**1. Introduction**

Foraminifera are one of the most abundant and diverse heterotrophic protists in the oceans consisting of a major group of calcareous marine microplankton [1]. Due to their great abundance and their good fossilization potential, foraminifera are commonly used for bio-ecostratigraphic [2–7], paleoceanographic/paleoclimatic [8–19], and/or paleobiogeographic [20–24] studies. Particularly, planktonic foraminifera are the most frequently applied microfossil group in this manner because they have an excellent fossil record with global distribution, high abundance in sedimentary archives, and further present an enhanced sensitivity to varying sea surface conditions [25,26]. The geographic ranges and abundance of these organisms can also provide valuable quantitative and qualitative proxy

data through the estimation of several paleoceanographic and paleoclimatic indices for reconstructing paleoenvironments [27–29]. Both their abundance and shape-size-related changes are strongly related to surface ocean physico-chemical properties, most notably temperature, but also nutrient and oxygen availability, water column stratification, salinity, turbidity, and carbonate saturation [8,25,30–38]. Elucidation of the factors governing their distribution, and processes involved in their ontogenetic development are therefore essential for reconstructing paleoceanographic conditions.

Compared to the large body of knowledge on the taxonomy, physiology, and ecology of planktonic foraminiferal species, their spatial distribution and size response to hydroclimate remains poorly studied, especially for the oceanic sub-basins and/or marginal seas, which are often more responsive to paleoceanographic and paleoclimatic changes than global oceans. Moreover, as a significant constituent of microzooplankton, they are key components of pelagic food webs and the main predators of phytoplankton in (sub)tropical oligotrophic waters regulating thus the carbon flux in such environments [39–43]. Although at a global scale, their abundance follows the overall pattern of primary productivity (PP; [44]), at a regional scale this relationship is weaker possibly due to the omnivorous diet of the planktonic foraminifera in the marine food web, and phase shifts in the production of phytoplankton and zooplankton [1]. Species abundance varies with season, water mass, and water depth [36]. Both the highest horizontal and vertical separation of species are recorded from temperate to subtropical waters, owing to a wider diversity of meso-scale local hydrographic features and biotic variables, which make their distribution patchy on temporal and spatial scales [45,46].

Based on the species-specific ecological tolerance limits of modern planktonic foraminifera [1,47], the potential reduction in abundance is related to their departure from optimum conditions [48], and the subsequent size-related and/or weight-related changes on the planktonic fauna [49], since planktonic foraminifera must compensate for their greater shell weight to maintain buoyancy [50]. Changes in size can be attributed to different processes (volume or surface area dependent) linked to the ecology of each species [51–53]. Particularly, isometric features increase with the cube of linear dimensions, while surface area-related features increase merely with the square of linear dimensions. Consequently, surface area-dependent processes, such as feeding, respiration, and skeletal support of foraminifera have to keep up with volume and weight changes.

The main goal of this study is to evaluate and further quantify the spatial and sizerelated distribution of modern planktonic foraminifera from the central Mediterranean Sea. In our attempt to better understand the paleoecological and paleobiogeographical significance of size variability in planktonic foraminiferal fauna of Adriatic and Ionian basins, the relative importance of environmental factors controlling rates, magnitudes, spatial scale, and biotic change are also examined. This allows for the establishment of a detailed reference record for the central Mediterranean in terms of planktonic foraminiferal ecosystem functioning (including species composition, diversity, and size variability), providing, on the one hand, a solid eco-morphological microplankton response to environmental conditions for the study area, and facilitating, on the other hand, paleoceanographic correlations at a local (e.g., eastern Mediterranean—Aegean and Levantine basins; [24]) and a global, e.g., [37], scale.


Type

Aerobic training should preferably be rhythmic in nature, repetitive, involve large muscle groups, and not require great experience. In view of this, the best training modes are walking, cycling, jogging, and swimming. Walking is a suitable mode of training for obese patients or untrained patients with a poor functional capacity. Exercise intensity is determined by walking speed, with brisk walking corresponding to moderate intensity [2]. Nordic walking—i.e., walking with poles—is recommended for elderly patients with gait or balance problems [19,20]. The most effective form of aerobic endurance training is jogging, and this is suitable only for patients with very good functional capacity. Stationary leg cycle ergometers allow for blood pressure and heart rate monitoring and for the recording of ECG; thus, they are suitable for group training.


