**1. Introduction**

Multiphase systems comprising of more than two phases are a common occurrence in the fields of chemical and bioprocess engineering. Such multiphase systems may be comprised of a gas phase, a liquid phase and a solid phase as is the case in froth flotation processes in the minerals sector [1, 2]. Alternatively, such multiphase systems may be comprised of a gas phase and two immiscible liquid phases as is often found in biological gas stripping [3] or biocatalysis [4]. Irrespective of the application field, a common expectation among such multiphase systems is that they are characterised by more complex hydrodynamics than two phase systems which are reasonably well understood [5, 6]. Similarly, mixing and mass transfer effects are expected to be more complex in such systems.

Given the above considerations, the design and analysis of multiphase systems requires the use of comprehensive frameworks that are capable of taking into account the various mechanisms of action that are at play. Computational Fluid Dynamics (CFD) has been proposed as one such framework since it is able to describe the hydrodynamics of multiphase systems based on fundamental equations of flows [7]. Furthermore, coupling of CFD simulations to sub-models of mass transfer, mixing or flotation can enable the description of these effects at finer

resolutions than can be obtained based on empirical modelling. Thus, significant effort has been recently directed towards the development and application of CFD techniques to simulate multiphase systems comprising of two phase reactors [8–10] as well as those with more than two phases [2, 11–13].

This chapter builds upon recent work by presenting a discussion on the use of CFD in the design and analysis of gas–liquid–liquid reactors within the context of mass transfer. Key considerations informing the modelling approach have been discussed with their implementation illustrated by the review of recently modelled case studies [12, 13]. Furthermore, the successes and challenges attending the CFDbased modelling of gas–liquid–liquid reactors have been highlighted and on the basis of these, recommendations have been given on areas requiring further investigation. The chapter thus addresses itself to graduate students, academics and industrial practitioners interested in a comprehensive modelling framework for the design and analysis of gas–liquid–liquid reactors.


**5.1 Using the modified winsorization with graphical diagnostic (MW-GD) method**

To evaluate the effectiveness of the modified winsorization with graphical diagnostic (MW-GD) method, two real samples (see [6] for the two data sets) were considered with the second used as a validation sample. The first training sample is from a renowned financial journal, among Japanese business leaders, which can be compared to the Economist, Financial Times, and Business Week in Europe and the United States of America. This dataset contains 50 observations from each of the two groups of Japanese financial institutions, each bank being evaluated using the following seven performance indexes: (1) return on total assets (= total profits/average total assets), (2) labor profitability (= total profits/total employees), (3) equity to total assets (= total equity/average total assets), (4) total net working capital, (5) return on equity (= earnings available for common/average equity), (6) cost-profit ratio (= total operating expenditures/total profits), and (7) bad loan ratio (= total bad loans/total loans). However, taking into account the beneficial effect of feature selection and outlier detection as a preprocessing step, a best subset and critical value of Mahalanobis distance were first obtained using the SPSS stepwise method, and its compute command for critical value of Mahalanobis distance. The stepwise approach produced a best subset which comprises return on total assets (*X*1), labor profitability (*X*2), equity to total assets (*X*3), and bad loan ratio (*X*7). The SPSS output of the constructed PDF based on the training sample of four variables given by:

$$Z = 0.005X\_1 + 0.006X\_2 + 0.004X\_3 + 0.005X\_7 \tag{10}$$

Thus, the classification accuracy of the PDF, *Z* (10) and its LOOCV estimate are given by Eqs. (11) and (12).

$$P^{(a)} = \frac{1}{N^t} \left(\sum\_{j=1}^{N^t} d\_j\right) \times 100 = \textbf{86.0\%}\tag{11}$$

$$\hat{P}\_{LOOCV}^{(a)} = \frac{1}{N} \left(\sum\_{j=1}^{n\_v} d\_j\right) \times 100 = 85\% \tag{12}$$

While the two critical values from the SPSS outputs for 0.95 and 0.99 used as the probabilities in the IDF.CHISQ function with four predictor variables were given as:

$$\text{COMPUTE} \text{Eritical} = \text{IDF.CHISQ} \ (0.95, 4) = 9.49$$

$$\text{COMPUTE} \text{Eritical} = \text{IDF.CHISQ} \ (0.99, 4) = 13.28$$

The Mahalanobis distance values for all cases, as reported in the case wise statistics table, were all lower than the two critical values. This means that there are neither outliers nor hidden influential observations in the dataset or training sample. Next, the MW-GD algorithm was applied to the training sample, *Dt N* consisting of four predictor variables.

At *step 1* of the MW-GD method, a total of five modified winsorized means for the four predictor variables were obtained. The summary of the winsorized means values is presented in **Table 1**.

To provide a meaningful interpretation of **Table 1**, the modified winsorized means or averages for both groups, as shown in **Table 1**, were plotted with a 2D area plot in Excel Package. The process involves entering the first variable *X*1 modified winsorized averages for both groups into Excel spreadsheet in pairs (with *X*1 values for group 1 and 2 occupying the first 2 columns from row 1 to row 6), followed by variable *X*2 (with *X*2 values for group 1 and 2 occupying column 3 to column 4 from row 8 to 13), followed by variables *X*3 and *X*7 proceeding downward in steps. The graphical representation is presented in **Figure 1**.

