Adopted notation
\r\n\tThe WHO classification in 2007; was based on the histogenesis and cell origin of the tumor. In the latest classification made in 2016; to better characterize the tumor and obtain better data on its prognosis; The combination of molecular and genetic biomarkers and histopathological features of the tumor was used. Despite all current treatment approaches, the median survival time is around 12 months in most GBM patients. Compared with the situation of some types of successfully treated cancers; the survival time of GBM patients is not at an acceptable level today. In the treatment of CNS tumors; surgery, chemotherapy, and radiation treatments (x-rays, gamma rays, electron and proton beams) are used. The therapeutic potential of chemotherapy; New strategies are needed to increase drug concentration at the diseased site, as this largely depends on the ability of the chemotherapeutic agent to achieve effective concentrations at tumor localization. Based on our better understanding of the genetic and molecular characteristics of CNS tumors; Targeted therapies, including vaccines, and treatment protocols such as immunotherapy are promising developments.
\r\n\r\n\tThis book supposes to be written by many authors who have an internationally honored place in their field to share their ideas about the treatment of CNS tumors. Surgery, Radiotherapy, Chemotherapy and Antiangiogenic Therapy Protocols, Immunotherapy, Molecular Therapy, Specific target-agents therapy with Nanoparticles and Gene Therapy for CNS tumors among the book chapters.
\r\n\tIn these sections; there are many practical pieces of information that can help the students who graduated from the Medicine Faculty and specialist doctors who are interested in Neurosurgery.
Multichannel (or multitemporal) InSAR techniques address the processing of interferometric data stacks obtained by combining multiple SAR acquisitions over the same area. These approaches can be essentially categorized in two main classes: persistent scatterers (PS) and small baseline (SB)-based techniques. The solution of the multichannel phase unwrapping (MCh-PhU) problem is generally required in multichannel InSAR techniques, whenever multidimensional SAR data set, conveying information about complex Earth’s crust events, have to be systematically investigated on suitable space-time scales for geospatial phenomena understanding [1–29]. In this chapter, we focus on two different related main issues.
Primarily, we present a rigorous gradient-based formulation of the MCh-PhU problem that is consistent with the theoretical foundation of the discrete calculus [30–34]. Emphasis is placed on the topological characterization of the underlying discrete setting provided by the
Then we present an innovative procedure to filter out the noise affecting the phase components of a redundant set of (multitemporal) multilook small-baseline interferograms. This is achieved by independently solving, for each pixel of the scene, a
In this Section, we review the mathematical formulation of the multichannel phase unwrapping (MCh-PhU) problem within the purview of the discrete calculus. As a matter of fact, a graph-based description naturally arises in formulating the MCh-PhU problem due to the underlying discrete irregular data structure. Indeed, as far as discrete settings (e.g., graphs) are concerned, resorting to the conventional vectorial calculus might not be adequate since it inherently implies a reference to an underlying continuum space. On the contrary,
A graph
with
Indeed, several methods for defining a cycle set have been studied, and they can be used to define incidence relations between edges and cycles. Specifically, the definition of a cycle set from the edge set can be obtained algebraically and geometrically (i.e., from an embedding). Algebraic methods find a suitable
Accordingly, the so defined
It is instructive to highlight that the topological operators
An example graph shown along with its edge–node incidence matrix,
As a final remark, some considerations on the
Once the basic concepts of the discrete calculus and graph theory are presented, we are in the position to frame the formulation of the MCh-PhU problem on an appropriate mathematical playground. Let us consider a set of
The final aim is to reconstruct the absolute (i.e., not restricted in the principal [–π,π) interval) interferometric phases values from the wrapped (i.e., observed only in the principal [–π,π) interval) interferometric phase pertinent to
The problem we are interested in can be naturally framed on a discrete setting. Indeed, one possibility is to regard the discrete set of
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
\n\t\t\t\t | \n\t\t\tNodes number of | \n\t\t\tNumber of SAR acquisitions | \n\t\t
\n\t\t\t\t | \n\t\t\tEdges Number of | \n\t\t\tNumber of interferometric pairs | \n\t\t
\n\t\t\t\t | \n\t\t\tDimension of the cycle space of | \n\t\t\t\n\t\t |
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\tDiscrete-gradient operator | \n\t\t
\n\t\t\t\t | \n\t\t\t\n\t\t\t | Discrete-divergence operator | \n\t\t
| \n\t\t\t\n\t\t\t\t | \n\t\t\tDiscrete-curl operator | \n\t\t
\n\t\t\t\t | \n\t\t\tNodes number of | \n\t\t\tNumber of selected pixels | \n\t\t
\n\t\t\t\t | \n\t\t\tEdges number of | \n\t\t\t\n\t\t |
\n\t\t\t\t | \n\t\t\tDimension of the cycle space of | \n\t\t\t\n\t\t |
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\tDiscrete-gradient operator | \n\t\t
\n\t\t\t\t | \n\t\t\t\n\t\t\t | Discrete-divergence operator | \n\t\t
| \n\t\t\t\n\t\t\t\t | \n\t\t\tDiscrete-curl operator | \n\t\t
Adopted notation
First of all, we consider the absolute phase relevant to the multichannel SAR acquisition as a node variable pertinent to both the graphs
where
Widely adopted global gradient-based PhU approaches, which have historically been developed for the single-channel case, generally consist in three processing steps [1, 29]. First, an estimation of the (wrapped) phase gradient is obtained; the estimated phase gradient is then suitably corrected (in terms of 2π multiples),and subsequently integrated to attain the unwrapped (absolute) phase.
Within the formulation of the MCh-PhU problem we concern [26], a twofold estimation of the discrete gradient field is carried out onto the considered two graphs
wherein the
Note also that
By applying the discrete gradient operator
As a result, by using Eq. (7), we get
Second, we consider the (wrapped) interferometric phase that is uniquely defined only in the principal value range since it is obtained as the phase of a complex function. Hence, it is convenient to formally introduce the non-injective (modulo-2π)
where
where we have exploited Eq. (11b). Note also that the
More specifically, whenever a possible spatial filtering (e.g., conventional multilooking followed by a noise-filtering step [42]) is
where we have used Eq. (11b) with
Finally, by substituting Eq. (13) in Eq. (15), we obtain
From Eq. (16), by using Eq. (11b), we get
where in the last equality we have noted that
In this Section, the nonlinear inversion MCh-PhU problem is reformulated as a (nonlinear) constrained optimization problem. According to the presented general formulation, we introduce in the following the MCh-PhU problem as the solution of the following matrix equation:
where the columns of
Similarly, by premultiplying both sides of the transposed version of Eq. (18) by
Constraints stated by Eq. (19) imply that the columns of
subject to
wherein
represents the weighted
In this Section, we review the basic concepts concerning the filtering of noise that corrupts a stack of multitemporal SAR interferograms. First, the noise-filtering operation for single-channel multilook interferograms is discussed; subsequently, the general framework of the multichannel noise-filtering (MC-NF) approach, which is intimately connected with the problem of multichannel phase unwrapping, is described.
