Impact of TP53INP1 deficiency (Loss of function) or over-expression (Gain of function) in different cell processes.
\r\n\tThe contents of the book will be written by multiple authors and edited by experts in the field.
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His research areas include cardiovascular biology and aging, atherosclerosis, and redox biology. He has authored of over 55 peer-reviewed publications, edited 8 books and chapters, and presented at over 30 international conferences. He has served as editorial board and reviewer of >20 prestigious journals, e.g. European Heart Journal, and professional member of over 10 international scientific associations, including American Heart Association (AHA), European Society of Cardiology (ESC) and European Atherosclerosis Society (EAS). He has been honored with numerous international and national awards (25 prizes), namely, including \\"Paul Dudley White International Award\\" from American Heart Association, \\"AGLA Walter Riesen Award\\" from Swiss Atherosclerosis Association, prize of \\"European Atherosclerosis Society Young Investigator Fellowship 2021\\", and \\"Harvard Postdoctoral Fellowship\\".',institutionString:"Harvard Medical School",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Harvard Medical School",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"177730",firstName:"Edi",lastName:"Lipovic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/177730/images/4741_n.jpg",email:"edi@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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In addition, cancer cells favor angiogenesis which is necessary for tumor survival, progression and dissemination. Genetic events leading to genome instability enable those cell deregulations, in particular gain of oncogenes and loss of tumor suppressors functions observed in all cancer cells. The tumor protein p53 is encoded by the tumor suppressor gene
Interestingly, dysfunction of cancer cells is both due to events intrinsic to these cells and to their response to signals generated by normal cells from their environment. In some circumstances, normal cells can even collaborate to neoplasia. This was shown for immune cells, which is paradoxical since they are known to play a crucial anti-tumoral role. Inflammatory immune cells secrete proinflammatory cytokines and chemokines, growth factors, matrix-remodelling proteins, as well as reactive oxygen species (ROS) and reactive nitrogen species (RNS) (collectively called RNOS). Although RNOS actively participate in a diverse array of biological processes including cell proliferation, cell death, and fight against infection, excessive RNOS levels damage cell macromolecular components therefore promoting oncogenesis [1-4]. DNA lesions form either directly when RNOS modify bases or indirectly as a consequence of lipid peroxidation, the resulting products reacting with DNA. DNA lesions may be genotoxic when error-free repair mechanisms fail to remove them leading to mutations. To summarize, clinical and epidemiological investigations have provided evidence supporting the role of RNOS in the etiology of cancer due to both endogenous and exogenous factors. In addition, cancer cells are frequently under persistent oxidative stress, which participates in cancer progression as well as in the selection of resistant cells that are unable to die by apoptosis.
In this chapter, we will describe the current knowledge on the relationship between p53 and redox, emphazing its complexity since on one hand p53 is regulated by redox and in the other hand p53 regulates cell redox status. We will then review the current knowledge on one of p53 target genes, Tumor Protein 53-Induced Nuclear Protein 1 (TP53INP1), which we have defined as a major actor in p53-driven oxidative stress response, even if the antioxidant role of TP53INP1 at the molecular level is still speculative and remains to decipher. Finally, we will describe some models of genetically engineered mutant mice and experimental inflammation settings which have provided important insights into the link between oxidative stress and cancer.
The p53 protein was discovered in 1979 by different research groups, in particular as interacting with oncogenic viral SV40 Large T antigen (for historical reviews, see [5, 6]). Its name is related to its apparent molecular weight of 53 kDa, which is grossly overestimated (p53 longest isoform is 393 aminoacids long) presumably owing to the presence of a proline-rich region that slows down the migration of the protein in SDS-polyacrylamide gels. Since its discovery, p53 has been the focus of a huge number of investigations. This protein is encoded by the
Interestingly, p53 was initially reported as a stress factor, highly induced upon stress events, and participating in stress resolution thus elimination of potential protumoral events towards cell homeostasis. In particular, it was shown to be induced in response to DNA damage then named “the guardian of the genome”, an expression that resumes its main physiological function. The DNA-damage response mediated by p53 is also an oncogene-induced barrier against progression of cancer beyond its early stages. p53 is necessary for silencing of mutant thus potentially cancerous cells by all means of tumor suppression, i.e. growth arrest, senescence and apoptosis.
More recent reports emphasize additional role of p53 in basal or low stress (“everyday life” stress) conditions, i.e. distinctly from conditions driving rapid and acute p53 induction in response to high levels of DNA damage. In particular, p53 is shown to be involved in embryonic development and energetic metabolism. In both settings however (acute stress or basal condition), p53 is sensitive and responsive to redox conditions. Thus, p53 is a fascinating multifaceted protein, besides a central player in the redox field.
p53 is complex at many levels. (1)
Complexity of p53 at different levels.
p53 is induced by different kinds of stress, either genotoxic (including oxidative lesions induced by ROS and RNS) or non-genotoxic (listed in Figure 2A). This induction relies mostly on structural modifications that turn p53 from dormant to active state via modifications in protein level, subcellular localisation, and interaction with itself (homotetramer) and other proteins (Figure 1). Dormant state of p53 is mostly due to its interaction with the E3 ubiquitine ligase MDM2 targeting p53 to permanent proteosomal degradation. Upon stress signal, p53 is post-translationally modified then stabilized by loss of interaction with MDM2 thus MDM2-driven degradation [6, 12].
p53 post-translational modifications are very diverse (listed in Figure 2B). They comprise also redox modifications on cysteine and tyrosine residues. Indeed p53 activity can be directly post-translationally modified via thiol redox modulation of critical cysteine residues in its DNA binding domain. The core domain of p53 holds a zinc atom that protects p53 from oxidation and is critical for DNA binding [13]. p53 oxidative modifications were extensively discussed in a recent review [8]. As proposed in this latter, p53 is at the core of a complex network of redox-dependent reactions. In addition, p53 activity can be indirectly modified via thiol redox modulation of kinases which post-translationally affect p53 via phosphorylation. The potential candidates are ATM, LKB1, AMPK, and JNK [14]. In summary, p53 structure can be redox-modified either directly or indirectly via redox-driven induction of kinases activity. Therefore, p53 is a ROS sensor.
Complexity of p53 with regards to multiple stress inducers (A), multiple post-translational modifications (B), and multiple transcriptional targets involved in redox control (C).
The first described molecular activity of p53 was its action as a transcription factor. p53 possesses two amino-terminus transactivation domains and a core DNA binding domain which can bind tightly to specific DNA sequences [8]. More than one hundred targets of p53 have been well characterized, for which transcription is more often activated. In parallel, p53 was also found to function as a transcriptional repressor. The transcriptional response to p53 induction is highly heterogeneous since it depends on the tissue/cell type and stress context [15]. The proteins encoded by p53-target genes are involved in many different cellular processes, favoring tumor suppression (cell-cycle arrest, senescence, apoptosis) or basal cell homeostasis (energy metabolism, autophagy, differentiation, …) [16].
