Formulas for the three spatial covariance functions used in this analysis.
\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"Milestone",originalUrl:"/media/original/124"}},components:[{type:"htmlEditorComponent",content:'
Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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CT has evolved into an indispensable imaging method in clinical routine. It was the first method to non-invasively acquire images of the inside of the human body that were not biased by superimposition of distinct anatomical structures. The first generation of CT scanners developed in the 1970s and numerous innovations have improved the utility and application field of the CT, such as the introduction of helical systems that allowed the development of the "volumetric CT" concept. In this book we want to explore the applications of CT from medical imaging to other fields like physics, archeology and computer aided diagnosis. Recently interesting technical, anthropomorphic, forensic and archeological as well as paleontological applications of computed tomography have been developed. These applications further strengthen the method as a generic diagnostic tool for non- destructive material testing and three-dimensional visualization beyond its medical use.',isbn:null,printIsbn:"978-953-307-378-1",pdfIsbn:"978-953-51-6624-5",doi:"10.5772/879",price:139,priceEur:155,priceUsd:179,slug:"computed-tomography-clinical-applications",numberOfPages:354,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"17fbe9a3e9ff839ef1c17f31a4d3c3cd",bookSignature:"Luca Saba",publishedDate:"January 5th 2012",coverURL:"https://cdn.intechopen.com/books/images_new/354.jpg",numberOfDownloads:75543,numberOfWosCitations:44,numberOfCrossrefCitations:19,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:43,numberOfDimensionsCitationsByBook:3,hasAltmetrics:0,numberOfTotalCitations:106,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 15th 2010",dateEndSecondStepPublish:"December 13th 2010",dateEndThirdStepPublish:"April 19th 2011",dateEndFourthStepPublish:"May 19th 2011",dateEndFifthStepPublish:"July 18th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"57078",title:"Dr.",name:"Luca",middleName:null,surname:"Saba",slug:"luca-saba",fullName:"Luca Saba",profilePictureURL:"https://mts.intechopen.com/storage/users/57078/images/1786_n.jpg",biography:"Dr. Luca Saba’s research fields are focused on neuroradiology, multi-detector-row computed tomography, magnetic resonance, ultrasound, and diagnostics in vascular sciences. His work, as lead author, has been published more than 80 high impact factor, peer-reviewed journals as American Journal of Neuroradiology, European Radiology, European Journal of Radiology, Acta Radiologica, Cardiovascular and Interventional Radiology, Journal of Computer Assisted Tomography, American Journal of Roentgenology, Neuroradiology, Clinical Radiology, Journal of Cardiovascular Surgery, Cerebrovascular Diseases. Dr. Saba has written 7 book chapters and he presented more than 400 papers in national and international congresses (RSNA, ESGAR, ECR, ISR, AOCR, AINR, JRS, SIRM, AINR). He has also won 6 scientific and extracurricular awards during his career. Dr. Saba is member of the Italian Society of Radiology (SIRM), European Society of Radiology (ESR), Radiological Society of North America (RSNA), American Roentgen Ray Society (ARRS) and European Society of Neuroradiology (ESNR).",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"University of Cagliari",institutionURL:null,country:{name:"Italy"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1008",title:"Radiology Diagnosis",slug:"radiology-diagnosis"}],chapters:[{id:"25697",title:"Computer-Aided Diagnosis for Acute Stroke in CT Images",doi:"10.5772/22232",slug:"computer-aided-diagnosis-for-acute-stroke-in-ct-images",totalDownloads:3008,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:0,abstract:null,signatures:"Yongbum Lee, Noriyuki Takahashi and Du-Yih Tsai",downloadPdfUrl:"/chapter/pdf-download/25697",previewPdfUrl:"/chapter/pdf-preview/25697",authors:[{id:"46880",title:"Dr.",name:"Yongbum",surname:"Lee",slug:"yongbum-lee",fullName:"Yongbum Lee"},{id:"59127",title:"Dr.",name:"Noriyuki",surname:"Takahashi",slug:"noriyuki-takahashi",fullName:"Noriyuki Takahashi"},{id:"59128",title:"Prof.",name:"Du-Yih",surname:"Tsai",slug:"du-yih-tsai",fullName:"Du-Yih Tsai"}],corrections:null},{id:"25698",title:"3D-CT Mammary Lymphography Facilitate the Endoscopic Sentinel Node Biopsy",doi:"10.5772/21666",slug:"3d-ct-mammary-lymphography-facilitate-the-endoscopic-sentinel-node-biopsy",totalDownloads:2202,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:null,signatures:"Koji Yamashita, Shunsuke Haga and Kazuo Shimizu",downloadPdfUrl:"/chapter/pdf-download/25698",previewPdfUrl:"/chapter/pdf-preview/25698",authors:[{id:"44172",title:"Prof.",name:"Koji",surname:"Yamashita",slug:"koji-yamashita",fullName:"Koji Yamashita"}],corrections:null},{id:"25699",title:"CT Aided Postoperative Breast Conservation Brachytherapy Irradiation",doi:"10.5772/21673",slug:"ct-aided-postoperative-breast-conservation-brachytherapy-irradiation",totalDownloads:2412,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"D. 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Estuarine salinity responds to dynamic meteorological and hydrological processes [1] with important consequences to physical features, such as vertical stratification, as well as living resources, such as the distribution, abundance and diversity of species [2, 3, 4, 5]. For example, relatively low mixing and subsequent salinity stratification can lead to hypoxia in areas where organically-rich sediments are not adequately re-oxygenated, causing emigration of mobile fauna and degradation of ecosystem functions [5, 6, 7, 8, 9]. Rapid salinity changes, such as those associated with large rainfall events or tropical cyclones, can cause death of postlarval stages that are sensitive to unusually low salinities [10], and mass seaward migration and subsequent hyper-aggregation of mobile, commercially important species that can result in (1) shifts of juveniles from primary nursery areas protected from trawling to secondary non-nursery areas vulnerable to fishing pressure [11], (2) overharvest of adults due to increases in fishery catchability [12], or (3) bias fishery-independent surveys that leads to over-inflated population abundance estimates [12]. Thus, the need to accurately predict the spatiotemporal dynamics of salinity is unprecedented. The specific goals of this study were to: (1) evaluate several statistical models to hindcast and forecast salinity in the second largest estuary and largest lagoonal estuary in the United States—Pamlico Sound, North Carolina, USA, and (2) assess salinity observations, predictions, and standard errors under five hydrologic scenarios characteristic of historic and future climate changes.
