Criteria for evaluating the covariance structure of the Gaussian process.
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These diseases account for more than
Vectors are living organisms that can transmit infectious pathogens between humans or from animals to humans. Many of these vectors are insects that ingest disease-causing microorganisms during a blood meal from an infected host and then transmit it to a new host after the pathogen has replicated. Another characteristic of arthropod vectors is that they are cold-blooded (ectothermic) and therefore very sensitive to climatic factors, although the climate is only one of many factors that influence vector distribution, as there are also geographic and sociodemographic factors [1].
In order to interpret the behavior of vector-borne diseases in the most accurate and simplified way possible, statistical models are used. A statistical model is a simplified representation of a phenomenon of interest [3, 4]. With their help, it is possible to model, predict and make inferences about natural phenomenons, biological systems, epidemiological studies, and others [5]. One of the most widely used statistical models is linear regression models, which predict a continuous target based on linear relationships between the target and one or more predictors. But there is another type of model that extends the general linear model, so that the dependent variable is linearly related to the factors and covariates by means of a certain link function, which is known as a generalized linear model [6].
Generalized Linear Models (GLMs) provide a collection of linear regression models including the exponential family, such as the Binomial and Poisson, which are distributions for counting data. The GLMs were introduced by Nelder in
There are three components in GLMs: A response variable distribution, a linear predictor, and a link function. A response variable
GLMs can help in numerous areas such as epidemiology, mining engineering, Earth and environmental sciences, ecology, biology, geography, economics, agronomy, forestry, image processing, and more [15, 16]. For epidemiology in particular, as it is about understanding diseases that affect a population, the most usual thing is to find a binary variable that represents the presence or absence of a disease or to count the events of a disease for certain areas.
Such is the case of a study conducted by Hashizume et al. [17] in Bangladesh,
An important extension of the GLMs is the Generalized Linear Mixed Models (GLMMs) [18]. GLMMs provide a range of analyses for those data that are correlated in space and belong to the exponential family (Gamma, Poisson, Binomial, among others) [19]. Generalized Linear Spatial Models (GLSMs) are basically GLMMs, since latent variables are derived from a spatial process. In recent years, there has been a growing interest in the analysis of spatial data in epidemiology, in order to predict the incidence of vector-borne diseases.
Using techniques available to epidemiologists and other health professionals, the potential of remote sensing, Geographic Information Systems (GIS), and spatial analysis of epidemiological data has been demonstrated by some authors such as those mentioned below; however, there are still few studies that adequately prove the potential of these tools, since they are still being exploited in the fight against diseases [20].
For instance, a Colombian paper published in
Another example is the work of Estallo et al. [22] in
As can be seen, spatial analysis is a powerful tool for the analysis of georeferenced data, as it can give health research a broader perspective of the occurrence of health events and diseases. Spatial statistical models are useful because they estimate the spatial variance inherent in the data, and can also be used to perform statistical inference throughout the study area. Spatial prediction can be made based entirely on a stochastic model or in combination with a deterministic trend [20, 23].
The aim of this chapter is to show an example of the application of spatial statistics, implementing a Generalized Linear Spatial Model for the prediction of dengue disease in the state of Chiapas. For this, there are considered patient age and the next information of each municipality: garbage disposal service, maximum environmental temperature, average monthly rainfall, and altitude as covariates. For the study of the disease in the
Space models have a simple structure, flexible enough to handle a variety of problems. The data may be continuous or discrete, present spatial aggregations, or be point observations in space. As for the spatial locations can be regular or irregular. A spatial model is usually used to predict sites where the study phenom was not observed.
Let
Structure 1, allows to differentiate and talk about problems with continuous spatial indexes, lattice, and point patterns giving rise to three types of data: geospatial, lattice data, and point patterns. In geospatial data,
Finally, the entangled data or also known as area data,
Knowing the type of variables with which they are working and taking into account their spatial dependence, helps to determine the regression technique that best fits the characteristics of the data [21]. For the study of spatial data Gaussian processes can be used, which are stochastic processes, a collection of variables. This allows any subset of finite random variables to have a multivariate Gaussian distribution. Gaussian processes can thus be thought of as distributions of random vectors or random functions [26]. Gaussian processes began to be studied in the
Gaussian stochastic processes are widely used as models for geostatic data. If a transformation of the original response variable is used, the scope of the Gaussian models can be amplified, and so with this extra flexibility the model provides a good empirical fit to the data.