**3. Biomechanical basis of the BDSF-method**

This method's innovation is laying of the three screws in two planes, which allows for the entry points of two of the implants to be placed much more distally, in the solid cortex of the proximal diaphysis, and also to lean onto the femoral neck distal cortex. Thus we establish two points of support. The solid distal cortex of the femoral neck acts as a medial supporting point for the screws, which works under pressure - *supporting point A*. The entry points of two of the screws (the distal and the middle one) in the thick cortex of the diaphysis, ensure a second solid supporting point for the screws – a lateral one, which works under tension (or pressure in proximal direction) - *supporting point B*. The position of the distal screw as well as of the middle screw, which are achieved by the method, in terms of the *statics*, turns them into *a simple beam with an overhanging end, loaded by a vertical force.* This beam with an overhanging end successfully supports the head fragment, bearing the body weight and transferring it to the diaphysis. Furthermore, due to the biplane placement, enough space for a third screw is provided, unlike the classic authors' models, where just one or maximum two implants are placed at an obtuse angle [7,11]. Another advantage of the method is that due to the increase in the distance between the two supporting points, the weight borne by the bone is significantly reduced *(see the static analysis)*. An advantage of the BDSF-method is that the entry points of the screws are positioned wide apart from each other, which ensures that upon weight bearing, the tensile forces spread over a greater surface of the lateral cortex and thus the risk of its fracturing decreases significantly. Another advantage with the BDSF is that the screw, placed at a highly increased angle, works in a direction close to the direction of the loading force, which guarantees better results for the screw in its role as a beam because the influence of its sagging decreases.

Biomechanics of the Fractured Femoral Neck –

*a* ;

The New BDSF-Method of Positioning the Implant as a Simple Beam with an Overhanging End 87

accuracy, with the BDSF-method, the static model is considered to be *a simple beam with an*

**Figure 3.** Static model of the new BDSF-method of fixation – the implant acts like a simple beam with

Applying the well-known equilibrium equations for a beam, we obtain the forces acting on

an overhanging end. *F = load; L = length of beam; a = distance between points A and B*

the smaller the weight at each of the two supporting points.

The load acting at point **A** is pressure in a distal direction and it equals to *FL A*

The load acting at point **B** is pressure in a proximal direction and it equals to *B* **=** *A – F*.

At the BDSF-method, due to the increase in the distance between the two supporting points, the weight borne by the bone is reduced. If we look at two cases of equal vertical weight but different distances between the supporting points, we will see that the greater the distance,

The average anatomical distance from the tip of the screw to the distal femoral neck cortex

With *conventional methods* **(case 1.)** the average distance from **point A** to the entry point of the screws in the lateral cortex (**point B**) is 5.5 cm (a = 5.5 cm). In order to make a comparison with the BDSF, when body weight of 100 kg is given, with conventional methods the load acting on the curve of the femoral neck distal cortex (if the screws lean on

the cortex at supporting points **A** and **B**.

curve (**point A**) is 3.5 cm (Fig. 4.).

this support at all) is estimated as

*overhanging end* (Fig.3). This beam is supported at points **A** and **B** only.


*Article* **EEG Based Classification of Long-Term Stress Using Psychological Labeling**

**Sanay Muhammad Umar Saeed 1, Syed Muhammad Anwar 2,3,\*, Humaira Khalid 4, Muhammad Majid 1 and Ulas Bagci 3**

Received: 18 February 2020; Accepted: 25 March 2020; Published: 29 March 2020

**Abstract:** Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via *t*-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

**Keywords:** long-term stress; electroencephalography; machine learning; perceived stress scale; expert evaluation


*6.4.1 Hardware*

*Control Theory in Engineering*

the first four out of five aims.

challenging and thus was not achieved in time.

aspect of the requirements.

a generic algorithm.

**6.3 Relation to MoSCoW and further improvements**

and thus be more applicable to real-world projects.

moving.

Can the vehicle utilise collision avoidance via ultrasonic sensors?

Can the vehicle recognise and stop at stop signs for a certain amount of time? Following the test phase, the vehicle has shown the capabilities required to pass

The vehicle fully utilises the neural network to accurately classify the real-time image to decide upon and then implement a movement instruction. This allows the vehicle to follow the track given any set layout and follow it until the procedure is manually stopped. A variety of obstacles have been placed in front of the vehicle during operation, and when this occurs, the ultrasonic sensors recognise it, and the vehicle stops within the specified distance in order to avoid collision. The vehicle remains stationary until the obstacle moves away or is removed from its path. If the obstacle is moved around incrementally, it will "follow" the object staying at a minimum of the specified distance away. This is a very useful functionality for potential additions to the project which is to have the vehicle following other moving vehicles or staying a specified distance away from the obstacle should it be

However, the stop sign detection capability was not implemented due to time constraints. To do this a Haar cascade qualifier must be used; this is an entirely different machine learning algorithm and thus would have to be implemented alongside the neural network. This naturally would have added many further functionalities. This was placed into the "would like to have this requirement later to develop the system design further" section of the MoSCoW analysis of the requirements because not only would this further implementation be time-consuming but finding small-scale stop signs that would be adequate for this task proved to be