Pamlico Sound (PS) is a relatively shallow estuary with a mean depth of 4 m and a maximum depth of 7 m. PS circulation is dominated by wind-driven currents and freshwater input [13, 14]. Seasonal cyclonic storms are also an important climatological component of the PS system. Since 1996, over three tropical storms or hurricanes have passed within 300 km of the North Carolina coast per year [10]. Given the important role that salinity plays in the abiotic and biotic system components of estuaries, and the likelihood that global climate change will increase the frequency of extreme weather events (e.g., floods, droughts, hurricanes—[9, 15, 16]), there is a critical need for models that can accurately forecast spatiotemporal variation in salinity (e.g., [17]). A recent review by Iglesias et al. [17] highlights the strengths of applying numerical modeling tools to characterize morpho-hydrodynamic processes in estuarine and coastal systems.
Producing retrospective salinity maps based on observational data does not require a statistical model based on hydrological mechanisms that affect salinity; it is possible to perform individual spatial interpolations for each time period of interest using an ordinary kriging model or a universal kriging model with a simple spatial trend. Predicting salinity under a hypothetical set of conditions, however, does require a model that can ‘learn’ about hydrological mechanisms based on retrospective data (e.g., [20, 21]). Thus, the more comprehensive goal of this study was to produce retrospective maps of salinity by developing a space-time statistical model in which the mean function represents the hydrological mechanisms that affect salinity, and a spatial covariance function makes up the difference between the observed salinity data and the mean function’s salinity prediction.
To create such a model, we constructed explanatory variables that accounted for the effect of riverine freshwater inflow (FWI), distance to inlet sources of oceanic saltwater, and hurricane incidence on salinities at different locations in PS. We used a forward-selection process to choose which of these variables to keep in the model. Standard errors based on the covariance function allowed for assessment of strengths and weaknesses of the representation of the hydrology in the mean function. Since an additional goal of this study was to provide a template for researchers to build process-based models of normally-distributed estuarine variables, we considered only models that could be fit using procedures in the SAS® software package, yet can be adopted to R-statistical software.
Other process-based models of PS salinity in the literature—all of which are differential-equation-based deterministic models—provided important insights into how different variables influenced spatiotemporal salinity variation in PS ([22, 23], and others). However, these models ultimately lacked the spatial resolution and/or coverage of the entire area of interest of this study, and none quantified uncertainty at every space-time prediction location. For example, Xu et al. [24] predicted surface and bottom salinity, and temperature at 30-second intervals over a spatial grid with varying cell size (200–800 m2) in the Pamlico River Estuary (PRE), a PS tributary, using a customized extension of the Environmental Fluid Dynamics Code [25] to incorporate FWI from major tributary rivers, as well as tide and wind effects on circulation. Although this model incorporated environmental variation and produced salinity predictions suitable to assess long-term space-time trends, the PRE makes up only 18% of the area of PS. Predicting salinity across the entire PS using this model would require spatial domain expansion and re-parameterization, and such extensions are not planned (J. Lin, NC State University, pers. comm. on behalf of Xu et al. [24]).
Though we are unaware of researchers that have constructed space-time statistical models of salinity in PS, there are examples of applying statistical models for spatial prediction of salinity in other estuaries. For example, Rathbun [26] used independent multiple linear regression models with spatially-correlated errors to predict salinity and dissolved oxygen (DO) in Charleston Harbor, SC over a two-week time period in 1988 as a function of spatial coordinates and distance to the estuary mouth. Chehata et al. [27] performed three-dimensional spatial interpolation of salinity and DO measurements in Chesapeake Bay. Qiu and Wan [20] developed a salinity model based on time series analyses of salinity data for the Caloosahatchee River Estuary, Florida, USA. The structure of their model consisted of an autoregressive term representing the system persistence and an exogenous term accounting for physical drivers including freshwater inflow, rainfall, and tidal water surface elevation that cause salinity to vary. The model was calibrated and validated using up to 20 years of measured data collected they found that the time series model offers comparable or superior performance compared with its 3-D, numerical counterpart. This model has been used as a tool for water resources management projects relating to ecosystem restoration and water control in south Florida [20]. Similarly, Ross et al. [21] examined the response of salinity in the Delaware Estuary, USA to climatic variations using statistical models and long-term (1950-present) records of salinity from the U.S. Geological Survey and the Haskin Shellfish Research Laboratory. The statistical models included non-parametric terms and were robust against auto-correlated and heteroscedastic errors. After using the models to adjust for the influence of streamflow and seasonal effects on salinity, several locations in the estuary showed significant upward trends in salinity. Insignificant trends are found at locations that are normally upstream of the salt front. The models indicate a positive correlation between rising sea levels and increasing residual salinity, with salinity rising from 2.5 to 4.4 psu per meter of sea-level rise. The results suggest that continued sea-level rise in the future will cause salinity to increase regardless of any variation in fresh water influx [21]. Urquhart et al. [28] present the results of multiple statistical models that predicted daily, gridded surface salinity at 1 km resolution across Chesapeake Bay, USA as a function of surface reflectance estimates of salinity from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Aqua platform satellite. Eight statistical methods were tested, and sea surface salinity was accurately predicted via remote sensed products with an accuracy that was more than sufficient for many physical and ecological applications [28].
None of these previous studies, however, attempted to explicitly represent the hydrological processes by which fresh and saltwater mixing affects estuarine salinity. In this paper, we describe the development of candidate explanatory variables to represent mechanisms affecting PS salinity and how that development led to consideration of two fundamentally different mean functions. We then describe the forward selection process by which candidate variables were chosen to be retained in the models, and how candidate covariance functions were selected to pair with each mean function. Next, we examined maps of salinity observations, predictions, and standard errors under five hydrologic scenarios, analyzed these results, and provided overall implications of the findings.
