Normalized pulse difference and adjacent pulse amplitude values for \n
\r\n\tFinally, I want to emphasize that, in this book, I expect to have excellent contributons on the subjects other than muscle systems, so that the book will be widely read by people interested in non-muscle motile systems as well as by muscle researchers.
",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:null,priceUsd:null,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"862ba53997da17b644b918fe44e97d4a",bookSignature:"Emeritus Prof. Haruo Sugi",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7021.jpg",keywords:"Musculo-skeletal system, Cardio-vascular system, Porter myosins, Cellular transport, Motile systems, cell division, Contractile ring formation, Mitotic apparatus, Ciliary Movement, Flagellar Movement, Amoeboid movement, Novel motile systems",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 13th 2018",dateEndSecondStepPublish:"September 3rd 2018",dateEndThirdStepPublish:"November 2nd 2018",dateEndFourthStepPublish:"January 21st 2019",dateEndFifthStepPublish:"March 22nd 2019",remainingDaysToSecondStep:"3 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"140827",title:"Emeritus Prof.",name:"Haruo",middleName:null,surname:"Sugi",slug:"haruo-sugi",fullName:"Haruo Sugi",profilePictureURL:"https://mts.intechopen.com/storage/users/140827/images/system/140827.jpg",biography:"Haruo Sugi was appointed instructor in the Depertment of Physiology of the University of Tokyoin 1962, and worked at Columbia University and the National Instututes of Health, USA, from 1965 to 1967. He was a professor and chairman of the Department of Physiology, Teikyo University Medical School from 1973 to 2004, when he became emeritus professor. Professor Sugi organized international symposia on muscle contraction seven times, each followed by publication of proceedings. He also edited 4 books. From 1995 to 2005, Sugi was Cairman of the Muscle Commission in the International Union of Physiological Sciences (IUPS).",institutionString:"Teikyo University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Tokyo",institutionURL:null,country:{name:"Japan"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"6",title:"Biochemistry, Genetics and Molecular Biology",slug:"biochemistry-genetics-and-molecular-biology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"220812",firstName:"Lada",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/220812/images/6021_n.jpg",email:"lada@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"2631",title:"Current Basic and Pathological Approaches to the Function of Muscle Cells and Tissues",subtitle:"From Molecules to Humans",isOpenForSubmission:!1,hash:"34fa138dc948d7121e2915ac84ea30cf",slug:"current-basic-and-pathological-approaches-to-the-function-of-muscle-cells-and-tissues-from-molecules-to-humans",bookSignature:"Haruo Sugi",coverURL:"https://cdn.intechopen.com/books/images_new/2631.jpg",editedByType:"Edited by",editors:[{id:"140827",title:"Emeritus Prof.",name:"Haruo",surname:"Sugi",slug:"haruo-sugi",fullName:"Haruo Sugi"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6694",title:"New Trends in Ion Exchange Studies",subtitle:null,isOpenForSubmission:!1,hash:"3de8c8b090fd8faa7c11ec5b387c486a",slug:"new-trends-in-ion-exchange-studies",bookSignature:"Selcan Karakuş",coverURL:"https://cdn.intechopen.com/books/images_new/6694.jpg",editedByType:"Edited by",editors:[{id:"206110",title:"Dr.",name:"Selcan",surname:"Karakuş",slug:"selcan-karakus",fullName:"Selcan Karakuş"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"59745",title:"Optimization Approaches in Sideband Calculations and a Non- Iterative Harmonic Suppression Strategy in 4D Arrays",doi:"10.5772/intechopen.74586",slug:"optimization-approaches-in-sideband-calculations-and-a-non-iterative-harmonic-suppression-strategy-i",body:'\nChannel capacity is one of the most critical constraints in wireless communication technology that both academic and industrial foundations are trying to efficiently use and/or to increase the capacity with the help of various multiplexing, duplexing and coding algorithms. Although software-based algorithms allow channel capacity to be efficiently used, when it is compared with the hardware modifications of antenna elements (e.g. adaptive smart antenna, MIMO, switched arrays, etc.), the gain stays at minor levels; however, it does not mean that either one of them is strictly selected. Instead, hardware- and software-based modifications should be mixed in order to obtain the best results [1, 2].
\nIn the late 1950s, Shanks and Bickmore introduced a new concept of antenna array design which is based on switching of individual array elements periodically [3]. The concept so-called time modulation enables time variable, t, as an antenna design parameter alongside of complex excitations and placement of the elements. While having a new design parameter, due to the nature of periodic excitations, infinite number of sideband radiations occurs at the multiplying frequencies of switching frequency. In a short while after the introduction of the time modulation concept, Kummer et al. analyzed the time-modulated arrays (i.e., TMAs) in terms of sideband radiations (i.e., SR) [4]. However, time modulation concept has not attracted much attention in scientific community, until the publication of a study on SR suppression with adjusting the sidelobe level using differential evolution algorithm by Yang et al. in 2002 [5]. Furthermore, with the emerging technology in computers and mobile communication infrastructures, TMAs gained popularity, and many studies have been conducted using different metaheuristic algorithms to suppress the harmonics [6, 7, 8, 9, 10, 11, 12] and using different excitation schemes so as to have more control parameters on array design such as shifting/splitting pulses and using different pulse amplitudes [6, 13, 14, 15, 16, 17]. Additionally, interference suppression and adaptive beamforming using TMAs have been studied in the literature [18, 19, 20, 21]. Moreover, TMAs having different geometries such as planar and conformal are analyzed for harmonic suppression [22, 23, 24], and closed form finite calculations have been done expressed in order to calculate total radiated power in SRs for different excitation schemes and geometries [25, 26, 27, 28, 29, 30, 31, 32].
\nAlthough there are many studies that are trying to suppress the all SRs since harmonic radiations consume main radiation power, the emerging concepts such as direction finding, spatial diversity and adaptive beamforming require harmonic radiations to be used in communication. In the literature, TMAs have been used for direction finding and direction of arrival estimation applications [33, 34, 35, 36, 37], harmonic beam steering, adaptive beamforming and communication over sidebands [38, 39, 40]. Furthermore, an overview about TMAs and additional enhancements for TMA concept have been published by Maneiro-Catoria
In this chapter, harmonic radiation suppression methods using metaheuristic algorithms will be examined in detail, total power calculations for SRs will be presented and novel non-iterative algorithmic method for harmonic suppression will be introduced.