In order to introduce the problem at hand, let us first consider one single-channel SAR interferogram obtained starting from two SAR (synthetic aperture radar) images, namely,
where
where
There are several causes that are responsible for coherence decrease. As matter of fact, the cross-correlation factor in Eq. (25) depends on different noise sources, and it can be conveniently factorized as follows [51]:
where
The term
The multilook operation, leading to the multilook phase
where
The pdf in Eq. (27) is sketched in Figure 2a for different values of
that shows that standard deviation depends on the coherence
Probability density function of the interferometric phase
In order to mitigate the effects of decorrelation noise artifacts affecting SAR interferograms, several noise-filtering techniques, mostly working on single-channel data, have been proposed in literature over the years [42, 54, 56, 57]. As shown in previous Section 3.1, the statistics of multilook interferograms can be characterized via a probability density function expressible in closed form (Eq. (27)), and the noise standard deviation generally depends upon the
Multilook interferogram computed by using different SAR data pairs: (a) July 11, 2011–August 16, 2011, X-band Cosmo-SkyMed (CSK); (b) September 15, 2004–October 29, 2004, C-band ASAR/ENVISAT; (c) July 30, 2007–September 14, 2007, L-band ALOS/PALSAR-1.
It should be emphasized that coherence and noise levels can also significantly change from one SAR system to another depending on the operational wavelength.Multilook processing has been proved to be effective for noise reduction, but this is paid in terms of a decrease of the image spatial resolution. Noise filtering constitutes a preliminary step before phase unwrapping. Indeed, the multilook operation usually involves an averaging on neighboring SAR pixels, hence reducing the spatial resolution of the interferograms. Several algorithms have been proposed in literature. The most commonly used noise filter is the
Multilook interferogram relevant to the SAR data pair July 11, 2011–August 16, 2011, X-band Cosmo-SkyMed (CSK): (a) Original, (b–d) Goldstein filtering, with (b)
The response of the
where
The noise-filtering methods discussed in the previous Section have historically been developed to analyse and filter out the noise affecting single interferograms, separately, thus without taking into account their mutual temporal relationships. A multichannel noise-filtering problem arises when a stack of SAR interferograms need to be jointly exploited. In this case, it is profitable to develop/use noise-filtering approaches that not only exploit spatial/frequency information but can also take into account temporal relationships among available multichannel interferograms, in order to identify and filter out the noise affecting the whole interferometric data stack in the more reliable way as possible. A specific multichannel noise-filtering (MCh-NF) method [43], which is based on using a stack of time-redundant multilook interferograms, is described in this Section. The MCh-NF method is here described by adopting the same rigorous formalism and terminology used for the topological description of multichannel phase unwrapping problem presented in Section 2. According to the adopted symbolism, let us consider
The resulting interferometric data stack of the
wherein
which represents the
where
wherein the generic elements
The evaluation of the weights for the optimization problem in Eq. (32) is now addressed. Let
where
where the symbol
Note that the exploitation of “conventional” small baseline multilook interferograms is the distinctive characteristic of MCh-NF approach with respect to other previous solutions, such as the
MCh-NF block diagram
We present in this section some results we obtained by processing a data set consisting of 39 SAR images (Track 308, Frame 2754), collected by the ENVISAT sensor between December 2002 and August 2010 over the Abruzzi region (Italy). The test-site area includes the city of L’Aquila and its surroundings, which were struck on 6 April 2009, by an Mw 6.3 earthquake that caused more than three hundred fatalities, thousands of evacuees, as well as severe industrial and residential building damages. Starting from the available SAR images, we retrieved a stack of 338 small baseline differential SAR interferograms with a maximum perpendicular baseline of 400 m and a maximum time span of 2000 days [43]. The interferograms have been computed by performing a complex multilook operation with 4 and 20 looks in the range and azimuth directions, respectively. For the interferogram generation, we used precise satellite orbit information and a three-arcsecond
Pseudo code of the MCh‐NF algorithm.
Comparison between the original (left column) and noise-filtered (right column) multilook interferograms relevant to the area of the Abruzzi region (Italy). (a–c) October 30, 2005, to November 8, 2009, August 21, 2005, to June 6, 2010, and August 1, 2004, to April 12, 2009, interferograms, characterized by perpendicular baseline values of 192, 145, and 395 m, respectively. (d–f) Noise-filtered multilook interferograms corresponding to the ones in panels a–c, respectively.
To investigate the performance of the presented noise-filtering approach, we applied the nonlinear minimization procedure in Eq. (32) to the stack of the generated (original) multilook small baseline interferograms. As a result, we retrieved a new set of noise-filtered interferograms that are characterized by generally improved coherence values. This is clearly visible in Figure 7a–f, where we compare three unfiltered (left side) interferograms with the corresponding (right side) noise-filtered interferograms. It is evident how the fringes due to the earthquake are well recovered. Such interferometric data stacks can then be used for the generation of surface deformation time series using the small baseline subset (SBAS) [6] processing chain, whose parallel version (P-SBAS) has been proposed in refs. [13, 14, 35, 36]. This is matter for the analysis presented in the next subsection.
Block diagram of the advanced EMCF-SBAS processing chain.
We present in this subsection how the MCh-NF algorithm can be efficiently used within the SBAS processing chain, where phase unwrapping procedures are implemented through the MCh-PhU technique known as
(a) Mean deformation velocity map (in color) of Yellowstone Caldera, computed in coherent pixels only and superimposed on the SAR amplitude image (gray-scale representation) of the zone, retrieved by applying the advanced EMCF-SBAS processing chain. The black square marks the location of the reference SAR pixel. (b–e) Deformation time series relevant to the four pixels identified via black stars in
Within the context of SAR interferometry, two main issues related to the multichannel phase unwrapping and noise filtering for interferometric data stacks processing have been addressed. First, a rigorous gradient-based formulation for the multichannel phase unwrapping (MCh-PhU) problem has been systematically established, thus providing a topological characterization of the problem within the purview of the theoretical foundation of the
This work was supported by the Italian Ministry of University and Research (MIUR) under the project “Progetto Bandiera RITMARE.” We would like to thank the European Space Agency for providing the ENVISAT ASAR data and the University of Delft, Delft, The Netherlands, for the related precise orbits. We would also like to thank Italian Space Agency (ASI), which has provided us the Cosmo-SkyMed SAR images under the framework of the European Union’s Seventh Program for research, technological development, and demonstration MED-SUV project (grant no. 308665). Finally, the authors thank the Japanese Space Agency (JAXA), which has provided the used ALOS-1 data through the project entitled “Advanced Interferometric SAR Techniques for Earth Observation at L-band” (ID project 1149) in the framework of “The 4-th ALOS Research Announcement for ALOS-2” call.
Eutrophication resulting from harmful cyanobacterial blooms is a frequent nuisance phenomenon in freshwater lakes and estuaries around the world, posing a serious threat to aquatic ecosystems and human health [1, 2]. Cyanobacteria thus constitute a global problem in freshwater ecosystems used for drinking water and recreational purposes [3]. The potential damage to water supplies, recreation, tourism, aquaculture, and agriculture could have a substantial economic and social impact. The most commonly occurring genera of cyanobacteria include
For well over a century, many animal and human poisonings have been associated with Cyanobacteria and their toxins; the death of livestock, wildlife, and pets due to ingestion of water containing toxic cyanobacterial cells or toxins released by the cells has been extensively documented. Human poisonings have also been reported [4]. The occurrence of toxic cyanobacteria has become a worldwide problem [5, 6].
One group of toxic compounds synthesized by several cyanobacteria (
In Argentina, in recent decades, blooms have been recorded in rivers, lakes, coastal lagoons, and estuaries throughout the country, demonstrating the geographical extent of the problem. An increase has been detected both in the number of responsible species and in the frequency and intensity of the harmful events. The genera most commonly associated with the development of toxic blooms are
Giannuzzi et al. [13] reported an acute case of cyanobacterial poisoning in the Salto Grande dam, Argentina, which occurred in January 2007. A young man accidentally became immersed in an intense bloom of
The duration of cyanobacterial blooms in temperate zones can last 2–4 months during the summer period, whereas in tropical and subtropical regions of Australia, China, and Brazil, they can sometimes persist all year round [15].
