Species list of known pollinators for global crop.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"6089",leadTitle:null,fullTitle:"Sensory Nervous System",title:"Sensory Nervous System",subtitle:null,reviewType:"peer-reviewed",abstract:"The sensory nervous system is of critical importance in our daily lives and contributes to our personal well-being and safety as well as communication with others. However, it is only when disease or injury impair its function that we fully appreciate the relevance of our sensory modalities. During the past decades, research of our senses has seen an ever-growing interest in this exciting field of study. This book provides the reader with an overview of the current state-of-the-art of research of our senses and focuses on the most important evidence-based developments in this area. This book addresses both the physiology and pathophysiology of our sensory nervous system ranging from molecular, cellular, and systems to cognitive and behavioral topics. Individual chapters focus on recent advances in specific areas of sensory systems in different model organisms and humans. All chapters represent recent contributions to the rapidly developing field of sensory science.",isbn:"978-1-78923-359-9",printIsbn:"978-1-78923-358-2",pdfIsbn:"978-1-83881-284-3",doi:"10.5772/68128",price:119,priceEur:129,priceUsd:155,slug:"sensory-nervous-system",numberOfPages:170,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"87928c04aaa3d4dc4117bd9bb6b599e7",bookSignature:"Thomas Heinbockel",publishedDate:"July 18th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6089.jpg",numberOfDownloads:7661,numberOfWosCitations:13,numberOfCrossrefCitations:13,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:24,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:50,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 5th 2017",dateEndSecondStepPublish:"July 26th 2017",dateEndThirdStepPublish:"October 22nd 2017",dateEndFourthStepPublish:"January 20th 2018",dateEndFifthStepPublish:"March 21st 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"70569",title:"Dr.",name:"Thomas",middleName:null,surname:"Heinbockel",slug:"thomas-heinbockel",fullName:"Thomas Heinbockel",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRm0fQAC/Profile_Picture_2022-08-12T10:38:02.jpg",biography:"Thomas Heinbockel, Ph.D., is a professor and interim chair, Department of Anatomy, Howard University College of Medicine, Washington, DC. He holds an adjunct faculty position in both the Department of Anatomy & Neurobiology and the Department of Physiology, University of Maryland School of Medicine. Dr. Heinbockel studied biology at the Philipps-University, Germany. His studies of the brain started during his MS thesis work at the Max Planck Institute for Behavioral Physiology, Germany. Dr. Heinbockel earned a Ph.D. in Neuroscience at the University of Arizona, USA. After graduating, he worked as a research associate at the Institute of Physiology, Otto-von-Guericke University, Germany. Dr. Heinbockelʿs research is focused on understanding how the brain processes information as it relates to neurological and psychiatric disorders. His laboratory at Howard University concentrates on foundational and translational topics such as drug development, organization of the olfactory and limbic systems, and neural signaling and synaptic transmission in the central nervous system.",institutionString:null,position:null,outsideEditionCount:null,totalCites:0,totalAuthoredChapters:"10",totalChapterViews:"0",totalEditedBooks:"8",institution:{name:"Howard University",institutionURL:null,country:{name:"United States of America"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"216",title:"Neuropsychology",slug:"life-sciences-neuroscience-neuropsychology"}],chapters:[{id:"61854",title:"Introductory Chapter: Organization and Function of Sensory Nervous Systems",doi:"10.5772/intechopen.78738",slug:"introductory-chapter-organization-and-function-of-sensory-nervous-systems",totalDownloads:1543,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:null,signatures:"Thomas Heinbockel",downloadPdfUrl:"/chapter/pdf-download/61854",previewPdfUrl:"/chapter/pdf-preview/61854",authors:[{id:"70569",title:"Dr.",name:"Thomas",surname:"Heinbockel",slug:"thomas-heinbockel",fullName:"Thomas Heinbockel"}],corrections:null},{id:"59650",title:"Long-Distance Modulation of Sensory Encoding via Axonal Neuromodulation",doi:"10.5772/intechopen.74647",slug:"long-distance-modulation-of-sensory-encoding-via-axonal-neuromodulation",totalDownloads:1140,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:1,abstract:"The neuromodulatory system plays a critical role in sensorimotor system function and animal behavior. Its influence on axons, however, remains enigmatic although axons possess receptors for a plethora of modulators, and pathologies of the neuromodulatory system impair neuronal communication. The most dramatic neuromodulatory effect on axons is ectopic spiking, a process common to many systems and neurons during which action potentials are elicited in the axon trunk and travel antidromically towards the site of sensory transduction. We argue that ectopic action potentials modify sensory encoding by invading the primary spike initiation zone in the periphery. This is a particularly intriguing concept, since it allows the modulatory system to alter sensory information processing. We demonstrate that aminergic modulation of a proprioceptive axon that elicits spontaneous ectopic action potentials changes spike frequency, which determines the burst behavior of the proprioceptor. Increasing ectopic spike frequency delayed the peripheral burst, caused reductions in spike number and burst duration, and changes in sensory firing frequency. Computational models show these effects depend on slow ionic conductances to modulate membrane excitability. Thus, axonal neuromodulation provides a means to rapidly influence sensory encoding without directly or locally affecting the sites of stimulus reception and spike initiation.",signatures:"Margaret L. DeMaegd and Wolfgang Stein",downloadPdfUrl:"/chapter/pdf-download/59650",previewPdfUrl:"/chapter/pdf-preview/59650",authors:[{id:"219774",title:"Prof.",name:"Wolfgang",surname:"Stein",slug:"wolfgang-stein",fullName:"Wolfgang Stein"},{id:"219775",title:"BSc.",name:"Margaret",surname:"DeMaegd",slug:"margaret-demaegd",fullName:"Margaret DeMaegd"}],corrections:null},{id:"60029",title:"Are Sensory Neurons in the Cortex Committed to Original Trigger Features?",doi:"10.5772/intechopen.74776",slug:"are-sensory-neurons-in-the-cortex-committed-to-original-trigger-features-",totalDownloads:1031,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Sensory cortices are inherently dynamic and exhibit plasticity in response to a variety of stimuli. Few studies have revealed that depending upon the nature of stimuli, excitation of the corresponding sensory region also evokes a response from other neighboring connected areas. It is even more striking, when somatosensory areas undergo reorganization as a result of an intentional disturbance and further explored as a paradigm to understand neuroplasticity. In addition, it has also been proved that drugs too can be used as a model to explore potential plasticity in sensory systems. To this aim, through electrophysiology in cats, we explored that visual neurons, throughout the cortical column, have a tendency to alter their inherent properties even when presented a non-visual stimulus. Furthermore, it was explored in mice, how the application of drugs (serotonin and ketamine) modulates potential plasticity within the visual system. Indeed, we found a shift in orientation tuning of neurons indicated by Gaussian tuning fits in both scenarios. These results together suggest that sensory cortices are capable of adapting to intense experiences by going through a recalibration of corresponding or neighboring sensory area(s) to redirect the sensory function and exhibit remarkable extent of neuroplasticity within the brain.",signatures:"Nayan Chanauria, Rudy Lussiez, Afef Ouelhazi and Stephane\nMolotchnikoff",downloadPdfUrl:"/chapter/pdf-download/60029",previewPdfUrl:"/chapter/pdf-preview/60029",authors:[{id:"145800",title:"Prof.",name:"Stephane",surname:"Molotchnikoff",slug:"stephane-molotchnikoff",fullName:"Stephane Molotchnikoff"},{id:"218367",title:"Ms.",name:"Nayan",surname:"Chanauria",slug:"nayan-chanauria",fullName:"Nayan Chanauria"},{id:"218513",title:"B.Sc.",name:"Rudy",surname:"Lussiez",slug:"rudy-lussiez",fullName:"Rudy Lussiez"},{id:"218514",title:"Ms.",name:"Afef",surname:"Ouelhazi",slug:"afef-ouelhazi",fullName:"Afef Ouelhazi"}],corrections:null},{id:"59930",title:"Olfaction, among the First Senses to Develop and Decline",doi:"10.5772/intechopen.75061",slug:"olfaction-among-the-first-senses-to-develop-and-decline",totalDownloads:1448,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:0,abstract:"Olfaction is one of the most conserved senses across species. It plays a crucial role in animals’ and humans’ life by influencing food intake, reproduction and social behavior. The olfactory system is composed of a peripheral neuroepithelium and a central olfactory nerve and is one of the few central nervous system (CNS) structures with direct access to the external environment without passage through the Blood Brain Barrier (BBB). This makes this nerve system of importance for understanding how exogenous stimuli may contribute to neuronal damage as well as for diagnostic and therapeutic purposes. Interestingly, olfactory activity physiologically declines with aging, but its alteration can be further impaired by various neurological conditions. For example, in progressive neurodegenerative disorders, such as Alzheimer’s disease (AD), olfaction is the first sense to be impaired before the onset of cognitive symptoms, suggesting that olfactory transmission may characterize early neural network imbalances. In this work, we will explore the main olfactory anatomical structures, the cytoarchitecture, the neurogenesis, several pathological conditions characterized by olfactory deficit and the potential use of this sense to diagnose and treat CNS pathologies.",signatures:"Emanuele Brai and Lavinia Alberi",downloadPdfUrl:"/chapter/pdf-download/59930",previewPdfUrl:"/chapter/pdf-preview/59930",authors:[{id:"208208",title:"Dr.",name:"Lavinia",surname:"Alberi",slug:"lavinia-alberi",fullName:"Lavinia Alberi"},{id:"208235",title:"Dr.",name:"Emanuele",surname:"Brai",slug:"emanuele-brai",fullName:"Emanuele Brai"}],corrections:null},{id:"59768",title:"Retinal Topographic Maps: A Glimpse into the Animals’ Visual World",doi:"10.5772/intechopen.74645",slug:"retinal-topographic-maps-a-glimpse-into-the-animals-visual-world",totalDownloads:1455,totalCrossrefCites:8,totalDimensionsCites:15,hasAltmetrics:0,abstract:"The vertebrates’ retina has a highly conserved laminar organization of 10 alternating nuclear and plexiform layers. Species differences in the retinal specializations, i.e., areas of higher cell density, among the species, represent specific regions of the visual field of higher importance for a better spatial resolution and indicate distinct evolutionary pressures on the structures of the visual system, which can be related to many aspects of the species evolutionary history. In this chapter, we analyzed the density and distribution of cells of the retinal ganglion cell layer (GCL) and estimated the upper limits of the spatial resolving power of 12 species of snakes from the Colubridae family, 6 diurnal and 6 nocturnal, which inhabit different habitats. Our results revealed lower visual acuity in nocturnal species, compared to diurnal, and we observed different types of retinal specialization, horizontal streak, area centralis, or scattered distribution, with higher cell density in different retinal regions, depending on the species. These variations may be related to ecological and behavioral features, such as daily activity pattern, habitat, and substrate preferentially occupied, hunting strategies and diet. This comparative study indicates the complexity of the adaptive strategies of the snakes’ visual system.",signatures:"Einat Hauzman, Daniela M.O. Bonci and Dora F. 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An individual CFM is composed of two orthogonal topographical representations, reflecting two essential aspects of a sensory feature space. Each CFM is thought to subserve a specific computation or set of computations that underlie particular perceptual behaviors by enabling the comparison and combination of the information carried by the various specialized neuronal populations within this cortical region. Multiple adjacent CFMs, in turn, have now been shown by multiple laboratories to be organized in visual and auditory cortex into a macrostructural pattern called the cloverleaf cluster. CFMs within cloverleaf clusters tend to share properties such as receptive field distribution, cortical magnification, and processing specialization. This chapter will review the evidence for CFM and cloverleaf cluster organization across human visual and auditory cortex and will discuss the utility of these measurements for determining cortical structure and function and for investigating what changes occur in sensory cortex following various types of trauma or disease.",signatures:"Alyssa A. 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He was an ENT registrar at the Royal Infirmary, Middlesbrough, United Kingdom in 1993 and subsequently a JW Fulbright scholar at the University of Pittsburgh, USA in 1997. During his Fulbright experience, he also worked at the Hospital of University of Pennsylvania (HUP), Philadelphia and St Joseph’s Hospital, Chicago, USA with sub-specialty interest in rhinology and aesthetic nasal surgery. Dr BS Gendeh retired after 38 years government service as a consultant ENT surgeon at the National University of Malaysia Medical Centre (UKMMC) in 2014, and presently is a Visiting Professor at the Department of Otorhinolaryngology-Head and Neck Surgery at UKMMC and is a resident ENT consultant at Pantai Hospital Kuala Lumpur since 2014. Is an executive member of numerous National and International bodies including Board Chairman of Malaysian American Commission on Educational Exchange (MACEE) from 2013-2015. Due to his vast contribution to the academia in research and clinical publication, he was elected as a Diploma of Fellowship Academy of Medicine Malaysia (FAMM) in October 2000, International Fellow of the Academy of Otolaryngology Head and Neck Surgery in April 2004, Fellow of the Academy of Sciences Malaysia (FASc) in April 2016 and as Fellow of Malaysian Scientific Association (FMSA) in September 2017. 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It is known to cause serious diseases in mammals and humans, most notably tuberculosis. It can be found in water, soil, and dust, and some can contaminate medications and products, including medical devices. Mycobacterium can be classified into several major groups for the purpose of diagnosis and treatment. Several species have been identified to cause a clinical threat to human beings and mammals.
