**3. Organoids in the immunology field**

The knowledge concerning the interactions of the immune system with other tissues has been gained mainly from animal models and/or cell lines co-cultures. Nevertheless, some interactions between human cells cannot be addressed with murine models or cell lines which are usually transformed or genetically modified [19]. For instance, a specific immune cell morphology is required to maintain the tissue properties and, moreover, the immune system needs of multiple cell types interactions for appropriate functioning. Similarly, there are some aspects that cannot be extrapolated in mice due to, for example, different protein pattern expressions in human and mice. Thus, immunology researchers are starting to get the benefits of using organoids, for a better comprehension of the immune cell interactions with other tissues, its development, homeostasis and in the bout of disease. The organoids approach maintains those cells in a near-native state, mimicking more accurately its original state and environment, providing researchers with a new effective tool.

The main challenge in the use of organoids in immunology resides in the fact that the organoids technology cultures only epithelial cells. However, a more complete resource for immunological research can be developed by co-culturing these organoids with other elements.

The number of publications showing multiple co-cultures has spiked up in the past decade, particularly in the last five years [20–22]. In order to develop effective interventions to preserve health and defeat diseases it is necessary to know how immune cells coordinate their activities to initiate, modulate, and terminate inflammation. Immune cells and molecules released by immune cells promote inflammation processes that are mediating the interactions between these cells [23].

These studies have revealed not only the importance of the presence (or absence) of immune cell derived factors in the epitheliums in culture, but also the need of the reciprocal communication with the immune system. A work concerning the role of macrophages and fibroblasts on myoblast proliferation and migration highlights the importance of multicellular communication [24]. Thus, co-culture of either macrophages or fibroblasts with myoblasts prompted a significant increase in myoblast proliferation. Conversely, in the triple co-culture, although macrophages continued promoting myoblast proliferation, they had a negative effect over the ability of fibroblasts to enhance myoblast migration [25]. Another study, using single-cell transcriptomics, highlighted that intestinal stem cells can function as non-classical antigen-presenting cells for CD4+ T cells. Moreover, these interactions, directly or through activated T cell-derived cytokines, seem to play a role in the intestinal epithelium differentiation [26].

The intestinal mucosal barrier function and the immune responses against invading pathogens seem to be regulated by the interaction between intestinal epithelial cells (IECs) and intraepithelial lymphocytes (IELs) [27]. IELs represent a heterogeneous population of activated and antigen-experienced T cells. A novel culture system of intestinal 'enteroids' has allowed the study of the complex interactions between IECs and immune peripheral T cells in long-term co-cultures. The development of these long-term co-cultures allowed the study of cell survival, proliferation, differentiation and IECs behavior. Moreover, IECs and T cells co-cultures revealed that peripheral T cells activated in the presence of enteroids acquire several features of IELs, including morphology, membrane markers and movement in the epithelial layer [27]. Similarly, mouse-derived enteroids cocultured with intestinal myofibroblasts and macrophages boosted their growth and differentiation [28].

In the same line, another study with intestinal organoids underlined the importance of the interactions between immune cells and other tissues for optimal maturation. In this work, the inclusion of the immune component (co-cultured with human T lymphocytes) into the differentiation protocol to form human pluripotent stem cell-derived intestinal organoids (hIOs) from hPSCs, enabled hIOs maturation. hIOs co-cultured with human T lymphocytes displayed expression levels of mature intestinal markers equivalent to adult intestinal epithelium, as well as increased intestine-specific functional activities, retaining their maturation status even after their *in vivo* engraftment. This study has proven the needless for animal models and *in vivo* maturation when working with organoids [29].

Holokai, L. *et al.* were among the first researchers to successfully obtain a multiple organoid-co-culture involving cytotoxic T lymphocytes (CTLs) and Helicobacter pylori-infected gastric organoids. CTLs express programmed death 1 (PD1) on the surface. When PD1 interacts with its ligand, CTLs cannot induce apoptosis. Thanks to this approach they discovered that PD-L1 signaling induces cellular proliferation and survival, leading to an increased expression of PD-1, IL-2 and IFNγ in lymphocytes [30].

Overall, epithelial organoid cultures, whether derived from iPSCs or AdSCs, constitute a promising platform for immunological research for several applications, allowing, among others, to study immune cell–epithelial cell interactions in the context of pathogenic infections or sterile tissue damage [19]. In this sense, the vast majority of organoid studies about the immune system and its effects on epithelial differentiation and function have been performed on intestine-like structures. However, it would be useful to have similar works with different organoid systems such as skin or lung, which also interact with both immune cells and commensal microorganisms [31].

Despite the amount of work already accomplished regarding the immune system, there is still a long way to go in inflammation research, due to the current lack of optimal immune cells organoids cultures.


**2. Model Formulation**

In contrast with the existing compartmental epidemiological models, we start the model derivation by the introduction of the class of newly infected individuals instead of the total number of infected individuals. This approach is appropriate to evaluate daily recovery and death rates. We recall in this section the model with a distributed recovery and death rate [31,33]. We then use this model to derive the delay model and study its properties in the next sections.


**5. Material characterization**

### **5.1 Density**

**Table 2** defines the comparison between the experimental measured density and the density achieved through theoretical calculations of the aluminum alloy and the manufactured FGM composites. By clearly visualizing the spectral images, we can clearly depict that the composites are prepared successfully with low porosity and microstructural imperfections. However, during the calculation we depicted that the measured values have attained lower values than those of the theoretical values. The depiction of low pores in the composite, low interstitial voids, agglomerations, and discontinuities, which are due to the impingement of reinforcements in proper weight percentages, is the main source during the compaction process. The theoretical and measured densities have been depicted in **Figure 7**

#### **Table 2.**

*Theoretical and measured densities of the alloy and FGM composites.*

**Figure 7.** *Theoretical density vs. practical density.*


**Author details**

Shui-Yuan Lu

Address all correspondence to<EMAIL_ADDRESS>
Applied Toxicology Division, Taiwan Agricultural Chemicals and Toxic Substances Research Institute, Council of Agriculture, Executive Yuan, Taichung, Taiwan