#### **Table 1.**

*Modified winsorized means for up to five pairs of winsorized values.*

*On the Use of Modified Winsorization with Graphical Diagnostic for Obtaining… DOI: http://dx.doi.org/10.5772/intechopen.104539*

A cursory look at **Figure 1** shows that the winsorized average values for the four predictor variables in both groups represented by the 2-D area plot have similar shape (or have similar variances within the groups) except for predictor variable *X*2. Also, in **Figure 1**, the bar shape of the predictor variable *X*2 in group 1 looks like a rectangle whereas in group 2 it looks like a trapezium. The observed difference in the shape of the variable *X*2 bar indicates that variable *X*2 does not have similar variances in the groups, and therefore becomes the only variable with legitimate contaminants. It therefore means that the training sample does not satisfy the assumption of homogeneity of variances. This finding was corroborated by the result of the Box M test for equality of variance-covariance matrices for this training sample, which was significant. Apart from manually replacing the extreme values on both ends with the median value, for each percentage of winsorization using R aggregate () function, the average calculation time needed to generate each row result for groups 1 and 2 in **Table 1** was 2 seconds.

At *step 2* of the MW-GD method, the modified winsorization process was performed for only variable *X*2. For each percent of winsorization, a PDF is constructed using all four predictor variables. The summary of hit rate results for each percent of winsorization is presented in **Table 2**. **Table 2** shows that the highest hit rate of 97.00 was achieved when 5 data points at both ends of the data were replaced by the median value. This means that all the legitimate contaminants in variable *X*2 was completely replaced at 20% winsorization. In other words, at 20% winsorization, the lack of homogeneity of variances observed in the variable *X*2 was taken into account, thus obtaining a near optimal training sample, *DN opt* ð Þ:

The optimized training sample, *DN opt* ð Þ was then used to construct a PDF. The SPSS output for the obtained PDF is given as:

$$Z\_{opt} = \; 0.003X\_1 + 0.018X\_2 + 0.001X\_3 + 0.004X\_7 \tag{13}$$

Thus, the PDF, *Zopt* (13) hit rate and its LOOCV estimate are given by Eqs. (14) and (15).

$$P^{(a)} = \frac{1}{N^t} \left(\sum\_{j=1}^{N^t} d\_j\right) \times 100 = 97.0\% \tag{14}$$

$$\hat{P}\_{LOOCV}^{(a)} = \frac{1}{N} \left(\sum\_{j=1}^{n\_v} d\_j\right) \times 100 = 91\% \tag{15}$$

#### **Table 2.**

*Summary of hit rate results for each percent of modified winsorization for predictor variable,* X*2.*

In addition to the dataset from Japanese banks, a second real dataset was used to validate the first sets of results (11), (12), (14), and (15). This validation sample was obtained from the academic records of junior secondary school (JSS) 2, University Demonstration Secondary School (UDSS), University of Benin, Nigeria. The dataset contains 30 observations for both classes: Science and Art. The dataset consists of average scores for the three consecutive terms obtained for eleven (11) subjects, including English Language (*X*1), Mathematics (*X*2), Integrated Science (*X*3), Social Studies (*X*4), Introductory Technology (*X*5), Business Studies (*X*6), Home Economics (*X*7), Agricultural Science (*X*8), Fine Art (*X*9), Physical and Health Education (*X*10), and Computer Studies (*X*11). Using the SPSS stepwise method, a subset of three variables comprising introductory technology (*X*5), physical and health education (*X*10), and computer science (*X*11) was obtained. The SPSS output for the PDF is given as:

$$Z = 0.135X\_{\\$} - 0.102X\_{10} + 0.058X\_{11} \tag{16}$$

Thus, the PDF, *Z* (16) hit rate and its LOOCV estimate are given by Eqs. (17) and (18).

$$P^{(d)} = \frac{1}{N^t} \left(\sum\_{j=1}^{N^t} d\_j\right) \times 100 = \textbf{85.096} \tag{17}$$

$$\hat{P}\_{LOAD}^{(a)} = \frac{1}{N} \left(\sum\_{j=1}^{n\_v} d\_j\right) \times 100 = \textbf{83\%}\tag{18}$$

Also, the two critical values from the SPSS outputs for 0.95 and 0.99 used as the probabilities in the IDF.CHISQ function with three predictor variables were given as:

$$\text{COMPUTE} \text{Eritical} = \text{IDF.CHISQ} \ (0.95, 4) = 7.81$$

$$\text{COMPUTE} \text{Eritical} = \text{IDF.CHISQ} \ (0.99, 4) = \text{11.34}$$

The Mahalanobis distance values for all cases, as reported in the case wise statistics table, were all lower than the two critical values. This means that there are neither outliers nor hidden influential observations in the dataset or training sample. Once again, the proposed algorithm is applied to this second training sample, *Dt N* consisting of three predictor variables.

At *step 1* of the MW-GD method, a total of five modified winsorized means for the three predictor variables were obtained. The summary of the modified winsorized means values is presented in **Table 3**.

Again, to interpret **Table 3**, the modified winsorized means or averages for both groups, as shown in column three of **Table 3**, were plotted with a 2D area plot in Excel Package. The graphical representation is presented in **Figure 2**.

