**4. Update on the clinical application of iNO in pediatric patients**

After NO was identified as an endothelial cell-derived relaxation factor and following preclinical studies, iNO therapy has been studied extensively in multicenter randomized trials as well as in early pilot studies of infants with severe hypoxemia associated with PH or infants with congenital diaphragmatic hernia (CDH) [60]. These studies have demonstrated improved oxygenation and reduction in the need for extracorporeal membrane oxygenation (ECMO) therapy, leading to the approval of iNO therapy by the Food and Drug Administration for use in patients at >34-week gestation with hypoxemic respiratory failure and persistent PH of the newborn (PPHN). Over the last two decades, the discussion of its application has been extended to premature infants and acute pulmonary vascular response testing to assess indications for specific pulmonary vasodilator therapy for patients with PAH or operability for children with congenital heart disease.

### **4.1 Role of inhaled NO in the prevention of bronchopulmonary dysplasia in premature newborns**

Bronchopulmonary dysplasia (BPD), which is characterized by impaired pulmonary development resulting from insults affecting the immature lung, including inflammation, hyperoxia, and mechanical ventilation, is associated with high mortality and adverse long-term neurological and respiratory outcomes in infants born very preterm. Although the effectiveness of iNO for the treatment of PPHN is largely due to its function as a selective pulmonary vasodilator, laboratory observations also suggest other important biological effects of NO, such as roles in decreasing lung inflammation (e.g., lung vascular protein leak; pulmonary neutrophil accumulation) [61], reducing oxidant stress [62], decreasing pulmonary vascular cell proliferation [63], and enhancing alveolarization and lung growth [64–66]. These observations have led to investigations into the use of iNO to prevent the development of BPD in premature newborns. In an initial randomized,

placebo-controlled study in a single center, 7 days of iNO prevented chronic lung disease in premature infants [67]. Despite promising findings in some subsequent studies showing a reduction in BPD in premature newborns [68, 69], later trials did not confirm the beneficial effects [70]. Meta-analyses of these studies have not found evidence for a net improvement in either chronic lung disease or developmental sequelae [71], leading to the conclusion by the National Institutes of Health Consensus Development Conference [72] and the American Academy of Pediatrics Committee on the Fetus and Newborn [73] that the use of iNO to prevent BPD is not supported by available evidence [74].

#### **4.2 iNO for the treatment of severe pulmonary hypertension in preterm infants**

In addition to the use of iNO for BPD prevention, its use in preterm infants for acute management of severe hypoxemic respiratory failure has been discussed. Several case series have described responses to iNO in premature newborns with PPHN associated with prolonged oligohydramnios and pulmonary hypoplasia. Chock et al. evaluated a subset of 12 premature newborns enrolled in the Preemie Inhaled Nitric Oxide Trial with pulmonary hypoplasia after preterm premature rupture of membranes (PPROM) [75]. Six infants were treated with iNO with a mortality rate of 33% compared with 67% mortality for six infants in the placebo control group. Shah and Kluckow described outcomes for infants with PPROM and reported that survival improved from 62 to 90% after the introduction of iNO and high-frequency oscillatory ventilation [76]. Semberova et al. reported a series of 22 premature infants with a history of PPROM, pulmonary hypoplasia, and PPHN who were treated with iNO, with a survival rate of 86% [77]. Thus, iNO therapy may have important benefits in subgroups of preterm infants with severe PH, especially in patients with oligohydramnios and lung hypoplasia. Further studies of the precise effects of iNO in premature neonates are needed.

#### **4.3 Role of iNO in newborns with congenital diaphragmatic hernia**

iNO in neonates with CDH has been evaluated in three randomized trials [78–80]. Finer and Barrington performed a Cochrane Review [81] of the use of iNO for respiratory failure in infants born at or near term. They concluded that while iNO might transiently improve oxygenation, its use is not recommended for infants with CDH because the risks of a composite of either death or ECMO are similar to or worse than those of controls [82].

Based on this evidence, iNO cannot be recommended for the routine treatment of PH in patients with CDH. However, iNO continues to be regularly used for CDH. Indeed, iNO was used at some point during preoperative stabilization in 36% (191/526) of infants with CDH from the population-based CAPSNet database. The ability of iNO to improve oxygenation and reduce the need for ECMO in non-CDH patients with PH explains its continued use in patients with CDH. The lack of a response to pulmonary vasodilators in CDH is speculated to be likely due to left atrial/pulmonary vein hypertension rather than to functional changes in the pulmonary arterial vasculature [83]. A recent study suggests that the response to pulmonary vasodilators in neonates with CDH may be limited by the severity of left ventricular (LV) dysfunction and/or hypoplasia, which impairs LV filling. Careful echocardiographic assessment, therefore, may guide treatment by identifying patients who may benefit from pulmonary vasodilators, including iNO [83].

**79**

**Figure 10.**

*to 60/14/32 mmHg, 3.14 Wood units m2*

*nitric oxide; AO, aorta; PA, pulmonary artery.*

*Endogenous and Inhaled Nitric Oxide for the Treatment of Pulmonary Hypertension*

*4.3.2 Acute vasoreactivity testing to assess operability of PAH associated with*

AVT (**Figure 10**) is also used to assess operability in children with PAH associated with a systemic-to-pulmonary shunt [87, 88]. Although pulmonary vasodilators other than iNO, such as inhaled iloprost or other orally or intravenously administered compounds (e.g., sildenafil and treprostinil), can be used for AVT, iNO ± oxygen is recommended [87]. The hemodynamic change that defines a positive response to AVT

*Acute vasoreactivity testingto assess operability. AVT is also used to assess operability in children with PAH associated with a systemic-to-pulmonary shunt [87, 88]. (Figure 10, unpublished). A 5-month-old infant with Down syndrome and an atrial septal defect was evaluated for operability by acute vasoreactivity testing using inhaled nitric oxide. Pulmonary hemodynamic parameters at baseline, including pulmonary arterial*

*to systemic vascular resistance (0.45), and the ratio of pulmonary to systemic blood flow (1.74), are changed*

*underwent surgical closure of the shunt, and no postoperative pulmonary hypertension was observed. NO,*

*, 0.20, and 2.2 after nitric oxide inhalation, respectively. The patient*

*), the ratio of pulmonary*

*pressure (76/29/49 mmHg), pulmonary vascular resistance index (6.7 Wood units m2*

*4.3.1 Acute vasoreactivity testing to assess prognosis and indications for specific PH*

The prognosis of children with PAH has improved in the past decade owing to new therapeutic agents and aggressive treatment strategies [84]. In idiopathic or heritable PAH (I/H-PAH), acute vasodilator testing (AVT) is recommended to identify patients who have a good long-term prognosis when treated with a long-term calcium channel blocker (CCB), accounting for 7–37% of children with PAH. For example, a >20% decrease in PAP or pulmonary vascular resistance (PVR) to inhaled NO accurately predicts a subsequent response to oral vasodilators, such as nifedipine. To identify such patients, the Sitbon criteria for positive AVT, as defined by a decrease in mean PAP by ≥10 mmHg to a value of <40 mmHg with an increased or unchanged cardiac output, is commonly used in adult I/H-PAH [85]. The Sitbon criteria can also be used to identify children who are expected to show a sustained response to CCB therapy [86]. Based on these data, the use of the Sitbon criteria is advised for AVT in children. Because only half of adult responders have a long-term hemodynamic and clinical improvement in response to CCB therapy,

*DOI: http://dx.doi.org/10.5772/intechopen.89381*

close long-term follow-up is required [87].

*congenital heart disease*

*therapy*

*Endogenous and Inhaled Nitric Oxide for the Treatment of Pulmonary Hypertension DOI: http://dx.doi.org/10.5772/intechopen.89381*

### *4.3.1 Acute vasoreactivity testing to assess prognosis and indications for specific PH therapy*

The prognosis of children with PAH has improved in the past decade owing to new therapeutic agents and aggressive treatment strategies [84]. In idiopathic or heritable PAH (I/H-PAH), acute vasodilator testing (AVT) is recommended to identify patients who have a good long-term prognosis when treated with a long-term calcium channel blocker (CCB), accounting for 7–37% of children with PAH. For example, a >20% decrease in PAP or pulmonary vascular resistance (PVR) to inhaled NO accurately predicts a subsequent response to oral vasodilators, such as nifedipine. To identify such patients, the Sitbon criteria for positive AVT, as defined by a decrease in mean PAP by ≥10 mmHg to a value of <40 mmHg with an increased or unchanged cardiac output, is commonly used in adult I/H-PAH [85]. The Sitbon criteria can also be used to identify children who are expected to show a sustained response to CCB therapy [86]. Based on these data, the use of the Sitbon criteria is advised for AVT in children. Because only half of adult responders have a long-term hemodynamic and clinical improvement in response to CCB therapy, close long-term follow-up is required [87].

