Velocity of the spread of shared information.
\\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:"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"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"7945",leadTitle:null,fullTitle:"Cryopreservation - Current Advances and Evaluations",title:"Cryopreservation",subtitle:"Current Advances and Evaluations",reviewType:"peer-reviewed",abstract:"Cryopreservation - Current Advances and Evaluations sheds light on storage of cells at subzero temperatures while ensuring that biological functionality is not compromised. Cryopreservation presents a perfect technique by which life can be preserved for posterity. However, there are many challenges to overcome and questions to answer, such as: Are organisms and metabolic systems functioning normally after cooling and thawing? This book provides comprehensive information on cryopreservation with a particular focus on cryoprotectant agents (CPAs). CPAs prevent ice from forming on cryogenically preserved cells, tissues, and organs, but can become toxic at high concentrations. As such, more research is needed to determine their precise mechanisms of action and to develop potential new CPAs that will not compromise the biology of cells. This book is an attempt in this direction.",isbn:"978-1-83880-206-6",printIsbn:"978-1-83880-205-9",pdfIsbn:"978-1-83880-630-9",doi:"10.5772/intechopen.77775",price:119,priceEur:129,priceUsd:155,slug:"cryopreservation-current-advances-and-evaluations",numberOfPages:190,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"0fe037813f921f4136cd393b7ff8dfe1",bookSignature:"Marian Quain",publishedDate:"June 10th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/7945.jpg",numberOfDownloads:6517,numberOfWosCitations:1,numberOfCrossrefCitations:7,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:16,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:24,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 1st 2019",dateEndSecondStepPublish:"August 29th 2019",dateEndThirdStepPublish:"October 28th 2019",dateEndFourthStepPublish:"January 16th 2020",dateEndFifthStepPublish:"March 16th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"72151",title:"Dr.",name:"Marian",middleName:"Dorcas",surname:"Quain",slug:"marian-quain",fullName:"Marian Quain",profilePictureURL:"https://mts.intechopen.com/storage/users/72151/images/system/72151.jpeg",biography:"Marian D. Quain is a biotechnologist whose research focuses on\nutilization of tissue culture techniques for production of clean\nplanting materials, germplasm conservation and use of molecular tools for crop diversity, disease diagnostics and molecular\nmarker assisted selection breeding using transcriptomics and\nproteomics, as well as application of recombinant gene technology for crop improvement. Marian was a Visiting Research Fellow\nat Leeds University, UK, from 2011 to 2013. In 2013, she was adjudged the National\nBest Research Scientist by the Ghanaian Ministry of Food and Agriculture (MoFA).\nShe is a 2017 Cochran Fellow and a fellow of the Africa Science Leadership Program. In 2018, she won the American Society for Plant Biologists Award for Excellence in Education for her outstanding contributions to plant biology education.",institutionString:"Council for Scientific and Industrial Research",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Council for Scientific and Industrial Research",institutionURL:null,country:{name:"Ghana"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"692",title:"Biotechnology",slug:"engineering-biomedical-engineering-biotechnology"}],chapters:[{id:"69120",title:"The Use of Chitooligosaccharides in Cryopreservation: Discussion of Concept and First Answers from DSC Thermal Analysis",doi:"10.5772/intechopen.89162",slug:"the-use-of-chitooligosaccharides-in-cryopreservation-discussion-of-concept-and-first-answers-from-ds",totalDownloads:672,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The use of dimethyl sulfoxide (Me2SO) as a cryoprotectant agent (CPA) is controversial. Indeed, this cryoprotectant agent (CPA) is cytotoxic and potentially mutagenic. Therefore, other cryoprotectants must be used to reduce the proportion of Me2SO in slow-freezing solutions. In this chapter, we propose to present the first evaluation of new non-penetrating cryoprotectants: the chitooligosaccharides (COS). These molecules are chitosan oligomers, which are biocompatible, antioxidant, and bacteriostatic. We first review the use of saccharides through cryopreservation processes. We question the possibility to reduce penetrating CPA during slow-freezing procedures. We propose to use COS as extracellular CPA to reduce the use of Me2SO. We question the biocompatibility of COS on mouse embryos through the analysis of the cells’ development. Next, we evaluate these molecules in slow-freezing solutions with a reduced quantity of Me2SO. Our experimental approach is a physical method often used to characterize slow-freezing solutions. Differential scanning calorimetry (DSC) allows to evaluate the crystallization and melting processes, the amount of crystallized water, and the equilibrium temperature and consequently to evaluate the impact of different cryoprotectants. This study gives a better understanding on how slow-freezing protocols could be improved with extracellular CPA.",signatures:"Hugo Desnos, Pierre Bruyère, Magda Teixeira, Loris Commin, Gérard Louis, Stephane Trombotto, Amani Moussa, Laurent David, Samuel Buff and Anne Baudot",downloadPdfUrl:"/chapter/pdf-download/69120",previewPdfUrl:"/chapter/pdf-preview/69120",authors:[null],corrections:null},{id:"69078",title:"Cryoprotection of Platelets by Grafted Polymers",doi:"10.5772/intechopen.89272",slug:"cryoprotection-of-platelets-by-grafted-polymers",totalDownloads:727,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Unlike red blood cells (RBC) which are stored at 4°C, platelets are stored at 22–24°C (room temperature) due to biophysical and biochemical changes induced by cold temperatures aggregately known as the ‘cold storage lesion’ (CSL). However, 22°C storage greatly increases the risk of microbial growth, thus limiting the safe storage of platelets to only 5–7 days (versus 42 days for RBC). Consequent to the short shelf life of platelets, blood services face chronic shortages of these life-saving cells. To overcome both the risk of microbial contamination and the constrained supplies of platelets, renewed research into attenuating the CSL and/or determining where cold stored platelets are clinically suitable are ongoing. In this chapter, we show that the covalent grafting of methoxypolyethylene glycol (mPEG), a biocompatible polymer, to the membrane of platelets attenuates the CSL. Moreover, the grafted mPEG serves as a potent cryoprotectant allowing platelets to be stored at 4°C, or frozen at −20°C, while retaining normal platelet counts and biologic function. The successful development of platelet PEGylation may provide a means by which the cold storage of platelets can be achieved with a minimal loss of platelet quality while improving both platelet microbial safety and inventory.",signatures:"Mark D. Scott, Nobu Nakane and Elisabeth Maurer-Spurej",downloadPdfUrl:"/chapter/pdf-download/69078",previewPdfUrl:"/chapter/pdf-preview/69078",authors:[null],corrections:null},{id:"71258",title:"Cryomedia Formula: Cellular Molecular Perspective",doi:"10.5772/intechopen.91382",slug:"cryomedia-formula-cellular-molecular-perspective",totalDownloads:622,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The growing market of cell therapy medicinal products (CTMPs) and biopharmaceuticals demand effective cryopreservation with greater safety, of which the currently available cryoprotective agents [CPAs (e.g., dimethyl sulfoxide, glycerol, trehalose, etc.)] alone are unable to provide. This is due to the need of applying high concentration of CPAs to achieve verification that concomitant oxidative damages. Formulating cocktail of compounds with anti-freezing and antioxidants properties found to be advantageous to overcome the resultant damages. Each cocktail, however, demonstrate overlapping and/or unique protective and modulation effect patterns. The advance technology and research tools (e.g., OMICs) provide a deep insight on how the formulation of cryomedia can influence the cellular pathways and molecular interactions. In fact, this shed the light over the uniqueness of cryomedia formulation and how can they serve various application purposes.",signatures:"Noha A. Al-Otaibi",downloadPdfUrl:"/chapter/pdf-download/71258",previewPdfUrl:"/chapter/pdf-preview/71258",authors:[null],corrections:null},{id:"70321",title:"Cryopreservation of Human Spermatozoa: A New Frontier in Reproductive