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1. Introduction
The extensive use of computers and information technology has led toward the creation of extensive data repositories from a very wide variety of application areas [1]. Such vast data repositories can contribute significantly towards future decision making provided appropriate knowledge discovery mechanisms are applied for extracting hidden, but potentially useful information embedded into the data [2].
Data mining (DM) is one of the phases in knowledge discovery in databases.It is the process of extracting the useful information and knowledge in which the data is abundant, incomplete, ambiguous and random [3], [4], [5]. DM is defined as an automated or semi-automated exploratory data analysis of large complex data sets that can be used to uncover patterns and relationships in data with an emphasis on large observational databases [6].Modern statistical and computational technologies are applied to the problem in order to find useful patterns hidden withina large database [7], [8], [9].To uncover hidden trends and patterns, DM uses a combination of an explicit knowledge base, sophisticated analytical skills, and domain knowledge.In effect, the predictive models formed from the trends and patterns through DM enable analysts to produce new observations from existing data. DM methods can also be viewed as statistical computation, artificial intelligence (AI) and database approach[10].However, these methods are not replacing the existing traditional statistics; in fact, it is an extension of traditional techniques.For example, its techniques have been applied to uncover hidden information and predict future trends in financial markets.Competitive advantages achieved by DM in business and finance include increased revenue, reduced cost, and improved market place responsiveness and awareness [11]. It has also been used to derive new information that could be integrated in decision support, forecasting and estimation to help business gain competitive advantage [9].In higher educational institutions, DM can be used in the process of uncovering hidden trends and patterns that help them in forecasting the students’ achievement.For instance, by using DM approach, a university could predict the accuracy percentage of students’ graduation status, whether students will or will not be graduated, the variety of outcomes, such as transferability, persistence, retention, and course success[12], [13].
The objective of this study is to investigate the impact of various data representations on predictive data mining models.In the task of prediction, one particular predictive model might give the best result for one data set but gives a poor results in another data set although these two datasets contain the same data with different representations [14],[15],[16], [17].This study focuses on two predictive data mining models, which are commonly used for prediction purposes, namely neural network (NN) and regression model.A medical data set (known as Wisconsin Breast Cancer) and a business data (German credit) that has Boolean targets are used for experimental purposes to investigate the impact of various data representation on predictive DM model. Seven data representations are employed for this study; they are As_Is, Min Max normalization, standard deviation normalization, sigmoidal normalization, thermometer representation, flag representation and simple binary representation.
This chapter is organized as follows.The second section describes data mining, and data representation is described in the third section.The methodology and the experiments for carrying out the investigations are covered in Section 4.The results are the subject of discussion which is presented in Section 5.Finally, the conclusion and future research are presented in Section 6.
2. Data mining
It is well known that DM is capable of providing highly accurate information to support decision-making and forecasting for scientific, physiology, sociology, the military and business decision making [13].DM is a powerful technology with great potential such that it helps users focus on the most important information stored in data warehouses or streamed through communication lines.DM has a potential to answer questions that were very time-consuming to resolve in the past.In addition, DM can predict future trends and behavior, allowing us to make proactive, knowledge-driven decisions [18].
NN, decision trees, and logistic regression are three classification models that are commonly used in comparative studies [19]. These models have been applied to a prostate cancer data set obtained from SEER (the Surveillance, Epidemiology), and results program of the National Cancer Institute. The results from the study show that NN performed best with the highest accuracy, sensitivity and specificity, followed by decision tree and then logistic regression.Similar models have been applied to detect credit card fraud. The results indicate that NN give better performance than logistic regression and decision tree [20].
3. Data representation
Data representation plays a crucial role on the performance of NN, “especially for the applications of NNs in a real world." In data representation study,[14] used NNs to extrapolate the presence of mercury in human blood from animal data.The effect of different data representations such as As-is, Category, Simple binary, Thermometer, and Flag on the prediction models are investigated.The study concludes that the Thermometer data representation using NN performs extremely well.
[16], [21] used five different data representations (Maximum Value, Maximum and Minimum Value, Logarithm, Thermometer (powers of 10), and Binary (powers of 2)) on a set of data to predict maize yield at three scales in east-central Indiana of the Midwest USA [17]. The data used to consist of weather data and yield data from farm, county and state levels from the year 1901 to 1996. The results indicate that data representation has a significant effect on NN performance.
In another study, [21] investigate the performance of data representation formats such as Binary and Integer on the classification accuracy of network intrusion detection system.Three data mining techniques such as rough sets, NN and inductive learning were applied on binary and integer representations. The experimental results show that different data representations did not cause significant difference to the classification accuracy.This may be due to the fact that the same phenomenon were captured and put into different representation formats [21]. In addition, the data was primarily discrete values of qualitative variables (system class), and different results could be obtained if the values were continuous variables.
Numerical encoding schemes (Decimal Normalization and Split Decimal Digit representation) and bit pattern encoding schemes (Binary representation, Binary Code Decimal representation, Gray Code representation, Temperature code representation, and Gray Coded Decimal representation) were applied on Fisher Iris data and the performance of the various encoding approaches were analyzed.The results indicate that encoding approaches affect the training errors (such as maximum error and root mean square error) and encoding methods that uses more input nodes that represent one single parameter resulted in lower training errors.Consequently, [22] work laid an important foundation for later research on the effect of data representation on the classification performance using NN.
[22] conducted an empirical study based on a theoretical provided by [15] to support the findings that input data manipulation could improve neural learning in NN.In addition, [15] evaluated the impact of the modified training sets and how the learning process depends on data distribution within the training sets.NN training was performed on input data set that has been arranged so that three different sets are produced with each set having a different number of occurrences of 1’s and 0’s. The Temperature Encoding is then employed on the three data sets and then being used to train NN again. The results show that by employing Temperature Encoding on the data sets, the training process is improved by significantly reducing the number of epochs or iteration needed for training. [15]’s findings proved that by changing input data representation, the performance in a NN model is affected.
4. Methodology
The methodology for this research is being adapted from [14] by using different data representations on the data set, and the steps involved in carrying out the studies are shown in Figure1 [14].The study starts with data collection, followed by data preparation stage, analysis and experiment stage, and finally, investigation and comparison stage.
Figure 1.
Steps in carrying out the study
4.1. Data collection
At this stage, data sets have been acquired through the UCI machine learning repository which can be accessed at http://archive.ics.uci.edu/ml/ datasets.html. The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for conducting empirical studieson machine learning algorithms. Two types of data have been obtained from UCI; they are Wisconsin Breast Cancer data set and German credit data set.
4.2. Data preparation
After the data has been collected in the previous stage, data preparation would be performed to prepare the data for the experiment in the next stage. Each attribute is examined and missing values are treated prior to training.
4.2.1. Data description
In this study, two sets of data are used, namely Wisconsin Breast Cancer and German Credit.Each data set is described in details in the following subsections.
4.2.1.1. Wisconsin breast cancer data set
Wisconsin breast cancer data set is originated from University of Wisconsin Hospitals, Madison donated by Dr. William H. Wolberg. Each instance or data object from the data represents one patient record.Each record comprises of information about Breast Cancer patient whose cancer condition is either benign or malignant.A total of 699 cases in the data set with nine attributes (excluding Sample Code Number) that represent independent variables and one attribute, i.e. Class represent the output or dependent variable.
Table 1 describes the attribute in the data set, code which represents the short form for this attribute, type, which shows the data type for particular attribute, domain, which represents the possible range in the value and the last column,showsthe missing values in all attributes in the study. From Table 1, only one attribute has been missing values (a total of 16 instances), and this attribute is Bare Nuclei.
No
Attribute description
Code
Type
Domain
Missing value
1
Sample code number
CodeNum
Continues
Id number
0
2
Clump Thickness
CTHick
Discrete
1 – 10
0
3
Uniformity of Cell Size
CellSize
Discrete
1 – 10
0
4
Uniformity of Cell Shape
CellShape
Discrete
1 – 10
0
5
Marginal Adhesion
MarAd
Discrete
1 – 10
0
6
Single Epithelial Cell Size
EpiCells
Discrete
1 – 10
0
7
Bare Nuclei
BareNuc
Discrete
1 – 10
16
8
Bland Chromatin
BLChr
Discrete
1 – 10
0
9
Normal Nucleoli
NormNuc
Discrete
1 – 10
0
10
Mitoses
Mito
Discrete
1 – 10
0
11
Class:
Cl
Discrete
2 for benign 4 for malignant
0
Table 1.
Attribute of Wisconsin Breast Cancer Dataset
Based on the condition of Breast Cancer patients, a total of 65.5% (458) of them has benign condition and the rest (34.5% or 241) is Malignant.
4.2.1.2. German credit dataset
German credit data set classifies applicants as good or bad credit risk based upon a set of attributes specified by financial institutions. The original data set is provided by Professor Hofmann contains categorical and symbolic attributes.A total of 1000 instances have been provided with 20 attributes, excluding the German Credit Class (Table 2). The applicants are classified as good credit risk (700) or bad (300) with no missing value in this data set.
