Class-wise distribution of image dataset.
\r\n\tApplied and basic studies - Field studies and lab assays of fungicides can be discussed. We also look for examples of application methods, which may include timing of application, tools for application, fungicide compatibility, phytotoxicity, etc. Field trials have to have at least two years of data;
\r\n\tAdaptation of Integrated Plant Disease Management - How the IPM practice has been adapted in the field. Application of disease risk models, or use of fungicide application aids, which can be hardware or software. The introduction of a new tool for growers can also be included;
\r\n\tNovel fungicides - In addition to the traditional chemical approach, alternative materials (enzymes, oils, extracts, etc.), biological control agents, or plant defense activators can be discussed;
\r\n\tAdaptation of new technologies - Examples will be the use of unmanned vehicles, sensor technologies, advanced sprayers, or disease forecast systems for precision agriculture;
\r\n\tFungicide resistance - Unfortunately, we cannot ignore the fact that fungicide-resistant strains are widespread. Documentation of fungicide-resistant strains, the introduction of new technologies and methods can be discussed.
One of the major functions of brain cells (neurons) is to receive, store, and participate in information retrieval – an important process for the successful daily activities of humans [1-3]. This function of neurons is termed ‘memory’ [1, 4, 5]. The present understanding of memory function is the product of the pioneering work of the German scientist Hermann Ebbinghaus [5]. Research suggests that many factors (both endogenous and exogenous) could affect memory function [6-8]. However, the effect of glucose on memory function remains extremely significant for the following reasons [9-12]. First, glucose is the vital energy substrate for neuronal functions [13, 14]. Second, inadequate level of glucose in the blood has been associated with a decrease in memory function [15]. Third, disorders in glucose metabolism have been related to various aspects of memory disorders [8, 16]. Furthermore, metabolic products of glucose in neurons themselves participate in one or more stages of memory formation [17-20]. Notwithstanding the significant accumulation of research data in last decades on the relationship between glycemiа and neuronal functions [11, 12, 21], the mechanisms of how glucose affect memory functions remains entirely not understood. In this chapter, we shall examine the possible mechanisms and processes involved in the glucose regulation of memory function. We shall elaborate on the effect of glucose on the major processes of memory functions, precisely on the formation and retrieval of “neural data” – memory.
More than 90% of human activities are dependent on higher integrative brain functions – a major subdivision, which is the topic of our discussion in the chapter. The higher integrative brain functions are the driving force during physical work. This is because the brain is the “chief” that directs resources for the successful completion of the task. Successful activities of humans are largely dependent on memory function [22]. This function of neurons becomes vividly indispensable in situations involving its disorder. Memory is that function of neurons that involve storage and retrieval of information [22]. Some researchers have argued “forgetting” as an important aspect of memory function [23, 24]. This is partly because without forgetting, some new information might hardly go into storage. Hence, there are theories of forgetting – the most known ones are the single-trace fragility theory, decay theory, retrieval failure, interference theory, repression, consolidation theory [22]. Generally, several concepts/theories/models/hypotheses have been used to explain memory function of neurons [22, 25-27]. However, with steady scientific progress it is becoming clearer that none of these gives a complete, and precise definition of memory. In this regard, we shall also discuss briefly on the modern concepts of memory function of neurons in relation to cerebral glucose metabolism.
Several factors affect memory functions, and they can either be endogenous or exogenous. Generally, the widely known substances/factors include narcotics, some prescription drugs, alcohol, some biomolecules (most notably glucose, fatty acids, amino acids), environmental factors, genetic and epigenetic factors [6-8, 21, 28]. Among the biomolecules that affect memory formation and retrieval, glucose is widely known and well-studied molecule. Glucose is the main substrate for memory formation and retrieval. Glucose not only provides the energy for memory formation and retrieval, but also, is involved in providing the necessary subunits or components for the formation of various neural components of the “neural data” – memory [9, 11, 12, 14, 21, 29, 30].
Decades of research have shown that a change in the glycemic level leads to a corresponding change in memory function of the brain [21, 29-41]. For example, decrease in blood glucose below the set point is reported to negatively affect memory function [9, 21, 29, 30]. Glycemia affect both memory formation and retrieval [9, 29].
Results of several studies have observed an inverted-U shaped dose-response relationship between glucose load and memory [31-34]. Recent study has shown that the optimum dose of glucose memory enhancement may differ under conditions of depleted glucose resources, and has other peculiarities [21].
Several controversies in the glucose memory facilitation effect remain. While some previous studies reported a “no effect relationship” between glucose and memory function [35, 36], others confirm this dose-response relationship [9, 31, 37, 38]. Researchers have suggested that this relationship is extremely dependent on the type of cognitive/memory task [39, 40]. Modulating factors of the glucose memory facilitation effect include physiological state (body mass index etc.), glucose dose, types of cognitive tasks used and cognitive demand [9, 39]. These factors are the possible sources of variance in the glucose facilitation of memory. Owen and colleagues (2008) investigated the dose response relationship of the glucose memory facilitation effect at glucose dosages of 0, 15, 25, 50 and 60 g [9]. They also examined the interactions between length of fasting interval (2 hours versus 12 hours) and the optimum dose of glucose. Their results revealed glucose facilitation of spatial working memory and verbal declarative memory following 25 g glucose. Furthermore, they observed that glucose memory facilitation effect is dependent on the following: the greater the length of fasting, the greater the glucose dose needed to facilitate memory [9]. So, at overnight fast (approximately 12 hours) the higher dose of glucose (i.e. 60 g) was needed to facilitate memory, whereas the lower dose (25 g) enhanced working memory performance following a 2 hour fast [9].
Comprehensive model of glucose memory facilitation
The mechanisms responsible for memory formation and retrieval are in constant perturbations of several factors (which might be competing factors, endogenous or exogenous in nature). The processes and mechanisms that ensure memory formation are the synthesis and activity of neurotransmitters (dopamine, d-serine, glutamate, acetylcholine etc), and receptor subunit systems; metabolic signaling pathways; LTP/LTD (long-term potentiation/long-term depression); genetic and epigenetic modifications. (Memory retrieval might involve the same systems and processes, but with different mechanisms). Both memory formation and retrieval involve other brain functions, including attention. The systems and processes earlier stated are affected by cerebral glucose, which can serve as a substrate or produce intermediate substrates for some stages of their syntheses. The cerebral glucose content is dependent on the plasma glucose, both of which are under constant regulation by the brain (hypothalamus), some internal organs (liver, kidney). The blood glucose is constantly regulated, also by the effect of the neuro-endocrine control on the gastrointestinal tract, organs (such as the liver and kidney), as well as the effect of the hypothalamus on these organs. The processes that are regulated in these organs by the higher regulatory centres (e.g. hypothalamus) are food intake, gluconeogenesis, glycogenolysis, glucose cycling – to ensure normal glycemic allostasis. These higher control centres, and the memory function are under constant pressure from modulating factors such as exogenous (e.g. environmental, ethanol), endogenous (ethanol, some physiological indices) – might affect the resultant effect of glucose on memory function. Alcohol actions [42-45] as represented on the model are one of a bi-directional effect of summation, meaning that alcohol affects memory, as well as glucose regulatory systems. The receptor systems of the brain could be modulated by both alcohol and glucose [46, 47]. Alcohol is a psychotic substance in widespread usage in the world. Importantly, this substance is also produced in vivo during biochemical reactions in an organism (including humans). In certain circumstances (varying physiological state, for instance during pregnancy, disease states), the level of endogenous ethanol produced significantly increases. This increase might have a protective effect, but the reason or mechanism on the general role of the increase in endogenous concentration ethanol is not fully known. Ethanol affects some neurotransmitters and receptor systems. Ethanol acts on ionotropic, metabotropic G-protein receptor, potassium ion channels [48-50]. Ethanol acts on metabotropic receptors of mGluR5, mGluR2/3, mGluR1 [51-53]. These metabotropic receptors (mGluR3 of the prefrontal cortex) have been also implicated in cognitive disorders in especially alcoholics [54]. mGluR5 and mGluR1 receptors have been recently implicated in cognition [53]. Ethanol causes hypoglycemia [43, 55]. Besides, it is reported that alcohol causes disorders in the expression of several genes, although the mechanisms remain not quite clear [56].
Glucose plays a pivotal role in memory and might enhance LTP/LTD [57] as hypoglycemia is associated with deficits in memory, and learning [58, 59]. Apart from producing ATP for neural energy, other substances may be synthesized from glucose that affects neuronal activity and functions (including memory) [60-62]. For example, it is known that d-serine (maybe synthesized from glucose molecule) affects LTP, synaptic plasticity, enhance information retrieval [60-64]. Hypoglycemia is associated with both d-serine and NO release aimed at enhancing LTP [58]. These substances can also regulate neuronal transcription factors [65]. A vast number of these signaling pathways, neurotransmitter and receptor systems, and are dependent on the activity level of neurons, and activity dependent transcriptions – activators and suppressor [66, 67]. Other brain cells (especially astrocytes) can modulate neuronal activity through various mechanisms, involving NMDA, d-serine, Ca2+, ATP, glutamate. Hence, these brain cells, which are affected by ethanol, might exert their resultant effect on neurons through astroglial linkages [68, 69].
