**5. Conclusions**

Based on the findings of this assessment, we could identify health facilities that fully met the targets and those that partially met or did not meet the targets. A wide range of performance was noted, and clear differences between facilities in meeting the targets were observed. Thus, healthcare policy makers are urged to develop WASH and IPC national policies and guidelines that set targets for all public and private healthcare facilities in the country. It is essential that healthcare providers in Jordan translate local and national IPC policies into their daily and regular practice. However, IPC policies should be enforced during the COVID-19 pandemic to control the spread of the virus. Developing and implementing a national IPC Action Plan (2021–2024) will assist the integration of IPC practices into the Jordanian healthcare system, which also identify, amend, and correct non-compliance practices with IPC standards. The action plan should be supervised by a national IPC unit, affiliated with, or as part of, the Ministry of Health.

Furthermore, stakeholders and policy makers are urged to institute a quality surveillance system through which standard precautions and transmission-based precautions can be implemented. This surveillance system assists healthcare facilities across Jordan to manage infections through early detection of patients with infectious diseases, immediate implementation of containment measures including the use of PPE and isolation; and measures required to control the spread of COVID-19.

The implementation of the surveillance system and WASH/IPC standards are possible only through capacity building with proper training that is carried out, based on international recommendations, like the WHO recommended procedures for PPE and WASH, for example.

Digital health solutions to enhance healthcare providers'skills and knowledge on WASH and IPC policies could be promising during the COVID-19 pandemic. Such digital health solutions can be designed to train healthcare providers to demonstrate evidence-based practices of infection control and to promote hygiene messages among patients to protect themselves and their families. However, the optimum benefits of precautionary measures and the sustainability of WASH and IPC targets are not achieved without the serious commitments from leaders and managers from all levels (national, provincial, and organizational). Skilful health management is necessary to officially mandate WASH and IPC practices and to provide and maintain necessary human and financial resources to conduct IPC activities. Moreover, medical leadership are expected to show tangible support and act as role models to drive a patient-safety culture, supporting WASH and IPC and all relevant subsequent actions. *Water, Sanitation, and Hygiene (WASH) and Infection Prevention and Control (IPC)… DOI: http://dx.doi.org/10.5772/intechopen.99523*


**1. Introduction**

124 Selected Topics on Optical Amplifiers in Present Scenario

Yoshida, H.; Yamashita, Y.; Kuwabara, M. & Kan, H. (2008b). Demonstration of an

93, No. 24, (December, 2008), 241106, ISSN 0003-6951

ultraviolet 336 nm AlGaN multiple-quantum-well laser diode. *Appl. Phys. Lett*., Vol.

Significant effort has been made in recent years to improve the Doped Fiber amplifier gain and noise figure. Extend the optical bandwidth of doped fiber amplifiers beyond the traditional 1550nm band, making the excellent EDFA characteristics available in a wider spectral region also was the main effort in optical amplifier fields. Several techniques have been developed to improve gain and shift the gain to the shorter wavelength region. In this chapter, the effects of external perturbation such as macro-bending and fiber length and internal perturbation such as transversal distribution profile and doped concentration on doped fiber performance have been demonstrated (S.D.Emami et al., 2010).

A macro-bending approach is demonstrated to increase a gain and noise figure at a shorter wavelength region of EDFA. The conventional double-pass configuration is used for the EDFA to obtain a higher gain with a shorter length and lower pump power. The macrobending suppresses the ASE at longer wavelength to achieve a higher population inversion at shorter wavelengths. Without the bending, the peak ASE at 1530nm, which is a few times higher than the ASE at the shorter wavelength, would deplete the population inversion and suppresses the gain in this region.

Macro-bending is introduced as a new method to increase gain flatness and bandwidth of EDFA in C-band region. Varying the bending radius and doped fiber length leads to the optimized condition with flatter and broader gain profile. Under the optimized condition, gain at shorter wavelengths is increased due to increment of population inversion which results in gain reduction in the longer wavelength regions. The balance of these two effects in the optimized condition has a significant result in achieving a flattened and broadened gain profile.

This technique is also capable to compensate the fluctuation in operating temperatures due to proportional temperature sensitivity of absorption cross section and bending loss of the aluminosilicate EDF . This new approach can be used to design a temperature insensitive EDFA for application in a real optical communication system which operates at different environments but still maintaining the gain characteristic regardless of temperature variations. The effect of macro-bending on high concentration EDFA using optimized

Doped Fiber Amplifier Characteristic Under Internal and External Perturbation 127

a higher attainable gain at a shorter wavelength region. This result shows that the distributed ASE filtering can be achieved by macro-bending of the fiber at an optimally chosen radius. This characteristic can be used in research of S-band EDFA and fiber lasers

Fig. 1. EDF bending loss profile (dB/m) against wavelength (nm) for different bending

**with macro-bending without macro-bending**

Fig. 2. Gain (solid symbols) and noise figure (hollow symbols) spectra with and without the macro-bending effect. The input signal and pump power is fixed at -30dBm and 100mW,

**1480 1490 1500 1510 1520 1530 1540 1550 1560 Signal Wavelength (nm)**

**Noise figure (dB)**

Fig. 2 shows the variation of gain and noise figure across the input signal wavelength for the double-pass EDFA with and without the macro-bending. The input signal and 980nm pump powers is fixed at -30 dBm and 100 mW respectively. The bending radius is set at 4 mm in case of the amplifier with the macro-bending. As shown in the figure, the gain enhancements of about 12 ~ 14 dB are obtained with macro-bending at wavelength region between 1480 nm and 1530 nm. This enhancement is attributed to macro-bending effect

(Daud, et al. 2008).

radius (3.5 mm, 4 mm and 5 mm).

**-40 -30 -20 -10 0 10 20 30 40 50**

**Gain (dB)**

respectively.

bending radius and length of the doped fiber is demonstrated. This gain increment compensates the gain reduction of the EDF before applying macro-bending and result in a flat and broad gain spectrum.

One of the many EDFA optimization parameters reported includes the Erbium Transversal Distribution Profile (TDP). The Erbium TDP is essential in determining the overlap factor, which affects the absorption and emission dynamics of the EDFA. At the end of this chapter, numerical models of different Erbium TDP is demonstrated and later verified by experiment. The model considers the overlap factor and absorption/ emission dynamics for different Erbium TDP. Results indicate a high performance EDFA is achievable with an optimized and yet realistic Erbium TDP.


**Abstract**

During the past decade, research in the area of synthesis and applications of nanostructured titanium dioxide (NS TiO2) has become tremendous. NS TiO2 materials have shown great potential and a wide range of applications. The decrease in the particle size and the increase of the surface/volume ratio lead to the increase of the specific surface and the modification of the physicochemical properties and the appearance of new interesting properties (photocatalytic, optical, magnetic, electronic...). Their new morphology even allows the appearance of new biological properties. NS TiO2 can thus be used for the same applications as those known for their precursors before transformation and their nanostructures are accompanied by new properties allowing applications. This chapter briefly describes the synthesis process of the different NS TiO2, their chemical and surface modifications, and their application. The preparation of NS TiO2, including nanoparticles, nanorods, nanowires, nanosheets, nanofibers, and nanotubes is described. This chapter discusses the effects of precursor properties and synthesis conditions on the structure, crystallinity, surface specificity, and morphology of titanium dioxide nanoparticles. Recent advances in NS TiO2 in nano-biosensing, medical implants, drug delivery, and antibacterial fields, pharmaceutical applications, as well as their toxicity and biocompatibility, were presented.

**Keywords:** titanium dioxide nanoparticle, syntheses process, chemical methods, physical methods, biosynthesis, environmental applications, biomedical applications, biocompatibility


*Article* **CRISPRLearner: A Deep Learning-Based System to Predict CRISPR**/**Cas9 sgRNA On-Target Cleavage E**ffi**ciency**

**Giovanni Dimauro 1,\*, Pierpasquale Colagrande 1, Roberto Carlucci 2, Mario Ventura 2, Vitoantonio Bevilacqua 3 and Danilo Caivano 1**

Received: 3 November 2019; Accepted: 30 November 2019; Published: 4 December 2019

**Abstract:** CRISPRLearner, the system presented in this paper, makes it possible to predict the on-target cleavage efficiency (also called on-target knockout efficiency) of a given sgRNA sequence, specifying the target genome that this sequence is designed for. After efficiency prediction, the researcher can evaluate its sequence and design a new one if the predicted efficiency is low. CRISPRLearner uses a deep convolutional neural network to automatically learn sequence determinants and predict the efficiency, using pre-trained models or using a model trained on a custom dataset. The convolutional neural network uses linear regression to predict efficiency based on efficiencies used to train the model. Ten different models were trained using ten different gene datasets. The efficiency prediction task attained an average Spearman correlation higher than 0.40. This result was obtained using a data augmentation technique that generates mutations of a sgRNA sequence, maintaining the efficiency value. CRISPRLearner supports researchers in sgRNA design task, predicting a sgRNA on-target knockout efficiency.