Among p53 target genes, several play a role in redox control (Figure 2). The link between sestrins family and p53 in redox regulation has been reviewed recently [14]. One of the key functions of sestrins is the regeneration of the peroxiredoxins antioxidant enzymes [17]. Besides this indirect antioxidant action, p53 is known to directly activate the transcription of the antioxidant enzymes GPx1, MnSOD (encoded by
In our laboratory, we identified a new target of p53 involved in oxidative stress response named TP53INP1. We recently demonstrated that TP53INP1 is able to mediate the antioxidant function of p53 (see part 2).
Besides its direct impact on the regulation of gene expression in the nucleus, p53 was found to possess non-transcriptional biochemical activities. These are very diverse and can be exerted both in the cytoplasm and the nucleus [20]. In particular, p53 influences mitochondrial functions such as apoptosis and respiration which is the most prominent source of ROS. p53 was shown to indirectly promote mitochondrial functions and inhibit glycolysis [21-23]. The consequence of this promotion of oxidative phosphorylation is a decrease in oxidative stress and thus prevention of DNA damage. In addition, by inhibiting glycolysis, p53 can prevent the Warburg effect which is one of the features of cancer cells [24].
The central role of p53 in human cancer makes it a target for cancer therapy development. This task is hindered by the fact that p53 is neither a cell surface protein nor an enzyme which are targetable by antibodies or inhibitors. Efforts have been undertaken in developing p53 gene therapy and restoring p53 activity [6]. Restoration of wild type p53 expression triggers elimination of tumors
Tomasini et al. showed induction of
Altogether, these data clearly indicate that
In turn, TP53INP1 is able to activate the transcriptional activity of p53, therefore being implicated in a positive feedback loop with p53.
How TP53INP1 activates the p53 response to stress? The phosphorylation of p53 on its serine 46 (Ser-46) seems to play a key role in the activation of p53-driven apoptosis by TP53INP1. It was well referenced that Ser-46 phosphorylation of p53 and induction of p53AIP1 are essential features to DNA damage response [37, 38]. Okamura et al. showed that co-expression of p53 and TP53INP1 enhances p53 Ser-46 phosphorylation, induces p53AIP1 and strongly increases apoptotic cell death, as observed by flow cytometry and Terminal deoxynucleotidyltransferase-mediated dUTP Nick End-Labeling (TUNEL) [36]. Moreover, inhibition of TP53INP1 expression by antisens oligonucleotides represses p53AIP1 expression. These observations suggest that TP53INP1 activates p53 protein toward activation of apoptosis by regulating phosphorylation at Ser-46, and that modified version of p53 activates transcription of apoptosis-inducing genes such as
Several proteins were shown to have a kinase activity on the Ser-46 of p53 and to promotes p53-dependent apoptosis: the homeodomain-interacting protein kinase-2 (HIPK2) [39, 40], the p38 MAPK [38], and the protein kinase C delta (PKCδ) [41]. Tomasini et al. demonstrated a direct interaction between TP53INP1, p53, and HIPK2 by GST-pulldown and co-immunoprecipitation assays [35]. Moreover, TP53INP1 co-localizes with HIPK2 and p53 in PML-NB, which are described to be the site where HIPK2 binds to p53 and phosphorylates its Ser-46. Using luciferase-reporter assays, the authors showed that TP53INP1 and HIPK2 can regulate the p53 activity on genes involved in cell cycle regulation (
Our molecular model is summarized in Figure 3. During a cellular stress,
Molecular model of p53-TP53INP1 functional interactions (positive feedback loop).
Study of TP53INP1 induction in MCF7 cells (expressing wild type p53) treated with several stress (γ-irradiation, UV radiation, adriamycin) showed that stress-triggered DNA double-strand breaks strongly induced TP53INP1 within 4h, whereas TP53INP1 is induced more slowly and to a lesser extent by UV radiation [36]. By contrast, p53 was induced similarly by both stresses. Moreover, DNA damage-induced cell death and cell cycle arrest (upon γ-irradiation and adriamycin treatment) were strongly decreased after inhibition of TP53INP1 expression by oligonucleotide antisens, whereas antisens had no effect on UV radiation-induced cell death. Those observations led Okamura and coll. to suggest that at least two different p53-dependent mechanisms are involved in TP53INP1 induction.
As described in the first part, p53 is a tightly regulated protein maintained at low levels under normal conditions. In response to stress, p53 is activated mainly by complex post-translational modifications, changes in protein-protein interaction and sub-cellular re-localization. This activation leads to transcription of several genes which will trigger a large panel of cellular processes, like cell cycle arrest, apoptosis, autophagy, DNA repair, senescence, or redox state regulation. To control this broad variety of mechanisms, the transcriptional activity of p53 is highly dependent on the promoter context and on the type of stimulus. All the presented data suggest that TP53INP1 is one of the p53 co-factors involved in such a regulation.
In addition to the role of TP53INP1 in the regulation of the p53-dependent response to stress, Tomasini et al. showed a p53-independent action of TP53INP1 [43]. This independency was initially suggested by the observation that TP53INP1 is induced in p53-/- mice during acute pancreatitis, and that TP53INP1 over-expression is able to trigger G1 cell cycle arrest in p53 deleted or mutated cell lines. The mechanistic explanation was provided by the demonstration that TP53INP1 is a target of p73. p73 belongs to the p53 family. It also encodes a nuclear transcription factor which shares structural and functional homologies with p53. Many isoforms of p73 exist, which result from alternative splicing and from differences in the initiation of transcription by different promoters. Some isoforms share functional similarities with p53 [11]. p73 is also known to be able to activate p53 target genes and to induce cell cycle arrest and apoptotic cell death. Tomasini et al. showed that p73α and β isoforms induce
As described above, TP53INP1 is a stress response protein. The expression of this gene is induced by a large panel of cellular stresses.
TP53INP1 is also induced by oncogenic stress (mutated RasV12D and viral E1A protein). Tomasini et al. suggested that this induction occurs through the activation of p53-dependent mechanisms in response to cell transformation [27]. Hershko et al. explain the E1A-induced expression of TP53INP1 by the disruption of RB/E2F complex by E1A, leading to deregulation of E2F activity, resulting in activation of TP53INP1 [42]. Therefore, TP53INP1 seems to be involved in all major stress pathways, induced both by genotoxic stress and oxidative stress, suggesting that this gene plays a central role in cellular response to damage.
First evidences of exacerbated oxidative stress in absence of TP53INP1 have been demonstrated
As TP53INP1 is induced by stress including oxidative stress, we postulated that this protein could be involved in cell redox homeostasis. Oxidative stress arises from an imbalance between oxidants and antioxidants in favor of the former, leading to an overload of ROS and RNS as described in the Introduction. To get further insights into the physiological role of TP53INP1 during oxidative stress, we first evaluated in TP53INP1 KO and WT mice the level of small anti-oxidant molecules such as plasmatic ascorbate (vitamin C), and lipid peroxide content as reflect of the total antioxidant capacity of the body. As altered ascorbic acid status has been reported in the mucosa [45] and plasma [46] in Inflammatory Bowel Diseases patients, measurements were carried out in colon and plasma of mice. ESR (Electron Spin Resonance) spectroscopy analyses demonstrated that TP53INP1 deficiency is associated with decreased ascorbate levels and increased lipid peroxide content in plasma [31]. Data obtained on colons of mice during colitis further confirmed these results as TP53INP1 KO mice displayed more colonic ROS than their WT counterparts. Interestingly, oxidative stress in the colon and plasma was also observed at basal state i.e. in the absence of induced colitis. This was the first demonstration of a chronic oxidative stress in TP53INP1-deficient mice.
