Major RNA types and their features
\r\n\tHuman-Robot Interaction (HRI) is defined as a field of study that is committed to realizing, creating, and testing robotic systems that interact with humans. HRI has emerged as a significant area of research due to its diverse applications in personal and societal domains.
\r\n\r\n\tThis book aims to present recent advances in this emerging area. More specifically, the book aims to look at research methods and domains in HRI including collaborative robots, humanoid robots, telerobotics, design and control, adaptation, and learning. The book also aims to look at some promising current and future applications of HRI including space, military, medical, personal, and societal applications. We welcome other novel applications that are not mentioned here.
\r\n\r\n\tThe book aims to close with some anticipated challenges in HRI and their integration into society. These challenges include standardizations, design evaluations, testing and validation, and contextualization. Overall, the goal of this book is to present comprehensive coverage from the contributions of leading researchers in this flourishing field of HRI.
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From 2008 to 2012, he worked as a postdoctoral research associate in the field of Brain-Machine Interfaces (BMI) to control prostheses at the School of Medicine, the University of Pittsburgh, where he received the Mary E Switzer Merit Fellowship from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR) in 2010. From 2012 to 2013, he worked as a research assistant professor in the Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, in the area of neuroprosthetics. He also worked as an assistant professor in the Department of Biomedical Engineering, Stevens Institute of Technology, Hoboken, New Jersey, from 2013 to 2020. He holds a secondary appointment as an adjunct assistant Professor at the Indian Institute of Technology, Hyderabad, India. He is currently an assistant professor in the Department of Computer Science and Electrical Engineering, University of Maryland, USA.\n\nIn 2018, Dr. Vinjamuri received the Harvey N Davis Distinguished Teaching Award for excellence in undergraduate and graduate teaching. He also received the National Science Foundation (NSF) CAREER Award in 2019 and an NSF Industry-University Cooperative Research Centers (IUCRC) Planning Grant in 2020. 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That is why genomic materials are commonly defined as dynamic entities and it is believed that they have been repeatedly altered and rearranged since the beginning of the life on the planet [1-4]. Understanding this dynamism is a valuable key to unlock the chest of the mysterious existence story in an evolutionary manner. Therefore, a lot of studies have been conducted on the dynamism of genomic materials in organisms and the count of related researches has gradually risen by the day. An enormous data from these studies call attention to recombinational, tranpositional and mutational processes as three main sources of genomic changes [1,2,5-18].
Recombinational changes of genomes are mainly dependent on internal factors which are closely associated with a great many of intracellular and intercellular interactions. Enzyme catalyzed pathways and predetermined timing are the most descriptive properties for many types of recombination events. For instance, usual meiotic crossing over, the best known recombinational event, always occurs under control of specified enzymatic reactions at a certain time period in the cell cycle [2,4,19-22].
Transpositional events are also important sources for sequential rearrangements in genomes and induced by external or internal genomic material pieces that are described as mobile or transposable elements. In mechanism of transposition, a transposable element changes its relative position within the genome. “Copy and Paste” or “Cut and Paste” postulates work in this process. A transpositional event occurring with the copy and paste mechanism is called as replicative transposition that a transposable element is duplicated during the process and copied sequence transferred into the target genomic sequence, and the other one with the cut and paste mechanism is called as non-replicative transposition that duplication of the transposable element does not occur and the original sequence is transferred from one region into another [5,23-24]. In both cases, a transpositional event is commonly resulted in a mutational phenomenon and alteration in genomic sizes that makes them attractive for genomic evolution studies [6-7,23-26].
Mutations are described as sudden changes in genomic materials induced by internal and external factors [27]. They have importance in medicinal, agricultural and other related researches due to their deleterious, beneficial or functional effects on organisms [5,9,28]. Moreover, enormous potential for construction of novel genes and other types of genomic sequences, they are considered as the most attractive subject for genome evolution [2,29-32].
Genetic recombination is a process that is catalyzed by many different enzymes called as recombinases. It can take place in all living cells from bacteria to eukaryota as well as viral genomes. This process mainly results in DNA repair, genomic rearrangements, variations and evolutional forces. Genetic recombinations are assigned to one of two groups according to their mechanism, which can be described as either homologous or non-homologous recombination [2,4,20,22,33-35].
Homologous recombinational events are sequential changes that occur between similar or identical parts of genomic material. In the beginning of 20th century, initial descriptions of homologous recombinations were introduced by W. Bateson and R. Punnett to explain diversions from predicted Mendelian inheritance phenotypic ratios [4,36-37]. This process, which is commonly found in many organisms from bacteria to higher organized eukaryotes, plays a significant role in DNA repair mechanisms and genome evolution by producing variations [2,38-40].
In prokaryotic cellular organisms, the most known types of homologous recombinational events are transformation, conjugation and transduction [41]. All of these events are resulted in genomic variations that have great value for evolution [42].
Transformation was discovered by Frederick Griffith in the late 1920s. His transformation experiments are considered as the beginning mile stone of the molecular biology discipline [5]. In the mechanism of natural prokaryotic transformation, a naked DNA fragment released from a cell is taken up by another under appropriate conditions, thus an exogenous genetic material is introduced into a prokaryotic cell that result in genomic variation. Transformation occurs in several groups of Gram positive, Gram negative and Archaea. A healthy double strand DNA molecule with a homological property and specific size (mostly smaller than 1000 nucleotides) is the most fundamental requirement for transformation [2,41]. Figure 1 illustrates a summarized scheme for transformation.
Simple mechanism of transformation
Bacterial conjugation, discovered in 1946 by Joshua Lederberg and Edward Tatum [43], is another process to transfer the genetic information in Prokaryotes. In its mechanism, the transfer of genetic material involves cell to cell contact and a plasmid encoded pathway. The process occurs between a donor cell, which includes a certain type of conjugative plasmid, and a recipient cell, which does not. In this process, the plasmid plays a key role by carrying all related genes on
An illustrative scheme for bacterial conjugation of F+ (a) and Hfr (b) cells
Transduction, initially discovered by Norton Zinder and Joshua Lederberg in 1951 [44], refers to virus-mediated transfers of genetic materials. There are two fundamental mechanisms as generalized and specialized transduction. In generalized transduction, any bacterial genomic sequence may be transferred to another bacterium via a modified bacteriophage that accidentally involves bacterial DNA instead of viral DNA. However, in specialized transduction, bacteriophage includes both bacterial and viral DNA at the same time [2,41]. Both types of transduction events are summarized at Figure 3.
In eukaryotic organisms, meiotic crossing over (chromosomal cross over) is the most well-known example for homologous recombination. This event occurs between homologous chromosomes at prophase I stage in meiosis and results in variation of genetic materials [2,5,45-46]. The scheme of meiotic crossing over is showed in Figure 4.
Mechanism of generalized (a) and specialized (b) transduction events
Mechanism of meiotic crossing over
Homologous recombination also plays a significant role in DNA repair mechanisms in both prokaryotic and eukaryotic organisms. It is one of the major DNA repair processes in bacteria [2,46]. For example, double-strand breaks in bacteria are repaired by the RecBCD pathway of homologous recombination [42,47-49]. Moreover, it is well known that similar mechanisms work in eukaryotic organisms.
Homologous recombination also includes non-allelic ones that have been not well documented. These events occur between sequences arisen from duplications or deletions that show high homology, but are not alleles. It is believed that non-allelic homologous recombination has a great importance for evolution due to generating a decrease or an increase in copy number of sequences [50-52].
Non-homologous recombination, also named as non-homologous end joining (NHEJ), is a pathway that mainly associated with DNA repair that especially works on double strand breaks. Contrary to the mechanisms of homologous recombination, it does not require sequential homology. However, this pathway has been identified in many groups of living organisms from bacteria to multicellular organisms, even in human being, recent studies have mainly focused on eukaryotes much more than bacteria. One reason for this is that prokaryotic DNA repair is heavily done by various processes of homologous recombination.
Nuclease, polymerase and ligase activities play the major role in NHEJ process. Despite its conservative mechanism, this process is generally resulting in variations of genetic materials [2,53-55].
Mobile genetic elements are described as DNA segments that can move within the genome. These include transposons, group II introns, plasmids and viral elements [56]. All these events result in genomic alterations that cause rising of evolutional forces [6,8,24-26,57-61].
Transposons, also named as transposable elements, are major forces in the evolution and rearrangement of genomes [6,26,56]. Discovery of transposable elements was achieved in 1943 by Barbara McClintock who was awarded with a Nobel Prize after 40 years in 1983 [2,58]. Since that time, the importance of transposons has been well established and much more attention has been given to their formation and consequences [62]. To get more easily comprehensive information, they are divided into three main groups as retrotransposons, DNA transposons and insertion sequences.