\r\n\r\n\tSome species were identified as non-pathogen and pathogens to humans and animals. If left undiagnosed and untreated may lead to major diseases that are a threat to global health—practicing healthy habits such as handwashing, cooking, and boiling water before use were observed as the best way of reducing the risk of spreading diseases caused by the pathogenic mycobacterium species. Antibiotics have been developed to treat diseases caused by this species. Most antibiotics treat the diseases well without any complications, but some can cause drug resistance if not taken properly. Adherence to medication is very important. Patients must be educated about the various diseases and medications to avoid and limit antimicrobial resistance – a major global problem.
\r\n\r\n\tThis book will provide an up-to-date update of these developments in the pathogenicity and immunology of Mycobacteria, coupled with allied advances in diagnosis and treatment, including the use of molecular techniques, pharmacogenomics, and genomics.
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She has a Diploma in Biotechnology from Durban University of Technology, South Africa, and was a Postdoctoral Research Fellow at the University of Pretoria. Her current research interest is the molecular epidemiology of Mycobacterium TB. She led a project on“The role of region-specific SNPs in virulence genes in Mycobacterium tuberculosis drug resistance.”",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"468847",title:"Dr.",name:"Awelani",middleName:null,surname:"Mutshembele",slug:"awelani-mutshembele",fullName:"Awelani Mutshembele",profilePictureURL:"https://mts.intechopen.com/storage/users/468847/images/system/468847.png",biography:"Dr. Mutshembele is a Specialist Scientist who joined the Tuberculosis Platform of SAMRC, South Africa as an Intramural Postdoctoral Research Fellow in 2017. 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Such mental simulation process is called mental imagery and resembles perception, but without the immediate actual corresponding sensory input. Typically, mental imagery is often referred to as the experience of seeing with the mind\'s eye, hearing with the mind\'s ear. In other words, mental images could be generated from any sensorial system. Imagery experience is usually understood as evocation, copy, or reconstruction of actual perceptual experience from the long term memory systems, which is first recalled and then internally reproduced within working memory. At other times, participants may also anticipate possible or forthcoming events through imagery as a consequence of what they are expecting. A key feature of this simulation process is that it gives the ability to mentally rehearse motor acts without overt body movement, which is known as the motor imagery (MI) experience. Practically, MI is more often used when the whole human body or body segments are involved, hence requiring participants to imagine the body as the generator of acting forces and not only the effects of these forces on the external world. In this latter case, the information from the body is mainly used to build the mental image of the movement, e.g. tactile or any kind of proprioceptive data, from the muscles, the joints or the inner ear. In the wealth of motor learning literature, it is now well-established that MI is a valuable complement to physical practice to enhance cognitive and motor performance (Driskell et al., 1994; Guillot & Collet, 2008), as well as to promote motor recovery (Sharma, Pomeroy, & Baron, 2006; de Vries & Mulder, 2007; Di Rienzo et al., 2014a). Interestingly, different types of MI can be described and easily combined. While people commonly report using visual imagery (either through a first-person or third-person perspective), auditory imagery, and kinaesthetic imagery (Kosslyn et al., 1990), each perceptual modality can be accompanied by imagery. On the other hand, and with reference to perception, mental imagery combines several cues from different sensorial systems, thus making the imagery a global sensory-motor experience. Thus, investigating and assessing MI remains difficult due to its concealed nature.
Early on, MI has attracted attention from cognitive neuroscientists. In particular, understanding the neural correlates of goal-directed action, whether executed or imagined, has been an important purpose of cognitive brain research since the advent of neuroimaging techniques such as functional Magnetic Resonance Imaging (fMRI). Providing physiological recordings that correlate to the MI experience was therefore a major issue of the past two decades. In short, but not only, neuroimaging research demonstrated that MI engages motor systems, and that the cerebral plasticity due to actual practice also occurred during MI as well. These findings help to explain why MI can improve actual performance, and further contribute to motor memory consolidation. The present chapter was designed to provide an overview of the neural correlates of MI, with a threefold aim:
The most common aim of fMRI data analysis is to investigate the neural processes mediating higher cognitive functions, and detect correlations between brain activations and the task the participants have to perform during the scan. Since the early 1980s, fMRI has come to dominate the MI-related brain mapping research. During typical fMRI image acquisitions, participants are asked to alternatively perform a movement, either physically or mentally. These experimental conditions are also compared with a rest/control condition.
There are two main types of experimental design in fMRI: the block design and the event-related design. The block design paradigm consists of several epochs representing experimental conditions, during which stimuli are presented and actual execution or MI are required from participants, which are interleaved with time blocks of rest. Event-related designs rather associate brain processes with discrete events, with stimulus events being randomly presented one at a time, and being separated by an inter-stimulus interval of a pre-determined length. Habituation effects can be reduced, and stimuli are usually presented either tens of seconds apart or at a faster rate (e.g., every second). It should be noted that event-related designs are more sensitive to the details of the hemodynamic response model use. Figure 1 provides a schematic illustration of these two experimental designs when comparing the neural networks mediating MI and actual execution of the same movement.
Schematic representation of a block design (a) and an event-related design (b) fMRI paradigm. In the block design paradigm, sustained periods of 20–30 s are used for actual execution (AE), motor imagery (MI) or control (C) conditions during which several trials are performed successively (vertical arrows). Before each block of practice, brief periods of time (2-3s) are necessary to provide participants some instructions related to the forthcoming condition. Brief ‘rest’ periods can also be included. In the event-related design, each experimental condition is presented at random (orange and red vertical bars and vertical arrows).
There is not a universal procedure yet to acquire and process functional data and many researchers independently developed their methods and analyses, using different software. Practically, the most commonly technique used to map brain activity is based on the Blood Oxygenation Level Dependent (BOLD) effect, measuring regional differences in hemoglobin oxygenation. Therefore, a considerable amount of experimental studies investigated BOLD changes during MI. They primarily aimed at comparing the neuronal activations recorded during MI and actual practice of the same movement. For example, Grèzes and Decety (2001) provided evidence of a functional equivalence between intending, simulating, observing, and performing an action, as shown by the great deal of overlap among the corresponding neural substrates. Interestingly, brain activations recorded during MI do however not exactly match those of actual movement execution (Lotze & Halsband, 2006). Some differences also appear when comparing different types of MI or with regard to the individual features of the sample of participants. Put simply, fMRI allows the measurement of different neuronal activity in participants with high vs. low MI ability (Guillot et al., 2008), as well as differences in the patterns of cerebral activity related to the degree of expertise of the participants with the motor task (Lotze et al., 2003; Ross et al., 2003; Milton et al., 2007, 2008). Based on such evidence, fMRI offers a way to evidence the MI experience (Guillot et al., 2008). Finally, recent fMRI data suggested that different types of imagery elicit different patterns of neural activation (Solodkin et al., 2004; Jackson et al., 2006; Guillot et al., 2009; Lorey et al., 2009), while others nicely demonstrated, through positron emission tomography (PET), that the cerebral plasticity occurring after physical practice was reflected during MI (Lafleur et al., 2002; Jackson et al., 2003), hence reinforcing the principle of functional equivalence between MI and physical performance of the same task. Before we consider these issues and review the main related findings in the next section, an important methodological question remains to be addressed. Specifically, what are the additional benefits provided by fMRI to the study of MI compared to other neuroimaging and electrophysiological techniques, and, in turn, what are the limitations of fMRI recordings?
A first advantage of fMRI, compared to PET, is its non-invasive nature. There is no radiation exposure, and no need for injection of radioactive materials with fMRI. Another strength is its high spatial resolution allowing the measurement of brain activity from deep structures. Unlike electrophysiological techniques such as electroencephalography (EEG) or even magnetoencephalography (MEG) which are somewhat biased towards the cortical surface, fMRI records signal from all regions of the brain, including subcortical structures. This is of particular importance when pinpointing the neural correlates of MI as activation of cerebellum and basal ganglia is commonly reported. As every technique, however, fMRI has also some methodological disadvantages or weaknesses for the study of MI (e.g., Dietrich, 2008). A first limit is related to the causality between brain activation and the task the participants perform in the scanner. Although the baseline activity is subtracted from that recorded during the task and the result is averaged for all participants, the causality between activated structures and characteristics of the task is not straightforward. In other words, some brain structures could remain activated although this activity is not directly related to the task. Such argument for the causality however also holds for other brain imaging techniques. Second, the temporal resolution, of about several seconds must also be considered, as fMRI cannot track the rapid temporal dynamics of the functioning brain compared to high temporal resolution methods, e.g. EEG or MEG. Neuromodulatory effects of some cognitive functions such as attention and memory are also likely to affect the spatiotemporal resolution of the signal as they can affect large masses of cells, and potentially induce larger changes in the fMRI signal than the sensory signals themselves (Logothetis, 2008). Fourth, regarding data interpretation, advanced statistical methods are required to identify active voxels, while modifications of the brain activity might be due either to an excitatory or inhibitory influence. Finally, the position in the scanner and the limitation of body movements make it difficult to investigate complex motor skills. Indeed, the participant is lying in supine position in the scanner, and then performs both actual execution and mental representation of the same movement. However, in such situation, only simple movement of fingers, hands, feet, or tongue, or more complex movements of limited amplitude can actually be studied.