A Gaussian process, {
Any such process is fully specified by the average function
A spatial Gaussian process is stationary if
Among the parametric functions for the covariance function [29] are the following:
Exponential:
Gaussian:
Matérn:
In these covariance functions (Eqs. (3)–(5))
There are several criteria in the literature to validate the covariance structure of a Gaussian process Eq. (2). Among the most used are: Mean Error (ME), Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Square Normalized Error (MSNE) (Table 1). ME and MSE should tend to zero when the covariance structure of the Gaussian process was correctly estimated. The MAE and RMSE criteria are considered as the most efficient criteria to validate the covariance structure of the Gaussian process. The RMSE is expected to be small like MAE, while the MSNE is expected to be close to
Measurement | Definition |
---|---|
Mean error | ME= |
Mean square error | MSE= |
Mean absolute error | MAE= |
Root mean square error | RMSE= |
Mean square normalized error | MSNE = |
Criteria for evaluating the covariance structure of the Gaussian process.
Spatial Generalized Linear Models were introduced by Diggle et al. in
Where
It is assumed that
To prove the existence of spatial dependence on a variable
Where
Several packages are available in statistical software R [34] to perform spatial modeling.
The
The
The geostatistical model assumes the response variable to be Gaussian, which may be an unrealistic assumption for some data sets. The GLSM provides a framework for analyzing Binomial and Poisson distributed data. The likelihood for such a model, in general, cannot be represented in closed form, since it is a high-dimensional integral
where
This section shows the application of a spatial model taking into account the social, climatic, and geographical characteristics of the municipalities of the state of Chiapas in relation to dengue virus infections registered from January to August of the year
Dengue disease is endemic to the state of Chiapas with scattered case reports, this is due to the different geographic characteristics of the state, such as the altitude of its municipalities and its border condition with the country of Guatemala. It is known that at different altitudes, in the regions, the climatic conditions tend to vary and this can favor the reproduction of the vector. The state of Chiapas is divided into
The data, which were collected at the municipal level, being 36 the municipalities that registered positive cases of dengue and were considered for the analysis, were obtained from different sources that are mentioned below.
The database with dengue cases registered in the state of Chiapas, during the period January–August
The climatic data were obtained from the World Meteorological Organization (WMO) [38], for each municipality of residence where the dengue cases were registered, working with the daily reports of average environmental temperature, maximum and minimum environmental temperature and average monthly rainfall. The climatic data were taken into account for the analysis,
Other factors related to infection were also considered in the analysis. Data on the population density and altitude of each municipality of residence per observed case were obtained from the INEGI, the other variables such as garbage disposal, contact with the mosquito, drinking water service, patient age, and sex were obtained from the original database of registered dengue cases provided by the secretary of health [37].
For the georeferencing of dengue cases registered in the period January–August
The database that is made up of
The spatial distribution of the
Georeferencing of cases
The Moran’s Index obtained, with the number of CDCs in the
After selecting the spatial model and the variables, we proceeded to estimate the covariance structure of the Gaussian process. For this, the Exponential, Gaussian, and Matérn covariance functions were tested, taking CDCs as the response variable, and measures of central tendency of the explanatory variables maximum environmental temperature, altitude, patient age, and average monthly rainfall were taken. Of the three functions, the Matérn covariance function generated the best value for
For the simulation and conditional prediction of the process Eq. (6) MCMC was used, since this provides a solution to the impediment of direct calculation of the predictive distribution due to the high dimensionality of the integral Eq. (8) [36]. For this,
For modeling the number of registered dengue cases in the
In Table 2, it is observed that the variables that have an effect on the cases of dengue observed are maximum environmental temperature, altitude of the municipalities, average monthly rainfall, and patient age. High temperatures and altitudes favor the presence of the disease, while young people will be preferred factors by the vector, as well as low rainfall because in seasons where there is no continuous flow of water in the rivers, stagnation causes an increase in the proliferation of
Parameter | Estimation coeff. | |
---|---|---|
Intercept ( | ||
Maximum temp. ( | ||
Altitude ( | ||
Rainfall ( | ||
Age ( |
Estimation of parameters and their confidence intervals of the selected model.