We used bottom salinity values measured by the North Carolina Division of Marine Fisheries (NC DMF) Pamlico Sound Trawl Survey Program 195 (
Pamlico Sound, NC and the Chowan, Roanoke, Pamlico, and Neuse Rivers. Green squares show the four river gauge stations used in this study. Purple dots indicate all P195 trawl survey sample stations for the 1987–2006 time domain. The pink star indicates the reference point from which northings and eastings were calculated. As referenced in Section 3.5, Parallel A is located at 35° 16′ N latitude and meridian B is at 75° 42′ W longitude.
The temporal domain contains
The fresh water influx (FWI) data represented watersheds of the Neuse, Pamlico, Roanoke, and Chowan rivers, which comprise 80% of the land draining into PS [29]. FWI observations were average daily river discharge rates collected by one US Geological Survey (USGS) gauge station per tributary (Figure 1): Neuse River (NR) station 02089500 in Kinston; Tar-Pamlico River (TPR) station 02083500 in Tarboro; Roanoke River (RR) station 02080500 in Roanoke Rapids; and Ahoskie Creek (AC) station 02053500 in Ahoskie, which gauges Chowan River inflow. Discharge rates in ft3/s for every day during the time domain (7305 days) were downloaded from the USGS Water Resources website for the state of North Carolina (USGS 2009) and were converted to m3/s. For each river, the gauge chosen was the furthest downstream gauge that recorded data over the entire temporal domain.
The creation of explanatory variables reflects the modeling context—the objectives, the geographical features of the spatial domain, and the space-time coverage and resolution of the data—but the general thought process can be modified by other researchers in a different context. We index the term
Sixty-one days is the average freshwater residence time of the four major rivers flowing into PS [30, 31, 32], accounting for the temporal lag between the upriver gauging of freshwater and the delivery of that water to
Since freshwater from river
The coordinates of each gauge station were used to calculate distance because the gauge was the location of the
The plot in Figure 2 of
Observed bottom salinity (psu) vs. the Roanoke River two-month relative freshwater influx index (
(A fortieth indicator variable was not used because it would create a non-full-rank design matrix, and the effect for the fortieth time period can be derived using the intercept.) This latter consideration led to the creation of two distinct mean function models: the
Although salinity on the inner-continental shelf of the U.S. Southeast Atlantic coast exhibits some spatial variability near PS [37], we follow Xie et al. [38] and assume constant open ocean salinity. This assumption allows for modeling the effect of ocean water mixing as a function of only the distance to inlet, as opposed to distance interacting with the salinity of the ocean water, from each spatial location in the sound to each of the major PS inlets: Oregon, Hatteras, and Ocracoke. Exploratory analyses reveal that models using a single variable (distance to the nearest inlet) rather than three variables (distances to each of the three inlets), explains the same amount of variability in salinity when other explanatory variables are also included. Therefore, we consider for inclusion in subsequent models the variable
A prevailing wind field that is north/northeast from March to August and south/southwest from September to February is the primary driver of currents in PS [39]. Thus, wind speed and direction were incorporated into the modeling process using the categorical variable
is used to examine the effects of seasonal wind patterns on the spatial distribution of salinity.
Holding other factors constant, sound-wide salinity in time periods that experience more evaporation of water from the surface of PS would likely be higher than those in time periods that experienced less evaporation, but no evaporation data were available for the space-time domain of interest. Salinity in time periods for which there was more direct precipitation into
Estuarine salinity varies over space such that functions of spatial coordinates might explain variability in salinity not accounted for by the other variables. Scatterplots of salinity versus easting and northing suggested that salinity is quadratic in the former and cubic in the latter. The quadratic function of easting can be explained by examining a west-to-east path through PS along the 35° 16′ N parallel (A in Figure 1): salinity should initially increase, reach a maximum at the saltwater plume near Ocracoke and Hatteras Inlets, and decrease again on the other side of the plume in the waters on the western shore of Hatteras Island near Buxton, NC. The cubic function of northing is best described by examining a north-to-south path along longitude of 75° 42′ W (B in Figure 1), where salinity should increase traveling south from Albemarle Sound, reach a local maximum near Oregon Inlet, decrease continuing past the saltwater inlet plume, and increase again as the Hatteras Inlet saltwater plume is reached. Thus,
Hurricanes can rapidly introduce large volumes of freshwater to estuaries via riverine influx, push large volumes of saltwater in through inlets via storm surge, and alter circulation patterns through abrupt changes in wind speed and direction [7, 10]. Hurricanes can also open new inlets to PS, which can alter current flow and increase saltwater intrusion [41]. The variable
Section 3 identifies 46 candidate explanatory variables for the process model mean function:
The results of eight separate ordinary least squares linear regression models of salinity make up the rows Table 1. The first five consist of an intercept and a single explanatory variable:
Adjusted R2 is a modification of R2 that penalizes the number of explanatory variables. While R2 increases as more variables are added to a model, adjusted R2 increases only if the added variable decreases the error sum of squares enough to offset the loss in error degrees of freedom.
The model with the long-term freshwater influx indices had the largest adjusted R2 at 0.38, followed by the model with the distance from the nearest inlet (0.34), and the model with the short-term FWI indices (0.27). None of the other four models explained more than 5% of the variability in salinity. We chose the model with the long-term freshwater influx indices as the base upon which to build the mean function.
To this base model we added the variable
Because the effect of FWI from one river on a given location in PS could change based on the FWI from another river during the same time period, we evaluated the addition of the 6 pair-wise interactions among the four
Spatial coordinate variables were evaluated last in groups according to their polynomial order, with squared and cubic terms added before interactions. We considered these variables last because we wanted to include them only if they explained additional variability in the response after more interpretable variables were included. We determined that including all variables except
To build the time model, we followed the same procedure described above, selecting for the base of the mean function a set of time period indicator variables because a linear regression of
The variable selection analyses above used ordinary least squares (OLS) regression to model salinity as a function of explanatory variables. That model can be written as
where
where bold print indicates vectors so that
Rarely, however, does the assumption of independent and identically distributed errors hold for observations of natural phenomena associated with locations in space and time. While it is intuitive that values of salinity located close together in space should be similar, it is also generally the case that the deviations from the mean function of observations located close together are similar. That similarity is referred to as spatial covariance, and the spatial covariance between deviations from the mean trend at two locations within the same time period can be modeled as a function of the distance separating them. Including in the overall model both a deterministic mean function and a spatial covariance function allowed predictions of salinity at locations where there were no observations.