Retrotransposons can be considered as the biggest group of transposable elements due to their abundance in many eukaryotic genomes (i.e. 49-78% of the total genome in maize and 42% in human) [63-64]. The term “retrotransposon” is attributed to the transposition mechanism that involves via RNA intermediates. In the mechanism, a retrotransposon is initially copied to RNA (transcription), then converted to DNA (reversetranscription) and finally inserted to the genome (integration), and this process is mainly under control of the gene region of retrotransposons encoding reverse transcriptase. These elements can increase genome size and induce mutational events by disturbing genes [2,24,26,56,59,62,65].
Retrotransposons are divided into three main groups according to the operation mechanisms: long terminal repeats (LTRs) encode reverse transcriptase, similar to retroviruses; long interspersed elements (LINEs) do not have LTRs and encode reverse transcriptase and small interspersed elements (SINEs) do not encode reverse transcriptase. LINEs and SINEs are transcripted by RNA polymerase II and III, respectively [66-68].
DNA transposons are the first discovered ones of transposable elements, initially named as “jumping genes” by Barbara McClintock in 1943 [69]. These are also called as Class II transposons, operate with a “cut and paste” mechanism. In this mechanism, transposition event mainly requires to transposase enzymes. Under control of the enzymatic processes, a DNA transposon is cut out of its location and inserted into a new location on the genome. Some transposases require a specific sequence as their target site; others can insert the transposon anywhere in the genomic material [2,24,41,62].
These are also known as IS elements. They are short DNA sequences that act as a simple form of transposable elements. Characterized properties of IS elements are that they have shorter sizes than other types of transposable elements (approximately 700 – 2500 bp), and carry some specific genes such as antibiotic resistance. Insertion sequences are usually flanked by inverted repeats [23,24,70].
Group II introns were discovered by Alexandre de Lencastre and his teammates in 2005 [71]. These elements, an important group of self-catalytic ribozymes, are generated during RNA splicing, and may cause genetic alterations [71].
Plasmids are circular and extra chromosomal genomic materials naturally found in bacteria, but rarely in several yeasts as eukaryotic organisms [41]. These elements show intracellular or intercellular mobility (see section 2.1.) that result in genomic alterations and evolutional forces.
Viral elements are genomic materials transferring between living organisms via virus infections. According to the mechanism of infection, viruses are divided into two categories as lytic and lysogenic. Lytic ones complete their eclipse phase in the cell and cause lysis of the host. However, lysogenic ones integrate their genomic materials into the host genome and directly cause genomic alterations [41]. For example, some retroviruses are common type of lysogenic viral elements and their effect mechanism is similar to retrotransposons.
The “Mutation” term was initially used by Hugo de Vries in 1905 to describe the phenotypic changes in evening-primrose plant (
Effect size of mutations on genomes is one of the most widely-accepted criteria for classification. According to this, mutations can be divided into two groups named as gene mutations and chromosome mutations [5,27].
Gene mutations are small-scale mutations that effect one or few bases in a genome. However, they can induce many important phenomenon depend on properties of effected genomic sequences. For example, a gene mutation in a protein coding region of genomic material can result in synthesis of a non-functional protein that mostly causes deleterious effects for the organism. Gene mutations are also divide subcategories as base substitution and insertion/deletion [2,5,27,34].
Base substitutions type of gene mutations
Insertion/Deletions type of gene mutations
Chromosomal mutations are described as phenomenon that causes bigger sequence alterations than gene mutations. These are also called as macro-mutations due to their microscopically examination capabilities. There are two main subcategories as structural and numerical alterations in chromosomal mutations [5,9,27,34].
These types of mutations mainly cause alterations in chromosome numbers in the living cells. Euploidy and aneuploidy are two essential subgroups.
These types of mutations do not change chromosome numbers. However, their effects are mainly on chromosomal structure. According to their effect mechanisms, structural mutations are grouped in four subcategories including deletions, inversions, duplications and translocations [5,9,27,72].
Structural chromosome mutations
The origin of life on the earth has always been an attractive subject for all human beings. The question about formation of the first active biomolecule is one of the most important perspectives in this subject, and has been heavily researched for many years. Initial studies referred to proteins as first biomolecules due to their catalytic activities that operates various reactions for maintaining of life. Although this view was confirmed for a long time, their lack of potential to carry genetic information was the major handicap. In 1982, the commonly accepted thought about the first biomolecule was drastically changed by Thomas Cech and co-workers who published a paper that demonstrate the single intron of the large ribosomal RNA of
Although the first genome has a potential to be ribonucleic acid form, instability and limited life of RNA molecules may have forced evolution of a more complex genomic material called as deoxyribonucleic acid (DNA). In this stage, there are several gaps and unanswered questions. However, the most discussed scenario about formation of DNA based genomes from initial RNA molecules (protogenome) proposes a phenomenon that is catalyzed by a reverse transcriptase [2,78,84].
Contrary to the high stability property, evolutional changes are continuously occurring in DNA based genomes that result in development of valuable features for adaptation. These changes have been mainly dependent on external forces since the beginning of the life on the planet (approximately 3.5 billion years ago) [2]. Understanding of this evolutional dynamism in genomic materials requires recognizing definitions of several important terms given in Table 2, prepared according to Eugene V. Koonin (2005) who is senior investigator at National Central of Biotechnology Information (NCBI) and studies on empirical comparative and evolutionary genomics [8].
Up to this point, all mentioned events cause changes in size and construction of genomic materials acting as evolutional forces. The genomic size is referred as “C value”. Although the genomic size may reduce via deletions, it has generally intended to increase when compared to the first genome of universal common ancestor (UCA). This expansion is controlled by rearrangement forces, especially duplications and mobile genetic elements. There are two fundamental hypotheses for why genome sizes vary. According to the “Selfish-DNA hypothesis”: genome size expansion is due to insertion and proliferation selfish genetic elements such as retrotransposons, and “Bulk-DNA hypothesis”: having more genetic bulk can be adaptive because genome size effects nuclear volume, cell size, cell division rate in turn effecting developmental rate and size at maturity, thus it results in organisms with larger body size have larger cell sizes, and organisms with larger cells generally have larger genomes [15,24-26,63,65,68,85-90]. In his paper, Zhang [88] underlined the positive correlation between duplicated gene amount and evolutional status of an organism. Table 3 represents prevalence of gene duplications in all three domains of life.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
mRNA (Messenger RNA) | \n\t\t\t- responsible for coding - represents 4% of whole RNA amount in a cell - called as hnRNA or pre-mRNA before processing in eukaryotes | \n\t\t\t[2] | \n\t\t
rRNA (Ribosomal RNA) | \n\t\t\t- composes ribosomes - the most abundant RNA in a cell (over 80%) - named as pre-rRNA before processing in all living organisms | \n\t\t\t[2] | \n\t\t
tRNA (Transfer RNA) | \n\t\t\t- responsible for carrying amino acids to ribosomal complexes - specific for each amino acid - named as pre-tRNA before processing and modification in all living organisms | \n\t\t\t[2] | \n\t\t
snRNA (Small Nuclear RNA) | \n\t\t\t- responsible for operation of splicing mechanism - found in nuclei of eukaryotes - also called as U-RNA - has a lot of sub-types with various catalytic activities | \n\t\t\t[2] | \n\t\t
snoRNA (Small Nucleolar RNA) | \n\t\t\t- responsible for chemical modification of rRNA - found in nucleolar region of eukaryotic nuclei - shows catalytic activities | \n\t\t\t[2] | \n\t\t
miRNA (MicroRNA) | \n\t\t\tresponsible for regulation of gene expression double strand molecule intracellular origin (nucleus) | \n\t\t\t[2] | \n\t\t
siRNA (Short Interfering RNA) | \n\t\t\t- responsible for regulation of gene expression - double strand molecule - extracellular origin (commonly synthetic) - called as small interfering or silencing RNA | \n\t\t\t[2] | \n\t\t
piRNA (Piwi-interacting RNA) | \n\t\t\t- interacts with piwi proteins - the largest class of small non-coding RNA molecules | \n\t\t\t[76] | \n\t\t
gRNA (Guide RNA) | \n\t\t\t- acts in mitochondrial mRNA processing - guides insertional or deletional events in mitochondrion | \n\t\t\t[77] | \n\t\t
tmRNA (Transfer-messenger RNA) | \n\t\t\t- have tRNA and mRNA properties - also known as 10Sa RNA - found in bacterial genomes | \n\t\t\t[78] | \n\t\t
shRNA (Small hairpin RNA) | \n\t\t\t- responsible for regulation of gene expression - makes a tight hairpin - extracellular origin | \n\t\t\t[79] | \n\t\t
stRNA (Small Temporal RNA) | \n\t\t\t- regulates gene expression (down regulation) | \n\t\t\t[80] | \n\t\t
Major RNA types and their features
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Orthologs | \n\t\t\tGenes originating from a single ancestral gene in the last common ancestor of the compared genomes. | \n\t\t
Pseudoorthologs | \n\t\t\tGenes that actually are paralogs but appeared to be orthologous due to differential, linage-specific gene loss. | \n\t\t
Xenologs | \n\t\t\tHomologous genes acquired via xenologous gene displacement (XGD) by one or both of the compared species but appearing to be orthologous in pairwise genome comparisons. | \n\t\t
Co-orthologs | \n\t\t\tTwo or more genes in one lineage that are, collectively, orthologous to one or more genes in another lineage due to a lineage-specific duplication(s). Members of a co-orthologous gene set are inparalogs relative to the respective speciation event. | \n\t\t
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Inparalogs (Symparalogs) | \n\t\t\tParalogs genes resulting from a lineage-specific duplication(s) subsequent to a given speciation event (defined only relative to a speciation event, no absolute meaning). | \n\t\t
Outparalogs (Alloparalogs) | \n\t\t\tParalogs genes resulting from a duplication(s) preceding a given speciation event (defined only relative to a speciation event, no absolute meaning) | \n\t\t
Pseudoparalogs | \n\t\t\tHomologous genes that come out as paralogs in a single-genome analysis but actually ended up in the given genome as a result of a combination of vertical inheritance and horizontal gene transfer. | \n\t\t
Homology: terms and definitions from Koonin 2005 [8].
