Vegetation cover for the state of Terengganu, Peninsular Malaysia, for years 2000, 2006 and 2017.
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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:"10859",leadTitle:null,fullTitle:"Data Mining - Concepts and Applications",title:"Data Mining",subtitle:"Concepts and Applications",reviewType:"peer-reviewed",abstract:"The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining.",isbn:"978-1-83969-267-3",printIsbn:"978-1-83969-266-6",pdfIsbn:"978-1-83969-268-0",doi:"10.5772/intechopen.95167",price:119,priceEur:129,priceUsd:155,slug:"data-mining-concepts-and-applications",numberOfPages:224,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"63a4e514e537d3962cf53ef1c6b9d5eb",bookSignature:"Ciza Thomas",publishedDate:"March 30th 2022",coverURL:"https://cdn.intechopen.com/books/images_new/10859.jpg",numberOfDownloads:2353,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:2,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:2,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 5th 2021",dateEndSecondStepPublish:"April 2nd 2021",dateEndThirdStepPublish:"June 1st 2021",dateEndFourthStepPublish:"August 20th 2021",dateEndFifthStepPublish:"October 19th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"43680",title:"Prof.",name:"Ciza",middleName:null,surname:"Thomas",slug:"ciza-thomas",fullName:"Ciza Thomas",profilePictureURL:"https://mts.intechopen.com/storage/users/43680/images/system/43680.jpeg",biography:"Dr. Ciza Thomas is currently Senior Joint Director at the Directorate of Technical Education, Government of Kerala, India. Her area of expertise is network security with research interest in the fields of information security, data mining, sensor fusion, pattern recognition, information retrieval, digital signal processing, and image processing. She has more than eighty journal papers and fifty conference publications to her credit. She has edited nine books and published sixteen book chapters. She is a reviewer of more than ten international journals including IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks, International Journal of Network Security, International Journal of Network Management, and Security and Communications Network. Dr. Thomas received an achievement award in 2010 and an e-learning IT award in 2014 from the Government of Kerala.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"6",institution:{name:"Government of Kerala",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"558",title:"Machine Learning and Data Mining",slug:"computer-science-and-engineering-machine-learning-and-data-mining"}],chapters:[{id:"78106",title:"The Concept of Data Mining",doi:"10.5772/intechopen.99417",slug:"the-concept-of-data-mining",totalDownloads:275,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Data mining is a technique for identifying patterns in large amounts of data and information. Databases, data centers, the internet, and other data storage formats; or data that is dynamically streaming into the network are examples of data sources. This paper provides an overview of the data mining process, as well as its benefits and drawbacks, as well as data mining methodologies and tasks. This study also discusses data mining techniques in terms of their features, benefits, drawbacks, and application areas.",signatures:"Julius Olufemi Ogunleye",downloadPdfUrl:"/chapter/pdf-download/78106",previewPdfUrl:"/chapter/pdf-preview/78106",authors:[{id:"354954",title:"Ph.D. Student",name:"Julius",surname:"Olufemi Ogunleye",slug:"julius-olufemi-ogunleye",fullName:"Julius Olufemi Ogunleye"}],corrections:null},{id:"77946",title:"Use Data Mining Cleansing to Prepare Data for Strategic Decisions",doi:"10.5772/intechopen.99144",slug:"use-data-mining-cleansing-to-prepare-data-for-strategic-decisions",totalDownloads:33,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Pre-processing data on the dataset is often neglected, but it is an important step in the data mining process. Analyzing data that has not been carefully screened for such challenges can produce misleading results. Thus, the representation and quality of data are first and foremost before running an analysis. In this paper, the sources of data collection to remove errors are identified and presented. The data mining cleaning and its methods are discussed. Data preparation has become a ubiquitous function of production organizations – for record-keeping and strategical making in supporting various data analysis tasks critical to the organizational mission. Despite the importance of data collection, data quality remains a pervasive and thorny challenge in almost any production organization. The presence of incorrect or inconsistent data can significantly distort the results of analyses, often negating the potential benefits of strategical making driven approaches. This tool has removed and eliminated errors, duplications, and inconsistent records on the datasets.",signatures:"Mawande Sikibi",downloadPdfUrl:"/chapter/pdf-download/77946",previewPdfUrl:"/chapter/pdf-preview/77946",authors:[{id:"357041",title:"M.A.",name:"Mawande",surname:"Sikibi",slug:"mawande-sikibi",fullName:"Mawande Sikibi"}],corrections:null},{id:"77726",title:"Privacy Preserving Data Mining",doi:"10.5772/intechopen.99224",slug:"privacy-preserving-data-mini-1",totalDownloads:253,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Data mining techniques provide benefits in many areas such as medicine, sports, marketing, signal processing as well as data and network security. However, although data mining techniques used in security subjects such as intrusion detection, biometric authentication, fraud and malware classification, “privacy” has become a serious problem, especially in data mining applications that involve the collection and sharing of personal data. For these reasons, the problem of protecting privacy in the context of data mining differs from traditional data privacy protection, as data mining can act as both a friend and foe. Chapter covers the previously developed privacy preserving data mining techniques in two parts: (i) techniques proposed for input data that will be subject to data mining and (ii) techniques suggested for processed data (output of the data mining algorithms). Also presents attacks against the privacy of data mining applications. The chapter conclude with a discussion of next-generation privacy-preserving data mining applications at both the individual and organizational levels.",signatures:"Esma Ergüner Özkoç",downloadPdfUrl:"/chapter/pdf-download/77726",previewPdfUrl:"/chapter/pdf-preview/77726",authors:[{id:"262719",title:"Dr.",name:"Esma",surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç"}],corrections:null},{id:"78473",title:"Multilabel Classification Based on Graph Neural Networks",doi:"10.5772/intechopen.99681",slug:"multilabel-classification-based-on-graph-neural-networks",totalDownloads:170,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Typical Laplacian embedding focuses on building Laplacian matrices prior to minimizing weights of connected graph components. However, for multilabel problems, it is difficult to determine such Laplacian graphs owing to multiple relations between vertices. Unlike typical approaches that require precomputed Laplacian matrices, this chapter presents a new method for automatically constructing Laplacian graphs during Laplacian embedding. By using trace minimization techniques, the topology of the Laplacian graph can be learned from input data, subsequently creating robust Laplacian embedding and influencing graph convolutional networks. Experiments on different open datasets with clean data and Gaussian noise were carried out. The noise level ranged from 6% to 12% of the maximum value of each dataset. Eleven different multilabel classification algorithms were used as the baselines for comparison. To verify the performance, three evaluation metrics specific to multilabel learning are proposed because multilabel learning is much more complicated than traditional single-label settings; each sample can be associated with multiple labels. The experimental results show that the proposed method performed better than the baselines, even when the data were contaminated by noise. The findings indicate that the proposed method is reliably robust against noise.",signatures:"Wei-Cheng Ye and Jia-Ching Wang",downloadPdfUrl:"/chapter/pdf-download/78473",previewPdfUrl:"/chapter/pdf-preview/78473",authors:[{id:"125911",title:"Prof.",name:"Jia-Ching",surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang"},{id:"420535",title:"Dr.",name:"Wei-Cheng",surname:"Ye",slug:"wei-cheng-ye",fullName:"Wei-Cheng Ye"}],corrections:null},{id:"77974",title:"DMAPT: Study of Data Mining and Machine Learning Techniques in Advanced Persistent Threat Attribution and Detection",doi:"10.5772/intechopen.99291",slug:"dmapt-study-of-data-mining-and-machine-learning-techniques-in-advanced-persistent-threat-attribution",totalDownloads:323,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Modern-day malware is intelligent enough to hide its presence and perform stealthy operations in the background. Advance Persistent Threat (APT) is one such kind of malware attack on sensitive corporate and banking networks to stay there for a long time undetected. In real-time corporate networks, identifying the presence of intruders is a big challenging task for security experts. Recent