The major factors that influence the growth of cyanobacteria are light, temperature, and the nutrients composition of the suspending medium.
High water temperatures have been known to lead to cyanobacterial bloom development in temperate [16, 17, 18] and semiarid regions [19]. Increasing air and water temperatures as a result of climate change are likely to promote a faster algal growth rate [20, 21].
Nitrogen (N) and/or phosphorus (P) levels can also positively affect cyanobacterial growth in lakes and river. The absolute and relative concentrations of these nutrients affect the growth rate, abundance, and composition of phytoplankton in lake water [22] as commonly measured in terms of their trophic state, defined as the total weight of biomass in a given water body at the time of measurement [23]. Many studies show that phosphorus is the limiting nutrient in freshwater bodies [24, 25], and other studies show the relationship between cyanobacterial abundance and phosphorus concentrations in lakes [26, 27].
The trophic state of a lake generally increases with increases in total nitrogen (TN) and total phosphorus (TP) concentrations. Resolving lake or river eutrophication problems calls for a better understanding of the water and air temperature-dependence of algal blooms.
A high P concentration is considered to be the main cause of
Provided factors such as illumination and nutrients remain saturating, and the photosynthetic and specific maximal growth rate responses of different algal species to temperature can be compared [34].
Physiological properties within a single species, including photosynthetic response, can change according to the growth conditions [37]. Photoperiodicity- and light intensity-dependent changes in photosynthetic parameters and different pigments such as chlorophyll a and phycocyanin are to be expected.
The general consensus is that the optimum growth temperature for cyanobacteria is higher than that for most algae. Paerl [38] reported the optimum temperature to be higher than 25°C, overlapping with that of green algae (27–32.8°C) but clearly differing from that of dinoflagellates (17–27°C) and diatoms (17–22°C). Crettaz Minaglia [39] reported the optimum growth temperature for native
Lürling [40] found similar optimal temperatures for two strains of
Paerl [41] reported that higher temperatures (up to 25°C) due to climate change may lead to increased cyanobacterial growth rates and thus higher cyanobacterial dominance in temperate water bodies [17, 20]. This trend would be further facilitated by cyanobacterial buoyancy, which aids their proliferation in increasingly stratified conditions because decreasing water viscosity at higher temperatures results in higher flotation velocities of buoyant cyanobacteria [19, 42].
Many authors describe an inverse relationship between temperature and microcystin production.
Crettaz Minaglia [39] found that the production of MC-LR decreased with increasing temperature, coinciding with the findings of [43, 44, 45, 46, 47, 48, 49, 50].
van der Westhuizen [51] reported that optimal growth conditions do not coincide with the production of toxins. Similarly, Gorham [43] affirmed that the optimum temperature for growth (30–35°C) differed from that for optimal toxicity (25°C).
In an interesting paper, Mowe et al. [52] suggest that higher mean water temperatures resulting from climate change will generally not affect
Further studies on the temperature dependency of the different physiological processes affecting growth (e.g., carbon fixation, photorespiration, and respiration) are required in order to better understand the differences in temperature sensitivity between
An evaluation of microbiological cyanobacterial processes calls for kinetics studies examining the rates of production of cells and their metabolites and the effects of various factors on these rates.
One of the basic tools in microbiology is growth kinetics, defined as the relationship between a specific growth rate and parameters such as temperature, pH, light intensity, short wavelength radiations, pH, and nutrients.
A convenient way to evaluate laboratory-based bacteria growth systems under different abiotic factors is to examine the parameters characterizing the three phases of bacterial growth: the lag phase, the exponential phase, and the stationary phase.
During the lag phase, which can last from 1 hour up to several days, there is very little change in the number of bacteria cells because while they are adapting to the growth conditions, they are still immature and unable to reproduce. This is the period when the synthesis of RNA, enzymes, and other molecules occurs.
The exponential phase is characterized by cell doubling. The number of new bacteria appearing per unit time is proportional to the present population. With no limitations in place, doubling continues at a constant rate, leading to a doubling of the number of cells and the rate of population increase with each consecutive time period. Plotting the logarithm of cell number against time produces a straight line, the slope of which indicates the specific growth rate of the organism, which is a measure of the number of divisions per cell per unit time. The actual rate of this growth depends on the growth conditions, which affect the frequency of cell division events and the probability of both daughter cells surviving. Under controlled conditions, the cyanobacteria population can be doubled four times a day and then tripled. However, this exponential growth eventually comes to an end when the medium becomes depleted of nutrients and enriched with waste.
The stationary phase results when the death rate is equal to the growth rate, often because of the depletion of an essential nutrient and/or the formation of an inhibitory product such as an organic acid, giving rise to a “smooth,” horizontal line on the curve.
The final phase is the death phase. Bacterial death can be the result of lack of nutrients, environmental temperature above or below the tolerance band for the species, or other deleterious conditions.
Modeling a cyanobacterial growth curve allows one to reduce recorded data to a limited number of parameters of interest such as the specific growth rate, lag phase duration, and maximum population density.
The growth models found in the literature describe only the number of organisms and do not include substrate consumption as would a model based on the Monod equation. However, the substrate level is not of interest in our application since we assume there to be sufficient substrate to allow cyanobacterial growth.
An assessment of natural populations of
Studying the growth kinetics of
In batch culture methods, the culture is not maintained at a specific growth stage with constant addition and removal of culture medium and cells [54].
This makes it an appropriate method for our
The basic batch culture growth model emphasizes aspects of bacterial growth, which may differ from the growth of other organisms. Plotting an experimentally determined cell number or cell mass concentration (or their logarithms) against time gives rise to a characteristic curve.
For the purpose of modeling the growth of
The simplest is a linear function based on the exponential or Malthus model, called a simple exponential growth model. This model assumes that the growth rate of the population is proportional to its density
where N(t) is the population at time t, N0 is the initial population, μ is a constant indicating the rate of growth, and t is time. The parameter μ is called the specific growth rate and is expressed in reciprocal time units.
This model is widely used in microorganisms and is also very useful for describing the population growth of many organisms over short periods of time, there being no space or resource limitations.
During a batch cultivation, the specific growth rate changes continuously from zero to a maximum value, μmax. The value of the maximum specific growth rate depends on the type of microorganism and on physical and chemical cultivation conditions (temperature, pH, medium composition, light, etc.). Under given culture conditions, it is constant and represents an important characteristic of the process.
The specific growth rate (μ) can be calculated between successive sampling points from a simple first-order rate law using the equation
where N0 is the cell number per mL culture at time t0 and N1 is the cell number at time t1.
The main parameter characterizing the growth rate is the specific growth rate, which can be used to express other growth parameters given below.
The relationship between the specific growth rate (μ) and cell number doubling time (td) can be obtained by inserting into the equation N = 2N0 and t = td.
Yet another model is the logistic or Verhulst model (Eq. (4)), a quadratic function based on the previous model under the assumption that the population cannot grow indefinitely and faster. In this model, μ is not a constant, but is a linearly increasing function of population density. This model has two equilibrium points defined as N values where the growth velocity is zero. These points correspond to N = 0 and N = K (load capacity). The load capacity refers to the maximum population that its environment can sustain in terms of resource or space availability.
where N(t) is the final population, N0 is the initial population, μ is a constant that indicates the growth rate, t is time, and K is the load capacity.