Medicine",doi:"10.5772/intechopen.90152",slug:"cryopreservation-of-human-spermatozoa-a-new-frontier-in-reproductive-medicine",totalDownloads:656,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:1,abstract:"Cryopreservation is a worldwide technique that makes it possible to preserve different living cells and tissues, including male and female gametes and embryos, in a structurally intact state using low temperature over time. Since the starting point of the cryopreservation era in 1776, until today, this was one of the most important steps in assisted reproductive techniques. Conventional slow freezing of spermatozoa is commonly used for cryopreservation of both ejaculated and surgically retrieved spermatozoa. The technique of the slow freezing is principally based on dehydration of cells which is performed through slow cooling combined with low concentrations of a cryoprotectant agent for achieving a balance. Besides of slow freezing, for more than a decade, many reports suggest the sperm vitrification technique as an alternative to slow freezing. Contrary to the slow freezing method, with vitrification, the effects of the cryoprotectants in spermatozoa are eliminated since this method is cryoprotectant-free. All of these interesting and promising protocols of vitrification, however, have not been implemented in the lab routine yet, and slow freezing remains the standard cryopreservation method in most laboratories worldwide.",signatures:"Nabil Sayme",downloadPdfUrl:"/chapter/pdf-download/70321",previewPdfUrl:"/chapter/pdf-preview/70321",authors:[null],corrections:null},{id:"70167",title:"Cryobiology and Cryopreservation of Sperm",doi:"10.5772/intechopen.89789",slug:"cryobiology-and-cryopreservation-of-sperm",totalDownloads:1247,totalCrossrefCites:1,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Low temperature has been utilized to keep living cells and tissues dormant but potentially alive for cryopreservation and biobanking with great impacts on scientific and biomedical applications. However, there is a critical contradiction between the purpose of the cryopreservation and experimental findings: the cryopreserved cells and tissues can be fatally damaged by the cryopreservation process itself. Contrary to popular belief, the challenge to the life of living cells and tissues during the cryopreservation is not their ability to endure storage at cryogenic temperatures (below −190°C); rather it is the lethality associated with mass and energy transport within an intermediate zone of low temperature (−15 to −130°C) that a cell must traverse twice, once during cooling and once during warming. This chapter will focus on (1) the mechanisms of cryoinjury and cryopretection of human sperm in cryopreservation, and (2) cryopreservation techniques and methods developed based on the understanding of the above mechanisms.",signatures:"Ali Erdem Öztürk, Mustafa Numan Bucak, Mustafa Bodu, Nuri Başpınar, İlhami Çelik, Zhiquan Shu, Nazan Keskin and Dayong Gao",downloadPdfUrl:"/chapter/pdf-download/70167",previewPdfUrl:"/chapter/pdf-preview/70167",authors:[null],corrections:null},{id:"69458",title:"Current Advancements in Pancreatic Islet Cryopreservation Techniques",doi:"10.5772/intechopen.89363",slug:"current-advancements-in-pancreatic-islet-cryopreservation-techniques",totalDownloads:881,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"There have been significant advancements in the research of pancreatic islet transplantations over the past 50 years as a treatment for Type 1 Diabetes Mellitus (T1DM). This work has resulted in hundreds of clinical islet transplantation procedures internationally. One limitation of the procedure includes effective storage techniques during donor-recipient cross-matching following islet isolation from deceased donor. Cryopreservation, which is heavily used in embryology research, has been proposed as a prospective method for pancreatic islet banking to bridge the temporal intervals between donor-recipient matching. The cryopreservation methods currently involve the freezing of islets to subzero (−80/−196°C) temperatures for storage followed by a thawing and warming period, which can be increasingly harmful to islet viability and insulin secretion capabilities. Recent advances in islet cryopreservation technologies have improved outcomes for islet health and survivability during this process. The aim of this chapter is to characterize aspects of the islet cryopreservation method while reviewing current procedural improvements that have led to better outcomes to islet health.",signatures:"Samuel Rodriguez, David Whaley, Michael Alexander, Mohammad Rezaa Mohammadi and Jonathan R.T. Lakey",downloadPdfUrl:"/chapter/pdf-download/69458",previewPdfUrl:"/chapter/pdf-preview/69458",authors:[null],corrections:null},{id:"71192",title:"Cryopreservation in Ophthalmology",doi:"10.5772/intechopen.91312",slug:"cryopreservation-in-ophthalmology",totalDownloads:773,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Amniotic membranes (AMs) and corneas are critical materials in ocular surface reconstruction. AM has specific structures (e.g., basement and two types of cells with stemness characteristics: amniotic epithelial cells and amniotic mesenchymal cells), which contribute to its attractive physical and biological properties that make it fundamental to clinical application. The corneal endothelial cell is a vital part of the cornea, which can influence postoperative vision directly. However, widespread use of fresh AM and cornea has been limited due to their short use span and safety concerns. To overcome these concerns, different preservation methods have been introduced. Cryopreservation is distinguished from many preservation methods for its attractive advantages of prolonged use span, optimally retained tissue structure, and minimized infection risk. This review will focus on recent advances of cryopreserved AM and cornea, including different cryopreservation methods and their indications in ophthalmology.",signatures:"Yuting Shao, Chao Chen, Qi Zhou, Jun Yang, Xiao Lv, Mingyue Lin and Yanlong Bi",downloadPdfUrl:"/chapter/pdf-download/71192",previewPdfUrl:"/chapter/pdf-preview/71192",authors:[null],corrections:null},{id:"69726",title:"Methods of Thermal Analysis as a Tool to Develop Cryopreservation Protocols of Vegetatively Propagated Crops",doi:"10.5772/intechopen.89723",slug:"methods-of-thermal-analysis-as-a-tool-to-develop-cryopreservation-protocols-of-vegetatively-propagat",totalDownloads:940,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Cryopreservation is considered to be a reliable biotechnological tool for the long-term conservation of vegetatively propagated plant germplasm. The technique is based on freezing plant tissues at an ultralow temperature. However, high water content in plant tissue can result in injury during the cooling and thawing processes. Water behavior in the process of cryopreservation can be assessed by the use of thermal analysis method. This chapter demonstrates how the use of heat flux-type differential scanning calorimetry (DSC) thermal analysis methods such as standard DSC, temperature-modulated DSC (TMDSC), and quasi-isothermal temperature-modulated DSC (QITMDSC) can be used to assess the amount of freezable water and verify if the tissue being used has reached glass transition as well as analyzing the thermal events during cooling and freezing to reduce crystallization and damage by frost. Here, you can find a guide on how these thermal analysis methods can be applied, through concrete examples of each method and their use in the development of a more reliable and precise cryopreservation protocol for vegetatively propagated plant species.",signatures:"Stacy D. Hammond, Miloš Faltus and Jiří Zámečník",downloadPdfUrl:"/chapter/pdf-download/69726",previewPdfUrl:"/chapter/pdf-preview/69726",authors:[null],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"509",title:"Biomedical Science, Engineering and Technology",subtitle:null,isOpenForSubmission:!1,hash:"eec1ae8717629372ed7f0c0499dee14c",slug:"biomedical-science-engineering-and-technology",bookSignature:"Dhanjoo N. Ghista",coverURL:"https://cdn.intechopen.com/books/images_new/509.jpg",editedByType:"Edited by",editors:[{id:"35845",title:"Prof.",name:"Dhanjoo N.",surname:"Ghista",slug:"dhanjoo-n.-ghista",fullName:"Dhanjoo N. 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Flight Deck Interval Management (FIM) that exchanges place as well as altitude information straight between planes is a major engineering for Continuous Descent Operation (CDO), resulting in extremely effective aircraft operations. Underneath CDO, arrival plane descends at a continuous velocity to the runway at a state close to idling; current study results have shown the power effectiveness of this particular strategy [1, 2].