\n\t\t\t | \n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t|
\n\t\t\t\t | \n\t\t\t\n\t\t\t | \n\t\t | |
\n\t\t\t\t | \n\t\t\t677 | \n\t\t\t298 (44) | \n\t\t|
\n\t\t\t\t | \n\t\t\t1590 | \n\t\t\t266 (17) | \n\t\t|
\n\t\t\t\t | \n\t\t\t1709 | \n\t\t\t284 (17) | \n\t\t|
\n\t\t\t | \n\t\t | ||
\n\t\t\t\t | \n\t\t\t2436 | \n\t\t\t719 (30) | \n\t\t|
\n\t\t\t | \n\t\t | ||
\n\t\t\t\t | \n\t\t\t6241 | \n\t\t\t1858 (30) | \n\t\t|
\n\t\t\t\t | \n\t\t\t18424 | \n\t\t\t8971 (49) | \n\t\t|
\n\t\t\t\t | \n\t\t\t13601 | \n\t\t\t5536 (41) | \n\t\t|
\n\t\t\t\t | \n\t\t\t25498 | \n\t\t\t16574 (65) | \n\t\t|
\n\t\t\t\t | \n\t\t\t40580a\n\t\t\t | \n\t\t\t15343 (38) | \n\t\t
Prevalence of gene duplications in all three domains of lifeb from Zhang 2003 [88].
a The most recent estimate is ~30000.
b Use of different computational methods or criteria results in slightly different estimates of the number of duplicated genes.
Besides, Xue et al. [91] laid emphasis on the roles of duplications in genomic size and compositional changes in their studies via exploring the evolution of segmental gene duplication in haploid and diploid populations by analytical and simulation approaches. The result of this study highlighted that duplications do not only cause alterations in genome size but they are also result in many recombinational events that closely related to formation of variations that have value in rising evolutional forces. In another paper, Force et al. [92] focused on the DDC (duplication-degeneration-complementation) model for the alternative fates (nonfunctionalization, neofuctionalization and subfuctionalization) of duplicate genes, and underlined their roles in genome evolution.
Mobile genetic elements also affect genome size. For example, horizontal transfer of transposable elements plays a key role in genome evolution. In their “copy-and-paste” operation mechanisms, retrotransposons, as common examples of mobile genetic elements that may cause horizontal gene transfer, transpose via an RNA-intermediated process, and this increases genomic material size [26,93-94]. Furthermore, all advanced biology sources covering microbial genetic title mention the role of other types of mobile genetic elements including plasmids and viral genomes in formation of variations in genomic size and structure [41].
On the other hand, reduction of genomic size in certain periods is an inevitable fact for genome evolution. In this manner, smaller genomes are more advantageous for selection than bigger ones due to their high replication potentials and metabolic inexpensiveness. Deletions can be given as the main force to diminish genomic size that causes gene losses [95-96]. In a recent paper, Pettersson and co-workers emphasized the role of deletions in regulation of genomic size and its coding density by using a mathematical model to determine the evolutionary fate [97].
A genomic material may accept deletions and reduce its size up to reach minimal genome limits that have the smallest number of genetic elements sufficient to build a modern-type free-living cellular organism. In addition, under some exceptional conditions, genomic materials of several endo-symbionts and co-symbionts carry much less genes than predicted minimal genome rates. For example, although
Contrary to the genomic material of
Recent attention of evolutionary studies has shifted to genetics, molecular and cellular biology as a result of finding out principles of genetics and DNA is the main molecule responsible for inheritance. Thus, the popularity of genome-wide studies has increased. In this regard, genomic rearrangement mechanisms (recombinations, mutations or mobility of several genetic elements) are major research topics for evolution of genomes because any change in the DNA molecule of the organisms may cause a valuable process for evolution when it has inheritable potential.
Thus, aim of the present study was conducted to emphasize potential value of genomic rearrangements for evolution, and therefore, basic rearrangement mechanisms were explained in detail, and their evolutionary effects on genomes were briefly discussed via giving important samples in this chapter.
Hyperspectral imaging (HSI), also known as imaging spectroscopy, is a technology capable of sampling hundreds of narrow spectral bands across the electromagnetic spectrum through the use of an optical element that disperses the incoming radiation into certain wavelengths [1]. This technology combines the main features of two existing technologies: imaging and spectroscopy, making possible to exploit both the morphological features and the chemical composition of objects captured by a camera. The interaction between electromagnetic radiation and matter is distinctive for each material, therefore by using this technology it is possible to discriminate among different materials [2]. The characteristic spectral curve associated with a certain material is called spectral signature or spectral fingerprint, and through its analysis it is possible to differentiate among different materials or substances. The data structure used in HSI comprises both the spectral and spatial features from a given scene, and is referred to as hyperspectral (HS) cube. Figure 1 shows a graphical representation of an HS cube with an example of a spectral signature for the top-right pixel.
Example of a HS cube with an example of a spectral signature.
Although historically HSI has been applied to remote sensing [3], in recent years this technology has become a trending topic in different research fields such as food quality analysis [4, 5], military and security applications [6] or agriculture [7, 8], among many others [9]. HSI is also an emerging imaging modality in the medical field. It has been proven that the interaction between the electromagnetic radiation and matter carries useful information for diagnostic proposes [10]. As an alternative diagnostic tool, one of the strengths offered by HSI is being completely non-invasive and label-free. In medical research applications, this technology has been employed for more than twenty years in different areas such as the analysis of cancerous tissues in
In this chapter, a survey of the most common processing frameworks employed in the literature for information extraction in medical HSI will be presented. First, a brief introduction of the optical properties of biological tissues is provided. Second, the most common information extraction methods employed for HSI medical processing are described and discussed, including optical inverse modeling and machine learning methods. The last section summarizes the conclusions reached in this literature analysis.
The interaction between light and biological tissues has been proven to be a useful tool to identify and classify several diseases. Absorption, refraction and scattering are the three different types of interaction that can be measured in biological tissues [15]. Light absorption measures the amount of light absorbed and transformed to energy by tissue molecules. Specific wavelengths of the spectrum will present absorption peaks related to the transitions between two energy levels in a molecule, which can provide tissue diagnostic information. Absorption is the inverse measurement of reflectance using HSI systems. The measurement of refraction and reflection of light is based on changes in speed and direction of the incident light into tissue. Particularly, hemoglobin (Hb) is the major component of the spectral signature between 450 and 600 nm of biological tissues, and spectral differences can be observed in the absorption/reflectance between oxygenated and deoxygenated Hb states [16]. A single absorbance peak is found at 560 nm in deoxygenated Hb, while two absorbance peaks are found at 540 and 580 nm in oxygenated Hb [17]. Figure 2 shows an example of these Hb signatures published in [18].