*2.1. Chemical Composition*

The proximate chemical composition of beetroot plant parts is shown in (Table 1). There are no significant differences (*p* > 0.05) detected between peels and pomace regarding all the chemical parameters, this might be due to the difficulty of removing the thin peel from the pulp. Consequently, the juice also

showed no significant differences (*p* > 0.05) with peels and pomace concerning total lipids, total sugars, and ash content. Interestingly, the leaves had the highest amount of protein (5.64%), which comprised a promising good source of protein. The obtained results were higher than that previously reported by Biondo et al. [8] where protein content was 3.81%. Furthermore, there was a discrepancy between the protein content of pomace (1.13%) in the present study and the other previous studies; It was much less compared to 45.53% by Shyamala and Jamuna [21], and slightly fewer than 1.6% as noted by Neha et al. [22]. These differences reasonably attributed to the different nitrogen content that resulted from the variations in nitrogen fertilization, the properties of the soil and other environmental conditions. It is also worth mentioning that leaves and stems exhibited the highest total lipids content (0.43 and 0.41%), respectively. These findings disagreed with Biondo et al. [8] who reported 0.78%, with a high concentration of the essential fatty acid (linolenic acid). Whereas, pomace contain fewer lipids portion (0.15%) which was in agreement with the USDA value (0.17%) described by Neha et al. [22], but it was lower than 0.31% as evaluated by Shyamala and Jamuna [21]. The sugar content in pomace and peel was 8.79% and 8.4%, respectively, which was slightly less than 9.56% in beetroot plant as reported by Neha et al. [22]. The sugar content in leaves was 0.44% which was nearly twenty times less than the sugar content in the pomace. These results were much less than 3.98% evaluated by Biondo et al. [8]. On the other hand, the juice sugar content was 4.8% which was higher than Kazimierczak et al. [23], who reported that the sugar content was 3.33% in the juice. The crude fiber was 2.6, 1.97, 2, and 2.15 in peels, pomaces, stems, and leaves, respectively. The value in pomace was less than the expected value (2.8%) as in USDA nutritional data reported by Neha et al. [22], and 35.53% as reported by Shyamala and Jamuna [21]. It might be the filtration of pomace during preparation decreased its fiber content. The obtained results revealed that the beetroots plant is a promising nutritional source for macronutrients which make it a good source for supporting several kinds of food products.

**Table 1.** Proximate chemical composition and minerals content of beetroot plant parts.

Mean values in a raw having different superscript are significantly different at (*p* ≤ 0.05); ND \* (Not detected); \*\* Total sugars calculated by difference; (1 ) proximate analysis parameters expressed in (g/100 g); (2 ) minerals content expressed in (mg/100 g).


**Abstract**

With the introduction of new digital technologies and the internet, traditional television started to share its audiences with a new technology called the OTT platforms. As of January 2023, Nigeria was home to 31.60 million social media users, equating to 14.3% of the total population. There have been studies in the area of OTT in Nigeria, but none have compared undergraduates' preference between traditional television and OTT platforms in watching movies among select universities in Nigeria. The study analysed undergraduates' preference between traditional television and OTT platforms in watching movies in select universities in Nigeria. A survey research method was used. The findings revealed that the respondents are highly aware of the existence of traditional television and OTT platforms. Further findings revealed that the undergraduates in the select universities in Nigeria preferred OTT platforms more than traditional television for watching movies. The reasons for their preference for OTT platforms for watching movies are it enables them to access a variety of movies, due to the convenience it presents and it is not costly. The researchers recommend that OTT platform operators in Nigeria should carry out effective awareness campaigns in other to promote their products and services to the people.

**Keywords:** undergraduate, preference, traditional television, OTT platform, universities


a. Systemic steroids

Due to their availability and quick therapeutic effect, are used as first-line therapy in acute inflammatory diseases. Oral prednisone treatment is usually started with a dose of 1 mg/kg/day (maximum 100 mg/day) and then gradually tapered depending on clinical response [1]. For severe PUK, which threatens vision, intravenous pulses of methylprednisolone (1 g/day for 3 days) are used, followed by a switch to orally administered prednisone and a gradual reduction in dose [48]. Still, the side effects of chronic steroid administration should be kept in mind: glucocorticoid effect, electrolyte disturbances, hypertension, and hyperglycemia. Adjuvant use of H2-blockers to prevent steroid-related gastric ulcers is advisable as well as calcium supplementation to prevent bone density reduction [66].


**3. Human expert Fuzzy Inference System**

Human expert's FIS uses a collection of fuzzy membership functions and rules to reason about student's performance. FIS consists of a fuzzification interface, a rule base, a database, a decision-making unit, and finally a defuzzification interface.

To compute the output of this fuzzy inference system given the inputs, four steps has to be followed (Norazah, 2005):

#### **3.1 Fuzzification**

66 Fuzzy Inference System – Theory and Applications

�� = ∑ �� � ��� �

The number of attempt (��) is determined by calculating the number of attempts made (��) to answer a given question and dividing it by the maximum number of attempts (��) allowed

> �� = �� ��

d. Help (H) is the average amount of help, *x4*, of a learning unit where it able to help student by giving some hints or notes to answer the question. The term sets involve:

The average amount of help (*x4*) is calculated as equation (6), by dividing the total amount of help accessed by a student in answering a set of given questions by the total number of

> �� = ∑ ℎ� � ��� �

The amount of help (ℎ�) is found by calculating the number of help (��) links that a student accessed while answering a given question and dividing it by the maximum number of help

> ℎ� = �� ����

The output consequent of the student model is the student's performance and can be represented as *has mastered* (*P1*), *moderately mastered* (*P2*) and *not mastered* (*P3*) for the output. A student is classified as *has mastered* in a particular learning unit, when the student earns high scores (i.e. greater than 75%) with below 40% of time spent, not exceeding 25% of number of tries needed and number of helps. Besides that, a student is classified as *moderately mastered* when the student earns a moderate score, with moderate time spent, tries more than once, and number of help needed. For example, a moderate score would be rated in between 35% and 75%, time spent between 40% and 60%, tries between 25% and 75%, and help between 25% and 75%. Furthermore, a student is classified as *not mastered*  when the student has a low score with a lot of time, many tries and many help needed. However, in acquiring knowledge from the human experts is that, they cannot decide on all

*ai* is the number of attempts made to answer a given question *Pi* is the maximum number of attempts allowed for the question

*little* (*H1*), *average* (*H2*),and *needed* (*H3*).

*hi* is the total amount of help accessed by a student

links (����) provided for a given question.

Where :

Where :

Where :

for the question.

questions in the set.

� is the total number of questions

� is total number of questions

(4)

(5)

(6)

(7)

In the fuzzification stage, the input and output of the fuzzy inference system are determined. Table 2 and Table 3 exhibit examples of the four input and one output

Table 2. The input variables of the Fuzzy Inference System

A Concise Fuzzy Rule Base to Reason Student Performance Based on Rough-Fuzzy Approach 69

Fuzzy rules are a collection of linguistic statements that describe how the fuzzy inference system should make a decision regarding classifying an input or controlling an output. Fig. 5 presents the four inputs and one output reasoning of the student's performance procedure

Fig. 5. Fuzzy reasoning procedures for Human Expert FIS model of Student's Performance

The rule *Ri* is the *i-*th rule in the fuzzy rule base system, the *µi* is the membership function of the antecedent part of the *i*-th rule for each input variable and *wi* is the weight of the consequent of each rule. For example, for input1 is score and the membership function can classified as low, moderate or high. If *score* is *high* and *time* is *fast* and *attempt* is *a few* and *help* is *little* then *student performance* is *has mastered*. This process of taking input such as *score* and processing it through membership functions to determine the "high" score is called fuzzification. Based on the human experts' experience and knowledge about the students' performance, 18 initial rules that are certain have been constructed as shown in Table 4.