## *4.3.2 Acute vasoreactivity testing to assess operability of PAH associated with congenital heart disease*

AVT (**Figure 10**) is also used to assess operability in children with PAH associated with a systemic-to-pulmonary shunt [87, 88]. Although pulmonary vasodilators other than iNO, such as inhaled iloprost or other orally or intravenously administered compounds (e.g., sildenafil and treprostinil), can be used for AVT, iNO ± oxygen is recommended [87]. The hemodynamic change that defines a positive response to AVT

#### **Figure 10.**

*Acute vasoreactivity testingto assess operability. AVT is also used to assess operability in children with PAH associated with a systemic-to-pulmonary shunt [87, 88]. (Figure 10, unpublished). A 5-month-old infant with Down syndrome and an atrial septal defect was evaluated for operability by acute vasoreactivity testing using inhaled nitric oxide. Pulmonary hemodynamic parameters at baseline, including pulmonary arterial pressure (76/29/49 mmHg), pulmonary vascular resistance index (6.7 Wood units m2 ), the ratio of pulmonary to systemic vascular resistance (0.45), and the ratio of pulmonary to systemic blood flow (1.74), are changed to 60/14/32 mmHg, 3.14 Wood units m2 , 0.20, and 2.2 after nitric oxide inhalation, respectively. The patient underwent surgical closure of the shunt, and no postoperative pulmonary hypertension was observed. NO, nitric oxide; AO, aorta; PA, pulmonary artery.*

in PAH associated with a shunt (a ratio of pulmonary to systemic blood flow >1.5) for children should be a >20% decrease in PVR index and a ratio of pulmonary to systemic vascular resistance with respective final values of <6 Wood units m2 and <0.3. However, specific criteria for defining a positive AVT response that predicts the reversal of PAH and good long-term prognosis have not been described. The pediatric task force of the Sixth World Symposium on Pulmonary Hypertension agreed on a general guidance for assessing operability in CHD-PAH but emphasized that the long-term impact of defect closure in the presence of PAH with increased PVR is unknown [84].


**Lydia Hangulu**

Discipline of Psychology, Health Promotion Programme, University of KwaZulu-Natal, Durban 4041, South Africa<EMAIL_ADDRESS>
Received: 10 July 2018; Accepted: 5 November 2018; Published: 12 November 2018

**Abstract:** There is lack of uniform nomenclature for healthcare waste (HCW) globally, which could undermine efforts to develop and implement appropriate policies relating to healthcare waste management (HCWM) in developing countries. This study sought to understand the terminologies used to describe HCW, including their definitions, categories, classification, and how they align with those that are provided by the World Health Organization (WHO)'s global manual for HCWM from healthcare facilities. The study first identified terms from the existing literature; then, it conceptually mapped the literature, and identified gaps and areas of further inquiry. Six electronic databases—EBSCOhost, Open Access, ProQuest, PubMed, Web of Science, and Google Scholar were used to search for literature. A total of 112 studies were included in the study. Despite having various nomenclature for HCW globally that align with those provided by the WHO manual, the use of varying nomenclature could create confusion among healthcare workers in the quest of managing HCW properly, especially in low and middle-income countries (LMICs). Further studies must be conducted to determine how these terminologies are interpreted and implemented in practice by healthcare workers. This will help to understand how their implementation aligns with the recommendations provided by the WHO manual.

**Keywords:** healthcare waste; healthcare workers; health sector; scoping review; WHO


**2. Mapping environmental diseases in the twenty-first century**

Most of the epidemiological studies that had mapped vector-borne diseases in the context of the environmental factors associated with those diseases [18–20] had based their assumptions on the established scientific evidence that those environmental factors were associated with the disease outcome of interest. To date an increasing number of disease mapping and epidemiology studies continue to use environmental and climatic data to map and predict disease distribution in defined geographic areas. Such studies would often be used to help guide and target the deployment of health interventions to those areas that had been identified to have high burden of the mapped disease. As the resolutions (both spatial and temporal) of remote sensing sensors had improved from the first generation of this technology, so has the interest and confidence in the use of their data products increased among the scientific community. As much as early studies were limited by computer processing power and storage, they were also limited by poor spectral bands of sensors which could not faithfully enhance delineation and demarcation of features. The spectral bands refer to the recorded wavelengths of the electromagnetic spectrum recorded by a sensor during image acquisition. Recently, sensor spectral bands have also improved and could now resolve and aid multicolor image display during feature analysis and identification. These advances in image processing, visualization and display have also supported and enabled the uptake and appreciation of research findings from mapping efforts as the resultant maps became more *beautiful* in addition to providing more information. **Figure 1** is an example of a high-resolution satellite imagery that had been used to develop a land cover classification map for part of Eswatini as shown in **Figure 2**.

In disease mapping, these advances in remote sensing and sensor technology meant that identification of spatial heterogeneities would be possible even at small geographic or local scales. These advances were pivotal for disease mapping as epidemiologist could identify important drivers of disease risk and thus be able to guide control programs more efficiently and with evidence-based decisions. Also, the costs of remotely sensed data products had been significantly reduced

#### **Figure 1.**

*An example of high-resolution true color image covering part of Eswatini used to develop a land cover classification.*

as more sensors had been launched thus stabilizing the demand for data products and reliance in only a few remote sensing agents. More and more countries and private companies have launched satellites into space in the twenty-first century and the resultant imagery data products have been availed to the research community [24]. In addition, archived remotely sensed data products had often been offered to researchers free of charge and this have enabled spatial analysts to perform various analysis techniques such as time-series analysis, data mining and other data learning techniques. The RS data and other end products had also been customized in terms of the derivation and calculation of vegetation indices used in mapping studies. This customization had enabled direct incorporation of such indices into models as interpretation became possible to the research community even though not being experts in the remote sensing field. Common indices that had been widely adopted into disease mapping models because of their ease in interpretation include those of the Normalized Difference Vegetation Index (NDVI), temperature and rainfall. These indices had often been supplied or archived in their complete processed (derived) and customized form, thus enabling researchers to easily access them from the hosting agencies and websites and directly incorporate them into their mapping models as they had become interoperable.

Advances made in mapping software, particularly geographic information system (GIS) software, had seen interest being stimulated among disease researchers and epidemiologist. Whereas, earlier software was mostly geared toward solely remote sensing experts, the availability of customized mapping platforms for spatial epidemiology meant that these software programs could be utilized even by

#### **Figure 2.**

*An example of high-resolution land cover classification in Eswatini.*

non-remote sensing experts. For instance, the public health community had been able to facilitate the development and customization of disease mapping software programs like; Health Mapper, Epi-Info, etc. Other GIS software programs such as ESRI ArcGIS had over the years added more customized mapping tools and extensions meant to support disease mapping and epidemiology efforts. Again, the high costs which were often associated with some of the commercial software had been reduced as more open source software became available. For example, GIS software programs such as QGIS had been availed as open source and could be directly downloaded and installed into any GIS capable computer. Also, true color visualization web-based software such as Google Earth which had even given mapping novices some level of confidence due to the fact that it had been made without any associated sophistication had contributed to the hype about disease mapping, rapid risk assessment, and prediction among epidemiologists. No mapping experts could leverage on such web-based imagery software programs and be able to identify, analyze, and interpret spatial phenomenon explicitly as it appears on the zoomed imagery on a computer screen. This way, disease experts had been able to explain some of the identified trends and patterns and also directly answer some of the pertinent questions associated with disease epidemiology such as clustering, severity variation and disease presence or absence *inter alia*.

#### *Remote Sensing Applications in Disease Mapping DOI: http://dx.doi.org/10.5772/intechopen.93652*

Again, a number of statistical software programs have also added mapping extensions which have enabled the analysis of environmental and climatic data to be undertaken using such software. This has resulted to a new field of research called geostatistics, which combines both geography and statistics during spatial analysis. For example, statistical software programs such as STATA, R, WINBUGS, and others have been used to process and analyze climatic data derived from remote sensing. In disease risk mapping, space and time analysis had often been conducted using these statistical software programs and they had been widely used as research methods in epidemiology and disease risk prediction in addition to the usage of mathematical models which attempt to explain the underlying factors and quantities in disease risk modeling. Geostatistics therefore had been pivotal in the application and incorporation of remotely sensed data products into disease mapping and epidemiology. The capability of Geostatistics to incorporate technical algorithms that could be used to forecast disease burden in space and time had also contributed to the wide adoption of such approaches as it meant that control programs could *a priori* be informed about disease risk and thus be better prepared to deal with disease outbreaks.

As already mentioned, advances in computer processing power had enabled the integration of computing methods based for instance on Bayesian inference approaches which had been previously limited due to poor computer performance. Data simulation methods such as Markov Chain Monte Carlo (MCMC) and the integrated Laplace approximation (INLA) had been widely used to estimate posterior distribution of geographic data in space and time. The results of which had been obtainable within reasonable time frames compared to earlier computation efforts of similar data. In statistics MCMC are methods comprised of a class of algorithms that sample from a probability distribution. MCMC uses simulation techniques to find a posterior distribution and sample from it. On the other hand INLA relies on analytical combinations that approximate and efficiently integrate numerical schemes to achieve highly accurate deterministic approximations of posterior quantities of interest [25, 26]. As a result of these statistical and computational advances, the integration of environmental and climatic data derived from remote sensing technology into disease mapping models had over the years markedly increased.

