**2. Role of cell adhesion molecules (CAMs) in proliferation of oral cancer**

CAMs are typically single-pass transmembrane receptors [17] compounded of three stored domains: an intracellular domain that interacts with a transmembrane domain, an extracellular domain, and the cytoskeleton. These domains can interact in several different ways [18]. The first way is through homophilic binding, where the same CAMs bind to each other. They are also capable of heterophilic binding, which means a CAM on one cell will bind to different CAMs on another cell. The third type of binding is that between cells and substrate, where a mutual extracellular ligand binds two different CAMs. There are four major superfamilies, or groups, of CAMs: the immunoglobulin super family of cell adhesion molecules (IgCAMs), the cadherins, the integrins, and the c-type lectin-like domain proteins (CTLDs). Proteoglycans are also believed to be a class of CAMs. One classification system involves distinction between calcium-dependent and calcium-independent CAMs [19]. The Ig-superfamily CAMs and integrins are not dependent on calcium ions, whereas selectins and cadherins are calcium-dependent. In addition, integrins participate in cell-matrix interactions, while other CAM families play some important roles in cell-to-cell interactions [20].

#### **2.1. Neural cell adhesion molecule (NCAM)**

With the rapid global population increase and aging, the rising significance of cancer as a leading cause of death is partly correlated with a marked decline in mortality rates due to stroke and coronary heart disease in many countries. In addition, tobacco and alcohol consumption are known to be etiologically associated with carcinogenesis. Cancer is both a genetic and progressively systemic disease. In all types of cancer, somatic cells begin to divide uncontrollably and spread into surrounding tissues. Cancer cells can arise almost anywhere in the human body. Normal cells grow and divide according to the body's needs, and when they age or become damaged, they die and are replaced by new cells. However, when cancer arises, this orderly process breaks down: as cellular abnormalities increase, old or damaged cells survive rather than being removed, and new cells form when they are not needed. These extra cells can divide uncontrollably and may form malignant tumors. Although many cancers form solid tumors composed of masses of tissue, cancers of the blood, such as leukemias, generally do not form solid tumors [2–4]. Head and neck cancer is a common neoplasm that encompasses epithelial malignant tumors of the nasopharynx, larynx, and mouth, representing about 6% of all cases and accounting for an estimated over 650,000 new cancer cases and over 350,000 cancer-related deaths worldwide every year [5, 6].  Oral cancers comprise two categories: those affecting the oral cavity (lips, inner lips, cheeks, teeth, gums, the anterior two-thirds of the tongue, the floor, and palate) and those affecting the oropharynx (middle region of the throat, including the tonsils and base of the tongue). Such cancers may arise in any location, although there are certain areas that are affected more frequently, such as the tongue and gingiva. These areas represent about 90% of all malignancies of the oral cavity [7, 8]. However, despite significant advances in surgery and chemotherapy over the last few decades [9], oral cancer is still characterized by a poor prognosis and a low

84 Prevention, Detection and Management of Oral Cancer

survival rate [10]. The 5-year survival rate of those diagnosed is ~ 60%.

lance [14–16].

on its diagnosis and treatments.

In patients diagnosed with oral cancers at an advanced stage, there is a high incidence of metastasis to surrounding tissues, lymph nodes, and distant organs [5]. Metastasis is recognized as a process, whereby genetic instability in the primary tumor accelerates cell heterogeneity, allowing a few metastatic clones to eventually emerge and be positively selected to disseminate cancer at a distance [11]. This is the most annihilating stage of malignancy and the leading cause of cancer-related death. In metastasis, cancer cells break away from the primary cancer, travel through the blood or lymph system, and form new metastatic tumors elsewhere in the body. Each metastatic tumor is the same type of cancer as the primary tumor [12, 13], and the cells of each resemble each other upon microscopic observation. Moreover, they usually share common molecular features, such as the presence of specific genetic changes. It is clear that only a minority of malignant cells participate in the process of metastasis, due to interaction with host tissues and the intrinsic characteristics of the cancer cells themselves; thus, metastasis may imply an escape of these cells from the hostile environment they have created, characterized by features such as hypoxia, inflammation, and immunological surveil-