Oxy-Hb (a) and deoxy-Hb (b) normalized absorption spectra, with Hb concentrations of 50 g/L and 68 g/L, respectively. The solid lines are experimentally measured, and the dotted black lines are the ideal. Oxy-Hb (c) and deoxy-Hb (d) measured and theoretical attenuation coefficients [
Regarding the measurement of light scattering, it is achieved when there is a spatial variation of the reflective index in the illuminated tissue. Scattering properties can be highly useful in diagnostic applications, since they provide different variations in tissue affected by a certain disease [19]. For example, the spectral range between 700 and 900 nm is related with the scattering dominant optical properties of collagen [20]. Also, the near-infrared spectral region is the scattering dominant region of fat, lipids, collagen, and water. Moreover, several tissues have fluorescence properties that can be revealed when such tissue is excited with certain wavelengths. As an example, ultraviolet light can be used to excite tissues, revealing the fluorescence emission of proteins and nucleic acids [21]. More details about biological tissue optical properties can be found in [19].
There are two main types of medical HSI processing: optical inverse modeling and machine learning approaches. In this section, both methods will be presented in detail, showing their main characteristics, as well as their advantages and disadvantages.
In optical inverse modeling techniques, a mathematical equation which models the interaction between the light and tissue is proposed, and the collected HS data is used to extract optical properties, such as the absorption or scattering of tissue. First, a physics-based model is proposed for the light propagation in tissues. Second, the HS data are used to extract optical properties from the proposed light propagation model. Although the number of studies which make use of this type of approach is limited, some researchers have used HS and light transport models in tissue to extract useful information for the detection of different diseases or conditions. Milanic
The use of optical inverse modeling for information extraction in medical HSI presents some advantages and challenges. The main advantage is to count with an established physical-based model for correlating measured data, which are theoretically strong and contain tissue optical parameters that can be used for diagnostics. The main disadvantage of this approach is the possibility of bias in the model development and over-simplification of complex physical processes, which could result in suboptimal performance for information extraction.
Machine Learning (ML) methods are algorithms able to learn from data. ML algorithms enable solutions to difficult tasks which usually cannot be performed by a traditionally designed computer program [25]. There are different ML algorithms depending on the task they perform. In regression problems, a numerical variable is estimated from the data. In the context of medical HSI, Arimoto
Example of classification and heat maps obtained through ML classification from (A) in-vivo brain tissue HS images [
ML algorithms can be classified as supervised and unsupervised. In unsupervised algorithms, the goal is to cluster similar data samples in groups, extracting the information from data features. In supervised algorithms, the data is comprised of the data features and associated labels [29]. For example, in the example of Figure 3A, the data features consist of the spectra of each pixel of the HS image, and the labels are the different categories into which each pixel can be categorized, i.e. normal tissue, tumor tissue, hypervascularized tissue and background. The main goal of supervised algorithms is to use data and their labels to train a model which can be used to perform predictions about new data. ML techniques can be categorized as Feature Learning (FL) or Deep Learning (DL) methods. In FL approaches, the inputs of a supervised classifier are given by features extracted from the data. For example, in an image processing framework, such features may be related to shape, texture or color. On the contrary, DL approaches are devoted to use all the data as input to a supervised classifier, and the important features to perform the classification task are learned by the supervised classifier.
There are challenges related with both types of ML approaches. On the one hand, in FL methods, the classification may be biased by which features are selected from the data for the classification, while the identification of features is performed automatically in a DL algorithm. On the other hand, DL methods usually require large amounts of data to succeed in the feature extraction and classification, while FL approaches may provide good performance with a limited dataset. Next, we provide a survey about the different ML approaches which are commonly used for HSI processing in medical applications.
In this section, we describe the most common FL approaches which have been employed for processing medical HS data. This section is categorized in three main categories, namely pixel-wise classification, feature extraction and selection methods, and the usage of both the spatial and spectral information.
In the HS literature, the concept of pixel-wise processing refers to the exclusive usage of the spectral information within an HS cube for extracting information from HS data. Recently, Ghamisi
SVM is a binary classification algorithm proposed by Vapnik [31]. The algorithm finds the optimal hyperplane that maximizes the margin between samples belonging to different classes. Although it was originally designed for linear classification, an SVM classifier can be used for nonlinear classification problems by using different kernels to map the data into a higher dimensional space. SVM has been shown to provide competitive classification performance on HS data even with a limited training sample size [32].
RF was firstly proposed by Breiman [33]. This algorithm consists of an ensemble of decision trees, where, in each decision tree, the training data are hierarchically partitioned into smaller homogeneous groups. In RF, different decision trees are generated from the training data, and the different classification results are combined by a voting process. The main advantage of RF is a reduced training time. RF has been successfully used for the classification of HS images [34].
Finally, MLR [35] approaches exploit the posterior class distributions of the training data for making predictions, and these methods have been successfully applied for the classification of HS images. The main advantages of MLR are fast computation for training and customizability, which allows modifications to the original algorithm to provide better generalization, e.g. sparsity constraints or multiple feature learning.
In the context of medical HS classification, several authors have utilized the spectral information for the diagnosis of different diseases in a pixel-wise manner. The most commonly used pixel-wise classifier in medical HSI is SVM. In the context of surgical guidance, Akbari
HS data are characterized by a high dimensionality. For this reason, instead of exploiting the complete spectral signature for image analysis, one trend in HSI processing is the use of Dimensionality Reduction (DR) methods. These methods are devoted to reduce dimensionality of the original data while preserving the most relevant information [45]. DR methods have been extensively used for HS image processing. There are two main types of DR approaches: feature extraction and feature selection methods.
On the one hand, in feature extraction methods, a transformation is applied to the data to generate a new representation with lower dimensionality, but similar information content. The most studied DR algorithm for HSI is Principal Component Analysis (PCA). The goal of PCA [46] is searching for a linear transformation of the data by using orthogonal projections which minimize the covariance matrix of the original data. On the other hand, several data transformation approaches have been proposed for dimensionality reduction, such as wavelet transformations [47], different orthogonal projection approaches, or the exploitation of manifold embedding [48].
Nevertheless, in feature extraction methods the data are transformed, and thus the physical information about specific wavelengths is lost, which means that the provided interaction between light and tissue cannot be analyzed, which may affect certain applications. For this reason, feature selection methods are devoted to find the most relevant features from the original data by keeping the most relevant information. In the context of HSI, feature selection methods are also known as band selection methods, which also seek to identify the most relevant spectral features for a certain application. There are several types of band selection methods. In this chapter, we only describe the most prominent methods used in medical HSI. In a large-dissimilarity criteria approach, the goal is to select the most dissimilar spectral bands. Conversely, in a low-correlation criterion, the spectral bands showing low correlation between each other are selected. An example of this kind of algorithm is Maximum Relevance Minimum Redundancy (mRMR). In search-based methods, the band selection is performed by solving an optimization problem driven by a given optimization function. These algorithms search for the best bands to solve such optimization problem. Some search-based methods used in HSI are Genetic Algorithm (GA) [49] or Particle Swarm Optimization (PSO) [50]. Further details about more sophisticated band selection techniques can be found in [51].
In the context of medical HSI, feature extraction methods are used both as standalone methods and as a preprocessing stage before further data analysis. The former approach is to enhance the visualization of data, while the latter reduces the complexity of the data for being processed by other machine learning approaches. As an example of the direct application of PCA for tissue visualization enhancement, Zuzak
The use of band selection methods for medical HSI applications is not as extended as in other fields, such as remote sensing. However, some researchers have successfully exploited different band selection methods in HSI. Goto
Both feature selection and feature extraction methods aim to reduce the dimensionality of HS data while retaining the most important information. Successful application of these techniques leads to reduced computational time, which is required in applications such as surgical guidance. Nevertheless, for biomedical HS applications, there are some relevant advantages of using band selection methods instead of feature extraction methods. The first advantage is that the information about the concrete wavelengths that are used is retained. This fact allows further analysis about the physical response of different tissues to specific wavelengths. The second advantage of band selection methods is the possibility of developing custom HS cameras which only captures the most relevant spectral channels for a given application. Such reduced-band cameras would be able to acquire HS video, which would be also convenient for some surgical guidance applications.
Although the aforementioned data processing methods rely on the spectral information, a HS cube is a 3D data structure containing both the spatial and the spectral information of a scene. In a recent review manuscript, He
In [65], the authors proposed a classification of spatial-spectral approaches in three main types, depending on how the spatial information is integrated in the processing framework. In pre-processing approaches, spatial and spectral features are extracted from the HS cube, and then such features are used for the classification. In integrated classification, both spatial and spectral features are used to train the classifier. Finally, in post-processing approaches, the spatial information is employed to refine the results of a pixel-wise processing of the HS cube.