Conventional analysis suggests that the Aircraft Surveillance Applications System (ASAS) may well not have the ability to deal with the projected intense increase in the variety of the plane until the authors are able to conquer the bottleneck of information sharing involving aircrafts. ASAS enables the surveillance of adjacent traffic flow in the environment with the traditional Automatic Dependent Surveillance Broadcast (ADS-B), which are only able to process incredibly limited quantities of info, which means that innovative correspondence is not possible [3].
Even though satellite and the ground-based internet for aircraft usually do not address whole routes, they offer non-mission-critical internet services to passengers. Additionally, they usually need the assembly of a new antenna on the plane.
In this analysis, the authors think about the setup of an ad hoc mesh system which employs the current aircraft communication tools almost as practical and will discuss adequate information for the FIM. This proposal is dependent on the concept that the existing aircraft facilities are untouched, making it possible for a system to be recognized without renovating the outside of the plane. Additionally, the proposed technique is powerful against unexpected accidents including equipment breakdown, since it does not need a predesigned topology and can transmit data redundantly throughout the mesh network.
This concept is complementary to the standard internet technology for aircraft like satellite web. It’s potential to run web-based FIM even once the plane is outside of the coverage of internet services.
The authors discuss about connected researches on candidates for inter-aircraft computer networks in terms of the Open Systems Interconnection (OSI) reference model. The authors likewise outline the current radio equipment found in a regular aircraft.
The authors discuss IEEE 802.11 (Wi-Fi), IEEE 802.16 m (WiMAX), and IEEE 802.15.4 and IEEE 802.22 that are standardized by the IEEE 802 standardization committee for local area networks (LAN) and metropolitan area networks (MAN). The authors subsequently present an overview of satellite internet as well as the ground-based internet. Lastly, the authors are going to touch on the communication technologies presently used in the plane.
Since satellite constellations are able to work with low-earth-orbit satellites, the correspondence latency is smaller than within the net service provided by fixed satellites. Because the places of those satellites are completely dynamical, an omnidirectional antenna is usually required, and far additional power is required than for communication with fixed satellites [13, 14].
The authors here shortly discuss about ground-based aircraft internet, aircraft-based internet, and latest developments in long-distance and low-speed network engineering for the Internet of Things (IoT).
References [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25] show other popular wide-area network technologies.
A voice communication channel using 118.0–136.975 MHz (VHF) is also used. The audio uses amplitude modulation (AM).
In the network layer protocol, the Internet Protocol (IP) is the factual standard whenever the datalink level has enough bandwidth. Based on the IP, the Space Communications Protocol Specifications (SCPS) may also be used when the band is extremely narrow, like for communication between the earth and other planets [26, 27].
It’s typical for the transport layer to ensure end-to-end communication. In networks based on the IP for the network layer, the Transmission Control Protocol (TCP) is often used for the transport layer [28]. Networks which do not make use of IP for the network layer, or networks which use IP but do not use TCP for the transport layer, occasionally have the own error-correction mechanism of theirs in the transport layer [29, 30].
The authors do not go over the session layer (or higher layers), as they are not the topic of the research.
In the prior section, the authors summarized the conventional wireless network technologies. They’re, nonetheless, unsatisfactory due to the constraints of the conventional aircrafts.
IEEE 802.11, IEEE 802.15.4, IEEE 802.16 m, and LTE will need the aircrafts to be engineered with freshly installed antennas and are probably jammed by high-speed movement and may experience radio interference since they are acting secondary modulation by OFDM.
As satellite internet utilizing geostationary satellites does not handle the polar regions, it cannot be put on to aircraft traveling near the North Pole. Furthermore, satellite internet utilizing satellite constellations necessitates broadcasting communication among the satellites, which is technically complicated.
Air-based internet and ground-based internet aren’t ideal for FIM because the coverage area of theirs is limited.
Long-distance, low-speed networks (excluding LoRa) aren’t ideal for FIM, because interaction with mobile vehicles is not taken into consideration in the design of theirs.
Consequently, the authors have created a brand new low-speed (1–10 kbps) wireless communication technology to allow the inter-aircraft flow of information. The authors assume that conventional wireless communications are used by the aircraft in the physical layer and the datalink layer for the best backward compatibility with existing aircrafts.
The authors present the following communication protocol and then verify the effectiveness of the protocol by simulations.
The authors assume narrowband wireless communication in the physical layer. In order to share the antenna with SSR [31, 32] the authors utilize the UHF band as well as frequency modulation (FM) that is ideal for digital signals, and they have high noise tolerance.
In thought of the practicableness of implementing this specific protocol on typical aircraft, the authors use precisely the same band as SSR (i.e., the UHF band) to enable the potential for diverting the SSR antenna.
As the current SSR uses a straightforward pulse-based communication protocol, the communication band is incredibly narrow. Thus, a bandwidth of roughly 10 kHz with adjacent frequency as being a carrier is available. The authors consider FM for the noise tolerance at this layer. Generally, AM is utilized for the lower selectivity of voice communication channels in aircraft communication, but as this proposal is restricted to digital communication, FM is adopted since it is more reluctant to interference compared to AM.
The datalink layer encodes/decodes the data we send/receive with metadata including data sender, time code, etc. These packed data are referred to as the packets.
Permit the size of the packet at the season of exchange from the datalink layer to the physical layer be near 256 [octet]. As the littlest metadata, the authors incorporate a “magic” code up to 4 [octet] specifying the character of the packet, a time code (4 [octet]), and a code of the aircraft (4 [octet]). By considering these headers, 240 [octet] is left inside the datagram. Expecting that 2 kbps is offered as the channel of the datalink layer, it will take 1 s to transmit a solitary packet. The transmission capacity of 2 kbps could be feasible.
