\r\n\tThe development of the interpersonal model and the Kleinian school in the second half of the last century allowed the emergence of an original understanding of the unconscious mind. Within the intersubjective paradigm, the psychoanalytic situation is conceptualized as an interpersonal field to which both the analyst and the patient contribute substantially. We have shown elsewhere how the failure to give a full account of such an intersubjective dimension in both psychoanalytic theory and practice amounts to a core liability in contemporary psychoanalytic discourse.
\r\n
\r\n\tThe present book will focus on a few areas where the insufficient development of our discipline is currently apparent: five wounds that mark the body of the psychoanalytic enterprise.
\r\n
\r\n\tNew contributions are particularly needed in the following areas: Current conceptualization of the unconscious mind is mechanistic and not suited to incorporate the full network of interpersonal exchanges which unfolds in the analytic room; Furthermore, the development of interpersonal psychoanalysis and the theory of the object relations warrants a greater appreciation of the impact of extratranference relations (e.g., couple, family, peers) on the patient's inner life both within and without the psychoanalytic situation.
\r\n
\r\n\tAn integration of theories and models from other psychological paradigms is clearly in order here; the book will also focus on Barangers’ theory of the bi-personal field that makes traditional unipersonal models of the psychoanalytic process untenable. Also, it will help in the understanding of the reciprocal interactions of the two partners in the psychoanalytic dyad in most psychoanalytic institutes the training format relies naively on models from the academic or the professional domains. This fosters rigidity, conformism, and a hierarchical organizational style in the institutional life; e) all over the long span of his creative life Freud showed consistent interest in the application of psychoanalysis to literature, the arts, religion, and politics. Contemporary psychoanalysis is getting more and shyer and is pressed at the margins of social and political debate. The psychoanalytic theory includes unique lore of knowledge about the conscious and unconscious mind. Without it, a comprehensive understanding of human reality will stay out of the reach of contemporary culture.
",isbn:"978-1-80356-882-9",printIsbn:"978-1-80356-881-2",pdfIsbn:"978-1-80356-883-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"c6a104ee38fec8d9ba8aa139a33003ce",bookSignature:"Dr. Paolo Azzone",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11591.jpg",keywords:"Unconscious, Repression, Conformism, Intersubjective Paradigm, Interpersonal Psychoanalysis, Object Relation Theory, Couple Therapy, Family Therapy, Psychoanalytic Process, Transference Interpretation, Resistance, Controtransference",numberOfDownloads:3,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 31st 2022",dateEndSecondStepPublish:"June 17th 2022",dateEndThirdStepPublish:"August 16th 2022",dateEndFourthStepPublish:"November 4th 2022",dateEndFifthStepPublish:"January 3rd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"14 days",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Paolo Azzone, M.D., is a psychiatrist and a psychoanalyst with over 20 years of experience in mental health topics. He was a tutor for the course in Clinical Psychiatry at the Medical School of the University of Milan and now is responsible for the Forensic Psychiatric Outpatient Program at the ASST-Rhodense Hospital in Milan, Italy. Azzone contributed to the establishment of a psychotherapy research tradition in Italy and is a co-editor and author of multiple works that are linked to psychoanalysis.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"324882",title:"Dr.",name:"Paolo",middleName:null,surname:"Azzone",slug:"paolo-azzone",fullName:"Paolo Azzone",profilePictureURL:"https://mts.intechopen.com/storage/users/324882/images/system/324882.jpg",biography:null,institutionString:"ASST-Rhodense Hospital",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:[{id:"82322",title:"A Psychoanalytic Approach to Identity Politics",slug:"a-psychoanalytic-approach-to-identity-politics",totalDownloads:3,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453624",firstName:"Martina",lastName:"Scerbe",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/453624/images/20399_n.jpg",email:"martina.s@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. 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1. Introduction
\n
In solving complex civil engineering problems, conventional analytical and empirical methodologies suffer from many difficulties. This is mainly because of the limitations of such methods in handling large, complex structures that may require time-consuming and exhausting tasks. In such situations, soft-computing techniques come into the picture. They are effective estimation tools that reduce the cost and time of design and analysis. Neural networks are useful soft-computing tools that can be used for classification and prediction in complex civil engineering problems [1–3].
\n
Sensitivity analysis is a necessary approach for understanding the relationship and the influence of each input parameter on the outputs of a problem. The key point behind sensitivity analysis is that by slightly varying each explicative input parameter and registering the response in the output, the explicative parameters with the highest sensitivity values gain the greatest importance. Sensitivity analysis of the most significant parameters can be very useful for analyzing complex engineering problems.
\n
Neural network-based parameter sensitivity analysis in civil engineering systems is gaining more importance due to the remarkable ability of neural networks to explain the nonlinear relationships between the explicative and response variables of a problem [1, 4]. Commonly, a specific training technique is used to develop one optimal neural network to be a system model, and this model is then used for sensitivity analysis [5–10]. Yet, it is relatively difficult to determine the most optimal neural network model, for reasons such as random initialization of the underlying connection weights in the neural network model, different features of various learning techniques used to train the neural network, the absence of a reliable technique for defining the optimal structure in neural network modeling, etc. To overcome these difficulties, two potential techniques, namely neural network committee (NNC)-based sensitivity analysis [1] and neural network ensemble (NNE)-based sensitivity analysis [11], are illustrated. These two paradigms utilize a group of pre-trained optimal neural networks to handle the neural network modeling, thereafter implementing parameter sensitivity analysis individually and lastly defining the sensitivity of parameters. This chapter is organized as follows. A complete explanation is given of some traditional neural network-based sensitivity analysis. Thereafter, the NNC-based parameter sensitivity analysis method is presented, followed by a geotechnical engineering case study of strata movement and two case studies related to classical civil engineering. Then, the NNE-based sensitivity analysis paradigm is described, followed by two illustrative case studies. Finally, a complete summary of the chapter is presented.
Many studies have been concerned with improving existing neural network-based sensitivity analysis methods [9]. Among the different techniques, the partial derivative algorithm [5] and the input perturbation algorithm [10] have superior performance compared to other techniques based on the magnitude of weights [6, 7]. Therefore, these two algorithms are explored in detail in this chapter, along with some other techniques.
\n
2.1. Partial derivative algorithm
\n
The partial derivative algorithm is a famous neural network-based sensitivity analysis technique [5, 11]. Its characteristics enable it to deal with neural networks that apply first-derivative activation functions, such as back-propagation neural networks (BPNNs) and radial basis function neural networks (RBFNNs) [1, 8]. By implementing the partial derivative algorithm, it is possible to identify the variations of output parameters of neural networks with respect to small changes in each input parameter, thereby defining the contribution of each such input on the output parameters. This can be done by deriving the output parameters of the neural network with respect to input parameters, in other words, by calculating the Jacobian matrix that contains the partial derivatives of outputs with respect to inputs [5, 11].
\n
For a successful BPNN model having input xi with ni as the total number of inputs and output yk with nk as the total number of outputs, the Jacobian matrix ∂yk∂xi can be defined by using the chain rule as [1]\n
where xi is the ith input variable hn,hn−1,…, and hi are the hidden neurons from the nth to the first hidden layer, respectively; yk, yhn, and yh1 are the output values for output neuron k, hidden neurons hn, and n1 in the respective nth and the first hidden layer; Whnk is the connection weight between the kth output neuron and the hidden neuron hn; Whn−1hn is the connection weight between the hidden neurons hn−1 and hn and Wih1 is the connection weight between the ith input neuron and the hidden neuron h1; Ok,Ohn, and Oh1 are the weighted sums of kth output neuron, the hidden neuron hn, and h1, respectively; f′ denotes the derivative of the activation function f. yk,yhn, and yh1 can be given as
where bk,bhn, and bh1 are the biases of the kth output neuron, the hidden neuron hn, and, h1, respectively.
\n
For p training samples of each input xi on the output yk of the neural network, cik can be calculated as\n
cik=∑p|(∂yk∂xi)p|E3
\n
For each input parameter, the value of c can be used as a factor for classification of the influence of total inputs on the outputs of the neural network model. The most important or crucial input parameter may have the highest c value [1].
\n
\n
2.2. Input perturbation algorithm
\n
The input perturbation algorithm is another common method for neural-network-based sensitivity analysis [6, 9]. It implements a small perturbation on each input of the neural network model and measures the corresponding change in the outputs. This perturbation is applied on one input individually at a time while all other inputs are fixed, and the response for perturbation of each output is registered. Sensitivity analysis is performed by giving a rank for each response of the output generated by the same perturbation in every input parameter. The input that has the highest effect on the outputs after perturbation is considered the most influential or important [1].
\n
In essence, when a larger amount of perturbation is added to the selected input parameter, the mean square error (MSE) of the neural network increases. The variance of the input parameter can be represented as xi=xi+Δxi, where xi is the current selected input variable and Δxi is the perturbation. The perturbation can be varied from 0 to 50% by steps of 5% of the input value. Depending on the increasing value of the MSE corresponding to each perturbed input, outputs can be ranked and thus sensitivity analyses are performed [1, 8].