Additional proofs supporting this observation came just four years later by studies achieved in our lab by N’guessan et al. by the use of DCF-DA (2’,7’-dichlorofluorescein diacetate) which is a cell permeable dye oxidized and retained within cell in DCF fluorescent probe [32]. Staining on total thymocytes of mice challenged or not with whole-body γ-irradiation (6 Grays) showed that absence of TP53INP1 increased ROS levels in the latter. This was further validated by ESR spectroscopy in thymocytes as well as in blood samples. Once it was proven that there was a deregulated redox status in TP53INP1 KO mice, it was important to assess whether this deregulation was linked with an overall deficit in antioxidant defenses, as demonstrated previously in the colon [31]. We confirmed that thymocytes, blood and different organs of TP53INP1-deficient mice (colon, intestine, spleen) were strongly depleted in ascorbate and glutathione [32]. Others organs have also been tested but displayed different ascorbate and glutathione profiles. No difference was seen in pancreas, and interestingly, in liver and thymus, levels of vitamin C were higher at basal state in KO mice. Our data suggest a higher de novo production of ascorbate in TP53INP1-deficient liver that could be due to a higher need owing to higher ROS level in TP53INP1 -/- mice. Regarding thymus, which displays a different pattern of oxidative defenses compared to thymocytes, we suggest a higher provision of ascorbate in TP53INP1-deficient thymus, further suggesting a protection of thymus against oxidative stress. Nevertheless, in spite of this protective microenvironment, irradiation stress induces a higher production of ROS in deficient thymocytes compared to WT. Taken together, our data demonstrate a profound dysregulation of antioxidant balances in the absence of TP53INP1.
In order to study more in depth and more easily the impact of TP53INP1 in the regulation of cellular redox status, primary Mouse Embryonic Fibroblasts (MEFs) were prepared from TP53INP1 WT and KO mice. Cano et al. demonstrated that what was observed
Same series of experiments was carried out on E1A-RasV12D transformed MEFs exposed to γ-irradiation (10 Grays) which is at the origin of a global oxidant stress [32]. The fact that ROS content was different between TP53INP1 WT and KO MEFs 24h after irradiation underscored dysfunction of ROS regulation in deficient cells. By contrast with primary MEFs, no significant difference was seen at basal state. Treatment with antioxidant NAC (N-acetylcysteine, a precursor of glutathione) significantly reduced ROS level in both genotypes. Interestingly, other anti-oxidants such as Trolox (a water-soluble vitamin E derivative) and Ebselen (organo-selenium compound possessing βantioxidant properties) were able to decrease ROS content in WT but not in TP53INP1-deficient cells. As neither Trolox nor Ebselen can correct a defect in glutathione and regarding our
The elucidation of the TP53INP1 mechanistic led us to assess the role of this protein in the cellular context. Tomasini et al. in 2001 first demonstrated that over-expression of exogenous TP53INP1 α and β in COS7 cells induced cell death via an apoptotic pathway [27]. Further works in our lab demonstrated that TP53INP1s and HIPK2 regulate the p53 transcriptional activity on genes involved in apoptosis (
Then, investigations have been performed to try to validate this hypothesis. We first showed that TP53INP1 was lost in human pancreatic and gastric cancer and that its restoration inhibited tumor development [48, 49]. In TP53INP1-deficient mice, we put in place three different models of induced tumorigenesis. i/ First model consisted in injection of transformed E1A-RasV12D MEFs in nude mice. TP53INP1-deficient MEFs revealed more aggressive than WTs [48]. ii/ In parallel, we developed a genetic model by crossing mice deficient for TP53INP1 with p53 KO mice: p53 heterozygous mice displayed an accelerated tumor development in absence of TP53INP1 and majority of tumor revealed to be lymphoma [30]. iii/ Last model consisted in induction of colorectal tumors by injection of carcinogen AOM (Azoxymethane) followed by a chronic colonic inflammation provoked by 3 ingestion cycles of DSS (Dextran Sulfate Sodium) assuring promotion of tumoral cells initiated by AOM. Our results clearly showed that TP53INP1 -/- mice were far more sensitive to development of induced colorectal tumors compared to WT [31]. All models strongly suggested an anti-tumoral role of TP53INP1.
As mentioned in the introduction, ROS have a promoting role in tumor initiation and promotion. As ROS regulation is impaired in absence of TP53INP1, this could at least partially explain its tumor suppressor role. To evaluate this supposed link, ROS implication has been considered in the two last tumorigenesis mouse models. Notably, in the absence of TP53INP1, oxidative stress-related lymphoma incidence was markedly increased in p53+/- mice (model ii), and oxidative stress-associated carcinogenesis in the colon was promoted (model iii). Altogether, these data showed that chronic oxidative stress in the absence of TP53INP1 played a crucial role in facilitating tumorigenesis.
To go more in depth in the link between TP53INP1, ROS and tumor suppression, experiments have been performed in MEFs cells and thymocytes
On the whole, we clearly demonstrated at cellular level the anti-tumoral role of TP53INP1 related with its function as antioxidant regulator.
Table 1 recapitulates the state of knowledge regarding the impact of TP53INP1 on cellular processes in the settings of gain of function (ectopic over-expression) and loss of function (deficient cells and mice).
Impact of TP53INP1 deficiency (Loss of function) or over-expression (Gain of function) in different cell processes.
As mentioned above, p53 plays its tumor suppressor role mainly via transcriptional induction of target genes involved in cell cycle, apoptosis, and regulation of cell redox status. p53 antioxidant function is dependent on its transcriptional activity and proceeds by sequential induction of antioxidant targets. However, none of the known p53 targets were able to fully recapitulate the p53-mediated antioxidant response in the p53-deficient cells. As a target of p53, TP53INP1 could be a major actor in p53-driven oxidative stress response.
Interestingly, we demonstrated that TP53INP1 absence confers increased thymocyte death sensitivity both in a context of p53-dependent cell death (irradiation, and etoposide treatment), and in a p53-independent cell death context (dexamethasone) [32]. Consistent with this, quantitative RT-PCR experiments did not show any difference in the induction of expression of
Figures 4 and 5 schematically recapitulate the state of knowledge regarding TP53INP1 activities. Figure 4 illustrates the dual (dependency/independency) relationship between TP53INP1 and p53. In low stress conditions, moderate amount of TP53INP1 located mostly in cytoplasm is involved in autophagy, and in consequence favors cell survival. By contrast, in high stress conditions, high levels of TP53INP1 would induce apoptosis both by promoting autophagy-dependent cell death in the cytoplasm and p53-driven cell death in nucleus.
Model of p53-dependent and -independent TP53INP1 activities.
Figure 5 takes Figure 4 forward by adding the setting of TP53INP1 absence observed in tumors and in experimental TP53INP1-deficient mice. Absence of TP53INP1 is associated with ROS increase which promotes cancer initiation and progression (Figure 5, left). Deficient cells lack the redox control activity of TP53INP1 which is schematically shown here as a direct activity but that can be an indirect effect. Furthermore, deficient cells lack the tumor suppressive pro-apoptotic activity of TP53INP1 which is induced during high stress situation (Figure 5, right).