No.
Attribute description
Code
Type
Domain
Missing value
1
Status of existing checking account
SECA
Discrete
1, 2, 3, 4
0
2
Duration in month
DurMo
Continuous
4- 72
0
3
Credit history
CreditH
Discrete
0, 1, 2, 3, 4
0
4
Purpose
Purpose
Discrete
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
0
5
Credit amount
CreditA
Continuous
250 - 18424
0
6
Savings account/bonds
SavingA
Discrete
1, 2, 3, 4, 5
0
7
Present employment since
EmploPe
Discrete
1, 2, 3, 4, 5
0
8
Instalment rate in percentage of disposable income
InstalRate
Continuous
2 – 4
0
9
Personal status
PersonalS
Discrete
1, 2, 3, 4, 5
0
10
Other debtors / guarantors
OtherDep
Discrete
1, 2, 3
0
11
Present residence since
PresentRe
Discrete
1 – 4
0
12
Property
Property
Discrete
1, 2, 3, 4
0
13
Age in years
Age
Continuous
19 – 75
0
14
Other instalment plans
OtherInst
Discrete
1, 2, 3
0
15
Housing
Housing
Discrete
1, 2, 3
0
16
Number of existing credits at bank
NumCBnk
Discrete
1,2,3
0
17
Job
Job
Discrete
1, 2, 3, 4
0
18
Number of people being liable to provide maintenance for
Numppl
Discrete
1, 2
0
19
Telephone
Telephone
Discrete
1, 2
0
20
Foreign worker
ForgnWor
Discrete
1, 2
0
21
German Credit Class
GCL
Discrete
1 good 2 bad
0
Table 2.
Attribute of German Credit Dataset
4.2.2. Data cleaning
Before using the data that has been collected in the previous stage, missing values should be identified. Several methods that could be performed to solve missing values on data, such as deleting the attributes or instances, replacing the missing values with the mean value of a particular attribute, or ignore the missing values. However, which action would be performed to handle the missing values depends upon the data that has been collected.
German credit application data set has no missing values (refer to Table 2); therefore, no action was taken on German credit data set. On the other hand, Wisconsin breast cancer data set has 16 missing values of an attribute Bare Nuclei (see Table 1). Therefore, these missing values have been resolved by replacing the mean value to this attribute. The mean value to this attribute is 3.54, since the data type for this attribute is categorical so the value was rounded to 4. Finally, all the missing values have been replaced by value 4.
4.3. Analysis and experiment
The data representations used for the experiments are described in the following subsections.
4.3.1. Data representation
Each data set has been transformed into data representation identified for this study, namely As_Is, Min Max Normalization, Standard Deviation Normalization, Sigmoidal Normalization, Thermometer Representation, Flag Representation and Simple Binary Representation.In As_Is representation, the data remain the same as the original data without any changes.The Min Max Normalization is used to transform all values into numbers between 0 and 1. The Min Max Normalization applies linear transformation on the raw data, keeping the relationship to the data values in the same range.This method does not deal with any possible outliers in the future value, and the min max formula [25] is written in Eqn. (1).
V\'=(v-Minvi)/(Maxvi-\n\tMinvi)E1
Where V’ is the new value,Min(v(i)) is the minimum value in a particular attribute, Max(v(i)) the maximum value in a particular attribute and v is the old value.
The Standard Deviation Normalization is a technique based on the mean value and standard deviation function for each attribute on the data set. For a variable v, the mean value Mean (v) and the standard deviation Std_dev(v) is calculated from the data set itself. The standard deviation normalization formula [25] is written as in Eqn. (2).
V\'=\n\t(v-meanv)std_dev(v)E2
where meanvv=\n\tSumvn\n\t std_dev(v)= sqr(sum(v2)-(sum(v)2/n)/(n-1))
The Sigmoidal Normalization transforms all nonlinear input data into the range between -1 and 1 using a sigmoid function. It calculates the mean value and standard deviation function value from the input data. Data points within a standard deviation of the mean are converted to the linear area of the sigmoid. In addition, outlier points to the data are compacted along the sigmoidal function tails. The sigmoidal normalization formula [25] is given by Eq. (3).
V\'=\n\t(v-meanv)std_dev(v)E3
Where a=v-meanvstddevvstd_dev(v)= sqr(sum(v2)-(sum(v)2/n)/(n-1))
In the Thermometer representation, the categorical value was converted into a binary form prior to performing analysis. For example, if the range of values for a category field is 1 to 6, thus value 4 can berepresented in thermometer format as "111100" [15].
In the Flag format, digit 1 is represented in the binary location for the value. Thus, following the same assumption that the range values in a category field is 1 to 6, if the value 4 needs to be represented in Flag format, the representation will be shown as "000100." The representation in Simple Binary is obtained by directly changing the categorical value into binary.Table 3 exhibits the different representations of Wisconsin Breast Cancer and German Credit data set.
Table 3.
Various dataset representations
4.3.2. Logistic regression
Logistic regression is one of the statistical methods used in DM for non-linear problems either to classify or for prediction.Logistic Regression is one of the parts of statistical models, which allows one to predict a discrete outcome (known as dependent variable), such as group membership, from a set of variables (also known as independent variables) that may be continuous, discrete, dichotomous, or a combination of any of these. The logistic regression aims to correctly predict the category of outcome for individual cases using the most parsimonious model. In order to achieve the goal, a model is created, which comprises of all predictor (independent) variables that are useful in predicting the desired target. The relationship between the predictor and the target is not linear instead; the logistic regression function is usedwhose equation can be written as Eqn. (4) [26].
θ=exp(β0+β1X1+…+βkxk)1+exp(β0+β1X1+…+βkxk)E4
Whereα = the constant from the equation andβ = the coefficient of the predictor variables. Alternatively, the logistic regression equation can be written as Eqn. (5).
logit[θ(x)]=log[θ(x)1−θ(x)]=α+(β0+β1X1+…+βkxk)E5
Anodd\'s ratio is formed from logistic regression that calculates the probability or success over the probability of failure. For example, logistic regression is often used for epidemiological studies where the analysis result shows the probability of developing cancer after controlling for other associated risks. In addition, logistic regression also provides knowledge about the relationships and strengths among the variables (e.g., smoking 10 packs a day increases the risk for developing cancer than working in asbestos mine)[27].
Logistic regression is a model which is simpler in terms of computation during training while still giving a good classification performance [28]. The simple logistic regression model has the form as in Eqn. (6), viz:
<&#OMath>meanv=\n\tSumvnE6
Taking the antilog of Eqn. (1) on both sides, an equation to predict the probability to the occurrence of the outcome of interest is as follows:
logitY=naturalloglogodds=lnϞ1-Ϟ=\n\tϏ+ϐXE7
WhereϞ=ProbabilityY=outcomeofinterestX=x,\n\taspecificvalueofX)=\n\teϏ+ϐx1+\n\t\t\teϏ+ϐsis theprobability for the outcome of interest or “event,” α is the intercept, ß is the regression coefficient, and e = 2.71828 is the base forthe system of natural logarithmsϞ can becategorical or continuous, but X is alwayscategorical.
For the Wisconsin Breast Cancer dataset, there are ten independent variables and one dependent variable for logistic regression as shown in Figure 2.However, the CodeNum is not included for analysis.
Figure 2.
Independent and dependent variables of Wisconsin Breast Cancer dataset
Similar approach is applied to German Credit dataset.
4.3.3. Neural network
NN or artificial neural network (ANN) are one of the DM techniques; defined as an information-processing system which is inspired from the function of the human brain whose performance characteristics are somehow in common with biologicalNN[30]. It comprises of a large number of simple processing units, called artificial neurons or nodes. All nodes are interconnected by links known as connections.These nodes are linked together to perform parallel distributed processing in order to solve a desired computational taskby simulating the learning process [3].
There are weights associated with the links that represent the connection strengths between two processing units. These weights determine the behavioron the network.The connection strengths determine the relationship between the input and the output for the network, and in a way represent the knowledge stored on the network. The knowledge is acquired by NN through a process of training during which the connection strengths between the nodes are modified. Once trained, the NN keeps this knowledge, and it can be used for the particular task it was designed to do [29].Through training, a network understands the relationship of the variables and establishes the weights between the nodes.Once the learning occurs, a new case can be loaded over the network to produce more accurate prediction or classification [31].
NN models can learn from experience, generalize and “see through” noise and distortion, and also abstract essential characteristics in the presence of irrelevant data [32].NN model is also described as a ‘black box’ approach which has great capacity in predictive modelling.NN models provide a high degree of robustness and fault tolerance since each processing node has primarily local connections[33]. NNs techniques are also advocated as a replacement for statistical forecasting methods because of its capabilities and performance [33], [34], [33]. However, NNs are very much dependent upon the problem at hand.
The techniques of NNs have been extensively used in pattern recognition, speech recognition and synthesis, medical applications (diagnosis, drug design), fault detection, problem diagnosis, robot control, and computer vision [36], [37]. One major application areas of NNs is forecasting, and the NNs techniques have been used as to solve many forecasting problems ([33], [36], [39], [38].