The projection of the model was carried out on a map of the state of Chiapas which was made based on the municipalities where the cases were registered, as can be seen in Figure 2, the prediction is divided by zones in shades of green to yellow with a contour delimited by contour lines that show the area in which the model predicts the number of cases for that area. As we can see, most of the predicted cases occur within the metropolitan area where the state capital Tuxtla Gutiérrez and the municipalities of Chiapa de Corzo, Berriozábal and Suchiapa are located, this corresponds to the observed data, since most of the cases occurred in the same area. On the other hand, it is observed that the prediction power is diminished in areas where no dengue cases were registered.
Prediction of confirmed dengue cases.
The purpose of this chapter is to present and expand the use of spatial statistics to contribute to public health and the epidemiology of vector-borne diseases, and for this reason, the example of the use of a GLSM was proposed to model the distribution of dengue in Chiapas, since this is one of the endemic diseases that cause numerous infections per year. Climatological, geographic, and sociodemographic variables were used for the modeling, where it was found that the maximum environmental temperature, altitude, patient age, and average monthly rainfall are the variables that best predict the spread of dengue.
Maximum environmental temperature is shown to have a significant effect on dengue cases, as it is an environmental risk factor for dengue transmission, higher temperatures increase viral replication in the vector in a shorter time and thus increase the potential for transmission of dengue viruses. This is described by a study on the extrinsic incubation period. Liu et al. [41] found that the virus remained in the midgut of the vector at
The altitude above sea level of each municipality was also an important variable in the study, which is consistent with the findings of the systematic review by Aswi [42], where this variable was used in different statistical models in order to describe the behavior of the disease, since the spread of the Aedes aegypti mosquitoes is limited by climatic conditions and this will be governed by the location of the geographical area and its altitude. The study of Reinhold et al. [43] alludes that Aedes Aegytpi cannot regulate its body temperature because it is an endothermic arthropod, and that is why its temperature is defined by the climatic conditions of its environment. Thus, geographic location and altitude are important variables for dengue disease.
On the other hand, we have average monthly rainfall, where we see a negative association, since the less rainfall, the more cases of dengue. This coincides with the results of the work of Hashizume et al. [17], where they indicate that dengue cases increase by
Finally, we have the variable patient age, as can be seen in the results, the correlation was negative too, due to the young population being preferred by the vector, since there is a greater number of cases at an average age of
Vector-borne diseases (VBD) are an important public health issue worldwide. The distribution of these diseases as well as their transmission and seasonality are known to be largely determined by environmental, geographic, and socio-demographic factors. GLSMs allow robust analysis of the complex and diverse factors that influence the occurrence of VBD, incorporating spatial dimensions. They can also be a valuable tool for targeting interventions in surveillance and control programs for VBD at the global or regional level. These analytical approaches have recently been used in the field of public health, but in Mexico there are still very few studies that contribute to this knowledge. For this reason, this chapter presents an example of the application of GLSM with a study of dengue, one of the most common VBD in Mexico, finding that the maximum temperature, altitude, and average monthly rainfall of each municipality, as well as patient age, are the factors that best predicted the presence of dengue cases in the state of Chiapas in the period from January to August
The authors declare no conflict of interest.
ARMA | Autoregressive Moving Average |
CAR | Conditional Autoregressive Model |
CDCs | Confirmed Dengue Cases |
DENV | Dengue Virus |
EW | Epidemiological Week |
GLM | Generalized Linear Model |
GLMM | Generalized Linear Mixed Model |
GLSM | Generalized Linear Spatial Model |
INEGI | National Institute of Statistic and Geography |
INLA | Integrated Nested Laplace Approximation |
MAE | Mean Absolute Error |
MCMC | Markov Chain Monte Carlo |
ME | Mean Error |
MI | Moran’s Index |
MSE | Mean Square Error |
MSNE | Mean Square Normalized Error |
RMSE | Root Mean Square Error |
SAR | Simultaneous Autoregressive Model |
VBD | Vector-Borne Diseases |
WMO | World Meteorological Organization |
ZCL | Zoonotic Cutaneous Leishmaniasis |
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\n\nBut, one thing we have in common is -- we are all scientists at heart!