**Keywords:** convolutional neural network; CRISPR; deep learning

### **1. Introduction**

### *1.1. Background*

Genetic engineering in different living beings has always been used for various tasks, such as treating particular diseases or creating species with particular genetic features. Editing and modifying these features can be accomplished with various biotechnology techniques, which, most of the time, are quite complex.

However, things started to get easier with the discovery of CRISPR, an acronym that stands for Clustered Regularly Interspaced Short Palindromic Repeats. CRISPR was originally discovered in bacteria and archaea in late 1990s and early 2000s as a family of DNA segments containing short repeated sequences. These sequences separate fragments of DNA acquired from viruses that previously attacked the cell, forming an adaptive immunity system. In fact, after a virus attack, new viral DNA is incorporated into the CRISPR locus in form of spacers. Researchers also found that this repeated cluster was accompanied by a set of genes, called CRISPR associated system (Cas) genes, used to generate Cas proteins. Once a virus attacks again, a portion of the CRISPR region is transcribed into CRISPR RNA, or crRNA, that gets joined to a trans-activating crRNA (tracrRNA). These sequences, forming a unique one, are then bound to a Cas9 protein, guiding it to the target site of the virus DNA. The Cas9 protein then unwinds the DNA and performs a double stranded cut, knocking out the virus. The sequence targeted by Cas9 is followed by a 2-6 base pair (bp) sequence called protospacer adjacent motif (PAM), which is part of the invading virus DNA, but not part of the CRISPR region, to prevent Cas9 from cutting the CRISPR locus itself. In fact, Cas9 will not bind to a target sequence if it is not followed by PAM.

Jennifer Doudna and Emmanuelle Charpentier re-engineered the Cas9 endonuclease fusing crRNA and tracrCRNA into a single RNA sequence called sgRNA (single guide RNA). This sequence, when bound with Cas9, can find and cut a target DNA specified by the sequence itself. By manipulating the sgRNA sequence, the artificial Cas9 system can recognize and cut any DNA sequence. CRISPR then becomes a powerful genome editing tool, called CRISPR/Cas9 [1,2]. Recognition and knockout occur via a 23-bp sequence composed by a 20-bp sequence followed by a 3-bp sequence, PAM.

Designing and developing this sequence is an important task because not all the sgRNAs designed to cut a target DNA are equally effective. The efficiency of CRISPR/Cas9 sgRNA depends on the features like the target site, the properties of the endonuclease, and the design of the sequence [3]. Additionally, when DNA gets cut, the cell tends to repair this cut, leading to more or less serious mutations. Predicting efficiency in cutting DNAs (on-target cleavage efficiency or on-target knockout efficiency) and its side effects and mutations (off-target profile or off-target effects) has an important role in sgRNA design task. Also, researchers will be able to obtain these sequence parameters without performing a physical genetic modification, saving time and resources for the actual experimentation. To refine sgRNA design task, various efficiency prediction systems have been developed, using various approaches. For example, locating PAM sequence (CasFinder [4]), scoring efficiencies empirically based on sequence key features (CHOPCHOP [5]), or predicting them with training models (sgRNA designer [6], sgRNA scorer [7,8], SSC [9], CRISPRscan [10]). However, prediction systems based on deep learning principles have surpassed their competitors in both predicting on-target and off-target efficiencies.

The repetitions in a CRISPR locus have variable size: they usually range from 28 to 37 bp. Much shorter repetitions (23 bp) have been discovered and we focused on these ones. Other authors focused on these lengths too, in these preliminary experiments. This paper is presenting CRISPRLearner, a system that uses a deep convolutional neural network (CNN) to extract and automatically learn sequence features and determinants and predict on-target cleavage efficiency of an up to 23-bp sgRNA.

### *1.2. Related Works*

Deep learning for sequencing data has been used [11–13]; it has also been used in some works to predict sgRNA on-target and off-target efficiency. For example, a deep learning approach to predict off-target effects is described by Lin in [14]. Another work that used deep learning to predict both off-target and on-target efficiencies is described in [15]. In this system, called DeepCRISPR, the sequence is encoded into a one-hot matrix, composed of 4 rows, one for each nucleobase, and 23 columns, one for each nucleobase in the 23-bp sgRNA sequence. The matrix gets augmented with additional rows corresponding to epigenetic features, to build a generalized model. This matrix then gets passed as input to a CNN, which is able to use both linear regression and classification to predict efficiencies. In the first case, the predicted value is a real value, while in second case a class is predicted (0 low efficiency, 1 high efficiency).

The system developed by Xue [16], called DeepCas9, uses a CNN too. A sequence up to 30-bp is encoded into a one-hot matrix using the same one-hot encoding scheme used in [15], but without adding additional rows for epigenetic features. The obtained matrix gets passed as input to the CNN that uses linear regression to predict efficiency represented by a real value.

All these three studies use similar encoding mechanism to transform each sgRNA sequence into a data format suitable for the CNN. In fact, CNNs take as input a matrix of values, corresponding to the pixel matrix of an image. Each cell of this matrix contains a value representing the color in the corresponding cell of the pixel matrix. For grey-level pictures, matrix cells only contain real values from 0 to 1, where 0 represents white, 1 represents black, and values in between them represent the shades of grey. In [14–16], the sgRNAs get encoded into matrixes where cells assume only 0 and 1 values. Each of them is then served as input to the CNN that makes predictions. Other techniques for comparing images can be found in [17] and an interesting application of NN-based sequencing system is in [18].

The system presented in this paper uses a CNN to predict a score for on-target knockout efficiency: 10 models regarding different type of organisms and cells have been trained using 10 different datasets. The efficiency of a sequence is calculated using one of these 10 models, depending on the organism the sequence has been designed for. In DeepCas9, the efficiency score is calculated with a weighted sum between the scores predicted by three trained models. Moreover, in DeepCas9, the sequence accepted are 30-bp sequence, which we found out was an uncommon type. In DeepCRISPR, different models are used to predict efficiency, some based on a binary classification (1 efficient, 0 not efficient) and some on regression. The system here presented system uses regression, and aims to output a more useful grade of efficiency instead of knowing just if a sequence is efficient or not.

In our work, the sgRNA sequence is encoded into a 4 × 23 one-hot matrix that is served as input to the CNN. We also implemented a particular technique to encode sequences with a length less than 23-bp. In addition, datasets are augmented with a particular technique. These details will be described further.

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

#### *2.1. Datasets*

To train and develop the system here presented, the datasets used by Xue in [16] were used, consisting in 10 sgRNA efficiency datasets covering several cell types of five species, which were collected by Haeussler in [19]. These ten datasets were:

These datasets were aggregated with others in [19], creating a dataset of 31625 sgRNAs with their relative knockout efficiencies. Unfortunately, we were not able to merge datasets from the same organisms (e.g., Human, Zebrafish datasets). In fact, human datasets were referring to different cell types while for zebrafish dataset were left separated since it was not possible to extract information about cell types or tissues. Each dataset had its own measurement scale for knockout efficiencies, producing a dataset with non-standardized knockout efficiency measurements. Moreover, some datasets presented sequences with a length less than 23-bp, meaning that these sequences were not in form of a 20-bp sequence followed by a 3-bp PAM sequence, leading to non-standardized sequences too. To solve these problems, we adopted some strategies that will be described later.

#### *2.2. Software*

The system was developed using Python language, because of its simplicity and popularity compared to other programming languages. The integrated development environment used to develop CRISPLearner was Pycharm, in combination with VSCode for minor modifications. Also, Git version control system was used, in combination with GitHub.

To develop the core of the system, the convolutional neural network behind the prediction task, Tensorflow has been used, including Keras. Keras uses a data structure called model to represent the way which network layers are organized. In this project, the sequential model has been used. Other libraries used are Scipy, an open library dedicated to scientific computing, NumPy, a library for scientific calculation that provides many functions for operations between matrices, Scikit-learn, a library for machine learning supporting algorithms and Python default libraries, like os, re, and shutil for various purposes.

#### **3. System Description**

This section will describe the techniques used to extract datasets from Haeussler [19] dataset, the strategies adopted to standardize sequences and efficiencies, along with the sequence representation technique used. In addition, the dataset augmentation technique adopted, the CNN architecture and the CLI (Command-Line Interface) implemented will be described.

#### *3.1. Dataset Creation*

This section will describe the procedure adopted to extract and prepare data.