There are two types of perceptron in NN, namely simple or linear perceptron and MLP. Simple perceptron consists of only two layers; the input layer and output layer. MLP consists of at least three layers input layer, hidden layer and output layer. Figure 3illustrates the two types of perceptron.
The basic operation of NN involves summing its input weights and the activation function is applied to these layers to yield the output. Generally, there are three types of activation functions used in NN, which are threshold function, Piecewise-linear function and Sigmoid function (Figure4).Among these sigmoid function is the most commonly used in NN.
Figure 3.
Simple and MLP architecture
Figure 4.
Activation function for BP learning
Multilayer Perceptron (MLP) is one of the most common NN architecture that has been used for diverse applications, particularly in forecasting problems [40]. The MLP network is normally composed of a number of nodes or processing units, and it is organized into a series of two or more layers. The first layer (or the lowest layer) is named as an input layer where it receives the external information while the last layer (or the highest layer) is an output layer where the solution to the problem is obtained. The hidden layer is the intermediate layer in between the input layer and the output layer, and may compose with one or more layers. The training of MLP could be stated as a nonlinear optimization problem. The objective of MLP learning is to find out the best weights that minimize the difference between the input and the output. The most popular training algorithm used in NN is Back propagation (BP), and it has been used in solving many problems in pattern recognition and classification. This algorithm depends upon severalparameters such as a number of hidden nodes at the hidden layers ‘learning rate, momentum rate, activation function and the number of training to take place. Furthermore, these parameters could change the performance on the learning from bad to good accuracy [23].
There are three stages involved when training the NN using BP algorithm[36]. The first step is the feed forward of the input training pattern, second is calculating the associated error from the output with the input. The last step is the adjustment to the weight. The learning process basically starts with feed forward stage when each of input units receives the input information and sends the information to each of the hidden units at the hidden layer. Each hidden unit computes the activation and sends its signal to each output unit, and applies the activation to form response of the net for given input pattern. The accuracy of NN is provided by a confusion matrix. In a confusion matrix, the information about actual values and the predictive values are illustrated in Table 4.Each row of the matrix represents the actual accounts of a class of target for the actual data, while each column represents the predictive value from the actual data. To obtain the accuracy of NN, the summation of the correct instance will be divided by the summation for all instances. The accuracy of NN is calculated using Eqn. (7).
YE8
Based on Table 4, the Percentage of correct is calculated as:
Experiments are conducted to obtain a set of training parameters that gives the optimum accuracy for both data sets.Figure.5shows general architecture of NN for the Wisconsin Breast Cancer data set.Note that the ID number is not including in the architecture.
Figure 5.
Neural Network architecture for Wisconsin Breast Cancer
Similar architecture can be drawn for German Credit dataset; however, the number of hidden units and output units will be different from the Wisconsin Breast Cancer.
4.4. Investigation and comparison
The accuracy results obtained from previous experiments are compared and investigated further.Two data sets are considered for this study, the Logistic regression and Neural Network.Logistic regression is a statistical regression model for binary dependent variables [24], which is simpler in terms of computation during training while still giving a good classification performance [27]. Figure 6shows the general steps involve in performing logistic regression and NN experiments using different data representations in this study.
Figure 6.
Illustration of Data Representation for NN/ Regression analysis experiments
5. Results
Investigating the prediction performance on different data sets involves many uncertainties for a different data type.In the task of prediction, one particular predictive model might give the best result for one data set but gives the poor results in another data set although these two data sets contain the same data with different representations [14],[15],[16], [17].
Initial experimental results of correlation analysis on Wisconsin Breast Cancer indicate that all attributes (independent variables)has significant correlation with the dependent variable (target).However, German Credit data set indicates otherwise.Therefore, for German Credit data set, two different approaches (all dependent variables and selected variables) were performed in order to complete the investigation.
Based on the results exhibited in Table 5, although NN obtained the same percentage of accuracy, As_Isachieved the lowest training results (98.57%, 96.24%).On the other hand, regression exhibits the highest percentage of accuracy for ThermometreandFlag representation (100%) followed by Simple Binary representation.
Referring to the result shown in Figure 7, similar observation has been noted for German Credit data set when all variables are considered for the experiments.As_Isrepresentation obtained the highest percentage of accuracy (79%) for NN model.For regression analysis, Thermometer and Flag, representation obtained the highest percentage of accuracy (80.1%).Similar to earlier observation on the Wisconsin Breast Cancer dataset. Simple Binary representation obtained the second highest percentage of accuracy (79.5%).
Wisconsin Breast Cancer
Neural Network
Regression
Train
Test
Accuracy
As_Is representation
96.24%
98.57%
96.9%
Min Max normalization
96.42%
98.57%
96.9%
Standard Deviation normalization
96.42%
98.57%
96.9%
Sigmoidal normalization
96.60%
98.57%
96.9%
Thermometer representation
97.14%
98.57%
100.0%
Flag representation
97.67%
98.57%
100.0%
Simple Binary representation
97.14%
98.57%
97.6%
Table 5.
Percentage of accuracy for Wisconsin Breast Cancer Dataset
Figure 7.
German Credit All Variables accuracy for Neural Network and Regression
When selected variables of German Credit data set was tested with NN, the highest percentage accuracy was obtained using As_Is representation (80%), followed by Standard Deviation Normalization (79%) Min Max Normalization (78%) and Thermometer (78%) representation.The regression results show similar patterns with results illustrated in Figure.In other words, the data representation techniques, namely Thermometer (77.4%) and Flag(77.4%) representations produce the highest and second highest percentage of accuracy for selected variables of German Credit.
Figure 8.
German Credit Selected Variables accuracy for Neural Network and Regression
For brevity, Table 6 exhibits NN parameters that produce the highest percentage of accuracy for Wisconsin Breast Cancer, and German Credit data set using all variables as well as selected variables in the experiments.
Neural Network
Wisconsin Breast lCancer
German credit using all variables
German credit using selected variables
Percentage of Accuracy
98.57%
80.00%
79.00%
Input units
9
20
12
Hidden units
2
6
20
Learning rate
0.1
0.6
0.6
Momentum rate
0.8
0.1
0.1
Number of epoch
100
100
100
Table 6.
The summary of NN experimental results using As_Is representation
The logistic regression and correlation results for Wisconsin Breast Cancer data set are exhibited in Table 7.Note that based on Wald Statistics, variables such as CellSize, Cellshape, EpiCells, NormNuc and Mito are not significant in the prediction model.However, these variables have significant correlation with Type of Breast Cancer.Thus, the logistic regression independent variables include all variables listed in Table 7.
Logistic Regression
Correlation
Variables
B
Sig.
R
p
CTHick
.531
.000
CellSize
.006
.975
.818(**)
.000
CellShape
.333
.109
.819(**)
.000
MarAd
.240
.036
EpiCells
.069
.645
.683(**)
.000
BareNuc
.400
.000
BLChr
.411
.009
NormNuc
.145
.157
.712(**)
.000
Mito
.551
.069
.423(**)
.000
Constant
-9.671
.000
Table 7.
List of variables included in logistic regression of Wisconsin breast cancer
For German Credit data set, NN obtained the highest percentage of accuracy when all variables are considered for the training (see Table 6).The appropriate parameters for this data set are also listed in the same table. The summary of logistic regression results is shown in Table 8.All shaded variables displayed in Table 8 are significant independent variables for determining whether a credit application is successful or not.
Note also that variable age is not significant to German Credit target.However, its correlation with the target is significant. Therefore, these are variable included in logistic regression equation that represents German credit application.
Regression (Thermometer representation)
German Credit using all variables (80%)
Variables
Logistic Regression
Correlation
B
Sig.
R
p
SECA
-.588
000
-.348(**)
.000
DurMo
.025
.005
.206(**)
.000
CreditH
-.384
.000
-.222(**)
.000
CreditA
-.384
.018
.087(**)
.003
SavingA
-.240
.000
-.175(**)
.000
EmploPe
-.156
.029
-.120(**)
.000
InstalRate
.300
.000
.074(**)
.010
PersonalS
-.267
.022
-.091(**)
.002
OtherDep
-.363
.041
-0.003
.460
Property
.182
.046
.141(**)
.000
Age
-.010
.246
-.112(**)
.000
OtherInst
-.322
.004
-.113(**)
.000
Forgn Work
-1.216
.047
-.082(**)
.005
Constant
4.391
.000
Table 8.
List of variables included in logistic regression of German Credit dataset
6. Conclusion and future research
In this study, the effect of different data representations on the performance of NN and regression was investigated on different data sets that have a binary or boolean class target.The results indicate that different data representation produces a different percentage of accuracy.
Based on the empirical results, data representation As_Isis a better approach for NN with Boolean targets (see also Table 9). NN has shown consistent performance for both data sets.Further inspection of the results exhibited in Table 6 also indicates that for German Credit data set, NN performance improves by 1%.This leads to suggestion that by considering correlation and regression analysis, both NN results using As_Isand Standard Deviation Normalization could be improved.For regression analysis, Thermometer, Flag and Simple Binary representations produce consistent regression performance.However, the performance decreases when the independent variables have been reduced through correlation and regression analysis.
