2.7.2. Trans-Corneal Permeation

Ex vivo permeation experiments were performed utilizing the vertical Franz diffusion cell using an isolated goat corneal membrane obtained from a local abattoir (Ahmedabad, India) held between the donor and receptor cell [31]. Testing were done using either optimized formulation (CL10) or control (0.25% drug solution equivalent to 2.5 mg of clarithromycin/mL). The receptor compartment was filled with simulated tear fluid (pH 7.4) with 1% Tween 80 (to maintain sink conditions), and temperature was set at 37 ± 0.5 ◦C. Suitable volumes of the samples were taken at specific time intervals and similar volumes of fluids at the same temperature. The samples withdrawn from the receptor compartment were appropriately diluted and estimated for clarithromycin by HPLC. The flux was determined as described in the literature [32].


**8. A Computational Example**

Let us consider the optimal control problem for two mobile robots. They have to exchange its position on the plane with obstacles.

Mathematical models of mobile robots [45] are given

$$\begin{array}{rcl} \dot{x}^{j} &=& 0.5(\boldsymbol{u}\_{1}^{j} + \boldsymbol{u}\_{2}^{j})\cos(\theta^{j}),\\ \dot{y}^{j} &=& 0.5(\boldsymbol{u}\_{1}^{j} + \boldsymbol{u}\_{2}^{j})\sin(\theta^{j}),\\ \dot{\theta}^{j} &=& 0.5(\boldsymbol{u}\_{1}^{j} - \boldsymbol{u}\_{2}^{j}),\end{array} \tag{64}$$

where **u***j* = [*uj* 1 *uj* 2] is a vector of control, *j* = 1, 2.

Control is restricted

$$1 - 10 = u\_i^- \le u\_i^j \le u\_i^+ = 10, \; j = 1, 2, \; i = 1, 2. \tag{65}$$

The initial conditions are set

$$\text{tr}^1(0) = 0, \, y^1(0) = 0, \, \theta^1(0) = 0, \, \text{x}^2(0) = 10, \, y^2(0) = 10, \, \theta^2(0) = 0. \tag{66}$$

The terminal conditions are set

$$\mathbf{x}^1(t\_f) = 10, \ y^1(t\_f) = 10, \ \theta^1(t\_f) = 0, \mathbf{x}^2(t\_f) = 0, \ y^2(t\_f) = 0, \ \theta^2(t\_f) = 0,\tag{67}$$

where

$$t\_f = \begin{cases} \ t \ \text{, if } t < t^+ \text{and } \Delta\_f(t) \le \varepsilon \\\ t^+ \text{, otherwise} \end{cases} \tag{68}$$

$$\Delta\_f(t) = \sqrt{(10 - \mathbf{x}^1(t))^2 + (10 - y^1(t))^2 + (\theta^1(t))^2 + (\mathbf{x}^2(t))^2 + (y^2(t))^2 + (\theta^2(t))^2} \tag{69}$$
  $t^+ = 2.4$  s,  $\varepsilon = 0.01$ .

The quality functional includes the time to reach the terminal state and penalty functions for violation of the accuracy of reaching the terminal state and for violation of static and dynamic phase constraints

$$J\_{\mathfrak{c}} = t\_f + w\_1 \Delta\_f(t\_f) + w\_2 \int\_0^{t\_f} \sum\_{i=1}^2 \sum\_{j=1}^2 \theta(\varphi\_{i,j}(t)) dt + \mathcal{O}(\delta)$$

$$w\_3 \int\_0^{t\_f} \theta(d^2 - (\mathbf{x}^1(t) - \mathbf{x}^2(t))^2 - (y^1(t) - y^2(t))^2) dt \to \min\_{\mathbf{u}^1, \mathbf{u}^2} \tag{70}$$

where *w*1 = 2.5, *w*2 = 3, *w*3 = 3,

$$\theta(\alpha) = \begin{cases} 1, \text{if } \alpha > 0 \\ 0, \text{otherwise} \end{cases} \tag{71}$$

$$\varphi\_{i,j}(t) = r\_i - \sqrt{(\mathbf{x}\_i - \mathbf{x}^j(t))^2 + (y\_i - y^j(t))^2}, \ i = 1, 2, \ j = 1, 2,\tag{72}$$

*r*1 = 3, *r*2 = 3, *x*1 = 5, *x*2 = 5, *y*1 = 9, *y*2 = 1, *d* = 2.

It is necessary to find such a control to move all robots from its initial conditions (66) to the terminal conditions (67) with the minimal value of the quality criterion (70).

To solve the optimal control problem (64)–(72) by the proposed synthesized optimal control method it is necessary to initially solve the control synthesis problem (45)–(48) for each robot. Since robots are similar, it is enough to solve the control synthesis problem once for one robot. For the solution of this problem, the symbolic regression method of Cartesian genetic programming is used.

In the result, the following control function was obtained:

$$u\_i^j = \begin{cases} u\_i^+ = 10, \text{if } u\_i^+ \le \bar{u}\_i^j \\\ u\_i^- = -10, \text{if } \bar{u}\_i^j \le u\_i^- \\\ \bar{u}\_{i'}^j, \text{otherwise} \end{cases}, i = 1, 2, \ j = 1, 2,\tag{73}$$

where

$$\bar{u}\_1^j = A + B + \rho\_\#(A), \; j = 1, 2,\tag{74}$$

$$\bar{u}\_1^j = B - A - \rho\_\#(A), \; j = 1, 2,\tag{75}$$

$$A = c\_1(\theta^\* - \theta^j) + \sigma\_\theta((\mathbf{x}^\* - \mathbf{x}^l)(\underline{y}^\* - \underline{y}^l)),\tag{76}$$

$$B = 2(\mathbf{x}^\* - \mathbf{x}^{\bar{j}}) + \text{sgn}(\mathbf{x}^\* - \mathbf{x}^{\bar{j}})c\_{2,\prime} \tag{77}$$

$$\rho\_{\theta}(a) = \begin{cases} \text{sgn}(a)B^{+}, \text{if } |a| > -\log(\delta^{-})\\ \text{sgn}(a)(\exp(|a|) - 1) \end{cases}, \; \sigma\_{\theta}(a) = \text{sgn}(a)\sqrt{|a|}. \tag{78}$$

*c*1 = 3.1094, *c*2 = 3.6289, *B*+ = 108, *δ*− = 10−8.

For solution of the synthesis problem eight initial conditions were used and the quality criterion took into account the speed and the accuracy of terminal position achievement

$$\mathbf{x}^\* = \begin{bmatrix} x^\* \ y^\* \ \theta^\* \end{bmatrix}^T. \tag{79}$$

In the result of the solution of control synthesis problem a stable equilibrium point in the state space is appeared. Position of the equilibrium point depends on the terminal vector (79).

In the second stage the set of four points (79) were searched for each robot on criterion (70)

$$\mathbf{X}^\* = \{ \mathbf{x}^{\*,1,1}, \dots, \mathbf{x}^{\*,1,4}, \mathbf{x}^{\*,2,1}, \dots, \mathbf{x}^{\*,2,4} \}. \tag{80}$$

These points were switching in some time interval Δ*t* = 0.6 s for control function (73) of each robot.

To search for the points the evolutionary algorithm of Grey wolf optimizer [46,47] was used. In result, after more than one hundred tests the following best points were found:

$$\begin{array}{rcl} \mathbf{x}^{\*,1,1} &=& [4.0159 \, 1.8954 \, 1.2397]^T, & \mathbf{x}^{\*,1,2} &=& [7.0890 \, 4.2341 \, 0.5270]^T, \\ \mathbf{x}^{\*,1,3} &=& [7.2194 - 0.4480 \, 1.3042]^T, & \mathbf{x}^{\*,1,4} &=& [11.9722 \, 9.4663 \, 0.1866]^T, \\ \mathbf{x}^{\*,2,1} &=& [5.3899 \, 4.0791 - 0.1208]^T, & \mathbf{x}^{\*,2,2} &=& [-0.6401 \, 4.3126 - 0.0176]^T, \\ \mathbf{x}^{\*,2,3} &=& [0.3103 \, 0.8955 \, 0.6335]^T, & \mathbf{x}^{\*,2,4} &=& [-0.0791 - 0.1518 \, 0.0195]^T. \end{array} \tag{81}$$

The algorithm simulated the system (64) with the control (73) for calculation of criterion values (70) in one test more than 500,000 times.