\n
\n
2.3. Weights method
\n
This method was proposed by Garson [12] and Goh [13]. In this method, for each hidden neuron, the connection weights are divided into components related to each input neuron. This method was simplified by Gevrey et al. [8] to give the same results as the initial method. For the purpose of illustration, a multilayer neural network with a single hidden layer is considered; thereafter, for each hidden neuron the following calculations are used:
\n
For i=1 to ni
\n\n
For j=1 to nj
\n
Dij=|Wij|∑i=1ni|Wij|E4
\n\n
End
\n\n
End
\n
where ni and nj are the number of input and hidden neurons, respectively; Wij is the weight corresponding to input neuron i and hidden neuron j. The percentage relative contribution of all inputs RCi is then calculated as
\n
For i=1 to ni\n
RCi=∑j=1njDij∑j=1nj∑i=1niDijE5
\n
End
\n
\n
2.4. Profile method
\n
This method was proposed by Lek et al. [14–16], and further explained by Gevrey et al. [8]. The key point behind this method is to analyze one particular input at a time while fixing the values of all other inputs. The procedure starts by dividing the value of each input parameter into equal subintervals, whereas all other inputs are set prior to minimum, quarter, half, three quarters of the maximum and maximum, respectively. At the end of this task, patterns of five values corresponding to different input parameters result and the median value for each pattern is calculated. The median values are plotted with respect to the subintervals to form a profile that explains the contribution of the input parameter. Finally, for all inputs, a set of curves explaining the relative importance for all input parameters is obtained [8].
\n
\n
2.5. Stepwise method
\n
In this method, one input parameter is blocked and the responses of the outputs are recorded. This process is performed step by step for all input parameters and the responses of the outputs are recorded by means of the MSE. Depending on the MSE, the relative importance of each input variable is ranked correspondingly. There are two main strategies for the stepwise method. The first is to construct a number of neural network models by evolving the input parameters one by one. This strategy is called forward stepwise, while the backward stepwise strategy can be implemented in the reverse way, that is, constructing neural network models by first using all input parameters and then blocking each input parameter [8, 17].
\n
This method can be improved to reduce the difficulty of producing many neural network models by using a single model. In this model, one input parameter is blocked and the MSE is calculated. The parameter with the maximum MSE value is ranked as the most important and can then be either removed from the model or fixed at its mean value so that the contribution of other parameters can be found, and so on.
Consider a neural network model with a sensitivity analysis-ranking vector R=[r1,r2,…,rn] and the actual sensitivity analysis-ranking vector R0=[a1,a2,…,an], where ri and ai are the calculated and actual ranks of ith input parameter, respectively, and n is the number of input parameters. To reduce the difference between R and R0 to minimum, it is not efficient to use single neural network model to perform sensitivity analysis. The reason is the absence of persistence in sensitivity analysis of one neural network model even when a major sensitivity analysis strategy is implemented. In recognition of this fact, it is more effective to utilize a set of good pre-trained neural network models instead of using a single optimal model for sensitivity analysis. This procedure is well used in neural network committee (NNC)-based sensitivity analysis [1].
\n
The mathematical foundation of NNC-based sensitivity analysis starts from the weak law of large numbers in probability. Having x1,x2,… infinite set of random variables with no correlation between any two of them, each having the exact value of μ and variance σ2, the sample average convergence in probability can be written as [18]\n
x¯n=(x1+x2+…+x)nE6
\n
or, in other words, for a small number ε, the following can be expressed:\n
limn→∞P(|x¯n|−μ<ε)=1E7
\n
By considering single neural network sensitivity analysis-ranking vector R, the elements r1,r2,… can be defined as random variables; in other words, R is composed of n random variables. In the case of neural network ensemble-based sensitivity analysis, a set of random variables ri1,ri2,…,rim related to ri are obtained. Depending on the weak law of large numbers, for a large number m, the mean of ri1,ri2,…,rim can converge to the actual ranking values ai in R0. Therefore, in NNC-based sensitivity analysis, it is possible to find a ranking vector R that is close to the actual ranking vector R0.
\n
As the number of input variables is specified and the input variables are not completely random, due to the many specifications that appear during neural network model training, the condition of the weak law of large numbers that is applied on an infinite number of random variables is not satisfied. For this reason, optimization strategy can be an efficient tool to select a number of good pre-trained neural network models and skip those with weak performance. By electing the best neural network elements and eliminating the bad ones, optimization can generate good predictions of sensitivity analysis-ranking vectors [1].
\n
Depending on the above principles, we can summarize NNC-based sensitivity analysis in three basic procedures. First, groups (seeds) of successful neural network models are prepared using neural network-training techniques such as back propagation (BP) or radial basis functions, etc. Then, a set of best-performance models are chosen to compose the optimal NNC that is used in performing ensemble neural network sensitivity analysis by individual applications of sensitivity analysis, giving large numbers of R. Finally, the mean of R is calculated to find the accurate approximation of R0. A schematic diagram of NNC-based sensitivity analysis strategy is given in Figure 1.
Figure 1.
A schematic diagram of NNC-based sensitivity analysis strategy.
\n
Figure 2.
NNC-based sensitivity analysis strategy stepwise procedure: A–N, the neural network model seeds; a1–am, b1–bm, and n1–nm, the candidate groups of neural network models; a1–aak,bb1–bbk, and nn1–nnk, the superior neural network models; ellipse refers to a sensitivity analysis ranking of an input parameter.
\n
The basic steps for the NNC-based sensitivity analysis algorithm are shown in Figure 2 and can be explained as follows:
\n\n
Select the best available types of neural network model empirically. These are called “seeds” for NNC-based sensitivity analysis.
Each seed involves a set of neural network models. These models are varied by means of a number of hidden neurons or hidden layers to produce a candidate group of neural network models.
Depending on the MSE, a subset k of superior neural network models is picked up, where k=310m has been experimentally specified and m is the number of neural network models in the candidate group. Thereafter, sensitivity analysis is employed on each model to generate a group of sensitivity analysis-ranking vectors R.
For each input parameter, the mean of the related ranking number in the sensitivity analysis-ranking vector R is calculated to form a predicted ranking vector close to the actual ranking vector R0, which is calculated as\n
ai≈a^i=1NK∑s=1N∑t=1Krist,i=1,2,…,lE8
where a^i is the predicted value of ai in R0 for variable xi in R, K is the number of elements in the candidate group of neural network models (committee), N is the number of neural network seeds, and l is the number of input parameters.
\n
\n
4. NNC-based sensitivity analysis of strata movement
\n
Strata movement is a critical problem in geotechnical engineering because of the complex highly nonlinear properties involved. It is necessary to define the most significant factors involved in strata movement. Therefore, NNC-based sensitivity analysis strategy is used. The dataset of strata movement is composed of 168 samples taken from multiple typical observation stations of earth surface movement above underground metal mines. The dataset has six input parameters and three output parameters as shown in Table 1. These parameters characterize the working operation of strata movement of underground metal mines.
\n\n
In NNC-based sensitivity analysis, four scenarios are chosen, depending on the output variables (Table 1): scenario (1) all output parameters, (2) only MAU, (3) only MAL, and (4) only AA. At the beginning, radial basis function and BP neural networks are selected as seeds, because of their proven ability to handle nonlinear features. Then, 50 neural network models are generated by each seed to construct two candidate sets of neural network models. Thereafter, 15 superior neural network models are chosen from each set to form a committee containing the best-performed neural network models. After that, sensitivity analysis is applied to each model by utilizing both a perturbation algorithm and a partial derivative algorithm to produce a group of ranking vectors R. Next, the sum of corresponding ranking numbers that is considered as a score for input parameters is calculated. The score is a reflection of the near actual ranking R0. The sum is used instead of the mean to prevent the repetition of the identical values for different parameters, in order to have fewer neural network models from which to decide the final ranking. The best-performed neural networks and the input parameter ranking for scenario (1) are illustrated in Table 2.
Sensitivity analysis rankings produced by best-performed neural network model groups [1].
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Scenarios
\n
Score and ranking
\n
MCU
\n
LCL
\n
SAO
\n
TO
\n
LO
\n
DE
\n
\n\n\n
\n
(1)
\n
Score
\n
101
\n
120
\n
80
\n
138
\n
148
\n
43
\n
\n
\n
\n
Ranking
\n
3
\n
4
\n
2
\n
5
\n
6
\n
1
\n
\n
\n
(2)
\n
Score
\n
96
\n
114
\n
64
\n
162
\n
145
\n
49
\n
\n
\n
\n
Ranking
\n
3
\n
4
\n
2
\n
6
\n
5
\n
1
\n
\n
\n
(3)
\n
Score
\n
96
\n
81
\n
87
\n
75
\n
155
\n
136
\n
\n
\n
\n
Ranking
\n
4
\n
2
\n
3
\n
1
\n
6
\n
5
\n
\n
\n
(4)
\n
Score
\n
109
\n
121
\n
113
\n
103
\n
86
\n
98
\n
\n
\n
\n
Ranking
\n
4
\n
6
\n
5
\n
3
\n
1
\n
2
\n
\n\n
Table 3.