Valid covariance functions ensure that the covariance matrix will be positive definite, which, in turn, ensures that variances will be non-negative. Each covariance function has a shape defined by a range parameter, a partial sill, and sometimes a nugget effect. Appendix Table A1 gives formulas for determining spatial covariance according to the exponential, Gaussian, and spherical covariance functions, each with and without a nugget effect. Figure 3 shows an example of the spherical covariance function—the solid red line—fit to a sample covariogram—the blue dots—of deviations from the process model for June 1994. The range parameter—
Sample covariogram for June 1994 calculated from process model residuals (blue dots). The solid red line illustrates a spherical covariance function fit to the covariogram. Covariance is in units of salinity squared.
Model (3), modified to include spatial correlation, becomes
where
where zero matrices for off-diagonal elements indicate that deviations in one time period are not correlated with those in another. We make this assumption partially due to the long time span separating June and September, but also because no SAS® procedure has the capacity to model such space-time correlation while at the same time allowing every time period to have different spatial covariance parameters and allowing a mean function to be fit. Diagonal elements
Understanding how predictions of salinity and prediction standard errors are generated from this model will make the results and analysis in Sections 6 and 7 easier to understand. To predict salinity at space–time locations where it is not observed, the following results are needed. Superscripts differentiate between locations where salinity is observed and unobserved. Model (4), represents observations of salinity (by virtue of the dimensions of the vectors and matrices), but we model salinity observations and unobserved values of salinity at other space-time locations using a similar model, the joint distribution of unobserved and observed salinity, given by
Here,
and
Let
The pipe symbol (|) means “given” or “conditioned on knowing the values of” the terms following the pipe symbol. The terms before the comma represent the mean of the multivariate normal distribution, which is used for the salinity prediction, and the terms after the comma represent the variance-covariance matrix, which is used for prediction standard errors. Salinity predictions are the sum of the mean trend,
The salinity predictor
is an
Salinity predictions are better when a spatial covariance function is combined with either mean function. For example, of the time models, the exponential covariance function with a nugget produced predictions with the lowest RMSE (2.1), slope closest to one (0.92), and intercept closest to zero (1.55). Comparing process models, the exponential and spherical, each with and without a nugget, performed equally well, and better than the time models. To select the best model from this group of four, we examined statistics based on how well the model fit the base dataset. The model with an exponential covariance function with a nugget had the lowest AIC (7580.0) and BIC (7711.7) and was thus chosen as the final model. It explained 89% of variability in the test dataset and generated predictions with RMSE 2.0.
Next, we fit this model using the full dataset, and produced retrospective maps of salinity predictions and standard errors at evenly spaced 1 nmi (1.85 km) increments for each time period. Forty-two salinity predictions—less than 0.1% of the total number of predictions—were negative and set to zero.
To examine variations in the spatial distribution of salinity under drought, average, and flood conditions, we classified freshwater influx from each river within each time period (
Salinity model predictions (left), prediction standard errors (bottom right), and P195 survey observations (top right) for June 2005, classified as moderate-to-moderate FWI.
Salinity model predictions (left), prediction standard errors (bottom right), and P195 survey observations (top right) for June 1999 (A) and June 2002 (B), both classified as low-to-low FWI.
Salinity model predictions (left), prediction standard errors (bottom right), and P195 survey observations (top right) for September 1999 (A) and June 2003 (B), both classified as flood-to-flood FWI.
The lower right pane of Figure 4 (as well as Figure 5A and B
This spatial trend in SEs is further illustrated by comparing locations of high SE in the same time period, which are also consistent over time. High SEs occur between the mouths of the Neuse and Pamlico Rivers and along a margin of varying width following the outline of the Outer Banks, areas within which sampling does not occur (Figure 1). We note here that because SEs increase as distance from sample site increases, we chose to generate only interpolated (and not extrapolated) salinity predictions. In June 2005, as in all other time periods, predictions were generated only for locations within
Though June 2002 salinity observations have a larger mean and greater variability, the majority of prediction standard errors are less than 1.01. In June 1999, however, SEs fell between 1.01 and 1.81 at all prediction locations except those that were very close to observations. This result shows that the conditions affecting salinity in PS were better represented by the mean function in June 2002 than they were in June 1999.
Because water exchange between lagoonal estuaries and the open ocean can be relatively restricted, there is a relatively high potential in systems like PS for changes in precipitation patterns and storm frequencies associated with global climate change to result in changes in salinity patterns and subsequent ecosystem alterations. Changes in precipitation will affect the amount and timing of river flow, which will impact nutrient cycling, estuarine flushing rates, and salinity. Increased storm activity may open new inlets, which would alter current flow, increase tidal action, and allow a greater influx of seawater that carries with it both different chemical signals and mobile species. Salinity is therefore a practical estuarine characteristic to use to study the impacts of these changes, as both effects mentioned above include enhanced water exchange that impacts overall estuarine salinity content [43, 44].
We developed and evaluated two statistical models, using the best model to hindcast salinity in PS. The process mean function combined with the exponential covariance with a nugget explained 89% of the variability in a test dataset with a RMSE of 2.0 and produced relatively accurate retrospective salinity maps under a wide range of freshwater influx and system-state scenarios. Much of this accuracy was due to allowing the range and partial sill parameters of the spatial covariance to be time-period specific. We then examined variations in the spatial distribution of salinity under varying freshwater influx (FWI) conditions such as drought, average FWI, and flood conditions, and identified the following patterns. In years with moderate FWI, the salinity gradient increased from west to east in PS as expected, and was highest adjacent to the major inlets, with highest salinities near Oregon Inlet. In years with low FWI indicative of drought conditions, the overall mean and variance in salinity increased in PS. In years with floods, salinities displayed a high degree of spatial variation, with salinities being lower near the tributaries as expected, yet also displaying occasional sharp increases in salinity near inlets due to influx of ocean water into PS via the major inlets.