\n\nSara Uhac, COO
\n\nSara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
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\n\nDr Alex Lazinica
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Production",slug:"sugarcane-bagasse-valorization-strategies-for-bioethanol-and-energy-production",totalDownloads:2094,totalCrossrefCites:18,totalDimensionsCites:30,abstract:"The use of sugarcane bagasse pith as solid substrate for fungi and microbial growth is well known, as well as a source of microorganisms that can be isolated from it. Pith has also been used as a bulking agent for soil bioremediation. More recently, bagasse pith has been used for bioethanol production involving pretreatment and hydrolysis followed by fermentation and dehydration. However, little is reported about biomass valorization for the development of environmentally sound and innovative strategies to process sugarcane bagasse from sugar mills. Incineration of sugarcane bagasse pith is a very common and mature technology for waste disposal and generation of electrical and thermal energy. However, this approach may not be satisfactory in organic waste management due to pollutant emissions, economic and labor costs, loss of energy, and bad odor. In addition, no valuable product is generated from its decomposition process. Instead of incineration, recent research has focused on its utilization as biofuel source. In this chapter, the use of sugarcane bagasse pith as a waste material for incineration versus biomass to produce bioethanol is discussed in terms of energy ratio and emissions, in addition to elucidate the potential of sugarcane bagasse valorization for a more sustainable society.",book:{id:"6315",slug:"sugarcane-technology-and-research",title:"Sugarcane",fullTitle:"Sugarcane - Technology and Research"},signatures:"Elias Martinez-Hernandez, Myriam Adela Amezcua-Allieri, Jhuma\nSadhukhan and Jorge Aburto Anell",authors:[{id:"16428",title:"Dr.",name:"Jorge",middleName:null,surname:"Aburto",slug:"jorge-aburto",fullName:"Jorge Aburto"},{id:"27622",title:"Dr.",name:"Myriam",middleName:null,surname:"Amezcua",slug:"myriam-amezcua",fullName:"Myriam Amezcua"},{id:"232272",title:"Dr.",name:"Elias",middleName:null,surname:"Martinez-Hernandez",slug:"elias-martinez-hernandez",fullName:"Elias Martinez-Hernandez"},{id:"232274",title:"Dr.",name:"Jhuma",middleName:null,surname:"Sadhukhan",slug:"jhuma-sadhukhan",fullName:"Jhuma Sadhukhan"}]},{id:"57762",doi:"10.5772/intechopen.71497",title:"Sugarcane Bagasse and Cellulose Polymer Composites",slug:"sugarcane-bagasse-and-cellulose-polymer-composites",totalDownloads:2252,totalCrossrefCites:12,totalDimensionsCites:26,abstract:"Waste recycling has been the main topic of various scientific researches due to environmental management. Renewable agricultural sources such as pineapple leaf, sisal, jute, piassava, coir, and sugarcane bagasse are among agro waste, normally known as biomass, which is recently used for reinforcing polymeric materials. Sugarcane bagasse fiber residues has been extensively investigated and employed as a source of reinforcement of polymers. The major residue is normally burnt for energy supply in the sugar and alcohol industries and as a result, tons of ash is created. The ash contained inorganic components which are valuable for reinforcement in polymeric materials. This chapter reports on the use of sugarcane bagasse, sugarcane bagasse ash (SBA) and its cellulose as reinforcing fillers for polymers.",book:{id:"6315",slug:"sugarcane-technology-and-research",title:"Sugarcane",fullTitle:"Sugarcane - Technology and Research"},signatures:"Teboho C. Mokhena, Mokgaotsa J. Mochane, Tshwafo E. Motaung,\nLinda Z. Linganiso, Oriel M. Thekisoe and Sandile P. Songca",authors:[{id:"212802",title:"Prof.",name:"Tshwafo",middleName:null,surname:"Motaung",slug:"tshwafo-motaung",fullName:"Tshwafo Motaung"},{id:"218007",title:"Dr.",name:"Linda",middleName:null,surname:"Linganiso",slug:"linda-linganiso",fullName:"Linda Linganiso"},{id:"220962",title:"Dr.",name:"Teboho",middleName:null,surname:"Mokhena",slug:"teboho-mokhena",fullName:"Teboho Mokhena"},{id:"220963",title:"Dr.",name:"Mokgaotsa",middleName:null,surname:"Mochane",slug:"mokgaotsa-mochane",fullName:"Mokgaotsa