Model recapitulating anti-tumor activities of TP53INP1 and the consequences of its absence in tumor cells.
The question whether TP53INP1 would be a direct ROS-detoxifying enzyme, or a co-transcription factor of genes implicated in ROS elimination remains unclear for the moment. The possibility that TP53INP1 is a ROS-sensor is high since both TP53INP1 isoforms are rich in cysteine residues. Both isoforms could therefore be post-translationally redox-modified, which would result in modifications of both their physical interaction with partners and their subcellular localization.
We have other propositions, implying the p53 tumor suppressor gene homologue p73 notably. As mentioned above, Tomasini et al. demonstrated in 2005 that
Mouse models targeting the
The
Reciprocally, different models of p53 transgenic mice have been generated, most of them showing an impact on life-span, either an increase or a decrease [56]. These observations illustrate the role of p53 in regulating organismal aging, related to its impact on redox control either as an antioxidant or a pro-oxidant [52].
Mouse models of oxidative stress were recently reviewed, illustrating several cases where inactivation of one antioxidant enzyme promotes cancer development [57, 58]. In addition, these reviews underscore other transcription factors than p53, such as JunD, FoxOs, Bmi1, and HIF-2α, also involved in the modulation of antioxidant enzymes expression. Deficiency of one of these transcription factors also favors oxidative stress and redox-driven tumorigenesis. Finally, deficiency in ATM, a sensor of DNA damage and involved in the DNA damage response upstream from p53, is also an oxidative stress-associated tumor prone mouse model.
As mentioned in the introduction, RNOS are found at high levels in inflammatory sites, participating in elimination of the inflammation cause (infection or wound). However, RNOS can be harmful depending on duration or intensity of inflammation. Indeed, chronic inflammation was demonstrated to be a risk for cancer development. For examples,
In this chapter, we recapitulate the state of knowledge regarding p53 antioxidant role, which rely on different activities, mainly transcriptional induction of antioxidant molecules and control of energetic metabolism. Furthermore, we resume the identification of p53-target TP53INP1 as a main actor in p53-driven redox control. Antioxidant activity of TP53INP1 at the molecular level is still elusive. We propose several hypotheses which deserve being studied further. Finally, we underscore the interest of mouse mutant mice endowed with a chronic oxidative stress, such as p53 and TP53INP1 deficient mice. These mice provide plenty of basic knowledge, and can be used as preclinical models in cancer research.
The authors are supported by Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Institut National du Cancer, Association pour la Recherche sur le Cancer, and La Ligue Nationale contre le Cancer. M.S. is supported by Ministère de la Recherche et de la Technologie, S.P. by La Ligue Nationale contre le Cancer.
Emotion plays a significant role in daily interpersonal human interactions. This is essential to our rational as well as intelligent decisions. It helps us to match and understand the feelings of others by conveying our feelings and giving feedback to others. Research has revealed the powerful role that emotion play in shaping human social interaction. Emotional displays convey considerable information about the mental state of an individual. This has opened up a new research field called automatic emotion recognition, having basic goals to understand and retrieve desired emotions. In prior studies, several modalities have been explored to recognize the emotional states such as facial expressions [1], speech [2], physiological signals [3], etc. Several inherent advantages make speech signals a good source for affective computing. For example, compared to many other biological signals (e.g., electrocardiogram), speech signals usually can be acquired more readily and economically. This is why the majority of researchers are interested in speech emotion recognition (SER). SER aims to recognize the underlying emotional state of a speaker from her voice. The area has received increasing research interest all through current years. There are many applications of detecting the emotion of the persons like in the interface with robots, audio surveillance, web-based E-learning, commercial applications, clinical studies, entertainment, banking, call centers, cardboard systems, computer games, etc. For classroom orchestration or E-learning, information about the emotional state of students can provide focus on the enhancement of teaching quality. For example, a teacher can use SER to decide what subjects can be taught and must be able to develop strategies for managing emotions within the learning environment. That is why learner’s emotional state should be considered in the classroom.
\nThree key issues need to be addressed for successful SER system, namely, (1) choice of a good emotional speech database, (2) extracting effective features, and (3) designing reliable classifiers using machine learning algorithms. In fact, the emotional feature extraction is a main issue in the SER system. Many researchers [4] have proposed important speech features which contain emotion information, such as energy, pitch, formant frequency, Linear Prediction Cepstrum Coefficients (LPCC), Mel-frequency cepstrum coefficients (MFCC), and modulation spectral features (MSFs) [5]. Thus, most researchers prefer to use combining feature set that is composed of many kinds of features containing more emotional information [6]. However, using a combining feature set may give rise to high dimension and redundancy of speech features; thereby, it makes the learning process complicated for most machine learning algorithms and increases the likelihood of overfitting. Therefore, feature selection is indispensable to reduce the dimensions redundancy of features. A review for feature selection models and techniques is presented in [7]. Both feature extraction and feature selection are capable of improving learning performance, lowering computational complexity, building better generalizable models, and decreasing required storage. The last step of speech emotion recognition is classification. It involves classifying the raw data in the form of utterance or frame of the utterance into a particular class of emotion on the basis of features extracted from the data. In recent years in speech emotion recognition, researchers proposed many classification algorithms, such as Gaussian mixture model (GMM) [8], hidden Markov model (HMM) [9], support vector machine (SVM) [10, 11, 12, 13, 14], neural networks (NN) [15], and recurrent neural networks (RNN) [16, 17, 18]. Some other types of classifiers are also proposed by some researchers such as a modified brain emotional learning model (BEL) [19] in which the adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) are merged for speech emotion recognition. Another proposed strategy is a multiple kernel Gaussian process (GP) classification [17], in which two similar notions in the learning algorithm are presented by combining the linear kernel and radial basis function (RBF) kernel. The Voiced Segment Selection (VSS) algorithm also proposed in [20] deals with the voiced signal segment as the texture image processing feature which is different from the traditional method. It uses the Log-Gabor filters to extract the voiced and unvoiced features from spectrogram to make the classification.
\nIn previous work [21], we present a system for the recognition of «seven acted emotional states (anger, disgust, fear, joy, sadness, and surprise)». To do that, we extracted the MFCC and MS features and used them to train three different machine learning paradigms (MLR, SVM, and RNN). We demonstrated that the combination of both features has a high accuracy above 94% on the Spanish database. All previously published works generally use the Berlin database. To our knowledge, the Spanish emotional database has never been used before. For this reason, we have chosen to compare them. In this chapter, we concentrate to improve accuracy; more experiments have been performed. This chapter mainly makes the following contributions:
The effect of speaker normalization (SN) is also studied, which removes the mean of features and normalizes them to unit variance. Experiments are performed under a speaker-independent condition.
Additionally, a feature selection technique is assessed to obtain good features from the set of features extracted in [21].
The rest of the chapter is organized as follows. In the next section, we start by introducing the nature of speech emotions. Section 3 describes features we extracted from a speech signal. A feature selection method and machine learning algorithms used for SER are presented. Section 4 reports on the databases we used and presents the simulation results obtained using different features and different machine learning (ML) paradigms. Section 5 closes this chapter by analyses and conclusion.