\nAntenna arrays having identical elements are examined independently of directional characteristics of the radiating elements (e.g., dipole, horn, etc.) since total electric field is calculated by multiplication of array factor with electric field of single element at reference point according to pattern multiplication. The time-modulation concept introduces a periodic switching for array elements and array factor, naturally, becomes dependent to excitation function unlike conventional arrays. Although it is shown that signal radiation in switched arrays (i.e., time-modulated arrays, TMAs) may be approximated to an unmodulated array under the condition of switching frequency being much smaller than carrier frequency by Bregains
In order to derive an average power density for TMAs, it is convenient to start the derivation from the Poynting vector, and the instantaneous power density is defined as
\nwhere \n
and
\nHere, \n
In order to obtain time average radiation power density, Poynting vector can be integrated over one period (i.e., \n
If \n
Once the \n
where \n
and
\nwhere \n
Since, \n
It must be noted that Eq. (11) is only valid under the condition of \n
where \n
Hence, in the light of the result obtained in Eq. (11), this array factor may be written in phasor form for a specific harmonic number \n
As shown in Eq. (14), in switched arrays, the excitation strategies directly affect both the main and sideband radiations. Every periodic function is eligible to be used for switching the array elements; however, once the constraint number that shapes the radiation patterns increases, excitation strategies are needed to be evolved in order to meet the requirements. Time modulation concept firstly introduced by binary switching scheme, which switches the elements between on (‘1’) and off (‘0’) (i.e., variable aperture size (VAS) time scheme), then, the ideas shifting (i.e., pulse shift (PS)) and splitting pulses (i.e., pulse split (PSp) time scheme) are raised in order to get more control on antenna array characteristics. Additionally, another scheme so-called variable pulse amplitude (VPA) has been recently introduced, but the definition of this scheme is left to the further sections. An illustrative representation of these schemes is depicted in Figure 1.
\nCommon excitation schemes - darker bars represent a higher amplitude level.
Metaheuristics may be defined as the nature-inspired algorithms which mimics a natural phenomenon in order to achieve a certain goal. To be specific, the main purpose of this kind of approach is to find a close-optimal parameter set over a specifically constructed mathematical space so-called a search space with some limiting constraints for the problem at hand. Most of the metaheuristic algorithms operate on a point-based search mechanism in order to keep the dimension space unchanged. In other words, these kinds of algorithms operate on a search space sampling strategy which forms a parallel optimal point search mechanism over the search space. These search space samples form a potential solution set called, usually, the population. If there is no pre information about search space behavior, usually, these optimizers start their search from a uniformly distributed search space sample set so-called the initial population.
\nHere, the search space mentioned above is a user-defined mathematical function space, usually called as cost function, fitness function, and so on, which varies depending on the problem construction. Definition of the search space is one of the key issues for these kinds of algorithms since it directly defines the problem. A poorly defined search space may lead inappropriate results even the algorithm works properly. A search space may be constructed in several ways but constructing it on an error function such as mean square or absolute error is a popular approach since the optimal point in an error function is predictable or known. However, using an error-based function is not a necessity, and some other well-defined functions may be used as the function that is to be optimized. If an error-based cost function is formed for a problem at hand, typically the problem becomes a minimization process, and the algorithm tries to find the possible solutions close to a desired one with a minimal error. A typical error-based cost function may be defined as
\nwhere \n
After constructing and sampling the search space, that is, defining the cost function and constructing the initial population, all point-based metaheuristics move these initial samples to different points by their predefined operators. Actually, at this point, it should be noted that mainly the algorithms differ from each other by their relocation processes. Each algorithm uses a different method to relocate the initial samples, and usually this relocation process gives the algorithms name. By the relocation of the initial samples, a newly generated sample points are created which stands for new solutions. Comparing these new solutions with old ones and keeping the suitable ones and discarding the others cause an overall improvement in the solution set. In other words, after creating new points by relocation process, comparing them with already known possible solutions and keeping the better ones provide an overall convergence to the optimal point. This comparing and selecting process may be conducted in several ways such as using a roulette wheel or a binomial selection. In any way, the basic idea behind this process is comparing the solutions and keeping the better ones in order to improve the solution quality at hand. The relocation and selection process continues until a satisfied convergence rate is achieved or an iteration limit is reached. By this way a close optimal point may be found after the overall process in an iterative manner.
\nThe basic idea behind the metaheuristics may be defined in main four steps as
As stated above,
As all metaheuristic approaches, definition of the problem is the key issue in sideband suppression problems. In TMA design which the sidebands are not to be used on specific purpose, the sideband radiation levels are preferred to be as low as possible in order to concentrate the total power in main radiation, lowering the total signal interference and unwanted signal radiations. However, suppressing sideband radiations is not the only problem while designing a switched antenna array. The most important problem needs to be solved is shaping main radiation beam with required side lobe level which may be a ‘must’ of design procedure since it affects communication quality directly. Then, other parameters which affect the performance such as harmonic-level suppression, harmonic beamforming and spatial filtering (i.e., adaptive beamforming to use the power effectively and null-steering to reduce interference) need to be handled to have the best fit design for application. Thus, if all the constraints are taken into consideration at the same time, different optimization algorithms become practical once today’s computers’ calculation speed, performance and technology are getting better day by day.
\nAs mentioned earlier, designing a switching array via a metaheuristic algorithm starts with the definition of the problem at hand which will directly appears as the cost function that the algorithm uses. After the problem definition, the switching strategy should be decided which directly affects the parameter vector construction. These two steps should be taken into account carefully. First, a possible mistake in problem definition causes a defected mathematical search space, actually the search space will not be mathematically defected, but wrong statements will lead completely different search space that does not related to the actual problem. The algorithm will produce some results but completely unrelated to the problem actually at hand. Second, an improper selection of the excitation strategy may lead the problem unsolvable. As a simple example to this situation, for a problem involving only the suppression of specific harmonic levels, the selection of a VAS or PS switching scheme makes the problem unsolvable, since the harmonic levels are bounded to each other in these schemes. Hence, the independent suppression of different harmonics is mathematically impossible for this problem using the mentioned time schemes. At this point, it is beneficial to decide which conventional array design parameters such as element orientation or excitation phase to use in addition to the ‘time’ parameter. Since many problems may be solved via only correct switching strategy, this selection is not crucial and the traditional parameters may be excluded from the problem. However, adding some extra degrees of freedom will relax the solution of the problem and in some specific cases such as main beam steering, inclusion of the excitation phase to the parameter vector is inevitable. From this point on, the rest of the procedure is the application of the optimizer to the problem. The key points in the TMA design may be summarized as
Defining the problem correctly,
Selecting the switching strategy correctly,
Deciding whether the conventional parameters are necessary.