Mochane"},{id:"220965",title:"Prof.",name:"Thekisoe",middleName:null,surname:"Oriel",slug:"thekisoe-oriel",fullName:"Thekisoe Oriel"},{id:"220966",title:"Prof.",name:"Songca",middleName:null,surname:"Sandile",slug:"songca-sandile",fullName:"Songca Sandile"}]},{id:"59075",doi:"10.5772/intechopen.73056",title:"Nematodes Affecting Potato and Sustainable Practices for Their Management",slug:"nematodes-affecting-potato-and-sustainable-practices-for-their-management",totalDownloads:2008,totalCrossrefCites:14,totalDimensionsCites:25,abstract:"Plant-parasitic nematodes are a significant factor limiting potato production and tuber quality in several regions where potato is produced. Overall, parasitic nematodes alone cause an estimated annual crop loss of $ 78 billion worldwide and an average crop yield loss of 10–15%. As a result, sustainable food production and food security are directly impacted by pests and diseases. Degrading land use with monocultures and unsustainable cropping practices have intensified problems associated with plant pathogens. Proper identification of nematode species and isolates is crucial to choose effective and sustainable management strategies for nematode infection. Several nematode species have been reported associated with potato. Among those, the potato cyst nematodes Globodera rostochiensis and G. pallida, the root-knot nematode Meloidogyne spp., the root lesion nematode Pratylenchus spp., the potato rot nematode Ditylenchus destructor and the false root-knot nematode Nacobbus aberrans are major species limiting potato yield and leading to poor tuber quality. Here, we report a literature review on the biology, symptoms, damage and control methods used for these nematode species.",book:{id:"6219",slug:"potato-from-incas-to-all-over-the-world",title:"Potato",fullTitle:"Potato - From Incas to All Over the World"},signatures:"Fábia S.O. Lima, Vanessa S. Mattos, Edvar S. Silva, Maria A.S.\nCarvalho, Renato A. Teixeira, Janaína C. Silva and Valdir R. Correa",authors:[{id:"191564",title:"Dr.",name:"Fábia",middleName:null,surname:"Lima",slug:"fabia-lima",fullName:"Fábia Lima"},{id:"191758",title:"Dr.",name:"Valdir",middleName:null,surname:"Correa",slug:"valdir-correa",fullName:"Valdir Correa"}]},{id:"59391",doi:"10.5772/intechopen.73113",title:"Sugarcane Production in China",slug:"sugarcane-production-in-china",totalDownloads:1914,totalCrossrefCites:12,totalDimensionsCites:19,abstract:"Sugarcane production in China has a prolonged history since fourth century BC. At present, China is the world’s third largest sugar producing country after Brazil and India. During the past decade, more than 90% of the sugar production was contributed by sugarcane. Guangxi is the dominant sugarcane and sugar producer, accounting for 65% of sugar production in China. China’s sugarcane production faced serious problems in the past several years, especially the rapid increase in the labor cost because of the manual harvest. Now, China requires changing their sugarcane practice from manual to mechanical in order to catch up with the international trends in worldwide industry. Many other challenges and constraints are becoming severe, including abiotic and biotic stress, cost escalation, over fertilization, poor ratooning, and single cultivar. New technologies will be applied to sugarcane production, including omics-based breeding, best management practices, and so on.",book:{id:"6315",slug:"sugarcane-technology-and-research",title:"Sugarcane",fullTitle:"Sugarcane - Technology and Research"},signatures:"Muqing Zhang and Muralidharan Govindaraju",authors:[{id:"211925",title:"Dr.",name:"Muqing",middleName:null,surname:"Zhang",slug:"muqing-zhang",fullName:"Muqing Zhang"},{id:"227160",title:"Dr.",name:"Govindaraju",middleName:null,surname:"Muralidharan",slug:"govindaraju-muralidharan",fullName:"Govindaraju Muralidharan"}]},{id:"57733",doi:"10.5772/intechopen.71496",title:"Biotechnological Interventions for the Improvement of Sugarcane Crop and Sugar Production",slug:"biotechnological-interventions-for-the-improvement-of-sugarcane-crop-and-sugar-production",totalDownloads:1904,totalCrossrefCites:9,totalDimensionsCites:15,abstract:"Sugarcane, not only fulfills 70% of world sugar needs but is also a prime potential source of