The outputs of all of the fuzzy rules must now be combined to obtain one fuzzy output distribution. The output membership functions on the right-hand side of the figure are combined using the fuzzy operator AND to obtain the output distribution shown on the lower right corner of the Fig. 5. For a zero-order Sugeno model, the output level *z* is a constant. The output level *zi* of each rule is weighted by the firing strength *wi* of the rule (Lin and Lu, 1996). For example, for an ∩ rule with input 1 = *x* and input 2 = *y*, the firing strength

 *wi* = *F1*(*x*) ∩ *F2*(*y*) (9)

*R*i have four input variables and one output variable as shown below:

*Ri*: IF *S* is *µi1* AND *T* is *µi2* AND *A* is *µi3* AND *H* is *µi4* THEN *P* is *wi*

**3.3 Combining outputs into an output distribution**

is as shown in equation (9).

for zero order Sugeno fuzzy model. Each input has its own membership function.

**3.2 Creating fuzzy rules**

variables, in which each of the variables consists of three term values and labels as discussed in Section 2. The fuzzy output follows the zero-order Sugeno style inference, in which the output value of each fuzzy rule is a constant (Sivanandam et al., 2007). Fig. 3 shows the four inputs and one single output for the Human Expert FIS.

Table 3. The output variables of the Fuzzy Inference System

Fig. 3. Four inputs and single output for the Human Expert FIS

The membership function of the input is expressed by a Gaussian function specified by two parameters {�� �}, and the membership value is derived by the formula in Fig. 4.

Fig. 4. Gaussian shape function ��������(�� ����)

$$\text{Gaussian}(\mathbf{x}; \ \sigma, \mathbf{c}) = \exp\left(-\left[\frac{\mathbf{x} - \mathbf{c}}{2\sigma}\right]^2\right) \tag{8}$$

Where :

*c* represents the membership function's center

*σ* determines the membership function's width

#### **3.2 Creating fuzzy rules**

68 Fuzzy Inference System – Theory and Applications

variables, in which each of the variables consists of three term values and labels as discussed in Section 2. The fuzzy output follows the zero-order Sugeno style inference, in which the output value of each fuzzy rule is a constant (Sivanandam et al., 2007). Fig. 3 shows the four

**Fuzzy output variable Fuzzy linguistic terms Numerical range(normalized)**

The membership function of the input is expressed by a Gaussian function specified by two

��������(�� �� �) � ��� �� ����

�� � �

� (8)

parameters {�� �}, and the membership value is derived by the formula in Fig. 4.

0.0 0.5 1.0

Moderately Mastered, Has Mastered}

inputs and one single output for the Human Expert FIS.

Performance (P) {Not Mastered,

Table 3. The output variables of the Fuzzy Inference System

Fig. 3. Four inputs and single output for the Human Expert FIS

Fig. 4. Gaussian shape function ��������(�� ����)

*c* represents the membership function's center *σ* determines the membership function's width

Where :

Fuzzy rules are a collection of linguistic statements that describe how the fuzzy inference system should make a decision regarding classifying an input or controlling an output. Fig. 5 presents the four inputs and one output reasoning of the student's performance procedure for zero order Sugeno fuzzy model. Each input has its own membership function.

Fig. 5. Fuzzy reasoning procedures for Human Expert FIS model of Student's Performance

*R*i have four input variables and one output variable as shown below:

*Ri*: IF *S* is *µi1* AND *T* is *µi2* AND *A* is *µi3* AND *H* is *µi4* THEN *P* is *wi*

The rule *Ri* is the *i-*th rule in the fuzzy rule base system, the *µi* is the membership function of the antecedent part of the *i*-th rule for each input variable and *wi* is the weight of the consequent of each rule. For example, for input1 is score and the membership function can classified as low, moderate or high. If *score* is *high* and *time* is *fast* and *attempt* is *a few* and *help* is *little* then *student performance* is *has mastered*. This process of taking input such as *score* and processing it through membership functions to determine the "high" score is called fuzzification. Based on the human experts' experience and knowledge about the students' performance, 18 initial rules that are certain have been constructed as shown in Table 4.

#### **3.3 Combining outputs into an output distribution**

The outputs of all of the fuzzy rules must now be combined to obtain one fuzzy output distribution. The output membership functions on the right-hand side of the figure are combined using the fuzzy operator AND to obtain the output distribution shown on the lower right corner of the Fig. 5. For a zero-order Sugeno model, the output level *z* is a constant. The output level *zi* of each rule is weighted by the firing strength *wi* of the rule (Lin and Lu, 1996). For example, for an ∩ rule with input 1 = *x* and input 2 = *y*, the firing strength is as shown in equation (9).

$$w\_i = F\_1(\mathbf{x}) \cap F\_2(\mathbf{y})\tag{9}$$

A Concise Fuzzy Rule Base to Reason Student Performance Based on Rough-Fuzzy Approach 71

rules and known facts. The ANFIS model is proposed to form a complete fuzzy rule bases so

It is necessary to take into consideration the scarcity of data and the style of input space partitions. For example, for a single input problem, usually 10 data points are necessary to come up with a good model (Jang et al., 1997). Details on ANFIS model structure will be

The ANFIS model structure consists of four nodes for input layer, the nodes of hidden layer and one node for output layer as presented in Fig. 6. The input layer represents the antecedent part of the fuzzy rule, which is the student's learning behavior such as the scores (*S*) earned, the time (*T*) spent, the attempts (*A*), and helps (*H*); the output layer represents the consequent part of the rule, i.e. the student's performance (*P*). The size of the hidden

In this work, the ANFIS model is trained with 18 fuzzy rules obtained from the human expert. These rules are considered as the rules that are certain. After that, 81 potential fuzzy rules are used for testing the network that represent the 3 3 3 3 rule antecedents.

From the Fig. 6, every nodes of the same layer have similar functions. Layer 1 is the input

layer and the neurons in this layer simply pass external crisp signals to Layer 2.

that all possible input conditions of the fuzzy rules are being generated.

described in section 4.1.

**4.1 ANFIS model structure**

layer is determined experimentally.

Fig. 6. ANFIS model structure

Where:

*F1* and *F2* are the membership functions for input 1 and 2, respectively

Table 4. Initial fuzzy rules determine by human experts

#### **3.4 Defuzzification of output distribution**

The input for the defuzzification process is a fuzzy set and the output is a single number crispness recovered from fuzziness. Given a fuzzy set that encompasses a range of output values, we need to return one number, thereby moving from a fuzzy set to a crisp output. The final output of the system is the weighted average of all rule outputs, computed as in equation (10).

$$Final\ Output = \frac{\sum\_{l=1}^{N} w\_{l}z\_{l}}{\sum\_{l=1}^{N} w\_{l}}\tag{10}$$

Finally, all the outputs of datasets for reasoning of the student's performance in the human expert FIS have been recorded.

Next section describes the ANFIS approach to form a complete fuzzy rule base to solve the problem of incomplete and vague decisions made by human.


*Aflatoxin Occurrence, Detection, and Novel Strategies to Reduce Toxicity in Poultry Species DOI: http://dx.doi.org/10.5772/intechopen.107438*

growth-selected lines of Japanese quails of USDA experimental facilities in 1980s. This observation had thereafter led to the genetic selection of Japanese quails for resistance to acute aflatoxicosis by breeding survivors from a population of quails, which were given a single oral dose of aflatoxin that resulted in high mortality [271]. After five generations of selection, an 11-fold increase in resistance was attained in one of the aflatoxin-resistant lines. The next group of workers observed genetic variation in certain physiological parameters of selected commercial broiler populations and suggested the feasibility of genetic selection of chickens for infusing resistance to aflatoxicosis [272]. Many other researchers had observed genetic variation in a nonselected population of chickens [273].