Recently, the capability of big data, machine learning and other location intelligence methods to handle a large array of data sets have contributed to the awareness about the application of geographic data as often models using these methods would be performed on geographic software. Big data refers to extremely large data sets that may be computationally analyzed to reveal patterns, trends, and associations relating to human behavior. Machine learning approaches focuses on computer programs that can assess data and use them to learn and improve from experience of them without being explicitly programmed [27]. These analysis methods are often conducted on GIS capable computers and they also rely on remote sensing products such as satellite imagery to analyze and reveal any patterns, trends and associations coming from the data. In disease mapping and epidemiology such analysis approaches are important in understanding risk of disease spread due to human behavior and their interaction with the environment. As a result, most of the diseases mapping efforts currently applied have either practically or theoretically ended up either utilizing remotely sensed data or its associated geographic data products into spatial analysis models as often the resultant modeling outputs would be displayed in a mapping environment.

## **2.1 Remote sensing data application in vector-borne disease surveillance**

Mapping of vector-borne diseases began around 1950 with the use of aerial photography and cartographic techniques. Early studies included those that focused on eradicating malaria, dengue fever and yellow fever whereby climatic factors were used to identify areas at risk of higher transmission. The Malaria Atlas Project founded in 2006 took over from previous mapping efforts and demonstrated the application of geography based variables to map and disseminate accurate information on malaria endemicity. Identifying and mapping vector habitats using climatic suitability was used to guide surveillance and control efforts. Different approaches were used to improve visualization and to produce detailed maps such as high-altitude color-infrared photography and also incorporating high-resolution images [28]. Mapping of vegetation types associated with some of the vector breeding habitat had been carried out since 1973. The techniques used in such analysis had been very important for surveillance support and for identification of vector oviposition habitats. The visualization and interpretation techniques used were based on tone and texture and were used to identify habitats associated with tick-borne disease in some areas based on the concepts of landscape epidemiology of disease [29].

In early 1970s, multispectral scanner data was first used to monitor and map environmental parameters required for the breeding of disease vectors. A combination of remote sensing data acquired from satellites as well as aircraft platforms was used for this task. Later in 1976, some studies demonstrated that computer processing techniques could be used to classify airborne multispectral scanner data for mapping and identifying vegetation types associated with certain disease-causing mosquitoes. Around 1984, remote sensing techniques were applied to describe and map geographic characteristics associated with schistosomiasis [30]. Temperature and precipitation data obtained from remote sensing was also used to estimate the probability of disease occurrence at unsampled locations. These data were also used to identify and map mosquito larval habitats and their association with certain environmental variables in space and time. A study by [31] identified tick habitats on the island of Guadeloupe using derived vegetation and moisture indices.

Most of the studies discussed above were primarily focused on the application of remote sensing to identify and map potential vector habitats and breeding sites based on vegetation, water, and soil. Identifying existing or potential habitats and breeding sites would not be enough to adequately guide surveillance and control efforts unless all possible affected areas were identified and mapped. Therefore most studies have also incorporated predictive techniques as part of their support of surveillance and control efforts. Consequently, it had been necessary for studies to go beyond mere habitat and potential breeding sites mapping and to make predictions of vector distributions in space and time often for the entire geographic area of interest. This often included identification and mapping areas where vector production and disease transmission risk would be greatest in defined time and space thresholds. **Figure 3** is an example of the spatial distribution of malaria vector breeding sites and their distance to subsistence farming in Eswatini.

### **2.2 Predicting diseases using remotely sensed data variables**

An important aspect in the application of RS data in disease mapping and epidemiology is their use as predictor variables in modeling. Disease mapping studies had often used environmental and climatic proxies derived from remote sensing in statistical regression models aiming to predict disease risk in both its spatial and temporal dynamics. These studies predict disease distributions, vector populations and disease transmission risk within the affected populations in specific areas. Common climatic variables used in disease predictive modeling studies often combine remote sensing measurements of vegetation, precipitation, and temperature to identify when and where conditions would be favorable for disease propagation. Other studies have attempted to use remote sensing to predict the temporal as well

**Figure 3.** *Example of mapped breeding sites and their distance to subsistence farming.*

as spatial patterns, of habitat development, vector populations, and disease transmission risk [32, 33].

Among the most common disease mapping studies where predictions had often been used included those of malaria. These studies also use remote sensing to predict which populations or villages are at risk of transmission. In these cases risk would often be defined by the proximity of a village to areas of heightened transmission as well as the breeding, feeding, and resting habitats required by the malaria vector *Anopheles* mosquito*.* In addition recent studies have focused on assessing the issue of predictive modeling and disease transmission risk based on the application of remote sensing and GIS technologies. To date, there have not yet been an alternative to disease mapping and prediction in space and time and to identify, characterize, and map the patterns of vector habitats other than using products derived from satellite imagery and remote sensing. For instance, identification and location of areas where vector survival rates are highest had been done using vegetation indices derived from remote sensing. **Figure 4** is an example of predicted potential malaria vector breeding sites in the northeastern part of Eswatini.

Also remote sensing data and GIS techniques had been used to identify and map landscape features associated with disease transmission risk. Landscape features such as brush, woodland and grassland and areas cleared for housing, roads, or trails and other similar locations identified through remote sensing had often been used for detecting intercept areas between human hosts, vectors and parasites. Other landscape features such as coniferous forest, deciduous forest, mixed forest,

**Figure 4.** *An example of identified potential malaria vector breeding sites in Eswatini.*

water bodies, glades, and housing developments had also been identified via remote sensing. Some studies have assessed these landscapes for their association with the presence of certain disease vectors and the proximity of housing as a measure of human exposure to those diseases. Such landscape epidemiology had therefore been used to identify areas where transmission risk is greatest by characterizing the mixture of deciduous forest and residential developments that bring diseases, vectors, and humans into contact.

Field studies had often been used to train spatial models that make use of remote sensing to map the distribution of disease and predict transmission risk areas. These studies also use GIS to capture groundtruthed coordinates or Global Positioning System point (GPS) which would then be used to assess the accuracy of predictive models. For instance, GIS and remote sensing had been used to investigate the adjacency of certain landscape features and residential properties with dense vegetation as a potential measure of human-vector contact. In this case, regression models would be used to assess general correlations between landscape and disease transmission risk. Evidently, remote sensing and GIS had been combined to study, for instance, the structure and composition of a landscape as it relates to the epidemiology of a disease. Again some studies have combined remote sensing and GIS analysis techniques, to assess various associations between diseases, vectors and human contacts.

Models based on remote sensing data and GIS techniques have also been used to study certain disease vector population dynamics using remote sensing and GIS technologies. Such studies use satellite imagery and GIS modeling techniques to distinguish between areas with either high or low disease-causing vectors. In these cases, ground data on vector populations are used jointly with remote sensing data combined via either a statistical or a GIS software. Ground data variables on vector presence or absence are analyzed in relation to the remotely-sensed spectral data captured via satellite imagery. The groundtruthed field measurement data, that

were observable using GPS, would then be used to evaluate the accuracy of the remote sensing based predictive models.


**1. Introduction**

Many studies have been conducted to describe transport phenomena in ladles and the results are summarized in several reviews [1–4]. The previous reviews have included mass transfer to a limited extent. The reviews from Mazumdar et al. [1,2] were focused on solid–liquid systems. Sichen [3] discussed limitations of the two-film theory and Liu et al. [4] summarized mixing phenomena by physical and mathematical modeling due to gas stirring in ladles, indicating that a limited number of studies involved mass transfer. Ghotli et al. [5] reviewed liquid–liquid mass transfer in mechanically stirred vessels. Mechanical and gas stirring involve different operational parameters. In steelmaking, in particular in ladle metallurgy, its rate of production and final steel quality rely on mixing efficiency both liquid steel and liquid slag. The current review will primarily focus on the physical and mathematical modeling work that involves liquid–liquid mass transfer due to bottom gas injection.

The molar flux of species j (Nj) is proportional to the concentration gradient according with Fick's law for diffusion without convection and shown below. This form of the equation is valid for dilute systems, typical of steelmaking. The proportionality constant is the *mass transfer coe*ffi*cient* (*mtc*). Depending on the experimental conditions the units for the *mtc* can change. To avoid confusion the units are briefly revised. If the concentration units for Cj are in mol/cm3 then the units for kj are cm/s [6].

$$\mathbf{N}\_{\rangle} = -\mathbf{D}\_{\rangle} \begin{pmatrix} \frac{\partial \mathbf{C}\_{\rangle}}{\partial \mathbf{y}} \end{pmatrix} = \begin{pmatrix} \mathbf{D}\_{\rangle} \\ \frac{\partial \mathbf{C}\_{\rangle}}{\partial \mathbf{m}} \Delta \mathbf{C}\_{\rangle} \end{pmatrix} = \mathbf{k}\_{\rangle} \begin{pmatrix} \mathbf{C}\_{\rangle}^{b} - \mathbf{C}\_{\rangle}^{cq} \end{pmatrix} \tag{1}$$

where Nj is the molar flux in mol/cm2·s, Cj is the concentration in mol/cm3, Dj is the diffusion coefficient in cm2/s, y is distance in cm, δm is the diffusion boundary layer thickness in cm, kj is the mass transfer coefficient in cm/s. Superscripts *b* and *eq* represent bulk and equilibrium values, respectively. Subscript j represents a chemical species.