In relation to the MoSCoW analysis, "all of the must have", "should have" and

Further developments may be made as specified to cover the "would like to"

Implementing a backpropagation algorithm manually as opposed to relying on the one provided from OpenCV, this would be desirable as it has the potential to be developed in a manner that may be more efficient for the problem at hand than just

A possible improvement would also be integrating the capability of designing, recognising and acting upon stop signs via a Haar cascade qualifier. This would be a further development to the device which could allow it to have more functionality

Another adaptation which would have been interesting to implement would be using a K-nearest neighbour algorithm for the machine learning part of the project. This could be used to compare the results from this to that of the neural network, to see which is more accurate. However, as aforementioned this was not implemented currently because of the much larger data set that would be required

The only final improvement which could be made to the system would be to improve the mathematical functions applied to the ultrasonic distance sensors, which will be discussed below. These are currently only accurate at reading distances up to 50 cm using a simple mathematic function which for the present problem is acceptable, but future adaptations to the device may require more accuracy

and thus more advanced mathematics to allow it to be accurate up to 5 m.

"could have" related requirements were fully met to a good standard.

**320**

to train it.

Hardware design encountered a variety of problems with the circuitry of the vehicle which connected the external components to the Pi. Using a breadboard to connect all the components was not viable in the long term, and testing proved that as the car rattled, wires would come free as they were not fixed in place.

This was fixed by having a permanent version of the circuit made and soldered in the workshops at the university, producing a finished product seen in **Figure 7**. This took a while to be perfect, and because of this, other tasks had to be put on hold pending the new board design. The new board completely solved the problem and provided both stability and much greater longevity to the product.


ii.Acidogenesis

Anaerobic activity of acetogens produces mostly organic acids, alcohols, hydrogen gas, and hydrogen sulfide throughout this process [33, 97]. In an acidic environment generated by the fermentative bacteria, hydrolysis products are degraded by acidogenic microorganisms to produce ammonia, carbon dioxide, hydrogen sulfide, carbonic acid, shorter-volatility fatty acids, alcohol, and other trace products based on the substrate composition and products of hydrolysis [97]. Acidogenesis byproducts are too large to be ideal for methane production. Thus, they engage in acetogenesis [67].

#### iii.Acetogenesis

The process of acetogenesis produces acetate from acetic acid [99]. Through anaerobic digestion, acetic acid, hydrogen, and carbon dioxide are generated during acidogenesis. The results of acetogenesis and other processes are digested to the point where methanogens can produce methane [67].

#### iv.Methanogenesis

Methane and carbon dioxide are created in the final stage, called methanogenesis, thanks to the activity of acetoclastic methanogens (AM) and carbon dioxide (CO2) reducing methanogens (CM), respectively. Finally, anaerobic bacteria called methanogens like *Methanosarcina barkeri*, *Methanosaeta concilii*, and *Methanococcus mazei* complete the methanogenesis process by producing methane from the byproducts of acetogenesis and other intermediate products of acidogenesis and hydrolysis [33, 97, 100]. The first three stages of methanogenesis (as shown in Eqs. (1) and (2)) involve the utilization of CO2 and acetic acid (CH3 COOH):

$$\text{CH}\_2 + 4\text{ H}\_2 \rightarrow \text{CH}\_4 + 2\text{H}\_2\text{O} \tag{1}$$

$$\text{CH}\_3\text{COOH} \rightarrow \text{CH}\_4 + \text{CO}\_2 \tag{2}$$

While (CO2) might theoretically be turned into water and methane, at this time the acetic acid pathway is the primary mechanism for methane generation. Therefore, (CO2) and (CH4) are produced as the primary products of anaerobic digestion via the acetic acid pathway [67]. H2 and CO2 gas combinations are transformed into biogas, which is composed of 60–70% CH4 and 40–60% CO2 [16]. The four steps of biogas production, together with associated activities and microorganisms, are depicted in **Figure 3**.

**Figure 3.** *Summary of the biogas formation process.*

**Figure 3** shows the main stages in biogas production being hydrolysis, acidogenesis, acetogenesis, and methanogenesis as the four primary stages in anaerobic digestion. Maximal biogas production requires careful management of the four main stages.