The first study dealing with harmonic suppression via an optimizer has been appeared by Yang
In the cost function of an optimizer, this SBL information may be directly added. As an example, for a linear array and a problem involving only sideband suppression, the cost function of the metaheuristic algorithm may be written as
\nwhere \n
Extracting harmonic information to use in an optimizer from harmonic pattern samples is a generic way, but it is not the only way to gather information about sidebands. Calculating the power in sidebands and forming a bound function may also be used in sideband suppression problems. These two methods will be discussed in separate sections.
\nIn 2008, Bregains
In this section, the general form of the harmonic power equation will be briefly derived. Before beginning derivation, some remarks should be noted. First of all, the equation derived here is an asymptotic approximation which the far field and \n
Under these approximations, assume that \n
Here, \n
where \n
Here, the Fourier coefficients depend on the switching strategy. If an ideal PS time scheme consisting of rectangular pulses is considered, the switching function may be modeled as
\nwhere \n
where \n
For \n
Hence, the infinite summation of this product turns out to be
\nwhere \n
For \n
By using relation for summable series, see [25, Appendix], Eq. (25) reduces to
\nFor above equation, there exist 53 different situations in terms of equality and inequality conditions of switching instants. However, considering the fact that \n
In this expression, the bar represents the duration of the intersection between nth and mth element’s pulses. For more detailed calculations, [28] may be followed. By substituting this result into Eq. (19), it becomes
\nwhere \n
and it should be solved. Solving this integral is not an easy task, and some manipulations should be conducted. It can be started from to write some exponential terms in terms of Bessel functions. To do that, the following fact may be used
\nBy using this conversion and integral definition of ordinary Bessel functions with \n
Here, \n
Here, \n
Once the \n
Here, \n
where
\nIn this expression, \n
Hence, for \n
and \n
If this result is substituted to Eq. (19) with Eq. (24) and Eq. (27), total power approximation may be finally written as
\nOnce more, in Eq. (40), \n
The first usage of total power in harmonic suppression problems was conducted by Poli
where \n
Without loss of generality for a problem concerning only harmonic reduction, the usage of Eq. (41) in a cost function may be written as
\nwhere \n
As shown in [30] that neither harmonic level reduction nor power reduction ensures the total suppression in terms of both power and communication level. Hence, a combined way may be a more suitable approach if the both level and power reduction is necessary. At this point, the question of ‘is there any simple method exist for the level calculation in order to avoid time consuming processes?’ may be emerged in mind, and it can be said that such studies exist in the literature as an answer to this question.
\nAs mentioned earlier, the SBL method extracts the harmonic level information in an algorithmic way similar to sidelobe calculations. Since there exists no analytical solution to find a maximum point in an unknown sidelobe region, the sidelobe calculations are being conducted such an operation involving sampling the pattern in sidelobe region and finding its maximum. In contrast, the sideband calculations are being conducted in whole visible region, which makes a difference in both SLL and SBL calculations. In other words, since the SBL calculations are being operated over a complete elevation and azimuth space, in some cases, the maximum of a pattern may be extracted analytically. Since the maximum points of all individual harmonic patterns can be calculated, they form a maximum sideband level set, and the maximum point of this set bounds the whole harmonic maxima. Hence, this maximum appears as a bound function covering all individual maximum harmonic levels which are infinite in number. By this way, lowering the total bound ensures that all harmonic levels are below this level.
\nFirst attempt to calculate a bound is conducted by Aksoy for linear arrays with a VAS time scheme [23]. In this attempt, the Poynting vector of a TMA has been considered, and the pattern equations are written as the normalized time average power densities. After this first attempt, Aksoy and Afacan published a proof including the planar case using the same idea [24]. By this way, an overall harmonic bound for a VAS switched time modulated linear and planar arrays has been written as
\nwhere \n
Like all switched array calculations, this technique also uses an asymptotic approach and valid only under far-field and \n
If the harmonic levels are wanted to be suppressed via the bound function in a metaheuristic approach, the result produced by the bound function may be used directly in the cost function of the optimizer. The aim of the optimization is to find a switch-on duration sequence to satisfy a desired level. Hence, the parameter vector should contain at least switch-on durations \n
where \n
Moreover, the equality has been derived for the VAS scheme, but it can be used for any switching strategy involving one pulse in one period. On the other hand, the efficiency of the bound function in shifted cases does not appear as good as in the VAS scheme, since the equality does not contain information about starting instants. In practice, the actual harmonic levels are usually less than, or at least equal to, the results produced by the bound function. In VAS scheme, the difference between actual and the bound is usually getting smaller as the iteration counts, but the same statement does not hold true for the PS scheme. Hence, using the bound function in shifted cases causes a random walk on starting instants, and this situation may lead to some impractical results.
\nInstead of using “on–off” switching scheme, a novel approach is proposed by Aksoy in 2014 named as “Variable Pulse Amplitude (VPA),” which is based on switching each element between amplifiers [12]. The main purpose of the newly proposed method is preventing the array silencing, which means all the elements are switched to “off” position and no communication at that instant (e.g., see Figure 1). The binary “On – off” switching with modifications such as shifting and splitting allows the array to be silenced even for a very short time instant and the received or transmitted data may be missed at defined instants that all the elements are switched off in every period of switching. It may be seemed as minor possibility, but it results in additional optimization constraint to be checked whether there is an instant that all the elements are turned-off or not.
\nIt is stated in previous sections that metaheuristic optimization algorithms do not guarantee to find the proper solution; however, they generally produce acceptable results. In addition, optimization time may be longer than usual according to host computer performance, optimization constraints and iteration count. On the contrary, a noniterative approach offers a fast and reliable solution with respect to metaheuristic methods, and it is preferable if exists.
\nIn this part of section, a novel approach in order to suppress the sideband levels using variable pulse amplitude excitation scheme will be presented with an explanatory example.