\nThis section is concerned with defining the term emotion, presenting its different models. Also for recognizing emotions, there are several techniques and inputs that can be used. A brief description of all of the techniques is presented here.
\nA definition is both important and difficult because the everyday word “emotion” is a notoriously fluid term in meaning. Emotion is one of the most difficult concepts to define in psychology. In fact, there are different definitions of emotions in the scientific literature. In everyday speech, emotion is any relatively brief conscious experience characterized by intense mental activity and a high degree of pleasure or displeasure [22, 23]. Scientific discourse has drifted to other meanings and there is no consensus on a definition. Emotion is often entwined with temperament, mood, personality, motivation, and disposition. In psychology, emotion is frequently defined as a complex state of feeling that results in physical and psychological changes. These changes influence thought and behavior. According to other theories, emotions are not causal forces but simply syndromes of components such as motivation, feeling, behavior, and physiological changes [24]. In 1884, in
The categorization of emotions has long been a hot subject of debate in different fields of psychology, affective science, and emotion research. It is mainly based on two popular approaches: categorical (termed discrete) and dimensional (termed continuous). In the first approach, emotions are described with a discrete number of classes. Many theorists have conducted studies to determine which emotions are basic [27]. A most popular example is Ekman [28] who proposed a list of six basic emotions, which are anger, disgust, fear, happiness, sadness, and surprise. He explains that each emotion acts as a discrete category rather than an individual emotional state. In the second approach, emotions are a combination of several psychological dimensions and identified by axes. Other researchers define emotions according to one or more dimensions. Wilhelm Max Wundt proposed in 1897 that emotions can be described by three dimensions: (1) strain versus relaxation, (2) pleasurable versus unpleasurable, and (3) arousing versus subduing [29]. PAD emotional state model is another three-dimensional approach by Albert Mehrabian and James Russell where PAD stands for pleasure, arousal, and dominance. Another popular dimensional model was proposed by James Russell in 1977. Unlike the earlier three-dimensional models, Russell’s model features only two dimensions which include (1) arousal (or activation) and (2) valence (or evaluation) [29].
\nThe categorical approach is commonly used in SER [30]. It characterizes emotions used in everyday emotion words such as joy and anger. In this work, a set of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) plus neutral, corresponding to the six emotions of Ekman’s model, were used for the recognition of emotion from speech using the categorical approach.
\nThere is vigorous debate about what exactly individual can express nonverbally. Humans can express their emotions through many different types of nonverbal communication including facial expressions, quality of speech produced, and physiological signals of the human body. In this section, we discuss each of these categories.
\nThe human face is extremely expressive, able to express countless emotions without saying a word [31]. And unlike some forms of nonverbal communication, facial expressions are universal. The facial expressions for happiness, sadness, anger, surprise, fear, and disgust are the same across cultures.
\nIn addition to faces, voices are an important modality for emotional expression. Speech is a relevant communicational channel enriched with emotions: the voice in speech not only conveys a semantic message but also the information about the emotional state of the speaker. Some important voice feature vectors that have been chosen for research such as fundamental frequency, mel-frequency cepstral coefficient (MFCC), prediction cepstral coefficient (LPCC), etc.
\nThe physiological signals related to autonomic nervous system allow to assess objectively emotions. These include electroencephalogram (EEG), heart rate (HR), electrocardiogram (ECG), respiration (RSP), blood pressure (BP), electromyogram (EMG), skin conductance (SC), blood volume pulse (BVP), and skin temperature (ST) [32]. Using physiological signals to recognize emotions is also helpful to those people who suffer from physical or mental illness thus exhibit problems with facial expressions or tone of voice.
\nOur SER system consists of four main steps. First is the voice sample collection. The second features vector that is formed by extracting the features. As the next step, we tried to determine which features are most relevant to differentiate each emotion. These features are introduced to machine learning classifier for recognition. This process is described in Figure 1.
\nBlock diagram of the proposed system.
The speech signal contains a large number of parameters that reflect the emotional characteristics. One of the sticking points in emotion recognition is what features should be used. In recent research, many common features are extracted, such as energy, pitch, formant, and some spectrum features such as linear prediction coefficients (LPC), mel-frequency cepstrum coefficients (MFCC), and modulation spectral features. In this work, we have selected modulation spectral features and MFCC, to extract the emotional features.
\nSchema of MFCC extraction [
In our research, we extract the first 12 order of the MFCC coefficients where the speech signals are sampled at 16 KHz. For each order coefficients, we calculate the mean, variance, standard deviation, kurtosis, and skewness, and this is for the other all the frames of an utterance. Each MFCC feature vector is 60-dimensional.
\nProcess for computing the ST representation [
As reported by Aha and Bankert [34], the objective of feature selection in ML is to “reduce the number of features used to characterize a dataset so as to improve a learning algorithm’s performance on a given task.” The objective will be the maximization of the classification accuracy in a specific task for a certain learning algorithm; as a collateral effect, the number of features to induce the final classification model will be reduced. Feature selection (FS) aims to choose a subset of the relevant features from the original ones according to certain relevance evaluation criterion, which usually leads to higher recognition accuracy [35]. It can drastically reduce the running time of the learning algorithms. In this section, we present an effective feature selection method used in our work, named recursive feature elimination with linear regression (LR-RFE).
\nMany machine learning algorithms have been used for discrete emotion classification. The goal of these algorithms is to learn from the training samples and then use this learning to classify new observation. In fact, there is no definitive answer to the choice of the learning algorithm; every technique has its own advantages and limitations. For this reason, here we chose to compare the performance of three different classifiers.
\nInputs: Class models \n
Output: Class of \n
1. \n
2. \n
3. Distance calculation between original and predicted response variables \n
4. Decision is made in favor of the class with the minimum distance \n
Figure 4 shows a basic concept of RNN implementation. Unlike traditional neural network that uses different parameters at each layer, the RNN shares the same parameters (U, V, and W are presented in Figure 4) across all steps. The hidden state formulas and variables are as follows:
\nA basic concept of RNN and unfolding in time of the computation involved in its forward computation [
where \n
The performance and robustness of the recognition systems will be easily affected if it is not well trained with a suitable database. Therefore, it is essential to have sufficient and suitable phrases in the database to train the emotion recognition system and subsequently evaluate its performance. There are three main types of databases: acted emotions, natural spontaneous emotions, and elicited emotions [27, 44]. In this work, we used an acted emotion databases because they contain strong emotional expressions. The literature on speech emotion recognition [45] shows that the majority of studies have been conducted with emotional acted speech. In this section, we detailed the two emotional speech databases used for classifying discrete emotions in our experiments: Berlin Database and Spanish Database.
\nThe Berlin database [46] is widely used in emotional speech recognition. It contains 535 utterances spoken by 10 actors (5 female, 5 male) in 7 simulated emotions (anger, boredom, disgust, fear, joy, sadness, and neutral). This database was chosen for the following reasons: (i) the quality of its recording is very good, and (ii) it is public [47] and popular database of emotion recognition that is recommended in the literature [19].