Growth rate (μ) is commonly expressed in the literature as a function of light, nutrient, pH, ionic conditions, and temperature. Modeling the growth rate is based on simply multiplying the functions upon which growth is dependent:
where μ (time−1) is the cyanobacterial growth rate and f(I), f(N), and f(T) are the effects of irradiance, nutrients, and temperature on the growth rate, respectively.
Another method to calculate the three parameters of the three phases of bacterial growth mentioned earlier is using the modified Gompertz equation, a double exponential function based on four parameters, which describes an asymmetric sigmoid curve Eq. (6) [56].
The Gompertz model is one of the most widely used and recommended models from which lag time, maximum growth rate, and maximum population density (stationary phase) can be obtained directly from nonlinear regression of the cell numbers versus time data [57, 58].
A Gompertz model describing the growth of
where log(N) is the decimal logarithm of the cell counts (log (cell mL−1), t is time (days), and a is the logarithm of the asymptotic counts when time decreases indefinitely (roughly equivalent to the logarithm of the initial levels of cyanobacteria (log (cell mL−1)), c is the logarithm of the asymptotic counts when time is increased indefinitely (the number of log cycles of growth) (log (cell mL−1)), b is the growth rate relative to time (days−1), and m is the required time to reach the maximum growth rate (days).
The maximum or specific growth rate (μ) value is defined as the tangent in the inflection point and was calculated as μ = b.c/e with e = 2.7182 (days−1). The lag phase duration (LPD) is defined as the t-axis intercept of this tangent, the asymptote, and was calculated as LPD = m − 1/b, (days); the maximum population density MPD = a + c (log (cell mL−1) was derived from these parameters [59].
The value of modeling has been recognized for a number of years. Accurate and well-validated models are able to predict the behavior of dynamically changing systems and provide data and insights that would be difficult or impossible to obtain by conventional field.
Figure 1 shows a detail of
Light microscopy image of a
In a previous work, we informed two additional kinetic parameters: generation time (GT) and the relative lag phase duration (RLPD). Generation time was defined as the time for the bacterial population to double in cell numbers and was calculated by dividing μ values by 0.301 (equivalent to log10 2); GT is thus a measure of the metabolic rate in a new environment. The RLPD, defined by the amount of work to be done in adjusting to a new environment and the rate at which that work is done, was calculated by dividing LPD by GT [39].
For
Effect of temperature on
By examining these parameters, the blooming behavior of
The Gompertz parameters of these curves were reported by Crettaz Minaglia et al. [39]. It can be seen that as the temperature increases, the value of μ increases and LPD decreases. Thus, when the temperature changed from 26 to 35°C, the μ values increased from 0.18 to 0.24 days−1, and LPD decreased from 4.10 to 0.75 days, nonsignificant differences were found for the MPD values (7.25–7.10 log CFU mL−1). The GT parameter ranged from 1.67 to 1.25 days, and RLPD from 2.40 to 0.60.
The ratio of the specific growth rate to the lag time duration was approximately constant, suggesting a linear relationship between lag phase and the reciprocal of the growth rate. This finding was corroborated by Crettaz Minaglia [39], who reported that the lag phase showed a linear behavior with the reciprocal specific growth rate for
Lyck [61] reported specific growth rate values ranging from 0.52 to 0.54 day−1 calculated between successive sampling times according to a simple first-order rate function using cell concentration (cells mL−1).
In reviewing available literature on the effects of temperature on growth rates, Canale and Vogel [65] concluded that as temperature increased, the highest growth rates for broad phytoplankton groups changed from diatoms, via green algae to cyanobacteria (blue-green algae). Species-specific responses are, however, highly variable [66].
The specific growth rate and lag phase duration are known to be affected by many variables, and the cyanobacterial responses to changes in the environment are complex and difficult to characterize. Figure 3 shows an example of the variation in the specific growth rate with changes in temperature (26, 30, and 36°C), nutrient (N/P 10, 100, and 150), and irradiance (30, 50, 70 μmol photon m−2 s−1) conditions for
Surface response plots showing the dependence of specific growth rate parameters of M. aeruginosa on temperature and N/P ratio: (a) 30, (b) 50, and (c) 70 μmol photons m−2 s−1.
The percent variance was very high at 98.8%, indicating a very good fit of the model to the data. The parameter K2 was 0.89, 2.25, and 1.56 for 30, 50, and 70 μmol photons m−2 s−1, respectively. The values of K2 = Ea/R where Ea is the activation energy of μ (KJ mol−1) applied Eq. (7) and R is the gas constant (8.31 KJ K−1 mol−1). In the present study, Ea was 7.40, 18.69, and 12.96 KJ K−1 mol−1 for 30, 50, and 70 μmol photons m−2 s−1, respectively.
Figure 3a–c shows examples of a surface response plot corresponding to Eq. (7) obtained by fitting ln μ of
Using the model reported here, we determined the combined effects of the N/P ratio and temperature on specific growth rates in controlled laboratory assays, thus enabling us to predict
The Gompertz model was successfully tested with the experimental data for
However, many open questions remain concerning the validity of applying laboratory-observed growth kinetics to environmental growth conditions, with diverging data being reported for pure cultures growing with single substrates.
Although our model only takes into account temperature and N/P ratio, it would be important to extend the modeling to other factors such as pH and elements such as metals (Fe, Mn, etc.). Further studies are required to gain deeper insight into the factors that influence growth in order to better predict aspects related to
Cyanobacterial blooms can lead to the accumulation of cyanotoxins in aquatic animals, eventually posing a high risk to human health as well.
In the current scenario of growing problems associated with cyanobacterial blooms and their toxins, an environmentally compatible control strategy is urgently required. The removal of harmful cyanobacterial blooms is a crucial step for the adequate maintenance of water supplies and for the safety of food and aquatic products. Controlling cyanobacterial blooms is likely to become an even more challenging task in the future due to global warming effects.
Despite the availability of control methods for cyanobacterial blooms, it has not yet been possible to prevent the excessive proliferation of these organisms, which have adapted so successfully to water surfaces. The effectiveness of control methods naturally varies according to the circumstances (type and size of the lake, retention time, degree of alteration, quantity of nutrient load, quality and quantity of sediments, season, amount of aquatic life, etc.); they are not universal and their use may be restricted to particular circumstances.
The preferred method for preventing these blooms is to reduce the availability of nutrients, especially phosphorus, the main cause of the massive presence of cyanobacteria. This implies the rehabilitation of point and nonpoint sources of nutrients (discharge of effluents, drift of chemical substances from agriculture, and erosion of urban and forest areas) [67]. In those cases where nutrient reduction is not possible, more drastic, short-term action has been proposed in the form of chemical, physical, and biological approaches [68], each with its advantages and disadvantages for application to the control of harmful algal blooms.
A widely adopted chemical approach is the addition of algaecide (copper sulfate), oxidants (chlorine, potassium permanganate), and flocculants (FeCl3, AlCl3, polyaluminum chloride) etc., all of which have proven to be efficient in removing cyanobacteria cells. However, though chemical approaches can take rapid effect in removing algal blooms, they can cause secondary pollution of aquatic environments [69]. Their main disadvantage is that they do not selectively target harmful cyanobacteria and can lead to the elimination or damage to nonharmful algae or beneficial organisms. Depending on the oxidant and cyanotoxin type, some oxidants can cause the release of toxins, and the subsequent rapid oxidation of the toxins must therefore be assured [70].
The application of chemical agents to lakes and water bodies often leads to the collapse of aquatic ecosystems.