While several studies have noted that glucose is a critical factor for memory function, what is not exactly clear is whether the effect is a direct or indirect one. In this section, we shall be mainly concerned with the mechanisms and processes of how glucose affects memory. Pertinent literature and latest developments in the field will be reviewed. It will be necessary to have in mind that memory function (formation and retrieval of neural data) is overlapped or is connected with other brain functions such as perception, attention etc. Therefore, glucose is a vital regulating factor for other brain functions. We shall consider the various views, concepts and models of how glucose affects memory function, and provide a comprehensive model of glucose memory facilitation effect (Figure 1).
Smith and colleagues (2011) suggested a conceptual model of glucose facilitation of memory. Their neurocognitive model stipulates that glucose or acute stress/emotional arousal increases the concentration of circulating glucose in the periphery, and subsequently, the central nervous system. This increase in glucose exerts its effects on insulin, acetylcholine (Ach) synthesis and/or KATP channel function which subsequently leads to memory enhancement. Research has confirmed that there is specific cognitive domain that is most amenable to the glucose memory facilitation effect. The domain is episodic memory [41].
Memory formation or retrieval involves the synthesis of many biomolecules related to glucose metabolism [41, 70-73]. Glucose memory facilitation effect is a complex phenomenon comprising of several players including organs/systems of glucose metabolism, several competing factors, both genetic and epigenetic [42, 46, 72, 74]. Based on available data, here we propose a comprehensive model of glucose memory facilitation.
Several neurotransmitter systems have been implicated in memory function. Here, we shall briefly consider a few of the principal neurotransmitter systems involved in memory function. The literatures report significant role of dopaminergic, glutamatergic, serotonergic, cholinergic, and noradrenergic systems in memory function [75-78]. We shall consider d-serine involvement in memory formation owing to the fact that its main receptor – the NMDA receptor is one of the key receptors involved in long-term memory formation (as a result of its long-term potentiation effect). Long-term potentiation, as opposed to long-term depression is an integral process necessary for memory formation (especially long-term memory) [68, 69]. In fact, the NMDA receptor itself is implicated as one of the “alcohol receptors” [79]. Therefore, bi-directional effect of summation might occur through alcohol effect on neurotransmitter receptor systems, and glucose metabolism. The resultant effect is aggravation of memory dysfunction.
Since glucose is a metabolic product or must be involved in the cell’s metabolic pathways before its usefulness is realized; therefore, it is necessary to assume that metabolic pathways, involving glucose molecule are those pathways crucial for memory formation or retrieval. Unfortunately, research in this aspect is scanty. A number of signaling pathways are involved in glucose metabolism, but there is no sufficient evidence on how they are associated with memory function [80]. The widely studied signaling pathways that have a relationship between glucose metabolism and memory functions [81, 82] include CREB pathway [83, 84], AMPK [85, 86], Notch signaling [87], mTOR pathway [88] etc. The mTOR pathway has been majorly implicated in both glucose and memory function. Importantly, it was reported that glucose specifically affects memory through this pathway [84, 88, 89].
The enhancement of memory by glucose might be related partly to the functions of activity dependent genes [90, 91], as well as epigenetic modifications (DNA methylation and histone modifications) by glucose or its metabolites [10, 91-94].
Since epigenetic profile of the cells play crucial role in glucose metabolism and neuronal cell functions, here, we would suggest that the initial epigenetic data (program) of the involved cells responsible for glucose memory facilitation are partly important for the differences reported in the literature. Epigenetic mechanisms of glucose metabolism and memory functions are regulated by the activity of transcription factors [10, 95]. Due to the importance of glucose in the functioning of the CNS [96], this regulation may be modulated by glucose molecule itself. For example, the data of Li et al. (2010) indicate that glucose regulates gene transcription in the liver by increasing the level of ATP, hence inhibiting AMP-activated protein kinase and inducing hepatocyte nuclear factor 4alpha to stimulate cytochrome P450 7A1 gene transcription. Glucose also increases histone acetylation and decreases H3K9 methylation in the cytochrome P450 7A1 chromatin [97].
Recent experiments show that glucose is involved in the regulation of functions even at the progenitor cell level. Metabolism-sensing factors have recently been implicated in the regulation of neural stem cell fate through epigenetics modification [92, 98]. Hayakawa et al. (2013) reported that in embryonic stem cell population, glucose metabolite induces switching from the inactive state by Ogt-Sirt1 to the active state by Mgea5, p300, and CBP at the Hcrt gene locus [92]. The many pathways of glucose metabolism allows for the inclusion of its metabolic products into numerous cellular activities. For example, substrates of glucose metabolic pathways (acetyl-CoA, ATP, NAD+, glutamine, UDP-N-acetyl-glucosamine, N-acetyl-D-mannosamine etc.) are candidates of epigenetic modifications. Acetyl-CoA is a donor of histone acetylation. NAD+regulates Sirt1, a member of the sirtuin family, which functions as histone deacetylase and is also a metabolic sensor [92] (for review see Hayakawa et al. 2013). Epigenetic regulation by glucose or its metabolites affects memory functions and glucose metabolism itself through a shift in the cellular concentrations of critical metabolites implicated in higher integrative brain functions and metabolism.
A key mechanism for this epigenetic regulation is executed by the peripheral circadian oscillation [99]. However, importantly the peripheral clock and the central one could have some kind of metabolic associations. The concentration of NAD+/NADH plays critical link between metabolism and circadian rhythm [99]. Glucose and other metabolic substances may modulate the circadian rhythm by fluctuations in NAD+/NADH ratio. Compelling evidences now indicate that circadian misalignment could cause serious metabolic problems. In fact, transgenerational inheritance in metabolic alterations could be related to some mechanisms of epigenetic origin modulated by circadian clocks. Methylation of the leptin gene is associated with impaired glucose tolerance in the period of gestation [100]. This and many other discoveries on transgenerational inheritance represent substantial contribution to understanding the pathogenesis of diabetes, obesity in children [100-102].
Epigenetic regulations are not only affected by metabolites, but also body mass index, intrauterine environment, exercise, and other environmental factors [101].
It might be possible that epigenetic dysregulation of cerebral glucose metabolism is the result of cognitive impairment since glucose metabolism is controlled by epigenetic mechanisms and is also associated with cognition. Emerging evidences indicate that metabolic regulation (through epigenetic mechanisms) might be involved in memory function disorders. Reports show that a major pathogenesis of the CNS disorder such as Alzheimer\'s disease involves metabolic alterations, especially in glucose metabolism and associated hormonal or peptide signaling. Metabolic disorders in CNS pathologies are associated with brain insulin signaling. For example, a substantial quantity of insulin receptors is located in the hippocampus (a brain region which is basically concerned with the acquisition, consolidation and recall of new information) [103]. Impaired brain insulin signaling is implicated in cognitive impairment. Moreover, cognitive impairment is associated with diabetes and obesity, which are metabolic disorders [104]. De la Monte (2009) reported that in the initial stage of Alzheimer\'s disease, cerebral glucose metabolism is reduced by 45% and cerebral blood flow approximately by 18% [104]. Earlier, Arnáiz et al. (2001) reported that among twenty patients with mild cognitive impairment, impaired cerebral glucose metabolism and cognitive functioning were able to predict deterioration in mild cognitive impairment [105]. Mild cognitive impairment is an important indicator of the development of Alzheimer\'s disease. Notably, impairment in cerebral glucose metabolism was even a better predictor (75%) compared to neurospcyhological tests (65%) widely used in the assessment of cognitive impairment [105]. The authors further concluded that measures of temporoparietal cerebral metabolism and visuospatial function may aid in predicting the evolution to Alzheimer\'s disease for patients with mild cognitive impairment [105].
These data are very important especially when we consider the increasing prevalence of cognitive disorders. For instance, it is estimated that in 2030 years, the cases of Alzheimer\'s disease in relation to 2012 will double (35.6 million). No doubts, research in this direction is exceedingly necessary [106]. Previously other authors have also reported that impairment in cerebral glucose metabolism is associated with decline in cognition and memory functions. Schapiro et al (1988) studied the rate of cerebral metabolism for glucose with positron emission tomography and [18F]2-fluoro-2-deoxy-D-glucose in a 47 year-old man with trisomy 21 Down\'s syndrome and Alzheimer related dementia, and reported poorer general intelligence, visuospatial ability, language, and memory function compared with younger (19-33 years) patients with Down\'s syndrome [107]. Cerebral metabolism for glucose in the older patient was 28% less than in the younger patients. Besides, hypometabolism was reported in the parietal and temporal lobes of the brain cortices. Importantly, the study of Schapiro et al (1988) was probably one of the most comprehensive investigations to show the association between different diseases involving CNS disorder and their relationship with cerebral glucose metabolism [107]. Approximately a decade after Schapiro et al.\'s (1988) work [107], Pietrini et al. (1997) reported another predictor method for Alzheimer\'s disease risk prior to dementia in patients with Down\'s syndrome who were above 40 years (mean of 50 years) of age [108]. Pietrini, et al. (1997) confirmed their hypothesis that despite normal cerebral glucose metabolism at rest, an audiovisual stimulation (was used as a stress test) revealed abnormalities in cerebral glucose metabolism before the development of dementia in the parietal and temporal cortices which represent most vulnerable regions to Alzheimer\'s disease [108].