\nThe array factor of time-modulated array is given in Eq. (14), and it is easy to understand that complex Fourier coefficients (CFC), \n
where \n
In this equation, while \n
where\n
To sum up, after some basic manipulations, CFCs for all radiation levels are expressed as
Without loss of generality, since discrete windowing functions (e.g., Chebyshev or Taylor \n
In order to shape the main radiation pattern and to suppress the harmonic level, according to the abovementioned definitions, steps to be followed are given as:
Using this approach, main radiation sidelobe level is directly adjusted according to windowing function parameters, and fundamental harmonic radiation is suppressed with the ratio of \n
Let us assume a 10-element -30 dB Chebyshev array whose elements are located along
Harmonic suppression ratio, \n
In this simple example, maximum harmonic levels are selected as \n
Results for
1 | \n2 | \n3 | \n4 | \n5 | \n6 | \n7 | \n8 | \n9 | \n10 | \n||
---|---|---|---|---|---|---|---|---|---|---|---|
\n\n | \n\n\n | \n0.258 | \n0.430 | \n0.669 | \n0.878 | \n1.000 | \n1.000 | \n0.878 | \n0.669 | \n0.430 | \n0.258 | \n
\n\n | \n0.233 | \n0.389 | \n0.605 | \n0.794 | \n0.905 | \n0.905 | \n0.794 | \n0.605 | \n0.389 | \n0.233 | \n|
\n\n | \n0.025 | \n0.041 | \n0.064 | \n0.084 | \n0.095 | \n0.095 | \n0.084 | \n0.064 | \n0.041 | \n0.025 | \n|
\n\n | \n\n\n | \n0.258 | \n0.430 | \n0.669 | \n0.878 | \n1.000 | \n1.000 | \n0.878 | \n0.669 | \n0.430 | \n0.258 | \n
\n\n | \n0.249 | \n0.417 | \n0.648 | \n0.851 | \n0.969 | \n0.969 | \n0.851 | \n0.648 | \n0.417 | \n0.249 | \n|
\n\n | \n0.008 | \n0.013 | \n0.021 | \n0.027 | \n0.031 | \n0.031 | \n0.027 | \n0.021 | \n0.013 | \n0.008 | \n
Normalized pulse difference and adjacent pulse amplitude values for \n
If \n
Results for
Nowadays, communication technology moving rapidly toward 5G, and frequency spectrum is one of the most important issues in terms of operational and capital expenses for industry. In order not to use redundant frequency bands and to make efficient use of channel capacity, it is preferred to suppress the unwanted harmonic radiations. In this study, a general overview on harmonic suppression in 4D arrays using optimization methods is given. A brief mathematical background for switched arrays and the optimization basics are explained. Furthermore, common excitation strategies and the techniques used in harmonic calculations are summarized. More importantly, a noniterative algorithmic suppression strategy is introduced and exemplified via a simple example of the harmonic suppression of a 10-element linear array with sidelobe control. The results of the introduced noniterative strategy seem quite satisfactory in terms of calculation complexity as compared to suppression via an optimizer.
\nDendritic cells (DCs) are professional antigen presenting cells (APCs), the only cells capable of specifically activating naïve T cells and are key orchestrators of an immune response. They are a rare, heterogeneous population of haematopoietic cells that are equipped to capture, process and present antigen (Ag) to the adaptive immune system.
In a non-inflamed or steady state setting, DCs constantly sample the local environment for Ags and have the potential to induce peripheral tolerance via T cell anergy or deletion [1]. DCs recognise danger via pattern recognition receptors (PRR) on their cell surface, the cytoplasm and within cellular organelles [2]. Ligation of PRRs by pathogen associated molecular patterns (PAMPs) or damage associated molecular patterns (DAMPs), activates DC and licences DC to upregulate co-stimulatory marker expression such as CD86 and CD80 on their cell surface and initiate immunogenic T cell priming.
DCs situated in non-lymphoid tissues, also known as migratory DCs, constantly migrate to draining lymph nodes (LNs), maturing during this process, to present Ag to naïve T cells. Resident DCs in lymphoid organs are immature and maintain tolerance during steady state, but can stimulate naïve T cells when activated
In humans, the majority of DC characterisation studies are of DCs isolated from the blood due to the rarity of the cell type and limited access to human tissue samples, although more investigations on non-lymphoid DCs in the skin, lung and liver have recently emerged [4, 5, 6, 7]. DCs in the blood comprise ~1% of total peripheral blood mononuclear cells (PBMCs) and are traditionally identified by the high expression of MHCII (HLA-DR) and the lack of lineage markers CD3, CD14, CD15, CD19, CD20 and CD56, although the latter marker has recently been shown to be expressed on gut and other non-lymphoid DCs [6].
Human blood DCs can be divided into conventional DCs (cDCs) and plasmacytoid DCs (pDCs), which are HLA-DRhiCD11c+123− and HLA-DRhiCD11c−123+ respectively. Human blood cDCs are further categorised into cDC1 and cDC2 subsets. Additionally, there are monocyte-derived DCs that originate separately from cDCs and pDC precursors. The recent use of whole population and single cell sequencing techniques has been instrumental in elucidating transcription factors and surface markers that are unique to each DC subset, which has helped identify relationships between DC subsets across species and tissues as well as corroborate DC functional analyses [6, 7, 8, 9], summarised in Table 1.
DC subsets | |||
---|---|---|---|
cDC1 | cDC2 | pDC | |
Surface phenotype | CD11c+HLA-DR+ CD123−CLEC9A+ XCR1+Necl2+ CD141+ CD11b−CD172α− | CD11c+HLA-DR+CD123− CD1c+CD11b+CD172α+ CLEC10A+ with further subdivision based on CD5hiCD32B+CD163−CD36− or CD5loCD32B−CD163+CD36+ | CD11c−HLA-DR+ CD123+CD303+ CD304+CD45RA+ CD2+/− |
Transcription factors | |||
PRR expression | TLR3, 8 | TLR2, cytosolic RNA sensors (RIG-I, MDA-5), STING | TLR7, 9, STING |
Ag presentation | Cross-presentation of cellular Ag | Cross-presentation of soluble Ag | CD4+ and CD8+ T cell priming* |
Roles in immunity | Potent producer of Type III IFN (after TLR3 stimulation), CTL priming, Th1 response | Th1, Th17 response | Potent producers of Type I and III IFN and mediating anti-viral immunity |
Key features of human DC subsets.
Previous Ag presentation abilities by pDCs are now suggested to be contributed by contaminating AXL+Siglec6+ (AS) DCs.
cDC1s constitute ~0.03% of PBMCs and are found in the blood, tonsil, spleen and non-lymphoid tissues such as the skin. They were classically defined by the high expression of CD141 (blood DC antigen 3 (BDCA3) or thrombomodulin) [10]. However, CD141 is not a completely specific marker for cDC1 as it is also expressed on endothelial cells, monocytes and other DC subsets [8]. Using phenotypic, transcriptional and functional assays, these CD141+ DCs have been further characterised as CD11c+HLA-DR+CD11b−CD172a− CLEC9a+XCR1+Necl2+ cells that lack monocytic markers CD14 and CD16 [4, 11] identifying them as human cDC1 [12, 13, 14, 15, 16].