bioethanol. It is majorly grown in tropical and subtropical regions. Researchers have improved this grass to great extent and have developed energy cane with ability to accumulate up to 18% sucrose in its Culm. Improvement of this crop is impeded by its complex genome, low fertility, long production cycle and susceptibility to various biotic and abiotic stresses. Biotechnological interventions hold great promise to address these impediments paving way to get improved sugarcane crop. Further, being vegetatively propagated in most of the agroecological regions, it has become more attractive plant to work with. This chapter highlights, how advanced knowledge of omics (genomics, transcriptomics, proteomics and metabolomics) can be employed to improve sugarcane crop. In addition, potential role of in vitro techniques and transgenic technology has also been discussed for developing improved sugarcane clones with enhanced sugar recovery.",book:{id:"6315",slug:"sugarcane-technology-and-research",title:"Sugarcane",fullTitle:"Sugarcane - Technology and Research"},signatures:"Ghulam Mustafa, Faiz Ahmad Joyia, Sultana Anwar, Aqsa Parvaiz\nand Muhammad Sarwar Khan",authors:[{id:"211046",title:"Dr.",name:"Ghulam",middleName:null,surname:"Mustafa",slug:"ghulam-mustafa",fullName:"Ghulam Mustafa"},{id:"212508",title:"Dr.",name:"Faiz",middleName:null,surname:"Ahmad",slug:"faiz-ahmad",fullName:"Faiz Ahmad"},{id:"212509",title:"Ms.",name:"Sultana",middleName:null,surname:"Anwar",slug:"sultana-anwar",fullName:"Sultana Anwar"},{id:"212510",title:"Ms.",name:"Aqsa",middleName:null,surname:"Pervaiz",slug:"aqsa-pervaiz",fullName:"Aqsa Pervaiz"},{id:"212511",title:"Prof.",name:"Muhammad Sarwar",middleName:null,surname:"Khan",slug:"muhammad-sarwar-khan",fullName:"Muhammad Sarwar Khan"}]}],mostDownloadedChaptersLast30Days:[{id:"59391",title:"Sugarcane Production in China",slug:"sugarcane-production-in-china",totalDownloads:1914,totalCrossrefCites:12,totalDimensionsCites:19,abstract:"Sugarcane production in China has a prolonged history since fourth century BC. At present, China is the world’s third largest sugar producing country after Brazil and India. During the past decade, more than 90% of the sugar production was contributed by sugarcane. Guangxi is the dominant sugarcane and sugar producer, accounting for 65% of sugar production in China. China’s sugarcane production faced serious problems in the past several years, especially the rapid increase in the labor cost because of the manual harvest. Now, China requires changing their sugarcane practice from manual to mechanical in order to catch up with the international trends in worldwide industry. Many other challenges and constraints are becoming severe, including abiotic and biotic stress, cost escalation, over fertilization, poor ratooning, and single cultivar. New technologies will be applied to sugarcane production, including omics-based breeding, best management practices, and so on.",book:{id:"6315",slug:"sugarcane-technology-and-research",title:"Sugarcane",fullTitle:"Sugarcane - Technology and Research"},signatures:"Muqing Zhang and Muralidharan Govindaraju",authors:[{id:"211925",title:"Dr.",name:"Muqing",middleName:null,surname:"Zhang",slug:"muqing-zhang",fullName:"Muqing Zhang"},{id:"227160",title:"Dr.",name:"Govindaraju",middleName:null,surname:"Muralidharan",slug:"govindaraju-muralidharan",fullName:"Govindaraju Muralidharan"}]},{id:"58251",title:"Management of Late Blight of Potato",slug:"management-of-late-blight-of-potato",totalDownloads:2951,totalCrossrefCites:7,totalDimensionsCites:11,abstract:"Potato (Solanum tuberosum L.) is the most important crop and Phytophthora infestans (Mont.) de Bary is the oomycete, which was responsible for infamous Irish potato famine during 1843–45 and it continues to cause worldwide devastation of the potato. Moreover, this disease is re-emerging in the forms of different genotypes and causes huge yield loss in the potato crop. The factors which are responsible for huge yield loss of potato are applied improper management strategies and pathogen behavior. Management strategies includes; forecasting, cultural, biological, varietal and chemical management. Forecasting is the better option for management of late blight, if accurately