The other successful directional selective breeding for AFB1 resistance was also reported [274]. Under their breeding trials, two populations of broiler chickens [Athens-Canadian (AC) versus another broiler commercial stock] were subjected to genetic selection for resistance to aflatoxicosis by exposing the respective chickens from each of the stocks (two) with a single oral dose of aflatoxin, which was capable of resulting in 40–70% mortality, otherwise. A simultaneous, non-selected control group was also maintained, which was not exposed to any AFB1. As for the selection method, the birds surviving the aflatoxin challenge were propagated as breeders, for subsequent generations. According to the outcome of their study, rapid progress was visible within the AC population for resistance to aflatoxin, whereas only moderate progress for AFB1 resistance was attained in the commercial broiler stock. After five generations of selection in the AC population, LD50 values of 9.42 and 17.05 milligrams aflatoxin per kg body weight (BW) were determined for both the non-selected and selected lines. Similarly, after four such generations of such selection in above commercial broiler population, LD50 values of 6.05 and 8.02 mg aflatoxin/kg BW were determined for the non-selected and selected lines, respectively. These experiments demonstrated that genetic progress for AFB1 tolerance could be achievable in chickens, but the quantum of such progress for resistance to AFB1 could be influenced by the population's background, meaning response to such genetic selection for such AFB1 resistance or tolerance was always a subject of genetic constitution of the hosts.

On a practical front, there have been couple of studies that attempted direct breeding of ducks for tolerance or resistance to AFB1's presence in diets, on selectivebreeding platforms, way back in 1980s, after which very little progress has been registered in duck-producing countries. The obvious interpretation could be that ensuring a diet with minimal cutoff levels for AFB1, which was achievable using toxin binders, mold inhibitors, etc., was probably preferred to (better than) raising stocks with resistance to AFB1.

#### *5.6.1 Epigenetic studies on aflatoxicosis*

Epigenetics is the study of heritable phenotypic alterations caused due to change in chromosomal topology rather than change in DNA sequence [275, 276]. The underlying epigenetic processes such as chromatin remodeling, non-coding RNAs (micro RNAs), DNA methylation, acetylation, deacetylation, histone modification, etc., are affected by prolonged exposure to aflatoxin, causing alteration in protein synthesis and thereby the gene expressions. Aflatoxin-B1 mainly induces DNA methylation, which plays a critical role in the development of all most all cancer types owing to its silencing effect on tumor suppressor genes [277]. In this process, the fifth carbon of the cytosine in dinucleotide 5'-CG-3′ is selectively methylated to form 5-mC [278, 279]. Aberrant methylation of promoters in eukaryotic cell may lead to silencing

#### *Aflatoxin Occurrence, Detection, and Novel Strategies to Reduce Toxicity in Poultry Species DOI: http://dx.doi.org/10.5772/intechopen.107438*

of regulatory genes especially tumor suppressor genes and thereafter, affect their signal pathways and lead to development of disease and cancers. Alteration in cellular epigenome compromises genomic stability and alters gene expression, which thereby affect the central dogma of molecular biology and ultimately phenotypic characters.

Few human studies have been reported in literature detailing the epigenetic changes which accompany aflatoxin-exposure, across various vital organs such as white blood cells, egg yolk, plasma, etc. It has been reported that maternal exposure to aflatoxin during early embryonic development leads to formation of aflatoxin albumin (AF-alb) adducts and genome-wide differential DNA methylation patterns of white blood cells for 71 CpG sites, including in genes related to growth and immune function [280].

Though epigenetic approaches raise hopes for a long-term strategy to overcome aflatoxicosis problems in ducks, the literature is just hollow, except some rudimentary reports. The ICAR-DPR's own annual report [95] indicated that most significant aflatoxicosis-induced production losses peaked and precipitated only in alternate generations/years, despite emergence of naturally arisen dietary aflatoxins (10–50 ppb ranges throughout the year) since last decade, which suggest that epigenetic sensitization of the ducklings/ducks every generation at early or perinatal stages, which are usually the phases of methylation-induction processes in an epigenetic regime. The RC, CARI (now a regional Station of Directorate on Poultry, Research, Bhubaneswar, India) has just concluded a large-funded program on epigenetics research in ducks, which has shown positive feedbacks through better egg-production recorded from AF-sensitized ducks versus the controls, thus signifying feasibility of such approaches in coming decades.

#### **6. Conclusions**

Almost all classes of poultry are physiologically vulnerable and susceptible to aflatoxins, especially the AFB1, which produces acute, chronic, mutagenic, and teratogenic toxicity along with causing millions of dollars per year damage to the poultry industry, worldwide. The high frequency and levels of AFB1 recently found in food supplies, particularly, poultry feed of various countries indicate wide exposure of poultry birds to this toxin, which still remain uncontrolled. The most appropriate analytical method differs according to the nature of detected mycotoxin, e.g., for AFs, ZEN, OTA, HPLC fluorescence, and LC-MS/MS are commonly used, while for trichothecenes, GC-MS is mainly preferred. Due to the increasing abundance of AFB1 in poultry feed, several approaches are being evolved to counter or eliminate poisoning/toxicity so as to improve safety and palatability of food products. Between pre- and post-harvest strategies, there are many options available to reduce the toxicity to a great extent. Large-scale implementation of these techniques could make a large impact worldwide to reduce the aflatoxin related toxicities such as growth impairment, histopathology of organs, and immunosuppression in poultry birds. Development of suitable method for detection of aflatoxin in field level and environment-friendly detoxification keeping in view the food safety will be beneficial strategies for achievement of poultry products, which will be safe and secured for human consumption. Quality control of feed ingredients; prevention of fungal growth with reduction in concomitant aflatoxin production; use of efficient detection method and suitable environment-friendly detoxification

methods, are essential to the feed manufacturers to reduce the exposure to aflatoxin and to make the poultry production a profitable enterprise. Among various classes of AFB1-susceptible poultry species, recent research on epigenetics in ducks has shown some positive feedbacks regarding feasibility of such approaches in upcoming decades, while needs to develop poultry species genetically resistant to Aflatoxins through direct selection may not find a great favor from primary breeders anymore, in twenty-first century despite promising results documented during late-twentieth century.


*4.5. Principal Component Analysis*

A principal component analysis (PCA) of the 20 impact factors was performed to group similar items into subgroups for data reduction, the results are shown in Table 6. It can be seen that the measured value of the sample data KMO (Kaiser-Meyer-Olkin Measure of Sampling Adequacy) was 0.802, The Barlett sphericity test was 1936.29 (*p* < 0.01), indicating the suitability for PCA. The PCA resulted in five factors with Eigen-values greater than 1.0 and explained 62% of the total variation. The Cronbach's alpha reliability scores for the five components ranged from 0.725 to 0.809.