The dependence of CD141+ DCs on Flt3 ligand (FL), an important DC developmental factor, has been demonstrated
PRRs expressed by human cDC1s are predominantly Toll-like receptor (TLR) 3, located in endosomes and which recognises double-stranded RNA and TLR8, also located in endosomes and which recognises bacterial ssRNA and mammalian mitochondrial RNA [10, 22]. In response to TLR3 signals [23] and also HCV
The cDC1s are superior to other DC subsets in their ability to present ex
Human cDC2, traditionally known as CD1c+ or BDCA1+ DCs, constitute ~1% of PBMCs and can be identified by the expression of CD11c, CD11b, CD13, CD33, CD172a, HLA-DR and CD45RO [2, 10, 26]. The phenotypic similarities between these DCs and moDCs, as well as the expression of CD1c on B cells and other DC subsets, have made the precise segregation of this subset quite difficult. Although previous studies have used CD64 to exclude monocytes from bonafide CD1c+ DCs in the blood, cDCs express low levels of this marker and cannot be definitively used to separate the cell populations [6, 7]. More recently, the use of single cell RNA sequencing techniques has identified additional surface phenotypic markers, such as
The cDC2 DCs highly express TLR2 and also express a range of cytosolic viral RNA sensors such as RIG-I [30, 31]. Different proposed cDC2 subsets also seem to have different PRR expression patterns. For example, CD5hi cDC2 express high levels of TLR7 and 8 compared to CD5lo cDC2 and CD32B+ cDC2 express higher levels of
Activated cDC2s can drive Th17 immune response and can also produce high levels of IL-12p70, potentially inducing Th1 differentiation [2, 29]. However, current data suggests Th17 versus Th1 driven responses may be independently driven by CD5+ versus CD5lo cDC2 subsets, respectively [8, 28].
Human cDC2s are able to cross-present
The pDCs constitute ~0.01–0.04% of PBMCs and commonly reside in secondary lymphoid organs localising in the follicular cortex, T cell nodules and around high endothelial venules [36, 37]. As their name suggests, pDCs are similar in morphology to that of plasma cells. Under light microscopy, pDCs are observed to be spherical in shape with a rounded nucleus, often predominant endoplasmic reticulum and present as clusters in T-cell rich regions of lymphoid tissue [36, 37, 38].
The pDCs, originally identified as ‘natural interferon producing cells’ (NIPC), are renowned for their ability to drive immense type I and type III IFN production via TLRs 7 and 9 [39, 40, 41]. This IFN production is essential to combat viral infection but pDC-derived IFN is also thought to contribute to disease in autoimmune diseases including systemic lupus erythematosus [42]. They are also thought to play a role in Th2 induction and asthma progression in humans [42]. Conversely, pDC have also been shown to play a major role in tolerance
pDCs are recognised as being CD11c−/loCD45RA+CD123+CD303+CD304+HLA-DR+ and can express CD56 (reviewed in [2]). pDCs may also be identified by their transcription factors including; TCF4 (also known as E2-2), SPIB, ZEB2, IRF8, IRF7 and IRF4 [43, 44, 45]. Haploinsufficiency in the
The pDCs can be divided into 2 subsets based on CD2 expression [47]. Recent single cell transcriptomic profiling of blood DCs from healthy donors has revealed that CD2+ ‘pDC’ also express AXL and SIGLEC6 (known as AS DCs). These AS DCs can stimulate CD4+ and CD8+ allogeneic T cell proliferation whereas the segregation of pDCs away from contaminating AS DCs demonstrated potent IFN-α production after TLR9 stimulation and a lack of T cell priming attributes [8]. Whether AS DCs and pDC are 2 distinct cell types or differentiation stages of one another is yet to be defined.
A rare and highly aggressive acute leukaemia known as Blastic Plasmacytoid Dendritic Cell Neoplasm (BPDCN) involves the malignancy of pDC precursors [48], driven, at least in part by the juxtaposition of the pDC-specific RUNX2 enhancer and the MYC promotor due to the chromosomal translocation (6;8)(p21;q24) [49]. The BPDCN can be reliably identified by immunohistochemical staining with TCF4 and CD123 antibodies [50]. BPDCNs most commonly present as skin lesions and may be accompanied by swelling of other organs such as the lymph nodes, bone marrow or spleen. Standard chemotherapy treatments for myeloid neoplasms often result in poor prognosis [51] although a toxin-conjugated anti-CD123 drug, tagraxofusp-erzs, has recently been approved as the first FDA-approved BPDCN-specific treatment [52].
Monocyte derived DC (moDC) refers to DCs induced from monocytes with GM-CSF
It still remains unclear whether the moDC actually represent an
While the
The ability to present Ag and activate the adaptive immune response makes DCs an attractive target to re-invigorate anti-cancer immunity. There are different types of DC vaccines, with the most common type involving the
Thus far, a wide variety of moDC vaccine strategies have been trialled [68]. moDCs have been differentiated and matured using monocyte conditioned medium with various supplements of cytokines (TNF-α, GM-CSF, IL-4, IFN-α), TLR agonists (LPS) and other factors such as prostaglandin E2 [67, 68, 69]. There is also variety in the type of Ags loaded into DCs such as peptides from tumour-associated Ags (TAA), TAA-encoding mRNA and whole tumour lysates [67]. More recently, the electroporation of synthetic mRNA encoding DC-maturation factors such as CD40 ligand, constitutively active TLR4 and CD70 together with fusion proteins DC-LAMP and melanoma-associated Ags into autologous moDCs (TriMixDC-MEL) have proven safe and immunogenic in phase 1 clinical trials in metastatic melanoma [70]. However, the variation in the aforementioned vaccine factors as well as the route of DC administration (intranodal, i.v.) and lack of standardised method of moDC generation has shown variable efficacies of moDC vaccines in clinical outcomes.
More recent clinical trials using naturally circulating blood DCs have turned to CliniMACS system by Miltenyi to isolate different DC subsets from patients (Figure 1). Two completed Phase I clinical trials have used CD1c+ DCs (cDC2) loaded with TAA peptides in hormone refractory metastatic prostate cancer and metastatic melanoma and observed good safety and immunogenicity [71, 72]. Another completed Phase I trial using pDCs showed the induction of tumour-Ag specific CTL response as well as an IFN signature [33]. On-going clinical trials, as summarised by Bol et al., are not only isolating single DC subsets for vaccination, but are also trying combination vaccines comprised of cDC2 and pDC subsets and using dual-activating maturation agonists such as single stranded RNA that stimulates TLR8 on cDC2 and TLR7 on pDCs (NCT-02993315, NCT-02574377, NCT-02692976) [67]. However, there are still many challenges in using naturally circulating blood DCs in tumour vaccinations. The methodology for isolation of sufficient CD141+ cDC1 DCs, which comprise only 0.03% PBMCs, is still lacking and will be important to harness due to their superior ability to cross-present dead and necrotic Ag. Furthermore, although improved over the years, the duration of DCs spent
Overview of potential roles of DC in cancer therapies. To improve current cancer treatments and the activation of tumour-specific CTL, DC may be directly targeted
Apart from the
The tumour microenvironment (TME) is a complex niche of tumour cells, stromal cells and tumour infiltrating myeloid and lymphoid immune cells. The dynamic nature of this niche varies with different types and stages of cancer, as well as between patients themselves. It has been established that the infiltration of CD8+ cytotoxic T cells have been associated with better treatment outcomes with checkpoint blockade therapies in a number of cancer types including metastatic melanoma [83]. However, the phenotype and role of tumour-infiltrating DCs (TIDCs) are less clear, possibly due to the lack of consistent markers probing DCs within the TME and the lack of distinctions between monocyte and putative DC subsets [84].