These CNS pathologies are now believed to be regulated by epigenetic mechanisms [109] and could have pretty good correlations with epigenetic mechanisms of cerebral glucose metabolism. Other CNS pathologies involving cognitive impairments such as epilepsy [110], schizophrenia [111, 112], Parkinson\'s disease [113], multiple sclerosis [114] had been associated with disturbances in glucose metabolism.
Interacting system (comprising of memory function, error monitoring and processing system, and modulators) of the reciprocability of neural systems of memory and the error monitoring and processing system. The modulators between the two reciprocals are glucose, other endogenous and exogenous substances/factors. N/B: Glucose can be an endogenous, as well as an exogenous factor; exogenous sources include per os administration of glucose, etc.; endogenous sources include gluconeogenetic production of glucose molecules, etc.
Our data and those of other authors show strong negative relations between glycemia and error commission. Whether this is due to the effect of glucose on memory or neural systems of error commission, is what is not exactly clear (see figure 2). There are no precise borders between the brain regions responsible for memory and error commission. Therefore, it is possible that the effect of glucose on error commission could be the resultant effect on the chief brain regions for memory function. Neural systems (or regions) of memory implicated in error commission have been linked to brain regions also involved in some aspects of memory function [115-117]. The brain systems concerned with error commission are referred to the error monitoring and processing system. The major regions of the brain concerned with error commission are the anterior cingulate cortex, basal ganglia, prefrontal cortex. These brain regions (especially the prefrontal cortex) are also implicated in memory function [45, 115, 117].
Alcohol is the most prevalent psychotic substance in the world. While alcohol affects glucose metabolism, memory also remains one of the most vulnerable functions of the brain that suffers from the negative effect of alcohol use [15, 16, 29, 30, 44, 45, 118]. Hence, there is the need to examine its effect on memory function and glucose regulatory mechanisms. Here, we view alcohol as a positive modulating factor for memory (especially at endogenous concentration), and as a psychopathological substance at blood concentrations higher than the normal physiological level.
Glucose is the foremost energy substrate for neuronal functions (memory). It provides the energy bonds needed for the formation of memory and takes part in information retrieval from neural stores. Both glucose and its metabolites are involved in different stages of memory formation and retrieval. Several factors such as ethanol, some physiological indices, and other competing factors modulate the effect of glucose on memory function.
Rust diseases are the fungal diseases of plants, mainly grasses, caused by fungi. They affect the aerial plant parts especially leaves but can also attack stems and even flowers and fruits. They bear complex life cycles that require two alternative unrelated hosts. Rusts produce spore pustules which vary in color according to the rust species. About 7000 rust species are known to affect a variety of host plants globally. They can cause a wide range of symptoms depending upon the host species like the formation of Galls or swellings on the branches, formation of Canker on the trunks, and formation of Spores on the surface of the leaf. Leaf rust is also known as bown rust due to the brown color of circular urediniospores on the surfaces of the leaf of the crop. Yellow rust or stripe rust is characterized by the yellow color of stripes on the surfaces of the leaf. Stem rust is also brown and characterized by the patches of brown color on the surface of stems. Many approaches are being deployed to combat the problem of these diseases which involves accurate phenotyping which means characterization of the diseases at field level followed by genotyping to find out the genes responsible for its cause. Many germplasm resources are being explored and screened by scientists worldwide to find new sources of resistance. Precision phenotyping is the key requirement to achieve the goals. So far there are manual interventions involved to screen these diseases. But manual scoring of these diseases is a cumbersome job in large pre-breeding and breeding programs. Therefore, there is a strong need for high precision phenomics which involves imaging using high-quality cameras or equipment followed by image analysis using newly developed software and tools. In today’s era of artificial intelligence, it is possible to explore high-end phenomics to achieve better yields of important crops like wheat. Many machine learning and deep learning models have been tested and tried to analyze and characterize wheat fungal diseases [1, 2, 3, 4]. One of the main reasons for the popularity of these techniques is the use of GPUs (graphics processing units). The classification tools, computer vision, and GPUs are combined in a single framework called deep learning [5]. Deep learning-based models have been used in the various applications of agriculture for end-to-end learning. With the use of GPUs, deep learning can give a better solution to the given problem in a shorter time [6]. The process of building such models is computationally challenging but using GPU power becomes very easy [7, 8]. Fungal diseases have been identified using image processing techniques on different horticulture and agriculture crops. Various feature extraction and classification algorithm have been used to detect the different types of fruits, vegetables, and cereal crops.
Among the various rust diseases, soybean-, coffee-, and wheat-rusts are the most damaging diseases. Therefore, the constant efforts are being done worldwide, to combat this problem. Wheat is one of the staple food crops in addition to rice and maize. The total area under wheat in the world is around 220 million hectares with a production of 772.64 million metric tons (2020–2021). Wheat rusts especially leaf rust, stem rust, and yellow rusts are major fungal diseases that affect the production of the wheat crop throughout the world particularly in South Asian countries [9]. As per the prediction of the Food and Agricultural Organization (FAO) of the United Nations, wheat production might not be fulfilled the requirement in near future due to rapid population growth [10, 11]. In this chapter we discuss the usefulness of deep learning-based algorithms to identify rust using wheat as a case study.
Human perception is based on the interaction between the brain and the eye. On the other hand, computer vision system (CVS) is used to emulate human vision for gathering information without physical interaction [12, 13].
It is also defined as the process of automatic acquisition, and analysis from image data. CVS emulates the dynamic vision system whose operation is very transparent and natural. The data is processed in various stages such as capturing, processing, and analysis of images. Figure 1 depicts the steps involved during image processing. In the first stage, image acquisition and pre-processing are involved. The images can be acquired using high-resolution cameras and sensors. Further, the images are pre-processed through data cleaning, background removing, adding/removing noise, and also enhancing the quality of images. In the second stage, the images are segmented. The segmentation process involves extracting only important and useful information from the whole image that further helps in the discrimination of classes. In the third stage, the high-level analysis is performed in which direct emphasis is done on the recognition (objects) and interpretation (making results). In a CVS, the following attributes contribute to decision-making: shape, color, texture, and also size. Figure 2 depicts the utilization of various artificial intelligence algorithms in plant disease detection. These algorithms are further divided into machine learning and deep learning-based classifiers. The description of these algorithms is illustrated in the coming subsections.
Steps of image processing techniques.
Description of machine learning and deep learning algorithm used for plant disease detection.
Classification is the process of dividing the dataset into different categories or groups by adding labels. Nowadays, the machine learning and deep learning approaches are performing well for classifying the algorithm images based on their category. Following are the machine learning algorithms which are used to classify plant disease and are based on supervised learning. Supervised learning is a type of learning where labels (category of images) are given along with input images.
It is the machine learning algorithm used for classification and calculated by
It is the algorithm of machine learning which comes under supervised learning to solve regression and classification-based problems. The decision tree is the graphical representation of pre-defined rules along with the solution. The graph of the decision tree has two types of nodes: one is decision nodes and another is leaf nodes. Additionally, the edges store the information of the answers to the questions, and leaf nodes store the actual output. In Sabrol and Kumar [16], Chopda et al. [17] and Rajesh et al. [18], the authors reported appreciable results in plant disease classification and recognition.
Support vector machine (SVM) is a very popular classifier used in statistical learning. The classifier aims to discriminate the classes from each other. In SVM, a hyperplane is used to discriminate one class from another. Those points which are close to the hyperplane are referred to as support vectors. The task of the SVM is to classify the different categories based on some features. Additionally, this algorithm performs well in extreme classes. Let us consider, color, texture, shape are some features of a particular plant. If we consider two features such as color and texture to classify diseased and healthy leaves. To classify them, the optimal decision boundary is required. Optimal decision boundaries could result in greater misclassification for the new instance. Therefore, the boundary support vectors are very important than all the training examples. This algorithm works well for linearly separating data points whereas in some cases if the data points are not linearly separable then 2-dimensional (2D) feature spaces are converted into 3-dimensional feature spaces. But the only problem is that it is computationally very expensive. In addition to that, it provides kernel function which can reduce the computational cost to convert 2D feature space to 3-dimensional feature space. Using kernel function the dot product is performed between two vectors. Especially, this is used to transform non-linear to linear transformation space. Various popular kernel functions are polynomial, radial basis, sigmoid kernels used to change 2D data to high dimensional feature space. Choosing the best kernel is a non-trivial task and is a hyper-parameter that can be selected by performing various experiments on the data. The main benefit of using SVM is that it is memory efficient and effective for high-dimensional feature space data.