Since the molar flux (Nj) is the amount of material transferred per unit area and unit time, the experimental measurement of the *mtc* requires knowledge of the interfacial area (A). In most cases this value is unknown. In this condition, the mass transfer coefficient is reported as the product, (kj·A) called *volumetric mass transfer coe*ffi*cient* (*vmtc*), with units cm3/min. If the volume of the liquid remains constant, we denote this *vmtc* as kj·*a* , with units min−1

$$\mathbf{N}\_{\mathbf{j}} = \frac{1}{\mathbf{A}} \frac{\partial \mathbf{n}\_{\mathbf{j}}}{\partial \mathbf{t}} = \mathbf{k}\_{\mathbf{j}} \Big(\mathbf{C}\_{\mathbf{j}}^{b} - \mathbf{C}\_{\mathbf{j}}^{c\eta}\Big) \tag{2}$$

$$\frac{\partial \mathbf{r}\_{\rangle}}{\partial \mathbf{t}} = \mathbf{V} \frac{\partial \mathbf{C}\_{\mathbf{j}}}{\partial \mathbf{t}} \ = \begin{pmatrix} \mathbf{k}\_{\rangle} \mathbf{A} \end{pmatrix} \begin{pmatrix} \mathbf{C}\_{\mathbf{j}}^{b} - \mathbf{C}\_{\mathbf{j}}^{cq} \end{pmatrix} \tag{3}$$

$$\frac{\partial \mathbf{C}\_{\mathbf{j}}}{\partial \mathbf{t}} = \left( \mathbf{k}\_{\mathbf{j}} \cdot \frac{\mathbf{A}}{\mathbf{V}} \right) \mathbf{C}\_{\mathbf{j}}^{b} - \mathbf{C}\_{\mathbf{j}}^{a\eta} \right) \\ = \left( \mathbf{k}\_{\mathbf{j}} \cdot \mathbf{a} \right) \left( \mathbf{C}\_{\mathbf{j}}^{b} - \mathbf{C}\_{\mathbf{j}}^{a\eta} \right) \tag{4}$$

The effect of the stirring conditions on the mass transfer coefficient has been experimentally measured for different systems: (i) gas–solid–liquid system: a typical example is the melting of additions by mechanical stirring (solid-liquid system) or by gas stirring (gas-solid-liquid system), (ii) gas–liquid system: some examples are gas absorption from the atmosphere into liquid steel, absorption of elements when different types of gases are injected (nitrogen, carbon dioxide, etc.) or oxygen injection for decarburization, and (iii) gas–liquid–liquid system: the main examples are slag/metal interfacial reactions. To clarify terms used in this work, in the gas–liquid system the gas is a reacting species that dissolves in the liquid in contrast to the gas–liquid–liquid system where the gas is an inert species and where an impurity dissolved in the lower liquid phase diffuses to the upper liquid phase. This review will focus primarily on the study of the *mtc* in the gas–liquid–liquid system, however, as an introduction the other systems are briefly reviewed in the beginning in Sections 2 and 3, followed by a detailed review in Section 4 on liquid–liquid mass transfer involving both physical and mathematical modeling studies. Section 5 provides a final assessment of our current understanding on this subject and suggest guidelines for further research.


**1. Introduction**

The aging of the world population has been demonstrated systematically by several demographic studies. Regrettably, increased life expectancy has led to an increased prevalence of age-related disorders including neurodegenerative diseases. In this regard, Alzheimer's and Parkinson's diseases constitute the most relevant issues for the public health system of different countries. Accordingly, during the last decades, significant efforts have been committed to improve our understanding of the molecular cascades responsible for an altered aging process as well as for the

establishment and progression of neurodegenerative disorders, mainly Alzheimer's disease (AD) and Parkinson's disease (PD) [1].

Relevantly, though AD and PD possess their very own pathological characteristics, the neuroinflammatory milieu has emerged as a central event of the chronic degenerative process. From the prodromal stage of these disorders up to the more advanced ones, inflammation seems to accompany or, in some cases, to drive the progression of AD and PD. Importantly, inflammation, as part of the nonspecific immune response, plays a critical role in maintenance of system homeostasis, allowing to prevent or to control the detrimental effects induced by a wide variety of xenobiotics to the cellular components of the biological systems. Remarkably, from an unspecific harmful stimulus, a whole range of responses are triggered including complement cascade activation and cytokines release as well as activation of the immune cells located at the site of the insult. To properly eliminate the initial cause of distress and to repair the damaged tissue, the inflammatory response must be delicately balanced considering not only pro-inflammatory but anti-inflammatory mediators as well. Tumor necrosis factor 1 (TNF-1α), interleukins (IL-1, IL-8, IL-10), interferon (INF-γ), transforming growth factor 1 (TGF-1), complement proteins, act together to develop a coordinated response against primary, unspecific stimuli [2, 3]. In this regard, the compromise of this essential system relates with severe, and often lethal, conditions including immunodeficiency syndromes as well as autoimmune diseases. Relevantly, during the last decades, a lot of attention has been also given to the effects of chronic inflammatory condition as the starting point of different degenerative conditions. Among these, neurodegenerative disorders, such as Alzheimer's and Parkinson's diseases, have found in the inflammatory response a critical milieu of events able to determinate the main molecular and cellular events verified during each of these diseases [4].

### **2. Central nervous system (CNS) immunocompetence**

CNS is a highly specialized structure whose functions require specific microenvironmental conditions. Moreover, neuronal activity and, thus, the health of the neuronal network depend on the maintenance of ion gradients whose concentration differs significantly from the rest of the body compartments [5]. To ensure these conditions, the CNS remains partially isolated by the existence of the blood-brain barrier (BBB), a highly complex semipermeable cellular barrier whose main function is to prevent both exogenous and endogenous elements to alter the brain homeostasis [5–7]. Regarding the immune response, microglial population constitutes the only immune system cellular representative within the brain with the astrocytes acting as a companion to exert immune surveillance and to act as the first line of response against harmful events within the brain. Although some additional peripheral immune cells including cluster of differentiation 11b and c (CD11b, CD11c)-positive cells localize to the CNS during some inflammatory conditions, it is believed that this situation is caused because of an altered permeability of the BBB as a consequence of the primary insult [8]. In this sense, the brain parenchyma has been defined as an anti-inflammatory environment due the increased levels of relevant anti-inflammatory mediators including the transforming growth factor β (TGFβ) and interleukin (IL)-10, preventing both the immune cell spreading across the CNS and an excessively strong immune response [8–10]. However, this latter condition does not imply that the CNS cannot answer to immune challenges; on the contrary, the CNS is a fully immunocompetent system, but with this mechanism being tightly controlled in order to avoid secondary damage caused by extensive inflammation and detrimental cell damage end products, such as reactive oxygen

**85**

*Toll-Like Receptors (TLRs) in Neurodegeneration: Integrative Approach to TLR Cascades…*

species (ROS). In this context, during the last years, it has been evidenced that sustained inflammatory challenge, whether systemic or local to the CNS, will alter significantly the neuronal environment affecting severely the neuronal function and the health of the neuronal network. Regrettably, it has been suggested that prior to AD and PD establishment as well as during their progression, a chronic inflammatory condition has developed, contributing to the molecular alterations

A significant characteristic of AD and PD is that these pathologies exhibit a strong inflammatory component even when both are sterile conditions. In this sense, the innate immune system works through the pattern recognition receptors (PRRs) which recognize molecular patterns related to pathogens (pathogen-associated molecular patterns, PAMPs) and to endogenous molecules indicative of cell damage (damageassociated molecular patterns, DAMPs), such as high-mobility group protein B1 (HMGB1), S100 proteins, heat shock proteins (HSPs), DNA, mitochondrial DNA (mt-DNA), and ATP [11–15]. The toll-like receptors (TLRs) family constitutes a highly relevant type of PRR necessary not only to unleash the initial immune response but also to connect the first unspecific defense with the secondary adaptive immunity [12]. Depending on the species, 11–13 TLR subtypes can be found. Relevantly, the localization of these receptors within the cells differs between the different TLRs. In this regard, while TLRs 1, 2, 4, 5, and 6 are expressed at the cell membrane, its main objective being to sense the extracellular compartment, the TLRs 3, 7, 8, and 9 are located inside the cells, mainly associated with endosomes and sensing the internal microenvironment for viral components, such as RNA and DNA [12, 16]. The presence of TLRs has been determined not only in several cell components of the peripheral immune system but also in the different cell types found in the brain including astrocytes, microglia, neurons, and oligodendrocytes, suggesting that each of these cell types can sense and trigger an immune response in the presence of different harmful molecular patterns. Interestingly, it has been demonstrated that not all the cells within the brain express the same pattern of TLRs. For example, microglia and neurons express all TLR subtypes, while astrocytes express a more limited repertoire, including TLR2, TLR3, TLR4, TLR9, and TLR11 [16, 17].