In detail, aircraft code (4 [octet]) contains the source aircraft code. A unique number for every aircraft must be allocated. The body (240 [octet]), followed by error-detection code, contains data body. The error-detection code (4 [octet]) contains error detection. CRC-32, which inspects the cyclic redundancy check, is used.
Packets having errors and packets older than a planned threshold are discarded. To scale back the load on the network layer, it manages “time to live” of the packet.
The network layer encodes/decodes the sent/received data by wrapping/unwrapping the datalink packet. Dropping any irregular data is done in the network layer. If there are not any irregularities, the data is passed to the upper layer, that is, the transport layer. If needed, the received data is retransmitted. A signal body of the transport layer is named a datagram.
The datagram includes a code (up to 4 [octet]) stating the character of the datagram, a destination aircraft code (4 [octet]), a source aircraft code (4 [octet]), and a time code (4 [octet]). The size of the user space of the datagram is 224 [octet].
In contrast to the internet, it’s really hard to assure end-to-end data transmission in aircraft communication. Therefore, the transport layer does not handle the end-to-end connection feature, that’s characteristic of TCP, like data retransmission requests. Yet, a comparatively powerful error-correction capability is enforced to the transport layer. The Reed-Solomon (RS) code is thought to be the promising candidate. When three datagrams are transmitted together with error-correction signals distributed in four datagrams, the typical quantity of data per datagram is 168 [octet].
The Reed-Solomon code, which is a more practical error-correction function than the cyclic redundancy check (CRC), is enforced within the transport layer. If three datagrams are coupled with the error-correction signal and, as a result, distributed to four datagrams to be transmitted, 168 [octets] per datagram are allotted to the implementer.
In order to confirm the feasibility of the protocol discussed in the prior section, the authors developed the model described below and conducted a simulation experiment.
Assume that
Let
When there’s no error in the communication route, the information
Let us denote the transmission rate of the communication route from aircraft
for some constants
Taking the transmission rate into account, Eq. (1) is modified to
Until now, the authors have dealt with only one-to-one communication from aircraft
Here, the attenuation term
In this report, to statistically investigate the arrival rate of data based on Eqs. (4) and (5), the authors performed computer simulation with the following parameters:
Simulation parameters (yellow,
For every simulation, the authors transmitted data from aircraft
Theoretically, the attenuation term
The authors have done the following two investigations.
Figure 1 shows results of Simulation 1. Figures 2 and 3 show the results of Simulation 2.
Number of aircraft reached.
Number of hops.
In Figure 2, the horizontal axis represents time steps, and the vertical axis represents the amount of aircraft (maximum 1000). The blue curve shows the case of
Typical values of Figure 2 are picked up in Table 1. Time steps 1, 30, and 60 correspond to 1, 30, and 60 s after the start of the communication, respectively.
Generation 1 | Generation 30 | Generation 60 | |
---|---|---|---|
0.1 | 98 | 871 | 953 |
0.316 | 269 | 977 | 992 |
1 | 612 | 996 | 1000 |
Velocity of the spread of shared information.
The horizontal axis and the vertical axis of Figure 3 represent generations and the overall range of hops needed for data transmission, respectively. The blue curve shows the case of
Simulation 1 under the condition of
By the outcomes of Simulation 2, the total amount of hops necessary for information transmission is likely to decrease with a rise in the quantity of time steps. In this particular operation, its anticipated attenuation term
As discussed above, even in a communication environment with extremely small reliability, it’s apparent that highly dependable communication is possible through the dispersed nature of the aircrafts in the airspace. Under the circumstances of this particular simulation, 97.7% of information was correctly shared among the aircrafts with 30 s of interaction. As the datagram size is no more than 224 [octet], aside from error correction in the transport layer, the data rate under a guaranteed transmission rate of 98% is approximately 60 bit/s in the worst case. (In the very best situation, the theoretical value is approximately 1.8 kbit/s).
Even though this appears to be an incredibly narrow communication band for contemporary wireless communication, it’s a feasible numerical value for mission-critical inter-aircraft. For communication which is not mission critical, we are able to consider satellite internet as a complementary protocol.
In this article, the authors reported an investigation of the information flow among aircrafts and proposed a new digital communication protocol which uses ad hoc mesh network technologies. The authors illustrated the proposed protocol could be operated utilizing conventional aircraft hardware and will accomplish extremely dependable interaction with a very short time period (a couple of tens of seconds). The simulations supported the strength of the suggested protocol.
If the ad hoc mesh system introduced in this article will come into reality, the application will cover not only CDO but also the autonomous management of associate adjacent craft. Moreover, computer networks that will manage ultrahigh-speed nodes like aircraft would contribute to conventional mobile digital networks that are actively studied.
This research was supported by
The simulation program used in this report is posted at https://github.com/kanaya. This program works with the Scheme interpreter (R5RS conforming or higher).
Rust diseases are the fungal diseases of plants, mainly grasses, caused by fungi. They affect the aerial plant parts especially leaves but can also attack stems and even flowers and fruits. They bear complex life cycles that require two alternative unrelated hosts. Rusts produce spore pustules which vary in color according to the rust species. About 7000 rust species are known to affect a variety of host plants globally. They can cause a wide range of symptoms depending upon the host species like the formation of Galls or swellings on the branches, formation of Canker on the trunks, and formation of Spores on the surface of the leaf. Leaf rust is also known as bown rust due to the brown color of circular urediniospores on the surfaces of the leaf of the crop. Yellow rust or stripe rust is characterized by the yellow color of stripes on the surfaces of the leaf. Stem rust is also brown and characterized by the patches of brown color on the surface of stems. Many approaches are being deployed to combat the problem of these diseases which involves accurate phenotyping which means characterization of the diseases at field level followed by genotyping to find out the genes responsible for its cause. Many germplasm resources are being explored and screened by scientists worldwide to find new sources of resistance. Precision phenotyping is the key requirement to achieve the goals. So far there are manual interventions involved to screen these diseases. But manual scoring of these diseases is a cumbersome job in large pre-breeding and breeding programs. Therefore, there is a strong need for high precision phenomics which involves imaging using high-quality cameras or equipment followed by image analysis using newly developed software and tools. In today’s era of artificial intelligence, it is possible to explore high-end phenomics to achieve better yields of important crops like wheat. Many machine learning and deep learning models have been tested and tried to analyze and characterize wheat fungal diseases [1, 2, 3, 4]. One of the main reasons for the popularity of these techniques is the use of GPUs (graphics processing units). The classification tools, computer vision, and GPUs are combined in a single framework called deep learning [5]. Deep learning-based models have been used in the various applications of agriculture for end-to-end learning. With the use of GPUs, deep learning can give a better solution to the given problem in a shorter time [6]. The process of building such models is computationally challenging but using GPU power becomes very easy [7, 8]. Fungal diseases have been identified using image processing techniques on different horticulture and agriculture crops. Various feature extraction and classification algorithm have been used to detect the different types of fruits, vegetables, and cereal crops.