It is the special type of machine learning algorithm used for classification. The researchers have been working on artificial neural networks (ANNs) since the beginning of the 1980s [19]. ANNs are a special type of classification algorithm and their structure is inspired by the human brain. ANNs takes input from the external world in the form of feature vector or patterns. Each input value is multiplied by their corresponding weights that are summing with the bias value. Further, the result is mapped to the activation function (binary, sigmoid) and produced the output. Other than these algorithms, there are various algorithms available that reported appreciable results in image recognition such as Random Forest, Naive Bayes, many more. Initially, we started with the study of traditional computer vision approaches used for plant disease detection. Plant disease can be caused by fungi, bacteria, and viruses from which fungi are the common disease organism. It is the type of disease that can be formed by taking energy from plants. The fungal disease has been identified using image processing techniques on different horticulture/agriculture crops [20]. To detect the different types of fruits, vegetable, commercial, and cereal crops that have been utilized using various feature extraction and classification algorithms. They achieved appreciable classification accuracy to identify the disease from horticulture/agriculture crops. Han et al. proposed a novel technique for feature extraction using super-pixel and marker-controlled segmentation methods for the classification of yellow rust and septoria diseases. They have used SVM and ANN for these disease classifications. Their experimentation concludes that SVM classifiers outperformed well than ANN classifiers for the classification of disease [21]. Su et al. experimented with the detection of fungal yellow rust disease on wheat crops. The author collected RGB images with a high-resolution camera and there are a total of three different classes present in region of interest (RoI) as rust, healthy, and background. To monitor the yellow rust, they used the U-Net deep learning architecture and the results were compared with the Random Forest algorithm. They found that U-Net-based segmentation outperformed spectral images. In their work, the average precision of 81.06%, recall of 90.10%, and F1-score of 84.00% have been achieved to segment the disease from spectral images [22]. An application of Fuzzy C-Means clustering has been proposed as the model to identify the wheat leaf disease [23]. In their work, they extracted inter- and intra-class features and further combined them to build a model for identifying the different wheat plant diseases. Although the traditional machine learning-based techniques are performing well for image classification, still there are certain limitations such as it requires manual feature extraction and is only suitable for small datasets, which may lead to the over-fitting problem [23, 24].
Convolutional neural network (CNN) is a popular neural network, designed for solving computer vision problems. The architecture of CNNs is shown in Figure 3. The images are represented in the form of pixel values. In the convolution layers, the operation of convolution is performed i.e., the kernel is slide over the input image after choosing the padding and stride values at each layer. Thereafter, the power of non-linearity is to give the non-linear mapping with the input images in such a way that after the non-linear mapping it becomes linearly separable. ReLU activation function is used to change all the negative values to positive values. With this, the pooling layer is used to down sample the different feature maps for getting the most prominent features i.e., the convolution layer performs these triplet operations like convolution followed by ReLU and ReLU followed by pooling one after another. These triplets operations are typically stacked one after another and also based on these triplets, the depth of the neural network has been defined. After these layers, the network is followed by one or more fully connected layers which are responsible for classification.
The basic architecture of CNNs.
To build the CNN model, all the above-mentioned parameters play a very important role. To build the custom CNN model, the numbers of convolution layers, max-pool layer, number of filter values, filter size, stride, padding, number of fully connected layers need to be specified. Increasing the number of convolution layers will produce different feature maps and also increasing the fully-connected layers increase the training time of the model. Although, the custom CNN model reported appreciable accuracy. The process of creating a custom CNN model takes more time. Therefore, the concept of transfer learning comes into the picture. Transfer learning is a concept of deep learning where the weights of pre-trained models are reused for a new problem. Every year, there is a competition held on the ImageNet dataset. Many researchers developed new models to classify the different objects of the ImageNet dataset and reported good classification accuracy and reduced error rate. There are variants of transfer learning models such as ResNet, GoogleNet, and EfficientNet varied in terms of the number of layers, filter size, number of filters used, stride, padding, and so on. Some of the few models are elaborated as given below:
Modern deep learning architectures are significantly popular to solve agriculture-related problems. Sladojevic et al. developed a CNNs based model for plant disease classification. The model recognized 13 different types of plants. In their work, they used 30,880 images in the training and 2589 images for validation and reported a classification accuracy of 96.30% [25]. Zhang et al. proposed a deep learning model for the detection of rust disease of wheat crop from hyperspectral images. In their work, they automate the process of detecting yellow rust-captured images from unmanned aerial vehicle (UAV). Yellow rust is a fungal disease that can cause 100% loss for the wheat crop. The author used the Inception-ResNet model for feature extraction and reported the highest accuracy of 85.00% when compared with the random forest that was 77.00% [26]. A deep learning model has been built for grading wheat stripe rust disease [27]. In their work, they used different mobile devices to capture images and build their dataset, referred WSRgrading. It contained 5242 wheat leaf images at six different levels. They build and proposed the model by adding an attention layer in the pre-trained DenseNet model and build a new model named as C-DenseNet which has been reported a good classification accuracy of 97.99%. Genaev et al. classify the rust disease from the wheat crop. In their work, they used the CGIAR dataset, containing three classes (healthy wheat, leaf rust, and stem rust). They implemented the DenseNet transfer learning model and reported the F1-score and AUC of 0.90 and 0.98, respectively [28]. Jia et al. in proposed the model for detection and segmentation of fruit features for optimal harvesting of apples using Mask R-CNN. ResNet model was used as the backbone of this network. The model was tested on 120 images and reported precision and recall rates of 97.31% and 95.70%, respectively [29]. The shortage of the wheat disease dataset motivated the researchers to create the dataset which should be publicly available for all [30]. They are motivated to collect more data that will help the research community for conducting the research competitions on wheat diseases classification. Finally, they attempted to prepare their WFD2020 dataset which contains 2414 images. They performed their experiments using the EfficientNet CNN-based model and reported 94.20% classification accuracy.
In the recent decade, deep learning techniques are highly utilized for image processing. Deep learning models are producing appreciable results than machine learning methods [31]. Figure 4 depicts the utilization of computer vision approaches (i.e., old machine learning methods and modern deep learning approaches) for the wheat crop. These statistics have been built based on work done from the period (2015 to July 2021) for classifying most of the wheat crop diseases. Deep learning approaches include CNN-based architecture such as VGG16, ResNet, Faster R-CNN, and so on. In different circumstances, the traditional machine learning approaches include SVM, Random Forest, and so on. The analysis concludes that the modern deep learning architectures have been utilized more for classifying most of the wheat crops diseases as compared to traditional machine learning approaches.
Year-wise statistics publication of wheat disease detection.
There are standard datasets that are publicly available for research experimentation in the computer vision and image processing domain, such as PASCAL VOC [32], ImageNet [33], IMDB-Wiki [34], CIFAR [35], and PlantVillage [36]. CGIAR dataset is one of the dataset publicly available on https://www.kaggle.com/shadabhussain/cgiar-computer-vision-for-crop-disease [37]. This dataset was further distributed in three different classes of wheat rust i.e., healthy wheat, leaf rust, and stem rust. A sample of each class is shown in Figure 5. Most of the images in this dataset were collected by CIMMYT and its partners from Ethiopia and Tanzania. Additionally, a few images were sourced from the Google image database. The images in this dataset have the specific characteristics like (i) all are colored (ii) mixed format, (iii) different orientation, (iv) variable quality, and captured with different resolutions. The datasets are already classified into two categories i.e., 876 images and 610 images for training and testing, respectively. From the training dataset (i.e., 876 images) a total of 863 images have been filtered and considered for training the model. In the present study, the 863 images dataset was further split for training and validation in the ratio of 3:1 (i.e., 75% data in training and 25% into validation). Table 1 describes the class-wise distribution of this dataset. It is a challenging task to build an efficient model that is capable to classify all three classes of images accurately.
Sampled images of (a) healthy wheat plant, (b) leaf rust, and (c) stem rust.
Class label | Images | Training set | Validation set |
---|---|---|---|
Healthy wheat | 142 | 105 | 36 |
Leaf rust | 345 | 258 | 86 |
Stem rust | 376 | 283 | 95 |
Total images | 863 | 646 | 217 |
Class-wise distribution of image dataset.
Deep learning is a popular methodology used for image processing. In deep learning models, features are extracted automatically and little human intervention is required to train the model. Deep learning models are quite efficient to discover the internal structure or patterns of high-dimensional data. However, directly processing the original images leads to inappropriate recognition results, therefore, it is necessary to pre-process the images before feeding them to the model. Pre-processing involves e.g. resizing, enhancing, or removing noise of the input images. It is worth mentioning that CNNs perform better for image recognition and classification. There are various transfer learning models which are based on CNNs like AlexNet, VGG16, GoogleNet, and Inception V3, that are pre-trained on the ImageNet dataset. ImageNet is the standard dataset that contains 1000 different categories of objects. CNN’s based transfer learning models reported appreciable results to classify 1000 different objects present in the ImageNet dataset. In the present study, the VGG16 model has been utilized and the architecture is depicted in Figure 6. This model is the composition of 16-layers (13 convolution layers, and 3 fully connected layers). In this model, the images are processed in standard size i.e., 224 × 224. The reason for resizing the fixed image size is to extract the uniform or equal feature maps at the end of the convolution process. This model used a fixed size of kernel i.e., 3 × 3. Sometimes, the kernel is referred to as a filter that is responsible for extracting features from the given images. These extracted patterns or features might be horizontal edges, vertical edges, and a combination of both. Initially, a convolution process has been performed to extract the features, and thereafter the classification is done. In the convolution operation, the kernel/filter is sliding over the image starting from the top left to the bottom right corner to extract the features.