#### 3.1.1. Data extraction

As said before, the datasets used to train the CNN are ten, which were collected with others in the Haeussler dataset [19]. Each row of this last dataset contained data regarding a single sgRNA sequence. The columns used to extract each dataset were four:

For each of the ten datasets, a file containing only a 23-bp sequence and its efficiency was created. To identify only the sequences from the datasets needed, the dataset column was used, which contained the dataset name. Using these names, ten files corresponding to the ten datasets were created. Each extracted dataset contained only two columns, a column containing the 23-bp sgRNA sequence and a column containing cleavage efficiency.

#### 3.1.2. Standardizing sgRNA Sequences

Some datasets include sequences with a length less than 23-bp. For example, Gandhi dataset had sequences of 22-bp, composed of a 19-bp sequence followed by a 3-bp PAM sequence. Instead, Doench A375 and Hart datasets had sequences of 20-bp not followed by a 3-bp PAM, as it should be.

Therefore, before extracting the datasets, 23-bp sgRNA sequences in form of 20-bp sequences followed by a 3-bp PAM sequence had to be extracted. In fact, in Doench A375 and Hart datasets, the missing PAM sequence was instead in the 3 nucleobases immediately following the seq sequence in longSeq100Bp extended sequence. So, the seq sequence was first found in longSeq100Bp sequence and then extracted along with the 3 immediately following nucleobases (the PAM sequence), obtaining a 23-bp sequence composed of a 20-bp sequence followed the 3-bp PAM sequence.

Instead, for Ghandi dataset, the seq sequence was first found in longSeq100Bp sequence and then extracted along with the first immediately preceding nucleobase.

After this operation, the ten files corresponding to the extracted datasets were created.

### 3.1.3. Standardizing Efficiency Measurement

Each dataset used a different scale to measure cleavage efficiency, based on the technique used to measure these values. In order to train a regression model, it was necessary to rescale each of these values to a standard measurement scale.

To do this, the same solution described by Xue in [16] was adopted. Each extracted dataset was rescaled using a min–max scaler mapping a value in the range [0, 1]. This function was defined as *fnk* = *fk*−*fmin fmax*−*fmin* , where *fmax* and *fmin* are, respectively, the maximum and minimum efficiency value of the dataset, *fk* is the original efficiency value to rescale and *fnk* is the rescaled value. This rescaling function was applied on each sequence of the ten extracted datasets. For each extracted dataset, a rescaled dataset file was created, containing a column with the 23-bp sequence and a column with its rescaled efficiency, rescaled using the minmax function described above. Efficiency was standardized in order to have a general measuring of the it, even if the datasets are not merged, so even if the original efficiency is in a different scale than [0, 1], the system will always output an efficiency included in this interval, no matter what the trained model is.

#### 3.1.4. Data augmentation Technique

During the experimentation, data from different cell types has been used to train the CNN model. Initially, only the rescaled datasets were used, but the results obtained were unsatisfactory, leading to an overfitted and less performing model. Modifying the CNN architecture or changing some of its hyperparameters did not improve model performances.

For this reason, a data augmentation technique was adopted. In particular, the data augmentation technique used by Chuai in [15] was adopted. In fact, it seems that mismatches in the PAM distal region, which is the 5 end of a 23-bp sgRNA, have no influence on the sequence efficiency [6,15,20], giving us the possibility to generate new sequences, starting from a single sgRNA, with the same efficiency value of the original one. The rescaled sets were then augmented, generating two mismatches in the first two nucleobases of the extracted sequences, obtaining sixteen new sequences for each original sgRNA sequence (one of them was the original sgRNA sequence), each of them having the same identical efficiency value.

Augmenting data has proved to be a key step in improving the CNN performances, as the problem of low performances and high losses seemed to reside in the data itself rather than in the CNN architecture. Table 1 resumes the number of sequences for each dataset before and after augmentation procedure (training on 80% of the sequences, see Section 4).

**Table 1.** Number of sequences in the original and augmented datasets.

### *3.2. Data Representation for Training*

To make the sequences usable for the CNN, each sequence was encoded in a one-hot matrix with 23 columns, corresponding to the nucleobases of the sequence, and 4 rows, corresponding to A, C, G, and T bases channels. CRISPRLearner supports sequence up to a maximum of 23 nucleobases. For sequences with less than 23 nucleobases, a different approach has been adopted. Since an sgRNA gets encoded into a one-hot manner, if a sequence as a length less than 23, some columns of zeros have been added at the beginning of the real one-hot matrix. So, for example, a sequence of 19-bp will be encoded into a 4 × 19 one-hot matrix and 4 (23 minus length of sequence) columns of zeros have been added at the start of the matrix, creating a matrix of 4 × 23 and making the matrix usable for the CNN. All the dataset that we used for these experiments contains only sequences of 23-bp, so this problem should never appear, since the system is designed to work efficiently with 23-bp sequences. However, we have left the possibility to experiment on shorter sequences if the PAM sequence cannot be extracted, retrieved or is omitted or if a researcher wants to experiment using simply shorter sequences. Figure 1 shows an example of one-hot encoding of a sequence that has a length less than 23-bp.

**Figure 1.** One-hot encoding of a 19-base pair (bp) sequence.

#### *3.3. Description of the Convolutional Neural Network Architecture*

The core of the system is a convolutional neural network that performs regression to predict sgRNA cleavage efficiency. Differently from Chuai et al. [15], this model is focused on a regression task instead of a classification task. This system uses only real value labels since it's based on regression instead of using class label. In regression, the labels are real values, indicating precisely the efficiency of the sgRNA on a range of real values between 0 and 1. In classification, labels are class labels, indicating only a binary efficiency, i.e., 0 if a sgRNA is not effective and 1 if it is effective.

The first layer is an input layer that takes as input a 4 × 23 × 1 one-hot matrix. Then, a convolution layer performs 50 convolutions with 4x4 kernel on the input matrix, producing 50 feature maps of size 1 × 20. After convolution, a ReLU activation layer removes outputs below 0, transforming them into zeros. After ReLU activation layer, a max pooling layer performs 1 × 2 max pooling of the feature maps produced by the convolution layer, producing 50 feature maps of size 1 × 10. After pooling, a flatten layer combines the pooling results in a vector with size of 500. Then, two fully connected layers are added, each with 128 nodes. Each fully connected layer is followed by a ReLU activation layer. Between the fully connected layers, a dropout layer is added, with a dropout rate of 0.3, to reduce overfitting. Then, a fully connected (or dense) layer serves as output layer followed by a linear regression activation layer. Table 2 resumes the CNN architecture.

Specifically, after being converted into a one-hot matrix with 4 rows and 23 columns, the sequence is passed to the CNN. The input layer reads the matrix as if it is a 4 × 23 black and white image. The first convolutional layer performs 50 convolutions with a 4 × 4 filter and a stride of 1, generating 50 feature maps of dimension 1 × 20. After convolution, the ReLU layer outputs results above threshold. The next pooling layer performs a 1 × 2 max pooling of each feature map using a stride of 1, producing 50 feature maps of size 1 × 10. All the pooling results are combined into a single vector by a flatten layer, resulting in a vector of 500 elements. The vector is then passed to two fully connected layers, each composed of 128 nodes, which have a ReLU layer and a dropout layer in between them, with a dropout rate of 0.3, to avoid overfitting. After the fully connected layers, the output layer outputs the prediction for on-target efficiency using linear regression.

**Table 2.** Layers of the convolutional neural network.

For each dataset, a model was trained on 250 epochs with a batch size of 32 and a learning rate of 0.001. An early stopping was also added to detect automatically the minimum validation loss, with a patience of 100 epochs. The models were also optimized with Adam algorithm, trained using 80% of each dataset and validated on the remaining 20% [26,27]. Each dataset was also randomly shuffled before training.

To evaluate each model, the loss function used was Mean Squared Error, while the metric used was Spearman correlation coefficient from Scipy library. The Spearman score of each model was calculated between the predicted efficiencies (on the 20% validation set) and the respective real efficiencies from the datasets. After each model training procedure, the model weights were saved.

### **4. Results**

The efficiency prediction of the system CRISPRLearner was evaluated using Spearman correlation coefficient and using Mean Squared Error loss. The system was trained on 80% of each dataset and tested on the 20% left, generating ten different models. Few evaluations made before adding the data augmentation technique previously described, pointed out low performances, with an average Spearman correlation coefficient of about 0.2 and a high loss. The models trained also showed overfitting and high validation losses. Adding data augmentation improved both losses and Spearman scores, avoiding overfitting and making the system more effective. In Table 3 it is reported a Spearman score comparison between this system and DeepCas9. The results are interesting, but it is clear that we cannot speak of a decisive improvement in performance in comparison with DeepCas9, which however in some datasets achieves better results. It is also interesting to compare the results with other competitors by analyzing Figure 2 in [16].