This chapter details recent findings on molecular markers that are involved in the mechanisms of proliferation and energy metabolism of oral cancer and provides new perspectives Neural cell adhesion molecule (NCAM) is a family of cell surface glycoproteins playing an important role in the development of the nervous system, fasciculation, axonal outgrowth, regulation of cell migration, and branching [21]. NCAM has several isoforms derived from alternative splicing of a single gene [22–24]. In particular, the three major isoforms with molecular weights of 120, 140, and 180 kDa have similar extracellular parts but differ in the disposition of their domains, which are cytoplasmic for the two larger polypeptides [25, 26]. Furthermore, the expression of NCAM is upregulated by transforming growth factor (TGF)-β1 [27–29]. Although NCAM was initially considered to exist only in neural tissue, it has since been observed in the human kidney, lung, fetal muscle, and colon, as well as in elements of the hemopoietic system. Furthermore, it has been described that NCAM is expressed by a variety of human tumors and associated with perineural invasion by various neoplasms, such as gallbladder cancer, melanoma, bile duct cancer, and adenoid cystic carcinoma of the head and neck [30–35]. We have also demonstrated previously that NCAM is sporadically found in the adenoid cystic carcinoma, derived from human submandibular salivary gland, *in vivo* [36]. NCAM is believed to mediate adhesion between cells through a calcium ion-independent homophilic (NCAM-NCAM) binding mechanism and to mediate adhesion between neurons and the extracellular matrix through heterophilic binding (NCAM to another ligand or counter-receptor) [21]. It has been described that exogenously added NCAM can inhibit the proliferation of cultured neonatal astrocytes and of astrocytes responding to a penetrating lesion in the adult rat brain, *in vivo* [37, 38], suggesting that these effects are mediated by homophilic binding to NCAM on the astrocyte membrane.

Adenoid cystic carcinoma (ACC) is a well-known and typical malignant salivary gland tumor. ACCs are biologically aggressive and can bring metastases even when many years have passed after excision of the primary tumor. Facial paralysis is especially frequent, causing perineural and/or neural invasion. We have attempted to examine the role of NCAM by investigating the effect of anti-NCAM antibody (MAb NCAM) and TGF-β1 in human salivary gland tumor cells. The expression and distribution of NCAM were also investigated in ACC tissues. We further found that apoptotic cell death was induced via a DNA damage signal through the mitochondria, inducing release of cytochrome *c* into the cytoplasm of salivary gland tumor cells [36]. However, MAb NCAM had no effect on human oral squamous cell carcinoma (HOSCC) cell lines, which do not express NCAM.As shown in **Figure 1**, these results indicate that the effect of MAb NCAM is specific to NCAM-expressing tumor cells, such as human salivary gland tumor cells; furthermore, blocking the ability of NCAM through MAb NCAM, as well as the homophilic (NCAM-NCAM) binding mechanism, rather than regulating a signaling pathway of cell proliferation, may in fact induce a negative signal such as apoptosis in human salivary gland tumor cells. In addition, homophilic (NCAM-NCAM) binding may activate multiple signaling pathways that differ among cell types. In view of the fact that NCAM expression on human salivary gland tumor cells is upregulated by TGF-β1, it can be hypothesized that a further homophilic (NCAM-NCAM) binding mechanism may be activated and that consequently the proliferative activity of HSG cells may also be upregulated by TGF-β1-mediated NCAM activity (**Figure 2**).

On the other hand, cimetidine, the most studied histamine type-2 receptor (H2R) antagonist used clinically, is commonly prescribed to treat gastroesophageal reflux disease as well as

gastric and duodenal ulcers [39]. Cimetidine has recently been shown to possess antitumor activity against gastric, kidney, and colon cancers, as well as melanomas [40–43]. A recent study has suggested that this behavior of cimetidine is mediated through three different effects: a direct inhibitory effect on tumor growth by blocking the cell growth activity of histamine via activation of H2 receptors and an indirect effect involving inhibition of tumorassociated angiogenesis, an immunomodulatory effect through augmentation of the host's immune response to tumor cells, and an inhibitory effect on cancer cell migration and adhesion to endothelial cells, thus inhibiting tumor angiogenesis and metastasis [44]. We have also examined the NCAM-associated impact of cimetidine on tumor growth and perineural/ neural invasion in salivary gland tumors using an in vitro cell culture system and an *in vivo* nude mouse cancer model. These experiments clearly indicated that cimetidine effectively downregulated the expression of NCAM by inhibiting NF-κB transactivation, subsequently blocking salivary gland tumor cell adhesion to neural cells, and ultimately inducing apoptosis in salivary gland tumor cells, thus preventing the growth of salivary gland tumor masses in nude mice [45]. Although malignant glandular tumors are commonly known to be resistant to chemotherapy and/or radiation, the clinical application of cimetidine as an anticancer drug might provide an integral part of future therapeutic strategies against NCAM-expressing

**Figure 2.** Schematic representation of TGF-β1-induced upregulation of NCAM expression and proliferative activity

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involved in homophilic (NCAM-NCAM) binding mechanism in NCAM-expressing cells.

tumors such as adenoid cystic carcinoma.

**Figure 1.** Schematic representation of MAb NCAM-induced apoptotic signal transduction pathways via the DNA damage signal through the mitochondria involved in apaf-1 and caspase activation in NCAM-expressing cells.