A cursory look at **Figure 2** shows that the winsorized average values for the four predictor variables in both groups represented by the 2-D area plot have similar shape (or have similar variances within the groups). The similar shape shown by the three variables in each group indicates that there are no legitimate contaminants in the training sample, *Dt N*. This implies that the fit between the training sample, *Dt N* and the basic assumptions of PDA is sufficient (in particular is the assumption of homoscedasticity) to construct a PDF whose hit rate can be said to be statistically optimal. Therefore, for this dataset, the MW-GD algorithm ends at *step 1*. The initial training

*On the Use of Modified Winsorization with Graphical Diagnostic for Obtaining… DOI: http://dx.doi.org/10.5772/intechopen.104539*

#### **Table 3.**

*Winsorized means for up to five pairs of winsorized values.*

**Figure 2.** *Graphical representation of winsorized mean values in Table 3.*

sample of three variables obtained from the second real dataset using SPSS stepwise method is therefore an optimal training sample.


**1.1. Surface functionalization of nonwovens by plasma techniques**

Nonwoven fabrics are produced by bonding or interlocking of fibers by different means such as mechanical, chemical, thermal, or physical. These fabrics may be designed to have a limited lifetime or as single-use fabrics, or very durable ones. The technologies enable the production from simple roll-goods to micro- and nanofiber webs. The structure of a typical nonwoven made from polypropylene fibers by melt-extrusion technologies is shown in Fig. 1. Nonwovens have been used in a wide range of products such as wipes, baby diapers, filtration media, garment interlinings, furniture padding, and many others. The end-use properties of nonwo‐ vens are determined by the properties of the fibers they are made from, the fabric structure, and other functionalities, such as absorbency, hydrophobicity, wettability, and antimicrobial

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

functional performance of the final product.

property, imparted by potential modifications. Surface modification applied to the fiber is a way to add value to the nonwoven fabric and enhance the functional performance of the final product. of the fibers they are made from, the fabric structure, and other functionalities, such as absorbency, hydrophobicity, wettability, and antimicrobial property, imparted by potential modifications. Surface modification applied to the fiber is a way to add value to the nonwoven fabric and enhance the

padding, and many others. The end-use properties of nonwovens are determined by the properties

Nonwoven fabrics are produced by bonding or interlocking of fibers by different means such as mechanical, chemical, thermal, or physical. These fabrics may be designed to have a limited lifetime or as single-use fabrics, or very durable ones. The technologies enable the production from simple roll-goods to micro- and nanofiber webs. The structure of a typical nonwoven made from polypropylene fibers by melt-extrusion technologies is shown in Fig. 1. Nonwovens have been used in

1. Surface functionalization of nonwovens by plasma techniques

Figure 1: Scanning electron microscopy images showing the surface texture of a nonwoven fabric **Figure 1.** Scanning electron microscopy images showing the surface texture of a nonwoven fabric

Plasma is the mixture of partially and fully ionized gas, photons, free electrons, and chemically reactive atoms and radicals [1]. Plasma is generated by raising the energy content of matter through Plasma is the mixture of partially and fully ionized gas, photons, free electrons, and chemically reactive atoms and radicals [1]. Plasma is generated by raising the energy content of matter through different methods such as dielectric barrier discharge, glow discharge, corona discharge, or the application of a high electric field.

different methods such as dielectric barrier discharge, glow discharge, corona discharge, or the application of a high electric field. The gas molecules become ionized when the kinetic energy of gas molecules exceeds their The gas molecules become ionized when the kinetic energy of gas molecules exceeds their ionization energy. The active species in plasma only interact with the outermost surface of the substrate, approximately 10 nm in depth, without altering the bulk properties of the substrate [2].

ionization energy. The active species in plasma only interact with the outermost surface of the substrate, approximately 10 nm in depth, without altering the bulk properties of the substrate [2]. Plasma treatment has been recognized as an alternative ecological surface treatment to Plasma treatment has been recognized as an alternative ecological surface treatment to conventional liquid-based textile-coating treatments, which use solvents, additives, and organic reagents and produce large volumes of liquid waste.

conventional liquid-based textile-coating treatments, which use solvents, additives, and organic Plasma treatment can be used to modify the surface properties of nonwovens for:

	- Enhancement of surface energy, thereby improving wettability and adhesion properties of nonwovens **•** Improvement of the surface properties of natural and synthetic fibers to improve wettability, printability, and dyeability of nonwovens
	- Improvement of the surface properties of natural and synthetic fibers to improve wettability, printability, and dyeability of nonwovens **•** Hydrophobization, oleophobization (dirt- and oil-repellent effect), and functional and bioactive coatings (antibacterial, fungicidal)

• Hydrophobization, oleophobization (dirt- and oil-repellent effect), and functional and bioactive coatings (antibacterial, fungicidal) With plasma, different reactions such as surface cleaning, etching, deposition, cross-linking, surface roughening, and grafting are possible [2]. Polymer surfaces can be cleaned or etched, mostly using nonpolymerizable gases such as O2, N2, H2, the noble gases, or gas mixtures [3].

2

Plasma etching has been used to remove contaminations such as lubricants, oils, surfactants from textile fibers and also to increase the wettability of the surface by changing the surface roughness [3].