NNC-based sensitivity analysis results for strata movement.
\n
The outcome sensitivity analysis for the four scenarios is illustrated in Table 3. It is clear from the table that for scenario (1), DE has the highest importance, followed by SAO, MCU, LCL, TO, and LO, respectively. In scenario (2), the degree of importance is the same as in scenario (1), but LO is more significant than TO. Nevertheless, in scenario (3), TO has the highest significance, above that of LCL, SAO, and MCU, which have approximately similar significance, and then DE and LO have the least significance. Finally, in scenario (4) LO has the highest contribution followed by DE, TO, MCU, SAO, and LCL, respectively. However, the contributions of DE and TO are very close to those of MCU, SAO, and LCL.
\n\n
Figure 3.
Activity analysis of dependent variables for strata movement based on NNC-based sensitivity analysis results [1].
\n
The working condition of strata movement is defined by the predictability of response parameter (output parameters). For this reason, the scores of the input variables after sensitivity analysis for three scenarios (MAU, MAL, and AA) that are related to the response variables are plotted in Figure 3. The response variable with the highest sensitivity against explicative variables has the highest predictability, and this can be calculated by finding the variance of the score vector of the explicative variables. The result of that procedure is 1965.6, 1068.4, and 150, corresponding to the response variables MAU, MAL, and AA, respectively. It is obvious that MAU has the highest predictability, followed by MAL and AA. Therefore, we can consider the angles of the upper wall rocks as the most significant feature, ahead of the lower wall rocks and the avalanche angle that are less important.
\n
\n
5. NNE-based parameter sensitivity analysis
\n
The NNE-based parameter sensitivity analysis technique is a modified version of the NNC-based sensitivity analysis. It reduces the time-consuming procedure of using different neural network types as seeds by using just one preferred neural network type as the seed [4]. NNE-based parameter sensitivity analysis incorporates the following steps: (1) one preferred type of neural network is considered as the seed, (2) a set of k-neural network models that are varied with regard to the number of hidden neurons and hidden layers is defined, (3) from k-neural network models, a group of n best-performed models (n<k) is picked up and the other poorly performed models are eliminated to form an NNE model, and (4) a sophisticated sensitivity analysis algorithm is performed on the NNE model to obtain a sensitivity ranking of all input variables of the engineering problem under consideration. A schematic diagram of NNE-based parameter sensitivity analysis is shown in Figure 4.
\n
Figure 4.
A schematic representation of NNE-based parameter sensitivity analysis.
\n
\n
6. Illustrative case studies
\n
To highlight the application of NNE-based parameter sensitivity analysis technique, two civil engineering case studies are explained. The first is the determination of the importance of material properties in the fracture failure of a notched concrete beam and the second is the specification of significant parameters in the lateral deformation of a deep-foundation pit [4].
\n
6.1. Fracture failure of notched concrete beam
\n
Fracture failure is the most common problem facing engineers in the analysis and usage of concrete structures [19,20]. Good knowledge of appropriate material properties is necessary during modeling of the fracture behavior of concrete structures. Such material properties are defined by a three-point bending of a notched concrete specimen. Therefore, the NNE-based parameter sensitivity analysis strategy is used to find the most crucial material properties in the fracture failure of a notched concrete beam. The geometry of the notched concrete beam is shown in Figure 5, with experimentally determined mean values of material properties [21]: modulus of elasticity Ec=35GPa, tensile strength ft=3MPa, compressive strength fc=65MPa, fracture energy Gf=100N/m, and compressive strain at compressive strength in the uniaxial compressive test ec=0.003. A group of 20 notched concrete beam samples is prepared depending on a stratified Monte Carlo-type simulation called Latin hypercube sampling (LHS) [22], using FReET software [23] with a correlation control procedure [24].
\n
Figure 5.
Notched concrete beam under three-point bending [4].
\n
Figure 6.
Force-displacement curves at the notch tip S from 20 simulated realizations of notched concrete beams [4].
\n
The 20 notched concrete beam samples are determined by employing the following steps: (1) material properties are considered as random variables and mean values are obtained by experiments; (2) for each property, the LHS stochastic simulation is utilized to produce 20 random realizations of {Ec,ft,fc,Gf,ec} that feature variation of 0.15 and that obey a rectangular probability distribution, to impose variability for the creation of the training set. Each random realization determines a numerical nonlinear fracture mechanic calculation of a notched concrete beam; and (3) the finite element method (FEM) software ATENA [25] is applied to each realization to simulate the tensile fracture of the corresponding notched concrete beam. The fracture failure is described by a force-displacement curve at the notch tip S (Figure 5). A set of 20 force-displacement curves is illustrated in Figure 6. This set can be used as input data for the NNE-based sensitivity analysis. These curves describe the correlation between material fracture-mechanical properties and the nonlinear response of the beam. The sensitivity of the material properties to tensile fracture is studied depending on three forces: F0.02, the force corresponding to 0.02-mm displacement; Fmax the maximum force; and F0.15 the force corresponding to 0.15-mm displacement. For each force, NNE-based parameter sensitivity analysis is applied to determine the significance of the material properties.
\n\n
\n
\n
\n
\n\n
\n
Parameter
\n
Characteristics
\n
Parameter type
\n
\n\n\n
\n
Ec
\n
Modulus of elasticity
\n
Input
\n
\n
\n
ft
\n
Tensile strength
\n
Input
\n
\n
\n
fc
\n
Compressive strength
\n
Input
\n
\n
\n
Gf
\n
Fracture energy
\n
Input
\n
\n
\n
εc
\n
Compressive strain
\n
Input
\n
\n
\n
F0.02
\n
Force at 0.02-mm displacement
\n
Output
\n
\n
\n
Fmax
\n
Maximum force
\n
Output
\n
\n
\n
F0.15
\n
Force at 0.15-mm displacement
\n
Output
\n
\n\n
Table 4.
Material properties in fracture failure of notched concrete beam [4].
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Force
\n
Ranking
\n
\n
\n
Ec
\n
ft
\n
fc
\n
Gf
\n
εc
\n
\n\n\n
\n
F0.02
\n
1
\n
2
\n
3
\n
4
\n
5
\n
\n
\n
Fmax
\n
2
\n
1
\n
4
\n
3
\n
5
\n
\n
\n
F0.15
\n
4
\n
2
\n
3
\n
1
\n
5
\n
\n\n
Table 5.
Sensitivity analysis results of material parameters in fracture failure [4].
\n
In NNE-based sensitivity analysis paradigm, a BP neural network with five input neurons and one output neuron (Table 4) is used as the seed to create a set of k-candidate neural network models. These models correlate the relationship between the material properties and the fracture failure. Depending on the performance of these models, the three best-performed neural network models are selected in the NNE model and the input perturbation algorithm is used for parameter sensitivity analysis. The result of the sensitivity analysis in this case is shown in Table 5. It is obvious from the table that ft, followed by Ec and Gf, are the most important parameters in the fracture failure of the notched concrete beam.
\n\n\n
\n
6.2. Lateral deformation of deep-foundation pit
\n
The construction of underground structures such as subway system tunnels, etc. requires deep-foundation pits. The working condition of a deep-foundation pit is usually defined by means of lateral deformation [26]. This lateral deformation usually involves a group of variables (Table 6), namely surface load q, deformation modulus of soil E, Poisson’s ratio λ, soil cohesion C, and internal friction angle of soil ϕ. To analyze the working process of the deep-foundation pit, it is essential to study the sensitivity of these variables in order. Therefore, NNE-based parameter sensitivity analysis is applied to determine the importance of parameters in the lateral deformation of deep-foundation pits. For such analysis, a deep polygon-shaped foundation pit, as in [27], is utilized, having an excavation depth of 9.71 m, a width of earth-retaining wall of 8.7 m, and a length of reinforcement piles of 19.0 m, with the insertion ratio about 1.0. For testing cases, an orthogonal design of experiments is used to generate 25 testing cases, as shown in Table 7 [27]. The testing cases are employed within the NNE-based sensitivity analysis paradigm to finally specify the contribution of each parameter to the lateral deformation y of the deep-foundation pits.
\n
\n
\n
\n
\n\n
\n
Parameter
\n
Characteristics
\n
Parameter type
\n
\n\n\n
\n
q
\n
Surface load
\n
Input
\n
\n
\n
E
\n
Deformation modulus of soil
\n
Input
\n
\n
\n
ε
\n
Poisson’s ratio
\n
Input
\n
\n
\n
C
\n
Soil cohesion
\n
Input
\n
\n
\n
ϕ
\n
Internal friction angle of soil
\n
Input
\n
\n
\n
y
\n
Lateral deformation of deep-foundation pit
\n
Output
\n
\n\n
Table 6.