**Table 3.** Performance comparison of CRISPRLearner with DeepCas9.

#### **5. Conclusion and Future Work**

As indicated in the Table 3, CNN-based systems perform better that other system on some of the datasets. This means that deep-learning based systems are generally performing better than machine-learning based systems and systems based on other techniques. There are some exceptions, like Doench mEl4, Doench A375, and Ghandi datasets, indicating that some other approaches still may perform better on some kind of data.

The system presented in this paper shows good performances, paying a lower generalization. However, allowing the user to train his own models based on the datasets he provides, leads to a generalization of the system, even if the model itself is not generalized. In fact, a single model was not trained for all the datasets, but for each dataset a model was trained and training more models on new datasets will allow the system to predict more and more sgRNA efficiencies of different genomes, cell types, and genes.

To support multiple cell identification, the user is free to train its own model using a different dataset, expanding the system and allowing it to predict sgRNA efficiencies regarding new cell types, gene, or genome types. This approach will allow researchers to contribute in the overall expansion and improvement of the system, adding new trained models to perform predictions on new types of cells.

A question could be why performance results were not better than the DeepCas9 in some datasets, but the answer could not be definitive. Performances can depend on type of data augmentation, on the number of original sequences, on the similarity between some sequences and obviously on the design details of each system. It is not easy to describe the behavior of the internal layers of the CNN, but we will deepen this argument in a further study.

The system here presented will be expanded, as just said, with more cell and genome types. Also, the ability to predict off-target efficiencies will be added to the system, transforming CRISPRLearner in a complete system for CRISPR/Cas9 sgRNA design. Also, deploying the system online will permit it to be expanded with new trained models more easily.

**Author Contributions:** Conceptualization, G.D. and P.C.; methodology, R.C., M.V. and G.D.; software, P.C. and D.C.; validation, V.B.; formal analysis, V.B.; investigation, P.C. and R.C.; resources, D.C.; data curation, P.C.; writing—original draft preparation, G.D. and P.C.; writing—review and editing, P.C., R.C., and M.V.; project administration, G.D.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**1. Introduction**

Type A aortic dissection (TAAD) represents a double challenge: a short-term challenge with a 30-day mortality around 25% [1] and a long-term challenge with an excess of morbidity and mortality for this population [2]. Concerning the shortterm evolution, the intrahospital mortality is mainly influenced by patient history and preoperative complications [2]. Concerning the long-term evolution, the excess of morbidity and mortality seems correlated to two main complications: complications of hypertension such as strokes and ischemic heart disease and complications of aneurysmal evolution of the descending aorta such as aortic rupture and reoperations [3]. Thus, the main challenge of the surgical management of the TAAD is to strike a balance between keeping an early mortality rate as low as possible and preventing long-term morbidity.


**Abstract**

This chapter proposes a new multiple-image encryption algorithm based on spectral fusion of watermarked images and new chaotic generators. Logistic-May (LM), May-Gaussian (MG), and Gaussian-Gompertz (GG) were used as chaotic generators for their good properties in order to correct the flaws of 1D chaotic maps (Logistic, May, Gaussian, Gompertz) when used individually. Firstly, the discrete cosine transformation (DCT) and the low-pass filter of appropriate sizes are used to combine the target watermarked images in the spectral domain in two different multiplex images. Secondly, each of the two images is concatenated into blocks of small size, which are mixed by changing their position following the order generated by a chaotic sequence from the Logistic-May system (LM). Finally, the fusion of both scrambled images is achieved by a nonlinear mathematical expression based on Cramer's rule to obtain two hybrid encrypted images. Then, after the decryption step, the hidden message can be retrieved from the watermarked image without any loss. The security analysis and experimental simulations confirmed that the proposed algorithm has a good encryption performance; it can encrypt a large number of images combined with text, of different types while maintaining a reduced Mean Square Error (MSE) after decryption.

**Keywords:** spectral fusion, chaotic generators, image encryption, watermarked images


**6.2. Waxes**

Waxes are lipoidal matter constituting mainly from long aliphatic chains that may contain one or more functional groups. They may contain hydroxyl groups as in the case of primary and secondary long-chain alcohols that are frequently present in the form of esters. Others contain unsaturated bonds, aromatic systems, amide, ketonic, aldehydic or carboxylic functional groups. On the other hand, synthetic waxes constitute of long-chain hydrocarbons (alkanes or paraffins) that lack functional groups. They are similar to the fixed oils and fats since they are esters of fatty acids, but with the difference that the alcohol is not glycerin. The seeds of *Simmondsia chinensis* yield the liquid wax, jojoba wax, which consists of straight chain esters of fatty acids and alcohols [42]. Jojoba wax has anti-inflammatory, anti-aging and wound healing activities, and hence it can be utilized in several skin conditions. Jojoba wax has also been used in topical medications to enhance drug absorption. In addition, it is used in skin care products and in cosmetics such as sunscreens and moisturizers [43].

### **6.3. Essential oils**

Essential oils are volatile aromatic complex mixtures of relatively low molecular weight compounds. Although they may contain up to 60 components, yet they are characterized by the presence of two or three major components at fairly high concentrations (20–70%) compared to other components present in trace amounts. For example, *Origanum compactum* essential oil contains carvacrol (30%) and thymol (27%) as major components. Linalol is the major component of *Coriandrum sativum* essential oil reaching up to 68%. Other examples are *Artemisia herba-alba* essential oil which contains α- and β-thuyone (57%) and camphor (24%) as major constituent, *Cinnamomum camphora* essential oil with 1,8-cineole (50%) as major constituent and finally *Mentha piperita* essential oil with menthol (59%) and menthone (19%) being the major constituent. Generally, these major components determine the biological properties of the essential oils [44]. They have many and important medical uses such as antiseptic, antimicrobial, analgesic, sedative, anti-inflammatory, spasmolytic and locally anesthesic remedies. They are also used as fragrances in embalmment and in food preservation [45].

surfaces or skin. Here, they produce a coating of "slime" that soothes and protects exposed or irritated surfaces of the gastrointestinal tract. They are used extensively in the management of inflammatory digestive disorders, especially when there is ulceration. Their relative indigest-

Plants Secondary Metabolites: The Key Drivers of the Pharmacological Actions of Medicinal Plants

http://dx.doi.org/10.5772/intechopen.76139

27

According to the abovementioned data, there are several classes of secondary plant metabolites that are responsible for the biological activities of herbal medicines. Eventually, secondary plant metabolites exert their action on molecular targets that differ from one case to the other. These targets may be enzymes, mediators, transcription factors or even nucleic acids. The use of herbal medicines should be based on comprehensive phytochemical studies for the determination of the chemical constituents of the herbs involved. Hence the knowledge of the resultant pharmacological and toxicological effects can be deduced, as well as the possible synergistic or antagonistic effects due to the use of multiple component herbal formulae. For this reason, the isolation and structural elucidation of secondary plant metabolites, though ancient, is still a huge and fast growing approach, and the techniques used for separation and

Pharmacognosy Department, Pharmaceutical Science Division, National Research Center,

[1] Seigler DS.Plant Secondary Metabolism. New York: Springer Science: Business Media; 1995. ISBN: 978-1-4613-7228-8; ISBN: 978-1-4615-4913-0 (eBook). DOI: 10.1007/978-1-4615-4913-0

[2] Jones ME, Kossel A. A biographical sketch. Yale Journal of Biology and Medicine.

[3] Bourgaud F, Gravot A, Milesi S, Gontier E. Production of plant secondary metabolites:

[4] Bennets HW, Underwood EJ, Shier FL. A specific breeding problem of sheep in subterranean clover pastures in Western Australia. Australian Veterinary Journal. 1946;**22**(1):2-12

[5] Goławska S, Sprawka I, Łukasik I, Goławski A. Are naringenin and quercetin useful chemicals in pest-management strategies? Journal of Pest Science. 2014;**87**(1):173-180

ibility and hydrophilic properties have important influences on bowel behavior [47].

**8. Conclusion**

**Author details**

Giza, Egypt

**References**

1953;**26**(1):80-97

analysis are advancing continuously.

Rehab A. Hussein\* and Amira A. El-Anssary

\*Address all correspondence to<EMAIL_ADDRESS>
A historical perspective. Plant Science. 2001;**161**:839-851


*3.6. Chlorophyll Distribution*

The spectral reflectance of the corn canopy in three growing stages was introduced into the chlorophyll content detection model. The distribution of chlorophyll in the field during the three growing periods was obtained (Figure 9).