For retrospective prediction purposes, model improvements could focus on improvements to the mean trend, the covariance, or both, and such improvements could be evaluated using the test dataset. A reasonable goal might be to increase
Though
Differences in both salinity values and SE estimates between early-stage drought during June 1999 and late-stage drought during June 2002 suggest accounting for effects of FWI over a longer duration than 61 days. Doing so might explain differences in salinity patterns seen in time periods with similar one-week and two-month FWI conditions. Molina [45] calculated an 11 month mean residence time for freshwater in PS. We could incorporate this effect by adding a third freshwater influx index to the mean function or by adding an autoregressive component to the model so that salinity in a given time period was a function of mean salinity in the previous time period. The first option would be tedious from a data-manipulation standpoint, but much easier from a mathematical model-fitting standpoint, because SAS® Proc Mixed could still be used. The second option necessitates a change in the covariance function, as we can no longer assume that salinity deviations from the mean function at a given space-time point were independent in time. This second option would also require specialized hand-written code, as no current SAS® Proc allows such a dynamic space-time model to be fit.
Differences in salinity patterns between June 1999 and June 2002, our two low-to-low FWI time periods, could be attributed to differences in FWI from the Roanoke River, one of the two northern rivers whose connection to PS is indirect. This observation warrants further investigation into the calculation of the FWII indices; namely, an investigation of water-path distance as a possible substitute for crow-flies distance between river gauges and sites in PS. Although we did not find a study that demonstrated marked predictive improvement using water-path distance under all circumstances ([36, 46], and others), it would be interesting in future work to compare differences in PS salinity predictions using both distance methods. Recall that Gardner et al. [34] noted more accurate predictions of stream temperatures when models incorporated water-path distance, but only when this distance was further modified and weighted by stream order. It might be the case that water-path distance out-performs crow-flies distance in predicting estuarine salinity when care is taken to make all explanatory variables as meaningful as possible. Development of an automated procedure for calculating water-path distances similar to the one used in [47] would make such an investigation more practically feasible.
Another way to achieve flexibility while still specifying a single covariance function for every time period, would be to allow an anisotropic covariance function. Geometric anisotropy allows for different range parameters in different directions. For example, if the water current in PS were flowing directly north-to-south, two points separated by a north-to-south vector might have more similar values of salinity than would two points separated by a west-to-east vector of the same length. Fortunately, the parameterization of a geometric anisotropic covariance function is such that if anisotropy were unnecessary, the parameters would take values that effectively result in an isotropic covariance function. The cost of this added flexibility is the need to estimate two additional covariance parameters per time period, for a total of 80 additional parameters. Computation time might be less here than for Matern, since anisotropic covariance functional forms are less complex.
We created a statistical model combining a process mean function with an exponential spatial covariance function with a nugget to predict salinity in a lagoonal estuary. This model can generate predictions of bottom salinity for Pamlico Sound, NC that are more spatially-resolute than any previous bottom salinity predictions encountered in the literature for this system. The salinity maps produced using the model are useful for researchers to build an intuitive understanding of salinity dynamics under PS conditions covered by these 40 time periods. Salinity predictions can also be used to inform future analyses including, but not limited to, the examination of historical distribution patterns of estuarine species relative to salinity variability and the prediction of salinity changes under various global climate change scenarios.
We thank the North Carolina Division of Marine Fisheries and the United States Geological Survey for providing datasets used in this study. We also thank editor A. Manning for helpful comments that improved the manuscript. Funding for this project was provided by the Environmental Defense Fund (Program Manager Pam Baker), North Carolina Coastal Recreational Fishing License Program (Grant No. 2010-H-004), North Carolina Sea Grant (R12-HCE-2) and the National Science Foundation (OCE-1155609) to D. Eggleston. A. Nail was supported as a VIGRE Postdoctoral Fellow by NSF grant DMS 0354189.
See Table A1.
Name of covariance function | ||
---|---|---|
With nugget effect | Without nugget effect | |
Exponential | ||
Gaussian | ||
Spherical | ||
Formulas for the three spatial covariance functions used in this analysis.
Explanatory variable or set of explanatory variables | Adj R2 |
---|---|
0.34 | |
0.049 | |
0.035 | |
0.029 | |
0.015 | |
0.27 | |
0.38 | |
0.41 |
Adjusted R2 for the eight initial linear regression models. All regressions include an intercept plus the variables listed.
Model type | −2 log likelihood | AIC | BIC | RMSE (psu) | Slope/β1 | Intercept/β0 | R2 | |
---|---|---|---|---|---|---|---|---|
Process | IID | 9935.9 | 9937.9 | 9943.5 | 2.9 | 0.98 | 0.84 | 0.74 |
Exponential | 7430.7 | 7584.7 | 7714.7 | 2.0 | 0.95 | 1.03 | 0.89 | |
Exponential + | 7424.0 | 7580.0 | 7711.7 | 2.0 | 0.96 | 0.96 | 0.89 | |
Gaussian | 8198.0 | 8356.0 | 8489.5 | 2.3 | 0.94 | 1.37 | 0.84 | |
Gaussian + | 7532.0 | 7686.0 | 7816.0 | 2.1 | 0.94 | 1.15 | 0.87 | |
Spherical | 7570.0 | 7722.0 | 7850.4 | 2.0 | 0.95 | 1.07 | 0.88 | |
Spherical + | 7571.6 | 7727.6 | 7859.3 | 2.0 | 0.96 | 0.93 | 0.89 | |
Time | IID | 7077.5 | 7079.5 | 7084.9 | 2.6 | 0.83* | 3.47* | 0.83 |
Exponential | Infinite | |||||||
Exponential + | 6217.1 | 6367.1 | 6493.7 | 2.1 | 0.92* | 1.55* | 0.87 | |
Gaussian | 6281.0 | 6433.0 | 6561.3 | 2.2 | 0.90* | 1.98* | 0.86 | |
Gaussian + | 6214.0 | 6366.0 | 6494.4 | 2.2 | 0.91* | 1.90* | 0.86 | |
Spherical | 6199.6 | 6315.6 | 6479.9 | 2.2 | 0.91* | 1.86* | 0.86 | |
Spherical + | 6201.3 | 6357.3 | 6489.1 | 2.2 | 0.91* | 1.86* | 0.86 |
Summary statistics comparing salinity observations in the test dataset to predictions based on fitting models to the base dataset.