**Author details**

Akinobu Ota\*, Md Wahiduzzaman and Yoshitaka Hosokawa

\*Address all correspondence to<EMAIL_ADDRESS>
Department of Biochemistry, Aichi Medical University School of Medicine, Nagakute, Aichi, Japan


Evolution of physical Ana BBots under selection for enhanced phototaxis and obstacle avoidance. (A) As detected by ANOVA (*p* < 0.05), fitness decreases over generational time. A significant onegeneration decrease between generations 4 and 5 (asterisk) is present as determined by *a priori* contrasts. Points are estimated marginal means ± 1 SE, with interactive and independent development pooled. (B) Interactive and independent thread developments are shown separately, even though they are not statistically distinct. (C) Selection differential, showing positive selection on fitness decreasing over generational time. The differential shown in a given generation is that applied to the next. (D) Variance in the populations, measured by SD, decreases by an order of magnitude over generational time. For the interactive development, the correlation between *S* and variance is 0.982; for independent development, the correlation is 0.974.

*p* = 0.023); also, there was a significant difference between the interactive and independent groups for the selection condition (*p* < 0.001), but not for the no-selection condition (*p* = 0.715). Selection and Generation also interacted significantly on number of crossover points, *p* = 0.019, with crossover points increasing more rapidly with Generation in the selection condition than in the no-selection condition. Additionally, Development Type had a significant effect on crossover (*p* < 0.001), explained by the independent group having significantly higher means (M = 4.407) than the interactive group (M = 3.786). Generation also had a significant effect (*p* < 0.001), with crossover points tending to increase with increasing generations. There was also a main effect of Selection (*p* < 0.001), with the selection group having more crossover points (M = 4.464) than the no-selection group (M = 3.729).

Given the importance of threads to the function of the robot, we had predicted a fitness landscape (see **Figure 7**). We see a precipitous decline in the number of threads in the EO population (interactive thread development) under selection from generations 2 to 3 (**Figure 10F**) that corresponds to a drop in fitness in the EO population under selection from generations 2 to 3 (**Figure 9B**). We do not see a similar drop in the number of threads or fitness under selection in the non-EO population (independent thread development) until generations 4 to 5. Also note that without selection the number of threads in the EO population plummets (**Figure 10E**). While these are qualitative results, they are important for three reasons: (1) the number of threads appears to be related to fitness, (2) the EO population responds differently than the non-EO population, and (3) selection changes the behavior of the EO population markedly.

To examine the evolution of threads in more detail, we examined the changes in their distribution patterns over generational time (**Figure 12**). Under selection, the distribution of the EO population changes more quickly than that of the non-EO population, with the two populations having overlapping distributions but different modes and skew after seven generations. Without selection, we see a similar rapid response of the EO population and different final distributions.

#### Crossover Point Constraints

To understand the importance of our decision to allow crossover points to be anywhere in the genome, we constrained crossover

wires decreases over generational time. (E,F) A significant three-way interaction (*p* < 0.05) indicates that under selection the number of threads in both types of development is not different and that the number of threads decreases over generational time. A univariate three-way fully factorial ANOVA was run on each phenotype. Scale of the ordinate is identical across rows. Points are estimated marginal means ± 1 SE.

points to positions between genes. These trials were run without selection. A 2 × 2 × 11 [Crossover Placement (constrained, unconstrained), Development Type (interactive, independent), Generation (1–11)] repeated-measures factorial ANOVA was run on the following measures: normalized performance, mean number of thread interactions, mean number of wires, mean number of crossover points, and mean number of threads. The genomic parameters of the "constrained" population were identical to those of the "unconstrained" population with the exception that crossover points were constrained to the intergenic regions in the former and not the latter.

For the number of interactions between threads, a significant Development Type by Generation interaction (*p*< 0.001) indicated that interactions generally decreased across generational time under interactive thread development, but tended to increase with generational time under independent thread development (**Figures 13A,B**). A Crossover Placement by Development Type interaction (*p* < 0.001) indicates that independent development populations have significantly more interactions (M = 1.300) than interactive development populations (M = 0.600), but only under unconstrained crossover placement. In general, interactive development populations had significantly more interactions (M = 1.159) than interactive development populations (M = 0.841; *p* < 0.001).

For the number of wires, a significant three-way interaction between Crossover Placement, Development Type, and

Generation on number of wires (*p* < 0.05) indicates that with constrained crossover placement the number of wires decreased more slowly for interactive thread development populations and increased less rapidly for independent thread populations (**Figure 13C**) when compared with unconstrained crossover placement (**Figure 13D**). There was a significant Development Type by Generation interaction (*p* < 0.001), indicating that wires tended to attenuate under interactive development, but stayed relatively stable under independent development. A significant Crossover Placement by Development Type interaction (*p* < 0.05) revealed a significantly larger gap in mean wires between independent development (M = 3.182) and interactive development (M = 1.218) under unconstrained crossover placement versus under constrained crossover placement (M = 3.364 and M = 2.400, respectively). The number of wires decreased with generation (*p* < 0.05), the independent group had significantly more wires (M = 3.273) than the interactive group (M = 1.809; *p* < 0.05), and that the mean number of wires was higher with constrained crossover placement (M = 2.882) than with unconstrained (M = 2.200; *p* < 0.05).