The architecture of VGG16 for wheat rust disease detection.
The movement of the kernel is either pixel-wise or by skipping some pixels using stride values. If the stride value is 1 then the movement of the kernel is shifted by one pixel after another and if the stride value is 2, then the movement of the kernel is shifted by two-pixel values during the operation of convolution. The convolution layers are used to identify the pattern or features from the images which further help in discriminating the classes. The initial layers extract the general features like edges and the subsequent layers extract the domain-specific features. Each convolution block is followed by the max-pool layer which is used for down-sampling the feature maps. In this process, the dimensionality of the image is reduced by retaining the most prominent feature. At the end of the convolution layers, different feature maps are generated as an output. These feature maps are further flattened and mapped with a fully connected layer in the classification module. Here, the model has a feature vector of size 4096 neurons also referred to as dense layer. This feature vector is further passed to the next dense layer of the same size. Finally, the last layer neurons are fully connected to the output neurons by using the soft-max activation function. However, in the current study, we considered the three classes classification problem. Therefore, the output layer changed to three classes using the soft-max probability function. The actual learning starts from data using forward and backward passes. In the forward pass, input neurons are multiplied with the weight values and also apply the activation function as ReLU. ReLU activation function adds non-linearity to the model i.e., all the negative pixel-values become positive after passing through it. On the other hand, in backward pass back-propagation is used to minimize the loss value. In this process, weights and biases are getting updated from the last to the initial layer by calculating the gradients at each layer using a convolution operator.
To summarize this model, the important and noticeable point is that this model has a total of 14,789,955 parameters but 75,267 are trainable parameters and the rest are non-trainable, the reason is that using transfer learning, the already trained weights have been used during building the model. Therefore, the model is trained in less time with fewer number parameters.
Hyper-parameter tuning is the backbone of any deep learning model. Finding the best parameters is a very tedious task, it needs many experiments to be performed while building the model. Hyper-parameters include learning rate, batch size, loss function, number of epochs, and optimizer is usually considered for tuning the model. To build the classification model for three classes each hyper-parameter is considered within a specific range. In this way, several experiments have been performed to build an efficient model. After performing some experiments with the variation in the given hyper-parameters, it was concluded that model accuracy is highly dependent on the batch size, learning rate, number of epochs, and size of the dataset. In the present study, the following hyper-parameters has been utilized:
As discussed in Section 3.1 image dataset of wheat disease classification has been utilized to train the model. We used the online google colab platform with GPU support. Among the performed experiments, we discuss the best one, which produces the highest training accuracy. Table 2 illustrates the training and validation accuracies obtained at different epochs (varied from 10 to 90) along with their loss values. Here, the training accuracy starts with 81.42% on 10 epochs and ends up with 99.54% on 80 epochs. We continued to compute the accuracy for the 90 epochs also but did not get any significant improvement in training accuracy. Although more experiments could be performed by increasing the number of epochs, the accuracy obtained at epoch 80 was quite promising. On the other hand, the validation accuracy fluctuating between 74.76% and 79.05% at different epochs, as shown in Figure 7. Similarly, it was observed that the training loss decreases at every increasing step of the epoch (from 10 to 80). Beyond that, the loss has started to increase. In contrast, the validation loss is fluctuating between 0.60 and 0.65 up to 40 epochs. Then, after 70 epochs it starts increasing rapidly (Figure 8).
Epochs | Training accuracy (in %) | Validation accuracy (in %) | Training loss | Validation loss |
---|---|---|---|---|
10 | 81.42 | 74.76 | 0.50 | 0.65 |
20 | 91.02 | 79.05 | 0.33 | 0.61 |
30 | 95.05 | 77.14 | 0.22 | 0.61 |
40 | 96.59 | 78.10 | 0.18 | 0.61 |
50 | 97.06 | 76.67 | 0.15 | 0.56 |
60 | 97.99 | 78.10 | 0.12 | 0.56 |
70 | 98.61 | 74.29 | 0.09 | 0.66 |
80 | 99.54 | 77.14 | 0.07 | 0.74 |
90 | 99.23 | 74.76 | 0.08 | 0.82 |
Comparison of training accuracy, validation accuracy, and training loss, and validation loss at different epochs.
Representation of the comparison of training and validation accuracy.
Representation of the comparison of training and validation loss.
To test the performance of the trained model, we performed the test experiments on the validation data (i.e., 25% of the total dataset). In this way, a total of 36 sample images of healthy leaf, 87 sample images of leaf rust, and 94 sample images of stem rust have been considered. The evaluation of the testing results was done using a confusion matrix. Figure 9 illustrates the accuracy and confusion with other intra-classes, wherein, it is shown that leaf rust class samples are confused with stem rust class samples due to less variation between classes.
Confusion matrix at epoch = 80.
To summarize this book chapter, different machine learning and deep learning-based models have been discussed to solve plant disease classification and detection problems. Considering a case study of wheat rust diseases, a deep learning-based model is proposed to classify the different wheat rust diseases using a pre-trained VGG16 model. Based on the CGIAR dataset with three classes (stem rust, leaf rust, and healthy wheat), the proposed model has been optimized and produced the classification accuracy of 99.54%, and when evaluated on unseen data it gave a validation accuracy of 77.14%. This model will further help farmers or experts to diagnose disease in the early stages. Although these models give good training accuracy, they were not appropriate to classify stem- and leaf rust when result plot on confusion metrics. This is due to the fact that some images in this dataset contained multiple diseases, meaning that one image contained the features of both leaf- and stem rust. Detection and classification of the wheat rust disease in the early stages lead to high yield at the production level [38]. In the future, we will extend this work by collecting real-time images of wheat rust disease and also incorporating object detection-based algorithms such as Yolov3, Faster R-CNN, and Mask R-CNN [39] to exactly localize the location of the disease in the image.
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Perveen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8929",title:"Modern Beekeeping",subtitle:"Bases for Sustainable Production",isOpenForSubmission:!1,hash:"cbf5aca68ed2c6690ad99f68aaaddcaf",slug:"modern-beekeeping-bases-for-sustainable-production",bookSignature:"Ramón Eduardo Rebolledo Ranz",coverURL:"https://cdn.intechopen.com/books/images_new/8929.jpg",editedByType:"Edited by",editors:[{id:"193813",title:"Dr.",name:"Ramón Eduardo",middleName:null,surname:"Rebolledo Ranz",slug:"ramon-eduardo-rebolledo-ranz",fullName:"Ramón Eduardo Rebolledo Ranz"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7561",title:"Beekeeping",subtitle:"New Challenges",isOpenForSubmission:!1,hash:"1c47c831256fe10ff19fb10f490930fc",slug:"beekeeping-new-challenges",bookSignature:"Ramón Eduardo Rebolledo Ranz",coverURL:"https://cdn.intechopen.com/books/images_new/7561.jpg",editedByType:"Edited by",editors:[{id:"193813",title:"Dr.",name:"Ramón Eduardo",middleName:null,surname:"Rebolledo Ranz",slug:"ramon-eduardo-rebolledo-ranz",fullName:"Ramón Eduardo Rebolledo Ranz"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6619",title:"Insect Science",subtitle:"Diversity, Conservation and Nutrition",isOpenForSubmission:!1,hash:"08241b041b2072a88452041f8fdebe7e",slug:"insect-science-diversity-conservation-and-nutrition",bookSignature:"Mohammad Manjur Shah and Umar Sharif",coverURL:"https://cdn.intechopen.com/books/images_new/6619.jpg",editedByType:"Edited by",editors:[{id:"94128",title:"Dr.",name:"Mohammad Manjur",middleName:null,surname:"Shah",slug:"mohammad-manjur-shah",fullName:"Mohammad Manjur Shah"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5163",title:"Beekeeping and Bee Conservation",subtitle:"Advances in Research",isOpenForSubmission:!1,hash:"fc469ff4d2cf6651cfdbf3c5cf90a469",slug:"beekeeping-and-bee-conservation-advances-in-research",bookSignature:"Emerson Dechechi Chambo",coverURL:"https://cdn.intechopen.com/books/images_new/5163.jpg",editedByType:"Edited by",editors:[{id:"94059",title:"Dr.",name:"Emerson",middleName:"Dechechi",surname:"Dechechi Chambó",slug:"emerson-dechechi-chambo",fullName:"Emerson Dechechi Chambó"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:5,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"50073",doi:"10.5772/62487",title:"Impacts of Pesticides on Honey Bees",slug:"impacts-of-pesticides-on-honey-bees",totalDownloads:3367,totalCrossrefCites:18,totalDimensionsCites:39,abstract:"This chapter focuses on the detrimental effects that pesticides have on managed honey bee colonies and their productivity. We examine first the routes of exposure of bees to agrochemicals used for crop protection and their application to crops, fate and contamination of water and plants around the fields. Most of the time, the exposure of bees to pesticides is through ingestion of residues