APT attacks like Carbanak, The Big Bang, and Red Echo attack (targeting the Indian power sector) are ringing alarms globally. New data exfiltration methods and advancements in malware techniques are the two main reasons for rapid and robust APT evolution. Although many traditional and hybrid methods are available to detect this stealthy malware, the number of target-specific attacks are increasing rapidly at global level. Attackers have been crafting payloads resistant to malware sandbox environments so that traditional sandboxing techniques may not work with these APT malware detection. In this paper, we shed light on various Data Mining, Machine Learning techniques and frameworks used in both Attribution and Detection of APT malware. Added to this, our work highlight GAP analysis and need for paradigm shift in existing techniques to deal with evolving modern APT malware.",signatures:"P.V. Sai Charan, P. Mohan Anand and Sandeep K. Shukla",downloadPdfUrl:"/chapter/pdf-download/77974",previewPdfUrl:"/chapter/pdf-preview/77974",authors:[{id:"356696",title:"Ph.D. Student",name:"P.V.",surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan"},{id:"357085",title:"Mr.",name:"P. Mohan",surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand"},{id:"357086",title:"Prof.",name:"Sandeep K.",surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla"}],corrections:null},{id:"78292",title:"Text Classification on the Instagram Caption Using Support Vector Machine",doi:"10.5772/intechopen.99684",slug:"text-classification-on-the-instagram-caption-using-support-vector-machine",totalDownloads:171,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Instagram is one of the world’s top ten most popular social networks. Instagram is the most popular social networking platform in the United States, India, and Brazil, with over 1 billion monthly active users. Each of these countries has more than 91 million Instagram users. The number of Instagram users shows the various reasons and goals for them to play this social media. Social Media Marketing does not escape being one of the purposes of using Instagram, with benefits to place a market for their products. Using text classification to categorize Instagram captions into organized groups, namely fashion, food & beverage, technology, health & beauty, lifestyle & travel, this paper is expected to help people know the current trends on Instagram. The Support Vector Machine algorithm in this research is used in 66171 post captions to classify trending on Instagram. The TF-IDF (Term Frequency times Inverse Document Frequency) method and percentage variations were used for data separation in this study. This study result indicates that the use of SVM with a percentage ratio 70% of dataset for training and 30% of dataset for testing produces a higher level of accuracy compared to the others.",signatures:"Setiawan Hadi and Paquita Putri Ramadhani",downloadPdfUrl:"/chapter/pdf-download/78292",previewPdfUrl:"/chapter/pdf-preview/78292",authors:[{id:"354126",title:"Dr.",name:"Setiawan",surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi"},{id:"414634",title:"Dr.",name:"Paquita",surname:"Putri Ramadhani",slug:"paquita-putri-ramadhani",fullName:"Paquita Putri Ramadhani"}],corrections:null},{id:"79615",title:"Computing on Vertices in Data Mining",doi:"10.5772/intechopen.99315",slug:"computing-on-vertices-in-data-mining",totalDownloads:110,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The main challenges in data mining are related to large, multi-dimensional data sets. There is a need to develop algorithms that are precise and efficient enough to deal with big data problems. The Simplex algorithm from linear programming can be seen as an example of a successful big data problem solving tool. According to the fundamental theorem of linear programming the solution of the optimization problem can found in one of the vertices in the parameter space. The basis exchange algorithms also search for the optimal solution among finite number of the vertices in the parameter space. Basis exchange algorithms enable the design of complex layers of classifiers or predictive models based on a small number of multivariate data vectors.",signatures:"Leon Bobrowski",downloadPdfUrl:"/chapter/pdf-download/79615",previewPdfUrl:"/chapter/pdf-preview/79615",authors:[{id:"357014",title:"Prof.",name:"Leon",surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski"}],corrections:null},{id:"78237",title:"Artificial Intelligence and Its Application in Optimization under Uncertainty",doi:"10.5772/intechopen.98628",slug:"artificial-intelligence-and-its-application-in-optimization-under-uncertainty",totalDownloads:382,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided.",signatures:"Saeid Sadeghi, Maghsoud Amiri and Farzaneh Mansoori Mooseloo",downloadPdfUrl:"/chapter/pdf-download/78237",previewPdfUrl:"/chapter/pdf-preview/78237",authors:[{id:"309983",title:"MSc.",name:"Saeid",surname:"Sadeghi",slug:"saeid-sadeghi",fullName:"Saeid Sadeghi"},{id:"310094",title:"M.Sc.",name:"Farzaneh",surname:"Mansoori Mooseloo",slug:"farzaneh-mansoori-mooseloo",fullName:"Farzaneh Mansoori Mooseloo"},{id:"415409",title:"Prof.",name:"Maghsoud",surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri"}],corrections:null},{id:"77913",title:"Practical Application Using the Clustering Algorithm",doi:"10.5772/intechopen.99314",slug:"practical-application-using-the-clustering-algorithm",totalDownloads:168,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter will survey the clustering algorithm that is unsupervised learning among data mining and machine learning techniques. The most popular clustering algorithm is the K-means clustering algorithm; It can represent a cluster of data. The K-means clustering algorithm is an essential factor in finding an appropriate K value for distributing the training dataset. It is common to find this value experimentally. Also, it can use the elbow method, which is a heuristic approach used in determining the number of clusters. One of the present clusterings applied studies is the particulate matter concentration clustering algorithm for particulate matter distribution estimation. This algorithm divides the area of the center that the fine dust distribution using K-means clustering. It then finds the coordinates of the optimal point according to the distribution of the particulate matter values. The training dataset is the latitude, longitude of the observatory, and PM10 value obtained from the AirKorea website provided by the Korea Environment Corporation. This study performed the K-means clustering algorithm to cluster feature datasets. Furthermore, it showed an experiment on the K values to represent the cluster better. It performed clustering by changing K values from 10 to 23. Then it generated 16 labels divided into 16 cities in Korea and compared them to the clustering result. Visualizing them on the actual map confirmed whether the clusters of each city were evenly bound. Moreover, it figures out the cluster center to find the observatory location representing particulate matter distribution.",signatures:"Yoosoo Oh and Seonghee Min",downloadPdfUrl:"/chapter/pdf-download/77913",previewPdfUrl:"/chapter/pdf-preview/77913",authors:[{id:"353307",title:"Prof.",name:"Yoosoo",surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh"},{id:"356823",title:"MSc.",name:"Seonghee",surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min"}],corrections:null},{id:"78984",title:"Leaching Mechanisms of Trace Elements from Coal and Host Rock Using Method of Data Mining",doi:"10.5772/intechopen.100498",slug:"leaching-mechanisms-of-trace-elements-from-coal-and-host-rock-using-method-of-data-mining",totalDownloads:151,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Coal and host rock, including the gangue dump, are important sources of toxic elements, which have high-contaminating potential to surface and groundwater. Surface water in the coal mine area and groundwater in the active or abandoned coal mines have been observed to be polluted by trace elements, such as arsenic, mercury, lead, selenium, cadmium. It is helpful to control pollution caused by the trace elements by understanding the leaching behavior and mechanism. The leaching and migration of the trace elements are controlled mainly by two factors, trace elements’ occurrence and the surrounding environment. The traditional method to investigate elements’ occurrence and leaching mechanism is based on the geochemical method. In this research, the data mining method was applied to find the relationship and patterns, which is concealed in the data matrix. From the geochemical point of view, the patterns mean the occurrence and leaching mechanism of trace elements from coal and host rock. An unsupervised machine learning method, principal component analysis was applied to reduce dimensions of data matrix of solid and liquid samples, and then, the re-calculated data were clustered to find its co-existing pattern using the method of Gaussian mixture model.",signatures:"Yao Shan",downloadPdfUrl:"/chapter/pdf-download/78984",previewPdfUrl:"/chapter/pdf-preview/78984",authors:[{id:"302698",title:"Dr.",name:"Yao",surname:"Shan",slug:"yao-shan",fullName:"Yao Shan"}],corrections:null},{id:"77605",title:"Tourist Sentiment Mining Based on Deep Learning",doi:"10.5772/intechopen.98836",slug:"tourist-sentiment-mining-based-on-deep-learning",totalDownloads:173,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection.",signatures:"Weijun