In the context of medical HSI processing, most of the spatial-spectral approaches have been focused in pre-processing and post-processing schemes. Some pre-processing approaches are the following. In leukemia detection in blood smear slides, Wang
Deep Learning is a family of machine learning algorithms that learn abstract features to best represent and make predictions about new data that is presented. More specifically, neural networks (NNs) consist of consecutive layers of neurons that have non-linear activations that connect the input data, extract features, and connect to logical outputs representing the classes of labels to provide prediction probabilities. Neural networks can have various dimensionalities, which largely depends on the size and dimensions of the input data. For example, utilizing only spectral signature information, a 1-D NN can extract features with fully-connected layers or 1-D convolutions. However, HS cameras acquire spatial information and spectral signatures simultaneously. Therefore, to exploit both sets of features, pseudo 3-D HS data can be input directly into a 2D-CNN and extract spatial features with learned convolutional kernels in the spatial domain, and these filters are connected across the entire spectral domain of the HS data. Lastly, 3D-CNN can utilize the full pseudo 3D HS data as input and extract spatial-spectral features with 3D convolutional kernels. There are numerous approaches, but these methods require more computational processing as more features and dimensions are involved.
The most widely used approach is 2D-CNNs. Aggressive brain tumors, such as glioblastoma, often require surgical resection for treatment, and surgeons often implement multiple imaging modalities, including fluorescence, to aid in this very challenging task. In a pilot study to aid brain surgeons with label-free HSI, Fabelo
Schematic diagram of the modified inception v4 CNN architecture. The CNN was customized to operate on the 25 × 25 × 91 patch-size selected. The receptive field size and number of convolutional filters is shown at bottom of each inception block. The convolutional kernel size used for convolutions is shown in italics inside each convolution box. Squeeze-and-excitation modules were added to the CNN to increase performance [
Another desired application of DL for HSI is semantic segmentation, which allows the entire scene to be classified altogether from spectral-spatial features in the entire scene. Semantic segmentation does not require image reconstruction like patch-based 2D-CNN approaches. The most commonly used method is the U-Net, as first used in HSI by Trajanovski
More recently, several modern DL approaches with origins in computer-vision have been applied to medical HSI experimentally. In [76], a generative adversarial network (GAN) was applied to use DL to learn the association of RGB images and HS images to learn the ability to generate HS digital histology images from standard RGB digital histology images of breast cancer. Another modern approach is long-short-term-memory (LSTM) and recurrent neural networks (RNN) which can utilize spatial-spectral and time-based inputs to operate in real-time video approaches. In [77], RNNs are compared to and outperform 2D- and 3D-CNN methods for in-vivo cancer detection with the goal of real-time video endoscopy.
The use of DL for HS processing is currently a hot topic in the research community in different fields. The main advantage of DL approaches in HSI is their capability to exploit jointly the spatial and the spectral information for image processing tasks. Currently, researchers are experimenting with different DL architectures in order to find the most appropriate DL model for HSI [78]. In the context of medical HSI, the use of DL in medical HS have shown good performance in different applications, but its usage is still limited compared to other ML approaches. The main reason is the limited number of data due to the novelty of the technology. More publicly available datasets with a large number of patients are required in order to definitively establish an adequate comparative of DL and traditional ML techniques.
In this book chapter, we provide a survey about the most common processing frameworks for information extraction in medical HSI. First, we show the main motivations on the usage of HS technology for biomedical data: the interaction between the light and tissue provides useful information for diagnostic applications. Second, we survey the most common approaches for HSI processing in the medical field: inverse optical modeling and machine learning approaches.
Within the ML approaches, we show there is a big variety in the methods which are used, mainly in two different types: traditional machine learning approaches based handcrafted features and recent DL techniques. Even within each subfield, the variety of options to extract information in medical HSI is still wide.
In Table 1 we provide a summary of the applications of the different methods which have been described in this book chapter. Such table relates the main information extraction methods and the biomedical applications of HSI. Further literature revision about the different biomedical HSI applications are out of the scope of this chapter. However, we recommend readers who are interested in further information about the usage of HSI for different biomedical applications to refer the different literature reviews in this context mentioned in the introduction section.
Information extraction method | Algorithm | Application | Ref. | |
---|---|---|---|---|
Optical inverse modeling | Light transport models and Monte Carlo Simulations | Cholesterol identification in skin | [22] | |
Arthritis identification in skin | [23] | |||
Blood volume fraction estimation in colon cancer samples | [24] | |||
Feature learning | Pixel-wise classification | SVM | Intestinal ischemia identification | [36] |
Gastric cancer detection | [37] | |||
Prostate cancer | [38] | |||
Tongue cancer | [39] | |||
Skin cancer | [40] | |||
RF | In-vivo oral cancer | [41] | ||
MLR | Ulcerative colitis in histological slides | [42] | ||
SVM, RF | Brain cancer in histological slides | [43] | ||
SVM, RF, LDA | Head and neck tumor | [44] | ||
Feature extraction and feature selection | PCA | Biliary trees visualization enhancement | [52] | |
PCA and false color | Melanocytic lesions visualization | [53] | ||
PCA and supervised classification | Detection of in-vivo oral cancer | [54] | ||
Prostate cancer in histological slides | [55] | |||
The identification of white blood cells in blood smear slides | [56] | |||
Intraoperative brain tumor delineation | [57] | |||
Orthogonal projections | Retina analysis for Alzheimer’s detection | [58] | ||
t-SNE and supervised classification | In-vivo brain tumor detection | [59] | ||
Wavelet transformation and supervised classification | Prostate cancer in mice models | [60] | ||
Fourier series and supervised classification | Breast cancer detection | [61] | ||
Band selection with Mahalanobis distance | Gastric cancer identification | [62] | ||
Band selection with mRMR | Ex-vivo breast cancer detection | [61] | ||
In-vivo head and neck cancer | [63] | |||
Band selection with optimization techniques | In-vivo brain tumor detection ‡ | [64] | ||
Spatial-spectral classification | Spatial and spectral features in supervised classification | Leukemia detection in blood smear | [66] | |
Red blood cell counting | [67] | |||
Superpixel segmentation and supervised classification | Brain tumor detection in histological slides | [68] | ||
Supervised classification and K-NN spatial filtering | In-vivo brain tumor detection‡ | [27] | ||
Deep learning | 2D-CNN and 1D-DNN | In-vivo brain tumor detection‡ | [69] | |
2D-CNN (Inception v4) | Head and neck cancer | [71] | ||
Salivary gland cancer | [72] | |||
2D-CNN and 3D-CNN | Head and neck cancer | [73] | ||
2D-CNN (U-Net) | Tongue cancer detection | [74] | ||
Breast cancer | [75] | |||
GAN | HS image generation from RGB | [76] | ||
RNNs, 2D-CNN and 3D-CNN | Head and neck cancer detection | [77] |
Summary of information extraction methods for medical HSI.
Publicly available datasets are marked with ‡.
The main challenge in HS medical image processing is to determine which processing framework is the most appropriate for clinical applications. Nowadays, the current trend for researchers working with medical HS data is to collect their own data, and then propose a processing framework to address a certain problem. Normally such processing frameworks are customized for their particular applications. In order to reach an agreement by the research community on the most successful information extraction methods for HSI, there is the need of further investigations with comparisons among the most promising processing approaches. To this end, the availability of large public datasets would help. However, although there is no general processing framework, the different information extraction techniques together with HS medical data have demonstrated several advantages for biomedical applications.
This research was supported in part by the Canary Islands Government through the ACIISI (Canarian Agency for Research, Innovation and the Information Society), ITHACA project under Grant Agreement ProID2017010164 and by the Spanish Government through PLATINO project (TEC2017-86722-C4-4-R). This research was supported in part by the Cancer Prevention and Research Institute of Texas (CPRIT) grant RP190588 and the U.S. National Institutes of Health (NIH) grants (R01CA156775, R01CA204254, R01HL140325, and R21CA231911). This work was completed while Samuel Ortega was beneficiary of a pre-doctoral grant given by the “
The authors declare that there are no conflicts of interest related to this chapter.