The Mechanisms of Proliferation and Energy Metabolism in Oral Cancer http://dx.doi.org/10.5772/intechopen.87091 87

after excision of the primary tumor. Facial paralysis is especially frequent, causing perineural and/or neural invasion. We have attempted to examine the role of NCAM by investigating the effect of anti-NCAM antibody (MAb NCAM) and TGF-β1 in human salivary gland tumor cells. The expression and distribution of NCAM were also investigated in ACC tissues. We further found that apoptotic cell death was induced via a DNA damage signal through the mitochondria, inducing release of cytochrome *c* into the cytoplasm of salivary gland tumor cells [36]. However, MAb NCAM had no effect on human oral squamous cell carcinoma (HOSCC) cell lines, which do not express NCAM.As shown in **Figure 1**, these results indicate that the effect of MAb NCAM is specific to NCAM-expressing tumor cells, such as human salivary gland tumor cells; furthermore, blocking the ability of NCAM through MAb NCAM, as well as the homophilic (NCAM-NCAM) binding mechanism, rather than regulating a signaling pathway of cell proliferation, may in fact induce a negative signal such as apoptosis in human salivary gland tumor cells. In addition, homophilic (NCAM-NCAM) binding may activate multiple signaling pathways that differ among cell types. In view of the fact that NCAM expression on human salivary gland tumor cells is upregulated by TGF-β1, it can be hypothesized that a further homophilic (NCAM-NCAM) binding mechanism may be activated and that consequently the proliferative activity of

86 Prevention, Detection and Management of Oral Cancer

HSG cells may also be upregulated by TGF-β1-mediated NCAM activity (**Figure 2**).

On the other hand, cimetidine, the most studied histamine type-2 receptor (H2R) antagonist used clinically, is commonly prescribed to treat gastroesophageal reflux disease as well as

**Figure 1.** Schematic representation of MAb NCAM-induced apoptotic signal transduction pathways via the DNA damage signal through the mitochondria involved in apaf-1 and caspase activation in NCAM-expressing cells.

**Figure 2.** Schematic representation of TGF-β1-induced upregulation of NCAM expression and proliferative activity involved in homophilic (NCAM-NCAM) binding mechanism in NCAM-expressing cells.

gastric and duodenal ulcers [39]. Cimetidine has recently been shown to possess antitumor activity against gastric, kidney, and colon cancers, as well as melanomas [40–43]. A recent study has suggested that this behavior of cimetidine is mediated through three different effects: a direct inhibitory effect on tumor growth by blocking the cell growth activity of histamine via activation of H2 receptors and an indirect effect involving inhibition of tumorassociated angiogenesis, an immunomodulatory effect through augmentation of the host's immune response to tumor cells, and an inhibitory effect on cancer cell migration and adhesion to endothelial cells, thus inhibiting tumor angiogenesis and metastasis [44]. We have also examined the NCAM-associated impact of cimetidine on tumor growth and perineural/ neural invasion in salivary gland tumors using an in vitro cell culture system and an *in vivo* nude mouse cancer model. These experiments clearly indicated that cimetidine effectively downregulated the expression of NCAM by inhibiting NF-κB transactivation, subsequently blocking salivary gland tumor cell adhesion to neural cells, and ultimately inducing apoptosis in salivary gland tumor cells, thus preventing the growth of salivary gland tumor masses in nude mice [45]. Although malignant glandular tumors are commonly known to be resistant to chemotherapy and/or radiation, the clinical application of cimetidine as an anticancer drug might provide an integral part of future therapeutic strategies against NCAM-expressing tumors such as adenoid cystic carcinoma.

Finally, it was suggested that NCAM might be associated with not only a cell-to-cell adhesion mechanism but also tumorigenesis, including the occurrence, development, and perineural/ neural invasion of human salivary gland tumors.

Further studies will be required to identify the signal transduction pathways by which treatment with cimetidine suppresses the growth of salivary gland tumors and to establish a strategy for cimetidine-based therapy for those tumors.

### **2.2. Coxsackievirus and adenovirus receptor (CAR/CXADR)**

Coxsackievirus and adenovirus receptor (CAR/CXADR), a transmembrane glycoprotein, was initially characterized as a viral attachment site on the surface of epithelial cells (**Figure 3**) [46]. Later it was identified as a component of the tight junction (TJ) complex, an interacting partner for a number of other TJ proteins and a regulator of TJ formation [47–52]. Furthermore, CAR is known to be a cell-cell adhesion molecule [53, 54]. In terms of function, loss of CAR has been considered to diminish intercellular adhesion, increase proliferation, and promote the migration as well as invasion of cancer cells [55, 56]. On the basis of these observations, a tumor-suppressive role of CAR in human cancers has speculated. Although it has recently been described [55–58] that CAR is observed in various organs, it is still unclear whether it is expressed in oral cancer. Therefore, we examined the role of CAR in SCC in the oral cavity (data not shown). This revealed that CAR was constitutively expressed in five oral SCC cell lines. To analyze the function of CAR, we then examined the proliferative activity of SAS cells