As for future research, more data sets will be utilized to investigate further on the effect of data representation on the performance of both NN and regression.One possible area is to investigate which cases fail during training, and how to correct the representation of cases such that the cases will be correctly identified by the model.Studying the effect of different data representations on different predictive models enable future researchers or data mining model\'s developer to present data correctly for binary or Boolean target in the prediction task.
German Credit All Variables
German Credit Selected Variables
Neural Network
Regn
Neural Network
Regn
Train
Test
Train
Test
As_Is representation
77.25
79.00
77.0
75.00
80.00
76.8
Min Max normalization
76.50
76.00
77.0
75.25
78.00
76.8
Standard Deviation normalization
76.75
77.00
77.0
75.13
79.00
76.8
Sigmoidal normalization
76.75
77.00
77.0
74.00
75.00
76.6
Thermometerrepresentation
78.38
78.00
80.1
77.00
78.00
77.4
Flag representation
76.75
77.00
80.1
75.13
73.00
77.4
Simple Binary representation
75.75
74.00
79.5
70.63
70.00
77.1
Table 9.
Summary of NN and regression analysis of German Credit dataset
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Rajib Hasan",authors:[{id:"17159",title:"Dr.",name:"Fadzilah",middleName:null,surname:"Siraj",fullName:"Fadzilah Siraj",slug:"fadzilah-siraj",email:"fadzilahsiraj@gmail.com",position:null,institution:null},{id:"157227",title:"Mr.",name:"Md Rajib",middleName:null,surname:"Hasan",fullName:"Md Rajib Hasan",slug:"md-rajib-hasan",email:"rajib@live.com.my",position:null,institution:{name:"Northern University of Malaysia",institutionURL:null,country:{name:"Malaysia"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Data mining",level:"1"},{id:"sec_3",title:"3. Data representation",level:"1"},{id:"sec_4",title:"4. Methodology",level:"1"},{id:"sec_4_2",title:"4.1. Data collection",level:"2"},{id:"sec_5_2",title:"4.2. Data preparation",level:"2"},{id:"sec_5_3",title:"Table 1.",level:"3"},{id:"sec_6_3",title:"4.2.2. Data cleaning",level:"3"},{id:"sec_8_2",title:"4.3. 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School of Computing, College of Arts and Sciences, University Utara Malaysia, Sintok, Kedah, Malaysia
School of Computing, College of Arts and Sciences, University Utara Malaysia, Sintok, Kedah, Malaysia
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1. Introduction
Plants often experience unfavorable environmental conditions such as high salinity, drought, cold, heat, depletion of soil nutrients, and excess of toxic ions, etc. that hamper the plant growth and development [1, 2, 3]. These stresses not only play a major role in determining the crop yield and productivity but they also contribute to the differential distribution of plant species across different parts of the earth [4]. About 90% of the arable lands around the globe are susceptible to one or more of the above stresses causing up to 70% annual yield loss of major food crops [5]. The changing climate is further aggravating the impact of abiotic stress factors on the overall growth and development of various crops [6]. It is believed that exposure to salt stress in irrigated lands has been increased by 37% during the last 20 years [7]. Moreover, the occurrence of drought is increased due to alteration in the evapotranspiration and pattern of precipitation caused by global warming [8]. As per a recent meta-analysis study, a further increase of 2.0 to 4.9°C in the average earth temperature by 2100 is speculated which will further impose a huge challenge for sustainable agriculture in the future [9].
Plants respond to different environmental constraints through complex intricate mechanisms [1]. The ability of plants to adjust to different environmental conditions is directly or indirectly related to two major plant strategies - plant stress avoidance and plant stress tolerance. Plant’s stress avoidance is a physiologically non- active phase like mature seeds, while stress tolerance is an active reversible adjustment which is generally referred to as acclimation [10]. Acclimation to stress is particularly mediated through profound changes at the level of gene expression which results in changes or modifications in the composition of plant transcriptome, proteome as well as metabolome [11]. During the last few decades, researchers have focused on recognizing and elucidating the different components and molecular partners underlying abiotic stress responses in plants [12]. Several attempts have been made to produce crops/species with improved abiotic stress adaptive traits including drought and salinity. However, one of the massive challenges in modern sustainable agriculture is the development of abiotic stress-resilient crops with new and desired agronomical traits using different approaches. For this purpose, understanding the mechanisms by which plants perceive stress signals and further transmit them to cellular machinery for activating adaptive responses is of huge importance [13, 14, 15, 16]. In this context, marrying the various physiological, biochemical, and gene regulatory network knowledge is essential that will aid up in the development of stress-tolerant high-yielding food crop cultivars [17, 18]. Therefore, a holistic understanding of the different responses associated with abiotic stress adaptation by taking advantage of various available high throughput tools like proteomics, metabolomics, and transcriptomics is critical. Hence, the present chapter deals with the various responses associated with abiotic stress stimuli in plants and the current status, and future prospects of different approaches used to date for developing stress-resilient crops.
2. Plant’s responses to abiotic stresses
Plants face several types of variations in their physical environment that hampers their growth and development. They respond to these oscillating environmental conditions through a series of external and internal changes [19, 20]. These stress-specific responses are associated with an array of molecular players that modulates the morphology, anatomy, and physiology of plants [12, 13].
2.1 Responses at the level of cellular membranes
Plant cells can sense changing environmental signals leading to significant changes in their physiology, metabolism, and gene expression [12, 13]. The stress stimuli are first perceived at the level of cellular membranes that initiates a cascade of events to transmit the signal to various organelles thus activating the appropriate molecular network [21]. In plants, the primary cell wall is composed of cellulose fibrils connected by hemicellulose tethers embedded in a pectin gel providing mechanical strength for load-bearing. It also contains several structural proteins, phenolics, and calcium [22]. These components are often modified when plants are exposed to abiotic stresses. The overall architecture of the cell wall is affected by exposure to abiotic stress depending upon the species, the stress intensity, plant phenotype, plant genotype as well as the age of plant. It appears to result in both loosening and tightening of the cell wall [23].
The viscoelastic properties of the primary cell wall are improved by elevating the levels of cell wall remodeling and biosynthetic enzymes, and by modulating the other cell wall loosening agents such as pectin, thus contributing to higher hydration status of the plant which aids up in maintaining turgor pressure necessary for growth [23]. The viscoelastic properties are also modulated by reinforcement of the secondary wall with the accumulation of cellulose and non-cellulosic components. In response to abiotic stress stimuli, the biosynthesis of xyloglucan (the most abundant non-cellulosic components of type I primary walls), and cellulose is induced [24, 25]. It is associated with an up-regulation of EXP (expansin), XTH (xyloglucan endo-β-transglucosylases/hydrolases) and Ces A (Cellulose Synthase) encoding genes [25] Moreover, the comparative analysis of changes in the cell wall of two- different drought-resistant varieties of wheat under stress showed an increase in pectin polymers RGI and RGII (rhamnogalacturonan I and II) side chains that probably leads to hydrogel formation of pectin, limiting the damage to the cells [26]. Also, methyl esterification of homogalacturonan (HG) levels regulated by PME (pectin methylesterase) reduces upon exposure to stress stimuli [27]. Such modifications in the cell wall architecture lead to relative maintenance of cell wall extensibility required to cope up with particular abiotic stress. Moreover, the genes encoding for cell wall proteins including arabinogalactan protein (AGP), glycine-rich protein (GRP), and proline-rich protein (PRP) are also induced in response to abiotic stress that could contribute to the cell wall strengthening [23].
One of the alternative responses against abiotic stress stimuli is to decrease the cell wall expansion and cell extensibility that can thus limit the water loss and prevent cell collapse due to dehydration stress [23, 28]. A decrease in cell wall extensibility or turgor pressure is often associated with the rigidification of the secondary cell wall by lignin deposition. As monolignols are the building blocks of lignin, they are synthesized from phenylalanine through the general phenylpropanoid and monolignol-specific pathways in the cytosol. The monolignols are then transported to the cell wall where they are polymerized by apoplastic peroxidase (PRX) and laccases into lignin [23].
A large number of integral plasma membrane proteins are also known to participate in stress perceptions which are the members of different receptor-like kinases RLKs (receptor-like kinases) [29]. Abiotic stresses are often responsible for alterations in wall-associated kinases (WAK) that are required for cell elongation and development [22]. In plants exposed to abiotic stresses, the expression of genes encoding for WAK proteins is up-regulated hinting towards the perception of stress at the cell wall or plasma membrane interface through the detection of released plant cell wall fragments [24, 30]. Thus, it can be concluded that modulation of the cell wall architecture is often a direct response that plays a vital role in the sensitization of the plant against abiotic stress stimuli. However, critical information on understanding this response comes from transcriptomics rather than biochemical analysis [26]. Therefore, a multidisciplinary approach is required for gaining an in-depth knowledge of this complex mechanism in the future.