When searching for points, the following constraints were used

$$-2 \le \mathbf{x}^\* \le 12, \ -2 \le \mathbf{y}^\* \le 12, \ -\pi/2 \le \theta^\* \le \pi/2. \tag{82}$$

In the Figure 1 the projections of optimal trajectories on the plane {*x*, *y*} are presented. The trajectories are black lines, red circles are obstacles, small black squares are projections of found points (81).

**Figure 1.** Optimal trajectories of robots on the plane {*x*, *y*} for synthesized optimal control.

The quality criterion (70) for found control was *Je* = 2.8914.

For comparative study of the obtained solution, the same optimal control problem was solved by a direct method. For this purpose control functions of robots were approximated by piece-wise linear functions of time. The interval of approximation was Δ*dt* = 0.4 s, therefore a number of intervals was

$$K = \left\lfloor \frac{t^+}{\Delta\_d t} \right\rfloor = \left\lfloor \frac{2.4}{0.4} \right\rfloor = 6.\tag{83}$$

For the approximation of control function, the values of parameters on the boundaries of intervals were searched. For each one control function it was necessary to find *K* + 1 = 7 parameters. Total vector of parameters had twenty eight components.

$$\mathbf{q} = [q\_1 \dots q\_{28}]^T.\tag{84}$$

The direct control has the following form

$$u\_i^j = \begin{cases} 10 = u\_i^+ \text{, if } u\_i^+ \le \bar{u}\_i^j \\\ -10 = u\_i^- \text{, if } \bar{u}\_i^j \le u\_i^- \text{, } i = 1, 2, \ j = 1, 2, \\\ \bar{u}\_{i'}^j \text{, otherwise} \end{cases} \tag{85}$$

where

$$
\mu\_1^1 = q\_s + (q\_{s+1} - q\_s) \frac{(t - s\Delta\_d t)}{\Delta\_d t},
\tag{86}
$$

$$
\overline{u}\_2^1 = q\_{s+L} + (q\_{s+L+1} - q\_{s+L}) \frac{(t - s\Delta\_d t)}{\Delta\_d t} \,\tag{87}
$$

$$
\overline{a}\_1^2 = q\_{s+2K} + (q\_{s+2L+1} - q\_{s+2L}) \frac{(t - s\Delta\_d t)}{\Delta\_d t},
\tag{88}
$$

$$
\Delta \overline{u}\_2^2 = q\_{s+3L} + (q\_{s+3L+1} - q\_{s+3L}) \frac{(t - s\Delta\_d t)}{\Delta\_d t},\tag{89}
$$

*s*Δ*dt* ≤ *t* ≤ (*s* + 1)Δ*dt*, *s* ∈ {1, . . . , 6}, *L* = *K* + 1 = 7.

To search for optimal parameters the same evolutionary algorithm of Grey wolf optimizer was used. In the result of more than one hundred tests the following best values of parameters were found:

$$\begin{array}{rcl} \mathbf{q} & = & [19.6125 \ 5.4318 \ 7.5921 \ 19.4020 \ 2.3928 \ 2.1627 \ 1.6976 \\ & 1.4941 \ 5.1828 \ 16.9087 \ 11.2478 & -2.4499 \ 17.7201 \ -0.6297 \\ & -0.9093 \ -1.6815 \ -19.5283 \ -16.4979 \ -0.2321 \ -11.4719 \ -17.7372 \\ & -1.4218 \ -18.0214 \ -3.7942 \ -3.0899 \ -13.3196 \ -9.7212 \ -0.3233]^T \\ \end{array} \tag{90}$$

The process of searching the parameters had restrictions

$$-20 = q^- \le q\_i \le q^+ = 20, \ i = 1, \dots, 28. \tag{91}$$

In one test, the algorithm simulated the system (64) with the control (85) for calculation of criterion values (70) more than 500,000 times. A value of quality criterion (70) for found control was *Je* = 2.5134.

In Figure 2, the projection of optimal trajectories of mobile robots on the horizontal plane {*x*, *y*} is presented.

**Figure 2.** Optimal trajectories of robots on the plane {*x*, *y*} for direct control.

To check the obtained solutions of sensitivity to perturbations, we included random functions of uncertainty into the model (64)

$$\begin{array}{rcl} \dot{x}^j &=& 0.5(\boldsymbol{\mu}\_1^j + \boldsymbol{\mu}\_2^j)\cos(\theta^j) + B\tilde{\xi}(t),\\ \dot{y}^j &=& 0.5(\boldsymbol{\mu}\_1^j + \boldsymbol{\mu}\_2^j)\sin(\theta^j) + B\tilde{\xi}(t),\\ \dot{\theta}^j &=& 0.5(\boldsymbol{\mu}\_1^j - \boldsymbol{\mu}\_2^j) + B\tilde{\xi}(t),\end{array} \tag{92}$$

where *j* = 1, 2, *ξ*(*t*) generates new random value in interval from −1 to 1 at every call.

Results of simulations with the found optimal controls and different levels of perturbations of the model are presented in the Table 1. The Table 1 includes average values of functional (70) on ten tests. As we can see, the synthesized optimal control is less sensitive to the perturbation of model. For the synthesized control with the level of perturbation *B* = 1.5, the average value of the functional is changed by no more than 30% and, for the direct control with the same level of perturbations, the functional is changed by more than 200%.

**Table 1.** The average values of functional (70).

In Figure 3, the trajectories for synthesized optimal control with model perturbations of level *B* = 1.5 are presented. In Figure 4, the trajectories for the direct control with the same level of perturbation *B* = 1.5 are presented.

As can be seen from Figures 3 and 4, the synthesized control does not change the nature of the motion of objects under large disturbances, and direct control first of all violates the accuracy of achieving the terminal conditions.

**Figure 3.** Optimal trajectories of robots on the plane {*x*, *y*} for synthesized control with *B* = 1.5.

**Figure 4.** Optimal trajectories of robots on the plane {*x*, *y*} for direct control with *B* = 1.5.


The TSEI-framework presented in this book helps to diagnose and advance Transformative Social-Ecological Innovation across sectors and disciplines and at different levels of governance. Predecessors of the TSEI-framework have been used successfully in environmental diplomacy and mediation processes in various parts of the world, as well as in advancing transformation processes and institutional change in water management, agriculture, and spatial planning. The TSEI-framework is proposed as an open framework, in the sense that it is open for additional predictors and moderators if they have a documented effect. To this end, it identifies intervention and leverage points and helps formulate sustainable solutions that can include different views as well as changing and competing needs. Overall, the concept of TSEI encourages public officials, business leaders, and the greater public to think more broadly about how society can rethink cooperation to address humankind's greatest challenges.

We now have an opportunity to make better decisions about how to organize our 21st-century society.

The aim of my research group, in collaboration with our partners, is to generate more insights into Transformative Social-Ecological Innovation (TSEI) for a sustainable, healthy, and just society. By doing so, we can together support our common quest for a Natural Social Contract and not simply for the benefit of ourselves but also for our planet and future generations (Fig. 8.1).

Earth, that's us.