found in the pollen and nectar of plants and in water. Honey bees are also exposed to pesticides used for the treatment of Varroa and other parasites. The basic concepts about the toxicity of the different kinds of pesticides are explained next. Various degrees of toxicity are found among agrochemicals, and emphasis is given to the classic tenet of toxicology, “the dose makes the poison,” and its modern version “the dose and the time of exposure makes the poison.” These two factors, dose and time, help us understand the severity of the impacts that pesticides may have on bees and their risk, which are analysed in the third section. Sublethal effects are also considered. The final section is devoted to some practical advice for avoiding adverse impacts of pesticides in beekeeping.",book:{id:"5163",slug:"beekeeping-and-bee-conservation-advances-in-research",title:"Beekeeping and Bee Conservation",fullTitle:"Beekeeping and Bee Conservation - Advances in Research"},signatures:"Francisco Sanchez-Bayo and Koichi Goka",authors:[{id:"74970",title:"Dr.",name:"Francisco",middleName:null,surname:"Sánchez-Bayo",slug:"francisco-sanchez-bayo",fullName:"Francisco Sánchez-Bayo"},{id:"192045",title:"Dr.",name:"Koichi",middleName:null,surname:"Goka",slug:"koichi-goka",fullName:"Koichi Goka"}]},{id:"59212",doi:"10.5772/intechopen.73864",title:"Insect Conservation for the Twenty-First Century",slug:"insect-conservation-for-the-twenty-first-century",totalDownloads:1941,totalCrossrefCites:7,totalDimensionsCites:14,abstract:"Insects have been immensely successful as an animal group. They dominate compositional diversity of all but the saltiest and coldest parts of the planet. Yet today insects are declining at a precipitous rate. This is of great concern in terms of impoverishment of Earth, and is also dire for us. Insects contribute to the maintenance of terrestrial and freshwater systems, their service delivery and their resilience. The meteoric impact of humans is challenging this dominance, yet so few people realize that the very fabric of life on which they depend is being unraveled at an alarming rate. Action is required, as are new perspectives, if we are to maintain insect diversity and services through the twenty-first century. Here, we review how we should view and act to have more effective insect diversity conservation based on six themes: (1) philosophy (establishing the ethical foundation), (2) research (the finding out), (3) policy (the framework for action), (4) psychology (understanding how to engage humans in insect conservation action), (5) practice (implementation of action), and (6) validation (establishing how well we are doing at conserving insects). We then overview some emergent challenges and solutions at both the species and landscape operational levels in agricultural, forestry, and urban environments.",book:{id:"6619",slug:"insect-science-diversity-conservation-and-nutrition",title:"Insect Science",fullTitle:"Insect Science-Diversity, Conservation and Nutrition"},signatures:"Michael J. Samways",authors:[{id:"233323",title:"Distinguished Prof.",name:"Michael",middleName:null,surname:"Samways",slug:"michael-samways",fullName:"Michael Samways"}]},{id:"79121",doi:"10.5772/intechopen.100416",title:"Botanical Insecticides Are a Non-Toxic Alternative to Conventional Pesticides in the Control of Insects and Pests",slug:"botanical-insecticides-are-a-non-toxic-alternative-to-conventional-pesticides-in-the-control-of-inse",totalDownloads:229,totalCrossrefCites:4,totalDimensionsCites:9,abstract:"Insect control for crops is one of the most critical global concerns. Pest management is an economic and ecological problem worldwide due to the human and environmental risks raised by most synthetic pesticide products. Botanical insecticides have resurfaced in popularity due to their low cost and low environmental impact, rather than their negative effects on human health. Botanical insecticides destroy only the insects they are meant to kill, leaving no residue on food or in the environment. Botanicals have long been used to combat pests. The compounds have many environmental advantages. However, as opposed to other bio-control pests and pathogens, their use was minimal during the twentieth century. In developing countries, botanical insecticides are well adapted for use in organic food production. Nonetheless, they may play a far bigger role in developed countries’ food production and post-harvest food protection. Consequently, the current chapter briefly addresses botanicals with active ingredients with insecticidal, antifeedant, or repellent properties.",book:{id:"10739",slug:null,title:"Global Decline of Insects",fullTitle:"Global Decline of Insects"},signatures:"Nazeer Ahmed, Mukhtar Alam, Muhammad Saeed, Hidayat Ullah, Toheed Iqbal, Khalid Awadh Al-Mutairi, Kiran Shahjeer, Rafi Ullah, Saeed Ahmed, Nibal Abd Aleem Hassan Ahmed, Hanem Fathy Khater and Muhammad Salman",authors:null},{id:"50307",doi:"10.5772/62654",title:"From Extraction to Meliponiculture: A Case Study of the Management of Stingless Bees in the West-Central Region of Mexico",slug:"from-extraction-to-meliponiculture-a-case-study-of-the-management-of-stingless-bees-in-the-west-cent",totalDownloads:2730,totalCrossrefCites:5,totalDimensionsCites:9,abstract:"Currently, stingless bees' populations are declining due to environmental degradation. In this context, the authors have developed a research project in the central-western region of Mexico with the goal to generate strategies for conservation and sustainable management of stingless bees. The chapter aims to present the process of this investigation and its main results in terms of a) local knowledge and management strategies of stingless bees, and b) the social process of technological appropriation of meliponiculture by beekeepers. We recognized specific knowledge on the biology and ecology of stingless bees that result in a system for identifying species and management strategies of wild populations of these bees based on the extraction of nests. The implementation of an innovative productive activity based on the principles of meliponiculture and current techniques has been well received by producers, which has led to the formation of the Meliponicultores Michoacanos del Balsas Association, which grows five species of stingless bees. The research suggests that conservation associated with the use of bees (integral meliponiculture) can be enhanced in the region. Faced with the loss of biodiversity and environmental crisis, it is essential to maintain and enhance local knowledge of stingless bees and management practices. This represents an alternative to develop management schemes that allow the raising and breeding of these bees, while its products are obtained.",book:{id:"5163",slug:"beekeeping-and-bee-conservation-advances-in-research",title:"Beekeeping and Bee Conservation",fullTitle:"Beekeeping and Bee Conservation - Advances in Research"},signatures:"Alejandro Reyes-González, Andrés Camou-Guerrero and Salvador\nGómez-Arreola",authors:[{id:"179951",title:"Dr.",name:"Andres",middleName:null,surname:"Camou-Guerrero",slug:"andres-camou-guerrero",fullName:"Andres Camou-Guerrero"},{id:"185413",title:"MSc.",name:"Alejandro",middleName:null,surname:"Reyes-González",slug:"alejandro-reyes-gonzalez",fullName:"Alejandro Reyes-González"},{id:"192049",title:"Dr.",name:"Salvador",middleName:null,surname:"Gómez-Arreola",slug:"salvador-gomez-arreola",fullName:"Salvador Gómez-Arreola"}]},{id:"50683",doi:"10.5772/63145",title:"Advances in Pharmacological Activities and Chemical Composition of Propolis Produced in Americas",slug:"advances-in-pharmacological-activities-and-chemical-composition-of-propolis-produced-in-americas",totalDownloads:2555,totalCrossrefCites:2,totalDimensionsCites:8,abstract:"Propolis is a resinous material produced by bees from the selective collection of plant exudates that are subsequently mixed with beeswax and salivary bee secretions. Propolis has been used in folk medicine, and certainly, several studies have validated its biological properties. The chemical composition and pharmacological activities of propolis collected through North (including Central America and Caribbean) and South America have been studied in the last years, and several papers have reported differences and similarities among the analysed geographical samples. Propolis has been classified according to its aspect and plant source; however, the ecological diversity present along the Americas provides a plethora of botanical resins. Herein, we summarize and discuss most of the studies performed at present on this profitable product for apiculture, attempting to compare the bioactivity, phytochemical diversity and botanical sources of honeybee propolis produced in Americas.",book:{id:"5163",slug:"beekeeping-and-bee-conservation-advances-in-research",title:"Beekeeping and Bee Conservation",fullTitle:"Beekeeping and Bee Conservation - Advances in Research"},signatures:"Efrain Alday, Moisés Navarro-Navarro, Adriana Garibay-Escobar,\nRamón Robles-Zepeda, Javier Hernandez and Carlos Velazquez",authors:[{id:"96966",title:"MSc.",name:"Moises",middleName:null,surname:"Navarro-Navarro",slug:"moises-navarro-navarro",fullName:"Moises Navarro-Navarro"},{id:"180409",title:"Dr.",name:"Carlos",middleName:null,surname:"Velazquez",slug:"carlos-velazquez",fullName:"Carlos Velazquez"},{id:"186351",title:"Dr.",name:"Ramón",middleName:null,surname:"Robles-Zepeda",slug:"ramon-robles-zepeda",fullName:"Ramón Robles-Zepeda"},{id:"186352",title:"MSc.",name:"Efrain",middleName:null,surname:"Alday",slug:"efrain-alday",fullName:"Efrain Alday"},{id:"186353",title:"Dr.",name:"Javier",middleName:null,surname:"Hernandez",slug:"javier-hernandez",fullName:"Javier Hernandez"},{id:"189161",title:"Dr.",name:"Adriana",middleName:null,surname:"Garibay-Escobar",slug:"adriana-garibay-escobar",fullName:"Adriana Garibay-Escobar"}]}],mostDownloadedChaptersLast30Days:[{id:"50170",title:"A Comprehensive Characterization