Among the various rust diseases, soybean-, coffee-, and wheat-rusts are the most damaging diseases. Therefore, the constant efforts are being done worldwide, to combat this problem. Wheat is one of the staple food crops in addition to rice and maize. The total area under wheat in the world is around 220 million hectares with a production of 772.64 million metric tons (2020–2021). Wheat rusts especially leaf rust, stem rust, and yellow rusts are major fungal diseases that affect the production of the wheat crop throughout the world particularly in South Asian countries [9]. As per the prediction of the Food and Agricultural Organization (FAO) of the United Nations, wheat production might not be fulfilled the requirement in near future due to rapid population growth [10, 11]. In this chapter we discuss the usefulness of deep learning-based algorithms to identify rust using wheat as a case study.
Human perception is based on the interaction between the brain and the eye. On the other hand, computer vision system (CVS) is used to emulate human vision for gathering information without physical interaction [12, 13].
It is also defined as the process of automatic acquisition, and analysis from image data. CVS emulates the dynamic vision system whose operation is very transparent and natural. The data is processed in various stages such as capturing, processing, and analysis of images. Figure 1 depicts the steps involved during image processing. In the first stage, image acquisition and pre-processing are involved. The images can be acquired using high-resolution cameras and sensors. Further, the images are pre-processed through data cleaning, background removing, adding/removing noise, and also enhancing the quality of images. In the second stage, the images are segmented. The segmentation process involves extracting only important and useful information from the whole image that further helps in the discrimination of classes. In the third stage, the high-level analysis is performed in which direct emphasis is done on the recognition (objects) and interpretation (making results). In a CVS, the following attributes contribute to decision-making: shape, color, texture, and also size. Figure 2 depicts the utilization of various artificial intelligence algorithms in plant disease detection. These algorithms are further divided into machine learning and deep learning-based classifiers. The description of these algorithms is illustrated in the coming subsections.
Steps of image processing techniques.
Description of machine learning and deep learning algorithm used for plant disease detection.
Classification is the process of dividing the dataset into different categories or groups by adding labels. Nowadays, the machine learning and deep learning approaches are performing well for classifying the algorithm images based on their category. Following are the machine learning algorithms which are used to classify plant disease and are based on supervised learning. Supervised learning is a type of learning where labels (category of images) are given along with input images.
It is the machine learning algorithm used for classification and calculated by
It is the algorithm of machine learning which comes under supervised learning to solve regression and classification-based problems. The decision tree is the graphical representation of pre-defined rules along with the solution. The graph of the decision tree has two types of nodes: one is decision nodes and another is leaf nodes. Additionally, the edges store the information of the answers to the questions, and leaf nodes store the actual output. In Sabrol and Kumar [16], Chopda et al. [17] and Rajesh et al. [18], the authors reported appreciable results in plant disease classification and recognition.
Support vector machine (SVM) is a very popular classifier used in statistical learning. The classifier aims to discriminate the classes from each other. In SVM, a hyperplane is used to discriminate one class from another. Those points which are close to the hyperplane are referred to as support vectors. The task of the SVM is to classify the different categories based on some features. Additionally, this algorithm performs well in extreme classes. Let us consider, color, texture, shape are some features of a particular plant. If we consider two features such as color and texture to classify diseased and healthy leaves. To classify them, the optimal decision boundary is required. Optimal decision boundaries could result in greater misclassification for the new instance. Therefore, the boundary support vectors are very important than all the training examples. This algorithm works well for linearly separating data points whereas in some cases if the data points are not linearly separable then 2-dimensional (2D) feature spaces are converted into 3-dimensional feature spaces. But the only problem is that it is computationally very expensive. In addition to that, it provides kernel function which can reduce the computational cost to convert 2D feature space to 3-dimensional feature space. Using kernel function the dot product is performed between two vectors. Especially, this is used to transform non-linear to linear transformation space. Various popular kernel functions are polynomial, radial basis, sigmoid kernels used to change 2D data to high dimensional feature space. Choosing the best kernel is a non-trivial task and is a hyper-parameter that can be selected by performing various experiments on the data. The main benefit of using SVM is that it is memory efficient and effective for high-dimensional feature space data.
It is the special type of machine learning algorithm used for classification. The researchers have been working on artificial neural networks (ANNs) since the beginning of the 1980s [19]. ANNs are a special type of classification algorithm and their structure is inspired by the human brain. ANNs takes input from the external world in the form of feature vector or patterns. Each input value is multiplied by their corresponding weights that are summing with the bias value. Further, the result is mapped to the activation function (binary, sigmoid) and produced the output. Other than these algorithms, there are various algorithms available that reported appreciable results in image recognition such as Random Forest, Naive Bayes, many more. Initially, we started with the study of traditional computer vision approaches used for plant disease detection. Plant disease can be caused by fungi, bacteria, and viruses from which fungi are the common disease organism. It is the type of disease that can be formed by taking energy from plants. The fungal disease has been identified using image processing techniques on different horticulture/agriculture crops [20]. To detect the different types of fruits, vegetable, commercial, and cereal crops that have been utilized using various feature extraction and classification algorithms. They achieved appreciable classification accuracy to identify the disease from horticulture/agriculture crops. Han et al. proposed a novel technique for feature extraction using super-pixel and marker-controlled segmentation methods for the classification of yellow rust and septoria diseases. They have used SVM and ANN for these disease classifications. Their experimentation concludes that SVM classifiers outperformed well than ANN classifiers for the classification of disease [21]. Su et al. experimented with the detection of fungal yellow rust disease on wheat crops. The author collected RGB images with a high-resolution camera and there are a total of three different classes present in region of interest (RoI) as rust, healthy, and background. To monitor the yellow rust, they used the U-Net deep learning architecture and the results were compared with the Random Forest algorithm. They found that U-Net-based segmentation outperformed spectral images. In their work, the average precision of 81.06%, recall of 90.10%, and F1-score of 84.00% have been achieved to segment the disease from spectral images [22]. An application of Fuzzy C-Means clustering has been proposed as the model to identify the wheat leaf disease [23]. In their work, they extracted inter- and intra-class features and further combined them to build a model for identifying the different wheat plant diseases. Although the traditional machine learning-based techniques are performing well for image classification, still there are certain limitations such as it requires manual feature extraction and is only suitable for small datasets, which may lead to the over-fitting problem [23, 24].
Convolutional neural network (CNN) is a popular neural network, designed for solving computer vision problems. The architecture of CNNs is shown in Figure 3. The images are represented in the form of pixel values. In the convolution layers, the operation of convolution is performed i.e., the kernel is slide over the input image after choosing the padding and stride values at each layer. Thereafter, the power of non-linearity is to give the non-linear mapping with the input images in such a way that after the non-linear mapping it becomes linearly separable. ReLU activation function is used to change all the negative values to positive values. With this, the pooling layer is used to down sample the different feature maps for getting the most prominent features i.e., the convolution layer performs these triplet operations like convolution followed by ReLU and ReLU followed by pooling one after another. These triplets operations are typically stacked one after another and also based on these triplets, the depth of the neural network has been defined. After these layers, the network is followed by one or more fully connected layers which are responsible for classification.
The basic architecture of CNNs.