after *CAR* gene knockdown. However, *CAR* knockdown did not promote the proliferative activity of SAS cells. Although the expression level of CAR was decreased by *CAR* knockdown, that of NF-κB p65 (RelA) showed little change. Furthermore, SAS cell numbers were notably reduced by CAR overexpression. Finally, it was suggested that the overexpression of CAR in SAS cells led to apoptosis via activation of caspase-9. In addition, the localizations of CAR and RelA in 40 samples of HOSCC at various stages were investigated using immunohistochemistry. A positive reaction for polyclonal antibody (PAb) CAR was weakly observed on the membrane of carcinoma cells in 19 of 40 cases (47.5%) of HOSCC. The immunoreactivity for CAR further tended to fade at the invasive front of oral SCC (**Figure 4**). In the meantime, RelA immunoreactivity was strongly positive, particularly on the nucleus of carcinoma cells at the invasive front, in 30 of 40 cases (75%) of HOSCC. These observations suggest that CAR

**Figure 4.** (A) The positive reaction for PAb CAR was observed on the membrane of tumor cells in 19 of 40 cases (47.5%) of SCCs. (B) NF-κB immunoreactivity were clearly detected in 30 of 40 cases (100%) in the HOSCC tissues. The immunoreactivity for CAR especially tended to fade away in the invasive front of oral SCC tissues. NF-kappaB (+)/CAR

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Cytokines are composed of a large family of secreted proteins that bind to and signal through defined cell surface receptors on a wide variety of target cells, playing an important role in

plays a significant role in the inhibition of oral cancer cell growth.

(−), 15/21 cases (71.4%).

**3. Role of cytokines in oral cancer cell proliferation**

**Figure 3.** Schematic representation of coxsackievirus and adenovirus receptor on the tumor cells.

**Figure 4.** (A) The positive reaction for PAb CAR was observed on the membrane of tumor cells in 19 of 40 cases (47.5%) of SCCs. (B) NF-κB immunoreactivity were clearly detected in 30 of 40 cases (100%) in the HOSCC tissues. The immunoreactivity for CAR especially tended to fade away in the invasive front of oral SCC tissues. NF-kappaB (+)/CAR (−), 15/21 cases (71.4%).

after *CAR* gene knockdown. However, *CAR* knockdown did not promote the proliferative activity of SAS cells. Although the expression level of CAR was decreased by *CAR* knockdown, that of NF-κB p65 (RelA) showed little change. Furthermore, SAS cell numbers were notably reduced by CAR overexpression. Finally, it was suggested that the overexpression of CAR in SAS cells led to apoptosis via activation of caspase-9. In addition, the localizations of CAR and RelA in 40 samples of HOSCC at various stages were investigated using immunohistochemistry. A positive reaction for polyclonal antibody (PAb) CAR was weakly observed on the membrane of carcinoma cells in 19 of 40 cases (47.5%) of HOSCC. The immunoreactivity for CAR further tended to fade at the invasive front of oral SCC (**Figure 4**). In the meantime, RelA immunoreactivity was strongly positive, particularly on the nucleus of carcinoma cells at the invasive front, in 30 of 40 cases (75%) of HOSCC. These observations suggest that CAR plays a significant role in the inhibition of oral cancer cell growth.


*<IP_ADDRESS> Calculation of T0*

The sample consists of only one layer (**Figure 1**) of thermal conductivity ks, thermal diffusivity Ds and thickness ls. It is fixed at backing of kb, Db, lb respectively thermal conductivity, thermal diffusivity and thickness. The sample and backing are in a fluid (air) of thermal conductivity kf, thermal diffusivity Df and thickness lf. To determine

We see that the normalized amplitude and the phase of the PTD signal are insensitive to variations of k (the three curves are coincident), which means that in this

**Figure 3** represents the theoretical variations normalized amplitude and phase curves for different values of thermal diffusivity D. These curves show a remarkable sensitivity of the PTD signal to variations in thermal diffusivity, which allows

The sample is composed of a layer deposed on a substrate (**Figure 4**). Fernelius [10] was developed the theoretical model of a layer deposed on a substrate by writing the heat equations in the four medium: fluid (Kf, Df, lf), sample (Ks, Ds, ls), black layer (Kc, Dc, lc) and backing (Kb, Db, lb). Assuming that all the light is absorbed only

by the black layer, the heat equations in the different media are given by:

*∂t if* 0≤ *z*≤*l f*

*αc z* 1 þ *e jωt if* � *lc* ≤*z*≤0

*∂t if* � *lc* � *ls* ≤*z*≤ � *ls*

*Theoretical variation of the normalized amplitude (a) and phase (b) of the photothermal signal with the*

*square root of modulation frequency for different values of thermal diffusivity.*

*∂t if* � *lc* � *ls* � *lb* ≤*z*≤ � *lc* � *ls*

(6)

case we cannot determine the thermal conductivity of the sample.

determining thermal diffusivity with great precision.