Fig. 8.1 "There is no planet B", by climate protesters (Shutterstock)

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

### References


**Abstract**

Alzheimer's disease is an age-related progressive neurodegenerative disorder. The two major neuropathologic hallmarks of Alzheimer's disease (AD) are extracellular Amyloid beta (Aβ) plaques and intracellular neurofibrillary tangles (NFTs). A number of additional pathogenic mechanisms, possibly overlapping with Aβ plaques and NFTs formation, have been described, including inflammation, oxidative damage, iron dysregulation, cholesterol metabolism. To date, only symptomatic treatments exist for this disease, all trying to counterbalance the neurotransmitter disturbance. To block the progression of the disease they have to interfere with the pathogenic steps responsible for the clinical symptoms, including the deposition of extracellular amyloid β plaques and intracellular neurofibrillary tangle formation, inflammation and stem cell. In this review, we discuss new potential disease-modifying therapies for AD that are currently being studied in phase I–III trials.

**Keywords:** Alzheimer, secretase modulators, anti-amyloid agents, stem cell

#### **1. Introduction**

Alzheimer's disease (AD) is an age-related progressive neurodegenerative disorder characterized by progressive memory loss, cognitive impairment and functional decline. AD is described as a multifactorial disease and several mechanisms significant roles in disease pathogenesis. Through an improved understanding of the molecular mechanisms underlying pathogenesis of AD, it is possible to develop novel, effective therapeutic methods in order to prevent onset and progression of AD. A better understanding of the molecular mechanisms underlying pathogenesis of AD makes available to a basis for development of novel, effective therapeutic strategies to prevent onset and progression of AD.

The formation of intracellular neurofibrillary tangles that are composed of hyperphosphorylated tau proteins [1] and accumulation of extracellular amyloid plaques are the fundamental neuropathological changes noticed in AD brain. Aβ and tau are two key/important proteins, have a main function in the pathogenesis of AD. Amyloid cascade hypothesis and tau hypothesis have been based on the causative factors in AD pathogenesis. While one of these hypothesis proposes that AD starts with the accumulation of Aβ, the other one suggests that AD starts with the accumulation of p-tau.

Amyloid cascade hypothesis: in 1992 Hardy and Higgins constructed the amyloid-cascade hypothesis [2]. According to this hypothesis, formation of pathological Aβ plaques, neurofibrillary tangles, synaptic loss, neurodegeneration and ultimately dementia in AD are caused by a cascade harming synapses and neurons has been triggered by Aβ and its aggregates. Aβ peptides are natural products of brain metabolism. AD is associated with the disruption of the balance between production and clearance of Aβ. Aβ accumulation in the brain induces oxidative stress and inflammatory response thus leads to neurotoxicity which contributes to impairment of cognitive functions. Several pathological events like excitotoxicity, synaptic and mitochondrial dysfunction, loss of calcium homeostasis, endoplasmic reticulum stress, oxidative stress and inflammation may occur as a result of Aβ aggregates. In spite of the role of Aβ in AD, only amyloid-cascade hypothesis is not sufficient to explain AD pathogenesis, because removal of Aβ did not halt AD pathology [3].

Tau hypothesis: tau is an intracellular protein which is a member of microtubuleassociated proteins family. This protein family promotes microtubule assembly and stabilization. Tau has neurotoxic effects when hyperphosphorylated due to loss of its normal function. Hyperphosphorylated tau promotes the formation of paired helical filaments which would eventually evolve into NFTs, dystrophic neurites, and neuropil threads [4]. Abnormal hyperphosphorylation of tau is a component of neurofibrillary tangles that is a key player of neurodegeneration and has been isolated from AD brain in the 1990s [5].

Although both hypotheses suggest primal roles of Aβ and tau protein in AD pathogenesis, increasing evidence suggests that there may be a crosstalk between two pathologies. However, the mechanisms linking Aβ toxicity and tau hyperphosphorylation have not been exactly clarified yet.

### **2. Pathogenic mechanisms in Alzheimer's disease**

#### **2.1 Oxidative stress**

Oxygen metabolism generates free radicals such as reactive nitrogen species (RNS) and reactive oxygen species (ROS) including superoxide anion and hydroxyl radical. One of the early changes observed in AD patients is increased oxidative damage. It has been shown that the percentages of 8-hydroxydeoxyguanosine (8OHdG) and 8-hydroxyguanosine (DNA and RNA oxidation markers), 4-hydroxynonenal, and F2-isoprostanes (lipid peroxidation markers), protein carbonyls and 3-nitrotyrosine (protein oxidation markers), and malondialdehyde (MDA), have been increased in AD brains [6]. Although the data is highly limited, oxidative stress may also influence hyperphosphorylation and polymerization of tau protein. Although oxidative stress has an important role in AD, it is still disputed whether it plays a causative role in the disease or secondary to the pathological changes observed in AD [7].

#### **2.2 Neuroinflammation**

Neuroinflammation is described as a process involving activation of natural immunity in the brain. The functions of neuroinflammation can be explained as protecting central nervous system from infectious insults, injury or diseases. Microglia are has a significant role in neuroinflammation. Transgenic animal models of AD have demonstrated that neuroinflammation is enhanced around amyloid plaques [8]. According to Bellucci et al. inflammation is the key player in the tauopathies for neurodegeneration [9]. It has been shown that production of enzymes

**85**

*Future Treatment of Alzheimer Disease DOI: http://dx.doi.org/10.5772/intechopen.85096*

**2.3 Metal toxicity**

researches in the field of AD.

**2.4 Mitochondrial dysfunction**

in mitochondrial dysfunction in synapse, indirectly.

**2.5 Brain insulin resistance and insulin deficiency**

strated in neither mild nor moderate forms of AD [11].

(COX-2) and proinflammatory cytokines (IL-1β) are boosted in tau-positive nerve cells in spinal cord and brainstem. Pursuant to these results of the research, neuroinflammation might be triggered through NFTs by activating microglia. It is found that suppression of neuroinflammation is related to improvements in behavioral and cognitive deficits in AD mouse models and is in harmony with decline in hyperphosphorylated tau and Aβ plaques in brain. It is efficient to treat with interleukin-1β (IL-1β) antibodies or anti-tumor necrosis factor-α (anti-TNF-α) in order to reduce the pathology in animal models of AD. It is noted that Aβ secretion and the expression and activity of β-secretase have been reduced by peroxisome proliferatoractivated receptor-γ [PPAR-γ] agonists and nonsteroidal anti-inflammatory drugs [NSAIDs] [10]. It is suggested that suppression of neuroinflammation with NSAIDs rescues memory and cognitive decline. While retrospective epidemiological studies have proven that prolonged treatment with NSAIDs delays onset of AD when initiated early stage or before disease initiation, its effectiveness has not been demon-

Iron, zinc and copper are important elements for neuronal function. During the aging, these metal ions accumulate in the brain, consequently contribute to neurodegeneration. Zinc, copper and iron have been found to be accumulated within the core and periphery of senile plaques and these metals have been suggested to be involved in Aβ aggregation and oxidative damage. Metal chelation is a therapy based on binding and removing to metal ions. This therapy can provide an advantageous against oxidative stress in AD. Desferrioxamine and clioquinol are several examples of treatment methods with metal chelators. And these methods have caught some success in order to alter the progression of AD [12]. Therapeutic approaches focusing on the improvement of metal balance are one of the popular subjects of current

Mitochondrial dysfunction has a significant function in brain aging and AD. Swerdlow and Kan suggested mitochondrial cascade hypothesis for sporadic form of AD in 2004 [13]. This hypothesis proposes that mitochondrial dysfunction exists early in disease pathogenesis and causes, NFT formation, Aβ deposition and synaptic loss, the mitochondria is vulnerable to oxidative stress because of lack of DNA repair activity and is the significant source of ROS in the central nervous system. Oxidation of mitochondrial DNA presents it vulnerable to somatic mutations which augments mitochondrial dysfunction. Mitochondrial dysfunction has been proposed to trigger onset of neuronal degeneration in AD. It is showed that Aβ accumulates in mitochondria from AD patients. Tau protein might also be included