Li, Qun Yang and Wencai Du",downloadPdfUrl:"/chapter/pdf-download/77605",previewPdfUrl:"/chapter/pdf-preview/77605",authors:[{id:"357198",title:"Prof.",name:"Wencai",surname:"Du",slug:"wencai-du",fullName:"Wencai Du"},{id:"357214",title:"Dr.",name:"Weijun",surname:"Li",slug:"weijun-li",fullName:"Weijun Li"},{id:"419199",title:"Dr.",name:"Qun",surname:"Yang",slug:"qun-yang",fullName:"Qun Yang"}],corrections:null},{id:"77997",title:"Data Mining Applied for Community Satisfaction Prediction of Rehabilitation and Reconstruction Project (Learn from Palu Disasters)",doi:"10.5772/intechopen.99349",slug:"data-mining-applied-for-community-satisfaction-prediction-of-rehabilitation-and-reconstruction-proje",totalDownloads:145,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Natural disasters can occur anytime and anywhere, especially in areas with high disaster risk. The earthquake that followed the tsunami and liquefaction in Palu, Indonesia, at the end of 2018 had caused tremendous damage. In recent years, rehabilitation and reconstruction projects have been implemented to restore the situation and accelerate economic growth. A study is needed to determine whether the rehabilitation and reconstruction that has been carried out for three years have met community satisfaction. The results of further analysis are expected to predict the level of community satisfaction for the implementation of rehabilitation and other reconstruction. The method used in this paper is predictive modeling using a data mining (DM) approach. Data were collected from all rehabilitation and reconstruction activities in Palu, Sigi, and Donggala with the scope of the earthquake, tsunami, and liquefaction disasters. The analysis results show that the Artificial Neural Network (ANN) and the support vector machine (SVM) with a DM approach can develop a community satisfaction prediction model to implement rehabilitation and reconstruction after the earthquake-tsunami and liquefaction disasters.",signatures:"Andri Irfan Rifai",downloadPdfUrl:"/chapter/pdf-download/77997",previewPdfUrl:"/chapter/pdf-preview/77997",authors:[{id:"226240",title:"Dr.",name:"Andri Irfan",surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:{id:"26",series:{id:"14",title:"Artificial Intelligence",issn:"2633-1403",editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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\nThe concept of resilience in relation to ecology and ecosystem is defined as the ability of the ecosystem to absorb the disturbance without shifting to an alternative state and losing function and services [1]. It is often used to describe the characteristic features of a system that are related to sustainability, and the concept of resilience is used in various interdisciplinary works, particularly in addressing the interactions between people and nature. Resilience is also associated with the capacity of the ecosystem to undergo disturbance and maintain its functions and controls [2]. For example, changes in grass species in the rangeland reduce its capacity to continue functioning ecologically (such as in water use and nutrient cycle) under heavy grazing by animals [3]. Ecosystem resilience can play a prime role in maintaining conditions that will sustain the provision of ecosystem services that contribute to the human well-being, in this case the well-being of coastal communities. The resilience of the ecosystem could directly affect the socioecological system. The objective of this chapter is to discuss the resilience of the coastal ecosystem of Terengganu, East Coast of Peninsular Malaysia, based on land use cover changes in Terengganu between years 2000 and 2017. Threats faced by the coastal ecosystem of Terengganu that may affect system resilience and ecosystem services are also discussed.
\nThe ecosystem services concept was coined to address various benefits and values provided to humankind by ecosystems, which arise from ecological functions and biodiversity [4, 5]. The benefits and values could be direct or indirect, covering a wide range of vital goods and services that are classified into various ecological functions, for example, the provisioning service (such as providing goods or raw materials), regulatory services (such as air and water purification, water and nutrient cycling and regulation, soil formation and retention, atmospheric carbon sequestration) and supporting services. The last is the prerequisite for three other sets of ecosystem services (provisioning, regulating and cultural). However, the classification and typology of ecosystem services are varied and sometimes debatable in terms of application and relevance [6]. Nevertheless, ecosystem services as a concept are acknowledged to be an important tool to raise awareness on ecosystem’s importance, particularly through identification of the goods and services made available by the ecosystem. The quantification of ecosystem services provide a monetary dimension, creating a potential link between biodiversity conservation and market value. In this chapter, we identify and discuss key ecosystem services of the coastal ecosystem of Terengganu and how it might have been interrupted by the changes.
\nWorldwide, coastal landscapes change tremendously due to urbanization and various other pressures both from anthropogenic-based and natural processes. Coastal landscapes are among the most densely populated zone as this zone provides enormous values and services to human population. Coastal ecosystem is commonly addressed together as ‘estuarine and coastal ecosystems’ (ECEs) due to their close connectivity and complexity in providing ecological services [7]. It not only holds high key economic values and vital ecosystem services but also supports integrated systems of social and ecological landscapes (SEL) [8]. At the global scale, coastal vegetation varies across geographical regions. In Malaysia and other tropical countries, there are three common vegetation types easily found in coastal zones, namely, mangroves, peat swamp forest and freshwater swamp. Coastal vegetation plays a significant role in stabilizing coastal ecosystems, for example, by modifying and stabilizing the physical environment [9]. The loss of coastal vegetation or changes in land use cover of terrestrial ecosystem could change the biomass and productivity leading to the changes in carbon cycling processes [10]. Coastal wetland reclamation causes loss in ecosystem services, for example, in Lianyungang Province in China [11]. Coastal wetland ecosystem varies in subtypes which comprises of estuaries, marshes, salt ponds, lagoons, mangroves, intertidal habitats and other coastal system subtypes. All wetland ecosystems vary in terms of unit value and ecosystem services rendered and even within the same eco-subtype; the unit value may vary with different space and time [4]. Thus, for the unit value of ecosystem services, different coastal wetland should be conserved and managed differently.
\nThe East Coast of Peninsular Malaysia coastal plain is originated from marine-based deposit arranged in a series of ridge and depression parallel to the shoreline [12]. This soil formation is classified as “beach ridges interspersed with swales” (
Examples of typical soil series (Rudua and Rhu Tapai soil series) under beach ridges interspersed with swales (BRIS) system in the East Coast of Peninsular Malaysia compose more than 90% of sand.
In the past decades, Terengganu has rapidly developed its overall economy through the federal government’s East Coast Economic Region (ECER) Master Plan that was launched in 2008 headed by the East Coast Economic Region Development Council (ECERDC) [18]. The development programs and projects, among others, aim to raise the income levels and reduce poverty of the Terengganu population by expanding employment prospects in the east coast regions. Many of the projects take place along the coast itself, for example, development of a new central business district (CBD) at the north and south estuary of the Kuala Terengganu town centre and the planned development of the 600-km east coast rail line (ECRL) planned for linking key industrial hubs in Terengganu with Kuantan Port in Pahang and Port Klang in Selangor, both to its south. Some developments in Terengganu are located on the shoreline itself, for example, hipster concept restaurants along the coast of Tok Jembal, in Kuala Nerus district. Looking at this trend, the future outlook for Terengganu coastal ecosystem is rather challenging based on the worrying fact that about 30% of Malaysia coastlines are exposed to erosion [15]. Terengganu coastline erosion or accretion is not only caused by large monsoon wave but also by a more complicated interaction of offshore bottom bathymetry and island shelters [19]. Coastal erosion then becomes more frequent as a result of major sea reclamation for an airport runway upgrading in 2008 [17]. Further development in the coastal zone of Terengganu needs in-depth analysis on the current physical setting to reduce impact on coastal environment and community. This paper discusses coastal changes in Terengganu by looking at land use changes in terms of vegetation cover, urbanization and water body from the years 2000 to 2017 and the impact of these changes to Terengganu coastal ecosystem resilience and ecosystem services.