As reported by Logothetis (2008), and despite the shortcomings mentioned above, fMRI remains probably the best current available technique for providing insights into brain function and formulating interesting testable hypotheses. Logothetis also stated that some limitations of fMRI are directly due to inappropriate experimental protocols that ignore the circuitry and complex functional organization of the human brain. This is a critical aspect that must be considered before drawing general conclusions and when comparing results across experimental studies. In particular, a specific attention should be paid to the control condition from which MI-related brain activity is reported. In fMRI data analyses, images of activity from experimental and control conditions are subtracted to determine significant peaks of activation. Hence, a difference which does not reach the significant threshold does not necessarily reflect a lack of activity, but can also be due to similar brain activations during both experimental and control tasks. In other words, if the control task is not thoroughly determined, the remaining activity of some brain regions might bias the pattern of activity observed during the experimental condition, i.e. MI. Inspection of the MI literature reveals great differences across studies. Among others, participants were asked either to listen for auditory stimuli without engaging in MI (perceptual control condition), having rest, projecting into a restful state, fixating a static stimulus on a screen, or even imagining a ‘neutral’ static motor task. Obviously, such differences might significantly influence the pattern of activity recorded when contrasting the experimental and the control conditions. This is likely to render the functional brain map, hence hindering the conclusions that one can reach. Another example is the fact that MI can be performed either with open or closed eyes. Marx et al. (2003) suggested that eye closure is likely to improve imagination, but in some circumstances, MI paradigms might require participants to open their eyes to fixate a screen and/or wait for instructions. As some activation or deactivation of cortical regions could be undetected with eyes open, however, MI should ideally be performed with closed eyes as it was asked for the rest condition (Marx et al., 2004).
MI research provided convincing evidence that there is a functional equivalence between MI and motor performance (e.g., Jeannerod, 1994; Grèzes & Decety, 2001; Holmes & Collins, 2001; Szameitat et al., 2012; Burianova et al., 2013; Hétu et al., 2014). It is therefore not surprising that movement execution and MI reveal a high overlap of active brain regions, i.e. imagery draws on almost the same neural network that is used in actual perception and motor control (Murphy et al., 2008). Considerable evidence in support of the functional equivalence theory comes from fMRI studies which demonstrated that both cortical and subcortical brain areas are activated during MI. All studies showed that secondary motor areas are recruited during MI, but that the primary motor cortex might also be part of the network involved in imagined actions. Cerebellar and basal ganglia activations are also consistently reported during MI (Gerardin et al., 2000; Munzert et al., 2009). Finally, apart from motor-related areas, other regions such as parietal lobules are activated during mental simulation of actions (Gao et al., 2011). Taken together, these data support that MI and motor performance share the same neural substrate (Figure 2), but interestingly there are also some differences within the pattern of activity in these areas, as well as with regards to the characteristics of the participants or the content of the MI task.
Schematization of brain activity during MI and possible roles of motor-related regions.
Since the pioneering PET study by Roland et al. (1980), who did not find a significant activation of the contralateral primary motor cortex (cM1) during MI, the question of the contribution of cM1 during imagined actions has attracted considerable attention and remains controversial (for reviews, see Lotze & Halsband, 2006; Sharma et al., 2008; Munzert et al., 2009; Lotze & Zentgraf, 2010). For example, Binkofski et al. (2000), Gerardin et al. (2000), Kuhtz-Bushbeck et al. (2003) and Hanakawa et al. (2003, 2008) failed to find peaks of activation in cM1, while Dechent et al. (2004) reported fleeting involvement. In contrast, several studies reported significant activation in cM1 during MI (Leonardo et al., 1995; Sabbah et al., 1995; Porro et al. 1996, 2000; Roth et al., 1996; Lotze et al., 1999; Ehrsson et al. 2003; Nair et al., 2003; Solodkin et al. 2004; Guillot et al., 2008, 2009). In a seminal paper, Ehrsson et al. (2003) even showed that the content of MI was reflected in the pattern of motor cortical activation. Accordingly, they found that MI of hand, foot and tongue movements specifically activated the corresponding hand, foot and tongue sections of cM1. Transcranial magnetic studies also revealed cM1 activation during MI (for review, see Stinear et al., 2010). Finally, and as previously reviewed by Munzert et al. (2009), similar cM1 activation was found during MI of movements with the impaired limb to that during motor execution with the unimpaired limb in amputees (Ersland et al., 1996). The same result was found in healthy controls during motor execution of the same movement and in patients with spinal cord injury (Alkadhi et al., 2005; Cramer et al., 2005). The fact that these patients do not need to inhibit the movement might have influenced the nature of the MI process (Munzert et al., 2009). A recent MEG study however revealed similar patterns of activity in cM1 during MI and actual practice of the same movement in a patient with spinal cord injury, while a loss of power was observed in the matched healthy control participant (Di Rienzo et al., 2011, 2014b). These latter data support fMRI findings and further confirm both the contribution of cM1 during MI and the weakened inhibitory processes during MI in patients with spinal cord injury. Cerebral activations governing the inhibition of action during MI have not been yet fully explored. This would probably offer fruitful fMRI investigations in the future.
With regards to cM1 activation, Sharma et al. (2006) suggested that discrepancies in results may be due, at least partially, to methodological differences and difficulties in monitoring compliance with MI instructions. Definitely, the advent of fMRI techniques to investigate the contribution of cM1 during MI brought significant data into the debate. Furthermore, Sharma and colleagues argued that previous experimental studies did not specifically address whether the subdivisions of cM1 were differentially involved during MI. Furthermore, the primary motor cortex can be subdivided into an anterior component, thought to be executive in nature, while its posterior part would be involved in cognitive tasks or non-executive functions (Sanes & Donoghue, 2000). Accordingly, Sharma et al. (2008) reported that the cluster distribution in the anterior part of cM1 was significantly reduced during MI compared to the physical execution, while that of the posterior part was similar. Accordingly, they proposed that the role of cM1 and its subdivisions may be related to spatial encoding. Despite these results, determining how MI participates to the activation of cM1 remains difficult. Typically, cM1 activation during MI is usually smaller compared to that during overt motor execution, and is not systematically observed in all participants (Munzert et al., 2009). It was also demonstrated that cM1 was increasingly involved along with the complexity of movements (Kuhtz-Buschbeck et al., 2003). As well, activation of cM1 might be differentially influenced by MI instructions, MI ability and motor expertise (Lotze & Zentgraf, 2010).
Taken together, and despite some controversial data, fMRI studies support that cM1 is actually activated during MI but more weakly than during actual movement. Such activation is not essential for imagery and the neurons are not in the same location than those active during movement execution (Lotze & Halsband, 2006). Moreover, cM1 might not only have an execution function for the motor system (Lotze & Zentgraf, 2010), and it is particularly preparation for MI that may impact significantly cM1 activation (Johnson et al., 2002; Zang et al., 2003; Munzert et al., 2009).
Unlike the contribution of cM1, there is a general consensus regarding the activation of secondary motor areas of the cerebral cortex during MI. Accordingly, fMRI studies provided strong evidence that motor-related areas such as the ventral and dorsal parts of the premotor cortex (PMC), the supplementary motor area (SMA) and the pre-SMA, are active during MI of both simple and complex movements as well as subcortical areas such as the cerebellum and the putamen. Overall, it was shown that MI might activate a subset of the areas required for movement execution, albeit to a lesser extent (e.g., Macuga & Frey, 2011). Conversely, other activations could also be more robust in MI than during motor execution (e.g., Gerardin et al., 2000) thus leading to a partial overlapping of the networks mediating MI and actual execution of the same movement.
The activation of the SMA is now well-established during MI (Guillot et al., 2008; 2009; Hanakawa et al., 2003; Lotze et al., 1999; Munzert et al., 2009; Olsson et al., 2008; Solodkin et al., 2004). As neurons in the SMA are integrated in the functional loop controlling motor actions planning, it is therefore quite reasonable that similar preparatory aspects of the movement are engaged during MI. Kasess et al. (2008) further demonstrated that the SMA may inhibit activity of cM1 to prevent movement execution during MI. Interestingly, some authors also reported that the overlap of SMA activity during MI and motor execution is partial, and that the pre-SMA might rather play a specific role during imagined movements. Hence, the pattern of activity in this region could be higher during MI than during actual practice of the same task (Gerardin et al., 2000). Pre-SMA is known as being involved in acquiring new sequences, planning spatio-temporal actions, and updating motor plans for subsequent temporally ordered movements. As suggested by Malouin et al. (2003), the pre-SMA might thus be activated during MI to provide proper movement sequencing and timing. Taken together, MI would activate both the SMA and pre-SMA, not only for the preparation of the movement, but also for its suppressive influence on cM1 thus preventing movement execution.
Overlapping activity during MI and motor execution was also found in the premotor cortex (PMC-Guillot et al., 2008; Lotze & Halsband, 2006; Munzert et al., 2009), with potential more robust activity in the ventral part of the PMC during MI (Gerardin et al., 2000). The dorsal part of the PMC is involved in the preparation and control of movement, while the ventral part may play a crucial role in the planning of movements. Neurons with mirror properties which are active during action imitation and recognition were found in this region neighbouring Brodmann areas 44/45 (Rizzolatti et al., 1998). This may partially explain why more robust activity was reported in this area during MI than during motor execution.