**Figure 9.** Field chlorophyll distribution in three growth stages.

#### **4. Discussion**

#### *4.1. Sensitive Spectral Wavelengths*

This study demonstrated that spectral measurements can be used for corn CCC detection. In the range of visible light, the chlorophyll content in the blue and red light regions was positively correlated with spectral reflectance, and reflection peaks were present; in the green light region, the chlorophyll content was negatively correlated with spectral reflectance, and an absorption valley was present [16,51,52]. When the maximum correlation coefficient method was used to filter the sensitive wavelengths, the 15 selected wavelengths were all in the absorption valley of green light, and a serious multicollinearity existed between them. The sensitive wavelengths screened by the local extremum of correlation coefficient method were relatively dispersed and distributed in the visible and near-infrared regions. In the visible light region, the chlorophyll was is characterized by the reflection peak in the red and blue light regions and the absorption valley in the green light region. The extremum characteristic wavelengths in the near-infrared region reflected the composition of other substances [53–55]. The reflectance of the 896 nm band indicates CH3 methyl groups, that in the 976 nm band reflects the moisture content, and that in the 1051 nm band reflects CH2 methylene groups [56–58]. These substances are relative to the canopy structure of the corn crop. Therefore, the characteristic wavelengths in the NIR region will improve the robustness of the chlorophyll detection model. The CA peak bands were more evenly distributed and had less redundant information compared with CA bands.

#### *4.2. Continuous Wavelet Analysis*

Each wavelet feature contains the information of scale and wavelength position, which corresponds to the state of the generating wavelet function in the process of CWT, namely the scaling factor and the position of shift. The physical meaning of the wavelet feature can be explained by plotting the generating wavelet function corresponding to the wavelet feature that is sensitive to the biochemical parameters. In this study, the Gaussian second derivative was chosen as the generating function of CWT. Each wavelet feature reflects the similarity between the generating wavelet function and the reflectivity spectrum at a specific wavelength position and scale. The absorption characteristics of biochemical parameters at different positions and intensities in the reflectivity spectrum were detected. This situation can be seen as the result of smoothing the spectrum at a particular wavelength and finding the second derivative. The spectral bending degree caused by the different absorption intensities of the biochemical parameters in various bands can be characterized. These two understandings of CWT can be combined to explain the physical meaning of wavelet transform.

The absorption of chlorophyll in the green light band was weaker than that in red and blue light regions. In the reflectivity spectrum, a reflection peak was formed in the green light band. The wavelet features sensitive to chlorophyll were located near the reflection peak of the green light band. Different chlorophyll contents can affect the shape and size of the reflection peak. These changes are easily captured by wavelet features in low scales. A small wavelet feature of medium and high scale near the green light band covered the whole visible band, thus providing the amplitude information of reflectance. The wavelet features at red edge and near-infrared band were stable at the leaf level, thereby indicating that this region is important for chlorophyll monitoring.

Wang et al. used a Mexican hat as the generating wavelet function to obtain the correlation between the wavelet coefficient and the SPAD value of wheat leaves, which can reflect the amount of chlorophyll content. The results showed that the wavelet features sensitive to chlorophyll were located in the red-edge band of 720–740 nm, which was consistent with the research results in References [59–62]. The multiple scattering of light inside the leaves leads to high reflectivity in the near-infrared region, resulting in the rapid increase of spectral reflectance of green vegetation at 680–750 nm, the "red edge" of the area. In the vegetation reflectance spectrum curve, the red edge is one of the most obvious spectral features, and it is an important indicator band used to describe the chlorophyll state of vegetation. The red-edge position (REP) is the wavelength position at which the reflectance of vegetation increases fastest in this interval, and it is also the inflection point of the first derivative of spectral reflectance in this interval. The position of the red edge is an important spectral parameter for detecting the chlorophyll content of vegetation. Liao et al. extracted the wavelet characteristics sensitive to the chlorophyll content of maize leaves in different layers on the basis of the canopy spectrum. The wavelet features sensitive to the chlorophyll content in the upper leaves were distributed in green light and red-edge bands. Meanwhile, the wavelet features sensitive to chlorophyll content in the middle and lower leaves were all located in the red-edge bands, and those in the green light bands disappeared [63]. This phenomenon could have been due to the strong absorption of chlorophyll in the visible range that made it difficult for the green light to penetrate to the middle and lower layers; the visible band of the canopy spectrum mainly contained information regarding chlorophyll in the upper leaves [64]. The comparison of existing literature indicated that the wavelet features at the green light band at the blade level perform better among different datasets. The wavelet features at the red edge perform better among the different datasets at the canopy level.

#### *4.3. Sensitive Wavelet Features*

The frequency-scale analysis indicated that CA-WFs were mainly distributed on the low frequency scale (1–4), and the other two types were distributed on the mid-frequency scale (5). The wavelength position analysis indicated that CA-WF1 and CA-WF10 were distributed in the blue light region, and CA-WF 3, CA-WF 5, CA-WF 6, CA-WF 8, CA-WF9, and CA-WF11 were distributed in the red light area. These regions are the strong absorption bands of leaf chlorophyll. CA-WF2, CA-WF4, and CA-WF7 were distributed in the green light regions. These regions are strong reflection bands of leaf chlorophyll. All wavelet features were distributed in the visible light region, which represented the pigment information of the leaves. CA-WFs selected using the maximum correlation coefficient method also had redundant information. For example, the 12 characteristic wavelet coefficients of CA-WF10 were distributed in 472–483 nm, and had serious variable autocorrelation. These autocorrelation problems also existed in CA-WF7, CA-WF8, and CA-WF9.

The frequency-scale analysis indicated that CA peaks WF1–WF4 were distributed in the low-frequency scale (1–4). Meanwhile, CA peaks WF5–WF7 were distributed in the medium-frequency scale (5–7), and CA peak WF8 was located in the high frequency scale (8). Most wavelet features were in the middle- and low-frequency scales, which was consistent with the result of Yao et al [47]. The wavelet features in middle and low frequencies could effectively detect the water content of wheat leaves.

The analysis from the distribution of characteristic wavelet features indicated that all the variables in CA-WFs were distributed in the visible light area and could only reflect the pigment information in the leaves. CA peak WFs were more evenly distributed compared with CA-WFs. The sensitive variable WF4—839 nm in the near-infrared region—reflected the molecular structure of RNHR. WF5—915 nm reflected the CH2 methylene group, and WF5—973 nm showed the moisture information. WF6—985 nm reflected the starch material, WF7—894 nm showed the CH3 methyl group, and WF8—909 nm provided protein information. CA peak WFs comprehensively reflected the material structural information of corn leaves, thereby improving the stability of the chlorophyll detection model.


*Review* **Ayurveda and Epigenetics**

#### **Hari Sharma 1,\* and Robert Keith Wallace 2,\***

Received: 5 November 2020; Accepted: 8 December 2020; Published: 11 December 2020

**Abstract:** Ayurveda is a comprehensive, natural health care system that originated in the ancient Vedic times of India. Epigenetics refers to the external modification of DNA that turns genes on and o ff, a ffecting gene expression. This occurs without changes in the basic structure of the DNA. This gene expression can have transgenerational e ffects. The major factors that cause epigenetic changes are lifestyle and behavior, diet and digestion, stress, and environmental factors. Ayurveda addresses these factors, thereby a ffecting the Deha (body) Prakriti (psychophysiological constitution), which corresponds to the phenotype, and indirectly the Janma (birth) Prakriti, which corresponds to the genotype. Thus, it is proposed that epigenetics is an important mechanism of Ayurveda. This correlation and understanding will lead to better communication and understanding with the current medical system, and lead to better integration of both sciences in the management of optimal health. In addition, research on Ayurvedic modalities a ffecting gene expression will further increase correlation and understanding between the current medical system and Ayurveda.

**Keywords:** Ayurveda; epigenetics; genotype; phenotype; Prakriti; doshas


**7. Potential of PSM to develop as a commercial level biofertiliser**

The distinctive and unique ability of PSMs to solubilise organic and inorganic phosphates, leads to their developed as commercial level bio fertilisers [10, 12]. Biofertilisers are microbiologically active, eco-friendly, low-cost products applied to soil expecting soil health and growth promotion of plants [10, 15]. Biofertilisers can be replaced by 50% instead of chemical fertilisers without any reduction of the yield [15]. These phosphate bio fertilisers are not crop-specific and able to be used for any type of plant, expecting growth promotion, high yield and crop quality through optimum phosphate absorption [10]. *Azotobacter* inoculation and *Bacillus* inoculants have success stories with yield increments of sugarcane and wheat. Application of the combination of *Bacillus megaterium* and *Azotobacter chroococcum* as biofertiliser remark the yield increment by 10–20%. *Bacillus circulans, Bacillus megaterium, Bacillus subtilis*, and *Pseudomonas striata* reach the commercial level in the phosphate biofertiliser industry [10].