The signaling cascade triggered after TLRs' activation involves the cross talk with several additional pathways able to critically modify cell physiology. In this sense, canonical TLR-mediated signaling involves the myeloid differentiation factor 88 (MyD88) cascade. In this pathway, TLR activation couples with MyD88 inducing the activation of interleukin-1 receptor-associated kinase (IRAK) and, subsequently, the activation of the tumor necrosis factor receptor-associated factor 6. This event allows the recruitment of the transforming growth factor-β-activated kinase-1 (TAK1) which, together with the TAK1-binding proteins, leads to the phosphorylation of IκB causing the activation of the IKK complex and the release of the nuclear factor-κB (NF-κB), triggering the NF-κB-dependent inflammatory response [11, 18, 19]. Importantly, TLR 3 and TLR 4 can also signal via the TIR-containing adaptor inducing interferon-β (IFN-β) (TRIF). In this additional pathway, additional to the release of NF-κB, it also causes an increased expression of IFN-β by means of the IKKε/TANK-binding kinase-1 (TBK1)-dependent phosphorylation of the interferon regulatory factor 3 and 7 (IRF3 and IRF7) [7, 19, 20].

*DOI: http://dx.doi.org/10.5772/intechopen.86167*

observed during these pathologies.

**3.1 TLR inflammatory cascade**

**3. Toll-like receptors**

*Toll-Like Receptors (TLRs) in Neurodegeneration: Integrative Approach to TLR Cascades… DOI: http://dx.doi.org/10.5772/intechopen.86167*

species (ROS). In this context, during the last years, it has been evidenced that sustained inflammatory challenge, whether systemic or local to the CNS, will alter significantly the neuronal environment affecting severely the neuronal function and the health of the neuronal network. Regrettably, it has been suggested that prior to AD and PD establishment as well as during their progression, a chronic inflammatory condition has developed, contributing to the molecular alterations observed during these pathologies.

## **3. Toll-like receptors**

*Toll-like Receptors*

disease (AD) and Parkinson's disease (PD) [1].

and cellular events verified during each of these diseases [4].

**2. Central nervous system (CNS) immunocompetence**

CNS is a highly specialized structure whose functions require specific microenvironmental conditions. Moreover, neuronal activity and, thus, the health of the neuronal network depend on the maintenance of ion gradients whose concentration differs significantly from the rest of the body compartments [5]. To ensure these conditions, the CNS remains partially isolated by the existence of the blood-brain barrier (BBB), a highly complex semipermeable cellular barrier whose main function is to prevent both exogenous and endogenous elements to alter the brain homeostasis [5–7]. Regarding the immune response, microglial population constitutes the only immune system cellular representative within the brain with the astrocytes acting as a companion to exert immune surveillance and to act as the first line of response against harmful events within the brain. Although some additional peripheral immune cells including cluster of differentiation 11b and c (CD11b, CD11c)-positive cells localize to the CNS during some inflammatory conditions, it is believed that this situation is caused because of an altered permeability of the BBB as a consequence of the primary insult [8]. In this sense, the brain parenchyma has been defined as an anti-inflammatory environment due the increased levels of relevant anti-inflammatory mediators including the transforming growth factor β (TGFβ) and interleukin (IL)-10, preventing both the immune cell spreading across the CNS and an excessively strong immune response [8–10]. However, this latter condition does not imply that the CNS cannot answer to immune challenges; on the contrary, the CNS is a fully immunocompetent system, but with this mechanism being tightly controlled in order to avoid secondary damage caused by extensive inflammation and detrimental cell damage end products, such as reactive oxygen

establishment and progression of neurodegenerative disorders, mainly Alzheimer's

Relevantly, though AD and PD possess their very own pathological characteristics, the neuroinflammatory milieu has emerged as a central event of the chronic degenerative process. From the prodromal stage of these disorders up to the more advanced ones, inflammation seems to accompany or, in some cases, to drive the progression of AD and PD. Importantly, inflammation, as part of the nonspecific immune response, plays a critical role in maintenance of system homeostasis, allowing to prevent or to control the detrimental effects induced by a wide variety of xenobiotics to the cellular components of the biological systems. Remarkably, from an unspecific harmful stimulus, a whole range of responses are triggered including complement cascade activation and cytokines release as well as activation of the immune cells located at the site of the insult. To properly eliminate the initial cause of distress and to repair the damaged tissue, the inflammatory response must be delicately balanced considering not only pro-inflammatory but anti-inflammatory mediators as well. Tumor necrosis factor 1 (TNF-1α), interleukins (IL-1, IL-8, IL-10), interferon (INF-γ), transforming growth factor 1 (TGF-1), complement proteins, act together to develop a coordinated response against primary, unspecific stimuli [2, 3]. In this regard, the compromise of this essential system relates with severe, and often lethal, conditions including immunodeficiency syndromes as well as autoimmune diseases. Relevantly, during the last decades, a lot of attention has been also given to the effects of chronic inflammatory condition as the starting point of different degenerative conditions. Among these, neurodegenerative disorders, such as Alzheimer's and Parkinson's diseases, have found in the inflammatory response a critical milieu of events able to determinate the main molecular

**84**

A significant characteristic of AD and PD is that these pathologies exhibit a strong inflammatory component even when both are sterile conditions. In this sense, the innate immune system works through the pattern recognition receptors (PRRs) which recognize molecular patterns related to pathogens (pathogen-associated molecular patterns, PAMPs) and to endogenous molecules indicative of cell damage (damageassociated molecular patterns, DAMPs), such as high-mobility group protein B1 (HMGB1), S100 proteins, heat shock proteins (HSPs), DNA, mitochondrial DNA (mt-DNA), and ATP [11–15]. The toll-like receptors (TLRs) family constitutes a highly relevant type of PRR necessary not only to unleash the initial immune response but also to connect the first unspecific defense with the secondary adaptive immunity [12].

Depending on the species, 11–13 TLR subtypes can be found. Relevantly, the localization of these receptors within the cells differs between the different TLRs. In this regard, while TLRs 1, 2, 4, 5, and 6 are expressed at the cell membrane, its main objective being to sense the extracellular compartment, the TLRs 3, 7, 8, and 9 are located inside the cells, mainly associated with endosomes and sensing the internal microenvironment for viral components, such as RNA and DNA [12, 16]. The presence of TLRs has been determined not only in several cell components of the peripheral immune system but also in the different cell types found in the brain including astrocytes, microglia, neurons, and oligodendrocytes, suggesting that each of these cell types can sense and trigger an immune response in the presence of different harmful molecular patterns. Interestingly, it has been demonstrated that not all the cells within the brain express the same pattern of TLRs. For example, microglia and neurons express all TLR subtypes, while astrocytes express a more limited repertoire, including TLR2, TLR3, TLR4, TLR9, and TLR11 [16, 17].

#### **3.1 TLR inflammatory cascade**

The signaling cascade triggered after TLRs' activation involves the cross talk with several additional pathways able to critically modify cell physiology. In this sense, canonical TLR-mediated signaling involves the myeloid differentiation factor 88 (MyD88) cascade. In this pathway, TLR activation couples with MyD88 inducing the activation of interleukin-1 receptor-associated kinase (IRAK) and, subsequently, the activation of the tumor necrosis factor receptor-associated factor 6. This event allows the recruitment of the transforming growth factor-β-activated kinase-1 (TAK1) which, together with the TAK1-binding proteins, leads to the phosphorylation of IκB causing the activation of the IKK complex and the release of the nuclear factor-κB (NF-κB), triggering the NF-κB-dependent inflammatory response [11, 18, 19]. Importantly, TLR 3 and TLR 4 can also signal via the TIR-containing adaptor inducing interferon-β (IFN-β) (TRIF). In this additional pathway, additional to the release of NF-κB, it also causes an increased expression of IFN-β by means of the IKKε/TANK-binding kinase-1 (TBK1)-dependent phosphorylation of the interferon regulatory factor 3 and 7 (IRF3 and IRF7) [7, 19, 20].

The main objective of these TLR-mediated processes is to regulate the expression of several pro-inflammatory and anti-inflammatory mediators including IL-1, IL-6, IL-10, IL-11, IL-12, tumor necrosis factor (TNF), TGF, IFN, CCL2, CCL5, CXCL8, and CXCL10, among others [4, 11, 18, 19].