\nAmong the most significant ecosystem services of coastal landscapes is perhaps coastal protection. The coastlines of eastern Peninsular Malaysia are directly exposed to the South China Sea’s strong winds and dynamic coastal processes. Coastal vegetation acts as a first line of defence from physical elements of wind and wave due to exposure to the annually occurring northeast monsoon. At the same time, coastal vegetation holds together structurally loose coastal sandy soil. The Terengganu coast is also blessed with a prominent stretch of pure stand of
Other than the hydrological aspect, some part of BRIS soil ecosystem is comprised of newly developed peat, which is an important form of carbon storage [23]. Soil carbon together with above and below ground biomass of plants is a very important carbon sink. Even though above ground carbon in the biomass of
Provisioning services of the coastal landscape of the East Coast of Peninsular Malaysia are closely related to support livelihoods of its fishery communities, for example, the utilization of the most abundant plant resources,
Indirect use of pure Gelam stand supports healthy populations of bees and stingless bees, giving a source of sought after honey, collected by the local fishermen as their side income [36]. In swampy part of coastal plain, gelam trees act as a key species in the swampy part of coastal plain, supporting a healthy population of freshwater fishes that are commonly caught by the locals for their ornamental (e.g. tigerbarb) and also for nutritional values (e.g. catfish, snake head and climbing perch). The fishes are abundant during the monsoon season in Terengganu. There are more than 60 species of ornamental freshwater fishes recorded in the riverine system and swamps of Terengganu [37, 38]. Other than supporting freshwater fishes, gelam swamp provides habitat for hydrophytes (submerged, emergent, floating rooted) and woody and nonwoody associated plants. Carnivorous plants of
The ridge areas on the dune which are dryer due to its loose sandy structure surprisingly support quite a number of adapted coastal vegetation [25], including more than 30 species of wild orchids [40](Figure 2). Thus, the Terengganu coastal plain could be an important gene bank for wild orchids that could support commercial orchid industry, one of the option values under the total economic valuation (TEV) [5]. The aesthetic value of this coastal ecosystem together with its natural flora, fauna and landscapes could potentially be conserved and highlighted as one of the many ecotourism products for Terengganu to add to the economic benefit to the coastal communities. This value could be a monetary trade-off for conserving Terengganu BRIS ecosystem. With all the outlined ecological values, services and potentials, gelam forest is no doubt a valuable premise for Terengganu’s coastal ecosystem resilience. Maintaining healthy Gelam forests will help maintain their ecological services for the benefit of the coastal environment that supports the livelihoods of coastal communities. Rather than being seen as unproductive and unimportant, gelam forest should be conserved for their values and services. Awareness on the importance of gelam forests to the sustainability of coastal ecosystem and people should be intensified. Factors contributing to the risk faced by the Gelam forest are outlined in the next section.
\nNatural vegetation on dry part (ridge) of BRIS soil ecosystem on Terengganu coast with a clumping pattern of vegetation (left image) and wild orchid species, Phalaenopsis pulcherrima, thriving well underneath vegetation clump (right image).
The coastal ecosystem of Terengganu is at risk of disappearing if there is no effort in conserving or managing this ecosystem in a sustainable way. Fragmentations of Terengganu coastal ecosystem are mainly due to reclaimation for housing or settlement on a private land, or a development of new township and infrastructure on the state owned land. This is primarily due to its strategic location along the main coastal road, as well as on the lower terrain. Failure in seeing the values of natural ecosystem, shadowed by the lack of value for agriculture, and BRIS soil ecosystem is considered as a barren land and wasteland that deserve to be converted to other land uses. This ecosystem is also threatened by illegal chemical and solid waste dumping, as observed in many areas along the coast of Setiu (north of Kuala Terengganu) and Marang (south of Kuala Terengganu) (Figure 3). The lack of public knowledge about the values of BRIS soil coastal ecosystem and low civic mindedness are identified as primary causes to this problem. Lack of human presence and visible activities in the ecosystem itself also encourage the act of illegal dumping. Frequent monitoring by local authority could help reduce the incidence of illegal waste dumping [20, 25].
\nThreats to coastal ecosystem of Terengganu, frequent fire occurrence particularly during drought or non-monsoon months (top row images), illegal sand mining (bottom row, left image) and illegal dumping (bottom row, right image).
BRIS soil vegetation can easily catch fire, particularly in non-monsoon months or drought season (Figure 3) which can be of natural process and human induced. High incidence of sunray and high temperature of sandy soil surface may initiate fire naturally. Fire can also occur simply from human reckless behaviour, for example, by throwing cigarette butts into the dry and sparse vegetation on BRIS soil ecosystem. There was an extensive fire occurrence recorded along Terengganu coast [41] and several places along coastal road in Setiu experiencing fire in 2016, coinciding with low rainfall and drought in 2014–2016 [30]. Fire is one of driven factors for ecological succession [42] and sometimes needed for vegetation regeneration [43]. However, with the presence of fire-adapted species, ecosystem resilience is negatively affected [44]. This brings us to the next threat faced by Terengganu coastal ecosystem, which is colonization of
The other major threat to BRIS soil ecosystem of Terengganu is sand mining, which commenced a few years back when there was a high demand for sand from the Terengganu coast as it contains high-quality silica. Sand is mined illegally and possibly being transported to the other states or countries to meet the demand. The illegal and small-scale sand mines operated by removing small patches of sand, usually not that far from the coast itself. However, there is one site being mined with the size as big as football field near Lembah Bidong in Setiu district (Figure 3). Experimental study at this degraded site indicates that regeneration of natural vegetation is low and occurs at a very slow rate. Thus, illegal small-scale sand mining could be interfering with key ecosystem services of the coastal ecosystem due to removal of sand and vegetation. In the case of legal and large-scale sand mining, currently Terengganu has two sites of sand mining, privately operated and declared as not detrimental to the coastal environment. However, it is doubtful that the impact of sand mining to the coastal ecosystem is low; rather, the extent of the impact is still uncertain and unknown, as the sand mining is a newly emerging economic activity in Terengganu. The hope is that this industry will be well regulated and monitored by the authority to minimize its impact on the coastal environment.
\nBased on images of Terengganu vegetation cover for years 2000 and 2017, it is clear that the coastal area of Terengganu is changing due to urbanization (Figure 4). Urban area has increased from about 3.3% in the year of 2000 to 33.6% in 2017 (Table 1). Even though the outline data does not specifically indicate differences contributed by the reduction of coastal area, it is clear that there is an increase in urbanization areas along the coast of Terengganu in 2017. Major changes to the Terengganu coastline begin in 2008 when parts of the sea off Terengganu were reclaimed for an airport runway upgrading [17]. Such major reclamation not only caused erosion but also halted the natural accretion process by disturbing sediment transport along the coastline [45]. Consequently, episodic erosion occurred in the northern part of the Terengganu coastline, and the most recent erosion occurs in Kampung Mengabang Telipot, north of Kuala Terengganu state capital [46].
\nMap of Peninsular Malaysia (top row) and vegetation cover in the state of Terengganu, East Coast of Peninsular Malaysia, for the years 2000 and 2017 (bottom row). Image source: Land Process Distributed Active Archive Centre (LPDAAC).
Vegetation cover for the state of Terengganu, Peninsular Malaysia, for years 2000, 2006 and 2017.
Note: Data in hectare are extracted from satellite images obtained from the Land Process Distributed Active Archive Centre (LPDAAC).