Process and time mean functions with no spatial covariance (IID) and with each of six covariance functions were used. The symbol “σ2n” indicates that a nugget was included. Stars (*) indicate rejection of the appropriate null hypothesis at the α = 0.05 level of significance: H01: σ2n = 0; H02: β1 = 1; H03: β0 = 0. The exponential plus nugget process model is highlighted as it was chosen as the best model of PS salinity for our modeling context.
Time periods and mean predicted salinity rank (mmyy, r) | ||
---|---|---|
Flood | Flood | |
High | none | |
Moderate | (0687, 28), (0689, 27) | |
Low | None | |
High | Flood | (0903*, 39), (0690, 29) |
High | (0904*, 32) | |
Moderate | (0698, 38), (0693, 36), (0697, 35) | |
Low | None | |
Moderate | Flood | (0996*, 33) |
High | (0696, 26), (0900, 24) | |
Moderate | ||
Low | (0694, 17) | |
Low | Flood | (0987, 18) |
High | (0695, 6) | |
Moderate | (0905, 20) | |
Low | (0997, 15), |
Sixteen combinations of 2mo_ and 1wk_FWIrt classifications; time periods that exhibit each set of conditions; and mean predicted salinity rank (1 = highest).
Only time periods that fit each scenario as defined in Section 6 are listed; the remaining 7 time periods were not classified. Boldfaced time periods are examined in Section 6. Stars (*) indicate time periods in which hurricanes occurred within the 61 days prior to the survey.
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Muñoz",authors:[{id:"125292",title:"Dr.",name:"Katy",middleName:null,surname:"Satué Ambrojo",slug:"katy-satue-ambrojo",fullName:"Katy Satué Ambrojo"}]},{id:"31163",doi:"10.5772/35840",title:"Intravascular Leukocyte Chemotaxis: The Rules of Attraction",slug:"intravascular-leukocyte-chemotaxis-the-rules-of-attraction",totalDownloads:2331,totalCrossrefCites:1,totalDimensionsCites:4,abstract:null,book:{id:"1830",slug:"hematology-science-and-practice",title:"Hematology",fullTitle:"Hematology - Science and Practice"},signatures:"Sara Massena and Mia Phillipson",authors:[{id:"106058",title:"Dr.",name:"Mia",middleName:null,surname:"Phillipson",slug:"mia-phillipson",fullName:"Mia Phillipson"},{id:"106366",title:"Dr.",name:"Sara",middleName:null,surname:"Massena",slug:"sara-massena",fullName:"Sara Massena"}]},{id:"37047",doi:"10.5772/32080",title:"Microparticles: Role in Haemostasis and Venous Thromboembolism",slug:"microparticles-role-in-haemostasis-and-venous-thromboembolism",totalDownloads:2440,totalCrossrefCites:0,totalDimensionsCites:4,abstract:null,book:{id:"832",slug:"pathophysiology-and-clinical-aspects-of-venous-thromboembolism-in-neonates-renal-disease-and-cancer-patients",title:"Pathophysiology and Clinical Aspects of Venous Thromboembolism in Neonates, Renal Disease and Cancer Patients",fullTitle:"Pathophysiology and Clinical Aspects of Venous Thromboembolism in Neonates, Renal Disease and Cancer Patients"},signatures:"Anoop K. Enjeti and Michael Seldon",authors:[{id:"90071",title:"Dr.",name:"Anoop",middleName:null,surname:"Enjeti",slug:"anoop-enjeti",fullName:"Anoop Enjeti"},{id:"151786",title:"Dr.",name:"Michael",middleName:null,surname:"Seldon",slug:"michael-seldon",fullName:"Michael Seldon"}]},{id:"59051",doi:"10.5772/intechopen.70937",title:"Acute Myeloid Leukemia in Pediatric Patients: A Review About Current Diagnostic and Treatment Approaches",slug:"acute-myeloid-leukemia-in-pediatric-patients-a-review-about-current-diagnostic-and-treatment-approac",totalDownloads:1545,totalCrossrefCites:1,totalDimensionsCites:4,abstract:"Acute leukemia is the most common childhood malignancy, accounting for almost 35% of all childhood cancers. Acute myeloid leukemia (AML) represents 15–20% of pediatric acute leukemia. Majority of AML cases appear de novo, however a minority of cases can present as a secondary malignancy. AML is a highly heterogeneous disease and its diagnosis involves a combination of diagnostic analyses including morphology, immunophenotyping, cytochemistry, and leukemic blasts derived from peripheral blood or bone marrow demonstrating cytogenic and molecular characteristics. Through the identification of recurrent genetic mutations, it has been made possible to refine individual prognosis and guide therapeutic management. The current survival rate of children with AML is approximately 70%. The standard therapeutic regimen is a combination of cytarabine- and anthracycline-based regimens with allogenic stem cell transplantation in appropriate patients. Relapse in pediatric patients suffering from AML occurs in approximately 30% of cases, whereas death occurs in 5–10% of patients as a result of disease complications or chemotherapeutic side effects. In understanding the genetic basis of AML, targeted therapies will have the ability to reduce treatment-related morbidity and mortality. Here, we provide a comprehensive review of AML, its biology, diagnosis and therapeutic management in pediatric patients.",book:{id:"6261",slug:"myeloid-leukemia",title:"Myeloid Leukemia",fullTitle:"Myeloid Leukemia"},signatures:"Katarzyna Derwich, Dorothy Mitkowski and Jolanta Skalska-\nSadowska",authors:[{id:"205540",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Derwich",slug:"katarzyna-derwich",fullName:"Katarzyna Derwich"},{id:"214057",title:"Dr.",name:"Dorothy",middleName:null,surname:"Mitkowski",slug:"dorothy-mitkowski",fullName:"Dorothy Mitkowski"},{id:"214058",title:"Dr.",name:"Jolanta",middleName:null,surname:"Skalska-Sadowska",slug:"jolanta-skalska-sadowska",fullName:"Jolanta Skalska-Sadowska"}]},{id:"59322",doi:"10.5772/intechopen.73862",title:"Hypogonadism in Female Patients with Beta Thalassemia Major",slug:"hypogonadism-in-female-patients-with-beta-thalassemia-major",totalDownloads:1144,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"Beta thalassemia is the most frequent hemoglobinopathy worldwide. In patients with beta thalassemia major (BTM), the consequence of long-term life-saving transfusions is iron overload in liver, heart and endocrine