2.2 Modulation of photosynthetic apparatus and gaseous parameters
Plants suffer numerous physiological reactions on exposure to environmental stress. These responses include alterations in photosynthetic rates, assimilate translocation, nutrient uptake and translocation, changes in water uptake, and evapotranspiration [31]. Among these, photosynthesis is one of the most critical plant processes affected by various abiotic stresses [31, 32]. These stresses negatively influence the photosystems (PS I and PS II) thus reducing the photosynthetic activity along with reduced chlorophyll biosynthesis, and photosynthetic electron transport. They also lead to impaired RuBp (ribulose 1,5-bisphosphate) regeneration that substantially affects the Rubisco activity. Generally, the stress-derived inhibitory effects on photosynthesis in plants may occur due to limitations in CO2diffusion factors and/or metabolic factors. Some reports provide evidence that stomatal closure is the key event under stress conditions resulting in a decrease in the sub-stomatal as well as chloroplast CO2 concentration (Ci and Cc, respectively) thus producing a decline in CO2 assimilation [32, 33, 34, 35, 36].
Under moderate drought stress, decreased stomatal conductance (gs) is considered as the primary cause of photosynthetic inhibition from reduced supply of CO2 to the intercellular space. In general, atmospheric CO2 diffuses to the intercellular space (i.e. stomatal limitation) through stomata and then across the mesophyll (mesophyll limitations) at the carboxylation site [31]. Thus, mesophyll conductance (gm) and biochemical limitation (bL) (often termed as non-stomatal limitations to photosynthesis mainly under high water stress) have gained importance in the recent years, however, their relative importance to photosynthesis limitation has been a subject of debate [31, 36, 37]. Although, the function of non-stomatal limitations to photosynthesis is evident, however, controversies still exist because of the error and assumptions in the estimation of gm and bL under stress conditions [38].
2.3 Ion stress signaling and homeostasis
Abiotic stresses particularly salt and heavy metal stress are majorly responsible for an imbalance in ionic composition inside the plant cells [10]. For a normal metabolic function of plants, cells need to maintain high K+ and low Na+ levels. Thus, systematic exclusion of excess Na+ ions from the cytoplasm or their accumulation within the vacuoles are the main adaptive mechanisms against ionic stress in plants [21]. This occurs through a highly sophisticated mechanism of ion homeostasis which involves the interplay of different molecular players. Ion homeostasis is maintained by ion pumps like symporters, antiporters, and carrier proteins located on the cell membranes [39]. At the plasma membrane of the cell, the stress signal is perceived by a sensor or a receptor which is generally regulated by the coordination of various ion pumps [40]. Exclusion of ions is typically carried out by transmembrane transport proteins excluding Na+ from the cytosol, however, compartmentalization is carried out by H+- pyrophosphatase proteins and vacuolar membrane H+ -ATPase [12].
Salt Overly Sensitive also known as SOS pathway is an excellent example of intracellular ion management or homeostasis which is turned ‘on’ after the activation of the receptor in response to stress and transcriptional induction of genes by signaling intermediate compounds along with certain downstream interacting partners which result in the efflux of excess ions [41]. SOS1, SOS2, and SOS3 are the three genes encoding for SOS proteins, which work in a synchronized manner and aids in the transportation of Na+ ions from the cytoplasm by effluxing excess of Na+ ions using a plasma membrane Na+/H+ antiporter. This pathway is triggered by the high concentrations of Na+ ions perceived by the intracellular calcium (Ca2+) ion signals. The high concentration of sodium chloride (NaCl) disturbs the intracellular levels of Ca2+ via hypothetical plasma membrane sensors. This Ca2+ signal is then recognized and interpreted by the SOS3 protein which belongs to the calcineurin B-like protein (CBLs) family which in association with SOS2 activates the SOS1 [42]. SOS1 encodes for a Na+/H+ antiporter and various studies have confirmed the functional role of SOS1 in maintaining the homeostatic balance of ions during salt stress adaptation [43]. The vacuolar Na+/H+ and H+/Ca2+ antiporters are also known to be differentially regulated by SOS2, thus contributing to enhanced Na+ ions sequestration in vacuole imparting salinity tolerance. Furthermore, the SOS2/SOS3 kinase complex is responsible for the down-regulation of the activity of Na+ ion transporters, mediating the entry of these ions into the cells of root tissue during salinity. Apart from the well-established function of ion homeostasis, SOS proteins have also been known to play novel functions during stress acclimatization including regulation of cell cytoskeleton dynamics, development of lateral roots via modulation of auxin gradients as well as maxima in roots under moderate salt stress [43].
In plants, potassium (K+) is one of the most abundant inorganic cations involved in various aspects of plant growth and development including abiotic stress management [44]. Thus, the maintenance of K+ homeostasis through K+ ion transporters and channels across the plasma membrane is necessary for the survival of plants, especially during stress conditions [45]. Plants have developed a unique transport system for K+ acquisition and release using the high-affinity K+ uptake transporters (HKTs) [46]. There are two sub-groups of these transporters (class I and class II) which have been identified to play a critical role in selective Na+ ion transport and cationic co-transport of Na+/K+, respectively [12]. They also play a significant role in the maintenance and distribution of Na+ ions between plant shoots and roots [47]. In Arabidopsis thaliana (Arabidopsis) knockout mutations in the AtHKT1 gene along with AtSOS1 gene {induced either by T-DNA insertion or ethyl methane sulphonate (EMS) treatment} lead to over-deposition of Na+ ions in leaves due to the decreased amount of Na+ ions in roots under salt stress [48].
Cl− is a plant micronutrient which regulates turgor pressure, leaf osmotic potential, and stimulates growth in plants by acting as a critical messenger in plant developmental processes [49]. Cl− ion signaling and transporters also regulate different pathways conferring abiotic stress tolerance in plants [50]. For instance, as an early salt stress response, the Cl− ion signal in the soil with elevated salt concentration has been connected to stomatal closure in an ABA dependent manner [21]. However, increased deposition of these ions during ionic stress is detrimental to plant growth and development [51]. Thus, plants tend to decrease the net levels of Cl− ions during stress through reduced net Cl− uptake by roots, decreased intracellular compartmentation, reduced net xylem loading of Cl−, and phloem recirculation and translocation [52]. Also, inside the cytosol, threshold levels of Cl− ions are maintained primarily through its sequestration with the help of ion transporters and voltage-gated ion channels inside the vacuole [53]. A voltage gradient is maintained between the vacuole and the cytoplasm because of a slightly positive charged vacuole and a negatively charged cytoplasm. Hence, a large number of the Cl− ions are sequestered through voltage-gated anion channels of the CLC family which are present on the tonoplast. Different CLC proteins function as anion/H+ exchangers or anion-selective channels. In reports, AtCLCa has been characterized as a two-anion/H+ exchanger which drives the active uptake of anions inside the vacuoles of Arabidopsis guard cells and mesophyll with higher selectivity for NO3− ions over Cl− ions [54]. Besides, CLCs play a vital role in loading anions in the vacuole of guard cells for stomatal opening in response to light and later releasing them during ABA-induced stomatal closure [55].
2.4 Intracellular osmotic adjustment and osmoprotectants
The intracellular water loss from the cell due to drought and salinity stress results in cellular dehydration thus imposing osmotic stress in plants [56]. To counteract the effects of osmotic stress, plants and bacteria accumulate certain organic solutes like quaternary ammonium compounds, polyamines, fructose, sucrose, sugar alcohols, trehalose, fructans, oxalate, malate, and many others. These metabolites are referred as osmoprotectants or compatible solutes and may accumulate in large quantities without disturbing the intracellular biochemistry [57]. Among these osmoprotectants, quaternary ammonium compounds including proline and glycine betaine (GB) abundantly accumulate in response to abiotic stresses. The imino acid proline is known to be deposited in considerable amounts in plant cells under the influence of drought, salinity, and other stresses [58]. It is synthesized inside the cytoplasm and plastids while it is degraded to glutamate (Glu) in the mitochondria. In addition to its role in osmotic adjustment, proline contributes in the stabilization of the cellular membranes and vital proteins by making clusters with water molecules that later get attached to membranes and proteins, thus, inhibiting their denaturation [59, 60]. Proline also scavenges free radicals to maintain or buffer the redox potential inside the cell under stressful conditions. It alleviates the cytoplasmic acidosis and sustains NADP+/NADPH ratios at required levels for cellular metabolism, hence, supporting redox cycling [60, 61]. Researchers have observed a positive correlation between proline deposition and tolerance against various abiotic stresses in plants [58]. Furthermore, the exogenous application of proline has been used as an effective approach to improve stress tolerance in plants [62].