To build the CNN model, all the above-mentioned parameters play a very important role. To build the custom CNN model, the numbers of convolution layers, max-pool layer, number of filter values, filter size, stride, padding, number of fully connected layers need to be specified. Increasing the number of convolution layers will produce different feature maps and also increasing the fully-connected layers increase the training time of the model. Although, the custom CNN model reported appreciable accuracy. The process of creating a custom CNN model takes more time. Therefore, the concept of transfer learning comes into the picture. Transfer learning is a concept of deep learning where the weights of pre-trained models are reused for a new problem. Every year, there is a competition held on the ImageNet dataset. Many researchers developed new models to classify the different objects of the ImageNet dataset and reported good classification accuracy and reduced error rate. There are variants of transfer learning models such as ResNet, GoogleNet, and EfficientNet varied in terms of the number of layers, filter size, number of filters used, stride, padding, and so on. Some of the few models are elaborated as given below:
Modern deep learning architectures are significantly popular to solve agriculture-related problems. Sladojevic et al. developed a CNNs based model for plant disease classification. The model recognized 13 different types of plants. In their work, they used 30,880 images in the training and 2589 images for validation and reported a classification accuracy of 96.30% [25]. Zhang et al. proposed a deep learning model for the detection of rust disease of wheat crop from hyperspectral images. In their work, they automate the process of detecting yellow rust-captured images from unmanned aerial vehicle (UAV). Yellow rust is a fungal disease that can cause 100% loss for the wheat crop. The author used the Inception-ResNet model for feature extraction and reported the highest accuracy of 85.00% when compared with the random forest that was 77.00% [26]. A deep learning model has been built for grading wheat stripe rust disease [27]. In their work, they used different mobile devices to capture images and build their dataset, referred WSRgrading. It contained 5242 wheat leaf images at six different levels. They build and proposed the model by adding an attention layer in the pre-trained DenseNet model and build a new model named as C-DenseNet which has been reported a good classification accuracy of 97.99%. Genaev et al. classify the rust disease from the wheat crop. In their work, they used the CGIAR dataset, containing three classes (healthy wheat, leaf rust, and stem rust). They implemented the DenseNet transfer learning model and reported the F1-score and AUC of 0.90 and 0.98, respectively [28]. Jia et al. in proposed the model for detection and segmentation of fruit features for optimal harvesting of apples using Mask R-CNN. ResNet model was used as the backbone of this network. The model was tested on 120 images and reported precision and recall rates of 97.31% and 95.70%, respectively [29]. The shortage of the wheat disease dataset motivated the researchers to create the dataset which should be publicly available for all [30]. They are motivated to collect more data that will help the research community for conducting the research competitions on wheat diseases classification. Finally, they attempted to prepare their WFD2020 dataset which contains 2414 images. They performed their experiments using the EfficientNet CNN-based model and reported 94.20% classification accuracy.
In the recent decade, deep learning techniques are highly utilized for image processing. Deep learning models are producing appreciable results than machine learning methods [31]. Figure 4 depicts the utilization of computer vision approaches (i.e., old machine learning methods and modern deep learning approaches) for the wheat crop. These statistics have been built based on work done from the period (2015 to July 2021) for classifying most of the wheat crop diseases. Deep learning approaches include CNN-based architecture such as VGG16, ResNet, Faster R-CNN, and so on. In different circumstances, the traditional machine learning approaches include SVM, Random Forest, and so on. The analysis concludes that the modern deep learning architectures have been utilized more for classifying most of the wheat crops diseases as compared to traditional machine learning approaches.
Year-wise statistics publication of wheat disease detection.
There are standard datasets that are publicly available for research experimentation in the computer vision and image processing domain, such as PASCAL VOC [32], ImageNet [33], IMDB-Wiki [34], CIFAR [35], and PlantVillage [36]. CGIAR dataset is one of the dataset publicly available on https://www.kaggle.com/shadabhussain/cgiar-computer-vision-for-crop-disease [37]. This dataset was further distributed in three different classes of wheat rust i.e., healthy wheat, leaf rust, and stem rust. A sample of each class is shown in Figure 5. Most of the images in this dataset were collected by CIMMYT and its partners from Ethiopia and Tanzania. Additionally, a few images were sourced from the Google image database. The images in this dataset have the specific characteristics like (i) all are colored (ii) mixed format, (iii) different orientation, (iv) variable quality, and captured with different resolutions. The datasets are already classified into two categories i.e., 876 images and 610 images for training and testing, respectively. From the training dataset (i.e., 876 images) a total of 863 images have been filtered and considered for training the model. In the present study, the 863 images dataset was further split for training and validation in the ratio of 3:1 (i.e., 75% data in training and 25% into validation). Table 1 describes the class-wise distribution of this dataset. It is a challenging task to build an efficient model that is capable to classify all three classes of images accurately.
Sampled images of (a) healthy wheat plant, (b) leaf rust, and (c) stem rust.
Class label | Images | Training set | Validation set |
---|---|---|---|
Healthy wheat | 142 | 105 | 36 |
Leaf rust | 345 | 258 | 86 |
Stem rust | 376 | 283 | 95 |
Total images | 863 | 646 | 217 |
Class-wise distribution of image dataset.
Deep learning is a popular methodology used for image processing. In deep learning models, features are extracted automatically and little human intervention is required to train the model. Deep learning models are quite efficient to discover the internal structure or patterns of high-dimensional data. However, directly processing the original images leads to inappropriate recognition results, therefore, it is necessary to pre-process the images before feeding them to the model. Pre-processing involves e.g. resizing, enhancing, or removing noise of the input images. It is worth mentioning that CNNs perform better for image recognition and classification. There are various transfer learning models which are based on CNNs like AlexNet, VGG16, GoogleNet, and Inception V3, that are pre-trained on the ImageNet dataset. ImageNet is the standard dataset that contains 1000 different categories of objects. CNN’s based transfer learning models reported appreciable results to classify 1000 different objects present in the ImageNet dataset. In the present study, the VGG16 model has been utilized and the architecture is depicted in Figure 6. This model is the composition of 16-layers (13 convolution layers, and 3 fully connected layers). In this model, the images are processed in standard size i.e., 224 × 224. The reason for resizing the fixed image size is to extract the uniform or equal feature maps at the end of the convolution process. This model used a fixed size of kernel i.e., 3 × 3. Sometimes, the kernel is referred to as a filter that is responsible for extracting features from the given images. These extracted patterns or features might be horizontal edges, vertical edges, and a combination of both. Initially, a convolution process has been performed to extract the features, and thereafter the classification is done. In the convolution operation, the kernel/filter is sliding over the image starting from the top left to the bottom right corner to extract the features.
The architecture of VGG16 for wheat rust disease detection.