Hydrogen peroxide (HP) is selective for cyanobacteria (vs. eukaryotic algae and higher plants) and poses no serious long-term threats to the system because of its rapid decomposition without producing persistent toxic chemicals or by-products that cause esthetic odor or color issues. It has been reported that HP has potential for removing
Physical approaches, such as mixing lake waters using an air compressor, ultrasonic damage to algal cells and pressure devices to collapse cyanobacterial gas vesicles, have also been proposed to control algal blooms. Other treatments such as the mechanical removal of cyanobacterial biomass and sediments and hypolimnetic aeration and oxygenation have also been described.
The most apparent merit of physical approaches for the removal of algae as opposed to chemical manipulations is that they are less likely to give rise to secondary pollution. However, the physical removal of algae is energy intensive and tends to be of low efficiency. Moreover, injury to nontarget organisms by energy-intensive treatments also limits the field application on a large scale [72].
Though biological approaches to controlling toxic cyanobacteria and harmful cyanobacterial blooms tend to be environmentally friendly, their efficiency is determined by many biotic and abiotic factors in the environment. It is well known that MCs can be degraded by local bacterial communities frequently exposed to cyanobacterial blooms.
The removal of MCs has been reported by a group of microorganisms generically referred to as a consortium [73].
Furthermore, a large group of bacteria able to degrade MCs has been isolated, Sphingomonadaceae being the most studied family. Most of these organisms have been identified as
Some biologically derived bioactive substances inhibit the growth of aquatic bloom-forming cyanobacteria [76, 77, 78], including plant extracts and identified natural chemicals from plants and microorganisms.
Aquatic plants such as
In view of the paucity of studies on the ecological and public health risks associated with most antialgal substances, their application should be very carefully evaluated. Only ecologically safe and easily applicable substances should be used for cyanobacterial growth control.
Mathematical modeling applied to
This study was financially supported by University National of La Plata (UNLP X646), National Agency of Scientific and Technical Research (PICT0861-2013), and CONICET (PIP0959).
No conflict of interest.
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It represents the chemical yield of crosslinks, scissions and double bonds, etc. For the crosslinked polymer, the crosslinking density increases with increasing the radiation dose, this is reflected by the swelling degree of the polymer while being immersed in a compatible solvent. If crosslinking predominates, the crosslinking density increases and the extent of swelling decreases. If chain scission predominates, the opposite occurs. 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The natural resources are limited, and due to the natural disasters like sudden and severe abiotic stress factors, excessive floods, etc., the production capacities are changed per year. In contrast, the yield potential should be significantly increased to cope with this problem. Despite rich genetic diversity, manipulation of the cultivars through alternative techniques such as mutation breeding becomes important. Radiation is proven as an effective method as a unique method to increase the genetic variability of the species. Gamma radiation is the most preferred physical mutagen by plant breeders. Several mutant varieties have been successfully introduced into commercial production by this method. Combinational use of in vitro tissue culture and mutation breeding methods makes a significant contribution to improve new crops. Large populations and the target mutations can be easily screened and identified by new methods. 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G. Iglesias-Andreu, P. Octavio-Aguilar and J. Bello-Bello",authors:[{id:"110581",title:"Dr.",name:"Lourdes",middleName:null,surname:"Iglesias-Andreu",slug:"lourdes-iglesias-andreu",fullName:"Lourdes Iglesias-Andreu"}]},{id:"58410",doi:"10.5772/intechopen.72074",title:"Radiation-Induced Degradation of Organic Compounds and Radiation Technologies for Purification of Aqueous Systems",slug:"radiation-induced-degradation-of-organic-compounds-and-radiation-technologies-for-purification-of-aq",totalDownloads:1435,totalCrossrefCites:8,totalDimensionsCites:13,abstract:"Environmental application of radiation technologies is an important part of radiation processing. Radiation treatment of aqueous systems contaminated with organic compounds is a promising method of water and wastewater purification and corresponding technologies are being developed. In this chapter, the following aspects of radiation treatment process are considered: sources of contamination and major contaminants of water and wastewater; primary processes in aqueous systems initiated by ionizing radiation; principal ways of contaminant conversion as consequences of primary processes (complete mineralization of organic compounds, partial decomposition of organic molecules resulted in detoxification, decolorization, disinfection of polluted water, and improvement in biological degradation of contaminant, polymerization of monomers’ contaminants, oxidation-reduction processes, and coagulation of colloids); sources of ionizing radiation; and main equipment applied in radiation technologies of aqueous system purification.",book:{id:"6149",slug:"ionizing-radiation-effects-and-applications",title:"Ionizing Radiation Effects and Applications",fullTitle:"Ionizing Radiation Effects and Applications"},signatures:"Igor E. Makarov and Alexander V. Ponomarev",authors:[{id:"213652",title:"Dr.",name:"Igor",middleName:null,surname:"Makarov",slug:"igor-makarov",fullName:"Igor Makarov"},{id:"213657",title:"Dr.",name:"Alexander",middleName:null,surname:"Ponomarev",slug:"alexander-ponomarev",fullName:"Alexander Ponomarev"}]}],mostDownloadedChaptersLast30Days:[{id:"32842",title:"Sterilization by Gamma Irradiation",slug:"sterilization-by-gamma-irradiation",totalDownloads:74818,totalCrossrefCites:37,totalDimensionsCites:85,abstract:null,book:{id:"1590",slug:"gamma-radiation",title:"Gamma Radiation",fullTitle:"Gamma Radiation"},signatures:"Kátia Aparecida da Silva Aquino",authors:[{id:"102109",title:"Dr.",name:"Katia",middleName:"Aparecida Da S.",surname:"Aquino",slug:"katia-aquino",fullName:"Katia Aquino"}]},{id:"32837",title:"Environmental Gamma-Ray Observation in Deep Sea",slug:"environmental-gamma-ray-observation-in-deep-sea-",totalDownloads:2931,totalCrossrefCites:4,totalDimensionsCites:6,abstract:null,book:{id:"1590",slug:"gamma-radiation",title:"Gamma Radiation",fullTitle:"Gamma Radiation"},signatures:"Hidenori Kumagai, Ryoichi Iwase, Masataka Kinoshita, Hideaki Machiyama, Mutsuo Hattori and Masaharu Okano",authors:[{id:"108174",title:"Dr.",name:"Hidenori",middleName:null,surname:"Kumagai",slug:"hidenori-kumagai",fullName:"Hidenori Kumagai"},{id:"108237",title:"Dr.",name:"Masa",middleName:null,surname:"Kinoshita",slug:"masa-kinoshita",fullName:"Masa Kinoshita"},{id:"137650",title:"Dr.",name:"Ryoichi",middleName:null,surname:"Iwase",slug:"ryoichi-iwase",fullName:"Ryoichi Iwase"},{id:"137656",title:"Dr.",name:"Hideaki",middleName:null,surname:"Machiyama",slug:"hideaki-machiyama",fullName:"Hideaki Machiyama"},{id:"146918",title:"Dr.",name:"Mutsuo",middleName:null,surname:"Hattori",slug:"mutsuo-hattori",fullName:"Mutsuo Hattori"},{id:"146919",title:"Dr.",name:"Masaharu",middleName:null,surname:"Okano",slug:"masaharu-okano",fullName:"Masaharu Okano"}]},{id:"58998",title:"Ionizing Radiation-Induced Polymerization",slug:"ionizing-radiation-induced-polymerization",totalDownloads:1820,totalCrossrefCites:8,totalDimensionsCites:17,abstract:"Ionizing radiation can induce some kinds of reactions, other than polymerization, such as dimerization, oligomerization, curing, and grafting. These reactions occur through a regular radical chain causing growth of polymer by three steps, namely, initiation, propagation, and termination. To understand ionizing