Several studies also reported that MI and motor execution activate subcortical regions such as the basal ganglia and the cerebellum (e.g., Gerardin et al., 2000; Guillot et al., 2008; Lotze & Halsband, 2006; Munzert et al., 2009; Nair et al., 2003). These structures are known to strongly contribute to motor learning and motor preparation, and are closely connected to cortical motor-related areas. More specifically, the cerebellum contributes to predict movement outcomes as well as to correct the movement on the basis of sensori-motor feedback. Evidence that the cerebellum participates in the internal representation of movement, and therefore to MI, also comes from studies showing that cerebellar stroke disrupts MI (Battaglia et al., 2006; Gonzalez et al., 2005). In a seminal paper, Lotze et al. (1999) even postulated that the posterior cerebellum might play a crucial role in the inhibition of the motor command during MI (see also Lotze & Halsband, 2006). Likewise, basal ganglia play a substantial role in the storage of learned motor sequences, as well as in motor preparation (Alexander & Crutcher, 1990). While there is a general consensus regarding the activation of the basal ganglia during MI, few studies were devised to determine the specific role of this region. Li (2004) demonstrated that lesion of the putamen is likely to impair MI. More generally, however, studies on the effect of basal ganglia dysfunction on MI revealed inconsistent results (Heremans et al., 2011), with possible, but not systematic, MI impairment in patients with Parkinson’s disease. Taken together, and even if experimental studies still have to understand in greater details how the cerebellum and the basal ganglia specifically contribute to the MI process, there is no doubt about their activation during imagined movements. Interestingly, distinct contributions of the cortico-striatal and cortico-cerebellar anatomical systems have been proposed in the motor learning literature (Doyon & Ungerleider, 2002; Doyon & Benali, 2005). Although functional interactions between these anatomical systems are essential at the beginning of the learning process, there is ample evidence that the cerebellum plays a weaker role when the sequence is well learned and has reached asymptotic performance (for review, see Doyon & Benali, 2005), while basal ganglia remain active during the later stages of motor learning. By comparing the pattern of cerebral activations in 13 skilled and 15 unskilled imagers during both physical execution and MI sequence of finger movements, Guillot et al. (2008) showed that poor imagers not only need to recruit the cortico-striatal system, but also activate the cortico-cerebellar system as well. Findings also suggested that compared to poor imagers, good imagers would have a more efficient recruitment of movement engrams. Although this remains a working hypothesis awaiting experimental investigation, the authors concluded that the pattern of cerebral activation recorded during MI in poor imagers might improve and evolve close to that observed in good imagers after MI training. This might suggest that the expected changes in subcortical brain activations during MI would reflect those elicited by the process of actual motor learning (see also Lafleur et al., 2002; Jackson et al., 2003).
Activation of parietal areas including possibly the somatosensory cortex, but more certainly the inferior and superior parietal lobules and the precuneus, is frequently reported during MI (Binkofski et al., 2000; Gerardin et al., 2000; Guillot et al., 2009; Hanakawa et al., 2003; Lotze et al., 1999; Munzert et al., 2009; Nair et al., 2003). More importantly, the parietal cortex, and perhaps predominantly the left parietal cortex, could play a critical role in the formation of mental images. Experimental studies in patients with lesions located in the superior region of the parietal cortex showed that the temporal congruence between actual and MI times, known as being a reliable measure of MI quality (Guillot & Collet, 2005; Guillot et al., 2011; Malouin et al., 2008), was affected (Malouin et al., 2004; Sabate et al., 2007; Sirigu et al., 1996). Sirigu et al. (1996) postulated that the parietal cortex might set up an internal model of the forthcoming movement making the participants to predict the expected movement outcome. After parietal damage, coding for the spatial properties of the movement to be imagined and predicting the temporal feature of the movement might therefore be more difficult, hence reflecting the critical role of the posterior parietal cortex in the use of MI.
Interestingly, bilateral parietal lesions resulted in a complete unawareness of movement execution during imagery (Schwoebel et al., 2002). In fact, the patient exhibited hand movements during the MI of the same body segments, but explicitly denied that they occurred. These data strongly support that parietal areas are critically involved in the generation and guidance of imagined movements. More importantly, they further suggest that the patient failed to inhibit the motor consequences of MI, and therefore, that parietal areas may also play a critical role in the inhibitory processes of the motor command during MI. Finally, and in conjunction with previous fMRI data (Gerardin et al., 2000), these findings suggest a functional dissociation between cortical areas underlying MI and motor execution. The primary sensory area would be more activated during motor execution, due to sensory feedback processes, whereas MI might elicit greater activity both in the inferior and superior parietal cortices, especially in the left hemisphere.
A limited number of fMRI studies examined the influence of MI content on brain activations, and most specifically whether visual imagery and kinaesthetic imagery recruit different neural networks. In a first study looking at this issue, Binkofski et al. (2000) reported that the anterior part of the intraparietal sulcus was more active during kinaesthetic imagery of finger movements, while visual imagery yielded stronger activation in the posterior part. Bilateral activations were also reported during kinaesthetic imagery in the opercular portion of the ventral PMC. In a seminal paper, Solodkin et al. (2004) later investigated the neural networks mediating physical execution, visual imagery and kinaesthetic imagery of hand movements. Although these two types of imagery shared similar neural substrates, including the connection from the superior parietal lobule to the SMA, the main difference was found in the inputs from the superior parietal lobule and the SMA to cM1, which were opposite to those observed during motor execution. In the same line of research, Guillot et al. (2009) examined whether the same group of participants with high imagery abilities recruited similar or distinct brain activations during visual imagery and kinaesthetic imagery of complex hand movements. Visual imagery activated predominantly the visual pathways including the occipital regions and the precuneus, whereas the pattern of kinaesthetic imagery involved mainly motor-associated structures and the inferior parietal lobule (Figure 3). These data support the hypothesis that visual imagery of a motor sequence refers to the visual properties of visual perception, while kinaesthetic imagery includes in greater extent motor simulation processes related to the form and timing of actual movements (Michelon et al. 2006). Although visual and kinaesthetic imagery shared common brain structures, these data provide strong evidence that the patterns of neural activity mediating the ability to perform a specific MI type are partially different. Practically, it might suggest that participants are able to favor one sensory modality to form mental images, although MI is a multimodal and multidimensional construct.
Schematization of brain activations during visual and kinaesthetic imagery. During kinaesthetic imagery (blue), the pattern of activity involves motor-related regions mainly, including cM1, PMC, SMA, cerebellum and basal ganglia (hidden from the view), as well as in the inferior parietal cortex. In contrast, more activity is observed during visual imagery (red) in occipital regions and superior parietal lobule, including the precuneus.
Similar data have been reported in regards to the visual imagery perspectives, that is, by comparing first and third person imagery perspectives. The first study looking at this issue, was conducted by Ruby and Decety (2001) with PET scan. They investigated the neural networks mediating MI when the participants were asked either to mentally simulate an action or to imagine someone else (
More generally, these results provide clear evidence that both imagery types (e.g. visual vs. kinaesthetic imagery) and perspectives (first-person vs. third-person imagery) are partially mediated through separate neural systems, which may therefore contribute differently during the process of motor learning and neurological rehabilitation. This assumption is supported by a remarkable fMRI study demonstrating that MI engages systematically the organized sections of cM1 in a somatotopic manner, i.e., that the content of the mental images is reflected in the pattern of motor cortical activation (Ehrsson et al., 2003).
The fact that MI efficacy depends on the individual ability to form accurate mental images is now well-established. Although not systematic, this capacity to mentally simulate forthcoming actions may be influenced by the individual level of expertise in the corresponding motor task. While the concepts of motor expertise and MI ability measurements have been considered early on, researchers are able, for a short time, to assess the content of MI objectively using thorough procedures, in particular since the advent of functional brain mapping studies (Guillot et al., 2010). Taken overall, fMRI data strongly support the existence of distinct neural mechanisms of expertise in MI, as a function of the individual skill level. For instance, lower cortical activation was recorded in professional violinists as compared to amateurs (Lotze et al., 2003). Similar differences were observed in the neural networks mediating MI in novice and expert athletes (Milton et al., 2008). By comparing brain activations of six golfers of various handicaps during MI of a golf swing, Ross et al. (2003) found decreased activations of the SMA and cerebellum as a function of golf skill level, i.e. an inverse relationship between brain activity and skill level. Also, golf swing MI yielded few peaks of activation in the basal ganglia and cingulate gyri across all skill levels. Milton et al. (2007) finally reported that the posterior cingulate, the amygdala-forebrain complex, and the basal ganglia were activated in novices but not in elite golfers during motor planning of a golf swing movement, hence confirming that the neural networks controlling both motor planning and MI are influenced by the individual skill level. As a whole, changes in cerebral activations confirmed previous investigations showing that levels of expertise during the motor learning process are supported by different neural networks (Doyon & Benali, 2005).
Analogously, researchers investigated the neural networks mediating musical experience, training onset, and training stages. Langheim et al. (2002) first showed in participants who imagined playing a musical selection with their instrument that an associative network including the superior parietal lobule, the inferior frontal gyrus and the bilateral lateral cerebellum was activated during. This network would be particularly activated during the coordination of the complex spatial and timing components of musical performance. As mentioned above, Lotze et al. (2003) compared the patterns of brain activation during auditory imagery in experienced and novice musicians, with the professionals reporting frequent use of imagery with high vividness. Interestingly, experienced musicians recruited very few cerebral areas, while amateurs manifested a widely distributed activation map. In the professional group, however, more activation was observed in the SMA, the PMC, the superior parietal lobule and the cerebellum. Finally, Kleber et al. (2007) reported an increased activation in the fronto-parietal regions during imagined singing, suggesting increased involvement of working memory processes during imagery
To summarize, fMRI data provided strong evidence that partially separate neural networks are activated during MI of both motor and musical performance in regards to the individual differences in the level of expertise. These findings therefore strongly support the hypothesis of distinct neural mechanisms for expertise in imagery, independently of the imagery type, with the network integrating the superior parietal lobule being seen as mediating the imagery activity of highly experienced people.
Apart from the level of expertise itself, the rate at which the movement is performed and imagined is also likely to recruit distinct neural patterns. Accordingly, Sauvage et al. (2013) nicely demonstrated that although execution and imagination of slow and fast movements activate a common neural substrate, the rate of movement differentially yielded associative cortical, striatal and cerebellar areas. Their data suggest that slow movement and motor imagery of both slow and fast movements activated fronto-polar, orbitofrontal and dorsolateral prefrontal cortices, while fast movements recruited more intensively the premotor cortex and the cerebellum.
Going beyond the usual comparison of brain activation patterns that are associated with MI and motor execution of the same movement is the next step in this field of research. To do so, researchers may take advantage of the recent methodological fMRI developments. Both the use of real time fMRI and the recording of changes in functional and effective connectivity in the neural networks activated during MI are of particular interest. Also, reporting more systematically negative BOLD responses might contribute to expand our knowledge on neuronal inhibition during MI.