Biofilm inoculants and nano-bio inoculants are ultra-modern techniques in biofertiliser formulation technology. Biofilm inoculants are the combination of two microorganisms, while one microorganism colonises over the other microorganism. The second microorganism act as a biotic surface for the first microorganism to develop a metabolically enhanced biofilm rather than a single culture [3]. Even though these relationships affect the high phosphate solubilisation and growth promotion of plants, the abundance in the soil is very low [3, 79]. Therefore, applying these biofilms as biofertilisers through artificial formulation is advantageous in agriculture [3]. *Pleurotus ostreatus*, *Xanthoparmelia mexicana* and *Penicillium* spp. are the most prominent PSMs, which have the potential to be used in biofilms for substantial impact [80].


**2. Materials and methods**

#### **2.1 Materials**

Tragacanth gum (T) and chitosan were purchased from Sigma-Aldrich Company. Reactive red 35 (RR35), reactive yellow 15 (RY15), and acid black 194 (AB194) were obtained from Burboya Company. Zinc nitrate hexahydrate (Zn(NO3)2·6H2O), glacial acetic acid (glacial 100%, pro analysis), and sodium hydroxide (NaOH) were purchased from Merck Company. MF-Millipore™ membrane filter was purchased from Merck Company. Ultrapure water was provided by a human ultrapure water system (water resistance: 18.3 Mohm) and was used for the preparation of all reactive dye solutions as analytes. All electrochemical gold transducers and voltammetric electrochemical workstation were purchased from Ebtro Electronics.

**5**

**Figure 1.**

*3D model structure and property of the reactive dyes.*

*A Novel Electrochemical Sensor for the Detection of Reactive Red Dye to Determine Water Quality*

Tragacanth gum/chitosan/ZnO nanoprisms were prepared using a green sonochemical method at 25°C (35 kHz frequency, 320 W, Sonoplus, Bandelin, Germany). In the first step, 0.1 g of tragacanth gum was dissolved in 50 ml of deionized pure water on a magnetic stirrer in 2 h at 25°C. In the second step, 0.1 g of chitosan was dissolved in 50 ml of 2% glacial acetic acid. In the third step, 25 ml of the prepared solutions was taken and mixed. In the fourth step, O.1 M of Zn(NO3)2·6H2O and 0.2 M of NaOH was prepared in deionized pure water. In the fifth stage, the solution of Zn(NO3)2·6H2O was added in the solutions, and then the solution of NaOH was added dropwise under the sonicator at 25°C for 40 min (%30 amp). The solution was kept at 24 h in the dark at room temperature. Then, it was first filtered through membrane filters of 0.45 μm and 0.22 μm pore size, respectively. The final solution (viscosity: 1.28 cP and pH 4) was stored at 25°C for

*DOI: http://dx.doi.org/10.5772/intechopen.92280*

use in the sterile container for analysis.

used are shown in **Figure 1**.

**2.2 Synthesis of tragacanth gum/chitosan/ZnO nanoprism**

**2.3 Preparation and measurements of electrochemical sensors**

Electrochemical gold transducers were rinsed with ultrapure water, dried with nitrogen gas, and coated with tragacanth gum/chitosan/ZnO nanoprism solution by dropping and drying the solution. The 3D model structure and the property of dyes

The potential of the working electrode was varied linearly with time, while the reference electrode was maintained at a constant potential. The potential was *A Novel Electrochemical Sensor for the Detection of Reactive Red Dye to Determine Water Quality DOI: http://dx.doi.org/10.5772/intechopen.92280*


2.6.2. Spatiotemporal Beamformer Classifier

Before calculating the spatiotemporal beamformer (STBF), the signal was downsampled to 32 Hz or twice the low-pass frequency 16 Hz, resulting in 17 time samples between 0.1 s and 0.6 s. According to the Nyquist theorem, more samples would not contain more information; hence, the minimum temporal resolution was chosen to reduce the dimensionality of the covariance. The activation pattern is the difference between the averages of epochs in response to cued targets and the averages of those in response to non-cued targets. We constructed three variations of the spatiotemporal beamformer: STBF with empirical covariance estimation (STBF-EMP) as in Section 2.3.1, STBF with LOOCV-shrunk covariance estimation (STBF-SHRUNK) as in Section 2.3.2, and STBF with Kronecker–Toeplitz-structured covariance estimation (STBF-STRUCT) with LOOCV shrinkage for the Kronecker factors as in Section 2.3.4.

## 2.6.3. Riemannian Geometry Classifier

We opted for a Riemannian geometry-based classifier to compare our results. The Riemannian model (xDAWN+RG) uses the xDAWN spatial filter combined with Riemannian geometry in tangent space as implemented by Barachant et al. [58]. This classifier uses four xDAWN spatial filters and each epoch's empirical spatial covariance matrix. The target with the maximum score is the prediction of the cued target. xDAWN+RG was trained and validated without downsampling using epochs at the original sample rate of 2048 Hz.


*4.1. Experimental Validation of the HPTO Model*

Figure 4A shows the hardware in the loop (HIL) test rig of the HPTO unit. In general, the HIL test rig was developed based on the design of WECs in Figure 1 and several sensors were installed to monitor the variations of HPTO force, oil pressure, the oil level in the oil tank and as the hydraulic motor shaft speed, as illustrated in Figure 4B. The servo-electric actuator was also installed in the HIL test rig to replicate wave-induced relative pitch motion to drive the HPTO unit to capture the wave energy. A servo motor controller based on Labview/Arduino integration and the data acquisition system for data collection from the sensors were placed in the control system unit. The relative pitch motion generated by the electric actuator is according to the input wave state. To simulate the condition that is close to real-world application, the hydrodynamic parameters from the CFD analysis and the feedback HPTO force were considered to calculate the produced excitation force applied to the floater's arm. The sinusoidal wave input with the amplitude and period of 0.4 m and 2.5 s were considered for the validation of HPTO model. The captured image of maximum upward and downward motions of the HIL test rig during the experimental validation of the HPTO unit is depicted in Figure 4C,D.

**Figure 4.** Experimental evaluation of WEC with hydraulic PTO unit. (**A**) A complete dry-lab test rig, (**B**) Enlarge image of HPTO unit setup, (**C**) Maximum upward, and (**D**) Maximum downward position of the WEC device.

> Figure 5 shows the results of the behaviour of the HPTO unit in a regular wave condition with an amplitude and period of 0.4 m and 2.5 s. From the figure, it can be seen that the simulation results of the developed WEC with the HPTO unit model in

MATLAB/Simulink is in good agreement with the results obtained from the HIL test rig. However, slight differences and some fluctuations in hydraulic motor speed and HPTO force results were obtained during the experiment, as depicted in Figure 5A,B. These differences and fluctuations may be attributed to the errors in the manufacture and assembly of the test rig, the measuring errors of the transducers, the vibration of the hydraulic motor, and the leakage in the hydraulic motor, cylinders and joints. Such a good agreement presented in Figure 5A,B indicates that the developed WEC with HPTO unit model in MATLAB/Simulink presented in the present study would be effective and reliable as a tool for predicting the amount of power that can be generated from the ocean waves.

**Figure 5.** Behaviour of HPTO unit in a regular wave condition. (**A**) Speed of hydraulic motor and (**B**) HPTO force applied to the floater's arm.

#### *4.2. Performance of WEC with HPTO Model in Five Irregular Sea States*

The simulations of the developed WEC with the HPTO unit model using different sea states was first carried out in the present study. This simulation was intended to evaluate the performance of the developed model against the different wave heights and periods. The simulation was started with the nominal sea state (sea state A) and the results from the simulation are presented in Figures 6 and 7. Figure 6A shows the responses of WEC and the hydraulic cylinder piston against the irregular wave input in sea state A. The figure shows that the displacement of the WEC device was slightly lower than the wave elevation, particularly during the upward motion. This is due to influencing factors such as the hydrostatic restoring moment, the moment due to the HPTO unit and the initial moments of floater and arm [36]. Based on the results, the average displacement of the WEC device and hydraulic piston was 70% and 15% of the wave elevation. The figure also depicts that the displacements of the WEC device and piston were slightly delayed from the wave elevation. Figure 6B presents the profile of the HPTO force applied to the WEC device. On average, the HPTO forces applied to the WEC device during upward and downward motion equaled 3.64 kN and 1.99 kN, respectively. The unbalanced HPTO force applied to the WEC device is due to the unsymmetrical effective area of the piston. A larger effective area of piston produced a higher force rather than a smaller effective area piston.

Figure 6C shows the profile of HPA pressure. From the figure, the pressure of HPA reached up to 49 bar several times, which was a 4.5% increased from its pre-charge pressure setting.