Properties in lateral deformation of deep-foundation pit [4].
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
No.
\n
q (kPa)
\n
E (kPa)
\n
ε
\n
C (kPa)
\n
ϕ (rad)
\n
y (cm)
\n
\n\n\n
\n
1
\n
1 (5.0)
\n
1 (3855)
\n
1 (0.325)
\n
1 (5.63)
\n
1 (0.1386)
\n
63.7
\n
\n
\n
2
\n
1
\n
2 (6168)
\n
2 (0.376)
\n
2 (7.44)
\n
2 (0.1834)
\n
35.3
\n
\n
\n
3
\n
1
\n
3 (7710)
\n
3 (0.410)
\n
3 (8.65)
\n
3 (0.2133)
\n
26.7
\n
\n
\n
4
\n
1
\n
4 (9252)
\n
4 (0.444)
\n
4 (9.86)
\n
4 (0.2432)
\n
20.9
\n
\n
\n
5
\n
1
\n
5 (11,565)
\n
5 (0.478)
\n
5 (11.68)
\n
5 (0.2731)
\n
12.5
\n
\n
\n
6
\n
2 (8.0)
\n
1
\n
2
\n
3
\n
4
\n
55.8
\n
\n
\n
7
\n
2
\n
2
\n
3
\n
4
\n
5
\n
32.1
\n
\n
\n
8
\n
2
\n
3
\n
4
\n
5
\n
1
\n
21.9
\n
\n
\n
9
\n
2
\n
4
\n
5
\n
1
\n
2
\n
16.3
\n
\n
\n
10
\n
2
\n
5
\n
1
\n
2
\n
3
\n
25.2
\n
\n
\n
11
\n
3 (10.0)
\n
1
\n
3
\n
5
\n
2
\n
47.8
\n
\n
\n
12
\n
3
\n
2
\n
4
\n
1
\n
3
\n
26.1
\n
\n
\n
13
\n
3
\n
3
\n
5
\n
2
\n
4
\n
16.2
\n
\n
\n
14
\n
3
\n
4
\n
1
\n
3
\n
5
\n
30.4
\n
\n
\n
15
\n
3
\n
5
\n
2
\n
4
\n
1
\n
22.1
\n
\n
\n
16
\n
4 (12.0)
\n
1
\n
4
\n
2
\n
5
\n
37.1
\n
\n
\n
17
\n
4
\n
2
\n
5
\n
3
\n
1
\n
18.0
\n
\n
\n
18
\n
4
\n
3
\n
1
\n
4
\n
2
\n
34.9
\n
\n
\n
19
\n
4
\n
4
\n
2
\n
5
\n
3
\n
25.8
\n
\n
\n
20
\n
4
\n
5
\n
3
\n
1
\n
4
\n
18.9
\n
\n
\n
21
\n
5 (15.0)
\n
1
\n
5
\n
4
\n
3
\n
25.2
\n
\n
\n
22
\n
5
\n
2
\n
1
\n
5
\n
4
\n
42.4
\n
\n
\n
23
\n
5
\n
3
\n
2
\n
1
\n
5
\n
30.1
\n
\n
\n
24
\n
5
\n
4
\n
3
\n
2
\n
1
\n
22.4
\n
\n
\n
25
\n
5
\n
5
\n
4
\n
3
\n
2
\n
15.6
\n
\n\n
Table 7.
Orthogonal experimental design for producing testing samples [27].
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Model
\n
Ranking
\n
\n
\n
q
\n
E
\n
λ
\n
C
\n
ϕ
\n
\n\n\n
\n
NNM1
\n
5
\n
1
\n
2
\n
3
\n
4
\n
\n
\n
NNM2
\n
5
\n
1
\n
2
\n
3
\n
4
\n
\n
\n
NNM3
\n
5
\n
1
\n
2
\n
3
\n
4
\n
\n\n
Table 8.
Sensitivity analysis results in lateral deformation of deep-foundation pit [4].
\n
As in the previous case study, a BP neural network is chosen as the seed in NNE-based sensitivity analysis to generate a set of k-candidate neural network models having five inputs and one output as listed in Table 6. By selecting three superior neural network models, namely NNM1, NNM2, and NNM3, and implementing input perturbation algorithm for sensitivity analysis, the ranking of each input parameter corresponding to each neural network model is shown in Table 8. It is clear that E is the most important parameter, followed by λ,C,ϕ, and q, respectively.
\n\n\n\n
\n
\n
7. Summary
\n
A short review of traditional neural network sensitivity analysis techniques was illustrated, followed by the presentation of two advanced techniques, NNC-based sensitivity analysis and NNE-based sensitivity analysis. These two techniques utilized selective superior neural network models along with some mathematical concepts to analyze the sensitivity of significant explicative variables. The efficiency of NNC-based sensitivity analysis paradigm was verified by studying the underlying influential parameters in strata movement. The effectiveness of NNE-based sensitivity analysis paradigm was proved by two case studies in civil engineering, the fracture failure of notched concrete beams and the lateral deformation of deep-foundation pits. These paradigms are essential for understanding the neural-network-based sensitivity analysis of critical engineering problems, due to their ability to determine the most and least important parameters, thereby reducing the inputs of neural network models to generate better predictability. They are good tools for analyzing the mechanism of engineering problems that black-box neural network models cannot explain.
\n
\n\n',keywords:"civil engineering, neural networks, sensitivity analysis, NNC-based sensitivity analysis, NNE-based sensitivity analysis",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/51330.pdf",chapterXML:"https://mts.intechopen.com/source/xml/51330.xml",downloadPdfUrl:"/chapter/pdf-download/51330",previewPdfUrl:"/chapter/pdf-preview/51330",totalDownloads:2442,totalViews:549,totalCrossrefCites:14,totalDimensionsCites:20,totalAltmetricsMentions:0,impactScore:7,impactScorePercentile:96,impactScoreQuartile:4,hasAltmetrics:0,dateSubmitted:"October 28th 2015",dateReviewed:"April 29th 2016",datePrePublished:null,datePublished:"October 19th 2016",dateFinished:"June 25th 2016",readingETA:"0",abstract:"Artificial neural networks (ANNs) are powerful tools that are used in various engineering fields. Their characteristics enable them to solve prediction, regression, and classification problems. Nevertheless, the ANN is usually thought of as a black box, in which it is difficult to determine the effect of each explicative variable (input) on the dependent variables (outputs) in any problem. To investigate such effects, sensitivity analysis is usually applied on the optimal pre-trained ANN. Existing sensitivity analysis techniques suffer from drawbacks. Their basis on a single optimal pre-trained ANN model produces instability in parameter sensitivity analysis because of the uncertainty in neural network modeling. To overcome this deficiency, two successful sensitivity analysis paradigms, the neural network committee (NNC)-based sensitivity analysis and the neural network ensemble (NNE)-based parameter sensitivity analysis, are illustrated in this chapter. An NNC is applied in a case study of geotechnical engineering involving strata movement. An NNE is implemented for sensitivity analysis of two classic problems in civil engineering: (i) the fracture failure of notched concrete beams and (ii) the lateral deformation of deep-foundation pits. Results demonstrate good ability to analyze the sensitivity of the most influential parameters, illustrating the underlying mechanisms of such engineering systems.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/51330",risUrl:"/chapter/ris/51330",book:{id:"5191",slug:"artificial-neural-networks-models-and-applications"},signatures:"Maosen Cao, Nizar F. Alkayem, Lixia Pan and Drahomír Novák",authors:[{id:"180549",title:"Prof.",name:"Maosen",middleName:null,surname:"Cao",fullName:"Maosen Cao",slug:"maosen-cao",email:"cmszhy@hhu.edu.cn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Nanjing University",institutionURL:null,country:{name:"China"}}},{id:"180560",title:"Dr.",name:"Lixia",middleName:null,surname:"Pan",fullName:"Lixia Pan",slug:"lixia-pan",email:"plx1@hhu.edu.cn",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"180562",title:"Dr.",name:"Nizar Faisal",middleName:null,surname:"Alkayem",fullName:"Nizar Faisal Alkayem",slug:"nizar-faisal-alkayem",email:"nizaralkayem@yahoo.in",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Typical neural networks-based sensitivity analysis algorithms",level:"1"},{id:"sec_2_2",title:"2.1. Partial derivative algorithm",level:"2"},{id:"sec_3_2",title:"2.2. Input perturbation algorithm",level:"2"},{id:"sec_4_2",title:"2.3. Weights method",level:"2"},{id:"sec_5_2",title:"2.4. Profile method",level:"2"},{id:"sec_6_2",title:"2.5. Stepwise method",level:"2"},{id:"sec_8",title:"3. Neural network committee-based sensitivity analysis",level:"1"},{id:"sec_9",title:"4. NNC-based sensitivity analysis of strata movement",level:"1"},{id:"sec_10",title:"5. NNE-based parameter sensitivity analysis",level:"1"},{id:"sec_11",title:"6. Illustrative case studies",level:"1"},{id:"sec_11_2",title:"6.1. Fracture failure of notched concrete beam",level:"2"},{id:"sec_12_2",title:"6.2. Lateral deformation of deep-foundation pit",level:"2"},{id:"sec_14",title:"7. Summary",level:"1"}],chapterReferences:[{id:"B1",body:'Cao MS, Qiao P. Neural network committee-based sensitivity analysis strategy for geotechnical engineering problems. Neural Computing & Applications. 