Erosion and accretion are natural processes and part of ecological coastal dynamic. However, severe erosion fundamentally indicates failure of managing coastal zone when longshore sediment transport is interrupted by engineering works such as construction of groynes and breakwaters along the northern Terengganu coast [47]; most possibly it is happening in recent breakwater establishment along the coast of Terengganu (Figure 5). Other possible causes of erosion are removal of natural vegetation that can dissipate the wave energy, reduction of sediment supply from engineering works in rivers such as dams and barrages, sand mining from river bed and unregulated or uncontrolled dredging and sand mining activities in near shore areas. All of these factors seem to be part of the contributing agents to Terengganu coastal erosion. It is a prime challenge for the authority of the state of Terengganu to find a creative engineering technique to solve this complicated ‘man versus nature’ situation. To ensure the sustainability of the coast, significant efforts should be made to maintain ecological infrastructures or multifunctional network of ecosystem provided by coastal wetlands [11]. Considering the dynamics of the Terengganu coast, it is recommended that the coastal sustainable land use planning (SLUP) strategy be adopted. SLUP is evident to enhance coastal resilience, so that coastal ecosystem could continue to provide key ecosystem services, particularly for the benefit of the coastal community [8, 48, 49].
\nCoastal erosion along the Universiti Malaysia Terengganu (UMT) campus in Kuala Nerus district, north of Kuala Terengganu (left image), and breakwaters constructed to solve erosion along north of Terengganu (right images). White arrows mark extension of airport runway in 2008. Source: Media Kreatif UMT (left image) and Mr. Mokhtar Ishak (right image).
The reduction of vegetation cover in some parts of the coastal areas of Terengganu is possibly due to vegetation removal for aquaculture activities and settlement construction. In the coastal areas of Terengganu, apart from mangrove trees and associated plants,
An interesting shift that has taken place in resilience thinking that is of relevance to this paper. The premise in resilient thinking that ecological resilience is key to the management of changes occurring in complex and dynamic systems of people and nature cannot be understood if there is little understanding of the social drivers of change that contributes to that ecological resilience [50]. ‘People do change the resilience of ecological systems’ ([50]:p.428).
\nComplexity and diversity as well as fragility are deemed to be the characteristics of both social and natural systems so that responses to interventions or encroachments are unpredictable. Ecological resilience taken to mean the capacity for renewal in a dynamic environment is required in order for the system to respond to the social drivers of change, albeit in an unpredictable manner. The major social drivers of change that are most mentioned in the literature, because of their generalized presence in landscapes and regions around the world, are acknowledged to be unsustainable land use, abandonment and urbanization [51]. These some drivers are also occurring in the coastal landscapes of Terengganu, as mentioned in earlier sections of this paper.
\nThe tendency to focus on man-made degradation of ecosystems in studies of resilience has been criticized. Instead, it is recommended that solutions should be focused on creative processes of accumulating natural capital developed and should include their intangible values. This is also due to the assessment practices that commonly focus on visible or tangible change (biodiversity loss, brittle stability, of ‘an accident waiting to happen’) [50]. Examples of intangible values are those associated with biodiversity conservation (for ecotourism, or for ecosystems services it renders to human populations).
\nSince human well-being is also linked to non-tangible (non-market values), there has been an increasing interest in cultural landscapes (heritage places, regions that have iconic value for identity formation—nationalism—such as Mount Kinabalu for Sabahans of Malaysia, the pastoral landscapes of England and many more). These non-market values are broadly captured by the literature on ‘cultural values’ [52, 53]. We will focus on one element of cultural values, namely, identity strengthening, which is linked to a sense of place. According to [52] the concept of a sense of place ‘embeds all dimensions of peoples’ perceptions and interpretations of the environment, such as attachment, identity or symbolic meaning, and has the potential to link social and ecological issues’. An example of a sense of place, in this instance the link between the Terengganu coastal system and the identity of fishers, can be taken as an example as below.
\nLivelihood security of artisanal fishers of Terengganu depends on the sea—near shore and further in the open South China Sea. However, the sea provides more than livelihoods to fishers. Anecdotal evidence from newspaper clippings indicate that despite risks from coastal erosion, many local residents find it difficult to leave because they claim that they have nowhere to go [54]. As well, among artisanal fishers of the Setiu wetlands in Terengganu, despite risks from weather disturbances and being employed in more stable occupations such as in aquaculture, many fishers maintain their fishing trips out to at least three to four times a week except during severe monsoons [55]. This maintenance of their connection with the sea is what distinguishes those who consider themselves as ‘real’ fishers versus those who are not (including those who have boats and equipment but do not pursue fishing seriously). The sea then carries the intangible value of providing some fishers with a mechanism for strengthening their cultural identity. Similar findings on the effects of place (whether marine or terrestrial) and identity are evident in many studies around the world [56]. For example, in Sabah, Bajau fishers identify themselves with the inland sea surrounding the Banggi Island chain and their identity found strength in seaweed cultivation, despite the fact that fishing as an activity provided them with a higher return for hours worked than labour intensive seaweed cultivation [57].
\nThe bio-security of Gelam forests depends on the degree of its resilience as well as the social resilience of the local communities that have lived alongside them or who are benefitted from the health of these forests. The ecosystem services provided by the wetlands and the dry swamp of Gelam forests are including uses in the construction of sea-going fishing boats. Freshwater fishes found in flooded lakes and riverine systems during the annual monsoon season provide extra source of nutrition to local communities as outlined in earlier sections of this paper. But the reverse provision of services by local communities, through their local knowledge in the sustainable management (through use) of natural resources from inland forests and seas, has not been well researched.
\nConsequently, a lot more research needs to be done on how local communities form knowledge about their landscapes. Secondly, given the understanding that throughout history there are very few landscapes in the world that have not been shaped by local communities [54], to what extent has local knowledge shaped the characteristics of the gelam landscape? These are valid questions to ask because despite the transformation of landscapes by drivers of development as the Terengganu coast has been, certain cultural values are not totally lost as viewed in fishers’ identity and place. As to why local knowledge research is important, there is a consensus that environmental degradation is not amenable for its solution to one body of knowledge alone but from a variety of knowledge types and disciplines.
\nThere is a reduction in vegetation cover in Terengganu from the years 2000 and 2017, and it coincides with the increase in coverage of urban areas. Even though our data do not particularly reflect specific changes to coastal areas, this reduction in vegetation cover deserves to be addressed. It is time that the complexity of coastal ecosystem be valued as a social ecological landscape. Sustainable land use planning (SLUP) may be a good model to be adopted in managing coastal ecosystem of Terengganu. Sustainable solutions should be applied to aim for social, economic and environmental benefits. In-depth research on each component of social and ecological system and their connectivity should be enhanced to further understand coastal ecosystem resilience and assist the authority in the planning and managing of coastal ecosystem [58]. Better valuation of the landscape could be conducted to include general public perception analysis in the development planning [59]. Local knowledge of the ecosystem ought to be encouraged for their value to planning.
\nThe authors would like to acknowledge the Director of Institute of Tropical Biodiversity and Sustainable Development (Bio-D Tropika) of Universiti Malaysia Terengganu, Dato’ Prof. Dr. Mohd Tajuddin Abdullah, for his support and encouragement in organizing the 20th Colloquium of Malaysia and Singapore Society of Australia (MASSA2018), February 8–9, 2018, where parts of this write up are presented. We would like to thank Media Kreatif UMT for the image of erosion along Terengganu coastline and Mr. Mokhtar Ishak for the drone images of breakwater along UMT beaches.
\nThere is no conflict of interest in this publication.