Using immunohistochemistry staining, many studies have previously used CD1a and S100 proteins to identify TIDCs. The higher density of these cells within tumours correlated with better clinical outcomes in melanoma and head and neck cancers [84, 85]. However, discrepancies in this correlation were reported in colon, breast, gastric, nasopharyngeal, lung and ovarian cancers [84, 86, 87, 88]. One major factor that could explain these reported discrepancies is the markers used to identify DCs. CD1a and S100 are expressed at different levels on Langerhans cells (LCs), interdigitating DCs and moDCs, but not on cDCs or pDCs and the expression of these markers on epithelial-tropic DCs such as LCs could account for the strong correlations observed in only the epithelial cancers [84]. Furthermore, DC activation markers CD83 and DC-LAMP were used to identify mature DCs, though CD83 is not expressed in all DC subsets [7, 84, 89]. In breast adenocarcinoma patients, immature DCs were found to localise within the tumour whereas CD83/DC-LAMP+ mature DCs localised in the peri-tumour edges [90]. Some studies have reported significant correlations between the intratumoral infiltration of mature DCs with better clinical outcomes. For example, a recent report showed that the recruitment of DC-LAMPhi cells into the tumour stroma exhibited strong correlations with significantly higher overall and relapse-free survival in high-grade serous ovarian carcinoma [91]. However, this correlation has also been inconsistent in a number of different cancers [85, 90, 92, 93, 94].
More recently, with the establishment of The Cancer Genome Atlas (TCGA) program, scientists are able to compare DC-specific signatures with a publicly available molecular and clinical database of a vast array of cancers. In melanoma and breast cancer patients, DC-specific genes such as
Whilst the recent data above points towards a benefit of the infiltration of conventional DC into tumour sites, the correlation between tumour infiltrating pDCs and poor survival prognosis is clear. This has been described in breast, head and neck, ovarian and lung cancers [100, 101, 102, 103] where it is thought that pDC-induced tolerance and impaired IFN-α production contributes to a suppressive, non-immunogenic TME. Indeed mouse studies point to a role of TGF-β in the tumour environment in preventing an activatory phenotype of pDC and favouring a tolerising, IDO producing phenotype [104].
Further factors within the TME that have been illustrated to correlate with DC infiltration or function include for example, vascular endothelial growth factor (VEGF), a tumour angiogenic factor, inversely correlated with DC density and overall survival in gastric adenocarcinoma tissues [87, 105]. High serum VEGF levels were also associated with low blood cDC1 and cDC2 numbers in colorectal and non-small cell lung cancers and treatment of VEGF decoy receptor, VEGF-Trap, increased the proportion of mature DCs, but not overall numbers or DC priming function in various solid cancer patients [106, 107, 108]. Direct evidence of VEGF-induced DC inhibition was also reported in DCs differentiated from CD34+ precursors and moDCs [105, 106, 109]. Other cytokines such as IL-6, IL-10 and TGFβ have also demonstrated DC-inhibitory effects in the TME [104, 110, 111, 112, 113, 114].
In metastatic melanoma patients, higher active β-catenin signalling within the tumour was associated with low cDC1 signatures and T cell signatures [115]. Furthermore, the expression of fatty acid synthase was inversely correlated with CD11c+ DC signatures in ovarian, prostate and bladder cancers [116].
Chemotherapy and radiotherapy have remained the core pillars of cancer treatments. However, the combination of these traditional therapies with immunotherapies targeting immune checkpoint receptors has greatly enhanced patient clinical outcomes, especially in patients with immunogenic cancers, summarised in Table 2.
Checkpoint inhibitor (CI) | CI cell expression | Ligand | Ligand cell expression | Anti-CI mAb clinical name | Clinical outcome |
---|---|---|---|---|---|
PD-1 | T, B, NK cells, DC | PD-L1/2 | PD-L1: DC, monocytes, Treg, cells, tumour; PD-L2: Activated cDC, moDCs | Pembrolizumab, Nivolumab | Approved for metastatic melanoma, renal cell carcinoma, squamous-cell carcinoma of head and neck, Hodgkin’s lymphoma, metastatic colorectal, non-small cell lung, Merkel cell and ovarian cancers Improved clinical outcomes in combination with peptide/vector vaccines for advanced solid cancers, metastatic melanoma and HPV-16-related cancers |
CTLA4 | T cells, activated moDCs | CD80/86 (B7.1/2) | APC | Ipilimumab, Tremelimumab | Approved for metastatic melanoma, renal cell carcinoma and colorectal cancer treatments Mixed results in combination with peptide and moDC vaccines |
TIM-3 | T, B cells, cDC, myeloid cells | Galectin-9, CEACAM-1, HMGB1, phosphatidylserine | Tumour | — (pre-clinical) | — |
LAG-3 | Activated T, NK cells, pDCs | MHCII | APC | LAG-3Ig fusion protein | Elevated clinical activity Phase I/II trial in combination with paclitaxel for metastatic breast carcinoma |
ICOS | Treg cells, activated T cells | ICOS-L | APC (especially activated pDCs) | MEDI-570 | Phase I Trial for T cell lymphoma (National Cancer Institute Clinical Trial NCT02520791) |
List of checkpoint inhibitors, their ligands, cell expression and clinical associations.
Immune checkpoints consist of a family of co-stimulatory and co-inhibitory receptors expressed by T cells that modulate their immune responses. Signalling from these receptors depends on their interaction with specific ligands present at the surface of various immune and non-immune cells. These regulatory pathways are a major cause of immune suppression during cancer due the high levels of co-inhibitory ligands being expressed in the tumour microenvironment, resulting in T cell immunosuppression. Monoclonal antibodies (mAb) blocking programmed cell death 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4), two co-inhibitory immune checkpoint receptors have become routine treatment against many malignancies and more therapeutic molecules against members of the immune checkpoint family are being trialled. Here we review the role of DC in the response to immune checkpoint therapies.