*Improvement of the Thermal Properties of Sorel Cements DOI: http://dx.doi.org/10.5772/intechopen.91774*

*3.2.2 Sample composed (layer deposed on a substrate)*

*∂T f*

*∂Tc ∂t* � *Ac e*

*∂Ts*

*∂Tb*

*<IP_ADDRESS> Calculation of T0*

*∂*2 *T f ∂z*2 ¼ 1 *Df*

*∂*2 *Tc ∂z*2 ¼ 1 *Dc*

*∂*2 *Ts ∂z*2 ¼ 1 *Ds*

*∂*2 *Tb ∂z*2 ¼ 1 *Db*

**Figure 3.**

**Figure 4.** *Different medium.*

**127**

#### **Figure 1.** *Different medium browsed by the heat.*

the periodic temperature T0, we solve the one-dimensional heat diffusion equation in the different media backing, sample and fluid. Assuming that only the sample is absorbing the pump light beam, the heat equations in the different media are given by:

$$\begin{aligned} \frac{\partial^2 T\_f}{\partial x^2} &= \frac{1}{D\_f} \frac{\partial T\_f}{\partial t} & \text{if } \ 0 \le z \le l\_f\\ \frac{\partial^2 T\_s}{\partial x^2} &= \frac{1}{D\_s} \frac{\partial T\_s}{\partial t} - A e^{ax} \left(1 + e^{j\alpha t}\right) & \text{if } -l\_s \le z \le 0\\ \frac{\partial^2 T\_b}{\partial x^2} &= \frac{1}{D\_b} \frac{\partial T\_b}{\partial t} & \text{if } -l\_s - l\_b \le x \le -l\_s \end{aligned} \tag{4}$$

where A = αsI0/2ks are constant numbers and αs is the optical absorption coefficient of the sample.

Indeed, the application of the boundary conditions of temperature and heat flux at the different interfaces allows us to determine the expression of the periodic temperature T0 at the sample surface:

$$\begin{aligned} T\_0 &= -E\left[ (\mathbf{1} - r)(\mathbf{1} + b) \, e^{\sigma\_l \, l\_t} - (\mathbf{1} + r)(\mathbf{1} - b) \, e^{-\sigma\_l \, l\_t} \\ &+ 2(r - b) \, e^{-a \, l\_t} \right] / \left[ (\mathbf{1} + \mathbf{g}) \left( \mathbf{1} + b \right) \, e^{\sigma\_l \, l\_t} - (\mathbf{1} - \mathbf{g})(\mathbf{1} - b) \, e^{-\sigma\_l \, l\_t} \right] \end{aligned} \tag{5}$$


**Meet the editor**

Dr. Imre Laszlo Szabo is a medical doctor holding a Ph. D. degree in Gastroenterology from the University of Pécs (Hungary, 2004), where he studied the mechanisms of cytoprotection and mucosal healing processes in different regions of human gastrointestinal system. He currently works as an Assistant Professor at the First Department of Medicine, University of Pécs and has

10 years of experience in teaching Internal Medicine and Gastroenterology at graduate and postgraduate level. His research in the United States included the study of ulcer healing, nonsteroidal gastroenteropathy in animal models and their molecular mechanisms in isolated cells. He has published his work in several peer reviewed journals and given oral and poster presentations at national and international conferences. In addition he has authored Hungarian grants and participated in grants from U.S. Veteran's Administration. His fields of special expertise include gastrointestinal mucosal injury, defensive and healing mechanisms.