It is now possible to image human brain functioning in real time with fMRI (Esposito et al., 2003; Weiskopf et al., 2003; for an extensive review, see deCharms, 2008). This technique provides a reconstruction of the raw data obtained with the brain scan, while the scan is going on. There are several exciting research perspectives for considering the contribution of real time fMRI dedicated to MI study. This technique may first be useful during the MI learning process, especially when the pattern of activation during mental simulation is not the one expected. Indeed, real time images are expected to provide participants some objective information related to the vividness of MI, since they have been educated to gain some familiarity with the neuroanatomy before neurofeedback fMRI sessions. Receiving a feedback from brain activation in predetermined regions of interest seems possible. An average 2-5s time lapse usually remains necessary, due to the physiological delay of the haemodynamic response (a more simple feedback can also be used, e.g. a simple score using a Likert-type scale). A well-known study supporting the use of real time fMRI was published by Yoo and Jolesz (2002), who used visual feedback of brain activation to guide participants to adjust their motor task performance and to achieve the desired modulation of cortical activity. For example, during simple hand movements, participants spontaneously involved more muscle groups and increased tapping frequency along fMRI sessions. The biofeedback given to participants with regard to the activated neural network helped them to modulate their cortical activation. A significant illustration of the strength of this methodology in the field of MI was offered by deCharms et al. (2004) during imagery of a manual action task. In this study, participants received feedback about the activation level in the somatomotor cortex with a simple virtual reality interface. The results showed that they enhanced the fMRI level of activation driven by MI in the somatomotor cortex through the course of training. Moreover, the activation of this region after MI training was as robust as that recorded during actual practice. In a more recent study, Yoo et al. (2008) showed that real time fMRI might help individuals to learn how to increase region-specific cortical activity associated with a MI task. Practically, the level of increased activation in motor areas was consolidated after the 2-week self-practice period. Regarding the method for presentation of neurofeedback (intermittent presentation or continuous presentation) and the nature of the neurofeedback being presented to participants during a MI task (true or false neurofeedback regarding brain activations), Johnson et al. (2010) further reported that the intermittent presentation of feedback was more effective than the continuous presentation in promoting self-modulation of brain activity. Accordingly, regular interruptions in neurofeedback presentation allowed central processing and integration of the information conveyed in the feedback regarding brain activations. The authors also reported that false feedback resulted in irrelevant brain activations with regards to the regions of interest targeted by experimental instructions. Finally, Xie et al. (2011) supported the effectiveness of delivering neurofeedback during MI using real time fMRI, and further provided evidence that the SMA was controllable by participants. These data strongly support that real time fMRI is a valuable technique to investigate whether participants are able to use a cognitive strategy to control a target brain region in real time. In the field of neurorehabilitation, for example, a similar approach could be used to learn how controlling pain by learning to control the brain regions that mediate pain perception (deCharms et al., 2005). Indeed, there are multiple therapeutic applications of real time fMRI but it is too early to predict success or failure and new experimental results are awaited.
Real time fMRI might also particularly useful in developing brain-computer interfaces (deCharms, 2008). A brain-computer interface is a novel communication system that translates human thoughts or intentions into a control signal without using any muscle activity (for review, see Pfurtscheller & Neuper, 2010). To date, many brain-computer interface systems have used MI tasks to modulate sensorimotor EEG activity taken to operate and control an external device. Pfurtscheller & Neuper (2010) nicely demonstrated how brain-computer interface systems using MI can contribute to help patients with various motor impairments and paralysis. Despite the temporal limitations of fMRI, using fMRI data for brain-computer interface remains plausible as many cognitive processes change slowly, over seconds or minutes (deCharms, 2008). Practically, the method is appealing although future experimental studies are necessary to determine its feasibility and effectiveness, such as during motor recovery of patients with motor impairment. Real time fMRI might also be used to explore the state of consciousness and communicate with patients in a persistent vegetative state. Accordingly, Owen et al. (2006) detected awareness in such a patient following instructions to mentally imagine moving around a house and playing tennis. Brain activations were observed in the parahippocampal gyrus, the posterior parietal lobe and the lateral PMC during MI of walking, and in the SMA when imagining playing tennis. These data provide evidence that real time fMRI might be used along with MI to actually communicate with people and/or patient who are physically or conventionally unable to interact with their environment (deCharms, 2008).
Creating functional connectivity maps of distinct spatial distributions of temporally correlated brain regions is another methodological advanced tool offered by fMRI. Functional and effective connectivity can be used to examine interactions among brain regions. Practically, these techniques go beyond the usual activation maps obtained through peak-detection methods, by looking respectively at temporal correlation between the time course of activation of two regions, and the influence of one neuronal population over another (Doyon & Benali, 2005; Friston & Büchel, 2003; Friston et al., 1995). Multiple innovative data-driven methods have been proposed to investigate the changes observed in cerebral networks over time, or to study functional and effective connectivity. Some reliable examples are the structural equation modeling and the dynamic causal modeling. In the field of MI, few researchers examined the inter-relationships among brain areas selectively activated along different experimental conditions, that is, the effective connectivity between network components. Solodkin et al. (2004) explored the effective connectivity of the neural networks mediating motor execution, visual imagery, and kinaesthetic imagery of a finger to thumb opposition task. Their results provided evidence that the networks underlying these behaviors were almost different, despite the extensive overlap between motor execution and kinaesthetic imagery. In particular, inputs from SMA and lateral PMC to cM1, which were facilitated during motor execution, exhibited the opposite activity during kinaesthetic imagery, suggesting a physiological mechanism whereby the system prevents overt movements. A second study looking at the effective connectivity between SMA and cM1 suggested that the lack of activation in cM1 during MI might result from a suppressive influence of the SMA (Kasess et al., 2008). More recently, Gao et al. (2008) and Chen et al. (2009) reported that, in right-handed participants, more brain regions showed effective connections to the SMA during right-hand MI than during left-hand MI, but that the strength of the casual influence was stronger during left-hand MI. Furthermore, they found forward and backward effective connectivity between the SMA and the bilateral dorsal PMC, the contralateral primary and somatosensory cortex, and cM1. A last study by the same group of researchers confirmed these findings, and further showed that motor execution has some increased causal connections because of additional processes for the overt behavior stage (Gao et al., 2011). Taken together, these experimental studies highlight the advantages of studying functional and/or effective connectivity through fMRI to expand our understanding of the neural underpinnings of MI (see also Szameitat et al., 2012).
Reporting an elevation in the fMRI BOLD signal, namely positive BOLD, has become a common routine for mapping neural activity in the human brain. Aside from positive BOLD signal changes, several studies have also observed negative BOLD responses. The negative BOLD signal is a physiologic process correlated with a corresponding decrease in cerebral blood flow, oxygen consumption, and neuronal activity (for review, see Shmuel et al., 2002). Practically, negative BOLD signal reflects less neural processing for a given task as compared to a baseline condition. The negative BOLD response might be caused by a reduction in cerebral blood flow and is associated with decreases in the rate of oxygen consumption. Its neurobiological mechanisms are yet not well understood, and much of the debate has centered on whether its source is primarily vascular or neuronal. The neuronal origin might reflects a suppression of the local neuronal activity and/or a reduction in the afferent input, whereas the vascular origin refers to a reduction in cerebral blood flow to the less demanding regions due to the increase in flow to the demanding areas, without a necessary decrease in neuronal activity in the negative regions (Schmuel et al., 2002). Until recently, very few experimental studies were designed to analyze negative BOLD responses during either visual or MMI tasks. An interesting and innovative paper by Amedi et al. (2005) investigated the pattern of brain deactivation during visual imagery and compared it to the neural correlates of visual perception. While they found that visual imagery and visual perception share similar neural substrate, these two conditions yielded different brain-deactivation profiles as shown by negative BOLD responses. Of particular interest is that the authors reported a robust deactivation in early auditory areas as well as a selective deactivation in the somatosensory cortex during visual imagery, hence supporting that visual imagery is associated with deactivation of non-visual sensory processing. Based on these data, they stated that that pure visual imagery might be characterized by an isolated activation of visual cortical areas with concurrent deactivation of sensory inputs that may potentially disrupt the image created by the mind’s eye. They also considered that deactivation could be the consequence of filtering out irrelevant stimuli. The correlation between the level of deactivation and the vividness of visual imagery might support the hypothesis that participants with high imagery ability are able to shut down or disconnect the ‘‘irrelevant’’ cortices. To the best of our knowledge, unfortunately, there are no experimental studies specifically designed to look for similar results in MI paradigms
The data reviewed in this chapter strongly support that recording human brain functioning with fMRI during MI provides an objective measurement of the neural networks underlying MI processes. Although they are not totally overlapping, it is now well-established that the neural substrates mediating MI and actual execution of the same task are quite similar. Spurred by recent fMRI methodological advances, the next step of MI research will certainly contribute to understand in greater details the neural correlates of imagined movements, as well as the inhibition of the motor command. MI studies could therefore benefit from the use of real time fMRI, effective connectivity, and also negative BOLD. This latter technique might be of particular interest in investigating the inhibitory processes of the motor command during MI. Cerebral regions which are inhibited during mental operations or human behavior are certainly of the same scientific interest than those which are activated.
Pollination is a multi-million-year-old ecosystem process from which both flowering plants and pollinators get benefitted. Pollinating animals come to flowers for a variety of reasons, including food and shelter. Pollen rubs or falls onto pollinator’s bodies when they visit flowers. As the pollinator passes from one flower to the next, it transfers the pollen to another flower. This transfer is important in the life cycle of all flowering plants because it is required to begin seed and fruit production. Pollinators are important for healthy, productive agricultural ecosystems and nature.
Indeed, the interactions between plants and their pollinators are among the most beautiful examples of coevolution on the planet. While some pollinators are generalists, visiting a wide variety of flowers, many pollinators have acquired preferences for certain flower kinds, and vice versa. Most pollinators have their favourite colour of flower: Bees prefer blue flowers, butterflies prefer pink and red flowers, flies choose yellow and white flowers, beetles and bats prefer white flowers, while hummingbirds prefer red flowers. In addition, the phenology, form, and food reward offered by the flower can all impact which pollinators visit [1]. Bees, for example, can see ultraviolet light and have a better sense of bilateral symmetry. As a result, flowers that want to attract bees will likely use these visual signals to lure the bee to the flower’s centre [2].
Though some plant species depend on wind or water currents to carry pollens from one flower to another, but majority of plant species (approx. 90%) prefer animal assistance in this task. Around 200,000 different species of animals do this task of pollen transfer. Out of these, 1,000 are of vertebrates (birds, bats and tiny mammals), with the remainder being invertebrates, such as moths, bees, flies, beetles and butterflies [3].
Plant-pollinator interactions may be one of the most ecologically significant types of animal–plant interactions: without pollinators, many plants would be unable to set seed or reproduce, and without plants to provide pollen, nectar, and other rewards, countless animal populations would decline, with knock-on effects for other species [4].
Plants and their pollinators have had a significant impact on each other’s growth, frequently leading to diversification and even an exclusive partnership. The Madagascar Star Orchid (
Mutualisms between plants and pollinators extend back to the Cretaceous period, when insects began to feed on flowers and flowers achieved higher reproductive success through the transfer of pollen by insects. At least 67 percent of blooming plants rely on insects for pollination today [5], with the rest relying on birds and mammals. Pollinators are just as important as light and water for these plants to survive [6].