radiation-induced polymerization, the water radiolysis must be taken into consideration. This chapter explores the mechanism of water molecules radiolysis paying especial attention to the basic regularities of solvent radicals’ interaction with the polymer molecules for forming the crosslinked polymer. Water radiolysis is the main engine of the polymerization processes, especially the “free-radical polymerization.” The mechanisms of the free-radical polymerization and crosslinking will be discussed in detail later. Since different polymers respond differently to radiation, it is useful to quantify the response, namely in terms of crosslinking and chain scission. A parameter called the G-value is frequently used for this purpose. It represents the chemical yield of crosslinks, scissions and double bonds, etc. For the crosslinked polymer, the crosslinking density increases with increasing the radiation dose, this is reflected by the swelling degree of the polymer while being immersed in a compatible solvent. If crosslinking predominates, the crosslinking density increases and the extent of swelling decreases. If chain scission predominates, the opposite occurs. A further detailed discussion of these aspects is presented throughout this chapter.",book:{id:"6149",slug:"ionizing-radiation-effects-and-applications",title:"Ionizing Radiation Effects and Applications",fullTitle:"Ionizing Radiation Effects and Applications"},signatures:"Mohamed Mohamady Ghobashy",authors:[{id:"212371",title:"Dr.",name:"Mohamed",middleName:null,surname:"Mohamady Ghobashy",slug:"mohamed-mohamady-ghobashy",fullName:"Mohamed Mohamady Ghobashy"}]},{id:"53780",title:"Gamma-Ray Spectrometry and the Investigation of Environmental and Food Samples",slug:"gamma-ray-spectrometry-and-the-investigation-of-environmental-and-food-samples",totalDownloads:2529,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Gamma radiation consists of high‐energy photons and penetrates matter. This is an advantage for the detection of gamma rays, as gamma spectrometry does not need the elimination of the matrix. The disadvantage is the need of shielding to protect against this radiation. Gamma rays are everywhere: in the atmosphere; gamma nuclides are produced by radiation of the sun; in the Earth, the primordial radioactive nuclides thorium and uranium are sources for gamma and other radiation. The technical enrichment and use of radioisotopes led to the unscrupulously use of radioactive material and to the Cold War, with over 900 bomb tests from 1945 to 1990, combined with global fallout over the northern hemisphere. The friendly use of radiation in medicine and for the production of energy at nuclear power plants (NPPs) has caused further expositions with ionising radiation. This chapter describes in a practical manner the instrumentation for the detection of gamma radiation and some results of the use of these techniques in environmental and food investigations.",book:{id:"5451",slug:"new-insights-on-gamma-rays",title:"New Insights on Gamma Rays",fullTitle:"New Insights on Gamma Rays"},signatures:"Markus R. Zehringer",authors:[{id:"311750",title:"Dr.",name:"Markus R.",middleName:null,surname:"Zehringer",slug:"markus-r.-zehringer",fullName:"Markus R. Zehringer"}]},{id:"54118",title:"Gamma Rays from Space",slug:"gamma-rays-from-space",totalDownloads:2089,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"An overview of gamma rays from space is presented. We highlight the most powerful astrophysical explosions, known as gamma-ray bursts. The main features observed in detectors onboard satellites are indicated. In addition, we also highlight a chronological description of the efforts made to observe their high energy counterpart at ground level. Some candidates of the GeV counterpart of gamma-ray bursts, observed by Tupi telescopes, are also presented.",book:{id:"5451",slug:"new-insights-on-gamma-rays",title:"New Insights on Gamma Rays",fullTitle:"New Insights on Gamma Rays"},signatures:"Carlos Navia and Marcel Nogueira de Oliveira",authors:[{id:"189908",title:"Dr.",name:"Carlos",middleName:null,surname:"Navia",slug:"carlos-navia",fullName:"Carlos Navia"},{id:"243084",title:"MSc.",name:"Marcel",middleName:null,surname:"De Oliveira",slug:"marcel-de-oliveira",fullName:"Marcel De Oliveira"}]}],onlineFirstChaptersFilter:{topicId:"227",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82739",title:"Experimental Breeder Reactor II",slug:"experimental-breeder-reactor-ii",totalDownloads:11,totalDimensionsCites:0,doi:"10.5772/intechopen.105800",abstract:"The Experimental Breeder Reactor II (EBR-II) operated from 1964 to 1994. EBR-II was a sodium-cooled fast reactor operating at 69 MWth producing 19 MWe. Rather than using a loop approach for the coolant, EBR-II used a pool arrangement where the reactor core, primary coolant piping, and primary reactor coolant pumps were contained within the pool of sodium. Also contained within the pool was a heat exchanger where primary coolant, which is radioactive, transferred heat to secondary, nonradioactive, sodium. The nuclear power plant included a sodium boiler building where heat from the secondary sodium generated superheated steam, which was delivered to a turbine/generator for electricity production. EBR-II fuel was metallic uranium alloyed with various metals providing significant performance and safety enhancements over oxide fuel. The most significant EBR-II experiments occurred in April 1986. Relying on inherent physical properties of the reactor, two experiments were performed subjecting the reactor to loss of primary coolant flow without reactor SCRAM and loss of the secondary system heat removal without reactor SCRAM. In both experiments, the reactor experienced no damage. This chapter provides a description of the most important design features of EBR-II along with a summary of the landmark reactor safety experiments.",book:{id:"10982",title:"Nuclear Reactors - Spacecraft Propulsion, Research Reactors, and Reactor Analysis Topics",coverURL:"https://cdn.intechopen.com/books/images_new/10982.jpg"},signatures:"Chad L. Pope, Ryan Stewart and Edward Lum"},{id:"82712",title:"Idaho State University AGN-201 Low Power Teaching Reactor: An Overlooked Gem",slug:"idaho-state-university-agn-201-low-power-teaching-reactor-an-overlooked-gem",totalDownloads:10,totalDimensionsCites:0,doi:"10.5772/intechopen.105799",abstract:"A category of reactors called university research and teaching reactors, includes relatively high-power pool-type and low-power solid-core reactors. Many high-power university reactors are largely used for irradiations and isotope production. Their almost constant operation tends to impede student access. A university reactor can be particularly relevant to the university’s mission of preparing well-rounded students who have theoretical knowledge, reinforced by focused laboratory reactor experience. The solid-core Idaho State University Aerojet General Nucleonics (AGN) model 201 reactor operates at such a low power (5 W maximum) that it is not useful for isotope production activities. However, the AGN-201 reactor is well suited for teaching and research activities. The solid-core AGN-201 reactor requires no active cooling system, uses a simple shielding arrangement, and the very low operating power results in trivial burnup providing an operating lifetime exceeding many decades. It is thus worthwhile to examine the Idaho State University AGN-201 nuclear reactor more closely because it offers a wide range of research and teaching capabilities while being widely available to students.",book:{id:"10982",title:"Nuclear Reactors - Spacecraft Propulsion, Research Reactors, and Reactor Analysis Topics",coverURL:"https://cdn.intechopen.com/books/images_new/10982.jpg"},signatures:"Chad L. Pope and William Phoenix"},{id:"81424",title:"Core Reload Analysis Techniques in the Advanced Test Reactor",slug:"core-reload-analysis-techniques-in-the-advanced-test-reactor",totalDownloads:13,totalDimensionsCites:0,doi:"10.5772/intechopen.103896",abstract:"Since becoming a national user facility in 2007, the type of irradiation campaigns the Advanced Test Reactor (ATR) supports has become much more diverse and complex. In prior years, test complexity was