Type 2 diabetes mellitus is a risk factor for AD and these two disorders share many common pathological pathways. Impaired glucose metabolism is related to rising oxidative stress and accumulated advanced glycation end products.  Insulin receptors are mostly located in the cerebral cortex, cerebellum, hypothalamus, hippocampus and olfactory bulb that are the cognition pertinent areas of the brain. Brain glucose utilization and insulin signaling are impaired in AD. AD is related to a reduction in the levels of insulin in

#### *Future Treatment of Alzheimer Disease DOI: http://dx.doi.org/10.5772/intechopen.85096*

(COX-2) and proinflammatory cytokines (IL-1β) are boosted in tau-positive nerve cells in spinal cord and brainstem. Pursuant to these results of the research, neuroinflammation might be triggered through NFTs by activating microglia. It is found that suppression of neuroinflammation is related to improvements in behavioral and cognitive deficits in AD mouse models and is in harmony with decline in hyperphosphorylated tau and Aβ plaques in brain. It is efficient to treat with interleukin-1β (IL-1β) antibodies or anti-tumor necrosis factor-α (anti-TNF-α) in order to reduce the pathology in animal models of AD. It is noted that Aβ secretion and the expression and activity of β-secretase have been reduced by peroxisome proliferatoractivated receptor-γ [PPAR-γ] agonists and nonsteroidal anti-inflammatory drugs [NSAIDs] [10]. It is suggested that suppression of neuroinflammation with NSAIDs rescues memory and cognitive decline. While retrospective epidemiological studies have proven that prolonged treatment with NSAIDs delays onset of AD when initiated early stage or before disease initiation, its effectiveness has not been demonstrated in neither mild nor moderate forms of AD [11].


**Author details**

Patrick Kareru1\*, Zacchaeus Kipkorir Rotich2 and Esther Wamaitha Maina1

\*Address all correspondence to<EMAIL_ADDRESS>
1 Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

2 Presbyterian Church of East Africa University, Kikuyu, Kenya


**6.4 Water**

### **6.4.1 Methodology**

Samples were collected from 17 different locations in Southeast Nigeria at various occasions covering the dry and wet seasons. In collecting samples from rivers, lakes and streams, the polyethylene sampling containers were dipped just below the surface to minimize the contamination of the water sample by surface films. For borehole samples, the mouth of the tap was cleaned with cotton wool and was left to run to waste for several minutes before collection while for spring water, samples were collected at different outlets of the spring. All the samples were collected with 2 L polyethylene cans which were leached with a 1:1 HCl and water and rinsed with distilled-de-ionized water (Okoye et al., 2010).

The samples were concentrated by evaporating 500 mL of water sample to about 100 mL followed by addition of 1 mL conc HCl and digesting until volume was about 15-20 mL. This was later made up to mark with distilled-de-ionized water in a 25 mL standard flask and later transferred into an acid-leached polyethylene bottle prior to analysis. Trace metals were determined with AAS (ALPHA Series 4200 CHEM TECH ANALYTICAL Ltd, UK) equipped with air-acetylene flame.


2.2.4. FoodWasteExplorer

Advances in food research are increasingly directed towards sustainability of food chains, including exploitation of unconventional foods/waste for biologically active compounds, and reuse or recycling to achieve a circular economy. FoodWasteEXplorer [78] brings together the compositions of some of the most common products and their associated side streams and was developed within the EU-founded project REFRESH (REFRESH: Resource Efficient Food and dRink for the Entire Supply cHain, Grant Agreement ID: 641933, https://eu-refresh.org/, accessed on 6 June 2022). Currently, FoodWasteEXplorer contains 27,069 datapoints, including 587 nutrients, 698 bioactives, and 49 toxicants, gathered from peer-reviewed papers, grey literature (e.g., manufacturers' data), and other sources (https://ws.eurofir.org/foodwasteexplorer/about, accessed on 6 June 2022). Food and side streams in FoodWasteEXplorer are searchable and grouped under areas of interest

such as wine and beer, spirits, cider, cereals, chocolate, (fruit and vegetable) juices, cheese, animal products, sugar, vegetable oil, and coffee production. They are also grouped into food categories, e.g., cereals; milk and dairy; eggs; fats and oils, nuts and seeds; fish and seafood; fruits and vegetables; beverages; and other (i.e., algae, frog, snail, etc.).  Other Developing/Ongoing Resources: FoodCASE

FoodCASE was developed by Premotec GmbH (CH) in partnership with EuroFIR AISBL to manage food composition, food consumption, total diet study (TDS), laboratory food analysis, and branded food data, assembling food information in one system to promote re-use by linking food lists to other datasets and resources [79,80]. This data management system has wizards to support advanced data operations such as data import and export, recipe calculations, dataset linkage, nutrient estimation, data issue, and data quality analysis. It also supports different processes involved in the acquisition, management, and processing of data and uses European and international standards for the different datasets [80].


**1. Introduction**

Microalgae are primarily found in aquatic ecosystems, living in both seawater and freshwater, and are photosynthetic eukaryotic organisms that contain chloroplasts and nuclei, similar to plants. Microalgae more e fficiently yield biomass than land-based plants owing to their higher performance in utilizing sunlight and CO2, leading to their extremely high growth rates [1]. Therefore, microalgae have been used in the food, pharmaceutical, and cosmetic industries, and their pigments, nutrients, bioactive compounds and whole biomass are already in use worldwide. Recently, various bioactive compounds and nutrients have been detected in both seawater and freshwater microalgae, including cyanobacteria. These compounds and nutrients have been reported to promote human health [1,2]. However, there is limited information regarding the bioactive compounds of freshwater-living *Chlorella* species, which are classified as green algae.

*Chlorella* species can be mass-cultured, and their dietary supplement products are commercially available worldwide. However, the commercial cultivation of their biomass has started only several years ago. *Chlorella vulgaris* was discovered and reported in 1890 by Dr. Martinus Willem Beijerinck, a famous microbiologist and botanist [3]. Another *Chlorella* species, distinguished by the presence pyrenoids in chloroplasts, was identified and accordingly named *C. pyrenoidosa* in 1903 [4]. Since then, more than 20 *Chlorella* species have been characterized, with over 100 strains described [5]. At present, *Chlorella* species are divided into three varieties: *C. vulgaris, C. lobophora, and C. sorokiniana* [6]. *C. sorokiniana* is a sub-species first isolated in 1953 by Sorokin and originally thought to be a

thermotolerant mutant of *C. pyrenoidosa* [7,8]. *C. pyrenoidosa*, the subject of many scientific studies, is now called *C. sorokiniana*.

Investigations of the dietary value of *Chlorella* in human health began in the early 1950s, when the use of *Chlorella* as a food source was initiated in the midst of a global food crisis [9]. *Chlorella* was first produced and consumed in Asia, mainly in Japan, and then used as a dietary supplement worldwide [10]. *Chlorella* is produced commercially for use in foods and as a source of its intrinsic compounds. Using large-scale cultivation technology, *C. vulgaris* and *C. pyrenoidosa* are prepared as commercial sources for dietary supplements [11]. Studies have shown that *Chlorella* cells contain a variety of nutrients and bioactive compounds that promote human health and prevent certain diseases [10,12], suggesting that *Chlorella*-derived natural compounds might provide substitutes for synthetic compounds or drugs. The content of natural compounds in *Chlorella* differs greatly between culture conditions and *Chlorella* species [13,14].

Here, we present updated information on the *Chlorella* content of nutrients and bioactive compounds that promote human health. However, at present, there is limited information available regarding the bioactive compounds responsible for its pharmacological activities, which might be due to the synergistic effects of various nutrients and antioxidant compounds in *Chlorella*.