*4.1.3. Effect on fruits and olive oils quality*

than vegetative growth. In the initial years of orchard establishment, when rapid vegetative growth is desirable in order to quickly obtain optimum tree size and canopy, as well as to begin fruit production as soon as possible, it is critical not to depress vegetative activity. For this reason, in commercial orchards, DI is commonly implemented only once, trees are fully grown to avoid negative effects on the formation of tree structure during the training period [102]. DI at early stages of tree development may result useful not only for water saving but also for controlling vigor in super high-density (SHD) orchards, in particular in regions where local conditions lead to excessive vegetative growth, such as in northern Argentina [103]. The choice and success of DI strategy is conditioned by tree density and rootzone size. It seems that SDI is more interesting when trees explore large volumes of soil, as in low-density orchards that maximize the availability of stored soil water per tree, compared to higher densities [97, 104]. Moreover, the success of SDI as compared to FI depends on the crop load of olive. About this issue, Martín-Vertedor et al.

of Spain. They observed that SDI (75% ETc) reduced yield in "on" years. Nevertheless, they reported that this DI could be advisable during "off" years, when a lower water use is observed, and trees are less sensitive to water deficit with low-crop load. There is still uncertainty about

Lavee et al. [107] suggested that the most efficient schedule for RDI irrigation was to withhold water till the end of endocarp hardening and then to apply full irrigation from that stage till

Often, DI strongly reduces vegetative growth, but only slightly reduces the final fruit volume. Water stress caused a higher reduction in fresh fruit yield than oil yield due to a higher oil concentration in DI irrigated trees "in Picual" (Spain), without differences between SDI and RDI [11]. Moreover, Iniesta et al. [11] observed that WP for oil production has tripled for a 25% decrease in total water applied. They conclude that both irrigation strategies may be used with moderate reductions (about 15%) in oil yield. Similarly, Fernandes-Silva et al. [12] ("Cobrançosa," Portugal) reported that for a SDI at 30% ETc, WP for oil is higher or very close to FI, depending on the year, and is more than double the one obtained in rainfed conditions; oil yield is reduced only 35% as compared to FI, while saving 60% of water applied. Nevertheless, oil concentration on a dry matter basis (DM) in SDI was 7–19% higher as compared to FI, hence oil yield reduction was lower than yield of fruit (DM). The higher oil yield observed in FI is mainly due to higher number of fruits, although under SDI, fruits have slight higher values of mesocarp (>3–5%) as compared to FI olives, mainly attributed to a higher crop load in FI olive trees. Fernandes-Silva et al. [12] founded a good relationship between the oil amount per mesocarp dry mass (g) (y = 0.83 × −0.17, r2 = 0.97). This may be useful in supporting the decision of the most suitable time for harvest to optimize oil productivity.

Irrigation is particularly an important component in SHD orchards as the trees are expected to have more reduced volume of the rootzone. There is not a consensus on the best irrigation approach for SHD olive orchards. A reduction in water applied up to 16% in July did not

), in the Southwest

) under FI [63],

[109].

[105, 106] conducted a long term studied in "Morisca" orchard (417 trees ha−1

The literature provides results, for low-density orchards (300–600 trees ha−1

SDI [12], RDI [11], and PRD [108] and for SHD olive orchards >1500 trees ha−1

which DI strategies are better, regarding SDI or RDI [58, 101].

2 weeks prior to harvest.

60 Irrigation in Agroecosystems

The concept of quality in fruit products is wide, complex, and dynamic. In the case of olive trees, two main products are obtained from olive fruits: virgin olive oil (the juice of the fruit) and table olives; both are staple foods of the Mediterranean diet. The quality attributes that are considered for each product largely differ from one another.

High irrigation rates are associated with a decrease mainly in minor compounds of virgin olive oil (VOO) as they are total polyphenols (TP), orto-diphenols (OD), tocopherols (TC) volatile compounds (VC) [16, 114] that have an important role in nutritional value, biological proprieties, and organoleptic characteristics of VOO. There is a controversy about the effect of irrigation in overall quality of VOO. In the literature, there are researchers who argue that FI lowers the quality of olive oil [115]. If this may be true for Cvs poor in TP, such as "Arbequina," FI may compromise the conditions necessary for virgin extra category and in other hand, decrease its self-live time. Nonetheless, in Cvs very rich in PT (>1000 mg/kg), such as "Cornicabra," VOO is very bitter and pungent, and therefore with poor acceptability by the consumer, FI may help to overcome this problem.

olives ("Manzanilla"), after processing that were previously submitted to three irrigation

with low stress at the end of flowering stage, and moderate during pit hardening. They observed that FI olives had the highest weight and size, and were rounded. Color coordinates

predominated in FI olive fruits, while terpenes and polyunsaturated fatty acids predominated

consumers due to fresh olive flavor, crunchiness, and global satisfaction. They argue that both RDIs are effective and can be a good alternative irrigation practice for this cultivar. However, these authors evaluated table olives quality after processing, an evaluation after harvest, that