limited by the computational ability to analyze the tests’ influence on the fuel. Large volume tests are irradiated in flux traps which are designed to receive excess neutrons from the surrounding fuel elements. Typically, fuel elements drive the test conditions, not vice versa. The computational tool, PDQ, was used for core physics analysis for decades. The PDQ code was adequate so long as the diffusion approximation between test and fuel element remained valid. This paradigm changed with the introduction of the Ki-Jang Research Reactor—Fuel Assembly Irradiation (KJRR-FAI) in 2015. The KJRR-FAI was a prototypic fuel element for the KJRR research reactor project in the Republic of Korea. The KJRR-FAI irradiation presented multiple modeling and simulation challenges for which PDQ was ill suited. To demonstrate that the KJRR-FAI could be irradiated and meet safety requirements, the modern neutron transport codes, HELIOS and MCNP, were extensively verified and validated to replace PDQ. The hybrid 3D/2D methodology devised with these codes made analysis of the ATR with KJRR-FAI possible. The KJRR-FAI was irradiated in 2015-2016.",book:{id:"10982",title:"Nuclear Reactors - Spacecraft Propulsion, Research Reactors, and Reactor Analysis Topics",coverURL:"https://cdn.intechopen.com/books/images_new/10982.jpg"},signatures:"Samuel E. Bays and Joseph W. Nielsen"},{id:"81555",title:"Nuclear Thermal Propulsion",slug:"nuclear-thermal-propulsion",totalDownloads:113,totalDimensionsCites:0,doi:"10.5772/intechopen.103895",abstract:"This chapter will cover the fundamentals of nuclear thermal propulsion systems, covering basic principles of operation and why nuclear is a superior option to chemical rockets for interplanetary travel. It will begin with a historical overview from early efforts in the early 1950s up to current interests, with respect to fuel types, core materials, and ongoing testing efforts. An overview will be provided of reactor types and design elements for reactor concepts or testing systems for nuclear thermal propulsion, followed by a discussion of nuclear thermal design concepts. A section on system design and modeling will be presented to discuss modeling and simulation of driving phenomena: neutronics, materials performance, heat transfer, and structural mechanics, solved in a tightly coupled multiphysics system. Finally, it will show the results of a coupled physics model for a conceptual design with simulation of rapid startup transients needed to maximize hydrogen efficiency.",book:{id:"10982",title:"Nuclear Reactors - Spacecraft Propulsion, Research Reactors, and Reactor Analysis Topics",coverURL:"https://cdn.intechopen.com/books/images_new/10982.jpg"},signatures:"Mark D. DeHart, Sebastian Schunert and Vincent M. Labouré"},{id:"81013",title:"Cyber-Informed Engineering for Nuclear Reactor Digital Instrumentation and Control",slug:"cyber-informed-engineering-for-nuclear-reactor-digital-instrumentation-and-control",totalDownloads:32,totalDimensionsCites:0,doi:"10.5772/intechopen.101807",abstract:"As nuclear reactors transition from analog to digital technology, the benefits of enhanced operational capabilities and improved efficiencies are potentially offset by cyber risks. Cyber-Informed Engineering (CIE) is an approach that can be used by engineers and staff to characterize and reduce new cyber risks in digital instrumentation and control systems. CIE provides guidance that can be applied throughout the entire systems engineering lifecycle, from conceptual design to decommissioning. In addition to outlining the use of CIE in nuclear reactor applications, this chapter provides a brief primer on nuclear reactor instrumentation and control and the associated cyber risks in existing light water reactors as well as the digital technology that will likely be used in future reactor designs and applications.",book:{id:"10982",title:"Nuclear Reactors - Spacecraft Propulsion, Research Reactors, and Reactor Analysis Topics",coverURL:"https://cdn.intechopen.com/books/images_new/10982.jpg"},signatures:"Shannon Eggers and Robert Anderson"},{id:"79671",title:"Fault Detection by Signal Reconstruction in Nuclear Power Plants",slug:"fault-detection-by-signal-reconstruction-in-nuclear-power-plants",totalDownloads:105,totalDimensionsCites:0,doi:"10.5772/intechopen.101276",abstract:"In this work, the recently developed auto associative bilateral kernel regression (AABKR) method for on-line condition monitoring of systems, structures, and components (SSCs) during transient process operation of a nuclear power plant (NPP) is improved. The advancement enhances the capability of reconstructing abnormal signals to the values expected in normal conditions during both transient and steady-state process operations. The modification introduced to the method is based on the adoption of two new approaches using dynamic time warping (DTW) for the identification of the time position index (the position of the nearest vector within the historical data vectors to the current on-line query measurement) used by the weighted-distance algorithm that captures temporal dependences in the data. Applications are provided to a steady-state numerical process and a case study concerning sensor signals collected from a reactor coolant system (RCS) during start-up operation of a NPP. The results demonstrate the effectiveness of the proposed method for fault detection during steady-state and transient operations.",book:{id:"10982",title:"Nuclear Reactors - Spacecraft Propulsion, Research Reactors, and Reactor Analysis Topics",coverURL:"https://cdn.intechopen.com/books/images_new/10982.jpg"},signatures:"Ibrahim Ahmed, Enrico Zio and Gyunyoung Heo"}],onlineFirstChaptersTotal:8},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:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,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:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,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:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,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:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261",scope:"Modern physiology requires a comprehensive understanding of the integration of tissues and organs throughout the mammalian body, including the cooperation between structure and function at the cellular and molecular levels governed by gene and protein expression. While a daunting task, learning is facilitated by identifying common and effective signaling pathways mediated by a variety of factors employed by nature to preserve and sustain homeostatic life. \r\nAs a leading example, the cellular interaction between intracellular concentration of Ca+2 increases, and changes in plasma membrane potential is integral for coordinating blood flow, governing the exocytosis of neurotransmitters, and modulating gene expression and cell effector secretory functions. Furthermore, in this manner, understanding the systemic interaction between the cardiovascular and nervous systems has become more important than ever as human populations' life prolongation, aging and mechanisms of cellular oxidative signaling are utilised for sustaining life. \r\nAltogether, physiological research enables our identification of distinct and precise points of transition from health to the development of multimorbidity throughout the inevitable aging disorders (e.g., diabetes, hypertension, chronic kidney disease, heart failure, peptic ulcer, inflammatory bowel disease, age-related macular degeneration, cancer). With consideration of all organ systems (e.g., brain, heart, lung, gut, skeletal and smooth muscle, liver, pancreas, kidney, eye) and the interactions thereof, this Physiology Series will address the goals of resolving (1) Aging physiology and chronic disease progression (2) Examination of key cellular pathways as they relate to calcium, oxidative stress, and electrical signaling, and (3) how changes in plasma membrane produced by lipid peroxidation products can affect aging physiology, covering new research in the area of cell, human, plant and animal physiology.",coverUrl:"https://cdn.intechopen.com/series/covers/10.jpg",latestPublicationDate:"July 