\nBioprocesses, which are consisted of a series of enzymatically catalyzed biochemical reactions in all living things, are necessary for survival. They have a high potential in terms of material synthesis, which has recently been performed by chemical techniques [1]. Furthermore, the advancement of heterologous production systems and genetic engineering techniques has resulted in pioneering initiatives to manufacture usable biomaterials [2]. These advancements also enabled the successful generation of primary and secondary metabolic pathway products in physiologically and genetically well-defined hosts, such as
NRPs are secondary metabolites that are synthesized outside of the ribosomal machinery and have a variety of properties such as cytostatics, immunosuppressants or anticancer agents, antibiotics, pigments, siderophores, toxins [3, 5, 6]. NRPs are typically produced by marine microorganisms and invertebrates, as well as soil-inhabiting microorganisms [5, 7, 8]. The majority of natural products produced by sponges, bryozoans, mollusks, and tunicates are members of the NRP or mixed polyketide–NRP families. Several of NRPs are being used in the development of new medicines for inflammatory, cancer, neurological diseases, and infectious disease nowadays [7].
Non-ribosomal peptide synthetases (NRPSs) enzymes are poly-functional mega-synthases that biosynthesize NRPs [7, 9, 10]. NRPSs, multi-modular enzyme or enzyme complexes from common bacteria, less common eukarya, and rare archaea, are capable of producing a wide range of natural pharmaceutical products (Bacitracin, antibiotics for skin infections; Bleomycin antitumor; Cyclosporin, antifungal, and immunosuppressive drugs; Daptomycin, antibiotics) [5, 7, 11]. NRPSs use both proteinogenic and nonproteinogenic amino acids (not encoded by DNA) as building blocks for the growing peptide chain [1, 7, 11, 12]. They catalyze multiple biosynthetic processes, each of which is responsible for particular one amino acid elongation on the growing peptide chain [10]. This chapter looks at the structure, function, and synthesis of NRPs, as well as producer microorganisms and their various applications.
NRPSs are responsible for nonribosomal peptide (NRP) synthesis. These are large multi-enzyme complexes that are modularly organized and serve as biosynthetic machinery and templates [5, 11, 12, 13, 14]. For example, a single NRPS of 1.6 MDa synthesizes the Cyclosporine A (7). In fungal systems, such as in the case of cyclosporine A (7), a single NRPS synthesizes peptides, whereas bacteria frequently use numerous NRPSs with genes grouped in an operon. NRPSs have a modular structure [14, 15].
In a genome mining research of 2699 genomes, Wang et al. found that more than half of the NRPS enzymes were non-modular NRPS enzymes [16]. Nonmodular NRPS enzymes can be found in siderophore biosynthesis pathways, such as EntE and VibH in enterobactin and VibE in vibriobactin, or as a standalone peptidyl carrier protein, such as BlmI from the bleomycin gene cluster. NRPS enzymes are found frequently in bacteria, less frequently in eukaryotes, and infrequently in archaea. Actinobacteria, Cyanobacteria, Firmicutes, and Proteobacteria were the phyla with the greatest number of these enzymes in the bacterial domain. There was a correlation between genome size and the number of NRPS clusters [5, 17].
A module is a part of the NRPS polypeptide chain that is in charge of integrating one amino acid into the final product. Modules can further be separated into domains (Figure 1), which represent enzyme units that catalyze distinct steps of NRP synthesis. On the protein level, domains are defined by distinctive, greatly conserved order of patterns known as “core motifs.” In certain instances, biochemical and structural data were used to confirm the involvement of greatly conserved residues in domain function (Table 1) [14].
Catalyzed reactions by various NRPS-domains [
A1 L(TS)YxEL A2 LKAGxAYL(VL)P(LI)D A3 LAYxxYTSG(ST)TGxPKG A4 FDxS A5 NxYGPTE A6 GELxJGx(VL)ARGYL A7 Y(RK)TGDL A8 GRxPxQVKIRGxRIELGEIE A9 LPxYM(IV)P A10 NGK(VL)DR | T LGG(DH)SL |
C1 SxAQxR(LM)(WY)xL C2 RHExLRTxF C3 MHHxISDG(WV)S C4 YxD(FY)AVW C5 (IV)GxFVNT(QL)(CA)xR C6 HN)QD(YD)PFE C7 RDxSRNPL | Te GxSxG |
E1 PIQxWF E2 HHxISDG(WV)S E3 DxLLxAxG E4 EGHGRE E5 RTVGWFTxxYP(YV)PFE E6 PxxGxGYG E7 FNYLG(QR) | Cy1 FPL(TS)xxQxAYxxGR Cy2 RHx(IM)L(PAL)x(ND)GxQ C3 D(NLI)xDxxS Cy3 LPxxPxLPLxxxP Cy4 (TS)(PA)3x(LAF)6x(IVT)LxxW Cy5 (GA)DFTxLxLL Cy6 PVVFTSxL Cy7 (ST)(QR)TPQVx(LI)D13xWD |
Ox1 KYxYxSxGxxY(PG)VQ Ox2 GxxxG(LV)xxGxYYY(HD)P Ox3 IxxxYG | M1 VL(DE)xGxGxG M2 NELSxYRYxAV M3 VExSxARQxGxLD |
R1 V(L)(L)TG(A)TG(F)(L)GxxLL R2 Vx(L)(L)VR(A) R3 GPL(G)x(P)x(L)GL R4 V(Y)PYxYLxx(P)NVxxT R5 GYxxSKW(A)(A)E R6 R(P)G R7 YxxxxG(LF)LxxP |
NPRS-domains’ core-motifs [14].
There are three domains in a module. These are 1) the adenylation (A) domain, 2) the peptidyl carrier protein (PCP) or thiolation (T) domain, and 3) the condensation (C) domain, all of which are responsible for the synthesis of NRPs. A module may include additional tailoring or altering domains incorporating epimerization (E), methylation and oxidation domains or a heterocyclization (Cy) domain in place of a C-domain. Finally, most NRPS termination modules have a TE-domain, which is in charge of releasing linear, cyclic, or branching cyclic peptides [5, 9, 10, 11, 15, 18, 19, 20, 21].
The order of the modules is frequently aligned with the sequences of the resulting peptides. NRP synthesis begins at the N-terminus and ends at the C-terminus, yielding peptides that are typically 3–15 amino acids long. The released peptides contain amino acids, that is, imino acids or ornithine and their structures are linear, cyclic-macrocyclic, branched-cyclic, branched-macrocyclic, dimers or trimers of identical structural elements [5].
The A-domain is responsible for the first step in biosynthesis, which involves recognizing and activating the amino acid substrate via adenylation with Mg-ATP, resulting in an aminoacyl adenylated intermediate. Around 550 amino acids make up domain A. It has 10 amino acid residues that serve as NRPS enzyme “codons” and are essential for substrate specificity. The D and L forms of the 20 amino acids used in ribosomal protein synthesis, as well as non-proteinogenic amino acids like imino acids, ornithine, and hydroxy acids like β-butyric acids and α-aminoadipic, are substrates recognized by the A-domain. The PCP-domain, which consists of about 80 amino acids and covalently attaches the activated amino acid to their cofactor 4′-phosphopantetheine (PP) arm via a thioester bond, completes the second step. And also, the active substrate and elongation intermediates are transferred to the C-domain via this domain. In the last step, C-domain, which contains approximately 450 amino acids, catalyzes the formation of peptide bonds between the carboxyl group of the incipiently synthesized peptide and the amino acid transported by the side module [5, 22]. Furthermore, this domain allows the expanding chain to be translocated to the next module. Following this step, the linear intermediate peptide is liberated in bacteria via internal cyclization or hydrolysis with the help of the Thioesterase (TE) domain. On the other hand, it appears less commonly in fungi’s NRPSs. Fungi use a variety of ways to release chains. The first is a thioesterase NADP(H)-dependent reductase domain (R), which catalyzes NADPH reduction to create an aldehyde and the second is a terminal C domain, which catalyzes release by forming intermolecular or intramolecular amide bonds. By N-, C-, and O-methylation, halogenation, acylation, hydroxylation, glycosylation, or heterocyclic ring formation, the primary product of this synthesis can be changed post-synthetically to reach its mature form by additional tailoring enzymes that are not part of the NRPS. The structural diversity of NRPs is formed in part by these enzymes and their reactions [5].