Water deficit effect could increase of PhytoPs content, chemical compounds analogs to prostaglandin, which belong to a novel family of plant effectors, may be related to the enhancement of reactive oxygen species (ROS) production under drought stress, which induce the formation of an array of lipid peroxidation products [123]. The phase II of fruit growth can be noncritical considering fruit yield or fruit size [124] but is clearly critical for PhytoPs formation. Thus, olive table trees under RDI can be considered as complementary actions to enhance the PhytoP content and hence their potential beneficial effects on human health as they play a role

RDI is based on restraining irrigation during certain periods of the vegetative cycle of the crop, therefore implying the knowledge of the several phases and sometimes its differences between genotypes, since the length of some phases (fruit development period and ripening) varies for early-maturing or late varieties [126]. The phenological stages of peach *Prunus persica* L. Batsch) can be depicted as shown in **Figure 1**. During the fruit growth period, three phases are classically considered: phases I and III, where rapid growth occurs and a phase II characterized by a plateau [127] having the growth curve, a double sigmoid pattern [128].

Several studies over the last decades have addressed the use of deficit irrigation, namely RDI, in peach. Ref. [31] have applied the method to peach during the phase of final swell and observed a significant production and fruit growth increase, if irrigation restrictions were applied while excessive vegetative vigor could be suppressed to favor fruit growth. Mitchell and Chalmers [32] have used RDI during the phase of fast vegetative growth, obtaining similar yield and fruit growth to a nonrestricted situation, while saving ca. 30% of irrigation water and controlling the vegetative growth. For the post-harvest phase, [129] observed that irrigation reduction decreased pruning requirements and increased flowering in the next season. For the same phase, and also during fruit development, [130] saved 40% of irrigation water with light implications in production and fruit size. More recently, the benefits of applying

with moderate stress during pit hardening (soft water stress) and RDI2

Deficit Irrigation in Mediterranean Fruit Trees and Grapevines: Water Stress Indicators and Crop…

olives. Aldehydes and monounsaturated fatty acids

olives, with high satisfaction degree among

olives. Sensory evaluation indi-

http://dx.doi.org/10.5772/intechopen.80365

63

treatments: FI; RDI1

L\* and b\* had the highest values in RDI2

in regulation of immune function [125].

*4.2.1. Vegetative and productive cycle*

*4.2.2. Peach response to water stress*

**4.2. Peach**

cated that global acceptance was higher for RDI1

is, before olives processing may be more interesting.

in T1 fruits, and saturated fatty acids were abundant in RDI2

Motilva et al. [116] observed that RDI strategies applied to "Arbequina" induced a significant increase in polyphenol concentration and oil stability. Fernandes-Silva et al. [16] found a strong relation (*r*2 = 0.715; *p* = 0.033) with TP and between water stress integral (WSI). Similarly, Pearson's correlation coefficients between oxidative stability (OS) and TP was high and significant (*p* = 0.026), but no significant correlation was found between OS and TC (*p* = 0.322). Moreover, Gómez del Campo [110] and García [117] observed that the application of RDI in summer produced a significantly higher OS, which coincided with a significantly higher content of TP derivatives. These compounds are of great interest because they influence the quality and the palatability of VOO and increase their self-life time by slowing the formation of polyunsaturated fatty acid hydroperoxides.

Irrigation regimes either equivalent to 30 or 100% of ETc, applied to olive trees, "Cobrançosa" affects significantly the activity of L-phenylalanine ammonia lyase (PAL, EC <IP_ADDRESS>), that is considered as the key enzyme in phenolic biosynthesis, the TP and amount of individual polyphenols [17]. Higher PAL activity, TP and individual polyphenol contents were observed for the rainfed conditions in the first picking date, and decreased with maturation of the olive fruits. Also, this effect was observed for the two irrigation regimes applied. The difference in the PAL activity, TP and individual polyphenol content between the three water regimes, decreases as olives become more mature.

Olive oil fatty acid composition is often not affected by RDI strategies [118], although other studies indicate that irrigation strategies cause small variations in the oleic and palmitic acids [16, 116]. Magliulo et al. [119] reported that olive oil fatty acid composition from two different cultivars ("Frantoio"; "Leccino") was more affected by varietal factors and climatic conditions of the year than by water regimes. Curiously, when cv "Arbequina," is cultivated in warm arid valleys of North Western Argentina, produced a lower content of 18:1 acid in relation to the Mediterranean region [120] and a decrease with increasing temperature during oil accumulation of 2% per °C was found.

DI can also influence the sensory attributes of olive oil. In cultivars such as "Arbequina," which normally has low-phenolic concentrations, DI is beneficial due to the greater polyphenol concentrations. More phenolics contribute to better balanced oils with a more sophisticated pungent and bitter flavor [114].