For the number of threads, a significant three-way interaction between Crossover Placement, Development Type, and Generation (*p* < 0.001) indicates that with interactive thread development, threads attenuated more rapidly under unconstrained crossover placement (**Figure 13F**) than constrained crossover placement (**Figure 13E**). A significant Development Type by Generation interaction (*p* < 0.001) reflects the tendency for thread count to decrease with generational time under interactive development, whereas thread count parabolically decreased then increased under independent development. There was also a Crossover Placement by Development Type interaction (*p* < 0.001), indicating that thread count is significantly higher under interactive development (M = 1.709) than independent development (M = 1.591) with constrained crossover placement, but significantly lower under unconstrained crossover placement (M = 1.055, M = 2.291, respectively). There was also a main effect of Generation (*p* < 0.05), reflecting the general upward parabolic change in thread count. Additionally, the independent development populations had significantly more threads (M = 1.941) than the interactive development populations (M = 1.382; *p* < 0.05).

For number of crossover points, a significant three-way interaction between Crossover Placement, Development Type, and Generation (*p* < 0.05) indicates that under unconstrained crossover, the number of crossover points generally increases faster with the interactive development group than with the independent group (**Figure 14A**), but this relationship switches with constrained crossover placement (**Figure 14B**). A significant two-way interaction between Development Type and Generation (*p* < 0.05) indicates that the independent development populations accrued crossover points more quickly than the interactive group. A significant Crossover Placement by Generation interaction (*p* < 0.05) indicates that populations with unconstrained crossover points accrue crossover points more rapidly than populations with constrained crossover points. In general, the number of crossover points increased across generations (*p* < 0.05) and populations with unconstrained crossover had more crossover points (M = 4.682) than constrained crossover populations (M = 3.041; *p* < 0.05).

### Simulated Robots

A 2 × 500 [Development (EO, GO), Generation (1–500)] ANOVA revealed a main effect of Development (*p* < 0*.*001) and Generation (*p*< 0.001) on the fitness of the best individual in each population. There was no interaction effect (*p* = 0.175). These results (**Figure 15**) suggest that while the fitness of both types of simulated populations increased with successive generations, the EO significantly reduced fitness in comparison to the GO (non-EO) condition. Finally, note that while the fitness in the simulated populations increased over time, the fitness decreased over time in the physical populations (compare **Figures 9** and **15**).

### DISCUSSION

In addition to the standard GOs of ER, EOs are a complementary class of mechanisms that alter the expression of the genome.

1 to 7, the mode of the population with independent development and no epigenetic operator (no EO, green) remained stable at two threads, with or without selection. From generations 1 to 7, the mode of the population with interactive development and an EO (red) changed from 2 to 0 without selection and from 2 to 1 with selection. For clarity, an individual with four threads in the EO population was omitted from (D).

Physically embodied EOs, as we model them, may have important evolutionary consequences, as determined by the specific effect of the EO, the shape of the fitness landscape, and the position of the population on that landscape (**Figure 1**). With this model in mind, we hypothesized that an EO will alter the evolutionary trajectory of a population. This hypothesis is supported by the results of preliminary experiments in two populations of physically embodied robots, one with and one without an *interactive thread development* EO, under selection for enhanced phototaxis and obstacle avoidance.

The generality of this result is extended by experiments run in simulation. The difference between the physical and simulated robots in terms of the direction of the change in fitness (decrease for physical, increase for simulated, **Figures 9** and **15**, respectively) may reflect different effective algorithms for information transmission in the neural networks. In the physical robots, signaling is implemented in hardware, while in the simulated case we used standard neural network updating models rather than trying to simulate the electronic components on the Ana BBot. However, the key point of comparison is between the EO (a.k.a. interactive thread development) and non-EO (a.k.a. independent thread development or GO) conditions; for both simulated and physical systems we find that the EO condition degrades the effect

of selection on the evolution of fitness and phenotypes, respectively. In this critical comparison, the two approaches produce consistent results.

While the changes in mean fitness are statistically indistinguishable in the EO and non-EO populations of physical robots (**Figure 9**), mean phenotypic values diverge quickly (**Figures 10** and **11**). Because of its functional role, the key phenotype is the number of threads, where threads are the genetically encoded wiring patterns that may connect sensors to motors on the robot's physical circuit board (**Figure 4**). The distributions of thread number within each population are identical initially, in the first generation, but then they diverge rapidly (**Figure 12**). This rapid divergence occurs in EO populations both with and without

Figure 14 | Crossover placement impacts the evolution of the crossover phenotypes without selection. With crossover placement constrained (A), the differences between types of development are eliminated and the growth of points over time is attenuated compared crossover placement being unconstrained (B). Points are estimated marginal means ± 1 SE.

selection, which indicates that the EO effect is not selection in disguise; moreover, the effects of EO and selection interact, in a statistical sense, which provides additional evidence supporting the main hypothesis (compare **Figure 10E** and **Figure 10F**). In this instance, differences in the evolution under selection of two otherwise identical populations appear to be caused by the embodied EO of interactive thread development (**Figure 16**).

From simulations, we have a complementary perspective from populations that are larger and evolve for much longer than those in the physical robots. Importantly, the hypothesis that an EO will alter the evolutionary trajectory of a population is upheld. In simulation, otherwise identical populations of Ana BBots with EOs evolve fitness more slowly and with lower magnitudes of fitness than those lacking them (**Figure 15**).