Because of their extensive multidomain organization, NRPS genes are easier to identify using recent genome mining technologies, and they are also relatively easy to detect. Secondary metabolites production genes are frequently found in bacterial and fungal gene clusters. The clusters’ core is thought to be NRPS genes. Nevertheless, they are linked to genes involved in building blocks synthesis, product ornamentation, self-resistance, and peptide export. For the purpose of analyzing and in silico exploration of NRPS pathways, advanced genome sequencing techniques have made genome mining methodologies available, which are assisted by a variety of bioinformatics tools, such as AntiSMASH, PRISM, and SMURF [23].
Nowadays, known NRP structures are divided into various categories, each with its own structural characteristics. Lipocyclopeptides with varied linkage patterns, such as fengycin, iturin, surfactin, and head-to-tail-cyclized peptides of varying ring sizes, such as cyclosporine, gramicidin S, maybe the largest group. There are also a lot of linear peptide configurations. They include tripeptides (such as sevadicin and bialaphos) as well as 20-mer peptides (e.g., alamethicin, peptaibols). The current highest size limit for NRPs is syringopeptin 25A, which has 25 amino acids (syringopeptin 25A). Tailoring enzymes modify the structure of some NRPs. The most structurally complicated molecules are probably bleomycins, ergopeptides, glycopeptide antibiotics, and β-lactams [23].
Figure 2 shows some NRPs with diverse structures and a wide spectrum of activities. ACV-tripeptide (6), for example, is a precursor to antibiotics of the penicillin and cephalosporin families. Gramicidin S (4), tyrocidine A (1), and vancomycin (5) are three other antibiotic-active substances. Cyclosporin A (7), an immunosuppressive drug, is used in the post-transplantation care of patients. Cancer is treated with cytostatic agents, such as bleomycin A2 (8) and epothilone (9). Enterobactin (10), bacillibactin (11), mixochelin A (12), yersiniabactin (13), and vibriobactin (14) are examples of iron chelating agents. These compounds, known as siderophores, are created in iron-deficient environments to provide bacteria with an iron source. Figure 2 also depicts the structures of pigments like indigodin (15), toxins like thaxtomin A (17), and peptides with uncertain functions like anabaenopeptilide 90-A (18) [14].
Some NRPs with structural diversity [
NRPs have a number of structural characteristics that distinguish them from ribosomal peptides. For example, non-proteinogenic amino acids, such as ornithine in 1, 2, and 4, hydroxyphenyl or dihydroxyphenyl-glycine in 5 and (4R)-4-[(E)-2-butenyl]-4-methyl-L. -threonine (Bmt) in 7, are included. Furthermore, the structures are frequently macrocyclic (1), branched macrocyclic (2), or dimers of two (4) or trimers of three (10, 11) structural components. Smaller heterocyclic rings, such as thiazole in 9, thiazoline in 13, or oxazoline in 14, are common structural properties of nonribosomal peptides. In addition, fatty acids (3), glycosylations (5), N-methylations (7), and N-formylations (18) may also be present, as well as the addition of propionate units (8) or acetate [14].
NRPs are typically produced by marine microorganisms, soil-inhabiting microorganisms, including
Novel peptide products’ biological functions are strictly associated with their chemical structure, which is constrained by a peptide sequence that ensures specific interaction with a specific molecular target. Chemical alterations, such as the incorporation of fatty acid chains, D-amino acids, glycosylated amino acids, and heterocyclic rings, as well as cyclization or oxidative cross-linking of side chains, add a lot to these unique interactions. Bacitracin, fengycin, pristinamycin, surfactin, tyrocidine, and vancomycin are examples of novel peptides with antibacterial and antifungal properties [25].
When the ribosomal code was deciphered in the 1960s, Tatum and coworkers discovered that ribosomes had no effect on cell-based tyrocidine production [23, 26]. The first NRPs agent is tyrocidine, a cyclic decapeptide that is biosynthesized outside of the
Compound | Biosynthetic class of agent | Source | Disease/Molecular target |
---|---|---|---|
Bacitracin | Cyclic peptide | Antibiotic/dephosphorylation of C55-isoprenyl pyrophosphate | |
Bleomycin | Hybrid peptide | Antibiotic/inhibition of DNA synthesis | |
Capreomycin | Cyclicpeptide | Antibiotic/protein synthesis inhibitor | |
Carbapenems | Synthetic thienamycin | Antibacterial (multidrug resistant)/bacterial cell-wall biosynthesis (peptidoglycan;β-lactamase inhibition) | |
Cephalosporin | Antibiotic/Alters bacterial outer membrane | ||
Chlorampheniol | Synthetic;further derivatives: thiamphenicol [c], florfenicol | Antibacterial/inhibition of ribosomal protein synthesis | |
Colistin (Polymyxin E) | — | Antibacterial/binding to lipopolysaccharide (outer membrane), interaction with the cytoplasmic membrane | |
Dalbavancin | Semisynthetic teicoplanin derivative | — | Antibacterial (Gram-positive)/membrane anchoring; disruption of cell membrane and inhibition of bacterial cell wall biosynthesis |
Daptomycin | Lipopeptide | Antibiotic (Gram-positive)/disrupts the cell membrane | |
Gramicidin | Linear pentadecapeptide | Antibiotic/ion-channel formation, increasing the permeability of the membrane | |
Lincomycin | — | Antibacterial (patients allergic to penicillin) inhibition of the ribosomal protein synthesis (50S-subunit, dissociation of peptidyl-tRNA from the ribosome) | |
Monobactams | — | Antibacterial (Gram-negative)/bacterial cell-wall biosynthesis | |
Oritavancin | — | Semi synthetic | Antibiotic/disrupts the cell membrane |
Polymyxin B | Polypeptides | Antibacterial (Gram-negative)/binding to lipopolysaccharide (outer membrane), interaction with cytoplasmic membrane | |
Pristinamycin | Depsipeptide | Antibacterial (Gram-positive)/ribosomal biosynthesis (50S-subunit, peptidyl transfer, and elongation of protein synthesis) | |
Teicoplanin | Glycopeptide | Antibiotic/inhibit cell wall synthesis | |
Telavancin | — | Antibacterial (Gram-positive) disruption of cell membrane and inhibition of bacterial cell-wall biosynthesis | |
Tyrothricin | — | Antibacterial (Gram-positive)/disruption of cell membrane | |
Vancomycin | Glycopeptide | Antibiotic/inhibit cell wall synthesis | |
Virginiamycin | — | Antibacterial/ribosomal biosynthesis (50S-subunit, peptidyl transfer, and elongation of protein synthesis) |
As demonstrated in Table 2, systemic and topical antibacterials are the most often used NRPs-based drugs, accounting for billions of dollars in the chemical and pharmaceutical industry sales. Table 3 lists their other applications, which include anticancer agents, antifungals, animal feed additives, immunosuppressants (cyclosporine), obstetrics (ergometrine), and pain management (ergotamine).