The five components were as follows:

Component 1 (departmental influence): four items—atmosphere of academic freedom, educational ideas and culture, academic systems and policies, and quality of student source.

Component 2 (research environment): five items—total research funds, prestige of colleges and departments, research equipment and library facilities, cooperation and relationship with colleagues, and extent of connection with academic circle.

Component 3 (geographic location and social atmosphere): five items—university geographic location, climate of university geographic location, social atmosphere, recreational and leisure facilities in the community, and educational facilities in the community.

Component 4 (income): three items—total personal income, potential income and insurance benefits, and housing size.

Component 5 (spouse): three items—spouse's career development opportunities, spouse's workplace, and spouse's total income.

**Table 6.** Principal component analysis.

Because of its strong correlation with income, we chose *mobility frequency* as the dependent variable for the regression model. Since housing size was not correlated with moving or not or with mobility frequency, it was not included in the model. The independent variables in the model included the income variables as well as academic titles and prestige of colleges and departments.

As noted in Table 7, a stepwise analysis was employed to assess the additive effects of the variables. The final model has good explanatory power (R-square = 0.698), indicating that the selected variables explain faculty mobility. Overall, the factors related to performance, income, institutional prestige and academic titles contribute to predicting faculty mobility.

Specifically, considering all publications at home and abroad in the past five years as an indicator of academic performance can demonstrate scholars' academic capability. In the regression model, the relationship between publication quantity and mobility frequency passed the significance level test. The income indicators also had strong explanatory power in this model, especially the direct income indicator, with a value reaching 13.2%, while the total research funds indicator for indirect income was over 10%. Another indirect income indicator, spouse's total income, had weak explanatory power, only 2.4%. This result reveals that in the faculty mobility process at Chinese research universities, income continues to play a very important role, which is reflected not only in scholars' personal income but also in their research funds. It is also partially related to the possession of research resources. As an indicator of the prestige of colleges and departments, types of doctoral degrees granted by the institution contributed significantly to this model. When we ranked the prestige of institutions from

high to low (based on a scale of 1–4), we found that the lower an institution's rank was, the less often one moved.

**Table 7.** Regression models of the impact factors at Chinese research universities.

*Levels:* \*\*\* *p* < 0.001, \*\* *p* < 0.01, \* *p* < 0.05.

This study included cumulative academic work time abroad in the regression model. The results indicate that there was a significant positive correlation between the experience of working abroad at academic institutions (regarded as "international reputation") and mobility frequency. The more cumulative time that was spent working abroad in academic institutions, the more times the faculty member moved. Additionally, academic titles also had an important influence on the regression model. With titles ranked from high to low (1 for professor, 5 for teaching assistant), we found that the higher one's title was, the more frequently he or she moved.

The regression models presented in Table 7 indicate that the effects of income on faculty mobility are weaker than the effects of academic performance. It should be noted that types of doctoral degrees granted by the institution not only contributed to the model but also reduced the explanatory power. This model intuitively demonstrates that among faculty members at Chinese research universities, neither direct nor indirect income is the core impact factor, although they play crucial roles in the mobility process.

#### **5. Discussion**

The findings of this study demonstrate that although income contributes to faculty mobility at major Chinese research universities, a number of additional factors remain important for faculty and their work, particularly for those who move to a different institution. The desire for academic performance and institutional prestige are equal to if not more important than income. In fact, Chinese scholars frequently have discussions about the impact factors for faculty mobility at the theoretical level, but most of the impact factors have not yet been tested, and non-monetary factors are considered key for influencing faculty mobility. The first of these is educational background and "guanxi". The Chinese academic labor market is not yet completely marketized [19,54,55], so positive "guanxi" is needed for some aspects, such as obtaining new job information [56], acquiring help in the mobility process [36] and career development following a move [6,57–59], and these close and even private academic contacts are based primarily on the relationship between supervisors and students as well as self-established relationship networks in academic circles. Another of our studies analyzed the curriculum vitae of faculty members from "211 Project" universities and found that the rate of academic inbreeding at a few research universities (even well-known universities at the top of the Shanghai JiaoTong University Ranking list) is still higher than 60%, so the low mobility rate of Chinese academics is related to academic inbreeding. In contrast, the collapse of or tension in "guanxi" at faculty members' original academic institutions is also important for faculty mobility. For example, an interview about faculty mobility among Chinese scholars suggested that academic organizational culture is a significant factor. Academic organizational culture will undoubtedly generate centrifugal forces if the team disagrees, if there are strains in interpersonal relationships and if team members defame each other [56]. The second factor is academic titles. Some studies [60,61] have asserted that

the pursuit of senior academic titles provides motivation to transition to a new academic position, but this notion is controversial and has not undergone comprehensive empirical testing. For instance, some studies have found that only a few faculty members transfer to lower-ranked universities because of the pressure of academic titles, while fewer studies have reported that the issue of academic titles is resolved by transferring to lower-ranked universities [62].


*11.1.1. Reverse transcriptase inhibitors*

Fanconi syndrome [177-179, 184]. Additionally, its use in PrEP is well tolerated, and the occurrence of headache, nausea, vomiting, abdominal pain, and weight loss may occur infrequently [177-179]. Nausea and vomiting affects about one in six patients at the beginning of the treatment, but these effects often reduce in the first month [177]. Although the use of

**Table 5.** Clinical trials with TDF/FTC (Truvada®) for pre-exposure prophylaxis (PrEP)(GRADE Criteria). Note: Grade

moderate=further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate;low= further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate; very low=any estimate of effect is very uncertain. \*All trials in this table

**trial\* Sample size Limitations Evidence**

Adherence High

Minimal High

Minimal High

Minimal High

Low

Low

Low

Placebo (n=199) -High loss to follow-up Moderate

concerns

drug

in both

TDF and TDF/FTC arms

Placebo (n=1248)

Placebo (n=199)

TDF(n=1589) TDF/FTC (n=1583) Placebo (n=1589)

Placebo (n=1058)

Placebo (n=467)

TDF(n=1007) TDF/FTC (n=1003) Placebo (n=1009)

Placebo (n=1207)

quality rating: high=further research is very unlikely to change our confidence in the estimate of effect;

**Study**

Among men who have **sex with men**

Among heterosexual men

Partners PrEP(n=4758) Phase lll trial

Among heterosexual Women

**and women**

VOICE(n=3019)

Among injection drug users

**Clinical**

148 Trends in Basic and Therapeutic Options in HIV Infection - Towards a Functional Cure

iPrEX trial (n=2499) Phase lll trial TDF/FTC (n=1251)

US MSM Trial(n=400) Phase ll trial TDF/FTC (n=201)

TDF2(n=1219) Phase ll trial TDF/FTC (n=201)

FEM-PrEP(n=2120) Phase lll trial TDF/FTC (n=1062)

West African(n=936) Phase ll trial TDF (n=469)

Phase llB trial

BTS(n=2411) Phase lll trial TDF (n=1204)

were randomized, double-blind, prospective clinical trials.