\nThe INTER1SP Spanish emotional database contains utterances from two professional actors (one female and one male speaker).The Spanish corpus that we have the right to access (free for academic and research use) [48] was recorded twice in the «six basic emotions plus neutral (anger, sadness, joy, fear, disgust, surprise and neutral/normal)». Four additional neutral variations (soft, loud, slow, and fast) were recorded once. This is preferred to other created database because it is available for researchers use and it contains more data (6041 utterances in total). This paper has focused on only seven main emotions from the Spanish database in order to achieve a higher and more accurate rate of recognition and to make the comparison with the Berlin database detailed above.
\nIn this section, experimentation results are presented and discussed. We report the recognition accuracy of using MLR, SVM, and RNN classifiers. Experimental evaluation is performed on the Berlin and Spanish databases. All classification results are obtained under tenfold cross-validation. Cross-validation is a common practice used in performance analysis that randomly partitions the data into N complementary subsets, with \n
\n | \n | \n | \n | Recognition rate (%) | \n\n | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Test | \nFeature | \nMethod | \nSN | \nA | \nE | \nF | \nL | \nN | \nT | \nW | \nAVG. | \n(\n | \n
#1 | \nMS | \nMLR | \nNo | \n45.90 | \n45.72 | \n48.78 | \n77.08 | \n59.43 | \n79.91 | \n75.94 | \n66.23 | \n(5.85) | \n
MFCC | \n56.55 | \n62.28 | \n45.60 | \n54.97 | \n57.35 | \n74.36 | \n91.37 | \n64.70 | \n(3.20) | \n|||
MFCC+SM | \n70.26 | \n73.04 | \n51.95 | \n82.44 | \n69.55 | \n82.49 | \n76.55 | \n73.00 | \n(3.23) | \n|||
#2 | \nMS | \nSVM | \nNo | \n56.61 | \n54.78 | \n51.17 | \n70.98 | \n67.32 | \n67.50 | \n73.13 | \n70.63 | \n(6.45) | \n
MFCC | \n73.99 | \n64.14 | \n64.76 | \n55.30 | \n62.28 | \n84.13 | \n83.13 | \n71.70 | \n(4.24) | \n|||
MFCC+SM | \n82.03 | \n68.70 | \n69.09 | \n79.16 | \n76.99 | \n80.89 | \n80.63 | \n81.10 | \n(2.73) | \n|||
#3 | \nMS | \nMLR | \nYes | \n48.98 | \n35.54 | \n32.66 | \n80.35 | \n55.54 | \n88.79 | \n85.77 | \n64.20 | \n(5.27) | \n
MFCC | \n59.71 | \n59.72 | \n48.65 | \n67.10 | \n67.98 | \n91.73 | \n87.51 | \n71.00 | \n(4.19) | \n|||
MFCC+SM | \n72.32 | \n68.82 | \n51.98 | \n82.60 | \n81.72 | \n91.96 | \n80.71 | \n75.25 | \n(2.49) | \n|||
#4 | \nMS | \nSVM | \nYes | \n62.72 | \n49.44 | \n37.29 | \n76.14 | \n71.30 | \n88.44 | \n80.15 | \n71.90 | \n(2.38) | \n
MFCC | \n70.68 | \n56.55 | \n56.99 | \n59.88 | \n68.14 | \n91.88 | \n85.44 | \n77.60 | \n(4.35) | \n|||
MFCC+SM | \n77.37 | \n69.67 | \n58.16 | \n79.87 | \n88.57 | \n98.75 | \n86.64 | \n81.00 | \n(2.45) | \n
Recognition results with MS, MFCC features, and their combination on Berlin database; AVG. denotes average recognition rate; \n
Berlin (a, fear; e, disgust; f, happiness; l, boredom; n, neutral; t, sadness; w, anger).
\n | \n | \n | \n | Recognition rate (%) | \n\n | \n | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Test | \nFeature | \nMethod | \nSN | \nA | \nD | \nF | \nJ | \nN | \nS | \nT | \nAVG. | \n(\n | \n
#1 | \nMS | \nMLR | \nNo | \n67.72 | \n44.04 | \n68.78 | \n46.95 | \n89.58 | \n63.10 | \n78.49 | \n69.22 | \n(1.37) | \n
MFCC | \n67.85 | \n61.41 | \n75.97 | \n60.17 | \n95.79 | \n71.89 | \n84.94 | \n77.21 | \n(0.76) | \n|||
MFCC+SM | \n78.75 | \n78.18 | \n80.68 | \n63.84 | \n96.80 | \n82.44 | \n89.01 | \n83.55 | \n(0.55) | \n|||
#2 | \nMS | \nSVM | \nNo | \n70.33 | \n69.38 | \n78.09 | \n60.97 | \n89.25 | \n69.38 | \n85.95 | \n80.98 | \n(1.09) | \n
MFCC | \n79.93 | \n79.02 | \n81.81 | \n75.71 | \n93.77 | \n80.15 | \n92.01 | \n90.94 | \n(0.93) | \n|||
MFCC+SM | \n84.90 | \n88.26 | \n89.44 | \n80.90 | \n96.58 | \n83.89 | \n95.63 | \n89.69 | \n(0.62) | \n|||
#3 | \nMS | \nMLR | \nYes | \n64.76 | \n49.02 | \n66.87 | \n44.52 | \n87.50 | \n58.26 | \n78.70 | \n67.84 | \n(1.27) | \n
MFCC | \n66.54 | \n57.83 | \n74.56 | \n56.98 | \n94.02 | \n72.32 | \n89.63 | \n76.47 | \n(1.51) | \n|||
MFCC+SM | \n77.01 | \n78.45 | \n80.50 | \n64.18 | \n94.42 | \n80.14 | \n91.29 | \n83.03 | \n(0.97) | \n|||
#4 | \nMS | \nSVM | \nYes | \n69.81 | \n70.35 | \n75.44 | \n52.60 | \n86.77 | \n66.94 | \n82.57 | \n78.40 | \n(1.64) | \n
MFCC | \n77.45 | \n77.41 | \n80.99 | \n69.47 | \n91.89 | \n75.17 | \n93.50 | \n87.47 | \n(0.95) | \n|||
MFCC+SM | \n85.28 | \n84.54 | \n84.49 | \n73.47 | \n93.43 | \n81.79 | \n94.04 | \n86.57 | \n(0.72) | \n
Recognition results with MS, MFCC features, and their combination on Spanish database.
Spanish (a, anger; d, disgust; f, fear; j, joy; n, neutral; s, surprise; t, sadness).
Dataset | \nFeature | \nSN | \nAverage (avg) | \nStandard deviation (\n | \n
---|---|---|---|---|
Berlin | \nMS | \nNo | \n66.32 | \n5.93 | \n
MFCC | \n69.55 | \n3.91 | \n||
MFCC+MS | \nYes | \n63.67 | \n7.74 | \n|
MS | \n68.94 | \n5.65 | \n||
MFCC | \n73.08 | \n5.17 | \n||
MFCC+MS | \n\n | 76.98 | \n4.79 | \n|
Spanish | \nMS | \nNo | \n82.30 | \n2.88 | \n
MFCC | \n86.56 | \n2.80 | \n||
MFCC+MS | \n90.05 | \n1.64 | \n||
MS | \nYes | \n82.14 | \n1.67 | \n|
MFCC | \n86.21 | \n1.22 | \n||
MFCC+MS | \n87.02 | \n0.36 | \n
Recognition results using RNN classifier based on Berlin and Spanish databases.