Contents

**Preface VII**

Milan

**Bowel Disease 3**

**and Disease 73**

Chapter 4 **Fibrosis in Crohn's Disease 151**

**Section 2 Management of Disease 175**

**Diseases 197**

Lauri Diehl

and Michael J. Wannemuehler

**and Future Directions 177** Rahul A. Sheth and Michael S. Gee

V. Surlin, C. Copaescu and A. Saftoiu

**Section 1 Pathogenesis of Inflammatory Bowel Disease 1**

Chapter 1 **Insights to the Ethiopathogenesis of the Inflammatory**

Ana Brajdić and Brankica Mijandrušić-Sinčić

Chapter 3 **The Role of the Microbiota in Gastrointestinal Health**

Chapter 2 **Gene Polymorphisms and Inflammatory Bowel Diseases 23**

Chapter 5 **The Imaging of Inflammatory Bowel Disease: Current Concepts**

Chapter 6 **An Update to Surgical Management of Inflammatory Bowel**

Bartosova Ladislava, Kolorz Michal, Wroblova Katerina and Bartos

Anne-Marie C. Overstreet, Amanda E. Ramer-Tait, Albert E. Jergens

## Contents

## **Preface XI**

## **Section 3 Future Therapeutic Directions in IDB 225**

Chapter 7 **Targeting Colon Drug Delivery by Natural Products 227** Hyunjo Kim

#### Chapter 8 **Food Nanoparticles and Intestinal Inflammation: A Real Risk? 259** Alina Martirosyan, Madeleine Polet, Alexandra Bazes, Thérèse

Sergent and Yves-Jacques Schneider

Preface

deposition of collagen.

Inflammatory bowel disease (IBD) is a term for a two very different and yet in many characteristics congruent chronic inflammatory disorder of the intestinal tract. Crohn's disease (CD) and ulcerative colitis are two principal components of IBD. Both CD and ulcerative colitis are considered as multifactoral diseases. For long period of time we keep these inflammatory intestinal disorders as the result of environmental factors and immunological disturbances manifested in persons with genetic predisposition. The pathogenesis of IBD has remained largely unknown, but surely involves environmental factors, immunological factors in a complex form. Increasing disease prevalence and

Epidemiological studies have shown that prevalence of IDB has dramatically increases in western world probably due to western lifestyle. Environmental factors are suggested to have major role in development of diseases connected to westernalization of the lifestyle. These factors include smoking, use of antibiotics and non-steroidal anti-inflammatory drugs, stress, various infections and diet. The initiating mechanisms of their actions are still not understood. Research has revealed several genetic factors contributing to IDB pathogenesis; more than a hundred IDB genes, loci and their allele variation have been

Intestinal stricture is a serious and late complication of CD. Recent studies have identified certain disease specific characteristics that may be used in identifying individuals having higher risk for stricture development. These are lower age at initial diagnosis of CD, need for steroid therapy at the diagnosis, perianal fistulizing disease or small intestinal localized disease. Anti-Saccharomyces cerevisiae antibody (ASCA) levels were also found to be correlated to fibrostenosing or penetrating disease behaviour. Recently, other serological markers (anti-I2 and anti-CBir1), fibronectin, bFGF and YKL-40 glycoprotein of the chitinase family have been shown to lead to the hyperplasia of the intestinal muscle layers and

Determination of gene polymorphism can also be important in regarding the prediction of therapy response. Since no curative therapy for IBD exists, pharmacological therapy mainly focuses on inflammation control. IBD pharmacotherapy utilizes a wide scale of drugs for inflammation reduction including aminosalicylates, glicocorticoids, immunomodulators and biological therapy. Their pharmocodinamics and pharmacokinetics can be altered by polymorphisms of certain gene coding proteins resulting faster drug elimination, tolerance or more side affect development. Namely, the form of N-acetyltransferase in slow acetylators can develop more side effects for sulfasalazine, mutation of glucocorticoid receptor-β lead to decreased affinity to exogenous glucocorticoids, mutations of thiopurine

defined (e.g. NOD2/CARD15, IL23, ATG16L1, IRGM, ICAM-1, CCR5, TLR4, TNFα).

gathered lot of new research data on IBD suggested the pretence for review.


**6. Bayesian Approach**

Bayesian inference has gained appeal in a variety of sectors in recent years, including engineering, clinical medicine, biology, and so on. Its capacity to analyze data using prior knowledge makes it valuable in dependability studies, where data availability is a major issue. The model parameters β1,β2,β3 and R Bayesian estimates, as well as the corresponding credible intervals, are derived in this section.

### *6.1. Prior Information and Loss Function*

Because the gamma distribution can take on different shapes based on the parameter values, using various gamma priors is simple and can result in more expressive posterior density estimates. As a result, we investigated gamma density priors, which are more adaptable than other challenging prior distributions and APE distribution under progressive first-failure censoring model parameters. As a result, under progressive first-failure censoring model parameters gamma aj, bj ; j = 1, ... , 4, independent gamma PDFs are assumed for the APE distribution. The joint prior is as follows

$$
\pi(\boldsymbol{\beta}\_{1}, \boldsymbol{\beta}\_{2}, \beta\_{3}, \boldsymbol{\aleph}) \propto \boldsymbol{\beta}\_{1}^{\ \mathbf{a}\_{1}-1} \mathbf{e}^{-\mathbf{b}\_{1}\beta\_{1}} \ \boldsymbol{\beta}\_{2}^{\ \mathbf{a}\_{2}-1} \mathbf{e}^{-\mathbf{b}\_{2}\beta\_{2}} \ \boldsymbol{\beta}\_{3}^{\ \mathbf{a}\_{3}-1} \mathbf{e}^{-\mathbf{b}\_{3}\beta\_{3}} \ \mathbf{R}^{\mathbf{a}\_{4}-1} \mathbf{e}^{-\mathbf{b}\_{4}\mathbf{R}},\tag{20}
$$

where aj, bj; j = 1, ... , 4 indicate prior knowledge of the unknown parameters β1,β2,β3 and R and are anticipated to be non-negative.