2008;17(5):509–519. DOI: 10.1007/s00521-007-0143-5'},{id:"B2",body:'Cao M, Qiao P, Ren Q. Improved hybrid wavelet neural network methodology for time-varying behavior prediction of engineering structures. Neural Computing and Applications. 2009;18(7):821–832. DOI: 10.1007/s00521-009-0240-8'},{id:"B3",body:'Waszczyszyn Z, Ziemiański L. Neural networks in mechanics of structures and materials—new results and prospects of applications. Computers & Structures. 2001;79(22-25):2261–2276. DOI: 10.1016/S0045-7949(01)00083-9'},{id:"B4",body:'Cao MS, Pan LX, Gao YF, Novák D, Ding ZC, Lehký D, Li XL. Neural network ensemble-based parameter sensitivity analysis in civil engineering systems. Neural Computing & Applications. Forthcoming. DOI: 10.1007/s00521-015-2132-4'},{id:"B5",body:'Dimopoulos Y, Bourret P, Lek S. Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Processing Letters. 1995;2(6):1–4. DOI: 10.1007/BF02309007'},{id:"B6",body:'Gedeon TD. Data mining of inputs: analysing magnitude and functional measures. International Journal of Neural Systems. 1997;8(2):209–218. DOI: 10.1142/S0129065797000227'},{id:"B7",body:'Wang W, Jones P, Partridge D. Assessing the impact of input features in a feedforward neural network. Neural Computing & Applications. 2000;9(2):101–112. DOI: 10.1007/PL00009895'},{id:"B8",body:'Gevrey M, Dimopoulos I, Lek S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling. 2003;160(3):249–264. DOI: 10.1016/S0304-3800(02)00257-0'},{id:"B9",body:'Montaño JJ, Palmer A. Numeric sensitivity analysis applied to feedforward neural networks. Neural Computing & Applications. 2003;12(2):119–125. DOI: 10.1007/s00521-003-0377-9'},{id:"B10",body:'Zeng X, Yeung DS. A quantified sensitivity measure for multilayer perceptron to input perturbation. Neural Computation. 2003;15(1):183–212. DOI: 10.1162/089976603321043757'},{id:"B11",body:'Yang Y, Zhang Q. A hierarchical analysis for rock engineering using artificial neural networks. Rock Mechanics and Rock Engineering. 1997;30(4):207–222. DOI: 10.1007/BF01045717'},{id:"B12",body:'Garson GD. Interpreting neural network connection weights. AI Expert. 1991;6(4):46–51.'},{id:"B13",body:'Goh ATC. Back-propagation neural networks for modelling complex systems. Artificial Intelligence in Engineering. 1995;9(3):143–151. DOI: 10.1016/0954-1810(94)00011-S'},{id:"B14",body:'Lek S, Belaud A, Baran P, Dimopoulos I, Delacoste M. Role of some environmental variables in trout abundance models using neural networks. Aquatic Living Resources. 1996;9(1):23–29.'},{id:"B15",body:'Lek S, Delacosteb M, Baranb P, Dimopoulosa I, Laugaa J, Aulagnierc S. Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling. 1996;90(1):39–52. DOI: 10.1016/0304-3800(95)00142-5'},{id:"B16",body:'Lek S, Belaud A, Dimopoulos I, Lauga J, Moreau J. Improved estimation, using neural networks, of the food consumption of fish populations. Marine and Freshwater Research. 1995;46(8):1229–1236 . DOI: 10.1071/MF9951229'},{id:"B17",body:'Sung AH. Ranking importance of input parameters of neural networks. Expert Systems with Applications. 1998;15(3-4): 405–411. DOI: 10.1016/S0957-4174(98)00041-4'},{id:"B18",body:'Durrett R. Probability: Theory and Example, 3rd ed. Pacific Grove, CA: Duxbury; 2004. 521 p.'},{id:"B19",body:'Carpinteri A, Ferro G. Size effects on tensile fracture properties: a unified explanation based on disorder and fractality of concrete microstructure. Materials and Structures. 1994;27(10):563–571. DOI: 10.1007/BF02473124'},{id:"B20",body:'Rott JG. Computational modelling of concrete fracture [dissertation]. Delft: Technische Hogeschool Delft; 1988.'},{id:"B21",body:'Novák D, Lehký D. ANN inverse analysis based on stochastic small-sample training set simulation. Engineering Applications of Artificial Intelligence. 2006;19(7):731–740. DOI: 10.1016/j.engappai.2006.05.003'},{id:"B22",body:'Stein M. Large sample properties of simulations using Latin hypercube sampling. Technometrics. 1987;29(2):143–151 . DOI: 10.2307/1269769'},{id:"B23",body:'Novák D, Vořechovský M, Teplý B. FReET: software for the statistical and reliability analysis of engineering problems and FReET-D: degradation module. Advances in Engineering Software. 2014;72:179–192. DOI: 10.1016/j.advengsoft.2013.06.011'},{id:"B24",body:'Vořechovský M, Novák D. Correlation control in small-sample Monte Carlo type simulations I: a simulated annealing approach. Probabilistic Engineering Mechanics. 2009;24(3):452–462. DOI: 10.1016/j.probengmech.2009.01.004'},{id:"B25",body:'Červenka V, Jendele L, Červenka J. ATENA Program Documentation, Part 1: Theory. Prague: Červenka Consulting; 2009. 94 p.'},{id:"B26",body:'Feng S, Wu Y, Li J, Li P, Zhang Z, Wang D. The analysis of spatial effect of deep foundation pit in soft soil areas. Procedia Earth and Planetary Science. 2012;5:309–313. DOI: 10.1016/j.proeps.2012.01.052'},{id:"B27",body:'XU C, YE GB. Parameter sensitivity analysis of numerical model by cross test design technique. Hydrogeology and Engineering Geology. 2004;1:95–97.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Maosen Cao",address:"cmszhy@hhu.edu.cn",affiliation:'
Department of Engineering Mechanics, Hohai University, Nanjing, People’s Republic of China
'},{corresp:null,contributorFullName:"Nizar F. Alkayem",address:null,affiliation:'
Department of Engineering Mechanics, Hohai University, Nanjing, People’s Republic of China
Faculty of Civil Engineering, Institute of Structural Mechanics, Brno University of Technology, Brno, Czech Republic
'}],corrections:null},book:{id:"5191",type:"book",title:"Artificial Neural Networks",subtitle:"Models and Applications",fullTitle:"Artificial Neural Networks - Models and Applications",slug:"artificial-neural-networks-models-and-applications",publishedDate:"October 19th 2016",bookSignature:"Joao Luis G. 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1. Introduction
Psoriasis is a chronic inflammatory skin disease that progresses with remission and exacerbations [1, 2]. It constitutes an important percentage, approximately 6–8% of patients who apply to dermatology clinics [3]. Due to its high prevalence and chronic course, it is important to diagnose it early and clearly to manage patient appropriately and avoid functional losses as much as possible. In addition, in some situations that should be intervened swiftly such as erythrodermic psoriasis or generalized pustular psoriasis; the sooner we diagnose, the better we take control of disease setting.
In diagnosis of psoriasis, usually clinical observation is enough; however, in doubtful cases, histopathological examination is required as gold standard technique. However, it requires an invasive procedure and needs time for pathological preparation. With dermoscopy, we can mostly distinguish psoriasis from other resembling diseases in clinic noninvasively. Despite it not being gold standard, easily applicable and noninvasive properties of dermoscopy make it a helpful diagnostic tool and reduce the need of performing biopsies.
2. Dermoscopy of psoriasis types and differentials
2.1 Plaque psoriasis
Plaque psoriasis is the most common clinical subtype of psoriasis with 90% of all cases [4]. It is characterized by erythematous, well-defined, and usually indurated plaques greater than 1 cm in size with white-silvery scales on them (Figure 1). They can vary in size and may coalesce. Especially rapidly progressing lesions can be seen in annular configuration (Figure 2) [4, 5]. Removal of psoriatic scales may cause pinpoint bleedings, which is called Auspitz sign. Psoriatic plaques are mostly located in the scalp, trunk, lumbosacral area, and extensor surfaces of extremities (Figure 3) [6].