Pollinators comprise a diverse group of animals that include species of butterflies, flies, moths, wasps, beetles, ants, birds, weevils, thrips, midges, bats, monkeys, marsupials, rodents, and reptiles, but are dominated by insects, particularly bees. Bees and flies visit more than 90% of the world’s major plant types, while the other species visit fewer than 6% of the crop varieties (Table 1). The western and eastern species of honey bees i.e.,
Many species of flower visitors have been reported to visit flowering crops in the literature. For instance, a mega-study that included 90 percent of all agricultural pollination studies from throughout the world discovered that 785 different bee species visit crop blooms [8]. Bees are the most prolific and diverse pollinators in most parts of the world, with over 20,000 species recorded [9, 10]. With over 1,20,000 species, flies are an important group in agriculture, although only a few families are effective pollinators [11]. In colder climates, such as high altitude/latitude environments, flies outweigh bees in both diversity and quantity as pollinators [12]. In addition to bees and flies, butterflies, beetles, moths, wasps, ants, thrips and vertebrates also pollinate plants, including some crops. Pollinating butterflies and moths are found all around the planet, but in the tropics they are more numerous and diversified [13]. The enormous variety of insect pollinators was discussed by Kevan and Baker [14]. Some birds and bats, in addition to insects, are essential pollinators [15, 16]. Bird pollinators are mostly found in warm (tropical/subtropical) climates, whereas bats pollinate tropical forests and some desert cactus. Pollinators that are less well-known have also been reported for a variety of plant species. These include, among others, cockroaches [17], mice [18], squirrels [19], lizards [20, 21, 22] and snails [23]. The less well known pollinators are not known to have major roles in supporting agricultural production.
Bees play a significant role in pollination in most terrestrial environments around the world. Honeybees and thousands of species of native bees pollinate garden crops, meadows and woodland plants in the United States. The majority of bees visit flowers in search of pollen or nectar to nourish themselves and their young ones. Crop pollination and honey production are significantly reliant on honeybees. Solitary bees are among the most common native pollinators and named because most of them live solitary lives and do not assemble to live in colonies. Blueberries, sunflowers, apples, watermelon, alfalfa and strawberries are among the commercial crops pollinated by solitary bees. Solitary bees build their nests in a variety of unusual locations, such as sticks, mud mounds, and termite holes. A few species build mud nests and saps, plant resins on the edge of rocks and trees to make domed nests. Many bees excavate their nests into the soft inner pith of stems and twigs, or exploit abandoned beetle burrows. Some solitary bees, on the other hand, create tunnels in bare or partially vegetated, well-drained soil to make their nests. These bees can be generalist or specialist feeders, depending on the species. Generalist bees visit a wide variety of floral types collect nectar and pollens. Being more hardy species, these are able to thrive in degraded settings dominated by weedy or invasive plants. While specialists are more vulnerable to the detrimental effects of landscape or habitat changes since they depend on a single plant species for nectar and pollen.
Bumblebees are social bees, which means these bees reside in colonies, share tasks, and have many generations that overlap in the spring, summer, and fall. The bumblebees require a suitable sized cavity in to build their nest. These bees usually build their nest underground in abandoned rat burrows and sometimes in hollow trees or walls or under a clump of grass above ground. Bumblebees usually feed on a wide variety of plants.
Ants are gregarious insects that enjoy nectar in large quantities. These active insects are frequently seen visiting flowers in search of energy-dense nectar. Ants do not have any wings, so they have to crawl into each bloom to get their meal. They are more likely to collect nectar from flowers that are not efficiently cross-pollinated. Ants are drawn to low-growing, inconspicuous blooms close to the stem. Small’s stonecrop (
Butterflies, like all pollinators, are inextricably related to their surroundings, and abrupt changes in the ecosystem can have fatal consequences for localised populations or species. The butterfly’s habitat requirements differ from stage to stage, and each has its own set of requirements that must be taken into account in order to create acceptable habitat. The life cycle of a butterfly is divided into four stages: egg, caterpillar, pupa, and adult. Butterfly deposit its eggs on leaves of trees and shrubs, flowers and grasses.
Being oligolectic, most butterfly species remain confined to one or a few closely related species of plants as these plant species effectively act as host plants for their caterpillars. The females usually lay their eggs on or near the host plant for the survival of their caterpillars. The caterpillars of monarch butterflies, for example, only consume milkweed, and adult females of monarch butterflies lay eggs on or near milkweed plants. Newly hatched caterpillars feed on the leaves, stalks, flowers and fruits of their host plants, which also act as a protective barrier against predators. Caterpillars begin to transform into adult forms after several weeks of eating and growing. This is the pupal stage of a butterfly’s life, which is a non-feeding, sedentary stage. Pupae do not require nourishment, but they do require a safe place to convert into their adult forms, such as sticks, tall grass or a pile of leaves.
Adult butterflies feed almost entirely on nectar. Butterflies prefer flowers that are brightly coloured, aromatic, and have flat, broad surfaces on which to land. Adult butterflies like the nectar of daisies such as zinnias, asters, marigolds, goldenrods, dahlias and asters, dogbane, butterfly weed, ironweed, phlox and milkweed. Rotting fruit, tree sap, mud puddles, animal excrement and urine are also sources of nutrients, minerals and salt for adult males of some species. Adult butterflies can feed, bask, and rest on the leaves and stems of the host plants, which provide perching locations. Wind, rain and predators can all be protected by vegetation and modest woodpiles.
The moths are nocturnal in nature and some species are pollinators of night-blooming flowering plants, especially in the southern United States and Mexico. The female yucca moth, for example, has mouthparts that allow her to capture pollen and lay her eggs in the stigma of the yucca flower. The life and propagation of yucca plants are entirely dependent on the yucca moth. Each flower’s pistil (female component) terminates in a three-lobed stigma. Pollen masses must be driven down into this centre stigmatic opening in order for pollination to occur. Using her particularly modified mouthparts, the female yucca moth collects pollen from flower anthers. She gathers the sticky pollen and rolls it into a ball. She then “stuffs” or “combs” the pollen ball into the stigmas of the flowers she visits. The yucca flower will not develop into a fruit or pod with seeds unless this procedure occurs.
When a female moth visits a flower, she walks up to the base of the flower and inserts her ovipositor into one or more of the six chambers to lay an egg. The egg is protected in the chamber while it develops. The yucca will have begun to grow a pod with little seeds by the time the egg hatches into a tiny caterpillar. In this association, both the yucca plant and the yucca moth benefit.
Flies and beetles are two important pollinator groups. Certain species of flies show resemblance with bees by mimicking bee coloration and patterns. Both bees and flies possess transparent membranous wings and but flies can be distinguished on the basis of having only one pair of wings. Some pollinating beetles are small in size and difficult to spot as these beetles resemble with the black specks present on the petals of flowers, while others are large and more colourful. There are hundreds of thousands of species of pollinating flies and beetles, many of which have yet to be documented. The habitat requirements of different species vary. For each of their life phases, such as egg, larva, pupa, and adult, flies and beetles require food, water, and cover in adequate quantity and quality. Pollination is greatly aided by syrphid flies.
Wasps, like bees, have extremely high energy requirements that must be satisfied in order for them to survive. Pollen and nectar from a variety of flowers are vital for wasps. True wasps have stingers, which they utilise to catch insects or spiders for their larvae to feed. Small fig wasps are common throughout the tropics. Many tropical ecosystems rely on figs as a keystone species. Fig wasps pollinate about 1,000 different varieties of figs.
Figs are unique because of how the flowers are contained within the immature fruit. To mate, lay eggs, and pollinate the small flowers, fig wasps enter through a tiny pore. Both are severe examples of obligatory symbiosis, in which the plant and the insect are entirely dependent on one another to survive.
These small insects perform one of the most important ecosystem services on the planet, ensuring that both our culinary experiences and the world’s environment flourish. Nearly 75% of the plant species cultivated for food, fibre, spices, beverages, condiments and pharmaceuticals are pollinated by animals (Table 2). The status of pollinator populations has huge economic impacts on agriculture. While some crops such as corn and wheat, are wind pollinated and some others like potatoes reproduce vegetatively, a whopping 35% of agricultural yield relies on animal pollinators [25]. Roubik published a comprehensive list of 1330 tropical crop species, including a list of viable breeding systems and pollinating taxa [24].
Sr. No. | Pollinator group | Species name |
---|---|---|
1. | Bumble bees | |
2. | Beetles | |
3. | Honey bees | |
4. | Hover flies | |
5. | Stingless bees | |
6. | Thrips | |
7. | Wasps |
Species list of known pollinators for global crop.
1. | Fruits, berries and nuts | Almonds, Apple, Apricot, Avocado, Blackberry, Blueberry, Cacao, Cashew, Cherry, Chestnut, Citrus, Coffee, Coconut, Cranberry, Date, Fig, Gooseberry, Grapes, Guava, Huckleberry, Kiwi, Litchi, Mango, Olive, Papaya, Peach, Pear, Plum, Pomegranate, Raspberry, Strawberry, Vanilla, Watermelon |
2. | Herbs and spices | Black Pepper, Cardamom, Chive, Clove, Coriander, Fennel, Lavender, Mustard, Nutmeg, Parsley, Pimento, Tea, White Pepper |
3. | Legumes | Beans, Cowpea, Lima Beans, Lupines, Mung Bean/Green or Golden Gram, Soybean |
4. | Seeds and grains | Alfalfa, Buckwheat, Canola, Flax, Oil Palm, Safflower, Sesame, Sunflower |
5. | Vegetables | Asparagus, Beet, Broccoli, Brussels Sprouts, Cantaloupes, Carrot, Cauliflower, Celeriac, Celery, Cucumber, Eggplant, Endive, Green Pepper, Leek, Lettuce, Okra, Onion, Parsnip, Pumpkin, Radish, Rutabaga, Squash, Tomato, Turnip, White Gourd |
6. | Others | Cotton, Kenaf |
Common agricultural crops benefited by insect pollination [24].
Williams examined the pollinator requirements for 264 crop species in Europe and found that 84 percent of them rely on animal pollination to some extent [26]. To put this in context, pollinators contribute over about $200 billion to the global economy [27].
The benefits of pollinators can easily be expanded to global biomes exceeding our gardens, kitchens, and dinner tables. With so many of the world’s plants depending on pollinators for reproduction, these flower-loving friends are inadvertently supporting soil stabilisation, carbon sequestration and animal habitats. Sustaining healthy pollinator populations leads to supporting healthy ecosystems. The native pollinators not only provide a significant portion of the food and add to the economy, but they also play an important part in the natural ecosystem. The native pollinators help to keep the plant communities healthy and able to reproduce. They also support plants to provide cover and food for wildlife, to prevent erosion and keep waterways clean. The fruits and seeds produced by pollinated plants form an important part of the diet of birds and mammals. Many insects, including butterflies, use flowering plants as egg laying and nesting places.
The significance to a plant or the loss of its pollinators depends on whether the pollination relationship is facultative or obligate [28]. Some plants grow as a result of vegetative reproduction and are thus unaffected by the loss of pollinators. Others have vast seed banks or live a long time, so they may not be in immediate risk of extinction if their pollinator goes extinct. Most plants have several pollinators, and most pollinators pollinate multiple plant species, rather than a rigid one-pollinator-one-plant relationship. The composition of communities varies with environment, and what appears to be a specific relationship between a plant and a pollinator species may shift over time. Plants that are dioecious and self-incompatible, those with a solitary pollinator, and those that proliferate only by seeds are the most vulnerable to pollinator loss.