GB is another critical compound that plays an important role in osmoprotection, stroma adjustment as well as protection of thylakoid membranes for maintaining the photosynthetic activity during stress conditions [63, 64]. It protects the photosystem II (PS-II) complex from the impact of abiotic stresses [65]. GB also possesses a protective role for Rubisco against heat-induced destabilization [65]. The increased accumulation of GB provides abiotic stress resistance in several agronomically important crops including tobacco, potato, tomato, barley, and maize [11, 66, 67]. Moreover, the Arabidopsis thaliana, Nicotiana tabacum, and Brassica napus plants transformed with bacterial choline oxidase cDNA were found to show 5 to 10% increased levels of GB than the naturally found levels of GB in them that moderately improved their tolerance against different abiotic stresses [68].
The content of soluble carbohydrates also varies in response to abiotic stresses in plants. Simple and complex carbohydrates such as sugars, starch, and sugar alcohols accumulate under stress conditions in plants [68]. The major roles of these biomolecules are osmotic adjustment, carbon storage, and free radical scavenging. Their pattern of accumulation in response to stress varies under short- and long-term reactions. In short-term water stress conditions, decreased content of sucrose and starch were observed in the case of Setaria sphacelata, which is a naturally adapted C4 grass whereas an increased amount of soluble sugars and decreased amount of starch were reported under long term stress imposition [69]. Trehalose is a rare non-reducing sugar that occurs in some desiccation-tolerant higher plants along with various bacterial and fungal species [70]. It shows significant accumulation in plants in response to various environmental stimuli and acts as an osmolyte thus protecting the plant cells. It also protects the protein functioning by reducing the aggregation of denatured proteins and safeguards the biological molecules from the changing environmental stresses through its reversible water-absorption capacity [68, 71]. The sugar alcohols also show considerable accumulation in response to abiotic stress in plants and help in osmotic adjustment [72]. Mannitol, a sugar alcohol, accumulates upon salt and water stress conditions in plants. Wheat transgenics, expressing the mtlD gene (mannitol-1-phosphate dehydrogenase) of Escherichia coli showed significantly more tolerance towards salt as well as water stress. Upon analysis, increased plant height, biomass, and the number of secondary stems were observed in transgenic wheat [72].
Polyamines are small organic molecules ubiquitously present in all living organisms which play a vital role in diverse cellular processes. They are positively charged at physiological pH and are regarded as growth substances [73, 74, 75]. Under stress conditions, different plant species respond differently to polyamines levels. Some of the plants might increase the content of polyamines under stress conditions whereas others decrease their levels of endogenous polyamines when exposed to severe environmental conditions [73]. Exogenous application of polyamine and/or inhibitors of enzymes which are involved in polyamine biosynthesis also hints towards a possible role of such compounds in plant adaptation or defense process in response to environmental stresses [76]. Moreover, studies involving either transgenic overexpression or loss of function mutants support the protective, adaptive, or defensive role of polyamines in plant’s response to various abiotic stresses [76, 77].
2.5 Reactive oxygen species (ROS) regulation during stress acclimation
Many evidences suggest that various environmental stresses lead to the generation of ROS in plants. Actually, in plants, each cellular compartment is equipped with its own ROS homeostasis control [78, 79, 80]. The ROS signaling is changed depending upon the cell type, developmental stage, and level of stress [81]. Under optimal growth conditions, ROS inside the cell is mainly produced at a low level in organelles like chloroplast, mitochondria, and peroxisomes [82]. It has been estimated that 1–2% of the O2 consumed by plant tissues, leads to the ROS formation that mainly involves 1O2, H2O2, O•−2, and OH• [83, 84]. At this low concentration, ROS acts as a signaling molecule that triggers signal transduction pathways involved in growth and development [21, 85]. However, in response to various abiotic stresses, the generation of increased levels of ROS causes irreversible damage to cells through their strong oxidative properties [86]. They possess lethal properties and cause extensive damage to DNA, proteins, and lipids thereby affecting normal cellular functioning [82]. Plants have developed an elaborate and efficient network of ROS generating and scavenging mechanisms to overcome this ROS toxicity. The two systems interplay with each other for maintaining a steady state in plants during stress acclimation [87, 88]. The delicate balance between the generation of ROS and its scavenging is responsible for duality in its function in plants which is orchestrated by a giant network of genes known as ‘ROS gene network’ [84].
Plant NADPH oxidases also referred as respiratory burst oxidase homologs (RBOHs) are the most studied enzymatic source of ROS in plants [88]. These are superoxide-producing enzymes that are widely involved in various processes including abiotic stress responses in plants [89]. The superoxide radical is a short-lived ROS molecule that is characterized by moderate reactivity and can trigger a series of reactions to produce other ROS species. It is produced inside mitochondria, chloroplasts, endoplasmic reticulum, and peroxisomes as a result of their normal metabolism [90]. The activity of plant NADPH oxidase is regulated by some key regulatory components like Ca2+, calcium-dependent protein kinases (CDPKs), Ca2+/CaM-dependent protein kinase, some small GTPases, and others. The production of ROS through NADPH oxidase may result in regulating the acclimation to abiotic stresses in plants. For instance, in barley, NADPH oxidase-mediated apoplastic ROS generation (acting upstream of xylem Na+ loading) that is linked to ROS-inducible Ca2+ uptake systems in the xylem parenchyma tissue is considered as a critical factor contributing to salt stress tolerance in plants [91]. In Arabidopsis, the double mutants of AtRbohD and AtRbohF genes with significantly inhibited ROS generation exhibited less growth and relatively higher cellular Na+ to K+ ratios than the wild-type (WT) as well as a single null mutant ATrbohd and ATrbohf plants under salt stress [92].
Superoxide ions generated by NADPH oxidase are converted to hydrogen peroxide (H2O2), catalyzed by the different isoforms of superoxide dismutase (SOD) enzyme [93]. H2O2 production in plant cells not only occurs under normal conditions but also by oxidative stress which is caused by different abiotic factors. The major sources of H2O2 production in plant cells comprises of the electron transport chain in the chloroplast, endoplasmic reticulum (ER), mitochondria, cell membrane, β-oxidation of fatty acid, and photorespiration along with various other sources including reactions comprising photo-oxidation by NADPH oxidase. The rates of H2O2 accumulation in peroxisomes, as well as chloroplasts, may be 30–100 times higher as compared with H2O2 generated in the mitochondria. It acts as a systemic signal that alerts various plant tissues to respond and adapt in response to the upcoming stress stimuli [94, 95]. H2O2 confer acclamatory stress tolerance by regulating osmotic adjustment, photosynthesis, ROS detoxification, and phytohormones signaling [95]. Studies have suggested that seeds pre-treated with H2O2, or together with the application of H2O2 and abiotic stress, induce an inductive pulse which aids up in protecting plants under abiotic stresses by the restoration of redox-homeostasis and mitigation of oxidative damage to membranes, lipids, and proteins by modulating the stress signaling pathways [95].
The stress-induced ROS activating responses occur rapidly with the appearance of the stress and it should decay immediately to protect the plants against their toxic effects. For this, plants are equipped with an array of ROS detoxifying proteins that mitigate the toxic effects of ROS generated as a result of different types of stresses [96]. In plants, the redox homeostasis during stressful conditions is maintained by the two arms of the antioxidant machinery—the enzymatic components consisting of the superoxide dismutase (SOD), guaiacol peroxidase (GPX), ascorbate peroxidase (APX), catalase (CAT), glutathione-S-transferase (GST), and the non-enzymatic molecular compounds like reduced glutathione (GSH), ascorbic acid (AA), α-tocopherol, phenolics, carotenoids, flavonoids, and proline. These antioxidant enzymes are situated in different sites of the plant cells and work together to detoxify ROS. The omnipresent behavior of both arms of the antioxidant machinery explains the basic necessity of detoxification of ROS for cell survival [97].
3. Strategies to combat abiotic stresses in plants
Various strategies have been undertaken by the researchers from time to time to improve the abiotic stress tolerance in plants, particularly crop plants [98]. Plant breeding is the most traditional and widely used method for achieving the desired trait in given plants including stress adaptation [99]. However, the success of crop-breeding programs greatly depends on the availability of natural genetic variations among the germplasm resources and tedious selection procedures that are too slow and equally expensive [100]. Moreover, the various environmental factors such as plant developmental stage along with the logistical constraints of physiological screening of large breeding populations on a field-scale can affect the differential selection of a particular stress tolerant plant. Thus, plant breeding is almost always limited by the genetic complexity of the underpinning mechanisms along with the potential interaction among genetic determinants [101]. In this regard, the identification and recognition of discrete chromosomal regions having a major effect on the specific tolerance trait via quantitative trait loci (QTL) mapping and marker-assisted selection remain a valuable option for the success of many breeding programs [102]. Although, QTL mapping holds great promise, but still it remains complicated as the introgression of QTL regions in elite lines is tedious due to linkage drag that may introduce non-target regions. As an alternative, the cellular-based mutant introduction and subsequent selection under controlled in vitro conditions offer a method to quickly screen large populations with homogeneous backgrounds for novel fortuitous changes related to tolerance. Subsequent field screening then ensures the adequate performance of the tolerance trait under the external potentially mitigating factors [103].