**Figure 6.** Performance of WEC with HPTO unit in sea state A, (**A**) Displacement of Wave, WEC and hydraulic cylinder piston, (**B**) HPTO force applied to WEC device, and (**C**) Pressure of high-pressure accumulator.

Meanwhile, Figure 7A,B show the pressure and speed profile of the HM. It can be seen from the figures that the pressure and the speed of the hydraulic motor reached up to 49 bar and 200 rpm. The smoothing effect of the HPA unit on the hydraulic motor pressure can be seen in Figure 7A. The HPA was able to reduce the fluctuation of the hydraulic motor pressure, particularly after 50 s of HPTO operation. Figure 7C illustrates the profile of the generated power from the HPTO unit. The average power generated from the generator was 70.9% of its rated capacity (100 W). The figure also demonstrates that some fluctuations exist in the generated power from the generator, particularly at the early stage of the operation. The comparison simulation results of the WEC with HPTO unit in each sea state are summarized in Table 5. From the table, the simulation result showed that the significant wave height and peak wave period were affected by the overall performance of the WEC with the HPO unit. First, the table reveals that the averaged angular displacement of the WEC device was increased and decreased, relatively, with increases and decreases of the significant wave height and peak wave period. From the table, the angular displacement of the WEC device in sea states B and D were reduced by 38% and 33% (upward) and 24% and 21% (downward) of its angular displacement in the nominal sea state. Meanwhile, the angular displacement in the sea states C and E were increased by 21% and 16% (upward) and 28% and 18% (downward), respectively. The increase and decrease of the angular displacements are due to the increase and decrease HPTO force applied to the WEC device, which can be obtained in Table 5. The increase and decrease of WEC displacement, relatively, also increase and decrease the generated output power from the HPTO unit. As can be seen, the average power generated from the HPTO unit in sea states B and D were decreased by 41% and 34% of power from sea state A, while 15.5% and 10.4% were increased in sea states C and E. Overall, the generated power from the HPTO unit in each sea state is below its rated capacity. Thus, several parameter optimization methods, as suggested in [20], can be further implemented to increase the generated power from the HPTO unit.

**Figure 7.** Performance of WEC with HPTO unit in sea state A (continue), (**A**) Pressure of hydraulic motor, (**B**) Speed of hydraulic motor, and (**C**) Electrical power generated from HPTO unit.


**4.1. SMEDDS vs SNEDDS**

vase®) containing medium‐chain mono‐ and diglycerides, povidone and α‐tocopherol was able to increase bioavailability threefold higher than Invirase® in humans [23, 24]. Several other published [10, 25, 26] and unpublished case studies are also available that established the significance of rational approach in designing SMEDDS/SNEDDS which can improve the *in vivo* absorption of the PWSDs. The commercial product such as amprenavir (agenerase), ciprofloxacin (cipro), fenofibrate (fenogal), liponavir/ritonavir (kaletra, norvir), etc., have been

**Figure 1.** The encapsulated SNEDDS designed for oral administration of PWSDs. \*Adapted with permission from Ref.

The potentiality of nanoemulsions within lipid‐based drug delivery systems was explored almost four decades ago. In simple term, nanoemulsions are the emulsions comprising nanosized droplets and they are well dispersed, transparent and kinetically stable for several months. Their physical stability can be improved by careful selection of surfactants and the ratio of oil/water/surfactant and also the efficiency of equipment used to reduce droplet sizes.

**4. Concept of nanoemulsions within lipid‐based formulation**

formulated using suitable SMEDDS/SNEDDS [24, 27].

36 Advanced Technology for Delivering Therapeutics

[21].

SMEDDS and SNEDDS are almost similar lipid dosage form which can be prepared from same materials comprising a simple mixture of oils, surfactants and possibly cosolvents. SMEDDS have the ability to form fine oil in water (O/W) microemulsion, and SNEDDS produce nanoemulsion upon mild agitation in the presence of an aqueous (preferably intestinal) media [28]. The structure provides both SMEDDS and SNEDDS as good candidates for oral delivery of PWSDs with adequate solubility in oil only or oil/surfactant blends and establishes the desired reproducible pharmacokinetic profile. Upon dilution, SMEDDS form transparent microemulsions, with a droplet size of <50 nm [11], while SNEDDS produce transparent dispersions of oil and water stabilized by surfactants, with droplet sizes between 20 and 250 nm and kinetically but not thermodynamically stable systems [29]. These two systems are the most famous colloidal dispersions within lipid‐based systems but physicochemically different. Structures and properties of nanoemulsion can be changed on long‐term storage but not for microemulsions at same temperature, pressure and composition. The formation of SMEDDS is spontaneous, and SNEDDS need high‐energy methods for their fabrication, but both systems need some external energy to overcome kinetic energy barriers and support mass transport. In comparison, SNEDDS need lesser surfactant‐to‐oil ratio than SMEDDS. The preparation of SNEDDS involve specific mixing order in which surfactant must be mixed first with oil phase, whereas SMEDDS do not need any specific mixing order for their preparation. Ternary phase diagrams are required to have a suitable selection of both systems which should be coherent with different phases involved in preparation.

An important best‐known example is Sandimmune Neoral® which was introduced in 1994 became the turning point for development of SMEDDS in oral lipid‐based formulations of PWSD [30]. This formulation contains Cremophor RH40 (polyoxyl hydrogenated castor oil), corn oil glycerides, propylene glycol and ethanol, which emulsifies spontaneously into a microemulsion with a particle size smaller than 100 nm. This new formulation (Sandimmune Neoral®) resulted in a twofold increase in the bioavailability compared to the earlier product Sandimmune® [31]. Recent years, SMEDDS and SNEDDS have gained lots of interest as potential drug delivery vehicles largely due to their clarity, simplicity of preparation, thermo‐ dynamic stability and their abilities to be filtered and to incorporate a wide range of drugs of varying lipophilicity.

## *4.1.1. SMEDDS/SNEDDS within lipid formulation classification systems*

By considering several factors in mind, Pouton [20, 32] introduced a lipid formulation classification system (LFCS) into four Types (I–IV) which differentiate lipid‐based formula‐ tions from one to another that is being used as a framework to categorize nanoformulations. These four Types of formulations were classified on the basis of formulation compositions, their aqueous dispersibility and the potential effects of lipid digestion and possible drug precipitation from lipids. Among the LFCS, Type III systems are the most attractive formula‐

tions as they produce microemulsions/nanoemulsions (SMEDDS and SNEDDS) of lipid‐ surfactant mixtures with particle sizes in the range of 0–250 nm upon dispersion. The microemulsions can be used for many other drug delivery/application systems, such as topical, intra venous, trans‐dermal, etc. There are several marketed products available which were developed as Type III formulations since the drugs may be absorbed from the microemulsions and or nanoemulsions without the digestion of lipids and/or surfactants present. Type III systems further divided into subtype IIIA and IIIB according to the hydrophilic content of the SMEDDS and SNEDDS. Type IV systems are efficient formulations as they also produce SMEDDS and/or SNEDDS and have high drug loading ability but may loss solvent capacity upon dilution with aqueous media.

## **4.2. Solidification of SMEDDS/SNEDDS**

The excipients commonly used in designing SNEDDS are liquid at room temperature, and their compatibility with semi‐solid and solid dosage forms allows encapsulating into soft/hard gelatin capsules for oral delivery. This could be a great challenge as the interaction between liquid formulation and capsule shell may result in either brittleness or softness of the shell [33]. In addition, the stability of liquid formulations could be another major issue (e.g., leaching and rancidity) since some drugs might suffer significant chemical instability in both aqueous and oily formulations. Apart from that, manufacturing liquid‐filled soft gelatin capsules is a slow process and requires specialized equipment, having risk of formulation components migrating into capsule shell [23].

Therefore, to address this limitation, incorporation of liquid lipid formulations into a solid dosage form is convincing and desirable. Liquid lipid formulations could be transformed into acceptable free flowing fine powder by loading the formulation on a suitable solid carrier as solid SNEDDS [34, 35]. Only few studies have attempted to investigate the conversion of such formulations into free flowing powders by adsorption using various inorganic high surface area materials (i.e., neusilin, syloid, aeroperal and aerosol) that are amenable to encapsulation or tableting [36, 37]. On the other hand, the final powder preparation should have acceptable flow properties to achieve the best content uniformity and weight variation. The current interest in solidification technique by both the industry and academia is raised enormously due to the attractive properties including independence of gastric transit, flexibility in dose dividing, decrease in intra‐ and inter‐subject variability, highest safety profile and physical/ chemical stability improvement.