The populations of simulated Ana BBots with interactive thread EOs—a destructive process predicted to reduce the number of threads (**Figure 1**)—are less evolvable than populations without them. Although both the EO and GO treatments allow for large amounts of neutral mutation, which has been cited as a contributor to increased evolvability (Smith et al., 2001; Wagner, 2008), the GO treatment may allow for more connections to be constructed between the sensor and motor layers, or perhaps for more efficient, and less self-interfering, networks. This may in turn provide more raw materials for subsequent evolutionary change. By contrast, the EO treatment may produce fewer overall connections between sensor and motor layers, which may in turn make any subsequent mutations that change the nature of this path more disruptive. Future work will involve more detailed analysis of how such pathways in both treatments do change—or fail to change—over evolutionary time.

We note that the evolutionary impact of the interactive thread EO depends on the shape of the fitness landscape and the location of the populations on that landscape (**Figure 7**). For example, if we shifted the starting populations to the right on the fitness landscape, more threads result in a loss of fitness as threads are redundant in their function or if their functions counteract each other. In this situation, the interactive thread EO would increase the fitness of the populations by pruning threads. Thus the identical EO can have opposite effects on evolution depending on where a population sits in a particular fitness landscape.

As we have narrowly defined it, an EO may be any mechanism inherent to an agent's developmental system that alters the expression of the genome. This leaves investigators with a daunting array of EOs from which to choose. To avoid an arbitrary decision, we let the physical embodiment of the Ana BBot (**Figure 2**) guide us. This is an analog robot that is programed using jumper wires to connect sensors to motors (**Figure 3**). With this in mind, we created a genome that encodes genes that governed multiple, separate wiring patterns called threads (**Figure 4**). Since only

a limited set of pins is available for threads, we created an EO, interactive thread development, that recognized this physical constraint: if different genes code for the same pin, then only the first gene expressed may use it and the expression of the other gene is left incomplete. In development without this EO, all genes are fully expressed by allowing threads that call for the same pin to switch to an alternate and functionally equivalent pin.

We recognize that this developmental system is extremely simple, particularly when compared to one that changes full-body morphology in simulated, embodied mobile robots (Bongard, 2011). But to our knowledge, interactive thread development is the first physically embodied EO used in the evolution of physically embodied robots. Thus the developmental engine of ER (Eiben et al., 2010) has its first physical instantiation, albeit a simple one; physical instantiation is a necessary condition for the complete life cycle of the evolution of things (Eiben and Smith, 2015) and in the physical evolution of ontogenies (Northcutt, 2002).

Embodied development means different things in different contexts. For researchers interested in social and socially assistive robots (Tapus et al., 2007), for example, development focuses on learning and interactive changes in an agent's cognition (Asada et al., 2001). Others focus on life cycle changes in an agent's morphology (Jin and Meng, 2011; Doursat et al., 2012). For researchers interested in ER, changes in morphology during a digitally simulated embodied agent's lifespan dramatically alter the impact of selection on the evolution of behavior (Bongard, 2011). To bridge the reality gap between the simulation of morphological changes and the physical instantiation of those changes, we can incorporate the methods of reconfigurable robots (Levi et al., 2014), self-assembling swarms (Rubenstein et al., 2014), and programmable matter (Toffoli and Margolus, 1991; Felton et al., 2014) to create PED. Combining PED with ER creates an approach, ERPED, exemplified in a preliminary and simple manner in this study.

### CONCLUSION

We have shown that an EO alters the evolution of populations of physical and simulated embodied robots under selection for enhanced phototaxis and object avoidance. While we must be cautious in drawing general conclusions from this preliminary result, the specific method employed is easily extended to other physically embodied robotic systems. Necessary to this extension is to make development explicit, genetic, and physical. When the expression of genes is altered by the physical rules and interactions governing the agent's physical construction, the genotypeto-phenotype mapping process becomes available as a creative tool to ER.

### AUTHOR CONTRIBUTIONS

JBr, AH, and KL conceived of and designed the experiments. JBr and AH programed the genetic and epigenetic systems, and validation of the code was overseen by EA and JL. JBr and AH

### REFERENCES

Kitano, H. (1990). Designing neural networks using genetic algorithms with graph generation system. *Complex Syst. J.* 4, 461–476.

ran the experiments and reduced the data on the physical robots. JL analyzed the results. JBr and JBo designed, built, ran, and analyzed the simulation. All the authors contributed to the writing of the manuscript.

### ACKNOWLEDGMENTS

Larry Doe was invaluable to the maintenance and modification of the Ana BBot. John Connell of Johuco Ltd. developed the Ana BBot and provided high-quality schematics. Nick Livingston provided invaluable assistance in the development of the project and revision of the manuscript. Anton Bernatskiy provided critical assistance in developing the simulation.

### FUNDING

This work was funded by the U.S. National Science Foundation (grant no. 1344227, INSPIRE, Special Projects).

Wagner, G. P., and Altenberg, L. (1996). Perspective: complex adaptations and the evolution of evolvability. *Evolution* 50, 967–976. doi:10.2307/2410639

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Brawer, Hill, Livingston, Aaron, Bongard and Long. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*


Preface

Antisense therapy, or oligotherapy, is an emerging field of disease treatment and a revolution that has completely changed gene therapy. Antisense oligonucleotides are small synthetic fragments of DNA that have potential to target and bind to messenger RNA of a particular gene causing a particular disease, and modulate specific protein production in human genome. Antisense therapy using oligonucleotides has many applications in clinical medicine and has great potential to change the therapeutic landscape for many disease conditions, including their prevention, treatment, and management. This book presents oligonucleotides as an important therapeutic option and discusses how they may be used to treat cancer and other diseases. It not only covers important developments in the field of molecular mechanisms, but also highlights novel therapeutic approaches in several human disorders including cancer. There was a need to bring this information together in a single volume, as much of the recent developments are dispersed throughout biomedical literature, largely in specialized journals.

