null,institution:null},{id:"240574",title:"MSc.",name:"Mingjie",middleName:null,surname:"Liu",fullName:"Mingjie Liu",slug:"mingjie-liu",email:"liumj@inha.edu",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related works",level:"1"},{id:"sec_3",title:"3. Proposed model for human action recognition",level:"1"},{id:"sec_3_2",title:"3.1. Multilevel action descriptor",level:"2"},{id:"sec_4_2",title:"3.2. Tracking by detection",level:"2"},{id:"sec_5_2",title:"3.3. Appearance-based temporal features",level:"2"},{id:"sec_6_2",title:"3.4. Multi-CNN action classifier",level:"2"},{id:"sec_8",title:"4. Experimental results",level:"1"},{id:"sec_8_2",title:"4.1. Evaluation metrics",level:"2"},{id:"sec_9_2",title:"4.2. Action recognition on ICVL dataset",level:"2"},{id:"sec_11",title:"5. Conclusions",level:"1"},{id:"sec_12",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Yamin H, Peng Z, Zhuo T, et al. Going deeper with two-stream ConvNets for action recognition in video surveillance. Pattern Recognition Letters. 2017 (available online). DOI: 10.1016/j.patrec.2017.08.015'},{id:"B2",body:'Andreas S, Rainer S. Pedestrian intention recognition using latent-dynamic conditional random fields. 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