With regard to the quality attributes of tables' olive, they are also affected by DI strategies. Cano-Lamadrid et al. [121] and Cano-Lamadrid et al. [122] evaluated the quality of table olives ("Manzanilla"), after processing that were previously submitted to three irrigation treatments: FI; RDI1 with moderate stress during pit hardening (soft water stress) and RDI2 with low stress at the end of flowering stage, and moderate during pit hardening. They observed that FI olives had the highest weight and size, and were rounded. Color coordinates L\* and b\* had the highest values in RDI2 olives. Aldehydes and monounsaturated fatty acids predominated in FI olive fruits, while terpenes and polyunsaturated fatty acids predominated in T1 fruits, and saturated fatty acids were abundant in RDI2 olives. Sensory evaluation indicated that global acceptance was higher for RDI1 olives, with high satisfaction degree among consumers due to fresh olive flavor, crunchiness, and global satisfaction. They argue that both RDIs are effective and can be a good alternative irrigation practice for this cultivar. However, these authors evaluated table olives quality after processing, an evaluation after harvest, that is, before olives processing may be more interesting.

Water deficit effect could increase of PhytoPs content, chemical compounds analogs to prostaglandin, which belong to a novel family of plant effectors, may be related to the enhancement of reactive oxygen species (ROS) production under drought stress, which induce the formation of an array of lipid peroxidation products [123]. The phase II of fruit growth can be noncritical considering fruit yield or fruit size [124] but is clearly critical for PhytoPs formation. Thus, olive table trees under RDI can be considered as complementary actions to enhance the PhytoP content and hence their potential beneficial effects on human health as they play a role in regulation of immune function [125].


**3. Results**

#### *3.1. Study Selection*

A total of 572 articles were identified in the first phase of the research. During the second phase, 94 articles were considered, and after full-text evaluation 16 studies were included in the review. Finally, five articles of interest were obtained through manual research obtaining a total of 21 articles were included in this review [17–37] (Figure 2).

**Figure 2.** Flow diagram of study inclusion.

#### *3.2. Study Methods and Characteristics*

Four studies were RCT [20,24,33,35], 2 were NRCT [21,28], 2 were prospective studies [22,32] and 13 were case series [17–19,23,25–27,29–31,34,36,37] and all of them were published between 2001 and 2021 (Table 1).

The studies were conducted in nine different countries, the total number of patients included was 382 (137 males, 193 females, and 52 non-specified) and a total of 416 titanium meshes.

There were four articles in which the gender was not specified [20,33–35]. The study with a higher number of patients was Miyamoto et al. (N = 41) [17] while the one with fewer patients was Ciocca et al. [29] with a total of nine patients. The mean age was 53.4 and four articles did not specify the mean age [24,27,34,35].

Regarding the characteristics of the surgeries performed, it was quantified the number of sites, and whether if it was in mandible or maxilla. In five studies, the number of sites was not specified [18,28,30,34,35], of which 164 sites belong to the maxilla and 129 to the mandible.

In 16 studies the graft material used was autologous bone; in six of them it was the only material used [17,18,20,21,23,28] (N = 120), and in the other 10 articles, autologous bone was associated with other graft materials such as inorganic bovine bone (ABB) [19,22,25,26,32,35,37] (N = 91), thermoplastic mouldable allograft paste (TMAP) [33] (N = 28), bovine porous bone protein (BPBM) [21] (N = 3), demineralized bovine bone mineral (DBBM) [27] (N = 13) or Hydroxyapatite [29] (N = 21). There were five studies in which autologous bone was not used and the regeneration was performed only with the use of ABB [24,30,34] (N = 67) or TMAP (36,31) (N = 39).

The removal of the mesh and the quantification of bone gains was performed during the second-stage surgery or also called in most studies as healing period, which was performed on average at 6.5 months.

#### *3.3. Quality Assessment and Risk of Bias within Studies*

The risk of bias of the RCT is presented in Table 2. Three RTCs were considered as having a low risk of bias even though there were some concerns about the blinding of participants and researchers [24,33,35] and Atef et al. did not report the blinding of outcome assessment and selective outcome reporting, potentially introducing selection bias [35]. One study was considered as having a high risk of bias since there were some concerns about the random sequence generation, the allocation concealment, and the blinding of participants and researchers [20].

**Table 2.** Risk of Bias of included randomized clinical trials.

L: Low; (?): Unclear; H: High.

Two non-randomized clinical trials were included, and the risk of bias is represented in Table 3. These two articles were considered as having a low risk of bias but there were some concerns about potential bias in the classification of interventions [21,28] and also due to deviations from intended interventions [21].

**Table 3.** Risk of Bias of included non-randomized clinical trials.

L: Low; (?): Unclear; H: High.