PD-1 is expressed by activated T cells and interacts with two ligands, PD-L1 (B7-H1/CD274) and PD-L2 (B7-DC/CD273). PD-1 engagement results in downregulation of T cell proliferation and function [117]. This inhibitory pathway is harnessed by tumour cells to escape attack by T cells through expression of PD-L1 on their cell surface. Anti-PD-1/PD-L1 therapies have shown considerable effects on patients with high PD-L1-expressing tumours, boosting the effector functions of tumour-associated CD8+ T cells inducing tumour regression. To date, two anti-PD-1 mAb (Pembrolizumab, Nivolumab) and three anti-PD-L1 mAb (Atezolizumab, Durvalumab, Avelumab) have been approved for the treatment of cancers including advanced melanoma, non-small-cell lung cancer, head and neck squamous cell carcinoma, Hodgkin lymphoma and renal carcinoma [118].
The ligands for PD-1 are abundant on DC. PD-L1 expression is on pDC and cDC subsets and upregulated in response to inflammatory stimuli and following exposure to platinum-based chemotherapy drugs [84, 119]. Furthermore, PD-L1 is also highly expressed on DC that infiltrate tumours as exemplified by the high PD-L1 expression measured on both pDC and multiple myeloma cells isolated from the bone-marrow of multiple myeloma patients [120]. PD-L2 is detectable at low levels on cDC only after activation and is highly expressed by moDC [121]. Whether PD-L2 is also expressed by DC in different TMEs and the effect of anti-PD-L2 therapies is yet to be defined.
cDC1 play a critical role in the efficacy of anti-PD-1/PD-L1 mAb therapies. Single cell mass spectrometry analyses of PBMC from patients with advanced melanoma, before and after anti-PD-1 therapy revealed that CD141 and CD11c, both expressed by cDC1 are strong predictive biomarkers of clinical response to anti-PD-1 treatments [122]. This is consistent with several mouse studies reporting that cDC1-deficient mice do not respond to immune checkpoint blockade using anti-PD-L1 or a combination of anti-PD-1 anti-CTLA4 mAb [123, 124]. Furthermore, mice that possess cDC1 defective in antigen cross-presentation fail to establish CTL responses and do not respond to anti-PD-1 blockade [125].
The success of anti-PD-1 therapy also depends on a cross-talk between cDC1 and T cells in the TME. In mouse models anti-PD-1 treatment induces IL-12 production by tumour-infiltrating cDC1 [124, 126] which amplifies T cell effector functions. In melanoma patients, the clinical electroporation of an IL-12 plasmid in the tumour lesions enhances the CTL gene signature, thus validating the role of this cytokine in supporting CTL responses [126], Figure 1.
In addition to its ligands, expression of the PD-1 receptor on DC has been reported during cancer. In hepatocellular carcinoma patients, detectable levels of PD-1 were reported on peripheral blood cDC1, cDC2 and pDC whereas PD-1 was only present on cDC1 in healthy donors. This was confirmed with microscopy analyses of cancerous liver tissues showing co-expression of PD-1 and the DC marker CD11c [127]. In line with this data, co-expression of PD-1 and PD-L1 was detected on CD11c+ DC isolated from the tumours of non-small cell lung cancer patients [128]. However, in this case, PD-1 was absent from DC isolated from the PBMC of either cancer patients or healthy donors, suggesting that PD-1 is upregulated locally on DC in response to the immunosuppressive tumour environment [128].
Mouse studies support an inhibitory role of PD-1 on DC [127]. This finding however contrasts with a recent study revealing that PD-1 can establish
Combining anti-PD-1/PD-L1 therapy with DC-based vaccines, or vaccines that target DC
The co-inhibitory immune checkpoint CTLA4 (CD152) is constitutively expressed by regulatory T cells (Treg) and by effector T cells upon activation. CTLA4 is highly homologous to the co-stimulatory receptor CD28 and binds the same ligands CD80 and CD86 (B7.2), however with a much higher affinity. As such, CTLA4 outcompetes CD28 for ligand binding and reduces CD28-mediated co-stimulation of T cell functions. CTLA4 blockade promotes anti-tumour immunity by increasing the activation of effector T cells and by depleting Treg in the TME. The CTLA4 blocking mAb Ipilimumab and Tremelimumab have been approved for the treatment of metastatic melanoma, renal cell carcinoma and colorectal cancer [118].
CTLA4 on T cells directly alters DC functions by removing the CTLA4 ligands (CD80/86) from their cell surfaces. When human moDC are co-cultured with CTLA4+ T cells, CD80/86 levels on DC decrease rapidly in a CTLA4-dependent manner. This mechanism, named trans-endocytosis, involves the physical capture of CTLA4 ligands by the receptor and their degradation. This process is upregulated by TCR engagement [139, 140]. Mouse
Besides their regulation through CTLA4-CD80/86 interaction, moDC also express the CTLA4 molecule upon activation by TLR stimuli. Treatment of these cells with an agonistic anti-CTLA4 Ab induced increased production of IL-10, reduced expression of IL-8 and IL-12 and decreased T cell stimulation capacity [145]. MoDC are also able to secrete CTLA4 in extracellular microvesicles. Microvesicular CTLA4 has been shown to downregulate CD80 and CD86 on moDC [146].
Combinatorial approaches of anti-CTLA4 mAb with cancer vaccines have been tested in clinics and have yielded mixed results. In melanoma patients, peptide vaccines, in combination with anti-CTLA4 Ipilimumab did not show better clinical outcomes compared to Ipilimumab alone [127, 147, 148]. However, other strategies using DC vaccines have provided promising results. For instance, the co-administration to melanoma patients of autologous moDC that have been pulsed with tumour peptide, together with Tremelimumab, resulted in objective and durable tumour responses [149]. Furthermore, a phase II study using Ipilimumab and moDC electroporated with synthetic mRNA (TriMixDC-MEL) has been tested in advanced melanoma patients and has shown an encouraging rate of highly durable tumour response [150].
T cell immunoglobulin mucin-3 (TIM-3) is a co-inhibitory immune checkpoint receptor expressed by all T cell populations as well as B cells and a large variety of myeloid cells. Four TIM-3 ligands have been identified, including Galectin-9, CEACAM-1, HMGB1 and phosphatidylserine. Engagement of TIM-3 on tumour-infiltrating T cells induces exhaustion and suppresses tumour immunity. Preclinical studies have reported high therapeutic activities of blocking anti-TIM-3 antibodies against various types of malignancies and clinical trials with TIM-3 inhibitors are currently underway [128].