Many pollinator habitats have been destroyed or disrupted as a result of human activities. Invasive plant species have fragmented and damaged many remaining habitat regions and such habitats become less suitable for pollinators and other wildlife. These habitat alterations may result decline in food sources, nesting and mating sites of native pollinators. Many pesticides have negative effects on pollinators and their habitats due to overuse and poor application. Herbicides diminish forage plant diversity by eliminating wildflowers, and some pesticides harm pollinators directly, particularly pollinating insects. Honeybees, for example, might outcompete indigenous pollinators for local nectar resources, putting them at greater risk of extinction. Pollinator populations have declined significantly as a result of habitat degradation and fragmentation. At least 185 pollinator species are designated as threatened or extinct by the International Union for Conservation of Nature (IUCN), and two bat species and 13 bird species are recognised as endangered in the United States.
A number of threats to pollinators have been identified. These include habitat alteration, habitat fragmentation, introduction of alien pollinators and pesticide poisoning [28].
Many bees not only require large numbers of flowers to provide nectar and pollen, but also need a variety of flowering plants for their sustainability throughout the growing season. Oligolectic insects, such as some bees and butterfly larvae depend on specific plants for survival and persistence of their populations.
In addition to food requirements, pollinating organisms often have specific nesting requirements. Some bee species nest in cavities in the ground such as old rodent burrows, spaces under rocks, or holes excavated in sand or soft dirt. Many other types of bees nest in hollow twigs. As land is developed for human activity, the availability of twigs, rodent burrows and suitable nesting substrates typically decrease.
In the present scenario, large-scale monoculture of crops and intensive cropping practices reduce the amount of land available to support wild vegetation. With the increasing mechanisation of agriculture, the decrease in number and area of hedgerows and uncultivated patches reduced the number of native plants available as pollen and nectar sources [29, 30].
Gess and Gess determined that grazing livestock alters habitat sufficiently to affect pollinators [31]. They documented changes in availability of nesting sites, water resources, and vegetation that have direct negative effects on species diversity and population size of bees and wasps. Trampling of vegetation by livestock can directly destroy the nests of ground-nesting species and can compact the soil, constraining nest formation. In addition, the people who tend livestock in these areas of South Africa collect wood for fuel, thus reducing the availability of hollow twigs that provide nesting sites for some bee pollinators. Grazing also affects bees by decreasing water availability. Both ground-nesting and cavity-nesting bees must collect water for use in nest construction. Most bees cannot obtain water from livestock water tanks with steep sides, or even ponds without sloping edges, but need to stand at the edge of shallow water.
Tampering with the natural water supply to provision cattle or produce crops often modifies water availability for bees. Dramatic reductions in bee number and species diversity have been documented in areas of the Guana caste Province of Costa Rica that were deforested to support cattle [32, 33]. Vinson
Although habitat fragmentation is a problem, preserving large tracts of a particular vegetation type may not be enough to maintain pollinator populations. Janzen and colleagues censured euglossine bee populations in parks and reserves in Costa Rica and determined that even within the same park, different habitats vary dramatically in bee diversity [36]. Many of the bee species travel long distances to pollinate plants that do not occur within the habitats in which they were collected. This finding indicates that preservation of diverse patches within an area may be essential to maintain adequate pollinator populations.
Several studies have indicated that introduced honeybees decrease the foraging success of native pollinators by competing with them for resources [37, 38, 39, 40, 41, 42]. Such example is provided by honeybees in Australia. Honeybees were introduced in Australia approximately 150 years ago, and so far they were considered beneficial to the native flora. However, Paron concluded in a recent study that honeybees may actually be harmful to the native flora as they may displace native pollinators, they may be ineffective at pollinating native flowers and they may interact in complex ways with native pollinators to reduce the amount and efficiency of pollen transfer [38].
Foraging on pesticide-treated plants is a major source of bee mortality, yet honeybees are often expected to pollinate crops that have been treated with pesticides. The susceptibility of bees to chemical poisoning is usually related to their surface area-volume ratio. Bumblebees are often more tolerant of pesticides than honeybees because of their smaller surface area-volume ratio and honeybees are in turn more tolerant than most small native bees. Chemical poisoning results in abnormal communication dances and mistakes in indicating distance and direction to food sources, in addition to direct mortality.
One source of pesticides that affects pollinators is the broad-spectrum insecticides used to control grasshoppers on rangelands in the South-Western United States. The rangelands are sprayed with these insecticides to save the grasses for cattle forage. The sprays kill many other insects in addition to grasshoppers, including local pollinators. The grasshopper-spraying campaigns overlap the flowering period of a number of endemic rangeland plants that grow among the grasses and many of these plants are listed as endangered or threatened [44]. Additionally, these campaigns also imbricate the period of emergence and active foraging of majority of the native bee species [45].
Another example of how pesticide application can affect plant reproductive success through its action on pollinators comes from the studies conducted in forests of New Brunswick, Canada [46]. These forest areas were sprayed with Matacil (aminocarb insecticide) to control spruce bud worm,
An area must have sufficient food, shelter, water, and nesting grounds to lure local pollinators. To ensure that habitat demands are met, habitat management actions should be implemented. For instance, landowners can acquire, build, or plant extra nesting sites for bees and butterflies. Depending on the type of native pollinator targeted, various habitat management strategies are used.
Plant-appropriate vegetation: Planting gardens or meadows with a variety of native wildflowers, trees, grasses and shrubs is the easiest approach to attract local pollinators. Wildflowers and indigenous grasses will offer food such as nectar, pollen and larval host plants. For pollinators, trees and dense shrubs provide crucial shelter, nesting and overwintering places. Considering pollinator species have different preferences, planted areas should have diverse amounts of vegetation and areas of light, full shade and partial shade. Planting should take place in wind-protected areas.
Native plants should be chosen since these have evolved with local pollinators and are adapted to local soils and temperature. Native plants should make up at least 75% of a habitat’s surface area. The cultivation of invasive species should not be avoided because such plants disrupt the ecosystem’s natural structure and composition resulting in degrading pollinator and other wildlife habitat. The area of mowed lawn should be restricted in favour of native wildflowers, shrubs, and grasses. The existing lawns should be mowed less frequently to allow plants to offer pollinator habitat. Annuals should be avoided in favour of perennials. Perennials are often higher in nectar content and provide a more reliable food source than annuals because they bloom year after year. Plants that reproduce in “doubles,” such as marigolds and roses, should be avoided because such plants are designed for ornamentation rather than pollen and nectar availability. The species of wildflowers should be grown in a clump to attract more pollinators and not grown individually. Throughout the growing season, nectar and pollen flowers should be available. The variation in flower shape and colour will deliver nectar and pollen to a variety of pollinators. Bell, tube, or trumpet-shaped flowers, as well as those with clusters of tubular florets, are favourites of birds and butterflies, especially when surrounded with a flat surface for perching. They favour flowers that are brilliantly coloured such as oranges, yellows and reds. Yellow, blue, and purple flowers are most appealing to bees. The flowers that bloom at night attract moths and bats.
Use pesticides carefully: Pesticides, the chemical toxins, do not distinguish between beneficial and harmful insects. As an insecticide is used to kill a crop-eating insect, it may also harm important natural pollinators. Pesticide treatment has the potential to harm or kill all pollinator species, as well as to effect other wildlife. Pollinators can be poisoned by such chemicals through contaminated food or directly from the contaminated surfaces of florets, leaves, soil, or other things when they come in contact with them. To sustain the whole spectrum of native pollinators, usage of such chemicals should be restricted or kept to a bare minimum. To address pest infestations, landowners should use non-chemical or organic methods.
Provide water: The pollinator species require water to survive. Bees and butterflies should be attracted to a source of pesticide-free water mud and other beneficial insects drawn to a birdbath, fountain, tiny pond, or mud puddle. For butterflies and bees, a moist salt lick can be made. A damp patch on the earth can be created by using a dripping hose, drip irrigation line, or birdbath and additionally, a small amount of sea salt or wood ashes can be mixed to meet the mineral needs of butterflies and bees.
Insects, being diverse and dominant, are the key component of a healthy ecosystem. Humans determine whether an insect is beneficial, benign or pestiferous. Majority of them are beneficial to humans either directly or indirectly as food, pollinators, pollution indicators, scavengers, for production of useful products etc. The insects represent their dominance as pollinator. Bees and flies visit more than 90% of the world’s major plant types, while the other species visit fewer than 6% of the crop varieties. The effectiveness of pollinators varies according to factors such as their abundance; their ability to reach individual plants of the same species and to collect, transfer and deposit the pollen to the appropriate plant organ. Insect pollinators are in decline which is tentative, considering the lack of comprehensive data [48], but it is still a matter of concern. Losses in diversity and abundance are particularly strong under intensive agricultural management [49, 50]. Despite their significance, pollinators are declining and often overlooked in terms of their contributions to healthy ecosystems. No pollinators would mean no seeds or fruits and therefore the collapse of agriculture. No plant reproduction in the wild means that many plants will become locally extinct. Human activities have destroyed and fragmented native pollinator habitats. This diversity needs protection by integrating conservation measures with sustainable agricultural practices, which may raise crop yields and protect both wild and managed species of bees and other pollinators.
A range of conservation measures in intensively-farmed regions can help to maintain diversity, by preserving the resources that pollinators need. Some of the measures are at farm-level such as planting flower strips among crops, reintroduction of hedges and planting trees while as others are implemented at landscape-level such as the conservation of natural and semi-natural habitats in agricultural landscapes. There is no “one size fits all” approach to conserve all species, due to their varying preferences for different food sources and nesting sites. Reversing the decline in pollinators is the key to feed mouths in future and must be seriously given a thought and action plan.
The authors declare no conflict of interest.