According to the literature, choosing the symmetric loss function (SLF), (squared loss function) (SEL) is a critical issue in Bayesian analysis. The SEL function is the most often utilized SLF in this study for estimating the considered unknown values.

$$\mathcal{L}\left(\mathbb{R},\mathbb{R}\right) = \left(\mathbb{R}-\mathbb{R}\right)^2,\\ \mathcal{L}\left(\mathbb{\beta}\_1,\overline{\beta\_1}\right) = \left(\overline{\beta\_1}-\beta\_1\right)^2,\\ \mathcal{L}\left(\mathbb{\beta}\_2,\overline{\beta\_2}\right) = \left(\overline{\beta\_2}-\beta\_2\right)^2,\\ \mathcal{L}\left(\mathbb{\beta}\_3,\overline{\beta\_3}\right) = \left(\overline{\beta\_3}-\beta\_3\right)^2,\\ \mathcal{L}\left(\mathbb{\beta}\_4,\overline{\beta\_4}\right) = \left(\overline{\beta\_4}-\beta\_4\right)^2,\\ \mathcal{L}\left(\mathbb{\beta}\_5,\overline{\beta\_5}\right) = \left(\overline{\beta\_5}-\beta\_5\right)^2$$

where R 5 , β 6 1, β 6 2 and β 6 3 are approximations of R, β1,β2 and β3. The posterior mean of R, θ1, θ2 and θ3 is utilized to compute the objective estimate of 5 R, θ 5 1, θ 5 2, and θ 5 3. In contrast, any other loss function can be easily incorporated.

### *6.2. Posterior Analysis by SLF*

Observing the APE distribution under progressive first-failure censoring sample data from the likelihood function and the prior knowledge given both yield the joint posterior density function.

$$\mathrm{L}(\mathsf{R},\ \beta\_{1},\beta\_{2},\beta\_{3}|\mathtt{f}) \propto \pi(\mathsf{R},\ \beta\_{1},\beta\_{2},\beta\_{3}) \prod\_{i=1}^{3} \prod\_{j=1}^{n\_{\mathtt{i}}} \mathrm{g}\left(\mathsf{t}\_{\mathtt{i}}\right) \left(1-\mathrm{G}\left(\mathsf{t}\_{\mathtt{i}}\right)\right)^{\varepsilon\_{\mathtt{i}}},\tag{21}$$

5

5

5

5

The Bayesian estimator of R, θ1, θ2 and θ3 such as R , θ 1, θ 2, and θ 3, under the SEL function, is the posterior expectation of R, θ1, θ2 and θ3. The marginal posterior distributions for each of the parameters (R, θ1, θ2 and θ3) must be gathered in order to generate

these estimates. However, due to the implied mathematical calculations, precise formulations for the marginal PDFs for each unknown parameter are plainly not realistic. As a result, we would like to generate Bayesian estimates and credible intervals utilizing simulation approaches such as MCMC.

The Metropolis–Hastings (MH) algorithm, which is used to generate random samples using the posterior density distribution and an independent proposal distribution to approximate Bayesian estimates and to create the associated Highest Posterior Density (HPD) credible intervals, is one of the most useful MCMC algorithms. In addition, this method provides a chain version of the Bayesian estimate that is simple to use in practice.


*Edited by Seth Appiah-Opoku*

This book discusses aspects of land use change and sustainability in ways that may generate further research ideas. It brings together discussions from leading researchers and scholars in the field of land use change and sustainability from five different countries including the USA, Ethiopia, Guyana, Taiwan, and Indonesia. Based on empirical research and case studies, the book is divided into two sections. The first section is subdivided into four chapters and discusses land use sustainability in the Northern Great Plains of the USA; effects of rural land use and tenure on sustainable management of mangroves in Corentyne, Guyana; the property formation process in peri-urban areas of Ethiopia; and the effects of green energy production on farmlands in the Yulin County of Taiwan. The second section of the book is subdivided into two chapters and discusses cases pertaining to land use mapping and sustainability including land cover/land use mapping using soft computing techniques with optimized features; and applying systems analysis to evaluate Jelutung as option for sustainable use of peat lands in Central Kalimantan, Indonesia. The book is insightful, thought provoking, concise, and easy to understand. It could serve as an important reference material on land use change and sustainability.