The movement of the kernel is either pixel-wise or by skipping some pixels using stride values. If the stride value is 1 then the movement of the kernel is shifted by one pixel after another and if the stride value is 2, then the movement of the kernel is shifted by two-pixel values during the operation of convolution. The convolution layers are used to identify the pattern or features from the images which further help in discriminating the classes. The initial layers extract the general features like edges and the subsequent layers extract the domain-specific features. Each convolution block is followed by the max-pool layer which is used for down-sampling the feature maps. In this process, the dimensionality of the image is reduced by retaining the most prominent feature. At the end of the convolution layers, different feature maps are generated as an output. These feature maps are further flattened and mapped with a fully connected layer in the classification module. Here, the model has a feature vector of size 4096 neurons also referred to as dense layer. This feature vector is further passed to the next dense layer of the same size. Finally, the last layer neurons are fully connected to the output neurons by using the soft-max activation function. However, in the current study, we considered the three classes classification problem. Therefore, the output layer changed to three classes using the soft-max probability function. The actual learning starts from data using forward and backward passes. In the forward pass, input neurons are multiplied with the weight values and also apply the activation function as ReLU. ReLU activation function adds non-linearity to the model i.e., all the negative pixel-values become positive after passing through it. On the other hand, in backward pass back-propagation is used to minimize the loss value. In this process, weights and biases are getting updated from the last to the initial layer by calculating the gradients at each layer using a convolution operator.
To summarize this model, the important and noticeable point is that this model has a total of 14,789,955 parameters but 75,267 are trainable parameters and the rest are non-trainable, the reason is that using transfer learning, the already trained weights have been used during building the model. Therefore, the model is trained in less time with fewer number parameters.
Hyper-parameter tuning is the backbone of any deep learning model. Finding the best parameters is a very tedious task, it needs many experiments to be performed while building the model. Hyper-parameters include learning rate, batch size, loss function, number of epochs, and optimizer is usually considered for tuning the model. To build the classification model for three classes each hyper-parameter is considered within a specific range. In this way, several experiments have been performed to build an efficient model. After performing some experiments with the variation in the given hyper-parameters, it was concluded that model accuracy is highly dependent on the batch size, learning rate, number of epochs, and size of the dataset. In the present study, the following hyper-parameters has been utilized:
As discussed in Section 3.1 image dataset of wheat disease classification has been utilized to train the model. We used the online google colab platform with GPU support. Among the performed experiments, we discuss the best one, which produces the highest training accuracy. Table 2 illustrates the training and validation accuracies obtained at different epochs (varied from 10 to 90) along with their loss values. Here, the training accuracy starts with 81.42% on 10 epochs and ends up with 99.54% on 80 epochs. We continued to compute the accuracy for the 90 epochs also but did not get any significant improvement in training accuracy. Although more experiments could be performed by increasing the number of epochs, the accuracy obtained at epoch 80 was quite promising. On the other hand, the validation accuracy fluctuating between 74.76% and 79.05% at different epochs, as shown in Figure 7. Similarly, it was observed that the training loss decreases at every increasing step of the epoch (from 10 to 80). Beyond that, the loss has started to increase. In contrast, the validation loss is fluctuating between 0.60 and 0.65 up to 40 epochs. Then, after 70 epochs it starts increasing rapidly (Figure 8).
Epochs | Training accuracy (in %) | Validation accuracy (in %) | Training loss | Validation loss |
---|---|---|---|---|
10 | 81.42 | 74.76 | 0.50 | 0.65 |
20 | 91.02 | 79.05 | 0.33 | 0.61 |
30 | 95.05 | 77.14 | 0.22 | 0.61 |
40 | 96.59 | 78.10 | 0.18 | 0.61 |
50 | 97.06 | 76.67 | 0.15 | 0.56 |
60 | 97.99 | 78.10 | 0.12 | 0.56 |
70 | 98.61 | 74.29 | 0.09 | 0.66 |
80 | 99.54 | 77.14 | 0.07 | 0.74 |
90 | 99.23 | 74.76 | 0.08 | 0.82 |
Comparison of training accuracy, validation accuracy, and training loss, and validation loss at different epochs.
Representation of the comparison of training and validation accuracy.
Representation of the comparison of training and validation loss.
To test the performance of the trained model, we performed the test experiments on the validation data (i.e., 25% of the total dataset). In this way, a total of 36 sample images of healthy leaf, 87 sample images of leaf rust, and 94 sample images of stem rust have been considered. The evaluation of the testing results was done using a confusion matrix. Figure 9 illustrates the accuracy and confusion with other intra-classes, wherein, it is shown that leaf rust class samples are confused with stem rust class samples due to less variation between classes.
Confusion matrix at epoch = 80.
To summarize this book chapter, different machine learning and deep learning-based models have been discussed to solve plant disease classification and detection problems. Considering a case study of wheat rust diseases, a deep learning-based model is proposed to classify the different wheat rust diseases using a pre-trained VGG16 model. Based on the CGIAR dataset with three classes (stem rust, leaf rust, and healthy wheat), the proposed model has been optimized and produced the classification accuracy of 99.54%, and when evaluated on unseen data it gave a validation accuracy of 77.14%. This model will further help farmers or experts to diagnose disease in the early stages. Although these models give good training accuracy, they were not appropriate to classify stem- and leaf rust when result plot on confusion metrics. This is due to the fact that some images in this dataset contained multiple diseases, meaning that one image contained the features of both leaf- and stem rust. Detection and classification of the wheat rust disease in the early stages lead to high yield at the production level [38]. In the future, we will extend this work by collecting real-time images of wheat rust disease and also incorporating object detection-based algorithms such as Yolov3, Faster R-CNN, and Mask R-CNN [39] to exactly localize the location of the disease in the image.