**8. References**

Experimental data conclude that the mixtures consisting of biocide with fungal suspensions has an major effect to increase the anode current density, leading to further degradation by corrosion of the AISI 304 Stainless Steel surfaces. The results showed that there is a synergic effect between the active substances from the disinfectant, fungal suspensions and applied electric potential. This chapter could be considered a pioneer research and brinks possible ideas for further researches and raises more issues that need to be explored on the food

This work has benefited from financial support through the 2010 POSDRU/89/1.5/S/52432 project, ORGANIZING THE NATIONAL INTEREST POSTDOCTORAL SCHOOL OF APPLIED BIOTECHNOLOGIES WITH IMPACT ON ROMANIAN BIOECONOMY, project co-financed by the European Social Fund through the Sectoral Operational Programme

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**8**

115

*Hungary*

**Biotechnological Utilisation of**

*1Department of MOL Hydrocarbon and Coal Processing*

Nándor Nemestóthy, Tamás Bányai,

 *Membrane Technology and Energetics,*

 **Fusel Oil for Biolubricant Production**

 Katalin Bélafi-Bakó, László Bartha1 and László Gubicza *University of Pannonia, Research Institute on Bioengineering,*

Complete utilisation of by-products from agricultural and food industrial production is an important task both from economical and environmental aspects. There are numerous possibilities for manufacture of bioproducts by biotechnological processes, among them biofuels attracted the greatest attention (Watanabe et al., 2000). Industrial by-products can be, however, processed by several biotechnological methods, for example their utilisation as a food additive, which requires intensive research and development work (Filipini & Hogg,

Fusel oil is a by-product of distilleries, its average composition is 10% ethanol, 13% npropanol, 15% i-butanol, 51% i-amyl-alcohol, 11% miscellaneous alcohols and water. Nowadays fusel oil is usually burned to cover the energy demand of the distilleries. Researches have been carried out to utilise it as an additive to improve octane number in gasoline or for production of natural flavours and lubricants (Özgülsün & Karaosmanoglu,

Esterification of fusel oil with oleic acid using sulphuric acid catalyst was studied by Turkish researchers and bio-lubricant—according to ASTM (American Society Testing and Materials) standard—was manufactured for industrial purposes. Pure, natural lubricants manufactured by environmental-safe processes, however, have gained more and more attention recently, since they do not contain toxic compounds and are biological degradable. The demands against a bio-lubricant are that it should provide maximal protection during its usage, do not pollute the environment and do not accumulate (Özgülsün et al., 2000). Unfortunately the used lubricants are usually deposited in the environment, endangering our planet. To solve the problem lubricants should be manufactured from plant oil derivatives. There are several industrial application possibilities for fatty acid esters, as natural compounds. Oleic acid (cis-9-octadecenoic acid) is one of the most important fatty acids in nature, it can be obtained from plant oils (Bélafi-Bakó et al., 1994), its esters

Modern enzymology has achieved improvements in the development and application of lipase as catalyst. New immobilisation techniques make possible to use enzymes in industrial processes in a similar way to the classical catalysis for heterogeneous reactions.

produced by enzyme catalysis can be applied as lubricant (Linko et al., 1998).

**1. Introduction**

1997, Demirbas, 2000).

1999).


**2. α-particle emitters**

Radionuclides emitting α-particles such as 225Ac (half-life 10 days), 211At (half-life 7.2 h), 212Bi (half-life 60.55 min) and 213Bi (half-life 45.6 min) are options for treatment of small tumor nests or single disseminated tumor cells. Alfa particle emitting radionuclides are short ranged, high-energy helium nuclei with a high linear energy transfer (LET). As a consequence, α-emitters have a high relative biological effectiveness (RBE) which means, if nuclear localisation is possible, fewer radionuclides per cell are needed (Fass, 2009).


**5. Evolution**

The evolution of the crust would refer to the gradual development of the crust over time. Geomorphically significant evolution of the Earth's crust falls into two main categories, endogenic processes from forces that originate within the Earth and exogenic processes that are a result of forces from above or on the planet surface.

#### **5.1 Endogenous factors**

Continental crust transforms into oceanic crust in a cyclic and dynamic process [23]. Where the old crust is being destroyed at convergent boundaries, new crust is being created at divergent boundaries. When rifting first occurs at divergent boundaries, the crust-mantle system transforms due to the temperature, and a rift forms. Subduction of the low-velocity zone in the upper part of the crust is the main mechanism overlooking the beginning of crustal attenuation. Intruding magma, originating from the mantle under the rift, modifies the intermediate and lower crustal layers. As the process continues, a "pseudo-oceanic" crust forms, which has an intermediate chemical composition. Before the new oceanic crust is created, the intermediate crust disappears completely, and the underneath crustal layer is critically modified by bouts of magma from the mantle sources. New oceanic crust is then produced from the ridge and spreads out from the spreading centre towards the subduction zone where the crust is eventually destroyed. Components of the crust will return to the upper crust in different forms such as igneous intrusions and contribute to the formation of new continental crust [21].Depending on the type of plate boundary and the types of plates involved, the resultant processes and landforms formed differ. The different phenomena that occur contribute to the evolution of the crust.

Another example of the evolution of the crust due to endogenous processes is volcanism, where material from the mantle or the deep crust is deposited onto the surface where it contributes in renewing the crust surface with new igneous rock and landforms. In some places the crust is weaker such as along plate boundaries, the magma forces its way through the rock, extruding rock and releasing pressure, which is why volcanic activity tends to occur near the borders of tectonic plates, for example, the Pacific Ring of Fire [22]. The composition and origin of the lava determine the type of volcanic landform created, with more fluid mafic lava forming structures such as shield volcanoes and more viscous felsic lava forming structures such as stratovolcanoes from the accumulation of ejecta. However, in cases where magma does not breach the surface, the magma in horns or magma chambers may solidify to form intrusive or plutonic rocks. Over time, the surrounding softer rock erodes away, revealing the harder plutonic rock beneath, which creates landforms such as plutons, batholiths, dykes, sills, laccoliths and volcanic necks.

### **5.2 Exogenous factors**

The evolutionary processes mentioned above were all a result of forces originating from within the Earth. However, the crust is also shaped by a multitude of processes from external forces such as climate and extraterrestrial material. An overt example of an extraterrestrial force on the crust would be an impact crater, in which materials from space such as asteroids, meteoroids or comets collide with the Earth, leaving scars on the surface. While fairly infrequent in recent geological time, impacts were a major force of change during the late heavy bombardment period of the Earth's history [24], as the orbital path of the planet had not been fully cleared. The size of the impactor and extension diameter of the resultant impact crater is a decisive factor on the type of crater formed, with crater diameters above 2 km for sedimentary rocks and 4 km for crystalline rocks having a more complex impact structure as opposed to a simple bowl shape [25].

Climate and weathering are also significant drivers in the continued evolution of the crust. And while the parameters that control climate are complex and not fully understood, its effects can be seen widely. These processes can be observed in many forms, such as the exposure of batholiths by the erosion of soft rock, the carving of the Grand Canyon or the deposition of sediment by fluvial processes to create river deltas [26].

Additionally, biological processes also play a role in weathering and erosion. For example, plant roots hold the soil together, providing resistance to erosion [25]. Plants and burrowing animals also contribute to the mechanical breakdown of rock through wedging caused by growth and burrowing, respectively. All of the above processes are but a fraction of the factors that keep the Earth's crust in a state of constant flux. And while we may be unable to observe all geological evolutionary phenomena in the span of a human lifetime, we have more than enough examples and evidence to show that truly drastic changes occur in geological time.

## **6. Summary and conclusion**

While the crust may only comprise the superficial layer of the Earth, it is truly a dynamic and fascinating thing to learn about.  From its early origins as a hot lifeless shell covering our planet to its current state as the home for all life on Earth, it has changed so much over the geological timespan of the Earth's history and continues to evolve to

**7**

**Author details**

Muhammad Nawaz

National University of Singapore, Singapore

provided the original work is properly cited.

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. 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,

*Introductory Chapter: Earth Crust - Origin, Structure, Composition and Evolution*

this day. It is fortunate that the Earth would coincidentally have the perfect chemical composition to form a crust suitable for life forms to exist, all in accordance with the physical laws that govern the formation of worlds. The crust is truly an amazing

*DOI: http://dx.doi.org/10.5772/intechopen.88100*

and astonishing thing to learn and behold.

*Introductory Chapter: Earth Crust - Origin, Structure, Composition and Evolution DOI: http://dx.doi.org/10.5772/intechopen.88100*

this day. It is fortunate that the Earth would coincidentally have the perfect chemical composition to form a crust suitable for life forms to exist, all in accordance with the physical laws that govern the formation of worlds. The crust is truly an amazing and astonishing thing to learn and behold.