(The systematic review was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) scale [13]) with 21 items.

*3.4. Characteristics of the Mesh*

Different types of meshes were used in the studies, all of them are summarized in Table 4.

**Table 4.** Characteristics of the meshes used.

NE: Not evaluated.


**2. Experimental**

carbon-carbon double bonds and carbonyl moieties [2]. The presence of these functional groups affords UV filters the ability to absorb photons and rapidly return to the ground state by thermally emitting the energy through vibrational relaxation [3]. This makes the com-

After application, UV-filters are washed off and enter the aquatic environmental directly or indirectly via wastewater effluent and recreational water systems [4]. They are also used as sun blocking agents in materials such as plastics, adhesive and rubber, this suggests that these compounds can also leach into the environmental matrices [5–7]. The amount and type of UV-filter used depends on the desired degree of protection, however combined concentra-

The main concern of the presence of these compounds is their potential toxicity and their effects as xenohormones (effect on reproductive activity) [9, 10]. These effects include estrogenic activity [11], effects on cell proliferation by 4-methylbenzylidene camphor (4-MBC), benzophenones, and octyl methoxycinnamate (OMC) [12]. Several studies have shown hormonal disruption in both in vivo and in vitro test systems in fish and mammals [13–16]. It has also been recently shown that besides estrogens, there are other hormonal targets affected by

A number of detection techniques have been used to quantify UV-filters in environmental water samples. These include techniques high performance liquid chromatography (HPLC) with UV or mass spectrometry detection [19, 20], HPLC-MS/MS [21], gas chromatography-mass spectrometry (GC-MS) and GC-MS/MS [22, 23]. However, the levels of these compounds in environmental waters are usual in the μg L−1 range. Therefore, sample cleanup/preconcentration techniques such stir-bar sorptive extraction [20], pressurised liquid extraction [1], dispersive liquid-liquid phase extraction [24], solid phase extraction [25], among others, have been used to improve sensitivity, LODs and to remove interferences prior to quantification with the different analytical techniques. Solid phase extraction is one of the most established preconcentration techniques used for the simultaneous extraction and analysis of organic compounds [26]. Mainly silica bonded phases such as C18 were formerly used for SPE, however recently modified and tuned solid phases can be used to achieve more specificity [27]. Advantages of SPE include the potential of simultaneous extraction, reduced labour and cost. Of the advantages of solid phase extraction, scientists are mostly attracted to the possibility of using reduced amount of organic solvents

and the fact that SPE is highly tuneable with regards to the adsorbents used [28].

pound to be able to mitigate the deleterious effects of UV radiation [1, 2].

**Figure 1.** Chemical structures of (a) benzophenone and (b) sulisobenzone.

46 Emerging Pollutants - Some Strategies for the Quality Preservation of Our Environment

tions should not exceed 10% with other organic or inorganic UV-filters [8].

UV-filters in fish and mammals [17, 18].


**5. Reverse pharmacology in drug discovery for infectious diseases**

HTS bioassay-guided identification and isolation of bioactive compounds from natural biodiversity is termed as "Reverse Pharmacognosy." Similarly, isolating a chemical entity and developing a pharmaceutical product from the clinically proven herbal remedy is termed "Reverse Pharmacology." Quinine and Artemisinin are the two well-known antimalarial lead molecules identified and isolated through this approach [32] which were optimized using HTS and chemistry approaches to various antimalarial drugs with a better pharmacokinetic and pharmacodynamic profile.


**4.4 Water resource depletion**

 While the debate and study of how water quality may be impacted by UOG activities continues, there is clear evidence that water abstraction for hydraulic fracturing in water-scarce areas can lead to increased competition and shortages. A report by Ceres, a sustainability non-profit organization formerly known as the Coalition for Environmentally Responsible Economies, examined the relationship between water use by UOG and other sectors in water-stressed regions and found that water resources are negatively impacted [42]. Nationwide, Ceres looked at nearly 110,000 UOG wells and estimated that 57% of oil and gas wells hydraulically fractured between 2011 and 2015 were in water-scarce regions and where water is a subject of competition among farmers, towns and cities, and the oil and gas industry. Overall, fracking-related water use during the 5-year study period totaled 358 billion gallons. Put into perspective, this amount of water is consumed by approximately 200 mid-sized U.S. cities. States with significant oil and gas production that are particularly impacted by the threat of increasing water competition are Texas, Colorado, and California. These states are also home of some of the leading shale plays, including the Eagle Ford and the Midland Play (part of the Permian Basin) in Texas. Other plays characterized by high water use are the Marcellus Shale and the Niobrara in Colorado, Wyoming, and Nebraska. The county with the highest number of UOG wells (~7000 wells) and water use for hydraulic fracturing (>16 billion gallons) is Weld County, CO [42].