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Although typical biological treatments of water offer some advantages such as low cost and operability, many investigations referring to the removal of pesticides have suggested that in many cases they have low effectiveness due to the limited biodegradability of many agrochemicals. In recent years, research for new techniques for water detoxification to avoid these disadvantages has led to processes that involve light, which are called advanced oxidation processes (AOPs). Among the different semiconductor (SC) materials tested as potential photocatalysts, titanium dioxide (TiO2) is the most popular because of its photochemical stability, commercial availability, non-toxic nature and low cost, high photoactivity, ease of preparation in the laboratory, possibility of doping with metals and non-metals and coating on solid support. 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Thus, in the present review, we provide an overview of the recent research being developed to photodegrade pesticide residues in water using TiO2 as photocatalyst.",book:{id:"6407",slug:"application-of-titanium-dioxide",title:"Application of Titanium Dioxide",fullTitle:"Application of Titanium Dioxide"},signatures:"Nuria Vela, Gabriel Pérez-Lucas, José Fenoll and Simón Navarro",authors:[{id:"202983",title:"Dr.",name:"Simón",middleName:null,surname:"Navarro",slug:"simon-navarro",fullName:"Simón Navarro"},{id:"202988",title:"Dr.",name:"Nuria",middleName:null,surname:"Vela",slug:"nuria-vela",fullName:"Nuria Vela"},{id:"202989",title:"Dr.",name:"José",middleName:null,surname:"Fenoll",slug:"jose-fenoll",fullName:"José Fenoll"},{id:"206059",title:"Dr.",name:"Gabriel",middleName:null,surname:"Pérez-Lucas",slug:"gabriel-perez-lucas",fullName:"Gabriel Pérez-Lucas"}]},{id:"49965",title:"Utilization of Apatite Ores",slug:"utilization-of-apatite-ores",totalDownloads:2590,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Phosphate rock is an important mineral commodity used in the chemical industry and production of food. The first section of ninth chapter of this book introduces utilization of apatite ores for manufacturing of phosphorus. The second part deals with production of phosphoric acid via wet and thermal process and utilization of byproducts such as phosphogypsum, phosphorous slag and ferrophosphorus. The last section of this chapter describes the methods for production of fertilizers, such as supephosphates, Thomas slag, ammonium phosphates, thermophosphates, etc., and the chapter ends with environmental demand of phosphate fertilizers.",book:{id:"4783",slug:"apatites-and-their-synthetic-analogues-synthesis-structure-properties-and-applications",title:"Apatites and their Synthetic Analogues",fullTitle:"Apatites and their Synthetic Analogues - Synthesis, Structure, Properties and Applications"},signatures:"Petr Ptáček",authors:[{id:"76186",title:"Associate Prof.",name:"Petr",middleName:null,surname:"Ptáček",slug:"petr-ptacek",fullName:"Petr Ptáček"}]},{id:"49970",title:"Introduction to Apatites",slug:"introduction-to-apatites",totalDownloads:3058,totalCrossrefCites:4,totalDimensionsCites:0,abstract:"Apatite is the generic name, which was first introduced by German geologist A.G. Werner. These minerals and their synthetic analogs represent a major class of ionic compounds and the most common crystalline form of calcium phosphates, which are of interest of many industrial branches and scientific disciplines. Since, apatite (fluorapatite) is the most abundant phosphate mineral, apatite bearing phosphate rocks represents an important source of inorganic phosphorus. First chapter of this book introduces the basic concepts of nomenclature, composition, classification, crystal structure, mineralogy and properties of minerals from the supergroup of apatite. Furthermore, the minerals from the group of apatite and polysomatic apatites are described. Since, the most of the topics mentioned in this chapter will be developed in the following chapters, the key concepts provided in this chapter are important to understood before proceeding further.",book:{id:"4783",slug:"apatites-and-their-synthetic-analogues-synthesis-structure-properties-and-applications",title:"Apatites and their Synthetic Analogues",fullTitle:"Apatites and their Synthetic Analogues - Synthesis, Structure, Properties and Applications"},signatures:"Petr Ptáček",authors:[{id:"76186",title:"Associate Prof.",name:"Petr",middleName:null,surname:"Ptáček",slug:"petr-ptacek",fullName:"Petr Ptáček"}]},{id:"39146",title:"ArF Excimer Laser Annealing of Polycrystalline Silicon Thin Film",slug:"crystallization-of-silicon-copper-film-by-excimer-laser-annealing",totalDownloads:7035,totalCrossrefCites:0,totalDimensionsCites:4,abstract:null,book:{id:"1980",slug:"crystallization-science-and-technology",title:"Crystallization",fullTitle:"Crystallization - Science and Technology"},signatures:"Noriah Bidin and Siti Noraiza Ab Razak",authors:[{id:"112868",title:"Prof.",name:"Noriah",middleName:null,surname:"Bidin",slug:"noriah-bidin",fullName:"Noriah Bidin"}]}],onlineFirstChaptersFilter:{topicId:"492",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:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,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:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. 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From\r\n1964 to 1974, he worked as Assistant in Biochemistry at the School of MedicineUniversidad Nacional de La Plata, Argentina. From 1974 to 1976, he was a Fellowof the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor oBiochemistry at the Universidad Nacional de La Plata, Argentina. He is Member ofthe National Research Council (CONICET), Argentina, and Argentine Society foBiochemistry and Molecular Biology (SAIB). His laboratory has been interested for manyears in the lipid peroxidation of biological membranes from various tissues and different species. Professor Catalá has directed twelve doctoral theses, publishedover 100 papers in peer reviewed journals, several chapters in books andtwelve edited books. Angel Catalá received awards at the 40th InternationaConference Biochemistry of Lipids 1999: Dijon (France). 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Dr. Chen\\'s research interests include bioactive compounds, chromatography techniques, in vitro culture, medicinal plants, phytochemicals, and plant biotechnology. He has published more than ninety scientific papers and serves as an editorial board member for Plant Methods, Biomolecules, and International Journal of Molecular Sciences.",institutionString:"National University of Kaohsiung",institution:{name:"National University of Kaohsiung",institutionURL:null,country:{name:"Taiwan"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:45,paginationItems:[{id:"82135",title:"Carotenoids in Cassava (Manihot esculenta Crantz)",doi:"10.5772/intechopen.105210",signatures:"Lovina I. Udoh, Josephine U. Agogbua, Eberechi R. Keyagha and Itorobong I. 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He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null},{id:"255360",title:"Dr.",name:"Usama",middleName:null,surname:"Ahmad",slug:"usama-ahmad",fullName:"Usama Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255360/images/system/255360.png",biography:"Dr. Usama Ahmad holds a specialization in Pharmaceutics from Amity University, Lucknow, India. He received his Ph.D. from Integral University, Lucknow, India, with his work titled ‘Development and evaluation of silymarin nanoformulation for hepatic carcinoma’. Currently, he is an Assistant Professor of Pharmaceutics, at the Faculty of Pharmacy, Integral University. He has been teaching PharmD, BPharm, and MPharm students and conducting research in the novel drug delivery domain. From 2013 to 2014 he worked on a research project funded by SERB-DST, Government of India. He has a rich publication record with more than twenty-four original journal articles, two edited books, four book chapters, and several scientific articles to his credit. He is a member of the American Association for Cancer Research, the International Association for the Study of Lung Cancer, and the British Society for Nanomedicine. Dr. Ahmad’s research focus is on the development of nanoformulations to facilitate the delivery of drugs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"333824",title:"Dr.",name:"Ahmad Farouk",middleName:null,surname:"Musa",slug:"ahmad-farouk-musa",fullName:"Ahmad Farouk Musa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333824/images/22684_n.jpg",biography:"Dato’ Dr Ahmad Farouk Musa\nMD, MMED (Surgery) (Mal), Fellowship in Cardiothoracic Surgery (Monash Health, Aust), Graduate Certificate in Higher Education (Aust), Academy of Medicine (Mal)\n\n\n\nDato’ Dr Ahmad Farouk Musa obtained his Doctor of Medicine from USM in 1992. He then obtained his Master of Medicine in Surgery from the same university in the year 2000 before subspecialising in Cardiothoracic Surgery at Institut Jantung Negara (IJN), Kuala Lumpur from 2002 until 2005. He then completed his Fellowship in Cardiothoracic Surgery at Monash Health, Melbourne, Australia in 2008. He has served in the Malaysian army as a Medical Officer with the rank of Captain upon completing his Internship before joining USM as a trainee lecturer. He is now serving as an academic and researcher at Monash University Malaysia. He is a life-member of the Malaysian Association of Thoracic & Cardiovascular Surgery (MATCVS) and a committee member of the MATCVS Database. He is also a life-member of the College of Surgeons, Academy of Medicine of Malaysia; a life-member of Malaysian Medical Association (MMA), and a life-member of Islamic Medical Association of Malaysia (IMAM). Recently he was appointed as an Interim Chairperson of Examination & Assessment Subcommittee of the UiTM-IJN Cardiothoracic Surgery Postgraduate Program. As an academic, he has published numerous research papers and book chapters. 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Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:318,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/101813",hash:"",query:{},params:{id:"101813"},fullPath:"/profiles/101813",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()