20th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:14,editor:{id:"35854",title:"Prof.",name:"Tomasz",middleName:null,surname:"Brzozowski",slug:"tomasz-brzozowski",fullName:"Tomasz Brzozowski",profilePictureURL:"https://mts.intechopen.com/storage/users/35854/images/system/35854.jpg",biography:"Prof. Dr. Thomas Brzozowski works as a professor of Human Physiology and is currently Chairman at the Department of Physiology and is V-Dean of the Medical Faculty at Jagiellonian University Medical College, Cracow, Poland. His primary area of interest is physiology and pathophysiology of the gastrointestinal (GI) tract, with the major focus on the mechanism of GI mucosal defense, protection, and ulcer healing. He was a postdoctoral NIH fellow at the University of California and the Gastroenterology VA Medical Center, Irvine, Long Beach, CA, USA, and at the Gastroenterology Clinics Erlangen-Nuremberg and Munster in Germany. He has published 290 original articles in some of the most prestigious scientific journals and seven book chapters on the pathophysiology of the GI tract, gastroprotection, ulcer healing, drug therapy of peptic ulcers, hormonal regulation of the gut, and inflammatory bowel disease.",institutionString:null,institution:{name:"Jagiellonian University",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"10",title:"Animal Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/10.jpg",editor:{id:"202192",title:"Dr.",name:"Catrin",middleName:null,surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland",profilePictureURL:"https://mts.intechopen.com/storage/users/202192/images/system/202192.png",biography:"Catrin Rutland is an Associate Professor of Anatomy and Developmental Genetics at the University of Nottingham, UK. She obtained a BSc from the University of Derby, England, a master’s degree from Technische Universität München, Germany, and a Ph.D. from the University of Nottingham. She undertook a post-doctoral research fellowship in the School of Medicine before accepting tenure in Veterinary Medicine and Science. Dr. Rutland also obtained an MMedSci (Medical Education) and a Postgraduate Certificate in Higher Education (PGCHE). She is the author of more than sixty peer-reviewed journal articles, twelve books/book chapters, and more than 100 research abstracts in cardiovascular biology and oncology. She is a board member of the European Association of Veterinary Anatomists, Fellow of the Anatomical Society, and Senior Fellow of the Higher Education Academy. Dr. Rutland has also written popular science books for the public. https://orcid.org/0000-0002-2009-4898. www.nottingham.ac.uk/vet/people/catrin.rutland",institutionString:null,institution:{name:"University of Nottingham",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"306970",title:"Mr.",name:"Amin",middleName:null,surname:"Tamadon",slug:"amin-tamadon",fullName:"Amin Tamadon",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002oHR5wQAG/Profile_Picture_1623910304139",institutionString:null,institution:{name:"Bushehr University of Medical Sciences",institutionURL:null,country:{name:"Iran"}}},{id:"251314",title:"Dr.",name:"Juan Carlos",middleName:null,surname:"Gardón Poggi",slug:"juan-carlos-gardon-poggi",fullName:"Juan Carlos Gardón Poggi",profilePictureURL:"https://mts.intechopen.com/storage/users/251314/images/system/251314.jpeg",institutionString:null,institution:{name:"Valencia Catholic University Saint Vincent Martyr",institutionURL:null,country:{name:"Spain"}}},{id:"245306",title:"Dr.",name:"María Luz",middleName:null,surname:"Garcia Pardo",slug:"maria-luz-garcia-pardo",fullName:"María Luz Garcia Pardo",profilePictureURL:"https://mts.intechopen.com/storage/users/245306/images/system/245306.png",institutionString:null,institution:{name:"Miguel Hernandez University",institutionURL:null,country:{name:"Spain"}}},{id:"283315",title:"Prof.",name:"Samir",middleName:null,surname:"El-Gendy",slug:"samir-el-gendy",fullName:"Samir El-Gendy",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRduYQAS/Profile_Picture_1606215849748",institutionString:null,institution:{name:"Alexandria University",institutionURL:null,country:{name:"Egypt"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",institutionString:"Kafkas University",institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}}]},{id:"11",title:"Cell Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/11.jpg",editor:{id:"133493",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",profilePictureURL:"https://mts.intechopen.com/storage/users/133493/images/3091_n.jpg",biography:"Prof. Dr. Angel Catalá \r\nShort Biography Angel Catalá was born in Rodeo (San Juan, Argentina). He studied \r\nchemistry at the Universidad Nacional de La Plata, Argentina, where received aPh.D. degree in chemistry (Biological Branch) in 1965. From\r\n1964 to 1974, he worked as Assistant in Biochemistry at the School of MedicineUniversidad Nacional de La Plata, Argentina. From 1974 to 1976, he was a Fellowof the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor oBiochemistry at the Universidad Nacional de La Plata, Argentina. He is Member ofthe National Research Council (CONICET), Argentina, and Argentine Society foBiochemistry and Molecular Biology (SAIB). His laboratory has been interested for manyears in the lipid peroxidation of biological membranes from various tissues and different species. Professor Catalá has directed twelve doctoral theses, publishedover 100 papers in peer reviewed journals, several chapters in books andtwelve edited books. Angel Catalá received awards at the 40th InternationaConference Biochemistry of Lipids 1999: Dijon (France). W inner of the Bimbo PanAmerican Nutrition, Food Science and Technology Award 2006 and 2012, South AmericaHuman Nutrition, Professional Category. 2006 award in pharmacology, Bernardo\r\nHoussay, in recognition of his meritorious works of research. Angel Catalá belongto the Editorial Board of Journal of lipids, International Review of Biophysical ChemistryFrontiers in Membrane Physiology and Biophysics, World Journal oExperimental Medicine and Biochemistry Research International, W orld Journal oBiological Chemistry, Oxidative Medicine and Cellular Longevity, Diabetes and thePancreas, International Journal of Chronic Diseases & Therapy, International Journal oNutrition, Co-Editor of The Open Biology Journal.",institutionString:null,institution:{name:"National University of La Plata",institutionURL:null,country:{name:"Argentina"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"186048",title:"Prof.",name:"Ines",middleName:null,surname:"Drenjančević",slug:"ines-drenjancevic",fullName:"Ines Drenjančević",profilePictureURL:"https://mts.intechopen.com/storage/users/186048/images/5818_n.jpg",institutionString:null,institution:{name:"University of Osijek",institutionURL:null,country:{name:"Croatia"}}},{id:"187859",title:"Prof.",name:"Kusal",middleName:"K.",surname:"Das",slug:"kusal-das",fullName:"Kusal Das",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBDeQAO/Profile_Picture_1623411145568",institutionString:"BLDE (Deemed to be University), India",institution:null},{id:"79615",title:"Dr.",name:"Robson",middleName:null,surname:"Faria",slug:"robson-faria",fullName:"Robson Faria",profilePictureURL:"https://mts.intechopen.com/storage/users/79615/images/system/79615.png",institutionString:null,institution:{name:"Oswaldo Cruz Foundation",institutionURL:null,country:{name:"Brazil"}}},{id:"84459",title:"Prof.",name:"Valerie",middleName:null,surname:"Chappe",slug:"valerie-chappe",fullName:"Valerie Chappe",profilePictureURL:"https://mts.intechopen.com/storage/users/84459/images/system/84459.jpg",institutionString:null,institution:{name:"Dalhousie University",institutionURL:null,country:{name:"Canada"}}}]},{id:"12",title:"Human Physiology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/12.jpg",editor:{id:"195829",title:"Prof.",name:"Kunihiro",middleName:null,surname:"Sakuma",slug:"kunihiro-sakuma",fullName:"Kunihiro Sakuma",profilePictureURL:"https://mts.intechopen.com/storage/users/195829/images/system/195829.jpg",biography:"Professor Kunihiro Sakuma, Ph.D., currently works in the Institute for Liberal Arts at the Tokyo Institute of Technology. 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