#### **2. Nutrients in Commercial** *Chlorella* **Products**


**5. Discussion**

Accurate and reliable information of land cover dynamics is essential for the sustainable management of tropical deltas and mangrove ecosystems and their capacity for ecosystem service provision. The 'traditional' remote sensing mapping approach involving the use of image mosaics of optical data from two dates, together with likelihood function maximisation image classification algorithms, is not reliable in the humid tropics due to cloud cover [29,31], data availability [27,28], and algorithm performance. This has led to conflicting land cover change estimates for the largest river delta in Africa and the failure to assess the extent of degradation of one of the most endangered ecosystems in the world [60]. Our results show that, by incorporating novel image compositing techniques, spectral-temporal metrics, and machine learning classification algorithms, a reliable assessment of the change dynamics over the Niger Delta Region can be made. Our accurate land cover estimates also allowed for a more comprehensive land change analysis that incorporates an assessment of change intensity and the fragmentation of a key component of the NDR: its mangrove forests.

#### *5.1. Land Cover and Change Dynamics*

There is an inherent di fficulty in mapping land cover in tropical deltas, in general, and mangrove forests, in particular, as they are a ffected by seasonal and intertidal e ffects, with pixels often comprising of a mixture of vegetation, soil, and water due to their location between land and sea and the average tidal range in the Niger Delta being 1.5 m [9]. Nevertheless, we mapped the eight main land cover types for the entire NDR, achieving high overall accuracies in all epochs (~79% for 1988, and 82% for 2000 and 2013; Table 2) and high producer's accuracies for all classes and years. With the exception of the grassland and bareland classes, user's accuracies were also high (from 70% to 91%). Our results compare favourably with other studies in the NDR [19,23,44,45]. Regarding the mapping of degraded mangroves, one of the main objectives of this paper, our study is the first to map this accurately with user's accuracies between 77% and 87% and producer's consistently above 82%. The only other study thatattemptedtomapdegradedmangrovesreportedverylowaccuracies[27].

The results reveal some interesting dynamics:

• There is consistent net loss in mangrove and woodland types and a consistent net gain of the urban class in both periods of study

More specifically, according to our findings, healthy mangroves reported a net loss in both study periods: 292 km2 in the first and 235 km2 in the second, while degraded mangroves consistently reported a net gain (21 km2 in the first and 97 km2 in the second). Interestingly, our study and the studies by Kuenzer et al. [27] and James et al. [9] found a similar decrease in the overall combined (degraded and non-degraded) mangrove area. According to our results, this area was 270 km2, while, in an almost identical period of study, Kuenzer et al. [27] found that the loss was 239 km2. In the James et al. [9] study between 1987 to 2002, the loss was 213 km2. However, our more accurate

findings identify the total areas covered by the mangrove classes to be very di fferent to the areas in the Kuenzer et al. [27] study: we found that mangroves and degraded mangroves occupied an area between 8697 and 8428 km2 in the two periods, while Kuenzer et al. [27] claim that these numbers were 10,311 and 10,072 km2, respectively. These figures di ffer by almost a fifth, and can play a significant role in the setting of conservation targets, management policies, and sustainability goals. Moreover, our mangrove results compare favourably with three studies that mapped mangroves as one class accurately: the study of Nwobi et al. [19], who found that mangroves occupied an area of 9115 km2 in 2007 and 8017 km2 in 2017; the study of Ayanlade and Drake [23] (9965 km2 in 1987, 9255 km2 in 2001, and 8430 km2 in 2011); and the study by James et al. [9] (7037 km2 in 1987 and 6824 km2 in 2002).

While it is relatively simple to compare the results on the extent of mangroves between the di fferent studies that mapped land cover change in the NDR, as this class is confined in the coastal belt and is always included within the study area, it is not as straightforward to compare the findings on other land cover types, as the study areas do not match. In the case of woodland, for example, the biggest land cover type in the NDR, our study found that it occupied 23,770 km2 in 1987 and su ffered net losses in both periods: ~700 km2 in the first and ~800 km2 in the second. The study by Ayanlade and Drake [23] also found net losses in both periods for the combined "lowland rainforest" and "freshwater forest" classes but found that these occupied 31,200 km2 in 1987, 25,400 km2 in 2001, and 21,470 km2 in 2011. However, their study area far exceeds the boundaries of our delineation of the NDR. The study by Kuenzer et al. [27] also agrees that "forest" and "swamp forest" experienced net losses in both periods. They report far smaller areas than both our study and the study by Ayanlade and Drake [23]: 18,325 km2 in 1987 and 15,408 km2 in 2013. Finally, the Nwobi et al. [19] study also agrees that "tropical forests" were reduced but reported that these occupied 29,000 km2 in 2007 and 25,500 km2 in 2017. As all of these studies, including ours, reported high per-class accuracies in the mapping of forests, it is di fficult to ascertain which on is closer to the true figure.

The di fficulty in comparing the findings of di fferent studies remains for the agricultural class, which we found to significantly increase in the first period (from 11,571 to 13,787 km2) and decrease in the second (12,645 km2 in 2013). An additional issue to the problem of relating to di fferent study areas around the NDR is the choice of land cover nomenclature. Based on our knowledge of the region and on the classification systems of the ESA 20m African land cover data for 2016 and the GlobeLand 30 m data for 2010, we included a grassland class in our mapping e fforts, which were found to decrease in the first period (from 9421 to 7089 km2) and increase in the second (8102 km2 in 2013). Our figures for the agricultural class are significantly lower to those in Ayanlade and Drake [23], Kuenzer et al. [27], and Nwobi et al. [19]. However, none of these studies included a separate class for grassland but, according to their spatial outputs, appear to have mapped this together with the agricultural class. We recognise that separating these classes poses di fficulties, as the spectral separability between them is low: our user's accuracies for grassland are testament to that (Table 2). However, we strongly believe that it is a shortcoming to map these two classes as one, as this precludes the identification of very important land cover dynamics between either of these classes and, for example, the woodland or urban classes. If summed together, our estimates of agricultural and grassland compare favourably with those of Nwobi et al. [19], who estimated the area covered by "agricultural land" as 21,733 km2 in 2007 and 24,179 km2 in 2017.

An important change that occurred in both periods is the expansion of the built-up areas: from 1990 km2 in 1988, to 2924 km2 in 2000, to 3728 km2 in 2013, i.e., an 87% increase. As in the previous land cover types, the di fference in the extent of the study area makes comparison to the other studies di fficult. For example, the Ayanlade and Drake [23] study reports much higher figures, but their study includes the city of Benin, the fourth largest Nigerian city, which lies outside of our delineation of the NDR. Similarities exist between our findings and the Nwobi et al. [19] study: their 'built-up-area' class occupied 3950 km2 in 2007 and 5938 km2 in 2017. Their higher estimates can be attributed to the fact that they include the city of Calabar and a number of built-up areas in the northeast of their study area that lie outside our delineation of the NDR.

According to the results of our intensity analysis (Figure 6b), in the first period of study, only mangroves and woodland demonstrated dominant gains, while all the other categories had active gains. Interestingly, only the grassland and bareland types had active annual change intensities, with the former having the largest size of losses in this period (Figure 6a). However, these two are the classes that scored lower user's accuracies and the respective intensity results need to be treated with caution. Notable results from this period are the ~5 times greater annual intensity of mangrove loss than gain and the ~10 times greater annual intensity of urban gain than loss. The intensity of agricultural expansion is also noteworthy, reporting ~2 times greater gain than loss.

In the first period, the land cover class that mangroves 'target' most intensively when they change is degraded mangroves, with a transition intensity of 1.57% of the total area of degraded mangroves in the end of the first period. This is much higher than the estimated uniform change intensity of 0.06%. An area of 535 km2 of mangroves was degraded by the year 2000. In the second period, this change is even more intense (1.80%, higher than the uniform intensity of 0.08%) and leads to a conversion of a total of 596 km2 of mangrove to degraded mangrove by 2013. Bareland is also found to be a targeted class for mangroves with an estimated transition intensity of 0.20% (221 km2). Water also targets bareland, as well as mangroves and degraded mangroves, with transition intensities higher than the estimated uniform change intensity. As this is the first paper to undertake an intensity analysis in the NDR, we are unable to compare our findings to existing studies.