It is my hope that readers will appreciate the complexity of oligonucleotides research, especially for healthcare treatment. In the era of precession medicine, targeted therapy plays an important role. Identifying disease-specific molecular changes and targets for precision treatment is critical. In theory, oligonucleotides are designed to control the transfer of genetic information, but the use of antisense therapy is likely to benefit the patient and help manage disease. This book summarizes the current state of the field of oligo-based therapy in cancer and is an ideal resource for clinicians and researchers interested in antisense therapy. It will also appeal to physicians, surgeons, residents, biomedical scientists, and students.

> **Shashwat Sharad, Ph.D.**  Center for Prostate Disease Research, Department of Surgery, Uniformed Services University of Health Sciences (USU) and Walter Reed National Military Medical Center (WRNMMC), Bethesda, Maryland, USA

Henry Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA

#### **Suman Kapur, Ph.D.**

Senior Professor, Department of Biological Sciences and Dean, International Programmes and Collaboration Division (University wide), Birla Institute of Technology and Science, Pilani Hyderabad Campus, Telangana, India

**1**

**Chapter 1**

**Abstract**

*Shashwat Sharad*

Antisense Therapy: An Overview

Nucleic acids are the backbone of antisense therapy. Antisense oligonucleotidebased therapeutics involves downregulation of gene expression. RNA-based drugs that include antisense oligonucleotides bear great therapeutic potential toward treatment of various diseases by altering RNA and/or reducing, restoring, and modifying protein expression through multiple molecular mechanisms. Pharmacology of targeted antisense therapy has provided the platform to translate its utility to the clinic. Over the years, chemical modifications of antisense oligonucleotides have not only enhanced the specificity and efficacy but also reduced the side effects. These have changed the whole clinical trial design and provide newer strategies for therapies. Improvement in antisense oligonucleotide therapy technology has allowed and brought research from bench to clinic. Additionally, the use of small interfering RNAs, micro RNAs, ribozymes, and other antisense compounds toward the treatment of deadly diseases like cancers have demonstrated both preclinical and clinical responses. Furthermore, antisense therapy has great potential to target specific genes of interest in the context of precision medicine. Optimization of enhanced delivery, specificity, affinity, and nuclease resistance with reduced toxicity is underway in different disease context. This chapter gives a complete overview of antisense therapy and highlights its potential. Here, we focused on the advances of the antisense technology, pharmacology, therapeutics, and drug discovery.

**Keywords:** antisense oligonucleotides, antisense therapy, antisense drug,

The advancement in the next-generation sequencing enables us to identify the genetic heritages of several diseases, such as cancer, Parkinson's, rheumatoid arthritis, and Alzheimer's, which brings to attention the development of personalized medicine [1]. This knowledge has been well adapted and accepted for diagnosis, but the field still lags toward pharmaceutical interventions to address the genetic defects underlying diseases. At present, small molecules and proteins are the two major classes of US Food and Drug Administration (FDA)-approved drugs [2]. Small-molecule drugs inhibit target proteins through competitive binding, whereas protein-based drugs (such as antibodies) can bind with high specificity to several targets. The size and stability of proteins are the major limitations of their utility for majority of disease targets [2], and both protein and small-molecule drugs cannot target every disease-relevant protein or gene. Thus, there is a current need to develop the drugs for personalized genomics. The mRNA- and DNA-based drugs are therapeutically more promising and have the great potential to cure the genetic defect [1]. The RNA-based drugs have emerged as a promising candidate to treat

antisense therapeutic, gene therapy

**1. Introduction: antisense technology**


**3. Magnesium for weight-saving and lowering energy for transportation**

Nowadays, the transportation sector impacts around 25% of direct CO2 emissions from fossil fuel combustion. Among the variety of transportation means, road vehicles, particularly passenger cars and freight vehicles such as heavy trucks, busses, and two-wheelers, are estimated by the International Energy Agency (IEA), accounting for nearly three-quarters of transport CO2 emissions. Although CO2 emissions from aviation and shipping have been increasing in the last decade, the road share of total transport sector emissions has fluctuated around 75% of total transport emissions for two decades. If several efforts and advancements have been made in road-vehicle electrification, otherwise larger (and heavier) vehicles are still preferred by lots of consumers. The worldwide market share of SUVs has increased in the last two decades, and in 2019, before the pandemic crisis, it represented nearly half of the global light-duty vehicle market in several countries. Growing demand for the urban transport of goods is rising, adversely affecting air quality, noise, safety, and liveability in the city. The automotive sector has been putting efforts for reversing CO2 emissions growth by several strategies; one of those strategies focuses on energy efficiency countermeasures that would be implemented in the form of:

managing/rationalizing travel habits to reduce the frequency and/or distance switching from high-energy-intensity modes (e.g., private car and or air) to most efficient methods (i.e., train for traveling long distances plus rented new efficient vehicles on local base).

deploying energy-efficient technologies for vehicles and fuels.

more stringent requirements on vehicle efficiency, namely, power consumption per km.