Additionally, TLR activation can also signal through complementary molecular pathways. Indeed, TAK1 activation also induces nemo-like kinase (NLK) and the c-Jun N-terminal kinases (JNK) pathway [18–21]. Similarly, MyD88 can signal through the phosphatidylinositide-3 kinase (PI3K)/Akt pathway, modulating the activity of the glycogen synthase kinase 3 β (GSK3β) [22, 23]. On the other hand, it has been demonstrated that TLR2 and TLR4 can activate the PI3K/Akt pathway through the Ras-related C3 botulinum toxin substrate 1 (Rac1), a member of the Rho family of GTPases [23]. Complimentarily, the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) (JAK/STAT) pathway is known to respond to a variety of PAMPs/DAMPs and cytokines, including different interleukins and INF-β. Relevantly, different studies have demonstrated that several members of the TLR family can phosphorylate different STAT members, suggesting a direct modulation of the JAK/STAT pathway [24, 25]. As is possible to observe, the molecular cascades that could be triggered secondarily to TLR activation are related with critical cellular processes including cell cycle modulation, apoptosis, and cytoskeleton remodeling, among others. This situation depicts the complexity of the immune response and the relevance of its modulation in the context of different pathologies including neurodegenerative ones, such as AD and PD.


**4. The investigative method**

In recent years, in the public debate on urban development and citizen involvement, there seems to be growing doubt as to whether the efforts to date will adequately respond to the challenges that are increasingly faced by larger cities. The criticism herein often focuses on existing efforts being reactive in the sense that they typically focus on solving acute problems, whereby the area's resources are often ignored, and the long-term development perspective fades [26]. It is clear from this debate that this tradition need-/problem-based approach to urban development often has several unintended adverse side effects, whereby efforts often create clients rather than active citizens—thus increasing reliance on external assistance in the district. According to McKnight [27], development assistance based on external assistance is a central part of this negative spiral, because local institutions and organizations are thereby pacified and lose functionality, which means that the district gradually loses its power of action and attracts resourceful citizens. With this in mind, it is therefore essential in urban development to allow the district to revitalize itself "from the inside" through local institutions and organizations actively using their existing resources and anchoring in the area to create quality of life and increase the attractiveness of the area. In this context, this process must take place in an autonomous manner for which the area's institutions are responsible—thus assuming ownership—for its creation. From empirical results, it is a central and well-documented point [27]. The above information is a necessity if the desired development process is to achieve sustainability that goes beyond consultancy assistance. If this is to be successful, then the development process must use an optic with a radically different focus than the problem-oriented approach. In this situation, the resource-based approach to urban development

**425**

*Urban Social Sustainability - Case Study; Gellerupparken–Denmark*

(asset-based community development) is one of the most well-founded [26]. Suggestions have been proposed for a development methodology that addresses the above issues as well as potentials, such as the method developed by McKnight [27]. Based on comparative studies of urban development methodologies—and their success rates—the following starting point for urban development is employed: Communities and boroughs can create development themselves by identifying and utilizing existing (but often unrecognized) resources in new ways. This process requires the borough's actors explicitly focus on finding—and using—the area's

Thus, according to [26], the resource-based approach to urban development can

• ABCD is an asset-based approach that uses methods to draw out strengths and successes in a community's shared history as its starting point for change (as in

• Among all the assets that exist in the community, ABCD pays particular attention to the assets inherent in social relationships, as evidenced in formal and informal associations and networks (recognized in research on *social capital*).

• ABCD's community-driven approach is in keeping with the principles and practice of *participatory approaches development* where active participation and empowerment (and prevention of disempowerment) are the basis of practice.

• ABCD is a strategy directed towards sustainable economic development that is community-driven. The reference to community economic development

• ABCD, as a strategy for sustainable economic development, relies on linkages between community-level actors and macrolevel actors in public and private sectors. In fostering these linkages, ABCD also fosters active citizenship engagement to ensure access to public goods and services and to ensure the accountability of local government. It, therefore, contributes to and benefits

Practical work on urban development from this perspective is based on several methodologies that are defined based on the above basic thinking. In this context, a particularly useful tool must be highlighted, which exemplifies the above starting point and thus can be used in development processes to promote resource-based development. One method of involving and engaging the citizen is, according to the American example, to apply a method called appreciative inquiry. The strength of this approach is that it is based on existing resources and potentials. All areas have positive sides and potentials, but it is often necessary to tend to overlook them and focus on the problems instead. People will naturally feel far more committed and eager to take responsibility if they are met with recognition and an attitude that they are a resource rather than a problem. Simultaneously, an appreciative and curious study of local citizens' experiences and opinions about the area will encourage far more participation than a laid back expert attitude. The proposal for a strategy to improve exposed residential areas, therefore, derives solely from the ideas, wishes, and narratives of citizens who either live or work in the area. Citizens have been asked several times and in many different ways about their perception of the

*DOI: http://dx.doi.org/10.5772/intechopen.93124*

be summarized in the description below [28]:

theory is therefore relevant to ABCD strategy.

from a strengthened *civil society*.

**4.1 Appreciative inquiry model**

*appreciative inquiry*).

resources [26, 27].

*Urban Social Sustainability - Case Study; Gellerupparken–Denmark DOI: http://dx.doi.org/10.5772/intechopen.93124*

(asset-based community development) is one of the most well-founded [26]. Suggestions have been proposed for a development methodology that addresses the above issues as well as potentials, such as the method developed by McKnight [27]. Based on comparative studies of urban development methodologies—and their success rates—the following starting point for urban development is employed:

Communities and boroughs can create development themselves by identifying and utilizing existing (but often unrecognized) resources in new ways. This process requires the borough's actors explicitly focus on finding—and using—the area's resources [26, 27].

Thus, according to [26], the resource-based approach to urban development can be summarized in the description below [28]:

## **4.1 Appreciative inquiry model**

Practical work on urban development from this perspective is based on several methodologies that are defined based on the above basic thinking. In this context, a particularly useful tool must be highlighted, which exemplifies the above starting point and thus can be used in development processes to promote resource-based development. One method of involving and engaging the citizen is, according to the American example, to apply a method called appreciative inquiry. The strength of this approach is that it is based on existing resources and potentials. All areas have positive sides and potentials, but it is often necessary to tend to overlook them and focus on the problems instead. People will naturally feel far more committed and eager to take responsibility if they are met with recognition and an attitude that they are a resource rather than a problem. Simultaneously, an appreciative and curious study of local citizens' experiences and opinions about the area will encourage far more participation than a laid back expert attitude. The proposal for a strategy to improve exposed residential areas, therefore, derives solely from the ideas, wishes, and narratives of citizens who either live or work in the area. Citizens have been asked several times and in many different ways about their perception of the

area and possible proposals for change in the future. No strategy has been adopted without the support of citizens, and it has always been possible to come up with amendments to both the primary strategy and the most concrete sub-proposals. Amendments, new ideas, and criticisms have been taken seriously and attempted to be incorporated. This approach has slowed down the process, but a human being cannot think that it can be avoided in any way—nor the future process.

Another aspect of the recognizable approach is that it encourages seeing opportunities, allowing the process participants to talk about hopes and dreams for the future. People's need to be recognized and heard, as well as the ability to imagine the best possible imaginable, releases a tremendous amount of energy when given room for it, an energy that is all too often lost in rapid decision-making and political power struggles. This brief presentation of the methodological approach is intended as part of the proposal for a strategy for the development of vulnerable residential areas. It is used extensively in Gellerupparken and other residential areas, and it is no doubt that an appreciative and positive approach will awaken people's desire to participate, which is an invaluable resource. Without citizens' involvement and local roots, development cannot be reversed. Additionally, as a basis for this answer, proposals for change lie in the establishment of close cooperation between book associations, business, and local authorities, which will be discussed in more detail later.

Appreciative inquiry (AI)—or friendly conversation—is a critical methodology in resource-based urban development. It is a transformative approach to change that focuses on collaboration and identifying and working with the positive aspects of organizations, or communities, rather than the problems [29]. This method is based on several research findings, all of which demonstrate that our notions about the future are mainly decisive for our present actions. Therefore, it is essential to focus on strengths and successes as a basis for development rather than problems and shortcomings [30]. What the appreciative approach seeks is to achieve the transformation of a culture from one that sees itself in largely negative terms, and moreover, is, therefore, inclined to become locked into its contrary construction of itself—to one that sees itself as having within it the capacity to enrich and enhance the quality of life of all its stakeholders—and therefore move towards this appreciative construction of itself [30]. AI as a development methodology is, thus, as illustrated below, in sharp contrast to traditional problem-/need-oriented perspectives in terms of the resource-oriented starting point. **Table 1** shows the explicative board based on the "release recognition" model.

The world-renowned Imagine Chicago project (see: www.imaginechicago.org) is an inspiring example of how AI and the resource-based approach have been used to revitalize Chicago neighborhoods characterized by ghetto education through the

**427**

*Urban Social Sustainability - Case Study; Gellerupparken–Denmark*

the continued sustainability of the result.

3.Citizens have a minimum right to be heard.