#### *5.2. Fragmentation and Degradation of the Niger Delta Mangrove Forest*

The Niger Delta's mangrove forest is a hub for substantial oil and gas deposits. As a consequence, it is highly vulnerable to activities of oil and gas extraction, e.g., land clearing, dredging, construction of flow stations, pipe and seismic lines, well blowouts, leakages or corrosion, equipment failure, error during operation or maintenance, accidents during transportation, sabotage, etc., as well as urbanisation, selective logging, and the proliferation of the invasive Nipa palm species (*Nypa fruticans*) that lead to the forest's destruction, fragmentation, and degradation [9,10,19,27,64].

Our land cover change and intensity analyses showed that degraded mangroves increased in both periods of study and mangroves losses were 5 times more intense than gains. To further assess the condition of the Niger Delta mangrove forest, we carried out the first ever fragmentation analysis of the area. Our fragmentation results show that the 'number of patches' (NP) for the healthy mangroves increased persistently while the 'total percentage of landscape' (PLAND) decreased (Figure 7a,b). The 'largest patch index' (LPI), a measure of dominance (Figure 7c), shows that in the second period, larger patches are on a decrease. The 'area weighted mean shape index' (SHAPE\_AM; Figure 7f) is also decreasing for the healthy mangroves, in both periods: this indicates that changes are happening in the perimeter of patches, uniformly. The 'area weighted mean Euclidean nearest neighbour distance' index (ENN\_AM; Figure 7d) is slightly decreasing, indicating less dispersion of the healthy mangrove patches. The standard deviation of this index (ENN\_SD; Figure 7h) is decreasing but with high values compared to the mean, which indicates a more uneven distribution of patches. The high and steady values of PLADJ (Figure 7g) confirm the ENN results: the healthy mangrove patches remain relatively aggregated throughout the study period. This was expected, as mangroves are very localised within the delta and naturally only occur by the coast.

Figure 7 also shows the change in landscape metrics through time for the degraded mangroves. The size of this class (PLAND; Figure 7a) is constantly increasing but shows some fluctuation in the number of patches (NP; Figure 7b). A divergent pattern is observed in the evolution of the number of patches and the median of patch area metrics (AREA\_MD; Figure 7e): NP increases in the first period and AREA\_MD decreases, while in the second period, this is reversed. The latter means that this class becomes less fragmented, with more patches and lower patch size in the first period. Between 2000 and 2013, there are fewer patches and larger patch sizes, indicating that some of the first period's patches have merged to form larger ones.

A visual examination of the land cover maps and derived change maps from these revealed three areas that demonstrate higher concentrations of degraded mangrove. One such area is in the eastern part of the NDR, around the city of Port-Harcourt and the towns of Bonny, Okrika, and Degema (Figure 8a). Mangrove degradation here can be attributed to the effects of rapid urbanisation and oil extractive activities [14,17], as demonstrated by the overlap with the locations of the oil wells, the pipelines, and the oil spills in Figure 8a. At the central part of the study area, mangrove degradation is mainly due to oil spills resulting from crude oil extractive activities, notably near River Bayelsa and the towns of Nembe, Southern Ijaw, Ekeremor, Brass, and Oloibiri, where oil extraction first began as early as the 1950s (Figure 8b). The highest concentration of degraded mangroves is, however, in the western part of the NDR, in the Delta state (Figure 8c). This area shows widespread degradation, with a notable increase in the second and third date around the towns of Wari South and Wari South West. Several oil spill and gas incidents have been reported in the literature around this area and period [14,15,17,18].

**Figure 8.** Oil wells, pipelines, oil spills, and mangrove degradation hotspots in three parts of the study area: (**a**) the eastern area, around the city of Port-Harcourt; (**b**) the central area, near the river Bayelsa, and (**c**) the western area around the cities of Wari South and Wari South West. U: Urban; M: Mangrove; DM: Degraded Mangrove. (Oil spill data: https://www.nosdra.gov.ng and https://oilspillmonitor.ng. Oil wells and pipeline data: https://www.shell.com.ng).


**5. References**

<sup>\*</sup> Corresponding Author

olive: findings from the 1990 freeze. *California. Agriculture* Vol., 47, pp. 1-12, ISSN 0008- 0845

Ercisli, S.; Barut, E. & Ipek A. (2009). Molecular characterization of olive cultivars using amplified fragment length polymorphism markers. *Genetics and Molecular Research* Vol. 8, pp. 414-419, ISSN 16765680

36 Olive Germplasm – The Olive Cultivation, Table Olive and Olive Oil Industry in Italy

*Horticulturae* Vol. 521, pp. 275-284, ISSN 0567-7572

invasiveness. *Euphytica*, Vol. 148: pp. 97-109, ISSN 0014-2336.

analysis, *Plant Cell Report* Vol. 30, pp. 449-462, ISSN 0721-7714

*Horticoltural Science* Vol. 20, pp. 100-102, ISSN 0862-867X

University Press, New York, ISBN 0-231-03880-1

*University of Calabria (UNICAL), Dep. of Ecology,Arcavacata di Rende (CS), Italy*

*Agricultural Research Council - Olive Growing and Oil Industry Research Centre, Rende (CS), Italy*

The authors thank the CERTOLIO project and University of Calabria funds for financial

Baldoni, L.; Pellegrini, M.; Mencuccini, M.; Mulas, M. & Angiolillo, A. (2000). Genetic relationships among cultivated and wild olives revealed by AFLP markers*. Acta*

Baldoni, L.; Tosti, N.; Ricciolini, C.; Belaj, A.; Arcioni, S.; Pannelli, G.; Germanà, MA.; Mulas, M. & Porceddu A. (2006). Genetic structure of wild and cultivated olives in the central Mediterranean basin. *Annals of Botany,* Vol. 98, No 5, (Novembre 2006), pp. 935-942,

Bartolini, G.; Petruccelli, R. & Tindall, HD.; (2002) In: Tindal HD.; Menini UG: (eds) Classification, origin, diffusion and history of the olive FAO Rome, Italy ISBN 978-92-5-

Bartolini, G.; Prevost, G.; Messeri, C.; Carignani, G. & Menini, UG. (1998). Olive germplasm: Cultivars and world-wide collections, FAO Rome, Italy ISBN 978-92-5-106534-1 Bass, DA.; Crossman, ND.; Latrie, SL. & Lethdridge, MR. (2006). The importance of population growth, seed dispersal and habitat suitability in determining plant

Bracci, T.; Busconi, M.; Fogher, C. & Sebastiani, L. (2011). Molecular studies in olive (*Olea europaea* L.): overview on DNA markers applications and recent advances in genome

Camarda, I. & Valsecchi, F. (1983). Alberi e arbusti spontanei della Sardegna. (ed.) Gallizzi,

Crisosto, C. & Suffer, EG. (1985). Improving "Manzanillo" olive seed germination.

Cronquist, A. (1981). An Integrated System of Classification of Flowering Plants. Columbia

Denney, JO.; Martin, GC.; Kammereck, R.; Ketchie, DO.; Connell, JH.; Krueger, WH.; Osgood, JW.; Sibbeft, GS. & Nour, GA. (1993). Freeze damage and coldhardiness in

**Author details**

Adriana Chiappetta\*

Innocenzo Muzzalupo

support.