glands. Hypogonadotropic hypogonadism is the most frequent endocrine complication. Recent progresses in the treatment of BTM dramatically improved life expectancy and quality of life of these patients, making the concern for fertility and pregnancy to gain importance. Therefore, we performed a review of the available data regarding hypogonadism in female patients with BTM. We found that hypogonadotropic hypogonadism is still frequently found in female patients with BTM. Pituitary iron overload seems to be the main factor contributing to hypogonadism occurrence, although iron-related damage of the ovaries and the genital tract cannot be excluded. The increased oxidative stress observed in BTM patients was hypothesized as a contributor to pituitary-gonadal dysfunction. Hypogonadism has significant consequences on quality of life, final height, bone health and fertility of the patients. Estro-progestative administration is essential in order to minimize consequences, although the best treatment regimen should be carefully weighted in each patient. Although spontaneous fertility is reduced by the presence of hypogonadism, it seems that ovulation-induction treatment with gonadotropins is effective in achieving pregnancies in majority of patients.",book:{id:"6210",slug:"thalassemia-and-other-hemolytic-anemias",title:"Thalassemia and Other Hemolytic Anemias",fullTitle:"Thalassemia and Other Hemolytic Anemias"},signatures:"Alice Ioana Albu and Dragos Albu",authors:[{id:"200829",title:"Dr.",name:"Alice",middleName:"Ioana",surname:"Albu",slug:"alice-albu",fullName:"Alice Albu"},{id:"207984",title:"Dr.",name:"Dragoș",middleName:null,surname:"Albu",slug:"dragos-albu",fullName:"Dragoș Albu"}]}],mostDownloadedChaptersLast30Days:[{id:"64871",title:"Diagnosis and Classification of Myelodysplastic Syndrome",slug:"diagnosis-and-classification-of-myelodysplastic-syndrome",totalDownloads:3212,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"Myelodysplastic syndrome (MDS) is a clonal hematopoietic stem cell disorder characterized by morphological dysplastic changes in one or more of the major hematopoietic cell lines. MDS can present with varying degrees of single or multiple cytopenias including neutropenia, anemia and thrombocytopenia. Presentation of MDS can range from asymptomatic to life threatening. MDS diagnosis and classification present important challenges, particularly in the distinction from benign conditions. French-American-British (FAB) classification proposed a classification based on easily obtainable laboratory information and was recommended in early and as modified by guidelines of new classification of World Health Organization (WHO). The strategy of diagnostic laboratory in MDS depends on morphological changes and is based on existence of dysplastic changes in the peripheral blood and bone marrow including peripheral blood smear, bone marrow aspirate smear and bone marrow trephine biopsy. The correct morphological interpretation and the use of cytogenetics, immunophenotyping, immunohistochemistry and molecular analysis will give valuable information on diagnosis and prognosis.",book:{id:"7138",slug:"recent-developments-in-myelodysplastic-syndromes",title:"Recent Developments in Myelodysplastic Syndromes",fullTitle:"Recent Developments in Myelodysplastic Syndromes"},signatures:"Gamal Abdul Hamid, Abdul Wahab Al-Nehmi and Safa Shukry",authors:[{id:"36487",title:"Prof.",name:"Gamal",middleName:null,surname:"Abdul Hamid",slug:"gamal-abdul-hamid",fullName:"Gamal Abdul Hamid"},{id:"273724",title:"Dr.",name:"Safa",middleName:null,surname:"Shukry",slug:"safa-shukry",fullName:"Safa Shukry"},{id:"277511",title:"Dr.",name:"Abdulwahab",middleName:null,surname:"Al-Nehmi",slug:"abdulwahab-al-nehmi",fullName:"Abdulwahab Al-Nehmi"}]},{id:"60442",title:"Invasive and Noninvasive Approaches in Prenatal Diagnosis of Thalassemias",slug:"invasive-and-noninvasive-approaches-in-prenatal-diagnosis-of-thalassemias",totalDownloads:1730,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Thalassemia is a significant health problem worldwide. There are two main classifications, α- and β-thalassemias, which are usually caused by the defective synthesis of the α-globin, and which are commonly caused by different mutations of the β-globin chain. Different hemoglobin mutations have been identified to date. Thalassemias can result in profound anemia from early life and, if not treated with regular blood transfusions, can lead to death in the first year. Prenatal diagnosis of thalassemia is the essential part of preventive medicine and is currently dependent on the use of invasive diagnostic tests within the first 2 months of pregnancy. These diagnostic techniques carry a small but significant risk of fetal loss up to 1%. Molecular diagnostic methods have been developed for genotyping thalassemias based on PCR techniques and high-throughput technologies. Noninvasive tests using cell-free DNA (cfDNA) from a maternal blood sample is also an alternative method, thus eliminating the risk of miscarriage. This chapter summarizes the current invasive approaches and the noninvasive methods using cell-free fetal DNA as new molecular diagnostic methods for genotypic diagnosis of thalassemia in clinical practice. Prevention strategies that encompass carrier screening, genetic counseling, and prenatal diagnosis are discussed.",book:{id:"6210",slug:"thalassemia-and-other-hemolytic-anemias",title:"Thalassemia and Other Hemolytic Anemias",fullTitle:"Thalassemia and Other Hemolytic Anemias"},signatures:"Abdullah Tuli and Ebru Dündar Yenilmez",authors:[{id:"183998",title:"Ph.D.",name:"Ebru",middleName:null,surname:"Dündar Yenilmez",slug:"ebru-dundar-yenilmez",fullName:"Ebru Dündar Yenilmez"},{id:"215677",title:"Prof.",name:"Abdullah",middleName:null,surname:"Tuli",slug:"abdullah-tuli",fullName:"Abdullah Tuli"}]},{id:"62044",title:"Sickle Cell Disease: A Genetic Disorder of