Reverse transcriptase inhibitors are antiretroviral with recognized efficacy and safety in the treatment of HIV-1 infection and prevention of mother-to-child HIV-1 transmission. This class of drugs has allowed the formulation of topical microbicide less toxic and more effective [194]. The nucleotide reverse transcriptase inhibitor tenofovir was the first antiretroviral drug to safely demonstrate in animal models both pre-exposure and post-exposure prophylaxis as proof-of-concept against the sexual transmission of HIV-1 [195, 196]. Two other compounds of class NNRTIs being studied as topical microbicides to prevent HIV-1 infection, are the TMC120 and UC781. Preclinical or clinical testing of these compounds as potential topical microbicides have several features in common, and both compounds show minimal systemic absorption, and good safety profiles in animal studies [200, 201]. *In vitro*, TMC120 and UC781 prevent cell-free and cell-associated virus from infecting co-cultures of monocyte-derived dendritic cells and T cells [202-204].

#### *<IP_ADDRESS>. Tenofovir*

Tenofovir is active as a diphosphate, rather than a triphosphate, which does not act via HIV DNA chain termination, coupled with the limited phosporylation ability of macrophages. This explains why the drug might be effective in macrophages and other non-dividing cells [197, 198]. Based on the animal studies and with an appreciation for tenofovir's relatively high barrier to resistance compared with other reverse transcriptase inhibitors [196], the compound became the first antiretroviral drug to be assessed as a VM in a clinical trial. In a phase I study (HPTN 050), 0 3% and 1% vaginal tenofovir gel, formulated as a diphosphate, was used once or twice daily for 14 days by HIV-1 infected and uninfected women. The gel was found to be safe, well tolerated, and acceptable to participants [199].

Note: NNRTI=non-nucleoside reverse transcriptase inhibitor; STI=sexually transmitted infection; SHIV=chimeric simian/ human immunodeficiency virus;HSV=herpes simplex virus.

\*NCT number: Clinical trials.gov website :http://www.clinicaltrials.gov.

**Table 6.** Specific and non-specific microbicides agents for prevention HIV-infection.


**2. FNAC: a vital diagnostic tool in salivary gland pathology**

FNAC is easy, minimally invasive, cost effective technique which provides a rapid initial preoperative diagnosis of SG tumours and has an impact on subsequent management and treatment [11]. The sensitivity and specificity of FNAC in diagnosing SG tumours is 85–100% and 90–100% respectively. The aspirated material obtained through FNAC can also be utilized for special staining such as Periodic acid-Schiff (PAS), Periodic acid-Schiff with diastase (PAS-D), Mucicarmine, Phosphotungstic acid-haematoxylin (PTAH), Acid fast bacilli (AFB) and Gram staining for further evaluation and diagnosis. PAS-D and mucicarmine are particularly useful for highlighting mucin containing cells in challenging cases of low-grade mucoepidermoid carcinoma (MEC). Similarly, PTAH stain can be applied on paraffin-embedded cell block preparation for identification oncocytic cells in challenging diagnosis of tumours with oncocytic differentiation. The cell blocks can also be utilized for Immunohistochemistry (IHC) for demonstrating epithelial and myoepithelial components in diagnosis of challenging tumours. The epithelial cells are positive for immunohistochemical markers such as cytokeratin and epithelial membrane antigen (EMA) and the myoepithelial cells show positivity for smooth muscle actin (SMA), calponin, p63 and S-100. Further, the aspirated material can also be used for microbiological culture, immunophenotyping and molecular analysis for confirming the cytological diagnosis. While the FNAC of palpable lesions in major SGs is relatively easy, the FNAC of intraoral SGs is challenging as many times aspirates are not cellular as these intraoral sites are often difficult to approach and sometimes inaccessible [10–12]. In such cases, radiological-guided FNAC may be advised for better yield of aspirates for subsequent cytological diagnosis. Also, while performing FNAC, there are chances of complications such as hemorrhage, nerve pain and damage and infection. There can be post FNAC induced changes in tissue such as squamous metaplastic changes, inflammation, granulation tissue formation and sometimes infarction, which may interfere with subsequent histological diagnosis. Therefore, familiarity with key cytological features with recognition of the subtle cytomorphological changes in cells is crucial for overcoming barriers and making a correct diagnosis.


*2.3.1. Continuous liquid interface production (CLIP)*

Continuous liquid interface production (CLIP) is a type of constrained surface DLP process, where a thin film between building platform and the material tray is not cured and remains liquid. The so-called dead zone at the interface can be generated by utilizing a vat with a floor that is permeable to oxygen. This inhibits curing, and the resin in contact with oxygen remains liquid. Recoating mechanisms are thereby superfluous and continuous elevation of the building platform can be achieved, which improves surface quality and drastically increases printing speed up to 500 mm/h [44]. Similarly, to attain a liquid interface film, a high-density inert and an immiscible liquid layer such as brine has been proposed [45]. CLIP has been commercialized by Carbon Inc., and is establishing itself in the AM market due to its reduced printing times [46].

#### **2.4. Liquid crystal display stereolithography**

Since its development in 1997 by Bertsch *et al.* [47], using an LCD device as a dynamic mask for SLA has been almost completely replaced by the DLP counterpart. The latter benefits are from superior switching speeds at higher accuracy [48]. Nevertheless, it merits mentioning as a low-cost alternative to DLP with commercially available LCD printers primarily catering to the laypersons demographic as opposed to the industry [49].


**2.1 Principle of phase-shifting digital holography (Yamaguchi et al., 1997; Zhang et al., 1998)**

As an example of phase-shifting digital holography, a Twyman-Green type interferometer shown in Figure 1 is used. A collimated light from a laser is divided into an object wave and a reference wave by a beam splitter. The phase of the reference wave is shifted by with a PZT stage. The value is set as 0, /2, and 3/2 in this phase-shifting digital holography. The four phase-shifted digital holograms are recorded on the CCD plane in a CCD camera without any focusing lens. The intensity of the hologram with a phase-shift value at the pixel coordinates (*X, Y*) on the CCD plane is expressed as *I*(*X, Y,* ). The amplitude *a*0(*X, Y*), the phase 0(*X, Y*) and the complex amplitude *g*(*X, Y*) of the object wave are expressed at the pixel coordinates (*X, Y*) on the CCD plane as follows, respectively.

holograms, a hologram is divided into several parts by superposing many different windows. The phase-difference values at the same reconstructed point obtained from any other different part of the hologram should be the same. If there is speckle noise, the phase-difference with higher intensity at a reconstructed point is more reliable than that with lower intensity. Therefore, the phase-difference at each point is selected when the intensity is the largest at the same point (Morimoto et al. 2004, 2005a), or the phasedifference is calculated by averaging the phase-differences obtained from all the windowed holograms by considering the weight of the intensity (Morimoto et al. 2005b,

In this study, in order to check the effect of the number *n* of the windowed holograms on accuracy, the number *n* is changed. The weight for averaging is also changed. When the weight is the *m*-th power of the absolute amplitude of the complex amplitude of the reconstructed object, the accuracy, that is, the standard deviation of the analyzed displacement error changes according to the power *m*. The optimal weight is studied experimentally for out-of-plane movement of a flat plate. The resolution becomes better when the number of windows becomes larger. However, the spatial resolution may be lower when the number of windows becomes larger. Therefore, the effects of the number of windows, the window size, the displacement resolution and the spatial resolution on the