**5. References**

ISSN 0305-7364

Sassari: pp. 403-412

 \*

Corresponding Author

106534-1

**4. Acknowledgement**

	- Quézel , P. (1965). La végétation du Sahara. Du Tchad à la Mauritanie. Stuttgart: Gustav Fischer Verlag, ISBN 3-437-30258-2

**Chapter 3**

© 2012 Briccoli Bati et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Briccoli Bati et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Cultivation Techniques**

Additional information is available at the end of the chapter

improve the quantity and quality of production.

http://dx.doi.org/10.5772/51932

**1. Introduction**

Caterina Briccoli Bati, Elena Santilli, Ilaria Guagliardi and Pietro Toscano

In an agricultural context, olive growing is emerging as a dynamic and interesting topic. The majority of Italian olive growing still requires organic renewal interventions such as farm restructuring and tree planting, varietal conversion, mechanization, technical assistance for the implementation of technological innovations, better organization and contractual weight in product marketing. Therefore, the main objective in this area can be obtained with a more economical management of olive orchards in order to achieve a high production per unit area at lower costs and while respecting the environment. Therefore, current olive growing must be based on two pillars such as the reduction of management costs and the use of cultivation techniques with a low environmental impact. More and more attention is being paid, by the EU, and also by Italy, to environmental sustainability, biodiversity and compliance requirements in agriculture. Indeed, in recent years the EU has issued a set of regulations aimed at environmental protection and enhancement of rural areas by improving the competitiveness of the agricultural sector in order to obtain high-quality products aimed at enhancing the peculiarities of the different territories of origin (PGI, PDO) and protecting agricultural and natural resources. Quality must always be considered a key resource for agriculture, which will enable farms to survive and compete in both Italian and International markets. The cultivation techniques used in olive orchards are directed to preserve and improve the physical and chemical characteristics of the soil (soil preparation and tillage, irrigation, fertilization) and to enhance plant production (training, pruning, fruiting, production and pesticide treatments). The knowledge of olive morphology and biology is a prerequisite for the rationalization of cultivation techniques to

Although the olive tree can be considered a hardy plant and is cultivated in marginal areas, it requires specific cultivation techniques coordinated and integrated with each other in order to exalt their productive potentialities. This is why the wise use of tools such as pruning, irrigation, nutrition and soil management plays an important role in achieving a

http://www.hear.org/starr/hiplants/reports


**4. Mineral, organic and synthetic substrates used in the substrate cultivation system of cucumbers**

As mentioned in the introduction different substrates can be used for cucumber cultivation in substrate culture systems. In principle, most substrates are suitable for successful cucumber growing, if adequately supplied with water and plant nutrients.

In one experiment were compared substrates from the three groups (**Table 1**), because the ecological aspects are becoming more and more important when choosing substrates, as well the economic efficiency is important. Substrate were filled in containers or available as mats, between 8 and 9 liters of substrate were available per plant. The aim was to use substrates with different physical and chemical parameters (**Table 3**).

Comparing the four substrates used, the significantly highest total yields of cucumbers were obtained in the variants 'organic substrate'. The differences in crop

#### *Cucumber Economic Values and Its Cultivation and Breeding*

The objective of this experiment was to investigate the development and yield of cucumber grown on sheep wool slabs in comparison with peat and coconut fiber slabs as well as Rockwool slabs and perlite in containers. Different sheep wool slabs in size and added components were tested, the highest stability was obtained with

*Growth Promoting and Stabilizing of Cucumber Plants Cultivated in Soilless Cultivation…*

• Does sheep wool slabs have appropriate physical parameters for cucumber

• How do the physical properties change in comparison with other organic and

• How do biostimulators influence cucumber plant growth in the investigated

Sheep wool slabs used in this experiment were compared with the following

• Coconut fiber (width 25 cm, length 100 cm, height after wetting 9 cm) from

• Peat slabs 'GroBoard' (width 20 cm, length 100 cm, height after wetting 9 cm)

In this experiment fifty percent of the cucumber plants of each substrate variant were treated three times with a biostimulator solution containing 0.08% K-Humate

) of *Bacillus subtilis* FZB 24®. To the plants were applied 20 ml of this

• Perlite, average grain size between 0.06 mm and 1.5 mm, properties see

• Rockwool slabs (width 20 cm, length 100 cm, height 7.5 cm) from the

(Fa. Humintech), 0.2% Lactofol (O) (Fa. ECOFOL) and a spore suspension

solution three times in weekly intervals starting with first treatment in 6–7 leaves stage, after transplanting. Plants of *Cucumis sativus* L. 'Indira' were used for the experiment. The experiment was conducted during two cultivation periods, first from November until April and second from June until November in the next year.

*5.1.1 Effects of biostimulators growing cucumbers in mineral and organic substrates*

For the soilless cultivation of the cucumber plants substrates was selected with different physical properties (**Table 5**). In general substrates for cucumbers should have high air capacity with a range between 20 to 40%, during the cultivation time often the air capacity is decreasing very much and water capacity increasing, therefore a certain stability is necessary in this regard. Sheep wool had the highest air capacity with about 70%, while peat had the lowest air capacities with 18%. The peat slabs were pressed to reduce the volume for the transport, the expansion of the slabs needs time. Because the low water capacity of sheep wool at the beginning, a water reserve is missing in case of low water availability. It seems that sheep wool and perlite requires a higher and more stable supply with nutrient solution than the other substrates. The physical properties after second use of the substrates

sheep wool slabs in combination with coconut fibers.

*DOI: http://dx.doi.org/10.5772/intechopen.96536*

mineral substrates?

the company 'Dutch Plantin'.

company 'Pargro' from Finland.

from the KEKKILÄ Oyj company Finland.

**Table 3** was filled in Container with 7 L volume.

substrates?

substrates [12]:

(107 cfu ml1

**73**

In this experiment following questions were investigated:

cultivation and how many months they can be used?

*WC = water capacity; AC = air capacity; PV = pore volume; CEC = cation exchange capacity. \* [1].*

#### **Table 3.**

*Physical and chemical characteristics of substrates used in experiments with cucumber growing in substrate culture.*

#### **Table 4.**

*Yield of greenhouse cucumber in mineral, artificial and organic substrate (kg/m2 ).*

yield of the investigated substrates in the examined months (**Table 4**) can be due, because different growing conditions, as:

The development of the leaf area of cucumber plants was examined for the variants with rockwool mats, urea foam and perlite. The following leaf areas were determined as the mean of all measuring dates: in rockwool 8215 cm2 , in urea foam 7889 cm2 , and in perlite 7438 cm2 .


**E**ffi**cient Removal of Ni(II) from Aqueous Solution by Date Seeds Powder Biosorbent: Adsorption Kinetics, Isotherm and Thermodynamics**

**Abubakr Elkhaleefa 1,2 , Ismat H. Ali 3,\* , Eid I. Brima 3,4 , A. B. Elhag 5,6 and Babiker Karama 7**

Received: 20 July 2020; Accepted: 14 August 2020; Published: 17 August 2020

**Abstract:** Adsorption investigations in batch approaches were performed to explore the biosorption of Ni(II) ions from aqueous solutions on date seeds powder. The effects of pH, particle size, initial concentration of Ni(II) ions, adsorbent mass, temperature, and contact on the adsorption efficacy were studied. The maximum removal obtained was 90% for an original Ni(II) ion solution concentration of 50 ppm was attained at pH 7 after 30 min and with 0.30 g of an added adsorbent. The four adsorption models, namely Freundlich, Langmuir, Dubinin–Radushkevich (D–R), and Temkin were examined to fit the experimental findings. The adsorption system obeys the Freundlich model. The system was found to follow the pseudo-second order kinetic model. Thermodynamic factors; entropy (∆S ◦ ), enthalpy (∆H◦ ), and Gibbs free energy (∆G◦ ) changes were also assessed. Results proved that adsorption of Ni(II) ions is exothermic and spontaneous. Sticking probability value was found to be less than unity, concluding that the process is dominated by physical adsorption.

**Keywords:** adsorption; date seeds; kinetics; isotherm; thermodynamics

### **1. Introduction**

The presence of heavy metals in water streams is among one of the most dangerous environmental problems arising from the disposal of untreated industrial effluents [1–4]. Many industries comprise of final treatment processes where discharged metal compounds may lead to pollution in the effluent water [2,5,6]. Most of these heavy metals are non-biodegradable or with long biological half-life leading to potential accumulation and human exposure through food or water [1].