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Her research interest is radiology and neuroscience technology and application. She had been trained as an imaging scientist at several prestigious institutes including Columbia University, the University of Pennsylvania, and the National Institutes of Health (NIH). Her research focuses on multi-modal neuroimaging integration such as MRI/PET and EEG/MEG instrumentation to make the best use of multiple modalities for better interpretation of underlying disease mechanisms. She is the author and editor of more than twelve books for well-known publishers including IntechOpen and Nova Science. She has published more than 100 papers and abstracts in many reputed international journals and conferences and served as reviewer and editor for several academic associations.",institutionString:"University of Southern California",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"University of Southern California",institutionURL:null,country:{name:"United States of America"}}},equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9629",title:"Electroencephalography",subtitle:"From Basic Research to Clinical Applications",isOpenForSubmission:!1,hash:"8147834b6c6deeeec40f407c71ad60b4",slug:"electroencephalography-from-basic-research-to-clinical-applications",bookSignature:"Hideki Nakano",coverURL:"https://cdn.intechopen.com/books/images_new/9629.jpg",editedByType:"Edited 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by"}}],booksByTopicTotal:65,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"46296",doi:"10.5772/57398",title:"Physiological Role of Amyloid Beta in Neural Cells: The Cellular Trophic Activity",slug:"physiological-role-of-amyloid-beta-in-neural-cells-the-cellular-trophic-activity",totalDownloads:5886,totalCrossrefCites:18,totalDimensionsCites:31,abstract:null,book:{id:"3846",slug:"neurochemistry",title:"Neurochemistry",fullTitle:"Neurochemistry"},signatures:"M. del C. Cárdenas-Aguayo, M. del C. Silva-Lucero, M. Cortes-Ortiz,\nB. Jiménez-Ramos, L. Gómez-Virgilio, G. Ramírez-Rodríguez, E. Vera-\nArroyo, R. Fiorentino-Pérez, U. García, J. Luna-Muñoz and M.A.\nMeraz-Ríos",authors:[{id:"42225",title:"Dr.",name:"Jose",middleName:null,surname:"Luna-Muñoz",slug:"jose-luna-munoz",fullName:"Jose Luna-Muñoz"},{id:"114746",title:"Dr.",name:"Marco",middleName:null,surname:"Meraz-Ríos",slug:"marco-meraz-rios",fullName:"Marco Meraz-Ríos"},{id:"169616",title:"Dr.",name:"Maria del Carmen",middleName:null,surname:"Cardenas-Aguayo",slug:"maria-del-carmen-cardenas-aguayo",fullName:"Maria del Carmen Cardenas-Aguayo"},{id:"169857",title:"Dr.",name:"Maria del Carmen",middleName:null,surname:"Silva-Lucero",slug:"maria-del-carmen-silva-lucero",fullName:"Maria del Carmen Silva-Lucero"},{id:"169858",title:"Dr.",name:"Maribel",middleName:null,surname:"Cortes-Ortiz",slug:"maribel-cortes-ortiz",fullName:"Maribel Cortes-Ortiz"},{id:"169859",title:"Dr.",name:"Berenice",middleName:null,surname:"Jimenez-Ramos",slug:"berenice-jimenez-ramos",fullName:"Berenice Jimenez-Ramos"},{id:"169860",title:"Dr.",name:"Laura",middleName:null,surname:"Gomez-Virgilio",slug:"laura-gomez-virgilio",fullName:"Laura Gomez-Virgilio"},{id:"169861",title:"Dr.",name:"Gerardo",middleName:null,surname:"Ramirez-Rodriguez",slug:"gerardo-ramirez-rodriguez",fullName:"Gerardo Ramirez-Rodriguez"},{id:"169862",title:"Dr.",name:"Eduardo",middleName:null,surname:"Vera-Arroyo",slug:"eduardo-vera-arroyo",fullName:"Eduardo Vera-Arroyo"},{id:"169863",title:"Dr.",name:"Rosana Sofia",middleName:null,surname:"Fiorentino-Perez",slug:"rosana-sofia-fiorentino-perez",fullName:"Rosana Sofia Fiorentino-Perez"},{id:"169864",title:"Dr.",name:"Ubaldo",middleName:null,surname:"Garcia",slug:"ubaldo-garcia",fullName:"Ubaldo Garcia"}]},{id:"58070",doi:"10.5772/intechopen.72427",title:"MRI Medical Image Denoising by Fundamental Filters",slug:"mri-medical-image-denoising-by-fundamental-filters",totalDownloads:2564,totalCrossrefCites:17,totalDimensionsCites:30,abstract:"Nowadays Medical imaging technique Magnetic Resonance Imaging (MRI) plays an important role in medical setting to form high standard images contained in the human brain. MRI is commonly used once treating brain, prostate cancers, ankle and foot. The Magnetic Resonance Imaging (MRI) images are usually liable to suffer from noises such as Gaussian noise, salt and pepper noise and speckle noise. So getting of brain image with accuracy is very extremely task. An accurate brain image is very necessary for further diagnosis process. During this chapter, a median filter algorithm will be modified. Gaussian noise and Salt and pepper noise will be added to MRI image. A proposed Median filter (MF), Adaptive Median filter (AMF) and Adaptive Wiener filter (AWF) will be implemented. The filters will be used to remove the additive noises present in the MRI images. The noise density will be added gradually to MRI image to compare performance of the filters evaluation. The performance of these filters will be compared exploitation the applied mathematics parameter Peak Signal-to-Noise Ratio (PSNR).",book:{id:"6144",slug:"high-resolution-neuroimaging-basic-physical-principles-and-clinical-applications",title:"High-Resolution Neuroimaging",fullTitle:"High-Resolution Neuroimaging - Basic Physical Principles and Clinical Applications"},signatures:"Hanafy M. Ali",authors:[{id:"213318",title:"Dr.",name:"Hanafy",middleName:"M.",surname:"Ali",slug:"hanafy-ali",fullName:"Hanafy Ali"}]},{id:"41589",doi:"10.5772/50323",title:"The Role of the Amygdala in Anxiety Disorders",slug:"the-role-of-the-amygdala-in-anxiety-disorders",totalDownloads:9671,totalCrossrefCites:4,totalDimensionsCites:28,abstract:null,book:{id:"2599",slug:"the-amygdala-a-discrete-multitasking-manager",title:"The Amygdala",fullTitle:"The Amygdala - A Discrete Multitasking Manager"},signatures:"Gina L. Forster, Andrew M. Novick, Jamie L. Scholl and Michael J. Watt",authors:[{id:"145620",title:"Dr.",name:"Gina",middleName:null,surname:"Forster",slug:"gina-forster",fullName:"Gina Forster"},{id:"146553",title:"BSc.",name:"Andrew",middleName:null,surname:"Novick",slug:"andrew-novick",fullName:"Andrew Novick"},{id:"146554",title:"MSc.",name:"Jamie",middleName:null,surname:"Scholl",slug:"jamie-scholl",fullName:"Jamie Scholl"},{id:"146555",title:"Dr.",name:"Michael",middleName:null,surname:"Watt",slug:"michael-watt",fullName:"Michael Watt"}]},{id:"26258",doi:"10.5772/28300",title:"Excitotoxicity and Oxidative Stress in Acute Ischemic Stroke",slug:"excitotoxicity-and-oxidative-stress-in-acute-ischemic-stroke",totalDownloads:7157,totalCrossrefCites:6,totalDimensionsCites:25,abstract:null,book:{id:"931",slug:"acute-ischemic-stroke",title:"Acute Ischemic Stroke",fullTitle:"Acute Ischemic Stroke"},signatures:"Ramón Rama Bretón and Julio César García Rodríguez",authors:[{id:"73430",title:"Prof.",name:"Ramon",middleName:null,surname:"Rama",slug:"ramon-rama",fullName:"Ramon Rama"},{id:"124643",title:"Prof.",name:"Julio Cesar",middleName:null,surname:"García",slug:"julio-cesar-garcia",fullName:"Julio Cesar García"}]},{id:"62072",doi:"10.5772/intechopen.78695",title:"Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment",slug:"brain-computer-interface-and-motor-imagery-training-the-role-of-visual-feedback-and-embodiment",totalDownloads:1439,totalCrossrefCites:13,totalDimensionsCites:23,abstract:"Controlling a brain-computer interface (BCI) is a difficult task that requires extensive training. Particularly in the case of motor imagery BCIs, users may need several training sessions before they learn how to generate desired brain activity and reach an acceptable performance. A typical training protocol for such BCIs includes execution of a motor imagery task by the user, followed by presentation of an extending bar or a moving object on a computer screen. In this chapter, we discuss the importance of a visual feedback that resembles human actions, the effect of human factors such as confidence and motivation, and the role of embodiment in the learning process of a motor imagery task. Our results from a series of experiments in which users BCI-operated a humanlike android robot confirm that realistic visual feedback can induce a sense of embodiment, which promotes a significant learning of the motor imagery task in a short amount of time. We review the impact of humanlike visual feedback in optimized modulation of brain activity by the BCI users.",book:{id:"6610",slug:"evolving-bci-therapy-engaging-brain-state-dynamics",title:"Evolving BCI Therapy",fullTitle:"Evolving BCI Therapy - Engaging Brain State Dynamics"},signatures:"Maryam Alimardani, Shuichi Nishio and Hiroshi Ishiguro",authors:[{id:"11981",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Ishiguro",slug:"hiroshi-ishiguro",fullName:"Hiroshi Ishiguro"},{id:"231131",title:"Dr.",name:"Maryam",middleName:null,surname:"Alimardani",slug:"maryam-alimardani",fullName:"Maryam Alimardani"},{id:"231134",title:"Dr.",name:"Shuichi",middleName:null,surname:"Nishio",slug:"shuichi-nishio",fullName:"Shuichi Nishio"}]}],mostDownloadedChaptersLast30Days:[{id:"29764",title:"Underlying Causes of