High TIM-3 expression has been reported on cDC1 and cDC2 from peripheral blood [151, 152, 153] and on tumour-associated cDC1 and cDC2 from mammary tumour biopsies [152]. Mouse models indicated that blocking TIM-3 on cDC1 leads to an increase in the T cell chemoattractant CXCL9. Moreover, cDC1 expressing TIM-3 correlated with CXCL9 expression in human breast cancer biopsies and was positively associated with CD8+ T cell infiltration. These data suggest that TIM-3 blocking in these cancers could potentially enhance CD8+ T cell recruitment to the TME [152].
Lymphocyte activation gene-3 (LAG-3) is a co-inhibitory immune checkpoint receptor expressed on activated T cells and NK cells that recognise MHCII molecules on APCs as a ligand. LAG3 negatively regulates T cell activation and is frequently co-expressed with PD-1 on exhausted T cells in the TME. Several LAG-3-targeting cancer immunotherapies are currently in different phases of clinical development [154].
The interaction between MHCII and LAG-3 not only has effects in T cells, but also induces reverse signalling in DCs that is stimulatory. This was shown using the soluble LAG-3-Ig fusion protein that activates moDC, as indicated by the upregulation of co-stimulatory molecules, the production of several pro-inflammatory cytokines and chemokines and increased allogenic T cell activation. However, Ab-mediated MHCII ligation does not activate moDC, thus showing that the MHCII: LAG-3 interaction is required in this process [155, 156, 157]. Soluble LAG-3-Ig fusion protein in combination with the chemotherapy drug Paclitaxel has demonstrated elevated clinical activity in metastatic breast carcinoma during a phase I/II trial. This treatment also strongly stimulated the patients’ APC, as evidenced by the increase in the number and activation of monocytes, pDC and cDCs [158].
Notably, LAG-3 itself has been found expressed by DC, specifically by a subpopulation of circulating pDC in healthy donors. LAG-3+ pDC are also found in the tumour lesions and in the tumour-draining lymph nodes of melanoma patients and are thought to contribute to the immunosuppressive environment. Engagement of LAG-3 on pDC provides an activating signal, independent of TLR signalling, inducing low IFN-α and high IL-6 expression [159]. Hence, LAG-3-specific mAb in cancer immunotherapies may enhance the anti-tumour immune response by inhibiting LAG-3 signalling in both T cells and DC.
Inducible T cell costimulatory (ICOS) belongs to the co-stimulatory immune checkpoint receptor family and similarly to CD28, enhances the proliferation and effector functions of T cells. ICOS is expressed on activated T cells and constitutively on a subpopulation of Treg [160] while ICOS-L is present at the surface of APC. High ICOS expression on T cells has been particularly observed during anti-CTLA4 therapies and the co-administration of agonistic ICOS-specific mAb further improves the efficacy to CTLA4 blockade [161].
pDC are able to induce immunosuppression though ICOS stimulation. ICOS-L is strongly upregulated by human blood pDC, but not CD11c+ cDC, in response to TLR stimuli or IL-3 [162]. Co-cultures of pDC with allogenic T cells induced IL-10 expression through a mechanism mediated by ICOS-L-ICOS interaction [162] and similar observations were reported with pDC isolated from ovarian carcinoma [163]. Furthermore, pDC are able to induce Treg proliferation though ICOS stimulation [160] and this mechanism likely explains the dramatic accumulation of ICOS+ Treg in ovarian, breast, liver and gastric tumour tissues, in close proximity with ICOS-L+ pDC [101, 164, 165, 166].
DCs are rare, heterogeneous cells with clear roles in anti-tumour immunity. As summarised in Figure 1, understanding how best to activate DC to gain optimal anti-tumour adaptive immune responses will likely involve careful optimisation of adjuvants, checkpoint immunotherapies and DC targeting strategies. Emerging studies will likely examine checkpoint receptors and their ligands on DC, lymphocytes and other cells in tumour environments, in order to design targeted therapies for optimal antigen presentation, DC activation and anti-tumour response.
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",metaTitle:"Team",metaDescription:"Advancing discovery in Open Access for the scientists by the scientist",metaKeywords:null,canonicalURL:"page/team",contentRaw:'[{"type":"htmlEditorComponent","content":"Our business values are based on those any scientist applies to their research. We have created a culture of respect and collaboration within a relaxed, friendly and progressive atmosphere, while maintaining academic rigour.
\\n\\nCo-founded by Alex Lazinica and Vedran Kordic: “We are passionate about the advancement of science. As Ph.D. researchers in Vienna, we found it difficult to access the scholarly research we needed. We created IntechOpen with the specific aim of putting the academic needs of the global research community before the business interests of publishers. Our Team is now a global one and includes highly-renowned scientists and publishers, as well as experts in disseminating your research.”
\\n\\nBut, one thing we have in common is -- we are all scientists at heart!
\\n\\nSara Uhac, COO
\\n\\nSara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
\\n\\nAdrian Assad De Marco
\\n\\nAdrian Assad De Marco joined the company as a Director in 2017. With his extensive experience in management, acquired while working for regional and global leaders, he took over direction and control of all the company's publishing processes. Adrian holds a degree in Economy and Management from the University of Zagreb, School of Economics, Croatia. A former sportsman, he continually strives to develop his skills through professional courses and specializations such as NLP (Neuro-linguistic programming).
\\n\\nDr Alex Lazinica
\\n\\nAlex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics researcher with the university's Intelligent Manufacturing Systems Group, as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and, most importantly, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
\\n"}]'},components:[{type:"htmlEditorComponent",content:"Our business values are based on those any scientist applies to their research. We have created a culture of respect and collaboration within a relaxed, friendly and progressive atmosphere, while maintaining academic rigour.
\n\nCo-founded by Alex Lazinica and Vedran Kordic: “We are passionate about the advancement of science. As Ph.D. researchers in Vienna, we found it difficult to access the scholarly research we needed. We created IntechOpen with the specific aim of putting the academic needs of the global research community before the business interests of publishers. Our Team is now a global one and includes highly-renowned scientists and publishers, as well as experts in disseminating your research.”
\n\nBut, one thing we have in common is -- we are all scientists at heart!
\n\nSara Uhac, COO
\n\nSara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
\n\nAdrian Assad De Marco
\n\nAdrian Assad De Marco joined the company as a Director in 2017. With his extensive experience in management, acquired while working for regional and global leaders, he took over direction and control of all the company's publishing processes. Adrian holds a degree in Economy and Management from the University of Zagreb, School of Economics, Croatia. A former sportsman, he continually strives to develop his skills through professional courses and specializations such as NLP (Neuro-linguistic programming).
\n\nDr Alex Lazinica
\n\nAlex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics researcher with the university's Intelligent Manufacturing Systems Group, as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and, most importantly, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
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