From Table 1, it can be concluded that applying SN improves recognition results for Berlin database. But this is not the case for the Spanish database, as demonstrated in Tables 2 and 3. Results are the same with the three different classifiers. This can be explained by the number of speakers in each database. The Berlin database contains 10 different speakers, compared to the Spanish database that contains only two speakers and probably the language impact. As regarding the RNN method, we found that combining both types of features has the worst recognition rate for the Berlin database, as shown in Table 3. That is because the RNN model has too many parameters (155 coefficients in total) and a poor training data. This is the phenomena of overfitting. This is confirmed by the fact that when we reduced the number of features from 155 to 59 features, the results show an increase of above 13%, as shown in Table 4. To investigate whether a smaller feature space leads to better recognition performance, we repeated all evaluations on the development set by applying a recursive feature elimination (LR-RFE) for each modality combination. The stability of RFE depends heavily on the type of model that is used for feature ranking at each iteration. In our case, we tested the RFE based on an SVM and regression models; we found that using linear regression provides more stable results. We observed from the previous results that the combination of the features gives the best results. So we applied LR-RFE feature selection only for this combination to improve accuracy. In this work, a total of 155 features were used; best features were chosen from feature selection. Fifty-nine features were selected by RFE feature selection method based on LR from the Berlin database and 110 features from the Spanish database. The corresponding results of LR-RFE can be seen in Table 4. For most setting using the Spanish database, LR-RFE does not significantly improve the average accuracy. However, for recognition based on Berlin database using the three classifiers, LR-RFE leads to a remarkable performance gain, as shown in Figure 5. This increases the average of MFCC combined with MS features from 63.67 to 78.11% for RNN classifier. These results are illustrated in Table 4. For the Spanish database, the feature combination of MFCC and MS after applying LR-RFE selection using RNN has the best recognition rate which is above 94.01%.
\nSN | \nClassifier | \nLR-RFE | \nBerlin | \nSpanish | \n
---|---|---|---|---|
No | \nMLR | \nNo | \n73.00 (3.23) | \n83.55 (0.55) | \n
Yes | \n79.40 (3.09) | \n84.19 (0.96) | \n||
SVM | \nNo | \n81.10 (2.73) | \n89.69 (0.62) | \n|
Yes | \n80.90 (3.17) | \n90.05 (0.80) | \n||
RNN | \nNo | \n63.67 (7.74) | \n90.05 (1.64) | \n|
Yes | \n78.11 (3.53) | \n|||
Yes | \nMLR | \nNo | \n75.25 (2.49) | \n83.03 (0.97) | \n
Yes | \n82.27 (1.12) | \n|||
SVM | \nNo | \n81.00 (2.45) | \n86.57 (0.72) | \n|
Yes | \n86.47 (1.34) | \n|||
RNN | \nNo | \n76.98 (4.79) | \n87.02 (0.36) | \n|
Yes | \n85.00 (0.93) | \n
Recognition results with combination of MFCC and MS features using ML paradigm before and after applying LR-RFE feature selection method (Berlin and Spanish databases).
Performance comparison of three machine learning paradigms (MLR, SVM, RNN) using speaker normalization (SN) and RFE feature selection (FS), for the Berlin database, is shown.
The confusion matrix for the best recognition of emotions using MFCC and MS features with RNN based on Spanish database is shown in Table 5. The rate column lists per class recognition rates and precision for a class are the number of samples correctly classified divided by the total number of samples classified to the class. It can be seen that
Emotion | \nAnger | \nDisgust | \nFear | \nJoy | \nNeutral | \nSurprise | \nSadness | \nRate (%) | \n
---|---|---|---|---|---|---|---|---|
Anger | \n||||||||
Disgust | \n0 | \n|||||||
Fear | \n0 | \n3 | \n||||||
Joy | \n3 | \n1 | \n1 | \n|||||
Neutral | \n2 | \n0 | \n1 | \n0 | \n||||
surprise | \n2 | \n1 | \n0 | \n3 | \n0 | \n|||
Sadness | \n0 | \n0 | \n1 | \n0 | \n2 | \n0 | \n||
Precision (%) | \n91.86 | \n91.78 | \n92.10 | \n94.66 | \n96.29 | \n95.23 | \n94.28 | \n\n |
Confusion matrix for feature combination after LR-RFE selection based on Spanish database.
In this current study, we presented an automatic speech emotion recognition (SER) system using three machine learning algorithms (MLR, SVM, and RNN) to classify seven emotions. Thus, two types of features (MFCC and MS) were extracted from two different acted databases (Berlin and Spanish databases), and a combination of these features was presented. In fact, we study how classifiers and features impact recognition accuracy of emotions in speech. A subset of highly discriminant features is selected. Feature selection techniques show that more information is not always good in machine learning applications. The machine learning models were trained and evaluated to recognize emotional states from these features. SER reported the best recognition rate of 94% on the Spanish database using RNN classifier without speaker normalization (SN) and with feature selection (FS). For Berlin database, all of the classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection (FS) are applied to the features. From this result, we can see that RNN often perform better with more data and it suffers from the problem of very long training times. Therefore, we concluded that the SVM and MLR models have a good potential for practical usage for limited data in comparison with RNN .
\nEnhancement of the robustness of emotion recognition system is still possible by combining databases and by fusion of classifiers. The effect of training multiple emotion detectors can be investigated by fusing these into a single detection system. We aim also to use other feature selection methods because the quality of the feature selection affects the emotion recognition rate: a good emotion feature selection method can select features reflecting emotion state quickly. The overall aim of our work is to develop a system that will be used in a pedagogical interaction in classrooms, in order to help the teacher to orchestrate his class. For achieving this goal, we aim to test the system proposed in this work.
\nAs an Open Access publisher, IntechOpen is dedicated to maintaining the highest ethical standards and principles in publishing. In addition, IntechOpen promotes the highest standards of integrity and ethical behavior in scientific research and peer-review. To maintain these principles IntechOpen has developed basic guidelines to facilitate the avoidance of Conflicts of Interest.
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\n\nAll Reviewers are required to declare possible Conflicts of Interest at the beginning of the evaluation process. If a Reviewer feels he or she might have any material, financial or any other conflict of interest with regards to the manuscript being reviewed, he or she is required to declare such concern and, if necessary, request exclusion from any further involvement in the evaluation process. A Reviewer's potential Conflicts of Interest are declared in the review report and presented to the Academic Editor, who then assesses whether or not the declared potential or actual Conflicts of Interest had, or could be perceived to have had, any significant impact on the review itself.
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\n\nAuthors are required to declare all potentially relevant non-financial, financial and material Conflicts of Interest that may have had an influence on their scientific work.
\n\nAcademic Editors and Reviewers are required to declare any non-financial, financial and material Conflicts of Interest that could influence their fair and balanced evaluation of manuscripts. If such conflict exists with regards to a submitted manuscript, Academic Editors and Reviewers should exclude themselves from handling it.