Figure 1.
Erythematous, well-defined indurated plaque with white scales.
Figure 2.
Erythematous, annular plaques with white scales.
Figure 3.
Psoriatic plaques located on the trunk and extensor surfaces of the arms.
2.1.1 Dermoscopy of plaque psoriasis
Dermoscopic examination of a psoriasis plaque should be done in three categories: background, vessels, and scales. Examination should be done with minimal pressure to visualize vessels better and with immersion oil if possible.
In dermoscopic examination of plaque psoriasis with handheld dermoscope, we usually see regularly distributed dotted vessels in a reddish-pinkish background and white scales (Figure 4) [7]. In some cases, background can be grayish-white due to highly hyperkeratotic scales (Figure 5).
Figure 4.
Regularly distributed dotted vessels on reddish background with patchy distributed white scales. Note dot blood hemorrhages (red circle). Anatomical localization: Upper extremity (×10).
Figure 5.
Background color can barely be seen due to diffuse thick white scales. Dotted vessels can be seen in the center. Note dot blood hemorrhages (red circle). Anatomical localization: Elbow (×10).
Apart from regular distribution, vessels can be distributed scattered, in clusters, in rings, and patchy (Figure 6a). In higher magnifications (with videodermoscopy), these dotted vessels can be seen as bushy capillaries, globules, radial capillaries, globular rings, hairpin capillaries, and comma vessels in descending order [8] (Figure 6b). Rarely dot blood hemorrhages can be seen in vessel locations (Figure 5). Scales can be distributed diffuse, patchy, central, or peripheral in descending order; however, white color is key point for scales [8, 9].
Figure 6.
a: Vessel distribution patterns (regular, scattered, in clusters, in rings, patchy, respectively). b: Vessels subtypes can be seen in higher magnifications (bushy, globular, radial, globular ring, hairpin, and comma vessels, respectively).
2.1.2 Dermoscopic differential diagnosis of plaque psoriasis
Differential diagnosis of plaque psoriasis should be done with skin diseases, which are characterized by erythematous plaques with scales such as dermatitis, tinea corporis, pityriasis rosea, pityriasis rubra pilaris, lichen planus, and non-pigmented squamous cell carcinoma in situ.
In dermoscopic examination of dermatitis, we usually see patchy or scattered distributed dotted vessels with yellow globules (corresponding to sero-crusts) [10]. Background can be erythematous or not depending on lesions phase (acute or chronic). Hemorrhagic crusts can be seen as well secondary to traumatization (Figure 7).
In dermoscopic examination of tinea corporis, we usually see peripherally located dotted vessels and rough white scales (Figure 8). In contrast with psoriasis, dotted vessels are not regularly distributed and not uniform. In addition, scales are only located peripherally, tend to peel outward, and shaped in moth-eaten pattern [11].
Figure 8.
Peripherally located dotted vessels and white scales. Note the moth-eaten pattern (red circle). Anatomical localization: Trunk (×10).
Pityriasis rubra pilaris shows dotted and more frequently linear vessels, perifollicular yellow-orange halos, follicular plugs with central hair on them (Figure 9). Scales can be yellowish or whitish. Background is usually dark or yellowish red [7, 12].
Figure 9.
Dotted vessels regularly distributed on pinkish background. Note the follicular plugs and central hairs (red circles). Anatomical localization: Elbow (×20).
Squamous cell carcinoma in situ and psoriasis can be challenging especially in solitary plaques. Dermoscopic clues for non-pigmented squamous cell carcinoma in situ are dotted or glomerular vessels in clusters in the center and arranged in lines at the periphery with yellowish white scales (Figure 10) [13, 14].
Figure 10.
Glomerular vessels in the center, white scales. Note the linear arrangement of dotted vessels at the periphery (red circles) and actinic keratosis area at top left. Anatomical localization: Forearm (×20).
Dermoscopic features of plaque psoriasis and its differentials are summarized in Table 1.
Background
Vessel types
Vessel arrangement
Scales
Additional features
Plaque psoriasis
Reddish-pinkish Whitish (due to hyperkeratotic scales)
Dotted
Regular
Whitish-grayish
Dermatitis
Skin colored-pinkish
Dotted
Scattered/patchy
Yellowish
Irregularly distributed dot blood hemorrhages due to traumatization
Tinea corporis
Reddish
Dotted
Peripheral
White and rough; peripheral; moth-eaten pattern; tend to peel outwards
Pityriasis rubra pilaris
Dark red/yellowish red
Linear and/or dotted
Scattered
Yellowish-whitish; follicular
Perifollicular yellow-orange halos, follicular plugs, central hair
Squamous cell carcinoma in situ
Pinkish
Glomerular or dotted
Regularly in center, may organize in lines at the periphery
Yellowish white scales
Peripheral actinic keratosis areas may help (white and wide follicular openings, rosettes)
Table 1.
Dermoscopic features of plaque psoriasis and its differentials.
2.2 Guttate psoriasis
Guttate psoriasis, a psoriasis variant that is more common in pediatric population and young adults. Distinctly from other variants, we know that guttate psoriasis is selectively triggered by beta hemolytic streptococcal infections [15]. It is characterized by erythematous, well-defined flat papules/plaques lower than 1 cm in size with white-silvery scales on them. Lesions mostly located in the trunk and extremities (Figure 11a and b).
Figure 11.
a: Slightly erythematous flat papules/plaques with white scales on them in an adolescent. Trunk localization. b: Slightly erythematous flat papules/plaques with white scales on them in an adolescent. Extremity localization.
2.2.1 Dermoscopy of guttate psoriasis
Dermoscopic features of guttate psoriasis are very similar with plaque psoriasis, which is characterized by regularly distributed dotted vessels in a reddish background and white scales on them (Figure 12). Due to guttate psoriasis’ smaller lesion sizes (lower than 1 cm in diameter), findings may be insignificant when compared with plaque psoriasis (Figure 13).
Figure 12.
Regularly distributed dotted vessels in reddish background. White scales. Anatomical localization: Upper extremity (×10).
Figure 13.
Regularly distributed dotted vessels on pinkish background. Scales are white, thin, and patchy. Anatomical localization: Upper extremity (×10).
2.2.2 Dermoscopic differential diagnosis of guttate psoriasis
Differential diagnosis of guttate psoriasis should be done with skin diseases, which are characterized by erythematous papules/small plaques with scales. Pityriasis rosea, lichen planus, nummular dermatitis, secondary syphilis, tinea corporis, pityriasis lichenoides chronica, and disseminated eruptive porokeratosis may count as differential. (Dermatitis and tinea corporis will not be mentioned because they were discussed above.)
Dermoscopic examination of pityriasis rosea shows irregular distributed dotted vessels and peripheral thin white scale (Figure 14) [10]. Scales tend to peel outward as in tinea corporis. But note the white scale is not rough and vessels are not in the same distribution with scales. Background is generally skin-colored or slightly pinkish.
Figure 14.
Patchy distributed dotted vessels and peripheral thin white scale. The configuration of the scales named “collarette sign.” anatomical localization: Back (×10).
In dermoscopic examination of lichen planus, key point is detecting Wichkam striaes, which cannot be seen macroscopically sometimes. In fair-skinned patients, dotted and linear vessels around Wickham striae make these structures more visible (Figure 15); however, in dark-skinned patients, absence of peripheral vascular structures around Wichkam striaes may lead to misdiagnosis [16].
Figure 15.
Reticular arranged white lines (Wickham striae). Note the dotted vessels around Wickham striae in this fair-skinned patient. Anatomical localization: Lower extremity (×10).
In dermoscopic examination of secondary syphilis, yellowish-orange background and absence of vascular structures are key points (Figure 16) [17]. Scales may be present, however, thinner and smaller when compared with psoriatic scales.
Figure 16.
Yellowish-orange structureless area with thin white scales. Note the absence of vascular structures. Anatomical localization: Back (×10).
In dermoscopic examination of pityriasis lichenoides chronica, we usually see orange-yellowish structureless areas and focally distributed dotted or linear vessels (Figure 17) [18].
Figure 17.
Yellowish-orange structureless areas with thin white scales. Note the focal dotted vessel areas (red circles). Anatomical localization: Hand dorsum (×10).
In dermoscopic examination of porokeratosis, key clue is peripheral double lines resembling railways (Figure 18). This feature is called “cornoid lamella” [19].
Figure 18.
Small white scales on yellowish-brown background. Note the railway-like “cornoid lamella” at the periphery (red arrows). Anatomical localization: Hand dorsum (×10).
Dermoscopic features of guttate psoriasis and its differentials are summarized in Table 2.