Ni(II) ions exist naturally in water as nitrates, sulfides, and oxides. Nickel intake above the permissible limit causes skin dermatitis, fibrosis, vomiting, pulmonary, nausea, and many other diseases [3,6–9]. Most of the methods used for Ni(II) removal from artificial wastewater-like cation exchange and precipitation are costly and produce toxic sludge [2,9]. Recently, some economical, renewable, and effective agricultural and natural materials have been studied as alternative biosorbents [2].

Date (*Phoenix dactylifera*) seeds, which are composed of lignin, hemicellulose, and cellulose are effective biosorbents; thus, eliminates various contaminants from wastewater [4,10]. The efficiency of this inexpensive biosorbent is due to oxygenated functional groups in the lignocellulosic materials such as cellulose and phenolic compounds [10]. The removal of Ni(II) ions onto bentonite/grapheme oxide was previously studied [11] and the results revealed that it follows Langmuir isotherm with high uptake capacity. Mehrmad et al. [12] have reported that the removal of Ni(II) ions by functionalized henna powder depends on the experimental circumstances, mainly the Ni(II) concentration, the biosorbent mass, and the pH of the medium, the Freundlich and Langmuir isotherm models were used to define the process. The kinetic data were fitted with the pseudo-second-order reaction. The adsorption of Ni(II) ions from wastewater by natural clay had previously been investigated [13] and it was reported that the process is controlled by the pH value of the medium; the sorption process was rapid whereas the maximum adsorption capacity had been attained within 120 min and it was found that the system follows a pseudo-second-order reaction.

The adsorbent used in this study was prepared by a direct, facile, and economic technique. The process does not require any chemicals addition nor high-temperature calcination. It is believed that only few adsorbents were tested without any modification in the removal of heavy metals from aqueous solutions while being powerfully recyclable. *Phoenix dactylifera* is one of the most plentiful plants in Saudi Arabia and the region. Using date seed powder as an adsorbent for Ni(II) ions is considered the novelty of this research.

This work aims to assess the potential of date seed powder to act as an inexpensive and environment-friendly material for the removal of Ni(II) from artificial wastewater. This study was designed to assess, compare, and characterize the adsorption of Ni(II) ions by date seeds powder (DSP) without the addition of any chemicals or thermal treatment. DSP was initially characterized using Brunauer–Emmett–Teller (BET) surface area, scanning electron microscope technique (SEM), and attenuated total reflection-Fourier transform infrared spectrometer (ATR-FTIR). The factors that influence the adsorption efficiency such as mass, concentration of Ni(II) ion, pH, temperature, and contact time, were investigated in this work.

#### **2. Materials and Methods**

#### *2.1. Collection and Treatment of Adsorbent*

Dates seeds were collected locally from Abha city, Saudi Arabia. Seeds were rinsed with tap water and then by deionized water, dried at room temperature and were then grounded to powder size using a ball mill before being sieved. The efficiency of the room-temperature dried DSP was compared with small amount of oven-dried DSP and no differences were noticed on the results. Thus, to minimize the cost, drying of DSP by oven was not used in this study.

#### *2.2. Reagents*

Deionized water (>18 Ω/cm, Milli-Q) was used throughout this work for the solutions and DSP preparations. A stock solution of 1000 ppm Ni(II) ions was prepared using NiNO3 (LOBA Chemie, Laboratory Reagents and fine Chemicals, Mumbai, India).

#### *2.3. Batch Adsorption*

Adsorption batch experiments were conducted at different operating conditions (pH, time, adsorbent dosage, adsorbent particles size, and temperature) by adding the desired amount of DSP to 50 mL of Ni(II) ion solution under each particular condition. NaOH (0.25 M) and/or HCl (0.25 M) were used to control the pH value. Mechanical thermostated shaker (WSB, Witeg, Belrose, Germany) was used throughout all the experiments. The solutions were filtered and then analyzed using Atomic

Absorption Spectroscopy (AAS) (SpectrAA 220, Varian, Australia) to measure the remaining Ni(II) ions concentrations. The removal efficiencies (R%) were calculated using Equation (1).

$$\text{R\%} = \frac{\text{C}\_{\text{o}} - \text{C}\_{\text{e}}}{\text{C}\_{\text{o}}} \times 100 \tag{1}$$

where C0 and Ce are the initial and equilibrium Ni(II) ion concentrations, respectively.

#### *2.4. Characterization of Biosorbent*

The Brunauer–Emmett–Teller (BET) surface area, pore size and pore volume after and before the adsorption process were investigated using Quanta Chrome NOVA 4200E Surface Area Analyzer. The morphology of the DSP was investigated using a scanning electron microscope technique (SEM) JEOL 6360 (Japan). Accelerating voltage of 20 kV was used. The functional groups of DSP before and after the adsorption process were investigated by ATR-FTIR (Cary 630 FTIR from Agilent) in the range of 4000–400 cm−1 at a spectral resolution of 8 cm−1 . DSP samples were analyzed without any pretreatment.

#### **3. Results and Discussion**

#### *3.1. Characterization of the DSP*

Table 1 displays the results of the analysis of DSP by surface area analyzer. Results prove that DSP poses a mesoporous arrangement. Mesopores are detected over the entire sample surface forming a highly uniform and interpenetrating permeable media. Moreover, results also confirm that DSP has a large surface area compared to some other adsorbents used to adsorb Ni(II) ions [13].

**Table 1.** Properties of the DSP surface.

#### *3.2. ATR—FTIR Spectrum*

Investigations of the ATR-FTIR spectra from DSP after and before the adsorption process (Figure 1) proves the presence of functional groups, which are among the major characteristic to DSP. The presence of developed aliphatic groups was identified by the absorption band ascribed to the stretching vibrations of carbon-hydrogen bonds in the range between (2980–2840 cm−1 ). OH functional groups were recognized by the broad band ascribed to stretching vibrations of oxygen-hydrogen bonds in the range from 3600 to 3100 cm−1 , whereas, ether structures were identified by the band ascribed to stretching vibrations of carbon-oxygen in the range (1100–1000 cm−1 ). Moreover, CO groups were also detected by the band ascribed to stretching vibrations of C=O bonds in the range from 1750 to 1500 cm−1 . On the other hand, the bands of aromatic compounds bond groups overlay with those obtained by the bonds of other structures. Generally, the FTIR spectrum proves the multiplicity of the structure of DSP. The presence of these aforementioned functional groups on the DSP surface indicates its potential ability to act as a promising adsorbent [14].

#### *3.3. Scanning Electron Microscope Technique (SEM)*

The SEM technique investigations provided an insight into the diverse morphology of the DSP where some larger constituents show asymmetrical form, some other constituents have an extended rod-like construction whereas other smaller constituents display rectangular form. Overall, most of the constituents have a reedy and coarse structure with irregular ends. Particle size of the DSP ranges from 5–15 µm. It can be noted from Figure 2a the availability of numerous available cavities and holes enabling Ni(II) ions to be adsorbed, while Figure 2b shows that these holes are occupied by Ni(II) ions indicating good adsorption capacity for DSP. *Processes* **2020**, *8*, x FOR PEER REVIEW 4 of 16

*Processes* **2020**, *8*, x FOR PEER REVIEW 4 of 16

**Figure 1.** Attenuated total reflection-Fourier transform infrared spectrometer (ATR-FTIR) spectra for **Figure 1.** Attenuated total reflection-Fourier transform infrared spectrometer (ATR-FTIR) spectra for the date seeds powder (DSP) before and after Ni(II) ions adsorption process. and holes enabling Ni(II) ions to be adsorbed, while Figure 2b shows that these holes are occupied by Ni(II) ions indicating good adsorption capacity for DSP.

the date seeds powder (DSP) before and after Ni(II) ions adsorption process.

**Figure 2. Figure 2.**SEM technique images of the DSP surface ( SEM technique images of the DSP surface ( **aa**) before and ( ) before and (**b b** ) after adsorption. ) after adsorption.