of the Honeybees in Siberia (Russia)",slug:"a-comprehensive-characterization-of-the-honeybees-in-siberia-russia-",totalDownloads:2274,totalCrossrefCites:4,totalDimensionsCites:8,abstract:"A comprehensive study of some populations of honeybee (332 colonies) in Siberia (Tomsk region, Krasnoyarsk Krai (Yenisei population), Altai) using morphometric and molecular genetic methods was conducted. Infestation of bees (132 colonies) by Nosema has also been studied. Three variants of the COI-COII mtDNA locus were registered: PQQ, PQQQ (typical for Apis m. mellifera), and Q (specific for southern races). It was established that 64% of bee colonies from the Tomsk region and all colonies studied from the Krasnoyarsk and the Altai territories originate from Apis m. mellifera on the maternal line. According to the morphometric study, the majority of bee colonies of the Tomsk region are hybrids; in some colonies the mismatch of morphometric and mtDNA data was observed. Moreover, the majority of bee colonies infected by Nosema were hybrids. Yenisei population may be considered as a unique Apis m. mellifera population. Microsatellite analysis (loci А008, Ap049, AC117, AC216, Ap243, H110, A024, A113) showed the specific distribution of genotypes and alleles for some loci in the bees, which differ by geographical location. Loci A024 and Ap049 are of considerable interest for further study as candidate markers for differentiation of subspecies; locus A008 can be considered informative for determining of different ecotypes of Apis m. mellifera.",book:{id:"5163",slug:"beekeeping-and-bee-conservation-advances-in-research",title:"Beekeeping and Bee Conservation",fullTitle:"Beekeeping and Bee Conservation - Advances in Research"},signatures:"Nadezhda V. Ostroverkhova, Olga L. Konusova, Aksana N. Kucher\nand Igor V. Sharakhov",authors:[{id:"180112",title:"Ph.D.",name:"Nadezhda",middleName:null,surname:"Ostroverkhova",slug:"nadezhda-ostroverkhova",fullName:"Nadezhda Ostroverkhova"},{id:"180249",title:"Ms.",name:"Olga",middleName:null,surname:"Konusova",slug:"olga-konusova",fullName:"Olga Konusova"},{id:"180342",title:"Prof.",name:"Aksana",middleName:null,surname:"Kucher",slug:"aksana-kucher",fullName:"Aksana Kucher"},{id:"180343",title:"Prof.",name:"Igor",middleName:null,surname:"Sharakhov",slug:"igor-sharakhov",fullName:"Igor Sharakhov"}]},{id:"70501",title:"Southeast Asian Meliponiculture for Sustainable Livelihood",slug:"southeast-asian-meliponiculture-for-sustainable-livelihood",totalDownloads:1235,totalCrossrefCites:2,totalDimensionsCites:5,abstract:"Stingless bees (Apidae: Meliponini) are one of the most important pollinators of native plants and economic crops in tropical and subtropical parts of the world. They not only establish large perennial colonies with complex social organization but also have a diverse nesting biology. The economic utilization of a total of 60 stingless bee species in Asia has been reported. The current status of meliponiculture in Southeast Asia is mainly focused on pollination utilization and honey and propolis production. This chapter shows that small-scale beekeeping of stingless bees, which is suitable for the flowering pattern in the tropics, is one of the best potential alternative opportunities. The cost-effectiveness analysis based on production yield, investment cost, and profit-return rate is reviewed. Finally, a sustainable utilization of stingless bees is considered to be an enhancer of pollination services both in an agricultural crop and natural ecosystem.",book:{id:"8929",slug:"modern-beekeeping-bases-for-sustainable-production",title:"Modern Beekeeping",fullTitle:"Modern Beekeeping - Bases for Sustainable Production"},signatures:"Atsalek Rattanawannee and Orawan Duangphakdee",authors:[{id:"283087",title:"Ph.D.",name:"Atsalek",middleName:null,surname:"Rattanawannee",slug:"atsalek-rattanawannee",fullName:"Atsalek Rattanawannee"},{id:"306411",title:"Dr.",name:"Orawan",middleName:null,surname:"Duangphakdee",slug:"orawan-duangphakdee",fullName:"Orawan Duangphakdee"}]},{id:"50073",title:"Impacts of Pesticides on Honey Bees",slug:"impacts-of-pesticides-on-honey-bees",totalDownloads:3367,totalCrossrefCites:18,totalDimensionsCites:39,abstract:"This chapter focuses on the detrimental effects that pesticides have on managed honey bee colonies and their productivity. We examine first the routes of exposure of bees to agrochemicals used for crop protection and their application to crops, fate and contamination of water and plants around the fields. Most of the time, the exposure of bees to pesticides is through ingestion of residues found in the pollen and nectar of plants and in water. Honey bees are also exposed to pesticides used for the treatment of Varroa and other parasites. The basic concepts about the toxicity of the different kinds of pesticides are explained next. Various degrees of toxicity are found among agrochemicals, and emphasis is given to the classic tenet of toxicology, “the dose makes the poison,” and its modern version “the dose and the time of exposure makes the poison.” These two factors, dose and time, help us understand the severity of the impacts that pesticides may have on bees and their risk, which are analysed in the third section. Sublethal effects are also considered. The final section is devoted to some practical advice for avoiding adverse impacts of pesticides in beekeeping.",book:{id:"5163",slug:"beekeeping-and-bee-conservation-advances-in-research",title:"Beekeeping and Bee Conservation",fullTitle:"Beekeeping and Bee Conservation - Advances in Research"},signatures:"Francisco Sanchez-Bayo and Koichi Goka",authors:[{id:"74970",title:"Dr.",name:"Francisco",middleName:null,surname:"Sánchez-Bayo",slug:"francisco-sanchez-bayo",fullName:"Francisco Sánchez-Bayo"},{id:"192045",title:"Dr.",name:"Koichi",middleName:null,surname:"Goka",slug:"koichi-goka",fullName:"Koichi Goka"}]},{id:"74836",title:"Chironomidae: Biology, Ecology and Systematics",slug:"chironomidae-biology-ecology-and-systematics",totalDownloads:426,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The family of Chironomidae is a group of Diptera insects belonging to the suborder of Nematocera, commonly called “non-biting midges” in the adult stage and “bloodworms” in the larval stage. The Chironomidae are often the most abundant group of macroinvertebrates, in number of species and individuals, encountered in all aquatic environments of freshwater, brackish, terrestrial and even the sea. Likewise, Chironomidae occur in all the continents. The Chironomidae family is divided into 11 sub-families that have diffrent ecological statues. Despite the wealth of data on Chironomidae in the Holarctic region, other parts of the world are poorly studied and few guides to identifying Chironomidae have been produced. This chapter includes a theoretical synthesis on the Chironomidae, it deals with the Biology (life cycle and description of different stages), description of all subfamilies and the ecology of this important family of Diptera.",book:{id:"10423",slug:"the-wonders-of-diptera-characteristics-diversity-and-significance-for-the-world-s-ecosystems",title:"The Wonders of Diptera",fullTitle:"The Wonders of Diptera - Characteristics, Diversity, and Significance for the World's Ecosystems"},signatures:"Zerguine Karima",authors:[{id:"334825",title:"Dr.",name:"Karima",middleName:null,surname:"Zerguine",slug:"karima-zerguine",fullName:"Karima Zerguine"}]},{id:"75438",title:"Characteristics of Dipteran Insects",slug:"characteristics-of-dipteran-insects",totalDownloads:485,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Diptera means two wings (Di: two, pteron: wing). They have complete metamorphosis and they are holometabolous insects which means there are 4 stages (egg, larvae, pupae and adult). The name of larval stage is “maggot”. Some of the dipteran insects cause damage in agricultural production. Some are harmful for humans. Dipteran insects have two wings. Hind wings are reduced and they are called “halteres”. Function of halteres is balancing when the insects fly. Except mosquitoes, dipteran insects have sponging-sucking mouthparts. Important examples for dipteran insects are Olive fruit fly and Medfly which cause damages in agricultural production. OFF is the most destructive pest in olive growing areas and Mediterranean fruit fly cause damages in fruit production.",book:{id:"10423",slug:"the-wonders-of-diptera-characteristics-diversity-and-significance-for-the-world-s-ecosystems",title:"The Wonders of Diptera",fullTitle:"The Wonders of Diptera - Characteristics, Diversity, and Significance for the World's Ecosystems"},signatures:"Murat Helvacı",authors:[{id:"301984",title:"Ph.D.",name:"Murat",middleName:null,surname:"Helvaci",slug:"murat-helvaci",fullName:"Murat Helvaci"}]}],onlineFirstChaptersFilter:{topicId:"35",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"78491",title:"Insect Conservation and Management: A Need of the Hour",slug:"insect-conservation-and-management-a-need-of-the-hour",totalDownloads:26,totalDimensionsCites:0,doi:"10.5772/intechopen.100023",abstract:"Insects play a very vital role in divergent ecosystems and have gained great economic and medical importance as pollinators, pests, predators, parasitoids, decomposers and vectors. With the large-scale practice of synthetic pesticides, the diminishing rate of beneficial and pollinator insects is increasing rapidly. Environmental pollution, climate change, global warming, urbanization, industrialization and some natural calamities like wildfires add more fuel to the acceleration of insect decline all over the world. Alternative steps should be employed to replace the toxic pesticides and implementation of integrated pest management (IPM) should be put forward to reduce the overuse of synthetic pesticides and fertilizers, which have a great impact on beneficial insects as well as birds, aquatic organisms, and also on human health. The present study aims to create awareness among