In the past few decades, the genetic engineering approach has attracted the interest of the research community for producing stress-tolerant elite crops [104]. Genetic transformation with stress-inducible genes has been employed by the researchers to gain an understanding of their functional role in stress tolerance and ultimately to improve the traits in the target genotype [105]. The genetic manipulation techniques including insertional mutagenesis have largely contributed to deciphering the function of genes and thereby identifying the suitable candidates for crop improvement [106]. However, though success has been achieved in introducing desired tolerance traits into various crop varieties from wild relatives like barley and tomato, a restricted success has been reported in achieving abiotic stress tolerance with elite germplasm [107]. Moreover, the integration of transgenes into the host genome is sometimes non-specific and unstable [108]. Recently, the use of targeted genome editing using clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein9 nuclease (Cas9) (CRISPR/Cas) has generated a lot of interest in various fields of plant biology including abiotic stress management [109]. CRISPR/Cas has been adopted in the field of plant developmental biology for characterizing genes as well as to underpin the molecular mechanisms behind various plant traits [110]. It has been used in the model plants such as Arabidopsis and tobacco earlier and likewise, now it is being utilized effectively for crop plants like sorghum, rice, wheat, maize, soybean as well as woody plants. Researchers have worked on the potential use of the CRISPR/Cas9 technique for the production of abiotic stress-tolerant crops by targeting the key sensitivity (S genes and cis-regulatory sequences) and tolerance (T genes) players. In general, T genes are deployed to achieve stress tolerance in plants; however, the S genes negatively regulates the biological function of the T genes. Therefore, the silencing of S genes to disturb their functioning can help plants to adjust their physiological and biochemical pathways for providing tolerance in response to abiotic stress [111]. Like S genes, various cis-regulatory sequences have also been identified that negatively regulates abiotic stress tolerance mechanisms. These sequences are highly conserved and help in the regulation of gene expression by interacting with specific transcription factors [111]. Thus, editing such cis-regulatory sequences can also serve as a potential strategy for improving stress tolerance in plants. However, one major limitation of genome editing is the off-target mutations that are caused by Cas9 in transgenic plants. This limitation has been overcome to a considerable extent by the advent of stress-inducible CRISPR/Cas9 technique which reduces the rate of off-target mutations to negligible levels [112]. Thus, we can consider stress-inducible CRISPR/Cas as a promising tool for precise and efficient genome editing in crop plants for numerous traits, including abiotic stress tolerance.
4. Conclusion
In the last few decades, significant progress has been made in our understanding of the complex mechanisms governing abiotic stress tolerance in plants. However, still we are far from pinning the exact battery of gene activation mechanisms responsible for providing tolerance to various abiotic stresses. Our struggle to understand the complex mechanisms is ongoing and recent development of new tools for high-throughput phenotyping and genotyping gives us a new ray of hope. A complete understanding of the physiological, biochemical and molecular mechanisms especially the signaling cascades in response to abiotic stresses in tolerant plants will help to manipulate susceptible crop plants and increase agricultural productivity in the near future. Moreover, advances in genomics strategies including genetic engineering and genome editing have provided new opportunities for crop improvement by employing precise genome engineering for targeted traits in crop plants. However, the selection of the key genes is critical for the success of these approaches.
Acknowledgments
Authors are thankful to the Department of Biotechnology, GOI, and Rashtriya Uchchattar Shiksha Abhiyan (RUSA-II) Program, Ministry of Human Resource Development (MHRD), GOI.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"plants, abiotic stress, photosynthesis, reactive oxygen species, ion transport, osmoregulation",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/73257.pdf",chapterXML:"https://mts.intechopen.com/source/xml/73257.xml",downloadPdfUrl:"/chapter/pdf-download/73257",previewPdfUrl:"/chapter/pdf-preview/73257",totalDownloads:113,totalViews:0,totalCrossrefCites:0,dateSubmitted:"June 13th 2020",dateReviewed:"August 31st 2020",datePrePublished:"October 1st 2020",datePublished:null,dateFinished:null,readingETA:"0",abstract:"Exposure to abiotic stresses has become a major threatening factor that hurdles the sustainable growth in agriculture for fulfilling the growing food demand worldwide. A significant decrease in the production of major food crops including wheat, rice, and maize is predicted in the near future due to the combined effect of abiotic stresses and climate change that will hamper global food security. Thus, desperate efforts are necessary to develop abiotic stress-resilient crops with improved agronomic traits. For this, detailed knowledge of the underlying mechanisms responsible for abiotic stress adaptation in plants is must required. Plants being sessile organisms respond to different stresses through complex and diverse responses that are integrated on various whole plants, cellular, and molecular levels. The advanced genetic and molecular tools have uncovered these complex stress adaptive processes and have provided critical inputs on their regulation. The present chapter focuses on understanding the different responses of the plants involved in abiotic stress adaptation and strategies employed to date for achieving stress resistance in plants.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/73257",risUrl:"/chapter/ris/73257",signatures:"Deeksha Marothia, Navdeep Kaur and Pratap Kumar Pati",book:{id:"10363",title:"Abiotic Stress in Plants",subtitle:null,fullTitle:"Abiotic Stress in Plants",slug:null,publishedDate:null,bookSignature:"Dr. Shah Fahad, Dr. Shah Saud, Prof. Yajun Chen, Dr. Chao Wu and Dr. Depeng Wang",coverURL:"https://cdn.intechopen.com/books/images_new/10363.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"194771",title:"Dr.",name:"Shah",middleName:null,surname:"Fahad",slug:"shah-fahad",fullName:"Shah Fahad"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Plant’s responses to abiotic stresses",level:"1"},{id:"sec_2_2",title:"2.1 Responses at the level of cellular membranes",level:"2"},{id:"sec_3_2",title:"2.2 Modulation of photosynthetic apparatus and gaseous parameters",level:"2"},{id:"sec_4_2",title:"2.3 Ion stress signaling and homeostasis",level:"2"},{id:"sec_5_2",title:"2.4 Intracellular osmotic adjustment and osmoprotectants",level:"2"},{id:"sec_6_2",title:"2.5 Reactive oxygen species (ROS) regulation during stress acclimation",level:"2"},{id:"sec_8",title:"3. Strategies to combat abiotic stresses in plants",level:"1"},{id:"sec_9",title:"4. Conclusion",level:"1"},{id:"sec_10",title:"Acknowledgments",level:"1"},{id:"sec_13",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Chang YN, Zhu C, Jiang J, Zhang H, Zhu JK, Duan CG. Epigenetic regulation in plant abiotic stress responses. 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International journal of molecular sciences. 2013 Apr;14(4):7660-80'},{id:"B48",body:'Wang Q , Guan C, Wang P, Ma Q , Bao AK, Zhang JL, Wang SM. The Effect of AtHKT1; 1 or AtSOS1 mutation on the expressions of Na+ or K+ transporter genes and ion homeostasis in Arabidopsis thaliana under salt stress. International journal of molecular sciences. 2019 Jan;20(5):1085'},{id:"B49",body:'Franco-Navarro JD, Brumós J, Rosales MA, Cubero-Font P, Talón M, Colmenero-Flores JM. Chloride regulates leaf cell size and water relations in tobacco plants. Journal of Experimental Botany. 2016 Feb 1;67(3):873-91'},{id:"B50",body:'Brini F, Masmoudi K. Ion transporters and abiotic stress tolerance in plants. ISRN molecular biology. 2012;2012'},{id:"B51",body:'White PJ, Broadley MR. Chloride in soils and its uptake and movement within the plant: a review. Annals of Botany. 2001 Dec 1;88(6):967-88'},{id:"B52",body:'Wu H, Li Z. 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Trehalose metabolism: from osmoprotection to signaling. International journal of molecular sciences. 2009 Sep;10(9):3793-810'},{id:"B71",body:'Suprasanna P, Nikalje GC, Rai AN. Osmolyte accumulation and implications in plant abiotic stress tolerance. InOsmolytes and plants acclimation to changing environment: Emerging omics technologies 2016 (pp. 1-12). Springer, New Delhi'},{id:"B72",body:'Abebe T, Guenzi AC, Martin B, Cushman JC. Tolerance of mannitol-accumulating transgenic wheat to water stress and salinity. Plant physiology. 