The latter strategy is thought a valuable approach for accompanying market migration from heavy vehicles powered by combustion engines fueled by gasoline toward cleaner electrified cars that could be likely powered by near-zero-emission electricity. During vehicle operation, the fuel consumption rate can be approximated as the sum of a linear function of the vehicle mass and—as a second contribution the loss in aerodynamic drag; both of them through coefficients that depends on several vehicle characteristics. Strategies approaching weight reduction are actually most effective during transient driving cycles; instead, during constant speed traveling, the vehicles' fuel efficiency mostly depends on aerodynamic drag forces. Global average fuel consumption of new cars has been too slowly decreased, less than 2% per year, setting around 7 L gasoline equivalent per 100 km (Lge/100 km). To get on track with 2030 targeted 4.5 Lge/100 km, expected standards will become significantly more stringent to achieve efficiency goals. In 2021 the European Commission proposed new CO2 emissions targets for 2030 and 2035 that require CO2 emissions reductions of half actual emissions for cars and vans.

Despite wide literature on life cycle assessment of on-road vehicles considers fossil fuel-powered vehicles, a similar approach is being deployed in the case of electric motor-powered vehicles (considering the energy efficiency of kWh per km traveled) or hydrogen-gas fueled road vehicles (considering hydrogen gas supplied to fuel cell unit per km traveled). Precisely for fully electric cars, the weight of full-electric vehicles is a sum of the mass of the vehicle's architecture and the mass of battery packs. Thus, its common sense considering that the travel range represents for the consumers the independence from the plug-in commences with battery size. That's the Achille's heel of plugged-in vehicles for fossil-fueled vehicle buyers. On the one hand, travel range increases with battery capacity, but on the other hand, larger battery capacity means a heavier vehicle to travel.

While vehicle downsizing improvements in fuel efficiency could be achieved, it appears in contrast with buyers' needs; thus, reducing size for reducing mass could not target a competitive strategy for automakers.

For this reason, a weight-saving strategy primarily implemented by extensive use of lightweight materials—better to say, by higher specific strength—is, therefore, most promising for pursuing consumers' satisfaction. Meanwhile, environmental aspects are successfully addressed, as they cannot be deferrable. It has been estimated that a 10% of vehicle weight curbing increases the vehicle's fuel efficiency by nearly 7%. But the ability to introduce new lightweight materials into vehicles is not a simple remove-and-replace process. Concerns about the impact of material changes on manufacturing lines, supplying network reliability, material cost stability, secure material availability in the marketplace are the main drivers in the material-shift decision process as they all could be more important for automakers than the percentage of weight saved. The potentiality of any lightweight scenarios steered by material replacement rates is based on the actual capability of lighter but weaker materials to safely replace heavier but stronger ferrous alloys, like steels and cast irons. As shown in **Table 3**, the weight-saving potentialities of lighter material depends on the specific substitution factor for the specific function, and it's a fact that the materials substitution factor strongly depends on: the physical properties of the material (e.g., its density and its elastic module as key-factor impacting on stiffness-limited design), the shaped part mechanical properties that are strongly dependent on the shaping process employed (e.g., fatigue limit obtained by cold pressure die-casting operations is different from fatigue limit obtained with low-pressurized die-casting), the geometrical constraints fixed by design (e.g., limited space of fixed boundaries to frame architecture).

As it is usual for any comparative analysis, we need a baseline and parameter to use in the calculation of data output to compare. The fuel consumption reduction coefficient is conventionally used as a measure of fuel-mass correlation. It provides the saving in specific consumption achieved through a 100 kg weight-saving. Recent literature set in the range 0.3–0.5 L/(100 km × 100 kg), varying with modeling assumptions, such as vehicle class, car model, driving cycle, the fuel consumption reduction coefficient for internal combustion engine vehicles [19], and values in the range of 0.47–1.17 kWh/(100 km × 100 kg) for electric vehicles [20].


**6. Case study 2: overseas flight**

A volunteer (a male aged 66) travelled by air from the Narita-Tokyo Airport to the Washington Dulles International Airport in order to attend a conference held in the USA. Using the iPod device, we recorded his EKGs and computed the scaling exponents as shown in **Figure 15**. Twenty-four SI measurements during the flight were documented and plotted, from which we found that mDFA accomplished understandable results similar to that shown in **Figure 13**.

We confirmed that the SI values can represent the internal world of the subject (see **Figure 15**). For example, when the subject was at an aroused state such as in the waiting lounge (see 1 in **Figure 15**), watching an exciting documentary (note: highly personalised expression), and preparing for landing (see 24 in **Figure 15**), the SI is near 1.0. In turn, when watching a movie which has an emotional involvement (note: highly personalised expression), the heartbeat of subject shows a lower SI values (see 18–20 in **Figure 15**). Finally, when the subject is at asleep condition, the SI decreases significantly (see 7–9 in **Figure 15**).

In conclusion, a happy life could fundamentally guarantee a healthy exponent. Anxiety and stress lowered the scaling exponent. mDFA might reflect psychological and physical internal bodily state. mDFA might look at the internal state through the heart. The heart is the window of the mind.