the researchers and general public by providing a brief review of insect importance, decline and conservation strategies.",book:{id:"10739",title:"Global Decline of Insects",coverURL:"https://cdn.intechopen.com/books/images_new/10739.jpg"},signatures:"Muzafar Riyaz, Rauf Ahmad Shah and Soosaimanickam Maria Packiam"},{id:"80971",title:"Agricultural Intensification Causes Decline in Insect Biodiversity",slug:"agricultural-intensification-causes-decline-in-insect-biodiversity",totalDownloads:47,totalDimensionsCites:0,doi:"10.5772/intechopen.101360",abstract:"The world’s population exceeded 7 billion in late 2011 and it is expected to reach 9.3 billion by 2050. Meanwhile, demand for food is predicted to increase between 50 and 100% by 2050. To meet the food demands of the increasing population, agricultural intensification practices including growing monocultures of high-yielding crop varieties and increased applications of fertilizers and pesticides have been used to increase productivity. These practices, however, impact negatively on biodiversity of existing flora and fauna, particularly causing huge declines in insect biodiversity. This chapter reviews present state of knowledge about agricultural intensification practices and global decline of insect biodiversity (i.e., pest and beneficial insect species) in intensive agricultural system and point out the likely drivers of these declines. It concludes the review by examining sustainable agricultural intensification practices that could be used to mitigate these biodiversity declines while maintaining productivity in intensive agricultural systems.",book:{id:"10739",title:"Global Decline of Insects",coverURL:"https://cdn.intechopen.com/books/images_new/10739.jpg"},signatures:"Mumuni Abudulai, Jerry Asalma Nboyine, Peter Quandahor, Ahmed Seidu and Fousséni Traore"},{id:"78872",title:"Diversity, Importance and Decline of Pollinating Insects in Present Era",slug:"diversity-importance-and-decline-of-pollinating-insects-in-present-era",totalDownloads:86,totalDimensionsCites:0,doi:"10.5772/intechopen.100316",abstract:"Pollination is a multi-million-year-old co-evolutionary process involving flowering plants and pollinators. It is one of the most important mechanisms in preservation and promotion of biodiversity as well as life on Earth. Pollinator diversity is essential for maintaining overall biological diversity in many habitats including agro-ecosystems. Pollinators are responsible for assisting reproduction in over 80% of the world’s flowering plants. In their absence, humans and wildlife would go hungry. Insects are the most efficient pollinators as they play a crucial part in pollination ecology. Pollinators and their habitats have ecological, economic, cultural and social benefits. Pollination efficiency is highly dependent on certain attributes and characteristics of pollinators such as vision, anatomy, food preferences, olfaction, behaviour and learning ability. With the rapid growth of human population, our demand for food has also risen. Our agricultural systems will need to produce more food in a sustainable manner in the future to cope with this. Pollinators play an important role in these ecosystems and will continue to do so in the future. Because pollinators are so important to agriculture, we need to learn more about which crops require specific pollinators and how to best maintain and promote both wild and controlled species. Their diversity needs protection because there are specific relationships between certain crops and pollinators. Pollinator communities are suffering as a result of man-made habitat disruptions, including severe biodiversity loss. This diversity must be protected by combining conservation measures with sustainable farming practices which could increase crop yields while protecting insect pollinator species.",book:{id:"10739",title:"Global Decline of Insects",coverURL:"https://cdn.intechopen.com/books/images_new/10739.jpg"},signatures:"Navkiran Kaur and Amritpal Singh Kaleka"},{id:"80012",title:"Impacts of Organic Farming on Insects Abundance and Diversity",slug:"impacts-of-organic-farming-on-insects-abundance-and-diversity",totalDownloads:108,totalDimensionsCites:0,doi:"10.5772/intechopen.102035",abstract:"Organic farming encourages maximum utilization of the natural biological processes to manage the farm in terms of soil fertilization and pest control, which implies using none or less synthetic fertilizers, pesticides, and plant and animal growth-promoting substances. All these practices increase arthropod diversity, particularly soil-dwelling insects. Intercropping, cover crops, and hedges, which are common practices in organic fields, provide alternative habitats for arthropod communities. The refugia also provide a good source of food for pollinators in terms of pollen grains and nectar. The interactions among the different plant and animal taxa (weeds, birds, mammals) that are found in the organic farming ecosystem have a great impact on insects’ abundance and diversity. This chapter summarizes the impacts of the organic farming system on the abundance and diversity of insects. The role of organic farming in mitigating the impact of agriculture intensification, urbanization, deforestation, and climate change on global insects’ decline and diversity loss is discussed.",book:{id:"10739",title:"Global Decline of Insects",coverURL:"https://cdn.intechopen.com/books/images_new/10739.jpg"},signatures:"Hamadttu Abdel Farag El-Shafie"},{id:"78945",title:"Botanical Insecticides and Their Potential as Anti-Insect/Pests: Are They Successful against Insects and Pests?",slug:"botanical-insecticides-and-their-potential-as-anti-insect-pests-are-they-successful-against-insects-",totalDownloads:205,totalDimensionsCites:7,doi:"10.5772/intechopen.100418",abstract:"In low-income countries, subsistence and transitional farms frequently use botanical insecticides. The shortage or high cost of industrial pesticides also prompts their use. Botanical insecticides are also prescribed by agricultural and development programs and certain development organizations. However, since insecticidal proof of their effectiveness and protection might not be sufficient or usable, this may be called into question. While insecticidal botanicals have been extensively studied, there has yet to be a fusion that focuses especially on the domestic synthesis of biopesticides that work infield and storage effectively. In this chapter, we look at the effectiveness of botanicals (neem, garlic, and essential oil) that are used as insecticides. In addition, this chapter also focuses on research carried out on the use of these essential oils as insecticides. Processes that use variable amounts of ingredients and concentrations and ratios of active ingredients can have varying impacts on the efficacy of plant-based biological insecticides. Finally, using home-made insecticides would reduce the losses that occur during food production and enable us to use environment-friendly pest management methods.",book:{id:"10739",title:"Global Decline of Insects",coverURL:"https://cdn.intechopen.com/books/images_new/10739.jpg"},signatures:"Toheed Iqbal, Nazeer Ahmed, Kiran Shahjeer, Saeed Ahmed, Khalid Awadh Al-Mutairi, Hanem Fathy Khater and Reham Fathey Ali"},{id:"79060",title:"Description of a New Species of the Genus Anagrus (Hymenoptera: Chalcidoidea: Mymaridae): A Biocontrol Agent as an Alternative to Insecticide Use",slug:"description-of-a-new-species-of-the-genus-anagrus-hymenoptera-chalcidoidea-mymaridae-a-biocontrol-ag",totalDownloads:76,totalDimensionsCites:0,doi:"10.5772/intechopen.99957",abstract:"Although insects are economically important as they produce honey, silk, act as pollinators and also play an important role in functioning of an ecosystem, yet insect population is declining very fast. One of the possible causes of insects decline is excessive use of pesticides. Control of pest with synthetic chemicals or pesticides result in several issues and complications. These chemical pesticides or insecticides can also cause toxic effects on beneficial organisms like honeybees and butterflies which are important pollinators. So, biocontrol agents can be used as best alternative to control pest without harming beneficial organism and non-target insects or other organism as majority of biocontrol agents are host specific. Biological control agents including predators and parasotoids are natural enemies of insect pests. Present chapter deals with the description and illustration of one new species Anagrus (Anagrus) sololinearis sp.nov from India. This new species belongs to genus Anagrus (Hymenoptera: Chalcidoidea: Mymaridae). Genus Anagrus is considered as one of the important and most promising biocontrol agents in insects as it is an egg parasitoid.",book:{id:"10739",title:"Global Decline of Insects",coverURL:"https://cdn.intechopen.com/books/images_new/10739.jpg"},signatures:"Shireen Saleem and Shoeba Binte Anis"}],onlineFirstChaptersTotal:10},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"June 25th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",isOpenForSubmission:!1,editor:null,editorTwo:null,editorThree:null},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",isOpenForSubmission:!0,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. Board Member and Chair of Mycology Group of Chinese Society of Dermatology.",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"5",title:"Parasitic Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",isOpenForSubmission:!0,editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. 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Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,series:{id:"11",title:"Biochemistry"}}},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:318,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/183092",hash:"",query:{},params:{id:"183092"},fullPath:"/profiles/183092",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()