forecasted and promptly information reaches to the end users. As infected potato tubers cause the primary sources of infection in next season. The cultural practices will also helpful in reducing inoculum load and managing the disease. The host resistance is best option for management of this disease. However, due to very divers’ virulence nature of P. infestans; the resistance of the varieties is wiped out within a decade. Several fungicides including contact, systemic and translaminar have been evaluated from time to time; however, the pathogen has shown a remarkable capacity for change with respect to host genotype and fungicides. Nowadays biological control is gaining importance due to its eco-friendly in nature.",book:{id:"6219",slug:"potato-from-incas-to-all-over-the-world",title:"Potato",fullTitle:"Potato - From Incas to All Over the World"},signatures:"Mehi Lal, Sanjeev Sharma, Saurabh Yadav and Santosh Kumar",authors:[{id:"186150",title:"Dr.",name:"Mehi",middleName:null,surname:"Lal",slug:"mehi-lal",fullName:"Mehi Lal"},{id:"194201",title:"Mr.",name:"Saurabh",middleName:null,surname:"Yadav",slug:"saurabh-yadav",fullName:"Saurabh Yadav"},{id:"210908",title:"Dr.",name:"Sanjeev",middleName:null,surname:"Sharma",slug:"sanjeev-sharma",fullName:"Sanjeev Sharma"},{id:"210909",title:"Dr.",name:"Santosh",middleName:null,surname:"Kumar",slug:"santosh-kumar",fullName:"Santosh Kumar"}]},{id:"57967",title:"Sugarcane Bagasse Valorization Strategies for Bioethanol and Energy Production",slug:"sugarcane-bagasse-valorization-strategies-for-bioethanol-and-energy-production",totalDownloads:2094,totalCrossrefCites:18,totalDimensionsCites:30,abstract:"The use of sugarcane bagasse pith as solid substrate for fungi and microbial growth is well known, as well as a source of microorganisms that can be isolated from it. Pith has also been used as a bulking agent for soil bioremediation. More recently, bagasse pith has been used for bioethanol production involving pretreatment and hydrolysis followed by fermentation and dehydration. However, little is reported about biomass valorization for the development of environmentally sound and innovative strategies to process sugarcane bagasse from sugar mills. Incineration of sugarcane bagasse pith is a very common and mature technology for waste disposal and generation of electrical and thermal energy. However, this approach may not be satisfactory in organic waste management due to pollutant emissions, economic and labor costs, loss of energy, and bad odor. In addition, no valuable product is generated from its decomposition process. Instead of incineration, recent research has focused on its utilization as biofuel source. In this chapter, the use of sugarcane bagasse pith as a waste material for incineration versus biomass to produce bioethanol is discussed in terms of energy ratio and emissions, in addition to elucidate the potential of sugarcane bagasse valorization for a more sustainable society.",book:{id:"6315",slug:"sugarcane-technology-and-research",title:"Sugarcane",fullTitle:"Sugarcane - Technology and Research"},signatures:"Elias Martinez-Hernandez, Myriam Adela Amezcua-Allieri, Jhuma\nSadhukhan and Jorge Aburto Anell",authors:[{id:"16428",title:"Dr.",name:"Jorge",middleName:null,surname:"Aburto",slug:"jorge-aburto",fullName:"Jorge Aburto"},{id:"27622",title:"Dr.",name:"Myriam",middleName:null,surname:"Amezcua",slug:"myriam-amezcua",fullName:"Myriam Amezcua"},{id:"232272",title:"Dr.",name:"Elias",middleName:null,surname:"Martinez-Hernandez",slug:"elias-martinez-hernandez",fullName:"Elias Martinez-Hernandez"},{id:"232274",title:"Dr.",name:"Jhuma",middleName:null,surname:"Sadhukhan",slug:"jhuma-sadhukhan",fullName:"Jhuma Sadhukhan"}]},{id:"59580",title:"In Vitro Propagation of Sugarcane for Certified Seed Production",slug:"in-vitro-propagation-of-sugarcane-for-certified-seed-production",totalDownloads:1675,totalCrossrefCites:2,totalDimensionsCites:1,abstract:"Micropropagation of sugarcane is important to obtain pathogen-free plants, genetically homogeneous and invigorate. The micropropagation procedure is divided into stages for the sake of better understanding. Micropropagation for large-scale sugarcane production using a temporary immersion system (TIS) is