5.Citizens must have real opportunities to participate.

processes in the future.

must be promoted.

evaluated and developed.

and developed.

citizen involvement:

application of the above principles. The experience of this project, which exemplifies the resource-based approach, has attracted international attention and points to the efforts of the future. With the above starting point, innovative research and international experience seem to be promising with respect to working with citizeninvolving processes in Gellerupparken and the surrounding area. This means that we see citizen involvement as citizen-driven development processes. In this perspective,

the successful citizen engagement process will have two significant benefits:

• In terms of yield, the process metaphorically described results in answers to important questions from the citizens of the area. Thus, the process makes a positive difference in the lives of citizens. Furthermore, this difference must be sufficiently significant that the citizens themselves will take responsibility for

• Process wise: Through the process, the involved parties increase their acting competence and position, so that they are strengthened with respect to similar

This understanding is basically in harmony with the city of Aarhus model of

1.Citizen involvement must be based on the values of the Aarhus municipality.

2.Citizen involvement is a mandatory consideration in the initial phase of a task.

4.If an existing plan is changed, it must state why the "agreement" is being changed.

6.The private involvement in the development of the municipality or local areas

7.Cooperation with councils, associations, and organizations must be maintained

8.Processes, methods, and professional competence must be continually

The understanding of citizen involvement on which this report is based can, based on the Aarhus model, be understood as an effort to explain and translate points 5–7 into a concrete basis for action. We strive to develop methods to create real citizen-driven processes, whereby, through interaction with local actors, we establish a self-sustaining practice that continuously promotes the development of local areas. This endeavor requires that, concerning the Aarhus model, we must perceive citizen involvement in a broader perspective. Generally, citizen involvement is perceived as a means of achieving a given content goal. However, this understanding does not include any focus on building the capacity of citizens to be able to handle similar challenges in the local area independently in the future. Ultimately, the development process itself does not then necessarily create more active citizens. In contrast, development processes in which this aspect is not considered, as pointed out in the above section, entail significant risks for citizens to be positioned

*DOI: http://dx.doi.org/10.5772/intechopen.93124*

#### **Table 1.**

*An explicative board based on the "release recognition" model.*

*Urban Social Sustainability - Case Study; Gellerupparken–Denmark DOI: http://dx.doi.org/10.5772/intechopen.93124*

application of the above principles. The experience of this project, which exemplifies the resource-based approach, has attracted international attention and points to the efforts of the future. With the above starting point, innovative research and international experience seem to be promising with respect to working with citizeninvolving processes in Gellerupparken and the surrounding area. This means that we see citizen involvement as citizen-driven development processes. In this perspective, the successful citizen engagement process will have two significant benefits:

This understanding is basically in harmony with the city of Aarhus model of citizen involvement:

The understanding of citizen involvement on which this report is based can, based on the Aarhus model, be understood as an effort to explain and translate points 5–7 into a concrete basis for action. We strive to develop methods to create real citizen-driven processes, whereby, through interaction with local actors, we establish a self-sustaining practice that continuously promotes the development of local areas. This endeavor requires that, concerning the Aarhus model, we must perceive citizen involvement in a broader perspective. Generally, citizen involvement is perceived as a means of achieving a given content goal. However, this understanding does not include any focus on building the capacity of citizens to be able to handle similar challenges in the local area independently in the future. Ultimately, the development process itself does not then necessarily create more active citizens.

In contrast, development processes in which this aspect is not considered, as pointed out in the above section, entail significant risks for citizens to be positioned as clients. With this starting point, we, therefore, consider citizen involvement as a process with a dual objective; a result must be created that is an answer to an essential question of the citizens. Simultaneously, the process must develop the trading skills of the parties involved. This understanding is translated into the following procedure for adjacent writing.


**2.6 Phenology**

Among the other abiotic stresses, salinity has a significant impact on the phenological attributes of the plant family Brassicaceae. Salt stress alters the duration of several physiological stages of plants, from seedling emergence, leaf unfolding, the appearance of first flowering, siliqua formation, grain filling to leaf color changing, and leaf senescence in the end. According to Mohamed et al. [43], 100 mM NaCl showed a significant effect on the duration of the flowering stage of two *B. napus* cultivars (Yangyoushuang2 and Xiangyouza553). Salinity delayed the first appearance date of flowering from 139 d to 143 d and from 141 d to 147 d in Yangyoushuang2 and Xiangyouza553 cultivars, respectively. Moreover, salinity increased the days to 50% flowering in both of the cultivars from 142 d to 146 d and from 144 d to 150 d. Another study by Pandey et al. [76] stated that under 50 mM salt stress, the cotyledonary leaf emergence rate of *B. juncea* was reduced by 30% to the control plants. According to several findings, extreme saline circumstances may bring delayed seed germination [77] because of decreased hydrolytic enzyme activity and seed metabolite mobilization [78]. *B. campestris* seed germination was delayed beyond 24 h under 120 mM salt stress level, as reported by Siddikee et al. [79].

Leaf senescence is another age-dependent phenological characteristic of plants that is related with a wide range of biochemical and molecular changes inside plant organs. Due to the decreased biosynthesis and enhanced breakdown of Chl molecules under salt stress, premature or early leaf senescence occurs in the plant life cycle. Alamri et al. [62] stated that the seedlings of *B. juncea* grown under 120 mM NaCl concentration experienced early leaf senescence.

### **2.7 Reproductive development**

Several additional reproductive stage characteristics, such as flower initiation, anther, and pollen grain development, fertilization, siliqua formation and development, and seed filling, are considerably influenced by the salinity in Brassicaceae plants. During the reproductive stage, *B. napus* seed production potential is indicated by pre-and post-flowering activities. It has been suggested that the reproductive stage is the most sensitive to stress [80]. Flowering and seed filling are the most vulnerable stages of the oilseed Brassicaceae family to extreme salinity stress than the earlier vegetative phases, such as seed germination and seedling growth. Salinity reduces plant fertility by affecting the development of male and female reproductive organs, which are very susceptible to stress [81]. The reproductive stage is intrinsically linked to seed production because fertilization and seed development occur during this stage. Therefore, tolerance to salt stress during this stage is crucial [82].

Arif et al. [83] experimented with BARI Sarisha-8 and BINA sharisha 5 cultivars of *B. napus* under 100 mM NaCl and observed that flowering and siliquae formation were significantly affected by salinity. Discoloration and rolling of the leaves and flowers, inhibition of new buds opening, and the death of young siliquae altogether, leaving the adult siliquae with wrinkled and immature growth. As a result, early maturity symptoms of the seed occurred. However, among two of those cultivars, BARI Sarisha-8 showed more sensitivity at the adult siliquae development stage, whereas the young leaves and siliquae were more vulnerable in BINA sharisha 5. Another experiment by Gyawali et al. [34] with 131 *B. napus* accession lines showed varied responses under different salinity levels (1.4, 5, 10, 15, 20, and 28 dS m−1 of NaCl). The number of branches and siliquae were affected more in two of the genotypes (Kuju 29 and Kuju 32). Similarly, in DH12075, there was a failure in fertile branches and siliquae production found under salt stress of more than 10 dS m−1. The number of fertile branches is also affected under salinity. A study from Chakraborty et al. [3] reported that with increasing salinity levels (1.65, 4.50, and 6.76 dS m−1 of NaCl), the number of siliquae on primary branches of seven cultivars, for example, CS 52, CS 54, Varuna, Pusa Jagannath, Pusa Agrani and T 9, Sagam of *B. juncea,* and *B. campestris*, were reduced. The reduction was almost 50% under 1.65 dS m−1, whereas, under 6.76 dS m−1, it was one-third of the control plants. Not only the reduction in fertile branch numbers and siliquae but salinity also caused wilting of the reproductive parts (mature flowers and fruits) of *B. napus* [84].

#### **2.8 Oxidative stress**

Salt stress leads to ionic toxicity due to higher accumulation of Na+ and Cl− ions and depleted potassium ion pool in plants. Disruption in ion homeostasis leads to stomatal closure by hampering the functioning of guard cells, which in turn decreased carbon fixation due to insufficient CO2 supply in the leaves, and accelerates the generation of the ROS such as H2O2, 1 O2, O2 •−, HO2 • , RO• , and • OH [7]. Another effect of salinity is to create drought-like conditions in plants due to lower water potentiality and is also responsible for the increase of ROS generation by disrupting photosynthetic activities [56]. Though ROS is beneficial for activating the stress signaling molecules at a certain level, after that it becomes phytotoxic and disrupts metabolic activities and also accountable for the breakdown of different cellular components, namely proteins, lipids, carbohydrates, and nucleic acids [85]. Thus, accelerated activities of ROS boosted protein denaturation, lipid peroxidation, and oxidation of carbohydrates and ultimately created oxidative stress in plants [7; **Figure 1**].