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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. 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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. 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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. Marker assisted selection and advanced techniques such as microarray, next generation sequencing methods to detect a specific mutant in a large population will help to the plant breeders to use ionizing radiation efficiently in breeding programs.",book:{id:"5451",slug:"new-insights-on-gamma-rays",title:"New Insights on Gamma Rays",fullTitle:"New Insights on Gamma Rays"},signatures:"Özge Çelik and Çimen Atak",authors:[{id:"147362",title:"Dr.",name:"Özge",middleName:null,surname:"Çelik",slug:"ozge-celik",fullName:"Özge Çelik"},{id:"147364",title:"Prof.",name:"Çimen",middleName:null,surname:"Atak",slug:"cimen-atak",fullName:"Çimen Atak"}]}],mostDownloadedChaptersLast30Days:[{id:"32842",title:"Sterilization by Gamma Irradiation",slug:"sterilization-by-gamma-irradiation",totalDownloads:74724,totalCrossrefCites:36,totalDimensionsCites:82,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:2897,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:1755,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:2476,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:2005,totalCrossrefCites:1,totalDimensionsCites:1,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:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,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:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{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"}}}},{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"}}}}]},series:{item:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517",scope:"Paralleling similar advances in the medical field, astounding advances occurred in Veterinary Medicine and Science in recent decades. These advances have helped foster better support for animal health, more humane animal production, and a better understanding of the physiology of endangered species to improve the assisted reproductive technologies or the pathogenesis of certain diseases, where animals can be used as models for human diseases (like cancer, degenerative diseases or fertility), and even as a guarantee of public health. Bridging Human, Animal, and Environmental health, the holistic and integrative “One Health” concept intimately associates the developments within those fields, projecting its advancements into practice. This book series aims to tackle various animal-related medicine and sciences fields, providing thematic volumes consisting of high-quality significant research directed to researchers and postgraduates. It aims to give us a glimpse into the new accomplishments in the Veterinary Medicine and Science field. 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After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",institutionURL:null,country:{name:"Portugal"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:3,paginationItems:[{id:"19",title:"Animal Science",coverUrl:"https://cdn.intechopen.com/series_topics/covers/19.jpg",isOpenForSubmission:!0,annualVolume:11415,editor:{id:"259298",title:"Dr.",name:"Edward",middleName:null,surname:"Narayan",slug:"edward-narayan",fullName:"Edward Narayan",profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",biography:"Dr. Edward Narayan graduated with Ph.D. degree in Biology from the University of the South Pacific and pioneered non-invasive reproductive and stress endocrinology tools for amphibians - the novel development and validation of non-invasive enzyme immunoassays for the evaluation of reproductive hormonal cycle and stress hormone responses to environmental stressors. \nDr. Narayan leads the Stress Lab (Comparative Physiology and Endocrinology) at the University of Queensland. A dynamic career research platform which is based on the thematic areas of comparative vertebrate physiology, stress endocrinology, reproductive endocrinology, animal health and welfare, and conservation biology. \nEdward has supervised 40 research students and published over 60 peer reviewed research.",institutionString:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},{id:"20",title:"Animal Nutrition",coverUrl:"https://cdn.intechopen.com/series_topics/covers/20.jpg",isOpenForSubmission:!0,annualVolume:11416,editor:{id:"175967",title:"Dr.",name:"Manuel",middleName:null,surname:"Gonzalez Ronquillo",slug:"manuel-gonzalez-ronquillo",fullName:"Manuel Gonzalez Ronquillo",profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",biography:"Dr. Manuel González Ronquillo obtained his doctorate degree from the University of Zaragoza, Spain, in 2001. He is a research professor at the Faculty of Veterinary Medicine and Animal Husbandry, Autonomous University of the State of Mexico. He is also a level-2 researcher. He received a Fulbright-Garcia Robles fellowship for a postdoctoral stay at the US Dairy Forage Research Center, Madison, Wisconsin, USA in 2008–2009. He received grants from Alianza del Pacifico for a stay at the University of Magallanes, Chile, in 2014, and from Consejo Nacional de Ciencia y Tecnología (CONACyT) to work in the Food and Agriculture Organization’s Animal Production and Health Division (AGA), Rome, Italy, in 2014–2015. He has collaborated with researchers from different countries and published ninety-eight journal articles. He teaches various degree courses in zootechnics, sheep production, and agricultural sciences and natural resources.\n\nDr. Ronquillo’s research focuses on the evaluation of sustainable animal diets (StAnD), using native resources of the region, decreasing carbon footprint, and applying meta-analysis and mathematical models for a better understanding of animal production.",institutionString:null,institution:{name:"Universidad Autónoma del Estado de México",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"28",title:"Animal Reproductive Biology and Technology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/28.jpg",isOpenForSubmission:!0,annualVolume:11417,editor:{id:"177225",title:"Prof.",name:"Rosa Maria Lino Neto",middleName:null,surname:"Pereira",slug:"rosa-maria-lino-neto-pereira",fullName:"Rosa Maria Lino Neto Pereira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9wkQAC/Profile_Picture_1624519982291",biography:"Rosa Maria Lino Neto Pereira (DVM, MsC, PhD and) is currently a researcher at the Genetic Resources and Biotechnology Unit of the National Institute of Agrarian and Veterinarian Research (INIAV, Portugal). 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Silva, Eliete A. Alvin, Lais S. de Jesus, Caio C.L. de França, Marílya P.G. da Silva, Samaysa L. Lins, Diógenes Meneses, Marcela R. Lemes, Rhanoica O. Guerra, Marcos V. da Silva, Carlo J.F. de Oliveira, Virmondes Rodrigues Junior, Renata M. Etchebehere, Fabiane C. de Abreu, Bruno G. Lucca, Sanívia A.L. Pereira, Rodrigo C. Rosa and Noelio O. 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Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University, Kuwait. His research interests include optimization, computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, and intelligent systems. Prof. Sarfraz has been a keynote/invited speaker at various platforms around the globe. He has advised/supervised more than 110 students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He has authored and/or edited around seventy books. Prof. Sarfraz is a member of various professional societies. He is a chair and member of international advisory committees and organizing committees of numerous international conferences. He is also an editor and editor in chief for various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:"Beijing University of Technology",institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Lakhno Igor Victorovich was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPhD – 1999, Kharkiv National Medical Univesity.\nDSc – 2019, PL Shupik National Academy of Postgraduate Education \nLakhno Igor has been graduated from an international training courses on reproductive medicine and family planning held in Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor of the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s a professor of the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics and gynecology department of Kharkiv Medical Academy of Postgraduate Education . He’s an author of about 200 printed works and there are 17 of them in Scopus or Web of Science databases. Lakhno Igor is a rewiever of Journal of Obstetrics and Gynaecology (Taylor and Francis), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for DSc degree \\'Pre-eclampsia: prediction, prevention and treatment”. Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: obstetrics, women’s health, fetal medicine, cardiovascular medicine.",institutionString:"V.N. Karazin Kharkiv National University",institution:{name:"Kharkiv Medical Academy of Postgraduate Education",country:{name:"Ukraine"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"243698",title:"M.D.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:"Shanxi Eye Hospital",institution:{name:"Shanxi Eye Hospital",country:{name:"China"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRZkkQAG/Profile_Picture_2022-05-09T12:55:18.jpg",biography:null,institutionString:null,institution:null},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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