Agent | Origin | Properties and area of application |
---|---|---|
Actinomycin D (Dactinomycin) | Antitumor/DNA intercalator, inhibition of transcription | |
Bialaphos | Herbicide/tripeptide prodrug, inhibitor of glutamine synthetase | |
Bleomycin A2, B2 | Antitumor/metal-dependent oxidative cleavage of DNA in presence of molecular oxygen | |
Capreomycin | Antituberculous/ inhibition of the ribosomal protein synthesis (16S and 23S-rRNA) | |
Carfilzomib | Synthetic derivative of epoxomycin ( | Anticancer/proteasome inhibitor |
Caspofungin | Antifungal (candidiasis, aspergillosis) fungal cell-wall integrity ((1-3)-β-D-glucan synthase) | |
Cyclosporine A | Immunosuppressant/cyclophilin binding, inhibition of IL-2 expression (inhibition of T-cell activation) | |
Emodepside | Anthelmintic/Slo-1 receptor (K+ channel) | |
Enduracidin (Enramycin) | Antibacterial, food additive/inhibition of MurG (essential for cell-wall biosynthesis in Gram positive bacteria), inhibition of the transglycosylation step of peptidoglycan biosynthesis | |
Enniatins (fusafungine) | Antibacterial (topical), antifungal, anti-inflammatory/ ionophore (NH4+) membrane depolarization | |
Ergometrine (ergonovine) | Obstetrics/interaction with a-adrenergic, dopaminergic and serotonin receptors | |
Ergotamine | Migraine vasoconstrictive (5-HT1B receptor, but also dopamine and noradrenaline receptors) | |
Romidepsin | Antitumor/histone deacetylase inhibitor (inducing apoptosis) | |
Trabectedin | Bacterial symbiont of | Antitumor (antiproliferative, treatment of soft tissue sarcoma) DNA binder, blocks binding of transcription factors |
Marketed-NRPs agents [23].
In the medical field, NRP-based marketed drugs, such as Cyclosporin A and Bleomycin A2, have high selling prices. The cost of these medicines is $107 for 25 mg of Cyclosporine A (98% purity) obtained from
The 70% discovery of NRPs with antibacterial, antiviral, cytostatic, immunosuppressive, antimalarial, antiparasitic, animal growth promoters, and natural insecticides activity is mostly attributed to marine organisms [13]. NRPs obtained from marine organisms (sponges, tunicates, and their associated phyla, such as Acidobacteria, Actinobacteria, Bacteriodetes, Chloroflexi, Cyanobacteria, Nitrospira, Planctomycetes, Poribacteria, Proteobacteria, Verrucomicrobia, and Archaea) have excellent binding properties, low off-target toxicity, and high stability and these properties make them a promising molecule for the development of new therapeutics pharmacologically active in many clinical searches. Table 4 shows the chemical structure and source of various NRPs isolated from marine sponges and tunicates.
NRPs agents | Chemical class | Origin | Target |
---|---|---|---|
Miraziridine A | Linear pentapeptide | Cancer/inhibit protease cathepsin B | |
Haligramides A-B | Cyclic hexapeptides | Cancer/A-549 (lung), HCT-15 (colon), SF-539 (CNS), SNB-19 (CNS) | |
Prepatellamide A | Cyclic peptide | Cancer/P388 murine leukemia cell lines | |
Tamandarins A-B | Depsipeptides | Cancer/pancreatic carcinoma BX-PC3, prostatic cancer DU-145, head and neck carcinoma UMSCC10b | |
Microsclerodermins F–I | Cyclic peptides | Cancer/HCT-116 cell line | |
Wainunuamide | Cyclic hexapeptide | Cancer/A2780 ovarian, K562 leukemia cancer cells | |
Leucamide A | Cyclic hexapeptide | Cancer/Tumor cell lines HM02, HepG2, Huh7 | |
Axinellin C | Cyclic octapeptide | Cancer/A2780 ovarian, K562 leukemia cancer cells | |
Milnamide D | Linearpeptide | Cancer/HCT-116 cells | |
Kapakahines E–G | — | Cancer/P388 murine leukemia cells | |
Didmolamides A-B | Cyclic hexapeptides | Cancer Tumor cell lines (A549, HT29 and MEL28) | |
Bistratamides E–J | Cyclic hexapeptides | Cancer/Human colon tumor (HCT-116) cell line | |
Milnamide C | — | Cancer/MDA-MB-435cancer cells | |
Scleritodermin A | Cyclic peptide | Cancer | |
Microcionamids A-B | — | Cancer/Human breast tumor cell lines MCF-7 and SKBR-3 | |
Kendarimide A | Linear peptide | Cancer/KB-C2 cells | |
Phakellistatin 14 | Cyclo heptapeptide | Cancer/Murine lymphocytic leukemia P388 cell line | |
Polytheonamides A-B | Polypeptides | Cancer/P388 murine leukemia cells | |
Neopetrosiamides A-B | Tricyclic peptides | Cancer | |
Seragamides A–F | Depsipeptides | Cancer | |
Theopapuamide | Cyclic depsipeptide | Cancer/CEM-TART, HCT-116 cell lines | |
Azumamide A-E | Cyclo tetrapeptides | Cancer | |
Callyaerin G | Cyclic peptide | Cancer/Mouselymphoma cell line (L5178Y) and HeLa cells | |
Stylopeptide 2 | Cyclo decapeptide | Cancer/BT-549 and HS578T breast cancer cell lines | |
Ciliatamides A-C | Lipopeptides | Cancer/HeLa cells | |
Diazonamides C–E | Macrocyclic peptides | Cancer/Human tumor cell lines (A549, HT29, MDA-MB231) | |
Rolloamide A-B | Cyclic heptapeptides | Cancer | |
Euryjanicin A | Cycloheptapeptide | Cancer | |
Callyaerin A–F and H | Cyclic peptides | Cancer/L5178Y cell line | |
Papuamides E-F | Depsipeptides | Cancer/Brine shrimp | |
Stylissamide X | Octapeptide | Cancer/HeLa cells | |
Gombamide A | Hexapeptide | Cancer/K562 and A549 cell lines | |
Microspinosamide | Cyclic depsipeptide | HIV | |
Neamphamide A | Cyclic depsipeptide | HIV | |
Mirabamides A-D | Cyclic depsipeptide | HIV | |
Homophymine A | Cyclodepsipeptide | HIV/PBMC cell line | |
Celebeside A-C | Depsipeptides | HIV/Colon carcinoma (HCT-116) cells | |
Theopapuamides B–D, Mutremdamide A, Koshikamides C-H | Cyclic depsipeptide | HIV | |
Ceratospongamide | Cyclic heptapeptide | Inflammation | |
Halipeptin A-B | Cyclic depsipeptide | Inflammation | |
Perthamide C-D | Cyclopeptide | Inflammation | |
Solomonamide A- B | Cyclic peptide | Inflammation | |
Stylissatin A | Cyclic peptide | Murine macrophage RAW264.7 | |
Dicynthaurin | — | Antimicrobial | |
Nagahamide A | Depsipeptide | Antibacterial | |
Plicatamide | Octapeptide | Antimicrobial | |
Callipeltins | — | ||
Citronamides A- B | — | ||
Renieramide | Cyclic tripeptide | — | |
Phoriospongins A-B | Depsipeptide | Nematocidal/ |
Agents produced from marine sponges and tunicates which are based on NRPs [7].
In the NCBI database, there are currently about 1.164 distinct non-ribosomal peptides that form over 500 different monomers including both proteinogenic and non-proteinogenic L- and D-amino acids, as well as amines and carboxylic acids. These complex secondary metabolites’ linear, cyclic, branching, or other complicated primary structures are frequently altered to enhance clinical qualities and/or bypass resistance mechanisms. Indeed, the nucleotide sequence modification of a native NRPS gene or mixing modules from multiple NRPSs makes them more efficient with pharmacological properties. Several bioengineering and molecular techniques have been developed during the last few decades to produce modified NRPs with improved physicochemical characteristics and bioactivity [13].
In this chapter, we discussed the significance, synthesis, and application areas of NRPs-based agents, which have received a lot of interest as a new source of pharmaceutical agents. NRPs with unique chemical structures and diverse biological actions, such as antibacterials (penicillin, vancomycin), anticancer compounds (bleomycin), and immunosuppressants (cyclosporine), have been researched as novel compounds for new drug discovery and development throughout the last several decades.
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