Salt induced oxidative stress due to higher accumulation of ROS observed in *Brassica* sp. However, the extent of salt-induced damage depends on the species, plant growth stage, ion strength, organ specificity, and the components of the salinizing solution [86; **Table 4**]. Sarwat et al. [95] observed that upon exposure to 100 and 200 mM NaCl, *B. juncea* plants resulted in upgraded levels of H2O2 content by 1.99 and 3.35-fold, respectively, with a maximum increase of malondialdehyde (MDA) content by 2.19-fold at 200 mM NaCl-treated plants. Salt-sensitive cultivar of *B. carinata* (cv. Adet) showed a higher accumulation of thiobarbituric acid reactive substances (TBARS) compared with the salt-tolerant one (cv. Merawi) in 150 mM NaCltreated mustard plants [41]. So, it can be stated that degree of salt-induced oxidative damages depends on the cultivar type, dose, and duration of stressed period. Sami et al. [14] found elevated production of H2O2 (by 46%) and O2 •− (by 47%) in 100 mM

*Oilseed* Brassica *Responses and Tolerance to Salt Stress DOI: http://dx.doi.org/10.5772/intechopen.109149*

#### **Figure 1.**

*Schematic representation of ROS-induced oxidative stress in plants and its consequences under salinity.*

#### **Table 4.**

*Salt-induced oxidative damages in oilseed Brassica sp.*

NaCl-treated *B. juncea* plants together with an augmented level of lipid peroxidation by 55% at a similar level of salinity.

#### **2.9 Yield and quality**

Plants exposed to salt stress undergo morphological, physiological, and biochemical changes. It leads to a deleterious influence on reproductive characteristics and ultimate yield reduction in plants [7]. As water is the key element for flowering and siliquae formation in oilseed *Brassica* plants, the accelerated water loss from plant cells induced by salt stress has a significant influence on the reproductive stages. Salinity shows a negative impact on the growth attributes, such as SL, RL, stem diameter, FW, DW, LN, leaf size, LA, and branch number, of oilseed *Brassica* plants. Similarly, it also affects the yield contributing attributes, such as the number of flowers, number of siliquae, seed yield, 1000-seed weight, and oil content of mustard. Moreover, salinity enforces osmotic stress on plants that adversely affect the water conductance status by altering the whole nutritional status of plants [4]. Thus, it causes growth retardation, ionic and nutritional imbalance, disrupted water relations, and photosynthetic inhibition, which subsequently affects the yield attributes in oilseed Brassicaceae family. While experimenting with seven cultivars (CS 52, CS 54, Varuna, Pusa Jagannath, Pusa Agrani, and T 9, Sagam) from *B. juncea* and *B. campestris*, Chakraborty et al. [3] found a significant reduction in seed yield (SY) and oil content in all the seven cultivars. They also reported a great extent of interspecific variation in response to salinity stress in oilseed *Brassica* plants [3, 83].

Likewise, in different studies, a wide range of oilseed *Brassica* cultivars were used to investigate their varied responses in yield attributes under salinity stress (**Table 5**). Reduction in oil content and oil quality is also a common response to salt stress in oilseed *Brassica* plants. Because of the smaller seed size and reduced cellular metabolic activities, total oil content, as well as lipid, protein, and fatty acid content in the oil, is also hampered (**Table 5**). For a balanced osmotic pressure in plant cells, soluble sugar and soluble protein contents play a very significant role. Upon exposure to 100 mM NaCl concentration, two *B. napus* cultivars, Yangyoushuang2 and Xiangyouza553, showed reduced crude oil percentage by 22% and 30%, respectively. Whereas the crude protein percentage was increased by 44% and 24% for both cultivars under the saline condition. Also, seed moisture content, saturated (palmitic and arachidic acids) and unsaturated fatty

#### **Table 5.**

*Changes in yield and quality traits of oilseed Brassica sp. upon salinity stress.*

acid concentrations, glucosinolate in both cultivars were greatly influenced under salt stress. But the oleic acid concentration in Xiangyouza553 cultivar remained unchanged under stress [43]. Six ecotypes of *B. napus* were tested under 50, 100, and 150 mM NaCl stress, which showed a similar response in yield attributing characters. The ecotypes were Super, Sandal, Faisal, CON-111, AC Excel, and Punjab, among which the number of pods per plant was reduced significantly under 150 mM NaCl in Punjab cultivar. Also, a similar trend in reduction was observed for 1000-seed weight in the same cultivar with increasing salt concentration, while the other varieties showed little to no response regarding those attributes [98].


*2.1. Data*

We retrieved the DNA sequence data packaged in the SPEID model of Singh, et al. [13] at the website http://genome.compbio.cs.cmu.edu/~{}sss1/SPEID/all\_sequence\_data.h5. Each sample represented an interacting/non-interacting pair of one-hot encoded DNA sequence centered at one enhancer (3000 bp) and one promoter (2000 bp), which were only local features compared to >10 kb distance between an enhancer and a promoter in the dataset [11] As for the local sequence data used previously [13,14], epigenomic data length was set to be 3000 bp for enhancers and 2000 bp for promoters in all epigenomic data types. We used the epigenomic features that are shared across the cell lines with most genomic features (K562, GM12878, HeLa-S3, and IMR90) based on Supplemental Table 2 in Whalen, et al. (2016). There are 22 epigenomic data types in total, including 11 histone mark peaks (H3K27ac,H3K27me3,H3K4me1, H2AZ, H3K4me2, H3K9ac, H3K4me3, H4K20me1, H3K79me2, H3K36me3, H3K9me3), 9 transcriptional factor bindings (POLR2A, CTCF, EP300, MAFK, MAZ, MXI1, RAD21, RCOR1, RFX5), DNase and methylation (ENCODE Project Consortium, 2007; Bernstein, 2010). Hence, the data dimensions were (# of samples)×3000×22 for enhancers and (# of samples×2000×22) for promoters (Figures 1–3). We extracted the epigenomic data as following. According to the enhancer/promoter genome coordinates available in the TargetFinder E/P dataset, the 3000 and 2000 genome window coordinates centered around the enhancer and promoter in each pair were calculated, then the data at each base pair were retrieved via those calculated window coordinates from 22 epigenomic data files across the whole genome for cell line K562 in the BigWig format, available at the ENCODE or NIH Roadmap Epigenomic projects [23,24]. We used package pyBigWig (http://dx.doi.org/10.5281/zenodo.45238) to read in BigWig files. However, one type of epigenomic data file in the BigWig format was often measured with multiple sample replicates, but the ENCODE or Roadmap project summarized those measurements only in BED format. The two file formats contain epigenomic feature information in different genome scales. Each unit in the BED file represents a small sampled genome sub-region with experimentally measured signals, thus the same base pair may be measured multiple times in multiple and different sub-region samples, which can be combined to map a unique signal value to each base pair by available tools for the whole genome. BigWig file, however, has a 1-1 correspondence between one base pair and a signal value across the whole genome. Thus, we obtained such 1-1 map between signals and genome in BigWig format data from BED files in Whalen, et al. [11] at https://github.com/shwhalen/targetfinder/blob/master/paper/targetfinder/K562/, which came from the cleaned peak files through the ENCODE or Roadmap. The Bedtools and bedGraphToBigWig software (http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86\_64/) was used to merge and convert BED files to the BigWig format [25]. To compare with TargetFinder, where data was summarized by computing the mean signal value across the whole local region of interest (3000/2000 bp), we also took the mean of the local epigenomic signals across each 3000/2000-bp window Later we will refer it as the TargetFinder-format data, which was 2-dimensional (Figure 4), and not suitable for CNNs. The epigenomic data were large to read in (13–23 GB) and often sparse in a 3000/2000-bp interval. Even when not sparse, the signal values often remained the same across multiple base pairs. The redundant and noisy data unnecessarily increased the number of parameters in prediction models. Therefore, we considered averaging the data signals through different sliding-window sizes and step sizes. Through the validation performance of CNNs, we found that a window/bin size of 50 and step size of 10 performed well among non-summarized and other forms of summarized data (Table S1). The data were later input into CNNs, hence we call it CNN-format data (Figure 2). After the sliding-window operation, the CNN-format data were reduced to 1–2 GB.

*Genes* **2020**, *11*, 41

**Figure 1.** Sequence model structures. (**a**) The structure followes a previously reported simple convolutional model [14]. We used shorter input sequences, each centered as the SPEID data, with attention modules (**b**) or ResNet (**c**).

To combine sequence and epigenomic data sources, a one-to-one mapping from enhancer-promoter pairs in the TargetFinder dataset to SPEID sequences was established. Although the exact procedure in the Singh, et al. [13] about sequence data generation from the TargetFinder E/P dataset was not available online, we inferred the relationship by matching sequences. Given the provided enhancer/promoter locations on the genome in TargetFinder, we first retrieved the enhancer and promoter sequence segments from the hg19 reference genome at http://genome.ucsc.edu/cgi-bin/das/hg19/dna?segment= chr1:6454864,6455189 (e.g., an enhancer was located on chromosome 1 from position 6454864 to 6455189 bp), then searched a matching SPEID enhancer/promoter sequence.

We always used the data on chromosomes 8 and 9 as the validation data; we used each of the remaining chromosome in turn as a test dataset while the other chromosomes (not the three chromosomes used as the validation and test data) as the training data. This avoided the bias issue of the original random data splitting [19].


# **Corrosion and Degradation of Materials**

Edited by Jian Chen, Yanxin Qiao, Fanjiang Meng and Yuxin Wang Printed Edition of the Special Issue Published in *Coatings*

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