described. In addition, the aim of this chapter is to report, from the laboratory to the field, the best way to establish and use basic seed (primary seed), semicommercial seed (foundation or secondary seed) and commercial seed production. In conclusion, commercial sugarcane micropropagation enables the massive multiplication of plants to obtain certified vitroplants and increase the sugarcane and sugar productivity per unit area.",book:{id:"6315",slug:"sugarcane-technology-and-research",title:"Sugarcane",fullTitle:"Sugarcane - Technology and Research"},signatures:"Jericó J. Bello-Bello, Maurilio Mendoza-Mexicano and Juan A. Pérez-\nSato",authors:[{id:"197218",title:"Dr.",name:"Jericó Jabín",middleName:null,surname:"Bello Bello",slug:"jerico-jabin-bello-bello",fullName:"Jericó Jabín Bello Bello"},{id:"215000",title:"Dr.",name:"Jericó Jabín",middleName:null,surname:"Bello Bello",slug:"jerico-jabin-bello-bello",fullName:"Jericó Jabín Bello Bello"},{id:"224304",title:"BSc.",name:"Maurilio",middleName:null,surname:"Mendoza-Mexicano",slug:"maurilio-mendoza-mexicano",fullName:"Maurilio Mendoza-Mexicano"},{id:"224306",title:"Dr.",name:"Juan Antonio",middleName:null,surname:"Pérez-Sato",slug:"juan-antonio-perez-sato",fullName:"Juan Antonio Pérez-Sato"},{id:"224307",title:"Dr.",name:"José Humberto",middleName:null,surname:"Caamal-Velázquez",slug:"jose-humberto-caamal-velazquez",fullName:"José Humberto Caamal-Velázquez"}]},{id:"57411",title:"Breeding Potato for Quality Improvement",slug:"breeding-potato-for-quality-improvement",totalDownloads:1796,totalCrossrefCites:4,totalDimensionsCites:10,abstract:"Potato is the most important non-cereal food crop in the world, that in general represent a non-fattening, nutritious and wholesome food, which supply important nutrients to the human diet. The potato tubers contain considerable amounts of carbohydrates, vitamin C, essential amino acids and minerals. The potato quality includes biological traits (e.g. proteins, carbohydrates and minerals); sensorial traits (e.g. flavor, texture); and industrial traits (e.g. tuber shape, cold sweetening and starch quality). These traits are deemed very important for fresh consumption, where they are most likely to influence consumer’s choice worldwide. Since most quality traits are genetically controlled, breeding work can successfully meet the quality of potato tubers and fulfills the needs of a changing and demanding world. Breeding potato for quality traits requires a continuous flow of new genes and allelic diversity into the Solanum tuberosum gene pool. However, recent advances in conventional and non-conventional breeding methods have significantly improved the possibilities of producing novel genetic variability for selection of new genotypes, especially when biotechnologists and plant breeders pool the existing resources. The genetics, biochemical and physiology of several quality traits is to be given equal importance that ultimately makes breeding efforts less empirical and more predictable.",book:{id:"6219",slug:"potato-from-incas-to-all-over-the-world",title:"Potato",fullTitle:"Potato - From Incas to All Over the World"},signatures:"Meenakshi Kumari, Manoj Kumar and Shashank Shekhar Solankey",authors:[{id:"210702",title:"Dr.",name:"Shashank Shekhar",middleName:null,surname:"Solankey",slug:"shashank-shekhar-solankey",fullName:"Shashank Shekhar Solankey"},{id:"211044",title:"Dr.",name:"Meenakshi",middleName:null,surname:"Kumari",slug:"meenakshi-kumari",fullName:"Meenakshi Kumari"},{id:"211045",title:"Mr.",name:"Manoj",middleName:null,surname:"Kumar",slug:"manoj-kumar",fullName:"Manoj Kumar"}]}],onlineFirstChaptersFilter:{topicId:"1412",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,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:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",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:"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:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:33,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-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"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). 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. 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