Paresthesia",slug:"underlying-causes-of-paresthesia",totalDownloads:192666,totalCrossrefCites:3,totalDimensionsCites:7,abstract:null,book:{id:"1069",slug:"paresthesia",title:"Paresthesia",fullTitle:"Paresthesia"},signatures:"Mahdi Sharif-Alhoseini, Vafa Rahimi-Movaghar and Alexander R. Vaccaro",authors:[{id:"91165",title:"Prof.",name:"Vafa",middleName:null,surname:"Rahimi-Movaghar",slug:"vafa-rahimi-movaghar",fullName:"Vafa Rahimi-Movaghar"}]},{id:"63258",title:"Anatomy and Function of the Hypothalamus",slug:"anatomy-and-function-of-the-hypothalamus",totalDownloads:4558,totalCrossrefCites:6,totalDimensionsCites:12,abstract:"The hypothalamus is a small but important area of the brain formed by various nucleus and nervous fibers. Through its neuronal connections, it is involved in many complex functions of the organism such as vegetative system control, homeostasis of the organism, thermoregulation, and also in adjusting the emotional behavior. The hypothalamus is involved in different daily activities like eating or drinking, in the control of the body’s temperature and energy maintenance, and in the process of memorizing. It also modulates the endocrine system through its connections with the pituitary gland. Precise anatomical description along with a correct characterization of the component structures is essential for understanding its functions.",book:{id:"6331",slug:"hypothalamus-in-health-and-diseases",title:"Hypothalamus in Health and Diseases",fullTitle:"Hypothalamus in Health and Diseases"},signatures:"Miana Gabriela Pop, Carmen Crivii and Iulian Opincariu",authors:null},{id:"57103",title:"GABA and Glutamate: Their Transmitter Role in the CNS and Pancreatic Islets",slug:"gaba-and-glutamate-their-transmitter-role-in-the-cns-and-pancreatic-islets",totalDownloads:3478,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"Glutamate and gamma-aminobutyric acid (GABA) are the major neurotransmitters in the mammalian brain. Inhibitory GABA and excitatory glutamate work together to control many processes, including the brain’s overall level of excitation. The contributions of GABA and glutamate in extra-neuronal signaling are by far less widely recognized. In this chapter, we first discuss the role of both neurotransmitters during development, emphasizing the importance of the shift from excitatory to inhibitory GABAergic neurotransmission. The second part summarizes the biosynthesis and role of GABA and glutamate in neurotransmission in the mature brain, and major neurological disorders associated with glutamate and GABA receptors and GABA release mechanisms. The final part focuses on extra-neuronal glutamatergic and GABAergic signaling in pancreatic islets of Langerhans, and possible associations with type 1 diabetes mellitus.",book:{id:"6237",slug:"gaba-and-glutamate-new-developments-in-neurotransmission-research",title:"GABA And Glutamate",fullTitle:"GABA And Glutamate - New Developments In Neurotransmission Research"},signatures:"Christiane S. Hampe, Hiroshi Mitoma and Mario Manto",authors:[{id:"210220",title:"Prof.",name:"Christiane",middleName:null,surname:"Hampe",slug:"christiane-hampe",fullName:"Christiane Hampe"},{id:"210485",title:"Prof.",name:"Mario",middleName:null,surname:"Manto",slug:"mario-manto",fullName:"Mario Manto"},{id:"210486",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Mitoma",slug:"hiroshi-mitoma",fullName:"Hiroshi Mitoma"}]},{id:"35802",title:"Cross-Cultural/Linguistic Differences in the Prevalence of Developmental Dyslexia and the Hypothesis of Granularity and Transparency",slug:"cross-cultural-linguistic-differences-in-the-prevalence-of-developmental-dyslexia-and-the-hypothesis",totalDownloads:3601,totalCrossrefCites:2,totalDimensionsCites:7,abstract:null,book:{id:"673",slug:"dyslexia-a-comprehensive-and-international-approach",title:"Dyslexia",fullTitle:"Dyslexia - A Comprehensive and International Approach"},signatures:"Taeko N. Wydell",authors:[{id:"87489",title:"Prof.",name:"Taeko",middleName:"N.",surname:"Wydell",slug:"taeko-wydell",fullName:"Taeko Wydell"}]},{id:"58597",title:"Testosterone and Erectile Function: A Review of Evidence from Basic Research",slug:"testosterone-and-erectile-function-a-review-of-evidence-from-basic-research",totalDownloads:1331,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Androgens are essential for male physical activity and normal erectile function. Hence, age-related testosterone deficiency, known as late-onset hypogonadism (LOH), is considered a risk factor for erectile dysfunction (ED). This chapter summarizes relevant basic research reports examining the effects of testosterone on erectile function. Testosterone affects several organs and is especially active on the erectile tissue. The mechanism of testosterone deficiency effects on erectile function and the results of testosterone replacement therapy (TRT) have been well studied. Testosterone affects nitric oxide (NO) production and phosphodiesterase type 5 (PDE-5) expression in the corpus cavernosum through molecular pathways, preserves smooth muscle contractility by regulating both contraction and relaxation, and maintains the structure of the corpus cavernosum. Interestingly, testosterone deficiency has relationship to neurological diseases, which leads to ED. Testosterone replacement therapy is widely used to treat patients with testosterone deficiency; however, this treatment might also induce some problems. Basic research suggests that PDE-5 inhibitors, L-citrulline, and/or resveratrol therapy might be effective therapeutic options for testosterone deficiency-induced ED. Future research should confirm these findings through more specific experiments using molecular tools and may shed more light on endocrine-related ED and its possible treatments.",book:{id:"5994",slug:"sex-hormones-in-neurodegenerative-processes-and-diseases",title:"Sex Hormones in Neurodegenerative Processes and Diseases",fullTitle:"Sex Hormones in Neurodegenerative Processes and Diseases"},signatures:"Tomoya Kataoka and Kazunori Kimura",authors:[{id:"219042",title:"Ph.D.",name:"Tomoya",middleName:null,surname:"Kataoka",slug:"tomoya-kataoka",fullName:"Tomoya Kataoka"},{id:"229066",title:"Prof.",name:"Kazunori",middleName:null,surname:"Kimura",slug:"kazunori-kimura",fullName:"Kazunori Kimura"}]}],onlineFirstChaptersFilter:{topicId:"18",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81646",title:"Cortical Plasticity under Ketamine: From Synapse to Map",slug:"cortical-plasticity-under-ketamine-from-synapse-to-map",totalDownloads:15,totalDimensionsCites:0,doi:"10.5772/intechopen.104787",abstract:"Sensory systems need to process signals in a highly dynamic way to efficiently respond to variations in the animal’s environment. For instance, several studies showed that the visual system is subject to neuroplasticity since the neurons’ firing changes according to stimulus properties. This dynamic information processing might be supported by a network reorganization. Since antidepressants influence neurotransmission, they can be used to explore synaptic plasticity sustaining cortical map reorganization. To this goal, we investigated in the primary visual cortex (V1 of mouse and cat), the impact of ketamine on neuroplasticity through changes in neuronal orientation selectivity and the functional connectivity between V1 cells, using cross correlation analyses. We found that ketamine affects cortical orientation selectivity and alters the functional connectivity within an assembly. These data clearly highlight the role of the antidepressant drugs in inducing or modeling short-term plasticity in V1 which suggests that cortical processing is optimized and adapted to the properties of the stimulus.",book:{id:"11374",title:"Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex",coverURL:"https://cdn.intechopen.com/books/images_new/11374.jpg"},signatures:"Ouelhazi Afef, Rudy Lussiez and Molotchnikoff Stephane"},{id:"81582",title:"The Role of Cognitive Reserve in Executive Functioning and Its Relationship to Cognitive Decline and Dementia",slug:"the-role-of-cognitive-reserve-in-executive-functioning-and-its-relationship-to-cognitive-decline-and",totalDownloads:23,totalDimensionsCites:0,doi:"10.5772/intechopen.104646",abstract:"In this chapter, we explore how cognitive reserve is implicated in coping with the negative consequences of brain pathology and age-related cognitive decline. Individual differences in cognitive performance are based on different brain mechanisms (neural reserve and neural compensation), and reflect, among others, the effect of education, occupational attainment, leisure activities, and social involvement. These cognitive reserve proxies have been extensively associated with efficient executive functioning. We discuss and focus particularly on the compensation mechanisms related to the frontal lobe and its protective role, in maintaining cognitive performance in old age or even mitigating the clinical expression of dementia.