Holography has basically three-dimensional information. It is useful to analyze threedimensional displacement and strain distributions of an object. (Zhang et al., 1998; Kolenovic et. al., 2003) In this study, the Windowed PSDHI is extended to analyze threedimensional displacement components. The authors developed three systems for digital holographic interferometry using three-directional illuminations. The first one was for a laboratory bench system using a microscope (Morimoto et al., 2008a). The second one used collimated light beams from one laser source and 3 shutters (Fujigaki et al., 2005). It provided a stable system for static measurement (Shiotani et al., 2008). The third one used three spherical waves from three laser sources. It provided a compact system for static and dynamic measurement. In this study, after discussing the relationship between phase differences and displacement components, examples of three measurements using microscope, collimated light beams, and three spherical waves from three laser sources, are

**2. Principle of phase-shifting digital holographic interferometry**

pixel coordinates (*X, Y*) on the CCD plane is expressed as *I*(*X, Y,*

the pixel coordinates (*X, Y*) on the CCD plane as follows, respectively.

**2.1 Principle of phase-shifting digital holography (Yamaguchi et al., 1997; Zhang et al.,**

As an example of phase-shifting digital holography, a Twyman-Green type interferometer shown in Figure 1 is used. A collimated light from a laser is divided into an object wave and a reference wave by a beam splitter. The phase of the reference wave is shifted by with a PZT stage. The value is set as 0, /2, and 3/2 in this phase-shifting digital holography. The four phase-shifted digital holograms are recorded on the CCD plane in a CCD camera without any focusing lens. The intensity of the hologram with a phase-shift value at the

0(*X, Y*) and the complex amplitude *g*(*X, Y*) of the object wave are expressed at

). The amplitude *a*0(*X, Y*),

2005c, 2007).

introduced.

**1998)**

the phase

accuracy are also studied.

$$a\_o(X,Y) = \frac{1}{4}\sqrt{\left[I(X,Y,\frac{3\pi}{2}) - I(X,Y,\frac{\pi}{2})\right]^2 + \left\{I(X,Y,0) - I(X,Y,\pi)\right\}^2} \tag{1}$$

$$\tan \Phi\_o(X, Y) = \frac{I(X, Y, \frac{3\pi}{2}) - I(X, Y, \frac{\pi}{2})}{I(X, Y, 0) - I(X, Y, \pi)}\tag{2}$$

$$\log(X,Y) = a\_o(X,Y)\exp\left\{i\Phi\_o(X,Y)\right\} \tag{3}$$

By calculating the Fresnel diffraction integral from the complex amplitudes *g*(*X, Y*) on the CCD plane, the complex amplitude *u*(*x, y*) of the reconstructed image at the position (*x, y*) on the reconstructed object surface being at the distance *R* from the CCD plane is expressed as follows.

$$u(\mathbf{x}, y) = \exp\left[\frac{ik(\mathbf{x}^2 + y^2)}{2R}\right] F\left\{\exp\left[\frac{ik(\mathbf{X}^2 + Y^2)}{2R}\right] \mathbf{g}(\mathbf{X}, Y)\right\} \tag{4}$$

where *k* and *F* denote the wave number of the light and the operator of Fourier transform, respectively. The optical axis is normal to the CCD plane and the origin is at the center of the CCD plane. By calculating the intensities of these complex amplitudes on the reconstructed object surface, a holographically reconstructed image is obtained.

Fig. 1. Twyman-Green type interferometer for phase-shifting digital holography

### **2.2 Principle of holographic interferometry**

Let us consider the deformation of an object (cantilever) shown in Figure 1. If the out-ofplane displacement *w*(*x, y*) of the object is small, the amplitudes of the reconstructed object before and after deformation are almost the same and only the phase changes by (*x, y*) i.e. the phase-difference before and after the deformation. The relationship between the out-ofplane displacement *w*(*x, y*) and the phase-difference (*x, y*) is expressed as follows.

$$
\Delta w(\mathbf{x}\_\prime \, y) = \frac{\lambda}{4\pi} \Delta \phi(\mathbf{x}\_\prime \, y) \tag{5}
$$

where is the wavelength of the light source.

### **2.3 Experiment of displacement measurement (Morimoto et al., 2007)**

As an experiment, the deflection of a cantilever is measured using the optical system shown in Figure 1. The light source is a He-Ne laser. The power is 8 mW and the wavelength is 632.8

Three-Dimensional Displacement and Strain Measurements

**3.1 Windowed hologram**

by Windowed Phase-Shifting Digital Holographic Interferometry 33

**3. Reduction method of speckle noise error by averaging phase-differences**

As mentioned above, the phase-difference is obtained from the complex amplitudes of the reconstructed holographic object before and after deformation. However, because coherent light is reflected from a rough surface, it provides random speckle patterns in the reconstructed image. And also, any measurement system has measurement error basically. Therefore, the obtained displacement distribution has also noise and the results are not so accurate. At the point where the intensity of the speckle is weak, the accuracy of the phase value of the light at the point becomes low. In holography, any part of a hologram has the optical information of the whole reconstructed image. By using this feature of holograms, the hologram is divided into many parts. The phase-difference at the same reconstructed point obtained from any part of the hologram should be the same if there is no error. The phase-difference obtained from the complex amplitude with high intensity is more reliable than the phase-difference obtained from the complex amplitude with low intensity. If there is speckle noise, among the phase-differences obtained from each divided hologram, the phase-difference with higher intensity at a reconstructed point is more reliable. Therefore, in our previous papers, the phase-difference was obtained by selecting the phase-difference with the maximum intensity at the same point (Morimoto et al. 2004, 2005a), or the phasedifference at the same point was obtained by averaging the phase-differences obtained from all the divided holograms by considering the weight of the intensity (Morimoto et al. 2005b,

**obtained by different windowed holograms (Morimoto et al., 2007)**

2005c, 2007). It provided the displacement distribution with high-resolution.

In this section, the divided holograms are considered as windowed holograms using some window functions. A window function with value 1 in a small part of the whole hologram area and value 0 in the remaining area is superposed on an original hologram. By multiplying the window function with values 1 and 0 by the complex amplitude of the original hologram, the windowed hologram is obtained. By changing the position of the area with value 1 in the window function, many windowed holograms are formed. The reconstructed object image is calculated from each windowed hologram using Eq. (4). A point of the reconstructed object image has a speckle pattern. The speckle patterns obtained by different windowed holograms are all different from each other. However, the speckle pattern does not move as a result of small deformation but the phase is changed by the deformation. The intensity distributions of the reconstructed object and the phase-difference distribution before and after deformation are obtained from the windowed holograms with the same window function before and after deformation. After calculating the average intensity before and after deformation from *n* sets of the windowed holograms obtained from the different window functions, the average value of the *n* phase-difference values weighted proportional to the average intensity before and after deformation is calculated at each reconstructed point. The resultant average phase-difference value is highly reliable. In this study, especially, the effect of the size or the number *n* of the windows is examined. At first, for an example, let us consider the case of *n*=16. The hologram is divided into 16 square areas. That is, a window with value 1 in a square of 240 x 240 pixels and value 0 in the other area in the 960 x 960 pixels is superposed on the original hologram with 960 x 960 pixels. By moving the area with value 1 in the window, 16 windowed holograms are obtained and they are numbered as shown in Figure 4(a). The reconstructed object image is calculated from each windowed hologram using Eq. (4). The 16 intensity distributions of the

nm. The pixel size of the CCD sensor is 4.65 µm by 4.65 µm. A captured image is sampled by 1280(V) by 960(H) pixels and the image is digitized with 8 bits. The image of 960(V) by 960(H) pixels near the center of the recorded image is used for the analysis in this study.