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The Gram-positive pathogen is armed with battery of virulence factors that facilitate to establish infections in the hosts. The organism is well known for its ability to acquire resistance to various antibiotic classes. The emergence and spread of methicillin-resistant S. aureus (MRSA) strains which are often multi-drug resistant in hospitals and subsequently in community resulted in significant mortality and morbidity. The epidemiology of MRSA has been evolving since its initial outbreak which necessitates a comprehensive medical approach to tackle this pathogen. Vancomycin has been the drug of choice for years but its utility was challenged by the emergence of resistance. In the last 10 years or so, newer anti-MRSA antibiotics were approved for clinical use. However, being notorious for developing antibiotic resistance, there is a continuous need for exploring novel anti-MRSA agents from various sources including plants and evaluation of non-antibiotic approaches.",book:{id:"5471",slug:"frontiers-in-i-staphylococcus-aureus-i-",title:"Frontiers in Staphylococcus aureus",fullTitle:"Frontiers in Staphylococcus aureus"},signatures:"Arumugam Gnanamani, Periasamy Hariharan and Maneesh Paul-\nSatyaseela",authors:[{id:"192829",title:"Dr.",name:"Arumugam",middleName:null,surname:"Gnanamani",slug:"arumugam-gnanamani",fullName:"Arumugam Gnanamani"},{id:"204388",title:"Dr.",name:"Periasamy",middleName:null,surname:"Hariharan",slug:"periasamy-hariharan",fullName:"Periasamy Hariharan"},{id:"204389",title:"Dr.",name:"Maneesh",middleName:null,surname:"Paul-Satyaseela",slug:"maneesh-paul-satyaseela",fullName:"Maneesh Paul-Satyaseela"}]},{id:"32282",doi:"10.5772/33983",title:"Bacteriophages of Ralstonia solanacearum: Their Diversity and Utilization as Biocontrol Agents in Agriculture",slug:"bacteriophages-of-ralstonia-solanacearum-their-diversity-and-utilization-as-biocontrol-agents-in-agr",totalDownloads:3757,totalCrossrefCites:7,totalDimensionsCites:23,abstract:null,book:{id:"555",slug:"bacteriophages",title:"Bacteriophages",fullTitle:"Bacteriophages"},signatures:"Takashi Yamada",authors:[{id:"98151",title:"Dr.",name:"Takashi",middleName:null,surname:"Yamada",slug:"takashi-yamada",fullName:"Takashi Yamada"}]},{id:"32276",doi:"10.5772/34642",title:"Bacteriophages and Their Structural Organisation",slug:"bacteriophages-and-their-structural-organisation-",totalDownloads:12434,totalCrossrefCites:9,totalDimensionsCites:17,abstract:null,book:{id:"555",slug:"bacteriophages",title:"Bacteriophages",fullTitle:"Bacteriophages"},signatures:"E.V. 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Clonal complex 398 (CC398), a predominant clonal lineage of livestock-associated-MRSA in domestic animals and retail meat, is capable of infecting humans. In order to monitor and prevent MRSA contamination, it is critical to understand its source and transmission dynamics. In this review, we describe MRSA in food-producing animals (pig, cattle, chicken), horses, pet animals (dogs, cats), and food products (pork, beef, chicken, milk, and fish).",book:{id:"5471",slug:"frontiers-in-i-staphylococcus-aureus-i-",title:"Frontiers in Staphylococcus aureus",fullTitle:"Frontiers in Staphylococcus aureus"},signatures:"Jungwhan Chon, Kidon Sung and Saeed Khan",authors:[{id:"189634",title:"Dr.",name:"Kidon",middleName:null,surname:"Sung",slug:"kidon-sung",fullName:"Kidon Sung"},{id:"190400",title:"Dr.",name:"Jungwhan",middleName:null,surname:"Chon",slug:"jungwhan-chon",fullName:"Jungwhan Chon"},{id:"190401",title:"Dr.",name:"Saeed",middleName:null,surname:"Khan",slug:"saeed-khan",fullName:"Saeed Khan"}]}],mostDownloadedChaptersLast30Days:[{id:"69731",title:"Isolation and Purification of Sulfate-Reducing Bacteria",slug:"isolation-and-purification-of-sulfate-reducing-bacteria",totalDownloads:1551,totalCrossrefCites:1,totalDimensionsCites:6,abstract:"Sulfate-reducing bacteria (SRB) are a widespread group of microorganisms that are often isolated from the anoxygenic environments (lake depths, soil, or swamps), and they are also present in the human and animal intestines. This group is often detected in patients with inflammatory bowel disease, including ulcerative colitis. That is why new rapid methods for their isolation, purification, and identification are important and necessary. In this chapter, the methods of mesophilic SRB isolation from various environments are described. Particular attention is paid to the purification of mesophilic SRB since they can be in close interaction with other microorganisms (Clostridium, Bacteroides, Pseudomonas, etc.), which are their frequent satellites. Moreover, the main methods of mesophilic SRB identification based on their morphological, physiological, biochemical, and genetical characteristics are presented.",book:{id:"8997",slug:"microorganisms",title:"Microorganisms",fullTitle:"Microorganisms"},signatures:"Ivan Kushkevych",authors:[{id:"252191",title:"Associate Prof.",name:"Ivan",middleName:null,surname:"Kushkevych",slug:"ivan-kushkevych",fullName:"Ivan Kushkevych"}]},{id:"65773",title:"Life Cycle of Trypanosoma cruzi in the Invertebrate and the Vertebrate Hosts",slug:"life-cycle-of-em-trypanosoma-cruzi-em-in-the-invertebrate-and-the-vertebrate-hosts",totalDownloads:1497,totalCrossrefCites:4,totalDimensionsCites:7,abstract:"Trypanosoma cruzi (T. cruzi) is a protozoan parasite that causes Chagas disease, a zoonotic disease that can be transmitted to humans by blood-sucking triatomine bugs. T. cruzi is a single-celled eukaryote with a complex life cycle alternating between reduviid bug invertebrate vectors and vertebrate hosts. This article will look at the developmental stages of T. cruzi in the invertebrate vector and the vertebrate hosts, the different surface membrane proteins involved in different life cycle stages of T. cruzi, roles of different amino acids in the life cycle, carbon and energy sources and gene expression in the life cycle of T. cruzi. The author will also look at extracellular vesicles (EV) and its role in the dissemination and survival of T. cruzi in mammalian host.",book:{id:"8806",slug:"biology-of-em-trypanosoma-cruzi-em-",title:"Biology of Trypanosoma cruzi",fullTitle:"Biology of Trypanosoma cruzi"},signatures:"Kenechukwu C. Onyekwelu",authors:[{id:"245368",title:"Dr.",name:"Kenechukwu C.",middleName:null,surname:"Onyekwelu",slug:"kenechukwu-c.-onyekwelu",fullName:"Kenechukwu C. 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The epidemiology of MRSA has been evolving since its initial outbreak which necessitates a comprehensive medical approach to tackle this pathogen. Vancomycin has been the drug of choice for years but its utility was challenged by the emergence of resistance. In the last 10 years or so, newer anti-MRSA antibiotics were approved for clinical use. However, being notorious for developing antibiotic resistance, there is a continuous need for exploring novel anti-MRSA agents from various sources including plants and evaluation of non-antibiotic approaches.",book:{id:"5471",slug:"frontiers-in-i-staphylococcus-aureus-i-",title:"Frontiers in Staphylococcus aureus",fullTitle:"Frontiers in Staphylococcus aureus"},signatures:"Arumugam Gnanamani, Periasamy Hariharan and Maneesh Paul-\nSatyaseela",authors:[{id:"192829",title:"Dr.",name:"Arumugam",middleName:null,surname:"Gnanamani",slug:"arumugam-gnanamani",fullName:"Arumugam Gnanamani"},{id:"204388",title:"Dr.",name:"Periasamy",middleName:null,surname:"Hariharan",slug:"periasamy-hariharan",fullName:"Periasamy Hariharan"},{id:"204389",title:"Dr.",name:"Maneesh",middleName:null,surname:"Paul-Satyaseela",slug:"maneesh-paul-satyaseela",fullName:"Maneesh Paul-Satyaseela"}]},{id:"55437",title:"Biological Control of Parasites",slug:"biological-control-of-parasites-2017-07",totalDownloads:4334,totalCrossrefCites:7,totalDimensionsCites:7,abstract:"Parasites (ectoparasites or endoparasites) are a major cause of diseases in man, his livestock and crops, leading to poor yield and great economic loss. To overcome some of the major limitations of chemical control methods such as rising resistance, environmental and health risks, and the adverse effect on non‐target organisms, biological control (biocontrol) is now at the forefront of parasite (pests) control. Biocontrol is now a core component of the integrated pest management. Biocontrol is defined as “the study and uses of parasites, predators and pathogens for the regulation of host (pest) densities”. Considerable successes have been achieved in the implementation of biocontrol strategies in the past. This chapter presents a review of the history of biocontrol, its advantages and disadvantages; the different types of biological control agents (BCAs) including predators, parasites (parasitoids) and pathogens (fungi, bacteria, viruses and virus‐like particles, protozoa and nematodes); the effect of biocontrol on native biodiversity; a few case studies of the successful implementation of biocontrol methods and the challenges encountered with the implementation of biocontrol and future perspectives.",book:{id:"5527",slug:"natural-remedies-in-the-fight-against-parasites",title:"Natural Remedies in the Fight Against Parasites",fullTitle:"Natural Remedies in the Fight Against Parasites"},signatures:"Tebit Emmanuel Kwenti",authors:[{id:"191763",title:"Dr.",name:"Tebit Emmanuel",middleName:null,surname:"Kwenti",slug:"tebit-emmanuel-kwenti",fullName:"Tebit Emmanuel Kwenti"}]},{id:"70336",title:"Plastics Polymers Degradation by Fungi",slug:"plastics-polymers-degradation-by-fungi",totalDownloads:1459,totalCrossrefCites:3,totalDimensionsCites:8,abstract:"The studies on plastic degradation are very important for the development of biodegradable plastics, and for reduction of pollution, since plastic waste can remain in the environment for decades or centuries. We have showed the degradation of oxo-biodegradable plastic bags and green polyethylene by Pleurotus ostreatus. This fungus can also produce mushrooms using these plastics. The plastic degradation was possibly by three reasons: (a) presence of pro-oxidant ions or plant polymer, (b) low specificity of the lignocellulolytic enzymes, and (c) the presence of endomycotic nitrogen-fixing microorganisms. In this chapter, the plastic bags’ degradation by abiotic and microbial process using the exposure to sunlight and the use of a white-rot fungus will described. The physical, chemical, and biological alterations of plastic were analyzed after each process of degradation. The degradation of plastic bags was more effective when the abiotic and biotic degradations were combined.",book:{id:"8997",slug:"microorganisms",title:"Microorganisms",fullTitle:"Microorganisms"},signatures:"José Maria Rodrigues da Luz, Marliane de Cássia Soares da Silva, Leonardo Ferreira dos Santos and Maria Catarina Megumi Kasuya",authors:[{id:"217699",title:"Dr.",name:"Jose Maria",middleName:null,surname:"Da Luz",slug:"jose-maria-da-luz",fullName:"Jose Maria Da Luz"}]}],onlineFirstChaptersFilter:{topicId:"151",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. 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. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,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. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. 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This group of bio-inspired metaheuristics solves multiple optimization problems by applying the metaphor of natural selection. It so far has solved problems such as resource allocation, routing, schedule planning, and engineering design. Moreover, in the field of machine learning, evolutionary computation has carved out a significant niche both in the generation of learning models and in the automatic design and optimization of hyperparameters in deep learning models. This collection aims to include quality volumes on various topics related to evolutionary algorithms and, alternatively, other metaheuristics of interest inspired by nature. 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. 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,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"111683",title:"Prof.",name:"Elmer P.",middleName:"P.",surname:"Dadios",slug:"elmer-p.-dadios",fullName:"Elmer P. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. 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Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation"},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. 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Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. 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