Background
Vessel types
Vessel arrangement
Scales
Additional features
Guttate psoriasis
Reddish-pinkish
Dotted
Regular
Whitish-grayish, thin and small
Pityriasis rosea
Pinkish
Dotted (vascularization is not dominant)
Patchy
White, thin, peripheral and tend to peel outwards “collarette sign”
Lichen planus
Pinkish, violaceus
Dotted or absent
Aroud Wickham striae
Whitish, patchy distributed, thin and small
Secondary Syphilis
Yellowish-orange
Absent
White and thin
Pityriasis lichenoides chronica
Pinkish, yellowish-orange
Dotted or linear
Focal
White, patchy, thin
Disseminated porokeratosis
Yellowish-brown
Unsignificant
White, small
Peripheral railway like double lines called “cornoid lamella”
Table 2.
Dermoscopic features of guttate psoriasis and its differentials.
2.3 Inverse psoriasis
Inverse psoriasis is another clinical variant of psoriasis, which involves flexural areas such as axillary, inguinal, and inframammary [20]. The prevalence of inverse psoriasis is not clear and varies in 3–36% because of diagnostic challenges [21]. And also it is controversial that if genital involvement is a part of inverse psoriasis; however, we include genital involvement under this topic for convenience of expression.
Inverse psoriasis is typically present with well-defined erythematous plaques located in flexural areas (Figure 19a and b). It can present with or without typical psoriasis plaques. In contrast with plaque and guttate psoriasis, scales are insignificant or absent.
Figure 19.
a: Erythematous plaque located in inframammary fold. b: Erythematous papules and plaques located in axillary fold. Note peripheral lesions have mild white scales.
Genital involvement shares similar clinical features with inverse psoriasis such as well-defined erythematous papules and plaques (Figure 20). However, occlusion in the genital areas is not as much as flexural areas, scales could be more visible in the genitals.
Figure 20.
Coalesced erythematous papules located in the glans penis and penile dorsum.
2.3.1 Dermoscopy of inverse psoriasis
Dermoscopic features of inverse psoriasis are characterized by regularly distributed dotted vessels on reddish background (Figure 21). In contrast with other variants, scales are absent. Absence of scales enhances visualization of vascular structures. Consequently, dermoscopic differential diagnosis of flexural dermatosis mainly leans on evaluation of vascular structures.
2.3.2 Dermoscopic differential diagnosis of inverse psoriasis
Differential diagnosis of inverse psoriasis should be done with skin diseases, which present with erythematous patches/plaques in flexural and genital areas. Mechanical intertrigo, seborrheic dermatitis, lichen planus inversus, and fungal/bacterial infections may count as differential. Because no clear dermoscopic features have been defined for mechanical intertrigo and flexural infections, we will discuss dermoscopic features of seborrheic dermatitis and lichen planus inversus under this topic.
The main dermoscopic features of seborrheic dermatitis of flexural areas are irregularly distributed linear, blurry vessels [22]. As we mentioned before, we do not see classical yellowish scales of seborrheic dermatitis in flexuras.
When we review the literature so far, there are only three reports about dermoscopic features of lichen planus inversus. In all of these reports, dermoscopic features of only pigmented variant of lichen planus inversus were evaluated and defined as diffuse brown patches containing multiple granular gray-brown dots [23, 24, 25]. In our clinical practice, we see non-pigmented lichen planus inversus more than pigmented subtype. According to our dermoscopic experience, Wickham striae, which is seen in lichen planus inversus, tends to be in “starry sky” or “radial streaming” pattern rather than reticular pattern. Background is usually pinkish or violaceous. Dotted vessels usually encircle Wickham striae (Figure 22).
Figure 22.
Wickham striae in “radial streaming” pattern (red circle) and “starry sky” pattern (blue circle). Dotted vessels surround Wickham striae in a patchy arrangement. Anatomical localization: Intermammary (×10).
Dermoscopic features of inverse psoriasis and its differentials are summarized in Table 3.
Background
Vessel types
Vessel arrangement
Additional features
Inverse psoriasis
Reddish-pinkish
Dotted
Regular
Seborrheic dermatitis of flexural areas
Pinkish
Linear, blurry vessels
Irregular
Lichen planus inversus
Pinkish, violaceus
Dotted or absent
Aroud Wickham striae
According to our dermoscopic experience, Wickham striae, which is seen in lichen planus inversus, tends to be in “starry sky” or “radial streaming” pattern
Table 3.
Dermoscopic features of inverse psoriasis and its differentials.
2.4 Pustular psoriasis
Pustular psoriasis is a rare clinical variant of psoriasis, which is characterized by sterile pustules on an erythematous skin (Figure 23). It could be either local or generalized [26]. In generalized pustular psoriasis, concomitant fever, malaise, dehydration may also be present [27].
Figure 23.
Small pustules and lake of pus on erythematous background.
2.4.1 Dermoscopy of pustular psoriasis
Dermoscopic features of pustular psoriasis are characterized by regularly distributed dotted vessels with milky globules (corresponding to sterile pustules) on reddish background (Figure 24) [28]. Attention should be paid on non-follicular localization of pustules. Typical vascular structures are seen. Nonspecific yellow crust may be seen. Dermoscopic features are same in both localized and generalized subtypes.
Figure 24.
Milky globules and regularly distributed dotted and bushy vessels on reddish background in pustular psoriasis. Anatomical localization: Trunk (×10).
2.4.2 Dermoscopic differential diagnosis of pustular psoriasis
Dermoscopic differential diagnosis of pustular psoriasis should be done with acute generalized exanthematous pustulosis (AGEP). In life-threatening clinical conditions such as generalized pustular eruptions, rapid and right diagnosis is essential, and dermoscope is very helpful at that point. In both pustular psoriasis and AGEP, pustules are sterile, disseminated, may coalesce, and be non-follicular. Thereby, we cannot distinguish these two situations by their clinical view only. In dermoscopic examination of both pustular psoriasis and AGEP, non-follicular milky globules on reddish background are seen [28]. Discriminately, in pustular psoriasis we see regularly distributed dotted vessels (Figure 24). In dermoscopic examination of AGEP, background is usually pinkish and vascular structures are absent (Figure 25) [29].
Figure 25.
Milky globules on reddish background. Globules are non-follicular (red circle). Note the absence of vessels. Anatomical localization: Trunk (×10).
2.5 Erythrodermic psoriasis
Erythroderma is a life-threatening condition, which is defined as desquamation and erythema of more than 90% of body surface area [30]. Erythrodermic variant of psoriasis (Figure 26) generally occurs due to poor control of disease, withdrawal of anti-psoriatic treatments, triggering drug intake, underlying systemic infections or conditions [31]. Clinical clues for erythrodermic psoriasis diagnosis are known history of psoriasis, psoriatic nail changes, presence of psoriatic arthritis. However, if none of the mentioned features is present, dermoscopy could be a game-changer.
2.5.1 Dermoscopy of erythrodermic psoriasis
Dermoscopic features of erythrodermic psoriasis are the same as other psoriasis variants. Regularly distributed dotted vessels on a reddish background, and patchy white scales are seen (Figure 27) [32].
Figure 26.
Desquamation and erythema of all body surfaces.
Figure 27.
Regularly distributed dotted vessels and white scales. Anatomical localization: Lower extremity (×20).
Figure 28.
Yellow globules, patchy distributed dotted vessels. Background is slightly pinkish. Tiny white scales are also present. Tiny white scales correspond to desquamation areas. Anatomical localization: Trunk (×10).
Figure 29.
Linear, serpiginous, and dotted vessels on pinkish background. Tiny white scales correspond to desquamation areas. Anatomical localization: Trunk (×10).
2.5.2 Dermoscopic differential diagnosis of erythrodermic psoriasis
Dermoscopic differential diagnosis of erythrodermic psoriasis includes dermatosis that can present with erythroderma such as atopic dermatitis, mycosis fungoides, and pityriasis rubra pilaris (Pityriasis rubra pilaris will not be mentioned because it was discussed above.)
Dermoscopic examination of atopic dermatitis shows typical dermatitis features. Yellowish globules (corresponding to sero-crusts) and patchy distributed dotted vessels on a pinkish background are demonstrative (Figure 28) [32].
Dermoscopic features of erythrodermic mycosis fungoides are a combination of linear and dotted vessels on a pale pinkish background (Figure 29) [32]. Some short linear vessels may be curved and named as “spermatozoon-like” vessels.
Dermoscopic features of erythrodermic psoriasis and its differentials are summarized in Table 4.
Background
Vessel types
Vessel arrangement
Scales
Additional features
Erythrodermic psoriasis
Reddish
Dotted
Regular
White, scattered-patchy scales
Erythrodermic atopic dermatitis
Pinkish
Dotted
Patchy
Yellowish sero-crusts
Erythrodermic mycosis fungoides
Pinkish (pale)
Linear and dotted
Scattered
Whitish scales can present.
.
Table 4.
Dermoscopic features of erythrodermic psoriasis and its differentials.