As a specimen, a cantilever shown in Figure 2 is analyzed. (In this experiment, there is not a reference plane shown in the figure. The reference plane is used in Section 4).  The cantilever size is 10 mm wide, 25 mm long and 1mm thick. The loading point is 20 mm from the fixed end. The displacement at the loading point is given by a micrometer with a wedge. To improve the reflection from the specimen, lusterless white lacquer is sprayed on the surface of the cantilever. The distance R from the CCD to the cantilever is 280 mm. The phase of the reference wave is shifted by every /2 using a mirror controlled with a PZT stage. Then four phase-shifted digital holograms for one cycle of the phase-shifting are recorded in the memory of a personal computer. The complex amplitude of the brightness at each pixel on the hologram is calculated using the phase-shifting method expressed in Equations (1) to (3). The reconstructed complex amplitude of the object wave at a point of the reconstructed object surface is calculated from the complex amplitudes of the holograms using the Fresnel diffraction integral expressed in Eq. (4).

After the cantilever is deformed, the reconstructed complex amplitude of the object waves at the same point of the reconstructed object is obtained similarly. The reconstructed images and the phase distributions are obtained from the holograms with 960 x 960 pixels before and after deformation. The phase-difference distribution before and after deformation, that is, the out-of-plane displacement distribution is shown in Figure 3(a).

Fig. 3. Phase-difference distributions obtained by digital holographic interferometry

Fig. 2. Specimen (Cantilever)


**Contents**

Reprinted from: *Cancers* **2022**, *14*, 3675, doi:10.3390/cancers14153675 ................ **121**

### **Nikitha Vobugari, Vikranth Raja, Udhav Sethi, Kejal Gandhi, Kishore Raja and Salim R. Surani** Advancements in Oncology with Artificial Intelligence—A Review Article Reprinted from: *Cancers* **2022**, *14*, 1349, doi:10.3390/cancers14051349 ................ **635**

## **About the Editors**

#### **Hamid Khayyam**

Hamid Khayyam received a B.Sc. degree (Hons.) from the University of Isfahan, a M.Sc. degree from the Iran University of Science and Technology, and a Ph.D. degree in Mechanical Engineering with specialization in intelligent systems from Deakin University, Australia. Dr. Khayyam has worked in automation and energy productivity in various industrial companies for more than ten years. In his previous position, he was leading the efforts on modeling the control and optimization of energy systems at the Carbon Nexus production line, Deakin University utilizing artificial intelligence and machine learning technologies. Dr. Khayyam is currently a Senior Lecturer in the Department of Mechanical Engineering, School of Engineering at the RMIT University, Australia. He has contributed more than 600 journal articles to professional journals as an editor and reviewer. Additionally, he has published more than 100 high-quality journal articles, 1 (sole) book, 9 book chapters, and currently serves on several Editorial Boards of ISI journals. Dr. Khayyam's research is focused on instituting new technologies to develop distinctive approaches for the integration of artificial intelligence and machine learning to solve complex systems. He is a keynote and invited speaker, in academic committees in international conferences, webinars, and workshops about technology development, artificial intelligence and machine learning. Dr. Khayyam is a senior academic member of the Intelligent Automation Research Group (IARG) at RMIT in Australia and The Materials and Manufacturing Research Institute (MMRI) at The University of British Columbia in Canada. Dr. Khayyam is a Senior Member of IEEE.

#### **Ali Madani**

Ali Madani is currently the Director of Machine Learning at Cyclica Inc, a leading Canadian biotechnology company focused on AI-based drug discovery. Dr. Madani is a Ph.D. graduate of the University of Toronto; alumnus of University of Waterloo School of Engineering; and attained a Master of Mathematics from the University of Waterloo. He also received his B.Sc. degree from the University of Tehran. He is an active member of the machine learning community in Toronto and speaks at world- and Canada-wide conferences, webinars, and workshops about technology development, artificial intelligence, drug discovery, and cancer therapeutics. He has published several scientific articles in high-impact factor journals and contributed to technological patents on these subjects. As a believer in industry-oriented education and the pro-democratization of knowledge, Dr. Madani has educated and mentored thousands of students and professionals on topics such as artificial intelligence, programming, and data science.

#### **Rahele Kafieh**

Rahele Kafieh received a BSc (2005) followed by an MSc (2008) and a Ph.D. (2014) in Biomedical Engineering from the Sahand University of Technology and Isfahan University of Medical Sciences in Iran. She obtained her first tenure post as an assistant professor in the Department of Advanced Technologies in Medicine at the Isfahan University of Medical Sciences in 2014 and remained there for 7 years, during which she obtained valuable experience in teaching and supervising projects at undergraduate and graduate levels. She has led many successful previously unexplored projects on medical images, including but not limited to classification and segmentation of retinal images with AI; applications of AI in CT-scan and X-ray images; and multi-modality MRI data analysis. Her two stays at the Charite university hospital, Germany and one stay at the Sabanci University in ´ Turkey were awarded by competitive research scholarships from the Einstein forum and TUBITAK, respectively. Then she moved to Newcastle University, where she worked as a Research Associate in an interdisciplinary team at Newcastle University, a position that provided her the great opportunity to conduct in-depth research on the role of artificial intelligence (AI) in the detection of neurological diseases from the eye. Since July 2022, she has been with the Department of Engineering at Durham University as an Assistant Professor. Her current research is on medical image processing in different organs (eye, chest, teeth, brain, heart, and breast), from different modalities (OCT, Fundoscopy, CT-scan, X-ray, cone-beam CT, MRI, fMRI, and infrared), facing challenges such as high dimensionality, noisiness, and imbalance.

#### **Ali Hekmatnia**

Ali Hekmatnia is a Professor of Radiology at the Isfahan University of Medical Sciences, Iran. He received his medical degree from the Shahid Beheshti University of Medical Sciences, Tehran in 1989, with an internship Radiology residency from the Isfahan University of Medical Sciences, Iran in 1995. He has had a visiting fellowship in pediatric radiology at the Great Ormond Street Hospital for Sick Children, London, UK in 2000. He had a visiting fellowship in neuroradiology at the National Hospital for Neurology and Neurosurgery, London, UK in 2000 too.He received a M.Sc. degree in Medical Education at the Isfahan University of Medical Sciences, Iran in 2007. Dr Hekmatnia has contributed in more than 120 peer-reviewed articles to professional journals. His research interests are in CT scan, MRI, pediatric radiology, and neuroradiology.