2003 Apr 1;131(4):1748-55'},{id:"B73",body:'Chen D, Shao Q , Yin L, Younis A, Zheng B. Polyamine function in plants: metabolism, regulation on development, and roles in abiotic stress responses. Frontiers in plant science. 2019 Jan 10;9:1945'},{id:"B74",body:'Kusano T, Berberich T, Tateda C, Takahashi Y. Polyamines: essential factors for growth and survival. Planta. 2008 Aug 1;228(3):367-81'},{id:"B75",body:'Yu Z, Jia D, Liu T. Polyamine oxidases play various roles in plant development and abiotic stress tolerance. Plants. 2019 Jun;8(6):184'},{id:"B76",body:'Groppa MD, Benavides MP. Polyamines and abiotic stress: recent advances. Amino acids. 2008 Jan 1;34(1):35'},{id:"B77",body:'Paschalidis K, Tsaniklidis G, Wang BQ , Delis C, Trantas E, Loulakakis K, Makky M, Sarris PF, Ververidis F, Liu JH. The interplay among polyamines and nitrogen in plant stress responses. Plants. 2019 Sep;8(9):315'},{id:"B78",body:'Gill SS, Tuteja N. Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant physiology and biochemistry. 2010 Dec 1;48(12):909-30'},{id:"B79",body:'Nadarajah KK. ROS Homeostasis in Abiotic Stress Tolerance in Plants. International Journal of Molecular Sciences. 2020 Jan;21(15):5208'},{id:"B80",body:'Miller GA, Suzuki N, Ciftci-Yilmaz SU, Mittler RO. Reactive oxygen species homeostasis and signalling during drought and salinity stresses. Plant, cell & environment. 2010 Apr;33(4):453-67'},{id:"B81",body:'Apel K, Hirt H. Reactive oxygen species: metabolism, oxidative stress, and signal transduction. Annu. Rev. Plant Biol.. 2004 Jun 2;55:373-99'},{id:"B82",body:'Das K, Roychoudhury A. Reactive oxygen species (ROS) and response of antioxidants as ROS-scavengers during environmental stress in plants. Frontiers in environmental science. 2014 Dec 2;2:53'},{id:"B83",body:'Thorpe GW, Reodica M, Davies MJ, Heeren G, Jarolim S, Pillay B, Breitenbach M, Higgins VJ, Dawes IW. Superoxide radicals have a protective role during H2O2 stress. Molecular biology of the cell. 2013 Sep 15;24(18):2876-84'},{id:"B84",body:'Karuppanapandian T, Moon JC, Kim C, Manoharan K, Kim W. Reactive oxygen species in plants: their generation, signal transduction, and scavenging mechanisms. Australian Journal of Crop Science. 2011 Jun;5(6):709'},{id:"B85",body:'Caverzan A, Casassola A, Brammer SP. Antioxidant responses of wheat plants under stress. Genetics and molecular biology. 2016 Mar;39(1):1-6'},{id:"B86",body:'Choudhury FK, Rivero RM, Blumwald E, Mittler R. Reactive oxygen species, abiotic stress and stress combination. The Plant Journal. 2017 Jun;90(5):856-67'},{id:"B87",body:'Huang H, Ullah F, Zhou DX, Yi M, Zhao Y. Mechanisms of ROS regulation of plant development and stress responses. Frontiers in Plant Science. 2019;10'},{id:"B88",body:'Kaur N, Dhawan M, Sharma I, Pati PK. Interdependency of reactive oxygen species generating and scavenging system in salt sensitive and salt tolerant cultivars of rice. BMC plant biology. 2016 Dec;16(1):1-3'},{id:"B89",body:'You J, Chan Z. ROS regulation during abiotic stress responses in crop plants. Frontiers in plant science. 2015 Dec 8;6:1092'},{id:"B90",body:'Jiménez-Quesada MJ, Traverso JÁ, Alché JD. NADPH oxidase-dependent superoxide production in plant reproductive tissues. Frontiers in Plant Science. 2016 Mar 31;7:359'},{id:"B91",body:'Zhu M, Zhou M, Shabala L, Shabala S. Physiological and molecular mechanisms mediating xylem Na+ loading in barley in the context of salinity stress tolerance. Plant, cell & environment. 2017 Jul;40(7):1009-20'},{id:"B92",body:'Ma L, Zhang H, Sun L, Jiao Y, Zhang G, Miao C, Hao F. NADPH oxidase AtrbohD and AtrbohF function in ROS-dependent regulation of Na+/K+ homeostasis in Arabidopsis under salt stress. Journal of Experimental Botany. 2012 Jan 1;63(1):305-17'},{id:"B93",body:'Ighodaro OM, Akinloye OA. First line defence antioxidants-superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPX): Their fundamental role in the entire antioxidant defence grid. Alexandria journal of medicine. 2018 Dec 1;54(4):287-93'},{id:"B94",body:'Si T, Wang X, Zhao C, Huang M, Cai J, Zhou Q , Dai T, Jiang D. The role of hydrogen peroxide in mediating the mechanical wounding-induced freezing tolerance in wheat. Frontiers in plant science. 2018 Mar 14;9:327'},{id:"B95",body:'Hossain MA, Bhattacharjee S, Armin SM, Qian P, Xin W, Li HY, Burritt DJ, Fujita M, Tran LS. Hydrogen peroxide priming modulates abiotic oxidative stress tolerance: insights from ROS detoxification and scavenging. Frontiers in plant science. 2015 Jun 16;6:420'},{id:"B96",body:'Sharma P, Jha AB, Dubey RS, Pessarakli M. Reactive oxygen species, oxidative damage, and antioxidative defense mechanism in plants under stressful conditions. Journal of botany. 2012;2012'},{id:"B97",body:'Gill, S. S., Khan, N. A., Anjum, N. A., & Tuteja, N. (2011). Amelioration of cadmium stress in crop plants by nutrients management: morphological, physiological and biochemical aspects. Plant Stress, 5(1), 1-23'},{id:"B98",body:'Ahmar S, Gill RA, Jung KH, Faheem A, Qasim MU, Mubeen M, Zhou W. Conventional and Molecular Techniques from Simple Breeding to Speed Breeding in Crop Plants: Recent Advances and Future Outlook. International Journal of Molecular Sciences. 2020 Jan;21(7):2590'},{id:"B99",body:'Govindaraj M, Vetriventhan M, Srinivasan M. Importance of genetic diversity assessment in crop plants and its recent advances: an overview of its analytical perspectives. Genetics research international. 2015;2015'},{id:"B100",body:'Wolter F, Schindele P, Puchta H. Plant breeding at the speed of light: the power of CRISPR/Cas to generate directed genetic diversity at multiple sites. BMC plant biology. 2019 Dec;19(1):1-8'},{id:"B101",body:'Nogué F, Mara K, Collonnier C, Casacuberta JM. Genome engineering and plant breeding: impact on trait discovery and development. Plant cell reports. 2016 Jul 1;35(7):1475-86'},{id:"B102",body:'Collard BC, Mackill DJ. Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences. 2008 Feb 12;363(1491):557-72'},{id:"B103",body:'Ulukapi K, Nasircilar AG. Induced mutation: creating genetic diversity in plants. InGenetic Diversity in Plant Species-Characterization and Conservation 2018 Nov 5. IntechOpen'},{id:"B104",body:'Jain M. Emerging role of metabolic pathways in abiotic stress tolerance. J. Plant Biochem. Physiol. 2013 Jun 15;1(108):10-4172'},{id:"B105",body:'Parmar N, Singh KH, Sharma D, Singh L, Kumar P, Nanjundan J, Khan YJ, Chauhan DK, Thakur AK. Genetic engineering strategies for biotic and abiotic stress tolerance and quality enhancement in horticultural crops: a comprehensive review. 3 Biotech. 2017 Aug 1;7(4):239'},{id:"B106",body:'Vij S, Tyagi AK. Emerging trends in the functional genomics of the abiotic stress response in crop plants. Plant biotechnology journal. 2007 May;5(3):361-80'},{id:"B107",body:'Zhang H, Mittal N, Leamy LJ, Barazani O, Song BH. Back into the wild—Apply untapped genetic diversity of wild relatives for crop improvement. Evolutionary Applications. 2017 Jan;10(1):5-24'},{id:"B108",body:'Stephens J, Barakate A. Gene editing technologies–ZFNs, TALENs, and CRISPR/Cas9.2017: 157-161'},{id:"B109",body:'N, Kaur G, Pati PK. Deciphering Strategies for Salt Stress Tolerance in Rice in the Context of Climate Change. InAdvances in Rice Research for Abiotic Stress Tolerance 2019 Jan 1 (pp. 113-132). Woodhead Publishing'},{id:"B110",body:'Li Q , Sapkota M, van der Knaap E. Perspectives of CRISPR/Cas-mediated cis-engineering in horticulture: unlocking the neglected potential for crop improvement. Horticulture Research. 2020 Mar 15;7(1):1-1'},{id:"B111",body:'Zafar SA, Zaidi SS, Gaba Y, Singla-Pareek SL, Dhankher OP, Li X, Mansoor S, Pareek A. Engineering abiotic stress tolerance via CRISPR/Cas-mediated genome editing. Journal of Experimental Botany. 2020 Jan 7;71(2):470-9'},{id:"B112",body:'Nandy S, Pathak B, Zhao S, Srivastava V. Heat-shock-inducible CRISPR/Cas9 system generates heritable mutations in rice. Plant direct. 2019 May;3(5):e00145'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Deeksha Marothia",address:null,affiliation:'
Department of Biotechnology, Guru Nanak Dev University, Amritsar-143005, Punjab, India
Department of Biotechnology, Guru Nanak Dev University, Amritsar-143005, Punjab, India
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As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 118,000 international scientists and researchers.
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The Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
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1,400 GBP Chapter - Edited Volume
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10,000 GBP Monograph - Long Form
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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