3. Conclusions
Psoriasis is a common skin disease with different clinical presentations. Generally, clinical evaluation is enough for diagnosis, though dermoscope is a helpful and noninvasive examination technique that enhances true diagnosis ratio. Knowing psoriasis’ and its differentials’ dermoscopic features may reduce requirement for histopathological examination and also makes rapid diagnosis possible in life-threatening conditions such as erythroderma. Note that regularly distributed dotted vessels on a reddish background are the most important clues for any variant of psoriasis. In doubtful cases, histopathological examination should be done for verifying the diagnosis as a gold standard technique.
Acknowledgments
All photos used in this chapter were taken by Dr. Ece Gokyayla with iPhone (XS) and dermatoscope (DermLite, DL4 model, 3Gen, USA) connected to an iPhone (XS) via adapter (DermLite Connection Kit MagnetiConnect). Immersion oil was not used. Written informed consent will be obtained from each patient in oral and written form. Only histopathologically or laboratorially confirmed cases’ photographs were included.
Conflict of interest
The authors declare no conflict of interest and no funding source.
\n',keywords:"psoriasis, dermoscopy, inflammoscopy",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80809.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80809.xml",downloadPdfUrl:"/chapter/pdf-download/80809",previewPdfUrl:"/chapter/pdf-preview/80809",totalDownloads:22,totalViews:0,totalCrossrefCites:0,dateSubmitted:"December 3rd 2021",dateReviewed:"February 2nd 2022",datePrePublished:"March 18th 2022",datePublished:null,dateFinished:"March 10th 2022",readingETA:"0",abstract:"Psoriasis is a chronic inflammatory skin disease, which is mainly characterized with erythematous indurated plaques with squams such as many other inflammatory skin diseases. Also different clinical subtypes of psoriasis can show distinctive clinical appearances. As an example, inverse psoriasis does not have squams and resemble erythema intertrigo; or erythrodermic variant cannot be distinguished from other erythroderma causes sometimes. From reasons above, differential diagnosis of psoriasis should be done carefully to manage a chronic and long-term treatment required disease appropriately. Histopathologial examination is gold standard technique for certain diagnosis; however, dermoscope is a noninvasive and easily applicable diagnostic tool with high specificity. In this chapter, we discuss dermoscopic differential diagnosis of psoriasis.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80809",risUrl:"/chapter/ris/80809",signatures:"Ece Gokyayla, Tubanur Cetinarslan and Aylin Turel Ermertcan",book:{id:"11087",type:"book",title:"Psoriasis",subtitle:null,fullTitle:"Psoriasis",slug:null,publishedDate:null,bookSignature:"Associate Prof. Shahin Aghaei",coverURL:"https://cdn.intechopen.com/books/images_new/11087.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-376-4",printIsbn:"978-1-80355-375-7",pdfIsbn:"978-1-80355-377-1",isAvailableForWebshopOrdering:!0,editors:[{id:"64024",title:"Associate Prof.",name:"Shahin",middleName:null,surname:"Aghaei",slug:"shahin-aghaei",fullName:"Shahin Aghaei"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Dermoscopy of psoriasis types and differentials",level:"1"},{id:"sec_2_2",title:"2.1 Plaque psoriasis",level:"2"},{id:"sec_2_3",title:"2.1.1 Dermoscopy of plaque psoriasis",level:"3"},{id:"sec_3_3",title:"Table 1.",level:"3"},{id:"sec_5_2",title:"2.2 Guttate psoriasis",level:"2"},{id:"sec_5_3",title:"2.2.1 Dermoscopy of guttate psoriasis",level:"3"},{id:"sec_6_3",title:"Table 2.",level:"3"},{id:"sec_8_2",title:"2.3 Inverse psoriasis",level:"2"},{id:"sec_8_3",title:"2.3.1 Dermoscopy of inverse psoriasis",level:"3"},{id:"sec_9_3",title:"Table 3.",level:"3"},{id:"sec_11_2",title:"2.4 Pustular psoriasis",level:"2"},{id:"sec_11_3",title:"2.4.1 Dermoscopy of pustular psoriasis",level:"3"},{id:"sec_12_3",title:"2.4.2 Dermoscopic differential diagnosis of pustular psoriasis",level:"3"},{id:"sec_14_2",title:"2.5 Erythrodermic psoriasis",level:"2"},{id:"sec_14_3",title:"2.5.1 Dermoscopy of erythrodermic psoriasis",level:"3"},{id:"sec_15_3",title:"Table 4.",level:"3"},{id:"sec_18",title:"3. Conclusions",level:"1"},{id:"sec_19",title:"Acknowledgments",level:"1"},{id:"sec_22",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Gül Ü. Psoriasis. Sağlığın Başkenti Dergisi. 2010;16:18-21'},{id:"B2",body:'Griffiths CEM, Camp RDR, Barker JNWN. Psoriasis. In: Burns T, Breathnach S, Cox N, Griffiths C, editors. Rook’s Textbook of Dermatology. 7th ed. Oxford: Blackwell; 2004. pp. 35.1-35.69'},{id:"B3",body:'Braun-Falco O, Plewig G, Wolff HH, Burgdorf WHC. Dermatology. 2nd ed. Berlin: Springer-Verlag Berlin Heidelberg; 2000. pp. 585-610'},{id:"B4",body:'Griffiths CE, Barker JN. Pathogenesis and clinical features of psoriasis. Lancet. 2007;370(9583):263-271. DOI: 10.1016/S0140-6736(07)61128-3'},{id:"B5",body:'Langley RG, Krueger GG, Griffiths CE. Psoriasis: Epidemiology, clinical features, and quality of life. Annals of the Rheumatic Diseases. 2005;64(Suppl 2):ii18-ii23; discussion ii24-5. DOI: 10.1136/ard.2004.033217'},{id:"B6",body:'Weigle N, McBane S. 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French. DOI: 10.1016/j.annder.2020.05.003'},{id:"B20",body:'Micali G, Verzì AE, Giuffrida G, Panebianco E, Musumeci ML, Lacarrubba F. Inverse psoriasis: From diagnosis to current treatment options. Clinical, Cosmetic and Investigational Dermatology. 2019;12:953-959. DOI: 10.2147/CCID.S189000'},{id:"B21",body:'Omland SH, Gniadecki R. Psoriasis inversa: A separate identity or a variant of psoriasis vulgaris? Clinics in Dermatology. 2015;33(4):456-461. DOI: 10.1016/j.clindermatol.2015.04.007'},{id:"B22",body:'Errichetti E, Lacarrubba F, Micali G, Stinco G. Dermoscopy of Zoon\'s plasma cell balanitis. Journal of the European Academy of Dermatology and Venereology. 2016;30(12):e209-e210. DOI: 10.1111/jdv.13538'},{id:"B23",body:'Robles-Méndez JC, Rizo-Frías P, Herz-Ruelas ME, Pandya AG, Ocampo CJ. Lichen planus pigmentosus and its variants: Review and update. International Journal of Dermatology. 2018;57(5):505-514. 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Dermatoscopy in life-threatening and severe acute rashes. Clinics in Dermatology. 2020;38(1):113-121. DOI: 10.1016/j.clindermatol.2019.10.013'},{id:"B29",body:'Jha AK, Sonthalia S, Lallas A. Non-follicular milky globules-dermoscopy saves the day. Dermatology Practical & Conceptual. 2017;7(2):35-36. DOI: 10.5826/dpc.0702a07'},{id:"B30",body:'Cuellar-Barboza A, Ocampo-Candiani J, Herz-Ruelas ME. A practical approach to the diagnosis and treatment of adult erythroderma. Actas Dermosifiliogr. 2018;109(9):777-790. DOI: 10.1016/j.ad.2018.05.011'},{id:"B31",body:'Raychaudhuri SK, Maverakis E, Raychaudhuri SP. Diagnosis and classification of psoriasis. Autoimmunity Reviews. 2014;13(4–5):490-495. DOI: 10.1016/j.autrev.2014.01.008'},{id:"B32",body:'Errichetti E, Piccirillo A, Stinco G. Dermoscopy as an auxiliary tool in the differentiation of the main types of erythroderma due to dermatological disorders. International Journal of Dermatology. 2016;55(12):e616-e618. DOI: 10.1111/ijd.13322'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Ece Gokyayla",address:null,affiliation:'
Medical Faculty, Department of Dermatology and Venereology, Manisa Celal Bayar University, Turkey
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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
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Then, multilayer perceptron (MLP) and learning vector quantization (LVQ) networks have their performances verified during the training, validation and test stages in the speech signal recognition, whose patterns are given by two-dimensional time matrices, result from mel-cepstral coefficients coding by the discrete cosine transform (DCT). In order to avoid the pattern separability problem, the patterns are modified by a nonlinear transformation to a high-dimensional space through a suitable set of Gaussian radial base functions (GRBF). The performance of MLP and LVQ experts is improved and configurations are trained with few examples of each modified pattern. 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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. 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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. 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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. 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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. 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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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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. 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