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Gabriela Álvares-Pereira, Carolina Maruta and Maria Vânia Silva-Nunes"},{id:"81488",title:"Aggression and Sexual Behavior: Overlapping or Distinct Roles of 5-HT1A and 5-HT1B Receptors",slug:"aggression-and-sexual-behavior-overlapping-or-distinct-roles-of-5-ht1a-and-5-ht1b-receptors",totalDownloads:20,totalDimensionsCites:0,doi:"10.5772/intechopen.104872",abstract:"Distinct brain mechanisms for male aggressive and sexual behavior are present in mammalian species, including man. However, recent evidence suggests a strong connection and even overlap in the central nervous system (CNS) circuitry involved in aggressive and sexual behavior. The serotonergic system in the CNS is strongly involved in male aggressive and sexual behavior. In particular, 5-HT1A and 5-HT1B receptors seem to play a critical role in the modulation of these behaviors. The present chapter focuses on the effects of 5-HT1A- and 5-HT1B-receptor ligands in male rodent aggression and sexual behavior. Results indicate that 5-HT1B-heteroreceptors play a critical role in the modulation of male offensive behavior, although a definite role of 5-HT1A-auto- or heteroreceptors cannot be ruled out. 5-HT1A receptors are clearly involved in male sexual behavior, although it has to be yet unraveled whether 5-HT1A-auto- or heteroreceptors are important. Although several key nodes in the complex circuitry of aggression and sexual behavior are known, in particular in the medial hypothalamus, a clear link or connection to these critical structures and the serotonergic key receptors is yet to be determined. This information is urgently needed to detect and develop new selective anti-aggressive (serenic) and pro-sexual drugs for human applications.",book:{id:"10195",title:"Serotonin and the CNS - New Developments in Pharmacology and Therapeutics",coverURL:"https://cdn.intechopen.com/books/images_new/10195.jpg"},signatures:"Berend Olivier and Jocelien D.A. Olivier"},{id:"81093",title:"Prehospital and Emergency Room Airway Management in Traumatic Brain Injury",slug:"prehospital-and-emergency-room-airway-management-in-traumatic-brain-injury",totalDownloads:49,totalDimensionsCites:0,doi:"10.5772/intechopen.104173",abstract:"Airway management in trauma is critical and may impact patient outcomes. Particularly in traumatic brain injury (TBI), depressed level of consciousness may be associated with compromised protective airway reflexes or apnea, which can increase the risk of aspiration or result in hypoxemia and worsen the secondary brain damage. Therefore, patients with TBI and Glasgow Coma Scale (GCS) ≤ 8 have been traditionally managed by prehospital or emergency room (ER) endotracheal intubation. However, recent evidence challenged this practice and even suggested that routine intubation may be harmful. This chapter will address the indications and optimal method of securing the airway, prehospital and in the ER, in patients with traumatic brain injury.",book:{id:"11367",title:"Traumatic Brain Injury",coverURL:"https://cdn.intechopen.com/books/images_new/11367.jpg"},signatures:"Dominik A. Jakob, Jean-Cyrille Pitteloud and Demetrios Demetriades"},{id:"81011",title:"Amino Acids as Neurotransmitters. The Balance between Excitation and Inhibition as a Background for Future Clinical Applications",slug:"amino-acids-as-neurotransmitters-the-balance-between-excitation-and-inhibition-as-a-background-for-f",totalDownloads:19,totalDimensionsCites:0,doi:"10.5772/intechopen.103760",abstract:"For more than 30 years, amino acids have been well-known (and essential) participants in neurotransmission. They act as both neuromediators and metabolites in nervous tissue. Glycine and glutamic acid (glutamate) are prominent examples. These amino acids are agonists of inhibitory and excitatory membrane receptors, respectively. Moreover, they play essential roles in metabolic pathways and energy transformation in neurons and astrocytes. Despite their obvious effects on the brain, their potential role in therapeutic methods remains uncertain in clinical practice. In the current chapter, a comparison of the crosstalk between these two systems, which are responsible for excitation and inhibition in neurons, is presented. The interactions are discussed at the metabolic, receptor, and transport levels. Reaction-diffusion and a convectional flow into the interstitial fluid create a balanced distribution of glycine and glutamate. Indeed, the neurons’ final physiological state is a result of a balance between the excitatory and inhibitory influences. However, changes to the glycine and/or glutamate pools under pathological conditions can alter the state of nervous tissue. Thus, new therapies for various diseases may be developed on the basis of amino acid medication.",book:{id:"10890",title:"Recent Advances in Neurochemistry",coverURL:"https://cdn.intechopen.com/books/images_new/10890.jpg"},signatures:"Yaroslav R. Nartsissov"},{id:"80821",title:"Neuroimmunology and Neurological Manifestations of COVID-19",slug:"neuroimmunology-and-neurological-manifestations-of-covid-19",totalDownloads:41,totalDimensionsCites:0,doi:"10.5772/intechopen.103026",abstract:"Infection with SARS-CoV-2 is causing coronavirus disease in 2019 (COVID-19). Besides respiratory symptoms due to an attack on the broncho-alveolar system, COVID-19, among others, can be accompanied by neurological symptoms because of the affection of the nervous system. These can be caused by intrusion by SARS-CoV-2 of the central nervous system (CNS) and peripheral nervous system (PNS) and direct infection of local cells. In addition, neurological deterioration mediated by molecular mimicry to virus antigens or bystander activation in the context of immunological anti-virus defense can lead to tissue damage in the CNS and PNS. In addition, cytokine storm caused by SARS-CoV-2 infection in COVID-19 can lead to nervous system related symptoms. Endotheliitis of CNS vessels can lead to vessel occlusion and stroke. COVID-19 can also result in cerebral hemorrhage and sinus thrombosis possibly related to changes in clotting behavior. Vaccination is most important to prevent COVID-19 in the nervous system. There are symptomatic or/and curative therapeutic approaches to combat COVID-19 related nervous system damage that are partly still under study.",book:{id:"10890",title:"Recent Advances in Neurochemistry",coverURL:"https://cdn.intechopen.com/books/images_new/10890.jpg"},signatures:"Robert Weissert"}],onlineFirstChaptersTotal:17},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:99,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:289,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:108,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:104,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:"2753-894X",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:12,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"May 18th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:17,paginationItems:[{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:12,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81557",title:"Object Tracking Using Adapted Optical Flow",doi:"10.5772/intechopen.102863",signatures:"Ronaldo Ferreira, Joaquim José de Castro Ferreira and António José Ribeiro Neves",slug:"object-tracking-using-adapted-optical-flow",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Information Extraction and Object Tracking in Digital Video",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",subseries:{id:"24",title:"Computer Vision"}}},{id:"81558",title:"Thresholding Image Techniques for Plant Segmentation",doi:"10.5772/intechopen.104587",signatures:"Miguel Ángel Castillo-Martínez, Francisco Javier Gallegos-Funes, Blanca E. Carvajal-Gámez, Guillermo Urriolagoitia-Sosa and Alberto J. Rosales-Silva",slug:"thresholding-image-techniques-for-plant-segmentation",totalDownloads:15,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Information Extraction and Object Tracking in Digital Video",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",subseries:{id:"24",title:"Computer Vision"}}}]},overviewPagePublishedBooks:{paginationCount:9,paginationItems:[{type:"book",id:"7723",title:"Artificial Intelligence",subtitle:"Applications in Medicine and Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7723.jpg",slug:"artificial-intelligence-applications-in-medicine-and-biology",publishedDate:"July 31st 2019",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"a3852659e727f95c98c740ed98146011",volumeInSeries:1,fullTitle:"Artificial Intelligence - Applications in Medicine and Biology",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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For example, some of the issues of interest could be the following: Advances in evolutionary computation (Genetic algorithms, Genetic programming, Bio-inspired metaheuristics, Hybrid metaheuristics, Parallel ECs); Applications of evolutionary algorithms (Machine learning and Data Mining with EAs, Search-Based Software Engineering, Scheduling, and Planning Applications, Smart Transport Applications, Applications to Games, Image Analysis, Signal Processing and Pattern Recognition, Applications to Sustainability).",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",hasOnlineFirst:!1,hasPublishedBooks:!0,annualVolume:11421,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. 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