**4. Conclusions and perspectives**

Altogether, we assume that the set of our studies are revealing the cellular and molecular dynamic interactions behind breast-cancer-derived pre-metastatic bone niche formation (**Figure 1**). There are still many questions about the factors that determine the chemotaxis of cells, which Ags or ligands are needed, as well as the modulating elements of this distant system. So far, we know, that the immune system is central. Multiple cues need to be investigated to translate our current knowledge toward clinical impact. If these immunophenotypes patterns are confirmed in human disease, this complex network can be used either as prognostic tools or even as therapeutic targets.

### **Figure 1.**

*Left panel: anti-osteoclastogenic cytokines produced by 67NR+ CD8+ T cells keep bone homeostasis mediated by OBs and OCs cross talk. Right panel: BM pre-metastatic niche formation by 4T1 Th17 RANKL+ CD4+ T cells and RANKL+ CD8+ T cells activities. DCs loaded with tumor Ags from primary tumor growth site prime naïve CD4+ and CD8+ T cells into draining LNs to differentiate into tumor-activated T cells producing RANKL and IL-17F, which in turn migrate to BM before tumor bone colonization. Migrating DCs can now activate the maintenance of tumor RANKL+ CD4+ Th17 T cells inside BM microenvironment dependent on IL-23 production. Dysregulation of bone homeostasis by an intense activation of BM-OCs by RANKL and M-CSF under breast tumor pre-metastatic osteolytic conditions induced by RANKL+ CD4+ Th17 T cells and RANKL+ CD8+ T cells. Both T cells activities support their osteoclastogenic potential in the establishment of the pre-metastatic niche. Left panel: Bone marrow scenario after subcutaneously 67NR non-metastatic tumor cells challenge. Production of anti-osteoclastogenic cytokines, IFN-γ + , IL-10, and anabolic levels of RANKL expressed by CD8+ T cells keep bone homeostasis mediated by OCs and OBs cross talk.*


2. Method, hypothesis and results

#### 2.1. Goals

The general goal of this study is to evaluate the impact of taking the SREU module on students from a variety of university courses, using a reliable and valid tool.

characteristic, in which the sample should be similar to the study population, second year

Social Responsibility among University Students: An Empirical Study of Spanish Samples

http://dx.doi.org/10.5772/intechopen.75115

27

The information was collected using an individual survey. The field work was performed two times: (1) in October 2012 it was performed on the students when teaching of the module had recently started (pretest) and (2) in May 2013 it was performed a second time when the students had completed the module (posttest). The samples from the pretest and the posttest are independent as the questionnaires are anonymous and the individuals who answered the pretest and posttest are not identified to make comparison of the samples possible. Table 1

A total of 757 surveys was eventually obtained: 404 surveys in the pretest (63.2% of the population) and 353 surveys in the posttest (55.2% of the population). The following profile of

The SPSS version 21 statistics program was used to codify and analyse the data (SPSS Inc., Chicago, USA). Descriptive analyses were performed (central tendency measures and standard deviation). To compare the significant differences at the statistical level between the two moments (before and after taking the module), Student's t-test for independent samples and one factor and two factor Analyses of Variance (ANOVA) parametric tests were used to analyse the differences. The assumptions of independence of the data (normality and homoscedasticity) were tested. Student's t-test was performed, without distinguishing between courses, on an aggregate dimension (the sum of the four dimensions). One-factor analyses of variance (ANOVA) were performed on each of the four dimensions and on the opinion item. For these six quantitative variables, two-factor ANOVA analyses were performed to evaluate the effects of the two possible sources of variance, as well as the interaction between them: (A) belonging to the pretest or posttest groups and (b) belonging to a particular course. One-factor ANOVA analyses were also performed on the four dimensions and the opinion item for each of the courses. A confidence level of 95% or higher was used in almost all of the analyses.

Universe Second year students at the UFV who are taking the social responsibility module

students at the UFV.

3.3. Data analysis process

Characteristics Survey

Sampling error 3%

Table 1. Technical details of the research project.

Sampling process By quotas Stratification by course Data collection Individual survey of the students in class Sample size 404 (pretest)/353 (posttest) surveys collected

Confidence level 95% for the most unfavourable case p = q = 0.5 Field Work November 2012 (pretest) and May 2013 (posttest)

shows the technical details of the sampling.

the sample was subsequently identified (Table 2).

The specific goals are:

#### 2.2. Hypothesis

There are three main hypotheses to compare in this study. They relate to three types of possible impact: (1) the module has a general impact on the students; (2) the different dimensions that make up SREU have different impacts on the students; and (3) the module has a different impact on the students in each course.