ISBN 978-1-78984-299-9

Land Use Change and Sustainability

Published in London, UK © 2020 IntechOpen © Gerson Repreza / unsplash


Objective Function

Optimal welfare will be reached by maximizing the sum of producer and consumer surplus, given by the integral of the aggregate demand curve less power production costs. The objective function is therefore expressed as:

$$Obj = \sum\_{n=1}^{|H|} \sum\_{i=k\_q}^{|E|} \int\_{q=0}^{\varepsilon\_k} \mathbb{C}\_k(q) dq - \sum\_{n=1}^{|H|} \sum\_{\psi=1}^{|\Psi|} \left( o\_{\psi^i} \cdot \upsilon^i \right) - \sum\_{\psi=1}^{|\Psi|} \left( \varphi^i \right) \tag{6}$$

Subject to

$$\sum\_{i=1}^{|E|} q\_{kn} \le \sum\_{\psi=1}^{|\Psi|} o\_{\psi} \uparrow 0 \le o\_n^i \le F(n, i) \,\,\forall 0 \le o\_n^i \le \overline{o^i}$$


*2.2. Experimental Design*

This experiment included four global change treatments: Control (CK), warming (W), N input (N), and a combination of warming and N input (WN). Each global change treatment included two AMF treatments: with AMF ('AM') and without AMF ('NM'); each treatment was replicated four times, i.e., a total of 32 microcosms.

Sterilized soil was placed in microcosm pots (2.5 kg of dry soil per pot), and 200 g of AMF inoculum was added to produce the mycorrhizal ('AM') treatment. The same amount of sterilized soil (121 ◦C for two hours) was placed in the microcosm pots to produce the nonmycorrhizal ('NM') treatment. To ensure the rhizosphere microbial communities in the NM treatment were consistent with those in the AM treatment, a microbial suspension was prepared by sequential filtration of a soil extract obtained by orbital shaking (150 rpm) of a nonsterile soil, sterile dH2O (1:9 w/v) mixture for 30 min. The AM treatment received 10 ml of deionized water, and the NM treatment received 10 ml of filtrates that were free of mycorrhizal propagules [35].

Before planting, the seeds were surface disinfected with 10% (v/v) hydrogen peroxide for 5 min and washed five times with deionized water. The seeds were allowed to germinate at 20 ◦C for 48 h. Uniform seedlings were transplanted after 3 days into the grassland microcosms that had a height of 23 cm and a volume of approx. 2.5 kg (based on soil dry weight). The microcosms contained *Setaria viridis* (10 plants per pot), *L. chinensis* (15 plants per pot), and *S. corniculata* (5 plants per pot) [35].

The experiments were performed in phytotrons (LT/ACR-2002, E-Sheng Tech., Beijing, China) from April to August 2014 at Northeast Normal University. The microcosms were placed in phytotrons under a light intensity of 350 μmol−2 S−1 (06:00–20:00) and a relative humidity of 40%–60%. The temperature of the microcosms matched the average summer temperature in the Songnen meadow over the last 10 years. The temperature and N input in the phytotrons were based on those described by Zhang et al. [35]. The control and N input treatments were set up as follows: 22 ◦C from 06:00–10:00, 25 ◦C from 10:00–15:00, 22 ◦C from 15:00–20:00, and 22 ◦C from 20:00–06:00. In the warming and combined warming and N input treatments, the temperature was increased by 3 ◦C in all time periods relative to the control and N input treatments. The N input and combined warming and N input treatments received N (10 g m−2 yr−1). The soil water content was maintained at 50%–60% of the field capacity by adding water every two days.

After twelve weeks of growth, we harvested the plants. The aboveground samples were cut at the soil surface, removed from the microcosms, rinsed with deionized water, dried at 65 ◦C for 48 h, and then weighed. The plant roots were collected and washed using deionized water to measure mycorrhizal colonization. To account for soil heterogeneity, soil was collected from three random locations in each microcosm and was sieved using a 2 mm soil sieve. Dried plant leaf and soil samples were milled in a ball mill prior to C, N, and P chemical analysis.

The total C and N contents in the soil and plant leaves were determined using a stable isotope mass spectrometer (Isoprime 100, Isoprime Ltd, Manchester, UK). Soil and plant leaves were digested in sulfuric acid, and then total P contents were determined photometrically using the molybdenum blue ascorbic acid method [42]. The root samples were cut into 1 cm segments and were cleared with 10% (w/v) KOH and then stained with trypan blue at 90 ◦C for 2 h. AMF colonization was estimated using a previously described method [35].