Beta-Globin",slug:"sickle-cell-disease-a-genetic-disorder-of-beta-globin",totalDownloads:1784,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Sickle cell disease (SCD) is a structural and monogenetic genetic disorder due to a mutation that occurs in the globin β-chain, resulting in the formation of hemoglobin S (Hb S), a protein composed of two normal, and two β-type mutant chains. Estimates indicate that the prevalence among live births is 4.4% in the world. The difficulty in circulating the sickle cell, its interaction with endothelial cells, leukocytes, platelets, endothelial dysfunction, and the abnormal expression of adhesion molecules permeate the beginning of the blood vessel occlusion process as well as pathophysiological aspects of SCD. Among the secondary complications are the stroke, pulmonary hypertension, leg ulcer, renal disorders, and all complications associated with vascular dysfunction. Clinical and biochemical markers of disease severity can be used to predict risk, prevent complications, and increase the expectation and quality of life of the SCD population. The entire scenario generated by Hb S has implications for the health and social inclusion of patients, so the treatment of the person with SCD needs an approach focused on the prevention of these complications in an individualized way.",book:{id:"6210",slug:"thalassemia-and-other-hemolytic-anemias",title:"Thalassemia and Other Hemolytic Anemias",fullTitle:"Thalassemia and Other Hemolytic Anemias"},signatures:"Karen Cordovil",authors:[{id:"228575",title:"M.D.",name:"Karen",middleName:null,surname:"Cordovil",slug:"karen-cordovil",fullName:"Karen Cordovil"}]},{id:"66394",title:"Diffuse Large B-Cell Lymphoma",slug:"diffuse-large-b-cell-lymphoma",totalDownloads:2451,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Diffuse large B-cell lymphoma (DLBCL) is a heterogenous class of aggressive lymphoma and is considered as the most common subtype of non-Hodgkin lymphomas (NHL). Several genetic anomalies such as point mutations, numerical alterations, and, more rarely, translocations and gene amplifications play a role in the pathogenesis of this class of B-cell lymphoma and have been related to specific histological and immunophenotypic subtypes. On the other hand, the treatment protocol in DLBCL did not witness significant changes during the last two decades. The widespread adoption of rituximab as an important adjuvant to standard chemotherapy protocol in CD20+ cases was a notable exception, which provided significant improvement in disease-free survival and overall survival, with limited toxicity. However, no less than 20% of patients diagnosed with DLBCL exhibit relapse after the initial response to R-CHOP regimen, while more than 15% of the patients exhibit primary refractory disease. This is the reason why a review of all the morphological, clinical, and therapeutic particularities of DLBCL is required.",book:{id:"8316",slug:"normal-and-malignant-b-cell",title:"Normal and Malignant B-Cell",fullTitle:"Normal and Malignant B-Cell"},signatures:"Patrascu Ana Maria, Ionela Rotaru, Valeriu Surlin and Stefan Patrascu",authors:[{id:"158096",title:"Associate Prof.",name:"Valeriu",middleName:null,surname:"Surlin",slug:"valeriu-surlin",fullName:"Valeriu Surlin"},{id:"194539",title:"Dr.",name:"Stefan",middleName:null,surname:"Patrascu",slug:"stefan-patrascu",fullName:"Stefan Patrascu"},{id:"290810",title:"Dr.",name:"Ana Maria",middleName:null,surname:"Patrascu",slug:"ana-maria-patrascu",fullName:"Ana Maria Patrascu"},{id:"292959",title:"Dr.",name:"Ionela",middleName:null,surname:"Rotaru",slug:"ionela-rotaru",fullName:"Ionela Rotaru"}]},{id:"61929",title:"Idiosyncratic Drug-Induced Severe Neutropenia and Agranulocytosis: State of the Art",slug:"idiosyncratic-drug-induced-severe-neutropenia-and-agranulocytosis-state-of-the-art",totalDownloads:1582,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"In this chapter, we report and discuss the diagnosis and management of idiosyncratic drug-induced, or drug-associated, severe neutropenia, and agranulocytosis (neutrophil count of <0.5 × 109/L). In this setting, neutropenia remains a potentially serious adverse event due to the frequency of severe sepsis, with severe deep tissue infections (e.g., pneumonia), life-threatening infections, septicemia, and septic shock in two-thirds of all hospitalized patients. Recently, several poor prognostic factors, impacting the hematological recovery, the duration of hospitalization, and the outcome have been identified that may be helpful when identifying “frailty” patients. These factors include: old age, poor performance status, septicemia or shock, comorbidities such as renal failure, and a neutrophil count below 0.1 × 109/L. recovery. In this situation, modern management, with broad-spectrum antibiotics in case of any sepsis sign and hematopoietic growth factors (HGF) (particularly G-CSF), is likely to improve the prognosis, with a current mortality rate around 5%.",book:{id:"6439",slug:"hematology-latest-research-and-clinical-advances",title:"Hematology",fullTitle:"Hematology - Latest Research and Clinical Advances"},signatures:"Emmanuel Andrès and Rachel Mourot-Cottet",authors:[{id:"143493",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Andrès",slug:"emmanuel-andres",fullName:"Emmanuel Andrès"}]}],onlineFirstChaptersFilter:{topicId:"1026",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},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:"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,numberOfPublishedBooks:32,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. 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,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. 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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:"chapter.detail",path:"/chapters/5183",hash:"",query:{},params:{id:"5183"},fullPath:"/chapters/5183",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)}()